Synergistic associations of physical activity and diet quality on cardiometabolic risk factors in overweight and obese postmenopausal women
Bibliographic record
Abstract
Healthy diet and physical activity are associated with a lower cardiometabolic risk (CMR). Little is known about whether they interact to improve CMR. The purpose of the present study was to determine the synergistic associations of diet quality and physical activity energy expenditure (PAEE) on CMR factors. The present study was an a posteriori analysis of two cross-sectional studies on 124 inactive non-diabetic postmenopausal women with a BMI ≥ 27 kg/m². The following factors were measured: diet quality (assessed by the Canadian Healthy Eating Index (C-HEI) from a 3 d food record); PAEE (doubly labelled water); body composition (dual-energy X-ray absorptiometry, computed tomography scan); lipoprotein profile (total, HDL- and LDL-cholesterol (HDL-C and LDL-C), non-HDL-C, total cholesterol:HDL-C, TAG, apoA1, apoB, apoA1:apoB and LDL-C:apoB); insulin sensitivity (homeostasis model assessment of insulin resistance and hyperinsulinaemic-euglycaemic clamp); inflammatory markers (high-sensitivity C-reactive protein (hs-CRP), haptoglobin, orosomucoid, IL-6 and leucocyte count). The association of the interaction PAEE × C-HEI and CMR factors was evaluated by hierarchical regressions. Fat mass-adjusted ANCOVA determined the interaction between PAEE and the C-HEI. In hierarchical regressions, the interaction PAEE × C-HEI was a correlate of more favourable values of HDL-C, apoB, apoA1:apoB and LDL-C:apoB ratios, and hs-CRP, while only PAEE was a negative correlate of haptoglobin. Compared with those in the low-PAEE/low-C-HEI group, women in the high-PAEE/high-C-HEI group had 10 % higher HDL-C, 13 % lower apoB, 11 % larger LDL particles and 28 % lower hs-CRP concentrations (P< 0·05). PAEE and the C-HEI have a synergistic association with the CMR profile. These results support the integration of both diet quality and physical activity in the management of CMR.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".