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Original Article Dietary and Urinary Metabonomic Factors Possibly Accounting for Higher Blood Pressure of Black Compared With White Americans Results of International Collaborative Study on Macro-/Micronutrients and Blood Pressure Jeremiah Stamler, Ian J. Brown, Ivan K.S. Yap, Queenie Chan, Anisha Wijeyesekera, Isabel Garcia-Perez, Marc Chadeau-Hyam, Timothy M.D. Ebbels, Maria De Iorio, Joram Posma, Martha L. Daviglus, Mercedes Carnethon, Elaine Holmes, Jeremy K. Nicholson, Paul Elliott, for the INTERMAP Research Group Downloaded from http://hyper.ahajournals.org/ by guest on May 5, 2017 Abstract—Black compared with non-Hispanic white Americans have higher systolic and diastolic blood pressure and rates of prehypertension/hypertension. Reasons for these adverse findings remain obscure. Analyses here focused on relations of foods/nutrients/urinary metabolites and higher black blood pressure for 369 black compared with 1190 non-Hispanic white Americans aged 40 to 59 years from 8 population samples. Multiple linear regression, standardized data from four 24-hour dietary recalls per person, two 24-hour urine collections, and 8 blood pressure measurements were used to quantitate the role of foods, nutrients, and metabolites in higher black blood pressure. Compared with non-Hispanic white Americans, blacks’ average systolic/diastolic pressure was higher by 4.7/3.4 mm Hg (men) and 9.0/4.8 mm Hg (women). Control for higher body mass index of black women reduced excess black systolic/diastolic pressure to 6.8/3.8 mm Hg. Lesser intake of vegetables, fruits, grains, vegetable protein, glutamic acid, starch, fiber, minerals, and potassium, and higher intake of processed meats, pork, eggs, and sugar-sweetened beverages, along with higher cholesterol and higher Na/K ratio, related to in higher black blood pressure. Control for 11 nutrient and 10 non-nutrient correlates reduced higher black systolic/diastolic pressure to 2.3/2.3 mm Hg (52% and 33% reduction in men) and to 5.3/2.8 mm Hg (21% and 27% reduction in women). Control for foods/urinary metabolites had little further influence on higher black blood pressure. Less favorable multiple nutrient intake by blacks than non-Hispanic white Americans accounted, at least in part, for higher black blood pressure. Improved dietary patterns can contribute to prevention/control of more adverse black blood pressure levels. (Hypertension. 2013;62:00-00.) Online Data Supplement • Key Words: African American ■ blood pressure A dverse blood pressure (BP) is an established major risk factor for cardiovascular diseases. Repeated US population surveys, including the International Collaborative Study on Macro-/Micronutrients and Blood Pressure (INTERMAP), document that BP is higher in black (African Americans: AA) than non-Hispanic white Americans (NHWA).1–3 Etiopathogenesis of this BP difference remains unexplained, that is, it continues to be—theoretically and practically—a major unsolved challenge for cardiovascular disease research. ■ eating ■ nutrients Here, we hypothesize that multiple black–NHWA differences in food/nutrient intake and urinary metabolites account for higher AA BP; we use INTERMAP data to test this hypothesis.3–6 Methods Population Samples, Field Methods (1996–1999) Participants were 369 AA and 1190 NHWA women and men aged 40 to 59 years recruited as 8 stratified random US population samples (Table S1 in the online-only Data Supplement). Participants attended Received June 4, 2013; first decision June 17, 2013; revision accepted September 5, 2013. From the Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL (J.S., M.L.D., M.C.); Department of Epidemiology and Biostatistics, School of Public Health (I.J.B., Q.C., M.C.-H., M.D.I., P.E.), and Section of Computational and Systems Medicine, Department of Surgery and Cancer (I.K.S.Y., A.W., I.G.-P., T.M.D.E., J.P., E.H., J.K.N.), Faculty of Medicine, Imperial College London, United Kingdom; Department of Life Sciences, School of Pharmacy and Health Sciences, International Medical University, Kuala Lumpur, Malaysia (I.K.S.Y.); Department of Statistical Science, University College, London, United Kingdom (M.D.I.); and MRC-HPA Centre for Environment and Health, Imperial College London, United Kingdom (Q.C., A.W., I.G.-P., M.C.-H., T.M.D.E., J.P., E.H., J.K.N., P.E.). The online-only Data Supplement is available with this article at http://hyper.ahajournals.org/lookup/suppl/doi:10.1161/HYPERTENSIONAHA. 113.01810/-/DC1. Correspondence to Jeremiah Stamler, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 680 N Lake Shore Dr, Suite 1400, Chicago, IL 60611. E-mail [email protected] © 2013 American Heart Association, Inc. Hypertension is available at http://hyper.ahajournals.org DOI: 10.1161/HYPERTENSIONAHA.113.01810 1 2 Hypertension December 2013 4 times—2 visits on consecutive days, 2 further visits on consecutive days on average 3 weeks later.3 Demographic data were obtained by interviewer-administered questionnaire. Height and weight were measured at 2 visits. Each participant provided two 24-hour urine collections; aliquots were air-freighted to the Central Laboratory (Leuven, Belgium) for urinary biochemistry and to Imperial College London for proton nuclear magnetic resonance spectroscopy.6 Dietary data were collected and computerized by a certified interviewer using the multipass 24-hour recall method.3,7 Institutional ethics committee approval was obtained for each site; all participants gave written informed consent. BP Measurements Systolic and diastolic BP (SBP, DBP; first and fifth Korotkoff sounds) were measured twice per visit by a certified technician using a random-zero sphygmomanometer—participant seated for ≥5 minutes in a quiet room, bladder empty, arm at heart level.3 Statistical Methods Downloaded from http://hyper.ahajournals.org/ by guest on May 5, 2017 Measurements per person were averaged for BP and nutrients across the 4 visits; for 24-hour urinary excretions, across the 2 collections.3,7 Interethnic differences were assessed for statistical significance by Student t test or χ2 test. Approximate reliability—observed univariate regression coefficient as percent of theoretical true coefficient—was estimated for relevant variables.8,9 Based on AA–NHWA differences in dietary and urinary variables, multiple linear regression was used to examine relations of these traits to interethnic differences in BP.5,6,10 Model A included age, sex, sample, and an indicator for AA; model B added other nondietary factors; model C added body mass index (BMI). Then each dietary/urinary factor was added to model C separately; percentage reduction from the model C AA–NHWA BP difference was calculated to assess the influence of the added variable on higher BP of AA. Finally, dietary/ urinary variables were included in combinations to assess joint impact on higher AA BP. Results Descriptive Statistics AA had higher average BP than NHWA and higher prevalence rates of hypertension (Table S2). AA women had higher average BMI and rates of overweight/obesity than NHWA women. AA and NHWA of both sexes differed in intake of multiple foods/nutrients (Tables S2 and S3). AA Intake Lower, Foods/Nutrients With Possible Favorable Relation to BP AA had lower intake of total and raw vegetables, fresh fruits, pasta/rice, total grains, vegetable protein, glutamic acid, starch, fiber, Ca, Mg, P, K, Fe, and nonheme Fe. AA Intake Higher, Foods/Nutrients With Possible Adverse Relation to BP AA had higher intake of processed meats, pork, eggs, sugarsweetened beverages, dietary cholesterol, total sugars, fructose/glucose/sucrose, glycine, and Na/K ratio. AA intakes were relatively favorable for only a few foods/ nutrients: 1.AA intake higher, foods/nutrients with possible favorable relation to BP: fish/fish roe/shellfish, poultry, polyunsaturated fatty acids, polyunsaturated fatty acid/saturated fatty acid ratio, oleic acid. 2.AA intake lower, foods/nutrients with possible adverse relation to BP: cream, cheese, dairy product recipes, and alcoholic beverages. Urinary Metabolites The median urinary 600-MHz proton nuclear magnetic resonance spectra for AA and NHWA subsamples are shown in the Figure. Urinary metabolites significantly12 higher in AA than in NHWA included creatinine, 3-hydroxyisovalerate, N-acetyls of glycoprotein fragments, dimethylglycine, lysine, N-acetyl neuraminic acid, leucine, dimethylamine, taurine, and 2-hydroxyisobutyrate; metabolites significantly higher in NHWA included trimethylamine, N-methyl nicotinic acid, hippurate, and succinate (Table 1). Hippurate and N-methyl nicotinic acid as quantified in 24-hour excretions are shown in Table S4. Figure. Median urinary proton nuclear magnetic resonance spectrum of INTERMAP US black and non-Hispanic white American (NHWA) participants (top), based on the first urine collection (n=1455). Manhattan plot indicating the significant spectral variables (bottom). Metabolites higher in black individuals compared with NHWA are shown in red; metabolites higher in NHWA individuals compared with blacks are shown in blue. 1 indicates leucine; 2, 3-hydroxyisovalerate; 3, 2-hydroxyisobutyrate; 4, N-acetyls of glycoprotein fragments; 5, N-acetyl neuraminic acid; 6, succinate; 7, dimethylamine; 8, trimethylamine; 9, dimethylglycine; 10, lysine; 11, creatinine; 12, hippurate; and 13, N-methyl nicotinic acid. Stamler et al Nutrition and Black Blood Pressure 3 Table 1. Proton Nuclear Magnetic Resonance–Derived Urinary Metabolites Differing Significantly* Between Black and NHWA Participants, All and by Gender Minimum P Value† All Metabolites Chemical Shifts, ppm (Multiplicity) Men Women First Collection Second Collection First Collection Second Collection First Collection Second Collection Higher in blacks (154 men and 184 women) Creatinine 3.05 (s); 4.05 (s) 3.8×10–25 1.8×10–20 4.4×10–13 1.1×10–11 5.8×10–14 9.3×10–10 3-Hydroxyisovalerate 1.27 (s); 2.37 (s) 1.0×10–23 4.6×10–24 4.1×10–13 3.6×10–17 1.8×10–11 4.3×10–9 1.6×10 1.5×10 3.9×10 1.5×10 –12 1.4×10 3.1×10–12 … 4.1×10–15 2.2×10–11 … … … … 4.3×10–10 2.7×10–9 … … … N-acetyls of glycoprotein fragments Dimethylglycine 2.02–2.04 (s) 2.93 (s); 3.72 (s) –22 –19 5.2×10–18 9.7×10–18 4.2×10 –12 … –9 Downloaded from http://hyper.ahajournals.org/ by guest on May 5, 2017 Lysine 3.02 (t) 1.3×10 N-acetyl neuraminic acid 2.06 (s) 2.9×10–14 3.0×10–11 Leucine 0.96 (d) 1.7×10–10 3.9×10–11 … … … Dimethylamine 2.73 (s) 3.3×10 –10 4.7×10–9 4.1×10–10 1.7×10–11 … … 2-Hydroxyisobutyrate 1.36 (s) 5.3×10–10 8.6×10–8 … … … … 2.6×10–15 9.5×10–12 9.8×10–8 4.0×10–9 … … 1.2×10 4.1×10 2.2×10–7 … … –9 –15 Higher in NHWA (594 men and 523 women) Trimethylamine 2.87 (s) N-methyl nicotinic acid 4.44 (s); 8.84 (t); 9.13 (s) 3.1×10 Hippurate 3.98 (d); 7.55 (t); 7.64 (t); 7.84 (d) 5.9×10–10 3.1×10–9 … … … … Succinate 2.41 (s) 1.5×10–9 4.0×10–10 … … 3.9×10–9 3.4×10–8 –13 –10 –8 d indicates doublet; NHWA, non-Hispanic white Americans; s, singlet; and t, triplet. *Mean population differences in peak intensity for spectral variables were assessed for statistical significance using family wise error rate <0.01 (P<4×10–6 for group mean population differences by Student t test) for the 2 urine collections considered separately. †Minimum P values for mean population differences in peak intensity assigned to a particular metabolite, obtained separately for first and second urine collections, give a ranking of the discriminatory strength of the metabolites. Partial Correlations Partial correlation coefficients >0.5 (adjusted for age, sex, sample) were recorded for many pairs of foods/nutrients (Table S5). Reliability of Data For foods, 29 of 68 reliability estimates were >50%, without apparent pattern across sex/ethnic strata (Table S6). Nutrient reliability estimates were generally higher than for foods; 75 of 108 were >50%, somewhat lower for AA than for NHWA. Reliability estimates were high for hippurate, N-methyl nicotinic acid, SBP, and DBP. Relation of Foods, Nutrients, and Urinary Metabolites to Higher AA BP With control for possible nondietary confounders, SBP/DBP was significantly higher for AA than NHWA by 4.7/3.4 mm Hg (men) and 9.0/4.8 mm Hg (women) (Table S6; row B). With BMI in the model (Table S7; row C), these differences remained about the same for men; for women, they were reduced to 6.8/3.8 mm Hg. Foods Considered Singly Of 17 foods with significantly different intakes by AA and NHWA, most had only a small influence on higher BP of AA (Table S7). Nutrients and Urinary Metabolites Considered Singly Addition of individual nutrients to the regression model generally led to greater influences on SBP and DBP (Table S7) than addition of individual foods; 24-hour urinary K excretion (inversely related to BP and lower in AA than NHWA) produced the largest effect in men, that is, reduction of AA SBP by 1.2 mm Hg (26%) and DBP by 0.8 mm Hg (24%), but without effect in women. Dietary glycine (% total protein; directly related to BP and higher in AA) had a similar SBP effect in men and the largest effect in women. Inclusion of Ca, Mg, P singly (inversely related to BP and lower in AA than NHWA) produced 0.5 to 0.6 mm Hg (>10%) reduction in higher SBP of AA men and qualitatively similar effects for AA women. With inclusion of urinary hippurate, which is lower in AA men (Table S4) and inversely related to BP, higher SBP and DBP of AA men and women was reduced by only 0.1 mm Hg (Table S7). With addition of N-methyl nicotinic acid to the model, which is lower in AA than NHWA men and women (reportedly not related to BP), higher SBP and DBP of AA men and women were increased, not decreased. Foods, Nutrients, and Urinary Metabolites in Combination With 10 foods considered together (all with significantly less favorable intake by AA than NHWA), effects on higher AA SBP/DBP were modest (Table 2; row D). With 13 nutrients combined (most with significantly less favorable intake 4 Hypertension December 2013 Table 2. Relation of Combinations of Foods/Nutrients/Urinary Metabolites, Significantly Different in Blacks and NHWA, to Higher BP of Blacks than NHWA, by Gender SBP, mm Hg Model BP Difference Z-Score DBP, mm Hg % Change From C* BP Difference Z-Score % Change From C* Men (n=165 blacks, 620 NHWA) A: age, sample 5.41 4.40 3.46 3.94 B: A plus medical history of CVD/diabetes mellitus, family history of hypertension, physical activity, special diet, supplement use 4.68 3.80 3.38 3.84 C: B plus BMI 4.76 4.04 3.43 4.05 3.64 3.00) −23.5 2.88 3.28 −16.1 E1: C plus vegetable protein, glutamic acid (%kcal), starch, fiber, Ca, nonheme Fe, riboflavin, urinary K, Σ long-chain PFA, dietary cholesterol, glycine (%protein), 14-day alcohol, urinary Na 2.28 1.72 −52.1 2.31 2.41 −32.7 E2: as E1 except removing 14-day alcohol and urinary Na 2.51 1.89 −47.3 2.43 2.53 −29.2 E3: as E1 except glutamic acid as %protein (instead of %kcal) 2.77 2.12 −41.9 2.61 2.75 −24.1 E4: as E1 except Mg instead of Ca 2.41 1.82 −49.4 2.44 2.55 −28.9 Combination of foods D: C plus raw vegetables, fresh fruit, pasta/rice, total grains, eggs, sugar-sweetened beverages, cream/cheese/ice cream/milk and cheese recipes, pork, processed meats, alcoholic beverages Combinations of nutrients Downloaded from http://hyper.ahajournals.org/ by guest on May 5, 2017 E5: as E1 except P instead of Ca 2.39 1.81 −49.9 2.41 2.52 −29.8 E6: as E1 except urinary Na/K ratio instead of urinary K and Na 2.47 1.88 −48.1 2.51 2.63 −27.0 E7: E1 plus Mg, P 2.29 1.72 −52.0 2.33 2.42 −32.2 F1: including variables with largest SBP differences for men, D plus E1 2.17 1.62 −54.3 2.17 2.04 −36.7 F2: as F1, excluding variables with partial correlation >0.5† 2.25 1.69 −52.7 2.14 2.21 −37.8 3.12 3.57 7.6 3.09 3.44 Combinations of foods/nutrients Men with quantitated urinary metabolites (n=146 blacks, 578 NHWA) Combinations of nutrients and urinary metabolite variables G: as C except based on above n 3.81 3.15 H: G plus hippurate, N-methylnicotinic acid 4.10 3.29 −0.8 I: as E1 except based on above n 2.05 1.51 −46.2 2.46 2.50 −21.1 J: I plus hippurate, N-methyl nicotinic acid 2.44 1.78 −36.0 2.55 2.56 −18.2 A: age, sample 9.66 8.02 5.03 6.52 B: A plus medical history of CVD/diabetes mellitus, family history of hypertension, physical activity, special diet, supplement use 9.00 7.42 4.83 6.20 C: B plus BMI 6.76 5.76 3.77 4.87 6.18 4.74 −8.5 3.58 4.14 −5.2 5.31 3.82 −21.4 2.76 3.01 −26.8 Women (n=204 blacks, 570 NHWA) Combination of foods D: C plus raw vegetables, fresh fruit, pasta/rice, total grains, eggs, sugar-sweetened beverages, cream/cheese/ice cream/milk and cheese recipes, pork, processed meats, alcoholic beverages Combinations of nutrients E1: C plus vegetable protein, glutamic acid (%kcal), starch, fiber, Ca, nonheme Fe, riboflavin, urinary K, Σ long-chain PFA, dietary cholesterol, glycine (%protein), 14-day alcohol, urinary Na (continued) Stamler et al Nutrition and Black Blood Pressure 5 Table 2. Continued Systolic BP, mm Hg Diastolic BP, mm Hg BP Difference Z-Score % Change From C* BP Difference Z-Score % Change From C* E2: as E1 except removing 14-day alcohol and urinary Na 5.22 3.78 −22.8 2.61 2.86 −30.9 E3: as E1 except glutamic acid as %protein (instead of %kcal) 5.42 3.90 −19.8 2.78 3.02 −26.2 E4: as E1 except Mg instead of Ca 5.46 3.94 −19.2 2.80 3.05 −25.8 Model E5: as E1 except P instead of Ca 5.39 3.89 −20.2 2.73 2.98 −27.6 E6: as E1 except urinary Na/K ratio instead of urinary K and Na 5.13 3.69 −24.1 2.65 2.88 −29.7 E7: E1 plus Mg, P 5.40 3.87 −20.2 2.84 3.09 −24.6 F1: including variables with largest SBP differences for women, D plus E6 5.38 3.79 −20.4 3.04 3.23 −19.4 F2: as F1, excluding variables with partial correlation >0.5‡ 5.14 3.68 −24.0 2.83 3.06 −25.0 3.78 4.60 Combinations of foods/nutrients Downloaded from http://hyper.ahajournals.org/ by guest on May 5, 2017 Women with quantitated urinary metabolites (n=188 blacks, 514 NHWA) Combinations of nutrients and urinary metabolite variables G: as C except based on above n 6.80 5.48 H: G plus hippurate, N-methyl nicotinic acid 6.92 5.52 1.6 4.01 4.83 6.0 I: as E1 except based on above n 5.37 3.63 −21.1 2.77 2.82 −26.8 J: I plus hippurate, N-methyl nicotinic acid 5.33 3.60 −21.7 2.80 2.86 −26.0 Units for nutrients are %kcal, g/1000 kcal, or mg/1000 kcal; for glutamic acid and glycine, % total protein; for urinary hippurate and N-methyl nicotinic acid, mmol/24 h. Z-score=regression coefficient/SE. |Z| ≥1.96, uncorrected P≤0.05; |Z| ≥2.58, uncorrected P≤0.01; |Z| ≥3.29, uncorrected P≤0.001. Σ indicates sum; BMI, body mass index; BP, blood pressure; Ca, calcium; CVD, cardiovascular diseases; DBP, diastolic blood pressuure; Fe, iron; K, potassium; Mg, magnesium; Na, sodium; NHWA, nonHispanic white Americans; P, phosphorus; PFA, polyunsaturated fatty acids; SBP, systolic blood pressure; and SFA, saturated fatty acids. *For rows H, I, and J, % change from row G. †Model F2 (men) includes raw vegetables, fresh fruit, pasta/rice, eggs, sugar-sweetened beverages, cream/cheese/ice cream/milk and cheese recipes, pork, processed meats, vegetable protein, glutamic acid (%kcal), riboflavin, urinary K, Σ long-chain PFA, glycine (%protein), 14-day alcohol, urinary Na. ‡Model F2 (women) includes raw vegetables, fresh fruit, pasta/rice, total grains, sugar- sweetened beverages, cream/cheese/ice cream/milk and cheese recipes, pork, processed meats, alcoholic beverages, glutamic acid (%kcal), cholesterol, riboflavin, glycine (%protein), urinary Na/K ratio. by AA than NHWA), effects on higher AA BP were larger, particularly for men; for example, male SBP/DBP difference reduced to 2.3/2.3 mm Hg (Z-scores, 1.72 and 2.41), as well as a decrease of 2.2/1.1 mm Hg (−52% and −33%; Table 2; row E1). Female AA SBP/DBP differences remained substantial, with nutrient-related smaller effect of 1.5/1.0 mm Hg (−21% and −27%). No significant gender interaction was found (data not tabulated). Combinations of nutrients and foods yielded little or no additional reduction in higher AA BP (Table 2; row F2). With the 2 quantified urinary metabolites in the model together (without and with nutrients), higher AA SBP/DBP was not reduced (Table 2; rows H, J). Discussion The main finding here on higher SBP/DBP of AA than NHWA was that less favorable multiple nutrient intake by AA than NHWA accounted, in part, for higher AA SBP/DBP. These include vegetable protein and its main amino acid (glutamic acid), starch, fiber, K, Ca or Mg or P, nonheme Fe; in addition, dietary cholesterol and glycine.11–25 To the best of our knowledge, these INTERMAP findings are unique. The Atherosclerosis Risk in Communities (ARIC) population study, involving >8000 nonhypertensive women and men aged 45 to 64 years, reported that whites consuming ≥3 daily servings of low-fat milk, compared with those consuming <1, had a 2.7 mm Hg smaller SBP increase with 9-year follow-up, an association not prevailing for AA.26 No data were revealed on relations of dietary variables to BP differences between AAs and whites. The CARDIA study reported that with 10-year follow-up, intake of low-fat dairy products was associated with lower incidence of high BP in AA and whites.27 The study did not assess whether these or other foods/nutrients related to higher AA BP. The third National Health and Nutrition Survey (NHANES III; 1988– 1994) reported lower serum 25-hydroxy vitamin D in AA than NHWA and an inverse relation of 25-hydroxy vitamin D to BP.28 These differences were estimated to explain about half the greater AA BP prevalence. No dietary data were revealed. If the INTERMAP findings here are reproducible, supporting the inference that they are etiologically significant, they have important implications, especially need for greater efforts to improve AA nutrition. For example, AA nutrition would be enhanced importantly by adoption of dietary recommendations for prevention/control of adverse BP, used in such studies as the Dietary Approaches to Stop Hypertension (DASH)-low Na trial or the Optimal Macronutrient Intake Trial for Heart Health (OMNIHEART) plus low Na.15,16 To 6 Hypertension December 2013 Downloaded from http://hyper.ahajournals.org/ by guest on May 5, 2017 extend their use among AA, specific factors influencing AA diet need to be considered, for example, ethnic traditions, lower average income, and reduced accessibility to modern supermarkets.29 For AA women, there is a particular need to reduce their higher BMI, with known relation to higher BP. Metabolome-wide association analysis revealed 12 urinary metabolites that differed significantly between AA and NHWA. Hippurate (higher in NHWA) is a gut microbial cometabolite produced by bacterial metabolism of plant phenols30,31; hippurate was related inversely to BP in INTERMAP participants.6 Observed differences in dimethylamine and dimethylglycine (both higher in AA) also likely relate to interethnic microbiotal differences.33 Creatinine and guanidinoacetate (involved in creatinine metabolism) were higher in AA than NHWA. Creatinine excretion is related to muscle turnover34; these metabolite differences could reflect greater AA physical activity and muscle mass. Lower AA excretion of N-methyl nicotinic acid, a product of nicotinic acid/nicotinamide metabolism, could reflect lower AA dietary intake of niacin and tryptophan (observed; data not tabulated). Trimethylamine (lower in AA) is linked to dietary cholineinduced atherosclerosis35; this difference could reflect lower AA dietary intake of B-complex vitamins. Multiple nutrients accounted only in part for higher AA BP. This may reflect regression dilution bias and other limitations in the nutrient data, despite their quality.5,9 In this regard, 2 previous INTERMAP investigations—on higher BP of less educated Americans, and higher BP of northern than southern Chinese—showed that multiple nutrients accounted completely for higher BP.13,25 Thus, the fact here that multiple foods/nutrients/metabolites apparently accounted only in part for higher AA BP suggests that other traits may operate, for example, in utero influences, early-life dietary patterns, psychosocial factors, and genetic factors.36–40 Study Strengths Findings here are solidly based, with participants from 8 diverse US random samples and dietary/metabolite/BP data collected by standardized, quality-controlled repetitive methods. Study Limitations Data are cross-sectional, subject to random regression dilution bias despite multiple measurements, and—in regard to the dietary data to nonrandom biases inherent in the methodology.9 Perspectives Delineation of factors responsible for the more adverse BP patterns of AA compared with other Americans is a major, long-term, unresolved research challenge. Its importance relates not only to the need to overcome this inequality in health of AA. It also stems from the likelihood that resolving this problem will clarify understanding on the etiopathogenesis of epidemic prehypertension/hypertension in all strata of the population. The INTERMAP data reported here show that about one fourth (SBP, women) to one half (SBP, men) of higher BP of AA is attributable to less favorable intake of multiple nutrients by AA , and that greater obesity among AA women also is a significant factor. Improved eating patterns can help prevent/control adverse AA BP patterns. Acknowledgments We dedicate this article to our longtime colleague Prof Hugo Kesteloot, who died on October 5, 2010. Proton nuclear magnetic resonance signal processing and inhouse software were developed by Drs Cloarec, Ebbels, Veselkov, Keun, and Rantalainen (Department of Surgery and Cancer, Imperial College London). This observational study is registered at www.ClinicalTrials.gov as NCT00005271. Sources of Funding Supported by grants (R01-HL50490 and R01-HL84228) from the National Heart, Lung, and Blood Institute, National Institutes of Health (NIH), and by the NIH Office on Dietary Supplements (Bethesda, MD); also by national agencies in Japan, People’s Republic of China, and the United Kingdom. Disclosures None. References 1.Stamler J, Stamler R, Neaton JD. Blood pressure, systolic and diastolic, and cardiovascular risks. US population data. Arch Intern Med. 1993;153:598–615. 2. Burt VL, Whelton P, Roccella EJ, Brown C, Cutler JA, Higgins M, Horan MJ, Labarthe D. 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Soc Sci Med. 2008;67:1604–1611. 40. Adeyemo A, Gerry N, Herbert A, Doumatey A, Huang H, Zhou J, Lashley K, Chen Y, Christman M, Rotimi C. A genome-wide association study of hypertension and blood pressure in African Americans. PLoS Genet. 2009;5:e1000564. Novelty and Significance What Is New? Summary • To the best of our knowledge, the data here are the first reported on mul- Data here from the population-based US samples of the International Collaborative Study on Macro-/Micronutrients and Blood Pressure (INTERMAP) study support the conclusion that the inordinately high rates of prehypertension/hypertension, common among blacks, can be ameliorated by improved nutrition. tiple foods/nutrients/urinary metabolites that apparently account, at least in part, for the more adverse blood pressure patterns of blacks compared with non-Hispanic white Americans. What Is Relevant? • These data point the way to specific dietary enhancements for the prevention/control of adverse BP patterns in blacks. Downloaded from http://hyper.ahajournals.org/ by guest on May 5, 2017 Dietary and Urinary Metabonomic Factors Possibly Accounting for Higher Blood Pressure of Black Compared With White Americans: Results of International Collaborative Study on Macro-/Micronutrients and Blood Pressure Jeremiah Stamler, Ian J. Brown, Ivan K.S. Yap, Queenie Chan, Anisha Wijeyesekera, Isabel Garcia-Perez, Marc Chadeau-Hyam, Timothy M.D. Ebbels, Maria De Iorio, Joram Posma, Martha L. Daviglus, Mercedes Carnethon, Elaine Holmes, Jeremy K. Nicholson and Paul Elliott Hypertension. published online October 7, 2013; Hypertension is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231 Copyright © 2013 American Heart Association, Inc. All rights reserved. Print ISSN: 0194-911X. Online ISSN: 1524-4563 The online version of this article, along with updated information and services, is located on the World Wide Web at: http://hyper.ahajournals.org/content/early/2013/10/07/HYPERTENSIONAHA.113.01810 Data Supplement (unedited) at: http://hyper.ahajournals.org/content/suppl/2013/10/07/HYPERTENSIONAHA.113.01810.DC1 Permissions: Requests for permissions to reproduce figures, tables, or portions of articles originally published in Hypertension can be obtained via RightsLink, a service of the Copyright Clearance Center, not the Editorial Office. Once the online version of the published article for which permission is being requested is located, click Request Permissions in the middle column of the Web page under Services. Further information about this process is available in the Permissions and Rights Question and Answer document. Reprints: Information about reprints can be found online at: http://www.lww.com/reprints Subscriptions: Information about subscribing to Hypertension is online at: http://hyper.ahajournals.org//subscriptions/ ONLINE DATA SUPPLEMENT Dietary and Urinary Metabonomic Factors Possibly Accounting for Higher Blood Pressure of African-Americans Compared to White Americans – – The INTERMAP Study Jeremiah Stamler, Ian J. Brown, Ivan K.S. Yap, Queenie Chan, Anisha Wijeyesekera, Isabel Garcia-Perez, Marc Chadeau-Hyam, Timothy Ebbels, Maria De Iorio, Joram Posma, Martha L. Daviglus, Mercedes Carnethon, Elaine Holmes, Jeremy Nicholson, Paul Elliott, for the INTERMAP Research Group 1 SUPPLEMENTARY METHODS Metabolome-Wide Analysis by 1H NMR Spectroscopy Preparation of urine specimens: Urine specimens were thawed completely prior to mixing. 500 µL of urine were mixed with 250 µL of phosphate buffer for urinary pH stabilization (pH 7.4) and 75 µL of the sodium 3-trimethylsilyl-(2,2,3,3-2H4)-1-propionate (TSP) in D2O. TSP served as a chemical shift reference, and D2O served as a fieldfrequency lock for the NMR spectrometer. The resulting solution was then transferred into a 96-well plate and left to stand for 10 min. before centrifuging at 1,500 g for a further 10 min. to remove any precipitate prior to NMR analysis. 1 H NMR spectroscopic analysis: The urine specimens were analyzed by 1H NMR spectroscopy at 600 MHz using a Bruker DRX-600 spectrometer (Bruker Biospin, Rheinstetten, Germany) operating in flow injection mode. Urine specimens were automatically delivered to the spectrometer by a Gilson robot incorporated into the Bruker Efficient Sample Transfer (BEST) system. One-dimensional (1D) 1H NMR spectra of urine were acquired using a standard 1D pulse sequence (recycle delay-90°t1-90°-tm-90°-acquisition) with water presaturation during both the recycle delay (2s) and the mixing time, tm, of 150ms. The 90° pulse length was set to ~10µs and an acquisition time of 2.73s was used. In total, 64 transients were collected into 32K data points using a spectral width of 20 ppm. 1 H NMR data pre-processing: All free induction decays were multiplied by an exponential function equivalent to a 0.3 Hz line-broadening factor prior to Fourier transformation. The spectra were referenced and corrected for phase and baseline distortion. The spectral region δ 4.5 - 6.4 containing residual water and urea resonances was removed prior to normalisation by probabilistic quotient method (S1). The remaining spectrum (δ 0.5 - 9.5, excluding δ 4.5 - 6.4) was digitized to 7,100 variables (bin width 0.005 ppm). Principal component analysis (PCA) was performed on the pareto-scaled NMR dataset to identify metabolic outliers. PCA was done separately for first and second urine specimens, and participants whose scores mapped outside of the 95% Hotelling’s T2 ellipse (S2) in either collection were excluded. Of 1,559 AA and NHWA INTERMAP participants, 1H NMR spectra were not acquired for one of the two urine collections from 13 participants and a further 91 participants were excluded following the PCA outlier analysis, leaving 1,455 individuals for the present report: 338 AA and 1,117 NHWA. Statistical methods: Logistic regression (MATLAB R2011a, MathWorks, Natick, MA), adjusted for age, gender, and sample, was used to identify urinary metabolites discriminating AA and NHWA participants among the subsample excluding metabolic outliers (N=338 AA, N=1,117 NHWA). Analyses were done separately for first and second 24-hour urine collections. Mean inter-ethnic differences in peak intensity for 7,100 spectral variables were assessed for statistical significance using a conservative Family Wise Error Rate of <0.01 to minimize false positive findings, corresponding to P <4×10-6 for group mean inter-ethnic differences for each of the two urine collections considered separately (S3). Each discriminatory metabolite comprises multiple spectral variables at 0.005 ppm resolution; the minimum P-value for mean differences between AA and NHWA in peak intensity among the spectral variables assigned to a particular metabolite was obtained separately for the first and second urine collections. This gave 2 a ranking of the discriminatory strength of the metabolites. A spectral variable was found to be significant if the p-value was <4×10-6 for both urine collections, the regression coefficients for both urine collections had the same sign and both adjacent spectral variables were significant according the previous rules as well. Structural identification of discriminatory metabolites was achieved by 2dimensional NMR analyses (S4), statistical total correlation spectroscopy (STOCSY) (S5), addition of known standards to the urine specimens, solid phase extraction chromatography, and mass spectrometry. SUPPLEMENTARY REFERENCES S1. Dieterle F, Ross A, Schlotterbeck G, Senn H. Probabalistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal Chem. 2006;78:4281-4290. S2. Hotelling H. The generalization of Student’s ratio. Ann Math Stat. 1931;2:360378. S3. Chadeau-Hyam M, Ebbels TMD, Brown IJ, Chan Q, Stamler J, Huang CC, Daviglus ML, Ueshima H, Zhao L, Holmes E, Nicholson JK, Elliott P, De Iorio M. Metabolic profiling and the metabolome-wide association study: significance level for biomarker identification. J Proteome Res. 2010;9:4620-4627. S4. Holmes E, Loo RL, Cloarec O, Coen M, Tang H, Maibaum E, Bruce S, Chan Q, Elliott P, Stamler J, Wilson ID, Lindon JC, Nicholson JK. Detection of urinary drug metabolite (xenometabolome) signatures in molecular epidemiology studies via statistical total correlation (NMR) spectroscopy. Anal Chem. 2007;79:2629-2640. S5. Cloarec, O, Dumas ME, Craig, A, Barton RH, Trygg J, Hudson J, Blancher C, Gauguier D, Lindon JC, Holmes E, Nicholson J. Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets. Anal Chem. 2005;77:1282-1289. 3 SUPPLEMENTARY TABLES Online Table S1. U.S. INTERMAP population samples by age, gender, and ethnicity Sample Non-Hispanic White Americans African Americans Gender Male Female Male Female Age 40-49 50-59 40-49 50-59 40-49 50-59 40-49 50-59 Baltimore 31 36 32 35 38 38 30 33 Chicago 39 71 38 65 19 6 25 6 Jackson 41 59 30 41 18 5 33 22 Minneapolis 47 47 51 42 13 16 12 22 Pittsburgh 57 62 58 58 8 2 5 6 Corpus Christi 65 65 58 62 1 1 7 3 4 Online Table S2. Descriptive statistics - - blood pressure, other variables, food/food subgroup intakes, African Americans (AA) and non-Hispanic White Americans (NHWA) by gender Variable Men Women African American Non-Hispanic White African American Non-Hispanic White (N=165) American (N=620) (N=204) American (N=570) Mean or % (SD) Mean or % (SD) P-value* Mean or % (SD) Mean or % (SD) P-value* Systolic blood pressure, mm Hg 124.2 (15.3) 120.0 (12.2) 123.9 (15.7) 114.5 (13.8) 0.0002 4.3×10-15 Diastolic blood pressure, mm Hg 78.4 (11.0) 75.9 (9.1) 75.1 (9.9) 70.6 (8.8) 0.003 1.9×10-9 Hypertension†, % 38.2 25.6 46.6 20.0 0.001 2.2×10-13 Severe hypertension‡, % 28.5 19.5 40.2 17.5 0.01 5.9×10-11 Antihypertensive drug treatment, % 26.7 19.0 38.2 17.2 0.03 7.6×10-10 Age, years 48.0 (5.5) 49.3 (5.3) 48.7 (5.6) 49.6 (5.5) 0.004 0.05 -6 14.3 (2.4) 14.9 (2.7) Education, years 14.7 (2.9) 15.9 (3.1) 0.01 3.6×10 2 Body mass index, kg/m 29.5 (5.5) 29.1 (5.1) 31.2 (6.9) 27.8 (6.3) 0.33 1.5×10-10 Physical activity, hours/day moderate or heavy 4.2 (3.3) 3.3 (3.1) 3.3 (2.9) 2.8 (2.8) 0.002 0.05 Special diet, % 21.2 12.4 24.5 23.2 0.004 0.70 CVD/diabetes diagnosis, % 18.8 13.1 18.6 14.0 0.06 0.12 Current drinker, % 65.5 79.0 49.0 77.4 0.0003 3.6×10-14 14-day alcohol, drinkers only, g/24-h 15.1 (23.8) 13.3 (16.3) 5.0 (9.8) 5.6 (8.4) 0.34 0.52 Energy, kcal/24-h 2635 (722) 2640 (682) 1896 (494) 1884 (468) 0.93 0.76 Foods with possible favorable relation to blood pressure – lower intake in AA than NHWA Total vegetables, g/1,000 kcal 107.2 (60.2) 127.2 (65.6) 133.9 (79.6) 144.5 (74.9) 0.0004 0.09 Raw vegetables, g/1,000 kcal 20.0 (23.2) 29.9 (32.6) 30.4 (39.5) 39.4 (38.7) 0.0003 0.005 Fresh fruit, g/1,000 kcal 42.1 (54.0) 53.0 (64.8) 57.4 (73.5) 73.2 (67.9) 0.047 0.005 Pasta/rice, g/1,000 kcal 31.1 (34.7) 29.8 (30.0) 26.4 (27.8) 33.7 (32.8) 0.64 0.004 Total grains, g/1,000 kcal 89.0 (40.4) 97.3 (36.4) 85.0 (33.8) 106.2 (37.0) 0.01 1.9×10-12 Bread/rolls/biscuits, g/1,000 kcal 30.7 (16.3) 38.2 (21.0) 2.7×10-5 31.4 (19.7) 39.4 (22.6) 1.1×10-5 Foods with possible unfavorable relation to blood pressure – higher intake in AA than NHWA Processed meats, g/1,000 kcal 11.1 (12.0) 9.9 (11.9) 10.3 (13.5) 6.9 (9.4) 0.27 0.0001 Pork, g/1,000 kcal 10.0 (13.2) 8.5 (11.8) 10.0 (15.5) 6.3 (9.9) 0.17 0.0001 Eggs, g/1,000 kcal 12.5 (13.1) 9.9 (9.9) 12.4 (12.7) 9.4 (9.1) 0.005 0.0003 Continued on next page 5 Online Table S2. continued, page 2 Fruit juices/drinks, g/1,000 kcal 122.5 (122.7) 69.9 (102.1) 2.5×10-8 Sugar-sweetened beverages, g/1,000 kcal 210.1 (165.6) 126.3 (150.9) 8.9×10-10 Foods with possible favorable relation to blood pressure – higher intake in AA than NHWA Fish/fish roe/shellfish, g/1,000 kcal 10.2 (16.3) 7.5 (12.8) 0.02 Poultry, g/1,000 kcal 27.6 (22.6) 17.1 (16.8) 7.6×10-11 Foods with possible unfavorable relation to blood pressure – lower intake in AA than NHWA Cream, g/1,000 kcal 1.6 (3.5) 3.0 (6.1) 0.005 Cheese, g/1,000 kcal 6.8 (6.4) 13.1 (12.1) 1.8×10-10 Cream/cheese/ice-cream/milk & cheese recipes, g/1,000 kcal 19.0 (22.1) 25.7 (21.2) 0.0004 Alcoholic beverages, g/1,000 kcal 70.3 (165.1) 72.5 (143.8) 0.87 110.1 (126.0) 66.4 (118.6) 1.0×10-5 194.7 (166.7) 76.7 (108.0) 3.3×10-28 9.9 30.1 (14.6) (23.7) 8.5 17.8 (13.7) (17.0) 0.24 7.5×10-15 1.7 9.2 (4.2) (9.3) 3.5 15.6 (6.7) (14.8) 0.0003 1.2×10-8 18.9 23.8 (22.0) (117.3) 28.5 28.7 (23.7) (88.0) 5.4×10-7 0.53 Foods not included since they were not significantly different for AA compared to NHWA of both genders and/or their possible relation to BP remains undefined: animal fat; beef; cooked vegetables; cocoa, milk-type beverages; diet beverages; egg recipes; fish, fish roe; fresh meat excluding poultry, fish; fresh meat/poultry; frozen yogurt; fruits, dried; fruits, sweetened; game meat; ice cream/ice milk/sherbet/milk shake; imitation eggs; lamb; margarines; mature dried beans/peas; meat/poultry/fish recipes; milk; milk/cheese recipes; milk/yogurt/frozen yogurt/cocoa; non-alcoholic beverages including soda, excluding tea/coffee; nuts/nut butters; nuts/legumes; oils; polyunsaturated vegetable fat dairy products; ready-to-eat cereals; salad dressing; saturated vegetable fat dairy products; shellfish; shortening; snacks/sweets; sweet breads/rolls/pancakes; table spreads; total animal fats/margarines/table spreads/oils/shortening/salad dressing; total fresh meat/fish; total fruits; veal; vegetable recipes; vegetarian meat substitutes/related products. * From Student’s t-test or χ2 test. † Hypertension is systolic blood pressure ≥140 mm Hg, and/or diastolic blood pressure ≥90 mm Hg, and/or antihypertensive drug treatment for high blood pressure. ‡ Severe hypertension is systolic blood pressure ≥160 mm Hg, and/or diastolic blood pressure ≥100, and/or antihypertensive drug treatment for high blood pressure. 6 Online Table S3. Descriptive statistics - - nutrient intakes, AA and NHWA by gender Variable Men African American Non-Hispanic White African American (N=165) American (N=620) (N=204) Mean or % (SD) Mean or % (SD) P-value* Mean or % (SD) Nutrients with possible favorable relation to blood pressure – lower intake in AA than NHWA Vegetable protein, %kcal 4.4 (1.7) 5.0 (1.5) 4.5 (1.3) 4.3×10-5 -7 Glutamic acid, %kcal 2.8 (0.5) 3.0 (0.6) 2.8 (0.6) 4.2×10 -15 19.1 (1.5) Glutamic acid, %protein 19.0 (1.4) 20.1 (1.6) 1.4×10 -5 Starch, %kcal 19.9 (5.4) 21.9 (5.0) 19.8 (5.0) 1.2×10 -6 Total dietary fiber, g/1,000 kcal 7.6 (3.4) 8.9 (3.3) 8.2 (3.4) 5.2×10 -15 Calcium, mg/1,000 kcal 288.9 (107.3) 382.3 (138.4) 3.1×10 308.8 (114.4) -12 Magnesium, mg/1,000 kcal 125.4 (37.0) 147.6 (35.1) 130.4 (37.6) 2.3×10 Iron, mg/1,000 kcal 6.9 (3.1) 7.8 (2.9) 7.0 (2.3) 0.0003 Non-heme iron, mg/1,000 kcal 6.3 (3.1) 7.3 (2.9) 6.5 (2.2) 0.0001 -12 Phosphorus, mg/1,000 kcal 523.9 (103.3) 600.9 (125.7) 1.0×10 534.4 (117.5) -7 Riboflavin, mg/1,000 kcal 0.82 (0.26) 0.94 (0.24) 0.82 (0.24) 2.3×10 Vitamin D, µg/1,000 kcal 2.0 (1.5) 2.4 (1.3) 2.1 (1.6) 0.004 -18 Urinary potassium, mmol/24-h 55.5 (17.9) 71.7 (21.2) 44.2 (15.9) 1.2×10 Nutrients with possible unfavorable relation to blood pressure – higher intake in AA than NHWA Dietary cholesterol, mg/1,000 kcal 140.7 (62.1) 123.6 (52.9) 141.9 (64.1) 0.0004 -20 Arachidonic acid, %kcal 0.08 (0.04) 0.05 (0.03) 0.08 (0.05) 4.2×10 Estimated total sugars, %kcal 28.6 (8.7) 26.6 (8.4) 29.8 (8.5) 0.007 -6 Σ fructose, glucose, sucrose, %kcal 24.2 (8.8) 20.7 (8.4) 25.1 (8.6) 2.7×10 Glycine, %kcal 0.7 (0.2) 0.6 (0.2) 0.7 (0.2) 0.0006 -34 Glycine, %protein 4.5 (0.4) 4.1 (0.4) 4.5 (0.5) 6.1×10 -17 Ratio, urinary sodium/potassium 3.55 (1.28) 2.75 (0.98) 3.69 (1.53) 1.6×10 Nutrients with possible favorable relation to blood pressure – higher intake in AA than NHWA Total PFA, %kcal 7.3 (2.7) 6.8 (2.1) 7.5 (2.5) 0.006 Total Omega-3 PFA, %kcal 0.8 (0.4) 0.7 (0.3) 0.8 (0.4) 0.01 -5 Σ long chain PFA, %kcal 0.10 (0.17) 0.05 (0.09) 0.09 (0.12) 2.2×10 Total Omega-6 PFA, %kcal 6.6 (2.5) 6.2 (2.0) 6.8 (2.2) 0.01 Linoleic acid, %kcal 6.5 (2.5) 6.1 (2.0) 6.7 (2.2) 0.02 Oleic acid, %kcal 12.0 (2.5) 11.8 (2.7) 12.0 (2.69) 0.32 Ratio, PFA/SFA 0.76 (0.31) 0.69 (0.27) 0.80 (0.32) 0.003 Women Non-Hispanic White American (N=570) Mean or % (SD) P-value* 5.4 3.1 20.6 23.2 9.9 430.1 159.7 8.3 7.8 629.9 1.02 2.6 56.8 (1.4) (0.5) (1.5) (5.0) (3.4) (147.4) (39.0) (2.8) (2.8) (127.9) (0.26) (1.4) (17.9) 8.2×10-15 4.7×10-11 2.2×10-30 6.4×10-16 1.5×10-9 7.7×10-25 1.5×10-19 3.3×10-8 3.6×10-10 1.0×10-19 6.1×10-19 1.5×10-5 2.9×10-18 117.4 0.05 27.7 20.8 0.6 3.9 2.68 (48.6) (0.03) (7.2) (6.9) (0.2) (0.4) (0.98) 2.2×10-8 8.8×10-25 0.0007 1.6×10-12 8.5×10-8 9.5×10-50 3.2×10-25 6.6 0.7 0.06 6.0 5.9 11.1 0.71 (2.1) (0.4) (0.09) (2.0) (2.0) (2.7) (0.28) 5.2×10-7 0.045 0.002 4.0×10-7 9.5×10-7 4.4×10-5 0.0004 Continued on next page Online Table S3. continued, page 2 Nutrients with possible unfavorable relation to blood pressure – lower intake in AA than NHWA None Abbreviations: Σ, sum of; SFA, saturated fatty acids; PFA, polyunsaturated fatty acids. Heavy physical activity: hours/day of heavy activity, work and leisure; special diet: weight loss, weight gain, vegetarian, saltreduced, diabetic, fat-modified, or other; CVD-DM diagnosis: history of heart attack, other heart disease, stroke, or diabetes; all nutrients are from four 24-hour dietary recalls/person. Nutrients not included since they were not significantly different for AA compared to NHWA of both genders and/or their possible relation to BP remains undefined: 14-day alcohol, all; alanine; animal protein; arginine; aspartic acid; beta-carotene; cystine; fructose; galactose; glucose; histidine; isoleucine; Keys dietary lipid score; lactose; leucine; linolenic acid; lysine; maltose; methionine; phenylalanine; proline; retinol; selenium; serine; sucrose; threonine; total available carbohydrate; total fatty acids; total monounsaturated fatty acids; total protein, total SFA; tryptophan; tyrosine; urinary Na; valine; vitamin A; vitamin C; vitamin E. * From Student’s t-test or χ2 test. 7 Online Table S4. Quantitated urinary hippurate and N-methyl nicotinic acid*, by ethnic group and gender Variable African American Non-Hispanic White P-value† (N AA / N NHWA) Mean (SD) Mean (SD) Men (146/578) Hippurate, mmol/24-h 2.91 1.51 4.08 2.28 4.7×10-9 N-methyl nicotinic acid, mmol/24-h 0.24 0.21 0.42 0.31 2.1×10-10 Women (188/514) Hippurate, mmol/24-h 2.32 1.45 3.30 2.01 1.6×10-9 N-methyl nicotinic acid, mmol/24-h 0.19 0.17 0.30 0.25 5.3×10-8 * quantified via integration of signals relative to the creatinine CH2 signal (δ 4.06), with creatinine measured by the Jaffé reaction (7). † From Student’s t-test. 8 Online Table S5. Partial correlations* of foods, nutrients, urinary metabolites, r > 0.5, AA and NHWA women and men combined (N=1,559†) Variable Partial correlation, r >0.5 Foods Total vegetables Fiber (0.57), beta-carotene (0.51) Fresh fruit Fiber (0.57), vitamin C (0.57) Total grains Vegetable protein (0.58), glutamic acid %protein (0.52), starch (0.79) Eggs Cholesterol (0.83) Sugar-sweetened beverages Estimated total sugars (0.53), Σ fructose, glucose, sucrose (0.63), fructose (0.73), glucose (0.72) Fish, fish roe, shellfish Σ long chain PFA (0.79) Poultry Arachidonic acid (0.52), glycine %kcal (0.53) Cheese Tyrosine (0.51) Alcoholic beverages 14-day alcohol, all (0.70), 14-day alcohol, drinkers only (0.70) Nutrients Vegetable protein Starch (0.66), fiber (0.77), magnesium (0.63), non-heme iron (0.51), total grains (0.58), methionine (-0.56), lysine (-0.55) Glutamic acid, %kcal Phosphorus (0.74), glycine %kcal (0.72), selenium (0.65), animal protein (0.72), total protein (0.92) Glutamic acid, %protein Starch (0.52), arachidonic acid (-0.51), glycine %kcal (-0.55), glycine %protein (-0.62), total grains (0.52), animal protein (-0.55), proline (0.80), phenylalanine (0.59), alanine (-0.78), threonine (-0.66), methionine (-0.51), lysine (-0.70), arginine (-0.53), aspartic acid (-0.70), total fresh meat, fish (-0.63), fresh meat, poultry (-0.60) Starch Vegetable protein (0.66), glutamic acid %protein (0.52), total grains (0.79), lysine (-0.51) Fiber Vegetable protein (0.77), magnesium (0.77), total vegetables (0.57), fresh fruit (0.57) Calcium Phosphorus (0.80), riboflavin (0.69), vitamin D (0.64), glycine %protein (-0.60), lactose (0.86), proline (0.53), leucine (0.62), valine (0.71), tyrosine (0.65), isoleucine (0.57), milk (0.82), milk, yogurt, frozen yogurt, cocoa (0.84) Magnesium Vegetable protein (0.63), fiber (0.77), iron (0.55), non-heme iron (0.56), phosphorus (0.66) Iron Magnesium (0.55), riboflavin (0.62), ready-to-eat cereals (0.67) Non-heme iron Vegetable protein (0.51), magnesium (0.56), riboflavin (0.62), ready-to-eat cereals (0.69) Phosphorus Glutamic acid %kcal (0.74), calcium (0.80), magnesium (0.66), riboflavin (0.72), vitamin D (0.63), animal protein (0.52), total protein (0.68), lactose (0.68), valine (0.55), isoleucine (0.50), milk (0.67), milk, yogurt, frozen yogurt, cocoa (0.69) Riboflavin Calcium (0.69), iron (0.62), non-heme iron (0.62), phosphorus (0.72), vitamin D (0.61), retinol (0.66), lactose (0.64), milk (0.64), milk, yogurt, frozen yogurt, cocoa (0.65), ready-to-eat cereals (0.60) continued on next page 9 Online Table S5. continued, page 2 Vitamin D Calcium (0.64), phosphorus (0.63), riboflavin (0.61), lactose (0.71), valine (0.51), isoleucine (0.50), milk (0.75), milk, yogurt, frozen yogurt, cocoa (0.73) Cholesterol Arachidonic acid (0.63), eggs (0.83), methionine (0.51) Arachidonic acid Glutamic acid %protein (-0.51), cholesterol (0.63), glycine %kcal (0.57), poultry (0.52), animal protein (0.55), proline (-0.53), alanine (0.57), methionine (0.54), lysine (0.50), total fresh meat, fish (0.60), fresh meat, poultry (0.51) Estimated total sugars Sugar-sweetened beverages (0.53), total fatty acids (-0.52) Σ fructose, glucose, sucrose Sugar-sweetened beverages (0.63) Glycine, %kcal Glutamic acid %kcal (0.72), glutamic acid %protein (-0.55), arachidonic acid (0.57), poultry (0.53), selenium (0.58), animal protein (0.86), total protein (0.90), proline (-0.57), alanine (0.63), threonine (0.52), lysine (0.58), total fresh meat, fish (0.86), fresh meat, poultry (0.79) Glycine, %protein Glutamic acid %protein (-0.62), calcium (-0.60), proline (-0.71), phenylalanine (-0.62), serine (-0.60), alanine (0.84), arginine (0.69), aspartic acid (0.54), tyrosine (-0.55), total fresh meat, fish (0.58), fresh meat, poultry (0.57) Total PFA Oleic acid (0.52), total MFA (0.51), vitamin E (0.50), total animal fats, margarines, table spreads, oils, shortening, salad dressing (0.62) Σ long chain PFA Fish, fish roe, shellfish (0.79), fish, fish roe (0.78) Total Omega-6 PFA Oleic acid (0.53), vitamin E (0.51), total animal fats, margarines, table spreads, oils, shortening, salad dressing (0.61) Linoleic acid Oleic acid (0.53), vitamin E (0.51), total animal fats, margarines, table spreads, oils, shortening, salad dressing (0.61) Oleic acid Total PFA (0.52), total Omega-6 FA (0.53), linoleic acid (0.53), total SFA (0.68), total available carbohydrate (-0.67) Urinary metabolites Hippurate N-methylnicotinic acid (0.63) Abbreviations: Σ, sum of; MFA, monounsaturated fatty acids; PFA, polyunsaturated fatty acids; SFA, saturated fatty acids. Units are: g/1,000 kcal (foods, food subgroups), %kcal or amount/1,000 kcal (nutrients); %kcal or %protein (glutamic acid, glycine); mmol/24-hour (urinary metabolites). * 125 variables were included in the partial correlation matrix, adjusted for age, gender, sample. 32 variables from Online Tables 1 and 2 and Table 2 with correlations >0.5 are listed here. No correlations >0.5 were observed for raw vegetables; pasta, rice; bread, rolls, biscuits; processed meats; pork; fruit juices, drinks; cream; cream, cheese, ice-cream, milk and cheese recipes; urinary potassium; urinary sodium/potassium ratio; total Omega-3 PFA; PFA/SFA ratio. List excludes correlations between sums or ratios and their constituent variables. † N for urinary metabolites is 1,426, 91.5% of these participants. 10 Online Table S6. Reliability data – foods, nutrients, two quantitated urinary metabolites, systolic and diastolic blood pressure Variable Men Women AA (N=165) NHWA (N=620) AA (N=204) NHWA (N=570) † Ratio* (% ) Ratio (%) Ratio (%) Ratio (%) Foods with possible favorable relation to blood pressure – lower intake in AA than NHWA Total vegetables 1.68 (54.4) 1.47 (57.7) 2.28 (46.7) 1.49 (57.3) Vegetables, raw 5.55 (26.5) 2.61 (43.4) 2.80 (41.6) 1.41 (58.7) Fruit, fresh 1.16 (63.3) 0.85 (70.3) 1.91 (51.2) 1.67 (54.5) Pasta, rice 1.12 (64.1) 3.28 (37.9) (-) 4.03 (33.1) Total grains 1.29 (60.9) 1.97 (50.4) 4.01 (33.3) 2.71 (42.5) Bread, rolls, biscuits, related products 5.08 (28.3) 1.44 (58.1) 1.61 (55.5) 1.84 (52.1) Foods with possible unfavorable relation to blood pressure – higher intake in AA than NHWA Processed meats 11.68 (14.6) 5.35 (27.2) 2.55 (43.9) 4.46 (31.0) Pork 8.32 (19.4) 9.36 (17.6) 4.09 (32.9) (-) Eggs 2.15 (48.2) 3.50 (36.4) 7.66 (20.7) 5.20 (27.8) Fruit juice, drinks 1.25 (61.5) 0.65 (75.4) 1.40 (58.7) 0.65 (75.5) Sugar sweetened beverages 0.68 (74.6) 0.44 (82.1) 0.60 (76.9) 0.75 (72.7) Foods with possible favorable relation to blood pressure – higher intake in AA than NHWA Fish, fish roe, shellfish 15.84 (11.2) 3.46 (36.6) (-) 5.11 (28.1) Poultry 2.69 (42.6) 7.46 (21.1) 4.86 (29.2) 6.98 (22.3) Foods with possible unfavorable relation to blood pressure – lower intake in AA than NHWA Cream 3.09 (39.3) 1.59 (55.7) 2.03 (49.6) 1.76 (53.1) Cheese 9.02 (18.2) 3.87 (34.1) 5.47 (26.8) 2.61 (43.4) Cream, cheese, ice cream, milk & cheese recipes 1.30 (60.6) 2.82 (41.5) 2.39 (45.6) 3.51 (36.3) Alcoholic beverages 0.21 (90.5) 0.54 (78.7) 0.23 (89.6) 0.27 (87.9) continued on next page 11 Online Table S6. continued, page 2 Nutrients with possible favorable relation to blood pressure – lower intake in AA than NHWA Vegetable protein 0.86 (70.0) 0.84 (70.5) 1.23 (61.9) Glutamic acid, %kcal 1.95 (50.6) 1.31 (60.4) 1.73 (53.7) Glutamic acid, %protein 4.13 (32.6) 1.97 (50.3) 2.31 (46.4) Starch 1.05 (65.5) 1.42 (58.5) 1.22 (62.2) Total dietary fiber 0.74 (73.0) 0.72 (73.5) 0.88 (69.4) Calcium 2.31 (46.4) 0.82 (70.9) 1.64 (55.0) Magnesium 0.76 (72.5) 0.90 (69.1) 1.22 (62.1) Iron 1.31 (60.5) 1.03 (65.9) 3.35 (37.4) Non-heme iron 1.27 (61.1) 1.01 (66.5) 2.82 (41.5) Phosphorus 2.07 (49.2) 0.85 (70.1) 1.14 (63.7) Riboflavin 1.95 (50.7) 0.87 (69.7) 2.42 (45.2) Vitamin D 4.93 (28.8) 1.57 (56.0) 1.74 (53.5) Urinary potassium 1.03 (65.9) 0.78 (72.0) 0.64 (75.7) Nutrients with possible unfavorable relation to blood pressure – higher intake in AA than NHWA Dietary cholesterol 2.10 (48.7) 1.89 (51.4) 2.09 (48.9) Arachidonic acid 5.43 (26.9) 2.70 (42.5) 6.59 (23.3) Estimated total sugars 0.67 (74.8) 0.53 (79.2) 0.77 (72.2) Σ fructose, glucose, sucrose 0.62 (76.3) 0.50 (80.0) 0.61 (76.5) Glycine, %kcal 1.97 (50.4) 1.78 (53.0) 2.50 (44.4) Glycine, %protein 2.77 (42.0) 1.80 (52.6) 3.18 (38.6) Ratio, urinary sodium/potassium 1.37 (59.3) 1.26 (61.4) 1.48 (57.5) Nutrients with possible favorable relation to blood pressure – higher intake in AA than NHWA Total PFA 1.35 (59.7) 1.87 (51.7) 2.00 (50.0) Total Omega-3 PFA 2.10 (48.8) 2.20 (47.7) 1.84 (52.1) Σ long chain PFA 5.76 (25.8) 5.54 (26.5) 3.72 (35.0) Total Omega-6 PFA 1.47 (57.6) 1.85 (52.0) 2.15 (48.2) Linoleic acid 1.47 (57.7) 1.84 (52.0) 2.15 (48.2) Oleic acid 1.82 (52.4) 1.45 (58.0) 1.46 (57.8) Ratio, PFA/SFA 1.65 (54.8) 2.39 (45.6) 2.18 (47.9) continued on next page 12 1.21 2.11 3.29 1.55 0.90 1.15 0.94 1.28 1.23 1.40 1.40 1.42 0.68 (62.4) (48.6) (37.8) (56.4) (68.9) (63.5) (68.0) (60.9) (61.8) (58.8) (58.9) (58.5) (74.5) 2.99 3.72 0.85 0.85 2.18 1.87 (40.1) (35.0) (70.1) (70.2) (47.8) (51.6) 1.66 (54.6) 1.88 1.07 5.93 1.89 1.89 1.11 2.81 (51.5) (65.0) (25.2) (51.4) (51.4) (64.3) (41.6) Online Table S6. continued, page 3 Nutrients with possible unfavorable relation to blood pressure – lower intake in AA than NHWA None Other variables‡ Urinary sodium 2.17 (48.0) 1.10 (64.5) 1.20 (62.5) 14-day alcohol, all 0.16 (92.7) 0.29 (87.2) 0.28 (87.8) § Urinary metabolites Urinary hippurate 1.26 (61.4) 0.69 (74.3) 0.66 (75.1) Urinary N-methyl nicotinic acid 0.53 (79.0) 0.31 (86.5) 0.46 (81.2) Blood pressure Systolic blood pressure 0.18 (91.6) 0.20 (91.0) 0.20 (91.1) Diastolic blood pressure 0.25 (88.9) 0.21 (90.3) 0.28 (87.8) 1.74 0.39 (53.5) (83.7) 0.61 0.25 (76.7) (89.1) 0.18 0.22 (91.7) (90.2) Abbreviations: Σ, sum of; MFA, monounsaturated fatty acids; PFA, polyunsaturated fatty acids; SFA, saturated fatty acids. Units are: g/1,000 kcal (foods, food subgroups), %kcal or amount/1,000 kcal (nutrients); %kcal or %protein (glutamic acid, glycine); mmol/24-hour (urinary NMR metabolites); mm Hg (blood pressure). * Ratio is intra-individual variance divided by inter-individual variance, for dietary variables and blood pressure this was calculated from mean values of visits 1 and 2 and visits 3 and 4 (to account for higher correlation between values on consecutive days); for urinary variables, from the first and second collections. It was not possible to calculate the ratio for variables and population substrata where a large proportion of participants reported zero intake (9-11). † Observed coefficient from univariate regression as a percentage of ‘true’ theoretical coefficient, calculated from the formula 1/[1+(ratio/2)]×100. ‡ Variables did not differ significantly between AA and NHWA but were included here due to their important established effects on blood pressure. § N for hippurate and N-methyl nicotinic acid is 146 AA and 578 NHWA men, 188 AA and 514 NHWA women. 13 Online Table S7. Relation of individual food/food subgroup intakes, significantly different in AA and NHWA, to higher BP of AA than NHWA, considered singly, by gender Model Systolic BP Diastolic BP BP % change BP % change difference (Z score) from C difference (Z score) from C Men (N=165 AA, 620 NHWA) A: age, sample 5.41 (4.40) 3.46 (3.94) B: A + medical history of CVD/diabetes, 4.68 (3.80) 3.38 (3.84) family history of hypertension, physical activity, special diet, supplement use C: B + BMI 4.76 (4.04) 3.43 (4.05) Foods with possible favorable relation to blood pressure – lower intake in AA than NHWA C + Total vegetables 4.50 (3.78) -5.6 3.31 (3.88) -3.6 C + Vegetables, raw 4.48 (3.78) -5.9 3.26 (3.83) -5.0 C + Fruit, fresh 4.65 (3.95) -2.4 3.40 (4.01) -1.1 C + Pasta/rice 4.79 (4.06) 0.7 3.43 (4.05) -0.1 C + Total grains 4.61 (3.91) -3.1 3.39 (3.99) -1.2 C + Bread/rolls/biscuits/related products 4.90 (4.11) 2.8 3.49 (4.07) 1.6 Foods with possible unfavorable relation to blood pressure – higher intake in AA than NHWA C + Processed meats 4.68 (3.99) -1.6 3.41 (4.03) -0.7 C + Pork 4.72 (3.99) -1.0 3.37 (3.97) -2.0 C + Eggs 4.68 (3.94) -1.7 3.42 (4.00) -0.6 C + Fruit juices/drinks 4.91 (4.11) 3.1 3.45 (4.02) 0.5 C + Sugar sweetened beverages 4.34 (3.64) -9.0 3.23 (3.76) -6.1 Foods with possible favorable relation to blood pressure – higher intake in AA than NHWA C + Fish/fish roe/shellfish 4.76 (4.03) -0.1 3.44 (4.05) 0.1 C + Poultry 4.98 (4.13) 4.6 3.78 (4.37) 10.0 Foods with possible unfavorable relation to blood pressure – lower intake in AA than NHWA C + Cream 4.87 (4.12) 2.3 3.50 (4.12) 2.0 C + Cheese 4.76 (3.94) -0.1 3.45 (3.98) 0.6 C + Cream/cheese/ice cream/milk & cheese recipes 4.71 (3.97) -1.1 3.49 (4.10) 1.7 C + Alcoholic beverages 4.62 (3.94) -3.0 3.35 (3.97) -2.4 continued on next page 14 Online Table S7. continued, page 2 Women (N=204 AA, 570 NHWA) A: age, sample 9.66 (8.02) 5.03 B: A + medical history of CVD/diabetes, 9.00 (7.42) 4.83 family history of hypertension, physical activity, special diet, supplement use C: B + BMI 6.76 (5.76) 3.77 Foods with possible favorable relation to blood pressure – lower intake in AA than NHWA C + Total vegetables 6.75 (5.75) -0.1 3.77 C + Vegetables, raw 6.71 (5.71) -0.7 3.76 C + Fruit, fresh 6.69 (5.70) -1.0 3.75 C + Pasta/rice 6.53 (5.56) -3.4 3.66 C + Total grains 6.27 (5.19) -7.3 3.41 C + Bread/rolls/biscuits/related products 7.13 (6.00) 5.5 3.84 Foods with possible unfavorable relation to blood pressure – higher intake in AA than NHWA C + Processed meats 6.47 (5.49) -4.3 3.66 C + Pork 7.05 (5.98) 4.3 3.94 C + Eggs 6.63 (5.62) -1.9 3.76 C + Fruit juices/drinks 7.13 (6.03) 5.5 4.01 C + Sugar sweetened beverages 6.46 (5.17) -4.4 3.78 Foods with possible favorable relation to blood pressure – higher intake in AA than NHWA C + Fish/fish roe/shellfish 6.82 (5.82) 0.9 3.80 C + Poultry 6.65 (5.52) -1.7 3.72 Foods with possible unfavorable relation to blood pressure – lower intake in AA than NHWA C + Cream 6.80 (5.77) 0.6 3.81 C + Cheese 6.74 (5.65) -0.3 3.68 C + Cream/cheese/ice cream/milk & 6.68 (5.61) -1.2 3.79 cheese recipes C + Alcoholic beverages 6.74 (5.75) -0.3 3.76 (6.52) (6.20) (4.87) (4.87) (4.85) (4.83) (4.72) (4.28) (4.89) 0.0 -0.3 -0.7 -2.9 -9.6 1.8 (4.70) (5.06) (4.83) (5.14) (4.58) -2.8 4.4 -0.2 6.4 0.2 (4.90) (4.68) 0.7 -1.4 (4.90) (4.67) (4.82) 1.0 -2.4 0.4 (4.86) -0.4 Unit for foods is g/1,000 kcal. Z score = regression coefficient/standard error. |Z| ≥ 1.96, uncorrected P ≤ 0.05; |Z| ≥ 2.58, uncorrected P≤0.01; |Z| ≥ 3.29, uncorrected P≤0.001. 15