Relation Between Insulin Sensitivity and Metabolic Abnormalities in Japanese Men With BMI of 23–25 kg/m<sup>2</sup>
Bibliographic record
Abstract
Although metabolic abnormalities are often developed in Asians with body mass index (BMI) of 23–25 kg/m2, the characteristics of the nonobese Asians with metabolic abnormality have not been fully understood. The aim of this study was to investigate the clinical significance of insulin sensitivity in Japanese men with BMI of 23–25 kg/m2. In this study, we defined hypertension, hyperglycemia, and dyslipidemia as cardiometabolic risk factors (CMRFs). We recruited subjects who met the following selection criteria: men with BMI of 21–23 kg/m2 and no CMRF (n = 24); men with BMI of 23–25 kg/m2 and no CMRF (n = 28), or one CMRF (n = 28), or at least two CMRFs (n = 14); and overweight men with metabolic syndrome (n = 20). Insulin sensitivity (IS) and ectopic fat content in muscle and liver were measured by two-step hyperinsulinemic-euglycemic clamp and 1H-magnetic resonance spectroscopy, respectively. Among subjects with BMI of 23–25 kg/m2, impaired IS in muscle, but not in liver, was found in those with even one CMRF, whereas impaired IS in both muscle and liver was observed in overweight men with metabolic syndrome. Liver fat accumulation and elevated liver enzymes were associated with impaired IS in both muscle and liver in those subjects. Among Japanese men with BMI of 23–25 kg/m2, muscle insulin resistance was present in those with even one CMRF. In this population, liver fat accumulation and/or elevated liver enzymes could be a good marker for impaired IS in both muscle and liver.
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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 | 0.001 |
| 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".