The research team, led by Tamiris Invencioni Moraes Stefani, Thiago Dominguez Crespo Hirata and Alvaro Cerda, set out to test whether variants in ten candidate genes—FTO, LEP, LEPR, ADRB3, ADIPOQ, TCF7L2, ENPP1, APOA5, PPARG and CYP11B2—were associated with metabolic syndrome and obesity-related traits. These genes were chosen because genome-wide association studies and candidate gene analyses had previously implicated them in obesity, diabetes, dyslipidemia and blood pressure regulation. The participants, aged 18 to 83, were randomly selected from a Brazilian observational cohort, with 179 diagnosed with metabolic syndrome under International Diabetes Federation criteria and 241 serving as controls. Individuals with kidney, liver, thyroid or adrenal disease, gonadal dysfunction, secondary obesity or pregnancy were excluded.

The methodological rigor of the study deserves attention. Each participant underwent a detailed clinical and anthropometric evaluation, including body mass index, waist circumference, waist-to-hip ratio, body fat percentage and basal metabolic rate. Fasting blood samples yielded an extensive laboratory panel: glucose, glycated hemoglobin, insulin, a full lipid profile, apolipoproteins, adipokines such as leptin and adiponectin, and a battery of inflammatory markers including high-sensitivity C-reactive protein, interleukin-6, interleukin-1 beta, tumor necrosis factor alpha and plasminogen activator inhibitor 1. Insulin resistance was quantified both by the HOMA-IR index and by the triglyceride-glucose index. Genomic DNA was extracted from leukocytes and variants were genotyped using TaqMan assays, with control samples in every run and twenty percent of samples re-analyzed with complete concordance.

When the researchers applied multivariate logistic regression—adjusting for age, ethnicity, sex, alcohol consumption, tobacco smoking, physical activity and family history of cardiovascular disease—two variants stood out as independent predictors of metabolic syndrome. The G allele of ADRB3 rs4994, a missense change known as Trp64Arg, more than tripled the odds of the syndrome, with an odds ratio of 3.71. The G allele of LEPR rs1137100, the Lys109Arg variant in the leptin receptor, nearly doubled the risk, with an odds ratio of 2.52. Both variants were also associated with hypertension, and the ADRB3 risk allele additionally predicted dyslipidemia, insulin resistance and obesity.

The biological logic behind these associations is compelling. The ADRB3 gene encodes the beta-3 adrenergic receptor, which sits on the surface of adipose tissue cells and drives lipolysis, thermogenesis and energy expenditure. The Trp64Arg substitution lies in the intracellular tail of the receptor and is thought to impair its signaling, effectively dampening the body’s ability to burn fat. Carriers of the risk allele in this study showed higher body mass index, larger waist circumference in women, greater body fat, elevated glucose, insulin, triglycerides and HOMA-IR, and a strikingly pro-inflammatory profile, with increased leptin, PAI-1, hsCRP, IL-1 beta, IL-6 and TNF-alpha. The leptin receptor variant, meanwhile, alters the stability of the receptor’s ligand-binding domain, potentially weakening the leptin-melanocortin pathway that governs satiety and body weight.

Beyond the two strongest hits, variants in FTO and PPARG shaped the anthropometric, metabolic and inflammatory landscape of the cohort. The FTO rs17817449-G allele predicted higher waist-to-hip ratio in women along with lower apolipoprotein AI and resistin concentrations, echoing earlier findings in Chinese adults and in Brazilians with severe obesity. FTO is an intronic risk locus whose variants are believed to disrupt regulatory interactions that control energy homeostasis. The PPARG rs1801282 variant, Pro12Ala in the nuclear receptor that orchestrates adipogenesis and insulin sensitivity, predicted higher body fat in women and higher leptin in men—a gender-dependent effect that underscores how genetic risk can be expressed differently across sexes.

The study’s most forward-looking contribution is its polygenic risk score. Rather than treating each variant in isolation, the team built a weighted score in which each individual’s risk alleles were summed using adjusted odds ratios derived from their own regression models. Because genotyping success varied across participants, the researchers also computed a normalized score, dividing each person’s total by the number of successfully genotyped variants. Both versions of the score told the same story: values were significantly higher in people with metabolic syndrome, and they climbed steadily as the number of metabolic syndrome components increased from zero to five. Quartile analysis revealed a dose-response relationship, with syndrome prevalence rising progressively from the lowest to the highest genetic burden category.

The discriminatory power of the score, however, was modest. Receiver operating characteristic analysis yielded an area under the curve of 0.598 for the raw score and 0.616 for the normalized version—statistically significant but far from a stand-alone diagnostic test. The authors are candid about why: the score was built from a restricted set of candidate genes rather than genome-wide data, and the cohort, while carefully phenotyped, was relatively small and cross-sectional. Large genome-wide polygenic models, such as a recent multivariate analysis of nearly five million people of European and East Asian ancestry, achieve better discrimination by leveraging hundreds of loci. Yet those models transfer poorly to admixed populations like Brazil’s, which is precisely where a biologically informed candidate gene approach offers complementary value.

The limitations of the study are acknowledged with unusual transparency. The cross-sectional design precludes causal inference. Multiple comparisons were corrected using the Benjamini-Hochberg method at a ten percent false discovery rate, but some associations may still be influenced by the sheer number of tests. Deviations from Hardy-Weinberg equilibrium were observed for several polymorphisms, notably in the two variants most strongly associated with the syndrome, where deviations appeared only in the control group—a pattern the authors interpret as consistent with differential selective pressure rather than genotyping error. Many participants, particularly those with metabolic syndrome, were taking antidiabetic, antihypertensive or lipid-lowering medications, which may have blunted some genotype-phenotype associations. Self-reported ethnicity, without ancestry-informative markers, limits full control of population stratification in this admixed cohort.

Even with these caveats, the findings carry real weight for precision medicine in underrepresented populations. They confirm that variants governing energy homeostasis—ADRB3, LEPR and FTO—and lipid metabolism through PPARG are not merely statistical signals but are reflected in measurable differences in body composition, insulin resistance, lipid transport and systemic inflammation. The clear gradient of polygenic risk across the number of metabolic syndrome components supports the idea that common variants with individually modest effects accumulate into meaningful susceptibility. As the authors conclude, further studies in diverse populations will be essential before such scores can enter clinical practice, but this work marks an important step toward genetically informed assessment of metabolic and cardiovascular risk in a population that genome-wide resources have long overlooked.

Subject of Research: Genetic variants and polygenic risk scores associated with obesity-related metabolic syndrome in Brazilian adults

Article Title: ADRB3, LEPR, FTO and PPARG variants contribute to obesity-related metabolic syndrome in Brazilian adults: a polygenic risk score approach

Article References: Stefani, T. I. M., Hirata, T. D. C., Cerda, A., de Oliveira, R., dos Santos, M. A., Fajardo, C. M., Dorea, E. L., Bernik, M. M. S., Hirata, M. H., & Hirata, R. D. C. (2026). ADRB3, LEPR, FTO and PPARG variants contribute to obesity-related metabolic syndrome in Brazilian adults: a polygenic risk score approach. International Journal of Obesity. https://doi.org/10.1038/s41366-026-02218-9

Image Credits: AI Generated

DOI: 10.1038/s41366-026-02218-9

Keywords: metabolic syndrome, obesity, polygenic risk score, ADRB3, LEPR, FTO, PPARG, genetic variants, insulin resistance, inflammation, Brazilian cohort, cardiovascular risk

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