Including sex as a factor in studies

💡 The importance of sex/gender and age disaggregated data in (biomedical & women's health) research When conducting systematic and scoping reviews, I frequently face the challenge of finding data disaggregated by age and/or sex/gender. Many studies either generalize across both sexes/genders or focus narrowly on women of reproductive age (15-49), often neglecting the distinct health needs of teenage girls, premenopausal, or older women. It's time to acknowledge the critical need to incorporate sex/gender and age considerations into our research. Here is why: - Both sex (biological) and gender (sociocultural) factors influence health, disease, and treatment responses. - Age disaggregation is crucial because it enables accurate tracking of health trends across the life course, aids in identifying age-specific risk factors and interventions, and supports more effective health policy planning and program evaluation. - Government/national funding agencies like NIH and the European Commission have begun to implement policies to integrate sex, gender, and, more recently, diversity analysis into the grant proposal process. Integrating sex/gender and age considerations will lead to more rigorous, reproducible, and relevant research - ultimately improving health outcomes for all. Let's discuss how we can drive this important change together. #research #womenshealth #gender

Sex differences in the brain explain less than 1% of gene-expression variation. And yet they might explain why your Alzheimer's drug failed. A new study in *Science* analyzed over one million brain cells from 30 individuals across six cortical regions. The finding that matters most is not the one that makes headlines. Yes, the team identified more than 100 genes with consistent expression differences between male and female brains. But the core quantitative result deserves more attention. Sex accounted for less than 1% of total variation in gene expression. More variation exists within a sex than between sexes. This is not a contradiction. It is the whole point. Small molecular differences do not mean irrelevant molecular differences. They mean context-dependent molecular differences. The kind that modulate disease risk without determining it. The kind that disappear in underpowered studies and reappear in well-designed ones. This matters for anyone working in preclinical neuroscience or drug development. If your study design treats sex as a confounder to control for rather than a biological variable to characterize, you are not reducing noise. You are discarding signal. The field has known for years that schizophrenia, ADHD, and Parkinson's skew male. That Alzheimer's, depression, and anxiety skew female. What was missing was a molecular handle. DeCasien et al. now provide one. Not a definitive mechanism. A starting coordinate. For translational researchers, the implication is operational. Stratifying by sex is not a regulatory checkbox. It is an analytical prerequisite. The 1% that distinguishes male from female gene expression may sit precisely in the pathway your compound targets. Ignoring small effects because they are small is not rigorous. It is the reason 95% of CNS drugs fail in translation. Article in first comment.

Sex and gender are widely acknowledged as important variables in #research. This paper shows how inconsistently they are actually studied. A new Nature Neuroscience Perspective by Michelle Roche et al., led by the international #PAINDIFF Network, brings much-needed methodological clarity to this gap. The recommendations are grounded in a global survey of 483 pain researchers, combined with an expert consensus process spanning preclinical, clinical, and translational research. 💡 Several findings motivating the recommendations stood out: • Most researchers report that sex is important, yet far fewer routinely include both sexes in study design • Even when both sexes are included, sex-disaggregated analysis and reporting remain inconsistent • Gender is rarely incorporated beyond basic demographics in human and clinical studies • Common barriers persist, including limited resources, uncertainty about relevance, and lack of clear guidance • In preclinical research, persistent assumptions about increased variability in females continue to shape design choices These gaps matter. Inconsistent inclusion and reporting limit reproducibility, complicate comparison across studies, and reduce translational value. In response, the authors propose a clear, pragmatic framework, including five universal recommendations that should apply to most studies: 1. Include males and females as standard practice, with explicit justification when only one sex is studied 2. Account for sex in randomization, counterbalancing, and testing order 3. Power studies to detect sex differences when sex is a primary variable or when prior evidence suggests sex-specific effects 4. Report experimental design in sufficient detail to support replication and pooled analyses 5. Analyze and report data disaggregated by sex, regardless of whether differences are statistically significant Additional recommendations address preclinical specifics, such as reporting the sex of cell lines and environmental conditions, and human research considerations, including how sex assigned at birth and gender identity are collected, reported, and ethically handled. Although this Perspective focuses on pain and related research, the challenges it identifies and the solutions it proposes are relevant across therapeutic areas and research domains where variability, rigor, and generalizability matter. At GSD Health Research, much of our work sits at this intersection of study design, real-world complexity, and methodological rigor, particularly when sex- and gender-related variability matters for interpretation and translation. 🔗 Nature Neuroscience (2025): “Recommendations for the inclusion and study of sex and gender in research” https://lnkd.in/dGdzdxpv

Imagine losing your mother, sister, or daughter, because the treatments meant to save her were never designed for women. For decades, women have been underrepresented in clinical trials and medical research, despite clear biological differences that impact how treatments affect us. The result? 🔹Many treatments & drugs considered safe & effective may be far less suitable (or even harmful) for half of the population ↪️ For instance, women metabolize some drugs differently from men, meaning dosages should be adapted, but often aren’t 🔹Conditions like endometriosis or autoimmune diseases (disproportionately affecting women) remain under-researched and underfunded, leaving millions of women suffering without effective treatments As surprising as it may sound, women are twice as likely as men to die from a heart attack. However: 🩵 Until 1993, women were rarely included in clinical trials 🩵 Today, women still make up only 22% of participants in heart disease clinical trials 🩵 Most cells used early on in clinical trials are male cells, and most lab mice are male too 🩵 According to a 2022 report, less than 5% of the money spent researching coronary artery diseases goes to projects focusing on women 𝑇ℎ𝑖𝑠 𝑖𝑠𝑛'𝑡 𝑗𝑢𝑠𝑡 𝑎 𝑔𝑎𝑝: 𝑖𝑡’𝑠 𝑎 𝑠𝑦𝑠𝑡𝑒𝑚𝑖𝑐 𝑏𝑖𝑎𝑠. From heart disease to chronic pain, while we know that down to the cellular level, men and women do differ, gender differences are overlooked, and lives are at stake. 𝐖𝐡𝐲 𝐡𝐚𝐯𝐞 𝐰𝐨𝐦𝐞𝐧 𝐛𝐞𝐞𝐧 𝐞𝐱𝐜𝐥𝐮𝐝𝐞𝐝 𝐟𝐫𝐨𝐦 𝐜𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐭𝐫𝐢𝐚𝐥𝐬? ➡️ Hormonal variations during the menstrual cycle were deemed “too complex” to study ➡️ Potential pregnancy risks led to women being sidelined for “ethical” reasons ➡️ Male subjects were seen as the “standard,” with the assumption that results would automatically apply to women 🙅♀️ We can’t accept our healthcare systems to routinely fail to account for half the population. 𝐖𝐡𝐚𝐭’𝐬 𝐭𝐡𝐞 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧? 🧐 Medical research must stop treating men as the default, which means: 1️⃣ 𝐈𝐧𝐯𝐞𝐬𝐭𝐢𝐧𝐠 𝐢𝐧 𝐬𝐭𝐮𝐝𝐢𝐞𝐬 𝐭𝐡𝐚𝐭 𝐩𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞 𝐰𝐨𝐦𝐞𝐧’𝐬 𝐡𝐞𝐚𝐥𝐭𝐡 and conditions disproportionately affecting them 2️⃣ 𝐀𝐧𝐚𝐥𝐲𝐳𝐢𝐧𝐠 𝐠𝐞𝐧𝐝𝐞𝐫-𝐛𝐚𝐬𝐞𝐝 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬 in all clinical trials (not just listing gender breakdowns, but investigating how drugs & treatments work for women vs. men) 3️⃣ 𝐄𝐧𝐬𝐮𝐫𝐢𝐧𝐠 𝐝𝐨𝐬𝐚𝐠𝐞 𝐜𝐚𝐥𝐢𝐛𝐫𝐚𝐭𝐢𝐨𝐧 for women vs. men, so treatments are most effective and safe for all 𝐻𝑒𝑎𝑙𝑡ℎ𝑐𝑎𝑟𝑒 𝑚𝑢𝑠𝑡 𝑠𝑒𝑟𝑣𝑒 𝑎𝑙𝑙 𝑜𝑓 𝑢𝑠; 𝑛𝑜𝑡 𝑗𝑢𝑠𝑡 𝑠𝑜𝑚𝑒 𝑜𝑓 𝑢𝑠. But to make that happen, we need systemic change in how treatments are researched, designed, and delivered. 𝐈𝐭’𝐬 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐞𝐪𝐮𝐢𝐭𝐲; 𝐢𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐬𝐚𝐯𝐢𝐧𝐠 𝐥𝐢𝐯𝐞𝐬.

MICROGLIA ACT DIFFERENTLY IN MALE & FEMALE BRAINS Microglia, the brain’s immune cells, play vital roles in clearing toxins and maintaining neuronal health but can also contribute to neurodegenerative diseases if overactive. New research reveals sex-based differences in how adult male and female microglia respond to the enzyme inhibitor PLX3397, a common tool in microglial research. While male microglia showed the expected depletion, female microglia employed alternative signaling pathways, leading to increased survival. These findings highlight the necessity of sex-specific research in diseases like Alzheimer’s and Parkinson’s, where microglial activity plays a significant role and diagnosis rates differ by gender. This breakthrough emphasizes the importance of tailoring therapies that target microglia based on sex. Further research aims to explore hormonal and inflammatory factors influencing these differences. 3 Key Facts: 1. Sex-Based Microglial Differences:  Male and female microglia respond differently to PLX3397, with females showing increased survival. 2. Neurodegenerative Impact: Findings could reshape how Alzheimer’s and Parkinson’s therapies are developed and studied. 3. Therapeutic Implications: Sex-specific microglial activity may require tailored treatment strategies. Source: https://lnkd.in/gNAQ6mZk

Ever wondered what a gender analysis actually looks like? This example walks you through it—real questions, real findings, real context. Here’s what you’ll learn from this document: How to structure your analysis ↳ From methodology to thematic areas like access to resources, decision-making, and cultural norms. The kinds of questions that reveal real gender dynamics ↳ Not just “Who does what?”—but “Who decides?” “Who benefits?” and “Who is excluded?” How to present sex-disaggregated data ↳ See how data is used to compare food security, land ownership, and livelihoods across gender lines. How to identify power imbalances and practical implications ↳ Understand how social norms, roles, and access shape food insecurity—especially for women and girls. What makes a gender analysis actionable ↳ Clear recommendations link the findings to future programming—so the analysis leads to change, not just reports. Use this document as a reference, inspiration, or starting point. #GenderAnalysis #gender 🔔 Follow me for similar content ♻️ Sharing is caring

🚀 Excited to share our new publication in Nature Communications, spearheaded by the amazing Hannah Oppenheimer! Sex steroids such as estradiol are often proposed as key contributors to why depression and Alzheimer’s disease disproportionately affect females. Yet, most existing evidence comes from observational studies, where confounding and reverse causation make it hard to draw firm conclusions. To address this, we used Mendelian Randomization (MR): a method that leverages genetic variants as natural experiments, similar to randomised controlled trials. Because genes are thought to be randomly assigned at conception, MR helps us estimate whether a biological factor (exposure) causally influences an outcome, offering stronger evidence than observational associations. In our study, we combined openly available and newly run and annotated genome wide association studies (GWAS) with MR. We tested whether genetically predicted estradiol exposure across multiple traits (e.g., estradiol levels pre- and post-menopause, reproductive span, age at menarche/menopause, number of childbirths, and more) causally influences: 🧠 Brain age gap (a machine-learning derived proxy of brain health) 🧬 Alzheimer’s disease risk 💭 Depression risk We also replicated relevant analyses in males. 🔍 Our key finding: Across all robust methods and samples, we found no evidence that genetically predicted (i.e., "absolute"/constant) estradiol levels causally affect brain aging, depression risk, or Alzheimer’s disease risk. 💡 What this suggests: Our findings strengthen the idea that "absolute" estradiol levels may not be the key driver. Instead, an individual’s sensitivity to hormonal fluctuations could be crucial in understanding hormone-brain interactions. This work highlights the importance of moving beyond average hormone levels toward more nuanced, dynamic measures - an exciting direction for future research. A huge thank you to all co-authors: Dennis van der Meer, Louise Schindler, Arielle Crestol, Alexey Shadrin, Ole Andreassen, Lars T. Westlye, Ann-Marie de Lange 📄 Read the full article here: https://lnkd.in/dyydRXYu

Applauding an elegant advance in #Alzheimer’s prediction model published on Nature Magazine. A blood-based “clock” based on p-tau217 levels that estimates when symptoms are likely to emerge marks a conceptual shift, from detecting disease to predict . If validated, such tools could redefine prevention, trial design, and the timeline of intervention. But precision prediction demands biological precision. In these models, key covariates, including sex, were deemed to add negligible predictive value beyond p-tau217 levels. This interpretation is striking given the extensive evidence that Alzheimer’s disease unfolds differently in women and men: women bear the majority of cases, exhibit faster trajectories of decline, and show higher tau burden, including in prior CSF biomarker studies conducted by Women's Brain Foundation. When well-established biological differences disappear in pooled analyses, the explanation is not necessarily absence but averaging. Treating sex as background noise rather than a dimension of disease risks obscuring meaningful heterogeneity. A single clock calibrated on population means may tick accurately on average while not so for specific groups. The science is well conducted, the promise may be real. We learned from failed trials that fast progressors, majority women, can lead to a trial fail. A model that predicts the future of Alzheimer’s must account for the biology of those most affected to be properly used in clinical settings. Eager to hear perspectives from the community Congratulations to the authors and our collaborator Marta Milà Alomà. #brainhealth #womenbrainhealth #womenhealth #precisionmedicine

Medicine is built on evidence. But what if the evidence itself is biased? For decades, women were excluded from clinical trials. A 1977 FDA rule banned almost all women of “childbearing potential” from early studies. It took until 1993 to change that. Still today, many trials include fewer than one in three women. And most results are not analyzed by sex. 𝗧𝗵𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲: → Drugs are tested on men → and prescribed to women. The effects are very real and very measurable: • Women are twice as likely to be hospitalized for complications. • They make up 60% of all adverse drug reaction reports. • And report 1.5 to 1.7 times more side effects than men.    Psychiatric disorders. Cardiovascular disease. Autoimmune and chronic pain conditions. In all these areas, women are underrepresented and underserved. And representation in research is not a “women’s issue”. It’s a scientific integrity issue. Medicine cannot call itself evidence-based, when half the population is missing from the data. → We need sex-based results that reflect reality. → We need balanced representation in 𝗲𝘃𝗲𝗿𝘆 study. → We need trials that have a diversity action plan and accountability Because when women are left out of research, everyone loses. Who’s ready to challenge the status quo of medical research? 💪 __ P.S. Here are the references: 1. https://lnkd.in/gnwYnY-x 2. https://lnkd.in/gc_UbsPC 3. https://lnkd.in/gsp5pGcY 4. https://lnkd.in/gMGq2rhJ 5. https://lnkd.in/gszKTsTd

🫀 Female athlete’s heart: are we building evidence on half the population? A new systematic review audits the entire literature on exercise-induced cardiac adaptation… and the message is uncomfortable. 📊 The numbers speak clearly 767 studies ~95,000 athletes Only 24% female participants 54% of studies = male-only Just 5% = female-only studies ➡️ We are still defining the “athlete’s heart” mostly on men. 🔍 Even when women are included… Only 7% of studies perform sex-specific analysis Only 3% directly compare males vs females 6% don’t even report sex ➡️ Inclusion without analysis = lost information 🧬 The biggest blind spot: physiology Only 10 studies reported menstrual status. None met best-practice standards ➡️ We are studying female physiology… without considering female biology. ⚠️ Why this matters clinically Cardiac adaptation is sex-specific Hormones influence structure + function Reference values derived from men may 👉 misclassify normal vs pathology in women ➡️ Risk: over- or under-diagnosis in female athletes 🌍 And it’s not just sex bias Ethnicity reported in only 17% of studies 80% of participants = Caucasian Para-athletes = 0.4% ➡️ Evidence is not just male-biased, but also not representative 💡 Take-home message We don’t have a true model of the female athlete’s heart yet. We have a male model applied to women. 👉 The next step in sports cardiology is not more data— it’s better, more inclusive data. #SportsCardiology #FemaleAthletes #CardiacImaging #CMR #Echo #WomenInScience #PrecisionMedicine 🫀🧬 https://lnkd.in/efb2ZhP9