P2-33 The impact of the metabolic syndrome on cardiometabolic and inflammatory profiles among Canadian adults
Bibliographic record
Abstract
Background The metabolic syndrome (MetSyn) is known as a precursor condition for a spectrum of cardiometabolic complications including type 2 diabetes and cardiovascular disease. The present analysis aimed to quantify the differences in serum levels of cardiometabolic and inflammatory markers across the number of MetSyn components among Canadian adults. Methods Serum levels of apolipoprotein A1 and B (ApoA1, B), creatine, total cholesterol/HDL cholesterol ratio (TC:HDL), C reactive protein (CRP), fibrinogen, glycosylated haemoglobin (HbA1c) and homocysteine were extracted from 1920 adults from the Canadian Health Measures Survey (CHMS). The definition of MetSyn components was based on the National Cholesterol Education Program, Adult Treatment Panel III criteria. Generalised linear models adjusted for age, sex, physical activity, smoking and ethnicity were used to quantify the relationship between select markers and number of MetSyn components. Results Among survey subjects, 11.4% had MetSyn with 59.6% having at least one component. We observed several significant relationships between markers with increasing numbers of MetSyn components. Mean levels of ApoB, creatine, (TC:HDL), CRP, fibrinogen, HbA1c increased significantly as the numbers of MetSyn components increased whereas levels of ApoA1 decreased (p<0.05). Conclusions Our results suggest that increasing numbers of metabolic syndrome components are associated with an elevated level of both markers of chronic low-grade inflammation and intermediate disease. Our findings support previous work showing that persons with MetSyn are a clinically relevant population with underlying pathogenesis that could benefit from early treatment.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".