Data Resource Profile: Nutrition Data in the VA Million Veteran Program
Main Article Content
Abstract
Introduction
The Department of Veterans Affairs (VA) Million Veteran Program (MVP) nutrition data is derived from dietary food/beverage intake information collected through a semiquantitative food frequency questionnaire (SFFQ).
Methods
Estimates of dietary energy, nutrient, and non-nutritive food components intakes data were derived from an extensively validated SFFQ, which assessed the habitual frequency of consumption of 61 food items, added sugar, fried food frequency, and 21 nutritional supplements over the 12 months preceding questionnaire administration.
Results
Complete nutrition data was available for 353,418 MVP participants as of 30th September 2021. Overall, 91.5% of MVP participants with nutrition data were male with an average age of 65.7 years at enrollment. Participants who completed the SFFQ were primarily White (82.5%), and Blacks accounted for 13.2% of the responders. Mean ± SD energy intake for 353, 418 MVP participants was 1428 ±616 kcal/day, which was 1434 ±617 kcal/day for males and 1364 ±601 kcal/day for females. Energy intake and information on 322 nutrients and non-nutritive food components is available through contact with MVP for research collaborations at www.research.va.gov/mvp.
Conclusions
The energy and nutrient data derived from MVP SFFQ are an invaluable resource for Veteran health and research. In conjunction with the MVP Lifestyle Survey, electronic health records, and genomic data, MVP nutrition data may be used to assess nutritional status and related risk factors, disease prevalence, and determinants of health that can provide scientific support for the development of evidence-based public health policy and health promotion programs and services for Veterans and general population.
Key features
- Estimated daily intake of energy, nutrients, and non-nutritive food components were derived from the semiquantitative food frequency questionnaire (SFFQ) of the Department of Veterans Affairs (VA) Million Veteran Program (MVP), which is a national voluntary research program designed to examine genetic influences on health with the goal of improving health for Veterans.
- As of September 2021, over 850,000 participants have enrolled in MVP with 377,811 participants completing the SFFQ, which assessed the habitual frequency of consumption of 61 food items in addition to questions about added sugar to diet, fried food frequency, and 21 nutritional supplements over the 12 months preceding questionnaire administration.
- Frequency of consumption for specified portions of each food/beverage item in the SFFQ were converted to average estimated daily intake for each participant. Energy intake and information on 322 nutrients and non-nutritive food components were then derived by multiplying the frequency of consumption for each food item by its energy and nutrient content using the Harvard University Food Composition Database and summing across all foods. Energy-adjusted nutrient intakes were estimated using a residual method on a log-scale for both energy (exposure) and nutrients (outcome).
- After removing participants with implausible SFFQ responses, two datasets consisting of information for 353,418 MVP participants were derived: one for raw nutrient intakes and another for energy-adjusted nutrient intakes. Both datasets have 353,418 rows and 325 columns including two IDs, which are linked to complementary MVP data on lifestyle, genetic and VA electronic health record (EHR) information.
- Data are currently available to VA and VA-affiliated investigators through approved MVP research projects. Interested future research collaborators can contact MVP at www.research.va.gov/mvp.
Background
The Department of Veterans Affairs (VA) Million Veteran Program (MVP) nutrition data is derived from dietary food/beverage intake information collected through a semiquantitative food frequency questionnaire (SFFQ) [1]. MVP is a national research program designed to examine how genes, lifestyle, military experiences, and exposures affect health and wellness in Veterans [1, 2]. MVP enrollment of active Veterans Health Administration (VHA) users began in 2011 with the goal of enrolling at least 1 million Veterans [1, 2]. Upon enrollment in MVP, the MVP Lifestyle Survey which includes the SFFQ is available for self-completion and is returned by mail approximately 4-8 weeks following enrollment.
In September 2021, SFFQ data on 377,811 individuals were combined with the Harvard Food Composition data [3] to derive estimates of daily consumption of energy, nutrients, and non-nutritive food components. MVP nutrition data can be directly linked to a range of health-related data sources including: i) electronic health records (EHRs) with detailed inpatient and outpatient interactions with the VA healthcare system [2]; ii) genetic information originating from blood samples provided by MVP participants [2]; and iii) information on a variety of demographic, lifestyle and health related factors originating from two self-administered questionnaires [1]. MVP nutrition data can also be linked to other outside data sources, such as data compiled from the National Death Index [4], as well as from Medicare and Medicaid [5].
Methods
Estimates of dietary energy, nutrient, and non-nutritive food components intake data were derived from an extensively validated SFFQ [6], which assessed the habitual frequency of consumption of 61 food items, added sugar, fried food frequency, and 21 nutritional supplements over the 12 months preceding questionnaire administration. We asked how often, on average, the participant had consumed a specified portion size of each of the 61 food items over the preceding year on the SFFQ. Pre-specified responses were: “Never or less than once per month”; “1–3 per month”; “once a week”; “2–4 per week”; “5–6 per week”; “once a day”; “2–3 per day”; “4–5 per day”; and “≥6 per day”. We also collected the number of teaspoons of sugar added to beverages or food each day, the frequency of eating fried food at home, and the frequency of eating fried food away from home. Frequency of consumption for specified portions of each food/beverage item in the SFFQ were converted to an average estimated daily intake for each participant. Average daily total energy intake was then calculated by multiplying the frequency of consumption of each item by its energy content from the Harvard University Food Composition Database [3] and summing across all foods.
In addition to survey questions regarding food frequency intake, we also asked about types of fats used for baking at home. Data collected was used to derive the nutrients for baking pies at home and the type of fats used for frying which was used to derive the nutrients for fried foods at home.
Supplement use included frequency of multi-vitamin consumption and intake of 20 individual nutritional supplements on a regular basis including Vitamin A, Vitamin C, Vitamin E, Vitamin B6, selenium, iron, zinc, calcium, Metamucil, Vitamin D, B-complex vitamin, cod liver oil, folic acid, omega-3 fatty acid, iodine, copper, Brewer’s yeast, beta-carotene, niacin, magnesium or other. For nutrients with supplements, we prepared two separate nutrient intake datasets: nutrient intake from foods and beverages only and nutrient intake with supplements, respectively.
In total, SFFQ data on 377,811 individuals were combined with food composition data to derive energy and nutrient intake. Participants were excluded if a full page was left blank (n = 12,969 SFFQ) or if energy intake was considered implausible which was defined as female energy intake outside the range of 400–4000 kcal/day or 450–4500 kcal/day for males. The final dataset of MVP Nutrition included 353,418 rows and 325 columns (energy intake, two identification numbers and 322 nutrients) with no missing data. This dataset contains absolute nutrient intake values.
For a second MVP nutrition dataset, we applied the residual method on a log-scale for both energy and nutrients of 1400 kcal energy intake per day for females and 1500 kcal per day for males to derive the energy-adjusted dietary intakes [7]. This second MVP nutrition dataset converted the absolute nutrient values in the first nutrient dataset to energy-adjusted nutrient values. Both the first dataset with absolute nutrient intake values and the second dataset with energy adjusted nutrient intake values have 353,418 rows and 325 columns. A comprehensive list of nutrient variables can be found in the online supplement.
Results
Nutrient data collection is ongoing in MVP. Baseline characteristics for the 353,418 MVP participants with available nutrition data as of 30th September 2021 are presented in Table 1. Overall, 91.5% were male. The majority of participants enrolled between 2013 and 2018 with an average age of 65.7 years at enrollment. Participants with Spanish, Hispanic or Latino ethnicity accounted 6% of the study population. Overall, participants who completed the SFFQ were primarily White (82.5%). Blacks accounted 13.2% of the responders with significant gender difference (18.6% of females vs. 9.6% of males, P < 0.001 using Chi-squared test). Female participants also had a relatively higher educational attainment compared to males: 64.9% of females and 45.3% of males had a college degree or above (P < 0.001 using Chisquared test). Approximately one third of MVP participants (32.5%) had an annual family income <$30,000, Table 1.
| Characteristics 2 | Male (n = 323,358) | Female (n = 28,730) | Total3 (n = 353,418) |
| Year of enrollment (n=353,418) | |||
| 2011–2012 | 2.1 | 1.6 | 2.1 |
| 2013 | 31.2 | 28.6 | 31.0 |
| 2014 | 14.4 | 14.4 | 14.4 |
| 2015 | 11.8 | 12.3 | 11.8 |
| 2016 | 10.9 | 11.3 | 10.9 |
| 2017 | 9.7 | 11.0 | 9.8 |
| 2018 | 9.0 | 9.9 | 9.1 |
| 2019 | 3.7 | 4.0 | 3.7 |
| 2020–2021 | 7.3 | 6.9 | 7.3 |
| Age (years, mean ± SD) | 66.7 ± 11.4 | 54.8 ± 13.0 | 65.7 ± 12.1 |
| Age category (n = 353,418) | |||
| 50 | 7.5 | 30.4 | 9.4 |
| 50–59 | 13.0 | 32.9 | 14.6 |
| 60–69 | 40.4 | 26.0 | 39.2 |
| 70–79 | 26.8 | 7.6 | 25.2 |
| ≥80 | 12.4 | 3.1 | 11.6 |
| Spanish, Hispanic or Latino (n = 348,023) | 5.9 | 7.5 | 6.0 |
| Race (n = 353,418) | |||
| White | 86.0 | 75.3 | 85.1 |
| Black | 9.8 | 18.9 | 10.6 |
| Asian | 0.9 | 1.3 | 0.9 |
| Others | 3.3 | 4.5 | 3.4 |
| Education (n = 312,336) | |||
| ≤ High school or GED | 24.7 | 8.7 | 23.4 |
| Some college | 30.0 | 26.4 | 29.7 |
| College or above | 45.3 | 64.9 | 46.9 |
| Annual family income (n = 282,878) | |||
| 30,000 | 32.4 | 33.2 | 32.5 |
| 30,000–59,000 | 36.1 | 33.3 | 35.8 |
| ≥60,000 | 31.5 | 33.5 | 31.7 |
Mean ± SD energy intake for 353, 418 MVP participants was 1428 ± 616 kcal/day, which was 1434 ± 617 kcal/day for males and 1364 ± 601 kcal/day for females, Table 2. Animal derived sources of food contributed 43.6 ± 12.8% to the total energy intake including 16.0 ± 10.5% from dairy and dairy products, 14.1 ± 8.4% from red meat, 9.8 ± 7.8% from chicken, 2.4 ± 2.8% from eggs and 1.4 ± 1.7% from fish. Plant-derived sources of food contributed 56.4 ± 12.8% to energy intake including 20.7 ± 9.4% from grains, cakes, and potatoes. Beverages contributed 8.8 ± 10.8% to energy intake among MVP participants, Figure 1. Selected absolute nutrient intakes are presented in Table 2. Energy contributions from protein, carbohydrate, and fats were 21.1 ± 5.2%, 42.5 ± 10.1% and 33.9 ± 7.1%, respectively, Table 2. Selected energy-adjusted nutrient intakes are presented in Table 3. The energy-adjusted average intake of sodium and potassium was 1258 ± 290 mg/day (1119 ± 259 for females; 1270 ± 290 for males) and 2622 ± 555 mg/day (2541 ± 569 for females and 2629 ± 553 for males), respectively, with an average sodium to potassium ratio (Na:K ratio) of 0.5 ± 0.2 for both female and males. Nutritional supplements were the major contributors to dietary vitamin intake, Table 3. A full list of the nutrients and mean intakes may be found in the Supplemental codebook (Supplementary Table 1).
Figure 1: food sources of energy intake (%).
| Nutrient | Male (n = 323,358) | Female (n = 28,730) | Total (n = 353,418)1 |
| Total energy, kcal/day | 1434 (617) | 1364 (601) | 1428 (616) |
| Protein, g/day | 74.6 (36.7) | 74.7 (38.5) | 74.7 (36.8) |
| Protein, % total energy | 21.0 (5.1) | 22.1 (5.7) | 21.1 (5.2) |
| Animal Protein, % total energy | 16.7 (5.5) | 17.7 (6.2) | 16.8 (5.6) |
| Vegetable Protein, % total energy | 4.3 (1.4) | 4.4 (1.6) | 4.3 (1.4) |
| Carbohydrate, g/day | 152.7 (78.5) | 151.8 (81.6) | 152.6 (78.7) |
| Carbohydrate, % total energy | 42.4 (10.0) | 44.2 (11.1) | 42.5 (10.1) |
| Starch, % total energy | 12.9 (4.9) | 12.3 (5.2) | 12.9 (4.9) |
| Added sugar, % total energy | 10.7 (7.5) | 12.5 (8.3) | 10.8 (7.6) |
| Natural sugar, % total energy | 11.2 (5.9) | 12.0 (6.3) | 11.3 (5.9) |
| Fiber, g/day | 13.3 (7.8) | 13.7 (8.7) | 13.3 (7.9) |
| Total fat, g | 53.8 (25.7) | 49.4 (24.5) | 53.5 (25.7) |
| Total fat, % total energy | 34.0 (7.1) | 32.8 (7.1) | 33.9 (7.1) |
| Saturated fat, % total energy | 11.7 (3.0) | 11.3 (3.1) | 11.7 (3.0) |
| Monounsaturated fat, % total energy | 12.6 (3.0) | 11.9 (2.9) | 12.5 (3.0) |
| Trans fat, % total energy | 0.56 (0.18) | 0.49 (0.17) | 0.55 (0.18) |
| Polyunsaturated fat, % total energy | 6.62 (1.80) | 6.55 (1.89) | 6.62 (1.81) |
| α-Linolenic acid (18:3n-3c), % total energy | 0.58 (0.16) | 0.56 (0.16) | 0.58 (0.16) |
| Long-chain n-3 fatty acids, % total energy | 0.25 (0.25) | 0.28 (0.28) | 0.25 (0.26) |
| Linoleic acid (18:2n-6cc), % total energy | 5.46 (1.60) | 5.38 (1.68) | 5.45 (1.60) |
| Arachidonic acid (20:4n-6c), % total energy | 0.16 (0.07) | 0.17 (0.09) | 0.16 (0.08) |
| Cholesterol, mg | 266.4 (164.5) | 252.2 (158.3) | 265.3 (164.1) |
| Alcohol, g | 7.9 (16.0) | 4.1 (10.3) | 7.6 (15.7) |
| Nutrient | Male (n = 323,358) | Female (n = 28,730) | Total (n = 353,418)1 |
| Protein, g/day | 78.2 (19.0) | 76.9 (19.9) | 78.1 (19.1) |
| Carbohydrate, g/day | 159.7 (37.7) | 155.5 (38.9) | 159.3 (37.9) |
| Total fat, g/day | 56.3 (11.7) | 50.8 (11.1) | 55.9 (11.8) |
| Sodium, mg/day | 1270 (290) | 1119 (259) | 1258 (290) |
| Potassium, mg/day | 2629 (553) | 2541 (569) | 2622 (555) |
| Sodium : potassium ratio | 0.5 (0.2) | 0.5 (0.2) | 0.5 (0.2) |
| Calcium, mg/day | 705.9 (330.5) | 700.9 (322.7) | 705.4 (329.8) |
| With supplements | 794.7 (408.0) | 895.8 (454.7) | 802.7 (412.9) |
| Magnesium, mg/day | 242.9 (50.9) | 238.5 (55.9) | 242.5 (51.3) |
| With supplements | 268.6 (105.9) | 277.1 (125.2) | 269.3 (107.6) |
| Zinc, mg/day | 10.7 (2.5) | 10.0 (2.3) | 10.6 (2.5) |
| With supplements | 14.3 (15.4) | 13.2 (14.5) | 14.2 (15.3) |
| Iron, mg/day | 11.2 (3.7) | 10.5 (3.5) | 11.2 (3.7) |
| With supplements | 14.2 (10.9) | 14.5 (12.3) | 14.2 (11.0) |
| Retinol Activity Equivalents, μg/day | 835 (584) | 875 (573) | 838 (583) |
| With supplements | 2021 (1984) | 1991 (1790) | 2018 (1969) |
| α-Carotene, μg/day | 745 (824) | 829 (949) | 752 (835) |
| β–Carotene, μg/day | 3960 (3751) | 4999 (4662) | 4046 (3847) |
| With supplements | 4253 (4342) | 5225 (4994) | 4333 (4409) |
| Lycopene, μg/day | 864 (1113) | 807 (1035) | 860 (1106) |
| Lutein-zeaxanthin, μg/day | 2397 (2600) | 3252 (3866) | 2468 (2738) |
| Vitamin B1, mg/day | 1.0 (0.2) | 0.9 (0.2) | 1.0 (0.2) |
| With supplements | 10.0 (21.1) | 12.0 (23.1) | 10.2 (21.3) |
| Vitamin B2, mg/day | 1.9 (0.6) | 1.8 (0.5) | 1.9 (0.6) |
| With supplements | 11.0 (21.0) | 12.8 (23.0) | 11.1 (21.2) |
| Vitamin B3, mg/day | 21.3 (5.5) | 20.5 (5.9) | 21.2 (5.5) |
| With supplements | 70.2 (138.6) | 60.9 (113.9) | 69.4 (136.7) |
| Vitamin B6, mg/day | 2.1 (0.5) | 2.0 (0.5) | 2.1 (0.5) |
| With supplements | 21.4 (45.5) | 24.5 (48.4) | 21.6 (45.7) |
| Vitamin B12, ug/day | 6.9 (5.1) | 6.0 (4.4) | 6.8 (5.1) |
| With supplements | 18.5 (22.8) | 19.7 (24.4) | 18.6 (23.0) |
| Vitamin C, mg/day | 77.6 (50.0) | 76.6 (49.3) | 77.6 (49.9) |
| With supplements | 218.7 (255.7) | 214.5 (252.1) | 218.3 (255.3) |
Discussion
The major strength of the MVP nutrition data is its comprehensive estimates of more than 200 nutrients and non-nutritive food components, which is based on food composition data that has been collected, updated, and measured for decades by Harvard dietitians and researchers. Additional strengths include the large sample size of participants with diverse socioeconomic and racial/ethnic backgrounds and the ability to link nutrition data to other datasets for the same participant (i.e. biomarkers, genetics, lifestyle factors, etc.). Lastly, nutrition data can also be linked with local, regional, and national health registries including the VA electronic health record system to provide a valuable resource for research designed to improve the health and healthcare of US Veterans [1, 2]. With ongoing data collection, the potential for repeated measurements in the future will further strengthen and enhance the quality and depth of the MVP nutrition data.
Key weaknesses include potential recall bias due to self-reported dietary intake and the potential for the abbreviated SFFQ in MVP to underestimate nutrient intakes. For example, some major sources of dietary sodium including salt added at the table and salt used in preparation or cooking were not included in the MVP SFFQ [9, 10]. Use of a self-reported questionnaire as the primary means to collect dietary data is also a potential source of measurement error. To control for measurement error to a large degree, we provided nutrients with energy adjustment that negates correlated errors in nutrient and energy intake assessments [7]. Lack of validation in the study population is another limitation that is under consideration in our future research plans.
MVP nutrient data have been used to examine different health conditions affecting Veterans. Based on MVP nutrition data, a study entitled “Dietary Sodium and Potassium Intake and Risk of Non-Fatal Cardiovascular Diseases” has been published [8], which demonstrated a linear dose-response association between dietary sodium intake and Na:K ratio with risk of cardiovascular disease (CVD) and a nonlinear inverse association between dietary potassium intake and risk of CVD. The observed associations were consistent across racial groups and participants with or without baseline cardiometabolic conditions, but appeared to be slightly stronger among Veterans with low dietary quality [8]. Additional studies have examined the association of dietary fatty acids with risk of atherosclerotic CVD [11] and dietary omega-3 fatty acid consumption and the incidence of atrial fibrillation [12].
Future studies will focus on deriving different dietary patterns based on dietary food intake and nutrient intake data. Dietary patterns include the Alternative Healthy Eating Index, the Alternative Mediterranean diet, Dietary Approaches to Stop Hypertension (DASH), Planetary Health Dietary Index, and the Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diet. Additional projects based on MVP nutrition and conjunct data also include different dietary patterns and risk of CVD, neurogenerative diseases, cancers, and mortality among the overall MVP population and among sub-groups with different comorbidities. MVP also anticipates administering a follow-up sFFQ to obtain repeated dietary and nutrient measures in the future.
Data resource access
As described in detail in a Data Resource Profile for MVP surveys [1] and on the MVP website (https://www.research.va.gov/mvp/), access to MVP data and/or MVP samples is governed by the scope of MVP informed consent and VA policies. As such, data/sample access requires scientific review by appropriate VA review committees. Data are currently available to VA investigators and other approved partners with plans for expanding to non-VA investigators in the future. Inquiries can be directed to Xuan-Mai Nguyen, PhD at xuan-mai.nguyen@va.gov. For requests to access this resource, a consortium approach is strongly encouraged and collaborators from university affiliates and other organizations working with VA investigators are encouraged (https://www.research.va.gov/MVP/research.cfm).
Conclusions
Nutritional epidemiology allows researchers to understand how dietary patterns, components, and nutrients are associated with health outcomes and disease etiology in populations. The energy and nutrient data derived from MVP SFFQ are an invaluable resource for Veteran health and research. In conjunction with the MVP Lifestyle Survey, electronic health records (EHR) and genomic data, MVP nutrition data can be used to assess nutritional status, its related risk factors, and disease prevalence to improve our understanding of Veteran health. MVP nutrition data may also provide scientific support for the development of evidence-based public health policy and health promotion programs and services for Veterans and the general US population.
Acknowledgements
The authors thank the members of the Million Veteran Program Core, those who have contributed to the Million Veteran Program, and especially the Veteran participants for their generous contributions.
Conflict of interest statement
None to declare.
Author contributions
Manuscript writing: X-MTN, YL, KLI, LD
Analytics and data summary: YL, X-MTN, KLI, KC, LD, FBH, WCW
MVP Recruitment and Enrollment: SBW, X-MTN, MG
Oversight and critical review of manuscript for content and clarity: LD, MG, KC, WCW
All authors have read and agreed to the published version of the manuscript.
Ethics statement
This research was conducted according to the guidelines of the Declaration of Helsinki and approved by the Department of Veteran Affairs Central IRB (protocol code MVP001 approved in 2010). Written informed consent has been obtained from the participants in accordance with all VA policies and under the authority of the VA Central IRB.
Publication consent
The authors have gained consent to publish detailed descriptions of the VA MVP nutrition data that is available for research as outlined in the Data Access section of this paper.
Funding statement
This research is based on data from the VA Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was funded by award MVP#000. This publication does not represent the views of the Department of Veterans Affairs or the U.S. government. The authors have no financial disclosures.
Supplementary appendices
Supplementary data are available at IJPDS online.
Data availability statement
Data cannot be shared publicly because of VA policies regarding data privacy and security. Data contain potentially identifying and sensitive patient information. All relevant summary level data are included in the manuscript. For investigators with appropriate authorizations within the Department of Veterans Affairs, requests for data access can be made.
Abbreviations
| VA | Veterans Affairs |
| MVP | Million Veteran Program |
| SFFQ | semiquantitative food frequency questionnaire |
| EHR | electronic health record |
| VHA | Veterans Health Administration |
| CVD | cardiovascular disease |
| DASH | Dietary Approaches to Stop Hypertension |
| MIND | Mediterranean-DASH Intervention for Neurodegenerative Delay |
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