Abstract and Introduction
Abstract
Associations between low birth weight (≤2,500 g) and increased risk of mortality and morbidity provided the foundation for the "developmental origins of health and disease" hypothesis. Previous between-family studies could not control for unmeasured confounders. Therefore, we compared differentially exposed siblings to estimate the extent to which the associations were due to uncontrolled factors. Our population cohort included 3,291,773 persons born in Sweden from 1973 to 2008. Analyses controlled for gestational age, among other covariates, and considered birth weight as both an ordinal and a continuous variable. Outcomes included mortality after 1 year, cardiac-related death, hypertension, ischemic heart disease, pulmonary circulation problems, stroke, and type 2 diabetes mellitus. We fitted fixed-effects models to compare siblings and conducted sensitivity analyses to test alternative explanations. Across the population, the lower the birth weight, the greater the risk of mortality (e.g., cardiac-related death (low birth weight hazard ratio = 2.69, 95% confidence interval: 2.05, 3.53)) and morbidity (e.g., type 2 diabetes mellitus (low birth weight hazard ratio = 1.79, 95% confidence interval: 1.50, 2.14)) outcomes in comparison with normal birth weight. All associations were independent of shared familial confounders and measured covariates. Results emphasize the importance of birth weight as a risk factor for subsequent mortality and morbidity.
Introduction
Based on the seminal work by Barker, the "developmental origins of health and disease" hypothesis postulates that low birth weight (LBW; ≤2,500 g), a proxy for fetal growth, causally impacts several of the most costly and burdensome noncommunicable diseases. LBW is associated with offspring mortality and physical morbidity, including increased risk of cardiovascular disease hypertension type 2 diabetes mellitus, and stroke. The associations are present across populations, and research has identified plausible biological mechanisms that mediate the associations.
The commonly held assumption that LBW causally influences the risk of adverse adult health outcomes needs to be rigorously tested, however, since alternative explanations exist. The field continues to struggle with inferring causation from correlation, and conflicting results across noncommunicable disease outcomes have been reported. The previously identified associations may be due to unmeasured selection factors, such as environmental confounding and/or shared genetic liability, that influence both the likelihood of experiencing LBW and the disease outcomes. For example, LBW is associated with environmental risk factors that are themselves predictive of subsequent adverse outcomes Family, twin, and genome-wide linkage analyses have indicated that genetic factors influence birth weight and fetal growth as well as the studied outcomes. As such, researchers need to rule out plausible environmental and genetic confounders that may be responsible for the associations between LBW and mortality and morbidity. Therefore, making accurate estimates of associations and testing alternative hypotheses is essential.
Medical reviews have specifically called for quasi-experimental studies, approaches that utilize design features to test alternative explanations by increasing control over unmeasured confounding factors. Co-twin control designs, for instance, compare associations between risks and outcomes among monozygotic and dizygotic twins. The systematic genetic and environmental similarities between twins help investigators draw conclusions about the mechanisms responsible for the associations found. One co-twin control study suggested that associations between birth weight and cardiovascular disease and stroke may be a result of genetic confounding.
Although comparing discordant twins improves internal validity, there are concerns about the external validity of the findings. Birth weight differences in twins may be etiologically distinct from differences in singletons, and twins have a greater risk of growth restriction in utero than singletons. Sibling-comparison designs, however, test alternative explanations in a population that is more generalizable to the public than twin populations because they account for all genetic and environmental factors that make siblings similar. Few studies, though, have been performed using a sibling-comparison approach and such studies have major limitations. One study predicted blood pressure in a relatively small sample of children, while another studied associations in males only. A third study had excellent follow-up but used a relatively small sample and predicted a combination cardiovascular outcome that was measured via self-report. In addition, all previous sibling-comparison studies have been limited by their sole use of a categorical representation of birth weight or fetal growth. Thus, the field would benefit from research using large data sets and powerful analyses to assess the robustness of associations.
In the current investigation, we sought to rigorously examine the associations between birth weight and mortality and physical morbidity related to cardiovascular disease, stroke, and type 2 diabetes mellitus using one of the most comprehensive population-based cohorts assembled to date, a Swedish population cohort of over 3.6 million births. We aimed to provide more accurate estimates of the specific associations by using the sibling-comparison design. We also controlled for measured covariates that varied within families and used both ordinally and continuously measured birth weight.