Association Between Plasma Proprotein Convertase Subtilisin/Kexin Type 9 and the Presence of Metabolic Syndrome in a Predominantly Rural-Based Sub-Saharan African Population
Bibliographic record
Abstract
BACKGROUND: The prevalence of metabolic syndrome (MetS) has increased dramatically in low- and middle-income countries. Proprotein convertase subtilisin/kexin type 9 (PCSK9) plays a major role in low-density lipoprotein receptor degradation, but its relationship with metabolic parameters is still poorly understood. We aimed to investigate the association between plasma PCSK9 and metabolic parameters in a Kenyan cohort. METHODS: Total plasma PCSK9 levels were measured in 2016 by an in-house enzyme-linked immunosorbent assay (ELISA) using a polyclonal antibody. The International Diabetes Federation (IDF) 2009 consensus statement criteria were used to assess the presence of MetS. RESULTS: In 1338 Kenyans, 11% of the cohort had ≥3 MetS criteria. Total plasma PCSK9 concentration was significantly higher in subjects with MetS than in the non-MetS group (166.8 ± 4.4 vs. 148.0 ± 1.3, P < 0.0001). A progressive increase in circulating PCSK9 was observed when subjects were stratified according to the number of MetS criteria (<3, 3, 4, or 5) [P of the analysis of variance (ANOVA) <0.0001]. In a model corrected for age, sex, lifestyle factors, and body mass index, PCSK9 concentration was a significant predictor of all MetS criteria taken individually, except for waist circumference. Plasma PCSK9 levels were significantly associated with low-density lipoprotein cholesterol, but the strongest association was seen with triglycerides even after multiple adjustments. CONCLUSIONS: The presence of MetS was significantly associated with the PCSK9 concentration. Further studies are needed to provide a molecular connection between PCSK9 and insulin, as well as triglyceride metabolism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".