[Association of dietary patterns and diabetes risk level among adult residents in Datong city of Shanxi Province]

Association between dietary patterns and prediabetes risk in a middle-aged Chinese population

Background

Information regarding dietary patterns associated with prediabetes in the Chinese population is lacking. The objective of the present study was to explore the association between major dietary patterns and the risk of prediabetes in a middle-aged Chinese population.

Methods

A total of 1761 participants (aged 45 to 59 years) were recruited in Hangzhou city, the capital of Zhejiang Province, China from June 2015 to December 2016. Dietary information was obtained by interview using a 138-item, validated semi-quantitative food frequency questionnaire (SQFFQ). Multivariate logistic regression models were used to analyze the associations between dietary patterns and the risk of prediabetes with adjustment of potential confounding variables.

Results

Three dietary patterns were ascertained by factor analysis and labeled as traditional southern Chinese, Western, and grains-vegetables patterns. After controlling of the potential confounders, participants in the top quartile of the Western pattern scores had greater odds ratio (OR) for prediabetes (OR = 1.54; 95% confidence interval (CI):1.068–2.059; P = 0.025) than did those in the bottom quartile. Compared with those in the bottom quartile, participants in the top quartile of the grains-vegetables pattern scores had a lower OR for prediabetes (OR = 0.83; 95% CI:0.747–0.965; P = 0.03). Besides, no statistically significant association was observed in the association between the traditional southern Chinese pattern and prediabetes risk (P > 0.05).

Conclusions

The findings of this study showed that the Western pattern was associated with higher risk, and the grains-vegetables pattern was associated with lower risk of prediabetes. Future prospective studies are required to validate our findings.

Nomogram-Based Prediction of the Risk of Diabetic Retinopathy: A Retrospective Study

OBJECTIVES

This study is aimed at developing a risk nomogram of diabetic retinopathy (DR) in a Chinese population with type 2 diabetes mellitus (T2DM).

METHODS

A questionnaire survey, biochemical indicator examination, and physical examination were performed on 4170 T2DM patients, and the collected data were used to evaluate the DR risk in T2DM patients. By operating R software, firstly, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to optimize variable selection by running cyclic coordinate descent with 10 times K cross-validation. Secondly, multivariable logistic regression analysis was applied to build a predicting model introducing the predictors selected from the LASSO regression analysis. The nomogram was developed based on the selected variables visually. Thirdly, calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis were used to validate the model, and further assessment was running by external validation.

RESULTS

Seven predictors were selected by LASSO from 19 variables, including age, course of disease, postprandial blood glucose (PBG), glycosylated haemoglobin A1c (HbA1c), uric creatinine (UCR), urinary microalbumin (UMA), and systolic blood pressure (SBP). The model built by these 7 predictors displayed medium prediction ability with the area under the ROC curve of 0.700 in the training set and 0.715 in the validation set. The decision curve analysis curve showed that the nomogram could be applied clinically if the risk threshold is between 21% and 57% and 21%-51% in external validation.

CONCLUSION

Introducing age, course of disease, PBG, HbA1c, UCR, UMA, and SBP, the risk nomogram is useful for prediction of DR risk in T2DM individuals.