Prevalence and correlates of traditional risk factors for cardiovascular disease in a Nigerian ART-naive HIV population: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: HIV infection environment presents a classic example of the interplay between infectious diseases and non-communicable diseases (NCDs). Traditional cardiovascular disease (CVD) risk factors abound in the HIV population even before initiation of antiretrovirals (ARVs) and predispose them to the development of stroke and myocardial infarction. This work focuses on determining the prevalence of traditional CVD risk factors among ARV-naive HIV individuals in southern Nigeria. METHODS: This was a cross-sectional study of ARV-naive patients initiating care at the University of Uyo Teaching Hospital HIV clinic cohort to determine the prevalence and correlates of hypertension, diabetes mellitus (DM), obesity and dyslipidaemia. RESULTS: The sample consisted of 4925 assessed for hypertension, 5223 for obesity, 1818 for DM and 926 for dyslipidaemia. Hypertension prevalence was 26.7% (95% CI 25.5% to 28.0%) with a male preponderance (p=0.02). DM was found in 5.6% (95% CI 4.5% to 6.7%), obesity in 8.3% (95% CI 7.6% to 9.1%) and dyslipidaemia in 29.1% (95% CI 26.1% to 32.1%) with a high prevalence of low high-density lipoprotein-c (42.6%). Hypertension was independently associated with age (OR 1.04 (95% CI 1.03 to 1.05), p<0.001) and body mass index (BMI) (OR 1.06 (95% CI 1.03 to 1.08), p<0.001), obesity with age (OR 1.02 (95% CI 1.01 to 1.03), p<0.001), male gender (OR 0.38 (95% CI 0.29 to 0.49), p<0.001) and CD4 count (OR 2.63 (95% CI 1.96 to 3.53), p<0.001) while dyslipidaemia was associated with BMI (OR 1.05 (95% CI 1.01 to 1.10), p=0.03). CONCLUSION: The prevalence of traditional CVD risk factors is high in this ART-naive HIV population. An integrated approach of HIV and NCD screening/treatment may be relevant for centres in sub-Saharan Africa.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".