The association between dietary patterns with type 2 diabetes mellitus and pre-diabetes in the Henan rural cohort study

Machine Learning Models Integrating Dietary Indicators Improve the Prediction of Progression from Prediabetes to Type 2 Diabetes Mellitus

Background: Diet plays an important role in preventing and managing the progression from prediabetes to type 2 diabetes mellitus (T2DM). This study aims to develop prediction models incorporating specific dietary indicators and explore the performance in T2DM patients and non-T2DM patients. Methods: This retrospective study was conducted on 2215 patients from the Henan Rural Cohort. The key variables were selected using univariate analysis and the least absolute shrinkage and selection operator (LASSO). Multiple predictive models were constructed separately based on dietary and clinical factors. The performance of different models was compared and the impact of integrating dietary factors on prediction accuracy was evaluated. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the predictive performance. Meanwhile, group and spatial validation sets were used to further assess the models. SHapley Additive exPlanations (SHAP) analysis was applied to identify key factors influencing the progression of T2DM. Results: Nine dietary indicators were quantitatively collected through standardized questionnaires to construct dietary models. The extreme gradient boosting (XGBoost) model outperformed the other three models in T2DM prediction. The area under the curve (AUC) and F1 score of the dietary model in the validation cohort were 0.929 [95% confidence interval (CI) 0.916-0.942] and 0.865 (95%CI 0.845-0.884), respectively. Both were higher than the traditional model (AUC and F1 score were 0.854 and 0.779, respectively, p < 0.001). SHAP analysis showed that fasting plasma glucose, eggs, whole grains, income level, red meat, nuts, high-density lipoprotein cholesterol, and age were key predictors of the progression. Additionally, the calibration curves displayed a favorable agreement between the dietary model and actual observations. DCA revealed that employing the XGBoost model to predict the risk of T2DM occurrence would be advantageous if the threshold were beyond 9%. Conclusions: The XGBoost model constructed by dietary indicators has shown good performance in predicting T2DM. Emphasizing the role of diet is crucial in personalized patient care and management.

An environment-wide association study for the identification of non-invasive factors for type 2 diabetes mellitus: Analysis based on the Henan Rural Cohort study

AIM

To explore the influencing factors of Type 2 diabetes mellitus (T2DM) in the rural population of Henan Province and evaluate the predictive ability of non-invasive factors to T2DM.

METHODS

A total of 30,020 participants from the Henan Rural Cohort Study in China were included in this study. The dataset was randomly divided into a training set and a testing set with a 50:50 split for validation purposes. We used logistic regression analysis to investigate the association between 56 factors and T2DM in the training set (false discovery rate < 5 %) and significant factors were further validated in the testing set (P < 0.05). Gradient Boosting Machine (GBM) model was used to determine the ability of the non-invasive variables to classify T2DM individuals accurately and the importance ranking of these variables.

RESULTS

The overall population prevalence of T2DM was 9.10 %. After adjusting for age, sex, educational level, marital status, and body measure index (BMI), we identified 13 non-invasive variables and 6 blood biochemical indexes associated with T2DM in the training and testing dataset. The top three factors according to the GBM importance ranking were pulse pressure (PP), urine glucose (UGLU), and waist-to-hip ratio (WHR). The GBM model achieved a receiver operating characteristic (AUC) curve of 0.837 with non-invasive variables and 0.847 for the full model.

CONCLUSIONS

Our findings demonstrate that non-invasive variables that can be easily measured and quickly obtained may be used to predict T2DM risk in rural populations in Henan Province.

Relationships of beans intake with chronic kidney disease in rural adults: A large-scale cross-sectional study

Background and aims

Dietary factors play an important role in the development of chronic kidney disease (CKD). However, evidence on the relationship of beans consumption with CKD remains limited and inconclusive, especially in the middle-and low-income populations. The current study aimed to investigate the relationships of beans intake with indicators of kidney injury and CKD prevalence in rural adults.

Methods

A total of 20,733 rural adults from the Henan Rural Cohort Study in 2018–2022 were included. The total beans intake was collected using a validated food frequency questionnaire. Indicators of kidney injury and CKD was determined by the estimated glomerular filtration rate and the urinary albumin to creatinine ratio. Generalized linear regression and logistic regression models were applied to estimate the relationship of beans intake with continuous and dichotomized indicators of renal function, respectively.

Results

Of the 20,733 participants, 2,676 (12.91%) subjects were identified as CKD patients. After adjusting for potential confounders, participants in the higher quartiles of beans intake had a lower prevalence of CKD (odds ratio and 95% confidence interval, OR (95%CI); Q2: 0.968(0.866–1.082); Q3: 0.836(0.744–0.939); Q4: 0.854(0.751–0.970)) and albuminuria (Q2: 0.982(0.875–1.102); Q3: 0.846(0.750–0.954); Q4: 0.852 (0.746–0.973)), compared with the Q1. Per 50 g/day increment in beans intake was significantly associated with a 5 and 4% decreased prevalence of albuminuria and CKD, respectively. These inverse relationships were also significant in the subgroups of men, elder, and high-income participants (p &lt; 0.05).

Conclusion

Dietary beans intake was inversely associated with the prevalence of albuminuria and CKD in rural adults, suggesting that promoting soy food intake might help reduce the occurrence of CKD in rural adults.

Rural-urban disparities in the associations of residential greenness with diabetes and prediabetes among adults in southeastern China

AIMS

Greenness offers health benefits to prevent diabetes in urban areas. However, urban-rural disparities in this association have not been explored, with the underlying pathways understudied as well. We aimed to investigate and compare the associations and potential pathways between residential greenness and the risks for diabetes and prediabetes in urban and rural areas.

METHODS

Diabetes and prediabetes were diagnosed by fasting blood glucose (FBG). The participants' residential greenness exposure was estimated by the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). The association of residential greenness with the risks for diabetes and prediabetes was estimated by logistic regression and the generalized additive model. The potential mediation effects of air pollution, body mass index (BMI), and physical activity (PA) were examined by causal mediation analysis.

RESULTS

Of the 50,593 included participants, and the prevalence of prediabetes and diabetes were 21.22 % and 5.63 %, respectively. Each 0.1-unit increase in EVI_500m and NDVI_500m for healthy people reduced the risk for prediabetes by 12 % and 8 %, respectively, and substantially reduced the risk for diabetes by 23 % and 19 %, respectively. For those with prediabetes, each 0.1-unit increase in EVI_500m and NDVI_500m reduced the diabetes risk by 14 % and 12 %, respectively. Compared to the risks for diabetes at the 25th percentile of EVI_500m/NDVI_500m, such risks significantly reduced when EVI_500m (NDVI_500m) increased over 0.43 (0.48) and 0.28 (0.39) in urban and rural areas, respectively. The residential greenness-prediabetes/diabetes associations were mediated by air pollution and PA in urban areas and by air pollution and BMI in rural areas.

CONCLUSIONS

Exposure to residential greenness was associated with a lower risk for prediabetes and diabetes in urban areas and, more strongly, in rural areas, which were partly mediated by air pollution, PA, and BMI.

Association of intestinal microbiota markers and dietary pattern in Chinese patients with type 2 diabetes: The Henan rural cohort study

Studies on intestinal microbiota in Chinese type 2 diabetes mellitus (T2DM) patients are scarce and correlation studies with dietary intake are lacking. The case-control study included 150 participants (74 T2DM patients and 76 controls) and microbiome analysis was performed using 16S rDNA sequencing. Principal component analysis was used to determine dietary patterns and correlation analysis was used to evaluate the associations between microbiota diversity, T2DM indicators and dietary variables. Compared to controls, the T2DM group had different gut flora characteristics, including lower alpha diversity, higher Firmicutes/Bacteroidetes ratios, statistically significant beta diversity and other specific bacterial species differences. Gut microbiota was associated with several diabetes-related metabolic markers including HOMA2-β, fasting plasma glucose, HbA1c and fasting insulin. Significant associations were also observed between dietary intake pattern and gut flora. The animal foods pattern scores were positively correlated with the relative abundance of the phylum Fusobacteria, and the vegetarian diet pattern scores were positively correlated with the relative abundance of the phylum Actinobacteria. Phylum Actinobacteria mediated the association of vegetarian diet pattern with fasting insulin and HOMA2-β (all P < 0.05). Composition of intestinal microbiota in Chinese T2DM patients differs from that of control population, and the intestinal flora is affected by dietary intake while being associated with several diabetes-related metabolic markers. The gut microbiota may play an important role in linking dietary intake and the etiology of T2DM.

Association of eating out frequency and other factors with serum uric acid levels and hyperuricemia in Chinese population

PURPOSE

A significant shift in meal pattern with frequent eating out was closely associated with multiple chronic outcomes, but with limited evidence on hyperuricemia. We aimed to explore the associations between eating out and serum uric acid (SUA) as well as hyperuricemia.

METHODS

A total of 29,597 participants were recruited from the Henan Rural Cohort Study. A validated food frequency questionnaire (FFQ) was used to collect dietary data, including the frequency of eating out. Linear regression models were used to examine the association of eating-out frequency with SUA and BMI. Logistic regression and restricted cubic spline were performed to assess the association and dose-response relationship between eating-out frequency and hyperuricemia. The mediation effect of BMI between eating out and the risk of hyperuricemia was evaluated.

RESULTS

Eating out was significantly associated with higher SUA levels in the total population and males (P < 0.001). Multivariate-adjusted odds ratios (ORs) with 95% confidence interval (CIs) of hyperuricemia were 1.26 (1.09, 1.46) for the total population and 1.18 (1.00, 1.40) for males (≥ 7 times/week vs 0 time/week). A non-linear positive dose-response relationship between eating-out frequency and hyperuricemia was observed. Furthermore, BMI played a partial mediating role in the relationship between eating out frequency and hyperuricemia, which explained 30.7% in the total population and 44.8% in males.

CONCLUSION

Our findings indicated that eating out was associated with increased SUA levels and elevated hyperuricemia risk in rural China, especially in males. Moreover, the relationship was partly mediated by BMI.

CLINICAL TRIALS

ChiCTR-OOC-15006699 (2015-07-06).