Literature DB >> 29934481

Global Diabetes Prevention Interventions: A Systematic Review and Network Meta-analysis of the Real-World Impact on Incidence, Weight, and Glucose.

Karla Ivette Galaviz1, Mary Beth Weber2, Audrey Straus3, Jeehea Sonya Haw4, K M Venkat Narayan2, Mohammed K Ali2.   

Abstract

OBJECTIVE: Understanding the real-world impacts of lifestyle modification (LSM) for diabetes prevention is imperative to inform resource allocation. The purpose of this study was to synthetize global evidence on the impact of LSM strategies on diabetes incidence and risk factors in one parsimonious model. RESEARCH DESIGN AND METHODS: PubMed, Embase, Cochrane Library, and ClinicalTrials.gov were searched for studies published between January 1990 and April 2015. Effectiveness/translation studies of any design testing LSM strategies, targeting high-risk populations (with prediabetes or diabetes risk factors), and reporting diabetes incidence, weight, or glucose outcomes were included. We extracted number of diabetes cases/incidence rates and mean changes in weight (kg), fasting blood glucose (FBG, mmol/L), 2-h postload glucose (mmol/L), and hemoglobin A1c (%). Pairwise random-effects and frequentist random-effects network meta-analyses were used to obtain pooled effects.
RESULTS: Sixty-three studies were pooled in the meta-analysis (n = 17,272, mean age 49.7 years, 28.8% male, 60.8% white/European). In analyses restricted to controlled studies (n = 7), diabetes cumulative incidence was 9% among intervention participants and 12% among control participants (absolute risk reduction 3%; relative risk 0.71 [95% CI 0.58, 0.88]). In analyses combining controlled and uncontrolled studies (n = 14), participants receiving group education by health care professionals had 33% lower diabetes odds than control participants (odds ratio 0.67 [0.49, 0.92]). Intervention participants lost 1.5 kg more weight [-2.2, -0.8] and achieved a 0.09 mmol/L greater FBG decrease [-0.15, -0.03] than control participants. Every additional kilogram lost by participants was associated with 43% lower diabetes odds (β = 0.57 [0.41, 0.78]).
CONCLUSIONS: Real-world LSM strategies can reduce diabetes risk, even with small weight reductions.
© 2018 by the American Diabetes Association.

Entities:  

Mesh:

Substances:

Year:  2018        PMID: 29934481      PMCID: PMC6463613          DOI: 10.2337/dc17-2222

Source DB:  PubMed          Journal:  Diabetes Care        ISSN: 0149-5992            Impact factor:   19.112


  49 in total

1.  The Estimating effectiveness from efficacy taxonomy (EFFECT): A tool to estimate the real-world impact of health interventions.

Authors:  Karla I Galaviz; Mohammed K Ali; Jeehea Sonya Haw; Matthew James Magee; Alysse Kowalski; Jingkai Wei; Audrey Straus; Mary Beth Weber; Theo Vos; Christopher Murray; K M V Narayan
Journal:  Diabetes Res Clin Pract       Date:  2019-05-29       Impact factor: 5.602

2.  MMiDaS-AE: Multi-modal Missing Data aware Stacked Autoencoder for Biomedical Abstract Screening.

Authors:  Eric W Lee; Byron C Wallace; Karla I Galaviz; Joyce C Ho
Journal:  Proc ACM Conf Health Inference Learn (2020)       Date:  2020-04-02

Review 3.  Effects of dietary and physical activity interventions on the risk of type 2 diabetes in South Asians: meta-analysis of individual participant data from randomised controlled trials.

Authors:  Anne Karen Jenum; Idunn Brekke; Ibrahimu Mdala; Mirthe Muilwijk; Ambady Ramachandran; Marte Kjøllesdal; Eivind Andersen; Kåre R Richardsen; Anne Douglas; Genevieve Cezard; Aziz Sheikh; Carlos A Celis-Morales; Jason M R Gill; Naveed Sattar; Raj S Bhopal; Erik Beune; Karien Stronks; Per Olav Vandvik; Irene G M van Valkengoed
Journal:  Diabetologia       Date:  2019-06-15       Impact factor: 10.122

Review 4.  mHealth prompts within diabetes prevention programs: a scoping review.

Authors:  Megan M MacPherson; Kohle J Merry; Sean R Locke; Mary E Jung
Journal:  Mhealth       Date:  2022-04-20

5.  Early Outcomes From the English National Health Service Diabetes Prevention Programme.

Authors:  Jonathan Valabhji; Emma Barron; Dominique Bradley; Chirag Bakhai; Jamie Fagg; Simon O'Neill; Bob Young; Nick Wareham; Kamlesh Khunti; Susan Jebb; Jenifer Smith
Journal:  Diabetes Care       Date:  2019-11-12       Impact factor: 19.112

Review 6.  Issues in Defining the Burden of Prediabetes Globally.

Authors:  Justin B Echouffo-Tcheugui; Andre P Kengne; Mohammed K Ali
Journal:  Curr Diab Rep       Date:  2018-09-19       Impact factor: 4.810

7.  Effects of "plate model" as a part of dietary intervention for rehabilitation following myocardial infarction: a randomized controlled trial.

Authors:  Ranil Jayawardena; Piumika Sooriyaarachchi; Pavani Punchihewa; Niroshan Lokunarangoda; Anidu Kirthi Pathirana
Journal:  Cardiovasc Diagn Ther       Date:  2019-04

Review 8.  Prediabetes and What It Means: The Epidemiological Evidence.

Authors:  Justin B Echouffo-Tcheugui; Elizabeth Selvin
Journal:  Annu Rev Public Health       Date:  2021-12-23       Impact factor: 21.981

Review 9.  Phenotyping the Prediabetic Population-A Closer Look at Intermediate Glucose Status and Cardiovascular Disease.

Authors:  Elena Barbu; Mihaela-Roxana Popescu; Andreea-Catarina Popescu; Serban-Mihai Balanescu
Journal:  Int J Mol Sci       Date:  2021-06-25       Impact factor: 5.923

10.  Prediction of type 2 diabetes mellitus based on nutrition data.

Authors:  Andreas Katsimpris; Aboulmaouahib Brahim; Wolfgang Rathmann; Anette Peters; Konstantin Strauch; Antònia Flaquer
Journal:  J Nutr Sci       Date:  2021-06-21
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.