Evidence mapPaperPMID 40749968Full record

ArticleThe American journal of clinical nutrition2025

Associations of diet composition and quality with continuous glucose monitor-derived glycemic metrics in a community-based cohort.

Bahar Bakhshi, Naznin Sultana, Honghuang Lin, David Fei, Valeria Vallejo, Abigail Gatanti, Devin W Steenkamp, Joanne M Murabito, Nicola M McKeown, Maura E Walker and 1 more

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Article in The American journal of clinical nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Bahar BakhshiSection of Endocrinology, Diabetes, Nutrition, and Weight Management, Boston University Chobanian and Avedisian School of Medicine (BUCASM) and Boston Medical Center, Boston, MA, United States. Electronic address: baharb@bu.edu.
Naznin SultanaDepartment of Biostatistics, Boston University School of Public Health (BUSPH), Boston, MA, United States.
Honghuang LinDepartment of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.
David FeiDepartment of Medicine, Section of General Internal Medicine, BUCASM and Boston Medical Center, Boston, MA, United States.
Valeria VallejoSection of Endocrinology, Diabetes, Nutrition, and Weight Management, Boston University Chobanian and Avedisian School of Medicine (BUCASM) and Boston Medical Center, Boston, MA, United States.
Abigail GatantiSection of Endocrinology, Diabetes, Nutrition, and Weight Management, Boston University Chobanian and Avedisian School of Medicine (BUCASM) and Boston Medical Center, Boston, MA, United States.
Devin W SteenkampSection of Endocrinology, Diabetes, Nutrition, and Weight Management, Boston University Chobanian and Avedisian School of Medicine (BUCASM) and Boston Medical Center, Boston, MA, United States.
Joanne M MurabitoDepartment of Medicine, Section of General Internal Medicine, BUCASM and Boston Medical Center, Boston, MA, United States; Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA United States.
Nicola M McKeownDepartment of Health Sciences, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, United States.
Maura E WalkerBoston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA United States; Department of Health Sciences, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA, United States.
Nicole L SpartanoSection of Endocrinology, Diabetes, Nutrition, and Weight Management, Boston University Chobanian and Avedisian School of Medicine (BUCASM) and Boston Medical Center, Boston, MA, United States; Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA United States.

Funding

Continuous glucose monitoring: determinants and prediction of diabetes mellitus development in the Framingham Heart StudyR01DK129305 · NIDDK · BOSTON UNIVERSITY MEDICAL CAMPUS · 2022 to 2025
$1.3M
THE FRAMINGHAM HEART STUDY-N01HC25195-268025195N01HC025195 · TRUSTEES OF BOSTON UNIVERSITY · 2002 to 2005
NHLBI NIH HHS 75N92019D00031NHLBI NIH HHS HHSN268201500001CNHLBI NIH HHS HHSN268201500001INHLBI NIH HHS N01 HC025195NIDDK NIH HHS R01 DK129305
6 · The paper itself

Abstract

backgroundContinuous glucose monitors (CGMs) offer real-time assessment of glucose concentrations, providing an opportunity to understand the determinants of glycemic variability.

objectivesWe aimed to cross-sectionally evaluate the associations between carbohydrate substitution, indices of diet, and carbohydrate quality with CGM-derived measures in individuals without diabetes.

methodsParticipants from the Framingham Heart Study, with ≥3 days of CGM data and ≥2 days of dietary records, were included in this analysis. CGM-derived glycemic traits were calculated using the "cgmanalysis" R package. Multivariable linear regression models were used to derive beta coeffcients and standard erros (SE) from the relationships of carbohydrate substitution, carbohydrate quality (fiber intake and carb-to-fiber ratio), and indices of diet quality (healthy eating index, alternate healthy eating index, dietary approaches to stop hypertension and alternate Mediterranean diet score) with CGM-derived measures among all participants and stratified by glycemic status. Least-squares means were estimated to visualize adjusted group differences of percent time spent above 140 mg/dL across quartiles of diet and carbohydrate quality metrics.

resultsWe included 677 individuals in this analysis (56.9% with normoglycemia, 59.4% female, mean age 60.1 y, mean glucose 117.7 mg/dL). Overall, higher diet and carbohydrate quality were associated with favorable CGM-derived measures. Replacing 5% energy intake from protein with the equivalent from carbohydrate was associated with 0.97 mg/dL higher CGM mean glucose (SE = 0.47; P = 0.04). The associations of diet quality with glycemic variability were typically more pronounced among those with normoglycemia, but for those with prediabetes, consuming a diet with >1 g fiber for every ∼9 g carbohydrates was associated with 7%-10% lower time spent >140 mg/dL compared with higher carb-to-fiber ratios (P-trend < 0.001).

conclusionsOur findings demonstrate that carbohydrate intake and quality, in addition to overall diet quality, are associated with dynamic fluctuations in glucose concentrations. Future prospective analysis should examine whether glycemic variability mediates the association between diet and incident type 2 diabetes.

Indexed as

Blood GlucoseBlood Glucose Self-MonitoringDietDietary CarbohydratesAgedCohort StudiesCross-Sectional StudiesDietary FiberDiet, HealthyFemaleGlycemic IndexHumansMaleMiddle AgedBlood GlucoseDietary CarbohydratesDietary Fibercarbohydrate qualityContinuous glucose monitoringdiet qualityglycemic variabilitytype 2 diabetes

Identifiers

PMID40749968
PMCPMC12495531

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.