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Aims: Long noncoding RNAs (lncRNAs) may be associated with the development of type 2 diabetes mellitus and its complications; however, the findings remain controversial. We aimed to synthesize the available data to assess the diagnostic utility of lncRNAs for identification of type 2 diabetes mellitus and its consequences. Materials and Methods: We performed a systematic review and meta‐analysis, searching PubMed, Embase, and Web of Science for articles published from September 11, 2015 to December 27, 2022. We evaluated human case–control or cohort studies on differential lncRNA expression in type 2 diabetes mellitus or its associated comorbidities. We excluded studies if they were non‐peer reviewed or published in languages other than English. From 2387 identified studies, we included 17 (4685 participants). Results: Analysis of the pooled data showed that lncRNAs had a diagnostic area under the curve (AUC) of 0.84 (95% CI: 0.80–0.87), with a sensitivity of 0.79 (95% CI: 0.74–0.83) and a specificity of 0.75 (95% CI: 0.69–0.80). LncRNAs had an AUC of 0.65 for the diagnosis of prediabetes, with 82% sensitivity and 65% specificity. Conclusions: LncRNAs may be promising diagnostic markers for type 2 diabetes mellitus and its complications. [ABSTRACT FROM AUTHOR]

Aims/Introduction: To compare the application value of different machine learning (ML) algorithms for diabetes risk prediction. Materials and Methods: This is a 3‐year retrospective cohort study with a total of 3,687 participants being included in the data analysis. Modeling variable screening and predictive model building were carried out using logistic regression (LR) analysis and 10‐fold cross‐validation, respectively. In total, six different ML algorithms, including random forests, light gradient boosting machine, extreme gradient boosting, adaptive boosting (AdaBoost), multi‐layer perceptrons and gaussian naive bayes were used for model construction. Model performance was mainly evaluated by the area under the receiver operating characteristic curve. The best performing ML model was selected for comparison with the traditional LR model and visualized using Shapley additive explanations. Results: A total of eight risk factors most associated with the development of diabetes were identified by univariate and multivariate LR analysis, and they were visualized in the form of a nomogram. Among the six different ML models, the random forests model had the best predictive performance. After 10‐fold cross‐validation, its optimal model has an area under the receiver operating characteristic value of 0.855 (95% confidence interval [CI] 0.823–0.886) in the training set and 0.835 (95% CI 0.779–0.892) in the test set. In the traditional LR model, its area under the receiver operating characteristic value is 0.840 (95% CI 0.814–0.866) in the training set and 0.834 (95% CI 0.785–0.884) in the test set. Conclusions: In the real‐world epidemiological research, the combination of traditional variable screening and ML algorithm to construct a diabetes risk prediction model has satisfactory clinical application value. [ABSTRACT FROM AUTHOR]

Background and Aims: Previous studies had shed a new light on the importance of multiple inflammatory mechanisms in the pathogenesis of arterial stiffness. The dietary inflammatory index (DII®) is a new tool for estimating the overall inflammatory potential of the diet. The aim of this study is to assess the association of the inflammatory potential of diet with peripheral arterial stiffness and renal function in women with diabetes and prediabetes.Methods and Results: This is an observational cross-sectional study. A total of 2644 females aged 45-75 years were included for the study. Dietary intake in the past 12 months was assessed by a validated China National Nutrition and Health Survey 2002 (CNHS2002) food-frequency questionnaire. Energy-adjusted DII (E-DII) scores were calculated from daily dietary information. In a multivariable linear regression analysis adjusted for potential confounders, E-DII was positively associated with brachial ankle pulse wave velocity (baPWV) in participants with diabetes (β = 12.820; 95% CI = 2.565, 23.076; P = 0.014) and prediabetes (β = 29.025; 95% CI = 1.110, 56.940; P = 0.042), but not in females with normal glucose homeostasis. In addition, per unit increase of E-DII was significantly associated with lower eGFR (β = -1.363; 95% CI = -2.335, -0.392; P = 0.006) in patients with diabetes.Conclusion: We identified a direct association between E-DII and arterial stiffness, decreasedeGFR in middle-aged and elderly women with diabetes or prediabetes. Future studies are needed to verify and clarify the role of E-DII as an intervention target for cardiorenal complications of chronic hyperglycemia. [ABSTRACT FROM AUTHOR]

Aimed to investigate the predictive value of procalcitonin (PCT) in early detection of infections in elderly patients with type 2 diabetes, and to discover the optimum cut-off points of PCT. A retrospective study was conducted with type 2 diabetic patients (≥65 years) with lung infection (LI), urinary tract infection (UTI) or skin and soft tissue infection (SSTI). The receiver operating characteristic (ROC) curves of the 3 markers (PCT, WBC count, and CRP) were constructed and compared to assess their accuracies in diagnosing. Among the three different groups with LI, UTI or SSTI, the area under the ROC curve (AUC) of PCT was 0.98 (95% confidence interval (CI): 0.96–0.99, p < 0.05) for the LI group, 0.98 (95% CI: 0.96–0.99, p < 0.05) for the UTI group, and 0.97 (95% CI: 0.94–1.00, p < 0.05) for the SSTI group. The optimum cut-off point of PCT level was 0.73 ng/mL (Sn 89.7%, Sp 97.7%) for the LI group, 1.48 ng/mL (Sn 88.9%, Sp 100%) for the UTI group, and 0.73 ng/mL (Sn 85.7%, Sp 97.7%) for the SSTI group. PCT demonstrated the strongest correlation with each of the infection types, indicating significant diagnostic value. Optimum cut-off points of PCT levels in elderly diabetes were higher. [ABSTRACT FROM AUTHOR]