Bayesian network analysis of factors influencing type 2 diabetes, coronary heart disease, and their comorbidities.

OBJECTIVE: Bayesian network (BN) models were developed to explore the specific relationships between influencing factors and type 2 diabetes mellitus (T2DM), coronary heart disease (CAD), and their comorbidities. The aim was to predict disease occurrence and diagnose etiology using these models, thereby informing the development of effective prevention and control strategies for T2DM, CAD, and their comorbidities. METHOD: Employing a case-control design, the study compared individuals with T2DM, CAD, and their comorbidities (case group) with healthy counterparts (control group). Univariate and multivariate Logistic regression analyses were conducted to identify disease-influencing factors. The BN structure was learned using the Tabu search algorithm, with parameter estimation achieved through maximum likelihood estimation. The predictive performance of the BN model was assessed using the confusion matrix, and Netica software was utilized for visual prediction and diagnosis. RESULT: The study involved 3,824 participants, including 1,175 controls, 1,163 T2DM cases, 982 CAD cases, and 504 comorbidity cases. The BN model unveiled factors directly and indirectly impacting T2DM, such as age, region, education level, and family history (FH). Variables like exercise, LDL-C, TC, fruit, and sweet food intake exhibited direct effects, while smoking, alcohol consumption, occupation, heart rate, HDL-C, meat, and staple food intake had indirect effects. Similarly, for CAD, factors with direct and indirect effects included age, smoking, SBP, exercise, meat, and fruit intake, while sleeping time and heart rate showed direct effects. Regarding T2DM and CAD comorbidities, age, FBG, SBP, fruit, and sweet intake demonstrated both direct and indirect effects, whereas exercise and HDL-C exhibited direct effects, and region, education level, DBP, and TC showed indirect effects. CONCLUSION: The BN model constructed using the Tabu search algorithm showcased robust predictive performance, reliability, and applicability in forecasting disease probabilities for T2DM, CAD, and their comorbidities. These findings offer valuable insights for enhancing prevention and control strategies and exploring the application of BN in predicting and diagnosing chronic diseases.

Associations between dietary inflammatory index and stroke risk: based on NHANES 2005-2018.

The dietary inflammatory index (DII) is a measure of the inflammatory potential of the diet and is closely associated with insulin resistance (IR) and stroke. And IR may play an important role in the development of stroke. Therefore, this study aimed to evaluate the relationship between DII and stroke risk while delving into the potential role of IR in this association. We analyzed data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018, performing weighted univariate analyses, logistic regression, and mediation analyses. At baseline, 3.89% of participants developed stroke, and we observed stroke patients exhibited higher DII scores. After adjusting for covariates, compared to participants in the first quartile of DII scores, those in the third quartile and fourth quartile had increased odds of experiencing a stroke (OR: 1.78, 95% CI: 1.18-2.68) and (OR: 1.70, 95% CI: 1.16-2.50), respectively. Moreover, a significant dose-response relationship was observed (P-trend < 0.05). However, there was no observed interaction between DII and homeostatic model assessment-IR (HOMA-IR) concerning stroke risk, and HOMA-IR did not mediate the association between DII and stroke. In summary, our study elucidated the significant association between DII and stroke risk, independent of IR. This insight suggests that an anti-inflammatory diet may serve as an effective strategy for stroke prevention.

Association between antioxidant nutrients and pregnancy complications: a Mendelian randomization study / 预防医学

Objective@#To investigate the causal relationship between antioxidant nutrients and pregnancy complications, so as to provide the reference for the prevention and treatment of pregnancy complications.@*Methods@#Data of seven antioxidant nutrients including vitamin A, vitamin C, vitamin E, selenium, zinc, copper and iron were collected from genome-wide association study (GWAS) Catalog database, and data of four pregnancy complications including gestational diabetes mellitus, pre-eclampsia, spontaneous abortion and preterm birth were collected from the Finland database. Single nucleotide polymorphism (SNP) data were collected, and 27 SNPS strongly correlated with seven antioxidant nutrients were selected as instrumental variables. Mendelian randomization (MR) analyses were performed using the inverse-variance weighted (IVW) method with seven antioxidant nutrients as exposures factors and four pregnancy complications as outcome variables. The heterogeneity was assessed using the Cochran's Q test, the horizontal pleiotropy was assessed using the MR-PRESSO test and MR-Egger regression, and the robustness of the results was verified with the leave-one-out.@*Results@#Cochran's Q test showed heterogeneity of MR results between vitamin C and gestational diabetes mellitus, preeclampsia and preterm birth, between vitamin E and iron and gestational diabetes (all P<0.05), and a random effect model was employed. There was no heterogeneity in other results (all P>0.05), and a fixed effect model was employed. MR analysis results showed that there was no causal association between seven antioxidant nutrients and the risk of four pregnancy complications (all P>0.05). MR-PRESSO test and the MR-Egger regression identified no horizontal pleiotropy of instrumental variables (both P>0.05).@*Conclusion@#This study did not find genetically predicted associations of antioxidant nutrients with pregnancy complications.

Genetically predicted lifestyle factors, socioeconomic status and risk of coronary artery disease in individuals with diabetes: a Mendelian randomization study.

Background: This study explores the causal links between genetically predicted lifestyle factors, socioeconomic status, and coronary artery disease (CAD) risk in individuals with diabetes using a bidirectional Mendelian-randomization approach. Methods: This study explored the potential causal relationships of lifestyle factors and socioeconomic status with the risk of CAD in diabetes patients by a bidirectional, two-sample Mendelian-randomization (MR) analysis. Results: Genetically predicted smoking initiation (p = 0.005, 95% CI: 1.08-1.55) and insomnia (p = 0.001, 95% CI: 1.06-1.29) were associated with a higher risk of CAD in individuals with diabetes, whereas educational attainment (p = 0.0001, 95% CI: 0.47-0.78) was associated with a lower risk of CAD. The lifetime smoking index (p = 0.016, 95% CI: 1.12-3.03) was suggestively associated with a higher risk of CAD, while household income before taxes (p = 0.048, 95% CI: 0.41-1.00) was suggestively associated with a lower risk of CAD. In addition, we observed a suggestive negative association between the genetically predicted risk of CAD and the lifetime smoking index (p = 0.016, 95% CI: 0.98-0.99) and a significant causal relationship between the risk of CAD and household income before taxes (p = 0.006, 95% CI: 0.97-0.99). Conclusion: The results of this study provide evidence that smoking initiation, lifetime smoking index and insomnia are associated with an increased risk of CAD in individuals with diabetes, educational attainment and household income before taxes are associated with a reduced risk of CAD in individuals with diabetes, and the possible role of lifetime smoking index and household income before taxes on the risk of CAD in individuals with diabetes. It provides an opportunity for the prevention and management of CAD in individuals with diabetes.

Association between systemic immune-inflammatory index and diabetes mellitus: mediation analysis involving obesity indicators in the NHANES.

Background: Inflammation and obesity have been widely recognized to play a key role in Diabetes mellitus (DM), and there exists a complex interplay between them. We aimed to clarify the relationship between inflammation and DM, as well as the mediating role of obesity in the relationship. Methods: Based on the National Health and Nutrition Examination Survey (NHANES) 2005-2018. Univariate analyses of continuous and categorical variables were performed using t-test, linear regression, and χ2 test, respectively. Logistic regression was used to analyze the relationship between Systemic Immune-Inflammatory Index (SII) or natural logarithm (Ln)-SII and DM in three different models. Mediation analysis was used to determine whether four obesity indicators, including body mass index (BMI), waist circumference (WC), visceral adiposity index (VAI) and lipid accumulation product index (LAP), mediated the relationship between SII and DM. Results: A total of 9,301 participants were included, and the levels of SII and obesity indicators (BMI, WC, LAP, and VAI) were higher in individuals with DM (p < 0.001). In all three models, SII and Ln-SII demonstrated a positive correlation with the risk of DM and a significant dose-response relationship was found (p-trend <0.05). Furthermore, BMI and WC were associated with SII and the risk of DM in all three models (p < 0.001). Mediation analysis showed that BMI and WC mediated the relationship between SII with DM, as well as Ln-SII and DM, with respective mediation proportions of 9.34% and 12.14% for SII and 10.23% and 13.67% for Ln-SII (p < 0.001). Conclusion: Our findings suggest that increased SII levels were associated with a higher risk of DM, and BMI and WC played a critical mediating role in the relationship between SII and DM.