Prevalence, correlates of dyslipidaemia, and 10-year cardiovascular risk among people living with HIV and people without HIV adults in Rwanda: insights from the NCOHIRWA cohort study
Bibliographic record
Abstract
INTRODUCTION: Noncommunicable diseases, primarily cardiovascular diseases (CVDs), are an emerging cause of morbidity in sub-Saharan Africa. Dyslipidaemia, a key modifiable CVD risk factor, is increasing but remains underdiagnosed and undertreated, particularly among people living with HIV (PLHIV), where HIV-related chronic inflammation, antiretroviral therapy (ART), and sociodemographic factors may contribute. This study assessed the prevalence, correlates of dyslipidaemia, and estimated 10-year CVD risk among PLHIV and people without HIV (PWoH) adults in Rwanda. METHODS: We analysed baseline data from 1,546 adults (1,234 PLHIV and 312 PWoH) enrolled in the NCOHIRWA Cohort from 12 Rwandan health facilities. Data were collected via standardised World Health Organisation (STEP) questionnaires. Dyslipidaemia was defined according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria, and 10-year CVD risk was calculated via the Framingham risk score. For group comparisons, chi-square tests were used. Multivariate logistic regression identified independent predictors of dyslipidaemia, with adjusted odds ratios and 95% confidence intervals reported. RESULTS: Overall, 979 participants (61.9%) had dyslipidaemia, with comparable prevalence rates among PLHIV (744/1234; 60.29%) and PWoH participants (205/312; 65.06%). The PWoH participants had higher LDL-C levels (59 (18.9%) vs. 120 (9.7%), p < 0.0001). Independent predictors of dyslipidaemia included female sex, older age, obesity, and widowhood. PLHIV had lower odds of having elevated total cholesterol (aOR = 0.68, 0.49-0.95). Based on the estimated 10-year CVD risk, 3.95% of the participants had high and 21.47% had very high 10-year CVD, respectively, which was concentrated among older, widowed women with low education levels. CONCLUSION: Dyslipidaemia and elevated CVD risk are highly prevalent among the study participants, with disparities based on HIV status, sex, and social vulnerability. Routine lipid screening and integrated HIV-CVD care, particularly for high-risk subgroups such as older women, are essential for reducing the long-term CVD burden.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".