Effect of a high-protein energy-restricted diet combined with resistance training on metabolic profile in older individuals with metabolic impairments
Bibliographic record
Abstract
Adequate protein intake and resistance training are effective strategies to maintain muscle mass, but the effect of their combination on metabolic profile during weight loss remains to be determined in older adults. The main objective of this study was to determine the effect of a 16-week high-protein caloric restriction combined with resistance training on chronic disease risk factors in obese older individuals with metabolic impairments. A total of 26 overweight adults aged between 60 and 75 years (BMI 32.4 ± 3.9 kg/m2) with at least 2 factors of the metabolic syndrome participated in this study and were randomized into two groups: 1) high-protein caloric restriction (HP; n= 12) and 2) high-protein caloric restriction combined with dynamic-resistance training (HP+RT; n=14). Caloric intake was reduced by 500 kcal/d in all participants and protein intake equated 25-30% of total calories (~1.4 g/kg/d). Exercise training consisted of 3 session/week of resistance training on pulley machines. Outcome measures included total and trunk fat mass (FM), total and appendicular lean body mass (LBM), fasting glucose level, lipid profile and blood pressure. Our results showed that total and trunk FM (all p<0.0001) as well as fasting glucose (p<0.0001), triglycerides (p=0.002) and total cholesterol (p=0.03) levels decreased similarly in both groups. However, total (p=0.04) and appendicular (p=0.02) LBM decreased in the HP group only. Our data show that high-protein energy restriction improves health profile of obese elderly at high risk of chronic disease but needs to be combined with resistance training to maintain LBM.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".