Prevalence of metabolic syndrome and diabetes mellitus type-2 and their association with intake of dairy and legume in Andean communities of Ecuador
Bibliographic record
Abstract
Metabolic syndrome (MetS) and type 2 diabetes (T2D) are metabolic alterations associated with high morbidity and mortality, particularly in low and middle-income countries. Diet has a significant impact on the risk to develop MetS and T2D; in this regard, consumption of fruits, vegetables, and protein rich foods (from plant and animals) are important to prevent and manage these pathologies. There are limited studies regarding the potential association between Andean foods rich in proteins and the presence of cardio-metabolic conditions in Ecuador. It is necessary to develop new low-cost, local-culturally acceptable strategies to reduce the burden of cardio-metabolic diseases. We describe the prevalence (baseline data) of MetS and T2D in the Ecuadorian cohort of the Prospective Urban and Rural Epidemiology (PURE) study and their potential association with the consumption of protein rich foods, including beef, white meat, dairy and legumes. In this cross-sectional study, we assessed 1,997 individuals aged 35-70 years (mean age 51 years, 72% women), included in the Ecuadorian cohort of the PURE study, from February to December 2018. The prevalence of MetS was 42% for male and 44% for female participants; the prevalence of T2D was 9% for male and 10% for female. Metabolic syndrome and T2D were more common in women older than 50 years of age with primary education or less, low economic income, and with obesity; MetS was more frequent in the rural area while T2D was more frequent in the urban area. Using logistic regression analysis, we observed a significant protective effect of higher consumption of dairy and legumes in the prevalence of MetS and T2D compared with low consumption. It will be important to develop policies for ample production and consumption of protein rich foods such as legumes and dairy, part of traditional diets, to reduce the burden of cardio-metabolic diseases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".