Identifying sex‐ and gender‐specific endocrinological, lifestyle, psychosocial, and socio‐cultural targets for Alzheimer's disease prevention in Africans: The Female Brain Health and Endocrine Research in Africa (FemBER‐Africa) project
Bibliographic record
Abstract
Dementia rates are rising globally, with the burden increasing most rapidly in low- to middle-income countries. Despite this, research into Alzheimer's disease and related dementias (ADRD) among African populations remains limited, with existing models based on Western cohorts that overlook sex-, gender-, and ancestry-specific factors. The Female Brain Health and Endocrine Research in Africa (FemBER-Africa) project, hosted at the Brain and Mind Institute, Aga Khan University, Kenya, will establish a deeply phenotyped cohort of 250 African individuals across the ADRD spectrum. It will assess sex-specific risk factors linked to ethnicity, lifestyle, and endocrinological variables using fluid-based biomarkers (blood and saliva), neuroimaging (magnetic resonance imaging and positron emission tomography), and culturally adapted cognitive tests. By comparing data with Western and diasporic cohorts, the study aims to identify ancestry-specific and shared mechanisms driving ADRD risk and progression. The findings will support targeted, culturally relevant prevention and intervention strategies, addressing the underrepresentation of African populations in global dementia research. HIGHLIGHTS: By 2030, > 78 million individuals are expected to have dementia, with the highest burden among women in low- to middle-income countries. Despite this, African populations remain underrepresented in Alzheimer's disease and related dementias (ADRD) research. Existing ADRD risk models fail to account for the unique influence of sex, gender, and ancestry on dementia risk. Female-specific reproductive and hormonal factors, including menopause transition and hormone therapy use, are poorly integrated into current models. The Female Brain Health and Endocrine Research in Africa (FemBER-Africa) project is the first large-scale study to examine sex- or gender-specific and endocrine contributors to ADRD in an African population, using advanced diagnostic, biomarker, and culturally adapted cognitive assessments. The study will assess how biological (hormonal, metabolic), lifestyle (physical activity, diet), and socio-cultural (education, health-care access) factors interact to influence ADRD risk in African women. Insights from FemBER-Africa will inform the development of sex- and gender-specific, culturally adapted ADRD prevention strategies, enhancing the precision and equity of dementia mitigation efforts globally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".