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Background: Analyzing longitudinal gene expression data is extremely challenging due to limited prior information, high dimensionality, and heterogeneity. Similar difficulties arise in research of multifactorial diseases such as Type 2 Diabetes. Clustering methods can be applied to automatically group similar observations. Common clinical values within the resulting groups suggest potential associations. However, applying traditional clustering methods to gene expression over time fails to capture variations in the response. Therefore, shape-based clustering could be applied to identify patient groups by gene expression variation in a large time metabolic compensatory intervention., Objectives: To search for clinical grouping patterns between subjects that showed similar structure in the variation of IL-1β gene expression over time., Methods: A new approach for shape-based clustering by IL-1β expression behavior was applied to a real longitudinal database of Type 2 Diabetes patients. In order to capture correctly variations in the response, we applied traditional clustering methods to slopes between measurements., Results: In this setting, the application of K-Medoids using the Manhattan distance yielded the best results for the corresponding database. Among the resulting groups, one of the clusters presented significant differences in many key clinical values regarding the metabolic syndrome in comparison to the rest of the data., Conclusions: The proposed method can be used to group patients according to variation patterns in gene expression (or other applications) and thus, provide clinical insights even when there is no previous knowledge on the subject clinical profile and few timepoints for each individual., (Copyright © 2023 International Federation of Clinical Chemistry and Laboratory Medicine (IFCC). All rights reserved.)

Background: Even before the onset of age-related diseases, obesity might be a contributing factor to the cumulative burden of oxidative stress and chronic inflammation throughout the life course. Obesity may therefore contribute to accelerated shortening of telomeres. Consequently, obese persons are more likely to have shorter telomeres, but the association between body mass index (BMI) and leukocyte telomere length (TL) might differ across the life span and between ethnicities and sexes., Objective: A collaborative cross-sectional meta-analysis of observational studies was conducted to investigate the associations between BMI and TL across the life span., Design: Eighty-seven distinct study samples were included in the meta-analysis capturing data from 146,114 individuals. Study-specific age- and sex-adjusted regression coefficients were combined by using a random-effects model in which absolute [base pairs (bp)] and relative telomere to single-copy gene ratio (T/S ratio) TLs were regressed against BMI. Stratified analysis was performed by 3 age categories ("young": 18-60 y; "middle": 61-75 y; and "old": >75 y), sex, and ethnicity., Results: Each unit increase in BMI corresponded to a -3.99 bp (95% CI: -5.17, -2.81 bp) difference in TL in the total pooled sample; among young adults, each unit increase in BMI corresponded to a -7.67 bp (95% CI: -10.03, -5.31 bp) difference. Each unit increase in BMI corresponded to a -1.58 × 10(-3) unit T/S ratio (0.16% decrease; 95% CI: -2.14 × 10(-3), -1.01 × 10(-3)) difference in age- and sex-adjusted relative TL in the total pooled sample; among young adults, each unit increase in BMI corresponded to a -2.58 × 10(-3) unit T/S ratio (0.26% decrease; 95% CI: -3.92 × 10(-3), -1.25 × 10(-3)). The associations were predominantly for the white pooled population. No sex differences were observed., Conclusions: A higher BMI is associated with shorter telomeres, especially in younger individuals. The presently observed difference is not negligible. Meta-analyses of longitudinal studies evaluating change in body weight alongside change in TL are warranted.