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Record W4417427934 · doi:10.2196/75672

Low-Carbohydrate Nutrition Counseling With Continuous Glucose Monitoring to Improve Metabolic Health Among Veterans With Type 2 Diabetes: Pilot Quality Improvement Initiative Study

2025· article· en· W4417427934 on OpenAlexvenueno aff
Cassie D. Turner, K Patel, K Freeman, Larry Ruff, Jamie Michaels, Timothy W. Bodnar, Laura R. Saslow, James Henderson, Lauren Oshman, Caroline R. Richardson, Devvrat Malhotra, A. Mark Fendrick, Garth W. Strohbehn, Dina Hafez

Bibliographic record

VenueJMIR Diabetes · 2025
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsnot available
FundersYale New Haven HospitalHealth Services Research and DevelopmentU.S. Department of DefenseNational Pharmaceutical CouncilAgency for Healthcare Research and QualityNational Institutes of HealthYale UniversityArnold VenturesEli Lilly and CompanyNational Institute of Diabetes and Digestive and Kidney DiseasesBoehringer IngelheimU.S. Department of Veterans Affairs
KeywordsQuality managementTest (biology)CohortContinuous glucose monitoringCohort studyBlood glucose monitoringType 2 diabetesMEDLINE

Abstract

fetched live from OpenAlex

Background: One in 4 Veterans who receive care through the Veterans Health Administration has type 2 diabetes (T2D). Dietary carbohydrate restriction can promote weight loss and improve blood glucose control, but Veterans taking certain medications (eg, insulin) may experience serious complications (eg, hypoglycemia) without adequate support and monitoring. Objective: This study aims to develop and evaluate the feasibility, acceptability, and clinical effectiveness of a pilot low-carbohydrate (LC) nutrition counseling program guided by continuous glucose monitoring (CGM) for Veterans with T2D receiving insulin (ie, LC-CGM). Methods: This is a pragmatic, nonrandomized, pre-post quality improvement pilot program. Eligible patients were Veterans with T2D who were prescribed ≥3 daily injections of insulin. The 24-week LC-CGM program consisted of virtual visits with a registered dietitian (RD) and clinical pharmacy practitioner (CPP); CGM data were used to guide tailored nutrition counseling and de-escalation or cessation of glucose-lowering medications. To evaluate changes from baseline, intention-to-treat analyses were conducted for all enrollees, with separate analyses for program completers. Primary outcomes were program feasibility and acceptability (ie, program enrollment and completion rates and mean number of RD and CPP visits). Secondary outcomes included mean weight change, percent weight loss, achievement of ≥5% and ≥10% weight loss, change in glucose-lowering medication use, and change in laboratory measures (eg, hemoglobin A1c [HbA1c]). Results: Program evaluation occurred from March 19, 2021, to May 3, 2024. Among 43 Veterans referred to the LC-CGM program, 38 (88%) enrolled. Most were men (37/38, 97%), white (29/38, 76%), with an average age of 63.7 (SD 9.6) years. Mean BMI and HbA1c were 38.1 (SD 5.8) kg/m2 and 7.8% (SD 1.3). Of 38 enrollees, 27 (71%) completed the program. Enrollees averaged 9.5 (SD 3.3) RD visits and 12.8 (SD 4.7) CPP visits. In intention-to-treat analyses, mean weight change was -11.5 kilograms (SD 8.7; 95% CI -14.4 to -8.6), corresponding to 9.5% weight loss (SD 7.2; 95% CI -14.9 to -4.2), with 58% (22/38) achieving ≥5% weight loss and 32% (12/38) achieving ≥10% weight loss. Overall, use of glucose-lowering medications decreased from 3.5 (SD 0.8) per patient at baseline to 2.4 (SD 0.9) per patient at 24 weeks (P<.001), with 72% (26/36) of Veterans discontinuing short-acting insulin and 50% (18/36; P<.001) discontinuing long-acting insulin. Use of glucagon-like peptide-1 receptor agonists increased from 39% (15/38) at baseline to 61% (23/38) at 24 weeks (P=.02). Among program completers (n=27), mean percent weight loss was -11.8% (SD 6.5) and median HbA1c decreased by 0.7% (95% CI -0.9 to -0.3; P=.001). Conclusions: This pilot program provides preliminary evidence that supports feasibility, acceptability, and clinical effectiveness among Veterans with T2D. Additional research is needed to rigorously test longer-term clinical and cost-effectiveness among a larger cohort of eligible Veterans.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.315
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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