Literature DB >> 33444159

Regression to the Mean Contributes to the Apparent Improvement in Glycemia 3.8 Years After Screening: The ELSA-Brasil Study.

Maria Inês Schmidt1, Paula Bracco2, Scheine Canhada2, Joanna M N Guimarães3, Sandhi Maria Barreto4,5, Dora Chor6, Rosane Griep6, John S Yudkin5, Bruce B Duncan2.   

Abstract

OBJECTIVE: Glycemic regression is common in real-world settings, but the contribution of regression to the mean (RTM) has been little investigated. We aimed to estimate glycemic regression before and after adjusting for RTM in a free-living cohort of adults with newly ascertained diabetes and intermediate hyperglycemia (IH). RESEARCH DESIGN AND METHODS: The Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) is a cohort study of 15,105 adults screened between 2008 and 2010 with standardized oral glucose tolerance test and HbA1c, repeated after 3.84 ± 0.42 years. After excluding those receiving medical treatment for diabetes, we calculated partial or complete regression before and after adjusting baseline values for RTM.
RESULTS: Regarding newly ascertained diabetes, partial or complete regression was seen in 49.4% (95% CI 45.2-53.7); after adjustment for RTM, in 20.2% (95% CI 12.1-28.3). Regarding IH, regression to normal levels was seen in 39.5% (95% CI 37.9-41.3) or in 23.7% (95% CI 22.6-24.3), depending on use of the World Health Organization (WHO) or the American Diabetes Association (ADA) definition, respectively; after adjustment, corresponding frequencies were 26.1% (95% CI 22.4-28.1) and 19.4% (95% CI 18.4-20.5). Adjustment for RTM reduced the number of cases detected at screening: 526 to 94 cases of diabetes, 3,118 to 1,986 cases of WHO-defined IH, and 6,182 to 5,711 cases of ADA-defined IH. Weight loss ≥2.6% was associated with greater regression from diabetes (relative risk 1.52, 95% CI 1.26-1.84) and IH (relative risk 1.30, 95% CI 1.17-1.45).
CONCLUSIONS: In this quasi-real-world setting, regression from diabetes at ∼4 years was common, less so for IH. Regression was frequently explained by RTM but, in part, also related to improved weight loss and homeostasis over the follow-up.
© 2020 by the American Diabetes Association.

Entities:  

Year:  2020        PMID: 33444159     DOI: 10.2337/dc20-2030

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


  2 in total

1.  Using Patient Health Profile Evaluation for Predicting the Likelihood of Retinopathy in Patients with Type 2 Diabetes: A Cross-Sectional Study Using Latent Profile Analysis.

Authors:  Shang-Jyh Chiou; Kuomeng Liao; Kuan-Chia Lin; Wender Lin
Journal:  Int J Environ Res Public Health       Date:  2022-05-17       Impact factor: 4.614

2.  The state of diabetes treatment coverage in 55 low-income and middle-income countries: a cross-sectional study of nationally representative, individual-level data in 680 102 adults.

Authors:  David Flood; Jacqueline A Seiglie; Matthew Dunn; Scott Tschida; Michaela Theilmann; Maja E Marcus; Garry Brian; Bolormaa Norov; Mary T Mayige; Mongal Singh Gurung; Krishna K Aryal; Demetre Labadarios; Maria Dorobantu; Bahendeka K Silver; Pascal Bovet; Jutta M Adelin Jorgensen; David Guwatudde; Corine Houehanou; Glennis Andall-Brereton; Sarah Quesnel-Crooks; Lela Sturua; Farshad Farzadfar; Sahar Saeedi Moghaddam; Rifat Atun; Sebastian Vollmer; Till W Bärnighausen; Justine I Davies; Deborah J Wexler; Pascal Geldsetzer; Peter Rohloff; Manuel Ramírez-Zea; Michele Heisler; Jennifer Manne-Goehler
Journal:  Lancet Healthy Longev       Date:  2021-05-21
  2 in total

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