Associations of leucocyte telomere length with cardio-metabolic risk profile in a South African HIV-infected population
Bibliographic record
Abstract
ABSTRACT: Leukocyte Telomere length (LTL) is an independent predictor of cardio-metabolic diseases (CMDs) and Human Immuno Virus (HIV) infection. However, studies are lacking on the association between LTL with CMD profile in people with HIV. Accordingly, we investigated the association between LTL and CMD profile in HIV-infected adult South Africans.This cross-sectional study included 728 HIV patients (20.6% men; median age 38 years) recruited across 17 public healthcare facilities in Cape Town. CMD markers were compared across quartiles of LTL, and spearman correlations assessed the continuous association of LTL with CMD markers. Linear and logistic regressions were then used to relate LTL with CMD risk profile, with appropriate adjustment for confounders.The prevalence of obesity, hypertension and diabetes were 34.8%, 36.8%, and 8.4%, respectively. In age, sex and body mass index adjusted models, increasing Log10LTL was associated with decreasing systolic (β = -10.52) and diastolic (β = -6.74) blood pressures, HOMA-β (β = -70.72), increasing total cholesterol (β = 0.544), non-high-density lipoprotein cholesterol (β = 0.472), and waist-to-height-ratio > 0.5 (odds ratio [OR] = 5.67), all P < .05. Compared to those in the bottom quarter, those in the top LTL quarter had lower prevalence of hypertension (OR = 0.65), and higher prevalence of total cholesterol > 5 mmol/L (OR = 1.94), and low-density lipoprotein-cholesterol > 3 mmol/L (OR = 1.62), all P < .05. LTL was not associated with diabetes nor general obesity. It was associated with Alanine Transaminase (ALT) and heart rate in univariable analyses.LTL shortening was associated with some CMD risk factors in HIV-infected adults on anti-retroviral therapy in South Africa. Prospective research is needed to explore the direction and implications of these associations.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".