Higher Protein Intake Is Associated with Diabetes Risk in South Asian Indians: The Metabolic Syndrome and Atherosclerosis in South Asians Living in America (MASALA) Study
Bibliographic record
Abstract
OBJECTIVE: Despite a high prevalence of type 2 diabetes in South Asian Indians, the impact of diet in this high-risk ethnic group has not been fully explored. The association of macronutrient intake and diabetes in South Asian Indians was examined in this cross-sectional study. METHODS: A population-based cohort of 146 South Asian Indians aged 45-79 years without existing cardiovascular disease living in the San Francisco Bay Area was recruited between August 2006 and October 2007. Macronutrient intake was assessed with a food-frequency questionnaire developed and validated in South Asians. Diabetes was defined by use of a hypoglycemic medication, a fasting plasma glucose level > or =126 mg/dL, or a 2-hour post-challenge glucose level > or =200 mg/dL. The association between energy-adjusted macronutrient intake and diabetes was explored using multivariable logistic regression models. RESULTS: Forty-one (28%) participants had type 2 diabetes; 20 were unaware of this diagnosis and were classified as having diabetes by laboratory testing. In a model fully adjusted for age, sex, waist circumference, and hypertension, there was a 70% increase in the odds of diabetes per standard deviation in gram of protein intake/day (standardized OR 1.70 [95% CI 1.08, 2.68], p = 0.02). There was a trend toward increased protein intake and diabetes in the subset of participants with previously unknown, laboratory-diagnosed diabetes. Results did not vary significantly by sex, body mass index, or dietary pattern. CONCLUSIONS: Higher level of protein intake was associated with increased odds of diabetes in this cohort of South Asian Indians. Diet may be a modifiable lifestyle factor in this high-risk ethnic group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".