Age and Sex Differences in the Clustering of Metabolic Syndrome Factors
Bibliographic record
Abstract
OBJECTIVE: The metabolic syndrome is a general term given to a clustering of cardiometabolic risk factors that may consist of different phenotype combinations. The purpose of this study was to determine the prevalence of the different combinations of factors that make up the metabolic syndrome as defined by the National Cholesterol Education Program and to examine their association with all-cause mortality in younger and older men and women. RESEARCH DESIGN AND METHODS: A total of 2,784 men and 3,240 women from the Third National Health and Nutrition Examination Survey with public-access mortality data linkage (follow-up=14.2±0.2 years) were studied. RESULTS: Metabolic syndrome was present in 26% of younger (aged≤65 years) and 55.0% of older (aged>65 years) participants. The most prevalent metabolic syndrome combination was the clustering of high triglycerides, low HDL cholesterol, and elevated blood pressure in younger men (4.8%) and triglycerides, HDL cholesterol, and elevated waist circumference in younger women (4.2%). The presence of all five metabolic syndrome factors was the most common metabolic syndrome combination in both older men (8.0%) and women (9.2%). Variation existed in how metabolic syndrome combinations were associated with mortality. In younger adults, having all five metabolic syndrome factors was most strongly associated with mortality risk, whereas in older men, none of metabolic syndrome combinations were associated with mortality. In older women, having elevated glucose or low HDL as one of the metabolic syndrome components was most strongly associated with mortality risk. CONCLUSIONS: Metabolic syndrome is a heterogeneous entity with age and sex variation in component clusters that may have important implications for interpreting the association between metabolic syndrome and mortality risk. Thus, metabolic syndrome used as a whole may mask important differences in assessing health and mortality risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".