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Dietary Nitrate Intake Is Positively Associated with Muscle Function in Men and Women Independent of Physical Activity Levels

Dietary Nitrate Intake Is Positively Associated with Muscle Function in Men and Women Independent... ABSTRACT Background Nitrate supplements can improve vascular and muscle function. Whether higher habitual dietary nitrate is associated with better muscle function remains underexplored. Objective The aim was to examine whether habitual dietary nitrate intake is associated with better muscle function in a prospective cohort of men and women, and whether the relation was dependent on levels of physical activity. Methods The sample (n = 3759) was drawn from the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab) (56% female; mean ± SD baseline age: 48.6 ± 11.1 y). Habitual dietary intake was assessed over 12 y by obtaining an average [of at least 2 time points, e.g., baseline (2000/2001) and 2004/2005 and/or 2011/2012] from a food-frequency questionnaire. Nitrate intake was calculated from a validated nitrate database and other published literature. Muscle function was quantified by knee extension strength (KES) and the 8-ft-timed-up-and-go (8ft-TUG) test performed in 2011/2012. Physical activity was assessed by questionnaire. Generalized linear models and logistic regression were used to analyze the data. Results Median (IQR) total nitrate intake was 65 (52–83) mg/d, with ∼81% derived from vegetables. Individuals in the highest tertile of nitrate intake (median intake: 91 mg/d) had 2.6 kg stronger KES (11%) and 0.24 s faster 8ft-TUG (4%) compared with individuals in the lowest tertile of nitrate intake (median intake: 47 mg/d; both P < 0.05). Similarly, individuals in the highest tertile of nitrate intake had lower odds for weak KES (adjusted OR: 0.69; 95% CI: 0.47, 0.73) and slow 8ft-TUG (adjusted OR: 0.63; 95% CI: 0.50, 0.78) compared with those in the lowest tertile. Physical activity did not influence the relationship between nitrate intake and muscle function (KES; P-interaction = 0.86; 8ft-TUG; P-interaction = 0.99). Conclusions Higher habitual dietary nitrate intake, predominantly from vegetables, could be an effective way to promote lower-limb muscle strength and physical function in men and women. muscle strength, physical function, nutrition, vegetables, healthy aging Introduction Diets rich in vegetables are widely promoted due to their beneficial effect on reducing the risk of various chronic diseases, particularly metabolic diseases (1). Although the range of bioactive phytochemicals found in vegetables is diverse, some phytochemicals are found in much higher concentrations in specific types of vegetables (2). One such bioactive phytochemical is nitrate, which is derived primarily from green-leafy vegetables and beetroot (2). Dietary nitrate is known to have numerous health benefits, including cardiovascular and metabolic regulation (3). Nitrate supplementation can enhance NO bioavailability through the nitrate-nitrite-NO pathway; NO is a potent cell-signaling molecule that plays a key role in vasodilation and blood vessel health (4). High-dosage nitrate supplementation (e.g., beetroot juice) in acute and short-term studies has been reported to reduce blood pressure (5) and improve vascular function (6). Such physiological benefits are supported by meta-analysis and systematic reviews (7, 8). A recent crossover, double-blind, randomized controlled trial (RCT) in older adults (n = 12; mean age: 71 y) also reported that acute nitrate-rich beetroot juice intake improved maximal knee extensor angular velocity (11%) and power (4%) (9). In younger populations, nitrate supplements have been linked to enhanced blood flow to exercising skeletal muscle, which is important for energy production (10). As such, nitrate was classified in 2018 by the International Olympic Committee (11) as an ergogenic aid for athletes (12). To date, most RCTs examining the effects of nitrate supplementation on physiological and health outcomes have predominantly used concentrated beetroot juice or nitrate salts to deliver large acute dosages (ranging from ∼300 to 800 mg) of nitrate (7, 8). Such supplements provide ∼4–7 times more nitrate than that typically consumed as part of the average diet (13). Thus, despite the reported health benefits for short-term, high-dose nitrate supplements, the impact of habitual dietary nitrate intake on health is less clear. In community-dwelling older women (≥70 y), we previously reported that higher dietary nitrate intake was associated with better muscle strength and function (13). Specifically, women with the highest nitrate intakes had stronger hand-grip strength and faster timed-up-and-go (TUG) performance. Such findings are especially important as poor muscle function is a key risk factors for falls, which are a major cause of fracture. To date, no such study has been undertaken in men. Furthermore, considering that physical activity is also known to improve muscle function, it is possible that the relation between dietary nitrate and muscle function may differ based on an individual's level of daily physical activity. The primary aim of this study was to examine if habitual dietary nitrate intake was associated with better muscle function in a large cohort of men and women with ages ranging across the adult lifespan. A secondary aim was to explore if this relation was dependent on the level of daily habitual physical activity. Methods Study population Participants included in this study were men and women from the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab). This is a national population-based survey of Australian adults aged ≥25 y (up to 85 y), recruited in 1999/2000 (AusDiab1, n = 11,247) with follow-up in 2004/2005 (AusDiab2, n = 6400) and 2011/2012 (AusDiab3, n = 4614). Methods and response rates have been described previously (14). An identical food-frequency questionnaire (FFQ) was administered at all 3 AusDiab time points. Objective measures of muscle function were only obtained during AusDiab3 (2011/2012). A total of 6633 individuals were excluded due to drop out from AusDiab1 to AusDiab3. An additional 300 individuals were excluded as they did not perform muscle function tests in 2011/2012. Individuals were also excluded if they did not complete the FFQ or had an implausible energy intake (<3300 kJ/d and >17,500 kJ/d for males or <2500 kJ/d and >14,500 kJ/d for females) at baseline (n = 153), AusDiab2 and/or AusDiab3 (n = 104). We further excluded individuals with a missing value for any baseline confounder (n = 147). Finally, pregnant women (n = 15) or individuals receiving dietary treatment (n = 136) at any of the 3 AusDiab visits were excluded. After the exclusions (n = 7488; Supplemental Figure 1 [CONSORT (Consolidated Standards of Reporting Trials) flow diagram], characteristics presented in Supplemental Table 1), data from 3759 participants were available for this analysis. All participants provided written informed consent. The study was approved by the Human Research Ethics Committees of the International Diabetes Institute, Alfred Hospital, and by the International Diabetes Institute Ethics Committee (Melbourne, Australia). Dietary assessment A semi-quantitative FFQ developed by the Cancer Council of Victoria was used to assess dietary intake (15–17). This validated FFQ measures the usual frequency of food intake during a period of 12 mo and comprises a list of 74 food items with 10 frequency response options ranging from “never” to “three or more times per day”; it is complemented by another 27 food and alcoholic beverage items that ask various questions, such as “How many different vegetables do you usually eat per day?” The FFQ calculates portion size by use of 3 photographs of scaled portions for 4 different commonly consumed food types. Nutrient intake calculations were analyzed by Cancer Council Victoria using the NUTTAB95 food nutrient database and were supplemented by other data where necessary. To obtain an estimate of habitual dietary intake, average nitrate intake was obtained from available FFQ data obtained from the 3 AusDiab time points (e.g., AusDiab1, AusDiab2, AusDiab3; or AusDiab1, AusDiab2; or AusDiab1, AusDiab3). Total dietary nitrate (milligrams/day) was derived from both vegetable and nonvegetable sources. Vegetable-derived nitrate was estimated from a comprehensive nitrate database for vegetables (18). The median nitrate value (milligrams/gram) for each vegetable in the FFQ was obtained from the database and multiplied by vegetable consumption (in grams/day) to determine nitrate intake. Nitrate intake from vegetables per day was calculated by totalling the nitrate intake from individual vegetables. The nitrate database used to estimate nitrate intake from vegetables has been validated in a study of men and women by estimating nitrate intake using 24-h dietary recalls and FFQs comparing these values with urinary nitrate excretion (18). An estimate of nitrate concentration (milligrams/gram) in each of the nonvegetable items listed in the FFQ was derived using estimates from 3 published sources (19–21). Nitrate concentration of 67 of the 77 nonvegetable items were obtained from Inoue-Choi et al. (21), 5 values were obtained from the Food Standards Australia New Zealand (FSANZ) survey of nitrates and nitrites in food and beverages in Australia (19), and 2 values were from Griesenbeck et al. (20). Where no value was available [3 foods: Vegemite (Bega Cheese, Australia), jam, soymilk], a value of 0 mg/g was used. Drinking water has been shown to contain very low concentrations of nitrate (∼0.3 mg/L) in Perth, Western Australia (22, 23). This is likely the situation across many other cities and territories in Australia. Therefore, nitrate intake from drinking water was not quantified from the multiple regions across Australia. Measures of muscle function Knee extension strength (KES) was used to capture the isometric muscle strength of the lower limbs (24). In brief, participants were seated on a stool with their hip and knee at 90° angles. KES was measured using Lord's strap assembly incorporating a strain gauge (Neuroscience Research Australia, Sydney, Australia). Specifically, a webbing strap with a Velcro fastener was attached to participants’ dominant leg between 5 and 10 cm above the lateral malleolus. Participants were instructed to perform 1 practice and 2 test trials by extending their leg against the strap with maximal force for 2–3 s (24). The highest score (in kilograms) of the 2 test trials was recorded. One minute of rest was undertaken between all trials. The KES test is reported to have good test-retest reliability [intraclass correlation coefficient (ICC) >0.9] (25), including construct validity with other measures of muscle strength (r = 0.77) (24). To derive a cutoff for weak KES, participants were first separated into 2 age groups (<65 y and ≥65 y) at the time of the muscle function tests in 2011/2012 and by gender (male and female). From each of the 4 groups created, the cutoff for weak KES was derived from the lowest quartile. For men aged <65 y and ≥65 y, cutoffs for weak KES were 25.1 and 18.4 kg, respectively. Weak KES cutoffs for women aged <65 y and ≥65 y were 15.1 and 10.3 kg, respectively. The 8-ft-TUG (8ft-TUG) test is commonly used to assess mobility as it requires both static and dynamic balance. A shorter time (seconds) to complete the 8ft-TUG indicates better dynamic gait speed and mobility across a combination of 3 commonly performed functional activities of daily living (sitting, standing, walking, and turning). Participants were seated in a chair that was placed at the end of a marked 8-ft (2.44 m) walkway. On the command “go,” participants were instructed to rise from the chair, walk at a comfortable speed for 8 ft, turn around, walk back, and sit down in the chair. Total time was calculated from the “go” command until participants were seated and their backs contacted the chair rest. Previously, the 8ft-TUG has been shown to have good reliability (ICC = 0.95) and validity against gait speed (r = 0.61) (26). To derive a cutoff for slow 8ft-TUG, participants were separated into 2 age groups (<65 y and ≥65 y) at the time of the muscle function tests in 2011/2012. From each of the 2 groups created, the cutoff for slow 8ft-TUG was derived from the lowest quartile. Of note, for physical function assessment, cutoffs for compromised performance are not differentiated by sex (27, 28). As such, for individuals aged <65 y and ≥65 y, cutoffs for slow 8ft-TUG were 6.25 and 8.00 s, respectively. Baseline demographic and clinical assessment A baseline household interview was used to collect demographic information including age (date of birth), sex (male/female), relationship status (de facto married, separated, divorced, widowed, never married), and educational level (never to some high school, completed university or equivalent). The Socio-Economic Indexes for Areas (SEIFA) as reported by the Australian Bureau of Statistics (29) was obtained and summarized for a range of information on the economic and social conditions of people and households. Detailed anthropometric measurements have been described elsewhere (14, 30). Briefly, height was measured to the nearest 0.5 cm without shoes using a stadiometer. Weight was measured without shoes and excess clothing to the nearest 0.1 kg using a mechanical beam balance (31). BMI was calculated as weight (kilograms) divided by height (meters squared). Total physical activity time was estimated for the previous week based on self-reported information using the Active Australia Survey Questionnaire (32) and previously reported (33). This information was subsequently used to categorize individuals into 3 groups: sedentary (0 min/wk), insufficient physical activity (<150 min/wk), or sufficient physical activity (≥150 min/wk). Smoking status was assessed by using an interviewer-administered questionnaire, as previously reported (14). Smoking status was categorized as follows: currently smoking (smoking at least daily), ex-smoker (smoking less than daily for at least the last 3 mo), and never smoked (<100 cigarettes during life) (34). Self-reported history of cardiovascular disease (yes/no) and diabetes (based on plasma glucose concentrations) was assessed (35). Statistical analysis Statistical analysis was performed using IBM SPSS Statistics for Windows, version 25.0 (IBM Corporation) and R software (version 3.4.2; R Foundation for Statistical Computing) (36). Descriptive statistics of normally distributed continuous variables were expressed as means ± SDs. Non–normally distributed continuous variables were expressed as medians and IQRs. Categorical variables were expressed as number and proportion (%). For all analysis, individuals were also grouped into tertiles based on total nitrate intake for data presentation purposes, not for modelling. The primary outcomes were KES and 8ft-TUG measured at the 12-y (2011/2012) follow-up. Since both muscle function tests were positively skewed, generalized linear models with a gamma distribution and log-link were used to examine the association between total nitrate intake (milligrams/day) with both KES and 8ft-TUG. To investigate potential nonlinearity of the relation between the exposure and the outcome, restricted cubic splines were used in the model in order to fully assess the functional form of the associations across the full range of nitrate intake. Logistic regression models were also used to investigate the relation between nitrate intake (milligrams/day) and binary measures of poor muscle function, including the cutoffs for weak KES and slow 8ft-TUG. ORs were then calculated, relative to a reference value of the median of the first tertile of the relevant exposure variable, and were plotted against the exposure variable, with 95% confidence bands provided. Specifically, ORs were extracted from the aforementioned fitted models, comparing the median of each tertile with the reference value of the median in tertile 1, and tabulated with 95% CIs. P values for ORs were obtained using Wald tests; we tested for nonlinearity using a likelihood ratio test to compare nested models with and without the nonlinear terms for the exposure. For visual simplicity, in all graphs presented, the x-axis was truncated at 3 SDs above the mean. Given the established positive effects of physical activity on muscle function, we assessed the heterogeneity of effects by including nitrate by physical activity interaction terms in the models to determine if relations were dependent on the level of physical activity. We also stratified our analysis and examined the relation between total nitrate intake and muscle function in 3 separate groups based on physical activity classification: sedentary (0 min/wk), insufficient physical activity (<150 min/wk), and sufficient physical activity (≥150 min/wk). Two models of adjustment were adopted: a minimally adjusted model that included age (years), sex (male/female), and BMI (kg/m2) and a multivariable-adjusted model that included the minimally adjusted model plus energy intake (kilojoules/day), relationship status (de facto married, separated, divorced, widowed, never married), physical activity levels (sedentary, insufficient, sufficient), level of education (never to some high school, completed university or equivalent), SEIFA, smoking status (current smoker, ex-smoker, nonsmoker), self-reported history of cardiovascular disease (yes/no), and diabetes. Statistical significance was set at a 2-sided type 1 error rate of P < 0.05 for all tests. Further analysis Interaction testing was also performed to examine if sex or age (years) altered the relation between total nitrate intake and muscle function. To further investigate the robustness of the relation between nitrate and muscle function, including the potential for confounding, we performed a range of sensitivity analyses. Specifically, Spearman's correlations were performed between total nitrate intake and various nutrients known to positively affect muscle function. These nutrients included total daily intakes for protein (37), magnesium (38), and calcium (39). Given the potential for multicollinearity between many of these confounders, we considered their impact by entering them into the multivariable-adjusted generalized linear models separately, each as a continuous variable, for KES and 8ft-TUG. Despite total nitrate intake being predominantly derived from vegetables, nitrate is also found in foods not considered to be part of a healthy diet (e.g., processed meat). Therefore, we examined the relation between vegetable-derived nitrate, non–vegetable-derived nitrate, and muscle function measures separately. Results Baseline characteristics are presented in Table 1. Vegetable-derived nitrate contributed 76%, 81%, and 85% of total daily nitrate intake for individuals in tertiles 1, 2, and 3, respectively. Individuals with the highest total nitrate intake (tertile 3) were slightly older, had higher daily energy consumption, and were more physically active compared with individuals with the lowest total nitrate intake (tertile 1; Table 1). TABLE 1 Demographic baseline characteristics by tertiles of total dietary nitrate intake among Australian men and women from the Australian Diabetes, Obesity, and Lifestyle Study1 . . Tertile of total nitrate intake . . All participants . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Sample number 3759 1253 1253 1253 Sex (women), n (%) 2105 (56.0) 702 (56.0) 720 (57.5) 683 (54.5) Age, y 48.6 ± 11.1 47.7 ± 11.5 48.3 ± 10.8 49.7 ± 10.8 BMI, kg/m2 26.4 ± 4.6 26.3 ± 4.8 26.4 ± 4.6 26.5 ± 4.4  Overweight, n (%) 1485 (39.5) 476 (38.0) 504 (40.2) 505 (40.3)  Obese, n (%) 698 (18.6) 226 (18.0) 229 (18.3) 243 (19.4) Physical activity, n (%)  Sedentary 569 (15.1) 220 (17.6) 211 (16.8) 138 (11.0)  Insufficient (<150 min/wk) 1172 (31.2) 415 (33.1) 405 (32.3) 352 (28.1)  Sufficient (≥150 min/wk) 2018 (53.7) 618 (49.3) 637 (50.8) 763 (60.9) Relationship status, n (%)  Married 2918 (77.6) 955 (76.2) 985 (78.6) 978 (78.1)  De facto 163 (4.3) 42 (3.4) 59 (4.7) 62 (4.9)  Separated 72 (1.9) 21 (1.7) 27 (2.2) 24 (1.9)  Divorced 209 (5.6) 77 (6.1) 68 (5.4) 64 (5.1)  Widowed 101 (2.7) 37 (3.0) 29 (2.3) 35 (2.8)  Single 296 (7.9) 121 (9.7) 85 (6.8) 90 (7.2) Level of education, n (%)  Never to some high school 1240 (33.0) 425 (33.9) 418 (33.4) 397 (31.7)  Completed university or equivalent 2519 (67.0) 828 (66.1) 835 (66.6) 856 (68.3) SEIFA index 1029 ± 80 1028 ± 80 1031 ± 78 1030 ± 82 Smoking status, n (%)  Current 403 (10.7) 151 (12.1) 127 (10.1) 125 (10.0)  Ex-smoker 1071 (28.5) 343 (27.4) 351 (28.0) 377 (30.1)  Nonsmoker 2285 (60.8) 759 (60.6) 775 (61.9) 751 (59.9) Self-reported history of CVD, yes, n (%) 144 (3.8) 54 (4.3) 44 (3.5) 46 (3.7) Diabetes mellites, n (%)  Yes 136 (3.6) 56 (4.5) 48 (3.8) 32 (1.4)  No 3623 (96.4) 1197 (95.5) 1205 (96.2) 1221 (98.6) Dietary intake  Total energy intake, MJ/d 8.62 (6.40–10.0) 7.40 (5.79–9.22) 7.99 (6.54–9.89) 8.85 (7.06–11.0)  Total nitrate intake, mg/d 65.3 (51.9–82.6) 46.5 (39.5–51.9) 65.3 (60.8–69.7) 91.2 (82.6–105.5)  Vegetable-derived nitrate, mg/d 52.9 (40.3–69.2) 35.5 (28.9–40.7) 52.9 (48.7–57.7) 77.1 (69.1–90.9)  Non–vegetable-derived nitrate, mg/d 12.1 (9.7–15.2) 10.3 (8.4–12.7) 12.0 (9.8–14.7) 14.3 (11.5–17.4) . . Tertile of total nitrate intake . . All participants . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Sample number 3759 1253 1253 1253 Sex (women), n (%) 2105 (56.0) 702 (56.0) 720 (57.5) 683 (54.5) Age, y 48.6 ± 11.1 47.7 ± 11.5 48.3 ± 10.8 49.7 ± 10.8 BMI, kg/m2 26.4 ± 4.6 26.3 ± 4.8 26.4 ± 4.6 26.5 ± 4.4  Overweight, n (%) 1485 (39.5) 476 (38.0) 504 (40.2) 505 (40.3)  Obese, n (%) 698 (18.6) 226 (18.0) 229 (18.3) 243 (19.4) Physical activity, n (%)  Sedentary 569 (15.1) 220 (17.6) 211 (16.8) 138 (11.0)  Insufficient (<150 min/wk) 1172 (31.2) 415 (33.1) 405 (32.3) 352 (28.1)  Sufficient (≥150 min/wk) 2018 (53.7) 618 (49.3) 637 (50.8) 763 (60.9) Relationship status, n (%)  Married 2918 (77.6) 955 (76.2) 985 (78.6) 978 (78.1)  De facto 163 (4.3) 42 (3.4) 59 (4.7) 62 (4.9)  Separated 72 (1.9) 21 (1.7) 27 (2.2) 24 (1.9)  Divorced 209 (5.6) 77 (6.1) 68 (5.4) 64 (5.1)  Widowed 101 (2.7) 37 (3.0) 29 (2.3) 35 (2.8)  Single 296 (7.9) 121 (9.7) 85 (6.8) 90 (7.2) Level of education, n (%)  Never to some high school 1240 (33.0) 425 (33.9) 418 (33.4) 397 (31.7)  Completed university or equivalent 2519 (67.0) 828 (66.1) 835 (66.6) 856 (68.3) SEIFA index 1029 ± 80 1028 ± 80 1031 ± 78 1030 ± 82 Smoking status, n (%)  Current 403 (10.7) 151 (12.1) 127 (10.1) 125 (10.0)  Ex-smoker 1071 (28.5) 343 (27.4) 351 (28.0) 377 (30.1)  Nonsmoker 2285 (60.8) 759 (60.6) 775 (61.9) 751 (59.9) Self-reported history of CVD, yes, n (%) 144 (3.8) 54 (4.3) 44 (3.5) 46 (3.7) Diabetes mellites, n (%)  Yes 136 (3.6) 56 (4.5) 48 (3.8) 32 (1.4)  No 3623 (96.4) 1197 (95.5) 1205 (96.2) 1221 (98.6) Dietary intake  Total energy intake, MJ/d 8.62 (6.40–10.0) 7.40 (5.79–9.22) 7.99 (6.54–9.89) 8.85 (7.06–11.0)  Total nitrate intake, mg/d 65.3 (51.9–82.6) 46.5 (39.5–51.9) 65.3 (60.8–69.7) 91.2 (82.6–105.5)  Vegetable-derived nitrate, mg/d 52.9 (40.3–69.2) 35.5 (28.9–40.7) 52.9 (48.7–57.7) 77.1 (69.1–90.9)  Non–vegetable-derived nitrate, mg/d 12.1 (9.7–15.2) 10.3 (8.4–12.7) 12.0 (9.8–14.7) 14.3 (11.5–17.4) 1 Values are means ± SDs or medians (IQRs) unless otherwise indicated. CVD, cardiovascular disease; SEIFA, Socio-Economic Indexes for Areas. Open in new tab TABLE 1 Demographic baseline characteristics by tertiles of total dietary nitrate intake among Australian men and women from the Australian Diabetes, Obesity, and Lifestyle Study1 . . Tertile of total nitrate intake . . All participants . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Sample number 3759 1253 1253 1253 Sex (women), n (%) 2105 (56.0) 702 (56.0) 720 (57.5) 683 (54.5) Age, y 48.6 ± 11.1 47.7 ± 11.5 48.3 ± 10.8 49.7 ± 10.8 BMI, kg/m2 26.4 ± 4.6 26.3 ± 4.8 26.4 ± 4.6 26.5 ± 4.4  Overweight, n (%) 1485 (39.5) 476 (38.0) 504 (40.2) 505 (40.3)  Obese, n (%) 698 (18.6) 226 (18.0) 229 (18.3) 243 (19.4) Physical activity, n (%)  Sedentary 569 (15.1) 220 (17.6) 211 (16.8) 138 (11.0)  Insufficient (<150 min/wk) 1172 (31.2) 415 (33.1) 405 (32.3) 352 (28.1)  Sufficient (≥150 min/wk) 2018 (53.7) 618 (49.3) 637 (50.8) 763 (60.9) Relationship status, n (%)  Married 2918 (77.6) 955 (76.2) 985 (78.6) 978 (78.1)  De facto 163 (4.3) 42 (3.4) 59 (4.7) 62 (4.9)  Separated 72 (1.9) 21 (1.7) 27 (2.2) 24 (1.9)  Divorced 209 (5.6) 77 (6.1) 68 (5.4) 64 (5.1)  Widowed 101 (2.7) 37 (3.0) 29 (2.3) 35 (2.8)  Single 296 (7.9) 121 (9.7) 85 (6.8) 90 (7.2) Level of education, n (%)  Never to some high school 1240 (33.0) 425 (33.9) 418 (33.4) 397 (31.7)  Completed university or equivalent 2519 (67.0) 828 (66.1) 835 (66.6) 856 (68.3) SEIFA index 1029 ± 80 1028 ± 80 1031 ± 78 1030 ± 82 Smoking status, n (%)  Current 403 (10.7) 151 (12.1) 127 (10.1) 125 (10.0)  Ex-smoker 1071 (28.5) 343 (27.4) 351 (28.0) 377 (30.1)  Nonsmoker 2285 (60.8) 759 (60.6) 775 (61.9) 751 (59.9) Self-reported history of CVD, yes, n (%) 144 (3.8) 54 (4.3) 44 (3.5) 46 (3.7) Diabetes mellites, n (%)  Yes 136 (3.6) 56 (4.5) 48 (3.8) 32 (1.4)  No 3623 (96.4) 1197 (95.5) 1205 (96.2) 1221 (98.6) Dietary intake  Total energy intake, MJ/d 8.62 (6.40–10.0) 7.40 (5.79–9.22) 7.99 (6.54–9.89) 8.85 (7.06–11.0)  Total nitrate intake, mg/d 65.3 (51.9–82.6) 46.5 (39.5–51.9) 65.3 (60.8–69.7) 91.2 (82.6–105.5)  Vegetable-derived nitrate, mg/d 52.9 (40.3–69.2) 35.5 (28.9–40.7) 52.9 (48.7–57.7) 77.1 (69.1–90.9)  Non–vegetable-derived nitrate, mg/d 12.1 (9.7–15.2) 10.3 (8.4–12.7) 12.0 (9.8–14.7) 14.3 (11.5–17.4) . . Tertile of total nitrate intake . . All participants . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Sample number 3759 1253 1253 1253 Sex (women), n (%) 2105 (56.0) 702 (56.0) 720 (57.5) 683 (54.5) Age, y 48.6 ± 11.1 47.7 ± 11.5 48.3 ± 10.8 49.7 ± 10.8 BMI, kg/m2 26.4 ± 4.6 26.3 ± 4.8 26.4 ± 4.6 26.5 ± 4.4  Overweight, n (%) 1485 (39.5) 476 (38.0) 504 (40.2) 505 (40.3)  Obese, n (%) 698 (18.6) 226 (18.0) 229 (18.3) 243 (19.4) Physical activity, n (%)  Sedentary 569 (15.1) 220 (17.6) 211 (16.8) 138 (11.0)  Insufficient (<150 min/wk) 1172 (31.2) 415 (33.1) 405 (32.3) 352 (28.1)  Sufficient (≥150 min/wk) 2018 (53.7) 618 (49.3) 637 (50.8) 763 (60.9) Relationship status, n (%)  Married 2918 (77.6) 955 (76.2) 985 (78.6) 978 (78.1)  De facto 163 (4.3) 42 (3.4) 59 (4.7) 62 (4.9)  Separated 72 (1.9) 21 (1.7) 27 (2.2) 24 (1.9)  Divorced 209 (5.6) 77 (6.1) 68 (5.4) 64 (5.1)  Widowed 101 (2.7) 37 (3.0) 29 (2.3) 35 (2.8)  Single 296 (7.9) 121 (9.7) 85 (6.8) 90 (7.2) Level of education, n (%)  Never to some high school 1240 (33.0) 425 (33.9) 418 (33.4) 397 (31.7)  Completed university or equivalent 2519 (67.0) 828 (66.1) 835 (66.6) 856 (68.3) SEIFA index 1029 ± 80 1028 ± 80 1031 ± 78 1030 ± 82 Smoking status, n (%)  Current 403 (10.7) 151 (12.1) 127 (10.1) 125 (10.0)  Ex-smoker 1071 (28.5) 343 (27.4) 351 (28.0) 377 (30.1)  Nonsmoker 2285 (60.8) 759 (60.6) 775 (61.9) 751 (59.9) Self-reported history of CVD, yes, n (%) 144 (3.8) 54 (4.3) 44 (3.5) 46 (3.7) Diabetes mellites, n (%)  Yes 136 (3.6) 56 (4.5) 48 (3.8) 32 (1.4)  No 3623 (96.4) 1197 (95.5) 1205 (96.2) 1221 (98.6) Dietary intake  Total energy intake, MJ/d 8.62 (6.40–10.0) 7.40 (5.79–9.22) 7.99 (6.54–9.89) 8.85 (7.06–11.0)  Total nitrate intake, mg/d 65.3 (51.9–82.6) 46.5 (39.5–51.9) 65.3 (60.8–69.7) 91.2 (82.6–105.5)  Vegetable-derived nitrate, mg/d 52.9 (40.3–69.2) 35.5 (28.9–40.7) 52.9 (48.7–57.7) 77.1 (69.1–90.9)  Non–vegetable-derived nitrate, mg/d 12.1 (9.7–15.2) 10.3 (8.4–12.7) 12.0 (9.8–14.7) 14.3 (11.5–17.4) 1 Values are means ± SDs or medians (IQRs) unless otherwise indicated. CVD, cardiovascular disease; SEIFA, Socio-Economic Indexes for Areas. Open in new tab Compared with individuals with the lowest total nitrate intake (tertile 1; median: 46.5 mg/d), individuals with the highest total nitrate intake (tertile 3; median: 91.2 mg/d) had 11% and 4% significantly stronger KES and faster 8ft-TUG performance, respectively (Table 2). Furthermore, KES was significantly higher in nitrate-intake tertile 3 compared with tertile 2 (Table 2). Note that the multivariable-adjusted relations between nitrate intake, KES, and 8ft-TUG were of a nonlinear nature (P-nonlinearity = 0.006 and 0.002, respectively; Figure 1). Specifically, the greatest benefits to muscle function were observed at nitrate intakes of ∼90 mg/d. The relations between total nitrate intake and the odds of weak KES and slow 8ft-TUG were both nonlinear (P-nonlinearity < 0.001 and 0.059, respectively; Figure 2), with intakes of ∼90 mg/d also appearing optimal. Compared with individuals with the lowest total nitrate intake (tertile 1), individuals in tertile 2 and tertile 3 had 20% and 31% lower odds of having weak KES and 24% and 37% had lower odds of having slow 8ft-TUG, respectively, in the multivariable-adjusted model (Table 3). FIGURE 1 Open in new tabDownload slide Multivariable-adjusted dose–response relation between total nitrate intake and (A) knee extension strength (n = 3470) and (B) 8ft-timed-up-and-go (n = 3750) obtained by generalized regression models in men and women with the exposure included as a restricted cubic spline. Shading represents 95% CIs. The rug plot along the bottom of each graph depicts each observation. FIGURE 1 Open in new tabDownload slide Multivariable-adjusted dose–response relation between total nitrate intake and (A) knee extension strength (n = 3470) and (B) 8ft-timed-up-and-go (n = 3750) obtained by generalized regression models in men and women with the exposure included as a restricted cubic spline. Shading represents 95% CIs. The rug plot along the bottom of each graph depicts each observation. FIGURE 2 Open in new tabDownload slide Restricted cubic splines based on multivariable-adjusted logistic regression models highlighting the relative odds between total nitrate intake and (A) weak knee extension strength (n = 3470) and (B) slow 8-ft-timed-up-and-go (n = 3750) in men and women. Shaded areas represent 95% CIs. The reference value is the value associated with the median intake (46.5 mg/d) for individuals in the lowest tertile of total nitrate intake. FIGURE 2 Open in new tabDownload slide Restricted cubic splines based on multivariable-adjusted logistic regression models highlighting the relative odds between total nitrate intake and (A) weak knee extension strength (n = 3470) and (B) slow 8-ft-timed-up-and-go (n = 3750) in men and women. Shaded areas represent 95% CIs. The reference value is the value associated with the median intake (46.5 mg/d) for individuals in the lowest tertile of total nitrate intake. TABLE 2 Estimated marginal means (95% CI) for knee extension strength and 8ft-timed-up-and-go performance for each tertile of total nitrate intake among Australian men and women from the Australian Diabetes, Obesity, and Lifestyle Study1 . Tertile of total nitrate intake . . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Knee extension strength,2 kg  Minimally adjusted 23.9 (23.5, 24.4) 24.6 (24.3, 25.0)3 25.6 (25.1, 26.1)3,4  Multivariable-adjusted 23.6 (22.9, 24.3) 24.7 (24.0, 25.3)3 26.2 (25.4, 26.7)3,4 8-ft-timed-up-and-go,5 s  Minimally adjusted 6.23 (6.16, 6.30) 6.14 (6.09, 6.19)3 6.02 (5.95, 6.09)3  Multivariable-adjusted 6.23 (6.13, 6.33) 6.06 (5.97, 6.15)3 5.99 (5.89, 6.09)3 . Tertile of total nitrate intake . . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Knee extension strength,2 kg  Minimally adjusted 23.9 (23.5, 24.4) 24.6 (24.3, 25.0)3 25.6 (25.1, 26.1)3,4  Multivariable-adjusted 23.6 (22.9, 24.3) 24.7 (24.0, 25.3)3 26.2 (25.4, 26.7)3,4 8-ft-timed-up-and-go,5 s  Minimally adjusted 6.23 (6.16, 6.30) 6.14 (6.09, 6.19)3 6.02 (5.95, 6.09)3  Multivariable-adjusted 6.23 (6.13, 6.33) 6.06 (5.97, 6.15)3 5.99 (5.89, 6.09)3 1 Means and 95% CIs were obtained from a generalized linear model with gamma distribution and log-link function for the median values of nitrate intake within each tertile (tertile 1 = 46.5 mg/d; tertile 2 = 65.3 mg/d; tertile 3 = 91.2 mg/d). Minimally adjusted: adjusted for age, sex, and BMI. Multivariable-adjusted: minimally adjusted + energy intake, relationship status, physical activity, level of education, SEIFA (Socio-Economical Index for Areas), smoking status, diabetes, and self-reported history of cardiovascular disease. 2 Assessed in n = 3479. 3 Significantly different (P < 0.05) from tertile 1. 4 Significantly different (P < 0.05) from tertile 2. 5 Assessed in n = 3750. Open in new tab TABLE 2 Estimated marginal means (95% CI) for knee extension strength and 8ft-timed-up-and-go performance for each tertile of total nitrate intake among Australian men and women from the Australian Diabetes, Obesity, and Lifestyle Study1 . Tertile of total nitrate intake . . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Knee extension strength,2 kg  Minimally adjusted 23.9 (23.5, 24.4) 24.6 (24.3, 25.0)3 25.6 (25.1, 26.1)3,4  Multivariable-adjusted 23.6 (22.9, 24.3) 24.7 (24.0, 25.3)3 26.2 (25.4, 26.7)3,4 8-ft-timed-up-and-go,5 s  Minimally adjusted 6.23 (6.16, 6.30) 6.14 (6.09, 6.19)3 6.02 (5.95, 6.09)3  Multivariable-adjusted 6.23 (6.13, 6.33) 6.06 (5.97, 6.15)3 5.99 (5.89, 6.09)3 . Tertile of total nitrate intake . . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Knee extension strength,2 kg  Minimally adjusted 23.9 (23.5, 24.4) 24.6 (24.3, 25.0)3 25.6 (25.1, 26.1)3,4  Multivariable-adjusted 23.6 (22.9, 24.3) 24.7 (24.0, 25.3)3 26.2 (25.4, 26.7)3,4 8-ft-timed-up-and-go,5 s  Minimally adjusted 6.23 (6.16, 6.30) 6.14 (6.09, 6.19)3 6.02 (5.95, 6.09)3  Multivariable-adjusted 6.23 (6.13, 6.33) 6.06 (5.97, 6.15)3 5.99 (5.89, 6.09)3 1 Means and 95% CIs were obtained from a generalized linear model with gamma distribution and log-link function for the median values of nitrate intake within each tertile (tertile 1 = 46.5 mg/d; tertile 2 = 65.3 mg/d; tertile 3 = 91.2 mg/d). Minimally adjusted: adjusted for age, sex, and BMI. Multivariable-adjusted: minimally adjusted + energy intake, relationship status, physical activity, level of education, SEIFA (Socio-Economical Index for Areas), smoking status, diabetes, and self-reported history of cardiovascular disease. 2 Assessed in n = 3479. 3 Significantly different (P < 0.05) from tertile 1. 4 Significantly different (P < 0.05) from tertile 2. 5 Assessed in n = 3750. Open in new tab TABLE 3 ORs (95% CIs) for weak knee extension strength and slow 8ft-timed-up-and-go by tertiles of total nitrate intake among Australian women and men from the Australian Diabetes, Obesity, and Lifestyle Study1 . Tertile of total nitrate intake . . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Weak knee extension strength,2 kg  Events, n (%) 344 (30.2) 279 (24.1) 246 (20.8)  Minimally adjusted 1.00 0.78 (0.70, 0.87)3 0.57 (0.46, 0.70)3  Multivariable-adjusted 1.00 0.80 (0.72, 0.89)3 0.69 (0.47, 0.73)3 Slow 8-ft-timed-up-and-go,4 s  Events, n (%) 356 (28.5) 296 (23.7) 267 (21.3)  Minimally adjusted 1.00 0.75 (0.67, 0.83)3 0.61 (0.49, 0.75)3  Multivariable-adjusted 1.00 0.76 (0.68, 0.85)3 0.63 (0.50, 0.78)3 . Tertile of total nitrate intake . . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Weak knee extension strength,2 kg  Events, n (%) 344 (30.2) 279 (24.1) 246 (20.8)  Minimally adjusted 1.00 0.78 (0.70, 0.87)3 0.57 (0.46, 0.70)3  Multivariable-adjusted 1.00 0.80 (0.72, 0.89)3 0.69 (0.47, 0.73)3 Slow 8-ft-timed-up-and-go,4 s  Events, n (%) 356 (28.5) 296 (23.7) 267 (21.3)  Minimally adjusted 1.00 0.75 (0.67, 0.83)3 0.61 (0.49, 0.75)3  Multivariable-adjusted 1.00 0.76 (0.68, 0.85)3 0.63 (0.50, 0.78)3 1 Estimated ORs and 95% CIs from logistic regression comparing the median nitrate intake from each tertile compared with tertile 1. Minimally adjusted: adjusted for age, sex, and BMI. Multivariable-adjusted: minimally adjusted + energy intake, relationship status, physical activity, level of education, SEIFA (Socio-Economical Index for Areas), smoking status, diabetes, and self-reported history of cardiovascular disease. Weak knee extension strength and slow 8-ft-timed-up-and-go were assessed in 3479 and 3750 individuals, respectively. 2 Cutoffs for weak knee extension strength for men aged <65 y and ≥65 y were 25.1 and 18.4 kg, respectively. For women aged <65 y and ≥65 y, cutoffs for weak knee extension strength were 15.1 and 10.3 kg, respectively. 3 Different from tertile 1, P < 0.05. 4 Cutoffs for slow 8-ft-timed-up-and-go for individuals aged <65 y and ≥65 y were 6.25 and 8.00 s, respectively. Median nitrate intakes for tertiles 1, 2, and 3 were 46.5, 65.3, and 91.2 mg/d, respectively. Open in new tab TABLE 3 ORs (95% CIs) for weak knee extension strength and slow 8ft-timed-up-and-go by tertiles of total nitrate intake among Australian women and men from the Australian Diabetes, Obesity, and Lifestyle Study1 . Tertile of total nitrate intake . . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Weak knee extension strength,2 kg  Events, n (%) 344 (30.2) 279 (24.1) 246 (20.8)  Minimally adjusted 1.00 0.78 (0.70, 0.87)3 0.57 (0.46, 0.70)3  Multivariable-adjusted 1.00 0.80 (0.72, 0.89)3 0.69 (0.47, 0.73)3 Slow 8-ft-timed-up-and-go,4 s  Events, n (%) 356 (28.5) 296 (23.7) 267 (21.3)  Minimally adjusted 1.00 0.75 (0.67, 0.83)3 0.61 (0.49, 0.75)3  Multivariable-adjusted 1.00 0.76 (0.68, 0.85)3 0.63 (0.50, 0.78)3 . Tertile of total nitrate intake . . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Weak knee extension strength,2 kg  Events, n (%) 344 (30.2) 279 (24.1) 246 (20.8)  Minimally adjusted 1.00 0.78 (0.70, 0.87)3 0.57 (0.46, 0.70)3  Multivariable-adjusted 1.00 0.80 (0.72, 0.89)3 0.69 (0.47, 0.73)3 Slow 8-ft-timed-up-and-go,4 s  Events, n (%) 356 (28.5) 296 (23.7) 267 (21.3)  Minimally adjusted 1.00 0.75 (0.67, 0.83)3 0.61 (0.49, 0.75)3  Multivariable-adjusted 1.00 0.76 (0.68, 0.85)3 0.63 (0.50, 0.78)3 1 Estimated ORs and 95% CIs from logistic regression comparing the median nitrate intake from each tertile compared with tertile 1. Minimally adjusted: adjusted for age, sex, and BMI. Multivariable-adjusted: minimally adjusted + energy intake, relationship status, physical activity, level of education, SEIFA (Socio-Economical Index for Areas), smoking status, diabetes, and self-reported history of cardiovascular disease. Weak knee extension strength and slow 8-ft-timed-up-and-go were assessed in 3479 and 3750 individuals, respectively. 2 Cutoffs for weak knee extension strength for men aged <65 y and ≥65 y were 25.1 and 18.4 kg, respectively. For women aged <65 y and ≥65 y, cutoffs for weak knee extension strength were 15.1 and 10.3 kg, respectively. 3 Different from tertile 1, P < 0.05. 4 Cutoffs for slow 8-ft-timed-up-and-go for individuals aged <65 y and ≥65 y were 6.25 and 8.00 s, respectively. Median nitrate intakes for tertiles 1, 2, and 3 were 46.5, 65.3, and 91.2 mg/d, respectively. Open in new tab Physical activity classification did not alter the relation between total nitrate intake with either KES (P-interaction = 0.864) or 8ft-TUG (P-interaction = 0.997). A graphic representation of the relation between nitrate intake and muscle function measures according to physical activity classification is presented in Supplemental Figure 2. Additional analysis Sex influenced the relation between total nitrate intake and KES (P < 0.001 for interaction) but not for 8ft-TUG (P = 0.927 for interaction). The relations between total nitrate intake and KES for men and women are presented separately in Supplemental Figure 3. The relation between total nitrate intake with KES (P = 0.95 for interaction) and 8ft-TUG (P = 0.75 for interaction) did not differ by age (<65 or ≥65 y). Total nitrate intake was significantly correlated with protein (ρ = 0.28), magnesium (ρ = 0.39), and calcium (ρ = 0.21) (all P < 0.001). Generalized linear models depicting the relation between total nitrate intake, KES, and 8ft-TUG with the separate inclusion of each of these nutrients in a multivariable-adjusted model are displayed in Supplemental Figure 4. Inclusion of these nutrients did not change the relation between total nitrate intake with KES and 8ft-TUG. The relation between total nitrate intake with KES and 8ft-TUG was driven predominantly by vegetable-derived nitrate as opposed to non–vegetable-based sources of nitrate (Supplemental Figure 5). Discussion This work highlights the potential benefits of higher habitual dietary nitrate, predominantly from vegetables, to support muscle function in adults across the lifespan, independently of physical activity levels. The aforementioned relation reached a plateau at nitrate intakes of ∼90 mg/d, suggesting that moderate intakes of nitrate may be sufficient to maximize benefits for muscle strength and physical function. This work builds on existing evidence specific to older women (13), highlighting potential benefits of higher habitual nitrate intake on muscle function in men and women of various ages. Ingesting dietary nitrate enhances NO bioavailability via the nitrate-nitrite-NO pathway (40). NO is involved in the modulation of skeletal muscle function (10), specifically linked to reduced ATP cost of muscle force production and increased efficiency of mitochondrial respiration and blood flow to the muscle (41). Other benefits of nitrate intake may include improved blood pressure regulation (5) and vascular function (6). Collectively, such physiological changes are likely to support musculoskeletal health, thus potentially explaining the better muscle strength and physical function observed in the current investigation. Despite established ergogenic benefits of nitrate supplements for athletic performance (11, 12), in nonathletic populations any such benefits remain less clear. Recently, however, a small double-blind, placebo-controlled crossover study in healthy older men and women (n = 12; mean age: 71 y) reported that an acute dose of nitrate (∼800 mg) improved maximal knee extension velocity and power by 10.9% and 4.4%, respectively (9). Our observed associations in the current study were of a comparable magnitude (∼11%), where KES was stronger in individuals in the highest tertile of nitrate intake (median value: 91 mg/d) compared with those in the lowest tertile (median value: 47 mg/d; 26.2 kg vs. 23.6 kg). To date, most studies highlighting benefits of nitrate for muscle function have adopted large acute doses only attainable through supplements. Nitrate supplements, such as concentrated beetroot juice, provide up to 12 times more nitrate than the median intake of a typical diet (e.g., 65 mg/d in the current study) (9). Although acute studies using nitrate supplements provide “proof of concept,” potential long-term impacts, especially when considered part of a normal diet, require observational work and long-term RCTs. In a cohort of older women (n = 1420, ∼75 y), we previously demonstrated that individuals with the highest nitrate intake (≥90 mg/d) had 4% stronger hand-grip strength and 5% faster TUG, compared with women with the lowest nitrate intake (<64 mg/d). We now expand these findings to a larger cohort of men and women with ages ranging across the adult lifespan. Here, we observed an 11% stronger KES and a 4% faster 8ft-TUG in individuals with the highest nitrate intake (tertile 3, ≥76 mg/d), in comparison to those with the lowest nitrate intake (tertile 1, <57 mg/d). Demonstrating this positive relation in men is an important advancement in the field as sex differences are proposed to influence nitrate metabolism (42). In this expanded analysis of men and women of various ages, the median nitrate intake for individuals with superior muscle function was comparable to our previous investigation (92 vs. 108 mg/d). Notably, such intakes are easily achieved by consuming ∼1 cup of nitrate-rich green-leafy vegetables daily (e.g., raw spinach, ∼81 mg; arugula, ∼196 mg; or lettuce, ∼85 mg) (18). This is especially important since a diet rich in vegetables, in conjunction with an active lifestyle, remains the cornerstone of public health messages. Performing regular physical activity is universally recognized as an important approach to prevent age-related declines in musculoskeletal function. A previous RCT also reported that low-intensity aerobic exercise (30% of heart rate reserve, 3–5 times/wk for 6 mo) prevented age‐related declines in indices of microvascular NO‐mediated vasodilator function in older individuals (43). It is possible that exercise, in combination with greater dietary nitrate bioavailability, may promote superior vascular function leading to better muscle health. However, in the current study, the positive influence of dietary nitrate on muscle function was independent of habitual physical activity. Since different forms of physical activity (walking vs. strength training) are likely to vary in their effectiveness for improving indices of muscle function, the magnitude of benefit from nitrate may also depend on the predominant type of activity performed. For example, individuals performing resistance training may experience the greatest benefit on physical function and muscle strength (44). In this study, only the duration and not the type of physical activity was considered, as these data were not available. Muscle strength is often used as a marker of overall health status across the lifespan (45, 46). For example, in a large cohort of healthy Japanese-Americans residing in Honolulu (n = 6089, aged 45–68 y), grip strength was highly predictive of functional limitations and disability 25 y later. This suggests that better midlife muscle strength may be protective against old-age disability by providing a greater “safety margin” above the threshold of disability (47). Our findings indicate that simple strategies, such as consuming a diet rich in vegetable-derived nitrate, may result in greater muscle strength, which plays a key role in functional movements, similar to those seen in the 8ft-TUG test. Specifically, TUG incorporates numerous movements (e.g., sitting down, standing, walking, and turning) that are critical to mobility and independence. Since compromised TUG is known to be predictive of 2-y incident disability (48), as well as 15-y injurious falls risk (49), results from the current investigation provide an additional strategy that could be incorporated into public health messages to support healthy aging. This is especially important from a public health perspective, as better muscle function is likely to reduce the risk of falls and associated injury such as fractures as we age. Presently, mechanisms by which nitrate may improve muscle function remain unclear [see (50) for review], especially in the context of habitual nitrate intake. Current reports also suggest contrasting mechanisms for improved contractile force in animals (e.g., better calcium handling) and humans (e.g., increased myofibrillar force production but not calcium handling) (51, 52). Furthermore, considering this prior work adopted acute supplementation regimes, the extrapolation of outcomes to the current investigation is limited. Hence, future investigation of the mechanisms underlying muscle function changes from habitual nitrate intake is required. Strengths of the current study include the recruitment of a large cohort of men and women with a wide age range across the AusDiab cohort spanning 12 y (baseline and 5-y and 12-y follow-ups), building on previous cross-sectional work in older women (13). Detailed information on potential lifestyle (e.g., physical activity, socioeconomic, education) and nutritional (protein, magnesium, and calcium) confounders was also considered. Habitual nitrate intake was estimated from a validated diet-assessment tool and food database using well-established methodology (13, 18, 22) from FFQs over an extended period of up to 12 y. Nevertheless, several limitations must be acknowledged. First, due to the observational nature of this investigation, causality cannot be established. Furthermore, a large proportion of individuals were lost to follow-up (at AusDiab3) from baseline (∼60%), which could limit the generalizability of our results. In addition, there may be bias due to residual confounding from measurement error and/or unobserved confounding. Specifically, lifestyle patterns (e.g., a healthy diet) linked to better muscle function may coincide with higher nitrate intake. However, we explored dietary confounders often associated with better muscle function and demonstrated minimal effects on the point estimates. We also performed analyses demonstrating a similar beneficial relation between nitrate intake and muscle function independent of physical activity levels. Information on cooking method is not included as part of the FFQ. Therefore, it is possible that absolute nitrate intake may be overestimated/imprecise. Nevertheless, nitrate intakes in the current investigation were comparable to previous work (22, 23). Based on information in our vegetable nitrate database (18), the loss in nitrate from boiling or cooking vegetables is ∼50%. Nonetheless, this would be consistent across nitrate intake tertiles. Finally, there currently is no known “gold standard” biomarker to validate long-term nitrate exposure as calculated from FFQs. In conclusion, this study provides evidence for the long-term benefits of higher habitual nitrate intake (at ∼90 mg/d), obtained predominantly from vegetables (∼85%), for muscle function in men and women of various ages. Considering that poor muscle function is linked to numerous adverse clinical outcomes, including mortality and injurious falls (49, 53), a diet with an abundance of nitrate-rich vegetables could be a novel strategy to promote muscle function. If supported by causal evidence, public health messages should continue to encourage higher vegetable intake, while highlighting the importance of nitrate-rich vegetables, such as green-leafy vegetables and beetroot, for musculoskeletal health to facilitate healthy aging. ACKNOWLEDGEMENTS The AusDiab study, initiated and coordinated by the International Diabetes Institute, and subsequently coordinated by the Baker Heart and Diabetes Institute, gratefully acknowledges the support and assistance given by the following: A Allman, B Atkins, S Bennett, S Chadban, S Colagiuri, M de Courten, M Dalton, M D'Emden, T Dwyer, D Jolley, I Kemp, P Magnus, J Mathews, D McCarty, A Meehan, K O'Dea, P Phillips, P Popplewell, C Reid, A Stewart, R Tapp, H Taylor, T Welborn, and F Wilson. Also, for funding or logistical support, we are grateful to The Commonwealth Dept of Health and Aged Care, Abbott Australasia Pty Ltd, Alphapharm Pty Ltd, AstraZeneca, Aventis Pharmaceutical, Bristol-Myers Squibb Pharmaceuticals, Eli Lilly (Australia) Pty Ltd, GlaxoSmithKline, Janssen-Cilag (Australia) Pty Ltd, Merck Lipha s.a., Merck Sharp & Dohme (Australia), Novartis Pharmaceutical (Australia) Pty Ltd., Novo Nordisk Pharmaceutical Pty Ltd, Pharmacia and Upjohn Pty Ltd, Pfizer Pty Ltd, Roche Diagnostics, Sanofi Synthelabo (Australia) Pty Ltd, Servier Laboratories (Australia) Pty Ltd, BioRad Laboratories Pty Ltd, HITECH Pathology Pty Ltd, the Australian Kidney Foundation, Diabetes Australia, Diabetes Australia (Northern Territory), Queensland Health, South Australian Department of Human Services, Tasmanian Department of Health and Human Services, Territory Health Services, Victorian Department of Human Services, and the Victorian OIS Program and Health Department of Western Australia. The authors’ responsibilities were as follows—MS, LCB, SR-B, PP, NPB, CPB, JRL, RMD, and JMH: designed the research; DJM, JES, and RMD: conducted research; MS, LCB, SR-B, NPB, RW, and KM: analyzed the data; MS and LCB: wrote the manuscript; MS: has primary responsibility for the final content; and all authors: read and approved the final manuscript. Data Availability Data described in the manuscript, code book, and analytic code will be made available from the corresponding author upon reasonable request. Notes This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. NPB is funded by a National Health and Medical Research Council (NHMRC) Early Career Fellowship (grant number APP1159914), Australia. LCB is supported by an NHMRC of Australia Emerging Leadership Investigator grant (ID: 1172987) and a National Heart Foundation of Australia Postdoctoral Research Fellowship (ID: 102498). JRL is supported by a National Heart Foundation of Australia Future Leader Fellowship (ID: 102817). JMH is supported by a NHMRC of Australia Senior Research Fellowship (grant number APP1116937). Author disclosures: The authors report no conflicts of interest. None of the funding agencies had any role in the conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript. Supplemental Figures 1–5 and Supplemental Table 1 are available from the “Supplementary data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/jn/. JRL, RMD, and JMH contributed equally to this work. Abbreviations used: AusDiab, Australian Diabetes, Obesity, and Lifestyle Study; FFQ, food-frequency questionnaire; ICC, intraclass correlation coefficient; KES, knee extension strength; RCT, randomized controlled trial; SEIFA, Socio-Economical Index for Areas; TUG, timed-up-and-go; 8ft-TUG, 8-ft-timed-up-and-go. References 1. van Berleere M , Dauchet L. Fruits, vegetables, and health: evidence from meta-analyses of prospective epidemiological studies . In: Vegetarian and plant-based diets in health and disease prevention, edited by François Mariotti. Amsterdam (the Netherlands): Elsevier ; 2017 . p. 215 – 48 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 2. Blekkenhorst LC , Sim M, Bondonno CP, Bondonno NP, Ward NC, Prince RL, Devine A, Lewis JR, Hodgson JM. Cardiovascular health benefits of specific vegetable types: a narrative review . Nutrients . 2018 ; 10 ( 5 ): 595 . Google Scholar Crossref Search ADS WorldCat 3. Lundberg JO , Carlström M, Weitzberg E. Metabolic effects of dietary nitrate in health and disease . Cell Metab . 2018 ; 28 ( 1 ): 9 – 22 . Google Scholar Crossref Search ADS PubMed WorldCat 4. Bondonno CP , Blekkenhorst LC, Liu AH, Bondonno NP, Ward NC, Croft KD, Hodgson JM. Vegetable-derived bioactive nitrate and cardiovascular health . Mol Aspects Med . 2018 ; 61 ( 6 ): 83 – 91 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 5. Kelly J , Fulford J, Vanhatalo A, Blackwell JR, French O, Bailey SJ, Gilchrist M, Winyard PG, Jones AM. Effects of short-term dietary nitrate supplementation on blood pressure, O2 uptake kinetics, and muscle and cognitive function in older adults . Am J Physiol . 2012 ; 304 ( 2 ): R73 – 83 . Google Scholar OpenURL Placeholder Text WorldCat 6. Rammos C , Hendgen-Cotta UB, Sobierajski J, Bernard A, Kelm M, Rassaf T. Dietary nitrate reverses vascular dysfunction in older adults with moderately increased cardiovascular risk . J Am Coll Cardiol . 2014 ; 63 ( 15 ): 1584 – 5 . Google Scholar Crossref Search ADS PubMed WorldCat 7. Jackson JK , Patterson AJ, MacDonald-Wicks LK, Oldmeadow C, McEvoy MA. The role of inorganic nitrate and nitrite in cardiovascular disease risk factors: a systematic review and meta-analysis of human evidence . Nutr Rev . 2018 ; 76 ( 5 ): 348 – 71 . Google Scholar Crossref Search ADS PubMed WorldCat 8. Blekkenhorst LC , Bondonno NP, Liu AH, Ward NC, Prince RL, Lewis JR, Devine A, Croft KD, Hodgson JM, Bondonno CP. Nitrate, the oral microbiome, and cardiovascular health: a systematic literature review of human and animal studies . Am J Clin Nutr . 2018 ; 107 ( 4 ): 504 – 22 . Google Scholar Crossref Search ADS PubMed WorldCat 9. Coggan AR , Hoffman RL, Gray DA, Moorthi RN, Thomas DP, Leibowitz JL, Thies D, Peterson LR. A single dose of dietary nitrate increases maximal knee extensor angular velocity and power in healthy older men and women . J Gerentol A . 2019 ; 75 ( 6 ): 1154 – 60 . Google Scholar Crossref Search ADS WorldCat 10. Jones AM . Dietary nitrate supplementation and exercise performance . Sports Med . 2014 ; 44 ( 1 ): 35 – 45 . Google Scholar OpenURL Placeholder Text WorldCat 11. Maughan RJ , Burke LM, Dvorak J, Larson-Meyer DE, Peeling P, Phillips SM, Rawson ES, Walsh NP, Garthe I, Geyer H. IOC consensus statement: dietary supplements and the high-performance athlete . Int J Sport Nutr Exerc Metab . 2018 ; 28 ( 2 ): 104 – 25 . Google Scholar Crossref Search ADS PubMed WorldCat 12. Peeling P , Binnie MJ, Goods PSR, Sim M, Burke LM. Evidence-based supplements for the enhancement of athletic performance . Int J Sport Nutr Exer Metab . 2018 ; 28 ( 2 ): 178 – 87 . Google Scholar Crossref Search ADS WorldCat 13. Sim M , Lewis JR, Blekkenhorst LC, Bondonno CP, Devine A, Zhu K, Peeling P, Prince RL, Hodgson JM. Higher dietary nitrate intake is associated with better muscle function in older women . J Cachexia Sarcopenia Muscle . 2019 : 10 ( 3 ): 601 – 10 . Google Scholar Crossref Search ADS PubMed WorldCat 14. Dunstan DW , Zimmet PZ, Welborn TA, Cameron AJ, Shaw J, de Courten M, Jolley D, McCarty DJ. The Australian Diabetes, Obesity and Lifestyle Study (AusDiab)—methods and response rates . Diabetes Res Clin Pract . 2002 ; 57 ( 2 ): 119 – 29 . Google Scholar Crossref Search ADS PubMed WorldCat 15. Ireland P , Jolley D, Giles G, O'Dea K, Powles J, Rutishauser I, Wahlqvist ML, Williams J. Development of the Melbourne FFQ: a food frequency questionnaire for use in an Australian prospective study involving an ethnically diverse cohort . Asia Pac J Clin Nutr . 1994 ; 3 ( 1 ): 19 – 31 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 16. Hodge A , Patterson AJ, Brown WJ, Ireland P, Giles G. The Anti Cancer Council of Victoria FFQ: relative validity of nutrient intakes compared with weighed food records in young to middle‐aged women in a study of iron supplementation . Aust NZ J Public Health . 2000 ; 24 ( 6 ): 576 – 83 . Google Scholar Crossref Search ADS WorldCat 17. Woods RK , Stoney RM, Ireland PD, Bailey MJ, Raven JM, Thien FCK, Walters EH, Abramson MJ. A valid food frequency questionnaire for measuring dietary fish intake . Asia Pac J Clin Nutr . 2002 ; 11 ( 1 ): 56 – 61 . Google Scholar Crossref Search ADS PubMed WorldCat 18. Blekkenhorst LC , Prince RL, Ward NC, Croft KD, Lewis JR, Devine A, Shinde S, Woodman RJ, Hodgson JM, Bondonno CP. Development of a reference database for assessing dietary nitrate in vegetables . Mol Nutr Food Res . 2017 ; 61 ( 8 ): 1600982 . Google Scholar Crossref Search ADS WorldCat 19. Food Standards Australia New Zealand . Survey of nitrates and nitrites in food and beverages in Australia . Canberra (Australia) : Food Standards Australia New Zealand ; 2011 . Google Scholar 20. Griesenbeck JS , Steck MD, Huber JC, Sharkey JR, Rene AA, Brender JD. Development of estimates of dietary nitrates, nitrites, and nitrosamines for use with the short Willet Food Frequency Questionnaire . Nutr J . 2009 ; 8 ( 1 ): 16 . Google Scholar Crossref Search ADS PubMed WorldCat 21. Inoue-Choi M , Virk-Baker MK, Aschebrook-Kilfoy B, Cross AJ, Subar AF, Thompson FE, Sinha R, Ward MH. Development and calibration of a dietary nitrate and nitrite database in the NIH–AARP Diet and Health Study . Public Health Nutr . 2016 ; 19 ( 11 ): 1934 – 43 . Google Scholar Crossref Search ADS PubMed WorldCat 22. Blekkenhorst LC , Bondonno CP, Lewis JR, Devine A, Woodman RJ, Croft KD, Lim WH, Wong G, Beilin LJ, Prince RL. Association of dietary nitrate with atherosclerotic vascular disease mortality: a prospective cohort study of older adult women . Am J Clin Nutr . 2017 ; 106 ( 1 ): 207 – 16 . Google Scholar Crossref Search ADS PubMed WorldCat 23. Bondonno CP , Blekkenhorst LC, Prince RL, Ivey KL, Lewis JR, Devine A, Woodman RJ, Lundberg JO, Croft KD, Thompson PL. Association of vegetable nitrate intake with carotid atherosclerosis and ischemic cerebrovascular disease in older women . Stroke . 2017 ; 48 ( 7 ): 1724 – 9 . Google Scholar Crossref Search ADS PubMed WorldCat 24. Bohannon RW . Measuring knee extensor muscle strength . Am J Phys Med Rehab . 2001 ; 80 ( 1 ): 13 – 8 . Google Scholar Crossref Search ADS WorldCat 25. Sole G , Hamrén J, Milosavljevic S, Nicholson H, Sullivan SJ. Test-retest reliability of isokinetic knee extension and flexion . Arch Phys Med Rehabil . 2007 ; 88 ( 5 ): 626 – 31 . Google Scholar Crossref Search ADS PubMed WorldCat 26. Rikli RE , Jones CJ. Development and validation of a functional fitness test for community-residing older adults . J Aging Phys Act . 1999 ; 7 ( 2 ): 129 – 61 . Google Scholar Crossref Search ADS WorldCat 27. Cruz-Jentoft AJ , Baeyens J-P, Bauer JM, Boirie Y, Cederholm T, Landi F, Martin FC, Michel J-P, Rolland Y, Schneider SM. Sarcopenia: European consensus on definition and diagnosis . Age Ageing . 2010 ; 39 ( 4 ): 412 – 23 . Google Scholar Crossref Search ADS PubMed WorldCat 28. Cruz-Jentoft AJ , Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, Cooper C, Landi F, Rolland Y, Sayer AA. Sarcopenia: revised European consensus on definition and diagnosis . Age Ageing . 2019 ; 48 ( 1 ): 16 – 31 . Google Scholar Crossref Search ADS PubMed WorldCat 29. Census of Population and Housing . Socio-Economic Indexes for Areas (SEIFA), Australia, 2016 . Canberra (Australia) : Australian Bureau of Statistics ; 2018 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 30. Dalton M , Cameron AJ, Zimmet PZ, Shaw JE, Jolley D, Dunstan DW, Welborn TA, Committee AS. Waist circumference, waist–hip ratio and body mass index and their correlation with cardiovascular disease risk factors in Australian adults . J Intern Med . 2003 ; 254 ( 6 ): 555 – 63 . Google Scholar Crossref Search ADS PubMed WorldCat 31. ELM Barr DM , Zimmet PZ, Polkinghorne KR, Atkins RC, Dunstan DW, Murray SG, Shaw JE. Available from Baker Institute: https://www.baker.edu.au/-/media/documents/impact/ausdiab/reports/ausdiab-report-2005.pdf?la=en. [Accessed 2020 Mar 1] . 32. Backholer K , Spencer E, Gearon E, Magliano DJ, McNaughton SA, Shaw JE, Peeters A. The association between socio-economic position and diet quality in Australian adults . Public Health Nutr . 2016 ; 19 ( 3 ): 477 – 85 . Google Scholar Crossref Search ADS PubMed WorldCat 33. Cameron AJ , Zimmet PZ, Dunstan DW, Dalton M, Shaw JE, Welborn TA, Owen N, Salmon J, Jolley D. Overweight and obesity in Australia: the 1999–2000 Australian Diabetes, Obesity and Lifestyle Study (AusDiab) . Med J Aust . 2003 ; 178 ( 9 ): 427 – 32 . Google Scholar Crossref Search ADS PubMed WorldCat 34. Diabetes A , Dunstan DW. Diabesity & Associated Disorders in Australia-2000: the accelerating epidemic . Victoria (Australia) : International Diabetes Institute ; 2001 . OpenURL Placeholder Text WorldCat 35. Dunstan DW , Zimmet PZ, Welborn TA, Cameron AJ, Shaw J, De Courten M, Jolley D, McCarty DJ, AusDiab Steering Committee. The Australian Diabetes, Obesity and Lifestyle Study (AusDiab)—methods and response rates . Diabetes Res Clin Pract . 2002 ; 57 ( 2 ): 119 – 29 . Google Scholar Crossref Search ADS PubMed WorldCat 36. R Core Team . R: a language and environment for statistical computing . Vienna (Austria) : R Foundation for Statistical Computing ; 2019. ; Available from: http://www.r-project.org/ . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 37. Deutz NE , Bauer JM, Barazzoni R, Biolo G, Boirie Y, Bosy-Westphal A, Cederholm T, Cruz-Jentoft A, Krznariç Z, Nair KS. Protein intake and exercise for optimal muscle function with aging: recommendations from the ESPEN Expert Group . Clin Nutr . 2014 ; 33 ( 6 ): 929 – 36 . Google Scholar Crossref Search ADS PubMed WorldCat 38. Veronese N , Berton L, Carraro S, Bolzetta F, De Rui M, Perissinotto E, Toffanello ED, Bano G, Pizzato S, Miotto F. Effect of oral magnesium supplementation on physical performance in healthy elderly women involved in a weekly exercise program: a randomized controlled trial . Am J Clin Nutr . 2014 ; 100 ( 3 ): 974 – 81 . Google Scholar Crossref Search ADS PubMed WorldCat 39. Berchtold MW , Brinkmeier H, Muntener M. Calcium ion in skeletal muscle: its crucial role for muscle function, plasticity, and disease . Physiol Rev . 2000 ; 80 ( 3 ): 1215 – 65 . Google Scholar Crossref Search ADS PubMed WorldCat 40. Duncan C , Dougall H, Johnston P, Green S, Brogan R, Leifert C, Smith L, Golden M, Benjamin N. Chemical generation of nitric oxide in the mouth from the enterosalivary circulation of dietary nitrate . Nat Med . 1995 ; 1 ( 6 ): 546 – 51 . Google Scholar Crossref Search ADS PubMed WorldCat 41. Bailey SJ , Fulford J, Vanhatalo A, Winyard PG, Blackwell JR, DiMenna FJ, Wilkerson DP, Benjamin N, Jones AM. Dietary nitrate supplementation enhances muscle contractile efficiency during knee-extensor exercise in humans . J Appl Physiol . 2010 ; 109 ( 1 ): 135 – 48 . Google Scholar Crossref Search ADS PubMed WorldCat 42. Wickham KA , Spriet LL. No longer beeting around the bush: a review of potential sex differences with dietary nitrate supplementation . Appl Physiol Nutr Metab . 2019 ; 44 ( 9 ): 915 – 24 . Google Scholar Crossref Search ADS PubMed WorldCat 43. Black MA , Green DJ, Cable NT. Exercise prevents age‐related decline in nitric‐oxide‐mediated vasodilator function in cutaneous microvessels . J Physiol . 2008 ; 586 ( 14 ): 3511 – 24 . Google Scholar Crossref Search ADS PubMed WorldCat 44. Hunter GR , McCarthy JP, Bamman MM. Effects of resistance training on older adults . Sports Med . 2004 ; 34 ( 5 ): 329 – 48 . Google Scholar Crossref Search ADS PubMed WorldCat 45. Peterson MD , Duchowny K, Meng Q, Wang Y, Chen X, Zhao Y. Low normalized grip strength is a biomarker for cardiometabolic disease and physical disabilities among US and Chinese adults . J Gerentol A . 2017 ; 72 ( 11 ): 1525 – 31 . Google Scholar Crossref Search ADS WorldCat 46. Lee W-J , Peng L-N, Chiou S-T, Chen L-K. Relative handgrip strength is a simple indicator of cardiometabolic risk among middle-aged and older people: a nationwide population-based study in Taiwan . PLoS One . 2016 ; 11 ( 8 ): e0160876 . Google Scholar Crossref Search ADS PubMed WorldCat 47. Rantanen T , Guralnik JM, Foley D, Masaki K, Leveille S, Curb JD, White L. Midlife hand grip strength as a predictor of old age disability . JAMA . 1999 ; 281 ( 6 ): 558 – 60 . Google Scholar Crossref Search ADS PubMed WorldCat 48. Donoghue OA , Savva GM, Cronin H, Kenny RA, Horgan NF. Using timed up and go and usual gait speed to predict incident disability in daily activities among community-dwelling adults aged 65 and older . Arch Phys Med Rehabil . 2014 ; 95 ( 10 ): 1954 – 61 . Google Scholar Crossref Search ADS PubMed WorldCat 49. Sim M , Prince R, Scott D, Daly R, Duque G, Inderjeeth C, Zhu K, Woodman R, Hodgson J, Lewis J. Utility of four sarcopenia criteria for the prediction of falls-related hospitalization in older Australian women . Osteoporos Int . 2019 ; 30 ( 1 ): 167 – 76 . Google Scholar Crossref Search ADS PubMed WorldCat 50. McIlvenna LC , Muggeridge DJ, Whitfield J. Exploring the mechanisms by which nitrate supplementation improves skeletal muscle contractile function: one fibre at a time . J Physiol . 2020 ; 598 ( 1 ): 25 – 7 . Google Scholar Crossref Search ADS PubMed WorldCat 51. Hernández A , Schiffer TA, Ivarsson N, Cheng AJ, Bruton JD, Lundberg JO, Weitzberg E, Westerblad H. Dietary nitrate increases tetanic [Ca2+] i and contractile force in mouse fast‐twitch muscle . J Physiol . 2012 ; 590 ( 15 ): 3575 – 83 . Google Scholar Crossref Search ADS PubMed WorldCat 52. Whitfield J , Gamu D, Heigenhauser GJ, Van Loon LJ, Spriet LL, Tupling AR, Holloway GP. Beetroot juice increases human muscle force without changing Ca2+-handling proteins . Med Science Sports Exer . 2017 ; 49 ( 10 ): 2016 – 24 . Google Scholar Crossref Search ADS WorldCat 53. Sim M PR , Scott D, Daly RM, Duque G, Inderjeeth CA, Zhu K, Woodman RJ, Hodgson JM, Lewis JR. Sarcopenia definitions and their associations with mortality in older Australian women . J Am Med Dir Assoc . 2019 ; 20 ( 1 ): 76 – 82.e2 . Google Scholar Crossref Search ADS PubMed WorldCat © The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Nutrition Oxford University Press

Dietary Nitrate Intake Is Positively Associated with Muscle Function in Men and Women Independent of Physical Activity Levels

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Oxford University Press
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Copyright © 2021 American Society for Nutrition
ISSN
0022-3166
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1541-6100
DOI
10.1093/jn/nxaa415
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Abstract

ABSTRACT Background Nitrate supplements can improve vascular and muscle function. Whether higher habitual dietary nitrate is associated with better muscle function remains underexplored. Objective The aim was to examine whether habitual dietary nitrate intake is associated with better muscle function in a prospective cohort of men and women, and whether the relation was dependent on levels of physical activity. Methods The sample (n = 3759) was drawn from the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab) (56% female; mean ± SD baseline age: 48.6 ± 11.1 y). Habitual dietary intake was assessed over 12 y by obtaining an average [of at least 2 time points, e.g., baseline (2000/2001) and 2004/2005 and/or 2011/2012] from a food-frequency questionnaire. Nitrate intake was calculated from a validated nitrate database and other published literature. Muscle function was quantified by knee extension strength (KES) and the 8-ft-timed-up-and-go (8ft-TUG) test performed in 2011/2012. Physical activity was assessed by questionnaire. Generalized linear models and logistic regression were used to analyze the data. Results Median (IQR) total nitrate intake was 65 (52–83) mg/d, with ∼81% derived from vegetables. Individuals in the highest tertile of nitrate intake (median intake: 91 mg/d) had 2.6 kg stronger KES (11%) and 0.24 s faster 8ft-TUG (4%) compared with individuals in the lowest tertile of nitrate intake (median intake: 47 mg/d; both P < 0.05). Similarly, individuals in the highest tertile of nitrate intake had lower odds for weak KES (adjusted OR: 0.69; 95% CI: 0.47, 0.73) and slow 8ft-TUG (adjusted OR: 0.63; 95% CI: 0.50, 0.78) compared with those in the lowest tertile. Physical activity did not influence the relationship between nitrate intake and muscle function (KES; P-interaction = 0.86; 8ft-TUG; P-interaction = 0.99). Conclusions Higher habitual dietary nitrate intake, predominantly from vegetables, could be an effective way to promote lower-limb muscle strength and physical function in men and women. muscle strength, physical function, nutrition, vegetables, healthy aging Introduction Diets rich in vegetables are widely promoted due to their beneficial effect on reducing the risk of various chronic diseases, particularly metabolic diseases (1). Although the range of bioactive phytochemicals found in vegetables is diverse, some phytochemicals are found in much higher concentrations in specific types of vegetables (2). One such bioactive phytochemical is nitrate, which is derived primarily from green-leafy vegetables and beetroot (2). Dietary nitrate is known to have numerous health benefits, including cardiovascular and metabolic regulation (3). Nitrate supplementation can enhance NO bioavailability through the nitrate-nitrite-NO pathway; NO is a potent cell-signaling molecule that plays a key role in vasodilation and blood vessel health (4). High-dosage nitrate supplementation (e.g., beetroot juice) in acute and short-term studies has been reported to reduce blood pressure (5) and improve vascular function (6). Such physiological benefits are supported by meta-analysis and systematic reviews (7, 8). A recent crossover, double-blind, randomized controlled trial (RCT) in older adults (n = 12; mean age: 71 y) also reported that acute nitrate-rich beetroot juice intake improved maximal knee extensor angular velocity (11%) and power (4%) (9). In younger populations, nitrate supplements have been linked to enhanced blood flow to exercising skeletal muscle, which is important for energy production (10). As such, nitrate was classified in 2018 by the International Olympic Committee (11) as an ergogenic aid for athletes (12). To date, most RCTs examining the effects of nitrate supplementation on physiological and health outcomes have predominantly used concentrated beetroot juice or nitrate salts to deliver large acute dosages (ranging from ∼300 to 800 mg) of nitrate (7, 8). Such supplements provide ∼4–7 times more nitrate than that typically consumed as part of the average diet (13). Thus, despite the reported health benefits for short-term, high-dose nitrate supplements, the impact of habitual dietary nitrate intake on health is less clear. In community-dwelling older women (≥70 y), we previously reported that higher dietary nitrate intake was associated with better muscle strength and function (13). Specifically, women with the highest nitrate intakes had stronger hand-grip strength and faster timed-up-and-go (TUG) performance. Such findings are especially important as poor muscle function is a key risk factors for falls, which are a major cause of fracture. To date, no such study has been undertaken in men. Furthermore, considering that physical activity is also known to improve muscle function, it is possible that the relation between dietary nitrate and muscle function may differ based on an individual's level of daily physical activity. The primary aim of this study was to examine if habitual dietary nitrate intake was associated with better muscle function in a large cohort of men and women with ages ranging across the adult lifespan. A secondary aim was to explore if this relation was dependent on the level of daily habitual physical activity. Methods Study population Participants included in this study were men and women from the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab). This is a national population-based survey of Australian adults aged ≥25 y (up to 85 y), recruited in 1999/2000 (AusDiab1, n = 11,247) with follow-up in 2004/2005 (AusDiab2, n = 6400) and 2011/2012 (AusDiab3, n = 4614). Methods and response rates have been described previously (14). An identical food-frequency questionnaire (FFQ) was administered at all 3 AusDiab time points. Objective measures of muscle function were only obtained during AusDiab3 (2011/2012). A total of 6633 individuals were excluded due to drop out from AusDiab1 to AusDiab3. An additional 300 individuals were excluded as they did not perform muscle function tests in 2011/2012. Individuals were also excluded if they did not complete the FFQ or had an implausible energy intake (<3300 kJ/d and >17,500 kJ/d for males or <2500 kJ/d and >14,500 kJ/d for females) at baseline (n = 153), AusDiab2 and/or AusDiab3 (n = 104). We further excluded individuals with a missing value for any baseline confounder (n = 147). Finally, pregnant women (n = 15) or individuals receiving dietary treatment (n = 136) at any of the 3 AusDiab visits were excluded. After the exclusions (n = 7488; Supplemental Figure 1 [CONSORT (Consolidated Standards of Reporting Trials) flow diagram], characteristics presented in Supplemental Table 1), data from 3759 participants were available for this analysis. All participants provided written informed consent. The study was approved by the Human Research Ethics Committees of the International Diabetes Institute, Alfred Hospital, and by the International Diabetes Institute Ethics Committee (Melbourne, Australia). Dietary assessment A semi-quantitative FFQ developed by the Cancer Council of Victoria was used to assess dietary intake (15–17). This validated FFQ measures the usual frequency of food intake during a period of 12 mo and comprises a list of 74 food items with 10 frequency response options ranging from “never” to “three or more times per day”; it is complemented by another 27 food and alcoholic beverage items that ask various questions, such as “How many different vegetables do you usually eat per day?” The FFQ calculates portion size by use of 3 photographs of scaled portions for 4 different commonly consumed food types. Nutrient intake calculations were analyzed by Cancer Council Victoria using the NUTTAB95 food nutrient database and were supplemented by other data where necessary. To obtain an estimate of habitual dietary intake, average nitrate intake was obtained from available FFQ data obtained from the 3 AusDiab time points (e.g., AusDiab1, AusDiab2, AusDiab3; or AusDiab1, AusDiab2; or AusDiab1, AusDiab3). Total dietary nitrate (milligrams/day) was derived from both vegetable and nonvegetable sources. Vegetable-derived nitrate was estimated from a comprehensive nitrate database for vegetables (18). The median nitrate value (milligrams/gram) for each vegetable in the FFQ was obtained from the database and multiplied by vegetable consumption (in grams/day) to determine nitrate intake. Nitrate intake from vegetables per day was calculated by totalling the nitrate intake from individual vegetables. The nitrate database used to estimate nitrate intake from vegetables has been validated in a study of men and women by estimating nitrate intake using 24-h dietary recalls and FFQs comparing these values with urinary nitrate excretion (18). An estimate of nitrate concentration (milligrams/gram) in each of the nonvegetable items listed in the FFQ was derived using estimates from 3 published sources (19–21). Nitrate concentration of 67 of the 77 nonvegetable items were obtained from Inoue-Choi et al. (21), 5 values were obtained from the Food Standards Australia New Zealand (FSANZ) survey of nitrates and nitrites in food and beverages in Australia (19), and 2 values were from Griesenbeck et al. (20). Where no value was available [3 foods: Vegemite (Bega Cheese, Australia), jam, soymilk], a value of 0 mg/g was used. Drinking water has been shown to contain very low concentrations of nitrate (∼0.3 mg/L) in Perth, Western Australia (22, 23). This is likely the situation across many other cities and territories in Australia. Therefore, nitrate intake from drinking water was not quantified from the multiple regions across Australia. Measures of muscle function Knee extension strength (KES) was used to capture the isometric muscle strength of the lower limbs (24). In brief, participants were seated on a stool with their hip and knee at 90° angles. KES was measured using Lord's strap assembly incorporating a strain gauge (Neuroscience Research Australia, Sydney, Australia). Specifically, a webbing strap with a Velcro fastener was attached to participants’ dominant leg between 5 and 10 cm above the lateral malleolus. Participants were instructed to perform 1 practice and 2 test trials by extending their leg against the strap with maximal force for 2–3 s (24). The highest score (in kilograms) of the 2 test trials was recorded. One minute of rest was undertaken between all trials. The KES test is reported to have good test-retest reliability [intraclass correlation coefficient (ICC) >0.9] (25), including construct validity with other measures of muscle strength (r = 0.77) (24). To derive a cutoff for weak KES, participants were first separated into 2 age groups (<65 y and ≥65 y) at the time of the muscle function tests in 2011/2012 and by gender (male and female). From each of the 4 groups created, the cutoff for weak KES was derived from the lowest quartile. For men aged <65 y and ≥65 y, cutoffs for weak KES were 25.1 and 18.4 kg, respectively. Weak KES cutoffs for women aged <65 y and ≥65 y were 15.1 and 10.3 kg, respectively. The 8-ft-TUG (8ft-TUG) test is commonly used to assess mobility as it requires both static and dynamic balance. A shorter time (seconds) to complete the 8ft-TUG indicates better dynamic gait speed and mobility across a combination of 3 commonly performed functional activities of daily living (sitting, standing, walking, and turning). Participants were seated in a chair that was placed at the end of a marked 8-ft (2.44 m) walkway. On the command “go,” participants were instructed to rise from the chair, walk at a comfortable speed for 8 ft, turn around, walk back, and sit down in the chair. Total time was calculated from the “go” command until participants were seated and their backs contacted the chair rest. Previously, the 8ft-TUG has been shown to have good reliability (ICC = 0.95) and validity against gait speed (r = 0.61) (26). To derive a cutoff for slow 8ft-TUG, participants were separated into 2 age groups (<65 y and ≥65 y) at the time of the muscle function tests in 2011/2012. From each of the 2 groups created, the cutoff for slow 8ft-TUG was derived from the lowest quartile. Of note, for physical function assessment, cutoffs for compromised performance are not differentiated by sex (27, 28). As such, for individuals aged <65 y and ≥65 y, cutoffs for slow 8ft-TUG were 6.25 and 8.00 s, respectively. Baseline demographic and clinical assessment A baseline household interview was used to collect demographic information including age (date of birth), sex (male/female), relationship status (de facto married, separated, divorced, widowed, never married), and educational level (never to some high school, completed university or equivalent). The Socio-Economic Indexes for Areas (SEIFA) as reported by the Australian Bureau of Statistics (29) was obtained and summarized for a range of information on the economic and social conditions of people and households. Detailed anthropometric measurements have been described elsewhere (14, 30). Briefly, height was measured to the nearest 0.5 cm without shoes using a stadiometer. Weight was measured without shoes and excess clothing to the nearest 0.1 kg using a mechanical beam balance (31). BMI was calculated as weight (kilograms) divided by height (meters squared). Total physical activity time was estimated for the previous week based on self-reported information using the Active Australia Survey Questionnaire (32) and previously reported (33). This information was subsequently used to categorize individuals into 3 groups: sedentary (0 min/wk), insufficient physical activity (<150 min/wk), or sufficient physical activity (≥150 min/wk). Smoking status was assessed by using an interviewer-administered questionnaire, as previously reported (14). Smoking status was categorized as follows: currently smoking (smoking at least daily), ex-smoker (smoking less than daily for at least the last 3 mo), and never smoked (<100 cigarettes during life) (34). Self-reported history of cardiovascular disease (yes/no) and diabetes (based on plasma glucose concentrations) was assessed (35). Statistical analysis Statistical analysis was performed using IBM SPSS Statistics for Windows, version 25.0 (IBM Corporation) and R software (version 3.4.2; R Foundation for Statistical Computing) (36). Descriptive statistics of normally distributed continuous variables were expressed as means ± SDs. Non–normally distributed continuous variables were expressed as medians and IQRs. Categorical variables were expressed as number and proportion (%). For all analysis, individuals were also grouped into tertiles based on total nitrate intake for data presentation purposes, not for modelling. The primary outcomes were KES and 8ft-TUG measured at the 12-y (2011/2012) follow-up. Since both muscle function tests were positively skewed, generalized linear models with a gamma distribution and log-link were used to examine the association between total nitrate intake (milligrams/day) with both KES and 8ft-TUG. To investigate potential nonlinearity of the relation between the exposure and the outcome, restricted cubic splines were used in the model in order to fully assess the functional form of the associations across the full range of nitrate intake. Logistic regression models were also used to investigate the relation between nitrate intake (milligrams/day) and binary measures of poor muscle function, including the cutoffs for weak KES and slow 8ft-TUG. ORs were then calculated, relative to a reference value of the median of the first tertile of the relevant exposure variable, and were plotted against the exposure variable, with 95% confidence bands provided. Specifically, ORs were extracted from the aforementioned fitted models, comparing the median of each tertile with the reference value of the median in tertile 1, and tabulated with 95% CIs. P values for ORs were obtained using Wald tests; we tested for nonlinearity using a likelihood ratio test to compare nested models with and without the nonlinear terms for the exposure. For visual simplicity, in all graphs presented, the x-axis was truncated at 3 SDs above the mean. Given the established positive effects of physical activity on muscle function, we assessed the heterogeneity of effects by including nitrate by physical activity interaction terms in the models to determine if relations were dependent on the level of physical activity. We also stratified our analysis and examined the relation between total nitrate intake and muscle function in 3 separate groups based on physical activity classification: sedentary (0 min/wk), insufficient physical activity (<150 min/wk), and sufficient physical activity (≥150 min/wk). Two models of adjustment were adopted: a minimally adjusted model that included age (years), sex (male/female), and BMI (kg/m2) and a multivariable-adjusted model that included the minimally adjusted model plus energy intake (kilojoules/day), relationship status (de facto married, separated, divorced, widowed, never married), physical activity levels (sedentary, insufficient, sufficient), level of education (never to some high school, completed university or equivalent), SEIFA, smoking status (current smoker, ex-smoker, nonsmoker), self-reported history of cardiovascular disease (yes/no), and diabetes. Statistical significance was set at a 2-sided type 1 error rate of P < 0.05 for all tests. Further analysis Interaction testing was also performed to examine if sex or age (years) altered the relation between total nitrate intake and muscle function. To further investigate the robustness of the relation between nitrate and muscle function, including the potential for confounding, we performed a range of sensitivity analyses. Specifically, Spearman's correlations were performed between total nitrate intake and various nutrients known to positively affect muscle function. These nutrients included total daily intakes for protein (37), magnesium (38), and calcium (39). Given the potential for multicollinearity between many of these confounders, we considered their impact by entering them into the multivariable-adjusted generalized linear models separately, each as a continuous variable, for KES and 8ft-TUG. Despite total nitrate intake being predominantly derived from vegetables, nitrate is also found in foods not considered to be part of a healthy diet (e.g., processed meat). Therefore, we examined the relation between vegetable-derived nitrate, non–vegetable-derived nitrate, and muscle function measures separately. Results Baseline characteristics are presented in Table 1. Vegetable-derived nitrate contributed 76%, 81%, and 85% of total daily nitrate intake for individuals in tertiles 1, 2, and 3, respectively. Individuals with the highest total nitrate intake (tertile 3) were slightly older, had higher daily energy consumption, and were more physically active compared with individuals with the lowest total nitrate intake (tertile 1; Table 1). TABLE 1 Demographic baseline characteristics by tertiles of total dietary nitrate intake among Australian men and women from the Australian Diabetes, Obesity, and Lifestyle Study1 . . Tertile of total nitrate intake . . All participants . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Sample number 3759 1253 1253 1253 Sex (women), n (%) 2105 (56.0) 702 (56.0) 720 (57.5) 683 (54.5) Age, y 48.6 ± 11.1 47.7 ± 11.5 48.3 ± 10.8 49.7 ± 10.8 BMI, kg/m2 26.4 ± 4.6 26.3 ± 4.8 26.4 ± 4.6 26.5 ± 4.4  Overweight, n (%) 1485 (39.5) 476 (38.0) 504 (40.2) 505 (40.3)  Obese, n (%) 698 (18.6) 226 (18.0) 229 (18.3) 243 (19.4) Physical activity, n (%)  Sedentary 569 (15.1) 220 (17.6) 211 (16.8) 138 (11.0)  Insufficient (<150 min/wk) 1172 (31.2) 415 (33.1) 405 (32.3) 352 (28.1)  Sufficient (≥150 min/wk) 2018 (53.7) 618 (49.3) 637 (50.8) 763 (60.9) Relationship status, n (%)  Married 2918 (77.6) 955 (76.2) 985 (78.6) 978 (78.1)  De facto 163 (4.3) 42 (3.4) 59 (4.7) 62 (4.9)  Separated 72 (1.9) 21 (1.7) 27 (2.2) 24 (1.9)  Divorced 209 (5.6) 77 (6.1) 68 (5.4) 64 (5.1)  Widowed 101 (2.7) 37 (3.0) 29 (2.3) 35 (2.8)  Single 296 (7.9) 121 (9.7) 85 (6.8) 90 (7.2) Level of education, n (%)  Never to some high school 1240 (33.0) 425 (33.9) 418 (33.4) 397 (31.7)  Completed university or equivalent 2519 (67.0) 828 (66.1) 835 (66.6) 856 (68.3) SEIFA index 1029 ± 80 1028 ± 80 1031 ± 78 1030 ± 82 Smoking status, n (%)  Current 403 (10.7) 151 (12.1) 127 (10.1) 125 (10.0)  Ex-smoker 1071 (28.5) 343 (27.4) 351 (28.0) 377 (30.1)  Nonsmoker 2285 (60.8) 759 (60.6) 775 (61.9) 751 (59.9) Self-reported history of CVD, yes, n (%) 144 (3.8) 54 (4.3) 44 (3.5) 46 (3.7) Diabetes mellites, n (%)  Yes 136 (3.6) 56 (4.5) 48 (3.8) 32 (1.4)  No 3623 (96.4) 1197 (95.5) 1205 (96.2) 1221 (98.6) Dietary intake  Total energy intake, MJ/d 8.62 (6.40–10.0) 7.40 (5.79–9.22) 7.99 (6.54–9.89) 8.85 (7.06–11.0)  Total nitrate intake, mg/d 65.3 (51.9–82.6) 46.5 (39.5–51.9) 65.3 (60.8–69.7) 91.2 (82.6–105.5)  Vegetable-derived nitrate, mg/d 52.9 (40.3–69.2) 35.5 (28.9–40.7) 52.9 (48.7–57.7) 77.1 (69.1–90.9)  Non–vegetable-derived nitrate, mg/d 12.1 (9.7–15.2) 10.3 (8.4–12.7) 12.0 (9.8–14.7) 14.3 (11.5–17.4) . . Tertile of total nitrate intake . . All participants . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Sample number 3759 1253 1253 1253 Sex (women), n (%) 2105 (56.0) 702 (56.0) 720 (57.5) 683 (54.5) Age, y 48.6 ± 11.1 47.7 ± 11.5 48.3 ± 10.8 49.7 ± 10.8 BMI, kg/m2 26.4 ± 4.6 26.3 ± 4.8 26.4 ± 4.6 26.5 ± 4.4  Overweight, n (%) 1485 (39.5) 476 (38.0) 504 (40.2) 505 (40.3)  Obese, n (%) 698 (18.6) 226 (18.0) 229 (18.3) 243 (19.4) Physical activity, n (%)  Sedentary 569 (15.1) 220 (17.6) 211 (16.8) 138 (11.0)  Insufficient (<150 min/wk) 1172 (31.2) 415 (33.1) 405 (32.3) 352 (28.1)  Sufficient (≥150 min/wk) 2018 (53.7) 618 (49.3) 637 (50.8) 763 (60.9) Relationship status, n (%)  Married 2918 (77.6) 955 (76.2) 985 (78.6) 978 (78.1)  De facto 163 (4.3) 42 (3.4) 59 (4.7) 62 (4.9)  Separated 72 (1.9) 21 (1.7) 27 (2.2) 24 (1.9)  Divorced 209 (5.6) 77 (6.1) 68 (5.4) 64 (5.1)  Widowed 101 (2.7) 37 (3.0) 29 (2.3) 35 (2.8)  Single 296 (7.9) 121 (9.7) 85 (6.8) 90 (7.2) Level of education, n (%)  Never to some high school 1240 (33.0) 425 (33.9) 418 (33.4) 397 (31.7)  Completed university or equivalent 2519 (67.0) 828 (66.1) 835 (66.6) 856 (68.3) SEIFA index 1029 ± 80 1028 ± 80 1031 ± 78 1030 ± 82 Smoking status, n (%)  Current 403 (10.7) 151 (12.1) 127 (10.1) 125 (10.0)  Ex-smoker 1071 (28.5) 343 (27.4) 351 (28.0) 377 (30.1)  Nonsmoker 2285 (60.8) 759 (60.6) 775 (61.9) 751 (59.9) Self-reported history of CVD, yes, n (%) 144 (3.8) 54 (4.3) 44 (3.5) 46 (3.7) Diabetes mellites, n (%)  Yes 136 (3.6) 56 (4.5) 48 (3.8) 32 (1.4)  No 3623 (96.4) 1197 (95.5) 1205 (96.2) 1221 (98.6) Dietary intake  Total energy intake, MJ/d 8.62 (6.40–10.0) 7.40 (5.79–9.22) 7.99 (6.54–9.89) 8.85 (7.06–11.0)  Total nitrate intake, mg/d 65.3 (51.9–82.6) 46.5 (39.5–51.9) 65.3 (60.8–69.7) 91.2 (82.6–105.5)  Vegetable-derived nitrate, mg/d 52.9 (40.3–69.2) 35.5 (28.9–40.7) 52.9 (48.7–57.7) 77.1 (69.1–90.9)  Non–vegetable-derived nitrate, mg/d 12.1 (9.7–15.2) 10.3 (8.4–12.7) 12.0 (9.8–14.7) 14.3 (11.5–17.4) 1 Values are means ± SDs or medians (IQRs) unless otherwise indicated. CVD, cardiovascular disease; SEIFA, Socio-Economic Indexes for Areas. Open in new tab TABLE 1 Demographic baseline characteristics by tertiles of total dietary nitrate intake among Australian men and women from the Australian Diabetes, Obesity, and Lifestyle Study1 . . Tertile of total nitrate intake . . All participants . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Sample number 3759 1253 1253 1253 Sex (women), n (%) 2105 (56.0) 702 (56.0) 720 (57.5) 683 (54.5) Age, y 48.6 ± 11.1 47.7 ± 11.5 48.3 ± 10.8 49.7 ± 10.8 BMI, kg/m2 26.4 ± 4.6 26.3 ± 4.8 26.4 ± 4.6 26.5 ± 4.4  Overweight, n (%) 1485 (39.5) 476 (38.0) 504 (40.2) 505 (40.3)  Obese, n (%) 698 (18.6) 226 (18.0) 229 (18.3) 243 (19.4) Physical activity, n (%)  Sedentary 569 (15.1) 220 (17.6) 211 (16.8) 138 (11.0)  Insufficient (<150 min/wk) 1172 (31.2) 415 (33.1) 405 (32.3) 352 (28.1)  Sufficient (≥150 min/wk) 2018 (53.7) 618 (49.3) 637 (50.8) 763 (60.9) Relationship status, n (%)  Married 2918 (77.6) 955 (76.2) 985 (78.6) 978 (78.1)  De facto 163 (4.3) 42 (3.4) 59 (4.7) 62 (4.9)  Separated 72 (1.9) 21 (1.7) 27 (2.2) 24 (1.9)  Divorced 209 (5.6) 77 (6.1) 68 (5.4) 64 (5.1)  Widowed 101 (2.7) 37 (3.0) 29 (2.3) 35 (2.8)  Single 296 (7.9) 121 (9.7) 85 (6.8) 90 (7.2) Level of education, n (%)  Never to some high school 1240 (33.0) 425 (33.9) 418 (33.4) 397 (31.7)  Completed university or equivalent 2519 (67.0) 828 (66.1) 835 (66.6) 856 (68.3) SEIFA index 1029 ± 80 1028 ± 80 1031 ± 78 1030 ± 82 Smoking status, n (%)  Current 403 (10.7) 151 (12.1) 127 (10.1) 125 (10.0)  Ex-smoker 1071 (28.5) 343 (27.4) 351 (28.0) 377 (30.1)  Nonsmoker 2285 (60.8) 759 (60.6) 775 (61.9) 751 (59.9) Self-reported history of CVD, yes, n (%) 144 (3.8) 54 (4.3) 44 (3.5) 46 (3.7) Diabetes mellites, n (%)  Yes 136 (3.6) 56 (4.5) 48 (3.8) 32 (1.4)  No 3623 (96.4) 1197 (95.5) 1205 (96.2) 1221 (98.6) Dietary intake  Total energy intake, MJ/d 8.62 (6.40–10.0) 7.40 (5.79–9.22) 7.99 (6.54–9.89) 8.85 (7.06–11.0)  Total nitrate intake, mg/d 65.3 (51.9–82.6) 46.5 (39.5–51.9) 65.3 (60.8–69.7) 91.2 (82.6–105.5)  Vegetable-derived nitrate, mg/d 52.9 (40.3–69.2) 35.5 (28.9–40.7) 52.9 (48.7–57.7) 77.1 (69.1–90.9)  Non–vegetable-derived nitrate, mg/d 12.1 (9.7–15.2) 10.3 (8.4–12.7) 12.0 (9.8–14.7) 14.3 (11.5–17.4) . . Tertile of total nitrate intake . . All participants . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Sample number 3759 1253 1253 1253 Sex (women), n (%) 2105 (56.0) 702 (56.0) 720 (57.5) 683 (54.5) Age, y 48.6 ± 11.1 47.7 ± 11.5 48.3 ± 10.8 49.7 ± 10.8 BMI, kg/m2 26.4 ± 4.6 26.3 ± 4.8 26.4 ± 4.6 26.5 ± 4.4  Overweight, n (%) 1485 (39.5) 476 (38.0) 504 (40.2) 505 (40.3)  Obese, n (%) 698 (18.6) 226 (18.0) 229 (18.3) 243 (19.4) Physical activity, n (%)  Sedentary 569 (15.1) 220 (17.6) 211 (16.8) 138 (11.0)  Insufficient (<150 min/wk) 1172 (31.2) 415 (33.1) 405 (32.3) 352 (28.1)  Sufficient (≥150 min/wk) 2018 (53.7) 618 (49.3) 637 (50.8) 763 (60.9) Relationship status, n (%)  Married 2918 (77.6) 955 (76.2) 985 (78.6) 978 (78.1)  De facto 163 (4.3) 42 (3.4) 59 (4.7) 62 (4.9)  Separated 72 (1.9) 21 (1.7) 27 (2.2) 24 (1.9)  Divorced 209 (5.6) 77 (6.1) 68 (5.4) 64 (5.1)  Widowed 101 (2.7) 37 (3.0) 29 (2.3) 35 (2.8)  Single 296 (7.9) 121 (9.7) 85 (6.8) 90 (7.2) Level of education, n (%)  Never to some high school 1240 (33.0) 425 (33.9) 418 (33.4) 397 (31.7)  Completed university or equivalent 2519 (67.0) 828 (66.1) 835 (66.6) 856 (68.3) SEIFA index 1029 ± 80 1028 ± 80 1031 ± 78 1030 ± 82 Smoking status, n (%)  Current 403 (10.7) 151 (12.1) 127 (10.1) 125 (10.0)  Ex-smoker 1071 (28.5) 343 (27.4) 351 (28.0) 377 (30.1)  Nonsmoker 2285 (60.8) 759 (60.6) 775 (61.9) 751 (59.9) Self-reported history of CVD, yes, n (%) 144 (3.8) 54 (4.3) 44 (3.5) 46 (3.7) Diabetes mellites, n (%)  Yes 136 (3.6) 56 (4.5) 48 (3.8) 32 (1.4)  No 3623 (96.4) 1197 (95.5) 1205 (96.2) 1221 (98.6) Dietary intake  Total energy intake, MJ/d 8.62 (6.40–10.0) 7.40 (5.79–9.22) 7.99 (6.54–9.89) 8.85 (7.06–11.0)  Total nitrate intake, mg/d 65.3 (51.9–82.6) 46.5 (39.5–51.9) 65.3 (60.8–69.7) 91.2 (82.6–105.5)  Vegetable-derived nitrate, mg/d 52.9 (40.3–69.2) 35.5 (28.9–40.7) 52.9 (48.7–57.7) 77.1 (69.1–90.9)  Non–vegetable-derived nitrate, mg/d 12.1 (9.7–15.2) 10.3 (8.4–12.7) 12.0 (9.8–14.7) 14.3 (11.5–17.4) 1 Values are means ± SDs or medians (IQRs) unless otherwise indicated. CVD, cardiovascular disease; SEIFA, Socio-Economic Indexes for Areas. Open in new tab Compared with individuals with the lowest total nitrate intake (tertile 1; median: 46.5 mg/d), individuals with the highest total nitrate intake (tertile 3; median: 91.2 mg/d) had 11% and 4% significantly stronger KES and faster 8ft-TUG performance, respectively (Table 2). Furthermore, KES was significantly higher in nitrate-intake tertile 3 compared with tertile 2 (Table 2). Note that the multivariable-adjusted relations between nitrate intake, KES, and 8ft-TUG were of a nonlinear nature (P-nonlinearity = 0.006 and 0.002, respectively; Figure 1). Specifically, the greatest benefits to muscle function were observed at nitrate intakes of ∼90 mg/d. The relations between total nitrate intake and the odds of weak KES and slow 8ft-TUG were both nonlinear (P-nonlinearity < 0.001 and 0.059, respectively; Figure 2), with intakes of ∼90 mg/d also appearing optimal. Compared with individuals with the lowest total nitrate intake (tertile 1), individuals in tertile 2 and tertile 3 had 20% and 31% lower odds of having weak KES and 24% and 37% had lower odds of having slow 8ft-TUG, respectively, in the multivariable-adjusted model (Table 3). FIGURE 1 Open in new tabDownload slide Multivariable-adjusted dose–response relation between total nitrate intake and (A) knee extension strength (n = 3470) and (B) 8ft-timed-up-and-go (n = 3750) obtained by generalized regression models in men and women with the exposure included as a restricted cubic spline. Shading represents 95% CIs. The rug plot along the bottom of each graph depicts each observation. FIGURE 1 Open in new tabDownload slide Multivariable-adjusted dose–response relation between total nitrate intake and (A) knee extension strength (n = 3470) and (B) 8ft-timed-up-and-go (n = 3750) obtained by generalized regression models in men and women with the exposure included as a restricted cubic spline. Shading represents 95% CIs. The rug plot along the bottom of each graph depicts each observation. FIGURE 2 Open in new tabDownload slide Restricted cubic splines based on multivariable-adjusted logistic regression models highlighting the relative odds between total nitrate intake and (A) weak knee extension strength (n = 3470) and (B) slow 8-ft-timed-up-and-go (n = 3750) in men and women. Shaded areas represent 95% CIs. The reference value is the value associated with the median intake (46.5 mg/d) for individuals in the lowest tertile of total nitrate intake. FIGURE 2 Open in new tabDownload slide Restricted cubic splines based on multivariable-adjusted logistic regression models highlighting the relative odds between total nitrate intake and (A) weak knee extension strength (n = 3470) and (B) slow 8-ft-timed-up-and-go (n = 3750) in men and women. Shaded areas represent 95% CIs. The reference value is the value associated with the median intake (46.5 mg/d) for individuals in the lowest tertile of total nitrate intake. TABLE 2 Estimated marginal means (95% CI) for knee extension strength and 8ft-timed-up-and-go performance for each tertile of total nitrate intake among Australian men and women from the Australian Diabetes, Obesity, and Lifestyle Study1 . Tertile of total nitrate intake . . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Knee extension strength,2 kg  Minimally adjusted 23.9 (23.5, 24.4) 24.6 (24.3, 25.0)3 25.6 (25.1, 26.1)3,4  Multivariable-adjusted 23.6 (22.9, 24.3) 24.7 (24.0, 25.3)3 26.2 (25.4, 26.7)3,4 8-ft-timed-up-and-go,5 s  Minimally adjusted 6.23 (6.16, 6.30) 6.14 (6.09, 6.19)3 6.02 (5.95, 6.09)3  Multivariable-adjusted 6.23 (6.13, 6.33) 6.06 (5.97, 6.15)3 5.99 (5.89, 6.09)3 . Tertile of total nitrate intake . . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Knee extension strength,2 kg  Minimally adjusted 23.9 (23.5, 24.4) 24.6 (24.3, 25.0)3 25.6 (25.1, 26.1)3,4  Multivariable-adjusted 23.6 (22.9, 24.3) 24.7 (24.0, 25.3)3 26.2 (25.4, 26.7)3,4 8-ft-timed-up-and-go,5 s  Minimally adjusted 6.23 (6.16, 6.30) 6.14 (6.09, 6.19)3 6.02 (5.95, 6.09)3  Multivariable-adjusted 6.23 (6.13, 6.33) 6.06 (5.97, 6.15)3 5.99 (5.89, 6.09)3 1 Means and 95% CIs were obtained from a generalized linear model with gamma distribution and log-link function for the median values of nitrate intake within each tertile (tertile 1 = 46.5 mg/d; tertile 2 = 65.3 mg/d; tertile 3 = 91.2 mg/d). Minimally adjusted: adjusted for age, sex, and BMI. Multivariable-adjusted: minimally adjusted + energy intake, relationship status, physical activity, level of education, SEIFA (Socio-Economical Index for Areas), smoking status, diabetes, and self-reported history of cardiovascular disease. 2 Assessed in n = 3479. 3 Significantly different (P < 0.05) from tertile 1. 4 Significantly different (P < 0.05) from tertile 2. 5 Assessed in n = 3750. Open in new tab TABLE 2 Estimated marginal means (95% CI) for knee extension strength and 8ft-timed-up-and-go performance for each tertile of total nitrate intake among Australian men and women from the Australian Diabetes, Obesity, and Lifestyle Study1 . Tertile of total nitrate intake . . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Knee extension strength,2 kg  Minimally adjusted 23.9 (23.5, 24.4) 24.6 (24.3, 25.0)3 25.6 (25.1, 26.1)3,4  Multivariable-adjusted 23.6 (22.9, 24.3) 24.7 (24.0, 25.3)3 26.2 (25.4, 26.7)3,4 8-ft-timed-up-and-go,5 s  Minimally adjusted 6.23 (6.16, 6.30) 6.14 (6.09, 6.19)3 6.02 (5.95, 6.09)3  Multivariable-adjusted 6.23 (6.13, 6.33) 6.06 (5.97, 6.15)3 5.99 (5.89, 6.09)3 . Tertile of total nitrate intake . . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Knee extension strength,2 kg  Minimally adjusted 23.9 (23.5, 24.4) 24.6 (24.3, 25.0)3 25.6 (25.1, 26.1)3,4  Multivariable-adjusted 23.6 (22.9, 24.3) 24.7 (24.0, 25.3)3 26.2 (25.4, 26.7)3,4 8-ft-timed-up-and-go,5 s  Minimally adjusted 6.23 (6.16, 6.30) 6.14 (6.09, 6.19)3 6.02 (5.95, 6.09)3  Multivariable-adjusted 6.23 (6.13, 6.33) 6.06 (5.97, 6.15)3 5.99 (5.89, 6.09)3 1 Means and 95% CIs were obtained from a generalized linear model with gamma distribution and log-link function for the median values of nitrate intake within each tertile (tertile 1 = 46.5 mg/d; tertile 2 = 65.3 mg/d; tertile 3 = 91.2 mg/d). Minimally adjusted: adjusted for age, sex, and BMI. Multivariable-adjusted: minimally adjusted + energy intake, relationship status, physical activity, level of education, SEIFA (Socio-Economical Index for Areas), smoking status, diabetes, and self-reported history of cardiovascular disease. 2 Assessed in n = 3479. 3 Significantly different (P < 0.05) from tertile 1. 4 Significantly different (P < 0.05) from tertile 2. 5 Assessed in n = 3750. Open in new tab TABLE 3 ORs (95% CIs) for weak knee extension strength and slow 8ft-timed-up-and-go by tertiles of total nitrate intake among Australian women and men from the Australian Diabetes, Obesity, and Lifestyle Study1 . Tertile of total nitrate intake . . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Weak knee extension strength,2 kg  Events, n (%) 344 (30.2) 279 (24.1) 246 (20.8)  Minimally adjusted 1.00 0.78 (0.70, 0.87)3 0.57 (0.46, 0.70)3  Multivariable-adjusted 1.00 0.80 (0.72, 0.89)3 0.69 (0.47, 0.73)3 Slow 8-ft-timed-up-and-go,4 s  Events, n (%) 356 (28.5) 296 (23.7) 267 (21.3)  Minimally adjusted 1.00 0.75 (0.67, 0.83)3 0.61 (0.49, 0.75)3  Multivariable-adjusted 1.00 0.76 (0.68, 0.85)3 0.63 (0.50, 0.78)3 . Tertile of total nitrate intake . . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Weak knee extension strength,2 kg  Events, n (%) 344 (30.2) 279 (24.1) 246 (20.8)  Minimally adjusted 1.00 0.78 (0.70, 0.87)3 0.57 (0.46, 0.70)3  Multivariable-adjusted 1.00 0.80 (0.72, 0.89)3 0.69 (0.47, 0.73)3 Slow 8-ft-timed-up-and-go,4 s  Events, n (%) 356 (28.5) 296 (23.7) 267 (21.3)  Minimally adjusted 1.00 0.75 (0.67, 0.83)3 0.61 (0.49, 0.75)3  Multivariable-adjusted 1.00 0.76 (0.68, 0.85)3 0.63 (0.50, 0.78)3 1 Estimated ORs and 95% CIs from logistic regression comparing the median nitrate intake from each tertile compared with tertile 1. Minimally adjusted: adjusted for age, sex, and BMI. Multivariable-adjusted: minimally adjusted + energy intake, relationship status, physical activity, level of education, SEIFA (Socio-Economical Index for Areas), smoking status, diabetes, and self-reported history of cardiovascular disease. Weak knee extension strength and slow 8-ft-timed-up-and-go were assessed in 3479 and 3750 individuals, respectively. 2 Cutoffs for weak knee extension strength for men aged <65 y and ≥65 y were 25.1 and 18.4 kg, respectively. For women aged <65 y and ≥65 y, cutoffs for weak knee extension strength were 15.1 and 10.3 kg, respectively. 3 Different from tertile 1, P < 0.05. 4 Cutoffs for slow 8-ft-timed-up-and-go for individuals aged <65 y and ≥65 y were 6.25 and 8.00 s, respectively. Median nitrate intakes for tertiles 1, 2, and 3 were 46.5, 65.3, and 91.2 mg/d, respectively. Open in new tab TABLE 3 ORs (95% CIs) for weak knee extension strength and slow 8ft-timed-up-and-go by tertiles of total nitrate intake among Australian women and men from the Australian Diabetes, Obesity, and Lifestyle Study1 . Tertile of total nitrate intake . . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Weak knee extension strength,2 kg  Events, n (%) 344 (30.2) 279 (24.1) 246 (20.8)  Minimally adjusted 1.00 0.78 (0.70, 0.87)3 0.57 (0.46, 0.70)3  Multivariable-adjusted 1.00 0.80 (0.72, 0.89)3 0.69 (0.47, 0.73)3 Slow 8-ft-timed-up-and-go,4 s  Events, n (%) 356 (28.5) 296 (23.7) 267 (21.3)  Minimally adjusted 1.00 0.75 (0.67, 0.83)3 0.61 (0.49, 0.75)3  Multivariable-adjusted 1.00 0.76 (0.68, 0.85)3 0.63 (0.50, 0.78)3 . Tertile of total nitrate intake . . Tertile 1: <56.7 mg/d . Tertile 2: ≥56.7 to <75.6 mg/d . Tertile 3: ≥75.6 mg/d . Weak knee extension strength,2 kg  Events, n (%) 344 (30.2) 279 (24.1) 246 (20.8)  Minimally adjusted 1.00 0.78 (0.70, 0.87)3 0.57 (0.46, 0.70)3  Multivariable-adjusted 1.00 0.80 (0.72, 0.89)3 0.69 (0.47, 0.73)3 Slow 8-ft-timed-up-and-go,4 s  Events, n (%) 356 (28.5) 296 (23.7) 267 (21.3)  Minimally adjusted 1.00 0.75 (0.67, 0.83)3 0.61 (0.49, 0.75)3  Multivariable-adjusted 1.00 0.76 (0.68, 0.85)3 0.63 (0.50, 0.78)3 1 Estimated ORs and 95% CIs from logistic regression comparing the median nitrate intake from each tertile compared with tertile 1. Minimally adjusted: adjusted for age, sex, and BMI. Multivariable-adjusted: minimally adjusted + energy intake, relationship status, physical activity, level of education, SEIFA (Socio-Economical Index for Areas), smoking status, diabetes, and self-reported history of cardiovascular disease. Weak knee extension strength and slow 8-ft-timed-up-and-go were assessed in 3479 and 3750 individuals, respectively. 2 Cutoffs for weak knee extension strength for men aged <65 y and ≥65 y were 25.1 and 18.4 kg, respectively. For women aged <65 y and ≥65 y, cutoffs for weak knee extension strength were 15.1 and 10.3 kg, respectively. 3 Different from tertile 1, P < 0.05. 4 Cutoffs for slow 8-ft-timed-up-and-go for individuals aged <65 y and ≥65 y were 6.25 and 8.00 s, respectively. Median nitrate intakes for tertiles 1, 2, and 3 were 46.5, 65.3, and 91.2 mg/d, respectively. Open in new tab Physical activity classification did not alter the relation between total nitrate intake with either KES (P-interaction = 0.864) or 8ft-TUG (P-interaction = 0.997). A graphic representation of the relation between nitrate intake and muscle function measures according to physical activity classification is presented in Supplemental Figure 2. Additional analysis Sex influenced the relation between total nitrate intake and KES (P < 0.001 for interaction) but not for 8ft-TUG (P = 0.927 for interaction). The relations between total nitrate intake and KES for men and women are presented separately in Supplemental Figure 3. The relation between total nitrate intake with KES (P = 0.95 for interaction) and 8ft-TUG (P = 0.75 for interaction) did not differ by age (<65 or ≥65 y). Total nitrate intake was significantly correlated with protein (ρ = 0.28), magnesium (ρ = 0.39), and calcium (ρ = 0.21) (all P < 0.001). Generalized linear models depicting the relation between total nitrate intake, KES, and 8ft-TUG with the separate inclusion of each of these nutrients in a multivariable-adjusted model are displayed in Supplemental Figure 4. Inclusion of these nutrients did not change the relation between total nitrate intake with KES and 8ft-TUG. The relation between total nitrate intake with KES and 8ft-TUG was driven predominantly by vegetable-derived nitrate as opposed to non–vegetable-based sources of nitrate (Supplemental Figure 5). Discussion This work highlights the potential benefits of higher habitual dietary nitrate, predominantly from vegetables, to support muscle function in adults across the lifespan, independently of physical activity levels. The aforementioned relation reached a plateau at nitrate intakes of ∼90 mg/d, suggesting that moderate intakes of nitrate may be sufficient to maximize benefits for muscle strength and physical function. This work builds on existing evidence specific to older women (13), highlighting potential benefits of higher habitual nitrate intake on muscle function in men and women of various ages. Ingesting dietary nitrate enhances NO bioavailability via the nitrate-nitrite-NO pathway (40). NO is involved in the modulation of skeletal muscle function (10), specifically linked to reduced ATP cost of muscle force production and increased efficiency of mitochondrial respiration and blood flow to the muscle (41). Other benefits of nitrate intake may include improved blood pressure regulation (5) and vascular function (6). Collectively, such physiological changes are likely to support musculoskeletal health, thus potentially explaining the better muscle strength and physical function observed in the current investigation. Despite established ergogenic benefits of nitrate supplements for athletic performance (11, 12), in nonathletic populations any such benefits remain less clear. Recently, however, a small double-blind, placebo-controlled crossover study in healthy older men and women (n = 12; mean age: 71 y) reported that an acute dose of nitrate (∼800 mg) improved maximal knee extension velocity and power by 10.9% and 4.4%, respectively (9). Our observed associations in the current study were of a comparable magnitude (∼11%), where KES was stronger in individuals in the highest tertile of nitrate intake (median value: 91 mg/d) compared with those in the lowest tertile (median value: 47 mg/d; 26.2 kg vs. 23.6 kg). To date, most studies highlighting benefits of nitrate for muscle function have adopted large acute doses only attainable through supplements. Nitrate supplements, such as concentrated beetroot juice, provide up to 12 times more nitrate than the median intake of a typical diet (e.g., 65 mg/d in the current study) (9). Although acute studies using nitrate supplements provide “proof of concept,” potential long-term impacts, especially when considered part of a normal diet, require observational work and long-term RCTs. In a cohort of older women (n = 1420, ∼75 y), we previously demonstrated that individuals with the highest nitrate intake (≥90 mg/d) had 4% stronger hand-grip strength and 5% faster TUG, compared with women with the lowest nitrate intake (<64 mg/d). We now expand these findings to a larger cohort of men and women with ages ranging across the adult lifespan. Here, we observed an 11% stronger KES and a 4% faster 8ft-TUG in individuals with the highest nitrate intake (tertile 3, ≥76 mg/d), in comparison to those with the lowest nitrate intake (tertile 1, <57 mg/d). Demonstrating this positive relation in men is an important advancement in the field as sex differences are proposed to influence nitrate metabolism (42). In this expanded analysis of men and women of various ages, the median nitrate intake for individuals with superior muscle function was comparable to our previous investigation (92 vs. 108 mg/d). Notably, such intakes are easily achieved by consuming ∼1 cup of nitrate-rich green-leafy vegetables daily (e.g., raw spinach, ∼81 mg; arugula, ∼196 mg; or lettuce, ∼85 mg) (18). This is especially important since a diet rich in vegetables, in conjunction with an active lifestyle, remains the cornerstone of public health messages. Performing regular physical activity is universally recognized as an important approach to prevent age-related declines in musculoskeletal function. A previous RCT also reported that low-intensity aerobic exercise (30% of heart rate reserve, 3–5 times/wk for 6 mo) prevented age‐related declines in indices of microvascular NO‐mediated vasodilator function in older individuals (43). It is possible that exercise, in combination with greater dietary nitrate bioavailability, may promote superior vascular function leading to better muscle health. However, in the current study, the positive influence of dietary nitrate on muscle function was independent of habitual physical activity. Since different forms of physical activity (walking vs. strength training) are likely to vary in their effectiveness for improving indices of muscle function, the magnitude of benefit from nitrate may also depend on the predominant type of activity performed. For example, individuals performing resistance training may experience the greatest benefit on physical function and muscle strength (44). In this study, only the duration and not the type of physical activity was considered, as these data were not available. Muscle strength is often used as a marker of overall health status across the lifespan (45, 46). For example, in a large cohort of healthy Japanese-Americans residing in Honolulu (n = 6089, aged 45–68 y), grip strength was highly predictive of functional limitations and disability 25 y later. This suggests that better midlife muscle strength may be protective against old-age disability by providing a greater “safety margin” above the threshold of disability (47). Our findings indicate that simple strategies, such as consuming a diet rich in vegetable-derived nitrate, may result in greater muscle strength, which plays a key role in functional movements, similar to those seen in the 8ft-TUG test. Specifically, TUG incorporates numerous movements (e.g., sitting down, standing, walking, and turning) that are critical to mobility and independence. Since compromised TUG is known to be predictive of 2-y incident disability (48), as well as 15-y injurious falls risk (49), results from the current investigation provide an additional strategy that could be incorporated into public health messages to support healthy aging. This is especially important from a public health perspective, as better muscle function is likely to reduce the risk of falls and associated injury such as fractures as we age. Presently, mechanisms by which nitrate may improve muscle function remain unclear [see (50) for review], especially in the context of habitual nitrate intake. Current reports also suggest contrasting mechanisms for improved contractile force in animals (e.g., better calcium handling) and humans (e.g., increased myofibrillar force production but not calcium handling) (51, 52). Furthermore, considering this prior work adopted acute supplementation regimes, the extrapolation of outcomes to the current investigation is limited. Hence, future investigation of the mechanisms underlying muscle function changes from habitual nitrate intake is required. Strengths of the current study include the recruitment of a large cohort of men and women with a wide age range across the AusDiab cohort spanning 12 y (baseline and 5-y and 12-y follow-ups), building on previous cross-sectional work in older women (13). Detailed information on potential lifestyle (e.g., physical activity, socioeconomic, education) and nutritional (protein, magnesium, and calcium) confounders was also considered. Habitual nitrate intake was estimated from a validated diet-assessment tool and food database using well-established methodology (13, 18, 22) from FFQs over an extended period of up to 12 y. Nevertheless, several limitations must be acknowledged. First, due to the observational nature of this investigation, causality cannot be established. Furthermore, a large proportion of individuals were lost to follow-up (at AusDiab3) from baseline (∼60%), which could limit the generalizability of our results. In addition, there may be bias due to residual confounding from measurement error and/or unobserved confounding. Specifically, lifestyle patterns (e.g., a healthy diet) linked to better muscle function may coincide with higher nitrate intake. However, we explored dietary confounders often associated with better muscle function and demonstrated minimal effects on the point estimates. We also performed analyses demonstrating a similar beneficial relation between nitrate intake and muscle function independent of physical activity levels. Information on cooking method is not included as part of the FFQ. Therefore, it is possible that absolute nitrate intake may be overestimated/imprecise. Nevertheless, nitrate intakes in the current investigation were comparable to previous work (22, 23). Based on information in our vegetable nitrate database (18), the loss in nitrate from boiling or cooking vegetables is ∼50%. Nonetheless, this would be consistent across nitrate intake tertiles. Finally, there currently is no known “gold standard” biomarker to validate long-term nitrate exposure as calculated from FFQs. In conclusion, this study provides evidence for the long-term benefits of higher habitual nitrate intake (at ∼90 mg/d), obtained predominantly from vegetables (∼85%), for muscle function in men and women of various ages. Considering that poor muscle function is linked to numerous adverse clinical outcomes, including mortality and injurious falls (49, 53), a diet with an abundance of nitrate-rich vegetables could be a novel strategy to promote muscle function. If supported by causal evidence, public health messages should continue to encourage higher vegetable intake, while highlighting the importance of nitrate-rich vegetables, such as green-leafy vegetables and beetroot, for musculoskeletal health to facilitate healthy aging. ACKNOWLEDGEMENTS The AusDiab study, initiated and coordinated by the International Diabetes Institute, and subsequently coordinated by the Baker Heart and Diabetes Institute, gratefully acknowledges the support and assistance given by the following: A Allman, B Atkins, S Bennett, S Chadban, S Colagiuri, M de Courten, M Dalton, M D'Emden, T Dwyer, D Jolley, I Kemp, P Magnus, J Mathews, D McCarty, A Meehan, K O'Dea, P Phillips, P Popplewell, C Reid, A Stewart, R Tapp, H Taylor, T Welborn, and F Wilson. Also, for funding or logistical support, we are grateful to The Commonwealth Dept of Health and Aged Care, Abbott Australasia Pty Ltd, Alphapharm Pty Ltd, AstraZeneca, Aventis Pharmaceutical, Bristol-Myers Squibb Pharmaceuticals, Eli Lilly (Australia) Pty Ltd, GlaxoSmithKline, Janssen-Cilag (Australia) Pty Ltd, Merck Lipha s.a., Merck Sharp & Dohme (Australia), Novartis Pharmaceutical (Australia) Pty Ltd., Novo Nordisk Pharmaceutical Pty Ltd, Pharmacia and Upjohn Pty Ltd, Pfizer Pty Ltd, Roche Diagnostics, Sanofi Synthelabo (Australia) Pty Ltd, Servier Laboratories (Australia) Pty Ltd, BioRad Laboratories Pty Ltd, HITECH Pathology Pty Ltd, the Australian Kidney Foundation, Diabetes Australia, Diabetes Australia (Northern Territory), Queensland Health, South Australian Department of Human Services, Tasmanian Department of Health and Human Services, Territory Health Services, Victorian Department of Human Services, and the Victorian OIS Program and Health Department of Western Australia. The authors’ responsibilities were as follows—MS, LCB, SR-B, PP, NPB, CPB, JRL, RMD, and JMH: designed the research; DJM, JES, and RMD: conducted research; MS, LCB, SR-B, NPB, RW, and KM: analyzed the data; MS and LCB: wrote the manuscript; MS: has primary responsibility for the final content; and all authors: read and approved the final manuscript. Data Availability Data described in the manuscript, code book, and analytic code will be made available from the corresponding author upon reasonable request. Notes This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. NPB is funded by a National Health and Medical Research Council (NHMRC) Early Career Fellowship (grant number APP1159914), Australia. LCB is supported by an NHMRC of Australia Emerging Leadership Investigator grant (ID: 1172987) and a National Heart Foundation of Australia Postdoctoral Research Fellowship (ID: 102498). JRL is supported by a National Heart Foundation of Australia Future Leader Fellowship (ID: 102817). JMH is supported by a NHMRC of Australia Senior Research Fellowship (grant number APP1116937). Author disclosures: The authors report no conflicts of interest. None of the funding agencies had any role in the conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript. Supplemental Figures 1–5 and Supplemental Table 1 are available from the “Supplementary data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/jn/. JRL, RMD, and JMH contributed equally to this work. Abbreviations used: AusDiab, Australian Diabetes, Obesity, and Lifestyle Study; FFQ, food-frequency questionnaire; ICC, intraclass correlation coefficient; KES, knee extension strength; RCT, randomized controlled trial; SEIFA, Socio-Economical Index for Areas; TUG, timed-up-and-go; 8ft-TUG, 8-ft-timed-up-and-go. References 1. van Berleere M , Dauchet L. Fruits, vegetables, and health: evidence from meta-analyses of prospective epidemiological studies . In: Vegetarian and plant-based diets in health and disease prevention, edited by François Mariotti. Amsterdam (the Netherlands): Elsevier ; 2017 . p. 215 – 48 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 2. Blekkenhorst LC , Sim M, Bondonno CP, Bondonno NP, Ward NC, Prince RL, Devine A, Lewis JR, Hodgson JM. Cardiovascular health benefits of specific vegetable types: a narrative review . Nutrients . 2018 ; 10 ( 5 ): 595 . Google Scholar Crossref Search ADS WorldCat 3. Lundberg JO , Carlström M, Weitzberg E. Metabolic effects of dietary nitrate in health and disease . Cell Metab . 2018 ; 28 ( 1 ): 9 – 22 . Google Scholar Crossref Search ADS PubMed WorldCat 4. Bondonno CP , Blekkenhorst LC, Liu AH, Bondonno NP, Ward NC, Croft KD, Hodgson JM. Vegetable-derived bioactive nitrate and cardiovascular health . Mol Aspects Med . 2018 ; 61 ( 6 ): 83 – 91 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 5. Kelly J , Fulford J, Vanhatalo A, Blackwell JR, French O, Bailey SJ, Gilchrist M, Winyard PG, Jones AM. Effects of short-term dietary nitrate supplementation on blood pressure, O2 uptake kinetics, and muscle and cognitive function in older adults . Am J Physiol . 2012 ; 304 ( 2 ): R73 – 83 . Google Scholar OpenURL Placeholder Text WorldCat 6. Rammos C , Hendgen-Cotta UB, Sobierajski J, Bernard A, Kelm M, Rassaf T. Dietary nitrate reverses vascular dysfunction in older adults with moderately increased cardiovascular risk . J Am Coll Cardiol . 2014 ; 63 ( 15 ): 1584 – 5 . Google Scholar Crossref Search ADS PubMed WorldCat 7. Jackson JK , Patterson AJ, MacDonald-Wicks LK, Oldmeadow C, McEvoy MA. The role of inorganic nitrate and nitrite in cardiovascular disease risk factors: a systematic review and meta-analysis of human evidence . Nutr Rev . 2018 ; 76 ( 5 ): 348 – 71 . Google Scholar Crossref Search ADS PubMed WorldCat 8. Blekkenhorst LC , Bondonno NP, Liu AH, Ward NC, Prince RL, Lewis JR, Devine A, Croft KD, Hodgson JM, Bondonno CP. Nitrate, the oral microbiome, and cardiovascular health: a systematic literature review of human and animal studies . Am J Clin Nutr . 2018 ; 107 ( 4 ): 504 – 22 . Google Scholar Crossref Search ADS PubMed WorldCat 9. Coggan AR , Hoffman RL, Gray DA, Moorthi RN, Thomas DP, Leibowitz JL, Thies D, Peterson LR. A single dose of dietary nitrate increases maximal knee extensor angular velocity and power in healthy older men and women . J Gerentol A . 2019 ; 75 ( 6 ): 1154 – 60 . Google Scholar Crossref Search ADS WorldCat 10. Jones AM . Dietary nitrate supplementation and exercise performance . Sports Med . 2014 ; 44 ( 1 ): 35 – 45 . Google Scholar OpenURL Placeholder Text WorldCat 11. Maughan RJ , Burke LM, Dvorak J, Larson-Meyer DE, Peeling P, Phillips SM, Rawson ES, Walsh NP, Garthe I, Geyer H. IOC consensus statement: dietary supplements and the high-performance athlete . Int J Sport Nutr Exerc Metab . 2018 ; 28 ( 2 ): 104 – 25 . Google Scholar Crossref Search ADS PubMed WorldCat 12. Peeling P , Binnie MJ, Goods PSR, Sim M, Burke LM. Evidence-based supplements for the enhancement of athletic performance . Int J Sport Nutr Exer Metab . 2018 ; 28 ( 2 ): 178 – 87 . Google Scholar Crossref Search ADS WorldCat 13. Sim M , Lewis JR, Blekkenhorst LC, Bondonno CP, Devine A, Zhu K, Peeling P, Prince RL, Hodgson JM. Higher dietary nitrate intake is associated with better muscle function in older women . J Cachexia Sarcopenia Muscle . 2019 : 10 ( 3 ): 601 – 10 . Google Scholar Crossref Search ADS PubMed WorldCat 14. Dunstan DW , Zimmet PZ, Welborn TA, Cameron AJ, Shaw J, de Courten M, Jolley D, McCarty DJ. The Australian Diabetes, Obesity and Lifestyle Study (AusDiab)—methods and response rates . Diabetes Res Clin Pract . 2002 ; 57 ( 2 ): 119 – 29 . Google Scholar Crossref Search ADS PubMed WorldCat 15. Ireland P , Jolley D, Giles G, O'Dea K, Powles J, Rutishauser I, Wahlqvist ML, Williams J. Development of the Melbourne FFQ: a food frequency questionnaire for use in an Australian prospective study involving an ethnically diverse cohort . Asia Pac J Clin Nutr . 1994 ; 3 ( 1 ): 19 – 31 . Google Scholar PubMed OpenURL Placeholder Text WorldCat 16. Hodge A , Patterson AJ, Brown WJ, Ireland P, Giles G. The Anti Cancer Council of Victoria FFQ: relative validity of nutrient intakes compared with weighed food records in young to middle‐aged women in a study of iron supplementation . Aust NZ J Public Health . 2000 ; 24 ( 6 ): 576 – 83 . Google Scholar Crossref Search ADS WorldCat 17. Woods RK , Stoney RM, Ireland PD, Bailey MJ, Raven JM, Thien FCK, Walters EH, Abramson MJ. A valid food frequency questionnaire for measuring dietary fish intake . Asia Pac J Clin Nutr . 2002 ; 11 ( 1 ): 56 – 61 . Google Scholar Crossref Search ADS PubMed WorldCat 18. Blekkenhorst LC , Prince RL, Ward NC, Croft KD, Lewis JR, Devine A, Shinde S, Woodman RJ, Hodgson JM, Bondonno CP. Development of a reference database for assessing dietary nitrate in vegetables . Mol Nutr Food Res . 2017 ; 61 ( 8 ): 1600982 . Google Scholar Crossref Search ADS WorldCat 19. Food Standards Australia New Zealand . Survey of nitrates and nitrites in food and beverages in Australia . Canberra (Australia) : Food Standards Australia New Zealand ; 2011 . Google Scholar 20. Griesenbeck JS , Steck MD, Huber JC, Sharkey JR, Rene AA, Brender JD. Development of estimates of dietary nitrates, nitrites, and nitrosamines for use with the short Willet Food Frequency Questionnaire . Nutr J . 2009 ; 8 ( 1 ): 16 . Google Scholar Crossref Search ADS PubMed WorldCat 21. Inoue-Choi M , Virk-Baker MK, Aschebrook-Kilfoy B, Cross AJ, Subar AF, Thompson FE, Sinha R, Ward MH. Development and calibration of a dietary nitrate and nitrite database in the NIH–AARP Diet and Health Study . Public Health Nutr . 2016 ; 19 ( 11 ): 1934 – 43 . Google Scholar Crossref Search ADS PubMed WorldCat 22. Blekkenhorst LC , Bondonno CP, Lewis JR, Devine A, Woodman RJ, Croft KD, Lim WH, Wong G, Beilin LJ, Prince RL. Association of dietary nitrate with atherosclerotic vascular disease mortality: a prospective cohort study of older adult women . Am J Clin Nutr . 2017 ; 106 ( 1 ): 207 – 16 . Google Scholar Crossref Search ADS PubMed WorldCat 23. Bondonno CP , Blekkenhorst LC, Prince RL, Ivey KL, Lewis JR, Devine A, Woodman RJ, Lundberg JO, Croft KD, Thompson PL. Association of vegetable nitrate intake with carotid atherosclerosis and ischemic cerebrovascular disease in older women . Stroke . 2017 ; 48 ( 7 ): 1724 – 9 . Google Scholar Crossref Search ADS PubMed WorldCat 24. Bohannon RW . Measuring knee extensor muscle strength . Am J Phys Med Rehab . 2001 ; 80 ( 1 ): 13 – 8 . Google Scholar Crossref Search ADS WorldCat 25. Sole G , Hamrén J, Milosavljevic S, Nicholson H, Sullivan SJ. Test-retest reliability of isokinetic knee extension and flexion . Arch Phys Med Rehabil . 2007 ; 88 ( 5 ): 626 – 31 . Google Scholar Crossref Search ADS PubMed WorldCat 26. Rikli RE , Jones CJ. Development and validation of a functional fitness test for community-residing older adults . J Aging Phys Act . 1999 ; 7 ( 2 ): 129 – 61 . Google Scholar Crossref Search ADS WorldCat 27. Cruz-Jentoft AJ , Baeyens J-P, Bauer JM, Boirie Y, Cederholm T, Landi F, Martin FC, Michel J-P, Rolland Y, Schneider SM. Sarcopenia: European consensus on definition and diagnosis . Age Ageing . 2010 ; 39 ( 4 ): 412 – 23 . Google Scholar Crossref Search ADS PubMed WorldCat 28. Cruz-Jentoft AJ , Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, Cooper C, Landi F, Rolland Y, Sayer AA. Sarcopenia: revised European consensus on definition and diagnosis . Age Ageing . 2019 ; 48 ( 1 ): 16 – 31 . Google Scholar Crossref Search ADS PubMed WorldCat 29. Census of Population and Housing . Socio-Economic Indexes for Areas (SEIFA), Australia, 2016 . Canberra (Australia) : Australian Bureau of Statistics ; 2018 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 30. Dalton M , Cameron AJ, Zimmet PZ, Shaw JE, Jolley D, Dunstan DW, Welborn TA, Committee AS. Waist circumference, waist–hip ratio and body mass index and their correlation with cardiovascular disease risk factors in Australian adults . J Intern Med . 2003 ; 254 ( 6 ): 555 – 63 . Google Scholar Crossref Search ADS PubMed WorldCat 31. ELM Barr DM , Zimmet PZ, Polkinghorne KR, Atkins RC, Dunstan DW, Murray SG, Shaw JE. Available from Baker Institute: https://www.baker.edu.au/-/media/documents/impact/ausdiab/reports/ausdiab-report-2005.pdf?la=en. [Accessed 2020 Mar 1] . 32. Backholer K , Spencer E, Gearon E, Magliano DJ, McNaughton SA, Shaw JE, Peeters A. The association between socio-economic position and diet quality in Australian adults . Public Health Nutr . 2016 ; 19 ( 3 ): 477 – 85 . Google Scholar Crossref Search ADS PubMed WorldCat 33. Cameron AJ , Zimmet PZ, Dunstan DW, Dalton M, Shaw JE, Welborn TA, Owen N, Salmon J, Jolley D. Overweight and obesity in Australia: the 1999–2000 Australian Diabetes, Obesity and Lifestyle Study (AusDiab) . Med J Aust . 2003 ; 178 ( 9 ): 427 – 32 . Google Scholar Crossref Search ADS PubMed WorldCat 34. Diabetes A , Dunstan DW. Diabesity & Associated Disorders in Australia-2000: the accelerating epidemic . Victoria (Australia) : International Diabetes Institute ; 2001 . OpenURL Placeholder Text WorldCat 35. Dunstan DW , Zimmet PZ, Welborn TA, Cameron AJ, Shaw J, De Courten M, Jolley D, McCarty DJ, AusDiab Steering Committee. The Australian Diabetes, Obesity and Lifestyle Study (AusDiab)—methods and response rates . Diabetes Res Clin Pract . 2002 ; 57 ( 2 ): 119 – 29 . Google Scholar Crossref Search ADS PubMed WorldCat 36. R Core Team . R: a language and environment for statistical computing . Vienna (Austria) : R Foundation for Statistical Computing ; 2019. ; Available from: http://www.r-project.org/ . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC 37. Deutz NE , Bauer JM, Barazzoni R, Biolo G, Boirie Y, Bosy-Westphal A, Cederholm T, Cruz-Jentoft A, Krznariç Z, Nair KS. Protein intake and exercise for optimal muscle function with aging: recommendations from the ESPEN Expert Group . Clin Nutr . 2014 ; 33 ( 6 ): 929 – 36 . Google Scholar Crossref Search ADS PubMed WorldCat 38. Veronese N , Berton L, Carraro S, Bolzetta F, De Rui M, Perissinotto E, Toffanello ED, Bano G, Pizzato S, Miotto F. Effect of oral magnesium supplementation on physical performance in healthy elderly women involved in a weekly exercise program: a randomized controlled trial . Am J Clin Nutr . 2014 ; 100 ( 3 ): 974 – 81 . Google Scholar Crossref Search ADS PubMed WorldCat 39. Berchtold MW , Brinkmeier H, Muntener M. Calcium ion in skeletal muscle: its crucial role for muscle function, plasticity, and disease . Physiol Rev . 2000 ; 80 ( 3 ): 1215 – 65 . Google Scholar Crossref Search ADS PubMed WorldCat 40. Duncan C , Dougall H, Johnston P, Green S, Brogan R, Leifert C, Smith L, Golden M, Benjamin N. Chemical generation of nitric oxide in the mouth from the enterosalivary circulation of dietary nitrate . Nat Med . 1995 ; 1 ( 6 ): 546 – 51 . Google Scholar Crossref Search ADS PubMed WorldCat 41. Bailey SJ , Fulford J, Vanhatalo A, Winyard PG, Blackwell JR, DiMenna FJ, Wilkerson DP, Benjamin N, Jones AM. Dietary nitrate supplementation enhances muscle contractile efficiency during knee-extensor exercise in humans . J Appl Physiol . 2010 ; 109 ( 1 ): 135 – 48 . Google Scholar Crossref Search ADS PubMed WorldCat 42. Wickham KA , Spriet LL. No longer beeting around the bush: a review of potential sex differences with dietary nitrate supplementation . Appl Physiol Nutr Metab . 2019 ; 44 ( 9 ): 915 – 24 . Google Scholar Crossref Search ADS PubMed WorldCat 43. Black MA , Green DJ, Cable NT. Exercise prevents age‐related decline in nitric‐oxide‐mediated vasodilator function in cutaneous microvessels . J Physiol . 2008 ; 586 ( 14 ): 3511 – 24 . Google Scholar Crossref Search ADS PubMed WorldCat 44. Hunter GR , McCarthy JP, Bamman MM. Effects of resistance training on older adults . Sports Med . 2004 ; 34 ( 5 ): 329 – 48 . Google Scholar Crossref Search ADS PubMed WorldCat 45. Peterson MD , Duchowny K, Meng Q, Wang Y, Chen X, Zhao Y. Low normalized grip strength is a biomarker for cardiometabolic disease and physical disabilities among US and Chinese adults . J Gerentol A . 2017 ; 72 ( 11 ): 1525 – 31 . Google Scholar Crossref Search ADS WorldCat 46. Lee W-J , Peng L-N, Chiou S-T, Chen L-K. Relative handgrip strength is a simple indicator of cardiometabolic risk among middle-aged and older people: a nationwide population-based study in Taiwan . PLoS One . 2016 ; 11 ( 8 ): e0160876 . Google Scholar Crossref Search ADS PubMed WorldCat 47. Rantanen T , Guralnik JM, Foley D, Masaki K, Leveille S, Curb JD, White L. Midlife hand grip strength as a predictor of old age disability . JAMA . 1999 ; 281 ( 6 ): 558 – 60 . Google Scholar Crossref Search ADS PubMed WorldCat 48. Donoghue OA , Savva GM, Cronin H, Kenny RA, Horgan NF. Using timed up and go and usual gait speed to predict incident disability in daily activities among community-dwelling adults aged 65 and older . Arch Phys Med Rehabil . 2014 ; 95 ( 10 ): 1954 – 61 . Google Scholar Crossref Search ADS PubMed WorldCat 49. Sim M , Prince R, Scott D, Daly R, Duque G, Inderjeeth C, Zhu K, Woodman R, Hodgson J, Lewis J. Utility of four sarcopenia criteria for the prediction of falls-related hospitalization in older Australian women . Osteoporos Int . 2019 ; 30 ( 1 ): 167 – 76 . Google Scholar Crossref Search ADS PubMed WorldCat 50. McIlvenna LC , Muggeridge DJ, Whitfield J. Exploring the mechanisms by which nitrate supplementation improves skeletal muscle contractile function: one fibre at a time . J Physiol . 2020 ; 598 ( 1 ): 25 – 7 . Google Scholar Crossref Search ADS PubMed WorldCat 51. Hernández A , Schiffer TA, Ivarsson N, Cheng AJ, Bruton JD, Lundberg JO, Weitzberg E, Westerblad H. Dietary nitrate increases tetanic [Ca2+] i and contractile force in mouse fast‐twitch muscle . J Physiol . 2012 ; 590 ( 15 ): 3575 – 83 . Google Scholar Crossref Search ADS PubMed WorldCat 52. Whitfield J , Gamu D, Heigenhauser GJ, Van Loon LJ, Spriet LL, Tupling AR, Holloway GP. Beetroot juice increases human muscle force without changing Ca2+-handling proteins . Med Science Sports Exer . 2017 ; 49 ( 10 ): 2016 – 24 . Google Scholar Crossref Search ADS WorldCat 53. Sim M PR , Scott D, Daly RM, Duque G, Inderjeeth CA, Zhu K, Woodman RJ, Hodgson JM, Lewis JR. Sarcopenia definitions and their associations with mortality in older Australian women . J Am Med Dir Assoc . 2019 ; 20 ( 1 ): 76 – 82.e2 . Google Scholar Crossref Search ADS PubMed WorldCat © The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Journal

The Journal of NutritionOxford University Press

Published: Mar 24, 2021

Keywords: nitrate; physical activity; vegetables; muscle function; muscle strength; diet; physical function; diabetes mellitus; diabetes mellitus, type 2; extension of knee; life style

References