Katherine A Sauder1,2,3, Jeanette M Stafford4, Natalie S The5, Elizabeth J Mayer-Davis6,7, Joan Thomas6, Jean M Lawrence8, Grace Kim9, Karen R Siegel10, Elizabeth T Jensen11, Amy S Shah12, Ralph B D'Agostino4, Dana Dabelea1,2,3. 1. Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado, Aurora, Colorado, USA. 2. Department of Pediatrics, University of Colorado, School of Medicine, Aurora, Colorado, USA. 3. Department of Epidemiology, Colorado School of Public Health, Aurora, Colorado, USA. 4. Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA. 5. Department of Health Sciences, Furman University, Greenville, South Carolina, USA. 6. Department of Nutrition, University of North Carolina, Chapel Hill, North Carolina, USA. 7. Department of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA. 8. Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California, USA. 9. Department of Pediatrics, University of Washington, Seattle, Washington, USA. 10. Division of Diabetes Translation, Centers for Disease Control and Prevention, Atlanta, Georgia, USA. 11. Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA. 12. Division of Endocrinology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
Abstract
AIMS: Examine associations of dietary strategies used to manage diabetes over time with hemoglobin A1c in youth-onset type 1 or type 2 diabetes. METHODS: The SEARCH for Diabetes in Youth observational study assessed dietary strategies used by 1814 participants with diabetes (n = 1558 type 1, n = 256 type 2) at two to three research visits over 5.5 years (range 1.7-12.2). Participants reported often, sometimes, or never using 10 different dietary strategies, and use over time was categorized into five mutually exclusive groups: often using across visits; started using at later visits; sometimes using across visits; stopped using at later visits; or never using across visits. General multivariable linear models evaluated most recent A1c by use category for each strategy. RESULTS: In type 1 diabetes, A1c was lower among those who starting tracking calories (-0.4%, Tukey P < .05), often counted carbs (-0.8%, Tukey P < .001), or sometimes chose low glycemic index foods (-0.5%, Tukey P = .02) vs those with less use, while participants who never drank more milk had the lowest A1c (-0.5%, Tukey P = .04). In type 2 diabetes, A1c was lower among those who often limited high fat foods (-2.0%, Tukey P = .02) or started counting carbohydrates (-1.7%, Tukey P = .07) than those who did so less. CONCLUSIONS: For several dietary strategies, more frequent use over time was related to lower A1c in youth-onset type 1 and type 2 diabetes, suggesting these strategies can likely support diabetes management for this population. Investigation into factors predicting receipt of advice for specific strategies and corresponding impact on intake might be considered.
AIMS: Examine associations of dietary strategies used to manage diabetes over time with hemoglobin A1c in youth-onset type 1 or type 2 diabetes. METHODS: The SEARCH for Diabetes in Youth observational study assessed dietary strategies used by 1814 participants with diabetes (n = 1558 type 1, n = 256 type 2) at two to three research visits over 5.5 years (range 1.7-12.2). Participants reported often, sometimes, or never using 10 different dietary strategies, and use over time was categorized into five mutually exclusive groups: often using across visits; started using at later visits; sometimes using across visits; stopped using at later visits; or never using across visits. General multivariable linear models evaluated most recent A1c by use category for each strategy. RESULTS: In type 1 diabetes, A1c was lower among those who starting tracking calories (-0.4%, Tukey P < .05), often counted carbs (-0.8%, Tukey P < .001), or sometimes chose low glycemic index foods (-0.5%, Tukey P = .02) vs those with less use, while participants who never drank more milk had the lowest A1c (-0.5%, Tukey P = .04). In type 2 diabetes, A1c was lower among those who often limited high fat foods (-2.0%, Tukey P = .02) or started counting carbohydrates (-1.7%, Tukey P = .07) than those who did so less. CONCLUSIONS: For several dietary strategies, more frequent use over time was related to lower A1c in youth-onset type 1 and type 2 diabetes, suggesting these strategies can likely support diabetes management for this population. Investigation into factors predicting receipt of advice for specific strategies and corresponding impact on intake might be considered.
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