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 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".