About the Author(s)


Mark Cucuzzella Email symbol
School of Medicine, West Virginia University, Morgantown, West Virginia, United States

Houlton Regional Hospital, Houlton Maine, United States

Bronson Dant symbol
Department of Training and Nutrition, BD Solutions LLC, Reston, United States

Linda M. Julian symbol
Department of Education, Kent State University, Kent, United States

Dawn R. White symbol
School of Health Professions, National University, San Diego, United States

Glaser Center for Grounded Theory, Institute for Research and Theory Methodologies, Poway, United States

Bernerd College, University of the Pacific, Stockton, United States

Tim A. White symbol
Glaser Center for Grounded Theory, Institute for Research and Theory Methodologies, Poway, United States

School of Health Sciences, American Public University System, Charles Town, United States

Department of Global Health Services and Administration, University of Maryland Global Campus, Adelphi, United States

Citation


Cucuzzella M, Dant B, Julian LM, White DR, White TA. Low-carb nutrition education for metabolic health in veterans: A Veterans Affair clinical audit. J. metab. health. 2026;9(1), a141. https://doi.org/10.4102/jmh.v9i1.141

Clinical Audit

Low-carb nutrition education for metabolic health in veterans: A Veterans Affair clinical audit

Mark Cucuzzella, Bronson Dant, Linda M. Julian, Dawn R. White, Tim A. White

Received: 03 Dec. 2025; Accepted: 31 Mar. 2026; Published: 19 May 2026

Copyright: © 2026. The Authors. Licensee: AOSIS.
This work is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/).

Abstract

Background: United States (US) veterans face high rates of obesity and type 2 diabetes (T2D), with traditional interventions like VA MOVE! achieving limited success. Non-prescriptive low-carbohydrate (LC) nutrition education may offer a scalable alternative but remains under-evaluated.

Aim: To describe metabolic outcomes observed following the introduction of informal LC nutrition education among veterans with obesity, prediabetes or T2D in a rural Veterans Affair (VA) primary care clinic.

Setting: This clinical audit was conducted in a rural Veterans Affairs primary care clinic at the Martinsburg Veterans Administration Medical Center, where LC nutrition education was delivered during routine care visits without structured programming or additional clinical resources.

Methods: A retrospective audit at the Martinsburg Veterans Administration Medical Centre included 481 veterans with obesity, prediabetes or T2D who received informal LC education during a routine primary care visit. No structured protocol was used. Metabolic markers were measured at baseline and at the final visit, with an average of 10 months later. All eligible patients receiving education were included.

Results: Following introduction of the LC education approach, there was a statistically significant reduction in hemoglobin A1c (HbA1c) and weight, as well as discontinuation or reduction of insulin in 23 or 38 insulin-using veterans and of non-insulin glucose-lowering medications in 45 of 123 veterans. These reductions occurred in the context of an average 10-pound weight loss and a mean HbA1c reduction of 0.43% in those with prediabetes or T2D. Additionally, there was a statistically significant reduction in diastolic blood pressure (BP), triglycerides and the triglyceride-to-high-density lipoprotein (HDL) ratio, along with a 4.2-point increase in HDL cholesterol.

Discussion: Observed improvements in metabolic markers and medication use suggest that informal LC education may be a promising low-burden approach within primary care settings. Larger randomised trials are needed to assess scalability and long-term outcomes.

Conclusion: Informal low-carbohydrate nutrition education may represent a feasible and scalable approach to improving metabolic health and reducing medication burden among veterans. Further controlled studies are needed to confirm these findings and assess long-term outcomes, adherence, and generalisability.

Keywords: low-carbohydrate diet; veterans; type 2 diabetes; T2D; prediabetes; obesity; metabolic syndrome; VA healthcare; veteran healthcare.

Background

Metabolic dysfunction, including obesity, prediabetes and type 2 diabetes (T2D), is a critical public health issue among US military veterans, driven by physical, psychological and socioeconomic challenges unique to this population. Over 40% of veterans are obese, with rates exceeding 50% among those aged 45–64, and T2D prevalence reaches 31% in high-risk areas like Martinsburg, West Virginia.1,2,3 These conditions contribute to cardiovascular disease, kidney failure and amputations, as well as unsustainable costs. Although the current cost of diabetes is ‘unknown’,4 ‘the direct cost of diabetes-related VA services for veterans totalled $1.67 billion in 1998’.5 Following national trends, this cost has likely increased by a factor of 4 since 1998.

The Weight Management Programme, VA MOVE!, which emphasises calorie restriction and physical activity, has limited long-term efficacy. Dropout rates remain high, with 60% – 70% dropout rates (> 90% for post 9/11 veterans). Among those who stay in the programme, only 20% – 26% achieve ≥ 5% weight loss at 12 months.6,7,8,9,10 Emerging evidence from the VA11 and a recent meta-analysis12 supports the use of low-carbohydrate (LC) diets to address insulin resistance, a key driver of T2D and obesity. Still, structured LC interventions require significant resources, limiting scalability in primary care settings.

Aim and setting

This study is a clinical audit describing metabolic outcomes observed after the introduction of informal LC nutrition education during routine primary care visits. The purpose was to address the evidence gap regarding whether informal, non-prescriptive LC nutrition education can produce clinically meaningful improvements in metabolic outcomes among rural veterans, a population with elevated rates of obesity, prediabetes and T2D. Together, the aim and purpose are to document how this low-burden educational approach was implemented in routine care and to describe the metabolic outcomes observed among veterans exposed to the intervention.

Methods

Study design and participants

This study is a retrospective interventional audit conducted at the Martinsburg VA Medical Centre, a rural facility in West Virginia serving 70 000 veterans.13 As a retrospective audit, the study used existing clinical records from routine care encounters and follow-up visits to evaluate practice patterns and metabolic changes over time. The participants were identified from approximately 1100 veterans seen at the Martinsburg VA Medical Centre Post-9/11 Clinic between February 2023 and February 2025. Patients who presented with obesity (body mass index [BMI] ≥ 30), prediabetes (HbA1c 5.7% – 6.4%) or T2D (HbA1c ≥ 6.5%) were identified during their primary care visit.

Patients had laboratory tests, such as HbA1c and lipid profiles, ordered based on clinical need and risk assessment as part of the standard of care. Patients meeting the criteria were provided with informal clinical education on LC nutrition. Veterans who returned for at least one follow-up visit were included in this study (n = 481).

Intervention

The intervention involved brief clinical education on LC nutrition provided during routine primary care visits. The education session, lasting approximately 10–15 min, included: (1) an overview of insulin resistance and potential benefits of low carb for managing obesity, prediabetes and T2D,14 (2) examples of LC foods and foods to avoid, (3) sharing the ‘Low Carb on Any Budget’1 book15 for education, and (4) recommendation to follow the food lists and eat mindfully to satiety. No meal plans or follow-up dietary counselling were prescribed, and patients were not referred to structured programmes or dietitians. Implementation of the provided information was left to the patients’ discretion. This non-prescriptive approach was intended to document outcomes following the implementation of minimal-intervention dietary education within the constraints of a busy primary care setting.

Data collection

Clinical data were collected at existing electronic health records (EHRs), documenting routine medical appointments between February 2023 and February 2025. Baseline measurements, such as weight and BP, were recorded at the initial and subsequent office visits, as part of standard clinical practice. Laboratory tests, including HbA1c and lipid panel, were ordered as clinically indicated during routine care, and the corresponding EHR results were extracted for the audit.

For this retrospective review, data were extracted on demographics and all available metabolic markers obtained during routine primary care appointments. Because the study sought to evaluate whether informal, non-prescriptive LC nutrition education could produce meaningful improvements in metabolic outcomes among rural veterans, all individuals who met the inclusion criteria (HbA1C ≥ 5.7 or BMI ≥ 30) had received brief clinical education on LC approaches during their initial visit. These routinely collected measures allowed assessment of metabolic change over time following exposure to the LC educational intervention. All data were de-identified to ensure that no personally identifiable information was included in the collection or analysis.

Data analysis

Baseline and follow-up measurements were extracted from the EHR for each participant’s initial visit and for any subsequent visits with valid HbA1c or weight with a mean difference between visits of 10 months. Statistical analyses were conducted using a two-tailed matched-pairs t-test in IBM SPSS Statistics (Version 30) to evaluate whether changes in metabolic markers over time were statistically significant. A significance level of p < 0.05 was used for all tests.

Figure 1 provides information on the study population and sample used. Because this study used data collected in the course of a general practice, not all participants were included in every analysis. Listwise deletion was used to exclude participants only from the specific analysis with no follow-up data, not from the study overall. Thus, the sample size for each t-test varies. These analyses were used to describe the magnitude and direction of observed changes within the cohort. Because the audit did not include a comparison group or adherence monitoring, statistical testing was not intended to establish causal inference. Missing data for each analysis were handled via listwise deletion, and a significance level of p < 0.05 was used for all tests.

FIGURE 1: PRISMA flowchart.

Ethical considerations

This audit was approved by the VA VISN 5 IRB as a Quality Improvement (QI) project, and used fully de-identified data with a waived consent for retrospective analysis. Patients were informed that anonymised clinical information could be used for research and quality improvement purposes. The audit followed the 2023 Americans with Disabilities Act (ADA) Standards of Care and the VA and Department of Defense (DOD) Clinical Practice Guidelines for Obesity and T2D Management, focusing on improved glycaemic control, weight loss, medication deintensification and lipid profiles to achieve a healthier triglyceride-to-HDL ratio, a marker of cardiometabolic health.

Results

Of the 481 study participants, 475 were male, and six were female. Table 1 provides descriptive statistics for each variable collected at the initial visit for patients identified as obese (BMI ≥ 30), prediabetic or T2D (HbA1c ≥ 5.7). The table presents the mean and standard deviation (s.d.) for each variable, along with the number of valid data points and the percentage of the population represented.

TABLE 1: Descriptive statistics for baseline cardiometabolic variables.

Table 2 presents the analysis of cardiometabolic markers at baseline and the final visit, the number and percentage of matched pairs used for each individual two-tailed paired t-test, the outcomes of the paired t-test, and the effect size measured by Cohen’s d. Each t-test was performed on the paired sample identified in Table 2. There were no multiple comparisons conducted and no omnibus null hypothesis; therefore, no adjustments were necessary to correct for Type I error.16 Patients’ mean weight loss of 9.5 pounds between baseline (mean 243.1, s.d.: 42.1) and the last visit (mean 233.6, s.d.: 43.7); t469 = 10.5, p < 0.001 was statistically significant with a mean effect size as measured by Cohen’s d (d = 49). There was a 0.03 reduction in HbA1c between baseline (mean 6.3, s.d.: 1.3) and the last visit (mean 5.9, s.d.: 1.15); t296 = 5.3, p < 0.001 representing a small effect size (d = 0.31). The difference between baseline diastolic BP (mean 84.2, s.d.: 10.4) and follow-up visit diastolic BP (Mean 83.1, s.d.: 9.5) was also statistically significant (t454 = 2.2, p = 0.02) with a negligible effect (d = 0.1). No statistically significant difference in systolic BP was found between baseline and follow-up visits (p = 0.08).

TABLE 2: Statistical analysis of cardiometabolic variables measured at baseline and final visit.

Figure 2 presents the results of a paired t-tests, on the lipid profile finding a statistically significant reduction in triglycerides at baseline (mean 227.1, s.d.: 164.7) and follow-up visit (mean 185.9, s.d.: 142.5); t176 = 3.6, p < 0.001 with a small effect (d = 0.27), and baseline triglyceride-to-high-density lipoprotein (HDL) ratio (mean 6.0. s.d.: 5.1) and follow-up visit (mean 4.6, s.d.: 4.1); t176 = 4.2 p < 0.001 representing a small effect (d = 0.32). There was also a significant increase in HDL cholesterol from baseline (mean 41.16, s.d.: 10.43) to follow-up (mean 45.4, s.d.: 20.9); t176 = 4.9, p < 0.001 and a very small effect (d = 0.22). No statistically significant difference was found between baseline and follow-up total cholesterol (p = 0.5).

FIGURE 2: Analysis of lipid profiles measured at baseline and final visit.

Figure 3 presents the outcomes for participants who, at intake, were classified as prediabetic or T2D (HbA1c ≥ 5.7). Included in this subpopulation were 238 veterans with a mean baseline HbA1c of 6.67, of whom 160 (67.2%) were categorised as prediabetic and 78 (32.8%) as T2D. At their last follow-up visit, 85 (39.4%) of these veterans had a normal HbA1c, 76 (35.2%) had HbA1c within the prediabetic range and 55 (25.5%) had HbA1c within the diabetic range.

FIGURE 3: Sub-group comparison of veterans with HbA1c ≥ 5.7 at baseline and last visit.

Figure 4 presents outcomes for the subpopulation of participants who, at intake, were identified as having a BMI ≥ 30. At baseline, 240 (52.3%) individuals were identified as Obesity Class 1, 115 (29.5%) as Obesity Class 2 and 71 (18.2%) as Obesity Class 3. At the follow-up visit, 3 (0.8%) individuals presented with a weight within the normal range, 59 (15.6%) presented as overweight, 173 (45.6%) presented as Obesity Class 1, 85 (22.4%) were Obesity Class 2 and 59 (15.6%) as Obesity Class 3.

FIGURE 4: Sub-group comparison of veterans with body mass index ≥ 30 at baseline and last visit.

Medication changes

Among veterans using insulin therapy at any time during the study (n = 38), 61% experienced a clinically meaningful reduction in insulin requirements following implementation of the LC nutrition intervention. Specifically, 12 individuals (32%) had their insulin dose decreased, and an additional 11 (29%) were able to discontinue insulin entirely. Seven participants (18%) maintained stable insulin dosing, while eight (21%) required an increase or initiation of insulin therapy (Figure 5).

FIGURE 5: Diabetic medication management: (a) percentage distribution, (b) absolute count.

In veterans using non-insulin glucose-lowering medications (n = 123), a similar pattern of de-prescribing was observed. During the intervention period, 45 (37%) veterans reduced their non-insulin diabetes medications, with 21 (17%) decreasing use and 24 (20%) discontinuing them entirely. The majority (n = 73; 59%) maintained stable therapy, and only a small proportion (n = 5; 4%) required an increase or addition of medication.

A small sub-group of veterans was receiving GLP-1 receptor agonist therapy during the intervention (n = 11). Five veterans discontinued therapy during follow-up, three initiated treatments because of worsening glycaemic control, and three remained on therapy from baseline to the final visit. These shifts appeared to represent routine clinical adjustments rather than an intervention effect and may be best interpreted as incidental findings.

Discussion

This audit evaluates a non-prescriptive, informal LC nutrition education intervention in a rural VA primary care setting, addressing the high prevalence of obesity and T2D among veterans. Medication deintensification was observed in a substantial proportion of patients, occurring alongside improvements in glycaemic markers and body weight. The pattern of medication changes also illustrates how simple dietary education can produce clinically meaningful shifts without the need for programme infrastructure.

Overall, the predominant trend in this cohort was movement towards reduced reliance on exogenous insulin, consistent with improved endogenous metabolic regulation and reduced glycaemic burden. With non-insulin medical therapy, nearly nine times as many veterans achieved medication reduction or elimination compared to escalation, consistent with improved metabolic markers observed during follow-up.

GLP-1 receptor agonist use was limited in this cohort, which likely reflects historic prescribing patterns in the VA system and the period before broader adoption of these agents for obesity and diabetes management. The low prevalence of GLP-1RA therapy suggests that the improvements observed in glycaemic control, weight, and medication deintensification were primarily driven by the nutritional intervention rather than by pharmacologic intensification. This highlights the potential value of lifestyle-based approaches within veteran populations where access, cost or clinical eligibility may restrict the use of newer therapeutic agents.

Notably, these reductions occurred alongside an average 10-pound weight loss and a mean HbA1c reduction of 0.4% in the prediabetes and T2D cohort, indicating that medication deintensification reflected genuine metabolic improvement rather than undertreatment. Together, these medication outcomes demonstrate that the nutritional intervention did not merely shift glycaemic markers but also reduced pharmacologic dependence. The concurrent improvement in body weight, glycaemic control and medication de-escalation suggests metabolic healing rather than progression of diabetes severity.

The LC approach’s simplicity offers a scalable alternative to a highly effective, but more resource-intensive, LC programme like Carbohydrate Reduction Empowering Wellness (CREW), which reported 9.4 lbs – 15 lbs. of weight loss but required 16-week structured interventions.17 Metabolic improvements align with studies such as OWNA Health,18 which reported a 1.0% drop in HbA1c with LC diets, and with Virta Health’s VA pilot,19 which achieved cardiometabolic benefits over three years. These interventions required opt-in and buy-in, which is often difficult to achieve.

Veterans struggle with adherence because of veteran-specific barriers – PTSD,20 depression,21,22 anxiety,23 sleep disturbances,24,25 emotional eating26 and musculoskeletal injuries.27 These conditions are known to have a significant impact on physical health, often contributing to poor metabolic outcomes.28,29,30 The LC education leverages patient autonomy, potentially enhancing engagement in a population with complex psychosocial needs.

Gaps

Low-carbohydrate education may improve participation through its low-burden approach, but further data are needed. Suboptimal engagement and lack of dietary adherence data limit outcome assessment. The intervention’s potential cardiovascular benefits (e.g. improvements in triglyceride-HDL ratios) are critical for veterans with high heart disease rates, though LDL monitoring is warranted. Barriers such as PTSD, rural access issues, and variable clinician expertise may reduce participation and adherence. Areas of good practice included the intervention’s integration into routine care and significant medication deintensification, demonstrating feasibility and clinical impact.

Limitations

The retrospective design and lack of a control group preclude causal inference. The male-predominant cohort restricts generalisability, as women may have distinct metabolic responses. An additional limitation is the inconsistent data collected on cardiometabolic factors. For example, not all veterans had follow-up HbA1C or lipid profiles, which prevented comparisons between all veterans on all cardiometabolic markers. The non-prescriptive approach lacks data on dietary adherence or carbohydrate intake, obscuring dose-response relationships. Additionally, there were no measurements for confounders (e.g. physical activity, PTSD treatment, socioeconomic factors) that may influence outcomes, given veterans’ complex risk profiles.

Conclusion

This audit documents a brief, non-prescriptive LC nutrition education is a feasible, scalable intervention for improving metabolic health in veterans with obesity, prediabetes or T2D at a rural VA clinic. The approach’s low resource requirements make it ideal for busy primary care settings, helping address the multibillion-dollar burden of diabetes care.4 While there are several limitations, including the retrospective design, male-predominant cohort, incomplete data on cardiometabolic markers, and lack of data on compliance, there are several strengths. The use of real-world data provides insight into outcomes observed following the introduction of this intervention within routine clinical care. The low cost of the study, the time span covered, and the number of patients included in this audit are also strengths. Clinical audits such as this can provide valuable real-world observations that may inform the design of future controlled trials. Suggestions include equipping clinicians with LC nutrition training, scaling telehealth capabilities, and embedding mental health resources into care pathways. This study supports a shift towards evidence-based, patient-driven dietary strategies in VA care to enhance metabolic health. Together, these findings underscore the potential for simple, targeted nutrition counselling to strengthen chronic disease management and guide future innovations in veteran-centred metabolic care.

Acknowledgements

The authors sincerely thank their veterans for their commitment to this research. Additionally, the authors extend their sincere thanks to the VISN 5 Institutional Review Board for its support of this quality improvement project.

Competing interests

The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.

CRediT authorship contribution

Mark Cucuzzella: Conceptualisation, Data curation, Formal analysis, Investigation, Methodology, Project administration Bronson Dant: Formal analysis, Resources, Writing-original draft. Linda M. Julian: Formal analysis, Software, Validation. Dawn R. White: Methodology, Project administration, Writing-original draft, Writing-review & editing. Tim A. White: Writing-review & editing. All authors reviewed the article, contributed to the discussion of results, approved the final version for submission and publication, and take responsibility for the integrity of its findings.

Funding information

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Data availability

The data that support the findings of this study are available on request from the corresponding author, Mark Cucuzzella.

Disclaimer

The views and opinions expressed in this article are those of the authors and are the product of professional research. They do not necessarily reflect the official policy or position of any affiliated institution, funder, agency or that of the publisher. The authors are responsible for this article’s results, findings, and content.

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Footnote

1. ‘Low Carb on Any Budget’ Guidebook – www.tinyurl.com/LCanybudget.



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