Genetics May Help Explain Why GLP-1 Weight-Loss Drugs Work Better for Some People Than Others
Key Takeaways:
- Researchers have identified two genetic variants that may help explain why people respond differently to GLP-1 weight-loss medications such as Wegovy and Mounjaro.
- One genetic variant was linked to slightly greater weight loss, while another appeared to increase the likelihood of nausea and vomiting in people taking tirzepatide.
- Experts say the findings are important for understanding treatment variability, but non-genetic factors such as sex, medication type, dosage and treatment duration still appear to play a much larger role.
Genetic differences may help explain variable responses to GLP-1 weight-loss drugs
Scientists have uncovered new evidence suggesting that genetics may partly explain why GLP-1 weight-loss medications produce very different results from one person to another.
The research, published in Nature, examined how specific genetic differences may influence both weight-loss outcomes and the risk of side-effects in people taking glucagon-like peptide-1 receptor agonists, commonly known as GLP-1 drugs.
The findings could eventually contribute to more personalised approaches to obesity treatment, where therapies are selected based on an individual’s biological profile. However, researchers and independent experts stressed that the genetic effects identified in the study were relatively modest and are not yet strong enough to guide routine clinical decisions.
GLP-1 medicines and their growing role in obesity care
GLP-1 receptor agonists, including semaglutide, sold under the brand name Wegovy, and tirzepatide, marketed as Mounjaro, mimic naturally occurring gut hormones involved in regulating appetite, digestion and insulin release.
These medicines have transformed obesity treatment in recent years and are now used by millions of people globally. By helping reduce appetite and slow gastric emptying, they can support significant weight loss in many individuals.
However, clinical experience and research have consistently shown substantial variation in treatment response. Some people lose large amounts of weight, while others experience more limited benefits. Similarly, side-effects such as nausea and vomiting can vary considerably between individuals.
Until now, the biological reasons behind these differences have remained poorly understood.
Large genetic analysis involving nearly 28,000 people
To investigate the issue, researchers from 23andMe and a nonprofit medical research institute analysed data from 27,885 people taking GLP-1 medications.
The study focused on variations in genes linked to gut hormone pathways that regulate appetite and digestion.
Researchers identified one GLP1 receptor variant, known as rs10305420, that was associated with slightly greater weight loss among people carrying the variant compared with those who did not carry it.
A second genetic variant, rs1800437, was linked to a greater likelihood of nausea and vomiting in people taking tirzepatide. However, this variant was not associated with the amount of weight lost.
The findings suggest that inherited genetic differences may contribute to how people respond to GLP-1 therapies, both in terms of effectiveness and tolerability.
Genetics appears to play only a modest role
Despite the findings, researchers emphasised that the overall contribution of genetics appeared relatively small.
Marie Spreckley, an obesity expert at the University of Cambridge who was not involved in the study, said the research offered biologically plausible evidence that genetic variation may influence treatment outcomes.
“However, the magnitude of these genetic effects is small in clinical terms,” she said. “Importantly, non-genetic factors such as sex, drug type, dose and duration appear to explain a substantially larger proportion of variability. The authors’ model suggests that most of the explained variance comes from these factors, with genetics adding only a modest incremental contribution.
“In terms of how this fits with the wider evidence, it reinforces that while there is substantial variability in response to GLP1 therapies, genetics is only one part of a much more complex picture. Behavioural, clinical and treatment-related factors remain the dominant drivers of outcomes.
“Overall, this is an important step toward understanding variability and the potential for future precision approaches, but the effects are modest and the evidence is not yet sufficient to support using genetic information to guide treatment decisions in routine clinical practice.”
Toward more personalised obesity treatment
The findings contribute to a growing body of research exploring precision medicine approaches in obesity care.
As scientists continue to investigate why individuals respond differently to treatments, future obesity management may increasingly incorporate biological, behavioural and clinical information to tailor therapies more effectively.
However, experts caution that current evidence does not support the use of genetic testing to determine which GLP-1 medication a person should receive.
Instead, the study primarily advances understanding of the complex biological factors that may influence treatment response, while reinforcing that genetics represents only one piece of a much larger puzzle involving lifestyle, clinical characteristics and medication-related factors.
Read More
Genetic Risk Scores Offer Improved Prediction of Obesity, Type 2 Diabetes and Long-Term Health Outcomes
Key Takeaways:
- A new polygenic risk score integrates genetic data from over 8.5 million people to better predict obesity and type 2 diabetes risk
- The model goes beyond traditional measures such as body mass index by incorporating multiple aspects of metabolic function
- Individuals with higher genetic risk were more likely to develop complications and require interventions such as GLP-1 therapy or bariatric surgery
A more comprehensive approach to metabolic risk
Obesity and type 2 diabetes are complex metabolic conditions influenced by a combination of environmental, behavioural and genetic factors. While traditional clinical measures such as body mass index have long been used to assess risk, they do not fully capture the biological complexity underlying these conditions.
In a new study published in Cell Metabolism, researchers from Mass General Brigham have developed an advanced polygenic risk score designed to improve prediction of both obesity and type 2 diabetes, as well as their long-term health consequences. Polygenic risk scores work by aggregating the effects of many genetic variants across the genome, providing an estimate of an individual’s predisposition to developing a given condition.
“Our intention was to not only capture the risk of being diagnosed with obesity or diabetes, but also to better predict health consequences across the life course by integrating many aspects of metabolic function,” said co-first author Min Seo Kim, MD, MSc. “In the future, this genomic approach could complement established clinical risk factors to inform patient care and preventative strategies.”
Building a next-generation polygenic risk score
The research team constructed two distinct metabolic risk scores – one optimised for obesity and another for type 2 diabetes. Unlike conventional models, these scores incorporate genetic signals linked to 20 different traits associated with metabolic health. These include factors such as fat distribution, insulin regulation and glucose control.
To build these models, the investigators drew on genome-wide association studies conducted across some of the largest biobank datasets globally, encompassing more than 8.5 million individuals. This scale allowed the researchers to capture a broad and diverse range of genetic influences.
Importantly, the model moves beyond reliance on body mass index alone, reflecting a growing recognition that metabolic health cannot be fully understood through weight-based measures in isolation.
Predicting disease progression and clinical outcomes
Beyond predicting the likelihood of developing obesity or type 2 diabetes, the new polygenic risk scores demonstrated the ability to forecast downstream health outcomes.
The researchers found that individuals identified as high risk were more likely to go on to develop complications such as cardiovascular disease and stroke. Even among people who were initially healthy, those with a high genetic risk score were approximately twice as likely to require clinical interventions over time.
Specifically, individuals with higher polygenic risk scores were about twice as likely to receive GLP-1 receptor agonist medications or undergo bariatric surgery compared with those with average risk scores, over a median follow-up period of 5.5 years.
These findings suggest that genetic profiling could help identify people at risk earlier in the disease trajectory, potentially enabling more proactive and targeted care.
Improved performance across diverse populations
A notable strength of the study lies in its use of multi-ancestry genetic data. By incorporating genome-wide association studies from a wide range of populations, including African, East Asian, South Asian and Middle Eastern groups, the researchers were able to develop risk scores that performed better across diverse populations than earlier models.
Historically, many genetic prediction tools have been less accurate in non-European populations due to limited representation in genomic datasets. This study represents a step towards addressing that imbalance and improving equity in precision medicine.
Towards more personalised prevention and treatment
The research team emphasises that this work is part of a broader effort to refine understanding of the genetic subtypes of obesity and type 2 diabetes. Improved classification of these conditions could support more precise patient stratification in clinical trials and, ultimately, more tailored interventions in routine care.
“We want clinicians to be able to think about metabolic conditions in terms beyond body mass index, with a focus more broadly on underlying genetic susceptibility,” said co-senior author Akl Fahed, MD, MPH, of the Cardiovascular Research Center at Massachusetts General Hospital and an interventional cardiologist with the Mass General Brigham Heart and Vascular Institute. “Early identification of people who are likely to have a worse trajectory of poor metabolic health, before they even develop these conditions, can help us improve prevention and clinical interventions. That is how we can cure disease, and that is the bold mission that we are after.”
Implications for clinical practice
While further validation and implementation work will be required, the findings highlight the potential role of genomic tools in enhancing current approaches to metabolic disease prevention and management. By complementing existing clinical risk factors, polygenic risk scores could support earlier identification of people at risk and enable more personalised, proactive care pathways.
As healthcare systems increasingly move towards precision medicine, integrating genetic insights with clinical decision-making may become an important step in improving outcomes for people living with obesity and type 2 diabetes.
CCH insights:
This is exciting research, and a big step towards precision obesity prevention, as it gives us an individual risk score for obesity and diabetes for each patient. However, it is only half the story – ideally we’d also like to be able to determine what type of interventions will work best for each individual (in terms of diet, lifestyle and medicine) in order to optimise their chances of good metabolic health and achieving a healthy weight. Hopefully the ability to do this is not too far away.
Read More
Genetic Links Between Obesity and Autoimmune Diseases Identified in Large European Study
Key Takeaways:
- Large-scale genetic analyses have identified a substantial shared genetic basis between obesity and several autoimmune disorders in people of European ancestry.
- Dozens of shared genetic loci and genes appear to influence both body weight regulation and immune system function, particularly pathways involved in immune homeostasis.
- The findings suggest potential causal links between obesity and certain autoimmune conditions, with implications for future therapeutic strategies.
Overview of the study
A recent study published in the Journal of Translational Medicine has identified important genetic links between obesity and autoimmune disorders, shedding new light on why these conditions often co-occur. The research, led by Jiang and colleagues, focused on individuals of European ancestry and used large-scale genomic datasets to explore how shared genetic factors may influence both excess body weight and immune-mediated disease.
Obesity and autoimmune disorders represent a significant comorbidity burden, yet until now their shared genetic architecture has remained poorly understood. By applying advanced cross-trait genome-wide association study (GWAS) methods, the researchers aimed to uncover pleiotropic genetic variants – genes or loci that influence more than one trait – that may contribute to both conditions.
Study methods and analytical approach
The researchers conducted a comprehensive cross-trait analysis using GWAS summary data for obesity and 17 autoimmune diseases. Genetic correlations between traits were assessed using LD score regression and high-definition likelihood methods, allowing the team to quantify the extent to which obesity and autoimmune conditions share inherited risk.
To identify specific shared genetic loci, the team employed Stratified Pleiotropic Locus Mapping (PLACO), followed by Bayesian colocalization analyses to confirm whether obesity and autoimmune diseases truly shared the same causal genetic variants. Further analyses examined gene-level effects and tissue-specific heritability, while potential drug targets were prioritised using summary-based Mendelian randomisation (SMR).
In addition, immune co-localization techniques and bidirectional Mendelian randomisation were used to explore immunological mechanisms and to clarify potential causal relationships between obesity and autoimmune diseases.
Key genetic findings
The analysis identified eight autoimmune diseases with significant genetic correlations to obesity. In total, researchers discovered 10,324 pleiotropic single-nucleotide polymorphisms (SNPs), which mapped to 52 independent risk loci. Of these, nine loci were confirmed as shared causal variants through colocalization analysis.
Gene-level investigations revealed 133 unique pleiotropic genes. Notably, genes such as CLN3, SH2B1, and MMEL1 were highlighted and found to be enriched in biological pathways related to hematopoietic cell differentiation and immune homeostasis. These pathways are central to both metabolic regulation and immune function, reinforcing the biological plausibility of a shared genetic basis.
Tissue and immune cell involvement
Tissue-specific heritability analyses showed that shared genetic effects were most prominent in immune-related tissues, particularly the spleen, whole blood, and Epstein–Barr virus (EBV)-transformed lymphocytes. This finding further supports the role of immune system regulation in the overlap between obesity and autoimmune disease risk.
Immune co-localization analyses implicated six traits related to IgD+ CD38− B cell subsets as key pathological conduits. These immune cells may represent an important link between metabolic dysfunction and autoimmune processes.
Evidence of causal relationships
Using bidirectional Mendelian randomisation, the study provided evidence that obesity may play a causal role in the development of certain autoimmune conditions, including hypothyroidism, psoriasis, and multiple sclerosis. Conversely, an inverse causal association was observed between type 1 diabetes and obesity risk, suggesting a more complex and condition-specific relationship.
Implications and conclusions
In their conclusions, the authors state:
“This study demonstrates a robust shared genetic foundation between obesity and multiple autoimmune diseases, pinpointing specific pleiotropic loci, genes, and immune cell subsets.”
By identifying shared genetic mechanisms, the research provides a clearer mechanistic framework for understanding why obesity and autoimmune disorders frequently coexist. Importantly, these findings also highlight potential molecular and immunological targets for future therapeutic intervention, with the potential to address both metabolic and autoimmune disease pathways simultaneously.
Overall, the study represents a significant step forward in understanding the complex interplay between body weight regulation and immune system dysfunction, and it opens new avenues for research into integrated prevention and treatment strategies.
Read More
Age and Sex Shape Obesity’s Impact on Major Diseases, Large Genetic Study Finds
Key Takeaways:
- A time-resolved genetic analysis of over 360,000 UK Biobank participants shows that obesity’s health risks vary substantially across age and between men and women.
- Higher BMI was causally linked to greater risk of type 2 diabetes, coronary artery disease, atrial fibrillation, and osteoarthritis, but the timing and intensity of these effects differed by condition.
- The study’s novel genetic approach revealed that preventive interventions such as statin or blood pressure treatment may temporarily dampen obesity-related cardiovascular risk in midlife.
Understanding obesity’s changing health risks
Nearly one billion adults globally live with obesity, making it a key driver of type 2 diabetes (T2DM), coronary artery disease (CAD), atrial fibrillation (AF), and osteoarthritis (OA). Yet researchers have long struggled to pinpoint when in life excess body weight does the most harm.
Most studies average risk across all adults, masking crucial age-specific patterns. Body mass index (BMI) remains the standard measure of obesity, but its health impact may shift as metabolism, hormones, behaviour, and medical care evolve through life. Moreover, traditional epidemiological studies cannot always distinguish correlation from causation.
Genetic studies using Mendelian randomisation (MR) can infer causal effects, but conventional MR assumes that risks remain constant over time. In a new paper published in Science Advances, researchers introduced a time-resolved MR framework that tracks how obesity’s effects on major diseases change with age and differ between sexes.
Study design and methods
The researchers analysed data from 361,906 unrelated adults of European ancestry within the UK Biobank, a large population-based health resource. Participants had linked genetic and medical record data, and follow-up continued until a median age of around 70 years, capped at 76 to avoid sparse data at older ages.
BMI at study entry was standardised within sex-by-age groups. The primary outcomes were first occurrences of T2DM, CAD, AF, and OA, identified using International Classification of Diseases (ICD-10) codes.
To establish causal relationships, the team employed MR using polygenic scores (PGS) as instruments. They performed genome-wide association studies (GWAS) for BMI in two independent subsamples (each ~180,953 participants) to identify genome-wide significant genetic variants.
To minimise reverse causation, disease-specific BMI PGS were filtered using the Steiger method, which excluded variants that explained more variation in disease outcomes than in BMI itself. The researchers then modelled time-to-event data using Aalen’s additive hazard model, estimating both cumulative (“life-course”) and age-specific (“momentary”) effects.
Sensitivity analyses accounted for potential biases, including lipid-lowering treatment among CAD-free participants, blood pressure (SBP) as an alternative exposure, and cohort selection effects.
Distinct patterns across diseases
Across adulthood, higher BMI was causally associated with increased rates of all four conditions, but with striking differences in timing and trajectory.
- Osteoarthritis (OA): BMI-related risk rose early in life, becoming significant over 20 years before risk for AF increased. This suggests that musculoskeletal strain and inflammatory pathways linked to obesity manifest relatively early.
- Atrial Fibrillation (AF): The risk associated with BMI intensified later in adulthood, suggesting that atrial and metabolic factors accumulate over time.
- Type 2 Diabetes (T2DM): The effect of BMI increased steadily from midlife but plateaued between ages 60 and 70, indicating that preventive measures or clinical interventions may mitigate risk during this period.
- Coronary Artery Disease (CAD): The most distinctive pattern emerged here – a U-shaped curve. Risk decreased markedly around ages 50 to 70 before rising again in older age. This midlife dip was not explained by study participation patterns but appeared more pronounced among individuals on lipid-lowering medication such as statins, suggesting that treatment may blunt BMI-related cardiovascular risk during this window.
When the researchers replaced BMI with systolic blood pressure (SBP) as the exposure, AF risk displayed a similar midlife trough, consistent with the effect of antihypertensive therapy. However, no comparable trough appeared for CAD, reinforcing the role of statins rather than blood pressure control in midlife coronary risk reduction.
Sex differences in risk
Sex-stratified analyses revealed generally stronger BMI effects in men for T2DM, CAD, and AF. Osteoarthritis was an exception: both sexes exhibited similar BMI-related risk until about age 60, after which the association appeared to decline slightly in women, although the results carried uncertainty due to diverging confidence intervals.
A particularly notable finding concerned T2DM. Women displayed a distinct, temporary reduction in BMI-related diabetes risk beginning around age 60 and lasting roughly a decade, whereas men’s risk continued to rise. This “female trough” was not accounted for by menopause timing or the use of hormone therapy, suggesting that behavioural or clinical factors – such as greater engagement with weight management or preventive health care – could play a role.
Genetic and methodological insights
Clustering of BMI-associated genetic variants revealed multiple mechanistic pathways underlying obesity’s effects. Different genetic clusters contributed distinct temporal risk patterns for CAD and T2DM. For instance, “high-risk” clusters largely accounted for the CAD trough and the sex differences seen in T2DM.
Importantly, the researchers verified that the strength of genetic effects on BMI declines with age, underscoring the need for age-sensitive models. Simulation studies confirmed that their time-resolved MR method accurately captured dynamic effects even when the genetic influence on BMI varied over time.
Adjustments for potential selection bias slightly reduced the overall magnitude of effects but preserved key age-related patterns, including the midlife risk reductions.
Clinical implications
The findings emphasise that the timing of prevention matters as much as the magnitude of obesity itself. Sustained high BMI elevates the risk for several major diseases, but the most effective period for intervention differs by condition and by sex.
For example:
- Lipid-lowering treatment in midlife may attenuate BMI-related CAD risk.
- Blood pressure control could moderate AF risk later in life.
- Women may experience a unique window in their 60s when obesity-related diabetes risk temporarily subsides.
The authors conclude that prevention strategies should be tailored to life stage and sex, targeting the periods when intervention can avert the greatest number of disease events.
They also note limitations, including the assumption of an immediate biological response to BMI changes and the reduced precision of genetic instruments for early-life BMI. Nonetheless, their time-resolved MR framework offers a powerful new approach for uncovering dynamic, age-specific health risks that static analyses may obscure.
Read More