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. 2025 Jul;68(7):1452-1462.
doi: 10.1007/s00125-025-06420-8. Epub 2025 Apr 10.

The genetics of low and high birthweight and their relationship with cardiometabolic disease

Gunn-Helen Moen  1   2   3   4 , Liang-Dar Hwang  5 , Caroline Brito Nunes  5 , Nicole M Warrington  5   6   7 , David M Evans  8   9   10
Affiliations

The genetics of low and high birthweight and their relationship with cardiometabolic disease

Gunn-Helen Moen et al. Diabetologia. 2025 Jul.

Abstract

Aims/hypothesis: Low birthweight infants are at increased risk not only of mortality, but also of type 2 diabetes mellitus and CVD in later life. At the opposite end of the spectrum, high birthweight infants have increased risk of birth complications, such as shoulder dystocia, neonatal hypoglycaemia and obesity, and similarly increased risk of type 2 diabetes mellitus and CVD. However, previous genome-wide association studies (GWAS) of birthweight in the UK Biobank have primarily focused on individuals within the 'normal' range and have excluded individuals with high and low birthweight (<2.5 kg or >4.5 kg). The aim of this study was to investigate genetic variation associated within the tail ends of the birthweight distribution, to: (1) see whether the genetic factors operating in these regions were different from those that explained variation in birthweight within the normal range; (2) explore the genetic correlation between extremes of birthweight and cardiometabolic disease; and (3) investigate whether analysing the full distribution of birthweight values, including the extremes, improved the ability to detect genuine loci in GWAS.

Methods: We performed case-control GWAS analysis of low (<2.5 kg) and high (>4.5 kg) birthweight in the UK Biobank using REGENIE software (Nlow=20,947; Nhigh=12,715; Ncontrols=207,506) and conducted three continuous GWAS of birthweight, one including the full range of birthweights, one involving a truncated GWAS including only individuals with birthweights between 2.5 and 4.5 kg and a third GWAS that winsorised birthweight values <2.5 kg and >4.5 kg. Additionally, we performed bivariate linkage disequilibrium (LD) score regression to estimate the genetic correlation between low/normal/high birthweight and cardiometabolic traits.

Results: Bivariate LD score regression analyses suggested that high birthweight had a mostly similar genetic aetiology to birthweight within the normal range (genetic correlation coefficient [rG]=0.91, 95% CI 0.83, 0.99), whereas there was more evidence for a separate set of genes underlying low birthweight (rG=-0.74, 95% CI 0.66, 0.82). Low birthweight was also significantly positively genetically correlated with most cardiometabolic traits and diseases we examined, whereas high birthweight was mostly positively genetically correlated with adiposity and anthropometric-related traits. The winsorisation strategy performed best in terms of locus detection, with the number of independent genome-wide significant associations (p<5×10-8) increasing from 120 genetic variants at 94 loci in the truncated GWAS to 270 genetic variants at 178 loci, including 27 variants at 25 loci that had not been identified in previous birthweight GWAS. This included a novel low-frequency missense variant in the ABCC8 gene, a gene known to be involved in congenital hyperinsulinism, neonatal diabetes mellitus and MODY, that was estimated to be responsible for a 170 g increase in birthweight amongst carriers.

Conclusions/interpretation: Our results underscore the importance of genetic factors in the genesis of the phenotypic correlation between birthweight and cardiometabolic traits and diseases.

Keywords: Birthweight; DOHaD; Developmental origins of health and disease; Diabetes; GWAS; Genome-wide association.

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Conflict of interest statement

Acknowledgements: This research has been conducted using the UK Biobank resource (Reference 53641). We thank L. Yengo (University of Queensland, Australia), R. Freathy (University of Exeter, UK) and K. Birkeland (University of Oslo, Norway) for interesting discussions. Some of the data were presented as an abstract at the EPEC meeting in 2023. Data availability: Human genotype and phenotype data from the UK Biobank on which the results of this study were based were accessed with accession ID 53641. The genotype and phenotype data are available upon application to the UK Biobank ( http://www.ukbiobank.ac.uk/ ). Requirements for data access to the UK Biobank are described at https://www.ukbiobank.ac.uk/ . Funding: Open Access funding enabled and organized by CAUL and its Member Institutions. DME and this work are funded by an Australian National Health and Medical Research Council Investigator Grant (APP2017942) and Ideas Grant (GNT1183074). G-HM is the recipient of an Australian Research Council Discovery Early Career Award (Project number: DE220101226) funded by the Australian Government and supported by the Research Council of Norway (Project Grant: 325640). L-DH is funded by an Australian Research Council Discovery Early Career Researcher Award (DE240100014). NMW is funded by an Australian National Health and Medical Research Council Investigator Grant (APP2008723). Authors’ relationships and activities: The authors declare that there are no relationships or activities that might bias, or be perceived to bias, their work. Contribution statement: G-HM made substantial contributions to the conception and design of the work, the acquisition, analysis and interpretation of data, as well as drafting the article. L-DH and NMW made substantial contributions to the interpretation of data and reviewing the article critically for important intellectual content. CBN made substantial contributions to the analysis of data and reviewing the article critically for important intellectual content. DME made substantial contributions to the conception and design of the work, the acquisition and interpretation of data, as well as drafting the article. All authors have approved the final version to be published. G-HM and DME are the guarantors of this work.

Figures

Fig. 1
Fig. 1
The −log10 p values of genome-wide significant SNPs from the deCODE GWAS of own birthweight in the full birthweight GWAS vs the truncated GWAS (a), the full birthweight GWAS vs the winsorised GWAS (b) and the truncated GWAS vs the winsorised GWAS (c). In (a), 117 SNPs had lower p values in the full birthweight GWAS and 79 had lower p values in the truncated GWAS. In (b), 180 SNPs had lower p values in the winsorised GWAS and 16 SNPs had lower p values in the full GWAS. In (c), 150 SNPs had lower p values in the winsorised GWAS and 46 SNPs had lower p values in the truncated GWAS
Fig. 2
Fig. 2
Genetic correlation (rG) between either high (triangles) or low (circles) birthweight and cardiometabolic-related phenotypes. The colour scale represents the strength of genetic correlation from −1 (dark blue) to 1 (dark red). A genetic correlation of exactly zero would be shown as white. BW, birthweight; T2D, type 2 diabetes
See this image and copyright information in PMC

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