The risk factors for type 2 diabetes mellitus (T2DM) have been increasingly researched, but the lack of systematic identification and categorization makes it difficult for clinicians to quickly and accurately access and understand all the risk factors, which are categorized in this paper into five categories: Social determinants, lifestyle, checkable/testable risk factors, history of illness and medication, and other factors, which are discussed in a narrative review. Meanwhile, this paper points out the problems of the current research, helps to improve the systematic categorisation and practicality of T2DM risk factors, and provides a professional research basis for clinical practice and industry decision-making.

The objective of the present study is to analyze the urinary metabolome profile of patients with obesity and overweight and relate it to different obesity profiles. This is a prospective, cross-sectional study in which patients with a body mass index (BMI) ≥25 kg/m were selected. Anthropometric data were assessed by physical examination and body composition was obtained by bioimpedance (basal metabolic rate, body fat percentile, skeletal muscle mass, gross fat mass and visceral fat). Urine was collected for metabolomic analysis. Patients were classified according to abdominal circumference measurements between 81 and 93, 94 and 104, and >104 cm; visceral fat up to 16 kilos and less than; and fat percentiles of <36%, 36-46% and >46%. Spectral alignment of urinary metabolite signals and bioinformatic analysis were carried out to select the metabolites that stood out. NMR spectrometry was used to detect and quantify the main urinary metabolites and to compare the groups. Seventy-five patients were included, with a mean age of 38.3 years, and 72% females. The urinary metabolomic profile showed no differences in BMI, abdominal circumference and percentage of body fat. Higher concentrations of trigonelline (p = 0.0488), sarcosine (p = 0.0350) and phenylalanine (p = 0.0488) were associated with patients with visceral fat over 16 kg. The cutoff points obtained by the ROC curves were able to accurately differentiate between patients according to the amount of visceral fat: sarcosine 0.043 mg/mL; trigonelline 0.068 mg/mL and phenylalanine 0.204 mg/mL. In conclusion, higher visceral fat was associated with urinary levels of metabolites such as sarcosine, related to insulin resistance; trigonelline, related to muscle mass and strength; and phenylalanine, related to glucose metabolism and abdominal fat. Trigonelline, sarcosine and phenylalanine play significant roles in regulating energy balance and metabolic pathways essential for controlling obesity. Our findings could represent an interesting option for the non-invasive estimation of visceral fat through biomarkers related to alterations in metabolic pathways involved in the pathophysiology of obesity.

Diabetes places a significant burden on personal and public health. However, a comprehensive assessment of the burden of diabetes in older adults is lacking. We aimed to estimate the global burden of diabetes and explore trends for the population aged ≥70 from 1990 to 2019.

Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, the prevalence, mortality, and disability-adjusted life-years (DALYs) of diabetes among people aged ≥70 were estimated by sex and age group in 2019. We also assessed the epidemiological trend of diabetes from 1990 to 2019.

In 2019, 110.1 million (95% uncertainty interval [UI]: 101.2-119.4) people aged ≥70 years were living with diabetes (types 1 and 2 combined) with a global prevalence of 23.7% (21.8%-25.8%). Worldwide, 181.9 deaths (163.0-194.7) per 100,000 population and 4512.3 DALYs (3861.3-5264.2) per 100,000 population occurred due to diabetes. In 2019, minor sex-related disparities in the burden of diabetes were identified among specific age and sex groups. From 1990 to 2019, the prevalence of diabetes increased by 39.7% (37.7%-41.7%), and the related mortality and DALY rates also increased (16.4% [9.43%-22.9%] and 22.3% [17.2%-27.0%], respectively).

The global burden of diabetes in adults aged ≥70 has increased markedly from 1990 to 2019. As the population continues to age, there is an urgent need to combat the increasing disease burden.

This study aimed to explore the association between controlling nutritional status (CONUT) score and chronic kidney disease (CKD) in type-2 diabetes mellitus (T2DM) patients.

This was a cross-sectional study based on the National Health and Nutrition Examination Survey (NHANES). The data on demographic characteristics, physical examination, lifestyle behaviors, comorbidities, medicine use, laboratory values, and energy were extracted. Nutritional status was assessed using CONUT score, and patients were divided into normal nutrition group and malnutrition group. Association between CONUT score and CKD in T2DM patients was assessed using logistic regression analysis, and odds ratio (OR) and 95% confidence intervals (CIs) were reported. Subgroup analysis based on age, body mass index (BMI), cardiovascular disease (CVD), diabetic retinopathy, and hyperlipidemia was performed.

A total of 4581 patients were finally included for analysis. In the adjusted model, high CONUT score was found to be associated with the high odds of CKD (OR = 1.28, 95% CI 1.05-1.56). Also, high CONUT score was associated with the high odds of CKD in T2DM patients with age ≥ 65 years, with BMI < 25 kg/m2, with BMI ≥ 25 kg/m2, without CVD, without diabetic retinopathy, with hyperlipidemia, or without hyperlipidemia (all P < 0.05).

Malnutrition was associated with the high odds of CKD in T2DM patients, indicating that actively monitoring the nutritional status is important for the management of CKD in T2DM patients.

The present study aimed to investigate the correlation between weight status and mortality in mechanically ventilated patients and explore the potential mediators.

Three medical centers encompassing 3301 critically ill patients receiving mechanical ventilation were assembled for retrospective analysis to compare mortality across various weight categories of patients using machine learning algorithms. Bioinformatics analysis identified genes exhibiting differential expression among distinct weight categories. A prospective study was then conducted on a distinct cohort of 50 healthy individuals and 193 other mechanically ventilated patients. The expression levels of the genes identified through bioinformatics analysis were quantified through enzyme-linked immunosorbent assay (ELISA).

The retrospective analysis revealed that overweight individuals had a lower mortality rate than underweight individuals, and body mass index (BMI) was an independent protective factor. Bioinformatics analysis identified matrix metalloproteinase 8 (MMP-8) as a differentially expressed gene between overweight and underweight populations. The results of further prospective studies showed that overweight patients had significantly lower MMP-8 levels than underweight patients ((3.717 (2.628, 4.191) vs. 2.763 (1.923, 3.753), ng/ml, P = 0.002). High MMP-8 levels were associated with increased mortality risk (OR = 4.249, P = 0.005), indicating that elevated level of MMP-8 predicts the mortality risk of underweight patients receiving mechanical ventilation.

This study provides evidence for a protective effect of obesity in mechanically ventilated patients and highlights the potential role of MMP-8 level as a biomarker for predicting mortality risk in this population.

To develop a personalized nomogram and risk score to predict the 5-year risk of diabetes among Chinese adults with prediabetes.

There were 26 018 participants with prediabetes at baseline in this retrospective cohort study. We randomly stratified participants into two cohorts for training (n = 12 947) and validation (n = 13 071). The least absolute shrinkage and selection operator (LASSO) model was applied to select the most significant variables among candidate variables. And we further established a stepwise Cox proportional hazards model to screen out the risk factors based on the predictors chosen by the LASSO model. We presented the model with a nomogram. The model's discrimination, clinical use and calibration were assessed using the area under the receiver operating characteristic (ROC) curve, decision curve and calibration analysis. The associated risk factors were also categorized according to clinical cut-points or tertials to create the diabetes risk score model. Based on the total score, we divided it into four risk categories: low, middle, high and extremely high. We also evaluated our diabetes risk score model's performance.

We developed a simple nomogram and risk score that predicts the risk of prediabetes by using the variables age, triglyceride, fasting blood glucose, body mass index, alanine aminotransferase, high-density lipoprotein cholesterol and family history of diabetes. The area under the ROC curve of the nomogram was 0.8146 (95% CI 0.8035-0.8258) and 0.8147 (95% CI 0.8035-0.8259) for the training and validation cohort, respectively. The calibration curve showed a perfect fit between predicted and observed diabetes risks at 5 years. Decision curve analysis presented the clinical use of the nomogram, and there was a wide range of alternative threshold probability spectrums. A total risk score of 0 to 2.5, 3 to 4.5, 5 to 7.5 and 8 to 13.5 is associated with low, middle, high and extremely high diabetes risk status, respectively.

We developed and validated a personalized prediction nomogram and risk score for 5-year diabetes risk among Chinese adults with prediabetes, identifying individuals at a high risk of developing diabetes. Doctors and other healthcare professionals can easily and quickly use our diabetes score model to assess the diabetes risk status in patients with prediabetes. In addition, the nomogram model and risk score we developed need to be validated in a prospective cohort study.

Evidence regarding the relationship between the triglyceride glucose-body mass index (TyG-BMI) and the risk of progression from prediabetes to diabetes remains limited. Our study aimed to investigate the relationship between them in patients with prediabetes.

In this retrospective cohort study, data were collected from 25,279 patients with prediabetes who received health checks between 2010 and 2016. We used a Cox proportional-hazards regression model to examine the relationship between TyG-BMI and diabetes risk. We used Cox proportional hazards regression with cubic spline functions and smooth curve fitting to identify the nonlinear relationship between them. In addition, A series of sensitivity and subgroup analyses were also conducted.

The mean age of the included participants was 49.29 ± 13.82 years old, and 1,6734 (66.2%) were male. The mean TyG-BMI was 219.47. The median follow-up time was 2.89 years, and 2,687 (10.63%) individuals had a final diagnosis of diabetes. After adjusting for covariates, TyG-BMI was positively linked with incident diabetes in patients with prediabetes (HR = 1.011, 95%CI 1.010-1.012). TyG-BMI had a non-linear connection with diabetes risk, and its inflection point was 231.66. Right and left effects sizes (HR) at the inflection point were 1.017 (95%CI:1.014-1.019) and 1.007 (95%CI:1.005-1.009), respectively. The sensitivity analysis demonstrated the robustness of these results.

This study demonstrated a positive, non-linear relationship between the TyG-BMI and diabetes risk in Chinese patients with prediabetes. When the TyG-BMI was <231.66, there was a significant positive association between TyG-BMI and the risk of progression from prediabetes to diabetes. This study serves as a reference to promote clinical consultation and optimize diabetes prevention decisions for patients with prediabetes.

Evidence regarding the relationship between body mass index (BMI) and reversion to normoglycemia from prediabetes is still limited. The purpose of our study is to survey the link of BMI on reversion to normoglycemia among patients with impaired fasting glucose (IFG).

This study, a retrospective cohort, covered 32 regions and 11 cities in China and collected 258,74 IFG patients who underwent a health check from 2010 to 2016. We investigated the association between baseline BMI and reversion to normoglycemia in patients with IFG using the Cox proportional-hazards regression model. The nonlinear relationship between BMI and reversion to normoglycemia was determined using a Cox proportional hazards regression with cubic spline functions and smooth curve fitting. In addition, we also performed a series of sensitivity analyses and subgroup analyses. A competing risk multivariate Cox regression was performed using progression to diabetes as a competing risk for reversal of normoglycemic events.

After adjusting covariates, the results showed that BMI was negatively related to the probability of reversion to normoglycemia (HR=0.977, 95%CI:0.971-0.984). Compared with participants with normal BMI(<24kg/m²), overweight (BMI:24-28kg/m²) participants with IFG had a 9.9% lower probability of returning to normoglycemia (HR=0.901,95%CI:0.863-0.939), while obese patients (BMI ≥ 28kg/m²) had a 16.9% decreased probability of reverting from IFG to normoglycemia (HR=0.831,95%CI:0.780-0.886). There was also a nonlinear relationship between them, and the inflection point of BMI was 21.7kg/m². The effect sizes (HR) on the left sides of the inflection point were 0.972(95%CI:0.964-0.980). The competing risks multivariate Cox's regression and sensitivity analysis demonstrated the robustness of our results.

This study demonstrates a negative and nonlinear relationship between BMI and reversion to normoglycemia in Chinese patients with IFG. Minimizing BMI to 21.7 kg/m² in patients with IFG through aggressive intervention may significantly increase the probability of returning to normoglycemia.