Acta Scientiarum Mathematicarum

Abstract Aims/hypothesis Glycaemic traits such as high fasting glucose levels and insulin resistance are positively associated with the risk of type 2 diabetes and other cardiometabolic diseases. Genetic association studies have identified hundreds of associations for each glycaemic trait, yet very few studies have involved continental African populations. We report the results of genome-wide association studies (GWASs) in a pan-African cohort for four glycaemic traits, namely fasting glucose, fasting insulin, insulin resistance (HOMA-IR) and beta cell function (HOMA-B), which are quantitative variables that affect the risk of developing type 2 diabetes. Methods GWASs for the four traits were conducted in approximately 10,000 individuals from the Africa Wits-INDEPTH Partnership for Genomics Studies (AWI-Gen) cohort, with participants from Burkina Faso, Ghana, Kenya and South Africa. Association testing was performed using linear mixed models implemented in BOLT-LMM, with age, sex, BMI and principal components as covariates. Replication, fine mapping and functional annotation were performed using standard approaches. Results We identified a novel signal (rs574173815) in the intron of the ankyrin repeat domain 33B (ANKRD33B) gene associated with fasting glucose, and a novel signal (rs114029796) in the intronic region of the WD repeat domain 7 (WDR7) gene associated with fasting insulin. SNPs in WDR7 have been shown to be associated with type 2 diabetes. A variant (rs74806991) in the intron of ADAM metallopeptidase with thrombospondin type 1 motif 16 (ADAMTS16) and another variant (rs6506934) in the β-1,4-galactosyltransferase 6 gene (B4GALT6) are associated with HOMA-IR. Both ADAMTS16 and B4GALT6 are implicated in the development of type 2 diabetes. In addition, our study replicated several well-established fasting glucose signals in the GCK-YTK6, SLC2A2 and THORLNC gene regions. Conclusions/interpretation Our findings highlight the importance of performing GWASs for glycaemic traits in under-represented populations, especially continental African populations, to discover novel associated variants and broaden our knowledge of the genetic aetiology of glycaemic traits. The limited replication of well-known signals in this study hints at the possibility of a unique genetic architecture of these traits in African populations. Data availability The dataset used in this study is available in the European Genome–Phenome Archive (EGA) database (https://ega-archive.org/) under study accession code EGAS00001002482. The phenotype dataset accession code is EGAD00001006425 and the genotype dataset accession code is EGAD00010001996. The availability of these datasets is subject to controlled access by the Data and Biospecimen Access Committee of the H3Africa Consortium. GWAS summary statistics are accessible through the NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/). Graphical Abstract

Abstract Aims/hypothesis We aimed to examine arrhythmias and hypoglycaemia among individuals with and without diabetes who are receiving haemodialysis and to investigate the association between arrhythmias and hypoglycaemia, hyperglycaemia and glycaemic variability. Methods This prospective multicentre cohort study included 70 participants on maintenance haemodialysis (35 with diabetes and 35 without diabetes). We employed implantable cardiac monitors for continuous heart rhythm monitoring in combination with periodic use of continuous glucose monitoring. Logistic-regression-type linear mixed models were used to examine associations between arrhythmias and glycaemic measures. Results During 18 months of follow-up, clinically significant arrhythmias (bradyarrhythmia and ventricular tachycardia) were identified in 12 (34%) participants with diabetes and 11 (31%) without diabetes. Atrial fibrillation was detected in 13 (37%) participants with diabetes and 14 (40%) without, while other supraventricular tachycardia was detected in seven (20%) and 11 (31%) participants with and without diabetes, respectively. Hypoglycaemia (sensor glucose <3.9 mmol/l) was observed in 27 (77%) participants with diabetes and 32 (91%) without diabetes. Compared with euglycaemia, hypoglycaemia was associated with an increased rate of arrhythmias among participants without diabetes (incidence rate ratio [IRR] 3.13 [95% CI 1.49, 6.55]), while hyperglycaemia (sensor glucose >10.0 mmol/l) was associated with a decreased rate of arrhythmias among participants with diabetes (IRR 0.58 [95% CI 0.37, 0.92]). Glycaemic variability showed no association with arrhythmias regardless of the presence of diabetes. Conclusions/interpretation Arrhythmias and hypoglycaemia were common in those undergoing haemodialysis regardless of diabetes status. Our data suggest a temporal relationship between arrhythmias and glucose level in both individuals with and without diabetes. Trial registration Clinicaltrials.gov: NCT04841304. Graphical Abstract

Abstract Aims/hypothesis While investigating markers for declining beta cell function in type 1 diabetes, we previously demonstrated 11 statistically significant protein associations with fasting C-peptide/glucose ratios in longitudinal serum samples from newly diagnosed (ND) individuals (n=86; 228 samples in total) participating in the INNODIA (Innovative approaches to understanding and arresting type 1 diabetes) study. Furthermore, comparison with protein measurements from age- and sex-matched autoantibody-negative unaffected family members (UFMs, n=194) revealed differences in the serum levels of 13 target proteins. To further evaluate these findings, we analysed longitudinal serum drawn during the first year after diagnosis from a new group of ND individuals subsequently enrolled in the study, together with samples from additional UFMs. Methods To validate the previously reported statistically significant protein associations with type 1 diabetes progression, selected reaction monitoring (SRM) MS analyses were carried out. Sera from individuals diagnosed with type 1 diabetes under the age of 18 years (n=146) were collected within 6 weeks of diagnosis and at 3, 6 and 12 months after diagnosis (560 samples in total). The resulting SRM data were compared with fasting C-peptide/glucose measurements, which were used as a proxy for beta cell function. The protein data were further compared with cross-sectional SRM measurements from age- and sex-matched UFMs (n=272). Results Our results confirmed the presence of significant (p<0.05) inverse associations between fasting C-peptide/glucose ratios and peptides from apolipoprotein B-100, apolipoprotein M and glutathione peroxidase 3 (GPX3) in ND individuals. Additionally, we observed consistent differences in the levels of ten of the 13 targeted proteins between individuals with type 1 diabetes and UFMs. These proteins included GPX3, transthyretin, prothrombin, apolipoprotein C1 and afamin. Conclusions/interpretation The validated results reflect the landscape of biological changes accompanying type 1 diabetes. For example, the association of the targeted apolipoproteins with fasting C-peptide/glucose ratios in the first year after diagnosis is likely to relate to lipid abnormalities observed in individuals with type 1 diabetes, and reiterates the connection of apolipoproteins with the underlying changes accompanying the disease. Further research is needed to explore the clinical value and relevance of these targets. Graphical Abstract

Abstract Aims/hypothesis The organisation and cellular architecture of islets of Langerhans are critical to the physiological regulation of hormone secretion but it is debated whether human islets adhere to the characteristic mantle–core (M-C) structure seen in rodents. It is also unclear whether inherent architectural changes contribute to islet dysfunction in type 1 diabetes, aside from the loss of beta cells. Therefore, we have exploited advances in immunostaining, spatial biology and machine learning to undertake a detailed, systematic analysis of adult human islet architecture in health and type 1 diabetes, by a quantitative analysis of a dataset of >250,000 endocrine cells in >3500 islets from ten individuals. Methods Formalin-fixed paraffin-embedded pancreatic sections (4 μm) from organ donors without diabetes and living donors with recent-onset type 1 diabetes were stained for all five islet hormones and imaged prior to analysis, which employed a novel automated pipeline using QuPath software, capable of running on a standard laptop. Whole-slide image analysis involved segmentation classifiers, cell detection and phenotyping algorithms to identify islets, specific cell types and their locations as (x,y)-coordinates in regions of interest. Each endocrine cell was categorised into binary variables for cell type (i.e. beta or non-beta) and position (mantle or core). A χ2 test for independence of these properties was performed and the OR was considered to estimate the effect size of the potential association between position and cell type. A quantification of the M-C structure at islet level was performed by computing the probability, r, that the observed number of non-beta cells in the mantle is due to a random arrangement. The distribution of the r values for the islets in the study was contrasted against the r values of a digital population of equivalent randomly arranged islets, termed digital siblings. Both distributions of r values were compared using the earth mover’s distance (EMD), a mathematical tool employed to describe differences in distribution patterns. The EMD was also used to contrast the distribution of islet size and beta cell fraction between type 1 diabetes and control islets. Results The χ2 test supports the existence of a significant (p<0.001) relationship between cell position and type. The effect size was measured via the OR <0.8, showing that non-beta cells are more likely to be found at the mantle (and vice versa). At the islet level, the EMD between the distributions of r values of the observed islets and the digital siblings was emd-1d=0.10951 (0<emd-1d<1). The transport plan showed a substantial group of islets with a small r value, thus supporting the M-C hypothesis. The bidimensional distribution (beta cell fraction vs size) of islets showed a distance emd-2d=0.285 (0<emd-2d<2) between the control and type 1 diabetes islets. The suffixes ‘-1d’ and ‘-2d’ are used to distinguish the comparison between the distribution of one and two variables. Conclusions/interpretation Using a novel analysis pipeline, statistical evidence supports the existence of an M-C structure in human adult islets, irrespective of type 1 diabetes status. The methods presented in the current study offer potential applications in spatial biology, islet immunopathology, transplantation and organoid research, and developmental research. Graphical Abstract

Abstract Aims/hypothesis Growing evidence suggests that timing may be an important aspect of physical activity that influences cardiometabolic health. However, the current literature is inconclusive regarding the time of day that physical activity offers the greatest metabolic advantages. We investigated associations between hourly physical activity levels and diabetes mellitus and glycaemic biomarkers in a cross-sectional and nationally representative sample of US adults. Methods We studied 7074 adults (mean age 48 years; 52% women) from the National Health and Nutrition Examination Survey (2011–2014). Physical activity was measured by actigraphy. A monitor-independent movement summary (MIMS) unit was used to derive the total activity level (divided into quintiles) for hourly windows that were defined relative to sleep timing and according to clock time. The primary outcome was prevalent diabetes, and secondary outcomes included fasting glucose, fasting insulin, HOMA-IR and 2 h OGTT results. Results Physical activity levels in late morning and late afternoon were associated with lower adjusted odds of diabetes. Specifically, in late morning (8:01–9:00 h after the sleep midpoint), the highest quintile of activity was associated with a 35% decrease (OR 0.65; 95% CI 0.44, 0.96) in the odds of diabetes when compared with the lowest quintile, while in late afternoon (11:01–17:00 h after the sleep midpoint), the highest quintiles were associated with 56% and 36% lower odds (OR 0.44; 95% CI 0.29, 0.69 and OR 0.64; 95% CI 0.43, 0.95). Higher night-time activity was associated with higher odds of diabetes. Similar patterns of results were observed with OGTT data and across subgroups of age, gender, race/ethnicity, chronotype and sleep duration. Conclusions/interpretation Our findings suggest that the timing of physical activity may modulate its metabolic effects. Graphical Abstract

Abstract Aims/hypothesis Type 1 diabetes is categorised into autoantibody positive and autoantibody negative. Most type 1 diabetes research has focused on European populations, leaving a gap in understanding in relation to other ethnic groups, including Thai populations. This lack of data is significant given Thailand’s poor prevention and therapeutic management strategies. We aimed to investigate the frequency and distribution of islet autoantibodies among Thai individuals with long-standing type 1 diabetes diagnosed before the age of 30 years. Methods We conducted a nationwide population-based study involving 48 hospitals in Thailand from May 2020 to September 2023, enrolling 953 participants. Demographic and clinical characteristics of individuals with autoantibody-positive and -negative type 1 diabetes were analysed. The autoantibodies GAD65, IA-2 and ZnT8 were measured using ELISA. A random C-peptide level was detected by electrochemiluminescence immunoassay. Results Thai individuals with autoantibody-negative type 1 diabetes comprised 34.2% of the population. Among all individuals, the frequency of GAD65, IA-2 and ZnT8 was 56%, 37% and 33%, respectively. Autoantibody-negative individuals with type 1 diabetes were older at diagnosis, had higher BMI and had higher random C-peptide levels compared with autoantibody-positive individuals with type 1 diabetes. Female individuals had a higher prevalence of type 1 diabetes than male individuals (58% vs 42%; p=1.531 × 10−5). The southern region of Thailand exhibited a distinct pattern of autoantibody frequency compared with other regions (p=0.0001561). Conclusions/interpretation The frequency, distribution and characteristics of autoantibody-positive and -negative long-standing type 1 diabetes in Thailand showed uniqueness from other populations. This provides insight into the disease that may have implications for type 1 diabetes prediction, treatment and pathogenesis, especially in the Southeast Asian population. Graphical Abstract