Literature DB >> 28711469

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.

Aaron Baum1, Joseph Scarpa2, Emilie Bruzelius3, Ronald Tamler4, Sanjay Basu5, James Faghmous2.   

Abstract

BACKGROUND: 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.
METHODS: We used causal forest modelling, which identifies HTEs, using a random half of the trial data (the training set). We applied Cox proportional hazards models to test the potential HTEs on the remaining half of the data (the testing set). The analysis was deemed exempt from review by the Columbia University Institutional Review Board, Protocol ID# AAAO3003.
FINDINGS: Between Aug 22, 2001, and April 30, 2004, 5145 patients with type 2 diabetes were enrolled in the Look AHEAD randomised controlled trial, of whom 4901 were included in the The National Institute of Diabetes and Digestive and Kidney Diseases Repository and included in our analyses: 2450 for model development and 2451 in the testing dataset. Baseline HbA1c and self-reported general health distinguished participants who differentially benefited from the intervention. Cox models for the primary composite cardiovascular outcome revealed a number needed to treat of 28·9 to prevent 1 event over 9·6 years among participants with HbA1c 6·8% or higher, or both HbA1c less than 6·8% and Short Form Health Survey (SF-36) general health score of 48 or more (2101 [86%] of 2451 participants in the testing dataset; 167 [16%] of 1046 primary outcome events for intervention vs 205 [19%] of 1055 for control, absolute risk reduction of 3·46%, 95% CI 0·21-6·73%, p=0·038) By contrast, participants with HbA1c less than 6·8% and baseline SF-36 general health score of less than 48 (350 [14%] of 2451 participants in the testing data; 27 [16%] of 171 primary outcome events for intervention vs 15 [8%] of 179 primary outcome events for control) had an absolute risk increase of the primary outcome of 7·41% (0·60 to 14·22, p=0·003).
INTERPRETATION: Look AHEAD participants with moderately or poorly controlled diabetes (HbA1c 6·8% or higher) and subjects with well controlled diabetes (HbA1c less than 6·8%) and good self-reported health (85% of the overall study population) averted cardiovascular events from a behavioural intervention aimed at weight loss. However, 15% of participants with well controlled diabetes and poor self-reported general health experienced negative effects that rendered the overall study outcome neutral. HbA1c and a short questionnaire on general health might identify people with type 2 diabetes likely to derive benefit from an intensive lifestyle intervention aimed at weight loss. FUNDING: None.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Mesh:

Year:  2017        PMID: 28711469      PMCID: PMC5815373          DOI: 10.1016/S2213-8587(17)30176-6

Source DB:  PubMed          Journal:  Lancet Diabetes Endocrinol        ISSN: 2213-8587            Impact factor:   32.069


  25 in total

1.  The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection.

Authors:  J E Ware; C D Sherbourne
Journal:  Med Care       Date:  1992-06       Impact factor: 2.983

Review 2.  Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults--The Evidence Report. National Institutes of Health.

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Journal:  Obes Res       Date:  1998-09

3.  Interpretation of subgroup analyses in randomized trials: heterogeneity versus secondary interventions.

Authors:  Tyler J VanderWeele; Mirjam J Knol
Journal:  Ann Intern Med       Date:  2011-05-17       Impact factor: 25.391

4.  Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes.

Authors:  Rena R Wing; Paula Bolin; Frederick L Brancati; George A Bray; Jeanne M Clark; Mace Coday; Richard S Crow; Jeffrey M Curtis; Caitlin M Egan; Mark A Espeland; Mary Evans; John P Foreyt; Siran Ghazarian; Edward W Gregg; Barbara Harrison; Helen P Hazuda; James O Hill; Edward S Horton; Van S Hubbard; John M Jakicic; Robert W Jeffery; Karen C Johnson; Steven E Kahn; Abbas E Kitabchi; William C Knowler; Cora E Lewis; Barbara J Maschak-Carey; Maria G Montez; Anne Murillo; David M Nathan; Jennifer Patricio; Anne Peters; Xavier Pi-Sunyer; Henry Pownall; David Reboussin; Judith G Regensteiner; Amy D Rickman; Donna H Ryan; Monika Safford; Thomas A Wadden; Lynne E Wagenknecht; Delia S West; David F Williamson; Susan Z Yanovski
Journal:  N Engl J Med       Date:  2013-06-24       Impact factor: 91.245

Review 5.  A review and meta-analysis of the effect of weight loss on all-cause mortality risk.

Authors:  Mary Harrington; Sigrid Gibson; Richard C Cottrell
Journal:  Nutr Res Rev       Date:  2009-06       Impact factor: 7.800

6.  Diabetes mellitus, fasting glucose, and risk of cause-specific death.

Authors:  Alexander Thompson; Emanuele Di Angelantonio; Pei Gao; Nadeem Sarwar; Sreenivasa Rao Kondapally Seshasai; Stephen Kaptoge; Peter H Whincup; Kenneth J Mukamal; Richard F Gillum; Ingar Holme; Inger Njølstad; Astrid Fletcher; Peter Nilsson; Sarah Lewington; Rory Collins; Vilmundur Gudnason; Simon G Thompson; Naveed Sattar; Elizabeth Selvin; Frank B Hu; John Danesh
Journal:  N Engl J Med       Date:  2011-03-03       Impact factor: 91.245

7.  Detecting Heterogeneous Treatment Effects to Guide Personalized Blood Pressure Treatment: A Modeling Study of Randomized Clinical Trials.

Authors:  Sanjay Basu; Jeremy B Sussman; Rod A Hayward
Journal:  Ann Intern Med       Date:  2017-01-03       Impact factor: 25.391

8.  Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD).

Authors:  Gary S Collins; Johannes B Reitsma; Douglas G Altman; Karel G M Moons
Journal:  Ann Intern Med       Date:  2015-05-19       Impact factor: 25.391

Review 9.  Machine Learning and Data Mining Methods in Diabetes Research.

Authors:  Ioannis Kavakiotis; Olga Tsave; Athanasios Salifoglou; Nicos Maglaveras; Ioannis Vlahavas; Ioanna Chouvarda
Journal:  Comput Struct Biotechnol J       Date:  2017-01-08       Impact factor: 7.271

10.  Pragmatic Trials for Noncommunicable Diseases: Relieving Constraints.

Authors:  Anushka Patel; Ruth Webster
Journal:  PLoS Med       Date:  2016-03-29       Impact factor: 11.069

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  23 in total

Review 1.  Obesity: Pathophysiology and Management.

Authors:  Kishore M Gadde; Corby K Martin; Hans-Rudolf Berthoud; Steven B Heymsfield
Journal:  J Am Coll Cardiol       Date:  2018-01-02       Impact factor: 24.094

2.  Clinical Value of Predicting Individual Treatment Effects for Intensive Blood Pressure Therapy.

Authors:  Tony Duan; Pranav Rajpurkar; Dillon Laird; Andrew Y Ng; Sanjay Basu
Journal:  Circ Cardiovasc Qual Outcomes       Date:  2019-03

3.  Using the Bayesian credible subgroups method to identify populations benefiting from treatment: An application to the Look AHEAD trial.

Authors:  Anna Coonan; Patrick Schnell; Joel Smith; John Forbes
Journal:  PLoS One       Date:  2020-04-21       Impact factor: 3.240

Review 4.  The New Possibilities from "Big Data" to Overlooked Associations Between Diabetes, Biochemical Parameters, Glucose Control, and Osteoporosis.

Authors:  Christian Kruse
Journal:  Curr Osteoporos Rep       Date:  2018-06       Impact factor: 5.096

5.  Heterogeneity of Treatment Effects From an Intensive Lifestyle Weight Loss Intervention on Cardiovascular Events in Patients With Type 2 Diabetes: Data From the Look AHEAD Trial.

Authors:  Tamar I de Vries; Jannick A N Dorresteijn; Yolanda van der Graaf; Frank L J Visseren; Jan Westerink
Journal:  Diabetes Care       Date:  2019-08-15       Impact factor: 19.112

6.  Heterogeneous Exposure Associations in Observational Cohort Studies: The Example of Blood Pressure in Older Adults.

Authors:  Michelle C Odden; Andreea M Rawlings; Abtin Khodadadi; Xiaoli Fern; Michael G Shlipak; Kirsten Bibbins-Domingo; Kenneth Covinsky; Alka M Kanaya; Anne Lee; Mary N Haan; Anne B Newman; Bruce M Psaty; Carmen A Peralta
Journal:  Am J Epidemiol       Date:  2020-01-31       Impact factor: 4.897

Review 7.  Effects of Different Weight Loss Approaches on CVD Risk.

Authors:  Peter M Clifton; Jennifer B Keogh
Journal:  Curr Atheroscler Rep       Date:  2018-04-25       Impact factor: 5.113

8.  Type 2 Diabetes Subgroups, Risk for Complications, and Differential Effects Due to an Intensive Lifestyle Intervention.

Authors:  Michael P Bancks; Haiying Chen; Ashok Balasubramanyam; Alain G Bertoni; Mark A Espeland; Steven E Kahn; Scott Pilla; Elizabeth Vaughan; Lynne E Wagenknecht
Journal:  Diabetes Care       Date:  2021-03-11       Impact factor: 19.112

Review 9.  Big Data, Data Science, and Causal Inference: A Primer for Clinicians.

Authors:  Yoshihiko Raita; Carlos A Camargo; Liming Liang; Kohei Hasegawa
Journal:  Front Med (Lausanne)       Date:  2021-07-06

10.  Association of type 2 diabetes remission and risk of cardiovascular disease in pre-defined subgroups.

Authors:  Hilda Hounkpatin; Beth Stuart; Andrew Farmer; Hajira Dambha-Miller
Journal:  Endocrinol Diabetes Metab       Date:  2021-06-19
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