Dataset Information

Type 1 Diabetes Risk Phenotypes Using Cluster Analysis.

ABSTRACT:

Background

Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal clinically meaningful clusters in the at-risk population and allow for non-linear relationships between predictors are lacking. We aimed to identify and characterize clusters of islet autoantibody-positive individuals that share similar characteristics and type 1 diabetes risk.

Methods

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 (PTP) study data (n=1127). The outcome of the analysis was time to type 1 diabetes and variables in the model included demographics, genetics, metabolic factors and islet autoantibodies. An independent dataset (Diabetes Prevention Trial of Type 1 Diabetes, DPT-1 study) (n=704) was used for validation.

Findings

The analysis revealed 8 clusters with varying type 1 diabetes risks, categorized into three groups. Group A had three clusters with high glucose levels and high risk. Group B included four clusters with elevated autoantibody titers. Group C had three lower-risk clusters with lower autoantibody titers and glucose levels. Within the groups, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels, age, and genetic risk. A decision rule for assigning individuals to clusters was developed. The validation dataset confirms that the clusters can identify individuals with similar characteristics.

Interpretation

Demographic, metabolic, immunological, and genetic markers can 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.

SUBMITTER: You L

PROVIDER: S-EPMC10593014 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Type 1 Diabetes Risk Phenotypes Using Cluster Analysis.

You Lu L Ferrat Lauric A LA Oram Richard A RA Parikh Hemang M HM Steck Andrea K AK Krischer Jeffrey J Redondo Maria J MJ

medRxiv : the preprint server for health sciences 20231012

<h4>Background</h4>Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal clinically meaningful clusters in the at-risk population and allow for non-linear relationships between predictors are lacking. We aimed to identify and characterize clusters of islet autoantibody-positive individuals that share similar characteristics and type 1 diabetes risk.<h4>Methods</h4>We tested a novel outcome-guided clustering method in initially non-diabetic au ...[more]

PMID: 37873281

Similar Datasets

Project description:BackgroundGenome-wide association (GWA) studies identified a series of novel type 2 diabetes risk loci. Most of them were subsequently demonstrated to affect insulin secretion of pancreatic beta-cells. Very recently, a meta-analysis of GWA data revealed nine additional risk loci with still undefined roles in the pathogenesis of type 2 diabetes. Using our thoroughly phenotyped cohort of subjects at an increased risk for type 2 diabetes, we assessed the association of the nine latest genetic variants with the predominant prediabetes traits, i.e., obesity, impaired insulin secretion, and insulin resistance.Methodology/principal findingsOne thousand five hundred and seventy-eight metabolically characterized non-diabetic German subjects were genotyped for the reported candidate single nucleotide polymorphisms (SNPs) JAZF1 rs864745, CDC123/CAMK1D rs12779790, TSPAN8/LGR5 rs7961581, THADA rs7578597, ADAMTS9 rs4607103, NOTCH2 rs10923931, DCD rs1153188, VEGFA rs9472138, and BCL11A rs10490072. Insulin sensitivity was derived from fasting glucose and insulin concentrations, oral glucose tolerance test (OGTT), and hyperinsulinemic-euglycemic clamp. Insulin secretion was estimated from OGTT data. After appropriate adjustment for confounding variables and Bonferroni correction for multiple comparisons (corrected alpha-level: p = 0.0014), none of the SNPs was reliably associated with adiposity, insulin sensitivity, or insulin secretion (all p > or = 0.0117, dominant inheritance model). The risk alleles of ADAMTS9 SNP rs4607103 and VEGFA SNP rs9472138 tended to associate with more than one measure of insulin sensitivity and insulin secretion, respectively, but did not reach formal statistical significance. The study was sufficiently powered (1-beta = 0.8) to detect effect sizes of 0.19 < or = d < or = 0.25 (alpha = 0.0014) and 0.13 < or = d < or = 0.16 (alpha = 0.05).Conclusions/significanceIn contrast to the first series of GWA-derived type 2 diabetes candidate SNPs, we could not detect reliable associations of the novel risk loci with prediabetic phenotypes. Possible weak effects of ADAMTS9 SNP rs4607103 and VEGFA SNP rs9472138 on insulin sensitivity and insulin secretion, respectively, await further confirmation by larger studies.

Project description:BackgroundMultiple clinical phenotypes have been proposed for coronavirus disease (COVID-19), but few have used multimodal data. Using clinical and imaging data, we aimed to identify distinct clinical phenotypes in patients admitted with COVID-19 and to assess their clinical outcomes. Our secondary objective was to demonstrate the clinical applicability of this method by developing an interpretable model for phenotype assignment.MethodsWe analyzed data from 547 patients hospitalized with COVID-19 at a Canadian academic hospital. We processed the data by applying a factor analysis of mixed data (FAMD) and compared four clustering algorithms: k-means, partitioning around medoids (PAM), and divisive and agglomerative hierarchical clustering. We used imaging data and 34 clinical variables collected within the first 24 h of admission to train our algorithm. We conducted a survival analysis to compare the clinical outcomes across phenotypes. With the data split into training and validation sets (75/25 ratio), we developed a decision-tree-based model to facilitate the interpretation and assignment of the observed phenotypes.ResultsAgglomerative hierarchical clustering was the most robust algorithm. We identified three clinical phenotypes: 79 patients (14%) in Cluster 1, 275 patients (50%) in Cluster 2, and 203 (37%) in Cluster 3. Cluster 2 and Cluster 3 were both characterized by a low-risk respiratory and inflammatory profile but differed in terms of demographics. Compared with Cluster 3, Cluster 2 comprised older patients with more comorbidities. Cluster 1 represented the group with the most severe clinical presentation, as inferred by the highest rate of hypoxemia and the highest radiological burden. Intensive care unit (ICU) admission and mechanical ventilation risks were the highest in Cluster 1. Using only two to four decision rules, the classification and regression tree (CART) phenotype assignment model achieved an AUC of 84% (81.5-86.5%, 95 CI) on the validation set.ConclusionsWe conducted a multidimensional phenotypic analysis of adult inpatients with COVID-19 and identified three distinct phenotypes associated with different clinical outcomes. We also demonstrated the clinical usability of this approach, as phenotypes can be accurately assigned using a simple decision tree. Further research is still needed to properly incorporate these phenotypes in the management of patients with COVID-19.

Project description:ObjectiveTo investigate the possible subgroups of patients with Cluster Headache (CH) by using K-means cluster analysis.MethodsA total of 209 individuals (mean (SD) age: 39.8 (11.3) years), diagnosed with CH by headache experts, participated in this cross-sectional multi-center study. All patients completed a semi-structured survey either face to face, preferably, or through phone interviews with a physician. The survey was composed of questions that addressed sociodemographic characteristics as well as detailed clinical features and treatment experiences.ResultsCluster analysis revealed two subgroups. Cluster one patients (n = 81) had younger age at diagnosis (31.04 (9.68) vs. 35.05 (11.02) years; p = 0.009), a higher number of autonomic symptoms (3.28 (1.16) vs. 1.99(0.95); p < 0.001), and showed a better response to triptans (50.00% vs. 28.00; p < 0.001) during attacks, compared with the cluster two subgroup (n = 122). Cluster two patients had higher rates of current smoking (76.0 vs. 33.0%; p=0.002), higher rates of smoking at diagnosis (78.0 vs. 32.0%; p=0.006), higher rates of parental smoking/tobacco exposure during childhood (72.0 vs. 33.0%; p = 0.010), longer duration of attacks with (44.21 (34.44) min. vs. 34.51 (24.97) min; p=0.005) and without (97.50 (63.58) min. vs. (83.95 (49.07) min; p = 0.035) treatment and higher rates of emergency department visits in the last year (81.0 vs. 26.0%; p< 0.001).ConclusionsCluster one and cluster two patients had different phenotypic features, possibly indicating different underlying genetic mechanisms. The cluster 1 phenotype may suggest a genetic or biology-based etiology, whereas the cluster two phenotype may be related to epigenetic mechanisms. Toxic exposure to cigarettes, either personally or secondarily, seems to be an important factor in the cluster two subgroup, inducing drug resistance and longer attacks. We need more studies to elaborate the causal relationship and the missing links of neurobiological pathways of cigarette smoking regarding the identified distinct phenotypic classes of patients with CH.

Dataset Information

Type 1 Diabetes Risk Phenotypes Using Cluster Analysis.

Background

Methods

Findings

Interpretation

Publications

Type 1 Diabetes Risk Phenotypes Using Cluster Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets