Association between Indices of Body Composition and Abnormal Metabolic Phenotype in Normal-Weight Chinese Adults
Bibliographic record
Abstract
We aimed to determine the association of indices of body composition with abnormal metabolic phenotype, and to examine whether the strength of association was differentially distributed in different age groups in normal-weight Chinese adults. A total of 3015 normal-weight adults from a survey of Chinese people encompassing health and basic physiological parameters was included in this cross-sectional study. We investigated the association of body composition measured by bioelectrical impedance analysis and conventional body indices with metabolically unhealthy normal-weight (MUHNW) adults, divided by age groups and gender. Associations were assessed by multiple logistic regression analysis. We found abnormal metabolism in lean Chinese adults to be associated with higher adiposity indices (body mass index, BMI), waist circumference, and percentage body fat), lower skeletal muscle %, and body water %. Body composition was differentially distributed in age groups within the metabolically healthy normal weight (MHNW)/MUHNW groups. The impact of factors related to MUHNW shows a decreasing trend with advancing age in females and disparities of factors (BMI, body fat %, skeletal muscle %, and body water %) associated with the MUHNW phenotype in the elderly was noticed. Those factors remained unchanged in males throughout the age range, while the association of BMI, body fat %, skeletal muscle %, and body water % to MUHNW attenuated and grip strength emerged as a protective factor in elderly females. These results suggest that increased adiposity and decreased skeletal muscle mass are associated with unfavorable metabolic traits in normal-weight Chinese adults, and that MUHNW is independent of BMI, while increased waist circumference appears to be indicative of an abnormal metabolic phenotype in elderly females.
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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 | 0.000 | 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) | 0.001 | 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".