Low Birthweight May Lead to Type 2 Diabetes

A genetic risk score (GRS) was calculated, based on five low-birthweight-related single nucleotide polymorphisms (SNPs). The researchers then assessed the evidence for causality, first by examining the association of the GRS and the individual SNPs with 2DM, and then by performing a Mendelian randomization analysis to estimate the potentially causal effect size of low birthweight on 2DM.

The results showed that for every one point increase in genetic risk score for low birthweight, there was an associated 6% higher risk of 2DM. For every one standard deviation of lower birthweight, the Mendelian randomization odds ratio was 2.94, providing support for the hypothesis that there is a causal relationship between a lower birthweight and an increased risk for developing 2DM. The association was stronger among female participants. The study was published on June 23, 2016, in Diabetologia.

“Type 2 diabetes was determined based on self-reports, and covariates such as smoking cigarettes, drinking alcohol, and doing physical activity were all assessed,” said lead author Lu Qi, MD, PhD, and colleagues of the School of Public Health and Tropical Medicine. “While previous studies have suggested a link between intrauterine malnutrition and type 2 diabetes, this study is unique in that it suggests a link that is specifically causal. Due to covariates including socioeconomic and lifestyle factors, it can be difficult to prove that causality does indeed exist.”

In epidemiology, Mendelian randomization is a method of using measured variation in genes of known function to examine the causal effect of a modifiable exposure on disease in non-experimental studies. It uses common genetic polymorphisms with well-understood effects on exposure patterns, or effects that mimic those produced by modifiable exposures. The genotype must only affect the disease status indirectly via its effect on the exposure of interest. Mendelian randomization was first described as a method for obtaining unbiased estimates of the effects of a putative causal variable, without conducting a traditional randomized trial.

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