Diabetologia最新文献

Aims/hypothesis

Pregnancy is accompanied by maternal metabolic adaptations to ensure fetal growth and development, including insulin resistance, which occurs primarily during the second and third trimesters of pregnancy, and a decrease in fasting blood sugar levels over the course of pregnancy. Glucose-related traits are regulated by genetic and environmental factors and modulated by physiological variations throughout the life course. We addressed the hypothesis that there are both overlaps and differences between genetic variants associated with glycaemia-related traits during and outside of pregnancy.

Methods

Genome-wide SNP data were used to identify genetic variations associated with glycaemia-related traits measured during an OGTT performed at ~28 weeks’ gestation in 8067 participants in the Hyperglycaemia and Adverse Pregnancy Outcome (HAPO) Study. Associations outside of pregnancy were determined in 3977 individuals who also participated in the HAPO Follow-Up Study at 11–14 years postpartum. A Bayesian classification algorithm was used to determine whether SNPs associated with fasting and 2 h glucose and fasting C-peptide during pregnancy had a pregnancy-predominant effect vs a similar effect during pregnancy and postpartum.

Results

SNPs in six loci (GCKR, G6PC2, GCK, PPP1R3B, PCSK1 and MTNR1B) were significantly associated with fasting glucose during pregnancy, while SNPs in CDKAL1 and MTNR1B were associated with 1 h glucose and SNPs in MTNR1B and HKDC1 were associated with 2 h glucose. Variants in CDKAL1 and MTNR1B were associated with insulin secretion during pregnancy. Variants in multiple loci were associated with fasting C-peptide during pregnancy, including GCKR, IQSEC1, PPP1R3B, IGF1 and BACE2. GCKR and BACE2 were associated with 1 h C-peptide and GCKR, IQSEC1 and BACE2 with insulin sensitivity during pregnancy. The associations of MTNR1B with 2 h glucose, BACE2 with fasting and 1 h C-peptide and insulin sensitivity, and IQSEC1 with fasting C-peptide and insulin sensitivity that we identified during pregnancy have not been previously reported in non-pregnancy cohorts. The Bayesian classification algorithm demonstrated that the magnitude of effect of the lead SNP was greater during pregnancy compared with 11–14 years postpartum in PCSK1 and PPP1R3B with fasting glucose, in three loci, including MTNR1B, with 2 h glucose, and in six loci, including IGF1, with fasting C-peptide.

Conclusions/interpretation

Our findings support the hypothesis that there are both overlaps and differences between the genetic architecture of glycaemia-related traits during an

The tight regulation of glucose and lipid metabolism is crucial for maintaining metabolic health. Dysregulation of these processes can lead to the development of metabolic diseases. Secreted factors, or hormones, play an essential role in the regulation of glucose and lipid metabolism, thus also playing an important role in the development of metabolic diseases such as type 2 diabetes and obesity. Given the important roles of secreted factors, there has been significant interest in identifying new secreted factors and new functions for existing secreted factors that control glucose and lipid metabolism. In this review, we evaluate novel secreted factors or novel functions of existing factors that regulate glucose and lipid metabolism discovered in the last decade, including secreted isoform of endoplasmic reticulum membrane complex subunit 10, vimentin, cartilage intermediate layer protein 2, isthmin-1, lipocalin-2, neuregulin-1 and neuregulin-4. We discuss their discovery, tissues of origin, mechanisms of action and sex differences, emphasising their potential to regulate metabolic processes central to diabetes. Additionally, we discuss the translational barriers, particularly the absence of identified receptors, that hamper their functional characterisation and further therapeutic development. Ultimately, the identification of new secreted factors may give insights into previously unidentified pathways of disease progression and mechanisms of glucose and lipid homeostasis.

Graphical Abstract

Aims/hypothesis: We compared the effects of sodium-glucose cotransporter 2 (SGLT2) inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) on renal outcomes in individuals with type 2 diabetes, focusing on the changes in eGFR and albuminuria.

Methods: This was a multicentre retrospective observational study on new users of diabetes medications. Participant characteristics were assessed before and after propensity score matching. The primary endpoint, change in eGFR, was analysed using mixed-effects models. Secondary endpoints included categorical eGFR-based outcomes and changes in albuminuria. Subgroup and sensitivity analyses were performed to assess robustness of the findings.

Results: After matching, 5701 participants/group were included. Participants were predominantly male, aged 61 years, with a 10 year duration of diabetes, a baseline HbA_1c of 64 mmol/mol (8.0%) and BMI of 33 kg/m². Chronic kidney disease (CKD) was present in 23% of participants. During a median of 2.1 years, from a baseline of 87 ml/min per 1.73 m², eGFR remained higher in the SGLT2i group compared with the GLP-1RA group throughout the observation period by 1.2 ml/min per 1.73 m². No differences were detected in albuminuria change. The SGLT2i group exhibited lower rates of worsening CKD class and favourable changes in BP compared with the GLP-1RA group, despite lesser HbA_1c decline. SGLT2i also reduced eGFR decline better than GLP-1RA in participants without baseline CKD.

Conclusions/interpretation: In individuals with type 2 diabetes, treatment with SGLT2i was associated with better preservation of renal function compared with GLP-1RA, as evidenced by slower decline in eGFR. These findings reinforce SGLT2i as preferred agents for renal protection in this patient population.

Aims/hypothesis: Clustering-based subclassification of type 2 diabetes, which reflects pathophysiology and genetic predisposition, is a promising approach for providing personalised and effective therapeutic strategies. Ahlqvist's classification is currently the most vigorously validated method because of its superior ability to predict diabetes complications but it does not have strong consistency over time and requires HOMA2 indices, which are not routinely available in clinical practice and standard cohort studies. We developed a machine learning (ML) model to classify individuals with type 2 diabetes into Ahlqvist's subtypes consistently over time.

Methods: Cohort 1 dataset comprised 619 Japanese individuals with type 2 diabetes who were divided into training and test sets for ML models in a 7:3 ratio. Cohort 2 dataset, comprising 597 individuals with type 2 diabetes, was used for external validation. Participants were pre-labelled (T2D_kmeans) by unsupervised k-means clustering based on Ahlqvist's variables (age at diagnosis, BMI, HbA_1c, HOMA2-B and HOMA2-IR) to four subtypes: severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD) and mild age-related diabetes (MARD). We adopted 15 variables for a multiclass classification random forest (RF) algorithm to predict type 2 diabetes subtypes (T2D_RF15). The proximity matrix computed by RF was visualised using a uniform manifold approximation and projection. Finally, we used a putative subset with missing insulin-related variables to test the predictive performance of the validation cohort, consistency of subtypes over time and prediction ability of diabetes complications.

Results: T2D_RF15 demonstrated a 94% accuracy for predicting T2D_kmeans type 2 diabetes subtypes (AUCs ≥0.99 and F1 score [an indicator calculated by harmonic mean from precision and recall] ≥0.9) and retained the predictive performance in the external validation cohort (86.3%). T2D_RF15 showed an accuracy of 82.9% for detecting T2D_kmeans, also in a putative subset with missing insulin-related variables, when used with an imputation algorithm. In Kaplan-Meier analysis, the diabetes clusters of T2D_RF15 demonstrated distinct accumulation risks of diabetic retinopathy in SIDD and that of chronic kidney disease in SIRD during a median observation period of 11.6 (4.5-18.3) years, similarly to the subtypes using T2D_kmeans. The predictive accuracy was improved after excluding individuals with low predictive probability, who were categorised as an 'undecidable' cluster. T2D_RF15, after excluding undecidable individuals, showed higher consistency (100% for SIDD, 68.6% for SIRD, 94.4% for MOD and 97.9% for MARD) than T2D_kmeans.

Conclusions/interpretation: The new ML model fo

A panel of primary care and diabetes specialists conducted focused literature searches on the current role of glycaemic control in the management of type 2 diabetes and revisited the evolution of evidence supporting the importance of early and intensive blood glucose control as a central strategy to reduce the risk of adverse long-term outcomes. The optimal approach to type 2 diabetes management has evolved over time as the evidence base has expanded from data from trials that established the role of optimising glycaemic control to recent data from cardiovascular outcomes trials (CVOTs) demonstrating organ-protective effects of newer glucose-lowering drugs (GLDs). The results from these CVOTs were derived mainly from people with type 2 diabetes and prior cardiovascular and kidney disease or multiple risk factors. In more recent years, earlier diagnosis in high-risk individuals has contributed to the large proportion of people with type 2 diabetes who do not have complications. In these individuals, a legacy effect of early and optimal control of blood glucose and cardiometabolic risk factors has been proven to reduce cardiovascular and kidney disease events and all-cause mortality. As there is a lack of RCTs investigating the potential synergistic effects of intensive glucose control and organ-protective effects of newer GLDs, this article re-evaluates the evolution of the scientific evidence and highlights the importance of integrating glycaemic control as a pivotal early therapeutic goal in most people with type 2 diabetes, while targeting existing cardiovascular and kidney disease. We also emphasise the importance of implementing multifactorial management using a multidisciplinary approach to facilitate regular review, patient empowerment and the possibility of tailoring interventions to account for the heterogeneity of type 2 diabetes.

Challenges and fears related to managing glucose levels around planned and spontaneous exercise affect outcomes and quality of life in people living with type 1 diabetes. Advances in technology, including continuous glucose monitoring, open-loop insulin pump therapy and hybrid closed-loop (HCL) systems for exercise management in type 1 diabetes, address some of these challenges. In this review, three research or clinical experts, each living with type 1 diabetes, leverage published literature and clinical and personal experiences to translate research findings into simplified, patient-centred strategies. With an understanding of limitations in insulin pharmacokinetics, variable intra-individual responses to aerobic and anaerobic exercise, and the features of the technologies, six steps are proposed to guide clinicians in efficiently communicating simplified actions more effectively to individuals with type 1 diabetes. Fundamentally, the six steps centre on two aspects. First, regardless of insulin therapy type, and especially needed for spontaneous exercise, we provide an estimate of glucose disposal into active muscle meant to be consumed as extra carbohydrates for exercise ('ExCarbs'; a common example is 0.5 g/kg body mass per hour for adults and 1.0 g/kg body mass per hour for youth). Second, for planned exercise using open-loop pump therapy or HCL systems, we additionally recommend pre-emptive basal insulin reduction or using HCL exercise modes initiated 90 min (1-2 h) before the start of exercise until the end of exercise. Modifications for aerobic- and anaerobic-type exercise are discussed. The burden of pre-emptive basal insulin reductions and consumption of ExCarbs are the limitations of HCL systems, which may be overcome by future innovations but are unquestionably required for currently available systems.

Aims/hypothesis: Type 1 diabetes is associated with excess coronary artery disease (CAD) risk even when known cardiovascular risk factors are accounted for. Genetic perturbation of haematopoiesis that alters leukocyte production is a novel independent modifier of CAD risk. We examined whether there are shared genetic determinants and causal relationships between type 1 diabetes, CAD and leukocyte counts.

Methods: Genome-wide association study summary statistics were used to perform pairwise linkage disequilibrium score regression and heritability estimation from summary statistics (ρ-HESS) to respectively estimate the genome-wide and local genetic correlations, and two-sample Mendelian randomisation to estimate the causal relationships between leukocyte counts (335,855 healthy individuals), type 1 diabetes (18,942 cases, 501,638 control individuals) and CAD (122,733 cases, 424,528 control individuals). A latent causal variable (LCV) model was performed to estimate the genetic causality proportion of the genetic correlation between type 1 diabetes and CAD.

Results: There was significant genome-wide genetic correlation (r_g) between type 1 diabetes and CAD (r_g=0.088, p=8.60 × 10^-3) and both diseases shared significant genome-wide genetic determinants with eosinophil count (r_g for type 1 diabetes [r_g(T1D)]=0.093, p=7.20 × 10^-3, r_g for CAD [r_g(CAD)]=0.092, p=3.68 × 10^-6) and lymphocyte count (r_g(T1D)=-0.052, p=2.76 × 10^-2, r_g(CAD)=0.176, p=1.82 × 10^-15). Sixteen independent loci showed stringent Bonferroni significant local genetic correlations between leukocyte counts, type 1 diabetes and/or CAD. Cis-genetic regulation of the expression levels of genes within shared loci between type 1 diabetes and CAD was associated with both diseases as well as leukocyte counts, including SH2B3, CTSH, MORF4L1, CTRB1, CTRB2, CFDP1 and IFIH1. Genetically predicted lymphocyte, neutrophil and eosinophil counts were associated with type 1 diabetes and CAD (lymphocyte OR for type 1 diabetes [OR_T1D]=0.67, p=2.02^-19, OR_CAD=1.09, p=2.67 × 10^-6; neutrophil OR_T1D=0.82, p=5.63 × 10^-5, OR_CAD=1.17, p=5.02 × 10^-14; and eosinophil OR_T1D=1.67, p=5.45 × 10^-25, OR_CAD=1.07, p=2.03 × 10^-4. The genetic causality proportion between type 1 diabetes and CAD was 0.36 ± 0.16 (p_LCV=1.30 × 10^-2), suggesting a possible intermediary causal variable.

Conclusions/interpretation: This study sheds light on shared genetic mechanisms underlying type 1 diabetes and CAD, which may contribute to their co-occurrence through regulation of gene expression and leukocyte counts and identifies cellular and molecular targets for fur