Genes Behind Pregnancy Inflammation Show Only Weak Ties to Gestational Diabetes

C-reactive protein has long been one of the most trusted signals in clinical medicine, a molecule produced by the liver that rises rapidly whenever the body mounts an inflammatory response. In pregnancy, elevated levels of this protein have repeatedly been linked to gestational diabetes mellitus, a condition that affects a substantial share of pregnancies and carries consequences for both mother and child. Yet a question has lingered beneath those observational findings: does the genetic machinery that sets a person’s baseline CRP level actually shape the risk of developing gestational diabetes? A new genome-wide investigation drawing on one of the most richly characterized pregnancy cohorts ever assembled suggests the answer is, at most, only weakly.

The study, published in BMC Genomics, took advantage of the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be, known as nuMoM2b, a multi-center prospective cohort that collected biospecimens in early pregnancy, genome-wide genotype data, and detailed clinical outcomes across multiple ancestry groups. Led by researchers at Indiana University and collaborators across the United States, the team performed genome-wide association studies of first-trimester CRP levels using maternal genotypes from 4,326 participants and offspring genotypes from 2,140 children. The design allowed them to ask two distinct questions at once: which parts of the genome influence CRP levels during early pregnancy, and whether the inherited propensity toward higher or lower CRP translates into a measurable shift in gestational diabetes risk.

The first answer came cleanly. In the European-ancestry maternal sub-cohort, the researchers identified three genome-wide significant loci associated with early-pregnancy CRP levels: the CRP gene itself, LEPR, which encodes the leptin receptor, and HNF1A, a transcription factor with a well-documented role in regulating hepatic CRP production. These findings align closely with what large GWAS in non-pregnant populations have reported, indicating that the fundamental genetic architecture of CRP regulation persists into pregnancy. That consistency matters, because it suggests the pregnancy state does not wholesale rewrite the genetic control of this inflammatory marker, even though pregnancy itself dramatically reshapes inflammatory physiology.

When the analysis was widened to the full multi-ancestry maternal cohort, two additional loci emerged: ENSG00000257703 and APOC1. The appearance of ancestry-informative signals underscores a recurring theme in human genomics: cohorts that concentrate on a single ancestry group miss variants that are common or consequential elsewhere. APOC1, involved in lipid metabolism, sits in a genomic region long known to influence CRP, and its detection here hints at connections between inflammatory and metabolic pathways that pregnancy may bring into sharper focus. By contrast, the offspring GWAS of CRP levels produced no genome-wide significant associations at all, a null result the authors interpret in light of the smaller sample size and the distinct biology of fetal and neonatal CRP regulation.

The more provocative part of the study concerns gestational diabetes. Using linkage disequilibrium score regression, a technique that estimates the genetic correlation between traits from summary statistics alone, the team found no significant genetic correlation between CRP and gestational diabetes when using the nuMoM2b CRP GWAS as the input. That absence of correlation would seem to close the door on the idea that genetically influenced CRP levels meaningfully shape GDM risk. But the picture grew more complicated: when the researchers substituted an external, much larger population-based CRP GWAS, a significant genetic correlation with gestational diabetes did appear.

That discrepancy is not a contradiction so much as a lesson in how context shapes genomic inference. Genetic correlations estimated from summary statistics can vary depending on the population studied, the environment in which the phenotype was measured, and the specific covariates and ascertainment of each cohort. A CRP GWAS measured in early pregnancy among nulliparous women captures a different biological moment than one measured in the general adult population, where inflammation reflects age, adiposity, infection history, and chronic disease. The authors suggest that the genetic overlap between CRP and gestational diabetes may therefore vary across study contexts, a caution that extends well beyond this particular pair of traits.

To probe the relationship more directly, the team constructed polygenic risk scores for CRP, aggregating the small effects of thousands of variants into a single inherited score per individual, and tested whether those scores predicted gestational diabetes in the nuMoM2b cohort. They did not, regardless of whether the scores were derived from the pregnancy-specific GWAS or from the external population-based one. In other words, even where a statistical genetic correlation could be detected at the level of populations, the aggregate inherited influence on CRP was not a useful predictor of who would develop gestational diabetes within this cohort. The polygenic signal, the study concludes, is limited.

For clinicians and researchers, the result is a sobering check on a tempting hypothesis. Elevated CRP in early pregnancy is associated with later gestational diabetes in observational studies, and it has been natural to wonder whether inflammation is part of the causal chain. This work indicates that the genetic component of CRP variation, at least as it can currently be measured, contributes little to that association. If CRP and gestational diabetes are connected, the link is more likely mediated by environmental factors, adiposity, insulin resistance, or the hormonal shifts of pregnancy than by inherited differences in inflammatory set-point. Genetic risk prediction for gestational diabetes will need to look elsewhere, most plausibly toward the substantial polygenic architecture of glycemic traits themselves.

The study also carries a broader methodological message. Pregnancy-specific genomic resources remain scarce relative to their clinical importance, and this analysis demonstrates both their value and their limits. The nuMoM2b cohort, with thousands of well-phenotyped pregnancies and paired maternal-offspring genotypes, was large enough to replicate known CRP loci and to detect new ones in a multi-ancestry framework, yet the authors are explicit that larger pregnancy cohorts are needed to fully untangle the relationship between inflammation and metabolic complications of pregnancy. Gestational diabetes affects millions of pregnancies worldwide each year, and its long-term sequelae, including elevated lifetime risk of type 2 diabetes in mothers and altered metabolic programming in offspring, make it a priority target for precision approaches.

What emerges from this work is a carefully bounded conclusion rather than a dramatic one. The genetic architecture of CRP in early pregnancy mirrors what has been mapped in non-pregnant populations, with CRP, LEPR, and HNF1A as anchor points and APOC1 and one additional locus joining the map in a multi-ancestry analysis. Offspring CRP genetics yielded no significant signals. Genetic correlation with gestational diabetes appears only under some analytic conditions and not others, and polygenic risk scores for CRP fail to predict the disease. In an era when inflammatory biomarkers are frequently proposed as early warning signs of pregnancy complications, the study offers a useful corrective: a biomarker can travel with a disease without its genes being responsible for it, and disentangling the two requires cohorts, ancestries, and analytic frameworks designed specifically for the biology of pregnancy.

Subject of Research: Genome-wide association study of C-reactive protein levels in pregnancy and its polygenic relationship with gestational diabetes mellitus

Article Title: Maternal and offspring genome-wide association study of C-reactive protein reveals limited polygenic association with gestational diabetes mellitus

Article References: Zhang, Y., Moore, A., Ryckman, K. K., Yan, Q., Guerrero, R. F., Li, M., Silver, R. M., Luo, J., Yee, L. M., Reddy, U. M., Feghali, M. N., Chung, J., Haas, D. M., Kua, K. L., & Liu, N. (2026). Maternal and offspring genome-wide association study of C-reactive protein reveals limited polygenic association with gestational diabetes mellitus. BMC Genomics, 27(1), Article 782. https://doi.org/10.1186/s12864-026-12878-6

Image Credits: AI Generated

DOI: 10.1186/s12864-026-12878-6

Keywords: C-reactive protein, gestational diabetes, GWAS, polygenic risk score, nuMoM2b, pregnancy, inflammation, genetic correlation, LEPR, HNF1A, APOC1, maternal-fetal health