Lenzini, P. | Search bioRxiv

bioRxiv2024

A Novel Gene ARHGAP44 for Longitudinal Changes in Glycated Hemoglobin (HbA1c) in Subjects without Type 2 Diabetes: Evidence from the Long Life Family Study (LLFS) and the Framingham Offspring Study (FOS)

Glycated hemoglobin (HbA1c) indicates average glucose levels over three months and is associated with insulin resistance and type 2 diabetes (T2D). Longitudinal changes in HbA1c ({Delta}HbA1c) are also associated with aging processes, cognitive performance, and mortality. We analyzed {Delta}HbA1c in 1,886 non-diabetic Europeans from the Long Life Family Study to uncover gene variants influencing {Delta}HbA1c. Using growth curve modeling adjusted for multiple covariates, we derived {Delta}HbA1c and conducted linkage-guided sequence analysis. Our genome-wide linkage scan identified a significant locus on 17p12. In-depth analysis of this locus revealed a variant rs56340929 (explaining 27% of the linkage peak) in the ARHGAP44 gene that was significantly associated with {Delta}HbA1c. RNA transcription of ARHGAP44 was associated with {Delta}HbA1c. The Framingham Offspring Study data further supported these findings on the gene level. Together, we found a novel gene ARHGAP44 for {Delta}HbA1c in family members without T2D. Follow-up studies using longitudinal omics data in large independent cohorts are warranted.

genetics↗

bioRxiv2024

Opaque Ontology: Neuroimaging Classification of ICD-10 Diagnostic Groups in the UK Biobank

BackgroundThe use of machine learning to classify diagnostic cases versus controls defined based on diagnostic ontologies such as the ICD-10 from neuroimaging features is now commonplace across a wide range of diagnostic fields. However, transdiagnostic comparisons of such classifications are lacking. Such transdiagnostic comparisons are important to establish the specificity of classification models, set benchmarks, and assess the value of diagnostic ontologies. ResultsWe investigated case-control classification accuracy in 17 different ICD-10 diagnostic groups from Chapter V (mental and behavioral disorders) and Chapter VI (diseases of the nervous system) using data from the UK Biobank. Classification models were trained using either neuroimaging (structural or functional brain MRI feature sets) or socio-demographic features. Random forest classification models were adopted using rigorous shuffle splits to estimate stability as well as accuracy of case-control classifications. Diagnostic classification accuracies were benchmarked against age classification (oldest versus youngest) from the same feature sets and against additional classifier types (K-nearest neighbors and linear support vector machine). In contrast to age classification accuracy, which was high for all feature sets, few ICD-10 diagnostic groups were classified significantly above chance (namely, demyelinating diseases based on structural neuroimaging features, and depression based on socio-demographic and functional neuroimaging features). ConclusionThese findings highlight challenges with the current disease classification system, leading us to recommend caution with the use of ICD-10 diagnostic groups as target labels in brain-based disease prediction studies.

neuroscience↗