Identification of biomarkers for glycaemic deterioration in type 2 diabetes.
Ontology highlight
ABSTRACT:
SUBMITTER: Slieker RC
PROVIDER: S-EPMC10156700 | biostudies-literature | 2023 May
REPOSITORIES: biostudies-literature
Publications
Identification of biomarkers for glycaemic deterioration in type 2 diabetes.
Slieker Roderick C RC Donnelly Louise A LA Akalestou Elina E Lopez-Noriega Livia L Melhem Rana R Güneş Ayşim A Abou Azar Frederic F Efanov Alexander A Georgiadou Eleni E Muniangi-Muhitu Hermine H Sheikh Mahsa M Giordano Giuseppe N GN Åkerlund Mikael M Ahlqvist Emma E Ali Ashfaq A Banasik Karina K Brunak Søren S Barovic Marko M Bouland Gerard A GA Burdet Frédéric F Canouil Mickaël M Dragan Iulian I Elders Petra J M PJM Fernandez Celine C Festa Andreas A Fitipaldi Hugo H Froguel Phillippe P Gudmundsdottir Valborg V Gudnason Vilmundur V Gerl Mathias J MJ van der Heijden Amber A AA Jennings Lori L LL Hansen Michael K MK Kim Min M Leclerc Isabelle I Klose Christian C Kuznetsov Dmitry D Mansour Aly Dina D Mehl Florence F Marek Diana D Melander Olle O Niknejad Anne A Ottosson Filip F Pavo Imre I Duffin Kevin K Syed Samreen K SK Shaw Janice L JL Cabrera Over O Pullen Timothy J TJ Simons Kai K Solimena Michele M Suvitaival Tommi T Wretlind Asger A Rossing Peter P Lyssenko Valeriya V Legido Quigley Cristina C Groop Leif L Thorens Bernard B Franks Paul W PW Lim Gareth E GE Estall Jennifer J Ibberson Mark M Beulens Joline W J JWJ 't Hart Leen M LM Pearson Ewan R ER Rutter Guy A GA
Nature communications 20230503 1
We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster pr ...[more]
Similar Datasets
Project description:Aims/hypothesisThere is considerable variability in how diabetes progresses after diagnosis. Progression modelling has largely focused on 'time to failure' methods, yet determining a 'coefficient of failure' has many advantages. We derived a rate of glycaemic deterioration in type 2 diabetes, using a large real-world cohort, and aimed to investigate the clinical, biochemical, pharmacological and immunological variables associated with fast and slow rates of glycaemic deterioration.MethodsAn observational cohort study was performed using the electronic medical records from participants in the Genetics of Diabetes Audit and Research in Tayside Study (GoDARTS). A model was derived based on an individual's observed HbA1c measures from the first eligible HbA1c after the diagnosis of diabetes through to the study end (defined as insulin initiation, death, leaving the area or end of follow-up). Each HbA1c measure was time-dependently adjusted for the effects of non-insulin glucose-lowering drugs, changes in BMI and corticosteroid use. GAD antibody (GADA) positivity was defined as GAD titres above the 97.5th centile of the population distribution.ResultsThe mean (95% CI) glycaemic deterioration for type 2 diabetes and GADA-positive individuals was 1.4 (1.3, 1.4) and 2.8 (2.4, 3.3) mmol/mol HbA1c per year, respectively. A younger age of diagnosis, lower HDL-cholesterol concentration, higher BMI and earlier calendar year of diabetes diagnosis were independently associated with higher rates of glycaemic deterioration in individuals with type 2 diabetes. The rate of deterioration in those diagnosed at over 70 years of age was very low, with 66% having a rate of deterioration of less than 1.1 mmol/mol HbA1c per year, and only 1.5% progressing more rapidly than 4.4 mmol/mol HbA1c per year.Conclusions/interpretationWe have developed a novel approach for modelling the progression of diabetes in observational data across multiple drug combinations. This approach highlights how glycaemic deterioration in those diagnosed at over 70 years of age is minimal, supporting a stratified approach to diabetes management.
| S-EPMC6448965 | biostudies-literature
Project description:IntroductionType 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised "bottom-up" approach, we attempt to group T2D patients based solely on -omics data generated from plasma.MethodsCirculating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics.ResultsFrom a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor.ConclusionsUsing an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.
| S-EPMC10951062 | biostudies-literature
Project description:Aims/hypothesisHere, we describe the characteristics of the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) epidemiological cohorts at baseline and follow-up examinations (18, 36 and 48 months of follow-up).MethodsFrom a sampling frame of 24,682 adults of European ancestry enrolled in population-based cohorts across Europe, participants at varying risk of glycaemic deterioration were identified using a risk prediction algorithm (based on age, BMI, waist circumference, use of antihypertensive medication, smoking status and parental history of type 2 diabetes) and enrolled into a prospective cohort study (n = 2127) (cohort 1, prediabetes risk). We also recruited people from clinical registries with type 2 diabetes diagnosed 6-24 months previously (n = 789) into a second cohort study (cohort 2, diabetes). Follow-up examinations took place at ~18 months (both cohorts) and at ~48 months (cohort 1) or ~36 months (cohort 2) after baseline examinations. The cohorts were studied in parallel using matched protocols across seven clinical centres in northern Europe.ResultsUsing ADA 2011 glycaemic categories, 33% (n = 693) of cohort 1 (prediabetes risk) had normal glucose regulation and 67% (n = 1419) had impaired glucose regulation. Seventy-six per cent of participants in cohort 1 was male. Cohort 1 participants had the following characteristics (mean ± SD) at baseline: age 62 (6.2) years; BMI 27.9 (4.0) kg/m2; fasting glucose 5.7 (0.6) mmol/l; 2 h glucose 5.9 (1.6) mmol/l. At the final follow-up examination the participants' clinical characteristics were as follows: fasting glucose 6.0 (0.6) mmol/l; 2 h OGTT glucose 6.5 (2.0) mmol/l. In cohort 2 (diabetes), 66% (n = 517) were treated by lifestyle modification and 34% (n = 272) were treated with metformin plus lifestyle modification at enrolment. Fifty-eight per cent of participants in cohort 2 was male. Cohort 2 participants had the following characteristics at baseline: age 62 (8.1) years; BMI 30.5 (5.0) kg/m2; fasting glucose 7.2 (1.4) mmol/l; 2 h glucose 8.6 (2.8) mmol/l. At the final follow-up examination, the participants' clinical characteristics were as follows: fasting glucose 7.9 (2.0) mmol/l; 2 h mixed-meal tolerance test glucose 9.9 (3.4) mmol/l.Conclusions/interpretationThe IMI DIRECT cohorts are intensely characterised, with a wide-variety of metabolically relevant measures assessed prospectively. We anticipate that the cohorts, made available through managed access, will provide a powerful resource for biomarker discovery, multivariate aetiological analyses and reclassification of patients for the prevention and treatment of type 2 diabetes.
| S-EPMC6677872 | biostudies-literature
Project description:The DIRECT (Diabetes Research on Patient Stratification) Study is part of a European Union Framework 7 Innovative Medicines Initiative project, a joint undertaking between four industry and 21 academic partners throughout Europe. The Consortium aims to discover and validate biomarkers that: (1) predict the rate of glycaemic deterioration before and after type 2 diabetes onset; (2) predict the response to diabetes therapies; and (3) help stratify type 2 diabetes into clearly definable disease subclasses that can be treated more effectively than without stratification. This paper describes two new prospective cohort studies conducted as part of DIRECT.Prediabetic participants (target sample size 2,200-2,700) and patients with newly diagnosed type 2 diabetes (target sample size ~1,000) are undergoing detailed metabolic phenotyping at baseline and 18 months and 36 months later. Abdominal, pancreatic and liver fat is assessed using MRI. Insulin secretion and action are assessed using frequently sampled OGTTs in non-diabetic participants, and frequently sampled mixed-meal tolerance tests in patients with type 2 diabetes. Biosamples include venous blood, faeces, urine and nail clippings, which, among other biochemical analyses, will be characterised at genetic, transcriptomic, metabolomic, proteomic and metagenomic levels. Lifestyle is assessed using high-resolution triaxial accelerometry, 24 h diet record, and food habit questionnaires.DIRECT will yield an unprecedented array of biomaterials and data. This resource, available through managed access to scientists within and outside the Consortium, will facilitate the development of new treatments and therapeutic strategies for the prevention and management of type 2 diabetes.
| S-EPMC4018481 | biostudies-literature
Project description:Aims/hypothesisOur study aims to uncover glycaemic phenotype heterogeneity in type 1 diabetes.MethodsIn the Study of the French-speaking Society of Type 1 Diabetes (SFDT1), we characterised glycaemic heterogeneity thanks to a set of complementary metrics: HbA1c, time in range (TIR), time below range (TBR), CV, Gold score and glycaemia risk index (GRI). Applying the Discriminative Dimensionality Reduction with Trees (DDRTree) algorithm, we created a phenotypic tree, i.e. a 2D visual mapping. We also carried out a clustering analysis for comparison.ResultsWe included 618 participants with type 1 diabetes (52.9% men, mean age 40.6 years [SD 14.1]). Our phenotypic tree identified seven glycaemic phenotypes. The 2D phenotypic tree comprised a main branch in the proximal region and glycaemic phenotypes in the distal areas. Dimension 1, the horizontal dimension, was positively associated with GRI (coefficient [95% CI]) (0.54 [0.52, 0.57]), HbA1c (0.39 [0.35, 0.42]), CV (0.24 [0.19, 0.28]) and TBR (0.11 [0.06, 0.15]), and negatively with TIR (-0.52 [-0.54, -0.49]). The vertical dimension was positively associated with TBR (0.41 [0.38, 0.44]), CV (0.40 [0.37, 0.43]), TIR (0.16 [0.12, 0.20]), Gold score (0.10 [0.06, 0.15]) and GRI (0.06 [0.02, 0.11]), and negatively with HbA1c (-0.21 [-0.25, -0.17]). Notably, socioeconomic factors, cardiovascular risk indicators, retinopathy and treatment strategy were significant determinants of glycaemic phenotype diversity. The phenotypic tree enabled more granularity than traditional clustering in revealing clinically relevant subgroups of people with type 1 diabetes.Conclusions/interpretationOur study advances the current understanding of the complex glycaemic profile in people with type 1 diabetes and suggests that strategies based on isolated glycaemic metrics might not capture the complexity of the glycaemic phenotypes in real life. Relying on these phenotypes could improve patient stratification in type 1 diabetes care and personalise disease management.
| S-EPMC11343912 | biostudies-literature
Project description:Type 2 diabetes (T2D) is a systematic chronic metabolic condition with abnormal sugar metabolism dysfunction, and its complications are the most harmful to human beings and may be life-threatening after long-term durations. Considering the high incidence and severity at late stage, researchers have been focusing on the identification of specific biomarkers and potential drug targets for T2D at the genomic, epigenomic, and transcriptomic levels. Microbes participate in the pathogenesis of multiple metabolic diseases including diabetes. However, the related studies are still non-systematic and lack the functional exploration on identified microbes. To fill this gap between gut microbiome and diabetes study, we first introduced eggNOG database and KEGG ORTHOLOGY (KO) database for orthologous (protein/gene) annotation of microbiota. Two datasets with these annotations were employed, which were analyzed by multiple machine-learning models for identifying significant microbiota biomarkers of T2D. The powerful feature selection method, Max-Relevance and Min-Redundancy (mRMR), was first applied to the datasets, resulting in a feature list for each dataset. Then, the list was fed into the incremental feature selection (IFS), incorporating support vector machine (SVM) as the classification algorithm, to extract essential annotations and build efficient classifiers. This study not only revealed potential pathological factors for diabetes at the microbiome level but also provided us new candidates for drug development against diabetes.
| S-EPMC8299781 | biostudies-literature
Project description:AimsWe aimed to identify long-term HbA1c trajectories and examine associated characteristics in an observational, childhood-onset (<17 years) type 1 diabetes cohort.MethodsData are from the Epidemiology of Diabetes Complications study, comprising 405 participants with ≥2 of seven possible HbA1c measurements over follow-up (1988-2013) and available DNA (baseline mean diabetes duration 21 years, 53% men). HbA1c trajectories were estimated using latent class growth models. Baseline and change in participant characteristics were compared across trajectories.ResultsFive HbA1c trajectories were identified: low (51%), intermediate stable (22%), improved (19%), high stable (6%), and worsened (2%; not included in analyses). Age, diabetes duration, diabetes onset age, and sex did not differ across trajectories. Characteristics did not differ significantly between intermediate stable and low trajectories at baseline, though albumin excretion rate (AER, p = 0.0002) and estimated glomerular filtration rate (eGFR, p = 0.001) worsened slightly more in intermediate stable over time. Improved and high stable trajectories had higher baseline LDL-c (p = 0.002 and 0.003, respectively). Improved trajectory increased median self-monitoring of blood glucose from <1 to 3.5 times/day (p < 0.0001) and had larger LDL-c improvement (p = 0.004) but greater worsening of AER (p < 0.0001) and eGFR (p < 0.0001) than low. The A allele of rs12970134 (near MC4R) was associated with improved (p = 0.0003) or high stable (p = 0.001) HbA1c trajectory, both patterns with high baseline HbA1c.ConclusionsLong-term HbA1c trajectories were primarily associated with modifiable factors in this type 1 diabetes cohort. The intermediate stable pattern had a risk factor profile that suggests some protection against adverse metabolic effects of chronic hyperglycaemia, warranting further study.
| S-EPMC8295176 | biostudies-literature
Project description:BackgroundType 1 diabetes mellitus (T1DM) is a metabolic disease in which the autoimmune destruction of pancreatic islet β-cells occurs. This study sought to investigate the role of autophagy-related genes and immune cells in the development of T1DM.MethodsWe acquired the raw gene expression profiles of 302 T1DM and 422 normal control peripheral blood samples from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using the Limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. The Search Tool for the Retrieval of Interacting Genes/Proteins (https://string-db.org/) and Cytoscape autophagy genes were intersected with the DEGs for the immune cell analysis and the correlation analysis.ResultsA total of 568 DEGs were identified in the T1DM and normal samples, of which 301 were upregulated and 267 were downregulated. The results of the functional and pathway enrichment analyses showed that the DEGs were closely associated with autophagy and immunity. Member RAS oncogene family (RAB11A), protein tyrosine phosphatase non-receptor type 11, lamin A/C, heat shock protein70, heat shock protein family A member 4, cluster of differentiation 8A, caspase 3 (CASP3), exportin 1, proto-oncogene, non-receptor tyrosine kinase, SMAD family member 4, and sirtuin 1 (SIRT1) were located at the center of the protein-protein interaction network as the core genes. The peripheral blood T cells were more elevated in the T1DM subjects than the normal subjects. RAB11A, CASP3, and SIRT1 are autophagy-associated genes. RAB11A and CASP3 were positively correlated with most immune cells, while SIRT1 was negatively correlated with most immune cells.ConclusionsAutophagy-related genes (i.e., RAB11A, CASP3, and SIRT1) and immune cells (i.e., T and B cells) may play important regulatory roles in the development of T1DM. Our findings provide novel insights into and potential targets for T1DM prediction and treatment.
| S-EPMC9263783 | biostudies-literature
Project description:AimsThere are several epidemiological studies on the association between statins and incident diabetes, but most of them lack details. In this study, we aimed to investigate the association of statin use with glycaemic traits and incident type 2 diabetes.MethodsUsing the prospective population-based Rotterdam Study, we included 9535 individuals free from diabetes at baseline (>45 years) during the study period between 1997 and 2012. Linear regression analysis was applied to examine the cross-sectional associations between statin use and glycaemic traits including fasting blood serum of glucose and insulin concentrations, and insulin resistance. In a longitudinal follow-up study, we applied a Cox regression analysis to determine adjusted hazard ratios (HR) for incident type 2 diabetes in new users of statins.ResultsThe mean age at baseline was 64.3 ± 10.1 years and 41.7% were men. In the fully adjusted model, compared to never users of statins, baseline use of statins was associated with higher concentrations of serum fasting insulin (β = 0.07; 95% CI: 0.02-0.13) and insulin resistance (β = 0.09; 95% CI: 0.03-0.14). Ever use of statins was associated with a 38% higher risk of incident type 2 diabetes (HR = 1.38; 95% CI: 1.09-1.74). This risk was more prominent in subjects with impaired glucose homeostasis and in overweight/obese individuals.ConclusionsIndividuals using statins may be at higher risk for hyperglycaemia, insulin resistance and eventually type 2 diabetes. Rigorous preventive strategies such as glucose control and weight reduction in patients when initiating statin therapy might help minimize the risk of diabetes.
| S-EPMC6475692 | biostudies-literature
Project description:ObjectiveTo assess the association between dietary patterns and glycaemic control among Qatari adults with type 2 diabetes (T2DM).DesignCross-sectional analysis using data from the Qatar Biobank Study. Poor glycaemic control was defined as HbA1c ≥7·0 %. Dietary patterns were constructed using factor analysis based on habitual food intake assessed by a FFQ. Medication use was based on self-report. Multivariable logistic regression was used to assess the association.SettingQatar.ParticipantsAdults aged ≥18 years (n 1000) with known diabetes.ResultThe mean age of the participants was 52·3 (sd 11·5) years. Overall, the prevalence of poor glycaemic control was 57·6 %, and 27·7 % of the participants were insulin users. Three dietary patterns were identified. The modern dietary pattern (high intake of fast food, croissants, white bread and cheese) was inversely associated with poor glycaemic control. The sd increments of the modern pattern had OR for poor glycaemic control of 0·86 (95 % CI 0·68, 1·08) in men and 0·76 (95 % CI 0·61, 0·95) in women. There was a significant interaction between the modern pattern and diabetes medication in men but not in women. In men without diabetes medication, the modern pattern was positively associated with poor glycaemic control with an OR of 2·35 (95 % CI 1·13, 4·87).ConclusionsMale diabetes patients took medication to control diabetes but ate more unhealthy food. In men who were not taking diabetes medication, modern dietary pattern was associated with poor glycaemic control. Promoting healthy eating should be encouraged especially among those under diabetes medication.
| S-EPMC10195290 | biostudies-literature