Identifying genetic variants associated with obesity risk could help facilitate prevention and guide personalized interventions, but doing so will require genomic databases that include information from more diverse populations, specialists said at the 2026 International Congress on Obesity in Mexico City, Mexico.

Among the topics discussed were research efforts in molecular genetics aimed at obesity prevention. Experts said that understanding the genetic alterations linked to obesity may create opportunities for earlier lifestyle interventions and, in turn, more personalized therapeutic approaches.

Despite progress in this field, important challenges remain, including the difficulty of determining the effects of specific genetic variants and how they relate to obesity risk, as well as the need for more diverse genomic data, because current biobanks still primarily contain information from European populations.

Genes Associated With Fat Distribution

Karen Mohlke, PhD, genetics researcher at the University of North Carolina at Chapel Hill, said that variants associated with the waist-to-hip ratio and abdominal fat distribution may help predict obesity risk, guide earlier lifestyle intervention, and guide treatment based on each patient’s characteristics.

Using gene-expression data and bioinformatics tools, her group has identified roughly 1200 genes that may influence waist-to-hip ratio, along with bone and muscle development and several metabolic pathways involving adipose tissue.

“These data are helping us understand the underlying physiology and why obesity develops,” she said.

Her group is also studying how BMI varies across populations by using data from the Genetic Investigation of Anthropometric Traits consortium, which includes genetic information from about 5 million people.

According to Mohlke, the field is moving toward a point where genetic information could be used to identify risk earlier and support earlier intervention, including lifestyle changes and possibly drug treatment. She said both rare and common variants associated with BMI and waist-to-hip ratio have now been identified.

Still, she stressed that prediction models will remain limited unless they are built on more diverse data. Most existing biobanks, she noted, still overrepresent individuals of European ancestry.

Why Local Genomic Data Matter

That lack of diversity is one reason projects such as oriGEN are gaining attention. Rocío Isabel Díaz de la Garza, PhD, who leads the Integrative Biology Unit at Tecnológico de Monterrey in Monterrey, Mexico, is part of the project, which is collecting biological samples and clinical measurements from about 100,000 people in 17 Mexican cities.

The goal is to estimate the frequency of genetic variants in the Mexican population and examine how they relate to obesity and other metabolic traits.

Her team has identified 86 genes involved in one-carbon metabolism. “We know this pathway is altered in metabolic disease, and that people with obesity often have lower circulating levels of folate and vitamin B12,” she said. “What we do not yet know is why that happens or what role one-carbon metabolism may play in these disorders.”

The researchers then used phenome-wide association studies, which test many phenotypes against a single genetic variant, to analyze those genes. They identified 470 variants and, after filtering for metabolic traits of interest and applying statistical analysis, found 58 significant associations.

One of the most notable findings was a previously unreported variant in DNMT3A associated with increased fat mass and other adiposity measures.

“It is especially interesting for us to find a variant linked to adiposity and higher fat mass in the Mexican population,” Díaz said.

She said the broader aim of oriGEN is to generate data that can help classify variants, build population-specific risk scores, and support more individualized dietary planning based on genetic profiles.

“We hope this knowledge will contribute to the development of markers for precision nutrition,” she said.

Sequencing Alone Is Not Enough

Amélie Bonnefond, PhD, research director at the French National Institute of Health and Medical Research (ie, INSERM) in Lille, France, said that although identifying genes associated with increased obesity risk could be highly useful for prevention, several challenges remain in detecting these variants and clarifying how they relate to metabolic processes.

She cautioned that, although next-generation sequencing has reduced costs and shortened analysis times, it does not guarantee a correct diagnosis. “It’s true that this technology has been a major advance: We used to rely on Sanger sequencing to detect rare pathogenic variants, and now the process is simpler, cheaper, and faster. However, it is not a magic solution.”

Bonnefond outlined three challenges in identifying genes associated with obesity. The first is technical and centers on correctly detecting genetic alterations because insertions and deletions can still be difficult to identify and may generate false-positive results.

“Next-generation sequencing is not perfect: its sensitivity and specificity continue to have limitations,” she added.

The second challenge is determining whether a variant is truly pathogenic. Although international criteria exist for classifying variants, interpretation requires combining numerous lines of evidence and performing complex bioinformatic analyses. “Now the problem is different: the main bottleneck lies in the analysis, especially the bioinformatic analysis,” she noted.

She added that a variant classified as pathogenic does not necessarily cause obesity. In her team’s research, only heterozygous variants of the PCSK1 gene with complete loss of function were associated with monogenic obesity. In contrast, heterozygous variants considered pathogenic in the POMC and LEPR genes showed no consistent association with obesity or a higher BMI.

A similar result was observed with the NCOA1 protein, whose loss-of-function variants had been proposed as causes of obesity but showed no association when researchers attempted to replicate the finding. “This demonstrates the importance of precisely defining what a ‘known disease mechanism’ means,” she added.

The third challenge arises when studies identify variants of uncertain significance that do not allow for a definitive diagnosis. Bonnefond suggested that, to resolve these cases, clinicians may test other family members to see whether the variant is present alongside the disease, although it is not always possible to obtain family samples. “Another alternative lies in functional assays, which reveal whether the alteration modifies the activity of a gene or protein and provide solid evidence for reclassifying it.”

She concluded that integrating clinical, familial, functional, and polygenic evidence is essential to avoid inaccurate diagnoses and to appropriately select the treatment that is most likely to benefit the patient.

Mohlke, Bonnefond, and Díaz disclosed no relevant financial relationships.

This story was translated from Medscape’s Spanish edition.