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Review
. 2017 Apr;17(4):22.
doi: 10.1007/s11892-017-0852-9.

How Can Genetic Studies Help Us to Understand Links Between Birth Weight and Type 2 Diabetes?

Affiliations
Review

How Can Genetic Studies Help Us to Understand Links Between Birth Weight and Type 2 Diabetes?

Robin N Beaumont et al. Curr Diab Rep. 2017 Apr.

Abstract

Purpose of review: In observational epidemiology, both low and high birth weights are associated with later type 2 diabetes. The mechanisms underlying the associations are poorly understood. We review evidence for the roles of genetic and non-genetic factors linking both sides of the birth weight distribution to risk of type 2 diabetes, focusing on contributions made by the most recent genome-wide association studies (GWAS) of birth weight.

Recent findings: There are now nine genetic loci robustly implicated in both fetal growth and type 2 diabetes. At many of these, the same alleles are associated both with a higher risk of type 2 diabetes and a lower birth weight. This supports the Fetal Insulin Hypothesis and reflects a general pattern for type 2 diabetes susceptibility alleles: genome-wide, there is an inverse genetic correlation with birth weight, and initial estimates suggest genetic factors explain a large part of the covariance between the two traits. However, the associations at individual loci show heterogeneity; some fetal risk alleles are associated with higher birth weight. For most of these, the association reflects their correlation with the maternal risk allele which raises maternal glucose, thus increasing fetal insulin-mediated growth. GWAS have improved our understanding of the mechanisms underlying associations between type 2 diabetes and birth weight but questions remain about the relative importance of genetic versus non-genetic factors and of maternal versus fetal genotypes. To answer these questions, future work will require well-powered analyses of parents and offspring.

Keywords: Birth weight; Fetal; Genetics; Genome-wide association study; Maternal; Type 2 diabetes.

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Conflict of interest statement

Conflict of Interest

Dr. Beaumont and Dr. Freathy report grant funding from Wellcome Trust and Royal Society. Dr. Horikoshi has nothing to disclose.

Prof. McCarthy reports grants and personal fees from Novo Nordisk, Eli Lilly, and Pfizer, and grants from Takeda, Roche, Sanofi Aventis, Boehringer Ingelheim, Servier, Janssens, Abbvie, and Astra Zeneca.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Figures

Fig. 1
Fig. 1
Genetic and non-genetic factors underlying the observational association between birth weight (BW) and type 2 diabetes (T2D). The plot is a schematic representation of the U-shaped relationship resulting from meta-analysis of multiple observational studies [5]
Fig. 2
Fig. 2
Diagram showing direct and indirect effects of parental genotype on fetal growth. The mother’s genotype may influence birth weight directly, by being inherited by the fetus, or indirectly via a primary effect on the intrauterine environment (e.g., glucose availability). The father’s genotype cannot influence the intrauterine environment in this way, so is expected only to influence birth weight directly, once inherited
Fig. 3
Fig. 3
Associations of SNPs at nine loci identified in GWAS with birth weight and type 2 diabetes. a Type 2 diabetes (T2D) odds ratio [1, 21] (y-axis) plotted against birth weight (BW) effect size in SD units (x-axis) of SNPs at nine loci implicated in GWAS of both traits. Solid circles indicate fetal genotype associations for peak (i.e., most strongly associated) SNPs identified in fetal GWAS of birth weight [••], while solid triangles indicate the corresponding maternal genotype associations at the same SNPs [••]. Open circles indicate fetal genotype associations for peak SNPs identified in GWAS of T2D [, –26]. The open triangles indicate corresponding maternal genotype associations (available only for GCK [27] and MTNR1B [••, 27]) at the same (or perfectly-correlated) SNPs. b Pairwise linkage disequilibrium (LD) estimates (in European samples) between peak birth weight SNPs [••] and peak T2D SNPs [, –26] at the nine loci implicated in both traits. An r 2 close to 1 implies that the peak SNPs for both traits provides almost identical information. For example, at CDKAL1, MTNR1B, ADCY5, and ANK1, the birth weight and type 2 diabetes peak SNPs are very closely correlated, so the birth weight and T2D association signals can be assumed to be tagging the same causal variant (also likely the case for HHEX-IDE and PEPD, where r 2 ≈ 0.4). Conversely, there is a low correlation between the BW and T2D peak SNPs at INS-IGF2, GCK, and HMGA2), raising the possibility that they could be tagging different association signals within the same region of LD (indicated by D’ of 1). In particular, the very different birth weight and T2D associations of the two GCK SNPs support this—the birth weight peak SNP from the fetal GWAS at this locus (also encompassing YKT6) is of low minor allele frequency (European MAF <1%) and shows no evidence of association with T2D, whereas the T2D peak SNP is common (European MAF = 18%), with some evidence of association between the maternal risk allele and higher birth weight. The latter is consistent with a primary effect on maternal hyperglycemia

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