| Title: | Complementary body weight and cardiometabolic benefits of higher GLP-1 and lower GIP: genetic evidence from large-scale phenomic analyses |
| Journal: | Metabolism |
| Published: | 30 Jul 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42532176/ |
| DOI: | https://doi.org/10.1016/j.metabol.2026.156711 |
Publication 21216
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Abstract
BACKGROUND: The long-term health effects of glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) remain unclear. Most importantly, it is highly controversial whether GIP agonists vs. antagonists should be developed and what their effects and/or potential side effects would be in the clinic. Further investigation is needed to explore the effects of GIP lowering alone or in combination with GLP-1 enhancing. Tree-structured phenotypic modeling (TreeWAS) and factorial Mendelian randomization (MR) analyses are valuable in this context, even though the highest circulating GLP-1 levels may reflect only a minimum activation i.e. levels achieved with pharmacological intervention with dipeptidyl peptidase 4 (DPP-4) inhibitors and not GLP-1 receptor agonists (GLP-1 RAs) which achieve much higher levels.</p>
METHODS: TreeWAS was first conducted to map associations of GLP-1 or GIP levels with a broad range of disease outcomes among 385,917 UK Biobank participants. Disease-trajectory analysis was applied to characterize temporal patterns of co-occurrence of multiple comorbidities related to GLP-1 or GIP or both. Subsequently, factorial MR was performed to investigate the joint effects of GLP-1 and GIP on the associated health outcomes. Finally, mediation analysis was employed to explore the underlying mechanisms through which GLP-1 and GIP exert their effects.</p>
RESULTS: TreeWAS analysis revealed associations of elevated genetically predicted GLP-1 levels, even within the normal range, with decreased risk of metabolic diseases (e.g. type 2 diabetes) and polyarthropathies (e.g. gout). While genetically determined low GIP levels were linked to reduced risk of obesity, metabolic diseases (e.g. disorders of lipoprotein metabolism and other lipidaemias), digestive/hepatic and cardiometabolic diseases. Consistently, disease-trajectory analysis identified four major comorbidity clusters among individuals with genetically proxied physiologically lower GLP-1 and higher GIP levels, primarily involving metabolic diseases (e.g. type 2 diabetes), musculoskeletal system diseases (e.g. gout), gastrointestinal and urinary system disorders. Furthermore, the combined exposure to genetically predicted higher GLP-1 and lower GIP levels were associated with complementary and additive reductions in the risk of obesity (HR = 0.93, 95%CI: 0.88-0.99, p = 0.018), CVD (HR = 0.97, 95%CI: 0.94-0.99, p = 0.021), venous thromboembolism (VTE) (HR = 0.91, 95%CI: 0.83-1.00, p = 0.039), and multiplicative effects on composite hepatic events (CHEs) (HR = 0.88, 95%CI: 0.77-0.99, p = 0.040). No side effects on bone metabolism were identified. Finally, mediation analysis revealed that genetically predicted lower physiological GIP levels were associated with lower risk of obesity, CVD, VTE, overall and cardiovascular-specific mortality, with body weight consistent with a potential mediating role in exploratory decomposition analyses.</p>
CONCLUSIONS: Our genetic findings are consistent with the hypothesis that GLP-1 levels in the higher physiological range and GIP levels in the lower physiological range, individually and jointly, may be associated with lower body weight and improved cardiometabolic and liver phenotypes, while these observations warrant mechanistic and clinical investigation.</p>
8 Keywords
- Body Weight
- Cardiovascular Diseases
- Diabetes Mellitus, Type 2
- Gastric Inhibitory Polypeptide
- Glucagon-Like Peptide 1
- Humans
- Mendelian Randomization Analysis
- Phenomics
17 Authors
- Lijuan Wang
- Fangyuan Jiang
- Shuai Yuan
- Jing Sun
- Jianhui Zhao
- Siyun Zhou
- Jingyi Liang
- Haiqing Li
- Peige Song
- Shengfeng Wang
- Jianzeng Dong
- Siyan Zhan
- Susanna C Larsson
- Yao Xie
- Yuan Ding
- Xue Li
- Christos S Mantzoros
1 Application
| Application ID | Title |
|---|---|
| 66354 | Investigating the genetic, environmental and clinical determinants and their interactions on cancer risk, and developing risk prediction and prognosis models for common cancer types |
Enabling scientific discoveries that improve human health