Diabetic Subgroups Research Articles

Exploring the role of vitamin D in cognitive function: mediation by depression with diabetes modulation in older U.S. adults, a NHANES weighted analysis.

The relationship between vitamin D levels, depressive symptoms, and cognitive function has yet to be definitively understood in the elderly, particularly when considering the impact of chronic diseases. This study focuses on how depression mediates the impact of 25-hydroxyvitamin D3 (25(OH)D3) on cognitive performance in older U.S. adults. We analyzed data from 2,745 elderly individuals extracted from the NHANES 2011-2014 cycles, applying weighted processing to account for the complex multi-stage sampling design characteristic of NHANES data. Utilizing weighted data for covariate and model selection, we conducted mediation analyses on both the overall population and subgroup data. Significant mediation pathways were validated using a stratified weighted bootstrap approach. For significant subgroup pathways, we explored interactive mechanisms through interactive mediation analysis. Mediation analyses, thoroughly accounting for the impact of chronic conditions, revealed significant pathways in both the weighted overall population and the weighted diabetes subgroup. After 1,000 stratified weighted bootstrap replications, the proportion of mediation effects were 10.6% [0.040, 0.268] and 20.9% [0.075, 0.663], respectively. Interactive mediation analysis for diabetes indicated that the interaction between diabetes and depression was not significant in the direct pathway (estimates = 0.050, p = 0.113) but was significant in the mediation pathway, yielding the largest effect size compared to other covariates (estimates = 0.981, p < 0.001). This study highlights the mediating role of depression in the relationship between vitamin D levels and cognitive function in the elderly, particularly emphasizing diabetes as a key moderator. Our findings suggest targeted interventions addressing both vitamin D sufficiency and depression could significantly benefit cognitive health, especially in diabetic individuals.

Association between hypertension and osteoporosis: a population-based cross-sectional study

BackgroundCurrent evidence suggests that metabolic dysregulation is inextricably linked to both hypertension and osteoporosis, but the correlation between hypertension and osteoporosis is still unclear. Therefore, in this study, we explored the correlation between hypertension and osteoporosis.MethodsA total of 37,807 participants from the National Health and Nutrition Examination Survey (1999–2010, 2013–2014, 2017–2018) were enrolled in this population-based cross-sectional study. Hypertension was considered an exposure factor and osteoporosis was considered an outcome factor. Logistic regression and subgroup analysis were used to assess the association between hypertension and osteoporosis.ResultsA total of 2,523 participants, with a mean age of 68.65 ± 12.21 years, suffered from osteoporosis, and 86.2% were female. Participants with osteoporosis had a greater prevalence of hypertension than participants without osteoporosis (p < 0.001). Participants with hypertension also had a greater prevalence of osteoporosis than participants without hypertension (p < 0.001). Univariate logistic regression analysis indicated that hypertension was associated with osteoporosis (OR: 2.693, 95% CI: 2.480–2.924, p < 0.001). Multivariate logistic regression analysis with a fully adjusted model indicated that hypertension was strongly associated with osteoporosis (OR: 1.183, 95% CI: 1.055–1.327, p = 0.004). Subgroup analysis revealed that the associations between hypertension and osteoporosis were significant in the younger than 60 years, male sex, diabetes subgroup and hypercholesterolemia subgroup (p < 0.05).ConclusionHypertension was independently associated with osteoporosis in the general population.

Alteration in gut microbial characteristics of patients with acromegaly.

Studies on intestinal microbiota in acromegaly are scant. This study aimed to characterize the gut microbiome in patients with acromegaly. Stool samples were collected from 11 patients newly diagnosed with acromegaly and 12 healthy controls matched for body mass index (BMI) and age after three days on a standard diet. Clinical and gut microbial composition assessments were performed for the two participant groups using 16S rRNA gene amplicon sequencing. There was no difference in the alpha diversity of the microbiota between the samples from patients with acromegaly and those from the healthy controls. Based on beta diversity measurements, differences in microbial community structures were found to be significant only when compared using the Jaccard similarity index. The corresponding Firmicutes/Bacteroidota ratio tended to be higher in individuals with acromegaly than in healthy controls. The mean relative abundance of Actinobacteriota was 2.3 times higher in the acromegaly patient group than in the control group. Eggerthellaceae, Christensenellaceae, and Bacteroidaceae were among the significantly abundant bacterial families in the samples from the acromegaly patient group, while Butyricicoccaceae and Tannerellaceae were decreased. At the level of the genus, the most discriminative features were the abundance of Prevotella 7, Bacteroides, Senegalimassilia, Enterohabdus, the Family XIII AD3011 group, Howardella, and Hungatella in the samples from the acromegaly patient group. In contrast, the Butyrivibrio and the Eubacterium eligens group were the most discriminative genera for the healthy controls and were significantly less abundant in patients with acromegaly. While there were no significantly differentiated taxa between the diabetic and non-diabetic subgroups, Prevotella_7 was significantly enriched in the osteoarthritis (OA) subgroup. No significant association was found between individual genera and growth hormone (GH) levels and insulin-like growth factor-1 (IGF-1) levels as well as the upper limit of normal (ULN). Although alpha and beta diversity were mainly similar between the two groups, significant differences were observed between the acromegaly group and the control group at the family and genus levels. These results suggest that the differences between the microbial communities in patients with acromegaly and those in healthy individuals consist primarily of compositional differences independent of abundance. Prospective studies are needed to further explore the clinical implications of gut microbiome dysbiosis in patients with acromegaly.

Identification and validation of gestational diabetes subgroups by data-driven cluster analysis

Aims/hypothesisGestational diabetes mellitus (GDM) is a heterogeneous condition. Given such variability among patients, the ability to recognise distinct GDM subgroups using routine clinical variables may guide more personalised treatments. Our main aim was to identify distinct GDM subtypes through cluster analysis using routine clinical variables, and analyse treatment needs and pregnancy outcomes across these subgroups.MethodsIn this cohort study, we analysed datasets from a total of 2682 women with GDM treated at two central European hospitals (1865 participants from Charité University Hospital in Berlin and 817 participants from the Medical University of Vienna), collected between 2015 and 2022. We evaluated various clustering models, including k-means, k-medoids and agglomerative hierarchical clustering. Internal validation techniques were used to guide best model selection, while external validation on independent test sets was used to assess model generalisability. Clinical outcomes such as specific treatment needs and maternal and fetal complications were analysed across the identified clusters.ResultsOur optimal model identified three clusters from routinely available variables, i.e. maternal age, pre-pregnancy BMI (BMIPG) and glucose levels at fasting and 60 and 120 min after the diagnostic OGTT (OGTT0, OGTT60 and OGTT120, respectively). Cluster 1 was characterised by the highest OGTT values and obesity prevalence. Cluster 2 displayed intermediate BMIPG and elevated OGTT0, while cluster 3 consisted mainly of participants with normal BMIPG and high values for OGTT60 and OGTT120. Treatment modalities and clinical outcomes varied among clusters. In particular, cluster 1 participants showed a much higher need for glucose-lowering medications (39.6% of participants, compared with 12.9% and 10.0% in clusters 2 and 3, respectively, p<0.0001). Cluster 1 participants were also at higher risk of delivering large-for-gestational-age infants. Differences in the type of insulin-based treatment between cluster 2 and cluster 3 were observed in the external validation cohort.Conclusions/interpretationOur findings confirm the heterogeneity of GDM. The identification of subgroups (clusters) has the potential to help clinicians define more tailored treatment approaches for improved maternal and neonatal outcomes.Graphical

Mediating effect of diabetes on the relationship between nonalcoholic fatty liver disease and atherosclerotic cardiovascular disease: a prospective cohort study.

This study explored the mediating effect of diabetes on the relationship between nonalcoholic fatty liver disease (NAFLD) and atherosclerotic cardiovascular disease (ASCVD). In this prospective community cohort study, 82 975 participants were enrolled, with the primary outcome being the incidence of new-onset ASCVD. Using the Cox proportional hazards model, the hazard ratio (HR) and 95% confidence interval (CI) for ASCVD occurrence were computed between NAFLD and non-NAFLD groups. The correlation between NAFLD and diabetes was assessed using a binary logistic regression model, and that between NAFLD, diabetes and ASCVD using a mediation model. During follow-up, 9471 ASCVD cases were observed. Compared with individuals without NAFLD, those with NAFLD showed an increased ASCVD risk (HR: 1.424; 95% CI: 1.363-1.488; P < 0.001). Stratifying NAFLD based on metabolic subphenotypes revealed a higher ASCVD risk in the NAFLD combined with diabetes subgroup than in the non-NAFLD subgroup (HR: 1.960; 95% CI: 1.817-2.115; P < 0.001). NAFLD was positively associated with baseline diabetes (odds ratio: 2.983; 95% CI: 2.813-3.163; P < 0.001). Furthermore, NAFLD severity was positively correlated with diabetes risk. Mediation analysis indicated that diabetes partially mediated the effect of NAFLD on ASCVD incidence, accounting for 20.33% of the total effect. NAFLD is an independent predictor of increased ASCVD risk, which may be slightly mediated by diabetes in patients with NAFLD. Evaluating NAFLD and diabetes may be crucial in the early screening and prevention of ASCVD.

Data-Driven Patient clusters of Type 2 Diabetes May Help to Identify Patients at Risk and Predict Metabolic Surgery Outcomes on Diabetes Remission and NAFLD Pregression

Abstract Background Precision medicine and customized medicine have gained enormous attention in recent years, especially in the treatment of type 2 diabetes (T2D). Different subgroups of diabetes have been identified by research employing data-driven cluster analysis, each with a unique diabetes progression and complication risk (Ahlqvist et al., Lancet Diabetes Endocrinol. 2018). Aims We aimed to apply the proposed cluster analysis to a patent population post metabolic surgery and investigate the association with T2D remission and presence of NAFLD. Methods We retrospectively linked newly defined clusters to metabolic surgery outcomes in 53 T2D patients. Utilizing k-means and hierarchical clustering, three clusters emerged based on glutamate decarboxylase antibodies, age, BMI, HbA1c, and homoeostatic model assessment estimates of β-cell function (HOMA2-B%) and insulin resistance (HOMA2-IR). Intraoperative liver biopsies assessed nonalcoholic fatty liver disease (NAFLD) presence differentiating between simple steatosis (NAFL) and steatohepatitis (NASH). Clinical and biochemical data were collected over two years, focusing on T2D remission and NAFLD improvement. Results Cluster 1, characterized by the lowest BMI, highest NASH rate, impaired beta-cell function, and increased insulin resistance, displayed a favorable response to surgery, indicating robust regeneration of beta-cells. Despite increased insulin production, T2D remission was surprisingly low at 44.0% after one year, in contrast to 75.00% in Cluster 2 and 100.00% in Cluster 3. Metabolic surgery notably reduced insulin resistance and promoted NASH remission, evidenced by a significant reduction in a non-invasive NASH detection score and liver enzyme levels across all three clusters. Conclusion Our findings suggest that patients in Cluster 1 already show a lack of beta-cell compensation being associated with a higher prevalence of NAFLD and poorer diabetic control and therefore especially might benefit from an earlier intervention. Data-driven classification might help to customize treatment plans and identify patients at higher risk of problems at diagnosis.

Geriatric nutritional risk index is associated with the occurrence of acute kidney injury in critically ill patients with acute heart failure

Background: During the acute heart failure (AHF), acute kidney injury (AKI) is highly prevalent in critically ill patients. The occurrence of the latter condition increases the risk of mortality in patients with acute heart failure. The current research on the relationship between nutritional risk and the occurrence of acute kidney injury in patients with acute heart failure is very limited. Methods: This retrospective cohort study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 2.1) database. We included adult patients with AHF who were admitted to the intensive care unit in the study. Results: A total of 1310 critically ill patients with acute heart failure were included. The AUC of geriatric nutritional risk index (GNRI) (0.694) is slightly superior to that of controlling nutritional status (CONUT) (0.656) and prognostic nutritional index (PNI) (0.669). The Log-rank test revealed a higher risk of acute kidney injury in patients with high nutritional risk (p < 0.001). Multivariate COX regression analysis indicated that a high GNRI (adjusted HR 0.62, p < 0.001) was associated with a reduced risk of AKI during hospitalization in AHF patients. The final subgroup analysis demonstrated no significant interaction of GNRI in all subgroups except for diabetes subgroup and ventilation subgroup (P for interaction: 0.057-0.785). Conclusion: Our study findings suggest a correlation between GNRI and the occurrence of acute kidney injury in patients hospitalized with acute heart failure.

Validation of a Prediction Model for Acute Kidney Injury after Cardiac Surgery in a Retrospective Asian Cohort.

Background: The incidence of postoperative acute kidney injury (AKI) is relatively high in some Asian regions. The objective of this study was to examine the performance of an AKI prediction model developed based on data from a White-dominant population in a retrospective Asian cohort of patients undergoing cardiovascular surgery. Methods: We retrospectively identified 549 patients who underwent elective major cardiovascular surgery (coronary artery bypass graft, valve surgery, and aorta surgery), and excluded those who underwent a percutaneous cardiovascular procedure. Patients with a baseline estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 were also excluded. AKI was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) definition. Performance of the prediction model for AKI was expressed as area under the receiver operating characteristic curve (AUC). Results: The prediction model had a good predictive accuracy for postoperative AKI (all AUC > 0.92). The AUC of the prediction model in subgroups of age (<65 years and ≥65 years), sex (male and female), hypertension, and diabetes were all >0.85 (all p values < 0.001). Conclusions: The model could be used to predict postoperative AKI in Asian patients undergoing cardiovascular surgery with a baseline eGFR ≥ 60 mL/min/1.73 m2.

Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes

Aims/hypothesisThis study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist’s novel diabetes subgroups and previously analysed by Slieker et al.MethodsWe used two Dutch and Scottish diabetes cohorts (N=3054 and 6145; median follow-up=11.2 and 12.3 years, respectively) and defined five subgroups by k-means clustering with age at baseline, BMI, HbA1c, HDL-cholesterol and C-peptide. We investigated differences between subgroups by trajectories of risk factor values (random intercept models), time to diabetes-related complications (logrank tests and Cox models) and medication patterns (multinomial logistic models). We also compared directly using the clustering indicators as predictors of progression vs the k-means discrete subgroups. Cluster consistency over follow-up was assessed.ResultsSubgroups’ risk factors were significantly different, and these differences remained generally consistent over follow-up. Among all subgroups, individuals with severe insulin resistance faced a significantly higher risk of myocardial infarction both before (HR 1.65; 95% CI 1.40, 1.94) and after adjusting for age effect (HR 1.72; 95% CI 1.46, 2.02) compared with mild diabetes with high HDL-cholesterol. Individuals with severe insulin-deficient diabetes were most intensively treated, with more than 25% prescribed insulin at 10 years of diagnosis. For severe insulin-deficient diabetes relative to mild diabetes, the relative risks for using insulin relative to no common treatment would be expected to increase by a factor of 3.07 (95% CI 2.73, 3.44), holding other factors constant. Clustering indicators were better predictors of progression variation relative to subgroups, but prediction accuracy may improve after combining both. Clusters were consistent over 8 years with an accuracy ranging from 59% to 72%.Conclusions/interpretationData-driven subgroup allocations were generally consistent over follow-up and captured significant differences in risk factor trajectories, medication patterns and complication risks. Subgroups serve better as a complement rather than as a basis for compressing clustering indicators.Graphical

Endothelin-converting Enzyme-1b Genetic Variants Increase the Risk of Coronary Artery Ectasia.

Coronary artery ectasia (CAE), defined as a 1.5-fold or greater enlargement of a coronary artery segment compared to the adjacent normal coronary artery, is frequently associated with atherosclerotic coronary artery disease (CAD). Membrane-bound endothelin converting enzyme-1 (ECE-1) is involved in the maturation process of the most potent vasoconstrictor ET-1. Polymorphisms in the endothelin (ET) gene family have been shown associated with the development of atherosclerosis. This study aims to investigate the effects of rs213045 and rs2038089 polymorphisms in the ECE-1 gene which have been previously shown to be associated with atherosclerosis and hypertension (HT), in CAE patients. Ninety-six CAE and 175 patients with normal coronary arteries were included in the study. ECE-1b gene variations rs213045 and rs2038089 were determined by real-time PCR. The frequencies of rs213045 C > A (C338A) CC genotype (60.4% vs. 35.4%, p < 0.001) and rs2038089 T > C T allele (64.58% vs. 35.42%, p = 0.017) were higher in the CAE group compared to the control group. The multivariate regression analysis showed that the ECE-1b rs213045 CC genotype (p = 0.001), rs2038089 T allele (p = 0.017), and hypercholesterolemia (HC) (p = 0.001) are risk factors for CAE. Moreover, in nondiabetic individuals of the CAE and control groups, it was observed that the rs213045 CC genotype (p < 0.001), and rs2038089 T allele (p = 0.003) were a risk factor for CAE, but this relationship was not found in the diabetic subgroups of the study groups (p > 0.05). These results show that ECE-1b polymorphisms may be associated with the risk of CAE and this relationship may change according to the presence of type II diabetes.

Association between atherogenic index of plasma and depressive symptoms in US adults: Results from the National Health and Nutrition Examination Survey 2005 to 2018

ObjectiveThis study, utilizing data from the U.S. National Health and Nutrition Examination Survey (NHANES) between 2005 and 2018, investigates the association between the atherogenic index of plasma (AIP), a lipid biomarker, and symptoms of depression in American adults. MethodsIn this cross-sectional study of 12,534 adults aged 20 years and older, depressive symptoms were measured utilizing the Patient Health Questionnaire-9 (PHQ-9) scale. Weighted logistic regression models were employed to scrutinize the independent relationship between AIP levels and the likelihood of developing such symptoms. Moreover, a series of subgroup analyses were conducted to delve deeper into these relationships. ResultsFollowing adjustment for confounders, logistic regression by grouping AIP into quartiles revealed a significant association between AIP and an augmented likelihood of self-reported depression. Participants in the fourth quartile (Q4) exhibited a higher odds ratio (OR = 1.34, 95 % CI: 1.02–1.75, p < 0.05) compared to those in the first quartile (Q1). Notably, subgroup analysis unveiled significant interactions involving the smoking and diabetes subgroups, indicating that smoking status and diabetes may modify the relationship between AIP and depression incidence. ConclusionThis study reveals a positive correlation between AIP and the self-reported likelihood of depression among US adults, thereby underscoring AIP's potential clinical utility as a biomarker for depressive disorders. Our findings emphasize the necessity to consider and optimize cardiovascular health factors within depression management strategies and offer fresh insights into the development of risk stratification and intervention methods for psychiatric conditions.

Diabetes subgroups and sociodemographic inequalities in Mexico: a cross-sectional analysis of nationally representative surveys from 2016 to 2022

Differences in the prevalence of four diabetes subgroups have been reported in Mexico compared to other populations, but factors that may contribute to these differences are poorly understood. Here, we estimate the prevalence of diabetes subgroups in Mexico and evaluate their correlates with indicators of social disadvantage using data from national representative surveys. We analyzed serial, cross-sectional Mexican National Health and Nutrition Surveys spanning 2016, 2018, 2020, 2021, and 2022, including 23,354 adults (>20 years). Diabetes subgroups (obesity-related [MOD], severe insulin-deficient [SIDD], severe insulin-resistant [SIRD], and age-related [MARD]) were classified using self-normalizing neural networks based on a previously validated algorithm. We used the density-independent social lag index (DISLI) as a proxy of state-level social disadvantage. We identified 4204 adults (median age: 57, IQR: 47-66, women: 64%) living with diabetes, yielding a pooled prevalence of 16.04% [95% CI: 14.92-17.17]. When stratified by diabetes subgroup, prevalence was 6.62% (5.69-7.55) for SIDD, 5.25% (4.52-5.97) for MOD, 2.39% (1.95-2.83) for MARD, and 1.27% (1.00-1.54) for SIRD. SIDD and MOD clustered in Southern Mexico, whereas MARD and SIRD clustered in Northern Mexico and Mexico City. Each standard deviation increase in DISLI was associated with higher odds of SIDD (OR: 1.12, 95% CI: 1.06-1.12) and lower odds of MOD (OR: 0.93, 0.88-0.99). Speaking an indigenous language was associated with higher odds of SIDD (OR: 1.35, 1.16-1.57) and lower odds of MARD (OR 0.58, 0.45-0.74). Diabetes prevalence in Mexico is rising in the context of regional and sociodemographic inequalities across distinct diabetes subgroups. SIDD is a subgroup of concern that may be associated with inadequate diabetes management, mainly in marginalized states. This research was supported by Instituto Nacional de Geriatría in Mexico.

Reduced muscle expression heterogeneity by categorising diabetic subjects resulted in improved statistical significance for trascriptome comparison

The information gained through studies investigating muscle gene expression profiles (GEPs) for diabetes biomarkers identification, has resulted in the general belief that muscle transcriptome is relatively unaltered while the subject gradually progresses from a healthy to a diabetic condition. In this study, we focused on identifying the diabetes subgroup based on the subject's muscle gene expression profile to reduce the expression heterogeneity and regain statistical significance in transcriptome comparison. Given the muscle GEPs of type-II diabetic (T2D) and normal glucose tolerant (NGT) subjects, the R-package WGCNA was used to identify a set of correlated genes termed a module. The Random Forest classifier trained over expression profiles of genes belonging to respective modules was used to classify subjects into subgroups. The study was able to categorize early-stage diabetic (ESD) and advance stage diabetic (ASD) subjects based on muscle gene expression profiles. The ASD subjects differed significantly from ESD subjects with respect to diabetes-related clinical traits. Comparison between ASD and NGT subjects showed significant up-regulation of muscle atrophy marker atrogin-1 along with the FOXO signaling pathway. The expression of PDK4, an important inhibitor of glycolytic flux was up-regulated whereas, glucose oxidation biomarkers, HK2 and LDHB were down-regulated in ASD subjects. The result obtained from this study highlights muscle-associated gene expression changes that could be used to categorize diabetic subjects into biologically relevant subgroups.

Psychological resilience mediates the relationship between diabetes distress and depression among persons with diabetes in a multi-group analysis

The aim to examine the link between diabetes distress and depression in individuals with diabetes, assess the mediating role of psychological resilience in this relationship, and analyses if these relationships differ between Type 1 and Type 2 diabetes. The study utilized a cross-sectional design. A total of 181 (age 33–72 years, mean = 54.76 years, and SD = 9.05 years) individuals diagnosed with diabetes who were receiving treatment from State Specialist Hospitals in Okitipupa were selected for the study using the convenient sampling technique. The data were analysed using Pearson Multiple correlation and multi-group mediation analysis. The analyses were carried out with Smartpls and IBM/SPSS Version 28.0. The results revealed a significant positive correlation between diabetes distress and depression (r = .80, p < .05), suggesting that higher levels of diabetes distress were associated with increased depression scores. Additionally, psychological resilience partially mediated the relationship between diabetes distress and depression (b = − 0.10, p < .05), signifying that resilience played a crucial role in mitigating the impact of diabetes distress on depression. Furthermore, a multi-group analysis was conducted to explore potential differences between Type 1 and Type 2 diabetes subgroups. The relationship between diabetes distress and depression was found to be more pronounced in the Type 1 subgroup (difference = 0.345, p < .05), while the relationship between psychological resilience and depression was negatively stronger in the Type 2 subgroup (difference = − 0.404, p < .05) compared to the Type 1 subgroup. There is an intricate linkage between diabetes distress, resilience, and depression, emphasizing the differential roles of resilience in Type 1 and Type 2 diabetes. The insights gleaned from this study underscore the importance of considering the type of diabetes when designing interventions and support mechanisms for individuals with diabetes who are also suffering from depression. By advancing our understanding of these dynamics, we can strive for more effective and personalized approaches to improve the overall well-being of those living with diabetes.

The clinical relevance of a polygenic risk score for type 2 diabetes mellitus in the Korean population

The clinical utility of a type 2 diabetes mellitus (T2DM) polygenic risk score (PRS) in the East Asian population remains underexplored. We aimed to examine the potential prognostic value of a T2DM PRS and assess its viability as a clinical instrument. We first established a T2DM PRS for 5490 Korean individuals using East Asian Biobank data (269,487 samples). Subsequently, we assessed the predictive capability of this T2DM PRS in a prospective longitudinal study with baseline data and data from seven additional follow-ups. Our analysis showed that the T2DM PRS could predict the transition of glucose tolerance stages from normal glucose tolerance to prediabetes and from prediabetes to T2DM. Moreover, T2DM patients in the top-decile PRS group were more likely to be treated with insulin (hazard ratio = 1.69, p value = 2.31E−02) than were those in the remaining PRS groups. T2DM PRS values were significantly high in the severe diabetes subgroup, characterized by insulin resistance and β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document}-cell dysfunction (p value = 0.0012). The prediction models with the T2DM PRS had significantly greater Harrel’s C-indices than did corresponding models without it. By utilizing prospective longitudinal study data and extensive clinical risk factor information, our analysis provides valuable insights into the multifaceted clinical utility of the T2DM PRS.

Classification of type 1 diabetes: A pathogenic and treatment-based classification

AimTo improve the diagnosis and classification of patients who fail to satisfy current type 1 diabetes diagnostic criteria. MethodsReview of the literature and current diagnostic guidelines. DiscussionWe propose a novel, clinically useful classification based on islet autoantibody status and non-fasting C-peptide levels. Notably, we discuss the subgroup of latent autoimmune diabetes in the young and propose a new subgroup classification of autoantibody negative type 1 diabetes in remission. ConclusionA novel classification system is proposed. Further work is needed to accurately diagnose and manage minority type 1 diabetes subgroups.

The effect of exogenous glucagon on circulating amino acids in individuals with and without type 2 diabetes and obesity.

In obesity and type 2 diabetes, hyperglucagonaemia may be caused by elevated levels of glucagonotropic amino acids due to hepatic glucagon resistance at the level of amino acid turnover. Here, we investigated the effect of exogenous glucagon on circulating amino acids in obese and non-obese individuals with and without type 2 diabetes. This was a post hoc analysis in a glucagon infusion study performed in individuals with type 2 diabetes (n = 16) and in age, sex, and body mass index-matched control individuals without diabetes (n = 16). Each group comprised two subgroups of eight individuals with and without obesity, respectively. All participants received a 1-h glucagon infusion (4 ng/kg/min) in the overnight fasted state. Plasma amino acid concentrations were measured with frequent intervals. Compared to the control subgroup without obesity, baseline total amino acid levels were elevated in the control subgroup with obesity and in the type 2 diabetes subgroup without obesity. In all subgroups, amino acid levels decreased by up to 20% in response to glucagon infusion, which resulted in high physiological steady-state glucagon levels (mean concentration: 74 pmol/L, 95% CI [68;79] pmol/L). Following correction for multiple testing, no intergroup differences in changes in amino acid levels reached significance. Obesity and type 2 diabetes status was associated with elevated fasting levels of total amino acids. The glucagon infusion decreased circulating amino acid levels similarly in all subgroups, without significant differences in the response to exogenous glucagon between individuals with and without obesity and type 2 diabetes. The hormone glucagon stimulates glucose production from the liver, which may promote hyperglycaemia if glucagon levels are abnormally elevated, as is often seen in type 2 diabetes and obesity. Glucagon levels are closely linked to, and influenced by, the levels of circulating amino acids. To further investigate this link, we measured amino acid levels in individuals with and without obesity and type 2 diabetes before and during an infusion of glucagon. We found that circulating amino acid levels were higher in type 2 diabetes and obesity, and that glucagon infusion decreased amino acid levels in both individuals with and without type 2 diabetes and obesity. The study adds novel information to the link between circulating levels of glucagon and amino acids.

Thyroid function and thyroid homeostasis parameters are associated with increased urinary albumin excretion in euthyroid individuals over 60 years old from NHANES.

The relationship between thyroid function/homeostasis parameters and renal function has been extensively studied. However, the relationship between thyroid function and thyroid homeostasis parameters with albuminuria among elderly individuals remains unclear. The population was divided into an albuminuria group and a non-albuminuria group for baseline characteristic difference analysis. Multivariable logistic regression was used to test the association between thyroid function, and thyroid homeostasis parameters and albuminuria. The nonlinear relationship was explored with restricted cubic splines. Meanwhile, we investigated whether the relationship also existed in the diabetes and hypertension subgroups. Receiver operating characteristic (ROC) curves were used to assess the effectiveness of the indices. FT4 and TFQIFT4 were positively correlated with albuminuria (OR = 1.12; 95% CI = 1.02-1.23, p = 0.02; OR = 1.79; 95% CI = 1.08-2.99, p = 0.03), and FT3/FT4 was negatively correlated with albuminuria (OR = 0.03; 95% CI = 0.00-0.26, p = 0.003). Additionally, the nonlinear relationship between FT3/FT4 as well as TSHI and albuminuria was approximately U-shaped. Similar results were observed in the hypertension subgroup but not in the diabetes subgroup. There was a U-shaped nonlinear relationship between FT3 and albuminuria in the diabetes group. In addition, FT3/FT4 performed better than TFQI, TT4RI, and TSHI in ROC analyses for albuminuria prediction. FT4, TFQIFT4, and a low FT3/FT4 ratio were risk factors for albuminuria in euthyroid individuals over 60 years old. However, FT3 was more associated with albuminuria in the diabetes subgroup. TSH was not associated with albuminuria in any analysis. In our study, we attempted to provide more reasonable thyroid parameters and basis for evaluating patients with underlying albuminuria. FT3/FT4 may be used as a helpful indicator to predict albuminuria and provide novel ideas for the evaluation and treatment of albuminuria.

Flow-mediated dilatation facilitates transradial coronary angiography: a comparative study.

Radial artery (RA) spasm is demonstrated to be one of the most common complications of transradial approach (TRA). We hypothesised that radial flow-mediated dilation (FMD) can be used as a preprocedural method to assess the likelihood of arterial spasm. The patients were divided into 2 groups: those with and without flow-mediated RA dilatation. A blood pressure cuff was placed on the upper part of the antecubital region of the patients in the FMD group and inflated for 10 min, allowing the pressure to rise to 30 mm Hg above the systolic blood pressure. RA diameters of the patients in both groups were measured via quantitive coronary angiography method before transradial coronary angiography. A total of 165 patients were included in the study, of whom 64 (38.8%) were women. The median age of the patients was 56 years (48-63). The mean RA diameter was significantly larger in the FMD group (3.44 ±0.48 vs. 2.96 ±0.46 mm, p < 0.001), and the number of punctures required for successful transradial cannulation was found to be significantly higher in the group without FMD (1.55 ±0.7 vs. 1.20 ±0.64; p < 0.001). Linear regression analysis revealed diabetes and FMD as independent predictors of RA diameter. In the diabetic subgroup, RA diameter remained larger in the FMD group (3.00 ±0.35 vs. 2.78 ±0.26, p = 0.036). Radial puncture attempts were significantly higher in the control group compared to the FMD group (1.55 ±0.7 vs. 1.20 ±0.64; p < 0.001). In our study, we demonstrated that FMD created by pressure application significantly increased RA diameter and reduced puncture attempt during TRA.

The serum uric acid-to-high-density lipoprotein cholesterol ratio is a predictor for all-cause and cardiovascular disease mortality: a cross-sectional study.

The exact relationship between the serum uric acid-to-HDL cholesterol ratio (UHR) and mortality rates remains enigmatic among American adults. This study aims to clarify the association between UHR and both all-cause and cardiovascular disease (CVD) mortality in US adults. This study enrolled 48054 patients from the National Health and Nutrition Examination Survey (NHANES). Mortality outcomes were determined by linking to National Death Index (NDI) records up to December 31,2019. Multivariate Cox proportional hazards models were constructed to analyze explore the associations between UHR and mortality. Dose-response relationships were explored using restricted cubic splines, and stratified analyses were conducted based on gender, age, race, education, PIR, smoking status, alcohol intake, physical activity, BMI, diabetes and hypertension. During the follow-up period, the overall mortality for all-cause and CVD was 10.9% and 2.7%, respectively. The adjusted HRs in the highest quintile were 1.16 (95% CI: 1.05, 1.29) for all-cause mortality and 1.2 (95% CI: 1, 1.45) for CVD mortality. In diabetes, obese, and CVD subgroups, significantly elevated adjusted HRs were observed for both all-cause and CVD mortality. Specifically, diabetes patients had adjusted HRs of 1.32 (95% CI: 1.11, 1.57) and 1.38 (95% CI: 1.01, 1.90), obese individuals had HRs of 1.32 (95% CI: 1.10, 1.58) and 1.55 (95% CI: 1.06, 2.28), and CVD patients had HRs of 1.29 (95% CI: 1.10, 1.50) and 1.38 (95% CI: 1.06, 1.79), respectively. A non-linear relationship between UHR and mortality was identified, with critical thresholds of 12.4 for all-cause mortality and 10.7 for CVD mortality in the general population. Significant interactions were observed between UHR and stratified variables, including gender, BMI, education, smoking, alcohol use, and hypertension for all-cause mortality, while significant interactions were observed based on gender, smoking, and alcohol intake for CVD mortality. Comparable trends were also observed in patient with diabetes, obese and CVD. In this cohort study, we provide novel insights into the association between serum UHR concentrations and mortality in the general population. UHR is a strong predictor of all-cause and cardiovascular mortality in the general population.