Diabetologia最新文献

{"title":"Genetic relations between type 1 diabetes, coronary artery disease and leukocyte counts.","authors":"Jolade Adebekun, Ajay Nadig, Priscilla Saarah, Samira Asgari, Linda Kachuri, David A Alagpulinsa","doi":"10.1007/s00125-024-06247-9","DOIUrl":"10.1007/s00125-024-06247-9","url":null,"abstract":"<p><strong>Aims/hypothesis: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>There was significant genome-wide genetic correlation (r<sub>g</sub>) between type 1 diabetes and CAD (r<sub>g</sub>=0.088, p=8.60 × 10<sup>-3</sup>) and both diseases shared significant genome-wide genetic determinants with eosinophil count (r<sub>g</sub> for type 1 diabetes [r<sub>g(T1D)</sub>]=0.093, p=7.20 × 10<sup>-3</sup>, r<sub>g</sub> for CAD [r<sub>g(CAD)</sub>]=0.092, p=3.68 × 10<sup>-6</sup>) and lymphocyte count (r<sub>g(T1D)</sub>=-0.052, p=2.76 × 10<sup>-2</sup>, r<sub>g(CAD)</sub>=0.176, p=1.82 × 10<sup>-15</sup>). 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<sub>T1D</sub>]=0.67, p=2.02<sup>-19</sup>, OR<sub>CAD</sub>=1.09, p=2.67 × 10<sup>-6</sup>; neutrophil OR<sub>T1D</sub>=0.82, p=5.63 × 10<sup>-5</sup>, OR<sub>CAD</sub>=1.17, p=5.02 × 10<sup>-14</sup>; and eosinophil OR<sub>T1D</sub>=1.67, p=5.45 × 10<sup>-25</sup>, OR<sub>CAD</sub>=1.07, p=2.03 × 10<sup>-4</sup>. The genetic causality proportion between type 1 diabetes and CAD was 0.36 ± 0.16 (p<sub>LCV</sub>=1.30 × 10<sup>-2</sup>), suggesting a possible intermediary causal variable.</p><p><strong>Conclusions/interpretation: </strong>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","PeriodicalId":11164,"journal":{"name":"Diabetologia","volume":" ","pages":"2518-2529"},"PeriodicalIF":8.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141975269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Publisher Correction: EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD): Executive Summary.","authors":"","doi":"10.1007/s00125-024-06258-6","DOIUrl":"10.1007/s00125-024-06258-6","url":null,"abstract":"","PeriodicalId":11164,"journal":{"name":"Diabetologia","volume":" ","pages":"2608"},"PeriodicalIF":8.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11519244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142343425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dietary patterns during pregnancy and maternal and birth outcomes in women with type 1 diabetes: the Environmental Determinants of Islet Autoimmunity (ENDIA) study.","authors":"Rebecca L Thomson, James D Brown, Helena Oakey, Kirsten Palmer, Pat Ashwood, Megan A S Penno, Kelly J McGorm, Rachel Battersby, Peter G Colman, Maria E Craig, Elizabeth A Davis, Tony Huynh, Leonard C Harrison, Aveni Haynes, Richard O Sinnott, Peter J Vuillermin, John M Wentworth, Georgia Soldatos, Jennifer J Couper","doi":"10.1007/s00125-024-06259-5","DOIUrl":"10.1007/s00125-024-06259-5","url":null,"abstract":"<p><strong>Aims/hypothesis: </strong>Dietary patterns characterised by high intakes of vegetables may lower the risk of pre-eclampsia and premature birth in the general population. The effect of dietary patterns in women with type 1 diabetes, who have an increased risk of complications in pregnancy, is not known. The aim of this study was to investigate the relationship between dietary patterns and physical activity during pregnancy and maternal complications and birth outcomes in women with type 1 diabetes. We also compared dietary patterns in women with and without type 1 diabetes.</p><p><strong>Methods: </strong>Diet was assessed in the third trimester using a validated food frequency questionnaire in participants followed prospectively in the multi-centre Environmental Determinants of Islet Autoimmunity (ENDIA) study. Dietary patterns were characterised by principal component analysis. The Pregnancy Physical Activity Questionnaire was completed in each trimester. Data for maternal and birth outcomes were collected prospectively.</p><p><strong>Results: </strong>Questionnaires were completed by 973 participants during 1124 pregnancies. Women with type 1 diabetes (n=615 pregnancies with dietary data) were more likely to have a 'fresh food' dietary pattern than women without type 1 diabetes (OR 1.19, 95% CI 1.07, 1.31; p=0.001). In women with type 1 diabetes, an increase equivalent to a change from quartile 1 to 3 in 'fresh food' dietary pattern score was associated with a lower risk of pre-eclampsia (OR 0.37, 95% CI 0.17, 0.78; p=0.01) and premature birth (OR 0.35, 95% CI 0.20, 0.62, p<0.001). These associations were mediated in part by BMI and HbA_1c. The 'processed food' dietary pattern was associated with an increased birthweight (β coefficient 56.8 g, 95% CI 2.8, 110.8; p=0.04). Physical activity did not relate to outcomes.</p><p><strong>Conclusions/interpretation: </strong>A dietary pattern higher in fresh foods during pregnancy was associated with sizeable reductions in risk of pre-eclampsia and premature birth in women with type 1 diabetes.</p>","PeriodicalId":11164,"journal":{"name":"Diabetologia","volume":" ","pages":"2420-2432"},"PeriodicalIF":8.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11519125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142105299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the influence of insulin type (ultra-rapid vs rapid insulin) and exercise timing on postprandial exercise-induced hypoglycaemia risk in individuals with type 1 diabetes: a randomised controlled trial.","authors":"Joséphine Molveau, Étienne Myette-Côté, Sémah Tagougui, Nadine Taleb, Roxane St-Amand, Corinne Suppère, Valérie Bourdeau, Elsa Heyman, Rémi Rabasa-Lhoret","doi":"10.1007/s00125-024-06234-0","DOIUrl":"10.1007/s00125-024-06234-0","url":null,"abstract":"<p><strong>Aims/hypothesis: </strong>The relationship between pre-meal insulin type, exercise timing and the risk of postprandial exercise-induced hypoglycaemia in people living with type 1 diabetes is unknown. We aimed to evaluate the effects of exercise timing (60 vs 120 min post meal) and different insulin types (aspart vs ultra-rapid aspart) on hypoglycaemic risk.</p><p><strong>Methods: </strong>This was a four-way crossover randomised trial including 40 individuals with type 1 diabetes using multiple daily injections (mean HbA_1c 56 mmol/mol [7.4%]). Participants, who were recruited from the Montreal Clinical Research Institute, undertook 60 min cycling sessions (60% of <math> <msub> <mrow><mover><mi>V</mi> <mo>˙</mo></mover> <mtext>O</mtext></mrow> <mrow><mn>2</mn> <mtext>peak</mtext></mrow> </msub> </math> ) after breakfast (60 min [EX60min] or 120 min [EX120min] post meal) with 50% of their usual insulin dose (aspart or ultra-rapid aspart). Eligibility criteria included age ≥18 years old, clinical diagnosis of type 1 diabetes for at least 1 year and HbA_1c ≤80 mmol/mol (9.5%). Participants were allocated using sequentially numbered, opaque sealed envelopes. Participants were masked to their group assignment, and each participant was allocated a unique identification number to ensure anonymisation. The primary outcome was change in blood glucose levels between exercise onset and nadir.</p><p><strong>Results: </strong>Prior to exercise onset, time spent in hyperglycaemia was lower for EX60min vs EX120min (time >10.0 mmol/l: 56.6% [1.2-100%] vs 78.0% [52.7-97.9%]; p<0.001). The glucose reduction between exercise onset and nadir was less pronounced with EX60min vs EX120min (-3.8±2.7 vs -4.7±2.5 mmol/l; p<0.001). A similar number of hypoglycaemic events occurred during both exercise timings. Blood glucose between exercise onset and nadir decreased less with ultra-rapid aspart compared with aspart (-4.1±2.3 vs -4.4±2.8 mmol/l; p=0.037). While a similar number of hypoglycaemic events during exercise were observed, less post-exercise hypoglycaemia occurred with ultra-rapid aspart (n=0, 0%, vs n=15, 38%; p=0.003). No interactions between insulin types and exercise timings were found.</p><p><strong>Conclusions/interpretation: </strong>EX60min blunted the pre-exercise glucose increase following breakfast and was associated with a smaller glucose reduction during exercise. Ultra-rapid aspart led to a smaller blood glucose reduction during exercise and might be associated with diminished post-exercise hypoglycaemia.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov NCT03659799 FUNDING: This study was funded by Novo Nordisk Canada.</p>","PeriodicalId":11164,"journal":{"name":"Diabetologia","volume":" ","pages":"2408-2419"},"PeriodicalIF":8.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141787539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Similar early metabolic changes induced by dietary weight loss or bariatric surgery.","authors":"Roy Taylor","doi":"10.1007/s00125-024-06268-4","DOIUrl":"10.1007/s00125-024-06268-4","url":null,"abstract":"","PeriodicalId":11164,"journal":{"name":"Diabetologia","volume":" ","pages":"2603-2604"},"PeriodicalIF":8.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142132118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Progression to type 1 diabetes in the DPT-1 and TN07 clinical trials is critically associated with specific residues in HLA-DQA1-B1 heterodimers.","authors":"Lue Ping Zhao, George K Papadopoulos, Jay S Skyler, Alberto Pugliese, Hemang M Parikh, William W Kwok, Terry P Lybrand, George P Bondinas, Antonis K Moustakas, Ruihan Wang, Chul-Woo Pyo, Wyatt C Nelson, Daniel E Geraghty, Åke Lernmark","doi":"10.1007/s00125-024-06274-6","DOIUrl":"10.1007/s00125-024-06274-6","url":null,"abstract":"<p><strong>Aims/hypothesis: </strong>The aim of this work was to explore molecular amino acids (AAs) and related structures of HLA-DQA1-DQB1 that underlie its contribution to the progression from stages 1 or 2 to stage 3 type 1 diabetes.</p><p><strong>Methods: </strong>Using high-resolution DQA1 and DQB1 genotypes from 1216 participants in the Diabetes Prevention Trial-Type 1 and the Diabetes Prevention Trial, we applied hierarchically organised haplotype association analysis (HOH) to decipher which AAs contributed to the associations of DQ with disease and their structural properties. HOH relied on the Cox regression to quantify the association of DQ with time-to-onset of type 1 diabetes.</p><p><strong>Results: </strong>By numerating all possible DQ heterodimers of α- and β-chains, we showed that the heterodimerisation increases genetic diversity at the cellular level from 43 empirically observed haplotypes to 186 possible heterodimers. Heterodimerisation turned several neutral haplotypes (DQ2.2, DQ2.3 and DQ4.4) to risk haplotypes (DQ2.2/2.3-DQ4.4 and DQ4.4-DQ2.2). HOH uncovered eight AAs on the α-chain (-16α, -13α, -6α, α22, α23, α44, α72, α157) and six AAs on the β-chain (-18β, β9, β13, β26, β57, β135) that contributed to the association of DQ with progression of type 1 diabetes. The specific AAs concerned the signal peptide (minus sign, possible linkage to expression levels), pockets 1, 4 and 9 in the antigen-binding groove of the α1β1 domain, and the putative homodimerisation of the αβ heterodimers.</p><p><strong>Conclusions/interpretation: </strong>These results unveil the contribution made by DQ to type 1 diabetes progression at individual residues and related protein structures, shedding light on its immunological mechanisms and providing new leads for developing treatment strategies.</p><p><strong>Data availability: </strong>Clinical trial data and biospecimen samples are available through the National Institute of Diabetes and Digestive and Kidney Diseases Central Repository portal ( https://repository.niddk.nih.gov/studies ).</p>","PeriodicalId":11164,"journal":{"name":"Diabetologia","volume":" ","pages":"2481-2493"},"PeriodicalIF":8.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11519105/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142361310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of type 1 diabetes risk phenotypes using an outcome-guided clustering analysis.","authors":"Lu You, Lauric A Ferrat, Richard A Oram, Hemang M Parikh, Andrea K Steck, Jeffrey Krischer, Maria J Redondo","doi":"10.1007/s00125-024-06246-w","DOIUrl":"10.1007/s00125-024-06246-w","url":null,"abstract":"<p><strong>Aims/hypothesis: </strong>Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal the heterogeneity of the at-risk population by identifying clinically meaningful clusters are lacking. We aimed to identify and characterise clusters of islet autoantibody-positive individuals who share similar characteristics and type 1 diabetes risk.</p><p><strong>Methods: </strong>We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention study data (n=1123). The outcome of the analysis was the time to development of type 1 diabetes, and variables in the model included demographic characteristics, genetics, metabolic factors and islet autoantibodies. An independent dataset (the Diabetes Prevention Trial of Type 1 Diabetes Study) (n=706) was used for validation.</p><p><strong>Results: </strong>The analysis revealed six clusters with varying type 1 diabetes risks, categorised into three groups based on the hierarchy of clusters. Group A comprised one cluster with high glucose levels (median for glucose mean AUC 9.48 mmol/l; IQR 9.16-10.02) and high risk (2-year diabetes-free survival probability 0.42; 95% CI 0.34, 0.51). Group B comprised one cluster with high IA-2A titres (median 287 DK units/ml; IQR 250-319) and elevated autoantibody titres (2-year diabetes-free survival probability 0.73; 95% CI 0.67, 0.80). Group C comprised four lower-risk clusters with lower autoantibody titres and glucose levels (with 2-year diabetes-free survival probability ranging from 0.84-0.99 in the four clusters). Within group C, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels and age. A decision rule for assigning individuals to clusters was developed. Use of the validation dataset confirmed that the clusters can identify individuals with similar characteristics.</p><p><strong>Conclusions/interpretation: </strong>Demographic, metabolic, immunological and genetic markers may be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.</p>","PeriodicalId":11164,"journal":{"name":"Diabetologia","volume":" ","pages":"2507-2517"},"PeriodicalIF":8.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141893111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based reproducible prediction of type 2 diabetes subtypes.","authors":"Hayato Tanabe, Masahiro Sato, Akimitsu Miyake, Yoshinori Shimajiri, Takafumi Ojima, Akira Narita, Haruka Saito, Kenichi Tanaka, Hiroaki Masuzaki, Junichiro J Kazama, Hideki Katagiri, Gen Tamiya, Eiryo Kawakami, Michio Shimabukuro","doi":"10.1007/s00125-024-06248-8","DOIUrl":"10.1007/s00125-024-06248-8","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Aims/hypothesis: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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&lt;sub&gt;kmeans&lt;/sub&gt;) by unsupervised k-means clustering based on Ahlqvist's variables (age at diagnosis, BMI, HbA&lt;sub&gt;1c&lt;/sub&gt;, 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&lt;sub&gt;RF15&lt;/sub&gt;). 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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;T2D&lt;sub&gt;RF15&lt;/sub&gt; demonstrated a 94% accuracy for predicting T2D&lt;sub&gt;kmeans&lt;/sub&gt; 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&lt;sub&gt;RF15&lt;/sub&gt; showed an accuracy of 82.9% for detecting T2D&lt;sub&gt;kmeans&lt;/sub&gt;, 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&lt;sub&gt;RF15&lt;/sub&gt; 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&lt;sub&gt;kmeans&lt;/sub&gt;. The predictive accuracy was improved after excluding individuals with low predictive probability, who were categorised as an 'undecidable' cluster. T2D&lt;sub&gt;RF15&lt;/sub&gt;, 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&lt;sub&gt;kmeans&lt;/sub&gt;.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions/interpretation: &lt;/strong&gt;The new ML model fo","PeriodicalId":11164,"journal":{"name":"Diabetologia","volume":" ","pages":"2446-2458"},"PeriodicalIF":8.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11519166/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142016689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Whole-exome and whole-genome sequencing of 1064 individuals with type 1 diabetes reveals novel genes for diabetic kidney disease.","authors":"Jani K Haukka, Anni A Antikainen, Erkka Valo, Anna Syreeni, Emma H Dahlström, Bridget M Lin, Nora Franceschini, Andrzej S Krolewski, Valma Harjutsalo, Per-Henrik Groop, Niina Sandholm","doi":"10.1007/s00125-024-06241-1","DOIUrl":"10.1007/s00125-024-06241-1","url":null,"abstract":"<p><strong>Aims/hypothesis: </strong>Diabetic kidney disease (DKD) is a severe diabetic complication that affects one third of individuals with type 1 diabetes. Although several genes and common variants have been shown to be associated with DKD, much of the predicted inheritance remains unexplained. Here, we performed next-generation sequencing to assess whether low-frequency variants, extending to a minor allele frequency (MAF) ≤10% (single or aggregated) contribute to the missing heritability in DKD.</p><p><strong>Methods: </strong>We performed whole-exome sequencing (WES) of 498 individuals and whole-genome sequencing (WGS) of 599 individuals with type 1 diabetes. After quality control, next-generation sequencing data were available for a total of 1064 individuals, of whom 541 had developed either severe albuminuria or end-stage kidney disease, and 523 had retained normal albumin excretion despite a long duration of type 1 diabetes. Single-variant and gene-aggregate tests for protein-altering variants (PAV) and protein-truncating variants (PTV) were performed separately for WES and WGS data and combined in a meta-analysis. We also performed genome-wide aggregate analyses on genomic windows (sliding window), promoters and enhancers using the WGS dataset.</p><p><strong>Results: </strong>In the single-variant meta-analysis, no variant reached genome-wide significance, but a suggestively associated common THAP7 rs369250 variant (p=1.50 × 10^-5, MAF=49%) was replicated in the FinnGen general population genome-wide association study (GWAS) data for chronic kidney disease and DKD phenotypes. The gene-aggregate meta-analysis provided suggestive evidence (p<4.0 × 10^-4) at four genes for DKD, of which NAT16 (MAF_PAV≤10%) and LTA (also known as TNFβ, MAF_PAV≤5%) are replicated in the FinnGen general population GWAS data. The LTA rs2229092 C allele was associated with significantly lower TNFR1, TNFR2 and TNFR3 serum levels in a subset of FinnDiane participants. Of the intergenic regions suggestively associated with DKD, the enhancer on chromosome 18q12.3 (p=3.94 × 10^-5, MAF_variants≤5%) showed interaction with the METTL4 gene; the lead variant was replicated, and predicted to alter binding of the MafB transcription factor.</p><p><strong>Conclusions/interpretation: </strong>Our sequencing-based meta-analysis revealed multiple genes, variants and regulatory regions that were suggestively associated with DKD. However, as no variant or gene reached genome-wide significance, further studies are needed to validate the findings.</p>","PeriodicalId":11164,"journal":{"name":"Diabetologia","volume":" ","pages":"2494-2506"},"PeriodicalIF":8.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11519100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141893114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Elevations in plasma glucagon are associated with reduced insulin clearance after ingestion of a mixed-macronutrient meal in people with and without type 2 diabetes.","authors":"Kieran Smith, Guy S Taylor, Wouter Peeters, Mark Walker, Simone Perazzolo, Naeimeh Atabaki-Pasdar, Kelly A Bowden Davies, Fredrik Karpe, Leanne Hodson, Emma J Stevenson, Daniel J West","doi":"10.1007/s00125-024-06249-7","DOIUrl":"10.1007/s00125-024-06249-7","url":null,"abstract":"<p><strong>Aims/hypothesis: </strong>The temporal suppression of insulin clearance after glucose ingestion is a key determinant of glucose tolerance for people without type 2 diabetes. Whether similar adaptations are observed after the ingestion of a mixed-macronutrient meal is unclear.</p><p><strong>Methods: </strong>In a secondary analysis of data derived from two randomised, controlled trials, we studied the temporal responses of insulin clearance after the ingestion of a standardised breakfast meal consisting of cereal and milk in lean normoglycaemic individuals (n=12; Lean-NGT), normoglycaemic individuals with central obesity (n=11; Obese-NGT) and in people with type 2 diabetes (n=19). Pre-hepatic insulin secretion rates were determined by the deconvolution of C-peptide, and insulin clearance was calculated using a single-pool model. Insulin sensitivity was measured by an oral minimal model.</p><p><strong>Results: </strong>There were divergent time course changes in insulin clearance between groups. In the Lean-NGT group, there was an immediate post-meal increase in insulin clearance compared with pre-meal values (p<0.05), whereas insulin clearance remained stable at baseline values in Obese-NGT or declined slightly in the type 2 diabetes group (p<0.05). The mean AUC for insulin clearance during the test was ~40% lower in the Obese-NGT (1.3 ± 0.4 l min^-1 m^-2) and type 2 diabetes (1.4 ± 0.7 l min^-1 m^-2) groups compared with Lean-NGT (1.9 ± 0.5 l min^-1 m^-2; p<0.01), with no difference between the Obese-NGT and type 2 diabetes groups. HOMA-IR and glucagon AUC emerged as predictors of insulin clearance AUC, independent of BMI, age or insulin sensitivity (adjusted R²=0.670). Individuals with increased glucagon AUC had a 40% reduction in insulin clearance AUC (~ -0.75 l min^-1 m^-2; p<0.001).</p><p><strong>Conclusions/interpretation: </strong>The ingestion of a mixed-macronutrient meal augments differing temporal profiles in insulin clearance among individuals without type 2 diabetes, which is associated with HOMA-IR and the secretion of glucagon. Further research investigating the role of hepatic glucagon signalling in postprandial insulin kinetics is warranted.</p><p><strong>Trial registration: </strong>ISRCTN17563146 and ISRCTN95281775.</p>","PeriodicalId":11164,"journal":{"name":"Diabetologia","volume":" ","pages":"2555-2567"},"PeriodicalIF":8.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11519192/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141975268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}