Diabetologia最新文献

{"title":"Targeting the NLRP3 inflammasome-IL-1β pathway in type 2 diabetes and obesity.","authors":"Daniel T Meier, Joyce de Paula Souza, Marc Y Donath","doi":"10.1007/s00125-024-06306-1","DOIUrl":"https://doi.org/10.1007/s00125-024-06306-1","url":null,"abstract":"<p><p>Increased activity of the NACHT, LRR and PYD domains-containing protein 3 (NLRP3) inflammasome-IL-1β pathway is observed in obesity and contributes to the development of type 2 diabetes and its complications. In this review, we describe the pathological activation of IL-1β by metabolic stress, ageing and the microbiome and present data on the role of IL-1β in metabolism. We explore the physiological role of the IL-1β pathway in insulin secretion and the relationship between circulating levels of IL-1β and the development of diabetes and associated diseases. We highlight the paradoxical nature of IL-1β as both a friend and a foe in glucose regulation and provide details on clinical translation, including the glucose-lowering effects of IL-1 antagonism and its impact on disease modification. We also discuss the potential role of IL-1β in obesity, Alzheimer's disease, fatigue, gonadal dysfunction and related disorders such as rheumatoid arthritis and gout. Finally, we address the safety of NLRP3 inhibition and IL-1 antagonists and the prospect of using this therapeutic approach for the treatment of type 2 diabetes and its comorbidities.</p>","PeriodicalId":11164,"journal":{"name":"Diabetologia","volume":null,"pages":null},"PeriodicalIF":8.4,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142575164","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":"Comment on the role of interferons in the pathology of beta cell destruction in type 1 diabetes.","authors":"Sigurd Lenzen","doi":"10.1007/s00125-024-06264-8","DOIUrl":"10.1007/s00125-024-06264-8","url":null,"abstract":"","PeriodicalId":11164,"journal":{"name":"Diabetologia","volume":null,"pages":null},"PeriodicalIF":8.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11519303/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142145373","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":"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":null,"pages":null},"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":"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":"&lt;p&gt;&lt;strong&gt;Aims/hypothesis: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;There was significant genome-wide genetic correlation (r&lt;sub&gt;g&lt;/sub&gt;) between type 1 diabetes and CAD (r&lt;sub&gt;g&lt;/sub&gt;=0.088, p=8.60 × 10&lt;sup&gt;-3&lt;/sup&gt;) and both diseases shared significant genome-wide genetic determinants with eosinophil count (r&lt;sub&gt;g&lt;/sub&gt; for type 1 diabetes [r&lt;sub&gt;g(T1D)&lt;/sub&gt;]=0.093, p=7.20 × 10&lt;sup&gt;-3&lt;/sup&gt;, r&lt;sub&gt;g&lt;/sub&gt; for CAD [r&lt;sub&gt;g(CAD)&lt;/sub&gt;]=0.092, p=3.68 × 10&lt;sup&gt;-6&lt;/sup&gt;) and lymphocyte count (r&lt;sub&gt;g(T1D)&lt;/sub&gt;=-0.052, p=2.76 × 10&lt;sup&gt;-2&lt;/sup&gt;, r&lt;sub&gt;g(CAD)&lt;/sub&gt;=0.176, p=1.82 × 10&lt;sup&gt;-15&lt;/sup&gt;). 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&lt;sub&gt;T1D&lt;/sub&gt;]=0.67, p=2.02&lt;sup&gt;-19&lt;/sup&gt;, OR&lt;sub&gt;CAD&lt;/sub&gt;=1.09, p=2.67 × 10&lt;sup&gt;-6&lt;/sup&gt;; neutrophil OR&lt;sub&gt;T1D&lt;/sub&gt;=0.82, p=5.63 × 10&lt;sup&gt;-5&lt;/sup&gt;, OR&lt;sub&gt;CAD&lt;/sub&gt;=1.17, p=5.02 × 10&lt;sup&gt;-14&lt;/sup&gt;; and eosinophil OR&lt;sub&gt;T1D&lt;/sub&gt;=1.67, p=5.45 × 10&lt;sup&gt;-25&lt;/sup&gt;, OR&lt;sub&gt;CAD&lt;/sub&gt;=1.07, p=2.03 × 10&lt;sup&gt;-4&lt;/sup&gt;. The genetic causality proportion between type 1 diabetes and CAD was 0.36 ± 0.16 (p&lt;sub&gt;LCV&lt;/sub&gt;=1.30 × 10&lt;sup&gt;-2&lt;/sup&gt;), suggesting a possible intermediary causal variable.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions/interpretation: &lt;/strong&gt;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":null,"pages":null},"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":"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":"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":null,"pages":null},"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":"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":null,"pages":null},"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":"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":null,"pages":null},"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}