how to write scientific journal article

This document originally came from the Journal of Mammalogy courtesy of Dr. Ronald Barry, a former editor of the journal.

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Welcome to the PLOS Writing Center

Your source for scientific writing & publishing essentials.

A collection of free, practical guides and hands-on resources for authors looking to improve their scientific publishing skillset.

ARTICLE-WRITING ESSENTIALS

Your title is the first thing anyone who reads your article is going to see, and for many it will be where they stop reading. Learn how to write a title that helps readers find your article, draws your audience in and sets the stage for your research!

The abstract is your chance to let your readers know what they can expect from your article. Learn how to write a clear, and concise abstract that will keep your audience reading.

A clear methods section impacts editorial evaluation and readers’ understanding, and is also the backbone of transparency and replicability. Learn what to include in your methods section, and how much detail is appropriate.

In many fields, a statistical analysis forms the heart of both the methods and results sections of a manuscript. Learn how to report statistical analyses, and what other context is important for publication success and future reproducibility.

The discussion section contains the results and outcomes of a study. An effective discussion informs readers what can be learned from your experiment and provides context for the results.

Ensuring your manuscript is well-written makes it easier for editors, reviewers and readers to understand your work. Avoiding language errors can help accelerate review and minimize delays in the publication of your research.

The PLOS Writing Toolbox

Delivered to your inbox every two weeks, the Writing Toolbox features practical advice and tools you can use to prepare a research manuscript for submission success and build your scientific writing skillset. 

Discover how to navigate the peer review and publishing process, beyond writing your article.

The path to publication can be unsettling when you’re unsure what’s happening with your paper. Learn about staple journal workflows to see the detailed steps required for ensuring a rigorous and ethical publication.

Reputable journals screen for ethics at submission—and inability to pass ethics checks is one of the most common reasons for rejection. Unfortunately, once a study has begun, it’s often too late to secure the requisite ethical reviews and clearances. Learn how to prepare for publication success by ensuring your study meets all ethical requirements before work begins.

From preregistration, to preprints, to publication—learn how and when to share your study.

How you store your data matters. Even after you publish your article, your data needs to be accessible and useable for the long term so that other researchers can continue building on your work. Good data management practices make your data discoverable and easy to use, promote a strong foundation for reproducibility and increase your likelihood of citations.

You’ve just spent months completing your study, writing up the results and submitting to your top-choice journal. Now the feedback is in and it’s time to revise. Set out a clear plan for your response to keep yourself on-track and ensure edits don’t fall through the cracks.

There’s a lot to consider when deciding where to submit your work. Learn how to choose a journal that will help your study reach its audience, while reflecting your values as a researcher.

Are you actively preparing a submission for a PLOS journal? Select the relevant journal below for more detailed guidelines. 

How to Write an Article  

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11 steps to structuring a science paper editors will take seriously

April 5, 2021 | 18 min read

By Angel Borja, PhD

Editor’s note:  This 2014 post conveys the advice of a researcher sharing his experience and does not represent Elsevier’s policy. However, in response to your feedback, we worked with him to update this post so it reflects our practices. For example, since it was published, we have  worked extensively with researchers to raise visibility of non-English language research  – July 10, 2019

Update:  In response to your feedback, we have reinstated the original text so you can see how it was revised. – July 11, 2019

How to prepare a manuscript for international journals — Part 2

In this monthly series, Dr. Angel Borja draws on his extensive background as an author, reviewer and editor to give advice on preparing the manuscript (author's view), the evaluation process (reviewer's view) and what there is to hate or love in a paper (editor's view).

This article is the second in the series. The first article was: "Six things to do before writing your manuscript."

When you organize your manuscript, the first thing to consider is that the order of sections will be very different than the order of items on you checklist.

An article begins with the Title, Abstract and Keywords.

The article text follows the  IMRAD format opens in new tab/window , which responds to the questions below:

I ntroduction: What did you/others do? Why did you do it?

M ethods: How did you do it?

R esults: What did you find?

D iscussion: What does it all mean?

The main text is followed by the Conclusion, Acknowledgements, References and Supporting Materials.

While this is the published structure, however, we often use a different order when writing.

General strcuture of a research article. Watch a related tutorial on Researcher Academy opens in new tab/window .

Steps to organizing your manuscript

Prepare the  figures and tables .

Write the  Methods .

Write up the  Results .

Write the  Discussion . Finalize the Results and Discussion before writing the introduction. This is because, if the discussion is insufficient, how can you objectively demonstrate the scientific significance of your work in the introduction?

Write a clear  Conclusion .

Write a compelling  Introduction .

Write the  Abstract .

Compose a concise and descriptive  Title .

Select  Keywords  for indexing.

Write the  Acknowledgements .

Write up the  References .

Next, I'll review each step in more detail. But before you set out to write a paper, there are two important things you should do that will set the groundwork for the entire process.

The topic to be studied should be the first issue to be solved. Define your hypothesis and objectives (These will go in the Introduction.)

Review the literature related to the topic and select some papers (about 30) that can be cited in your paper (These will be listed in the References.)

Finally, keep in mind that each publisher has its own style guidelines and preferences, so always consult the publisher's Guide for Authors.

Step 1: Prepare the figures and tables

Remember that "a figure is worth a thousand words." Hence, illustrations, including figures and tables, are the most efficient way to present your results. Your data are the driving force of the paper, so your illustrations are critical!

How do you decide between presenting your data as tables or figures? Generally, tables give the actual experimental results, while figures are often used for comparisons of experimental results with those of previous works, or with calculated/theoretical values (Figure 1).

Figure 1. An example of the same data presented as table or as figure. Depending on your objectives, you can show your data either as table (if you wish to stress numbers) or as figure (if you wish to compare gradients).

Whatever your choice is, no illustrations should duplicate the information described elsewhere in the manuscript.

Another important factor: figure and table legends must be self-explanatory (Figure 2)

Figure 2. Figures must be self-explanatory.

When presenting your tables and figures, appearances count! To this end:

Avoid crowded plots (Figure 3), using only three or four data sets per figure; use well-selected scales.

Think about appropriate axis label size

Include clear symbols and data sets that are easy to distinguish.

Never include long boring tables (e.g., chemical compositions of emulsion systems or lists of species and abundances). You can include them as supplementary material.

Figure 3. Don't clutter your charts with too much data.

If you are using photographs, each must have a scale marker, or scale bar, of professional quality in one corner.

In photographs and figures, use color only when necessary when submitting to a print publication. If different line styles can clarify the meaning, never use colors or other thrilling effects or you will be charged with expensive fees. Of course, this does not apply to online journals. For many journals, you can submit duplicate figures: one in color for the online version of the journal and pdfs, and another in black and white for the hardcopy journal (Figure 4).

Figure 4. Using black and white can save money.

Another common problem is the misuse of lines and histograms. Lines joining data only can be used when presenting time series or consecutive samples data (e.g., in a transect from coast to offshore in Figure 5). However, when there is no connection between samples or there is not a gradient, you must use histograms (Figure 5).

Figure 5. Use the right kind of chart for your data.

Sometimes, fonts are too small for the journal. You must take this into account, or they may be illegible to readers (Figure 6).

Figure 6. Figures are not eye charts - make them large enough too read

Finally, you must pay attention to the use of decimals, lines, etc.

Step 2: Write the Methods

This section responds to the question of how the problem was studied. If your paper is proposing a new method, you need to include detailed information so a knowledgeable reader can reproduce the experiment.

However, do not repeat the details of established methods; use References and Supporting Materials to indicate the previously published procedures. Broad summaries or key references are sufficient.

Reviewers will criticize incomplete or incorrect methods descriptions and may recommend rejection, because this section is critical in the process of reproducing your investigation. In this way, all chemicals must be identified. Do not use proprietary, unidentifiable compounds.

To this end, it's important to use standard systems for numbers and nomenclature. For example:

For chemicals, use the conventions of the International Union of Pure and Applied Chemistry opens in new tab/window and the official recommendations of the IUPAC–IUB Combined Commission on Biochemical Nomenclature opens in new tab/window .

For species, use accepted taxonomical nomenclature ( WoRMS: World Register of Marine Species opens in new tab/window , ERMS: European Register of Marine Species opens in new tab/window ), and write them always in italics.

For units of measurement, follow the International System of Units (SI).

Present proper control experiments and statistics used, again to make the experiment of investigation repeatable.

List the methods in the same order they will appear in the Results section, in the logical order in which you did the research:

Description of the site

Description of the surveys or experiments done, giving information on dates, etc.

Description of the laboratory methods, including separation or treatment of samples, analytical methods, following the order of waters, sediments and biomonitors. If you have worked with different biodiversity components start from the simplest (i.e. microbes) to the more complex (i.e. mammals)

Description of the statistical methods used (including confidence levels, etc.)

In this section, avoid adding comments, results, and discussion, which is a common error.

Length of the manuscript

Again, look at the journal's Guide for Authors, but an ideal length for a manuscript is 25 to 40 pages, double spaced, including essential data only. Here are some general guidelines:

Title: Short and informative

Abstract: 1 paragraph (<250 words)

Introduction: 1.5-2 pages

Methods: 2-3 pages

Results: 6-8 pages

Discussion: 4-6 pages

Conclusion: 1 paragraph

Figures: 6-8 (one per page)

Tables: 1-3 (one per page)

References: 20-50 papers (2-4 pages)

Step 3: Write up the Results

This section responds to the question "What have you found?" Hence, only representative results from your research should be presented. The results should be essential for discussion.

However, remember that most journals offer the possibility of adding Supporting Materials, so use them freely for data of secondary importance. In this way, do not attempt to "hide" data in the hope of saving it for a later paper. You may lose evidence to reinforce your conclusion. If data are too abundant, you can use those supplementary materials.

Use sub-headings to keep results of the same type together, which is easier to review and read. Number these sub-sections for the convenience of internal cross-referencing, but always taking into account the publisher's Guide for Authors.

For the data, decide on a logical order that tells a clear story and makes it and easy to understand. Generally, this will be in the same order as presented in the methods section.

An important issue is that you must not include references in this section; you are presenting  your  results, so you cannot refer to others here. If you refer to others, is because you are  discussing  your results, and this must be included in the Discussion section.

Statistical rules

Indicate the statistical tests used with all relevant parameters: e.g., mean and standard deviation (SD): 44% (±3); median and interpercentile range:  7 years (4.5 to 9.5 years).

Use mean and standard deviation to report normally distributed data.

Use median and interpercentile range to report skewed data.

For numbers, use two significant digits unless more precision is necessary (2.08, not 2.07856444).

Never use percentages for very small samples e.g., "one out of two" should not be replaced by 50%.

Step 4: Write the Discussion

Here you must respond to what the results mean. Probably it is the easiest section to write, but the hardest section to get right. This is because it is the most important section of your article. Here you get the chance to sell your data. Take into account that a huge numbers of manuscripts are rejected because the Discussion is weak.

You need to make the Discussion corresponding to the Results, but do not reiterate the results. Here you need to compare the published results by your colleagues with yours (using some of the references included in the Introduction). Never ignore work in disagreement with yours, in turn, you must confront it and convince the reader that you are correct or better.

Take into account the following tips:

Avoid statements that go beyond what the results can support.

Avoid unspecific expressions  such as "higher temperature", "at a lower rate", "highly significant". Quantitative descriptions are always preferred (35ºC, 0.5%, p<0.001, respectively).

Avoid sudden introduction of new terms or ideas;  you must present everything in the introduction, to be confronted with your results here.

Speculations on possible interpretations are allowed, but these should be rooted in fact, rather than imagination.  To achieve good interpretations think about:

How do these results relate to the original question or objectives outlined in the Introduction section?

Do the data support your hypothesis?

Are your results consistent with what other investigators have reported?

Discuss weaknesses and discrepancies. If your results were unexpected, try to explain why

Is there another way to interpret your results?

What further research would be necessary to answer the questions raised by your results?

Explain what is new without exaggerating

Revision of Results and Discussion is not just paper work.  You may do further experiments, derivations, or simulations. Sometimes you cannot clarify your idea in words because some critical items have not been studied substantially.

Step 5: Write a clear Conclusion

This section shows how the work advances the field from the present state of knowledge. In some journals, it's a separate section; in others, it's the last paragraph of the Discussion section. Whatever the case, without a clear conclusion section, reviewers and readers will find it difficult to judge your work and whether it merits publication in the journal.

A common error in this section is repeating the abstract, or just listing experimental results. Trivial statements of your results are unacceptable in this section.

You should provide a clear scientific justification for your work in this section, and indicate uses and extensions if appropriate. Moreover, you can suggest future experiments and point out those that are underway.

You can propose present global and specific conclusions, in relation to the objectives included in the introduction

Step 6: Write a compelling Introduction

This is your opportunity to convince readers that you clearly know why your work is useful.

A good introduction should answer the following questions:

What is the problem to be solved?

Are there any existing solutions?

Which is the best?

What is its main limitation?

What do you hope to achieve?

Editors like to see that you have provided a perspective consistent with the nature of the journal. You need to introduce the main scientific publications on which your work is based, citing a couple of original and important works, including recent review articles.

However, editors hate improper citations of too many references irrelevant to the work, or inappropriate judgments on your own achievements. They will think you have no sense of purpose.

Here are some additional tips for the introduction:

Never use more words than necessary (be concise and to-the-point). Don't make this section into a history lesson. Long introductions put readers off.

We all know that you are keen to present your new data. But do not forget that you need to give the whole picture at first.

The introduction must be organized from the global to the particular point of view, guiding the readers to your objectives when writing this paper.

State the purpose of the paper and research strategy adopted to answer the question, but do not mix introduction with results, discussion and conclusion. Always keep them separate to ensure that the manuscript flows logically from one section to the next.

Hypothesis and objectives must be clearly remarked at the end of the introduction.

Expressions such as "novel," "first time," "first ever," and "paradigm-changing" are not preferred. Use them sparingly.

Step 7: Write the Abstract

The abstract tells prospective readers what you did and what the important findings in your research were. Together with the title, it's the advertisement of your article. Make it interesting and easily understood without reading the whole article.  Avoid using jargon, uncommon abbreviations and references.

You must be accurate, using the words that convey the precise meaning of your research. The abstract provides a short description of the perspective and purpose of your paper. It gives key results but minimizes experimental details. It is very important to remind that the abstract offers a short description of the interpretation/conclusion in the last sentence.

A clear abstract will strongly influence whether or not your work is further considered.

However, the abstracts must be keep as brief as possible. Just check the 'Guide for authors' of the journal, but normally they have less than 250 words. Here's a  good example on a short abstract opens in new tab/window .

In an abstract, the two  whats  are essential. Here's an example from an article I co-authored in  Ecological Indicators opens in new tab/window :

What has been done?   "In recent years, several benthic biotic indices have been proposed to be used as ecological indicators in estuarine and coastal waters. One such indicator, the AMBI (AZTI Marine Biotic Index), was designed to establish the ecological quality of European coasts. The AMBI has been used also for the determination of the ecological quality status within the context of the European Water Framework Directive. In this contribution, 38 different applications including six new case studies (hypoxia processes, sand extraction, oil platform impacts, engineering works, dredging and fish aquaculture) are presented."

What are the main findings? "The results show the response of the benthic communities to different disturbance sources in a simple way. Those communities act as ecological indicators of the 'health' of the system, indicating clearly the gradient associated with the disturbance."

Step 8: Compose a concise and descriptive title

The title must explain what the paper is broadly about. It is your first (and probably only) opportunity to attract the reader's attention. In this way, remember that the first readers are the Editor and the referees. Also, readers are the potential authors who will cite your article, so the first impression is powerful!

We are all flooded by publications, and readers don't have time to read all scientific production. They must be selective, and this selection often comes from the title.

Reviewers will check whether the title is specific and whether it reflects the content of the manuscript. Editors hate titles that make no sense or fail to represent the subject matter adequately. Hence, keep the title informative and concise (clear, descriptive, and not too long). You must avoid technical jargon and abbreviations, if possible. This is because you need to attract a readership as large as possible. Dedicate some time to think about the title and discuss it with your co-authors.

Here you can see some examples of original titles, and how they were changed after reviews and comments to them:

Original title: Preliminary observations on the effect of salinity on benthic community distribution within a estuarine system, in the North Sea

Revised title: Effect of salinity on benthic distribution within the Scheldt estuary (North Sea)

Comments: Long title distracts readers. Remove all redundancies such as "studies on," "the nature of," etc. Never use expressions such as "preliminary." Be precise.

Original title: Action of antibiotics on bacteria

Revised title: Inhibition of growth of Mycobacterium tuberculosis by streptomycin

Comments: Titles should be specific. Think about "how will I search for this piece of information" when you design the title.

Original title: Fabrication of carbon/CdS coaxial nanofibers displaying optical and electrical properties via electrospinning carbon

Revised title: Electrospinning of carbon/CdS coaxial nanofibers with optical and electrical properties

Comments: "English needs help. The title is nonsense. All materials have properties of all varieties.  You could examine my hair for its electrical and optical properties! You MUST be specific. I haven't read the paper but I suspect there is something special about these properties, otherwise why would you be reporting them?" – the Editor-in-Chief.

Try to avoid this kind of response!

Step 9: Select keywords for indexing

Keywords are used for indexing your paper. They are the label of your manuscript. It is true that now they are less used by journals because you can search the whole text. However, when looking for keywords, avoid words with a broad meaning and words already included in the title.

Some journals require that the keywords are not those from the journal name, because it is implicit that the topic is that. For example, the journal  Soil Biology & Biochemistry  requires that the word "soil" not be selected as a keyword.

Only abbreviations firmly established in the field are eligible (e.g., TOC, CTD), avoiding those which are not broadly used (e.g., EBA, MMI).

Again, check the Guide for Authors and look at the number of keywords admitted, label, definitions, thesaurus, range, and other special requests.

Step 10: Write the Acknowledgements

Here, you can thank people who have contributed to the manuscript but not to the extent where that would justify authorship. For example, here you can include technical help and assistance with writing and proofreading. Probably, the most important thing is to thank your funding agency or the agency giving you a grant or fellowship.

In the case of European projects, do not forget to include the grant number or reference. Also, some institutes include the number of publications of the organization, e.g., "This is publication number 657 from AZTI-Tecnalia."

Step 11: Write up the References

Typically, there are more mistakes in the references than in any other part of the manuscript. It is one of the most annoying problems, and causes great headaches among editors. Now, it is easier since to avoid these problem, because there are many available tools.

In the text, you must cite all the scientific publications on which your work is based. But do not over-inflate the manuscript with too many references – it doesn't make a better manuscript! Avoid excessive self-citations and excessive citations of publications from the same region.

As I have mentioned, you will find the most authoritative information for each journal’s policy on citations when you consult the journal's Guide for Authors. In general, you should minimize personal communications, and be mindful as to how you include unpublished observations. These will be necessary for some disciplines, but consider whether they strengthen or weaken your paper. You might also consider  articles published on research networks opens in new tab/window  prior to publication, but consider balancing these citations with citations of peer-reviewed research. When citing research in languages other than English, be aware of the possibility that not everyone in the review process will speak the language of the cited paper and that it may be helpful to find a translation where possible.

You can use any software, such as  EndNote opens in new tab/window  or  Mendeley opens in new tab/window , to format and include your references in the paper. Most journals have now the possibility to download small files with the format of the references, allowing you to change it automatically. Also, Elsevier's  Your Paper Your Way  program waves strict formatting requirements for the initial submission of a manuscript as long as it contains all the essential elements being presented here.

Make the reference list and the in-text citation conform strictly to the style given in the Guide for Authors. Remember that presentation of the references in the correct format is the responsibility of the author, not the editor. Checking the format is normally a large job for the editors. Make their work easier and they will appreciate the effort.

Finally, check the following:

Spelling of author names

Year of publications

Usages of "et al."

Punctuation

Whether all references are included

In my next article, I will give tips for writing the manuscript, authorship, and how to write a compelling cover letter. Stay tuned!

References and Acknowledgements

I have based this paper on the materials distributed to the attendees of many courses. It is inspired by many Guides for Authors of Elsevier journals. Some of this information is also featured in Elsevier's  Publishing Connect tutorials opens in new tab/window . In addition, I have consulted several web pages:  https://owl.english.purdue.edu/owl/ opens in new tab/window , www.physics.ohio-state.edu/~wilkins/writing/index.html.

I want to acknowledge Dr.  Christiane Barranguet opens in new tab/window , Executive Publisher of Aquatic Sciences at Elsevier, for her continuous support. And I would like to thank Dr. Alison Bert, Editor-in-Chief of Elsevier Connect; without her assistance, this series would have been impossible to complete.

Contributor

Angel Borja, PhD

How to write and structure a journal article

Sharing your research data  can be hugely  beneficial to your career , as well as to the scholarly community and wider society. But before you do so, there are some important ethical considerations to remember.

What are the rules and guidance you should follow, when you begin to think about how to write and structure a journal article? Ruth First Prize winner Steven Rogers, PhD said the first thing is to be passionate about what you write.

Steven Nabieu Rogers, Ruth First Prize winner.

Let’s go through some of the best advice that will help you pinpoint the features of a journal article, and how to structure it into a compelling research paper.

Planning for your article

When planning to write your article, make sure it has a central message that you want to get across. This could be a novel aspect of methodology that you have in your PhD study, a new theory, or an interesting modification you have made to theory or a novel set of findings.

2018 NARST Award winner Marissa Rollnick advised that you should decide what this central focus is, then create a paper outline bearing in mind the need to:

Isolate a manageable size

Create a coherent story/argument

Make the argument self-standing

Target the journal readership

Change the writing conventions from that used in your thesis

Get familiar with the journal you want to submit to

It is a good idea to choose your target journal before you start to write your paper. Then you can tailor your writing to the journal’s requirements and readership, to increase your chances of acceptance.

When selecting your journal think about audience, purposes, what to write about and why. Decide the kind of article to write. Is it a report, position paper, critique or review? What makes your argument or research interesting? How might the paper add value to the field?

If you need more guidance on how to choose a journal,  here is our guide to narrow your focus.

Once you’ve chosen your target journal, take the time to read a selection of articles already published – particularly focus on those that are relevant to your own research.

This can help you get an understanding of what the editors may be looking for, then you can guide your writing efforts.

The  Think. Check. Submit.  initiative provides tools to help you evaluate whether the journal you’re planning to send your work to is trustworthy.

The journal’s  aims and scope  is also an important resource to refer back to as you write your paper – use it to make sure your article aligns with what the journal is trying to accomplish.

Keep your message focused

The next thing you need to consider when writing your article is your target audience. Are you writing for a more general audience or is your audience experts in the same field as you? The journal you have chosen will give you more information on the type of audience that will read your work.

When you know your audience, focus on your main message to keep the attention of your readers. A lack of focus is a common problem and can get in the way of effective communication.

Stick to the point. The strongest journal articles usually have one point to make. They make that point powerfully, back it up with evidence, and position it within the field.

How to format and structure a journal article

The format and structure of a journal article is just as important as the content itself, it helps to clearly guide the reader through.

How do I format a journal article?

Individual journals will have their own specific formatting requirements, which you can find in the  instructions for authors.

You can save time on formatting by downloading a template from our  library of templates  to apply to your article text. These templates are accepted by many of our journals. Also, a large number of our journals now offer  format-free submission,  which allows you to submit your paper without formatting your manuscript to meet that journal’s specific requirements.

General structure for writing an academic journal article

The title of your article is one of the first indicators readers will get of your research and concepts. It should be concise, accurate, and informative. You should include your most relevant keywords in your title, but avoid including abbreviations and formulae.

Keywords are an essential part of producing a journal article. When writing a journal article you must select keywords that you would like your article to rank for.

Keywords help potential readers to discover your article when conducting research using search engines.

The purpose of your abstract is to express the key points of your research, clearly and concisely. An abstract must always be well considered, as it is the primary element of your work that readers will come across.

An abstract should be a short paragraph (around 300 words) that summarizes the findings of your journal article. Ordinarily an abstract will be comprised of:

What your research is about

What methods have been used

What your main findings are

Acknowledgements

Acknowledgements can appear to be a small aspect of your journal article, however it is still important. This is where you acknowledge the individuals who do not qualify for co-authorship, but contributed to your article intellectually, financially, or in some other manner.

When you acknowledge someone in your academic texts, it gives you more integrity as a writer as it shows that you are not claiming other academic’s ideas as your own intellectual property. It can also aid your readers in their own research journeys.

Introduction

An introduction is a pivotal part of the article writing process. An introduction not only introduces your topic and your stance on the topic, but it also (situates/contextualizes) your argument in the broader academic field.

The main body is where your main arguments and your evidence are located. Each paragraph will encapsulate a different notion and there will be clear linking between each paragraph.

Your conclusion should be an interpretation of your results, where you summarize all of the concepts that you introduced in the main body of the text in order of most to least important. No new concepts are to be introduced in this section.

References and citations

References and citations should be well balanced, current and relevant. Although every field is different, you should aim to cite references that are not more than 10 years old if possible. The studies you cite should be strongly related to your research question.

Clarity is key

Make your writing accessible by using clear language. Writing that is easy to read, is easier to understand too.

You may want to write for a global audience – to have your research reach the widest readership. Make sure you write in a way that will be understood by any reader regardless of their field or whether English is their first language.

Write your journal article with confidence, to give your reader certainty in your research. Make sure that you’ve described your methodology and approach; whilst it may seem obvious to you, it may not to your reader. And don’t forget to explain acronyms when they first appear.

Engage your audience. Go back to thinking about your audience; are they experts in your field who will easily follow technical language, or are they a lay audience who need the ideas presented in a simpler way?

Be aware of other literature in your field, and reference it

Make sure to tell your reader how your article relates to key work that’s already published. This doesn’t mean you have to review every piece of previous relevant literature, but show how you are building on previous work to avoid accidental plagiarism.

When you reference something, fully understand its relevance to your research so you can make it clear for your reader. Keep in mind that recent references highlight awareness of all the current developments in the literature that you are building on. This doesn’t mean you can’t include older references, just make sure it is clear why you’ve chosen to.

How old can my references be?

Your literature review should take into consideration the current state of the literature.

There is no specific timeline to consider. But note that your subject area may be a factor. Your colleagues may also be able to guide your decision.

Researcher’s view

Grasian Mkodzongi, Ruth First Prize Winner

Top tips to get you started

Communicate your unique point of view to stand out. You may be building on a concept already in existence, but you still need to have something new to say. Make sure you say it convincingly, and fully understand and reference what has gone before.

Editor’s view

Professor Len Barton, Founding Editor of Disability and Society

Be original

Now you know the features of a journal article and how to construct it. This video is an extra resource to use with this guide to help you know what to think about before you write your journal article.

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[How to write a scientific article]

Affiliation.

• 1 Croatian Medical Journal, Medicinski fakultet Sveucilista u Zagrebu, Zagreb, Hrvatska. [email protected]
• PMID: 16526311

Scientific articles published in science journals are the main means of communicating scientific information today. A scientific article as a type of publication is characterized by a specific design and content. It is defined as a special type of publication intended to convey scientific information. There are different types of scientific articles: original scientific article, review article, systematic review and meta-analysis, case report, etc. Original scientific article and how to write it is the main topic of this article. It is considered a primary scientific publication that brings research results that have not been published before and contains enough data for other researchers to assess the presented evidence, repeat the study, and critically assess the conclusions. Original scientific article usually follows the IMRaD structure, i.e., it consists of four main sections as follows: introduction, methods, results, and discussion. Each section has a different purpose and brings different part of information on the study that was conducted. Furthermore, other elements of a scientific article are presented and explained, such as a title, abstract, key words, and reference format. Hopefully useful suggestions are offered about the use of scientific language and style.

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• Teaching how to prepare a manuscript by means of rewriting published scientific papers. Tomaska L. Tomaska L. Genetics. 2007 Jan;175(1):17-20. doi: 10.1534/genetics.106.066217. Epub 2006 Nov 16. Genetics. 2007. PMID: 17110479 Free PMC article. Review.
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Top Tips to Master Scientific Writing for Researchers

Like any other form of writing, scientific writing aims to convey knowledge or discuss something. Scientific writing is very technical because scientists use it to communicate their findings, methodologies, or results to other scholars or people in their community. 

This form of writing will be seen in documents, research papers, scientific journals, or books.

Now you need to be extra careful with your words because there is little room for error in this form of writing. It is a must that you keep your tone formal and choose the exact meaning of words. This is mainly because it involves writing peer reviews, summaries of research findings, or asking for grant requests. 

Goals of Scientific Writing

Here are some of the goals or you can say features of scientific documentation, to help you get some basic understanding. 

1. Precision

Scientific writing should be precise, for example, if you are explaining a topic or summarizing your research in a document you need to explain everything in detail.

2. Clarity 

The clarity in your writing shows how clear your concepts are. If you are including any jargon or uncommon scientific terms, you need to clarify so that your writing is easy to understand.    

3. Objectivity

You need to be objective when writing scientific research or any other document. You can only present facts about a particular subject or your analysis. There is no need to give your opinions. 

Tip 1: Research Your Audience

This shouldn’t come as a surprise to you if you are a researcher. As a researcher, you might already know how important it is to research your audience to make sure you get accurate data every time. 

Start by developing an understanding of your target audience first. It will also help you write your document effectively since you already have some idea of who will be reading your work.

See if your audience is a student, a scholar, or a researcher or if you are addressing the scientific community. 

Getting to know your audience will help you choose an appropriate tone or language that is related to their expertise or skill set.

You must narrow down your audience in the early stages of your scientific writing career. It is more effective to reach a particular group of people rather than catering to a broader community. Most researchers don’t even know about it till late in their careers. But this is how you can target your audience and even leverage tools for effective personal branding in the scientific community. 

Tip 2: Structure Your Research Document

It doesn’t matter how impactful your research is, people won’t be able to get it if you don’t present it in an organized form. Irrespective of what your field or research topic is, a scientific paper usually has the following sections:

The first section is the title. It needs to be concise and informative, generally, it reflects the whole topic of the research.

2. Abstract 

An abstract is a summary of the research objectives, methods, results, and conclusions that you present in your document. 

3. Introduction 

In the introduction section, you need to give an overview of your research. It includes a background of the research, its importance, etc.

4. Methodology 

This part is a detailed description of the research methodologies you applied and the materials you used for your research.

In this section, you need to present your research findings. Researchers also add tables, figures, and graphs to back their research.

6. Discussion

In this section, you must interpret your results, and the significance of the findings and discuss further research that can be done.

7. Conclusion

Conclusion is a summary of the key findings of your research and you can include any other implications related to your research topic here.

8. References

Reference is the part of the research paper where you need to add citations of all sources and the material you used.  

Tip 3: Give Evidence to Back Your Claims

Even if you are an accomplished researcher, no one expects you to know everything. So it is better that you always provide evidence in your research to support your claims. People will only trust your findings if you have strong evidence to back them up.  

In a research document, this particularly means the data you use in your research. But that’s not all you will also be giving citations or references of the sources of the materials you used in your research. 

There are certain rules or formats for notating these citations or references. These include:

1) APA (American Psychological Association) 

2) Chicago Style.

3) Harvard Style

4) MLA (Modern Language Association Style)

You might be thinking why bother with this? Well, citing your sources is mandatory in your research. If you don’t give credit to its actual owner, it will be considered as plagiarised content in your research. 

Plagiarism won’t only ruin your chance of getting your research published but it will also hurt your reputation as an authentic researcher.  

Tip 4: Incorporate Visuals in Your Research

Visuals are easier to understand than text, this is also true for your research. That’s why in a research document there is a whole section dedicated where you can add visual representations of your data. 

This can be anything from data tables, figures like graphs charts, diagrams, or anything that is going to help you explain your research findings clearly. 

Including these pictorial representations also impacts your research as people are going to take your findings more seriously. 

So now you know why these are so impactful. Here are some tips to keep in mind while including these visuals in your research. 

Firstly make sure that you label your grapes or diagrams correctly. Don’t draw complicated illustrations and always add text to label your graphics so people can easily understand it.  

Tip 5: Grab Your Readers With a Compelling Abstract

The abstract is the most important section of your research, that’s why it is at the beginning of your research document. 

This part is a summary of your entire research paper. It includes details related to every part of your research, including your research questions, the methodologies you used, your results or findings, and finally the conclusion part.

What’s more, most people will only read your abstract section to tell if your research is worth giving a read. Since this is not the most technical part of your research you have an open window to be a little creative here. 

You can use compelling sentences to pique the interest. You can also add relevant keywords or phrases related to your research so the readers can know what topic you will be covering.  

If you want to dive deeper into the topic of how you can write effective abstracts , read this comprehensive article on abstract and its main parts.

To Summarize

So there you go! We have discussed all the pro tips that you can use the next time you plan to write your scientific research paper. You must use all the principles of effective scientific writing like precision, clarity, and objectivity in your documentation to communicate your findings.  

Use these tips whenever you feel ‘like your writing is lacking that spark and the only way to find out if these tips will work is to try them for yourself!

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How to Write a Cover Letter for Your Manuscript? Here are the Tips and Examples

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A cover letter is often the first thing an editor reads when reviewing your submission. As your first pitch to the editor, the cover letter helps them gauge the suitability of your manuscript for publication in their journal. Imagine your work shaping the future of your field, gathering citations, and sparking discussions. A powerful cover letter is thus the first step to making that vision into a reality.   

In this article, we will guide you through the process of writing an effective cover letter and explain how you can get it right every time with examples. First, let us get started with the basics!  

Getting the Basics Right  

When writing a cover letter, it is crucial to address the editor by their correct and complete name¹ . If there are multiple co-editors, you can address your letter to the right person, based on their specialization or designated responsibilities. If unsure, it is okay to go with a more general salutation, such as “Dear Editors”¹ .   

Presenting your Research  

Provide a clear and concise title for your submission and specify whether it is an article, communication, review, perspective, or a manuscript belonging to some other category. If the journal guideline recommends, consider including a list of all authors in the manuscript.   

After covering the preliminary information, briefly explain your paper’s central theme or focus to give the editor an idea of its contents. Ensure this stays a brief outline, without going into too much detail.   

Conveying the Importance of Your Work  

How you communicate the impact of your work can make or break your cover letter. To make a strong impression on the editor, articulate the significance of your research clearly, emphasizing its relevance to the field. Additionally, show how your work aligns with the journal’s scope and mission.  

Including a Formal Declaration  

Some journals require a set of declarations from you to ensure that your manuscript adheres to its ethical code and the larger ethical standards of scientific publishing. Here are the required declarations in a cover letter:  

• Originality of work:  
• Confirm that your work is original and has not been published elsewhere. This tells the editor your research is unique.  
• Conflict of interest statement:  
• Be clear about any potential conflicts of interest. This includes any personal, financial, or professional connections that might affect your research.  
• Funding source (if applicable):  
• Tell where your research funding came from, if any. This includes any support or grants from organizations.   

Including Personal Suggestions for Reviewers on a Separate Page (optional)  

If there is no part of the submission process that collects researcher suggestions for reviewers, and there are special requests from the researcher for reviewers (e.g., recommending the inclusion or suggesting the exclusion of a specific reviewer, etc.), you may also make a note about this in the cover letter.  

Combining these five points, here is a good example of a cover letter for researchers’ reference:  

(This image is intended to demonstrate the norms of formatting and tone of expression in a cover letter, it is to be used only by the researcher as a reference in writing² .)  

Conclusion  

A strong cover letter can go a long way in ensuring success for researchers looking to publish their manuscripts! Your cover letter is the opening act, setting the stage for how editors perceive your manuscript. So, look at it not as just another formality but as a crucial opportunity to make a strong impression.   

Understanding what to include, what is optional, and what is best left unsaid can be tricky. That is where our team of experts at Elsevier Language Services can step in. We will provide personalized recommendations and expert guidance to help you craft a cover letter that perfectly complements your manuscript. Reach out to us today to make a great first impression and embark on a successful academic journey!  

Reference  

• Nicholas, D. (2019). How to choose a journal and write a cover letter. Saudi Journal of Anaesthesia, 13(5), 35. https://doi.org/10.4103/sja.sja_691_18  
• Loyola University Chicago. (n.d.). JCSHESA Sample Cover Letter. https://ecommons.luc.edu/jcshesa/cover_letter_template.pdf  

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5 Science-Backed Ways to Write Clearly

If you want to become a better writer, ignore the lore and follow the science..

Posted June 24, 2024 | Reviewed by Devon Frye

• We read sentences written with active voice faster and comprehend content better than passive sentences.
• Studies document that we read and recall sentences with less effort when they turn content into micro-stories.
• Pronouns as subjects send readers backward, but readers comprehend sentences through prediction.
• Action verbs activate the brain's motor systems, creating semantic richness and enabling rapid comprehension.

Most writers assume they write well. Yet most writers grapple with the reality of writing as a black box.

That is, we know that writing works, but we’re a bit fuzzy on what makes readers grasp the meaning of some sentences instantly and without noticeable effort, while we find others difficult to understand after repeat re-readings. And contrary to popular belief, clear writing has virtually nothing to do with content, sentence length, or writing style.

Instead, we perceive sentences as clear when they map onto the methods our reading brains use to make sense of writing. Knowing the most important ones, including the below, could help make you a better writer.

1. Active voice makes sentences easier to read.

In dozens of studies, researchers have found that readers comprehend sentences more rapidly when sentences reflect the causal order of events. Two factors determine these outcomes.

First, human brains naturally perceive cause and effect, a likely survival mechanism. In fact, infants as young as six months can identify cause and effect, registered as spikes in heart rate and blood pressure.

Second, English sentence structure reflects causes and effects in its ordering of words: subject-verb-object order. In key studies, participants read sentences with active voice at speeds one-third faster than they read sentences in passive voice. More significantly, these same participants misunderstood even simple sentences in passive voice about 25 percent of the time.

As readers, we also perceive active sentences as both shorter and easier to read because active voice typically makes sentences more efficient. Consider the difference between the first sentence below, which relies on passive voice, and the second, which uses active voice.

• Passive: Among board members, there was an instant agreement to call for a pause in negotiations.
• Active: Board members instantly agreed to call for a pause in negotiations.

2. Actors or concrete objects turn sentences into micro-stories.

We read sentences with less effort—or cognitive load—when we can clearly see cause and effect, or, “who did what to whom,” as Ina Bornkessel-Schlesewsky puts it.

Bornkessel-Schlesewsky, a professor of cognitive neuroscience at the University of South Australia, used functional Magnetic Resonance Imaging (fMRI), to spot brains reacting to meaning and word order in sentences. Unsurprisingly, when the subjects of sentences are nouns clearly capable of performing actions, readers process sentences with greater speed and less effort. For actors, writers can choose people, organizations, publications—any individual, group, or item, intentionally created, that generates impact.

In addition to our unconsciously perceiving these sentences as easy to read and recall, we can also more readily identify actors in sentences. Furthermore, these nouns enhance the efficiency of any sentence by paring down its words. Take the examples below:

• Abstract noun as subject: Virginia Woolf’s examination of the social and economic obstacles female writers faced due to the presumption that women had no place in literary professions and so were instead relegated to the household, particularly resonated with her audience of young women who had struggled to fight for their right to study at their colleges, even after the political successes of the suffragettes.
• Actor as subject: In A Room of One’s Own , Virginia Woolf examined social and economic obstacles female writers faced. Despite the political success of the suffragettes, writers like Woolf battled the perception that women had no place in the literary professions. Thus Woolf’s book resonated with her audience, young women who had to fight for the right to study at their colleges.

3. Pronouns send readers backward, but readers make sense of sentences by anticipating what comes next.

Writers typically love to use pronouns as the subjects of sentences, especially the demonstrative pronouns this, that, these, those, and it , believing that these pronouns help link their sentences. Instead, pronouns save writers time and effort—but significantly cost readers for two likely reasons.

First, readers assume that pronouns refer to a singular noun, rather than a cluster of nouns, a phrase, or even an entire sentence. Second and more importantly, when writers use these pronouns without anchoring nouns, readers slow down and frequently misidentify the pronoun referents. In fact, readers rated writing samples with high numbers of sentences using demonstrative pronouns as being less well-written than sentences that used actors as subjects or pronouns anchored by nouns.

Pronoun as subjects: [Katie Ledecky] estimated that she swims more than 65,000 yards—or about 37 miles—a week. That adds up to 1,900 miles a year, and it means eons of staring at the black line that runs along the bottom of a pool. Actor as subject: [Katie] Ledecky swims up to 1,900 miles a year, mileage that entails seeming aeons of staring at the black line that runs along the bottom of a pool.

4. Action verbs make sentences more concrete, memorable, and efficient.

For years, old-school newspaper and magazine editors urged writers to use action verbs to enliven sentences.

However, action verbs also offer readers and writers significant benefits in terms of their memorability, as revealed in one study of readers’ recall of verbs. Of the 200 verbs in the study, readers recalled concrete verbs and nouns more accurately than non-action verbs.

In fact, when we read concrete verbs, our brains recruit the sensory-motor system, generating faster reaction times than abstract or non-action verbs, processed outside that system . Even in patients with dementia , action verbs remain among the words patients can identify with advanced disease, due to the richness of semantic associations that action verbs recruit in the brain.

• Non-action verbs: That the electric trolleys being abandoned in Philadelphia were greener and more efficient was not an insight available at that time.
• Action Verbs: Philadelphia scrapped its electric trolleys, decades before urban planners turned to greener, more efficient forms of transport.

5. Place subjects and verbs close together.

Over the past 20 years, researchers have focused on models of reading that rely on our understanding of sentence structure, a focus validated by recent studies.

As we read, we predict how sentence structure or syntax unfolds, based on our encounters with thousands of sentences. We also use the specific words we encounter in sentences to verify our predictions, beginning with grammatical subjects, followed by verbs.

As a result, readers struggle to identify subjects and verbs when writers separate them—the more distance between subjects and verbs, the slower the process of identifying them correctly. Moreover, readers make more errors in identifying correct subjects and verbs—crucial to understanding sentences—with increases in the number of words between subjects and verbs, even with relatively simple sentence structure.

Ironically, as writers tackle increasingly complex topics, they typically modify their subjects with phrases and adjective clauses that can place subjects at one end of the sentence and verbs at the opposite end. This separation strains working memory , as readers rely on subject-verb-object order in English to understand the sentence’s meaning. Consider, for example, this sentence from an online news organization:

In Florida, for instance, a bill to eliminate a requirement that students pass an Algebra I end-of-course and 10th-grade English/language arts exams in order to graduate recently cleared the Senate’s education committee.

On the other hand, when we place the subject and verb close together and use modifiers after the verb, we ease readers’ predictions and demands on working memory:

In Florida, the Senate’s education committee recently cleared a bill to eliminate two graduation requirements: an Algebra I end-of-course and 10th-grade English language arts.

Jane Yellowlees Douglas, Ph.D. , is a consultant on writing and organizations. She is also the author, with Maria B. Grant, MD, of The Biomedical Writer: What You Need to Succeed in Academic Medicine .

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At any moment, someone’s aggravating behavior or our own bad luck can set us off on an emotional spiral that could derail our entire day. Here’s how we can face triggers with less reactivity and get on with our lives.

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• Introduction
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• Article Information

a Inclusion criteria for tau pathology: low/medium or high tau indicated by standardized uptake value ratio >1.10 or positive visual read assessed by 18 F-flortaucipir positron emission tomography (PET) imaging.

b Inclusion criteria for amyloid pathology (≥37 Centiloids) assessed with 18 F-florbetapir or 18 F-florbetaben PET.

c Inclusion criteria for Mini-Mental State Examination: score of 20 to 28.

d Phosphorylated tau 181 (P-tau181) screening criterion was not implemented for the entire trial duration (eMethods in Supplement 3 ).

e Exclusion criteria for MRI include presence of amyloid-related imaging abnormalities of edema/effusion, >4 cerebral microhemorrhages, >1 area of superficial siderosis, and any intracerebral hemorrhage >1 cm or severe white matter disease.

f Summary of other screen failure can be found in eTable 3 in Supplement 3 (lists reason if ≥20 participants).

g Stratified by baseline tau categorization and enrolling sites.

h One additional death occurred after treatment completion and in the follow-up period.

i Alzheimer disease progression to a degree prompting study discontinuation, per investigator judgment.

j Treatment completion criteria: amyloid plaque level of 11 Centiloids on any single scan or 11 to <25 Centiloids on 2 consecutive scans.

k Participants who met treatment completion criteria are included in discontinuation and completion numbers.

l Percentage calculated as No./total No. of participants with a PET scan at visit: n = 761 at 24 wk, n = 672 at 52 wk, and n = 620 at 76 wk. Corresponding number of participants and percentages for the low/medium tau population were 20.3% (n = 106) at 24 wk, 51.9% (n = 241) at 52 wk, and 73.5% (n = 321) at 76 wk.

A, 35.1% slowing (95% CI, 19.90%-50.23%) of clinical progression. B, 22.3% slowing (95% CI, 11.38%-33.15%) of clinical progression. C, 36.0% slowing (95% CI, 20.76%-51.15%) of clinical progression. D, 28.9% slowing (95% CI, 18.41%-39.44%) of clinical progression. iADRS data were analyzed using the natural cubic spline model with 2 degrees of freedom (NCS2) and CDR-SB data were analyzed with mixed models for repeated measures (MMRM). For MMRM analyses, 95% CIs for least-squares mean changes were calculated with the normal approximation method. For the Alzheimer Disease Cooperative Study—Instrumental Activities of Daily Living, 13-item cognitive subscale of the Alzheimer Disease Assessment Scale, and CDR-SB clinical assessments analyzed with NCS2, see eFigure 1 (low/medium tau population) and eFigure 2 (combined population) in Supplement 3 and Table 2. For all clinical assessments analyzed with MMRM, see eFigure 3 (low/medium tau population) and 4 (combined population) in Supplement 3 and Table 2. P  < .001 for all 76 week time points.

Biomarker data shown were analyzed using mixed models for repeated measures (MMRM). For MMRM analyses, 95% CIs for the least-squares mean changes were calculated with the normal approximation method. P  < .001 for all time points in panels A-D. B, P value is from Fisher exact test comparing the percent amyloid negative by treatment groups at each visit. E and F, The analysis was conducted using a Cox proportional hazards model. There were 163 events among 573 participants in the placebo group and 100 events among 555 participants in the donanemab group in the low/medium tau population and 288 events among 844 participants in the placebo group and 186 events among 805 participants in the donanemab group in the combined population. CDR-G indicates Clinical Dementia Rating Global Score.

Trial protocol

Statistical analysis plan

Nonauthor collaborators

Data sharing statement

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• Amyloid-Targeting Monoclonal Antibodies for Alzheimer Disease JAMA Editorial August 8, 2023 Gil D. Rabinovici, MD; Renaud La Joie, PhD
• Novel Alzheimer Disease Treatments and Reconsideration of US Pharmaceutical Reimbursement Policy JAMA Editorial August 8, 2023 Meredith B. Rosenthal, PhD
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• Role of Registries in Medicare Coverage of New Alzheimer Disease Drugs JAMA Viewpoint October 10, 2023 This Viewpoint discusses how the design of the Centers for Medicare & Medicaid Services (CMS) registry could impact Medicare’s ability to evaluate whether monoclonal antibodies are reasonable and necessary for patients with Alzheimer disease and help physicians understand when the drug is most beneficial. Ilina C. Odouard, MPH; Mariana P. Socal, MD, PhD; Gerard F. Anderson, PhD
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• Use of Donanemab in Early Symptomatic Alzheimer Disease—Reply JAMA Comment & Response December 19, 2023 Cynthia D. Evans, PhD; John R. Sims, MD
• Use of Donanemab in Early Symptomatic Alzheimer Disease JAMA Comment & Response December 19, 2023 Nunzio Pomara, MD; Bruno Pietro Imbimbo, PhD
• Risks of Harm in Alzheimer Disease by Amyloid Lowering JAMA Viewpoint June 18, 2024 This Viewpoint discusses how data gaps in published research impede clinicians’ ability to clearly discuss the risks and benefits of amyloid-lowering drugs for treating Alzheimer disease. Madhav Thambisetty, MD, PhD; Robert Howard, MD

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Sims JR , Zimmer JA , Evans CD, et al. Donanemab in Early Symptomatic Alzheimer Disease : The TRAILBLAZER-ALZ 2 Randomized Clinical Trial . JAMA. 2023;330(6):512–527. doi:10.1001/jama.2023.13239

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Donanemab in Early Symptomatic Alzheimer Disease : The TRAILBLAZER-ALZ 2 Randomized Clinical Trial

• 1 Eli Lilly and Company, Indianapolis, Indiana
• 2 Boston Center for Memory and Boston University Alzheimer’s Disease Center, Boston, Massachusetts
• 3 Department of Neurology and Department of Psychiatry, Alpert Medical School of Brown University, Providence, Rhode Island
• 4 Butler Hospital, Providence, Rhode Island
• 5 Department of Neurology, Indiana University School of Medicine, Indianapolis
• 6 Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden; Memory Clinic, Skåne University Hospital, Lund, Sweden
• 7 Scottish Brain Sciences, Edinburgh, United Kingdom
• Editorial Donanemab for Alzheimer Disease—Who Benefits and Who Is Harmed? Jennifer J. Manly, PhD; Kacie D. Deters, PhD JAMA
• Editorial Amyloid-Targeting Monoclonal Antibodies for Alzheimer Disease Gil D. Rabinovici, MD; Renaud La Joie, PhD JAMA
• Editorial Novel Alzheimer Disease Treatments and Reconsideration of US Pharmaceutical Reimbursement Policy Meredith B. Rosenthal, PhD JAMA
• Editorial Ushering in a New Era of Alzheimer Disease Therapy Eric W. Widera, MD; Sharon A. Brangman, MD; Nathaniel A. Chin, MD JAMA
• Viewpoint Role of Registries in Medicare Coverage of New Alzheimer Disease Drugs Ilina C. Odouard, MPH; Mariana P. Socal, MD, PhD; Gerard F. Anderson, PhD JAMA
• Medical News & Perspectives Who Should Get the New Alzheimer Disease Drug? Rita Rubin, MA JAMA
• Comment & Response Use of Donanemab in Early Symptomatic Alzheimer Disease—Reply Cynthia D. Evans, PhD; John R. Sims, MD JAMA
• Comment & Response Use of Donanemab in Early Symptomatic Alzheimer Disease Nunzio Pomara, MD; Bruno Pietro Imbimbo, PhD JAMA
• Viewpoint Risks of Harm in Alzheimer Disease by Amyloid Lowering Madhav Thambisetty, MD, PhD; Robert Howard, MD JAMA

Question   Does donanemab, a monoclonal antibody designed to clear brain amyloid plaque, provide clinical benefit in early symptomatic Alzheimer disease?

Findings   In this randomized clinical trial that included 1736 participants with early symptomatic Alzheimer disease and amyloid and tau pathology, the least-squares mean change in the integrated Alzheimer Disease Rating Scale score (range, 0-144; lower score indicates greater impairment) at 76 weeks was −6.02 in the donanemab group and −9.27 in the placebo group for the low/medium tau population and −10.19 in the donanemab group and −13.11 in the placebo group in the combined study population, both of which were significant differences.

Meaning   Among participants with early symptomatic Alzheimer disease and amyloid and tau pathology, donanemab treatment significantly slowed clinical progression at 76 weeks.

Importance   There are limited efficacious treatments for Alzheimer disease.

Objective   To assess efficacy and adverse events of donanemab, an antibody designed to clear brain amyloid plaque.

Design, Setting, and Participants   Multicenter (277 medical research centers/hospitals in 8 countries), randomized, double-blind, placebo-controlled, 18-month phase 3 trial that enrolled 1736 participants with early symptomatic Alzheimer disease (mild cognitive impairment/mild dementia) with amyloid and low/medium or high tau pathology based on positron emission tomography imaging from June 2020 to November 2021 (last patient visit for primary outcome in April 2023).

Interventions   Participants were randomized in a 1:1 ratio to receive donanemab (n = 860) or placebo (n = 876) intravenously every 4 weeks for 72 weeks. Participants in the donanemab group were switched to receive placebo in a blinded manner if dose completion criteria were met.

Main Outcomes and Measures   The primary outcome was change in integrated Alzheimer Disease Rating Scale (iADRS) score from baseline to 76 weeks (range, 0-144; lower scores indicate greater impairment). There were 24 gated outcomes (primary, secondary, and exploratory), including the secondary outcome of change in the sum of boxes of the Clinical Dementia Rating Scale (CDR-SB) score (range, 0-18; higher scores indicate greater impairment). Statistical testing allocated α of .04 to testing low/medium tau population outcomes, with the remainder (.01) for combined population outcomes.

Results   Among 1736 randomized participants (mean age, 73.0 years; 996 [57.4%] women; 1182 [68.1%] with low/medium tau pathology and 552 [31.8%] with high tau pathology), 1320 (76%) completed the trial. Of the 24 gated outcomes, 23 were statistically significant. The least-squares mean (LSM) change in iADRS score at 76 weeks was −6.02 (95% CI, −7.01 to −5.03) in the donanemab group and −9.27 (95% CI, −10.23 to −8.31) in the placebo group (difference, 3.25 [95% CI, 1.88-4.62]; P  < .001) in the low/medium tau population and −10.2 (95% CI, −11.22 to −9.16) with donanemab and −13.1 (95% CI, −14.10 to −12.13) with placebo (difference, 2.92 [95% CI, 1.51-4.33]; P  < .001) in the combined population. LSM change in CDR-SB score at 76 weeks was 1.20 (95% CI, 1.00-1.41) with donanemab and 1.88 (95% CI, 1.68-2.08) with placebo (difference, −0.67 [95% CI, −0.95 to −0.40]; P  < .001) in the low/medium tau population and 1.72 (95% CI, 1.53-1.91) with donanemab and 2.42 (95% CI, 2.24-2.60) with placebo (difference, −0.7 [95% CI, −0.95 to −0.45]; P  < .001) in the combined population. Amyloid-related imaging abnormalities of edema or effusion occurred in 205 participants (24.0%; 52 symptomatic) in the donanemab group and 18 (2.1%; 0 symptomatic during study) in the placebo group and infusion-related reactions occurred in 74 participants (8.7%) with donanemab and 4 (0.5%) with placebo. Three deaths in the donanemab group and 1 in the placebo group were considered treatment related.

Conclusions and Relevance   Among participants with early symptomatic Alzheimer disease and amyloid and tau pathology, donanemab significantly slowed clinical progression at 76 weeks in those with low/medium tau and in the combined low/medium and high tau pathology population.

Trial Registration   ClinicalTrials.gov Identifier: NCT04437511

Deposition of β-amyloid in the brain is an early event in Alzheimer disease that leads to neurofibrillary tangles composed of tau protein and other characteristic brain changes referred to as the amyloid cascade . 1 , 2 Abnormal β-amyloid is a key pathological hallmark of Alzheimer disease defined by the 2018 National Institute on Aging and the Alzheimer’s Association Research Framework 3 and is one of the major targets in Alzheimer disease research and drug development.

Over the past decade, considerable advances occurred in testing the amyloid cascade hypothesis in Alzheimer disease clinical trials. Numerous amyloid-targeting therapy trials failed to show appreciable slowing of clinical disease progression 4 - 7 ; however, aducanumab, lecanemab, and donanemab recently showed promising amyloid plaque clearance, potentially benefitting patients. 8 - 10

Donanemab is an immunoglobulin G1 monoclonal antibody directed against insoluble, modified, N-terminal truncated form of β-amyloid present only in brain amyloid plaques. Donanemab binds to N-terminal truncated form of β-amyloid and aids plaque removal through microglial-mediated phagocytosis. 11 In the phase 2 TRAILBLAZER-ALZ trial of donanemab vs placebo, the primary outcome was met, as measured by the integrated Alzheimer Disease Rating Scale (iADRS), an integrated assessment of cognition and daily function. 9 Adverse events of interest included amyloid-related imaging abnormalities and infusion-related reactions. 9 To confirm and expand results from TRAILBLAZER-ALZ, we report results from TRAILBLAZER-ALZ 2, a global phase 3 randomized clinical trial that assessed donanemab efficacy and adverse events in a larger group of participants with low/medium tau pathology (the population studied in the phase 2 trial) and in a combined population including those with high tau pathology, a population hypothesized to be more difficult to treat due to more advanced disease.

TRAILBLAZER-ALZ 2 was a 76-week, phase 3, randomized, double-blind, parallel, multicenter, placebo-controlled trial with participants screened at 277 sites in 8 countries (eTable 1 in Supplement 3 ). Enrollment began June 19, 2020, and ended November 5, 2021, and database lock/unblinding (double-blind phase) occurred on April 28, 2023. The trial was originally designed as a phase 2 trial but was subsequently amended to a larger phase 3 trial in February 2021 in an effort to confirm and expand the results of the previous TRAILBLAZER-ALZ trial. The trial was conducted according to the Declaration of Helsinki, the International Conference on Harmonization Good Clinical Practice Guideline, and local regulatory requirements. An independent ethics committee/institutional review board at each site approved the study protocol ( Supplement 1 ), which is provided alongside the statistical analysis plan ( Supplement 2 ). Participants and study partners provided written consent. An independent data and safety monitoring board provided trial oversight.

The trial included participants aged 60 to 85 years with early symptomatic Alzheimer disease (mild cognitive impairment [MCI] 12 or Alzheimer disease with mild dementia). 3 P-tau181 screening was removed in an early protocol amendment (eMethods in Supplement 3 ). Eligible participants had screening Mini-Mental State Examination (MMSE) scores of 20 to 28, amyloid pathology (≥37 Centiloids) assessed with 18 F-florbetapir 13 or 18 F-florbetaben 14 positron emission tomography (PET), and presence of tau pathology assessed by 18 F-flortaucipir PET imaging with central image evaluation. 13 , 15 Tau PET scans were categorized as low/medium or high tau by visual and quantitative reads as previously described 16 - 20 ( Supplements 1 and 2 ). Screening procedures also included magnetic resonance imaging (MRI), and key exclusion criteria included presence of amyloid-related imaging abnormalities of edema/effusion, more than 4 cerebral microhemorrhages, more than 1 area of superficial siderosis, and any intracerebral hemorrhage greater than 1 cm or severe white matter disease on MRI. For all eligibility criteria, see Supplement 1 . Demographic information, including race and ethnicity, was collected to potentially understand any differences in disease course, treatment effects, or adverse events. The participants self-reported race and ethnicity based on fixed categories.

Eligible participants were randomly assigned in a 1:1 ratio ( Figure 1 ) by a computer-generated sequence using interactive web response systems, with stratification by baseline tau categorization and enrolling sites; the randomization block size was 4. Randomized participants received either donanemab (700 mg for the first 3 doses and 1400 mg thereafter) or placebo, administered intravenously every 4 weeks for up to 72 weeks. If amyloid plaque level (assessed at 24 weeks and 52 weeks) was less than 11 Centiloids on any single PET scan or less than 25 but greater than or equal to 11 Centiloids on 2 consecutive PET scans (TRAILBLAZER-ALZ cutoffs 9 ), donanemab was switched to placebo in a blinded procedure. Final adverse events and efficacy assessments were performed at 76 weeks. Amyloid-related imaging abnormality monitoring occurred with scheduled MRIs at 4, 12, 24, 52, and 76 weeks and unscheduled MRIs at investigator discretion. Any participant with detected amyloid-related imaging abnormalities had imaging every 4 to 6 weeks until resolution or stabilization. Amyloid-related imaging abnormality management and treatment interruption guidelines (eTable 2 in Supplement 3 ) depended on severity and symptoms. If infusions were held, investigators were advised to await resolution of amyloid-related imaging abnormalities of edema/effusion on radiographic imaging and stabilization of amyloid-related imaging abnormalities of microhemorrhages and hemosiderin deposits before resuming infusions. Permanent discontinuation was advised for macrohemorrhages. Investigators made final amyloid-related imaging abnormality management decisions.

The primary outcome was change in the iADRS score from baseline to 76 weeks in either the low/medium tau population or combined (low/medium and high tau) population. The iADRS is an integrated assessment of cognition and daily function from the 13-item cognitive subscale of the Alzheimer Disease Assessment Scale (ADAS-Cog 13 ) and Alzheimer Disease Cooperative Study—Instrumental Activities of Daily Living (ADCS-iADL), measuring global disease severity across the Alzheimer disease continuum as a single summary score. The iADRS is validated and captures clinical progression from MCI due to Alzheimer disease through moderate dementia due to Alzheimer disease, and treatment effects have been demonstrated across MCI and Alzheimer disease with mild dementia. 6 , 9 , 21 - 27 The possible scores on the iADRS range from 0 to 144 (lower scores indicate greater impairment), and the meaningful within-patient change (MWPC) is a change of 5 points for those with Alzheimer disease with MCI and 9 points for those with Alzheimer disease with mild dementia. The MWPC, or minimal clinically important difference (MCID) as in Supplement 1 and 2 , is a threshold for outcome scores (either patient-reported or physician-measured) above which a patient or physician would consider the change meaningful. 28

Prespecified secondary outcomes included changes from baseline to 76 weeks by sum of boxes of the Clinical Dementia Rating Scale (CDR-SB), the ADAS-Cog 13 , the ADCS-iADL, and MMSE in the low/medium tau or combined population. Amyloid plaque reduction at 76 weeks, percentage of participants reaching amyloid clearance (<24.1 Centiloids measured by amyloid PET 9 , 29 ) at 24 weeks and 76 weeks, tau PET 1 (frontal cortical regions) change, volumetric MRI (vMRI; whole brain, hippocampus, and ventricles) change, and adverse events were additional secondary outcomes. Supplement 1 provides a complete listing and methodology of adverse events assessments. Amyloid-related imaging abnormalities of edema/effusion, amyloid-related imaging abnormalities of microhemorrhages and hemosiderin deposits, and infusion-related reactions were adverse events of special interest because they were considered class effects or observed in previous trials. 9 , 30 - 32 Secondary outcomes related to pharmacokinetics and antidrug antibodies were also prespecified and are planned for subsequent studies. Exploratory outcomes included change in plasma P-tau217 (C 2 N Diagnostics) at 76 weeks and time-based analyses: progression risk using the CDR Global score (CDR-G; progression defined as any increase from baseline in CDR-G at consecutive visits), participants with no progression at 1 year on the CDR-SB, and clinical progression delay (ie, months saved with treatment) on the iADRS and CDR-SB. Additional information about outcome measures, including score ranges and MWPCs, is provided in eMethods in Supplement 3 .

Prespecified primary and secondary outcomes were controlled for multiplicity (gated) at 76 weeks ( Supplement 2 and eMethods in Supplement 3 ) except for MMSE, changes in vMRI measurements, and adverse event assessments. Additional time points were gated for amyloid clearance and P-tau217. Nominal P values are reported for gated and nongated outcomes.

The trial was originally designed as a phase 2 trial with a plan to enroll 500 participants and assess CDR-SB as the primary outcome, but was subsequently amended to a phase 3 trial assessing the iADRS score as the primary outcome in February 2021 in an effort to confirm and expand the results of the TRAILBLAZER-ALZ trial. No unblinded data analysis of TRAILBLAZER-ALZ 2 was performed or used to inform design or analyses. Further details regarding major protocol or study adjustments are in eMethods in Supplement 3 and the trial protocol in Supplement 1 .

Revised study sample size and power calculations were based on the primary results from the TRAILBLAZER-ALZ trial, 9 where mean progression in the placebo and donanemab groups on iADRS was −10.06 and −6.86 (approximately 32% slowing of disease progression) over 76 weeks, respectively. Multiple longitudinal data sets were simulated and the natural cubic spline model with 2 degrees of freedom (NCS2) was fit to each sample to determine the power. The powering and sample size determination of the trial was based on the low/medium tau population. With a sample size of approximately 1000 randomized participants in the low/medium tau population and an assumed 30% discontinuation rate, the NCS model provided greater than 95% power to achieve statistical significance at a 2-sided α level of .05. The total planned enrollment (including both the low/medium and high tau populations) was 1800.

Most statistical analyses were done with SAS version 9.4 (SAS Institute). Some time-based progression analyses were analyzed with R Project version 4.3.0 (R Foundation).

The efficacy analyses were conducted by using the evaluable efficacy population (participants with a baseline and at least 1 postbaseline efficacy measurement based on randomized treatment). A prespecified gated testing scheme 33 , 34 was used to control for study-wise type I error rate at 2-sided α level of .05, with 80% of initial α spend (.04) for multiplicity control allocated to the low/medium tau population and 20% of initial α spend (.01) for multiplicity control allocated to the combined population (testing scheme in Supplement 2 ; eMethods in Supplement 3 also describes time-based analyses not described below).

Clinical outcomes (except for CDR-SB) were primarily analyzed using an NCS2 model. The protocol-specified week value for each participant was used as a continuous variable to create NCS basis functions with knot locations at 0 weeks, the median observation time, and 76 weeks. The model restricted baseline estimates to be the same for treatment and placebo groups. The baseline score and each scheduled postbaseline score were dependent variables in the model. The model’s independent variables included NCS basis expansion terms (2 terms), NCS basis expansion term × treatment interaction (2 terms), baseline age, concomitant acetylcholinesterase inhibitor and/or memantine use at baseline (yes/no), and randomization stratifying factors (pooled site and baseline tau category [baseline tau category in combined population only]). An unstructured variance covariance matrix was used to model the within-participant errors using restricted maximum likelihood. The Kenward-Roger approximation was used to estimate the denominator degrees of freedom.

The MMRM was used to primarily assess CDR-SB, plasma P-tau217, amyloid PET, and vMRI. The analysis model used change from baseline as the dependent variable. The model was adjusted for age, baseline value, visit as a categorical variable, treatment, baseline × visit interactions, treatment × visit interactions, concomitant acetylcholinesterase inhibitor/memantine use at baseline (CDR-SB only), and randomization stratifying factors of pooled site and, for combined population only, baseline tau category. For vMRI, only age and baseline brain volumes were covariates. The covariance matrix structure used was the same as NCS. Plasma P-tau 217 value was log 10 -transformed to meet the normality assumption.

Both the NCS2 and MMRM use the same protocol-specified time values for each participant in the analysis; the NCS2 model makes additional parametric assumptions for the shape of the longitudinal mean structure that can lead to increased efficiency.

The percent slowing relative to placebo was calculated by dividing the least-squares mean (LSM) change from baseline treatment differences at 76 weeks by the LSM change from baseline with placebo at 76 weeks and multiplying by 100.

ANCOVA analysis was conducted for tau PET standardized uptake value ratio (SUVR), with change from baseline to 76 weeks as the dependent variable and covariates of baseline tau SUVR, age, and, for the combined population, tau burden.

MMRM, NCS with 3 degrees of freedom model (NCS3), and bayesian disease progression model (DPM) were applied as sensitivity analyses for the primary outcome. DPM was applied to measure the proportion of disease progression in donanemab-treated participants relative to placebo-treated participants using a disease progression ratio, as previously described. 35 Details on sensitivity analyses for censoring after amyloid-related imaging abnormalities or infusion-related reactions, per-protocol analysis, and analysis of study completers are in eMethods in Supplement 3 . Details of subgroup analyses and time-based analyses are also described in eMethods in Supplement 3 .

Cox proportional hazard models were applied to CDR-G (gated), iADRS (nongated), and CDR-SB (nongated). Progression to next clinical stage was defined as any increase in CDR-G at 2 consecutive visits from baseline. MWPC was established as an iADRS change of greater than or equal to 5 for those with Alzheimer disease with MCI and greater than or equal to 9 points for those with Alzheimer disease with mild dementia and a CDR-SB change of greater than or equal to 1 point for those with Alzheimer disease with MCI and greater than or equal to 2 points for Alzheimer disease with mild dementia at 2 consecutive visits from baseline.

Analyses of the high tau population alone (ie, not combined with the low/medium tau population) for primary and secondary outcomes was performed post hoc.

Adverse events were evaluated in all participants exposed to study drug and were summarized according to event frequency by treatment assignment.

If less than 30% of the ADCS-iADL, 3 or fewer items of the ADAS-Cog 13, or 1 box of the CDR were missing, the total score for these assessments was imputed. If more items were missing than defined, the total score at that visit was considered missing ( Supplement 2 ). If either the ADCS-iADL or ADAS-Cog 13 scores were missing, the iADRS score was considered as missing. The missing data for NCS and MMRM analyses were handled by the likelihood-based mixed-effect model and the model parameters were estimated using restricted likelihood estimation incorporating all the observed data.

All presented primary, secondary, and exploratory outcomes were controlled for multiplicity (gated) in at least 1 population except for MMSE, vMRI measurements, and safety assessments. Of the 24 gated outcomes (eMethods in Supplement 3 ), 23 were statistically significant.

Of 8240 participants screened, 1736 were enrolled (mean age, 73.0 years; 996 [57.4%] women) and 76% completed the trial: 860 were assigned to receive donanemab and 876 were assigned to receive placebo ( Figure 1 ). Baseline characteristics are summarized by treatment groups in both low/medium tau (n = 1182) and combined populations (n = 1736) ( Table 1 ). As expected, the combined population had higher tau biomarkers at baseline due to the inclusion of participants with high tau pathology and showed greater impairment across baseline clinical assessments.

In the low/medium tau population, LSM change from baseline in the iADRS score at 76 weeks was −6.02 (95% CI, −7.01 to −5.03) in the donanemab group and −9.27 (95% CI, −10.23 to −8.31) in the placebo group (difference, 3.25 [95% CI, 1.88-4.62]; P  < .001), representing a 35.1% (95% CI, 19.90%-50.23%) slowing of disease progression ( Figure 2 , Table 2 ).

In the combined population, LSM change from baseline in the iADRS score at 76 weeks was −10.19 (95% CI, −11.22 to −9.16) in the donanemab group and −13.11 (95% CI, −14.10 to −12.13) in the placebo group (difference, 2.92 [95% CI, 1.51-4.33]; P  < .001), representing a 22.3% (95% CI, 11.38%-33.15%) slowing of disease progression ( Figure 2 , Table 2 ).

In the low/medium tau population, the differences between treatment groups in the LSM change from baseline at 76 weeks was −0.67 (95% CI, −0.95 to −0.40) (36.0% [95% CI, 20.76%-51.15%] slowing of clinical progression) for CDR-SB, 1.83 (95% CI, 0.91-2.75) (39.9% [95% CI, 19.15%-60.58%] slowing of clinical progression) for ADCS-iADL, and −1.52 (95% CI, −2.25 to −0.79) (32.4% [95% CI, 16.55%-48.35%] slowing of clinical progression) for ADAS-Cog 13 ( Figure 2 , Table 2 ; eFigure 1 and 3 in Supplement 3 ).

In the combined population, the differences in the LSM change from baseline to 76 weeks between the donanemab and placebo groups were −0.70 (95% CI, −0.95 to −0.45) (28.9% [95% CI, 18.26%-39.53%] slowing of clinical progression) for CDR-SB, 1.70 (95% CI, 0.84-2.57) (27.8% [95% CI, 13.48%-42.13%] slowing of clinical progression) for ADCS-iADL, and −1.33 (95% CI, −2.09 to −0.57) (19.5% [95% CI, 8.23%-30.83%] slowing of clinical progression) for ADAS-Cog13 ( Figure 2 , Table 2 ; eFigures 2 and 4 in Supplement 3 ).

At 76 weeks, brain amyloid plaque level decreased by 88.0 Centiloids (95% CI, −90.20 to −85.87) with donanemab treatment and increased by 0.2 Centiloids (95% CI, −1.91 to 2.26) in the placebo group in the low/medium tau population; in the combined population, amyloid plaque level decreased by 87.0 Centiloids (95% CI, −88.90 to −85.17) with donanemab treatment and decreased by 0.67 Centiloids (95% CI, −2.45 to 1.11) in the placebo group ( Figure 3 A). The percentages of donanemab-treated participants in the low/medium tau population who reached amyloid clearance 29 , 38 were 34.2% (95% CI, 30.22%-38.34%) at 24 weeks and 80.1% (95% CI, 76.12%-83.62%) at 76 weeks compared with 0.2% (95% CI, 0.03%-1.02%) at 24 weeks and 0% (95% CI, 0.00%-0.81%) at 76 weeks of placebo-treated participants. In the combined population, amyloid clearance was reached in 29.7% (95% CI, 26.56%-33.04%) of participants at 24 weeks and 76.4% (95% CI, 72.87%-79.57%) at 76 weeks of donanemab-treated participants compared with 0.2% (95% CI, 0.07%-0.90%) at 24 weeks and 0.3% (95% CI, 0.08%-1.05%) at 76 weeks of placebo-treated participants ( Figure 3 B).

Evaluation of the LSM change from baseline to 76 weeks in frontal tau SUVR (cerebellar gray reference) did not show a significant difference in the low/medium tau or in the combined population (eFigure 5 in Supplement 3 ). The difference in LSM change in tau SUVR from placebo in the frontal lobe at 76 weeks was −0.0002 (95% CI, −0.01 to 0.01; P  = .97) in the low/medium tau population and −0.0041 (95% CI, −0.01 to 0.01; P  = .45) in the combined population.

For both the low/medium tau and combined populations, at 76 weeks, vMRI (a non-gated secondary outcome) showed a greater decrease in whole brain volume, a lesser decrease in the hippocampal volume, and a greater increase in ventricular volume in the donanemab group than in the placebo group (eFigure 6 in Supplement 3 ).

P-tau217 was significantly reduced from baseline with donanemab treatment compared with placebo in the low/medium tau and combined population. The difference in LSM change in tau SUVR (log 10 -based) vs placebo was −0.25 (95% CI, −0.28 to −0.22; P  < .001) in the low/medium tau population and −0.22 (95% CI −0.24 to −0.20; P  < .001) in the combined population at 76 weeks ( Figure 3 C and D).

There was a 38.6% (CDR-G hazard ratio, 0.61 [95% CI, 0.47-0.80]; P  < .001) lower risk of disease progression in the low/medium tau population and a 37.4% (CDR-G hazard ratio, 0.63 [95% CI, 0.51-0.77; P  < .001) lower risk of disease progression in the combined population with donanemab treatment compared with placebo over the 18-month trial ( Figure 3 E and F; see eFigure 7 in Supplement 3 for nongated disease progression analyses of iADRS and CDR-SB). Substantial decline in the low/medium tau population occurred in 100 (18%) donanemab-treated participants and 163 (28%) placebo-treated participants and, in the combined population, occurred in 186 (23%) donanemab-treated and 288 (34%) placebo-treated participants. In addition, in the low/medium tau population, an estimated 47% of participants were stable (showed no decline in CDR-SB from baseline) with donanemab at 1 year compared with 29% of participants receiving placebo ( P  < .001) (eTable 6 in Supplement 3 ). At 76 weeks, disease progression with donanemab treatment in the low/medium tau population was delayed by 4.36 months (95% CI, 1.87-6.85) on the iADRS and 7.53 months (95% CI, 5.69-9.36) on the CDR-SB.

Sensitivity analyses of the iADRS score (eFigure 8 in Supplement 3 ) using NCS3, MMRM, and DPM analyses, NCS2 in the completers and per protocol populations, and censoring change scores after amyloid-related imaging abnormalities edema/effusion and/or infusion-related reaction observations were consistent with the primary analysis (33.4%-39.6% slowing of clinical progression).

The findings as measured by iADRS and CDR-SB were generally consistent across baseline characteristic subgroups where the subgroup was sufficiently large (eFigure 9 in Supplement 3 ).

Analysis of the smaller (n = 552) high tau population alone (ie, not combined with the low/medium tau population) for all primary and secondary outcomes was completed post hoc. The difference between the donanemab and placebo groups in the LSM change from baseline at 76 weeks was 1.26 (95% CI, −1.77 to 4.28; P  = .42) for the iADRS score and −0.69 (95% CI −1.19 to −0.20; P  = .006) for the CDR-SB score. For additional assessments in the high tau population, see eTables 4, 5, and 10 and eFigures 10-13 in Supplement 3 .

The incidence of death was 1.9% in the donanemab group and 1.1% in the placebo group, while the incidence of serious adverse events was 17.4% in the donanemab group and 15.8% in the placebo group ( Table 3 ). In the donanemab group, 3 participants with serious amyloid-related imaging abnormalities subsequently died (2 APOE ε4 heterozygous carriers and one noncarrier; none were prescribed anticoagulant or anti-platelet medications; one resumed treatment after resolution of severe amyloid-related imaging abnormalities edema/effusion that was accompanied by severe amyloid-related imaging abnormalities microhemorrhages and hemosiderin deposits and one had superficial siderosis at baseline) (eTable 9 in Supplement 3 ). Treatment-emergent adverse events were reported by 759 of 853 participants (89.0%) receiving donanemab and 718 of 874 participants (82.2%) receiving placebo. Treatment discontinuation due to adverse events was reported in 112 participants receiving donanemab and 38 participants receiving placebo. The most common adverse events that led to treatment discontinuation were infusion-related reactions, either amyloid-related imaging abnormalities edema/effusion or microhemorrhages and hemosiderin deposits, and hypersensitivity (eTable 7 in Supplement 3 ).

Either amyloid-related imaging abnormalities of edema/effusion or microhemorrhages and hemosiderin deposits occurred in 314 participants (36.8%) receiving donanemab and 130 (14.9%) receiving placebo. Amyloid-related imaging abnormalities of edema/effusion, determined via MRI, occurred in 205 participants (24.0%) in the donanemab group and in 18 (2.1%) in the placebo group. Most amyloid-related imaging abnormalities of edema/effusion events were mild to moderate (see eTable 2 in Supplement 3 ) (n = 188 [93.1%] in the donanemab group; n = 17 [100%] in the placebo group). Symptomatic amyloid-related imaging abnormalities of edema/effusion were reported by 52 participants (6.1%) in the donanemab group (25.4% of those with amyloid-related imaging abnormalities of edema/effusion), with 45 participants (86.5%) having symptom resolution. Most cases (57.9%) of first amyloid-related imaging abnormalities of edema/effusion occurred after receiving up to 3 donanemab infusions. Serious amyloid-related imaging abnormalities of edema/effusion (see Table 3 ) occurred in 13 participants (1.5%) receiving donanemab. First events of amyloid-related imaging abnormalities of edema/effusion radiographically resolved in 198 (98.0%) donanemab-treated participants and 11 (64.7%) placebo-treated participants, with a mean amyloid-related imaging abnormalities of edema/effusion resolution time of 72.4 days for those receiving donanemab and 63.5 days for those receiving placebo. Edema/effusion were numerically less common among APOE ε4 noncarriers than carriers, with higher frequency among homozygotes than heterozygotes ( Table 3 ; further details in eTable 8 in Supplement 3 ).

The incidence of amyloid-related imaging abnormalities of microhemorrhages and hemosiderin deposits, determined via MRI, was higher in the donanemab group than the placebo group (268 participants [31.4%] vs 119 participants [13.6%]). Incidence of amyloid-related imaging abnormalities of microhemorrhages and hemosiderin deposits in the absence of amyloid-related imaging abnormalities of edema/effusion was not different between treatments (12.7% in the donanemab group vs 12.4% in the placebo group). The incidence of microhemorrhage and superficial siderosis was greater in the donanemab group than in the placebo group (microhemorrhage: 26.8% vs 12.5%; superficial siderosis: 15.7% vs 3.0%). Three intracerebral hemorrhages greater than 1 cm were recorded in the donanemab group and 2 were recorded in the placebo group ( Table 3 ).

Infusion-related reactions were reported by 74 participants (8.7%) in the donanemab group and 4 (0.5%) in the placebo group. Serious infusion-related reactions or hypersensitivity occurred in 3 participants (0.4%) in the donanemab group. Most infusion-related reactions were mild to moderate and occurred during or within 30 minutes of the end of the infusion and between the second and fifth infusion (73.6%). Anaphylactic reaction occurred in 3 participants (0.4%) in the donanemab group and were considered to be mild to moderate.

In this phase 3 trial, donanemab significantly slowed Alzheimer disease progression, based on the iADRS score, compared with placebo in the low/medium tau and combined tau populations and across secondary clinical outcomes of CDR-SB, ADAS-Cog 13 , and ADCS-iADL scores.

Donanemab treatment resulted in clinically meaningful benefit (considered to be >20% slowing of clinical progression 39 - 41 ) on the iADRS and CDR-SB scales for both the low/medium tau and combined populations, regardless of statistical model. Additional support for clinical relevance is the 38.6% risk reduction of disease progression as measured on the CDR-G score and the 4.4 to 7.5 months saved over the 18-month study (low/medium tau population). Furthermore, an estimated 47% of participants receiving donanemab had no change in the CDR-SB at 1 year (no disease progression), compared with 29% of participants receiving placebo.

This trial used a definition of a MWPC 28 based on any incremental change on the CDR-G scale (Alzheimer disease with MCI to mild Alzheimer disease or mild Alzheimer disease to moderate Alzheimer disease) or point changes of −5 on the iADRS and 1 on the CDR-SB for those with Alzheimer disease with MCI or −9 on the iADRS and 2 on the CDR-SB for those with Alzheimer disease with mild dementia at consecutive visits from baseline. In analyses assessing whether individual participants reached thresholds of clinically important progression over the course of the trial, donanemab resulted in significantly lower risk of meaningful change on the CDR-G as well as the prespecified nongated analyses of the iADRS and CDR-SB outcomes.

These clinical outcomes were achieved in 52% of low/medium tau participants completing donanemab treatment by 1 year, based on when a participant met amyloid clearance criteria. Limited-duration dosing was a distinct trial design feature reflecting donanemab binding specificity for amyloid plaque and implemented to decrease burden, cost, and potentially unnecessary treatments. 11 Early significant changes on both brain amyloid PET scans and P-tau217 blood test results suggest opportunities for clinical monitoring of therapy. Donanemab treatment resulted in significantly reduced brain amyloid plaque in participants at all time points assessed, with 80% (low/medium tau population) and 76% (combined population) of participants achieving amyloid clearance at 76 weeks. Clearance beyond 76 weeks, and associated Alzheimer disease biomarkers levels, are currently being studied in the ongoing extension phase. The lack of response in frontal tau-PET is inconsistent with the TRAILBLAZER-ALZ phase 2 results. 9 , 38 Additional regions have yet to be analyzed and reported. Factors resulting in this inconsistency will be examined. Changes in vMRI (including a greater decrease in whole brain volume in the donanemab group) were consistent with previous reports 9 , 42 and would benefit from further exploration.

The general belief is that treating Alzheimer disease at the earliest disease stage is likely to result in more clinically meaningful effects. 43 , 44 Post hoc evaluation in only high tau participants demonstrated no differences ( P < .05) on the primary outcome or on most secondary clinical outcomes in donanemab-treated compared with placebo-treated participants within the 18-month trial, with the exception of CDR-SB. Compared with significant differences in the low/medium tau population, this supports the hypothesis that a greater benefit from amyloid-lowering therapies may occur when initiated at an earlier disease stage.

Similar to other amyloid-lowering drugs, and the phase 2 TRAILBLAZER-ALZ trial, amyloid-related imaging abnormalities are an associated adverse event. When amyloid-related imaging abnormalities occur, they are mostly asymptomatic and resolve in approximately 10 weeks. When symptoms occur, they are usually mild, consisting of a headache or increase in confusion, but can have more severe symptoms such as seizures. In some instances, these events can be life-threatening and result in, or lead to, death. For 1.6% of participants in the donanemab treatment group, amyloid-related imaging abnormalities led to serious outcomes, such as hospitalization, and required supportive care and/or corticosteroid use. It is also important to note that 3 deaths in TRAILBLAZER-ALZ 2 occurred after serious amyloid-related imaging abnormalities. Further evaluation of the risks associated with serious and life-threatening amyloid-related imaging abnormalities will be important to identify the best approaches for managing risks and maximizing benefit, in addition to earlier treatment of the disease when less amyloid pathology is present and, theoretically, when amyloid-related imaging abnormalities risk is lower.

This study has several limitations. First, an inherent limitation to limited-duration dosing was variability in total donanemab doses received and/or duration of donanemab dosing. Second, data collection was for 76 weeks, limiting long-term understanding of donanemab; however, a study extension is ongoing. Third, the studied populations were primarily White (91.5%), which may limit generalizability to other populations due to a lack of racial and ethnic diversity. Fourth, although no related protocol amendments were necessary, this trial was conducted during the COVID-19 pandemic, and COVID-19 was the most commonly reported adverse event across treatment groups (see eMethods in Supplement 3 ). Fifth, direct comparison of results to other amyloid-targeting trials is not possible due to trial design differences such as stratification by baseline tau PET category. Sixth, amyloid-related imaging abnormality and infusion-related reaction occurrences may have caused participants and investigators to infer treatment assignment; attempts to minimize bias included blinding CDR raters to adverse event information and, based on sensitivity analyses, censoring change scores after the first observation of amyloid-related imaging abnormalities of edema/effusion and/or infusion-related reactions did not impact the results.

Among participants with early symptomatic Alzheimer disease and amyloid and tau pathology, donanemab significantly slowed clinical progression at 76 weeks in those with low/medium tau and in the combined low/medium and high tau pathology population.

Accepted for Publication: June 28, 2023.

Published Online: July 17, 2023. doi:10.1001/jama.2023.13239

Corresponding Author: John R. Sims, MD, Eli Lilly and Company, Lilly Corporate Center DC 1532, Indianapolis, IN 46285 ( [email protected] ).

Author Contributions: Dr Solomon had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Sims, Zimmer, Ardayfio, Sparks, Wessels, Wang, Collins, Salloway, Mintun, Skovronsky.

Acquisition, analysis, or interpretation of data: Sims, Zimmer, Evans, Lu, Ardayfio, Sparks, Wessels, Shcherbinin, Wang, Nery, Collins, Solomon, Apostolova, Hansson, Ritchie, Brooks, Mintun, Skovronsky.

Drafting of the manuscript: Sims, Evans, Ardayfio, Wang, Nery, Collins, Ritchie, Skovronsky.

Critical review of the manuscript for important intellectual content: Sims, Zimmer, Ardayfio, Wessels, Shcherbinin, Salloway, Apostolova, Ritchie, Mintun, Skovronsky.

Statistical analysis: Lu, Ardayfio, Sparks, Wang, Salloway, Skovronsky.

Obtained funding: Sims, Brooks, Mintun, Skovronsky.

Administrative, technical, or material support: Zimmer, Evans, Wessels, Shcherbinin, Collins, Salloway, Brooks, Mintun, Skovronsky.

Supervision: Sims, Wessels, Nery, Collins, Solomon, Brooks, Mintun, Skovronsky.

Other - imaging and biomarker analysis: Collins.

Other - suggested additional analyses: Apostolova.

Conflict of Interest Disclosures: Dr Sims reported being an employee of Eli Lilly and Company during the conduct of the study. Dr Zimmer reported receiving personal fees from and being a shareholder in Eli Lilly and Company during the conduct of the study. Dr Evans reported being an employee of and minority shareholder in Eli Lilly and Company during the conduct of the study. Dr Lu reported being an employee of and stockholder in Eli Lilly. Dr Ardayfio reported being an employee of and stockholder in Eli Lilly during the conduct of the study. Dr Wessels reported being a minor shareholder in Eli Lilly and Company outside the submitted work. Dr Shcherbinin reported being an employee of and stockholder in Eli Lilly and Company during the conduct of the study and Eli Lilly and Company having patents pending relevant to this research. Dr Nery reported being an employee of and shareholder in Eli Lilly and Company during the conduct of the study. Dr Collins reported being an employee of and stockholder in from Eli Lilly and Company during the conduct of the study. Dr Salloway reported receiving personal fees and grants from Biogen, Eli Lilly, Genentech, Avid, Roche, Eisai, Novartis, Acumen, NovoNordisk, and Prothena during the conduct of the study. Dr Apostolova reported receiving grants from NIA, Alzheimer Association, AVID Radiopharmaceuticals, Life Molecular Imaging, and Roche Diagnostics and personal fees from Eli Lilly, Biogen, Two Labs, IQVIA, Genentech, Siemens, Corium, GE Healthcare, Eisa, Roche Diagnostics, Alnylam, Alzheimer Association, and from the US Food And Drug Administration outside the submitted work. Dr Hansson reported personal fees from AC Immune, Amylyx, Alzpath, BioArtic, Biogen, Cerveau, Eisai, Eli Lilly, Fujirebio, Merk, Novartis, Novo Nordisk, Roche, Sanofi, and Siemens outside the submitted work. Dr Ritchie reported receiving personal fees from Actinogen, Biogen, Cogstate, Eisai, Eli Lilly, Janssen Cilag, Merck, Novo Nordisk, Roche Diagnostics, and Signant and being founder of and majority shareholder in Scottish Brain Sciences outside the submitted work. Dr Brooks reported being an employee of and shareholder in Eli Lilly and Company. Dr Mintun reported being an employee of and shareholder in Eli Lilly and Company and having a patent pending with Eli Lilly and Company. Dr Skovronsky reported being an employee of and shareholder in Eli Lilly and Company. No other disclosures were reported.

Funding/Support: This work was funded by Eli Lilly and Company.

Role of the Funder/Sponsor: Eli Lilly and Company was responsible for design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Group Information: The TRAILBLAZER-ALZ 2 Investigators appear listed in Supplement 4 .

Data Sharing Statement: See Supplement 5 .

Additional Contributions: We thank all the trial participants and their families and caregivers who participated in the TRAILBLAZER-ALZ 2 trial as well as the site staff, raters, and site investigators (see list in Supplement 4 ); members of the data and safety monitoring board; vendor partners including BioAgilitix, Clario, Clinical Trial Media, Cogstate, C 2 N Diagnostics, Invicro, IQVIA, Labcorp and Quanterix. The authors would like to thank the following salaried employees of Eli Lilly and Company for their contributions to TRAILBLAZER-ALZ 2, for which they received no additional compensation: Andrea Abram, MBA; Hrideep Antony, BS; Anupa Arora, MD; Theresa Bauer, BS; Jude Burger, MS; Yang Dai, MS; Russell A Delgiacco, MS; Marybeth Devine, BS; Dawn East, BS; Tim Edison, PharmD; Naohisa Hatekeyama, MS; Jeremy T Hemiup, MS, MBA; Stacy A Huckins, BS; Blaire Iris Kaufman, BS; Rashna Khanna, MD; Min Jung Kim, MS; Albert Lo, MD, PhD; Dedeepya Masarapu, B Pharm; Shoichiro Sato, MD, PhD; Adam Schaum, MAS; Linda Shurzinske, MS; Andrea L Speas, RNN, BSN; LisaAnn Trembath, MS; Giulia Tronchin, PhD; Melissa Veenhuizen, DVM, MS; Wen Xu, PhD; and Wei Zhou, MS. The authors would like to acknowledge Paula Hauck, PhD; Deirdre Hoban, PhD; and Carmen Deveau, PhD, salaried employees of Eli Lilly and Company, for project management support, and strategic scientific communication expertise, for which they received no additional compensation.

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How to Compose, Write and Publish a Scientific or Professional Communication

Milivoj boranic.

Professor of Pediatrics and Oncology, retired from Institute “Rudjer Boskovic”, Zagreb, Croatia and Faculty of medicine, Osijek, Croatia (till 2004) and Faculty of medicine, Mostar, Bosnia and Herzegovina (till 2013). ORCID ID: http://www.orcid.org/0000-0003-2780-7165

There is an ample number of recommendations, guides and monographs addressing the art of composing and publishing written, oral or visual communications in science and various professions. In order to write an article, autors have to follow certain rules. Presentation of the article (oral or poster presentation) also requires skill, meaning that you have to fulfill certain guidelines and regulations.

1. INTRODUCTION

There is an ample number of recommendations, guides and monographs addressing the art of composing and publishing written, oral or visual communications in science and various professions (see e. g. references 1-8).

Useful guidelines in our language are available on the internet, in monographs and as the instructions to authors in medical journals (e. g., see references 9-18). On the basis those guidelines, as well as from personal experience that I have acquired as author, mentor, reviewer, and above all as a chairman of the committee evaluating doctor-of-science and master-of-science theses at a medical faculty, I am glad, for this occasion, to present a personal view of essential steps in producing useful communications.

2. STEPS IN ARTICLE WRITING

Before embarking on the composing of a scientific or professional communication, observe following considerations:

• What is the reason for your effort? Is there anything important or new that you would like to convey? Are you forced to write an article or deliver an oral presentation for the sake of personal promotion? Is the communication needed for your professional advancement? “Forced” communications are likely to miss the point.
• Read relevant literature covering the field of your interest. Access journal databases (eg. PubMed, Web of Science, or Scopus (authorization is needed for the latter two). Ask for help your library service, if needed and if available. Pay attention to relevant and up-to-date communications. Consider meta-analyses. Information about actual knowledge and/or problems provides your work with appropriate background. Keep record of relevant articles, save their abstracts and/or jot down their summaries emphasizing points of your particular interest. This material will be used later when writing the Introduction and discussion of your communication.
• Collect your data and summarize them in tables and/or graphs. Provide approipriate legends. Carry out statistical analysis. Try to make the tables and/or graphs (with their legends) self-explanatory. If needed, choose appropriate illustrations (scans, histology, etc) and provide explanatory legends. Keep eye on that material when writing the Results section.
• Now set out to compose your communication by starting with the Summary, not the Introduction! This will help you make a general idea of what you are going to communicate before embarking on having it on paper in extenso. The summary may be amended later, after completing the communication. This summary may also be used with the applications for scientific or professional meetings.
• Compose the Material and Methods section. This helps you get going. Be meticulous, pay attention to details. Avoid detailed description of well-known routine procedures. Provide name(s) and addresse(s) of the manufactures and vendors. Keep in mind that detailed description of materials and methods shows your scientific sincerity.
• Writing the Introduction is a demanding task. NEVER start by writing it the first. Otherwise you get entangled with (often) useles reiteration of known facts and are likely to lose momentum before embarking on the results. Write the Introduction after the Results section. Avoid redundant reiteration of common knowledge, give only a general outline of well-known facts; concentrate on the area of your work. Describe current state-of-the-art in your area of interest and emphasize problems, unsolved questions and controversies. Here you may resort to the material (abstracts, summaries) acquired by means of the literature search (see section 2).
• Describe the Results looking at your tables, graphs and/or pictures. The material should be self-explanatory so that the text itself may point out to the essential facts only. Emphasize major findings.
• Discussion should put your findings, observations or research into the perspective of the knowledge and facts outlined in the Introduction. Do not repeat results, rather explain and comment them. Concentrate on your contribution to the field. Discuss controversies.
• Literature should be organized according to the Vancouver system. Be meticulous with citing! Avoid unnecessary or redundant citations. Consult the official list of journal title abbreviations.
• Add Summary written before; if needed, improve it. Provide standard keywords (see Medical Standard Headings - MeSH). If needed, add summary in the national language (as required e.g. for dissertations) and provide translated MeSH titles.
• Have your text edited by a language professional.
• If you intend to publish a scientific article, choose appropriate journal taking into account its rank and scope. Consult the list of journals with their impact factors at. See useful advice at. Do not hesitate to proffer your manuscript to a good journal having a high impact factor; the editor may turn it down, but you are likely to receive useful review(s). Improved paper may then be profferred to a less prestigious journal. Do not proffer your paper simultaneously to two journals! It is allowed, however, to publish in domestic journal a translation of an article already published in an international journal, with proper reference to the original.

3. ORAL PRESENTATION

• Organize your speech so as to observe the allotted time limit. Exercise aloud in advance!
• If you use the PowerPoint, make the written text on slides as succint as possible and legible from distance. Do not clog the slides with redundant text in small letters! Simplify tables and graphs, make them easily legible and understandable. Complicated graphs and tables with too many items cannot be understood by the audience. Avoid too many fonts, colours and other embellishments.
• When speaking, address the audience and not the screen!
• Summarize your presentation with a succint repetition of your message

See useful advices in references 19 and 20.

4. POSTER PRESENTATIONS - GUIDELINES

• Make the posters self-explanatory and legible from reasonable distance.
• Emphasize the Aim and Conclusions of your work.
• Do not clog the poster with differing fonts, too many colors and other embellishments, since that distracts from the message.
• A short and easily legible abstract may be added.

See numerous advices, e.g., in references 21 - 25.

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High Lead Levels in 2 Independent and Authenticated Locks of Beethoven’s Hair

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Nader Rifai, William Meredith, Kevin Brown, Sarah A Erdahl, Paul J Jannetto, High Lead Levels in 2 Independent and Authenticated Locks of Beethoven’s Hair, Clinical Chemistry , Volume 70, Issue 6, June 2024, Pages 878–879, https://doi.org/10.1093/clinchem/hvae054

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To the Editor:

The composer Ludwig van Beethoven’s health issues throughout his life are well documented, with many conjectures on the cause of his death from liver and kidney disease. The presence of high hair lead concentration in Beethoven led to speculation by Reiter, who claimed that it caused the composer’s ailments, including his deafness, and demise ( 1). However, genomic studies demonstrated that the Hiller (formerly known as the Guevara) Lock used in that study belonged to a woman ( 2).

Here we describe, for the first time, high lead concentrations in 2 independent and authenticated locks of hair from Beethoven, the Bermann (0.0413 g collected between late 1820 and March 1827) and the Halm-Thayer (0.0284 g collected April 1826) Locks. The authentication of the 2 locks was previously confirmed in a landmark report of the sequencing of Beethoven’s whole genome ( 2). Five of the 8 locks of Beethoven’s hair examined by Beggs et al. ( 2), including the Bermann and the Halm-Thayer Locks used in the current study, shared identical mitochondrial genomes of haplogroup H1b1 + 16,362C with a private mutation at C16,176T and had male XY karyotypes, demonstrating that the samples either came from a single individual or monozygotic twins. All 5 matching samples showed DNA damage that is consistent with an origin in the early 19th century. Furthermore, the Halm-Thayer Lock is one of the only 2 with an intact chain of custody; Beethoven hand-delivered the lock himself to the pianist Anton Halm in April 1826 ( 3). Therefore, it was concluded that these findings presented compelling evidence for the identity of the 5 independent locks of hair. Although the analysis revealed several significant genetic risk factors for liver disease and evidence of an infection with hepatitis B virus that may have contributed to his death, it did not shed light on the definitive causes of his deafness and gastrointestinal problems.

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• Published: 10 June 2024

Evidence for transient morning water frost deposits on the Tharsis volcanoes of Mars

• A. Valantinas   ORCID: orcid.org/0000-0001-9995-3335 1 , 2 ,
• N. Thomas 1 ,
• A. Pommerol   ORCID: orcid.org/0000-0002-9165-9243 1 ,
• O. Karatekin 3 ,
• L. Ruiz Lozano   ORCID: orcid.org/0000-0002-8084-5438 3 ,
• C. B. Senel   ORCID: orcid.org/0000-0002-7677-9597 3 , 4 ,
• O. Temel 3 , 5 ,
• E. Hauber   ORCID: orcid.org/0000-0002-1375-304X 6 ,
• D. Tirsch   ORCID: orcid.org/0000-0001-5905-5426 6 ,
• V. T. Bickel   ORCID: orcid.org/0000-0002-7914-2516 7 ,
• G. Munaretto 8 ,
• M. Pajola   ORCID: orcid.org/0000-0002-3144-1277 8 ,
• F. Oliva   ORCID: orcid.org/0000-0002-6271-3722 9 ,
• F. Schmidt   ORCID: orcid.org/0000-0002-2857-6621 10 , 11 ,
• I. Thomas   ORCID: orcid.org/0000-0003-3887-6668 12 ,
• A. S. McEwen 13 ,
• M. Almeida 1 ,
• M. Read 1 ,
• V. G. Rangarajan   ORCID: orcid.org/0000-0003-3694-7696 14 ,
• M. R. El-Maarry   ORCID: orcid.org/0000-0002-8262-0320 15 ,
• F. G. Carrozzo 9 ,
• E. D’Aversa   ORCID: orcid.org/0000-0002-5842-5867 9 ,
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• M. R. Patel   ORCID: orcid.org/0000-0002-8223-3566 16 ,
• G. Bellucci   ORCID: orcid.org/0000-0003-0867-8679 9 ,
• J. J. Lopez-Moreno   ORCID: orcid.org/0000-0002-7946-2624 17 ,
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• G. Cremonese 8  

Nature Geoscience ( 2024 ) Cite this article

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• Atmospheric dynamics
• Climate sciences
• Cryospheric science
• Geomorphology
• Inner planets

The present-day water cycle on Mars has implications for habitability and future human exploration. Water ice clouds and water vapour have been detected above the Tharsis volcanic province, suggesting the active exchange of water between regolith and atmosphere. Here we report observational evidence for extensive transient morning frost deposits on the calderas of the Tharsis volcanoes (Olympus, Arsia and Ascraeus Montes, and Ceraunius Tholus) using high-resolution colour images from the Colour and Stereo Surface Imaging System on board the European Space Agency’s Trace Gas Orbiter. The transient bluish deposits appear on the caldera floor and rim in the morning during the colder Martian seasons but are not present by afternoon. The presence of water frost is supported by spectral observations, as well as independent imagery from the European Space Agency’s Mars Express orbiter. Climate model simulations further suggest that early-morning surface temperatures at the high altitudes of the volcano calderas are sufficiently low to support the daily condensation of water—but not CO 2 —frost. Given the unlikely seasonal nature of volcanic outgassing, we suggest the observed frost is atmospheric in origin, implying the role of microclimate in local frost formation and a contribution to the broader Mars water cycle.

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The Tharsis Rise is a large volcanic province in the tropics of Mars 1 (latitude range: ±40° N, longitude range: 220–300° E). It is a broad topographic dome that rises about 5 km above the surrounding terrain and covers a region 5,000 km wide 2 . It contains some of the Solar System’s largest and tallest volcanoes 3 , such as Olympus Mons (21 km altitude), Arsia Mons (18 km), Ascraeus Mons (18 km) and Pavonis Mons (14 km), but also smaller shield volcanoes such as Ceraunius Tholus (9 km). Volcanic activity on Mars has been concentrated predominantly in this region throughout the planet’s geological history, persisting into current times, as evidenced by lava flows that are as recent as 2.4 million years old 4 . No current volcanic activity has been detected in Tharsis, although recent geophysical data show that Mars is still geodynamically active 5 , 6 , 7 .

Notable orographic water ice clouds and other atmospheric phenomena have been observed in Tharsis 8 , 9 , 10 , 11 , 12 . Water ice clouds play a fundamental role in cycling water on Mars, moving moisture for thousands of kilometres from polar regions to relatively dry equatorial areas 13 , 14 . In addition, Tharsis is situated along the route of an important cross-equatorial exchange of water vapour, where approximately 10 12  kg of water is annually transferred between the northern and southern hemispheres through the solstitial Hadley cells 15 . Atmospheric observations 16 have revealed a localized enrichment in water vapour above the Tharsis volcanoes, suggesting that an active exchange of water vapour between the regolith and the atmosphere may be ongoing, probably facilitated by desorption from the regolith and/or sublimation of frost. A subsequent study 17 confirmed the water-vapour enrichment over these areas but hypothesized that the local circulation pattern typical of the volcanic region is possibly responsible for the enrichment as it may carry considerable amounts of water vapour upslope.

Apart from the polar regions, water ice manifests on the surface as seasonal frost in mid- and low-latitude locations. NASA’s (the National Aeronautics and Space Administration’s) Viking 2 lander detected water frost at ~48° N 18 , 19 , 20 . In addition, orbital observations from a variety of instruments revealed that water frost can occur up to 13° S in the southern hemisphere and as low as 32° N on shaded pole-facing slopes 21 , 22 , 23 . However, the presence of frost at the tropics (~0° N latitude) was not expected because of higher average surface temperatures 24 and lower humidity 25 . Some studies predicted that on most of Mars’s surface, small amounts of H 2 O frost can condense nightly if radiative cooling is strong enough 26 , 27 . For example, extremely small amounts of water frost have been observed to condense near the equator on the high thermal emissivity calibration targets of NASA’s Opportunity rover 28 , 29 .

Most of the Martian atmosphere is composed of CO 2 gas, and therefore CO 2 frost can also form if surface temperatures are low enough 30 . On the basis of nightly surface temperatures and thermal modelling, it was shown that in the equatorial regions CO 2 frost may condense diurnally 29 , 30 , 31 , 32 . Predictions 30 , 31 indicated that putative CO 2 frost deposits may persist for only a few minutes after sunrise (~6:00 Local Solar Time ( lst )) before sublimating back into the atmosphere. Follow-up global surveys, utilizing early-morning colour observations from the Thermal Emission Imaging System (THEMIS 33 ) were conducted to search for these frosts in the equatorial regions, but no evidence of morning CO 2 frost was identified 34 .

Observations by the Colour and Stereo Surface Imaging System (CaSSIS 35 ) on board the European Space Agency’s (ESA’s) Trace Gas Orbiter (TGO) provide strong evidence for morning frost deposition on the equatorial Tharsis volcanoes. We present here these observations coupled with supporting evidence from other instruments and modelling.

Observations of frost

Early-morning images ( lst  = 7:11; latitude = 18.5° N, longitude = –133.5° E; spatial resolution = 4.5 m pixel –1 ) of Olympus Mons caldera acquired by CaSSIS (at utc 2022 November 25) in the late northern winter (solar longitude (Ls) ~345°) on Mars year (MY) 36 first revealed bluish deposits (at ~500 nm) on sections of the caldera floor and rim (Fig. 1 ). The CaSSIS observation suggests a spatial correlation between the bluish deposits and topography (Fig. 1d ). The deposits are concentrated on the caldera floor but are absent on well-illuminated warm slopes and farther north on the volcano flank. The finding was confirmed five days later with a High Resolution Stereo Camera (HRSC) 36 observation acquired on 2022 November 30 ( lst  = 7:20; latitude = 18.2° N, longitude = −133.2° E; spatial resolution = 800 m pixel –1 ), which revealed that the diffuse bluish ‘halo’ deposit was ubiquitous on the entire caldera floor and rim (Fig. 1b,c ). The halo is absent on the volcano flanks and is concentrated only at the mountain summit. During the CaSSIS detection, the Nadir and Occultation for Mars Discovery (NOMAD 37 ) spectrometer was operating and acquired a ride-along observation (instantaneous field of view = 17.5 km × 0.5 km). The nadir spectral data acquired in the NOMAD limb nadir and solar occultation (LNO) channel revealed that the deposit is frost (Fig. 1e ) as indicated by the elevated ice index values (more than 3 σ confidence; Methods and Supplementary Figs. 1 and 2 ).

a , Global view of Mars with white box marking the location of Olympus Mons. b , HRSC wide-angle image of Olympus Mons acquired in the early morning ( lst  = 7:20, Ls = 346.7°, latitude = 18.2° N, longitude = −133.2° E). The black dashed line indicates the orbit of the TGO corresponding to the images in d and e . The white box highlights the close up in c . c , Zoomed-in view of the Olympus Mons caldera. The white and blue dashed rectangles show the footprints of the CaSSIS and NOMAD-LNO observations, respectively. d , High-resolution (4.5 m pixel –1 ) CaSSIS colour image of frost on the caldera floor and northern rim of Olympus Mons ( lst  = 7:11, Ls = 344.1°). Frost is absent on the well-lit steep slopes. The blue rectangle marks the footprint of the one NOMAD-LNO observation that falls within the frost-covered area. e , NOMAD-LNO channel observation of the Olympus Mons caldera. The ice index values ( Methods ) indicate the presence of frost over the caldera floor (> µ  + 3 σ ). The coloured areas on the plot indicate the confidence intervals. HRSC image ID: hn889_0000 ( b , c ). CaSSIS colour image ID: MY36_022332_162_0_NPB ( d ). NOMAD-LNO observation ID: 20221125_082524 ( e ). Credit: b , ESA/DLR/FU Berlin; d , ESA/TGO/CaSSIS under a Creative Commons license CC-BY-SA 3.0 IGO .

Repeat imaging by HRSC shows that the frost deposits on top of Olympus Mons (Fig. 1b ) appear only in the early Martian morning ( lst  = ~7:00–7:30; latitude = 18.2° N, longitude = −133.2° E) and are spatially correlated with a geological bright halo unit (Extended Data Fig. 1a,d ). This unit may be dust that is relatively brighter than the surrounding material due to different grain size or texture 38 . This bright halo unit is also observed in Context Camera 39 images (Extended Data Fig. 2a ). Materials consisting of smaller particles may exhibit different thermophysical properties such as lower thermal conductivity 40 and high thermal emissivity 41 . Surfaces with such properties cool down more at night and warm up more slowly in the morning, further enhancing the likelihood and duration of frost formation. This latter point is illustrated by CaSSIS observations of frost on dust deposits that have not been removed by winds (Extended Data Fig. 2b,c ). As shown by CaSSIS, frost may also condense leeward of small craters where air-fall dust can accumulate and is perhaps less compact (Extended Data Fig. 2d ). Porous and less-compact materials provide more nucleation sites for frost formation 42 . Outside of the bright halo, frost is found near the northern rim of Olympus Mons, but its emplacement is more localized (Extended Data Fig. 2e–i ). In conclusion, the observed frost patterns on Olympus Mons, particularly in areas with geologically distinct bright dust deposits, underscore the importance of thermophysical properties such as low thermal conductivity and high thermal emissivity, as well as surface texture, in governing the formation, distribution and persistence of frost on Mars.

Within the CaSSIS database, 13 instances of frost have been found (Extended Data Fig. 3 ). These include detections not only on the largest Tharsis volcanoes of Olympus, Ascraeus and Arsia Montes but also on the smaller-sized Ceraunius Tholus shield volcano (Extended Data Fig. 4 ). In one case, the frost deposits on Arsia Mons (Fig. 2a ) are observed in the early Martian morning and during southern winter solstice ( lst  = ~8:00, Ls = ~90°, latitude = −8.7° N, longitude = −121.1° E). The frost line dividing warm and shadowed slopes is, however, not observed in the repeat CaSSIS observations of this location, which were acquired during late southern spring (Fig. 2b–d ). Photometric analysis shows that frost is associated with an increase in ratioed reflectance of up to 20% at wavelengths (Fig. 2e ; CaSSIS blue (BLU) filter bandwidth is 390–570 nm (ref. 43 )). The fact that frosty surfaces are sometimes brighter only at blue wavelengths, implying a lower spectral slope, can also be observed in a linearly stretched CaSSIS BLU filter image (Extended Data Fig. 5 and Methods ) and average spectra from a k -means clustering analysis 44 , 45 applied on a topographically corrected and photometrically normalized CaSSIS cube (Supplementary Fig. 3 ). The photometric and clustering analyses suggest that the frost deposits are probably very thin.

a , Frost on the shadowed slope of the crater in an early-morning observation during southern winter in MY 35 (latitude = −8.74° N, longitude = −121.14° E). b – d , No frost in an early-morning observation ( b ) and no frost in afternoon observations ( c , d ) during late southern spring in MY 36. The spectral profile along the black line in a is shown in e and reveals a marked increase in reflectance up to 20% in the BLU filter when frost is present. Errors are from the uncertainty in the absolute calibration of the instrument and are about ~3% (ref. 43 ). The illumination direction is indicated by the arrows in the bottom right corner of each image. North is up in all panels. The CaSSIS image IDs are shown in order ( a – d ): MY35_008465_192_0_NPB, MY36_020297_350_3_NPB, MY36_020366_190_1_NPB and MY36_020478_190_3_NPB. Credit: a , ESA/TGO/CaSSIS under a Creative Commons license CC-BY-SA 3.0 IGO .

CaSSIS observations of Olympus and Arsia Montes indicate diurnal and possibly seasonal trends in frost deposition (Fig. 3 ). The four detections in Olympus Mons (Fig. 3a,b ) are clustered around the early-morning hours ( lst   =  ~7:00–7:30) and northern spring equinox (Ls = ~320–40°). Similarly, the four detections in Arsia Mons (Fig. 3c–d ) fall within a slightly wider time range ( lst  = ~ 7:00–8:30) but around the southern winter solstice (Ls = ~45–145°). The early-morning non-detections in Arsia Mons fall within the southern summer period, which suggests seasonality (Extended Data Fig. 6b ), but the lack of early-morning observations in the northern summer precludes us from making the same conclusion for the detections in Olympus Mons (Extended Data Fig. 6a ). We removed observations at extremely high solar incidence angles (>85°) because of low image signal-to-noise ratio (SNR), and therefore there is an observational bias towards lst s at about 6:00 ( Methods ). Collectively, CaSSIS observations suggest that the frost cycle over Martian volcanoes is ephemeral and exhibits variability on multiple timescales. It appears to be influenced by diurnal patterns, probably reflecting daily temperature fluctuations. In addition, there is a probable control by the Martian seasons, indicating a longer-term variation in the frost cycle. On the basis of the CaSSIS observations, while there are indications of diurnal and seasonal influences on frost deposition on Martian volcanoes, these observations alone cannot definitively determine the composition of the frost. Therefore, we use simulations of surface temperatures as a proxy for frost composition.

a – d , Rose diagrams showing the seasonal ( a , c ) and diurnal ( b , d ) frost detections by CaSSIS over Olympus Mons ( a , b ) and Arsia Mons ( c , d ). The width of each bin reflects the number of CaSSIS observations. Frost is detected around northern spring equinox (Ls = ~0°) on Olympus Mons and around southern winter solstice (Ls = ~90°) on Arsia Mons. Frost is detected only in the early-morning hours (~7:00–8:00  lst ). The negative detections in the early morning bins correspond to observations that were acquired in warmer seasons.

Surface temperatures indicate water frost

At the time of CaSSIS frost detections in Olympus Mons (Fig. 1 ) and Arsia Mons (Fig. 2 ), the surface temperatures calculated by the general circulation model (GCM 46 ) via the Mars Weather Research and Forecasting (WRF 47 ) model are inconsistent with CO 2 frost. The stability of CO 2 frost at higher altitudes necessitates exceptionally low temperatures, specifically below 140 K, to maintain its solid state 30 . The predicted surface temperatures (at ~150 km model resolution) are ~150 K and 185 K at ~7:00  lst in Olympus Mons and at ~8:00  lst in Arsia Mons, respectively (Fig. 4 ). In addition, advanced high-resolution mesoscale modelling, with a model resolution of 5.47 km, reveals a substantial temperature difference between the surface temperature and the local CO 2 frost point at the locations of CaSSIS observations, with a difference of approximately 10 K at Olympus Mons (Fig. 5d ) and over 55 K at Arsia Mons (Extended Data Fig. 7d ). In fact, the surface temperatures predicted at each CaSSIS frost location (Table 1 ) consistently exceed the CO 2 frost point, corresponding to the mean surface temperature ~162 K (excluding C3 and C6). The stratification of water vapour in Mars’s atmosphere, especially near the surface, is not well understood 48 , making the determination of the H 2 O frost point challenging due to its considerable variability; however, it is generally accepted that this point occurs at around 180 K (ref. 14 ). Since the predicted surface temperatures at the time of CaSSIS, HRSC and NOMAD observations are too warm, this suggests that CO 2 frost is unlikely, hence providing support for the presence of water frost. At these seasons (Ls = 346.7° for Olympus Mons and Ls = 93.8° for Arsia Mons), CO 2 frost was also not observed by the Thermal Emission Imaging System 29 or by the Emirates Mars InfraRed Spectrometer 32 . Interestingly, the GCM also predicts that some CO 2 frost may be present at Ls = ~0–150° and at around sunrise (5:00–6:00  lst ) in Arsia Mons (Fig. 4b ). This result is consistent with previous studies indicating CO 2 frost formation from minutes to tens of minutes after sunrise in the equatorial regions 29 , 30 , 31 . However, such potential CO 2 frost deposits would sublime very quickly and would be difficult to detect by cameras and spectrometers due to low SNR 34 . In addition, we investigated the possible role of CO 2 frost in regolith gardening and slope streak formation on Mars 30 , 34 , 49 . We found no slope streaks on the calderas of the largest Tharsis volcanoes or any obvious differences in talus boulder shapes and sizes ( Methods and Extended Data Fig. 8 ). These results suggest that the diurnal CO 2 or H 2 O frost cycle plays a minor (if any) role in landscape evolution at these sites.

a , b , Annual surface temperatures at four different local mean solar times (LMST). c , d , Diurnal surface temperatures at Olympus Mons (Ls = 350°) ( c ) and Arsia Mons (Ls = 90°) ( d ) as predicted by the GCM. In c , d , blue and red horizontal dashed lines depict CO 2 30 and H 2 O frost point 14 , respectively. On both volcano calderas at the time of CaSSIS image acquisition, CO 2 frost point is not reached. This indicates favourable conditions for H 2 O ice. The simulations were conducted at geographical coordinates 18.75° N, −133.75° E for Olympus Mons and −8.75° N, −121.25 °E for Arsia Mons.

a – d , Utilizing MarsWRF high-resolution mesoscale modelling (at the time of CaSSIS observation Fig. 1d ), this figure presents the influence of Olympus Mons’s topography on its local climate, as shown by elevation gradients ( a ), surface atmospheric pressure ( b ), near-surface horizontal wind patterns ( c ) and the deviation in temperature between the Martian surface and the local CO 2 frost point ( d ). The topography of the Olympus Mons caldera is demonstrated to cause noticeable variations in local pressure, wind velocities and temperature gradients. The black outline across all panels highlights the boundary of the Olympus Mons caldera, while the black dashed rectangle marks the area observed by CaSSIS, as referenced in Fig. 1d . The CO 2 frost point in the area of CaSSIS observation is exceeded by about 10 K. By contrast, the CO 2 frost point in Arsia Mons is exceeded by around 60 K (Extended Data Fig. 7d ).

Microclimate and water ice amount

Our high-resolution mesoscale simulations reveal the distinct microclimatic conditions induced by the topography of the Tharsis volcanoes, as shown in Fig. 5 and Extended Data Fig. 7 . Specifically, within the calderas of Olympus Mons and Arsia Mons, we observe a substantial reduction in surface atmospheric pressure and near-surface horizontal wind speeds compared with the surrounding areas. For example, within the caldera of Olympus Mons (Fig. 5b ), the atmospheric pressure is estimated at only 110 Pa, compared with 160 Pa at the mountain’s base. Similarly, in the area of Arsia Mons (Extended Data Fig. 7b ), the pressure is about 100 Pa, notably lower than the over 200 Pa found in the adjacent plains. Moreover, the near-surface horizontal wind speeds within Olympus Mons (Fig. 5c ) are estimated at less than 10 m s –1 , in stark contrast to the approximately 30 m s –1 observed along the volcano’s flanks. In the case of Arsia Mons (Extended Data Fig. 7c ), the wind speeds are below 5 m s –1 within the caldera, compared with roughly 20 m s –1 on the flanks, highlighting the profound impact of volcanic topography on localized weather patterns.

Furthermore, our GCM simulations suggest that the thickness of water frost deposits is on the order of 1 µm ( Methods ). However, this estimate carries considerable uncertainty due to the unknown quantities of water-vapour-column abundances. To refine this estimate, we reference radiative transfer calculations 50 , 51 , which suggest a minimum thickness of 100 µm, while laboratory experiments 52 imply a thickness of about 10 µm ( Methods ). By adopting the median thickness of 10 µm for the water frost, and considering that the frost deposits are confined to the calderas of Olympus, Arsia, Ascraeus Montes and Ceraunius Tholus, we estimate that there is a transfer of approximately 1.5 × 10 8  kg of water ice between the surface and the atmosphere ( Methods ).

Possible sources of water vapour

The seasonal trends as shown by the set ( n  = 13) of CaSSIS observations suggests an atmospheric phenomenon driven by water transport due to large-scale seasonal changes, such as sublimation of the seasonal ice cap in the opposite hemisphere and transportation of humid air into the volcano calderas by upslope winds. Seasonal processes have been observed at a wide range of Martian latitudes 53 and may also apply to the Tharsis region. For example, the activity of the Aphelion Cloud Belt peaks at Ls ∼ 40°–140° (ref. 54 ), and in general little cloud activity is observed at Ls ~245°–320° 10 . Similarly, afternoon orographic clouds have been detected by Mars Reconnaissance Orbiter’s Mars Color Imager 55 over the Tharsis volcanoes 10 . The seasonal observation of water-vapour enrichment over Tharsis 17 shows increased abundances around northern spring equinox (Ls 0°), consistent with the CaSSIS detections of frost close to this season in Olympus Mons. Therefore, we hypothesize that this water-vapour enrichment 17 may be the source of the frost deposits detected in our study. The transport of water vapour from high latitudes to the Tharsis highlands could be facilitated by large-scale atmospheric eddies 56 . This process could be further augmented by strong upslope winds, driven by a combination of thermal effects and mountain gravity waves 57 , facilitating the movement of moisture over the volcano calderas. The local topography-induced circulation 57 and microclimatic conditions within the caldera (shown in Fig. 5 and Extended Data Fig. 7 ) may create favourable conditions for water frost condensation during the cold Martian nights. Within these calderas, ~150,000 tons of water ice is exchanged daily between the regolith and the atmosphere during the cold Martian seasons. Although this amount is relatively a small fraction of the seasonal inventory of water vapour in the Martian atmosphere (~10 12  kg) (ref. 14 ), it is important in the context of localized Martian environmental processes. Understanding these micro-environments is crucial for a comprehensive understanding of Mars’s hydrological cycle.

It is conceivable that dormant volcanoes can emit CO 2 , water vapour and minor amounts of SO 2 (ref. 58 ) via diffuse outgassing from the regolith 59 , 60 . If the observed water frost deposits are of volcanic origin, their distribution may constrain models for present-day outgassing from the interior. However, on Mars, SO 2 has not been detected 61 and no thermal hotspots have been found 62 . A volcanic source for the condensate cannot completely be ruled out, but further tests for trace species (CO 2 , H 2 S and SO 2 ) would be useful to explore the likelihood of this potential mechanism. Consequently, we conclude here that the newly detected frosts on Tharsis volcano calderas are probably of atmospheric origin.

CaSSIS frost observations

We surveyed ~4,200 CaSSIS images (acquired up to 2022 February 05) with illumination geometries of 50–90° incidence within dusty, low thermal inertia (<100 TIU) regions (60° N—30° S). Only images that include the latest CaSSIS radiometric and absolute calibration were used in this study 43 , 63 , 64 .

The images used in this study consisted of early (6:00–9:00  lst ) and late (15:00–18:00  lst ) times. Analysis and comparison in these two local time regimes may help the distinction between early-morning and late-afternoon phenomena. During the survey, it was noticed that most CaSSIS images acquired at extremely high solar incidence angles of 85–90° contain colour and calibration artefacts due to the decrease in SNR and/or an increase in aerosol contribution from the atmosphere 63 . Consequently, the images with colour artefacts were labelled as ambiguous and were not used for further analysis.

Frost detections relied on the use of CaSSIS NPB (near infrared (NIR) = 940, panchromatic (PAN) = 670, blue (BLU) = 497 nm) and synthetic RGB (red–green–blue; PAN and BLU only) products. These filter configurations allow a convenient separation between frosty and frost-free terrains. In CaSSIS colour products, frosty areas appear bluish, and/or whitish, and sometimes are bright only in the BLU filter (relative to frost-free areas; also see Supplementary Figs. 9 and 10 ). In support, we observe bluish frost deposits in HRSC colour images shown in Fig. 1b and Extended Data Fig. 1 (composites of blue (440 nm), green (530 nm) and red (750 nm) channels).

As shown by previous studies 21 , 65 deposits are usually correlated with topography (prefer poleward-sloping terrains). Therefore, if both conditions were met (colour and topographic correlation), it was considered a strong indication of surface frost. As a final procedure, each of these candidate detections was then analysed using a spectral profile tool in the Environment for Visualizing Images software. This procedure extracts the pixel irradiance over flux ( I/F ) values between two manually selected points crossing the potentially frosted region in each filter. The profiles were then normalized by a mean I/F of a nearby frost-free, relatively flat region of interest (ROI), a well-established method to cancel out some of the atmospheric and topographic effects 49 , 66 , 67 , 68 , 69 , 70 . If the frost deposits were brighter in the BLU filter than the surrounding frost-free terrains by at least 3% (within CaSSIS absolute uncertainty 43 ), then such images were flagged as potential frost detections. This survey yielded many frosty sites (not shown here) at latitudes ~40° N and ~30° S. However, because these latitude bands are dominated by known seasonal frost deposits 21 , 23 , 65 and we do not have a robust method to distinguish between seasonal and diurnal frost, we further narrowed our filtering criteria. The final frost detections analysed here were restricted to equatorial ~20° N to ~10° S latitudes (outside of the seasonal mid-latitude regions). In this work, only equatorial sites that included visible evidence of frost are considered.

The spectral profile shown in Fig. 2e was computed by dividing each pixel along the profile by an average pixel value extracted from an ROI in Extended Data Fig. 5d . The ROI (>100 pixels in size) was selected on a frost-free and relatively flat terrain as suggested by the low slope values in the CaSSIS digital elevation model of this site. CaSSIS digital elevation models were produced by a pipeline developed at the Astronomical Observatory of Padova, National Institute for Astrophysics 71 , 72 .

NOMAD-LNO spectral processing

The NOMAD instrument is a suite of three high-resolution spectrometers also on board TGO, offering nadir infrared observations through its LNO channel 37 , 73 . This channel covers the 2.2–3.8 µm spectral range where several spectral features of ice are distributed over different wavelengths. Nevertheless, the NOMAD-LNO spectrometer has the particularity of not observing the entire spectral range at once. The data are acquired through small spectral windows, representing specific diffraction orders of the diffraction grating. Each LNO observation can select a maximum number of 6 diffraction orders every 15 seconds to ensure the best possible SNR 74 , 75 , 76 . The LNO footprint (instantaneous field of view) is 17.5 km × 0.5 km (ref. 75 ), which provides enough spatial scale to resolve the caldera of Olympus Mons. In this work, we use spectrally and radiometrically calibrated LNO data converted into a reflectance factor. The 2.7 µm ice band is the strongest in the LNO spectral range, resulting from both CO 2 and H 2 O ice absorption. Although the use of this band is not suitable for quantifying the amount of ice (easily saturated), it is effective for detecting homogeneous deposits (both CO 2 and H 2 O ice), as demonstrated with the ice index value 77 . This spectral parameter uses two diffraction orders. It is based on the combination of high reflectivity at continuum wavelengths with a more pronounced absorption in the 2.7 μm band. Initially defined as the spectral ratio between the reflectance factors of order 190 (continuum part, 2.32–2.34 µm) and 169 (short wavelengths shoulder of the 2.7 µm band, 2.61–2.63 µm) (ref. 77 ), we adjust the ice index by considering the available orders of the joint CaSSIS–NOMAD observations, that is, orders 190 and 168 (2.64–2.65 µm).

In nadir mode, the variability in the reflectance factors is caused mainly by the surface albedo variations resulting from the different absorption of the Martian surface mineralogy 78 , 79 , 80 . To remove spatial albedo variations over the explored Martian surface, we normalize the LNO reflectance factors to the Martian albedo. The adjusted ice index (II) can thus be defined as:

where R i is the LNO reflectance factor value averaged around the central wavelength i of the LNO spectrum, fitted by a third-degree polynomial to mitigate the spectral oscillations resulting from the instrumental characteristics of the LNO channel, which become significant on the edges of each order (Supplementary Figs. 1 and 2 ). OMEGA i is the OMEGA albedo map 80 based on reflectance spectra in the near infrared as NOMAD-LNO R i . Two OMEGA albedo maps are used in this work: one defined at 2.32 µm for order 190 and the other defined at 2.62 µm for order 168. Studies have shown that this spectral parameter identifies spatially extensive and abundant ice deposits when the index values are three sigma higher than their average value over ice-free mid-latitude terrain 77 , 81 .

Mars GCM modelling

We perform the Martian GCM simulation for the entire MY 36 using the MarsWRF model, which is the Mars adaptation of the general-purpose planetary atmosphere model, planetWRF 47 . Here the GCM set-up is based on a previous study 46 examining the Martian planetary boundary and dust–turbulence interaction over a decade, from MY 24 to MY 34, which hosted three global dust storms. The reference model set-up 46 was validated against NASA’s Mars Climate Sounder (MCS) observations on board the Mars Reconnaissance Orbiter, radio occultation observations from ESA’s Mars Express orbiter, as well as the in situ observations from NASA’s Mars Science Laboratory Curiosity rover. This model set-up consists of a semi-interactive two-moment dust transport model 46 within the MarsWRF framework, in a way that the dust is lifted, mixed by model winds and sedimented, as guided by observed maps of column-integrated dust optical thickness 82 , 83 . Via this method, model processes govern the vertical dust distribution and related dust radiative heating, yet the horizontal dust distribution is guided to match the orbiter observations. In this model, the horizontal dust distribution is constrained to follow observations. In this model, the two-stream correlated k -distribution scheme is used for the short-wave and long-wave radiative transfer 84 . We use a Mars-specific boundary-layer turbulence parameterization scheme, which allows us to obtain the surface–atmosphere exchange coefficients 85 Surface properties of the MarsWRF model, such as the topography, albedo, emissivity and thermal inertia, are acquired from the datasets of the Mars Orbiter Laser Altimeter 2 and Thermal Emission Spectrometer (TES 78 ) observations, where the details are presented in another study 47 . Here we increased the horizontal model grid spacing of the GCM from 5° × 5° to 2.5° × 2.5° (ref. 46 ), enabling better spatial coverage to provide more realistic boundary and initial conditions to our mesoscale simulations. We used 52 vertical sigma layers extending up to the model top of 100 km. The predicted surface temperatures are shown in Table 1.

Our modelling methodology is based on a previous study by MarsWRF 85 , 86 . Mesoscale simulations for Fig. 5 and Extended Data Fig. 7 were forced with initial and boundary conditions acquired by GCM simulations corresponding to the same seasonal conditions of CaSSIS observations shown in Figs. 1 and 2 . The plots we present in terms of winds, pressure and temperature correspond to the local hours of observations. We nested three mesoscale domains in our GCM domain (see Supplementary Fig. 12 for details). Mesoscale domains use prescribed boundary conditions, derived either from GCM predictions (as in the case of d2) or from another mesoscale domain (d3 and d4). The GCM grid has a horizontal resolution of approximately 150 km. We progressively increased the horizontal resolution with a factor of three for our nested mesoscale domains. Our innermost domain, d4, has a horizontal resolution of 5.47 km. To assess the accuracy of our mesoscale predictions, we compared MarsWRF surface temperature predictions with the surface temperature observations by MCS and TES available for Olympus Mons and Arsia Mons regions at around 3:00  lst (Supplementary Fig. 13 ). We considered a sufficient Ls range of MCS and TES observations (Ls 310–360 for Olympus Mons and Ls 75–100 for Arsia Mons) to provide a sufficient set of observations to acquire a temperature map to be compared with MarsWRF simulations. These observations range from 1:00  lst to 4:00  lst , and MarsWRF estimations at the corresponding local times are compared for validation. The modelled surface temperatures for Olympus Mons caldera are within 10 K of the observations and within a few degrees Kelvin for Arsia Mons. It is important to note that these predictions carry uncertainties, particularly in regions with complex topography such as the Tharsis volcanoes.

Surface frost thickness and mass estimations

The MarsWRF GCM incorporates the phase transition and transport mechanisms of water vapour and ice, facilitating a parameterization of the Martian hydrological cycle that aligns with the methodologies outlined by previous studies 87 . This parameterization enables the model to approximate the surface frost layer thickness to about 1 μm at the locations in our study. However, it is important to acknowledge the inherent uncertainties associated with such estimations, particularly due to the limitations of physical parameterizations within Martian atmospheric models. These uncertainties are most pronounced in the prediction of atmospheric variables in regions lacking empirical observational data, such as the deposition rates of atmospheric volatiles.

In a recent experimental investigation, one study 52 systematically evaluated the interaction between water frost deposition and the optical properties of a Martian soil simulant, specifically Mars Global Simulant (MGS-1 88 ). The experimental design involved the controlled deposition of water frost on the surface of the simulant, followed by precise measurements of both the spectral reflectance and the thickness of the frost layer. The findings indicate that a frost layer thickness ranging from approximately 10 to 20 μm is required to significantly attenuate the characteristic red slope of the spectral reflectance, aligning with the observed morning frost brightening in the blue wavelengths by approximately 10–20% as detected by the CaSSIS instrument. Furthermore, the study demonstrates that a relatively thin frost layer of about 100 μm is sufficient to flatten the visible spectrum, effectively neutralizing the spectral features.

Radiative transfer models 50 , 51 can provide an additional constraint on the frost thickness estimation via the minimum optical depth ( τ ) necessary for frost visibility at CaSSIS visible and LNO near-infrared wavelengths. For example, with a τ of 10 –2 , we anticipate a minimal impact on albedo, less than 0.1 at CaSSIS visible wavelengths and negligible at LNO near-infrared wavelengths, given the single-scattering co-albedo is around 10 −6 for visible light and less than 10 −1 at 2.6 μm. However, LNO observations indicate a discernible albedo reduction at near-infrared wavelengths, suggesting a higher optical depth than 10 −2 . This implies that the frost’s grain radius and/or thickness must exceed 5 μm and 1 μm, respectively. If the grain radius is about 1 μm, then the frost layer’s thickness could be significantly greater, approximately 100 μm.

To conduct a preliminary quantification of the frost mass, we assumed a uniform frost layer thickness across all identified frost-covered regions, as observed by CaSSIS. The geographical extent of the frost coverage was approximated to the combined surface areas of the calderas of Martian volcanoes such as Arsia Mons, Olympus Mons, Ascraeus Mons and Ceraunius Tholus. By integrating the uniform frost thickness with the delineated area and adopting the density value for pure ice, we derived an initial estimate of the total frost mass. This approach provides a rudimentary yet insightful approximation of the frost mass, acknowledging the broad-scale estimative nature of this calculation.

Boulder size measurements

To investigate a potential effect of the diurnal frost cycle on the overall geomorphology and landscape evolution, we studied the shape of mass-wasted boulders across six sites of interest. Here we compare the sizes of boulders on volcanoes with frost as determined by CaSSIS (two sites in Olympus Mons and one in Arsia Mons) and on volcanoes where frost has not been detected (Tharsis Mons, Jovis Tholus and Ulysses Tholus). Because frost accumulates preferentially on poleward-facing slopes on Mars 29 , here we focused only on north-facing and south-facing slopes. This might reveal whether there are considerable differences in boulder sizes due to frost weathering 89 .

We used eight map-projected High Resolution Imaging Science Experiment (HiRISE) 90 images in Geographic Information System (QGIS) to determine the three principal dimensions of each identified boulder. The first dimension is defined as the longest distance between two points on the boulder as visible from orbit. Similarly, the second dimension is defined as the diameter of the boulder orthogonal to the first dimension. Last, the third dimension is defined as the height of the boulder as estimated using shadow length and solar incidence angle. In total, we identified and measured 63 boulders across the six sites. All derived measurements were plotted on ternary diagrams 91 using the Tri-Plot software 92 . These diagrams relate the three principal dimensions of each boulder, visualizing its overall shape as well as similarities and differences within and across the studied sites.

Data availability

CaSSIS data can be found on the University of Bern repository ( https://observations.cassis.unibe.ch/ ) and the ESA’s Planetary Science Archive ( https://archives.esac.esa.int/psa ). NOMAD-LNO observations are also found on the ESA’s Planetary Science Archive.

Code availability

The PlanetWRF model for Martian GCM and mesoscale simulations is accessible by request at https://planetwrf.com/ .

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Acknowledgements

CaSSIS is a project of the University of Bern and funded through the Swiss Space Office via ESA’s PRODEX programme. The instrument hardware development was also supported by the Italian Space Agency (ASI) (ASI-INAF agreement no. 2020-17-HH.0), INAF/Astronomical Observatory of Padova and the Space Research Center (CBK) in Warsaw. Support from SGF (Budapest), the University of Arizona (Lunar and Planetary Lab.) and NASA is also gratefully acknowledged. Operations support from the UK Space Agency under grant ST/R003025/1 is also acknowledged. The NOMAD experiment is led by the Royal Belgian Institute for Space Aeronomy (IASB-BIRA), assisted by Co-PI teams from Spain (IAA-CSIC), Italy (INAF-IAPS) and the United Kingdom (Open University). This project acknowledges funding by the Belgian Science Policy Office (BELSPO), with the financial and contractual coordination by the ESA Prodex Office (PEA 4000103401, 4000121493) and Italian Space Agency through grant 2018-2-HH.0. Operations and science support from the UK Space Agency under grants ST/X006549/1, ST/Y000234/1, ST/V005332/1 and ST/V002295/1 is also acknowledged. This research is financially supported by the Research Foundation-Flanders (FWO) with grant 12AM624N to C.B.S., and grant 12ZZL23N to O.T. J.J.L.-M. acknowledges financial support from the Severo Ochoa grant CEX2021-001131-S funded by MCIN/AEI/ 10.13039/501100011033 and by Spanish MICIIN through Plan Nacional and European funds. F.S. acknowledges support from the Institut National des Sciences de l’Univers (INSU), the Centre National de la Recherche Scientifique (CNRS) and Centre National d’Etudes Spatiales (CNES) through the Programme National de Planétologie. MRELM acknowledges funding from the KU internal grant (8474000336-KU-SPSC).

Open access funding provided by University of Bern.

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Space Research and Planetary Sciences, Physics Institute, University of Bern, Bern, Switzerland

A. Valantinas, N. Thomas, A. Pommerol, M. Almeida & M. Read

Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA

A. Valantinas

The Royal Observatory of Belgium (ROB-ORB), Brussels, Belgium

O. Karatekin, L. Ruiz Lozano, C. B. Senel & O. Temel

Archaeology, Environmental Changes & Geo-chemistry (AMGC) Research Unit, Vrije Universiteit Brussel, Brussels, Belgium

C. B. Senel

Institute of Astronomy, KU Leuven, Leuven, Belgium

Institute of Planetary Research, German Aerospace Center (DLR), Berlin, Germany

E. Hauber & D. Tirsch

Center for Space and Habitability, University of Bern, Bern, Switzerland

V. T. Bickel

INAF-Osservatorio Astronomico di Padova, Padova, Italy

G. Munaretto, M. Pajola, C. Re & G. Cremonese

Istituto di Astrofisica e Planetologia Spaziali, IAPS-INAF, Rome, Italy

F. Oliva, F. G. Carrozzo, E. D’Aversa & G. Bellucci

GEOPS, Université Paris-Saclay, CNRS, Orsay, France

Institut Universitaire de France, Paris, France

Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium

I. Thomas, F. Daerden, B. Ristic & A. C. Vandaele

Lunar and Planetary Laboratory, University of Arizona, Tuscon, AZ, USA

A. S. McEwen

Institute for Earth and Space Exploration, Department of Earth Sciences, Western University, London, Ontario, Canada

V. G. Rangarajan

Space and Planetary Science Center and Department of Earth Sciences, Khalifa University, Abu Dhabi, United Arab Emirates

M. R. El-Maarry

School of Physical Sciences, The Open University, Milton Keynes, UK

M. R. Patel

Instituto de Astrofísica de Andalucia, Granada, Spain

J. J. Lopez-Moreno

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A.V. led conceptualization, CaSSIS data collection and analysis, and writing. N.T. and A.P. led conceptualization, design and production of the CaSSIS instrument and its operation. O.T., C.B.S. and O.K. performed mesoscale and global circulation model simulations with MarsWRF, did post-processing of the modelling results and compared model predictions with MCS and TES observations, which were analysed by O.T. and O.K. L.R.L. and F.O. performed NOMAD spectral analysis. G.M. and M.P. performed CaSSIS clustering analysis and photometry. V.T.B. processed HiRISE data, performed boulder size measurements and analyzed boulder shapes. L.R.L., V.T.B., G.M., M.P., F.S., A.P., A.S.M., M.R.E.-M., V.G.R., N.T. and I.T. contributed to writing. F.G.C., A.P., N.T., G.B. and E.D. contributed to discussions and assisted with data interpretation. C.R., G.B. and I.T. contributed to data processing. A.C.V., J.J.L.-M., F.D. and M.R.P. contributed to the design and production of the NOMAD instrument and its operation. D.T., E.H., M.R., M.A., I.T. and B.R. participated in instrument operations and planning of the observations. N.T. and G.C. acquired funds for the development of the CaSSIS instrument and the generation of DEMs. A.C.V. and F.D. acquired funds for the development of the NOMAD instrument.

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Correspondence to A. Valantinas .

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Extended data

Extended data fig. 1 diurnal variations of frost halo on olympus mons..

HRSC images of Olympus Mons (lat = 18.2°N, lon = −133.2°E) acquired at different local times in MY 36. ( a, b ) Late morning images showing no evidence of frost on the bright halo deposit surrounding the volcano caldera. ( c, d ) Early morning images revealing the presence of frost on the bright halo deposit. The bright halo deposit is likely composed of fine-grained dust with low thermal conductivity, which facilitates frost formation. North is up in all panels. HRSC image IDs: hn705_0000 (a), hn772_0000 ( b ), hn889_0000 ( c ), hn948_0000 ( d ). Credit: ESA/DLR/FU Berlin.

Extended Data Fig. 2 Irregular frost distribution on the outskirts of Olympus Mons caldera.

( a ) Bright halo visible in the CTX global mosaic (Dickson et al., 2018). The bright halo deposit is also visible in HRSC non-detections in Extended Data Fig. 1 . ( b ) Dark windstreak and triangular bright unremoved dust deposits seen in CTX. ( c ) CaSSIS morning observation of froststreaks (lat/lon = 18.28°N, −134.24°E) that correlate with bright dust deposits seen in CTX. ( d, e ) Frost deposits leeward of small craters (lat/lon: 18.27°N, −134.24°E and 18.83°N, −133.65°E respectively). ( f, g ) Froststreaks that are parallel to local winds (lat/lon: 18.90°N, −133.65°E and 18.9°N −133.74°E respectively). ( h, i ) Small frost deposits on the rim of a collapse pit (lat/lon: 18.95°N, −133.69°E) and on the levee of a lava channel (lat/lon: 19.00°N, −133.71°E). North is up in all panels. CaSSIS IDs: MY37_023825_162_0 (LST =7:13AM; Ls = 42.84; c,d) and MY36_015229_160_0 (LST = 6:57AM; Ls = 35.24 ; e-i). Credit: a , b , NASA/JPL/MSSS/The Murray Lab; c – l , ESA/TGO/CaSSIS under a Creative Commons license CC-BY-SA 3.0 IGO .

Extended Data Fig. 3 CaSSIS frost detections in the Tharsis volcanic region.

Frost was detected only on and around the calderas of the three largest volcanoes such as Olympus, Arsia and Ascraeus Montes, but also on the smaller Ceraunius Tholus volcano. Frost has not been observed yet on Pavonis Mons and other Tharsis volcanoes. The basemap is the color hillshade MOLA data at 64 pixels per degree resolution. Credit: NASA/JPL/GSFC.

Extended Data Fig. 4 Frost on the caldera floor of Ceraunius Tholus volcano.

( a ) Wide angle view of Ceraunius Tholus (lat = 24.0°N, lon = −97.1°E) with CaSSIS early morning observation overlain on the CTX mosaic. ( b ) Zoomed in view of (a). White rectangle marks the close up in (c). ( c ) Ubiquitous frost coverage on the caldera floor and the apparent absence of frost on the caldera rim. ( d ) CaSSIS color NPB image of the Ceraunius Tholus caldera acquired at a different local time featuring no frost. Both CaSSIS images in (b) and (d) are acquired at similar incidence (and phase) angles, which suggests that photometric effects are not the cause of surface blueing. North is up in all panels. CaSSIS image IDs in order: MY37_023134_024_3_NPB ( a−c ) and MY36_022599_024_0_NPB ( d ). a , b , d , NASA/JPL/MSSS/The Murray Lab; a , b , c , d , ESA/TGO/CaSSIS under a Creative Commons license CC-BY-SA 3.0 IGO .

Extended Data Fig. 5 Greyscale CaSSIS filter images of a small crater presented in Fig. 2 .

( a–c ) Three individual filters: NIR (860–1100 nm), PAN (550–800 nm) and BLU (390–570 nm). ( d ) CaSSIS DEM of the same scene with the location of the spectral profile and the frost-free ROI used in the reflectance ratio ( Methods ) overlaid. The frost deposits are brighter and visible in the CaSSIS BLU filter. North is up in all panels. CaSSIS ID: MY35_008465_192_0 ( a − c ) and DEM ID: CAS-DTM-MY36_020366_190_1-OPD-03–01 ( d ). Credit: ESA/TGO/CaSSIS under a Creative Commons license CC-BY-SA 3.0 IGO .

Extended Data Fig. 6 Seasonal and local time coverage of CaSSIS early morning observations of Olympus Mons and Arsia Mons.

CaSSIS image coverage over Olympus Mons ( a ) and Arsia Mons ( b ). Based on these observations frost is not detected during late morning hours in Olympus Mons and around southern summer solstice (Ls ~270°) in Arsia Mons. The shaded grey region shows the CaSSIS observational bias (from 90 − 85° solar incidence) due to the low signal-to-noise ratio. Most observations were discarded in this region due to spectral ambiguity. The black lines mark the local sunrise time.

Extended Data Fig. 7 Microclimatic conditions simulated over Arsia Mons.

This figure illustrates the impact of Arsia Mons′ topography on localized atmospheric conditions (at 8AM, Ls = 90°, Fig. 2a ), depicted through ( a ) elevation gradients, ( b ) surface atmospheric pressure, ( c ) near-surface horizontal wind patterns, and ( d ) the temperature differential between the Martian surface and the local CO 2 frost point. The complex caldera topography of Arsia Mons is shown to substantially influence local pressure distributions, wind velocities, and thermal gradients. Notably, surface temperatures at sites identified by CaSSIS for frost presence exceed the CO 2 frost point by approximately 60 K, suggesting the predominance of H 2 O ice in these frost deposits.

Extended Data Fig. 8 Triangular (ternary) diagrams of boulder shape across six sites of interest.

Boulder shape and size analysis for Olympus Mons ( a , b ), Arsia Mons ( c ), Tharsis Tholus ( d ), Jovis Tholus ( e ), and Ulysses Tholus ( f ). The slope aspect of each site is indicated on the respective panel. The Olympus Mons ( a , b ) and Arsia Mons ( c ) sites were found to feature a distinct early morning frost signature (positives, blue), the other three sites not (negatives, red)( d – f ) – yet there is no obvious difference in boulder shape across those sites (the colored polygons underline the distribution of points). C = compact, P = platy, B = bladed, E = elongated, V = very; a = longest boulder dimension, b = intermediate boulder dimension, c = smallest boulder dimension. HiRISE image IDs ( a – f ): ESP_014275_1990_RED, ESP_043272_1980_RED, ESP_047439_1990_RED, PSP_009884_1980_RED, ESP_057843_1715_RED, ESP_012612_1940_RED, ESP_033711_1985_RED, and ESP_045619_1835_RED.

Supplementary information

Supplementary information.

Supplementary Figs. 1–13 and Text.

Supplementary Table 1

Additional CaSSIS observation details and GCM estimated surface temperatures.

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Valantinas, A., Thomas, N., Pommerol, A. et al. Evidence for transient morning water frost deposits on the Tharsis volcanoes of Mars. Nat. Geosci. (2024). https://doi.org/10.1038/s41561-024-01457-7

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Received : 01 June 2023

Accepted : 16 April 2024

Published : 10 June 2024

DOI : https://doi.org/10.1038/s41561-024-01457-7

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