TY - JOUR

T1 - Genetic associations vary across the spectrum of fasting serum insulin

T2 - results from the European IDEFICS/I.Family children’s cohort

AU - Mehlig, Kirsten

AU - Foraita, Ronja

AU - Nagrani, Rajini

AU - Wright, Marvin N.

AU - De Henauw, Stefaan

AU - Molnár, Dénes

AU - Moreno, Luis A.

AU - Russo, Paola

AU - Tornaritis, Michael

AU - Veidebaum, Toomas

AU - Lissner, Lauren

AU - Kaprio, Jaakko

AU - Pigeot, Iris

AU - on behalf of the I.Family consortium

N1 - Publisher Copyright: © 2023, The Author(s).

PY - 2023

Y1 - 2023

N2 - Aims/hypothesis: There is increasing evidence for the existence of shared genetic predictors of metabolic traits and neurodegenerative disease. We previously observed a U-shaped association between fasting insulin in middle-aged women and dementia up to 34 years later. In the present study, we performed genome-wide association (GWA) analyses for fasting serum insulin in European children with a focus on variants associated with the tails of the insulin distribution. Methods: Genotyping was successful in 2825 children aged 2–14 years at the time of insulin measurement. Because insulin levels vary during childhood, GWA analyses were based on age- and sex-specific z scores. Five percentile ranks of z-insulin were selected and modelled using logistic regression, i.e. the 15th, 25th, 50th, 75th and 85th percentile ranks (P15–P85). Additive genetic models were adjusted for age, sex, BMI, survey year, survey country and principal components derived from genetic data to account for ethnic heterogeneity. Quantile regression was used to determine whether associations with variants identified by GWA analyses differed across quantiles of log-insulin. Results: A variant in the SLC28A1 gene (rs2122859) was associated with the 85th percentile rank of the insulin z score (P85, p value=3×10−8). Two variants associated with low z-insulin (P15, p value <5×10−6) were located on the RBFOX1 and SH3RF3 genes. These genes have previously been associated with both metabolic traits and dementia phenotypes. While variants associated with P50 showed stable associations across the insulin spectrum, we found that associations with variants identified through GWA analyses of P15 and P85 varied across quantiles of log-insulin. Conclusions/interpretation: The above results support the notion of a shared genetic architecture for dementia and metabolic traits. Our approach identified genetic variants that were associated with the tails of the insulin spectrum only. Because traditional heritability estimates assume that genetic effects are constant throughout the phenotype distribution, the new findings may have implications for understanding the discrepancy in heritability estimates from GWA and family studies and for the study of U-shaped biomarker–disease associations. Graphical Abstract: [Figure not available: see fulltext.].

AB - Aims/hypothesis: There is increasing evidence for the existence of shared genetic predictors of metabolic traits and neurodegenerative disease. We previously observed a U-shaped association between fasting insulin in middle-aged women and dementia up to 34 years later. In the present study, we performed genome-wide association (GWA) analyses for fasting serum insulin in European children with a focus on variants associated with the tails of the insulin distribution. Methods: Genotyping was successful in 2825 children aged 2–14 years at the time of insulin measurement. Because insulin levels vary during childhood, GWA analyses were based on age- and sex-specific z scores. Five percentile ranks of z-insulin were selected and modelled using logistic regression, i.e. the 15th, 25th, 50th, 75th and 85th percentile ranks (P15–P85). Additive genetic models were adjusted for age, sex, BMI, survey year, survey country and principal components derived from genetic data to account for ethnic heterogeneity. Quantile regression was used to determine whether associations with variants identified by GWA analyses differed across quantiles of log-insulin. Results: A variant in the SLC28A1 gene (rs2122859) was associated with the 85th percentile rank of the insulin z score (P85, p value=3×10−8). Two variants associated with low z-insulin (P15, p value <5×10−6) were located on the RBFOX1 and SH3RF3 genes. These genes have previously been associated with both metabolic traits and dementia phenotypes. While variants associated with P50 showed stable associations across the insulin spectrum, we found that associations with variants identified through GWA analyses of P15 and P85 varied across quantiles of log-insulin. Conclusions/interpretation: The above results support the notion of a shared genetic architecture for dementia and metabolic traits. Our approach identified genetic variants that were associated with the tails of the insulin spectrum only. Because traditional heritability estimates assume that genetic effects are constant throughout the phenotype distribution, the new findings may have implications for understanding the discrepancy in heritability estimates from GWA and family studies and for the study of U-shaped biomarker–disease associations. Graphical Abstract: [Figure not available: see fulltext.].

KW - Biomarkers

KW - BMI

KW - Dementia

KW - Genetics

KW - Genome-wide association analysis

KW - Insulin

KW - Metabolic traits

KW - Obesity

KW - Quantile regression

KW - SNP

KW - Type 2 diabetes

U2 - 10.1007/s00125-023-05957-w

DO - 10.1007/s00125-023-05957-w

M3 - Journal article

C2 - 37420130

AN - SCOPUS:85164193974

VL - 66

SP - 1914

EP - 1924

JO - Diabetologia

JF - Diabetologia

SN - 0012-186X

ER -