Simple cardiovascular risk stratification by replacing total serum cholesterol with anthropometric measures: The MORGAM prospective cohort project
Bibliographic record
Abstract
To assess whether anthropometric measures (body mass index [BMI], waist-hip ratio [WHR], and estimated fat mass [EFM]) are independently associated with major adverse cardiovascular events (MACE), and to assess their added prognostic value compared with serum total-cholesterol. The study population comprised 109,509 individuals (53% men) from the MORGAM-Project, aged 19–97 years, without established cardiovascular disease, and not on antihypertensive treatment. While BMI was reported in all, WHR and EFM were reported in ∼52,000 participants. Prognostic importance of anthropometric measurements and total-cholesterol was evaluated using adjusted Cox proportional-hazards regression, logistic regression, area under the receiver-operating-characteristic curve (AUCROC), and net reclassification improvement (NRI). The primary endpoint was MACE, a composite of stroke, myocardial infarction, or death from coronary heart disease. Age interacted significantly with anthropometric measures and total-cholesterol on MACE (P ≤ 0.003), and therefore age-stratified analyses (<50 versus ≥ 50 years) were performed. BMI, WHR, EFM, and total-cholesterol were independently associated with MACE (P ≤ 0.003) and resulted in significantly positive NRI when added to age, sex, smoking status, and systolic blood pressure. Only total-cholesterol increased discrimination ability (AUCROC difference; P < 0.001). In subjects < 50 years, the prediction model with total-cholesterol was superior to the model including BMI, but not superior to models containing WHR or EFM, while in those ≥ 50 years, the model with total-cholesterol was superior to all models containing anthropometric variables, whether assessed individually or combined. We found a potential role for replacing total-cholesterol with anthropometric measures for MACE-prediction among individuals < 50 years when laboratory measurements are unavailable, but not among those ≥ 50 years.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".