Lipid accumulation product: a simple and accurate index for predicting metabolic syndrome in Taiwanese people aged 50 and over
Bibliographic record
Abstract
BACKGROUND: Lipid accumulation product (LAP) has been advocated as a simple clinical indicator of metabolic syndrome (MS). However, no studies have evaluated the accuracy of LAP in predicting MS in Taiwanese adults. The aim of our investigation was to use LAP to predict MS in Taiwanese adults. METHODS: Taiwanese adults aged 50 years and over (n = 513) were recruited from a physical examination center at a regional hospital in southern Taiwan. MS was defined according to the MS criteria for Taiwanese people. LAP was calculated as (waist circumference [cm] - 65) × (triglyceride concentration [mM]) for men, and (waist circumference [cm] - 58) × (triglyceride concentration [mM]) for women. Simple logistic regression and receiver-operating characteristic (ROC) analyses were conducted. RESULTS: The prevalence of MS was 19.5 and 21.5% for males and females, respectively. LAP showed the highest prediction accuracy among adiposity measures with an area under the ROC curve (AUC) of 0.901. This was significantly higher than the adiposity measure of waist-to-height ratio (AUC = 0.813). CONCLUSIONS: LAP was a simple and accurate predictor of MS in Taiwanese people aged 50 years and over. LAP had significantly higher predictability than other adiposity measures tested.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".