Dietary Sodium and Potassium Intake and Risk of Non-Fatal Cardiovascular Diseases: The Million Veteran Program
by
Dong D. Wang
1,2,3,*, 
Yanping Li
1,3,
Xuan-Mai T. Nguyen
1,4,5,
Rebecca J. Song
1,6,
Yuk-Lam Ho
1,
Frank B. Hu
2,3,7,
Walter C. Willett
2,3,7,
Peter W. F. Wilson
8,9,
Kelly Cho
1,4,5,
J. Michael Gaziano
1,4,5,
Luc Djoussé
1,3,4,5 and
on behalf of the VA Million Veteran Program
† 1
Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02111, USA
2
The Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
3
Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
4
Division of Aging, Department of Medicine, Brigham and Women′s Hospital, Boston, MA 02115, USA
5
Harvard Medical School, Boston, MA 02115, USA
6
Department of Epidemiology, Boston University School of Public Health, Boston, MA 02115, USA
7
Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
8
Atlanta VA Medical Center, Atlanta, GA 30033, USA
9
Emory Clinical Cardiovascular Research Institute, Atlanta, GA 30033, USA
*
Author to whom correspondence should be addressed.
†
VA Million Veteran Program is provided in the Supplementary Material.
Nutrients 2022, 14(5), 1121; https://doi.org/10.3390/nu14051121
Submission received: 25 January 2022
/
Revised: 2 March 2022
/
Accepted: 3 March 2022
/
Published: 7 March 2022
(This article belongs to the Section Nutrition and Public Health)
Abstract
:Objective: To examine the association between intakes of sodium and potassium and the ratio of sodium to potassium and incident myocardial infarction and stroke. Design, Setting and Participants: Prospective cohort study of 180,156 Veterans aged 19 to 107 years with plausible dietary intake measured by food frequency questionnaire (FFQ) who were free of cardiovascular disease (CVD) and cancer at baseline in the VA Million Veteran Program (MVP). Main outcome measures: CVD defined as non-fatal myocardial infarction (MI) or acute ischemic stroke (AIS) ascertained using high-throughput phenotyping algorithms applied to electronic health records. Results: During up to 8 years of follow-up, we documented 4090 CVD cases (2499 MI and 1712 AIS). After adjustment for confounding factors, a higher sodium intake was associated with a higher risk of CVD, whereas potassium intake was inversely associated with the risk of CVD [hazard ratio (HR) comparing extreme quintiles, 95% confidence interval (CI): 1.09 (95% CI: 0.99–1.21, p trend = 0.01) for sodium and 0.87 (95% CI: 0.79–0.96, p trend = 0.005) for potassium]. In addition, the ratio of sodium to potassium (Na/K ratio) was positively associated with the risk of CVD (HR comparing extreme quintiles = 1.26, 95% CI: 1.14–1.39, p trend < 0.0001). The associations of Na/K ratio were consistent for two subtypes of CVD; one standard deviation increment in the ratio was associated with HRs (95% CI) of 1.12 (1.06–1.19) for MI and 1.11 (1.03–1.19) for AIS. In secondary analyses, the observed associations were consistent across race and status for diabetes, hypertension, and high cholesterol at baseline. Associations appeared to be more pronounced among participants with poor dietary quality. Conclusions: A high sodium intake and a low potassium intake were associated with a higher risk of CVD in this large population of US veterans.
1. Introduction
High sodium intake has been shown to be associated with a higher risk of hypertension and cardiovascular morbidity and mortality in an approximately linear dose-response manner [1,2,3]. For example, a recent meta-analysis of 261,732 participants with 10,150 cases of stroke reported that dietary sodium intake and sodium-to-potassium ratio were positively associated with risk of stroke without evidence of nonlinearity [4]. Based on this dose-response relationship, modeling-based studies estimated that 1.65 million cardiovascular deaths globally [1] and more than 50,000 myocardial infarctions and 30,000 strokes per year in the US were attributable to high sodium consumption, defined as intake level above 2.0 g per day [2]. However, several recent studies have challenged the aforementioned evidence and reported a J-shaped dose-response relationship with the lowest risk of death and cardiovascular events observed at a urinary sodium excretion level of 3 to 6 g/day [5].
This inconsistency of the evidence may be partially explained by measurement errors embedded in the ascertainment of urinary sodium level, especially in those studies that relied on 24-h urinary sodium level imputed from spot urine samples [6,7]. In addition, 24-h urinary sodium excretion can only reflect short-term intake level, whereas long-term usual intakes of sodium and potassium are biologically more relevant for CVD with a long induction period. To address these gaps, we examined the associations of long-term intakes of sodium and potassium, measured by a semi-quantitative food frequency questionnaire (sFFQ), with incident myocardial infarction and stroke in a large cohort of US veterans with diverse socioeconomic and racial/ethnic backgrounds.
2. Methods
2.1. Study Population
Million Veteran Program (MVP) is a nationally representative, prospective cohort study of veterans designed to study genetic and nongenetic determinants of chronic diseases. Recruitment and enrollment for MVP began in early 2011 among eligible Veterans receiving routine primary care within the US Department of Veterans Affairs Healthcare System. Participant data is collected through self-reported surveys and electronic health records (EHR). Details of the study design can be found elsewhere [8]. All participants gave informed consent, and the Veterans Affairs Central Institutional Review Board approved the study protocol.
As of 2020, MVP consisted of 819,417 enrollees of which 379,852 participants completed the MVP Lifestyle Survey. For the current analysis, we excluded participants with a self-reported history of cancer (n = 100,994) or CVD (n = 52,913) at baseline where CVD history included self-reported diagnosis of stroke, coronary artery/coronary heart disease (includes angina) or confirmed MI or AIS. Participants who responded to the MVP Lifestyle Survey after December 2018 (n = 28,854) and whose last clinical visit was before or on the date of MVP Baseline Survey completion (n = 4827) were also excluded. The MVP Lifestyle Survey includes a sFFQ with a total of 61 food items. Those who did not provide dietary information or reported implausible dietary intakes at baseline (n = 12,108) were not included in this study, yielding a final population of 180,156 participants (Figure 1).
2.2. Assessment of Exposure and Covariates
Dietary intake was self-reported through a sFFQ, which has been previously validated in other cohorts [9]. Participants were asked “how often” they consume a standard portion of each food item in the past year. 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”. Frequencies and portions of each food item were converted to average daily intake for each participant. Nutrient intake and total energy intake were calculated by multiplying the frequency of consumption for each food item by its energy and nutrient content from the Harvard University Food Composition Database [10] and summing across all foods. Nutrients were adjusted for total energy intake using a residual method [11]. Baseline data on age, sex, race, family income, education, body mass index, alcohol consumption, exercise, smoking, dietary supplements, comorbidities, and medications were taken from the self-reported MVP Baseline Survey.
2.3. Assessment of Cardiovascular Diseases
Using combined information from the VA Corporate Data Warehouse (CDW) which is the VA EHR, Centers for Medicaid & Medicare Services database, and National Death Index database [12,13], we focused on cases of non-fatal myocardial infarction (MI) and acute ischemic stroke (AIS) [14,15,16]. MI and AIS cases were identified by applying the Surrogate-Assisted Feature Extraction (SAFE) method, a validated high-throughput phenotyping algorithms approach, using a combination of (International Classification of Diseases) ICD codes, natural language processing, and medical record review labels [17,18].
2.4. Statistical Analysis
Person-years of follow-up were calculated from sFFQ assessment to the first occurrence of MI or AIS, death, the end of follow-up (31 December 2018), or last visit recorded in CDW. We used Cox proportional hazards models to estimate hazard ratios (HRs) with 95% confidence intervals (CIs), comparing higher quintiles to the lowest quintile of dietary indices with simultaneous adjustment for covariates. Covariates included age (continuous), sex (male or female), race (white or other), education level (≤high school or GED (General Educational Diploma), some colleague, or college or above), income level (<$30,000, $30,000–$59,000 or ≥$60,000), marital status (currently married or not), smoking status (current, former or never smoking), frequency of alcohol consumption (never, <1 time/week or ≥1 time/week), frequency of vigorous exercise (never/rarely, 1–4 times/month, 2–4 times/week, or ≥5 times/week), dietary energy intake (quintiles), body mass index (continuous), and the presence of diabetes, hypertension or high cholesterol at baseline.
To quantify a linear trend of relative risk of CVD across quintiles, we assigned the median within each quintile and modeled this variable continuously; the Wald test was used to assess statistical significance. We also tested for potential nonlinearity in the association between dietary indices and the risk of CVD. Restricted cubic spline regression with 3 knots was applied to flexibly model the association between the dietary indices and risk of CVD with the first percentile of each dietary indices as the reference. Nonlinearity in the dose-response relationship of the dietary indices with the risk of CVD was evaluated by comparing the model with the linear term to the model with the linear and cubic spline terms using the likelihood ratio test [19].
In secondary analyses, we examined the association between sodium to potassium ratio and the risk of CVD among White and Black participants separately. We also conducted stratified analyses to examine the association between the ratio of sodium to potassium and the risk of CVD across different participant subgroups: with and without baseline diabetes, hypertension, or high cholesterol and by overall dietary quality. Overall dietary quality was evaluated by Dietary Approaches to Stop Hypertension (DASH) score [20]. We tested for interactions between the ratio of sodium to potassium and the stratification variables by adding a product term of the two variables and their main effects in the multivariable model.
In sensitivity analyses, we further adjusted for the number of medical diagnoses (0–2, 2–4, 5–6, ≥7) and medications (0, 1, 2, ≥3) at baseline for chronic health conditions in addition to the number of dietary supplements that were taken regularly to test the robustness of findings when we considered multi-medication, multi-morbidity, and dietary supplementation uses in our study population [21]. All analyses were completed using SAS Enterprise Guide 8.2.
3. Results
The average energy intake was 1372 kcal/day for women and 1447 kcal/day for men. The observed energy-adjusted average intakes of sodium and potassium were 1246 mg/day (1112 for women; 1262 for men) and 2600 mg/day (2534 for women and 2608 for men), respectively, with an average sodium to potassium ratio (Na/K ratio) of 0.5 for both women and men. Table 1 presents baseline characteristics of participants according to quintiles of sodium, potassium, and Na/K ratio. Participants with higher sodium intake and Na/K ratio were more likely to be inactive, have lower educational attainment, lower annual family income, more chronic conditions, and poor dietary quality.
During a mean follow-up of 3.5 years (up to 8 years), we documented 4090 CVD events, including 2499 MI and 1712 AIS cases (121 participants developed both MI and AIS). After adjusting for known and suspected confounding variables and risk factors, a higher sodium intake was associated with a higher risk of CVD (HR comparing extreme quintiles = 1.09, 95% CI: 0.99–1.21, p trend = 0.01). Cubic spline model revealed an approximately linear dose-response association between sodium intake and CVD risk without any indication of nonlinearity (p for non-linear = 0.35 and p for linear = 0.002). Across quintiles, a higher potassium intake was associated with a 13% lower risk of total CVD (HR comparing extreme quintiles = 0.87, 95% CI: 0.79–0.96, p trend = 0.005). The dose-response association between potassium and risk of CVD (p for non-linear = 0.07 and p for linear = 0.002) was slightly nonlinear; the decreasing trend plateaued after about 3 g/day. We found that Na/K ratio was positively associated with the risk of total CVD (HR comparing extreme quintiles = 1.26, 95% CI: 1.14–1.39, p trend < 0.0001). Similar to the association of sodium intake and CVD risk, the association between Na/K ratio and risk of CVD was linear (p for non-linear = 0.15 and p for linear < 0.0001, Figure 2). When modeling intake level continuously, every 1-SD increment in intake level was associated with a 6% higher risk of CVD (HR = 1.06, 95% CI: 1.01–1.10) for sodium, a 5% lower risk of CVD (HR = 0.95, 95% CI: 0.91–0.98) for potassium, and an 11% higher risk of CVD for Na/K ratio (HR = 1.11, 95% CI: 1.06–1.16) (Table 2). The associations did not materially change after further adjustment for baseline health conditions, medication usage, and dietary supplements (every 1-SD increment in sodium HR = 1.05, 95% CI: 1.01–1.10; potassium HR = 0.95, 95% CI: 0.92–0.99; Na/K ratio HR = 1.10, 95% CI: 1.05–1.16).
We found similar associations between Na/K ratio and subtypes of CVD. Every 1-SD increment in Na/K ratio was associated with HRs (95% CI) of 1.12 (1.06–1.19) for non-fatal MI and 1.11 (1.03–1.19) for non-fatal AIS (Table 3). The association between Na/K ratio and CVD risk was consistent between White and Black participants and between participants with or without diabetes, hypertension, or high cholesterol at baseline whereas the association appeared to be slightly pronounced in participants with poor dietary quality (all Ps for interaction > 0.05, Table 4).
4. Discussion
In this large cohort of US veterans, we observed that higher dietary sodium intake and Na/K ratio were each significantly associated with a higher risk of CVD in a linear dose-response manner. In addition, we observed a nonlinear inverse association between dietary potassium intake and risk of CVD; the lowest risk was observed for 3 g/day. 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 a low dietary quality. We found similar positive associations of the Na/K ratio with both MI and AIS.
Our findings are consistent with previous reports assessing the relationship between sodium intake and CVD [22,23]. For example, Cook et al. [22] reported a linear association of sodium with cardiovascular events where every 1000 mg/d increase in sodium was associated with a 17% higher risk (p = 0.05). In addition, high blood pressure has been positively associated with CVD risk [24] and the DASH (Dietary Approaches to Stop Hypertension)-Sodium clinical trial demonstrated beneficial effects of sodium reduction on blood pressure, even at very low levels of sodium intake (1500 mg/d). [25]. However, several studies challenged this evidence and proposed a J-shaped dose-response relationship with the lowest risk of death and cardiovascular events observed at a urinary sodium excretion level of 3 to 6 g/day. Potential limitations of these studies include measurement errors in sodium excretion, reverse causation, and residual confounding [3]. In contrast, our data do not support any potential risk associated with very low sodium intake (1500 to 2300 mg/d). In addition, a systematic review by the Institute of Medicine concluded that the underlying data supporting the potential high risk of CVD associated with very low sodium intake were of insufficient quality [26].
High sodium and low potassium intakes have been associated with increased CVD risk through potential elevated blood pressure effects [1,2,3,4,5]. In a meta-analysis of randomized clinical trials, reduced sodium intake, increased potassium intake, and use of potassium-containing salt substitutes in the diet reduced blood pressure [27]. A recent meta-analysis of prospective and retrospective observational studies reported that a higher sodium intake and higher sodium-to-potassium ratio were associated with a higher risk of stroke [5]. Our observed positive association between the ratio of sodium to potassium and CVD is consistent with previous studies based on FFQ, dietary records, 24 h urine excretion, and biomarker-calibrated intakes [28,29,30,31].
The current study has several strengths including a prospective cohort design with a large sample size of participants with diverse socioeconomic and racial/ethnic backgrounds, a large number of confirmed CVD cases, and comprehensive measurements and careful adjustment for many potential confounders, and complete ascertainment of CVD events via EHR. Our study has several limitations. First, the abbreviated FFQ in MVP likely underestimates the dietary sodium and potassium intakes. Some major sources of dietary sodium were missed in the MVP FFQ, such as salt added at the table and salt used in preparation or cooking [32,33]. Therefore, it is hard to distinguish whether the findings reflect the effects of sodium intake per se or a dietary pattern high in sodium. Second, our single assessment of dietary intake precluded the assessment of changes in sodium and potassium intakes over time. In addition, our use of a self-reported questionnaire as the primary means to collect dietary data is a limitation; measurement errors in dietary assessment are inevitable. However, we were able to control for measurement error to a large degree by applying energy adjustment that negated correlated errors in nutrient and energy intake assessments [11]. Third, the prevalence of major chronic diseases was high in the MVP population at baseline. Participants with concerns about an illness might have changed their dietary behaviors, potentially leading to biased results. However, to minimize this bias, we excluded participants with major chronic disease at baseline and performed statistical adjustment for diabetes, hypertension, and dyslipidemia. Lastly, hemorrhagic stroke was not included in our CVD identification because phenotyping efforts for this outcome are still ongoing in MVP; we expect to examine sodium and potassium intake in relation to hemorrhagic stroke in a future project.
In conclusion, our data showed that while sodium intake is positively associated with CVD risk, the reverse is true for potassium intake among US veterans. These findings support the recommendation of dietary patterns with lower dietary sodium to potassium ratio to prevent major cardiovascular diseases. Furthermore, our findings support the Dietary Guideline for Americans [34] and the World Health Organization guideline of reducing sodium intake and increasing potassium intake to reduce the risk of CVD, stroke, and coronary heart disease in adults [35,36].
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu14051121/s1, VA Million Veteran Program.
Author Contributions
Conceptualization, D.D.W., L.D., Y.L. and X.-M.T.N.; methodology, D.D.W. and Y.L.; software, Y.L. and Y.-L.H.; validation, Y.L. and L.D.; formal analysis, D.D.W., Y.L., Y.-L.H. and R.J.S.; investigation, D.D.W., Y.L., L.D. and X.-M.T.N.; resources, K.C., P.W.F.W., J.M.G. and L.D.; data curation, Y.L., Y.-L.H., X.-M.T.N., R.J.S. and L.D.; writing—original draft preparation, D.D.W. and Y.L.; writing—review and editing, D.D.W., Y.L., X.-M.T.N., Y.-L.H., R.J.S., K.C., J.M.G., P.W.F.W., F.B.H., W.C.W. and L.D.; visualization, D.D.W. and Y.L.; supervision, L.D., J.M.G., F.B.H. and W.C.W.; project administration, X.-M.T.N.; funding acquisition, K.C., J.M.G. and L.D. All authors have read and agreed to the published version of the manuscript.
Funding
This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was funded by award #MVP000. Additional support for this project was provided by VA Merit Awards BX004831 (Wilson/Cho) and CX001025 (Wilson/Cho). None of the authors have financial conflicts of interest with the work reported in this manuscript. This publication does not represent the views of the Department of Veterans Affairs or the United States Government.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki and approved by the VA Central Institutional Review Board (protocol code: 10-02; date of approval: 2010).
Informed Consent Statement
Informed consent was obtained from all participants.
Data Availability Statement
Data will not be made available in order to comply with current VA privacy regulations.
Acknowledgments
We acknowledge the Support for VA/CMS data provided by the Department of Veterans Affairs, VA Health Services Research and Development Service, VA Information Resource Center (Project Numbers SDR 02-237 and 98-004) and important contributions by the Million Veteran Program (MVP) Consortium. The authors would also like to 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.
Conflicts of Interest
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
References
- Mozaffarian, D.; Fahimi, S.; Singh, G.M.; Micha, R.; Khatibzadeh, S.; Engell, R.E.; Lim, S.; Danaei, G.; Ezzati, M.; Powles, J.; et al. Global sodium consumption and death from cardiovascular causes. N. Engl. J. Med. 2014, 371, 624–634. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bibbins-Domingo, K.; Chertow, G.M.; Coxson, P.G.; Moran, A.; Lightwood, J.M.; Pletcher, M.J.; Goldman, L. Projected effect of dietary salt reductions on future cardiovascular disease. N. Engl. J. Med. 2010, 362, 590–599. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ma, Y.; He, F.J.; Sun, Q.; Yuan, C.; Kieneker, L.M.; Curhan, G.C.; MacGregor, G.A.; Bakker, S.J.; Campbell, N.R.; Wang, M.; et al. 24-Hour Urinary Sodium and Potassium Excretion and Cardiovascular Risk. N. Engl. J. Med. 2022, 386, 252–263. [Google Scholar] [CrossRef] [PubMed]
- Jayedi, A.; Ghomashi, F.; Zargar, M.S.; Shab-Bidar, S. Dietary sodium, sodium-to-potassium ratio, and risk of stroke: A systematic review and nonlinear dose-response meta-analysis. Clin. Nutr. 2019, 38, 1092–1100. [Google Scholar] [CrossRef] [PubMed]
- Kieneker, L.M.; Eisenga, M.F.; Gansevoort, R.T.; de Boer, R.A.; Navis, G.; Dullaart, R.P.; Joosten, M.M.; Bakker, S.J. Association of Low Urinary Sodium Excretion With Increased Risk of Stroke. Mayo Clin. Proc. 2018, 93, 1803–1809. [Google Scholar] [CrossRef]
- O’Donnell, M.; Mente, A.; Rangarajan, S.; McQueen, M.J.; Wang, X.; Liu, L.; Yan, H.; Lee, S.F.; Mony, P.; Devanath, A. Urinary sodium and potassium excretion, mortality, and cardiovascular events. N. Engl. J. Med. 2014, 371, 612–623. [Google Scholar] [CrossRef] [Green Version]
- Mente, A.; O’Donnell, M.; Rangarajan, S.; Dagenais, G.; Lear, S.; McQueen, M.; Diaz, R.; Avezum, A.; Lopez-Jaramillo, P.; Lanas, F. Associations of urinary sodium excretion with cardiovascular events in individuals with and without hypertension: A pooled analysis of data from four studies. Lancet 2016, 388, 465–475. [Google Scholar] [CrossRef]
- Gaziano, J.M.; Concato, J.; Brophy, M.; Fiore, L.; Pyarajan, S.; Breeling, J.; Whitbourne, S.; Deen, J.; Shannon, C.; Humphries, D.; et al. Million Veteran Program: A mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 2016, 70, 214–223. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Salvini, S.; Hunter, D.J.; Sampson, L.; Stampfer, M.J.; Colditz, G.A.; Rosner, B.; Willett, W.C. Food-based validation of a dietary questionnaire: The effects of week-to-week variation in food consumption. Int. J. Epidemiol. 1989, 18, 858–867. [Google Scholar] [CrossRef]
- Harvard, T.H. Chan School of Public Health Nutrition Department. Food Composition Table. 15 January 2015. Available online: https://regepi.bwh.harvard.edu/health/nutrition/ (accessed on 16 July 2021).
- Willett, W.C.; Howe, G.R.; Kushi, L.H. Adjustment for total energy intake in epidemiologic studies. Am. J. Clin. Nutr. 1997, 65 (Suppl. 4), 1220S–1228S. [Google Scholar] [CrossRef]
- US Department of Veterans Affairs. System of Records Notice 97va10p1: Consolidated Data Information System-VA. 76 fr 25409; 4 May 2011. Amended 3 March 2015; US Department of Veterans Affairs: Washington, DC, USA, 2015.
- Center of Excellence for Suicide Prevention. Joint Department of Veterans Affairs (VA) and Department of Defense (DoD) Suicide Data Repository–National Death Index (NDI). Available online: http://vaww.virec.research.va.gov/Mortality/Overview.htm (accessed on 30 March 2021).
- Vassy, J.L.; Lu, B.; Ho, Y.-L.; Galloway, A.; Raghavan, S.; Honerlaw, J.; Tarko, L.; Russo, J.; Qazi, S.; Orkaby, A.R.; et al. Estimation of Atherosclerotic Cardiovascular Disease Risk Among Patients in the Veterans Affairs Health Care System. JAMA Netw. Open 2020, 3, e208236. [Google Scholar] [CrossRef] [PubMed]
- Price, L.E.; Shea, K.; Gephart, S. The Veterans Affairs’s corporate data Warehouse: Uses and implications for nursing research and practice. Nurs. Adm. Q. 2015, 39, 311–318. [Google Scholar] [CrossRef] [PubMed]
- US Department of Veterans Affairs. Frequently Asked Questions—2019 National Veteran Suicide Prevention Annual Report; US Department of Veterans Affairs: Washington, DC, USA, 2019.
- Yu, S.; Chakrabortty, A.; Liao, K.P.; Cai, T.; Ananthakrishnan, A.N.; Gainer, V.S.; Churchill, S.E.; Szolovits, P.; Murphy, S.N.; Kohane, I.S.; et al. Surrogate-assisted feature extraction for high-throughput phenotyping. J. Am. Med. Inform. Assoc. 2016, 24, e143–e149. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Y.; Cai, T.; Yu, S.; Cho, K.; Hong, C.; Sun, J.; Huang, J.; Ho, Y.-L.; Ananthakrishnan, A.N.; Xia, Z.; et al. High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP). Nat. Protoc. 2019, 14, 3426–3444. [Google Scholar] [CrossRef]
- Durrleman, S.; Simon, R. Flexible regression models with cubic splines. Stat. Med. 1989, 8, 551–561. [Google Scholar] [CrossRef]
- Djoussé, L.; Ho, Y.; Nguyen, X.T.; Gagnon, D.R.; Wilson, P.W.; Cho, K.; Gaziano, J.M.; Halasz, I.; Federman, D.; Beckham, J.; et al. DASH Score and Subsequent Risk of Coronary Artery Disease: The Findings From Million Veteran Program. J. Am. Heart Assoc. 2018, 7, e008089. [Google Scholar] [CrossRef]
- Scicchitano, P.; Cameli, M.; Maiello, M.; Modesti, P.A.; Muiesan, M.L.; Novo, S.; Palmiero, P.; Saba, P.S.; Pedrinelli, R.; Ciccone, M.M. Nutraceuticals and dyslipidaemia: Beyond the common therapeutics. J. Funct. Foods 2014, 6, 11–32. [Google Scholar] [CrossRef]
- Cook, N.R.; Appel, L.J.; Whelton, P.K. Lower Levels of Sodium Intake and Reduced Cardiovascular Risk. Circulation 2014, 129, 981–989. [Google Scholar] [CrossRef] [Green Version]
- Mills, K.T.; Chen, J.; Yang, W.; Appel, L.J.; Kusek, J.W.; Alper, A.; Delafontaine, P.; Keane, M.G.; Mohler, E.; Ojo, A.; et al. Sodium Excretion and the Risk of Cardiovascular Disease in Patients With Chronic Kidney Disease. JAMA J. Am. Med. Assoc. 2016, 315, 2200–2210. [Google Scholar] [CrossRef]
- Fuchs, F.D.; Whelton, P.K. High Blood Pressure and Cardiovascular Disease. Hypertension 2020, 75, 285–292. [Google Scholar] [CrossRef] [PubMed]
- Sacks, F.M.; Svetkey, L.P.; Vollmer, W.M.; Appel, L.J.; Bray, G.A.; Harsha, D.; Obarzanek, E.; Conlin, P.R.; Miller, E.R.; Simons-Morton, D.G.; et al. Effects on Blood Pressure of Reduced Dietary Sodium and the Dietary Approaches to Stop Hypertension (DASH) Diet. N. Engl. J. Med. 2001, 344, 3–10. [Google Scholar] [CrossRef] [PubMed]
- Institute of Medicine. Sodium Intake in Populations: Assessment of Evidence; The National Academies Press: Washington, DC, USA, 2013. [Google Scholar] [CrossRef]
- Newberry, S.J.; Chung, M.; Anderson, C.A.M.; Chen, C.; Fu, Z.; Tang, A.; Zhao, N.; Booth, M.; Marks, J.; Hollands, S.; et al. Sodium and Potassium Intake: Effects on Chronic Disease Outcomes and Risks; Agency for Healthcare Research and Quality (US): Rockville, MD, USA, 2018.
- Okayama, A.; Okuda, N.; Miura, K.; Okamura, T.; Hayakawa, T.; Akasaka, H.; Ohnishi, H.; Saitoh, S.; Arai, Y.; Kiyohara, Y.; et al. Dietary sodium-to-potassium ratio as a risk factor for stroke, cardiovascular disease and all-cause mortality in Japan: The NIPPON DATA80 cohort study. BMJ Open 2016, 6, e011632. [Google Scholar] [CrossRef] [PubMed]
- Yang, Q.; Liu, T.; Kuklina, E.V.; Flanders, W.D.; Hong, Y.; Gillespie, C.; Chang, M.-H.; Gwinn, M.; Dowling, N.; Khoury, M.J.; et al. Sodium and potassium intake and mortality among US adults: Prospective data from the Third National Health and Nutrition Examination Survey. Arch. Intern. Med. 2011, 171, 1183–1191. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cook, N.R.; Obarzanek, E.; Cutler, J.A.; Buring, J.E.; Rexrode, K.M.; Kumanyika, S.K.; Appel, L.J.; Whelton, P.K. Joint effects of sodium and potassium intake on subsequent cardiovascular disease: The Trials of Hypertension Prevention follow-up study. Arch. Intern. Med. 2009, 169, 32–40. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Prentice, R.L.; Huang, Y.; Neuhouser, M.L.; Manson, J.E.; Mossavar-Rahmani, Y.; Thomas, F.; Tinker, L.F.; Allison, M.; Johnson, K.C.; Wassertheil-Smoller, S.; et al. Associations of biomarker-calibrated sodium and potassium intakes with cardiovascular disease risk among postmenopausal women. Am. J. Epidemiol. 2017, 186, 1035–1043. [Google Scholar] [CrossRef] [PubMed]
- Firestone, M.J.; Beasley, J.M.; Kwon, S.C.; Ahn, J.; Trinh-Shevrin, C.; Yi, S.S. Asian American dietary sources of sodium and salt behaviors compared with other racial/ethnic groups, NHANES, 2011–2012. Ethn. Dis. 2017, 27, 241. [Google Scholar] [CrossRef] [PubMed]
- Bailey, R.L.; Parker, E.; Rhodes, D.G.; Goldman, J.D.; Clemens, J.C.; Moshfegh, A.J.; Thuppal, S.V.; Weaver, C.M. Estimating Sodium and Potassium Intakes and Their Ratio in the American Diet: Data from the 2011–2012 NHANES. J. Nutr. 2015, 146, 745–750. [Google Scholar] [CrossRef]
- US Department of Health and Human Services and US Department of Agriculture. Dietary Guidelines for Americans. 2015. Available online: https://health.gov/our-work/nutrition-physical-activity/dietary-guidelines/previous-dietary-guidelines/2015 (accessed on 12 October 2020).
- World Health Organization. Guideline: Sodium Intake for Adults and Children; World Health Organization: Geneva, Switzerland, 2012. [Google Scholar]
- World Health Organization. Guideline: Potassium Intake for Adults and Children; World Health Organization: Geneva, Switzerland, 2012. [Google Scholar]
Figure 1.
Participant Flow Chart.
Figure 2.
Dose-response relationship between dietary intakes of sodium, potassium, and ratio of sodium to potassium with risk of non-fatal cardiovascular diseases in the Million Veteran Program (The dose-response relationship was quantified by Cox proportional hazards models with restricted cubic spline with 3 knots specified. The first percentile of each dietary indices was used as reference level for calculating hazard ratios. We tested nonlinearity in the dose-response relationship of the dietary indices with CVD risk by comparing the model with only the linear term to the model with the linear and the cubic spline terms and using the likelihood ratio test. All the models simultaneously adjusted for age (continuous), sex (male or female), race/ethnicity, education level, income, and marital status, smoking status, frequency of alcohol consumption, frequency of exercise vigorously, total energy intake (in quintiles), body mass index (continuous), and the presences of diabetes, high cholesterol and hypertension at baseline (each Yes vs. No).).
Figure 2.
Dose-response relationship between dietary intakes of sodium, potassium, and ratio of sodium to potassium with risk of non-fatal cardiovascular diseases in the Million Veteran Program (The dose-response relationship was quantified by Cox proportional hazards models with restricted cubic spline with 3 knots specified. The first percentile of each dietary indices was used as reference level for calculating hazard ratios. We tested nonlinearity in the dose-response relationship of the dietary indices with CVD risk by comparing the model with only the linear term to the model with the linear and the cubic spline terms and using the likelihood ratio test. All the models simultaneously adjusted for age (continuous), sex (male or female), race/ethnicity, education level, income, and marital status, smoking status, frequency of alcohol consumption, frequency of exercise vigorously, total energy intake (in quintiles), body mass index (continuous), and the presences of diabetes, high cholesterol and hypertension at baseline (each Yes vs. No).).
Table 1.
Baseline characteristics of 180,156 participants in the Million Veteran Program according to energy-adjusted dietary intakes of sodium and potassium and ratio of sodium to potassium.
Table 1.
Baseline characteristics of 180,156 participants in the Million Veteran Program according to energy-adjusted dietary intakes of sodium and potassium and ratio of sodium to potassium.
| Sodium | Potassium | Ratio of Sodium to Potassium | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Q1 | Q3 | Q5 | Q1 | Q3 | Q5 | Q1 | Q3 | Q5 | |
| N | 36,031 | 36,032 | 36,031 | 36,031 | 36,032 | 36,031 | 36,031 | 36,032 | 36,031 |
| Sodium, mg/d | 865 | 1231 | 1666 | 1181 | 1271 | 1251 | 973 | 1242 | 1532 |
| Potassium, mg/d | 2478 | 2634 | 2628 | 1857 | 2575 | 3406 | 3102 | 2596 | 2112 |
| Ratio of sodium to potassium | 0.4 | 0.5 | 0.7 | 0.6 | 0.5 | 0.4 | 0.3 | 0.5 | 0.7 |
| Age, years | 59.9 | 62.3 | 64.2 | 59.3 | 62.6 | 64.2 | 62.2 | 62.4 | 61.9 |
| Men, % | 80.8 | 90.5 | 95.7 | 87.0 | 89.6 | 90.8 | 84.0 | 89.9 | 92.8 |
| White, % | 75.4 | 83.0 | 86.7 | 75.1 | 83.5 | 85.4 | 79.3 | 83.5 | 80.9 |
| Black, % | 18.3 | 13.1 | 10.1 | 20.0 | 12.5 | 10.3 | 14.9 | 12.4 | 15.6 |
| Married, % | 51.9 | 60.2 | 59.5 | 49.1 | 61.0 | 60.9 | 56.3 | 61.0 | 54.1 |
| Education level, % | |||||||||
| ≤High school or GED | 19.3 | 21.4 | 28.1 | 27.4 | 20.9 | 21.0 | 17.6 | 21.6 | 29.2 |
| Some colleague | 29.3 | 30.1 | 31.9 | 32.6 | 29.4 | 29.2 | 28.0 | 30.4 | 32.6 |
| College or above | 51.4 | 48.5 | 39.9 | 40.0 | 49.7 | 49.8 | 54.4 | 48.0 | 38.3 |
| Annual family income, % | |||||||||
| <30,000 | 32.9 | 31.9 | 39.1 | 40.8 | 31.5 | 32.8 | 31.4 | 31.5 | 41.2 |
| 30,000–59,000 | 33.8 | 35.6 | 35.9 | 33.7 | 35.5 | 35.5 | 34.0 | 35.9 | 34.5 |
| ≥60,000 | 33.3 | 32.4 | 24.9 | 25.6 | 33.0 | 31.7 | 34.6 | 32.6 | 24.2 |
| Smoking status, % | |||||||||
| Never smoking | 35.7 | 33.4 | 27.8 | 30.3 | 33.3 | 32.6 | 36.6 | 32.5 | 29.1 |
| Former smoker | 56.7 | 59.1 | 64.5 | 59.7 | 59.5 | 61.3 | 57.4 | 60.0 | 62.0 |
| Current smoking | 7.6 | 7.5 | 7.7 | 10.0 | 7.2 | 6.1 | 6.0 | 7.5 | 8.9 |
| Vigorously exercise 1, % | |||||||||
| Never/rarely | 27.1 | 26.5 | 33.7 | 37.4 | 26.2 | 24.2 | 22.6 | 27.6 | 36.9 |
| 1–4 times/month | 24.5 | 27.3 | 27.2 | 27.8 | 27.8 | 23.9 | 23.0 | 27.7 | 27.5 |
| 2–4 times/week | 31.9 | 31.9 | 26.5 | 23.7 | 31.9 | 33.5 | 35.0 | 31.0 | 24.1 |
| ≥5 times/week | 16.5 | 14.3 | 12.7 | 11.1 | 14.1 | 18.4 | 19.4 | 13.7 | 11.6 |
| Alcohol drinking, % | |||||||||
| Never | 34.0 | 39.1 | 50.7 | 37.5 | 39.2 | 48.1 | 40.2 | 39.1 | 46.5 |
| <1 times/week | 23.7 | 29.6 | 30.3 | 24.3 | 28.5 | 30.5 | 27.1 | 28.6 | 28.2 |
| ≥1 times/week | 42.3 | 31.3 | 19.0 | 38.2 | 32.3 | 21.4 | 32.7 | 32.3 | 25.3 |
| Energy intake, kcal/day | 1432 | 1438 | 1441 | 1431 | 1457 | 1419 | 1410 | 1459 | 1425 |
| BMI, kg/m2 | 28.6 | 29.6 | 30.6 | 29.4 | 29.7 | 29.6 | 28.8 | 29.7 | 30.3 |
| Hypertension, % | 48.5 | 54.4 | 62.1 | 54.2 | 54.4 | 55.2 | 49.5 | 55.4 | 58.9 |
| High cholesterol, % | 45.4 | 50.0 | 54.9 | 47.0 | 50.2 | 51.9 | 47.5 | 50.9 | 51.4 |
| Diabetes, % | 12.9 | 20.7 | 35.6 | 17.3 | 23.2 | 26.2 | 17.2 | 22.8 | 27.7 |
| DASH score | 21.7 | 21.8 | 20.7 | 16.9 | 21.9 | 25.5 | 24.9 | 21.7 | 18.0 |
| Number of baseline diseases and conditions | |||||||||
| 0–2 | 32.1 | 29.9 | 28.1 | 30.6 | 29.3 | 29.5 | 31.7 | 29.2 | 29.2 |
| 3–4 | 24.8 | 26.3 | 25.6 | 25.1 | 26.0 | 25.4 | 25.1 | 25.8 | 25.3 |
| 5–6 | 18.4 | 19.1 | 19.8 | 18.8 | 19.3 | 19.2 | 18.5 | 19.6 | 19.5 |
| ≥7 | 24.7 | 24.7 | 26.5 | 25.4 | 25.3 | 25.9 | 24.8 | 25.5 | 26.0 |
| Number of medication types | |||||||||
| 0 | 31.4 | 29.6 | 27.3 | 28.9 | 29.1 | 29.8 | 31.6 | 28.5 | 27.8 |
| 1 | 22.0 | 22.7 | 23.2 | 22.4 | 22.7 | 22.3 | 22.2 | 22.9 | 22.9 |
| 2 | 15.5 | 16.7 | 16.7 | 16.0 | 16.8 | 16.0 | 15.4 | 16.5 | 16.5 |
| ≥3 | 31.0 | 30.9 | 32.8 | 32.7 | 31.4 | 32.0 | 30.7 | 32.1 | 32.7 |
| Number of dietary supplement types | |||||||||
| 0 | 24.3 | 25.3 | 27.1 | 31.3 | 24.7 | 21.7 | 20.9 | 25.3 | 30.1 |
| 1 | 21.1 | 22.6 | 24.6 | 24.6 | 22.8 | 21.5 | 20.2 | 23.0 | 25.2 |
| 2–3 | 29.0 | 29.4 | 29.1 | 26.9 | 29.8 | 29.8 | 29.9 | 29.8 | 27.7 |
| ≥4 | 25.5 | 22.7 | 19.2 | 17.2 | 22.7 | 27.0 | 28.9 | 21.9 | 17.0 |
Unless otherwise indicated, data are expressed as means; DASH: Dietary Approaches to Stop Hypertension; 1 Exercise vigorously enough to work up a sweat.
Table 2.
Hazard ratios (95% CI) for cardiovascular disease by quintiles (or per SD) of dietary sodium, potassium, and their ratio in 180,156 participants from the Million Veteran Program (2011 to 2018).
Table 2.
Hazard ratios (95% CI) for cardiovascular disease by quintiles (or per SD) of dietary sodium, potassium, and their ratio in 180,156 participants from the Million Veteran Program (2011 to 2018).
| Quintiles of Dietary Indices | ptrend | HR (95%CI) | |||||
|---|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | Q5 | Per 1-SD Increment | ||
| Sodium Intakes | |||||||
| Median | 898 | 1095 | 1231 | 1373 | 1605 | ||
| Cases | 679 | 705 | 766 | 888 | 1052 | ||
| IR (/103 PYs) | 5.47 | 5.66 | 6.16 | 7.12 | 8.45 | ||
| Model 1 | 1.0 (ref.) | 0.98 (0.88, 1.09) | 1.02 (0.92, 1.14) | 1.15 (1.04, 1.27) | 1.33 (1.21, 1.46) | <0.0001 | 1.16 (1.11, 1.21) |
| Model 2 | 1.0 (ref.) | 0.97 (0.88, 1.08) | 1.00 (0.90, 1.11) | 1.10 (0.99, 1.21) | 1.19 (1.07, 1.31) | <0.0001 | 1.10 (1.05, 1.14) |
| Model 3 | 1.0 (ref.) | 0.97 (0.87, 1.08) | 0.99 (0.90, 1.10) | 1.08 (0.98, 1.20) | 1.17 (1.05, 1.29) | 0.0001 | 1.09 (1.04, 1.13) |
| Model 4 | 1.0 (ref.) | 0.96 (0.86, 1.06) | 0.97 (0.88, 1.08) | 1.04 (0.94, 1.16) | 1.09 (0.99, 1.21) | 0.01 | 1.06 (1.01, 1.10) |
| Potassium Intakes | |||||||
| Median | 1920 | 2306 | 2575 | 2854 | 3299 | ||
| Cases | 830 | 808 | 767 | 833 | 852 | ||
| IR (/103 PYs) | 6.85 | 6.62 | 6.21 | 6.60 | 6.59 | ||
| Model 1 | 1.0 (ref.) | 0.90 (0.82, 1.00) | 0.81 (0.74, 0.90) | 0.84 (0.76, 0.93) | 0.83 (0.75, 0.91) | <0.0001 | 0.93 (0.90, 0.96) |
| Model 2 | 1.0 (ref.) | 0.97 (0.88, 1.07) | 0.89 (0.81, 0.99) | 0.93 (0.85, 1.03) | 0.90 (0.82, 0.99) | 0.03 | 0.96 (0.93, 1.00) |
| Model 3 | 1.0 (ref.) | 0.96 (0.87, 1.06) | 0.89 (0.80, 0.98) | 0.92 (0.84, 1.02) | 0.89 (0.81, 0.98) | 0.02 | 0.96 (0.92, 0.99) |
| Model 4 | 1.0 (ref.) | 0.95 (0.86, 1.05) | 0.87 (0.79, 0.96) | 0.90 (0.82, 1.00) | 0.87 (0.79, 0.96) | 0.005 | 0.95 (0.91, 0.98) |
| Ratio of Sodium to Potassium | |||||||
| Median | 0.33 | 0.41 | 0.48 | 0.55 | 0.69 | ||
| Cases | 698 | 785 | 756 | 832 | 1019 | ||
| IR (/103 PYs) | 5.48 | 6.26 | 6.07 | 6.75 | 8.37 | ||
| Model 1 | 1.0 (ref.) | 1.12 (1.01, 1.24) | 1.09 (0.98, 1.20) | 1.21 (1.10, 1.34) | 1.52 (1.38, 1.67) | <0.0001 | 1.23 (1.18, 1.29) |
| Model 2 | 1.0 (ref.) | 1.12 (1.01, 1.24) | 1.04 (0.94, 1.16) | 1.12 (1.01, 1.24) | 1.31 (1.19, 1.45) | <0.0001 | 1.14 (1.08, 1.19) |
| Model 3 | 1.0 (ref.) | 1.12 (1.01, 1.24) | 1.04 (0.94, 1.15) | 1.11 (1.00, 1.23) | 1.30 (1.18, 1.43) | <0.0001 | 1.13 (1.08, 1.18) |
| Model 4 | 1.0 (ref.) | 1.10 (0.99, 1.22) | 1.02 (0.92, 1.13) | 1.08 (0.98, 1.20) | 1.26 (1.14, 1.39) | <0.0001 | 1.11 (1.06, 1.16) |
Abbreviation: IR: Incidence rate; PYs: person years. Model 1: age (continuous) and sex (male or female) adjusted relative risk. Model 2 further adjusted for race/ethnicity (European Americans or other), education level (≤high school or GED, some colleague, or college or above), income level (<$30,000, $30,000–$59,000 or ≥$60,000) and marital status (currently married or not), smoking status (current, former or never smoking), frequency of alcohol consumption (never, <1 times/week or ≥1 times/week), frequency of exercise vigorously (never/rarely, 1–4 times/month, 2–4 times/week, or ≥5 times/week). Model 3 further adjusted total energy intake (in quintiles), and body mass index (continuous). Model 4 further adjusted the presences of diabetes, high cholesterol, and hypertension at baseline (each Yes vs. No).
Table 3.
Hazard ratios (95% CI) for subtypes of cardiovascular disease by quintiles (or per SD) of dietary sodium, potassium, and their ratio in 180,156 participants from the Million Veteran Program (2011 to 2018).
Table 3.
Hazard ratios (95% CI) for subtypes of cardiovascular disease by quintiles (or per SD) of dietary sodium, potassium, and their ratio in 180,156 participants from the Million Veteran Program (2011 to 2018).
| Quintile of Dietary Indices | ptrend | HR (95%CI) | ||||||
|---|---|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | Q5 | Per 1-SD Increment | |||
| Sodium | ||||||||
| Non-fatal MI | Cases | 393 | 422 | 489 | 554 | 641 | ||
| Model1 | 1.0 (ref.) | 1.01 (0.88, 1.15) | 1.12 (0.98, 1.28) | 1.23 (1.08, 1.40) | 1.38 (1.21, 1.56) | <0.0001 | 1.17 (1.11, 1.24) | |
| Model2 | 1.0 (ref.) | 0.99 (0.86, 1.14) | 1.07 (0.94, 1.23) | 1.13 (1.00, 1.29) | 1.18 (1.04, 1.35) | 0.001 | 1.09 (1.04, 1.15) | |
| Model3 | 1.0 (ref.) | 0.98 (0.86, 1.13) | 1.06 (0.93, 1.21) | 1.11 (0.98, 1.27) | 1.15 (1.01, 1.31) | 0.005 | 1.08 (1.02, 1.14) | |
| Model4 | 1.0 (ref.) | 0.97 (0.85, 1.12) | 1.04 (0.91, 1.19) | 1.08 (0.95, 1.23) | 1.09 (0.96, 1.24) | 0.06 | 1.05 (1.00, 1.11) | |
| Non-fatal AIS | Cases | 306 | 300 | 297 | 363 | 446 | ||
| Model1 | 1.0 (ref.) | 0.93 (0.79, 1.09) | 0.89 (0.76, 1.05) | 1.06 (0.91, 1.23) | 1.27 (1.1, 1.47) | <0.0001 | 1.14 (1.07, 1.22) | |
| Model2 | 1.0 (ref.) | 0.94 (0.8, 1.11) | 0.9 (0.77, 1.06) | 1.05 (0.9, 1.23) | 1.2 (1.04, 1.4) | 0.0018 | 1.11 (1.04, 1.18) | |
| Model3 | 1.0 (ref.) | 0.94 (0.8, 1.11) | 0.9 (0.77, 1.06) | 1.05 (0.9, 1.22) | 1.2 (1.03, 1.39) | 0.0026 | 1.11 (1.04, 1.18) | |
| Model4 | 1.0 (ref.) | 0.93 (0.79, 1.09) | 0.88 (0.75, 1.03) | 1 (0.85, 1.17) | 1.1 (0.94, 1.28) | 0.0754 | 1.06 (0.99, 1.13) | |
| Potassium | ||||||||
| Non-Fatal MI | Cases | 502 | 490 | 464 | 554 | 489 | ||
| Model1 | 1.0 (ref.) | 0.91 (0.80, 1.03) | 0.82 (0.72, 0.93) | 0.93 (0.82, 1.05) | 0.79 (0.70, 0.89) | 0.001 | 0.93 (0.88, 0.97) | |
| Model2 | 1.0 (ref.) | 0.96 (0.85, 1.09) | 0.89 (0.78, 1.01) | 1.01 (0.89, 1.14) | 0.84 (0.74, 0.96) | 0.03 | 0.95 (0.91, 0.99) | |
| Model3 | 1.0 (ref.) | 0.96 (0.84, 1.08) | 0.88 (0.77, 1.00) | 1.00 (0.88, 1.13) | 0.83 (0.73, 0.95) | 0.02 | 0.94 (0.90, 0.99) | |
| Model3 | 1.0 (ref.) | 0.95 (0.84, 1.07) | 0.86 (0.76, 0.98) | 0.98 (0.87, 1.11) | 0.82 (0.72, 0.93) | 0.007 | 0.94 (0.89, 0.98) | |
| Non-fatal AIS | Cases | 348 | 342 | 326 | 316 | 380 | ||
| Model1 | 1.0 (ref.) | 0.91 (0.78, 1.06) | 0.82 (0.71, 0.95) | 0.75 (0.65, 0.88) | 0.87 (0.75, 1.01) | 0.02 | 0.94 (0.88, 0.99) | |
| Model2 | 1.0 (ref.) | 0.99 (0.85, 1.15) | 0.92 (0.79, 1.08) | 0.87 (0.74, 1.01) | 0.98 (0.85, 1.14) | 0.49 | 0.98 (0.93, 1.04) | |
| Model3 | 1.0 (ref.) | 0.99 (0.85, 1.15) | 0.92 (0.79, 1.07) | 0.86 (0.74, 1.01) | 0.98 (0.85, 1.14) | 0.48 | 0.98 (0.93, 1.04) | |
| Model3 | 1.0 (ref.) | 0.97 (0.84, 1.13) | 0.90 (0.77, 1.05) | 0.84 (0.72, 0.98) | 0.95 (0.82, 1.11) | 0.26 | 0.97 (0.91, 1.02) | |
| Ratio of Sodium to Potassium | ||||||||
| Non-fatal MI | Cases | 404 | 470 | 468 | 542 | 615 | ||
| Model1 | 1.0 (ref.) | 1.16 (1.01, 1.32) | 1.15 (1.01, 1.32) | 1.35 (1.18, 1.53) | 1.55 (1.37, 1.76) | <0.0001 | 1.25 (1.18, 1.32) | |
| Model2 | 1.0 (ref.) | 1.14 (1.00, 1.30) | 1.09 (0.95, 1.25) | 1.23 (1.08, 1.40) | 1.32 (1.17, 1.50) | <0.0001 | 1.14 (1.08, 1.21) | |
| Model3 | 1.0 (ref.) | 1.13 (0.99, 1.30) | 1.08 (0.95, 1.24) | 1.22 (1.07, 1.38) | 1.31 (1.15, 1.49) | <0.0001 | 1.14 (1.07, 1.21) | |
| Model3 | 1.0 (ref.) | 1.12 (0.98, 1.28) | 1.06 (0.93, 1.22) | 1.19 (1.04, 1.35) | 1.27 (1.12, 1.45) | 0.0001 | 1.12 (1.06, 1.19) | |
| Non-Fatal AIS | Cases | 312 | 330 | 314 | 317 | 439 | ||
| Model1 | 1.0 (ref.) | 1.06 (0.91, 1.24) | 1.02 (0.87, 1.19) | 1.05 (0.90, 1.23) | 1.49 (1.29, 1.72) | <0.0001 | 1.22 (1.14, 1.31) | |
| Model2 | 1.0 (ref.) | 1.08 (0.92, 1.26) | 1.00 (0.86, 1.17) | 0.99 (0.85, 1.16) | 1.32 (1.14, 1.53) | 0.0005 | 1.13 (1.06, 1.22) | |
| Model3 | 1.0 (ref.) | 1.08 (0.92, 1.26) | 1.00 (0.85, 1.17) | 0.98 (0.84, 1.15) | 1.31 (1.13, 1.52) | 0.0006 | 1.13 (1.05, 1.21) | |
| Model4 | 1.0 (ref.) | 1.06 (0.91, 1.24) | 0.97 (0.83, 1.14) | 0.95 (0.81, 1.11) | 1.26 (1.08, 1.46) | 0.005 | 1.11 (1.03, 1.19) | |
Table 4.
Stratified analysis of the associations between dietary ratio of sodium to potassium and cardiovascular diseases in 180,156 participants from the Million Veteran Program (2011 to 2018).
Table 4.
Stratified analysis of the associations between dietary ratio of sodium to potassium and cardiovascular diseases in 180,156 participants from the Million Veteran Program (2011 to 2018).
| Quintiles of Dietary Indices | ptrend | HR (95%CI) | |||||
|---|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | Q5 | Per 1-SD Increment | ||
| Sex | p for interaction = 0.99 | ||||||
| Female | 1.0 (ref.) | 0.96 (0.64, 1.42) | 0.66 (0.41, 1.04) | 0.96 (0.62, 1.49) | 1.36 (0.90, 2.06) | 0.23 | 1.14 (0.92, 1.42) |
| Male | 1.0 (ref.) | 1.12 (1.00, 1.24) | 1.04 (0.94, 1.16) | 1.09 (0.98, 1.21) | 1.26 (1.14, 1.40) | <0.0001 | 1.11 (1.06, 1.16) |
| Race | p for interaction = 0.92 | ||||||
| White | 1.0 (ref.) | 1.09 (0.98, 1.23) | 1.03 (0.92, 1.15) | 1.08 (0.97, 1.21) | 1.28 (1.15, 1.43) | <0.0001 | 1.12 (1.07, 1.18) |
| Black | 1.0 (ref.) | 1.25 (0.95, 1.64) | 0.96 (0.72, 1.28) | 1.18 (0.90, 1.54) | 1.29 (1.00, 1.66) | 0.07 | 1.12 (0.99, 1.26) |
| Baseline diabetes | p for interaction = 0.99 | ||||||
| No | 1.0 (ref.) | 1.02 (0.89, 1.17) | 0.94 (0.82, 1.08) | 1.04 (0.91, 1.19) | 1.21 (1.06, 1.38) | 0.002 | 1.11 (1.04, 1.18) |
| Yes | 1.0 (ref.) | 1.20 (0.97, 1.48) | 1.08 (0.88, 1.33) | 1.15 (0.94, 1.40) | 1.29 (1.06, 1.56) | 0.02 | 1.11 (1.01, 1.21) |
| Baseline high cholesterol | p for interaction = 0.24 | ||||||
| No | 1.0 (ref.) | 1.17 (0.99, 1.38) | 1.02 (0.85, 1.21) | 1.06 (0.89, 1.26) | 1.37 (1.17, 1.61) | 0.0004 | 1.15 (1.06, 1.24) |
| Yes | 1.0 (ref.) | 1.00 (0.86, 1.16) | 0.96 (0.82, 1.11) | 1.07 (0.92, 1.24) | 1.12 (0.97, 1.30) | 0.04 | 1.07 (1.00, 1.15) |
| Baseline hypertension | p for interaction = 0.41 | ||||||
| No | 1.0 (ref.) | 0.99 (0.82, 1.20) | 0.89 (0.73, 1.09) | 0.96 (0.79, 1.16) | 1.27 (1.06, 1.53) | 0.008 | 1.13 (1.03, 1.24) |
| Yes | 1.0 (ref.) | 1.12 (0.97, 1.28) | 1.03 (0.90, 1.19) | 1.12 (0.98, 1.29) | 1.22 (1.06, 1.39) | 0.005 | 1.09 (1.03, 1.16) |
| DASH score | p for interaction = 0.07 | ||||||
| Low (≤21) | 1.0 (ref.) | 1.07 (0.87, 1.32) | 0.99 (0.81, 1.21) | 1.07 (0.88, 1.29) | 1.25 (1.04, 1.49) | 0.0003 | 1.14 (1.06, 1.22) |
| High (>21) | 1.0 (ref.) | 1.09 (0.97, 1.23) | 1.00 (0.88, 1.13) | 1.02 (0.89, 1.17) | 1.10 (0.94, 1.28) | 0.50 | 1.02 (0.95, 1.10) |
Adjusted for age (continuous), sex (male or female, race/ethnicity (European Americans or other), education level (≤high school or GED, some colleague, or college or above), income level (<$30,000, $30,000–$59,000 or ≥$60,000) and marital status (currently married or not), smoking status (current, former or never smoking), frequency of alcohol consumption (never, <1 times/week or ≥1 times/week), frequency of exercise vigorously (never/rarely, 1–4 times/month, 2–4 times/week, or ≥5 times/week), total energy intake (in quintiles), and body mass index (continuous), presences of diabetes, high cholesterol, and hypertension at baseline (each Yes vs. No), except the stratified factor.
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Wang, D.D.; Li, Y.; Nguyen, X.-M.T.; Song, R.J.; Ho, Y.-L.; Hu, F.B.; Willett, W.C.; Wilson, P.W.F.; Cho, K.; Gaziano, J.M.; et al. Dietary Sodium and Potassium Intake and Risk of Non-Fatal Cardiovascular Diseases: The Million Veteran Program. Nutrients 2022, 14, 1121. https://doi.org/10.3390/nu14051121
AMA Style
Wang DD, Li Y, Nguyen X-MT, Song RJ, Ho Y-L, Hu FB, Willett WC, Wilson PWF, Cho K, Gaziano JM, et al. Dietary Sodium and Potassium Intake and Risk of Non-Fatal Cardiovascular Diseases: The Million Veteran Program. Nutrients. 2022; 14(5):1121. https://doi.org/10.3390/nu14051121
Chicago/Turabian StyleWang, Dong D., Yanping Li, Xuan-Mai T. Nguyen, Rebecca J. Song, Yuk-Lam Ho, Frank B. Hu, Walter C. Willett, Peter W. F. Wilson, Kelly Cho, J. Michael Gaziano, and et al. 2022. "Dietary Sodium and Potassium Intake and Risk of Non-Fatal Cardiovascular Diseases: The Million Veteran Program" Nutrients 14, no. 5: 1121. https://doi.org/10.3390/nu14051121
APA StyleWang, D. D., Li, Y., Nguyen, X. -M. T., Song, R. J., Ho, Y. -L., Hu, F. B., Willett, W. C., Wilson, P. W. F., Cho, K., Gaziano, J. M., Djoussé, L., & on behalf of the VA Million Veteran Program. (2022). Dietary Sodium and Potassium Intake and Risk of Non-Fatal Cardiovascular Diseases: The Million Veteran Program. Nutrients, 14(5), 1121. https://doi.org/10.3390/nu14051121
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