This study aimed to assess the associations between multidimensional sleep features and type 2 diabetes mellitus (T2DM). We conducted a systematic search across the PubMed, Embase, Web of Science, and Scopus databases for observational studies examining the association between nighttime sleep duration, nighttime sleep quality, sleep chronotype, and daytime napping with type 2 diabetes mellitus (T2DM), up to October 1, 2024. If I² < 50%, a combined analysis was performed based on a fixed-effects model, and vice versa, using a random-effects model. Our analysis revealed that a nighttime sleep duration of less than 7 h (odds ratio [OR] = 1.18; 95% CI = 1.13, 1.23) or more than 8 h (OR = 1.13; 95% CI = 1.09, 1.18) significantly increased the risk of T2DM. Additionally, poor sleep quality (OR = 1.50; 95% CI = 1.30, 1.72) and evening chronotype (OR = 1.59; 95% CI = 1.18, 2.13) were associated with a notably greater risk of developing T2DM. Daytime napping lasting more than 30 min augments the risk of T2DM by 7-20%. Interactively, the incidence of T2DM was most significantly elevated among individuals with poor sleep quality and nighttime sleep duration of more than 8 h (OR = 2.15; 95% CI = 1.19, 3.91). A U-shaped relationship was observed between sleep duration and type 2 diabetes mellitus (T2DM), with the lowest risk occurring at a sleep duration of 7 to 8 h. Additionally, poor sleep quality, evening chronotypes, and daytime napping exceeding 30 min emerged as potential risk factors for T2DM. These high-risk sleep characteristics interacted with one another, amplifying the overall risk of developing the disease.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

16S rRNA sequencing data of fecal samples from three breeds of boars at time points before, during and after the use of amoxicillin. A total of 103 stud boars of 3 breeds were used as experimental animals in this study, including 40 large white (LW), 47 landrace (LAN) and 16 duroc (DUR) stud boars. The starting time of the experiment was defined as Day 0 (D0). Amoxicillin was added to the diet at a dose of 100 mg/kg feed and started on day 3 (D3), with all other conditions remaining the same. The administration of amoxicillin was stopped on the seventh day (D7) of the experiment and the normal diet was used until the 14th day (D14). Fresh fecal samples were collected on days 0, 7 and 14, frozen in liquid nitrogen

16S rRNA sequencing data of fecal samples from three breeds of boars at time points before, during and after the use of amoxicillin. A total of 103 stud boars of 3 breeds were used as experimental animals in this study, including 40 large white (LW), 47 landrace (LAN) and 16 duroc (DUR) stud boars. The starting time of the experiment was defined as Day 0 (D0). Amoxicillin was added to the diet at a dose of 100 mg/kg feed and started on day 3 (D3), with all other conditions remaining the same. The administration of amoxicillin was stopped on the seventh day (D7) of the experiment and the normal diet was used until the 14th day (D14). Fresh fecal samples were collected on days 0, 7 and 14, frozen in liquid nitrogen

Genome-wide association studies have provided profound insights into the genetic aetiology of metabolic syndrome (MetS). However, there is a lack of machine-learning (ML)-based predictive models to assess individual genetic susceptibility to MetS. This study utilized single-nucleotide polymorphisms (SNPs) as variables and employed ML-based genetic risk score (GRS) models to predict the occurrence of MetS, bringing it closer to clinical application. Feature selection was performed using Least Absolute Shrinkage and Selection Operator. Six ML algorithms were employed to construct GRS models. A fivefold cross-validation was utilized to aid in the internal validation of models. The receiver operating characteristic (ROC) curve was used to select the better-performing GRS model. The SHapley Additive exPlanations (SHAP) was then applied to interpret the model. After extracting GRS, stratified analysis of BMI, age and gender was performed. Finally, these conventional risk factors and GRS were integrated through multivariate logistic regression to establish a combined model. A total of 17 SNPs were selected for analysis. Among the GRS models, the extreme gradient boosting (XGBoost) model demonstrated superior discriminative performance (AUC = 0.837). The XGBoost’s optimal robustness was also validated through five-fold cross-validation (mean ROC-AUC = 0.706). The XGBoost-based SHAP algorithm not only elucidated the global effects of 17 SNPs across all samples, but also described the interaction between SNPs, providing a visual representation of how SNPs impact the prediction of MetS in an individual. There was a strong correlation between GRS and MetS risk, particularly observed among young individuals, males and overweight individuals. Furthermore, the model combining conventional risk factors and GRS exhibited excellent discriminative performance (AUC = 0.962) and outstanding robustness (mean ROC-AUC = 0.959). This study established a reliable XGBoost-based GRS model and a GRS prediction platform (https://metabolicsyndromeapps.shinyapps.io/geneticriskscore/) to assess individual genetic susceptibility to MetS. This model has high interpretability and can provide personalized reference for determining the necessity of primary prevention measures for MetS. Additionally, there may be interactions between traditional risk factors and GRS, and the integration of both in a comprehensive model is useful in the prediction of MetS occurrence.

Genome-wide association studies have provided profound insights into the genetic aetiology of metabolic syndrome (MetS). However, there is a lack of machine-learning (ML)-based predictive models to assess individual genetic susceptibility to MetS. This study utilized single-nucleotide polymorphisms (SNPs) as variables and employed ML-based genetic risk score (GRS) models to predict the occurrence of MetS, bringing it closer to clinical application. Feature selection was performed using Least Absolute Shrinkage and Selection Operator. Six ML algorithms were employed to construct GRS models. A fivefold cross-validation was utilized to aid in the internal validation of models. The receiver operating characteristic (ROC) curve was used to select the better-performing GRS model. The SHapley Additive exPlanations (SHAP) was then applied to interpret the model. After extracting GRS, stratified analysis of BMI, age and gender was performed. Finally, these conventional risk factors and GRS were integrated through multivariate logistic regression to establish a combined model. A total of 17 SNPs were selected for analysis. Among the GRS models, the extreme gradient boosting (XGBoost) model demonstrated superior discriminative performance (AUC = 0.837). The XGBoost’s optimal robustness was also validated through five-fold cross-validation (mean ROC-AUC = 0.706). The XGBoost-based SHAP algorithm not only elucidated the global effects of 17 SNPs across all samples, but also described the interaction between SNPs, providing a visual representation of how SNPs impact the prediction of MetS in an individual. There was a strong correlation between GRS and MetS risk, particularly observed among young individuals, males and overweight individuals. Furthermore, the model combining conventional risk factors and GRS exhibited excellent discriminative performance (AUC = 0.962) and outstanding robustness (mean ROC-AUC = 0.959). This study established a reliable XGBoost-based GRS model and a GRS prediction platform (https://metabolicsyndromeapps.shinyapps.io/geneticriskscore/) to assess individual genetic susceptibility to MetS. This model has high interpretability and can provide personalized reference for determining the necessity of primary prevention measures for MetS. Additionally, there may be interactions between traditional risk factors and GRS, and the integration of both in a comprehensive model is useful in the prediction of MetS occurrence.

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An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.