21-LB: Prevalence of Atherosclerotic Cardiovascular Diseases in Women with Type 2 Diabetes across the Middle East and Africa—Primary Gender Analysis of the PACT-MEA Study
Bibliographic record
Abstract
Objectives: Contemporary data on cardiovascular (CV) burden in women with type 2 diabetes (T2D) is lacking, particularly in the Middle East and Africa where the incidence and prevalence of T2D is one of the highest globally. We report prevalence of ASCVD/ASCVD risk stratified by gender to identify opportunities for improving T2D care. Methods: PACT-MEA (PACT-MEA; NCT05317845) is a cross-sectional, observational study of adults (≥18 years) with T2D from 55 clinics in Bahrain, Egypt, Jordan, Kuwait, Qatar, South Africa, and United Arab Emirates. Medical history, demographics, clinical information, and laboratory values were collected from medical charts of patients at a clinic visit in 2022. We report the prevalence (95% CI) of established atherosclerotic cardiovascular disease (eASCVD) and ASCVD risk, the latter defined by the 2021 European Society of Cardiology (ESC) Guidelines on CV Disease Prevention in Clinical Practice (risk categories: moderate, high, very high [including eASCVD]). Prevalence estimates were weighted according to the size of the diabetes population in each country. Results: Of the 3,726 patients enrolled, 47% were female, with a median age of 59.0; 35% were <55 years old. Across the seven PACT-MEA countries, prevalence of eASCVD was 16.0% (95% CI: 13.4-18.6) among females and 26.6% (23.7-29.5) among males. Per ESC guidelines, 0.6% (0.0-1.2) of females were at moderate risk for ASCVD, 72.5% (69.0-75.9) were at high risk, and 26.9% (23.5-30.3) were at very high risk. Among males, 0.7% (0.0-1.5), 65.9% (62.4-69.4), and 33.4% (29.9-36.8) were at moderate, high, and very high ASCVD risk, respectively. Conclusions: We found 1 in 6 women with T2D in the Middle East and Africa have eASCVD and nearly all are at high/very high ASCVD risk. This highlights the need for better screening and timely management of T2D in women and men to minimize CV risk, as well as the importance of gender inclusiveness in clinical studies. Disclosure F. Alawadi: Research Support; Novo Nordisk. E. Mashaki ceyhan: None. H. M. Sabbour: Research Support; Novo Nordisk. S. Salek: None. G. Yadav: Employee; Novo Nordisk. S. Verma: Advisory Panel; Amgen Canada, AstraZeneca, Bayer Inc., Boehringer Ingelheim and Eli Lilly Alliance, HLS Therapeutics Inc., Janssen Pharmaceuticals, Inc., Novartis Canada, Novartis, Novo Nordisk, Novo Nordisk Canada Inc., Consultant; AstraZeneca, Other Relationship; Amarin Corporation, AstraZeneca, Bayer Inc., Boehringer Ingelheim and Eli Lilly Alliance, Boehringer Ingelheim International GmbH, Canadian Medical and Surgical Knowledge Translation Research Group, EOCI Pharmacomm, HLS Therapeutics Inc., Janssen Pharmaceuticals, Inc., Novartis Canada, Novo Nordisk, Novo Nordisk Canada Inc., Pfizer Inc., PhaseBio Pharmaceuticals, Inc., S & L Solutions Event Management Inc, Sanofi, Sun Pharmaceutical Industries Ltd., Toronto Knowledge Translation Working Group, Research Support; Amarin Corporation, Amgen Canada, AstraZeneca, Bayer Inc., Boehringer Ingelheim International GmbH, HLS Therapeutics Inc., Novartis, Novo Nordisk, Pfizer Inc., PhaseBio Pharmaceuticals, Inc. S. H. Assaad-khalil: Board Member; AstraZeneca, Merck & Co., Inc., Servier Laboratories, Sanofi, Consultant; AstraZeneca, Servier Laboratories, Sanofi, Merck & Co., Inc., Research Support; Novo Nordisk, Eva Pharma. W. Almahmeed: None. N. Alamuddin: Consultant; Novo Nordisk, Speaker's Bureau; Novo Nordisk. H. Alkandari: None. J. A. Haddad: None. L. N. Husemoen: Employee; Novo Nordisk A/S, Stock/Shareholder; Novo Nordisk A/S. L. Lombard: None. R. A. Malik: None.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".