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Targeting weight loss interventions to reduce cardiovascular complications of type 2 diabetes: a machine learning-based post-hoc analysis of heterogeneous treatment effects in the Look AHEAD trial.

PubMed ID: 28711469
Public Release Type: Journal
Publication Year: 2017
Affiliation: Department of Health System Design and Global Health, Arnhold Institute for Global Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: aaron.baum@mssm.edu.; Department of Health System Design and Global Health, Arnhold Institute for Global Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.; Department of Health System Design and Global Health, Arnhold Institute for Global Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Epidemiology, Joseph L Mailman School of Public Health, Columbia University, New York, NY, USA.; Division of Endocrinology, Diabetes, and Bone Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA.; Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.; Department of Health System Design and Global Health, Arnhold Institute for Global Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
DOI: https://doi.org/10.1016/S2213-8587(17)30176-6
Authors: Baum Aaron, Scarpa Joseph, Bruzelius Emilie, Tamler Ronald, Basu Sanjay, Faghmous James
Request IDs: 20676 , 21387
Studies: Look AHEAD: Action for Health in Diabetes

Abstract

The Action for Health in Diabetes (Look AHEAD) trial investigated whether long-term cardiovascular disease morbidity and mortality could be reduced through a weight loss intervention among people with type 2 diabetes. Despite finding no significant reduction in cardiovascular events on average, it is possible that some subpopulations might have derived benefit. In this post-hoc analysis, we test the hypothesis that the overall neutral average treatment effect in the trial masked important heterogeneous treatment effects (HTEs) from intensive weight loss interventions.

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Targeting weight loss interventions to reduce cardiovascular complications of type 2 diabetes: a machine learning-based post-hoc analysis of heterogeneous treatment effects in the Look AHEAD trial.

Abstract