What is the HORIZON-HLTH-2026-01-DISEASE-11 Horizon Europe call about?
Opening
10 February 2026
Deadline
16 April 2026
Keywords
Cluster 1 – Health
cardiovascular diseases
RIA
sex-specific mechanisms
gender-specific risk factors
risk modelling
hormone-linked pathways
HORIZON-HLTH-2026-01-DISEASE-11
It is a Research and Innovation Action (RIA) under Horizon Europe Cluster 1 – Health, Destination 3: « Tackling diseases and reducing disease burden. » The topic funds projects that generate actionable evidence on how sex (biological) and gender (sociocultural) differences shape cardiovascular disease determinants, risk factors and pathological pathways — and that translate this evidence into improved prevention, detection, diagnosis and treatment strategies.
Cardiovascular diseases remain the leading cause of death in Europe, with 1.71 million EU deaths from circulatory diseases in 2021 alone — 32% of all mortality. Yet most diagnostic criteria, risk scores and treatment protocols were derived from male-dominated cohorts. The Commission is funding work that moves beyond « sex as a subgroup analysis » toward mechanistic understanding of why CVD manifests, progresses and responds to treatment differently depending on sex and gender.
With EUR 39.30 million in total and approximately 6 projects expected at EUR 6–7 million each, the call demands proposals that integrate molecular biology, epidemiology, behavioural science and clinical research into a coherent chain from determinants to deployable strategies.
Discover more!
Administrative facts: what do we know about the HORIZON-HLTH-2026-01-DISEASE-11 call?
Which call is it under?
- Call name: Cluster 1 – Health (Single stage – 2026)
- Call identifier: HORIZON-HLTH-2026-01
Which destination?
- Destination 3: Tackling diseases and reducing disease burden
Topic identifier and title
- HORIZON-HLTH-2026-01-DISEASE-11 – Understanding of sex and/or gender-specific mechanisms of cardiovascular diseases: determinants, risk factors and pathways
Type of action
- RIA – Research and Innovation Action (100% funding rate)
Budget
- Overall budget: EUR 39.30 million
- Expected budget per project: EUR 6–7 million
- Indicative number of funded projects: 6
Timeline
- Opening date: 10 February 2026
- Deadline: 16 April 2026, 17:00 Brussels time
- Single-stage submission
Evaluation criteria
- Excellence, Impact, Implementation – standard Horizon Europe RIA weighting
- Independent expert evaluation based on scientific quality, methodological rigour, relevance to health burden reduction and translational credibility
Scientific range: what the Commission expects from the HORIZON-HLTH-2026-01-DISEASE-11 topic
Why does the EU fund this topic now?
Despite decades of CVD research, most clinical evidence remains biased toward male populations. Sex-specific conditions — pregnancy-related complications, menopause transition, hormonal therapies — are recognised risk amplifiers that current prevention frameworks largely ignore. Gender-related variables (occupational exposure, health-seeking behaviour, psychosocial stress patterns) further modify risk in ways that standard models miss. The Commission wants proposals that close this evidence gap with data strong enough to reshape clinical practice.
What outcomes are expected?
Evaluators will verify that your proposal addresses several of the following:
- Mechanistic understanding of sex- and/or gender-specific health determinants, risk factors and pathways for CVDs — usable by researchers, intervention developers and healthcare professionals.
- Sex- and/or gender-tailored risk models that are validated, clinically interpretable and designed for integration into prevention, detection, diagnosis and treatment strategies.
- Novel sex- and/or gender-specific strategies adopted by healthcare systems to reduce CVD burden across diverse populations.
What scientific directions are explicitly encouraged?
The topic text expects proposals to cover most of the following:
- Mechanisms: Structural, hormonal and biological distinctions between sexes/genders linked to CVD pathophysiology — including endothelial function, inflammation, thrombosis, arrhythmia substrate, microvascular disease and cardiac remodelling.
- Risk modelling: Development and validation of sex- and/or gender-tailored risk models. Not retrospective subgroup plots — validated, calibrated, externally tested models with fairness checks and defined clinical utility.
- Determinants and pathways: Identification and validation of sex-/gender-specific determinants using integrated, multidisciplinary data: molecular biology, behavioural science, nutrition, clinical research, social and environmental epidemiology, exposure sciences, genetics and epigenetics.
- Women-specific and life-stage risks: Pregnancy complications, menopause transition, hormone-related effects and comorbidities treated as first-class scientific questions, not secondary analyses.
Cross-cutting requirements
- FAIR data principles applied to all generated datasets — link to registries, cohorts and biobanks.
- Digital technologies including AI must be specified where they add measurable value (not as buzzwords).
- SSH expertise is expected to shape study design and data interpretation, not just dissemination.
- Stakeholder engagement: clinicians, prevention actors and patient voices must be integrated to ensure strategies are implementable.
Scientific strategy: How can you enhance your chances of being funded through HORIZON-HLTH-2026-01-DISEASE-11?
- Make sex and gender mechanistic drivers, not subgroup variables
The single most common mistake in CVD sex/gender proposals is treating sex as a covariate in a regression model. This topic demands mechanistic hypotheses: how do oestrogen receptor signalling, X-chromosome dosage effects, or androgen-mediated inflammatory pathways causally shape CVD onset, progression and treatment response? Define sex variables (biological) and gender variables (sociocultural) separately. Map each to hypotheses, endpoints and datasets. If you cannot draw a causal diagram linking your sex/gender variable to a CVD mechanism to a clinical outcome, your Excellence section is incomplete.
- Build a multi-layer causal chain
Evaluators want to see: Determinants → pathways → biomarkers/phenotypes → risk models → prevention or diagnostic decisions. Each arrow must be supported by a method, a dataset and a deliverable. Proposals that stop at « we will identify sex-specific biomarkers » without connecting them to a risk model or clinical decision will score poorly on Impact.
- Make risk modelling credible
Plan external validation on an independent cohort. Include calibration assessment. Run fairness checks across intersectional subgroups (sex × age × ethnicity × socioeconomic status). Define clinical interpretability: what specific clinical decision changes when the model is applied? If the answer is « more research is needed, » your model has no impact pathway.
- Treat women-specific risks as first-class science
Pregnancy complications (preeclampsia, gestational diabetes, peripartum cardiomyopathy), menopause transition, hormone replacement therapy effects, polycystic ovary syndrome — these are not niche conditions. They affect millions of women and are explicitly highlighted in the Work Programme. Proposals that marginalise these into a secondary work package will look like they missed the topic. Make them central scientific questions with dedicated methods, cohorts and deliverables.
- Specify where AI and digital tools add value
The topic mentions digital technologies and AI. Evaluators will reject vague « AI-enabled risk prediction » claims. Specify: which algorithm family, which training data, which validation protocol, which clinical deployment scenario. If you use machine learning for multi-omics integration, state the interpretability method. If you build a clinical decision support tool, define the user, the setting and the integration pathway into existing clinical workflows.
- Integrate SSH from design stage
Social sciences and humanities expertise is expected to shape how gender variables are operationalised, how health-seeking behaviour differences are captured, how psychosocial stress is measured and how prevention strategies account for structural inequalities. SSH partners who only appear in the dissemination work package will be flagged by evaluators. Give them co-design authority over study instruments, recruitment strategies and interpretation frameworks.
- Connect to adoption pathways
The Impact section must show how sex-/gender-tailored risk models and prevention strategies can enter clinical practice. Name the guideline bodies you will engage (ESC, national cardiology societies). Describe the format: risk calculator, decision tree, screening protocol modification. Quantify the expected improvement: earlier detection by X months, Y% reduction in misclassification, Z lives saved per 100,000 screened. Without adoption specifics, the proposal reads as basic science with an impact paragraph attached.
Consortium & proposal-writing plan: what works best with this type of Health RIA topic?
- Build the « triangle + bridge » consortium
The core triangle: (1) Cardiovascular clinical expertise — cardiology departments, hospital networks with access to sex-stratified patient cohorts. (2) Epidemiology and public health — population registries, longitudinal cohorts, exposure science capabilities. (3) Molecular and systems biology — omics platforms, genetics/epigenetics, hormonal biology expertise.
The bridge: (4) Data science, AI and biostatistics — risk model development, validation, fairness testing and deployment. (5) SSH experts — gender studies, behavioural science, health inequalities, shaping study design and interpretation. (6) Patient organisations and prevention stakeholders — uptake, relevance, ethics and feasibility assessment.
Discrete but effective: include an innovative SME that can translate evidence into deployable tools — validated analytical pipelines, clinical-grade decision support software, or experimental platforms for mechanistic validation. This strengthens the Impact spine and aligns with the Work Programme’s encouragement of SME participation.
- Structure the proposal around evaluator logic
Use a question-led structure that mirrors how evaluators read: What mechanism are we proving? → How will it change risk stratification? → What is the adoption pathway? Convert every major claim into a chain: Method → dataset → metric → deliverable → end-user. Include a one-page logic model mapping inputs → work packages → outputs → outcomes → impacts. Build the risk register around three categories: scientific risk (biological hypotheses may not hold), bias risk (cohorts may under-represent specific populations) and data access risk (registry access may be delayed).
- Align work packages to the topic language
WP1: Sex-/gender-specific determinants and pathways — mechanistic discovery. WP2: Risk model development and validation — external validation, calibration, fairness. WP3: Translation to prevention, detection, diagnosis and treatment strategies. WP4: SSH integration, stakeholder co-design and uptake assessment. WP5: Data management, FAIR compliance, registry linkage. WP6: Dissemination, exploitation, training and cross-project networking.
Common pitfalls that destroy DISEASE-11 proposals
- Treating sex/gender as a retrospective subgroup analysis rather than a mechanistic driver with dedicated hypotheses, methods and endpoints.
- Promising clinical impact without defining which guideline, which screening protocol or which clinical pathway would change.
- Marginalising SSH expertise into dissemination tasks instead of giving it co-design authority over study instruments and interpretation.
- Building risk models without external validation, calibration or fairness assessment across intersectional subgroups.
- Listing women-specific conditions (preeclampsia, menopause, hormonal effects) as secondary analyses instead of primary scientific questions.
- Using « AI-enabled prediction » as a buzzword without specifying the algorithm, training data, interpretability method and clinical deployment scenario.
- Assembling a consortium that is clinically strong but lacks epidemiological depth, or vice versa — missing the multidisciplinary integration the topic demands.
- Writing a risk register that lists generic project management risks instead of addressing the specific scientific challenges: cohort availability, sex-stratified sample size, hormone measurement timing, gender variable operationalisation.
- Overpromising on cohort access without confirmed data sharing agreements or letters of support from biobank/registry custodians.
How would microfluidics contribute to this topic?
Microfluidics enters this topic not as a core deliverable but as a high-value mechanistic validation layer. When clinical cohort data reveals sex-specific associations, the causal question remains open. Microfluidic platforms provide the controlled experimental environment to isolate and test specific biological mechanisms under physiologically relevant conditions.
Vessel-on-chip and endothelium-on-chip models. These platforms allow direct testing of how sex hormones (oestrogen, testosterone, progesterone) modulate endothelial dysfunction, inflammatory signalling, thrombotic tendency and vascular permeability. By exposing male- and female-derived endothelial cells to controlled shear stress and hormone concentrations, you generate mechanistic evidence that epidemiological data alone cannot provide. This directly feeds the « determinants → pathways » chain the topic requires.
Immune-vascular microphysiological systems. Sex-differentiated immune responses are increasingly implicated in atherosclerotic plaque stability, microvascular disease and cardiac inflammation. Microfluidic co-culture systems combining vascular endothelium with circulating immune cells under flow allow real-time observation of sex-specific immune–vascular interactions — monocyte adhesion kinetics, neutrophil extracellular trap formation, platelet aggregation dynamics — under controlled hormonal and inflammatory conditions.
Cardiac tissue platforms. For proposals addressing sex-specific arrhythmia substrates, drug cardiotoxicity or cardiac remodelling, microfluidic cardiac tissue chips enable electrophysiological measurement and contractility analysis of male- versus female-derived cardiomyocytes (including iPSC-derived). This is particularly relevant for studying QT interval sex differences, hormone-dependent repolarisation changes and sex-specific adverse drug reactions.
Microfluidic integration with multi-omics. On-chip sample preparation and multiplexed detection enable generation of controlled-condition multi-omics signatures (proteomics, metabolomics, secretomics) from sex-stratified cell populations. These chip-derived molecular profiles can serve as mechanistic priors for the risk models the topic demands — bridging experimental biology and computational epidemiology with biologically interpretable features.
Bridging to risk models. The strategic positioning: microfluidic-derived mechanistic parameters (hormone dose-response curves, sex-specific inflammatory thresholds, endothelial barrier metrics) can be fed as interpretable features or biological constraints into sex-/gender-tailored risk models. This strengthens biological plausibility, improves model generalisability and gives evaluators confidence that the risk model is grounded in causal biology, not just statistical association.
The MIC already brings its expertise in microfluidics to Horizon Europe:
H2020-NMBP-TR-IND-2020
The MIC developed microfluidic organ-on-chip platforms for studying tumour-immune interactions in controlled vascular environments. The expertise in designing compartmentalised chips with physiological flow conditions directly transfers to the vessel-on-chip and immune-vascular models required for sex-specific CVD mechanism research under DISEASE-11.
H2020-LC-GD-2020-3
Through the LIFESAVER project, the MIC contributed placenta-on-chip technology for pharmaceutical toxicology assessment. This work involved sex-hormone-sensitive tissue models under microfluidic perfusion — a direct methodological precursor to the hormone-modulated vascular and cardiac platforms needed for studying sex-specific CVD pathophysiology.
H2020-LC-GD-2020-3
In the ALTERNATIVE project, the MIC built heart-on-chip tissue models for environmental toxicology assessment. The cardiac microfluidic platform — designed for electrophysiological and contractility measurements — provides the technical foundation for sex-stratified cardiac tissue studies addressing drug response differences and arrhythmia substrate characterisation.
FAQ - HORIZON-HLTH-2026-01-DISEASE-11
What is HORIZON-HLTH-2026-01-DISEASE-11 about?
It is a Research and Innovation Action (RIA) under Horizon Europe Cluster 1 – Health that funds projects generating actionable evidence on sex- and/or gender-specific determinants, risk factors and pathways in cardiovascular diseases. The goal is to translate mechanistic understanding into sex-/gender-tailored prevention, detection, diagnosis and treatment strategies that can be adopted by healthcare systems. The total budget is EUR 39.30 million for approximately 6 projects.
Who should apply and how much funding is available?
The call targets multi-stakeholder consortia combining cardiovascular clinical expertise, epidemiology, molecular biology, data science, SSH and patient organisations. Budget per project is EUR 6–7 million. It is an RIA with 100% funding rate, single-stage submission, deadline 16 April 2026. With approximately 6 projects funded from a EUR 39.30 million envelope, competition will be significant — expect 30–50 proposals for a success rate around 12–20%.
Why is this topic important for the EU?
Cardiovascular diseases caused 1.71 million deaths in the EU in 2021 — 32% of all mortality. Despite this burden, most diagnostic criteria, risk scores and treatment protocols derive from male-dominated study populations. Sex-specific conditions like preeclampsia, menopause transition and hormone-related effects are recognised risk amplifiers that current frameworks largely ignore. Gender-related factors — health-seeking behaviour, occupational exposure, psychosocial stress — further modify risk in ways standard models miss. The Commission funds this topic to close the evidence gap and produce strategies that account for biological and sociocultural differences.
What scientific directions are evaluators expecting?
The Work Programme expects proposals to cover most of the following: (1) Mechanisms — structural, hormonal and biological distinctions between sexes/genders linked to CVD pathophysiology, including endothelial function, inflammation, thrombosis and cardiac remodelling. (2) Risk modelling — validated, externally tested, sex-/gender-tailored risk models with calibration and fairness checks. (3) Determinants and pathways — identified and validated using integrated multidisciplinary data across molecular biology, behavioural science, nutrition, clinical research, epidemiology, genetics/epigenetics and exposure sciences. (4) Women-specific and life-stage risks treated as primary scientific questions: pregnancy complications, menopause, hormonal effects and associated comorbidities.
How should we structure the scientific work?
Structure work packages around the causal chain, not around disciplines. WP1: Mechanistic discovery — sex-/gender-specific determinants and biological pathways, including experimental validation. WP2: Risk model development — training, external validation, calibration, fairness assessment and clinical interpretability testing. WP3: Translation — converting risk models and mechanistic evidence into prevention, screening and treatment strategies with defined adoption pathways. WP4: SSH co-design — gender variable operationalisation, behavioural factor measurement, equity analysis, stakeholder engagement shaping study design. WP5: Data management — FAIR compliance, registry linkage, cohort harmonisation. WP6: Dissemination, exploitation, training and cross-project networking. Build 12-month decision gates where major hypotheses are tested and weak lines are either redirected or terminated.
Where does microfluidics fit in a CVD sex/gender proposal?
Microfluidics provides the controlled experimental platform to isolate and test sex-specific CVD mechanisms that observational data alone cannot resolve. Vessel-on-chip models test how sex hormones modulate endothelial dysfunction, thrombosis and inflammatory signalling under physiological flow. Immune-vascular co-culture systems probe sex-differentiated immune responses affecting plaque stability. Cardiac tissue chips enable sex-stratified electrophysiology and drug response testing. Microfluidic multi-omics sample preparation generates controlled-condition molecular signatures that feed into risk models as biologically interpretable features. The positioning is not « we add a chip » but « we provide the causal validation layer that makes mechanistic claims credible and risk models biologically grounded. »
What type of consortium scores well for DISEASE-11?
Strong DISEASE-11 consortia have a triangle-plus-bridge structure. The triangle (core science): cardiovascular clinical expertise with sex-stratified patient cohorts, epidemiology and public health with access to population registries and longitudinal data, molecular and systems biology covering omics, genetics/epigenetics and hormonal biology. The bridge (translation): data science and AI for risk model development and validation, SSH experts shaping study design and gender variable operationalisation (not just dissemination), patient organisations and prevention stakeholders for uptake and feasibility. An innovative SME contributing experimental validation tools (microfluidic platforms, analytical pipelines, decision-support prototypes) strengthens the Impact section and demonstrates the consortium can translate evidence into deployable outputs.
What is strictly in and out of scope?
In scope: Mechanistic research on sex- and/or gender-specific CVD determinants, risk factors and pathways. Development and validation of sex-/gender-tailored risk models. Translation into prevention, detection, diagnosis and treatment strategies. Women-specific and life-stage conditions (pregnancy, menopause, hormonal effects) as primary research questions. Multi-disciplinary integration across molecular, clinical, epidemiological, behavioural and social sciences. FAIR data, AI with defined clinical utility, SSH co-design.
Out of scope: Generic CVD research without sex/gender specificity. Sex as a retrospective covariate without mechanistic hypotheses. Risk models without external validation or fairness assessment. AI claims without specified algorithms, training data and interpretability methods. Proposals that treat SSH as a dissemination task. Clinical trials (this is an RIA, not a clinical trial action). Work that does not connect mechanistic findings to actionable clinical or public health strategies.
What pitfalls destroy CVD sex/gender RIA proposals?
The most frequent failures: (1) Treating sex/gender as a subgroup analysis — no dedicated mechanistic hypotheses, no separate endpoints, no causal framework. (2) Promising clinical impact without specifying which guideline, screening protocol or clinical decision pathway changes. (3) Marginalising SSH into dissemination instead of giving it authority over study design, variable operationalisation and interpretation. (4) Risk models without external validation, calibration assessment or intersectional fairness checks. (5) Listing women-specific conditions as secondary analyses rather than primary science. (6) « AI-enabled prediction » without algorithm specification, training data description, interpretability method or deployment scenario. (7) Assembling a clinically strong consortium without epidemiological depth, or vice versa. (8) Generic risk registers that ignore the specific challenges: sex-stratified sample sizes, hormone measurement timing, gender variable definitions, registry access delays. (9) Overpromising cohort access without confirmed data sharing agreements.
How do milestones demonstrate credibility?
Month 6: Cohort access confirmed and data sharing agreements signed. Sex/gender variable definitions finalised. Study instruments validated by SSH partners. Regulatory/guideline body engagement initiated. Month 12: First decision gate — primary mechanistic hypotheses tested in discovery cohort. Preliminary sex-stratified omics data available. Experimental platforms (including any microfluidic systems) operational and producing reproducible results. Month 18: Risk model prototype trained on discovery data. External validation cohort access confirmed. Experimental validation of at least two sex-specific mechanisms completed. Month 24: Mid-term review — external validation of risk model initiated. Translation pathway defined with named guideline body or clinical partner. Month 30: Fairness and calibration assessment completed. Experimental evidence package consolidated. Month 36: Risk model validated. Prevention/diagnosis strategy prototype tested with end-users. Month 42: Guideline body or clinical pathway engagement formalised. Exploitation outputs defined. Month 48: Final evidence package delivered — validated risk model, mechanistic evidence dossier, strategy recommendation for clinical adoption. Show budget allocation tracking milestone-linked deliverables, not just calendar time.