Dietary intake and cardiometabolic biomarkers in relation to insulin resistance and hypertension in a middle-aged and elderly population in Beijing, China
Bibliographic record
Abstract
This study investigated the relationships of dietary intake and cardiometabolic biomarkers with insulin resistance and hypertension in rural middle-aged and elderly people in China. One hundred and eight middle-aged and elderly adults were recruited in Zhangfang village in May 2014. We measured blood pressure, anthropometric parameters, and biochemical indexes, including high-sensitivity C-reactive protein (hsCRP), soluble intercellular adhesion molecule-1 (sICAM-1), glucose, insulin, and blood lipids. Homeostasis model assessment of insulin resistance index (HOMA-IR) was assessed on the basis of fasting glucose and insulin. We recorded participant demographic characteristics, dietary intake, and lifestyle using questionnaires. Hypertensive participants had higher levels of triglycerides (TG), hsCRP, sICAM-1, body fat percentage (BF%), arm muscle circumference (AMC) and HOMA-IR than nonhypertensive individuals. Hypertensive participants had higher carbohydrate intake but lower intakes of protein and fat. Carbohydrate intake was positively correlated with hsCRP, sICAM-1, TG, BF%, and HOMA-IR, and was negatively correlated with AMC. Protein and fat intakes were negatively correlated with hsCRP and sICAM-1. Protein intake was also significantly negatively correlated with TG and HOMA-IR, and positively correlated with AMC. HOMA-IR was positively correlated with hsCRP, sICAM-1, TG and BF%, and negatively correlated with AMC. Multivariable linear regression indicated that TG, sICAM-1, and hsCRP were significantly associated with HOMA-IR. In conclusion, in a rural Chinese population, high intake of carbohydrate and low intake of fat and protein were associated with insulin resistance and hypertension, possibly by increasing inflammatory factors such as sICAM-1 and hsCRP, increasing BF% and increasing the level of plasma TG.
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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 | 0.001 | 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".