Genetic associations vary across the spectrum of fasting serum insulin: results from the European IDEFICS/I.Family children’s cohort
Research output: Contribution to journal › Journal article › Research › peer-review
Standard
Genetic associations vary across the spectrum of fasting serum insulin : results from the European IDEFICS/I.Family children’s cohort. / Mehlig, Kirsten; Foraita, Ronja; Nagrani, Rajini; Wright, Marvin N.; De Henauw, Stefaan; Molnár, Dénes; Moreno, Luis A.; Russo, Paola; Tornaritis, Michael; Veidebaum, Toomas; Lissner, Lauren; Kaprio, Jaakko; Pigeot, Iris; on behalf of the I.Family consortium.
In: Diabetologia, Vol. 66, 2023, p. 1914–1924.Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
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 -
ID: 361309987