Body Weight Reduction Results in Favorable Changes in Blood Pressure, Serum Lipids, and Blood Sugar in Middle-Aged Japanese Persons: A 5-Year Interval Observational Study of 26,824 Cases
Bibliographic record
Abstract
OBJECTIVE: We investigated the relationships between body weight (BWt) and metabolic syndrome (MS) risk factors to elucidate the effect of BWt (?BWt) change and body mass index (BMI) on these factors in the Japanese population. METHODS: Data were collected on MS-related parameters measured during two annual examinations of 16,640 men (mean age: 41.7±11.6 years) and 10,184 women (mean age: 45.0±12.2 years) without prior treatment of hypertension, diabetes mellitus, or dyslipidemia in 2006 and 2011 in Fukuoka, Japan. The subjects were divided into three groups according to BMI in 2006 (low, middle and high BMI) and into three groups according to change in BMI between 2006 and 2011 (decreased, stable, and increased BMI). Mean values for blood pressure (BP), systolic BP (SBP), diastolic BP (DBP), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), hemoglobin A1c (HbA1c), and fasting blood glucose (FBG) for each group were determined by sex and subjected to statistical analysis for comparison. RESULTS: High BMI (>26) was associated with higher SBP, LDL-C, FBG, and TG in both sexes. An increase≥1.1 BMI units in 5 years was associated with increased DBP, LDL-C, TG, HbA1c, and FBG and decreased HDL-C. In contrast, decreased BMI was associated with decreased BP and LDL-C and increased HDL-C in both sexes, and decreased TG in men and FBG in women. CONCLUSIONS: Maintaining a desirable weight or losing weight may help prevent hypertension and MS, even in non-obese individuals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".