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Background Rapeseed oil is widely recognized for its health benefits; however, the relationships between its metabolites and factors such as ecotypes, geographical regions, plant traits, and diabetes risk remain poorly understood. This study delves into the metabolic diversity of rapeseed across various ecotypes and geographical origins, while also examining its potential associations with plant traits and diabetes incidence rates. Results Comprehensive metabolomic analysis of 125 rapeseed accessions reveals substantial variations in 2,603 out of 6,048 detected metabolites, encompassing 107 fatty acyls, 124 flavonoids, and 10 phenolic compounds. These metabolic variations likely stem from the complex interplay of genetic divergence, historical selection, environmental adaptability of varieties, and other contributing factors. Penalized regression analysis reveals 24 metabolites associated with the length of the main inflorescence and 26 metabolites linked to the silique count of the main inflorescence, highlighting the possible metabolic underpinnings of these structural traits. Notably, specific metabolites–identified as docosatrienoic acid (M335T887_POS), uridine monophosphate (UMP, M340T917_NEG), and rosmarinate (M719T323_NEG)–are significantly associated with diabetes incidence. These associations suggest a potential link between the consumption of rapeseed and diabetes risk. Conclusions Our findings illustrate the putative links between agricultural production, plant metabolism, and human health. The study emphasizes the potential to enhance rapeseed’s nutritional profile and improve health outcomes through targeted breeding or metabolic engineering of specific metabolites. Further research is crucial to unravel the underlying mechanisms and to develop sustainable food strategies aimed at optimizing health benefits.

Wolfram syndrome 1 (WS1) is a rare autosomal recessive neurodegenerative disease characterized by diabetes insipidus, diabetes mellitus, optic atrophy, deafness, and other abnormalities. WS1 usually results in death before the age of 50 years. The pathogenesis of WS1 is ascribed to mutations of human WFS1 gene on chromosome 4p encoding a transmembrane protein called wolframin which has physiological functions in membrane trafficking, secretion, processing, and/or regulation of ER calcium homeostasis. Different types of WFS1 mutations have been identified, and some of these have been associated with a dominant, severe type of WS.

Background: Smoking is emerging as a public health concern in India, with 30% of the population (majority males) consuming tobacco products. The risk of metabolic syndrome is 1.07–1.66 times higher in smokers. Both smoking and diabetes are expected to rise in India. Aims and Objectives: The association of smoking with glycated hemoglobin (HBA1C) was analyzed in the present study. The effect of tobacco on total leucocyte count (TLC) and HBA1C was also studied. Materials and Methods: The study included 150 male diabetics grouped into smoker and non-smoker categories. Currents were further categorized as per smoking intensity. TLC and HBA1C were evaluated, and the data were analyzed using appropriate statistical tests in SPSS 21.0 and Microsoft Excel. A P < 0.05 was taken as significant. Results: HBA1C increased significantly (P < 0.0101) with an increase in intensity of smoking, but the difference was insignificant between non-smokers and mild (P = 0.125) to moderate (P = 0.07) intensity smokers. TLC increased significantly with an increase in smoking intensity compared to non-smokers (P < 0.001). Differences in TLC were insignificant between mild smokers and non-smokers (P = 0.114). HBA1C and TLC were significantly (P < 0.001) raised in current smokers as compared to ex-smokers and non-smokers, but the difference between HBA1C in non-smokers and ex-smokers was insignificant (P = 0.534). TLC increased insignificantly in ex-smokers as compared to non-smokers (P = 0.129). Regression analysis showed that HBA1C was significantly higher in ex-smokers (β = 0.0438, P = 0.021) and current smokers (β = 0.682, P = 0.001) than non-smokers. With the increase in the severity of smoking, HBA1C was higher than in non-smokers, but the association was insignificant. A non-significant positive association was found between TLC and HBA1C in current smokers (r = 0.049, P = 0.781) and ex-smokers (r = 0.036, P = 0.824), and a non-significant (P = 0.745) negative association (r= −0.070) was found between two non-smokers. Conclusion: In smokers, HBA1C and TLC are higher and are further raised with increased smoking intensity.

Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data. We used data from The Maastricht Study, an observational population-based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman's correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6). Models trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%). Machine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.

ObjectivesObesity is a risk factor for type 2 diabetes mellitus. Among obesity, visceral fat obesity, and ectopic fat obesity, it has been unclear which has the greatest effect on incident diabetes.MethodsIn this historical cohort study of 8430 men and 7034 women, we investigated the effect of obesity phenotypes on incident diabetes. Obesity, visceral fat obesity, and ectopic fat obesity were defined as body mass index ≥25 kg/m2, waist circumference ≥90 cm in men or ≥80 cm in women, and having fatty liver diagnosed by abdominal ultrasonography, respectively. We divided the participants into eight groups according to the presence or absence of the three obesity phenotypes.ResultsDuring the median 5.8 years follow-up for men and 5.1 years follow-up for women, 286 men and 87 women developed diabetes. Compared to the non-obese group, the hazard ratios (HRs) of incident diabetes in the only-obesity, only-visceral fat obesity, only-ectopic fat obesity groups, and with all-three types of obesity group were 1.85 (95%CI 1.06–3.26, p = 0.05) in men and 1.79 (0.24–13.21, p = 0.60) in women, 3.41 (2.51–4.64, p < 0.001) in men and 2.30 (0.87–6.05, p = 0.12) in women, 4.74 (1.91–11.70, p < 0.001) in men and 13.99 (7.23–27.09, p < 0.001) in women and 10.5 (8.02–13.8, p < 0.001) in men and 30.0 (18.0–50.0, p < 0.001) in women. Moreover, the risk of incident diabetes of the groups with ectopic fat obesity were almost higher than that of the four groups without ectopic fat obesity.ConclusionEctopic fat obesity presented the greatest risk of incident type 2 diabetes.

Background Adolescent diabetes is one of the major public health problems worldwide. This study aims to estimate the burden of type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) in adolescents from 1990 to 2021, and to predict diabetes prevalence through 2030. Methods We extracted epidemiologic data from the Global Burden of Disease (GBD) on T1DM and T2DM among adolescents aged 10–24 years in 204 countries and territories worldwide. This study calculated the age-standardized prevalence rate (ASPR) and age-standardized DALY rate (ASDR) in adolescents based on the world standard population for cross-country comparisons. Average annual percentage changes (AAPC) in age-standardized rate were calculated by linkage point regression. Correlation analyses were used to identify the relationship between age-standardized rate and sociodemographic index (SDI). The Bayesian age-period-cohort (BAPC) model was used to predict changes in the diabetes prevalence among adolescents from 2022 to 2030. Results In 2021, 3.4 million adolescents were living with T1DM, with an ASPR of 180.96 (95% CI 180.77–181.15), and 14.6 million were living with T2DM, with ASPR of 1190.73 (1190.13–1191.34). As national and territory SDI levels rise, the prevalence rate of T1DM increases ( r  = 0.44, p  < 0.01), and the prevalence rate of T2DM decreases ( r  = − 0.18, p  < 0.01). Compared with males, females had a greater age-standardized prevalence of T1DM (185.49 [185.21–185.76] vs. 176.66 [176.39–176.92]), whereas males had a greater ASPR of T2DM than females did (1241.45 [1240.58–1242.31] vs. 1138.24 [1137.40–1139.09]). This study found a negative correlation between the SDI and the ASDR for both T1DM ( r  = − 0.51, p  < 0.01) and T2DM ( r  = − 0.62, p  < 0.01) in adolescents. For T2DM patients, 32.84% of DALYs were attributed to high BMI, which increased by 40.78% during the study period. By 2030, 3.7 million people are projected to have T1DM, and 14.6 million are projected to have T2DM. Conclusions Among adolescents, the burden of T1DM and T2DM is increasing and varies by region, sex, and SDI. Therefore, targeted interventions based on regional features are needed to prevent and control adolescent diabetes. Moreover, more efforts are needed to control climate change and obesity to reduce the adolescent diabetes burden.

The gut microbiota is hypothesized to have a critical role in metabolic diseases, including type 2 diabetes (T2D). A traditional Chinese herbal formula, Gegen Qinlian Decoction (GQD), can alleviate T2D. To find out whether GQD modulates the composition of the gut microbiota during T2D treatment, 187 T2D patients were randomly allocated to receive high (HD, n =44), moderate (MD, n =52), low dose GQD (LD, n =50) or the placebo ( n =41) for 12 weeks in a double-blinded trial. Patients who received the HD or MD demonstrated significant reductions in adjusted mean changes from baseline of fasting blood glucose (FBG) and glycated hemoglobin (HbA1c) compared with the placebo and LD groups. Pyrosequencing of the V3 regions of 16S rRNA genes revealed a dose-dependent deviation of gut microbiota in response to GQD treatment. This deviation occurred before significant improvement of T2D symptoms was observed. Redundancy analysis identified 47 GQD-enriched species level phylotypes, 17 of which were negatively correlated with FBG and 9 with HbA1c. Real-time quantitative PCR confirmed that GQD significantly enriched Faecalibacterium prausnitzii , which was negatively correlated with FBG, HbA1c and 2-h postprandial blood glucose levels and positively correlated with homeostasis model assessment of β-cell function. Therefore, these data indicate that structural changes of gut microbiota are induced by Chinese herbal formula GQD. Specifically, GQD treatment may enrich the amounts of beneficial bacteria, such as Faecalibacterium spp. In conclusion, changes in the gut microbiota are associated with the anti-diabetic effects of GQD.

Glycosylated haemoglobin (HbA1c) measurement is used to diagnose and to guide treatment of diabetes mellitus. Within-subject variability in measured HbA1c affects its clinical utility and interpretation, but no comprehensive systematic review has described within-subject variability. A systematic review and meta-analysis was performed of within-subject variability of HbA1c. Multiple databases were searched from inception to November 2022 for follow-up studies of any design in adults or children, with repeated measures of HbA1c or glycosylated haemoglobin. Title and abstract screening was performed in duplicate, full text screening and data extraction by one reviewer and verified by a second. Risk of bias of included papers was assessed using a modified consensus-based standards for the selection of health measurement Instruments (COSMIN) tool. Intraclass correlation coefficient (ICC) results were pooled with a meta-analysis and coefficient of variation (CV) results were described by median and range. Of 2675 studies identified, 111 met the inclusion criteria. Twenty-five studies reported variability data in healthy patients, 19 in patients with type 1 diabetes and 59 in patients with type 2 diabetes. Median within-subject coefficient of variation (CV) was 0.070 (IQR 0.034 to .09). For healthy subjects the median CV for HbA1c % was 0.017 (IQR 0.013 to 0.022), for patients with type 1 diabetes 0.084 (IQR 0.067 to 0.89) and for type 2 diabetes 0.083 (IQR 0.06 to 0.10). CV increased with mean population HbA1c. Assessment of variability was not the main aim of many of the included studies and some relevant papers may have been missed. Many included papers had few participants or few repeated measurements. Within-subject variability of HbA1c is higher for patients with than without diabetes and increases with mean population HbA1c. This may confound observed relationships between HbA1c variability and health outcomes. Because of its importance in clinical decision-making there is a need for better estimates and understanding of factors associated with of HbA1c variability.

The presence of hypertension and various acute or chronic complications may affect the renin-angiotensin-aldosterone system (RAAS) in patients with type 2 diabetes mellitus (T2DM), which plays a crucial role in the regulation of glucose metabolism. However, the quantitative distribution of the RAAS components in relation to the progression of T2DM and the treatment of hyperglycemia and hypertension, as well as their association with different stages of complications and glucose metabolism, has not been well studied. We enrolled a total of 151 patients with T2DM and essential hypertension, 40 patients with T2DM and normotension, and 46 healthy controls in the study. They were categorized into subgroups based on criteria for diabetic complications. Statistical analyses, including Spearman rank correlation and multiple linear regression, were conducted to assess the relationship between RAAS components and glucose metabolism indexes such as HbA1c, FBG, CP, HOMA-β, HOMA-IR, and UACR. The results revealed significant differences in AII, ALD, REN, and ARR levels across various complication subgroups. Notably, the concentrations of ALD and REN exhibited a consistent trend, while ARR showed an opposite trend to the REN concentration. More than 60% of hypertensive patients were treated with ACEI/ARBs and calcium channel blockers, while 29.8% of the patients were prescribed β-blockers, resulting in decreased REN and increased ARR levels. All T2DM patients received antidiabetic treatment, among which 95 (49.7%) took SGLT-2is, 40 (20.9%) took GLP-1RAs injection and 55(28.8%) took DPP-4is. The subsequent analysis revealed that SGLT-2is, GLP-1RAs, DPP-4is and other glucose-lowering agents had no statistically significant effect on the RAAS system (p > 0.05). The correlation matrix analysis indicated positive associations between ALD, REN, CP, and HOMA-IR. Furthermore, the REN levels were negatively correlated with UACR in the hypertensive group and positively correlated with HbA1c and FBG levels in the normotensive group. Multiple linear regression analysis demonstrated that ALD levels increased with higher levels of CP and HOMA-IR, independently of the RAAS system, anti-RAAS treatment and antidiabetic therapy. REN levels decreased with increasing UACR and β-blocker usage in the hypertensive group, while they increased with higher levels of HbA1c, FBG, and HOMA-IR in the normotensive group, independently of the RAAS system and antidiabetic therapy. The activation status of the RAAS system varied among T2DM patients with different complications, highlighting the need for clinical differentiation. ALD was positively associated with insulin resistance and glucose metabolism impairment, while REN exhibited negative correlations with urinary microalbumin and β-blocker usage, and positive correlations with hyperglycemia and insulin resistance. Blocking the RAAS system holds promise for improving insulin sensitivity and β-cell function, and potentially reversing abnormal glucose tolerance or ameliorating glucose metabolism disorders.

The aim of this study was to examine the association of insulin resistance (evaluated by the short insulin tolerance test [SITT]) with parameters related to obesity and insulin resistance. We prospectively recruited controls and patients with type 2 diabetes mellitus (T2DM), subjected them to the SITT, and calculated the K indices of the intravenous insulin tolerance test (K ITT (iv)) and the subcutaneous insulin tolerance test (K ITT (sc)). We compared K ITT (iv) results between the volunteers and patients and examined its correlation with K ITT (sc). We also examined the association of K ITT (iv) with obesity, insulin resistance-related parameters, and the insulin dose required for glycemic control. A total of 24 participants (seven controls and 17 patients with T2DM) were studied. The mean K ITT (iv) was significantly lower in patients with T2DM than in the controls (2.5%±2.1% vs. 4.5%±1.8%). In all participants, K ITT (iv) was significantly correlated with the homeostasis model assessment for insulin resistance (HOMA-IR) values (r = −0.601, p<0.05) but not with K ITT (sc) (p = 0.62). K ITT (iv) was correlated positively with the serum adiponectin concentration, but negatively with the visceral fat area and serum concentrations of tumor necrosis factor-α and branched-chain amino acids. In patients with T2DM, K ITT (iv) and HOMA-IR values were significantly correlated with the total insulin dose required for glycemic control. Insulin resistance evaluated using K ITT (iv) was correlated with the HOMA-IR values, but not with the resistance evaluated using K ITT (sc). The degree of insulin resistance was associated with biomarkers, such as adiponectin, tumor necrosis factor-α, branched-chain amino acids, the visceral fat area, and the dose of insulin required for glycemic control.