Analysis of Dietary Factors Affecting Body Mass Index in Elderly Patients With Type 2 Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND: Body mass index (BMI) is correlated with the outcomes of various metabolic and pathological conditions. To elucidate the factors affecting BMI in elderly persons, we studied elderly persons with and without diabetes mellitus for BMI management target values using receiver operating characteristic (ROC) analysis. METHODS: We conducted a dietary survey targeting 60 elderly outpatients with type 2 diabetes mellitus (diabetes group, 70.1 ± 7.8 years) and 66 elderly persons who participated in a health class offered by the municipality (health class group, 72.5 ± 5.7 years). RESULTS: In the diabetes group, positive correlations were observed between BMI and several variables including blood glucose levels (all P < 0.05), whereas BMI had negative correlations with the third principal component (positive weight for oils and fats). In addition, BMI was negatively correlated with the intake of oils and fats. In the health class group, BMI was positively correlated (all P < 0.05) with grip strength/sixth principal component (positive weight for sweets)/condiments. An analysis of dietary patterns revealed that dietary factors correlated with BMI in each group. The cutoff value of BMI was suggested to be near the normal upper limit or slightly higher in the subject group. CONCLUSION: We considered that BMI management was useful as an indicator for maintaining grip and muscle strength in elderly persons and as an indicator for diabetes care management. From the present study, we may propose the utility of a careful dietary survey as one of the approaches for these aims.
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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 | 0.002 |
| 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".