Abstract
The current study evaluated the impact of a whole-foods, plant-based nutrition intervention on metabolic markers of patients with chronic disease in an underserved community setting. A retrospective analysis of metabolic biomarkers preintervention and postintervention was conducted on 31 patients with metabolic disease who attended an Eating for Life group visit series. Significant decreases were found for body mass index (BMI; −0.66 [−0.91 to −0.40] kg/m2), systolic blood pressure (−12 [−19 to −5] mm Hg), total cholesterol (−20 [−29 to −10] mg/dL), low-density lipoprotein (LDL; −11.6 [−17.5 to −5.5] mg/dL), and high-density lipoprotein (HDL; −3.2 [−5.7 to −0.7] mg/dL, all
The whole-foods, plant-based (WFPB) dietary pattern has been shown to prevent and reverse multiple chronic medical conditions.
Research indicates that 80% of chronic diseases are preventable with simple lifestyle changes, such as tobacco cessation, improved diet, moderate exercise, and maintenance of a healthy weight. 1 Given that 6 of 10 adults in the United States have at least 1 chronic condition and at least 40% have 2 or more, 2 the benefits of lifestyle changes are encouraging. In particular, diet plays a central role in our current chronic disease crisis. Since 2017, diet has been the leading cause of premature morbidity and mortality in the United States, surpassing tobacco. 3 In general, the standard American diet is high in added salt, sugar, and saturated fats. This predominant dietary pattern means that only about 1 in 10 adults in the United States eat the recommended number of servings of fruits and vegetables, and more than 50% of all calories consumed come from ultraprocessed foods.4,5 These dietary trends contribute significantly to the alarming situation in the United States where over 40% of adults are obese. 6
There are many reasons for this poor diet. For instance, nutritionally poor food choices are usually the easiest because they are immediately available, low cost, and aggressively marketed to consumers. Processed foods are also carefully formulated with specific proportions of salt, sugar, and fat to make them addictive.7,8 These factors put lower-income communities at a particular risk since they often have less geographic access to healthy foods, smaller budgets to invest in diet, and lack of time to prepare foods at home.9-11 All these constraints put lower-income communities at disproportionate risk for chronic disease.
The whole-foods, plant-based (WFPB) dietary pattern has been shown to prevent and reverse multiple chronic medical conditions.12-14 It involves eating high-fiber, low-fat foods and minimizes animal-based products and processed foods. Various studies have demonstrated benefits of adherence to this dietary pattern, including weight loss,15,16 decreased hemoglobin A1C, 17 decreased cholesterol, 18 decreased risk of cardiovascular events,18-20 decreased insulin resistance, 15 and improved subjective quality of life.21,22
Several studies have investigated general dietary interventions specifically in higher-risk populations. For example, a retrospective study evaluating the effects of a cooking class on hemoglobin A1C of veterans found class attendance had a positive effect on hemoglobin A1C changes. 23 Another study investigated the effect of a 5-week educational program in Latinx patients with a hemoglobin A1C greater than 6.4%. 17 A statistically significant decrease was found for hemoglobin A1C in the treatment group. 17 At federally qualified health centers (FQHCs), documented interventions include broad dietary changes concurrently with exercise education programs. Studies have shown increased consumption of fruits and vegetables and improved metabolic markers as a result of these programs.24,25 Although evidence suggests benefits from the WFPB diet,12-20 interventions advocating a transition to this diet are uncommon at FQHCs because of their perceived cost. There may also be a perception that such interventions are irrelevant to FQHC patients.
In general, many providers working in the FQHC setting report difficulty addressing food security issues. Therefore, advocating for the WFPB diet can feel unrealistic. However, the Eating for Life dietary model has been developed to help patients with metabolic disease in underserved communities increase their intake of WFPB foods. The purpose of the current study was to retrospectively evaluate the effectiveness of the Eating for Life model on patient metabolic markers in a real-world FQHC setting.
Methods
Study Design and Participants
The current study used a retrospective chart review to evaluate weekly attendance at an Eating for Life group visit (intervention) and metabolic markers in participants with metabolic disease. Data were collected from electronic health records (Citrix Cerner PowerChart) from February 2017 through November 2018. Eligibility criteria included attendance at two-thirds of group visits and at least 1 documented diagnosis of hypertension, hyperlipidemia, obesity, elevated hemoglobin A1C, prediabetes, or type 2 diabetes mellitus. Institutional review board approval was obtained from AT Still University AZ IRB. The requirement for informed consent was waived because the study was retrospective; participants provided general consent when they enrolled as patients of the FQHC.
Eating for Life Model
The Eating for Life model consists of a hybridized approach to better dietary habits by using individual and group visits. Specifically, this model includes a one-on-one visit before and after a series of group visits. Participants were first evaluated with a regular one-on-one visit to obtain a thorough medical history, build rapport, and help them mentally prepare for making dietary shifts. Those with conditions such as insulin-dependent diabetes or renal insufficiency were more closely monitored throughout the intervention and adjustments were made, as necessary. Medications were not changed unless necessary due to a drop in blood pressure or blood sugar level that either brought the value into the abnormal range and/or made a participant symptomatic. Metabolic markers were evaluated at the beginning and end of the intervention, so participants could see the changes they were able to make. At the end of the intervention, a final one-on-one visit was scheduled to review metabolic marker results and formulate a plan with the participant about how to move forward for lasting success. Participants were also informed that they could always return for individual nutritional visits as needed but should refer to their primary care provider for regular care.
The group visit component of the Eating for Life intervention consisted of 6 to 8 weekly nutrition sessions at the FQHC. Group visits were originally intended to occur for 6 weeks but were later extended to 8 weeks to better cover material. Group visit sessions focused on helping participants incorporate more whole plant foods into their diet in order to improve their chronic disease. Each weekly visit followed a consistent format: check-in with each participant, a 20-minute educational session, a cooking demonstration, and an activity or discussion on the specifics of changing behavior. Additional activities included a guided grocery store tour with education on reading and interpreting food labels. These group visits exposed participants to others with different metabolic diagnoses, and they collectively learned how poor diet manifests as different components of metabolic syndrome and how these conditions are related. During the educational sessions, the pros and cons of dietary choices were addressed for each of the metabolic conditions included in the current study. Weekly visits lasted for 2 to 2.5 hours, and participants were provided with a prepared meal at each visit.
The group visit was taught by a physician and nurse practitioner, who had extensive knowledge of the WFPB diet. The cooking demonstrations and provided meals were intended to show participants quick, easy, and inexpensive meal options. Participants were given recipes of the various meals from each weekly visit, so they could replicate them at home. The price of the meal was also provided to reinforce the affordable nature of this eating pattern. To make this dietary program accessible to participants, all meals were designed to cost less than $3.00 per serving. We based this price on local policy relative to a maximal food stamp benefit. Assuming participants had only that benefit to pay for food, they could spend an average of $7.50 per day on food or $2.50 per meal.
Cultural differences were considered by recommending choices in general food categories, such as using “cruciferous vegetables” or “fruit” rather than specific foods. Thus, participants were able to identify foods within their cultural cuisine, and continued consumption of those foods was encouraged. We also emphasized general food storage and preparation principles rather than specific dishes, which allowed participants to continue eating many familiar foods but with slightly different preparation methods. Finally, discussions focused on general approaches to maximizing nutrient intake and absorption so participants could incorporate the concepts when preparing their preferred foods. If participants lacked a developed repertoire of recipes and meals, cookbooks of a variety of cuisines were made available.
During the Eating for Life intervention, no food categories were eliminated. Although participants were encouraged to obtain 90% to 95% of calories from whole plant foods, as evidenced by the literature, they were allowed to move at their own pace when making dietary changes. Some participants chose to increase their intake of whole plant foods without becoming entirely WFPB. This partial approach was fully supported while also providing participants with relevant information from the current literature about their health condition.
Study Procedures
Patient electronic health records were used to obtain the following participant data: sex, age, race/ethnicity, body mass index (BMI), date range of group visits, number of group visits attended, clinical diagnosis, and preintervention and postintervention metabolic markers (blood pressure, lipid panels, and hemoglobin A1C values). Age was grouped by range (<55 years, 55-64 years, or >64 years) and BMI by normal (≤24.9 kg/m2), overweight (25-29.9 kg/m2), or obese (≥30.0 kg/m2). Metabolic markers were considered clinically normal or abnormal. For blood pressure, data were grouped by systolic blood pressure (normal, <120 mm Hg; abnormal, ≥120 mm Hg) and diastolic blood pressure (normal, <80 mm Hg; abnormal, ≥80 mm Hg); those with abnormal values were classified as having hypertension. Lipid panel data were grouped by total cholesterol (normal, <200 mg/dL; abnormal, ≥200 mg/dL), low-density lipoprotein (LDL; normal, <100 mg/dL; abnormal, ≥100 mg/dL), and high-density lipoprotein (HDL; normal female, ≥50 mg/dL; normal male, ≥40 mg/dL; abnormal female, <50 mg/dL; abnormal male, <40 mg/dL); those with abnormal values were classified as having hyperlipidemia. Hemoglobin A1C data were grouped as normal (<5.7%) or abnormal (≥5.7%). Participants were asked to complete preintervention laboratory tests for the metabolic markers within 1 week of starting group visits and postintervention laboratory tests within 2 weeks of completion of group visits.
Statistical Analysis
Data were summarized using frequency and percentage or mean or median and associated 95% confidence intervals (CI). The Mantel-Haenszel χ2 test, the χ2 test, or the Fisher exact test was used to compare preintervention demographic characteristics (sex, age, race/ethnicity, BMI, and hypertension) between participants included and excluded from analysis. Data for lipid panels and hemoglobin A1C were missing for some excluded participants and were omitted from this analysis. A paired t test or Wilcoxon signed rank test was used to compare changes between preintervention and postintervention in BMI and metabolic markers overall and for each subgroup (BMI range or clinically normal or abnormal marker). For nonnormally distributed changes, median and distribution-free 95% CI were reported for all dependent variables (preintervention, postintervention, change, and percent change). Percent change was defined as the change relative to preintervention. SAS version 9.4 software (SAS Institute, Inc, Cary, NC) was used to conduct the analyses. A P value <.05 was considered statistically significant.
Results
Of the 84 participants recruited for the current study, 24 were excluded because laboratory tests for their metabolic markers were not completed, and 29 were excluded because they did not attend at least two-thirds of the group visits. Therefore, data from 31 participants were included in our analyses. Of these, 77.4% (24) were female and 71.0% (22) aged over 55 years (Table 1). Consistent with the patient population of the FQHC, most participants were White (74.2%, 23/31), and 9.7% (3/31) identified as American Indian or Alaska Native. The majority of participants were obese (61.3%, 19/31). All 31 participants had hypertension, and the majority had hyperlipidemia (63.3%, 19/30) or elevated hemoglobin A1C levels (53.3%, 16/30).
Comparison of Preintervention Demographic and Metabolic Characteristics for Those Included or Excluded From the Current Study.
Abbreviations: BMI, body mass index; NA, data not available.
Mantel-Haenszel χ2 test, χ2 test, or Fisher exact tests were used for comparisons.
Although 53 participants were excluded from analyses, metabolic markers were unavailable for 2 participants, so comparisons were based on the remaining 51 excluded participants. Laboratory test data were missing for hyperlipidemia or diabetes/prediabetes markers for excluded participants and are not reported.
Hyperlipidemia was defined as low-density lipoprotein ≥100 mg/dL.
n = 30. One participant had high triglycerides, making low-density lipoprotein unmeasurable.
n = 30. One participant was missing preintervention hemoglobin A1C data.
When comparing preintervention demographic characteristics between included and excluded study participants, excluded participants were more likely than included participants to have normal BMI (31.4% vs 9.7%), indicate unspecified/other for race/ethnicity (15.7% vs 3.2%), or have no hypertension (60.8% vs 100%) (Table 1). Of the 8 excluded participants who indicated unspecified/other for race/ethnicity, 5 declined to identify race/ethnicity; 1 identified as Asian, 1 as other, and 1 as more than 1 race.
Results for preintervention and postintervention BMI and metabolic markers are presented in Table 2. When comparing mean (95% CI) changes between preintervention and postintervention BMI and metabolic markers overall, significant decreases were found for BMI (−0.66 [−0.91 to −0.40] kg/m2), systolic blood pressure (−12 [−19 to −5] mm Hg), total cholesterol (−20 [−29 to −10] mg/dL), LDL −11.6 mg/dL [−17.5 to −5.5] mg/dL), and HDL (−3.2 [−5.7 to −0.7] mg/dL, all Ps < .01). Percent change reduction from preintervention to postintervention was 2.1% for BMI, 7.4% for systolic blood pressure, 10.0% for total cholesterol, 10.6% for LDL, and 5.9% for HDL.
Preintervention and Postintervention Changes in Body Mass Index and Metabolic Markers From Participation in the Eating for Life Program Overall and by Clinically Normal or Abnormal Subgroups.
Abbreviations: BMI, body mass index; DBP, diastolic blood pressure; HDL, high-density lipoprotein; LDL, low-density lipoprotein; NA, data not available; SBP, systolic blood pressure.
A paired t test or Wilcoxon signed rank test was used for comparisons.
One participant had a high triglyceride level, making LDL undetectable.
Normal was ≥50 mg/dL for females and ≥40 mg/dL for males.
Abnormal was <50 mg/dL for females and <40 mg/dL for males.
One participant was missing preintervention hemoglobin A1C data.
The Wilcoxon signed rank test with median and associated distribution-free 95% CI was used due to nonnormal distribution.
For subgroup comparisons, participants with clinically normal preintervention BMI or metabolic markers ranged from 0% (0/31) for systolic blood pressure to 61% (19/31) for HDL (Table 2). The mean (95% CI) change increased significantly for diastolic blood pressure (6.0 [1.9 to 10.1] mm Hg) and decreased for total cholesterol (−17 [−26 to −8] mg/dL, both Ps ≤ .008).
For subgroup comparisons, participants with clinically abnormal preintervention BMI or metabolic markers ranged from 39% (12/31) for HDL to 100% (31/31) for systolic blood pressure (Table 2). The mean (95% CI) change significantly decreased for overweight (−0.45 [−0.85 to −0.05]) and obese (−0.76 [−1.13 to −0.39]) BMI, systolic blood pressure (−12 [−19 to −5] mm Hg), total cholesterol −22 [−40 to −4] mg/dL), and LDL (−15.6 [−23.8 to −7.4] mg/dL, all Ps ≤ .03).
Discussion
The current study retrospectively evaluated the effectiveness of the Eating for Life model on patient metabolic markers in the FQHC setting. Results suggested our targeted intervention advocating for transition to a WFPB dietary pattern significantly improved overall outcomes for BMI, systolic blood pressure, total cholesterol, LDL, and HDL. No significant changes were found overall for diastolic blood pressure or hemoglobin A1C.
Instead of the expected decrease in diastolic blood pressure between preintervention and postintervention, this metabolic marker increased from 79.8 to 81.8 mm Hg, but the change was not statistically significant. Combined with the already normal nature of the preintervention value, the mild increase in diastolic blood pressure was likely caused by chance and lacks clinical significance.
Although seemingly undesirable, HDL also decreased. This outcome was expected since HDL decreases when there is an overall reduction in total cholesterol. 26 In the current study, total cholesterol decreased 10.0%, LDL 10.6%, and HDL 5.9%. Because the HDL reduction was smaller than the LDL reduction, the overall metabolic profile improved. It would have been helpful to be able to assess triglyceride values as well. However, given that many patients did not come in with fasting blood work, it would not have been possible to accurately assess the difference between these preintervention and postintervention values. Furthermore, requiring fasting blood work would necessitate a separate trip to the FQHC and likely reduce the number of study participants.
The current study found no statistically significant differences between preintervention and postintervention for hemoglobin A1C. This result was not surprising since hemoglobin A1C represents the change in glycation of red blood cells through their 120-day life span. In general, the blood work for laboratory tests was performed at the start of the study intervention and approximately 7 weeks later, on the last day of the group visit. We used this timing for metabolic marker data because of the transient nature of the FQHC’s patient population and related difficulty of obtaining follow-up laboratory tests. Accordingly, the study intervention was not long enough to fully capture physiological changes in hemoglobin A1C.
It is promising to find these results in an underserved community setting where patients often do not have the time and/or budget to attend private classes. Although our intervention provided guidance for adoption of the WFPB diet, participants were required to purchase and prepare their own food, forcing them to find ways to make sustainable changes. Furthermore, while the class advocated a WFPB approach, we were cognizant of the challenges in making such changes quickly. A review of group visit notes showed that only a few participants completely changed their diet to 100% plant-based in the first week. In fact, by the end of the class series, a majority of participants were still attempting to become at least 90% WFPB. Our metabolic marker results suggested that even these incremental changes had substantial health benefits.
For example, in the current study, we found a reduction of 11.7 mm Hg for systolic blood pressure. A 2016 meta-analysis reported that every 10 mm Hg reduction in systolic blood pressure significantly reduced the risk of major cardiovascular disease events, coronary heart disease, stroke, and heart failure. 26 Overall, this reduction led to an estimated 13% reduction in all-cause mortality. 26
Our findings were also similar to other interventions advocating the WFPB eating pattern. For instance, the Complete Health Improvement Plan (CHIP), a lifestyle-based program initiated in 1986, has been implemented around the world and is a private program that requires participants to pay for the course. Comparing data from over 4500 participants of the professionally delivered CHIP program 27 to the current study indicated similar findings. Percent reduction outcomes were nearly the same between our study and the CHIP program for BMI (2.1 vs 3.5), total cholesterol (10.0 vs 11.3), LDL (10.6 vs 12.5), and HDL (5.9 vs 9.1). 27 Of note, the HDL outcome for the CHIP program 27 decreased as in our study, likely for the same reason. Overall results suggest that the Eating for Life intervention can be as effective as a private pay program offered to a population with more resources.
The current study had several limitations. For instance, there are challenges that are inherent in a community-based intervention. We had to exclude 24 participants because they were unable to complete the required metabolic marker laboratory tests. Often, lack of insurance or insurance limitations prevented them from completing the tests. As an example, sometimes insurance will not pay for tests repeated within 3 months. Another limitation was poor attendance at group visits. About a third of recruited participants did not attend at least two-thirds of group visits. To better evaluate whether the Eating for Life model tended to exclude certain demographic groups, we compared those who successfully completed the study with those who did not. Excluded participants were more likely to be younger with a normal BMI and no hypertension. Perhaps a healthier personal profile was a driving factor for those who did not complete the intervention. We found no difference between these groups based on sex or race/ethnicity; the 2 largest minority communities served by the study’s FQHC are Hispanic/Latinx and American Indian or Alaska Native, and participants with those race/ethnicities successfully completed the study intervention. To address these limitations and potentially improve participant retention, future studies should consider assessing insurance status of participants, lack of attendance at group visits because of conflicts with employment or childcare, and perceived benefit of the intervention information.
To determine the long-term effects of the Eating for Life model, longitudinal studies could be conducted with follow-up laboratory tests or interviews with participants. To address problems with the costs of laboratory tests in underserved populations, researchers could pursue grant funding to provide financial assistance to participants when insurance will not cover the tests. Future Eating for Life model interventions should consider offering the program in multiple languages. In the current study, our intervention was only offered in English, but about a tenth of the FQHC’s population prefers another language. To better assess the applicability of this intervention, future studies should offer it in other languages, such as Spanish or Navajo. Making the intervention more accessible to a variety of participants with different life circumstances may increase recruitment and retention outcomes.
Conclusion
Our retrospective evaluation of the effectiveness of the Eating for Life model on patient metabolic markers in the FQHC setting found improvements in BMI, systolic blood pressure, total cholesterol, and LDL. These findings support data from other interventions advocating a WFPB eating pattern among participants with the resources to pay for a private course. To our knowledge, the current study is the first to document the use of a WFPB intervention in an FQHC setting. These results suggested that group visits advocating for a WFPB diet can be effective in low-income, underserved communities to decrease risk factors contributing to chronic diseases.
Footnotes
Acknowledgements
We would like to thank Shalini Bhatia, Joyce Davidson, Deborah Goggin, Asta Jakobsson, Steve King, Vanessa Pazdernik, and Vinh Vu.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
