Search PubMed⌕ Search
Home/Archive/PubMed

PubMed · 41870969

Agnostic polygenic prediction of weight loss after bariatric surgery.

Bastien Vallée Marcotte·Juan de Toro-Martín·André Tchernof·Louis Pérusse·Simon Marceau·Marie-Claude Vohl
View original source ↗Export metadata ↓

Abstract

A large interindividual variability in weight loss outcomes following bariatric surgery is reported. To ensure optimal management of patients, it is crucial to accurately identify candidates most likely to benefit the most from the intervention. Since genetic variants largely contribute to surgery response, polygenic scores (PGS) derived from genome-wide association studies (GWAS) could constitute valuable tools for clinical decision making. We developed and evaluated PGS to predict the weight loss response in 540 patients with a body mass index (BMI) of 35 kg/m2 or higher who underwent biliopancreatic diversion with duodenal switch. Summary statistics derived from BMI-derived GWAS, together with summary statistics from previously published GWAS of BMI and adiposity features, were used to construct, evaluate, and benchmark weight loss PGS. The full-adjusted BMI PGS model built in the entire cohort explained 39.6% of the mean-over-time excessive body weight loss (%EBWL), while the BMI-PGS built in the training dataset explained 38.9%. All benchmarked PGS based on BMI showed a significant relationship with mean-over-time %EBWL. These findings highlight the potential of BMI PGS in predicting weight loss after bariatric surgery and support their use as promising tools to improve the effectiveness of future antiobesity treatments.

Explore related subjects

HumansWeight LossBariatric SurgeryBody Mass IndexGenetic Risk ScoreFemaleGenome-Wide Association StudyMaleAdultMiddle AgedMultifactorial InheritanceObesity, MorbidTreatment OutcomeGastroenterologyGenetic risk factorsGeneticsMetabolismObesitySurgeryJournal ArticleResearch Support, Non-U.S. Gov't

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bastien Vallée Marcotte, Juan de Toro-Martín, André Tchernof, Louis Pérusse, Simon Marceau, Marie-Claude Vohl. 2026-03-19. Agnostic polygenic prediction of weight loss after bariatric surgery.. https://doi.org/10.1172/jci.insight.198133

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2027

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans↗
PubMed2027

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans↗
PubMed2027

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans↗