Associations of dietary magnesium intake with all-cause and cause-specific mortality among individuals with gout and hyperuricemia

Associations of dietary magnesium intake with all-cause and cause-specific mortality among individuals with gout and hyperuricemia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Associations of dietary magnesium intake with all-cause and cause-specific mortality among individuals with gout and hyperuricemia Xuanni Lu, Anqi Wang, Ke Liu, Ying Chen, Weiwei Chen, Yingying Mao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4430372/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: We aimed to evaluate the relationship of dietary magnesium intake with all-cause and cause-specific mortality among patients with gout and hyperuricemia (HUA). Methods: We analyzed data of 1171 gout patients and 6707 patients with HUA from the National Health and Nutrition Examination Survey (NHANES) 2007-2018 and 2001-2018, respectively. Dietary intake data were obtained from 24-hour dietary recall interviews. Mortality status was determined using the NHANES public-use linked mortality fill. We used Cox regression model and restricted cubic spline analysis to probe the association of dietary magnesium intake and mortality among individuals with gout and HUA. Results: During 7081 person-years of follow-up, 257 deaths were documented in gout patients, among which 74 died from cardiovascular disease (CVD) and 48 died from cancer. For HUA patients followed up for 58,216 person-years, 1315 all-cause deaths occurred, including 411 CVD deaths and 224 cancer deaths. After multifactorial adjustments, higher dietary magnesium intake was associated with lower risk of all-cause mortality among participants with gout and HUA. Restricted cubic splines showed a nonlinear inverse association between dietary magnesium intake with CVD mortality among HUA patients ( P for nonlinear < 0.05), with the inflection point of 272mg/d. The results were robust in subgroup and sensitivity analyses. Conclusions: High dietary magnesium intake was associated with decreased risk of all-cause mortality among patients with gout and HUA, and had a nonlinear inverse association with CVD mortality in HUA patients. The results highlight the potential advantages of assessing dietary magnesium intake in preventing all-cause and CVD mortality in patients with gout and HUA. Diet Magnesium Gout Hyperuricemia Mortality Cardiovascular disease Cancer Figures Figure 1 Figure 2 Figure 3 Background Gout is a common disease caused by the deposition of sodium urate crystals in both articular and nonarticular structures and is manifested by intermittent attacks of severe painful arthritis[ 1 ]. Recent advances in epidemiological studies indicate gout is the most prevalent inflammatory arthritis[ 2 ], and even the incidence and prevalence are increasing worldwide[ 3 ]. Existing studies show that gout is associated with increased all-cause mortality and cause-specific mortality related to cardiovascular disease (CVD), infections, and digestive diseases[ 4 ]. The standardized all-cause mortality is 2.21 times higher in gout patients than general population[ 5 ]. The 2017 Global Burden of Disease (GBD) study estimates that disability-adjusted life-years (DALYs) of gout are 952,931 in men and 332,022 in women, respectively[ 6 ]. Therefore, urgent measures are needed to reduce the global burden of gout. The principal etiology of hyperuricemia (HUA) in gout has been well-established[ 7 ]. Nutrient intake and dietary patterns are reported to be closely related with the risk of gout and HUA[ 8 ]. Magnesium is an essential mineral and cofactor involved in many cellular reactions, such as ion transport, signal transduction, cell proliferation, and energy metabolism[ 9 ]. However, 79% of adults in United States (US) do not conform to the recommended dietary magnesium intake[ 10 ]. Epidemiological studies have shown that low magnesium intake increases the risk of chronic diseases, including CVD, hypertension, stroke, type 2 diabetes (T2D)[ 11 – 14 ], thus exploration of reasonable dietary magnesium intake and provision of reasonable dietary guidelines for the public can effectively reduce the incidence and prevalence of related diseases caused by magnesium deficiency. High dietary magnesium intake has been found to reduce the risk of death among the general population, as well as patients with diabetes and cancer[ 15 – 17 ]. Although previous studies have suggested that dietary magnesium intake is negatively associated with mortality[ 18 ], the association between dietary magnesium intake and mortality in patients with gout and HUA has not been reported, and the dose-response relationship has not been summarized, which need further research to explore. To fill these knowledge gaps, we prospectively investigated the associations and exact dose-relationship of dietary magnesium intake with all-cause and cause-specific mortality in US adults with gout and HUA. Methods Data source and study population The National Health and Nutrition Examination Survey (NHANES) is a nationally representative survey reflecting the health and nutritional status of the non-institutionalized citizens in the US with a multistage probability sampling design[ 19 , 20 ]. Details of survey design and data files is publicly available at NHANES website ( https://www.cdc.gov/nchs/nhanes/index.htm ). NHANES has been approved by the Research Ethics Review Board of National Center for Health Statistics (NCHS), and informed written consent was obtained from all participants[ 21 ]. In this study, we obtained data from six cycles of NHANES during 2007–2018 on gout patients, who were identified from self-reported personal interview data on a variety of health conditions. Initially, there were 1656 gout patients aged over 20 years. After excluding 185 patients with missing dietary magnesium intake data, 298 patients with cancer at baseline, and 2 patients with missing all-cause mortality data, 1171 gout patients were ultimately included (Fig. 1 ). For HUA patients, information was obtained from nine cycles of NHANES during 2001–2018. HUA was defined as the serum uric acid (SUA) levels ≥ 420 µmol/L in men and ≥ 360 µmol/L in women[ 22 ]. The SUA concentrations were measured by a timed endpoint method, which were performed with Beckman Synchron LX20 in 2001–2007, Beckman Coulter UniCel DxC 800 Synchron in 2008–2014, both Beckman Coulter UniCel DxC 800 Synchron and Beckman Coulter UniCel DxC 660i Synchron in 2015–2018. A total of 8167 HUA patients aged over 20 years were identified. Of these, we eliminated 503 patients with missing dietary magnesium intake data, 946 patients with cancer at baseline, 11 patients without all-cause mortality data, thus 6707 HUA patients were involved in the final analysis (Fig. 1 ). Collection of dietary intake Dietary intake data were obtained from 24-hour dietary recall interviews. The 2001–2002 NHANES cycle included only one 24-hour dietary recall, while the 2003–2018 NHANES cycles included an in-person interview in the mobile examination center (MEC) and a follow-up interview collected by telephone a few days later. When the second recall was not available, data from the first recall were used, otherwise we estimated the average dietary intake from two recall periods. Details of the interview process have been described in the dietary interview section of the NHANES website[ 23 , 24 ]. There were 121 gout patients and 1421 HUA patients with only the first 24-hour dietary recall interview, and 1050 gout patients and 5286 patients with HUA had two dietary recall interviews. Ascertainment of mortality We determined mortality status based on the 2001–2018 NHANES public-use linked mortality file, which has linked data from NCHS with death certificate records from the National Death Index (NDI). Cause-specific deaths were identified according to the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10): diseases of the heart (I00-I09, I11, I13, I20-I51) and malignancies (C00-C97). The follow-up time was from interview date to the date of death or end of follow-up (December 31, 2019). Assessment of covariates Sociodemographic characteristics including age, gender, race/ethnicity, education level, family poverty income ratio (PIR), smoking status, alcohol consumption and disease status (hypertension, diabetes) were obtained from household interviews through the standardized questionnaires. Body weight, height and waist circumference were measured by physical examinations in the MEC. Body mass index (BMI) was calculated as the ratio of weight (kg) to the square of height (m 2 ). Race/ethnicity was classified as Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, other Hispanic or others. Education levels were categorized as less than high school, high school or equivalent, college or above. PIR was defined as ratio of family income to poverty threshold. Alcohol consumption was defined as average daily alcohol consumption over the past 12 months, and classified by whether ≥ 4 drinks/day. Smoking status was classified as never (smoking less than 100 cigarettes in lifetime), former (smoking more than 100 cigarettes in lifetime and now not smoking at all), and current (smoking more than 100 cigarettes in lifetime and now smoking some days or every day)[ 25 ]. Statistical analyses Due to the complex survey designs of NHANES, all analyses accounted for dietary sampling weights, clustering, and stratification[ 26 ]. Continuous variables were presented as mean ± standard deviation (SD), and discontinuous variables were presented as percentage (%). Analysis of variance (ANOVA) test for continuous variables and chi-square (χ2) test for categorical variables were used to calculate the differences across the four groups of dietary magnesium intake. Cox regression models were performed to investigate the association between dietary magnesium intake and mortality. Model 1 was crude, Model 2 was adjusted for age, gender, race/ethnicity, and Model 3 was further adjusted for education level, PIR, BMI, energy, smoking status, alcohol consumption, hypertension and diabetes. The linear trend was tested for statistical significance according to the median of dietary magnesium intake in four groups. Furthermore, restricted cubic spline models fitted for Cox proportional hazards models were conducted to investigate the dose-response relationship between dietary magnesium intake and mortality among gout and HUA patients. In this study, we used restricted cubic spline models with three knots, corresponding to the 10th, 50th, and 90th percentiles[ 27 ] , [ 28 , 29 ]. If the relationship was nonlinear, we conducted a recursion algorithm to calculate the inflection point of the association between dietary magnesium intake and mortality. Stratified analyses were performed based on age (< 60 or ≥ 60 years old), gender (male, female), race/ethnicity (Whites or non-Whites), hypertension (yes or no), diabetes (yes or no) and BMI (< 30.00kg/m 2 or ≥ 30.00kg/m 2 ). Cochran’s Q test was used to assess the heterogeneity between different strata, and P value of less than 0.05 indicated the presence of potential heterogeneity. The significance of the interactions were also tested by the product terms between dietary magnesium intake and stratified variables. To assess the robustness of our findings, we conducted a series of sensitivity analyses. First, given the potential reverse causation bias, participants who died within the first 2 years of follow-up were excluded. Second, participants aged 80 and older were excluded to reduce the possibility of survival bias among the extremely elderly. Third, repeated analyses were conducted based on weighted quartiles of dietary magnesium intake[ 30 ]. Forth, we performed the main analyses according to the quintiles of dietary magnesium intake[ 31 ]. Fifth, NHANES survey cycles were additionally adjusted, considering the different measurement methods of SUA [ 30 , 32 , 33 ].Sixth, given that some dietary factors might influence the association of interest[ 34 – 39 ], intake of total folate, fiber, sodium, potassium, calcium and phosphorus were further adjusted. Seventh, to control the confounding effect of cardiometabolic markers[ 40 , 41 ], total cholesterol, high-density lipoprotein (HDL), glucose and triglycerides were further adjusted. Eighth, as renal dysfunction may affect the absorption and use of magnesium[ 42 ], kidney function assessed by estimated glomerular filtration rate (calculated by the improved modification of diet in renal disease formula) was further adjusted. Results Baseline characteristics of study participants According to the quartile of dietary magnesium intake, the demographics and characteristics of the study population are displayed in Table 1 . In this study, the analysis consisted of 1171 gout patients (mean age: 62.09 ± 13.10 years; 29.90% females) and 6707 HUA patients (mean age: 53.37 ± 17.73 years; 43.40% females). Moreover, gout and HUA patients with higher dietary magnesium intake tended to be male, younger, non-Hispanic White, had higher education levels, family income and daily energy intake, were less likely to have diabetes ( P < 0.05). Table 1 Baseline characteristics of participants Characteristic Patients with gout Patients with hyperuricemia Dietary magnesium intake (mg/d) P -value Dietary magnesium intake (mg/d) P -value Total Quartile 1 (< 193.25) Quartile 2 (193.25–261.50) Quartile 3 (261.50-344.50) Quartile 4 (≥ 344.50) Total Quartile 1 (< 187.00) Quartile 2 (187.00-251.50) Quartile 3 (251.50-336.25) Quartile 4 (≥ 336.25) N (%) 1171 293 289 295 294 6707 1675 1677 1678 1677 Age (years) 62.09 ± 13.10 64.87 ± 12.37 64.02 ± 12.79 60.44 ± 13.49 59.07 ± 12.90 0.001 53.37 ± 17.73 56.80 ± 18.25 55.14 ± 18.03 52.50 ± 17.11 49.06 ± 16.54 < 0.001 Gender (%) < 0.001 < 0.001 Male 821 (70.10) 162 (55.30) 190 (65.70) 213 (72.20) 256 (87.10) 3796 (56.60) 706 (42.10) 814 (48.50) 999 (59.50) 1277 (76.10) Female 350 (29.90) 131 (44.70) 99 (34.30) 82 (27.80) 38 (12.90) 2911 (43.40) 969 (57.90) 863 (51.50) 679 (40.50) 400 (23.90) Race/ethnicity (%) < 0.001 < 0.001 Mexican American 92 (7.90) 15 (5.10) 20 (6.90) 32 (10.80) 25 (8.50) 855 (12.70) 167 (10.00) 190 (11.30) 237 (14.10) 261 (15.60) Other Hispanic 84 (7.20) 15 (5.10) 24 (8.30) 24 (8.10) 21 (7.10) 457 (6.80) 113 (6.70) 108 (6.40) 129 (7.70) 107 (6.40) Non-Hispanic White 515 (44.00) 107 (36.50) 135 (46.70) 131 (44.40) 142 (48.30) 2996 (44.70) 648 (38.70) 761 (45.40) 786 (46.80) 801 (47.80) Non-Hispanic Black 341 (29.10) 132 (45.10) 85 (29.40) 63 (21.40) 61 (20.70) 1749 (26.10) 639 (38.10) 479 (28.60) 349 (20.80) 282 (16.80) Other race (including multi-racial) 139 (11.90) 24 (8.20) 25 (8.70) 45 (15.30) 45 (15.30) 650 (9.70) 108 (6.40) 139 (8.30) 177 (10.50) 226 (13.50) Education (%) 0.001 < 0.001 Less than high school 307 (26.20) 104 (35.50) 91 (31.50) 70 (23.70) 42 (14.30) 1699 (25.40) 565 (33.90) 430 (25.70) 378 (22.50) 326 (19.40) High school or equivalent 297 (25.40) 88 (30.00) 78 (27.00) 69 (23.40) 62 (21.10) 1692 (25.30) 464 (27.80) 436 (26.00) 416 (24.80) 376 (22.40) College or above 567 (48.40) 101 (34.50) 120 (41.50) 156 (52.90) 190 (64.60) 3308 (49.40) 640 (38.30) 810 (48.30) 883 (52.70) 975 (58.10) Smoking status (%) 0.790 0.002 Never 496 (42.40) 126 (43.00) 118 (40.80) 125 (42.40) 127 (43.20) 3430 (51.20) 838 (50.10) 885 (52.80) 853 (50.80) 854 (51.00) Former 475 (40.60) 111 (37.90) 121 (41.90) 122 (41.40) 121 (41.20) 1995 (29.80) 480 (28.70) 500 (29.80) 512 (30.50) 503 (30.00) Current 200 (17.10) 56 (19.10) 50 (17.30) 48 (16.30) 46 (15.60) 1278 (19.10) 355 (21.20) 291 (17.40) 313 (18.70) 319 (19.00) Alcoholic ≥ 4 drinks/day (%) 0.073 0.014 Yes 156 (23.30) 25 (20.20) 32 (20.10) 38 (21.60) 61 (29.00) 1164 (27.80) 207 (24.20) 226 (22.90) 312 (27.90) 419 (34.20) No 513 (76.70) 99 (79.80) 127 (79.90) 138 (78.40) 149 (71.00) 3021 (72.20) 649 (75.80) 760 (77.10) 806 (72.10) 806 (65.80) Family poverty income ratio 2.47 ± 1.60 1.96 ± 1.42 2.30 ± 1.55 2.53 ± 1.60 3.07 ± 1.63 < 0.001 2.50 ± 1.59 2.12 ± 1.47 2.44 ± 1.53 2.61 ± 1.61 2.84 ± 1.65 < 0.001 Waist circumference (cm) 109.31 ± 16.72 110.15 ± 17.58 109.69 ± 17.26 108.62 ± 16.27 108.86 ± 15.86 0.875 107.54 ± 16.23 107.40 ± 16.25 108.20 ± 15.74 107.89 ± 16.50 106.71 ± 16.37 0.184 BMI (kg/m 2 ) 32.11 ± 7.59 32.70 ± 8.16 32.59 ± 8.05 31.88 ± 7.33 31.29 ± 6.69 0.712 32.37 ± 7.71 32.47 ± 7.81 32.90 ± 7.98 32.41 ± 7.59 31.69 ± 7.43 0.001 Daily energy intake (kcal/d) 1945.44 ± 832.73 1226.55 ± 453.64 1723.91 ± 477.62 2085.14 ± 562.09 2739.45 ± 897.33 < 0.001 2007.04 ± 875.02 1283.81 ± 480.91 1741.22 ± 514.05 2143.73 ± 608.84 2858.48 ± 927.30 < 0.001 Hypertension (%) 842 (71.90) 229 (78.20) 215 (74.40) 195 (66.10) 203 (69.00) 0.669 3583 (53.60) 1021 (61.10) 942 (56.40) 868 (51.80) 752 (45.00) < 0.001 Diabetes (%) 373 (31.90) 116 (39.60) 93 (32.30) 94 (31.90) 70 (23.80) 0.014 1130 (16.90) 338 (20.20) 328 (19.60) 270 (16.10) 194 (11.60) < 0.001 Direct HDL-cholesterol (mg/dL) 48.33 ± 15.09 48.70 ± 16.06 47.31 ± 14.66 48.25 ± 13.51 49.01 ± 16.05 0.530 48.87 ± 15.24 50.13 ± 16.71 48.93 ± 14.65 48.36 ± 14.75 48.06 ± 14.69 0.219 Total cholesterol (mg/dL) 187.34 ± 43.76 185.88 ± 45.76 186.92 ± 45.09 186.45 ± 41.33 190.03 ± 43.03 0.486 200.16 ± 44.11 199.28 ± 43.33 199.20 ± 45.82 201.97 ± 44.70 200.17 ± 42.49 0.297 Glycohemoglobin (%) 6.17 ± 1.29 6.28 ± 1.38 6.17 ± 1.16 6.26 ± 1.46 5.99 ± 1.13 0.441 5.85 ± 0.99 5.93 ± 1.09 5.92 ± 1.03 5.84 ± 0.94 5.71 ± 0.88 < 0.001 Glucose (mg/dL) 117.96 ± 52.66 122.41 ± 54.02 118.12 ± 50.22 121.41 ± 63.48 110.13 ± 39.45 0.404 105.58 ± 34.86 107.55 ± 39.26 107.97 ± 35.99 104.71 ± 33.46 102.09 ± 29.73 < 0.001 Triglycerides (mg/dL) 190.87 ± 150.53 181.87 ± 147.78 192.29 ± 121.20 190.19 ± 139.23 198.71 ± 185.73 0.446 184.75 ± 150.57 165.37 ± 104.81 186.43 ± 172.50 193.58 ± 156.15 193.58 ± 158.29 < 0.001 Mean (S.D.) for continuous variables and numbers (percentages) for discontinuous variable. Continuous variables were compared using analysis of variance (ANOVA) test and categorical variables were compared using the chi-square (χ2) test. All estimates accounted for complex survey designs. Relationships of dietary magnesium intake with mortality During 7081 person-years of follow-up, there were 257 documented deaths in gout patients, including 74 CVD deaths and 48 cancer deaths. After multivariate adjustments, the hazard ratios (HRs) and 95% confidence intervals (CIs) of all-cause mortality for Q2 (193.25–261.50 mg/d), Q3 (261.50-344.50 mg/d) and Q4 (≥ 344.50 mg/d) were 1.44 (95% CI 0.69, 3.00), 0.42 (95% CI 0.16, 1.11), and 0.26 (95% CI 0.08, 0.88) when compared with Q1 (< 193.25 mg/d). The trend test revealed statistical significance ( P trend = 0.003). In terms of cause-specific mortality, no similar trend was found for CVD mortality ( P trend = 0.119) and cancer mortality ( P trend = 0.449) (Table 2 ). Table 2 HRs (95% CIs) for mortality according to dietary magnesium intake among participants with gout Dietary magnesium intake (mg/d) Quartile 1 Quartile 2 Quartile 3 Quartile 4 P trend a All-cause mortality Number of deaths (%) 86 (29.40) 80 (27.70) 58 (19.70) 33 (11.20) Model 1 1.00 0.84 (0.54,1.29), 0.424 0.45 (0.28,0.72), 0.001 0.19 (0.11,0.32), < 0.001 < 0.001 HR (95% CI), P -value Model 2 1.00 0.88 (0.56,1.38), 0.569 0.55 (0.32,0.93), 0.025 0.27 (0.15,0.48), < 0.001 < 0.001 HR (95% CI), P -value Model 3 1.00 1.44 (0.69,3.00), 0.331 0.42 (0.16,1.11), 0.080 0.26 (0.08,0.88), 0.030 0.003 HR (95% CI), P -value CVD mortality Number of deaths (%) 27 (9.20) 23 (8.00) 17 (5.80) 7 (2.40) Model 1 1.00 0.67 (0.33,1.37), 0.274 0.32 (0.16,0.66), 0.002 0.14 (0.05,0.40), < 0.001 < 0.001 HR (95% CI), P -value Model 2 1.00 0.79 (0.38,1.63), 0.516 0.42 (0.18,0.97), 0.043 0.22 (0.08,0.63), 0.005 0.001 HR (95% CI), P -value Model 3 1.00 0.62 (0.20,1.93), 0.411 0.19 (0.02,1.76), 0.142 0.11 (0.01,1.88), 0.126 0.119 HR (95% CI), P -value Cancer mortality Number of deaths (%) 16 (5.50) 12 (4.20) 10 (3.40) 10 (3.40) Model 1 1.00 0.82(0.25,2.64), 0.733 0.75 (0.20,2.84), 0.673 0.52 (0.21,1.26), 0.146 0.172 HR (95% CI), P -value Model 2 1.00 0.80 (0.26,2.48), 0.703 0.83 (0.25,2.83), 0.770 0.71 (0.25,2.03), 0.523 0.632 HR (95% CI), P -value Model 3 1.00 4.88 (1.06,22.52), 0.042 1.66 (0.23,11.79), 0.615 7.10 (0.69,72.70), 0.099 0.449 HR (95% CI), P -value Model 1: Non-adjusted Model 2: Adjusted for age, gender, race/ethnicity Model 3: Adjusted for age, gender, race/ethnicity, education level, PIR, BMI, energy, smoking status, alcohol consumption, hypertension, diabetes a P value for trend was tested according to the statistical significance of the median value for category variables For HUA patients followed up for a total of 58,216 person-years, 1315 all-cause deaths occurred, among which 411 died from CVD and 224 died from cancer. Compared with the reference (< 187.00mg/d), the risks of all-cause mortality among the other three comparison groups (187.00-251.50, 251.50-336.25, and ≥ 336.25mg/d) were 1.02 (95% CI 0.70, 1.49), 0.69 (95% CI 0.46, 1.04), and 0.47 (95% CI 0.26, 0.84), respectively, with a significant trend across quartiles ( P trend = 0.003). However, there was no significant trend for CVD mortality ( P trend = 0.064) and cancer mortality ( P trend = 0.182) (Table 3 ). Table 3 HRs (95% CIs) for mortality according to dietary magnesium intake among participants with hyperuricemia Dietary magnesium intake (mg/d) Quartile 1 Quartile 2 Quartile 3 Quartile 4 P trend a All-cause mortality Number of deaths (%) 477 (28.50) 368 (21.90) 265 (15.80) 205 (12.20) Model 1 1.00 0.81 (0.67,0.99), 0.040 0.58 (0.48,0.71), < 0.001 0.39 (0.31,0.50), < 0.001 < 0.001 HR (95% CI), P -value Model 2 1.00 0.89 (0.72,1.09), 0.248 0.74 (0.61,0.90), 0.002 0.63 (0.48,0.84), 0.001 0.001 HR (95% CI), P -value Model 3 1.00 1.02 (0.70,1.49), 0.924 0.69 (0.46,1.04), 0.078 0.47 (0.26,0.84), 0.011 0.003 HR (95% CI), P -value CVD mortality Number of deaths (%) 141 (8.40) 122 (7.30) 105 (6.30) 43 (2.60) Model 1 1.00 1.08 (0.78,1.51), 0.634 0.40 (0.25,0.64), < 0.001 0.55 (0.33,0.92), 0.023 < 0.001 HR (95% CI), P -value Model 2 1.00 1.17 (0.83,1.64), 0.363 1.27 (0.89,1.82), 0.194 0.58 (0.36,0.93), 0.025 0.026 HR (95% CI), P -value Model 3 1.00 1.47 (0.73,2.96), 0.279 1.61 (0.76,3.42), 0.214 0.45 (0.17,1.22), 0.117 0.064 HR (95% CI), P -value Cancer mortality Number of deaths (%) 85 (5.10) 61 (3.60) 33 (2.00) 45 (2.70) Model 1 1.00 0.49 (0.33,0.73), < 0.001 0.35 (0.20,0.61), < 0.001 0.35 (0.22,0.56), < 0.001 < 0.001 HR (95% CI), P -value Model 2 1.00 0.55 (0.36,0.82), 0.004 0.46 (0.27,0.81), 0.007 0.60 (0.35,1.04), 0.070 0.075 HR (95% CI), P -value Model 3 1.00 0.72 (0.39,1.31), 0.277 0.52 (0.22,1.22), 0.133 0.57 (0.25,1.30), 0.181 0.182 HR (95% CI), P -value Model 1: Non-adjusted Model 2: Adjusted for age, gender, race/ethnicity Model 3: Adjusted for age, gender, race/ethnicity, education level, PIR, BMI, energy, smoking status, alcohol consumption, hypertension, diabetes a P value for trend was tested according to the statistical significance of the median value for category variables The detection of dose–response relationship After full adjustments for potential confounders, linear negative associations of dietary magnesium intake with all-cause mortality were demonstrated for gout (Fig. 2 ) and HUA patients (both P for overall effect 0.05) (Fig. 3 ). Moreover, we observed a nonlinear negative association between magnesium intake and CVD mortality in participants with HUA, with the inflection point of 272.00 mg/d ( P for overall effect < 0.05, P for nonlinear < 0.05). When dietary magnesium intake exceeded 272.00 mg/d, the risk of CVD mortality decreased with the increase of dietary magnesium intake. Stratified analyses and sensitivity analyses In stratification analyses by age, gender, race/ethnicity, hypertension, diabetes and BMI, statistically significant inverse associations of dietary magnesium intake and all-cause mortality were found in the subgroups of non-white and hypertensive patients with gout and HUA (all P trend < 0.05, Supplementary Table 1–2 ). Furthermore, there was statistically significant heterogeneity in the subgroups stratified by hypertension in HUA patients ( P for heterogeneity < 0.05), suggesting the association of dietary magnesium intake and all-cause mortality differed by hypertension. However, no significant interactions were detected in the association between dietary magnesium intake and these stratifying variables for all-cause mortality among gout patients (all P interaction > 0.05). Among participants with HUA, we observed a significant interaction between dietary magnesium intake and hypertension for all-cause mortality ( P interaction < 0.05). In the sensitivity analyses, similar results were demonstrated after excluding participants who died within the first 2 years of follow-up or those aged 80 and older ( Supplementary Table 3–6 ). The protective effect on all-cause mortality remained steady when dietary magnesium intake was categorized into weighted quartiles or quintiles, even the inverse association of dietary magnesium intake with CVD mortality strengthened and reached statistical significance in HUA patients ( Supplementary Table 7–10 ). Consistent results were observed when we further adjusted for survey cycles, dietary intake of vitamins and minerals, cardiometabolic markers, or estimated glomerular filtration rate ( Supplementary Table 11–12 ). Discussion In this prospective cohort study, we examined the association between dietary magnesium intake and mortality of US adults with gout and HUA. After multivariate adjustments, negative linear associations were found between dietary magnesium intake and all-cause mortality among gout and HUA patients. There was also a nonlinear negative association between dietary magnesium intake and CVD mortality in HUA patients. Sensitivity analysis and stratified analysis confirmed the robustness of the results. These findings could provide a new indicator for the evaluation of magnesium in patients with gout and HUA. In this study, we observed that high dietary magnesium intake was associated with decreased risk of all-cause mortality among patients with gout and HUA. The consistent association was also found in a nationally representative sample of 30,899 US adults with a median follow-up of 6.1 years[ 15 ]. Similarly, previous studies based on patients with cancer and chronic disease demonstrated the protective effect of dietary magnesium intake on all-cause mortality[ 16 , 17 , 43 ]. Furthermore, the association between dietary magnesium intake and CVD mortality among gout and HUA patients varied in restricted cubic spline regression. There was a nonlinear inverse relationship between dietary magnesium intake and CVD mortality among HUA patients. It tended to better reflect the true effect of dietary magnesium intake in the development of disease, which has also been reported in studies on the risk of CVD, diabetes, and rheumatoid arthritis[ 44 – 46 ]. However, we did not found statistically significant dose-response relationship in gout patients, which may be due to reduced power caused by insufficient samples. Additionally, we found that the inflection point for dietary magnesium intake was 272 mg/day identified from the nonlinear curve. However, there was still controversy regarding the optimal dietary magnesium intake. Dietary reference intakes (DRIs) suggest that adult dietary magnesium intake is 420 mg/day for men and 320 mg/day for women[ 47 ]. A meta-analysis of 313,041 individuals reported the optimal effect of dietary magnesium intake at 250 mg/d on ischemic heart disease mortality[ 11 ]. A prospective Spanish study showed an obvious lower risk of CVD mortality with dietary magnesium intake of 442 mg/day[ 48 ]. This inconsistency may partly account for the differences in target population, sample size, disease types, and underlying health conditions. The role of HUA in increasing the risk of death has been established[ 49 ], and dietary magnesium intake still needs to be further confirmed in clinical trials to guide a more reasonable intake. Our findings can help us reduce the risk of mortality in patients with gout and HUA by increasing dietary magnesium intake in clinical practice, with practical public health implications. Considering that age, gender, race/ethnicity, hypertension, diabetes and BMI may be confounding factors for the association between dietary magnesium intake and all-cause mortality, stratified analyses were performed to test the robustness of the results. We found consistent inverse associations in the subgroup analyses, but the associations of several subgroups did not reach statistical significance. According to the heterogeneity test, the lack of significance in subgroups stratified by age, gender, race/ethnicity, diabetes and BMI, may due to the limited sample size rather than true heterogeneity within the strata. However, when the analysis was stratified by hypertension, we observed statistically significant heterogeneity and interaction between dietary magnesium intake and hypertension on all-cause mortality among HUA patients. To be specific, high dietary magnesium intake had a greater advantage in reducing all-cause mortality in hypertension patients, consistent with a previous study[ 50 ]. Hypertension is considered as a major cause of all-cause and CVD mortality worldwide[ 51 ], and can cause damage to a series of target organs, such as heart failure, renal insufficiency and atherosclerosis[ 52 ]. The effectiveness of high-magnesium diets in reducing blood pressure has been demonstrated[ 53 , 54 ], and hypertension patients are often recommended to consume more dietary magnesium, with greater benefits in reducing all-cause mortality. Therefore, in the treatment of HUA patients, dietary magnesium intake should be reasonably supplemented in clinical health management, especially in patients with hypertension. The protective effect of high dietary magnesium intake on all-cause mortality in patients with gout and HUA may be related to inflammatory mechanisms. Previous studies have shown high dietary magnesium intake is inversely associated with low C-reactive protein (CRP)[ 55 – 59 ], which is considered as a marker of inflammation and cytokine release[ 60 ]. A systematic review and meta-analysis of seven cross-sectional studies also confirmed the association between magnesium and CRP[ 61 ]. High dietary magnesium intake can mediate a series of chain reactions to inhibit CRP synthesis and improve inflammatory response[ 61 ]. Moreover, accumulating evidence has showed that magnesium deficiency can interfere with insulin receptor function, promoting insulin resistance[ 62 , 63 ], which leads to increased SUA levels[ 64 ]. Furthermore, the kidney is an important regulating organ of circulating uric acid levels, responsible for 60–70% of systemic uric acid excretion[ 65 , 66 ]. Magnesium can protect the kidney through a variety of molecular and cellular effects to maintain normal excretion of uric acid[ 67 ], and is considered to have a potential role in increasing uric acid excretion[ 68 ]. The inverse relationship between magnesium and uric acid levels has been demonstrated in patients with diabetic retinopathy[ 69 ]. A cross-sectional study of 26,796 US adults also showed that increased magnesium intake was associated with a reduced risk of HUA[ 68 ]. Appropriate increase of dietary magnesium intake may slow down the development or deterioration of gout and HUA by reducing uric acid, and reduce the risk of mortality. For different types of CVD, one of the important risk factors is insufficient intake of magnesium, and the prevalence of magnesium deficiency in CVD patients is much higher than in other patients[ 70 ]. This may be related to magnesium's ability to enhance endothelium-dependent vasodilation, improve blood pressure, and regulate physiological mechanisms such as arrhythmia, inflammatory response and platelet aggregation[ 71 – 73 ]. Based on recommendations of DRIs, more than half (56%) of the US population got less than the required amount of magnesium from food in 2001–2002, and the number declined to forty-eight percent in 2005–2006[ 47 , 74 ]. Various dietary surveys conducted since 2009 indicated that about 20% of the population consistently consume less than the recommended amount of magnesium[ 75 ]. Although the dietary magnesium intake has increased, there is still a gap with the reference level. Therefore, concern should be raised about adequate dietary magnesium intake. In clinical practice, all-cause and CVD mortality of gout and HUA patients can be reduced by increasing dietary magnesium intake, which is useful for the establishment of clinical policies and guidelines. Strengths of our study included the use of a nationally representative sample of US adults, adjustment for many potential confounding factors, and a long follow-up period, which enhanced the reliability of the conclusions. Nonetheless, there were some limitations to our study. First, the role of confounding by genetic susceptibility, psychosocial stress, or other variables could not be excluded. Second, the diagnosis of gout was based on a simple self-reported question, "Doctors ever told you had gout?". However, we did not have further medical records of the participants, which probably led to bias in diagnosis determination. Third, the number of deaths from CVD or cancer included in this study was relatively small, and the statistical power to analyze the association between dietary magnesium and cause-specific mortality might be insufficient. Fourth, the NHANES were all US residents, thus this conclusion probably cannot be applied to ethnically diverse populations. Conclusion High dietary magnesium intake was associated with decreased risks of all-cause mortality in US adult patients with gout and HUA, and had a nonlinear inverse association with CVD mortality in HUA patients, with the inflection point of 272mg/d. These findings could provide dietary assistance in preventing premature death in patients with gout and HUA. Abbreviations HUA: Hyperuricemia; CVD: cardiovascular disease; GBD: Global Burden of Disease; DALYs: disability-adjusted life-years; US: United States; T2D: type 2 diabetes; SUA: serum uric acid; NHANES: National Health and Nutrition Examination Survey; NCHS: National Center for Health Statistics; MEC: Mobile examination center; PIR: Poverty income ratio; BMI: Body mass index; SD: standard deviation; HDL: high-density lipoprotein; DRIs: Dietary reference intakes; CRP: C-reactive protein. Declarations Ethics approval and consent to participate The NHANES was reviewed and approved through the NCHS Research Ethics Review Board, and each participant provided informed consent (https://www.cdc.gov/nchs/nhanes/irba98.htm). Additionally, all NHANES data released by the NCHS is de-identified, and remained anonymous during data analysis. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are publicly available and accessible. Competing interests The authors declare that they have no competing interests. Funding This work was supported by grants from National Natural Science Foundation of China (82204843), Zhejiang Province Traditional Chinese Medicine Science and Technology Plan Project (2023ZR084), Science and Technology Project of Zhejiang Provincial Health Commission (2023KY845). Authors’ contributions Xuanni Lu: Conceptualization, Methodology, Formal analysis, Writing-original draft, Visualization. Anqi Wang: Formal analysis, Data curation, Writing-original draft. Ke Liu: Formal analysis, Data curation, Writing-original draft. Ying Chen: Conceptualization. Wei-Wei Chen, Project administration. Ying-Ying Mao: Supervision, Project administration, Funding acquisition. 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Rondanelli M, Faliva MA, Tartara A, Gasparri C, Perna S, Infantino V, Riva A, Petrangolini G, Peroni G: An update on magnesium and bone health. BIOMETALS 2021;34:715-736. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4430372","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":308136818,"identity":"1d10f404-0bac-4189-9323-3f9f197b706a","order_by":0,"name":"Xuanni Lu","email":"","orcid":"","institution":"Zhejiang Chinese Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuanni","middleName":"","lastName":"Lu","suffix":""},{"id":308136819,"identity":"62e40079-ed48-47ad-a6af-acf7894b2b8b","order_by":1,"name":"Anqi Wang","email":"","orcid":"","institution":"Zhejiang Chinese Medical 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20:11:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":214020,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between dietary magnesium intake and all‐cause (A), CVD (B) and cancer mortality (C) in gout patients.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4430372/v1/dfb9a6e13fb4cfd3c025cfa7.jpg"},{"id":58151449,"identity":"ba44ef45-b4ff-44e8-a7d8-d1885f4e26d2","added_by":"auto","created_at":"2024-06-11 20:11:13","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":231151,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between dietary magnesium intake and all‐cause (A), CVD (B) and cancer mortality (C) in hyperuricemia patients.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4430372/v1/4ae30682e8834c389151bf35.jpg"},{"id":58568129,"identity":"e64cf230-b8c1-492a-b81a-af81301363cc","added_by":"auto","created_at":"2024-06-18 10:26:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1974915,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4430372/v1/da5dca45-6f7e-4eb9-b738-5a05e45bba29.pdf"},{"id":58151451,"identity":"10ef2349-ba11-4013-8d0e-cc4e1beb26cd","added_by":"auto","created_at":"2024-06-11 20:11:13","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":53548,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4430372/v1/235d1570fca73d33c796145e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations of dietary magnesium intake with all-cause and cause-specific mortality among individuals with gout and hyperuricemia","fulltext":[{"header":"Background","content":"\u003cp\u003eGout is a common disease caused by the deposition of sodium urate crystals in both articular and nonarticular structures and is manifested by intermittent attacks of severe painful arthritis[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Recent advances in epidemiological studies indicate gout is the most prevalent inflammatory arthritis[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and even the incidence and prevalence are increasing worldwide[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Existing studies show that gout is associated with increased all-cause mortality and cause-specific mortality related to cardiovascular disease (CVD), infections, and digestive diseases[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The standardized all-cause mortality is 2.21 times higher in gout patients than general population[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The 2017 Global Burden of Disease (GBD) study estimates that disability-adjusted life-years (DALYs) of gout are 952,931 in men and 332,022 in women, respectively[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, urgent measures are needed to reduce the global burden of gout.\u003c/p\u003e \u003cp\u003eThe principal etiology of hyperuricemia (HUA) in gout has been well-established[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Nutrient intake and dietary patterns are reported to be closely related with the risk of gout and HUA[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Magnesium is an essential mineral and cofactor involved in many cellular reactions, such as ion transport, signal transduction, cell proliferation, and energy metabolism[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, 79% of adults in United States (US) do not conform to the recommended dietary magnesium intake[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Epidemiological studies have shown that low magnesium intake increases the risk of chronic diseases, including CVD, hypertension, stroke, type 2 diabetes (T2D)[\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], thus exploration of reasonable dietary magnesium intake and provision of reasonable dietary guidelines for the public can effectively reduce the incidence and prevalence of related diseases caused by magnesium deficiency. High dietary magnesium intake has been found to reduce the risk of death among the general population, as well as patients with diabetes and cancer[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Although previous studies have suggested that dietary magnesium intake is negatively associated with mortality[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], the association between dietary magnesium intake and mortality in patients with gout and HUA has not been reported, and the dose-response relationship has not been summarized, which need further research to explore.\u003c/p\u003e \u003cp\u003eTo fill these knowledge gaps, we prospectively investigated the associations and exact dose-relationship of dietary magnesium intake with all-cause and cause-specific mortality in US adults with gout and HUA.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source and study population\u003c/h2\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) is a nationally representative survey reflecting the health and nutritional status of the non-institutionalized citizens in the US with a multistage probability sampling design[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Details of survey design and data files is publicly available at NHANES website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). NHANES has been approved by the Research Ethics Review Board of National Center for Health Statistics (NCHS), and informed written consent was obtained from all participants[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we obtained data from six cycles of NHANES during 2007\u0026ndash;2018 on gout patients, who were identified from self-reported personal interview data on a variety of health conditions. Initially, there were 1656 gout patients aged over 20 years. After excluding 185 patients with missing dietary magnesium intake data, 298 patients with cancer at baseline, and 2 patients with missing all-cause mortality data, 1171 gout patients were ultimately included (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor HUA patients, information was obtained from nine cycles of NHANES during 2001\u0026ndash;2018. HUA was defined as the serum uric acid (SUA) levels\u0026thinsp;\u0026ge;\u0026thinsp;420 \u0026micro;mol/L in men and \u0026ge;\u0026thinsp;360 \u0026micro;mol/L in women[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The SUA concentrations were measured by a timed endpoint method, which were performed with Beckman Synchron LX20 in 2001\u0026ndash;2007, Beckman Coulter UniCel DxC 800 Synchron in 2008\u0026ndash;2014, both Beckman Coulter UniCel DxC 800 Synchron and Beckman Coulter UniCel DxC 660i Synchron in 2015\u0026ndash;2018. A total of 8167 HUA patients aged over 20 years were identified. Of these, we eliminated 503 patients with missing dietary magnesium intake data, 946 patients with cancer at baseline, 11 patients without all-cause mortality data, thus 6707 HUA patients were involved in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCollection of dietary intake\u003c/h2\u003e \u003cp\u003eDietary intake data were obtained from 24-hour dietary recall interviews. The 2001\u0026ndash;2002 NHANES cycle included only one 24-hour dietary recall, while the 2003\u0026ndash;2018 NHANES cycles included an in-person interview in the mobile examination center (MEC) and a follow-up interview collected by telephone a few days later. When the second recall was not available, data from the first recall were used, otherwise we estimated the average dietary intake from two recall periods. Details of the interview process have been described in the dietary interview section of the NHANES website[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. There were 121 gout patients and 1421 HUA patients with only the first 24-hour dietary recall interview, and 1050 gout patients and 5286 patients with HUA had two dietary recall interviews.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAscertainment of mortality\u003c/h2\u003e \u003cp\u003eWe determined mortality status based on the 2001\u0026ndash;2018 NHANES public-use linked mortality file, which has linked data from NCHS with death certificate records from the National Death Index (NDI). Cause-specific deaths were identified according to the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10): diseases of the heart (I00-I09, I11, I13, I20-I51) and malignancies (C00-C97). The follow-up time was from interview date to the date of death or end of follow-up (December 31, 2019).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of covariates\u003c/h2\u003e \u003cp\u003eSociodemographic characteristics including age, gender, race/ethnicity, education level, family poverty income ratio (PIR), smoking status, alcohol consumption and disease status (hypertension, diabetes) were obtained from household interviews through the standardized questionnaires. Body weight, height and waist circumference were measured by physical examinations in the MEC. Body mass index (BMI) was calculated as the ratio of weight (kg) to the square of height (m\u003csup\u003e2\u003c/sup\u003e). Race/ethnicity was classified as Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, other Hispanic or others. Education levels were categorized as less than high school, high school or equivalent, college or above. PIR was defined as ratio of family income to poverty threshold. Alcohol consumption was defined as average daily alcohol consumption over the past 12 months, and classified by whether \u0026ge;\u0026thinsp;4 drinks/day. Smoking status was classified as never (smoking less than 100 cigarettes in lifetime), former (smoking more than 100 cigarettes in lifetime and now not smoking at all), and current (smoking more than 100 cigarettes in lifetime and now smoking some days or every day)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eDue to the complex survey designs of NHANES, all analyses accounted for dietary sampling weights, clustering, and stratification[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and discontinuous variables were presented as percentage (%). Analysis of variance (ANOVA) test for continuous variables and chi-square (χ2) test for categorical variables were used to calculate the differences across the four groups of dietary magnesium intake. Cox regression models were performed to investigate the association between dietary magnesium intake and mortality. Model 1 was crude, Model 2 was adjusted for age, gender, race/ethnicity, and Model 3 was further adjusted for education level, PIR, BMI, energy, smoking status, alcohol consumption, hypertension and diabetes. The linear trend was tested for statistical significance according to the median of dietary magnesium intake in four groups. Furthermore, restricted cubic spline models fitted for Cox proportional hazards models were conducted to investigate the dose-response relationship between dietary magnesium intake and mortality among gout and HUA patients. In this study, we used restricted cubic spline models with three knots, corresponding to the 10th, 50th, and 90th percentiles[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003csup\u003e,\u003c/sup\u003e [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. If the relationship was nonlinear, we conducted a recursion algorithm to calculate the inflection point of the association between dietary magnesium intake and mortality. Stratified analyses were performed based on age (\u0026lt;\u0026thinsp;60 or \u0026ge;\u0026thinsp;60 years old), gender (male, female), race/ethnicity (Whites or non-Whites), hypertension (yes or no), diabetes (yes or no) and BMI (\u0026lt;\u0026thinsp;30.00kg/m\u003csup\u003e2\u003c/sup\u003e or \u0026ge;\u0026thinsp;30.00kg/m\u003csup\u003e2\u003c/sup\u003e). Cochran\u0026rsquo;s Q test was used to assess the heterogeneity between different strata, and \u003cem\u003eP\u003c/em\u003e value of less than 0.05 indicated the presence of potential heterogeneity. The significance of the interactions were also tested by the product terms between dietary magnesium intake and stratified variables.\u003c/p\u003e \u003cp\u003eTo assess the robustness of our findings, we conducted a series of sensitivity analyses. First, given the potential reverse causation bias, participants who died within the first 2 years of follow-up were excluded. Second, participants aged 80 and older were excluded to reduce the possibility of survival bias among the extremely elderly. Third, repeated analyses were conducted based on weighted quartiles of dietary magnesium intake[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Forth, we performed the main analyses according to the quintiles of dietary magnesium intake[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Fifth, NHANES survey cycles were additionally adjusted, considering the different measurement methods of SUA [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].Sixth, given that some dietary factors might influence the association of interest[\u003cspan additionalcitationids=\"CR35 CR36 CR37 CR38\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], intake of total folate, fiber, sodium, potassium, calcium and phosphorus were further adjusted. Seventh, to control the confounding effect of cardiometabolic markers[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], total cholesterol, high-density lipoprotein (HDL), glucose and triglycerides were further adjusted. Eighth, as renal dysfunction may affect the absorption and use of magnesium[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], kidney function assessed by estimated glomerular filtration rate (calculated by the improved modification of diet in renal disease formula) was further adjusted.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of study participants\u003c/h2\u003e \u003cp\u003eAccording to the quartile of dietary magnesium intake, the demographics and characteristics of the study population are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In this study, the analysis consisted of 1171 gout patients (mean age: 62.09\u0026thinsp;\u0026plusmn;\u0026thinsp;13.10 years; 29.90% females) and 6707 HUA patients (mean age: 53.37\u0026thinsp;\u0026plusmn;\u0026thinsp;17.73 years; 43.40% females). Moreover, gout and HUA patients with higher dietary magnesium intake tended to be male, younger, non-Hispanic White, had higher education levels, family income and daily energy intake, were less likely to have diabetes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003ePatients with gout\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e \u003cp\u003ePatients with hyperuricemia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eDietary magnesium intake (mg/d)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e \u003cp\u003eDietary magnesium intake (mg/d)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuartile 1 \u003c/p\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;193.25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuartile 2 \u003c/p\u003e \u003cp\u003e(193.25\u0026ndash;261.50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQuartile 3 \u003c/p\u003e \u003cp\u003e(261.50-344.50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQuartile 4 \u003c/p\u003e \u003cp\u003e(\u0026ge;\u0026thinsp;344.50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQuartile 1 \u003c/p\u003e \u003cp\u003e(\u0026lt;\u0026thinsp;187.00)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eQuartile 2 \u003c/p\u003e \u003cp\u003e(187.00-251.50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eQuartile 3 \u003c/p\u003e \u003cp\u003e(251.50-336.25)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eQuartile 4 \u003c/p\u003e \u003cp\u003e(\u0026ge;\u0026thinsp;336.25)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.09\u0026thinsp;\u0026plusmn;\u0026thinsp;13.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.87\u0026thinsp;\u0026plusmn;\u0026thinsp;12.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.02\u0026thinsp;\u0026plusmn;\u0026thinsp;12.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.44\u0026thinsp;\u0026plusmn;\u0026thinsp;13.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.07\u0026thinsp;\u0026plusmn;\u0026thinsp;12.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e53.37\u0026thinsp;\u0026plusmn;\u0026thinsp;17.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e56.80\u0026thinsp;\u0026plusmn;\u0026thinsp;18.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55.14\u0026thinsp;\u0026plusmn;\u0026thinsp;18.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e52.50\u0026thinsp;\u0026plusmn;\u0026thinsp;17.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e49.06\u0026thinsp;\u0026plusmn;\u0026thinsp;16.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e821 (70.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162 (55.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e190 (65.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e213 (72.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e256 (87.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3796 (56.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e706 (42.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e814 (48.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e999 (59.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1277 (76.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e350 (29.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131 (44.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99 (34.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82 (27.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38 (12.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2911 (43.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e969 (57.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e863 (51.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e679 (40.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e400 (23.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace/ethnicity (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (7.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (5.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (6.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (10.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25 (8.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e855 (12.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e167 (10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e190 (11.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e237 (14.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e261 (15.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (7.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (5.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (8.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (8.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21 (7.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e457 (6.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e113 (6.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e108 (6.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e129 (7.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e107 (6.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e515 (44.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 (36.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135 (46.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e131 (44.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e142 (48.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2996 (44.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e648 (38.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e761 (45.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e786 (46.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e801 (47.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e341 (29.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132 (45.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85 (29.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63 (21.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61 (20.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1749 (26.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e639 (38.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e479 (28.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e349 (20.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e282 (16.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther race (including multi-racial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139 (11.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (8.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (8.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45 (15.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45 (15.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e650 (9.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e108 (6.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e139 (8.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e177 (10.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e226 (13.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e307 (26.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (35.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91 (31.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70 (23.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42 (14.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1699 (25.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e565 (33.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e430 (25.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e378 (22.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e326 (19.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e297 (25.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78 (27.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69 (23.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62 (21.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1692 (25.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e464 (27.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e436 (26.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e416 (24.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e376 (22.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e567 (48.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (34.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120 (41.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e156 (52.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e190 (64.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3308 (49.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e640 (38.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e810 (48.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e883 (52.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e975 (58.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e496 (42.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126 (43.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118 (40.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e125 (42.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e127 (43.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3430 (51.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e838 (50.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e885 (52.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e853 (50.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e854 (51.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e475 (40.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111 (37.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121 (41.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122 (41.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e121 (41.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1995 (29.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e480 (28.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e500 (29.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e512 (30.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e503 (30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200 (17.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (19.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (17.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48 (16.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46 (15.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1278 (19.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e355 (21.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e291 (17.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e313 (18.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e319 (19.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcoholic\u0026thinsp;\u0026ge;\u0026thinsp;4 drinks/day (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156 (23.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (20.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (20.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (21.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61 (29.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1164 (27.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e207 (24.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e226 (22.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e312 (27.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e419 (34.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e513 (76.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (79.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127 (79.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138 (78.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e149 (71.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3021 (72.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e649 (75.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e760 (77.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e806 (72.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e806 (65.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily poverty income ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWaist circumference (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109.31\u0026thinsp;\u0026plusmn;\u0026thinsp;16.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110.15\u0026thinsp;\u0026plusmn;\u0026thinsp;17.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109.69\u0026thinsp;\u0026plusmn;\u0026thinsp;17.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108.62\u0026thinsp;\u0026plusmn;\u0026thinsp;16.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e108.86\u0026thinsp;\u0026plusmn;\u0026thinsp;15.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e107.54\u0026thinsp;\u0026plusmn;\u0026thinsp;16.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e107.40\u0026thinsp;\u0026plusmn;\u0026thinsp;16.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e108.20\u0026thinsp;\u0026plusmn;\u0026thinsp;15.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e107.89\u0026thinsp;\u0026plusmn;\u0026thinsp;16.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e106.71\u0026thinsp;\u0026plusmn;\u0026thinsp;16.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.11\u0026thinsp;\u0026plusmn;\u0026thinsp;7.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.70\u0026thinsp;\u0026plusmn;\u0026thinsp;8.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.59\u0026thinsp;\u0026plusmn;\u0026thinsp;8.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.88\u0026thinsp;\u0026plusmn;\u0026thinsp;7.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.29\u0026thinsp;\u0026plusmn;\u0026thinsp;6.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32.37\u0026thinsp;\u0026plusmn;\u0026thinsp;7.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.47\u0026thinsp;\u0026plusmn;\u0026thinsp;7.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e32.90\u0026thinsp;\u0026plusmn;\u0026thinsp;7.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e32.41\u0026thinsp;\u0026plusmn;\u0026thinsp;7.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e31.69\u0026thinsp;\u0026plusmn;\u0026thinsp;7.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDaily energy intake (kcal/d)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1945.44\u0026thinsp;\u0026plusmn;\u0026thinsp;832.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1226.55\u0026thinsp;\u0026plusmn;\u0026thinsp;453.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1723.91\u0026thinsp;\u0026plusmn;\u0026thinsp;477.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2085.14\u0026thinsp;\u0026plusmn;\u0026thinsp;562.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2739.45\u0026thinsp;\u0026plusmn;\u0026thinsp;897.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2007.04\u0026thinsp;\u0026plusmn;\u0026thinsp;875.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1283.81\u0026thinsp;\u0026plusmn;\u0026thinsp;480.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1741.22\u0026thinsp;\u0026plusmn;\u0026thinsp;514.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2143.73\u0026thinsp;\u0026plusmn;\u0026thinsp;608.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2858.48\u0026thinsp;\u0026plusmn;\u0026thinsp;927.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e842 (71.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e229 (78.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e215 (74.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e195 (66.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e203 (69.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3583 (53.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1021 (61.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e942 (56.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e868 (51.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e752 (45.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e373 (31.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116 (39.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (32.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94 (31.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70 (23.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1130 (16.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e338 (20.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e328 (19.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e270 (16.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e194 (11.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDirect HDL-cholesterol (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.33\u0026thinsp;\u0026plusmn;\u0026thinsp;15.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.70\u0026thinsp;\u0026plusmn;\u0026thinsp;16.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.31\u0026thinsp;\u0026plusmn;\u0026thinsp;14.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.25\u0026thinsp;\u0026plusmn;\u0026thinsp;13.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.01\u0026thinsp;\u0026plusmn;\u0026thinsp;16.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48.87\u0026thinsp;\u0026plusmn;\u0026thinsp;15.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e50.13\u0026thinsp;\u0026plusmn;\u0026thinsp;16.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e48.93\u0026thinsp;\u0026plusmn;\u0026thinsp;14.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e48.36\u0026thinsp;\u0026plusmn;\u0026thinsp;14.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e48.06\u0026thinsp;\u0026plusmn;\u0026thinsp;14.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal cholesterol (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187.34\u0026thinsp;\u0026plusmn;\u0026thinsp;43.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e185.88\u0026thinsp;\u0026plusmn;\u0026thinsp;45.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e186.92\u0026thinsp;\u0026plusmn;\u0026thinsp;45.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e186.45\u0026thinsp;\u0026plusmn;\u0026thinsp;41.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e190.03\u0026thinsp;\u0026plusmn;\u0026thinsp;43.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e200.16\u0026thinsp;\u0026plusmn;\u0026thinsp;44.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e199.28\u0026thinsp;\u0026plusmn;\u0026thinsp;43.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e199.20\u0026thinsp;\u0026plusmn;\u0026thinsp;45.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e201.97\u0026thinsp;\u0026plusmn;\u0026thinsp;44.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e200.17\u0026thinsp;\u0026plusmn;\u0026thinsp;42.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlycohemoglobin (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlucose (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117.96\u0026thinsp;\u0026plusmn;\u0026thinsp;52.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122.41\u0026thinsp;\u0026plusmn;\u0026thinsp;54.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118.12\u0026thinsp;\u0026plusmn;\u0026thinsp;50.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e121.41\u0026thinsp;\u0026plusmn;\u0026thinsp;63.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110.13\u0026thinsp;\u0026plusmn;\u0026thinsp;39.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e105.58\u0026thinsp;\u0026plusmn;\u0026thinsp;34.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e107.55\u0026thinsp;\u0026plusmn;\u0026thinsp;39.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e107.97\u0026thinsp;\u0026plusmn;\u0026thinsp;35.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e104.71\u0026thinsp;\u0026plusmn;\u0026thinsp;33.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e102.09\u0026thinsp;\u0026plusmn;\u0026thinsp;29.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriglycerides (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190.87\u0026thinsp;\u0026plusmn;\u0026thinsp;150.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181.87\u0026thinsp;\u0026plusmn;\u0026thinsp;147.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e192.29\u0026thinsp;\u0026plusmn;\u0026thinsp;121.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e190.19\u0026thinsp;\u0026plusmn;\u0026thinsp;139.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e198.71\u0026thinsp;\u0026plusmn;\u0026thinsp;185.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e184.75\u0026thinsp;\u0026plusmn;\u0026thinsp;150.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e165.37\u0026thinsp;\u0026plusmn;\u0026thinsp;104.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e186.43\u0026thinsp;\u0026plusmn;\u0026thinsp;172.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e193.58\u0026thinsp;\u0026plusmn;\u0026thinsp;156.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e193.58\u0026thinsp;\u0026plusmn;\u0026thinsp;158.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eMean (S.D.) for continuous variables and numbers (percentages) for discontinuous variable.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eContinuous variables were compared using analysis of variance (ANOVA) test and categorical variables were compared using the chi-square (χ2) test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eAll estimates accounted for complex survey designs.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eRelationships of dietary magnesium intake with mortality\u003c/h2\u003e \u003cp\u003eDuring 7081 person-years of follow-up, there were 257 documented deaths in gout patients, including 74 CVD deaths and 48 cancer deaths. After multivariate adjustments, the hazard ratios (HRs) and 95% confidence intervals (CIs) of all-cause mortality for Q2 (193.25\u0026ndash;261.50 mg/d), Q3 (261.50-344.50 mg/d) and Q4 (\u0026ge;\u0026thinsp;344.50 mg/d) were 1.44 (95% CI 0.69, 3.00), 0.42 (95% CI 0.16, 1.11), and 0.26 (95% CI 0.08, 0.88) when compared with Q1 (\u0026lt;\u0026thinsp;193.25 mg/d). The trend test revealed statistical significance (\u003cem\u003eP\u003c/em\u003e trend\u0026thinsp;=\u0026thinsp;0.003). In terms of cause-specific mortality, no similar trend was found for CVD mortality (\u003cem\u003eP\u003c/em\u003e trend\u0026thinsp;=\u0026thinsp;0.119) and cancer mortality (\u003cem\u003eP\u003c/em\u003e trend\u0026thinsp;=\u0026thinsp;0.449) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHRs (95% CIs) for mortality according to dietary magnesium intake among participants with gout\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eDietary magnesium intake (mg/d)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuartile 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuartile 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuartile 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQuartile 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u003csub\u003etrend\u003c/sub\u003e \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of deaths (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86 (29.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80 (27.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58 (19.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33 (11.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.84 (0.54,1.29), 0.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.45 (0.28,0.72), 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.19 (0.11,0.32), \u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.88 (0.56,1.38), 0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.55 (0.32,0.93), 0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.27 (0.15,0.48), \u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.44 (0.69,3.00), 0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.42 (0.16,1.11), 0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.26 (0.08,0.88), 0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCVD mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of deaths (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27 (9.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17 (5.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7 (2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.67 (0.33,1.37), 0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.32 (0.16,0.66), 0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.14 (0.05,0.40), \u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.79 (0.38,1.63), 0.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.42 (0.18,0.97), 0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.22 (0.08,0.63), 0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.62 (0.20,1.93), 0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.19 (0.02,1.76), 0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.11 (0.01,1.88), 0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of deaths (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (5.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (4.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10 (3.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10 (3.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.82(0.25,2.64), 0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.75 (0.20,2.84), 0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.52 (0.21,1.26), 0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.80 (0.26,2.48), 0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.83 (0.25,2.83), 0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.71 (0.25,2.03), 0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4.88 (1.06,22.52), 0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.66 (0.23,11.79), 0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e7.10 (0.69,72.70), 0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 1: Non-adjusted\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 2: Adjusted for age, gender, race/ethnicity\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 3: Adjusted for age, gender, race/ethnicity, education level, PIR, BMI, energy, smoking status, alcohol consumption, hypertension, diabetes\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e value for trend was tested according to the statistical significance of the median value for category variables\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor HUA patients followed up for a total of 58,216 person-years, 1315 all-cause deaths occurred, among which 411 died from CVD and 224 died from cancer. Compared with the reference (\u0026lt;\u0026thinsp;187.00mg/d), the risks of all-cause mortality among the other three comparison groups (187.00-251.50, 251.50-336.25, and \u0026ge;\u0026thinsp;336.25mg/d) were 1.02 (95% CI 0.70, 1.49), 0.69 (95% CI 0.46, 1.04), and 0.47 (95% CI 0.26, 0.84), respectively, with a significant trend across quartiles (\u003cem\u003eP\u003c/em\u003e trend\u0026thinsp;=\u0026thinsp;0.003). However, there was no significant trend for CVD mortality (\u003cem\u003eP\u003c/em\u003e trend\u0026thinsp;=\u0026thinsp;0.064) and cancer mortality (\u003cem\u003eP\u003c/em\u003e trend\u0026thinsp;=\u0026thinsp;0.182) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHRs (95% CIs) for mortality according to dietary magnesium intake among participants with hyperuricemia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eDietary magnesium intake (mg/d)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuartile 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuartile 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuartile 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQuartile 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u003csub\u003etrend\u003c/sub\u003e \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause mortality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of deaths (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e477 (28.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e368 (21.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e265 (15.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e205 (12.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.81 (0.67,0.99), 0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.58 (0.48,0.71), \u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.39 (0.31,0.50), \u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.89 (0.72,1.09), 0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.74 (0.61,0.90), 0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.63 (0.48,0.84), 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.02 (0.70,1.49), 0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.69 (0.46,1.04), 0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.47 (0.26,0.84), 0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCVD mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of deaths (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e141 (8.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122 (7.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105 (6.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43 (2.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.08 (0.78,1.51), 0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.40 (0.25,0.64), \u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.55 (0.33,0.92), 0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.17 (0.83,1.64), 0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.27 (0.89,1.82), 0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.58 (0.36,0.93), 0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.47 (0.73,2.96), 0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.61 (0.76,3.42), 0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.45 (0.17,1.22), 0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of deaths (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85 (5.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61 (3.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33 (2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45 (2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.49 (0.33,0.73), \u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.35 (0.20,0.61), \u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.35 (0.22,0.56), \u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.55 (0.36,0.82), 0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.46 (0.27,0.81), 0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.60 (0.35,1.04), 0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.72 (0.39,1.31), 0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.52 (0.22,1.22), 0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.57 (0.25,1.30), 0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (95% CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 1: Non-adjusted\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 2: Adjusted for age, gender, race/ethnicity\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 3: Adjusted for age, gender, race/ethnicity, education level, PIR, BMI, energy, smoking status, alcohol consumption, hypertension, diabetes\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e value for trend was tested according to the statistical significance of the median value for category variables\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe detection of dose\u0026ndash;response relationship\u003c/h2\u003e \u003cp\u003eAfter full adjustments for potential confounders, linear negative associations of dietary magnesium intake with all-cause mortality were demonstrated for gout (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e and HUA patients (both \u003cem\u003eP\u003c/em\u003e for overall effect\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cem\u003eP\u003c/em\u003e for nonlinear\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Moreover, we observed a nonlinear negative association between magnesium intake and CVD mortality in participants with HUA, with the inflection point of 272.00 mg/d (\u003cem\u003eP\u003c/em\u003e for overall effect\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cem\u003eP\u003c/em\u003e for nonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.05). When dietary magnesium intake exceeded 272.00 mg/d, the risk of CVD mortality decreased with the increase of dietary magnesium intake.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStratified analyses and sensitivity analyses\u003c/h2\u003e \u003cp\u003eIn stratification analyses by age, gender, race/ethnicity, hypertension, diabetes and BMI, statistically significant inverse associations of dietary magnesium intake and all-cause mortality were found in the subgroups of non-white and hypertensive patients with gout and HUA (all \u003cem\u003eP\u003c/em\u003e trend\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cb\u003eSupplementary Table\u0026nbsp;1\u0026ndash;2\u003c/b\u003e). Furthermore, there was statistically significant heterogeneity in the subgroups stratified by hypertension in HUA patients (\u003cem\u003eP\u003c/em\u003e for heterogeneity\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting the association of dietary magnesium intake and all-cause mortality differed by hypertension. However, no significant interactions were detected in the association between dietary magnesium intake and these stratifying variables for all-cause mortality among gout patients (all \u003cem\u003eP\u003c/em\u003e interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Among participants with HUA, we observed a significant interaction between dietary magnesium intake and hypertension for all-cause mortality (\u003cem\u003eP\u003c/em\u003e interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eIn the sensitivity analyses, similar results were demonstrated after excluding participants who died within the first 2 years of follow-up or those aged 80 and older (\u003cb\u003eSupplementary Table\u0026nbsp;3\u0026ndash;6\u003c/b\u003e). The protective effect on all-cause mortality remained steady when dietary magnesium intake was categorized into weighted quartiles or quintiles, even the inverse association of dietary magnesium intake with CVD mortality strengthened and reached statistical significance in HUA patients (\u003cb\u003eSupplementary Table\u0026nbsp;7\u0026ndash;10\u003c/b\u003e). Consistent results were observed when we further adjusted for survey cycles, dietary intake of vitamins and minerals, cardiometabolic markers, or estimated glomerular filtration rate (\u003cb\u003eSupplementary Table\u0026nbsp;11\u0026ndash;12\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this prospective cohort study, we examined the association between dietary magnesium intake and mortality of US adults with gout and HUA. After multivariate adjustments, negative linear associations were found between dietary magnesium intake and all-cause mortality among gout and HUA patients. There was also a nonlinear negative association between dietary magnesium intake and CVD mortality in HUA patients. Sensitivity analysis and stratified analysis confirmed the robustness of the results. These findings could provide a new indicator for the evaluation of magnesium in patients with gout and HUA.\u003c/p\u003e \u003cp\u003eIn this study, we observed that high dietary magnesium intake was associated with decreased risk of all-cause mortality among patients with gout and HUA. The consistent association was also found in a nationally representative sample of 30,899 US adults with a median follow-up of 6.1 years[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Similarly, previous studies based on patients with cancer and chronic disease demonstrated the protective effect of dietary magnesium intake on all-cause mortality[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Furthermore, the association between dietary magnesium intake and CVD mortality among gout and HUA patients varied in restricted cubic spline regression. There was a nonlinear inverse relationship between dietary magnesium intake and CVD mortality among HUA patients. It tended to better reflect the true effect of dietary magnesium intake in the development of disease, which has also been reported in studies on the risk of CVD, diabetes, and rheumatoid arthritis[\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. However, we did not found statistically significant dose-response relationship in gout patients, which may be due to reduced power caused by insufficient samples.\u003c/p\u003e \u003cp\u003eAdditionally, we found that the inflection point for dietary magnesium intake was 272 mg/day identified from the nonlinear curve. However, there was still controversy regarding the optimal dietary magnesium intake. Dietary reference intakes (DRIs) suggest that adult dietary magnesium intake is 420 mg/day for men and 320 mg/day for women[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. A meta-analysis of 313,041 individuals reported the optimal effect of dietary magnesium intake at 250 mg/d on ischemic heart disease mortality[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A prospective Spanish study showed an obvious lower risk of CVD mortality with dietary magnesium intake of 442 mg/day[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This inconsistency may partly account for the differences in target population, sample size, disease types, and underlying health conditions. The role of HUA in increasing the risk of death has been established[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], and dietary magnesium intake still needs to be further confirmed in clinical trials to guide a more reasonable intake. Our findings can help us reduce the risk of mortality in patients with gout and HUA by increasing dietary magnesium intake in clinical practice, with practical public health implications.\u003c/p\u003e \u003cp\u003eConsidering that age, gender, race/ethnicity, hypertension, diabetes and BMI may be confounding factors for the association between dietary magnesium intake and all-cause mortality, stratified analyses were performed to test the robustness of the results. We found consistent inverse associations in the subgroup analyses, but the associations of several subgroups did not reach statistical significance. According to the heterogeneity test, the lack of significance in subgroups stratified by age, gender, race/ethnicity, diabetes and BMI, may due to the limited sample size rather than true heterogeneity within the strata. However, when the analysis was stratified by hypertension, we observed statistically significant heterogeneity and interaction between dietary magnesium intake and hypertension on all-cause mortality among HUA patients. To be specific, high dietary magnesium intake had a greater advantage in reducing all-cause mortality in hypertension patients, consistent with a previous study[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Hypertension is considered as a major cause of all-cause and CVD mortality worldwide[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], and can cause damage to a series of target organs, such as heart failure, renal insufficiency and atherosclerosis[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The effectiveness of high-magnesium diets in reducing blood pressure has been demonstrated[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], and hypertension patients are often recommended to consume more dietary magnesium, with greater benefits in reducing all-cause mortality. Therefore, in the treatment of HUA patients, dietary magnesium intake should be reasonably supplemented in clinical health management, especially in patients with hypertension.\u003c/p\u003e \u003cp\u003eThe protective effect of high dietary magnesium intake on all-cause mortality in patients with gout and HUA may be related to inflammatory mechanisms. Previous studies have shown high dietary magnesium intake is inversely associated with low C-reactive protein (CRP)[\u003cspan additionalcitationids=\"CR56 CR57 CR58\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], which is considered as a marker of inflammation and cytokine release[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. A systematic review and meta-analysis of seven cross-sectional studies also confirmed the association between magnesium and CRP[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. High dietary magnesium intake can mediate a series of chain reactions to inhibit CRP synthesis and improve inflammatory response[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Moreover, accumulating evidence has showed that magnesium deficiency can interfere with insulin receptor function, promoting insulin resistance[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], which leads to increased SUA levels[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Furthermore, the kidney is an important regulating organ of circulating uric acid levels, responsible for 60\u0026ndash;70% of systemic uric acid excretion[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Magnesium can protect the kidney through a variety of molecular and cellular effects to maintain normal excretion of uric acid[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], and is considered to have a potential role in increasing uric acid excretion[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. The inverse relationship between magnesium and uric acid levels has been demonstrated in patients with diabetic retinopathy[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. A cross-sectional study of 26,796 US adults also showed that increased magnesium intake was associated with a reduced risk of HUA[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Appropriate increase of dietary magnesium intake may slow down the development or deterioration of gout and HUA by reducing uric acid, and reduce the risk of mortality. For different types of CVD, one of the important risk factors is insufficient intake of magnesium, and the prevalence of magnesium deficiency in CVD patients is much higher than in other patients[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. This may be related to magnesium's ability to enhance endothelium-dependent vasodilation, improve blood pressure, and regulate physiological mechanisms such as arrhythmia, inflammatory response and platelet aggregation[\u003cspan additionalcitationids=\"CR72\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on recommendations of DRIs, more than half (56%) of the US population got less than the required amount of magnesium from food in 2001\u0026ndash;2002, and the number declined to forty-eight percent in 2005\u0026ndash;2006[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Various dietary surveys conducted since 2009 indicated that about 20% of the population consistently consume less than the recommended amount of magnesium[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Although the dietary magnesium intake has increased, there is still a gap with the reference level. Therefore, concern should be raised about adequate dietary magnesium intake. In clinical practice, all-cause and CVD mortality of gout and HUA patients can be reduced by increasing dietary magnesium intake, which is useful for the establishment of clinical policies and guidelines.\u003c/p\u003e \u003cp\u003eStrengths of our study included the use of a nationally representative sample of US adults, adjustment for many potential confounding factors, and a long follow-up period, which enhanced the reliability of the conclusions. Nonetheless, there were some limitations to our study. First, the role of confounding by genetic susceptibility, psychosocial stress, or other variables could not be excluded. Second, the diagnosis of gout was based on a simple self-reported question, \"Doctors ever told you had gout?\". However, we did not have further medical records of the participants, which probably led to bias in diagnosis determination. Third, the number of deaths from CVD or cancer included in this study was relatively small, and the statistical power to analyze the association between dietary magnesium and cause-specific mortality might be insufficient. Fourth, the NHANES were all US residents, thus this conclusion probably cannot be applied to ethnically diverse populations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHigh dietary magnesium intake was associated with decreased risks of all-cause mortality in US adult patients with gout and HUA, and had a nonlinear inverse association with CVD mortality in HUA patients, with the inflection point of 272mg/d. These findings could provide dietary assistance in preventing premature death in patients with gout and HUA.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHUA: Hyperuricemia; CVD: cardiovascular disease; GBD: Global Burden of Disease; DALYs: disability-adjusted life-years; US: United States; T2D: type 2 diabetes; SUA: serum uric acid; NHANES: National Health and Nutrition Examination Survey; NCHS: National Center for Health Statistics; MEC: Mobile examination center; PIR: Poverty income ratio; BMI: Body mass index; SD: standard deviation; HDL: high-density lipoprotein; DRIs: Dietary reference intakes; CRP: C-reactive protein.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES was reviewed and approved through the NCHS Research Ethics Review Board, and each participant provided informed consent (https://www.cdc.gov/nchs/nhanes/irba98.htm). Additionally, all NHANES data released by the NCHS is de-identified, and remained anonymous during data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are publicly available and accessible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from National Natural Science Foundation of China (82204843), Zhejiang Province Traditional Chinese Medicine Science and Technology Plan Project (2023ZR084), Science and Technology Project of Zhejiang Provincial Health Commission (2023KY845).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eXuanni Lu: Conceptualization, Methodology, Formal analysis, Writing-original draft, Visualization. Anqi Wang: Formal analysis, Data curation, Writing-original draft. Ke Liu: Formal analysis, Data curation, Writing-original draft. Ying Chen: Conceptualization. Wei-Wei Chen, Project administration. Ying-Ying Mao: Supervision, Project administration, Funding acquisition. Ding Ye: Conceptualization, Writing-review \u0026amp; editing, Methodology, Supervision, Project administration, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely thank the researchers and participants of the original articles for their collection and management of data resources.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDalbeth N, Gosling AL, Gaffo A, Abhishek A: Gout. LANCET 2021;397:1843-1855.\u003c/li\u003e\n\u003cli\u003eAbhishek A, Roddy E, Doherty M: Gout - a guide for the general and acute physicians. 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MOL CELL BIOCHEM 2002;238:163-179.\u003c/li\u003e\n\u003cli\u003eSanjuliani AF, de Abreu FV, Francischetti EA: Effects of magnesium on blood pressure and intracellular ion levels of brazilian hypertensive patients. INT J CARDIOL 1996;56:177-183.\u003c/li\u003e\n\u003cli\u003eRosanoff A, Weaver CM, Rude RK: Suboptimal magnesium status in the united states: are the health consequences underestimated? NUTR REV 2012;70:153-164.\u003c/li\u003e\n\u003cli\u003eRondanelli M, Faliva MA, Tartara A, Gasparri C, Perna S, Infantino V, Riva A, Petrangolini G, Peroni G: An update on magnesium and bone health. BIOMETALS 2021;34:715-736.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diet, Magnesium, Gout, Hyperuricemia, Mortality, Cardiovascular disease, Cancer","lastPublishedDoi":"10.21203/rs.3.rs-4430372/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4430372/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e We aimed to evaluate the relationship of dietary magnesium intake with all-cause and cause-specific mortality among patients with gout and hyperuricemia (HUA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We analyzed data of 1171 gout patients and 6707 patients with HUA from the National Health and Nutrition Examination Survey (NHANES) 2007-2018 and 2001-2018, respectively. Dietary intake data were obtained from 24-hour dietary recall interviews. Mortality status was determined using the NHANES public-use linked mortality fill. We used Cox regression model and restricted cubic spline analysis to probe the association of dietary magnesium intake and mortality among individuals with gout and HUA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e During 7081 person-years of follow-up, 257 deaths were documented in gout patients, among which 74 died from cardiovascular disease (CVD) and 48 died from cancer. For HUA patients followed up for 58,216 person-years, 1315 all-cause deaths occurred, including 411 CVD deaths and 224 cancer deaths. After multifactorial adjustments, higher dietary magnesium intake was associated with lower risk of all-cause mortality among participants with gout and HUA. Restricted cubic splines showed a nonlinear inverse association between dietary magnesium intake with CVD mortality among HUA patients (\u003cem\u003eP\u003c/em\u003e for nonlinear \u0026lt; 0.05), with the inflection point of 272mg/d. The results were robust in subgroup and sensitivity analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e High dietary magnesium intake was associated with decreased risk of all-cause mortality among patients with gout and HUA, and had a nonlinear inverse association with CVD mortality in HUA patients. The results highlight the potential advantages of assessing dietary magnesium intake in preventing all-cause and CVD mortality in patients with gout and HUA.\u003c/p\u003e","manuscriptTitle":"Associations of dietary magnesium intake with all-cause and cause-specific mortality among individuals with gout and hyperuricemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-11 20:11:08","doi":"10.21203/rs.3.rs-4430372/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5ddeb149-ecf8-46e4-a88d-b93287e16141","owner":[],"postedDate":"June 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-25T11:50:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-11 20:11:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4430372","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4430372","identity":"rs-4430372","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}