This study aimed to investigate the relationship between 25-hydroxyvitamin D (25OHD) and the onset of ketosis in newly diagnosed patients with ketosis-prone type 2 diabetes (KPT2D). Patients and Methods: A total of 162 patients with non-autoimmune newly diagnosed diabetes mellitus were included in this crosssectional study. Patients were classified into KPT2D (n = 71) or non-ketotic type 2 diabetes (NKT2D, n = 91). Anthropometric parameters, islet functions, biochemical parameters, and body composition were determined in both KPT2D and NKT2D groups. Correlation analysis was performed to determine the associations between 25OHD and plasma ketones. The risk factors associated with ketosis episodes in patients with new-onset KPT2D were evaluated using binary logistic regression analysis. Results: Vitamin D deficiency was observed in both patients with KPT2D and NKT2D. Compared with the NKT2D group, serum 25OHD values were lower in the participants of the KPT2D group [14.20 (10.68, 19.52) vs 16.98 (13.54,2.96) ng/mL, P = 0.011]. Serum 25OHD was associated with plasma ketones (R = −0.387). Serum 25OHD is an independent protective factor for ketosis or ketoacidosis episodes in patients with new onset of KPT2D (P = 0.037, OR = 0.921). Conclusion:Vitamin D levels are associated with ketosis episodes in patients with KPT2D. Serum 25OHD is an independent protective factor for ketosis episodes in patients with KPT2D.
Objective We aimed to examine the association between bone morphogenetic protein-9 (BMP-9) and type 2 diabetes mellitus (T2DM) in conjunction with non-alcoholic fatty liver disease (NAFLD) and insulin resistance (IR) and to identify evidence supporting the potential role of BMP-9 in the clinical prevention and treatment of T2DM in conjunction with NAFLD. Methods One hundred and twenty subjects were included in this study. We sorted all of the subjects into four groups of equal size (n=30 each). A trained expert assessed the height, weight, systolic blood pressure (SBP), and diastolic blood pressure (DBP) of the subjects and computed the body mass index (BMI). All subjects had their fasting blood glucose (FBG), fasting insulin (FINS), serum BMP-9, and biochemical indices assessed. Results Significant variations were observed in BMI, SBP, DBP, ALT, TC, TG, HDL-C, LDL-C, ApoB, FBG, FINS, HOMA-IR, and serum BMP-9 among the four groups (P<0.05). The level of serum BMP-9 was positively correlated with HDL-C, while the level of serum BMP-9 was negatively correlated with BMI, SBP, DBP, ALT, TC, TG, LDL-C, FBG, FINS, and HOMA-IR. Multiple stepwise regression analyses revealed that FINS, LDL-C, HDL-C, and BMI were independent factors impacting serum BMP-9 levels (P<0.05). Logistic regression analyses revealed that BMP-9 was a protective factor for T2DM paired with NAFLD, while HOMA-IR was a risk factor. Conclusion Serum BMP-9 levels are significantly lower in the T2DM+NAFLD group when compared to other groups, and BMP-9 is an independent risk factor for T2DM paired with NAFLD.
The triglyceride-glucose index (TyG) is positively correlated with serum uric acid (SUA) in patients with type 2 diabetes mellitus (T2DM). However, whether this relationship exists in non-obese T2DM patients remains unknown. The study investigated the relationship between TyG and SUA in Chinese non-obese T2DM patients and examined the prognostic value of TyG in hyperuricemia (HUA). Patients and Methods: In total, 719 T2DM patients who were not obese were enrolled from among those who visited the Hebei General Hospital. The patients were categorized into groups according to their SUA levels. The relationship between TyG and clinical parameters was examined through correlation analysis. To consider covariates and examine the independent impact of TyG on HUA, logistic regression was performed. The receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value of TyG and homeostasis model assessment of insulin resistance (HOMA-IR) for HUA. Results: The HUA prevalence was 12.10%. TyG was statistically different among the four SUA groups, with lower TyG levels in the Q1, Q2, and Q3 groups than that in the Q4 group. TyG was positively correlated with SUA (r = 0.176, P < 0.001). Logistic regression exhibited that TyG and SUA were independently correlated (OR = 2.427, 95% CI = 1. 134-5.195, P = 0.022) even after adjustment for confounding factors. The ROC curve showed that the predictive value of TyG for HUA was higher than that of HOMA-IR (AUROC = 0.613, P = 0.001). Conclusion: TyG was positively correlated with SUA in non-obese T2DM patients. TyG may better predict HUA in non-obese T2DM patients than HOMA-IR.
Background and Aims. Previous studies have reported a correlation between vitamin D levels and seasonality in healthy populations. However, there are few studies on the seasonal variation in vitamin D levels and its relationship with glycosylated hemoglobin (HbA1c) in patients with type 2 diabetes mellitus (T2DM). The objective of this study was to investigate seasonal changes in serum 25-hydroxyvitamin D [25(OH)D] levels and the associations between these vitamin D concentrations and HbA1c levels in T2DM patients in Hebei, China. Methods. A cross-sectional study of 1,074 individuals with T2DM was conducted from May 2018 to September 2021. Levels of 25(OH)D in these patients were assessed based on both sex and season, and relevant clinical or laboratory variables that could impact vitamin D status were also considered. Results. In the T2DM patient cohort, the mean blood 25(OH)D levels were 17.05 ng/mL. A total of 698 patients (65.0%) had insufficient serum 25(OH)D levels. The vitamin D deficiency rates were significantly higher in the winter and spring compared to the autumn ( P < 0.05 ), indicating that seasonal fluctuations can have a significant impact on 25(OH)D levels. The levels of vitamin D inadequacy were highest in the winter (74%), and females were more likely than males to be deficient (73.4% vs. 59.5%, P < 0.001 ). In comparison to the winter and spring, both males and females showed higher 25(OH)D levels in the summer ( P < 0.001 ). HbA1c levels were 8.9% higher in those with vitamin D deficiencies than in nondeficient patients ( P < 0.001 ). HbA1c and vitamin D levels were negatively correlated (r = −0.119, P < 0.001 ). Conclusion. Vitamin D deficiencies are particularly prevalent among T2DM patients in Hebei, China, with exceptionally high rates in the winter and spring. Female T2DM patients were at an elevated risk of vitamin D deficiency, and vitamin D levels were negatively correlated with HbA1c.
Purpose Non-alcoholic fatty liver disease (NAFLD) and type 2 diabetes mellitus (T2DM) are frequently co-occurring diseases. Liver fibrosis (LF), with increasing incidence, has a prognostic value for NAFLD mortality. Our study aimed to investigate the relevant factors for FL in T2DM individuals with NAFLD. Patients and Methods A total of 565 T2DM patients with NAFLD from Hebei General Hospital participated in the study. Patients underwent an abdominal ultrasound, a questionnaire and laboratory tests. The fibrosis-4 index (FIB-4) was used to evaluate LF, with FIB ≥1.3 indicating LF and FIB ≥2.67 indicating F3-4 fibrosis. Results Compared with NLF group, LF group had higher levels of systolic blood pressure (SBP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and γ-glutamyl transpeptidase (GGT). The glomerular filtration rate (GFR), low-density lipoprotein cholesterol (LDL), glycated hemoglobin (HbA1c), and platelets (PLT) in LF patients were lower than those without LF. Patients with LF were older than those without LF. ALT, AST, and GGT in patients with severe LF were higher than those with mild LF, while platelet was lower. Age, SBP, duration of diabetes, ALT, AST, and GGT were positively correlated with FIB-4, while eGFR, TC, LDL, and HbA1c were negatively correlated with FIB-4. Logistic regression showed that age, SBP, ALT, GGT, LDL, and PLT were independently associated with LF. Conclusion For T2DM patients combined with NAFLD, older age, higher SBP, higher ALT, higher GGT, lower LDL, and lower PLT were relevant factors for LF.
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