This was an observational study based on the National Health and Nutrition Examination Survey (NHANES) and National Death Index (NDI) 2009–2014 which aimed to validate whether a proinflammatory diet may increase mortality risk in patients with diabetes mellitus. Dietary inflammatory potential was assessed by dietary inflammatory index (DII) based on 24 h dietary recall. Mortality follow-up information was accessed from NDI, which was then merged with NHANES data following the National Center for Health Statistics (NCHS) protocols. For 15,291 participants from the general population, the average DII was 0.37 ± 1.76 and the prevalence rate of diabetes was 13.26%. DII was positively associated with fasting glucose (β = 0.83, 95% CI: 0.30, 1.36, p = 0.0022), glycohemoglobin (β = 0.02, 95% CI: 0.01, 0.03, p = 0.0009), and the risk of diabetes (OR = 1.05, 95% CI: 1.01, 1.09, p = 0.0139). For 1904 participants with diabetes and a median follow-up of 45 person-months, a total of 178 participants with diabetes died from all causes (mortality rate = 9.34%). People with diabetes who adhered to a proinflammatory diet showed a higher risk of all-cause mortality (HR = 1.71, 95%CI: 1.13, 2.58, p = 0.0108). In summary, DII was positively associated with diabetes prevalence and a proinflammatory diet may increase mortality risk in patients with diabetes mellitus.

Background: The triglyceride–glucose (TyG) index is a simple, novel and reliable surrogate marker of insulin resistance. However, evidence for the prognostic impact of an elevated TyG index on IgA nephropathy (IgAN) is limited. Therefore, we evaluated the relationship between the TyG index and the risk of renal progression in IgAN. Method: This cohort study involved biopsy-proven IgAN between January 2009 and December 2018 in West China Hospital, in which patients were assigned to two groups based on the cut-off value of TyG using receiver operating characteristic (ROC) curves. A 1:1 matched-pair analysis was established to optimize the bias in IgAN by propensity score matching (PSM). The TyG index was calculated as ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. The composite endpoint was defined by eGFR decreased ≥50% of the baseline level, end-stage kidney disease (ESKD), renal transplantation and/or death. Univariable and multivariable Cox proportional hazard models were applied to confirm the predictive value of the optimal marker. Results: Before PSM, a total of 1210 participants were ultimately included. During a median follow-up period of 55.8 months (range 37.20–79.09 months), 129 participants progressed to the composite endpoint (10.7%). After PSM, 366 patients were enrolled in the matched cohort, of whom 34 (9.3%) patients reached the endpoints. Based on the cut-off value of the TyG index, patients were divided into the low TyG index group (TyG ≤ 8.72, n = 690) and the high TyG index group (TyG > 8.72, n = 520). Further analysis demonstrated that a higher TyG index was significantly associated with a higher risk of reaching composite endpoints in IgAN patients in both the unmatched and matched cohorts (before PSM: HR 2.509, 95% CI 1.396–4.511, p = 0.002; after PSM: HR 2.654, 95% CI 1.299–5.423, p = 0.007). Conclusion: A high TyG index is associated with a higher risk of renal progression.

Organochlorine pesticides, with their environmental persistence and bioaccumulation potential, have gained significant attention. This study explores the impact of organochlorine pesticides on mortality and chronic diseases, investigates their link to inflammatory states, and examines the role of anti-inflammatory diets in mitigating adverse reactions to these pesticides. This study, with 2,847 participants, used gas chromatography and mass spectrometry to measure organochlorine pesticide exposure in NHANES data. Conventional statistical methodologies, encompassing survival curves, Cox proportional hazards regression, regression analysis, and restricted quadratic spline analysis, were employed to investigate the association between pesticides and mortality, chronic ailments, and inflammation. Furthermore, machine learning techniques, comprising RF, AdaBoost, Extra-Trees, LightGBM, and BPNN, were leveraged to evaluate the impact of pesticides on chronic disease and mortality prognostication. Organochlorine pesticides were significantly and positively correlated with increased mortality (p<0.05). Additionally, these pollutants were linked to the incidence of chronic diseases such as chronic kidney disease, diabetes, and hypertension (p< 0.05). Our study, utilizing various machine learning models, also showed a notable increase in the Area Under the Curve when incorporating organochlorine pesticide indicators into the model as opposed to excluding them. Furthermore, strong correlations were observed between serum c-reactive protein (CRP) and CRP to serum albumin ratio (CAR) concentrations with these substances, demonstrating their pro-inflammatory effects at specific concentrations. Interestingly, cutting down on dietary inflammation through changes in diet effectively reduced the risk of death at high organochlorine pesticide exposure levels, but the effect was less noticeable at low to moderate exposure levels. Exposure to organochlorine pesticides was linked to a higher risk of mortality, likely due to an increased prevalence of chronic diseases. In this context, inflammation played a crucial role, and adopting an anti-inflammatory diet significantly reduced the mortality risk associated with these pesticides.

Background: Traditional risk models for immunoglobulin A nephropathy (IgAN), which primarily rely on renal indicators, lack comprehensive assessment and therapeutic guidance, necessitating more refined and integrative approaches. Objective: This study integrated network biomarkers with unsupervised learning clustering (k-means clustering based on network biomarkers [KMN]) to refine risk stratification in IgAN and explore its clinical value. Methods: Involving a multicenter prospective cohort, we analyzed 1460 patients and validated the approach externally with 200 additional patients. Deeper metabolic and microbiomic insights were gained from 2 distinct cohorts: 63 patients underwent ultraperformance liquid chromatography–mass spectrometry, while another 45 underwent fecal 16S RNA sequencing. Our approach used hierarchical clustering and k-means methods, using 3 sets of indicators: demographic and renal indicators, renal and extrarenal indicators, and network biomarkers derived from all indicators. Results: Among 6 clustering methods tested, the KMN scheme was the most effective, accurately reflecting patient severity and prognosis with a prognostic accuracy area under the curve (AUC) of 0.77, achieved solely through cluster grouping without additional indicators. The KMN stratification significantly outperformed the existing International IgA Nephropathy Prediction Tool (AUC of 0.72) and renal function-renal histology grading schemes (AUC of 0.69). Clinically, this stratification facilitated personalized treatment, recommending angiotensin-converting enzyme inhibitors or angiotensin receptor blockers for lower-risk groups and considering immunosuppressive therapy for higher-risk groups. Preliminary findings also indicated a correlation between IgAN progression and alterations in serum metabolites and gut microbiota, although further research is needed to establish causality. Conclusions: The effectiveness and applicability of the KMN scheme indicate its substantial potential for clinical application in IgAN management.