insitro Completes First AI-Enabled Human Genetics Study of Brown Adipose Tissue, Shares Differentiated Targets with Anti-Obesity Effects

Summary

Share AI-enabled brown adipose tissue (BAT) phenotype unlocks population-scale human genetic discovery BAT-01 knockdown drives 15% body-weight reduction in preclinical models through peripheral adipose beiging, preserving lean mass Key findings presented at the Keystone Symposia on Obesity Therapeutics SOUTH SAN FRANCISCO, Calif. – Feb 2, 2026 – insitro, the AI therapeutics company built on causal biology, today reported research demonstrating that artificial intelligence enables population-scale genetic analysis in tissues that have historically been challenging to study in large cohorts. In a first-of-its-kind genome-wide association study (GWAS) of brown adipose tissue (BAT) quantified using computer vision, insitro identified genetic loci associated with BAT biology and subsequently validated a prioritized target, BAT-01, the modulation of which produced significant weight loss in diet-induced obese mice while preserving lean mass potentially through a peripheral mechanism distinct from centrally acting appetite suppressants. The findings were presented by David Lloyd, Ph.D., Senior Vice President, Metabolic Disease and Translational Pharmacology, at the Keystone Symposia on Obesity Therapeutics. BAT has become increasingly implicated in metabolic health, but its anatomical diffuseness and functional heterogeneity have limited understanding of its genetic regulation in humans. “To power human genetics, you need tens of thousands of people – but brown fat measurements have historically required specialized approaches such as PET scans, which are hard to acquire at scale,” said Daphne Koller, Ph.D., founder and CEO of insitro. “AI enables these measurements to be derived from broadly available MRIs, unlocking a first-ever analysis of human genetics for BAT. Our results provide strong evidence for the important role that BAT plays across multiple metabolic health outcomes, and potentially reveal novel peripheral mechanisms for fat reduction, orthogonal to the central appetite pathways dominating current obesity therapies.” Leveraging its ClinML™ platform, insitro developed a machine learning-derived BAT imaging phenotype from UK Biobank Dixon MRI fat-signal fraction maps (n=69,598), using the delta between abdominal and supraclavicular adipose fat-signal fraction, to estimate brown fat content. Before launching the GWAS, the team established biological spe…