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OBJECTIVE The present study aims to prospectively examine the association between post-diagnosis low carbohydrate diet (LCD) patterns and mortality among individuals with type 2 diabetes (T2D).
RSEARCH DESIGN AND METHODS Among participants with incident diabetes identified in the Nurses’ Health Study and Health Professionals Follow-Up Study, an overall total low carbohydrate diet score (TLCDS) was calculated based on the percentage of energy as total carbohydrates. In addition, vegetable (VLCDS), animal (ALCDS), healthy (HLCDS), and unhealthy LCDS (ULCDS) were further derived that emphasized different sources and quality of macronutrients. Multivariable-adjusted Cox model were used to assess the association between the LCDS and mortality.
RESULTS Among 10,101 incident T2D cases contributing 139,407 person-years during follow-up, we documented 4,595 deaths of which 1,389 cases were attributed to cardiovascular disease (CVD) and 881 to cancer. The pooled multivariable-adjusted hazard ratios (HRs, 95% CIs) of total mortality per 10 points increment of post-diagnosis LCDS were 0.87 (0.82,0.92) for TLCDS, 0.76 (0.71,0.82) for VLCDS, and 0.78 (0.73,0.84) for HLCDS. Both VLCDS and HLCDS were also associated with significantly lower CVD and cancer mortality. Each 10 points increase of TLCDS, VLCDS, and HLCDS from pre-diagnosis to post-diagnosis period was associated with 12% (7%, 17%), 25% (19%, 30%), and 25% (19%, 30%) lower total mortality, respectively. No significant associations were observed for ALCDS and ULCDS.
CONCLUSIONS Among people with T2D, greater adherence to LCD patterns that emphasize high quality sources of macronutrients was significantly associated with lower total, cardiovascular, and cancer mortality.
Yang Hu, Gang Liu, Edward Yu, Biqi Wang, Clemens Wittenbecher, JoAnn E. Manson, Eric B. Rimm, Liming Liang, Kathryn M. Rexrode, Walter C. Willett, Frank B Hu, Qi Sun (2023). Low-carbohydrate diet scores and mortality among adults with incident type 2 diabetes. , DOI: https://doi.org/10.2337/figshare.21965207.v1.
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Type
Preprint
Year
2023
Authors
12
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.2337/figshare.21965207.v1
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