Metabolomic approaches to dissect dysregulated metabolism in the progression of pre-diabetes to T2DM.

Many individuals with pre-diabetes eventually develop diabetes. Therefore, profiling of prediabetic metabolic disorders may be an effective targeted preventive measure. We aimed to elucidate the metabolic mechanism of progression of pre-diabetes to type 2 diabetes mellitus (T2DM) from a metabolic perspective. Four sets of plasma samples (20 subjects per group) collected according to fasting blood glucose (FBG) concentration were subjected to metabolomic analysis. An integrative approach of metabolome and WGCNA was employed to explore candidate metabolites. Compared with the healthy group (FBG < 5.6 mmol L-1), 113 metabolites were differentially expressed in the early stage of pre-diabetes (5.6 mmol L-1 ⩽ FBG < 6.1 mmol L-1), 237 in the late stage of pre-diabetes (6.1 mmol L-1 ⩽ FBG < 7.0 mmol L-1), and 245 in the T2DM group (FBG ⩾ 7.0 mmol L-1). A total of 27 differentially expressed metabolites (DEMs) were shared in all comparisons. Among them, L-norleucine was downregulated, whereas ethionamide, oxidized glutathione, 5-methylcytosine, and alpha-D-glucopyranoside beta-D-fructofuranosyl were increased with the rising levels of FBG. Surprisingly, 15 (11 lyso-phosphatidylcholines, L-norleucine, oxidized glutathione, arachidonic acid, and 5-oxoproline) of the 27 DEMs were ferroptosis-associated metabolites. WGCNA clustered all metabolites into 8 modules and the pathway enrichment analysis of DEMs showed a significant annotation to the insulin resistance-related pathway. Integrated analysis of DEMs, ROC and WGCNA modules determined 12 potential biomarkers for pre-diabetes and T2DM, including L-norleucine, 8 of which were L-arginine or its metabolites. L-Norleucine and L-arginine could serve as biomarkers for pre-diabetes. The inventory of metabolites provided by our plasma metabolome offers insights into T2DM physiology metabolism.

Improving health and reducing health inequality: An innovation of digitalization?

RATIONALE: The association between digitalization and individual health has attracted increasing attention from both scholars and policymakers. Existing research, however, has not agreed on whether digitization can improve health or reduce health inequality. OBJECTIVE: The purpose of this study is to clarify whether and how the development of digitalization may be related to health and health disparities. METHODS: We rely on China Family Panel Studies (CFPS) surveys from 2012 to 2018 to obtain a sample of 82,471 observations to explore the impact of digitalization on self-rated health and health inequality and its transmission mechanisms. The hypotheses are tested by Ordinary Least Squares Modeling. RESULTS: As expected, digitalization is significantly and positively correlated with self-rated health. Furthermore, the development of digitalization has led to a notable decrease in health inequality. The influencing mechanisms of digitalization include income, healthcare consumption and health behaviors. Both dimensions of digitalization-internet development and digital finance-generate significant effects and the effects of internet development are greater. CONCLUSIONS: This study is the first to systematically investigate the impact of digitalization development on health and health inequality. Our findings provide evidence for the health promotion theory by clarifying the benefits of digitalization in improving residents' health and reducing health inequality. Therefore, utilizing the tools of digitalization efficiently could be a focus of policymakers aiming to accomplish the SDGs' health targets.

The association between complement C1q tumour necrosis factor-related protein-1 (CTRP-1) level and metabolic syndrome.

INTRODUCTION: Complement C1q tumour necrosis factor-related protein (CTRP-1) is a member of the C1q protein superfamily that plays a role in metabolism. This retrospective study aimed to investigate associations between CTRP-1 and metabolic syndrome (MetS). MATERIAL AND METHODS: This study screened subjects who had undergone regular health examinations at the Physical Examination Centre in the First People's Hospital of Yinchuan (the Second Affiliated Hospital of Ningxia Medical University) between November 2017 and September 2020. The total recruited population included 430 subjects who had undergone regular health examinations, excluding 112 subjects with high glycated haemoglobin (HbA1c ≥ 7). Finally, the data of 318 participants were further analysed. Non-diabetic subjects were divided into 2 groups: one with MetS and one without MetS (controls). Serum CTRP-1 concentrations were evaluated using an enzyme-linked immunosorbent assay. RESULTS: A total of 318 subjects were included, among whom 176 were diagnosed with MetS (MetS group) and 142 were not (non-MetS controls). The MetS group had significantly lower CTRP-1 levels than non-MetS controls (128.51 [111.56-143.05] vs. 138.82 [122.83-154.33] ng/mL, p < 0.001). Correlation analysis showed that serum CTRP-1 levels correlated negatively with body mass index (r = -0.161, p = 0.004), waist circumference (r = -0.191, p = 0.001), systolic blood pressure (r = -0.198, p < 0.001), diastolic blood pressure (r = -0.145, p = 0.010), fasting blood glucose (FBG) (r = -0.562, p < 0.001), fasting insulin (FIns) (r = -0.424, p < 0.001), and homeostasis model assessment of insulin resistance (HOMA-IR) (r = -0.541, p < 0.001). Multiple linear regression models showed that CTRP-1 levels were associated with MetS (p < 0.01). The lipid profile area under the curve (AUC) was comparable to those for FBG and FIns, and it was significantly higher than the AUCs for demographic variables. CONCLUSIONS: The results of this study suggest that the serum CTRP-1 level is negatively associated with MetS. CTRP-1 is a potential metabolism-related protein and is likely to be associated with lipid profiles in MetS.

Ultra-high-performance liquid chromatography-tandem mass spectrometry analysis of serum metabolomic characteristics in people with different vitamin D levels.

Vitamin D is a fat-soluble vitamin with multiple functions. However, the metabolism of people with different vitamin D concentrations is still unclear. Herein, we collected clinical data and analysed the serum metabolome of people with 25-hydroxyvitamin D (25[OH]D) ≥40 ng/mL (A), 30 ng/mL ≤25(OH)D <40 ng/mL (B) and 25(OH)D <30 ng/mL (C) by the ultra-high-performance liquid chromatography-tandem mass spectrometry method. We found that haemoglobin A1c, fasting blood glucose, fasting insulin, homeostasis model assessment of insulin resistance and thioredoxin interaction protein were enhanced, while HOMA-ß was reduced with the decrease of 25(OH)D concentration. In addition, people in the C group were diagnosed with prediabetes or diabetes. Metabolomics analysis showed that seven, thirty-four and nine differential metabolites were identified in the groups B vs A, C vs A and C vs B, respectively. Metabolites associated with cholesterol metabolism and bile acid biosynthesis, such as 7-ketolithocholic acid, 12-ketolithocholic acid, apocholic acid, N-arachidene glycine and d-mannose 6-phosphate, were significantly upregulated in the C group compared with the A or B groups. In conclusion, the disorder of vitamin D metabolism may be related to cholesterol metabolism and bile acid biosynthesis. This study provided a basis for exploring the possible mechanism leading to abnormal vitamin D metabolism.

Vitamin D deficiency leads to the abnormal activation of the complement system.

Vitamin D deficiency can damage the human immune system, and the complement system is a key component of the immune system. This study aimed to elucidate the mechanism by which vitamin D affects the immune system by analyzing the changes in the protein expression of the complement system under different vitamin D levels. We selected 40 participants and divided them into three groups according to their serum levels of 25-hydroxyvitamin D (25(OH)VD): group A, 25(OH)VD ≥ 40 ng/mL; group B, 30 ng/mL ≤ 25(OH)VD < 40 ng/mL; and group C, 25(OH)VD < 30 ng/mL. Serum samples were subjected to biochemical analysis, followed by proteomic analysis using high-throughput untargeted proteomic techniques. Vitamin D deficiency increased the levels of fasting blood sugar, fasting serum insulin, and homeostasis model assessment (HOMA) of insulin resistance and decreased the secretion of HOMA of ß-cell function, which led to insulin resistance and glucose metabolism disorder. Moreover, vitamin D deficiency resulted in the abnormal expression of 56 differential proteins, among which the expression levels of complement factor B, complement component C9, inducible co-stimulator ligand, and peptidase inhibitor 16 significantly changed with the decrease in vitamin D content. Functional enrichment analysis of these differential proteins showed that they were mainly concentrated in functions and pathways related to insulin secretion and inflammation. In conclusion, vitamin D deficiency not only contributes to insulin resistance and glucose metabolism disorder but also causes abnormal protein expression, resulting in the abnormal activation of the complement system. This study provides a novel theoretical basis for further studies on the relationship between vitamin D and the immune system.

Analysis of protein expression changes in patients with prediabetes using proteomics approaches.

RATIONALE: Proteomics and metabolomics are widely used in the study of diabetes, but rarely in prediabetes research. This study aimed to explore the mechanisms of early-onset type 2 diabetes mellitus (T2DM) by analyzing proteomic changes at different stages of glucose metabolism. METHODS: A total of 40 individuals undergoing routine physical health examinations between December 2016 and April 2017 were enrolled. Subjects were divided into four groups based on fasting blood glucose (FPG) levels: FPG < 5.6 mmol/L (group A); FPG ≥ 5.6 mmol/L and <6.1 mmol/L (group B); FPG ≥ 6.1 mmol/L and <7.0 mmol/L (group C); and FPG ≥ 7.0 mmol/L (group D). Each group had 10 cases. Sera from these 40 subjects were analyzed by label-free quantitative liquid chromatography coupled with tandem mass spectrometry (LC/MS/MS). LC/MS/MS with selected reaction monitoring mode was also performed for qualitative and quantitative metabolomics analysis. Differentially expressed proteins were identified. Partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to analyze the differentially expressed metabolites. RESULTS: A total of 202 differentially expressed proteins were screened and were identified as mainly secreted proteins. Comparing group A with group B, 32 proteins were up-regulated and 18 proteins were down-regulated. Comparing group A with group C, 24 proteins were up-regulated and 24 proteins were down-regulated. Comparing group A with group D, 19 proteins were up-regulated and 17 proteins were down-regulated. The fold change for up-regulated proteins was >1.2, p < 0.05, while the fold change for down-regulated proteins was <-1.2, p < 0.05. PLS-DA and OPLS-DA revealed 113 differentially expressed metabolites. Correlation analysis of differentially expressed metabolites of group A versus group B revealed that among the down-regulated differential proteins, transforming growth factor ß-induced protein ig-h3 correlated negatively with metabolite L-saccharin, while among the up-regulated differential proteins, apolipoprotein C-IV correlated negatively with metabolite 3-methyloxindole. Among all differentially expressed proteins, 19 proteins were associated with early initiation of chronic inflammation, including CD14 and CSF-1R, which were newly identified in the early onset of T2DM. CONCLUSIONS: Many proteins are differentially expressed between prediabetes and after T2DM diagnosis, although the specific mechanism remains unclear. The expression level of CD14 was significantly up-regulated and that of CSF-1R was significantly down-regulated when FPG was ≥5.6 mmol/L, suggesting that CD14 and CSF-1R may be important markers for early-onset T2DM and may serve as new targets for T2DM treatment.

Changes in plasma IRAK-M in patients with prediabetes and its relationship with related metabolic indexes: a cross-sectional study.

OBJECTIVE: To investigate whether IL-1R-associated kinase (IRAK)-M is associated with prediabetes and type 2 diabetes (T2D). METHODS: In this cross-sectional study, enrolled subjects were assigned to different groups according to their fasting plasma glucose (FPG) values. IRAK-M and metabolic parameters, including fasting insulin (FINS), glycosylated hemoglobin (HbA1c), homeostasis model assessment of insulin resistance (HOMA-IR) and beta-cell function (HOMA-ß), and thioredoxin-interacting protein (TXNIP), were evaluated. The area under the receiver operating characteristic curve of IRAK-M and TXNIP for prediabetes and T2D was determined. RESULTS: IRAK-M decreased significantly with increasing FPG levels. IRAK-M was negatively correlated with TXNIP, FPG, FINS, HbA1c, and HOMA-IR and positively correlated with HOMA-ß. The diagnostic cutoff value of IRAK-M was 3.76 ng/mL for prediabetes and 3.45 ng/mL for T2D. After stratifying by IRAK-M (<3.76 and ≥3.76 ng/mL), patients with a higher TXNIP level showed a greater risk of prediabetes or T2D in the subgroup with low IRAK-M (<3.76 ng/mL). CONCLUSIONS: IRAK-M is independently and positively associated with prediabetes and T2D, while TXNIP is independently and negatively associated with prediabetes and T2D. IRAK-M and TXNIP serve as diagnostic factors for prediabetes.

Quantitative Evaluation of TOD Performance Based on Multi-Source Data: A Case Study of Shanghai.

Transit-oriented development (TOD) has been widely adopted as a primary urban planning strategy to better integrate transit and land use; further, the pedestrian-oriented perspective has been receiving increasing attention. However, most studies so far have only focused on few features and fail to capture comprehensive perceptions in the transportation (T), pedestrian-oriented accessibility (O), and urban development (D) dimensions. New emerging urban datasets provide a more refined and systematic approach to quantify the characteristics of metro station areas. This study offers a more efficient and convenient process and comprehensive approach to measure TOD performance. With a combination of traditional data collected by an official department, high-resolution open data, and innovative technology, large-scale analyses of 347 metro stations in Shanghai were conducted. Fifteen indicators for T, O, and D were chosen to categorize TOD performance into five clusters. Radar charts, boxplots, and colored maps were used to display numerous quantitative factors for each type. Combining the results with the Shanghai Comprehensive Plan (2017-2035) showed that the majority of Cluster 4 is located at the center of the Five New Towns. The correlation analysis between ridership and TOD performance showed that the transportation dimension indicator has a strong correlation with daily ridership, followed by the O and D indicators. Moreover, ridership per capita was found to be affected by resident density, employment density, O value, and D value, whereas no significant correlation was found between ridership per capita and T value. Population plays a pivotal role in metro passenger traffic, indicating ridership per capita had a high, strong correlation with resident density, with R = 0.658 for weekdays and R = 0.654 for weekends. This study reinterpreted the node-place method and 5Ds framework, resulting in a renewal method with new datasets and analysis tools. It contributes to providing pedestrian-oriented TOD planning methodology for urban planners and policymakers by combining T, O, and D dimensions and visualizing the results with current urban planning.

Early Predictors in the Onset of Type 2 Diabetes at Different Fasting Blood Glucose Levels.

PURPOSE: This study investigates the possible roles and potential prediction ability of metabolic parameters in the early development of T2D by detecting their serum levels at different fasting blood glucose (FBG) levels. METHODS: The subjects were included and divided into normal glucose tolerance (NGT), prediabetes (PD), and T2Dsubgroups. Apart from detecting the levels of routine biochemical parameters, fasting serum insulin (FINS), 25(OH)D, thioredoxin-interacting protein (TXNIP), thioredoxin (TRX), and NOD-like receptor family, pyrin domain-containing 3 (NLRP3) were detected. ß-cell dysfunction (HOMA-ß) and insulin resistance (HOMA-IR) were assessed by homeostasis model assessment. Both univariate and multivariate logistic regression analyses were used to estimate the risk of metabolic parameters, and their optimal cut-off values were obtained in the receiver operating characteristic (ROC) curve analysis and the Youden index. RESULTS: Among the 207 subjects, aged from 20 to 60 years (44.62+12.92) contain 118 males and 89 females. There was a significantly lower trend of TRX, HOMA-ß, and 25(OH)D following the higher FBG level among these three subgroups, while a significantly higher trend of all the other metabolic parameters. The multivariate analysis showed that subjects with higher values of TRX, HOMA-ß, and 25(OH)D had a significantly lower risk for patients to be diagnosed as PD (aOR: 0.945, 0.961, and 0.543) and T2D (aOR: 0.912, 0.947, 0.434). Under the reliable 95% CI, TXNIP with a cut-off value of 119.27 showed the highest AUC value, sensitivity, and specificity (AUC: 0.981, 95% CI: 0.8524-0.9839, 91.49%, and 83.33%) to diagnose PD. FINS with a cut-off value of 28.1 also showed the highest ones (AUC=0.9872, 95% CI: 0.9753-0.9992, 100%, and 92.91%) to diagnose T2D. CONCLUSION: Early prediction of T2D is vital for timely intervention. Based on the FBG ≥100.8 mg/dl, the results provide evidence that 25(OH)D might be the protective factor in the early development of T2D. Besides, TXNIP and FINS might be the predictor for PD and T2D, respectively.