U-shaped Association Between Plasma Magnesium and First Stroke: A Community Based Nested Case-control Study

U-shaped Association Between Plasma Magnesium and First Stroke: A Community Based Nested Case-control Study | 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 Article U-shaped Association Between Plasma Magnesium and First Stroke: A Community Based Nested Case-control Study Wei Zhou, Minghui Li, Lishun Liu, Yun Song, Binyan Wang, Xiping Xu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4732467/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: Previous studies on the association between plasma magnesium concentrations and risk of first stroke were limited. We aimed to evaluate the association between plasma magnesium concentrations and the risk of first stroke in a community based Chinese population. Methods: The study sample population was drawn from “H-type Hypertension Prevention and Control Public Service Project” in China. We conducted a nested case–control analysis and matched 1255 cases with an equal number of controls for age ± 1 years, sex and study sites. Conditional logistic regression models was used to estimate the association of plasma magnesium with the risk of stroke and stroke subtypes (ischemic stroke and hemorrhagic strokes). Results: Using restricted cubic splines, there was a U-shaped association of plasma magnesium concentrations with risk of first stroke and ischemic stroke. Compared with the middle tertile (19.14 -< 20.67 mg/L), the multivariate-adjusted ORs (95% confidence interval [CI]) of stroke and ischemic stroke in the lowest tertile plasma magnesium were 1.37 (1.10, 1.70) and 1.36 (1.07, 1.72), in the highest tertile of plasma magnesium were 1.28 (1.03, 1.60) and 1.31 (1.03, 1.67), respectively. Furthermore, a stronger positive association between low tertile of plasma magnesium and first stroke was found in participants with current smoking than without ( P -interaction=0.035). No significant effect modifications were observed in subgroup analysis. Conclusions: Our study indicated a U-shaped association between plasma magnesium and first stroke, especially among current smoker. plasma magnesium first stroke hypertension U-shaped curve Figures Figure 1 Introduction Stroke is the third most common cause of death across the world [ 1 ] which has also become a serious public health concern in China [ 2 ]. Therefore, identifying novel, modifiable markers to inform the risk of stroke is an issue of critical importance. Recently, the effects of nutrition elements on risk of stroke have received great attention [ 3 – 5 ]. Magnesium, the second most abundant intracellular cation in the body, which can influence the cardiovascular system through vascular tone, blood pressure (BP), cardiac arrhythmias, and insulin metabolism [ 6 – 8 ]. Epidemiologic studies have reported that plasma magnesium was independent associated with cardiovascular disease (CVD) [ 9 – 13 ]. Although several studies found inverse associations [ 14 , 15 ], there were still several studies showed null association [ 5 , 16 ]. More recently, a prospective cohort study found a non-linear association between plasma magnesium and stroke [ 17 ]. Therefore, studies concerning magnesium and stroke risk have still yielded inconsistent results, especially few study in Chinese population. In addition, few studies have comprehensively examined potential modifiers of the association between copper and first stroke risk. For further evidence, we investigated the association between plasma magnesium and risk of first stroke (ischemic stroke and hemorrhagic stroke), and any possible effect modifiers in a community-based population of China. Methods Study population and design The present study was performed using data from the H-type Hypertension Prevention and Control Public Service Project, which is a ongoing community-based prospective observational longitudinal registry study. Patients were enrolled in 2 provinces of China (Rongcheng county, Shandong and Lianyungang, Jiangsu). The study purpose was to assess the prevalence and treatment of hypertension, and to examine the related factors affecting its prognosis in China. Finally, to establish the risk prediction model of cardio-cerebral and renal vascular diseases. Eligible patients had essential hypertension who aged 18 years or older, Hypertension was defined as the usual 140/90 mmHg threshold, self-report history of hypertension, and the use of antihypertensive drug at baseline. Exclusion criteria were participants who had psychological or nervous system impairment that inability to sign informed consent or unable finished follow-up. The investigators completed standardized electronic medical record collection at baseline and followed up every 3 months. At each visit, physical examination, and clinical outcomes were recorded. In this analysis, we assessed the relationship between plasma magnesium and first stroke used a nested case-control study design in this community-based study. This study matched 1255 stroke cases with an equal number of controls (patients without stroke) for age ± 1 years, sex, and village who participated between January 2016 and December 2018 in Rongcheng county, Shandong province, China. Patients with stroke data from the Chinese centers for disease control and prevention (CDC, 2016–2018) who had complete records (physical exam, questionnaire, biological samples) were selected as cases. Outcome The primary outcome of this study was a first stroke, which included first ischemic stroke and first hemorrhagic stroke. Information on incidence of stroke for all participants was obtained via the Center for Disease Control and Prevention of Rongcheng counties, and checked against the national health insurance system with electronic linkage to all hospitalizations, or ascertained through active follow-up. Diseases were coded according to the International Classification of Diseases, 10th Revision (ICD-10). Secondary outcomes included first ischemic stroke (I63) and first hemorrhagic stroke (I60-I61). The primary outcome included first ischemic stroke (I63), first hemorrhagic stroke (I60-I61) and no type stroke (I64). As shown in the government documentation local authorities from medical institutions were required to report all new cases of stroke to the local Center for Disease Control and Prevention. A report card which includes information on demographics, diagnostic basis and date of stroke is required to be submitted on the 28th of each month. Quality control, including finding and deleting repeated cases, error checking, and determining any missed cases, was completed by trained officials. Furthermore, the local Center for Disease Control and Prevention was also responsible for deleting repeated cases and finding logistical errors and missed cases. In addition, 5% of all uploaded cases are randomly chosen for further confirmation by phone or door-to-door interviews. Laboratory assays Serum total homocysteine (tHcy), fasting lipids, plasma calcium, and glucose concentrations at baseline were measured using automatic clinical analyzers (Beckman Coulter) at the Evergreen Research Institute in Shenzhen, China. The eGFR was estimated by the Chronic Kidney Disease Epidemiology Collaboration equation. Plasma magnesium was measured by inductively coupled plasma mass spectrometry (ICP-MS) in a commercial lab (Beijing DIAN Medical Laboratory, China). The intra-assay CV for magnesium ranged from 1.05–6.00%, while the inter-assay CV for magnesium ranged from 2.90–3.22%. Statistical analysis Baseline characteristics are presented as mean ± standard deviation (SD) for continuous variables and proportions for categorical variables. Differences in baseline characteristics between cases and controls were compared using chi-square tests for categorical variables and generalized paired t-tests for continuous variables. Variables that are known as traditional or suspected risk factors for stroke [ 18 ], matched variables, or variables that showed significant differences between cases and controls were chosen as the covariates in our current study. Odd ratios (ORs) of first stroke, ischemic stroke, and hemorrhagic stroke were estimated by modeling plasma magnesium as tertile using conditional logistic regression, except for matched variables (sex, age, study sites), without and with adjustment for, body mass indx (BMI), smoking status, alcohol consumption, self-reported hypertension, self-reported diabetes, labor intensity, baseline systolic blood pressure (SBP), baseline diastolic blood pressure (DBP), baseline fasting blood glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), total homocysteine, serum calcium, estimated glomerular filtration rate (eGFR) at baseline, antihypertensive drugs, lipoprotein-lowering drugs, antiplatelet drugs, and glucose-lowering drugs use. In addition, possible modifications of the association between magnesium and first stroke were also assessed for the following variables: age (< 70 compared with ≥ 70 years), sex (male compared with female), BMI (< 24 compared with ≥ 24), SBP (< 140 compared with ≥ 140mmHg), fasting glucose (< 7.0 compared with ≥ 7.0 mmol/L or self-reported diabetes or use glucose-lowering drugs), current smoking (yes compared with no), current alcohol drinking (yes compared with no), total homocysteine (< 15 compared with ≥ 15 umol/L), TC (median, < 5.78 compared with ≥ 5.78 mmol/L) at baseline. Potential interactions were examined by including the interaction terms into those logistic regression models with the greatest number of confounding variables. A 2-tailed P < 0.05 was considered to be statistically significant in all analyses. R software version 3.4.3 ( www.R-project.org ) and Empower version 2.17.8 ( www.empowerstats.com , X&Y Solutions, Inc.) were used for all statistical analyses. Results Study participants and baseline characteristics Among 1255 first stroke case, 1113 were ischemic stroke, 174 were hemorrhagic stroke and 5 no type stroke. The mean (SD) age at baseline was 70.75 (8.06) years and 50.52% were females. The differences of risk factors for stroke between cases and controls were shown in Table 1 . The median plasma magnesium levels did not differ between stroke cases and controls (19.87 vs. 19.99 mg/L in each; P = 0.123) in univariate analyses. Subjects in the lowest tertile of plasma magnesium, which were more likely to have higher rate of diabetes mellitus and use glucose-lowering drugs, to have higher fasting glucose concentrations, SBP, and eGFR, and to have lower triglycerides compared with subjects in the highest tertile (Supplemental table 1 ). The mean plasma magnesium levels in the lowest and highest tertile, were 17.95 and 21.95 mg/L, respectively. Table 1 Baseline Characteristics by cases and control* Characteristics Controls (n = 1255) Case (n = 1255) P -value Age, y 70.76 ± 8.06 70.75 ± 8.07 0.987 Female, n(%) 634 (50.52) 634 (50.52) 1.000 BMI, kg/m 2 25.87 ± 3.73 26.51 ± 4.42 < 0.001 SBP, mm Hg 149.34 ± 21.66 157.17 ± 23.81 < 0.001 DBP, mm Hg 83.31 ± 11.43 87.20 ± 12.82 < 0.001 Smoking, n (%) 0.048 Never or former 1000 (79.68) 959 (76.41) Current 255 (20.32) 296 (23.59) Drinking, n (%) 0.306 Never or former 938 (74.74) 960 (76.49) Current 317 (25.26) 295 (23.51) Labor intensity, n (%) 0.003 Light 907 (72.27) 978 (77.93) Moderate 267 (21.27) 221 (17.61) Severe 81 ( 6.45) 56 ( 4.46) Self-reported Hypertension, n (%) 566 (45.10) 746 (59.44) < 0.001 Self-reported Diabetes, n (%) 163 (12.99) 261 (20.80) < 0.001 Medication use, n (%) Antihypertensive drugs 488 (38.88) 664 (52.91) < 0.001 Glucose-lowering drugs 108 ( 8.61) 193 (15.38) < 0.001 Lipoprotein-lowering drugs 22 ( 1.75) 22 ( 1.75) 1.000 Antiplatelet drugs 23 ( 1.83) 60 ( 4.78) < 0.001 Laboratory results Total cholesterol, mmol/L 5.85 ± 1.20 5.84 ± 1.21 0.768 Triglycerides, mmol/L 1.31 ± 0.77 1.49 ± 0.92 < 0.001 HDL cholesterol, mmol/L 1.66 ± 0.40 1.59 ± 0.38 < 0.001 Glucose, mmol/L 5.98 ± 2.05 6.54 ± 2.53 < 0.001 Total homocysteine, µmol/L 13.47 ± 6.22 14.26 ± 7.94 0.006 Magnesium levels, mg/L 19.99 ± 1.85 19.87 ± 1.95 0.123 Plasma calcium, mmol/L 2.34 ± 0.21 2.35 ± 0.18 0.046 eGFR, mL · min − 1 · 1.73 m − 2 93.29 ± 13.40 91.69 ± 15.31 0.005 Abbreviations: BMI = body mass index, SBP = systolic blood pressure, DBP = diastolic blood pressure, HDL = high density lipoprotein, eGFR = estimated glomerular filtration rate. *Data are presented as number (%) or mean ± standard deviation. Association between plasma magnesium and first stroke The association of plasma magnesium with the risk of first stroke and first ischemic stroke remained U-shaped, with increased risk at low and high plasma magnesium levels in adjusted model (Fig. 1 A and Fig. 1 B). As shown in Table 2 . Compared to tertile 2 [T2] of plasma magnesium levels (19.14 -< 20.66 mg/L), the ORs and 95% confidence interval [CI] for the lowest tertile (tertile 1 [T1] plasma magnesium < 19.14 mg/L) was 1.37 (95% CI: 1.10, 1.70). Likewise, the highest tertile (tertile 3 [T3] plasma magnesium ≥ 20.66 mmol/L) was also associated with an increased risk for first stroke (OR: 1.28, 95% CI: 1.03, 1.60). Similarly, in the fully adjusted model (Model 2), participants in the T1 and T3 had a higher risk of first ischemic stroke (T1: 1.36, 95% CI: 1.07, 1.72; T3: 1.31, 95% CI: 1.03, 1.67, respectively) than those in the reference tertile (T2). However, no significant association was found between either low or high tertile of plasma magnesium and risk of first hemorrhagic stroke (OR: 1.34, 95% CI: 0.70, 2.55; OR: 1.33, 95% CI: 0.72, 2.49, respectively). Table 2 Risk of stroke associated with plasma magnesium concentrations Magnesium, mg/L Cases/ controls Model 1 Model 2 OR (95% CI) P -value OR (95% CI) P -value Stoke Tertile T 1 (< 19.14) 456/381 1.47 (1.21, 1.79) < 0.001 1.37 (1.10, 1.70) 0.005 T 2 (≥ 19.14,<20.67) 376/460 Ref Ref T 3 (≥ 20.67) 423/414 1.26 (1.03, 1.54) 0.023 1.28 (1.03, 1.60) 0.028 Ischemic stroke Tertile T 1 (< 19.14) 404/336 1.49 (1.21, 1.84) < 0.001 1.36 (1.07, 1.72) 0.011 T 2 (≥ 19.14,<20.67) 317/390 Ref Ref T 3 (≥ 20.67) 358/353 1.26 (1.01, 1.57) 0.037 1.31 (1.03, 1.67) 0.031 Hemorrhagic stroke Tertile T 1 (< 19.14) 51/42 1.52 (0.88, 2.61) 0.131 1.34 (0.70, 2.55) 0.379 T 2 (≥ 19.14,<20.67) 55/69 Ref Ref T 3 (≥ 20.67) 65/60 1.37 (0.82, 2.30) 0.232 1.33 (0.72, 2.49) 0.365 Model 1 is conditioned on the matching factors of age, sex, and study site Model 2 is conditioned on the matching factors of age, sex, and study site, and adjusted for BMI, smoking status, alcohol consumption, self-reported hypertension, self-reported diabetes, Labor intensity, baseline SBP, baseline DBP, baseline fasting blood glucose, total cholesterol, triglycerides, HDLC, total homocysteine, serum calcium, eGFR, antihypertensive drugs, lipoprotein-lowering drugs, antiplatelet drugs, glucose-lowering drugs. Stratified analysis by potential effect modifiers Interaction analyses were presented in Table 3 . No significant effect-modifications were found for: age (< 70 compared with ≥ 70 years; P -interaction = 0.851), sex (male compared with female; P -interaction = 0.643), BMI (< 24 compared with ≥ 24; P -interaction = 0.363), SBP (< 140 compared with ≥ 140; P -interaction = 0.087), fasting glucose (< 7.0 compared with ≥ 7.0 mmol/L or self-reported diabetes or use glucose-lowering drugs; P -interaction = 0.727), current alcohol drinking (yes compared with no; P -interaction = 0.400), total homocysteine (< 15 compared with ≥ 15 umol/L; P -interaction = 0.863) or TC (median, < 5.78 compared with ≥ 5.78 mmol/L; P -interaction = 0.841) at baseline, on the association between plasma magnesium (T2 vs . T1 and T3) and the risk of first stroke. Table 3 Multivariate odds ratios of stroke associated with low and high versus medium plasma magnesium, stratified by other risk factors Subgroups cases/ controls OR (95% CI) * of Magnesium Tertiles P for interaction T1 < 19.14 T2 ≥ 19.14, < 20.67 T3 ≥ 20.67 Age, years 0.851 < 70 588/590 1.39 (1.02, 1.90) Ref 1.31 (0.96, 1.77) ≥ 70 667/665 1.28 (0.97, 1.69) Ref 1.22 (0.93, 1.60) Sex 0.643 male 621/621 1.22 (0.92, 1.62) Ref 1.21 (0.90, 1.62) female 634/634 1.45 (1.08, 1.96) Ref 1.30 (0.98, 1.72) BMI, kg/m 2 0.363 < 24 328/404 1.56 (1.06, 2.28) Ref 1.17 (0.79, 1.72) ≥ 24 927/851 1.22 (0.96, 1.56) Ref 1.31 (1.03, 1.66) SBP, mmHg 0.087 <140 313/449 1.84 (1.25, 2.70) Ref 1.33 (0.91, 1.93) ≥140 942/806 1.19 (0.93, 1.52) Ref 1.24 (0.97, 1.58) Fasting glucose, mmol/L 0.727 < 7.0 896/1032 1.29 (1.02, 1.63) Ref 1.22 (0.97, 1.52) ≥ 7.0 or history of diabetes or use glucose-lowering drugs 359/223 1.60 (1.05, 2.43) Ref 1.45 (0.88, 2.39) current smoking 0.035 No 959/1000 1.22 (0.96, 1.54) Ref 1.32 (1.05, 1.66) Yes 296/255 2.04 (1.30, 3.20) Ref 1.02 (0.64, 1.60) Current alcohol drinking 0.400 No 960/938 1.41 (1.11, 1.79) Ref 1.23 (0.98, 1.56) Yes 295/317 1.28 (0.85, 1.92) Ref 1.46 (0.96, 2.24) Homocysteine, umol/L 0.863 < 15 869/940 1.31 (1.03, 1.66) Ref 1.22 (0.96, 1.55) ≥ 15 386/315 1.31 (0.88, 1.94) Ref 1.36 (0.93, 2.00) Cholesterol, mmol/L 0.841 < 5.78 641/613 1.24 (0.93, 1.65) Ref 1.25 (0.93, 1.67) ≥ 5.78 614/642 1.42 (1.06, 1.92) Ref 1.27 (0.95, 1.69) ORs of first stroke in relation to plasma concentrations of magnesium (tertiles) were calculated using multivariate logistic regression models. Each subgroup analysis adjusted, if not stratified, for age, sex, BMI, smoking status, alcohol consumption, self-reported hypertension, self-reported diabetes, Labor intensity, baseline SBP, baseline DBP, baseline fasting blood glucose, total cholesterol, triglycerides, HDLC, total homocysteine, serum calcium, eGFR, antihypertensive drugs, lipoprotein-lowering drugs, antiplatelet drugs, glucose-lowering drugs. However, a stronger positive association between the lowest tertile of plasma magnesium and first stroke was found in participants with current smoking (OR: 2.04; 95% CI: 1.30, 3.20) compared to those without current smoking (OR:1.22; 95% CI: 0.96, 1.54; P -interaction = 0.035). Discussion In this large prospective nested case-control study we found that the association of plasma magnesium concentrations with risk of first stroke and ischemic stroke followed a U-shaped curve, both low and high levels of plasma magnesium were associated with high risk of first stroke and ischemic stroke. In addition, current smoking was a modifier for the association between low plasma magnesium and first stroke. Previous studies have reported conflicting results for the relationships between plasma magnesium and the risk of stroke. The ARIC study [ 16 ] based on 577 ischemic stroke cases with 16 years of follow-up, found that the association between plasma magnesium and ischemic stroke was eliminated after adjustment for hypertension and diabetes mellitus. The REGARDS study [ 5 ] conducted in 27,770 participants, consistently reported that plasma magnesium had no significant effect on the risk of stroke with a median follow up of 5.5 years. However, a mendelian randomization analysis in European reported that each 0.1 mmol/L increase in genetically predicted serum magnesium concentrations were 0.78 (95% CI: 0.69–0.89) for all ischemic stroke [ 14 ]. Additionally, a nested case-control study from the Nurses’ Health Study (NHS, 459 ischemic stroke cases) [ 17 ], conducted in a female population, with a mean follow-up duration of 16 years, found that compared with participants in quintile 5 (0.95 -< 1.15 mmol/L), the lowest risk of ischemic stroke was found in quintile 4 (0.90 -< 0.95 mmol/L; RR: 0.75, 95% CI: 0.48–1.16), but not in the lowest quintile (0.7 -< 0.82 mmol/L; RR: 1.34, 95% CI: 0.82–2.17). It suggested that the association between plasma magnesium and risk of first stroke is not a simple, linear one. Therefore, the association between plasma magnesium and the risk of stroke remains uncertain. Moreover, few studies to date have examined the relation between plasma magnesium and the risk of first stroke [ 5 , 16 , 17 ]. In the current study, two new insights were demonstrated. Firstly, we found that there was a significant U-shaped relation between plasma magnesium concentration and first stroke and first ischemic stroke risk in this population. Consistent with several previous studies [ 19 – 21 ], the risk of first stroke significantly increased in participants with low magnesium levels. Generally, Low plasma magnesium levels was associated with higher hypertension, diabetes mellitus and atrial fibrillation risk [ 22 – 26 ]. Moreover, a direct role of low magnesium has been observed in endothelial dysfunction [ 27 ], lipoprotein peroxidation [ 28 ], and platelet aggregation and coagulation [ 7 , 29 ]. These were already known to be associated with stroke. Additionally, the risk of first stroke also significantly increased in participants with high magnesium levels. High magnesium concentrations can increase the risk of soft tissue calcification via parathyroid hormone inhibition and less bone turnover [ 30 ], and possibly associated with old age and renal impairment [ 31 ], both of them were associated with stroke. Moreover, high plasma magnesium may altered ion channel properties, induced by an altered oxido-redox state, lead to proarrhythmic conditions with normal hearts [ 32 , 33 ]. Thus, high magnesium levels might be a mediator factors or directly influence stroke. However, our findings seemed to be biologically plausible, more studies are needed to verify our results and to further examine the biological mechanisms. Although the normal reference range currently recommended for magnesium in blood serum is 0.76–1.15 mmol/L in general population [ 34 – 36 ]. However, there is no acceptable value or range for plasma manganese according to different populations and health outcomes (i.e., atrial fibrillation [ 37 , 38 ], coronary heart disease [ 39 ], type 2 diabetes [ 40 ] and hospitalized patients [ 41 ]). In our study, 82.35% of participants were in the normal reference range also had a high risk of first stroke, the lowest risk of stroke was found in the second tertile of plasma magnesium, this suggests that this “medium” plasma magnesium concentration may be more suitable for Chinese middle-aged and elderly populations with regards to prevention of first stroke. Therefore, there is a future study need to confirm the association of dietary and plasma magnesium with stroke in additional large prospective studies. Secondly, current smoking was a significant effect modifier: a strong association between the lowest tertile of plasma magnesium and risk of stroke was found among current smokers. China, the world's most populous nation, is also the largest consumer of cigarettes. A recent meta analysis [ 42 ] revealed that current smokers had a 92% (95% CI: 1.49–2.48) increased risk of stroke. The potential mechanisms underlying a smoking × low magnesium interaction is unknown. In our current study, a plausible biological explanation for the interaction may be due to the fact that low plasma magnesium and smoking could possibly share some cellular and molecular mechanisms for the pathogenesis of stroke. Smoking also has been proven to be related to diabetes, high blood pressure and atrial fibrillation [ 43 – 45 ], which are strong risk factors for stroke. Additionally, smoking elevates the levels of homocysteine and fibrinogen, and is associated with the development of atherosclerosis. [ 46 , 47 ] Further studies are needed to verify this hypothesis. The strengths of our study include the large number of participants and objectively measured plasma magnesium levels at baseline. Our study participants with stroke were confined to newly diagnosed and plasma magnesium measured before stoke diagnosis. Our study also have several potential limitations. First, we used a single assessment of plasma magnesium at baseline to analyze the associations between plasma or dietary magnesium and stroke incidence. It was known that the levels of plasma magnesium in the body vary constantly over time. Second, given the relatively small number of hemorrhagic stroke cases, we had limited power to explore the association between plasma magnesium and the risk of first hemorrhagic stroke. Our results warrant further investigations, including large-scale cohort studies and randomized trials. Third, our study focused on a middle-aged and elderly Chinese Han ethnicity population, therefore our results may not be suitable to younger populations or other racial and ethnic groups. Conclusions Both low and high plasma magnesium levels were positively associated with first stroke risk, taking on a U-shaped trend in the large nested case-control study in a middle-aged and elderly Chinese population. Declarations Acknowledgements We acknowledge the contribution the all staff who participated in this study as well as the study participants who shared their time with us. Authors’ Contributions HB, XC and XX designed the study and directed its implementation, including quality assurance and control. WZ and ML conducted data analysis and wrote the manuscript. LL, YS, XW and BW provided scientific comments and advice. All authors read and approved the final manuscript. Funding The study was supported by funding from the following: the National Key Research and Development Program (2018ZX09301034003); the 111 project from the Education Ministry of China (B18053); the National Natural Science Foundation of China (81960074, 81500233); Jiangxi Provincial Drug Administration Science and Technology Project (2022JS41,2023JS26). Availability of data and materials The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. Ethics approval and consent to participate This study was conducted according to the principles laid down in the Declaration of Helsinki, and approved by the Ethics Committee of the Institute of Biomedicine, Anhui Medical University, Hefei, China. All participants signed written informed consent. Consent for publication Not applicable. Competing interests All authors declare that they have no competing interests Author details 1 Center for Prevention and Treatment of Cardiovascular Diseases, the Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China. 2 Department of Cardiovascular Medicine, the Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China. 3 State Key Laboratory of Cardiovascular Disease, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100037, China 4 Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100091, China. 5 Shenzhen Evergreen Medical Institute, Shenzhen, Guangdong 518038, China. 6 Institute of Biomedicine, Anhui Medical University, Hefei 230032, China. 7 National Clinical Research Study Center for Kidney Disease; the State Key Laboratory for Organ Failure Research; Renal Division, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, China 8 Department of Population, Family and Reproductive Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore MD21218, USA. References GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018;392:1736-1788. doi: 10.1016/S0140-6736(18)32203-7 Zhou M, Wang H, Zeng X, Yin P, Zhu J, Chen W, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2019;394:1145-1158. doi: 10.1016/S0140-6736(19)30427-1 Adebamowo SN, Spiegelman D, Willett WC, Rexrode KM. Association between intakes of magnesium, potassium, and calcium and risk of stroke: 2 cohorts of US women and updated meta-analyses. Am J Clin Nutr 2015;101:1269-1277. doi: 10.3945/ajcn.114.100354 Larsson SC, Orsini N, Wolk A. Dietary magnesium intake and risk of stroke: A meta-analysis of prospective studies. Am J Clin Nutr 2012;95:362-366. doi:10.3945/ajcn.111.022376 Merrill PD, Ampah SB, He K, Rembert NJ, Brockman J, Kleindorfer D, et al. Association between trace elements in the environment and stroke risk: The reasons for geographic and racial differences in stroke (REGARDS) study. J Trace Elem Med Biol 2017;42:45-49. doi:10.1016/j.jtemb.2017.04.003 Volpe SL. Magnesium, the metabolic syndrome, insulin resistance, and type 2 diabetes mellitus. Crit Rev Food Sci Nutr 2008;48:293-300. doi:10.1080/10408390701326235 Shechter M. Magnesium and cardiovascular system. Magnes Res 2010;23:60-72. doi:10.1684/mrh.2010.0202 Kolte D, Vijayaraghavan K, Khera S, Sica DA, Frishman WH. Role of magnesium in cardiovascular diseases. Cardiol Rev 2014;22:182-92. doi:10.1097/CRD.0000000000000003 Del GL, Imamura F, Wu JH, de Oliveira OM, Chiuve SE, Mozaffarian D. Circulating and dietary magnesium and risk of cardiovascular disease: A systematic review and meta-analysis of prospective studies. Am J Clin Nutr 2013;98:160-173. doi:10.3945/ajcn.112.053132 Joosten MM, Gansevoort RT, Mukamal KJ, van der Harst P, Geleijnse JM, Feskens EJ, et al. Urinary and plasma magnesium and risk of ischemic heart disease. Am J Clin Nutr 2013;97:1299-1306. doi:10.3945/ajcn.112.054114 Chambers EC, Heshka S, Gallagher D, Wang J, Pi-Sunyer FX, Pierson RJ. Serum magnesium and type-2 diabetes in African Americans and Hispanics: A New York cohort. J Am Coll Nutr 2006;25:509-513.doi:10.1080/07315724.2006.10719566 Xu T, Chen GC, Zhai L, Ke KF. Nonlinear reduction in risk for type 2 diabetes by magnesium intake: An updated Meta-Analysis of prospective cohort studies. Biomed Environ Sci 2015;28:527-534. doi:10.3967/bes2015.075 Sarrafzadegan N, Khosravi-Boroujeni H, Lotfizadeh M, Pourmogaddas A, Salehi-Abargouei A. Magnesium status and the metabolic syndrome: A systematic review and meta-analysis. Nutrition 2016;32:409-417. doi:10.1016/j.nut.2015.09.014 Larsson SC, Traylor M, Burgess S, Boncoraglio GB, Jern C, Michaelsson K, et al. Serum magnesium and calcium levels in relation to ischemic stroke: Mendelian randomization study. Neurology 2019;92:e944-950. doi:10.1212/WNL.0000000000007001 Zhang X, Xia J, Del GL, Hruby A, Dai Q, Song Y. Serum magnesium concentrations and all-cause, cardiovascular, and cancer mortality among U.S. Adults: Results from the NHANES I Epidemiologic Follow-up Study. Clin Nutr 2018;37:1541-1549. doi:10.1016/j.clnu.2017.08.021 Ohira T, Peacock JM, Iso H, Chambless LE, Rosamond WD, Folsom AR. Serum and dietary magnesium and risk of ischemic stroke: The Atherosclerosis Risk in Communities Study. Am J Epidemiol 2009;169:1437-1444. doi:10.1093/aje/kwp071 Adebamowo SN, Jimenez MC, Chiuve SE, Spiegelman D, Willett WC, Rexrode KM. Plasma magnesium and risk of ischemic stroke among women. Stroke 2014;45:2881-2886. doi:10.1161/STROKEAHA.114.005917 Meschia JF, Bushnell C, Boden-Albala B, Braun LT, Bravata DM, Chaturvedi S, et al. Guidelines for the primary prevention of stroke: A statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2014;45:3754-3832. doi:10.1161/STR.00 00000000000046 Altura BT, Memon ZI, Zhang A, Cheng TP, Silverman R, Cracco RQ, et al. Low levels of serum ionized magnesium are found in patients early after stroke which result in rapid elevation in cytosolic free calcium and spasm in cerebral vascular muscle cells. Neurosci Lett 1997;230:37-40. doi:10.1016/s0304-3940(97)00471-0 Feng P, Niu X, Hu J, Zhou M, Liang H, Zhang Y, et al. Relationship of serum magnesium concentration to risk of short-term outcome of acute ischemic stroke. Blood Press 2013;22:297-301. doi:10.3109/08037051.2012.759696 Cojocaru IM, Cojocaru M, Burcin C, Atanasiu NA. Serum magnesium in patients with acute ischemic stroke. Rom J Intern Med 2007;45(3):269-273. Khan AM, Lubitz SA, Sullivan LM, Sun JX, Levy D, Vasan RS, et al. Low serum magnesium and the development of atrial fibrillation in the community: The Framingham Heart Study. Circulation 2013;127:33-38. doi:10.1161/CIRCULATIONAHA.111.082511 Misialek JR, Lopez FL, Lutsey PL, Huxley RR, Peacock JM, Chen LY, et al. Serum and dietary magnesium and incidence of atrial fibrillation in whites and in African Americans--Atherosclerosis Risk in Communities (ARIC) study. Circ J 2013;77(2):323-329. doi:10.1253/circj.cj-12-0886 Palmer BF, Clegg DJ. Electrolyte and Acid-Base disturbances in patients with diabetes mellitus. N Engl J Med 2015;373:548-559. doi: 10.1056/NEJMra1503102 Ramadass S, Basu S, Srinivasan AR. SERUM magnesium levels as an indicator of status of Diabetes Mellitus type 2. Diabetes Metab Syndr 2015;9:42-45. doi:10.1016/j.dsx.2014.04.024 Peacock JM, Folsom AR, Arnett DK, Eckfeldt JH, Szklo M. Relationship of serum and dietary magnesium to incident hypertension: The Atherosclerosis Risk in Communities (ARIC) Study. Ann Epidemiol 1999;9:159-165. doi:10.1016/s1047-2797(98)00040-4 Maier JA, Malpuech-Brugere C, Zimowska W, Rayssiguier Y, Mazur A. Low magnesium promotes endothelial cell dysfunction: Implications for atherosclerosis, inflammation and thrombosis. Biochim Biophys Acta 2004;1689:13-21. doi:10.1016/j.bbadis.2004.01.002 Blache D, Devaux S, Joubert O, Loreau N, Schneider M, Durand P, et al. Long-term moderate magnesium-deficient diet shows relationships between blood pressure, inflammation and oxidant stress defense in aging rats. Free Radic Biol Med 2006;41:277-284. doi:10.1016/j.freeradbiomed. 2006.04.008 Kolte D, Vijayaraghavan K, Khera S, Sica DA, Frishman WH. Role of magnesium in cardiovascular diseases. Cardiol Rev 2014;22:182-192. doi:10.1097/CRD.0000000000000003 Kanbay M, Goldsmith D, Uyar ME, Turgut F, Covic A. Magnesium in chronic kidney disease: Challenges and opportunities. Blood Purif 2010;29:280-292. doi:10.1159/000276665 Clark BA, Brown RS. Unsuspected morbid hypermagnesemia in elderly patients. Am J Nephrol 1992;12:336-343. doi:10.1159/000168469 Santulli G, Pagano G, Sardu C, Xie W, Reiken S, D'Ascia SL, et al. Calcium release channel RyR2 regulates insulin release and glucose homeostasis. J Clin Invest 2015;125:1968-1978. doi: 10.1172/JCI79273 Sardu C, Carreras G, Katsanos S, Kamperidis V, Pace MC, Passavanti MB, et al. Metabolic syndrome is associated with a poor outcome in patients affected by outflow tract premature ventricular contractions treated by catheter ablation. BMC Cardiovasc Disord 2014;14:176. doi: 10.1186/1471-2261-14-176 Grober U, Schmidt J, Kisters K. Magnesium in prevention and therapy. Nutrients 2015;7:8199-8226. doi: 10.3390/nu7095388 Ismail Y, Ismail AA, Ismail AA. The underestimated problem of using serum magnesium measurements to exclude magnesium deficiency in adults; A health warning is needed for "normal" results. Clin Chem Lab Med 2010;48:323-327. doi: 10.1515/CCLM.2010.077 Svagzdiene M, Sirvinskas E, Baranauskiene D, Adukauskiene D. Correlation of magnesium deficiency with C-reactive protein in elective cardiac surgery with cardiopulmonary bypass for ischemic heart disease. Medicina (Kaunas) 2015;51:100-06. doi: 10.1016/j.medici.2015.03.003 Misialek JR, Lopez FL, Lutsey PL, Huxley RR, Peacock JM, Chen LY, et al. Serum and dietary magnesium and incidence of atrial fibrillation in whites and in African Americans--Atherosclerosis Risk in Communities (ARIC) study. Circ J 2013;77:323-329. doi: 10.1253/circj.cj-12-0886 Khan AM, Lubitz SA, Sullivan LM, Sun JX, Levy D, Vasan RS, et al. Low serum magnesium and the development of atrial fibrillation in the community: The Framingham Heart Study. Circulation 2013;127:33-38. doi: 10.1161/CIRCULATIONAHA.111.082511 Kieboom BC, Niemeijer MN, Leening MJ, van den Berg ME, Franco OH, Deckers JW, et al. Serum magnesium and the risk of death from coronary heart disease and sudden cardiac death. J Am Heart Assoc 2016;5:e002707. doi: 10.1161/JAHA.115.002707 Pham PC, Pham PM, Pham SV, Miller JM, Pham PT. Hypomagnesemia in patients with type 2 diabetes. Clin J Am Soc Nephrol 2007;2:366-373. doi: 10.2215/CJN.02960906 Cheungpasitporn W, Thongprayoon C, Qian Q. Dysmagnesemia in hospitalized patients: Prevalence and prognostic importance. Mayo Clin Proc 2015;90:1001-1010. doi: 10.1016/j.mayocp.2015.04.023 Pan B, Jin X, Jun L, Qiu S, Zheng Q, Pan M. The relationship between smoking and stroke: A meta-analysis. Medicine (Baltimore) 2019;98:e14872. doi: 10.1097/MD.0000000000014872 Aune D, Schlesinger S, Norat T, Riboli E. Tobacco smoking and the risk of atrial fibrillation: A systematic review and meta-analysis of prospective studies. Eur J Prev Cardiol 2018;25:1437-1451. doi: 10.1177/2047487318780435 Yoon U, Kwok LL, Magkidis A. Efficacy of lifestyle interventions in reducing diabetes incidence in patients with impaired glucose tolerance: A systematic review of randomized controlled trials. Metabolism 2013;62:303-14. doi: 10.1016/j.metabol.2012.07.009 Bowman TS, Gaziano JM, Buring JE, Sesso HD. A prospective study of cigarette smoking and risk of incident hypertension in women. J Am Coll Cardiol 2007;50(21):2085-2092. doi: 10.1016/j.jacc.2007.08.017 Panagiotakos DB, Pitsavos C, Chrysohoou C, Skoumas J, Masoura C, Toutouzas P, et al. Effect of exposure to secondhand smoke on markers of inflammation: The ATTICA study. Am J Med 2004;116:145-150. doi: 10.1016/j.amjmed.2003.07.019 Hackshaw A, Morris JK, Boniface S, Tang JL, Milenkovic D. Low cigarette consumption and risk of coronary heart disease and stroke: Meta-analysis of 141 cohort studies in 55 study reports. BMJ 2018;360:j5855. doi: 10.1136/bmj.j5855 Additional Declarations No competing interests reported. Supplementary Files Supplementaltable1.doc 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-4732467","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":338441723,"identity":"4183339d-85b9-455b-b6be-c403418d0e4a","order_by":0,"name":"Wei Zhou","email":"","orcid":"","institution":"the Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhou","suffix":""},{"id":338441724,"identity":"0f8e1a65-9862-4363-9e99-a989b2600cbc","order_by":1,"name":"Minghui Li","email":"","orcid":"","institution":"Chinese Academy of Medical Science and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Minghui","middleName":"","lastName":"Li","suffix":""},{"id":338441727,"identity":"c71a2102-bac0-42e5-8ca6-673265f3e7c3","order_by":2,"name":"Lishun Liu","email":"","orcid":"","institution":"China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Lishun","middleName":"","lastName":"Liu","suffix":""},{"id":338441730,"identity":"1ab54b6a-d054-46a1-83ef-381452b4f14b","order_by":3,"name":"Yun Song","email":"","orcid":"","institution":"China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Song","suffix":""},{"id":338441731,"identity":"7a135983-830a-46c2-b1a3-f88b4c0dcfe1","order_by":4,"name":"Binyan Wang","email":"","orcid":"","institution":"Shenzhen Evergreen Medical Institute","correspondingAuthor":false,"prefix":"","firstName":"Binyan","middleName":"","lastName":"Wang","suffix":""},{"id":338441732,"identity":"12f70fe6-65dc-44e3-ab86-4ef32805810b","order_by":5,"name":"Xiping Xu","email":"","orcid":"","institution":"China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xiping","middleName":"","lastName":"Xu","suffix":""},{"id":338441733,"identity":"928b81db-c8a0-4d5e-8b73-d6ef2b6ae1ff","order_by":6,"name":"Xiaobin Wang","email":"","orcid":"","institution":"Johns Hopkins University Bloomberg School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Xiaobin","middleName":"","lastName":"Wang","suffix":""},{"id":338441734,"identity":"26dbcac6-0a9a-445d-973d-c06ea7cd5d4c","order_by":7,"name":"Hui-hui Bao","email":"","orcid":"","institution":"the Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Hui-hui","middleName":"","lastName":"Bao","suffix":""},{"id":338441735,"identity":"73f35705-0948-4c30-b8c2-10b9d0cd1334","order_by":8,"name":"Xiao-shu Cheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIie2QMUvDUBDHLxw8lxezvhAofoSDB1mab9IlIRCnaKFrh0Agjl0j+CEqLm4WDuwidA24JN+gIAhBQdOpILxUN4f3gztuuB9/7gAslv8IDuUUh8lpASiaeIjc/lJBAphn2r8RGZ1MOip7TmgnL9TYNm2x6/pHnnlB6bzNCWPNEgiW0cyk+KXQ2n3h/PaOMahJXIXsblp4zvLCoHgIYeBUnK+b600gSS5CPo/JKdioCDx79/tBeWpS/JCkkody6GOKhzJU7iFFpWJIoWSNJxS/lAvtVpd53aRiKinWiocnxyO30G573/XVNF/VKb7Kz6+Jt2Ju98vIqBiI/7ZusVgslh98A95gVffSsHjDAAAAAElFTkSuQmCC","orcid":"","institution":"the Second Affiliated Hospital of Nanchang University","correspondingAuthor":true,"prefix":"","firstName":"Xiao-shu","middleName":"","lastName":"Cheng","suffix":""}],"badges":[],"createdAt":"2024-07-12 20:06:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4732467/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4732467/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63277968,"identity":"addf1c87-285d-46aa-a6a1-ddabfae4d2cb","added_by":"auto","created_at":"2024-08-26 12:32:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":258042,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe association between baseline plasma magnesium and incident risk of first stroke (A) and first ischemic stroke (B). \u003c/strong\u003eIn addition to the matching factors (age, sex, study site), the splines also adjusted for BMI, smoking status, alcohol consumption, self-reported hypertension, self-reported diabetes, Labor intensity, baseline SBP, baseline DBP, baseline fasting blood glucose, total cholesterol, triglycerides, HDLC, total homocysteine, serum calcium, eGFR, antihypertensive drugs, lipoprotein-lowering drugs, antiplatelet drugs, glucose-lowering drugs.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4732467/v1/0eb5d705f32fc0102ac1848e.png"},{"id":70423396,"identity":"3ec51616-64f2-4ef5-ab5d-bd507ab32552","added_by":"auto","created_at":"2024-12-03 04:47:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1032532,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4732467/v1/030920ed-e98f-4a96-b8fe-2b133c197e4e.pdf"},{"id":63277970,"identity":"b24ae227-aaa9-48fe-8582-47e2acc14031","added_by":"auto","created_at":"2024-08-26 12:32:35","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":51769,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaltable1.doc","url":"https://assets-eu.researchsquare.com/files/rs-4732467/v1/4ceaac6ce906b7e2a04e70d1.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"U-shaped Association Between Plasma Magnesium and First Stroke: A Community Based Nested Case-control Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke is the third most common cause of death across the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] which has also become a serious public health concern in China [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Therefore, identifying novel, modifiable markers to inform the risk of stroke is an issue of critical importance. Recently, the effects of nutrition elements on risk of stroke have received great attention [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMagnesium, the second most abundant intracellular cation in the body, which can influence the cardiovascular system through vascular tone, blood pressure (BP), cardiac arrhythmias, and insulin metabolism [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Epidemiologic studies have reported that plasma magnesium was independent associated with cardiovascular disease (CVD) [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Although several studies found inverse associations [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], there were still several studies showed null association [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. More recently, a prospective cohort study found a non-linear association between plasma magnesium and stroke [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Therefore, studies concerning magnesium and stroke risk have still yielded inconsistent results, especially few study in Chinese population. In addition, few studies have comprehensively examined potential modifiers of the association between copper and first stroke risk.\u003c/p\u003e \u003cp\u003eFor further evidence, we investigated the association between plasma magnesium and risk of first stroke (ischemic stroke and hemorrhagic stroke), and any possible effect modifiers in a community-based population of China.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy population and design\u003c/h2\u003e\n \u003cp\u003eThe present study was performed using data from the H-type Hypertension Prevention and Control Public Service Project, which is a ongoing community-based prospective observational longitudinal registry study. Patients were enrolled in 2 provinces of China (Rongcheng county, Shandong and Lianyungang, Jiangsu). The study purpose was to assess the prevalence and treatment of hypertension, and to examine the related factors affecting its prognosis in China. Finally, to establish the risk prediction model of cardio-cerebral and renal vascular diseases. Eligible patients had essential hypertension who aged 18 years or older, Hypertension was defined as the usual 140/90 mmHg threshold, self-report history of hypertension, and the use of antihypertensive drug at baseline. Exclusion criteria were participants who had psychological or nervous system impairment that inability to sign informed consent or unable finished follow-up. The investigators completed standardized electronic medical record collection at baseline and followed up every 3 months. At each visit, physical examination, and clinical outcomes were recorded.\u003c/p\u003e\n \u003cp\u003eIn this analysis, we assessed the relationship between plasma magnesium and first stroke used a nested case-control study design in this community-based study. This study matched 1255 stroke cases with an equal number of controls (patients without stroke) for age\u0026thinsp;\u0026plusmn;\u0026thinsp;1 years, sex, and village who participated between January 2016 and December 2018 in Rongcheng county, Shandong province, China. Patients with stroke data from the Chinese centers for disease control and prevention (CDC, 2016\u0026ndash;2018) who had complete records (physical exam, questionnaire, biological samples) were selected as cases.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eOutcome\u003c/h2\u003e\n \u003cp\u003eThe primary outcome of this study was a first stroke, which included first ischemic stroke and first hemorrhagic stroke. Information on incidence of stroke for all participants was obtained via the Center for Disease Control and Prevention of Rongcheng counties, and checked against the national health insurance system with electronic linkage to all hospitalizations, or ascertained through active follow-up. Diseases were coded according to the International Classification of Diseases, 10th Revision (ICD-10). Secondary outcomes included first ischemic stroke (I63) and first hemorrhagic stroke (I60-I61). The primary outcome included first ischemic stroke (I63), first hemorrhagic stroke (I60-I61) and no type stroke (I64).\u003c/p\u003e\n \u003cp\u003eAs shown in the government documentation local authorities from medical institutions were required to report all new cases of stroke to the local Center for Disease Control and Prevention. A report card which includes information on demographics, diagnostic basis and date of stroke is required to be submitted on the 28th of each month. Quality control, including finding and deleting repeated cases, error checking, and determining any missed cases, was completed by trained officials. Furthermore, the local Center for Disease Control and Prevention was also responsible for deleting repeated cases and finding logistical errors and missed cases. In addition, 5% of all uploaded cases are randomly chosen for further confirmation by phone or door-to-door interviews.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eLaboratory assays\u003c/h2\u003e\n \u003cp\u003eSerum total homocysteine (tHcy), fasting lipids, plasma calcium, and glucose concentrations at baseline were measured using automatic clinical analyzers (Beckman Coulter) at the Evergreen Research Institute in Shenzhen, China. The eGFR was estimated by the Chronic Kidney Disease Epidemiology Collaboration equation. Plasma magnesium was measured by inductively coupled plasma mass spectrometry (ICP-MS) in a commercial lab (Beijing DIAN Medical Laboratory, China). The intra-assay CV for magnesium ranged from 1.05\u0026ndash;6.00%, while the inter-assay CV for magnesium ranged from 2.90\u0026ndash;3.22%.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eBaseline characteristics are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for continuous variables and proportions for categorical variables. Differences in baseline characteristics between cases and controls were compared using chi-square tests for categorical variables and generalized paired t-tests for continuous variables.\u003c/p\u003e\n \u003cp\u003eVariables that are known as traditional or suspected risk factors for stroke [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e], matched variables, or variables that showed significant differences between cases and controls were chosen as the covariates in our current study. Odd ratios (ORs) of first stroke, ischemic stroke, and hemorrhagic stroke were estimated by modeling plasma magnesium as tertile using conditional logistic regression, except for matched variables (sex, age, study sites), without and with adjustment for, body mass indx (BMI), smoking status, alcohol consumption, self-reported hypertension, self-reported diabetes, labor intensity, baseline systolic blood pressure (SBP), baseline diastolic blood pressure (DBP), baseline fasting blood glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), total homocysteine, serum calcium, estimated glomerular filtration rate (eGFR) at baseline, antihypertensive drugs, lipoprotein-lowering drugs, antiplatelet drugs, and glucose-lowering drugs use. In addition, possible modifications of the association between magnesium and first stroke were also assessed for the following variables: age (\u0026lt;\u0026thinsp;70 compared with \u0026ge;\u0026thinsp;70 years), sex (male compared with female), BMI (\u0026lt;\u0026thinsp;24 compared with \u0026ge;\u0026thinsp;24), SBP (\u0026lt;\u0026thinsp;140 compared with \u0026ge;\u0026thinsp;140mmHg), fasting glucose (\u0026lt;\u0026thinsp;7.0 compared with \u0026ge;\u0026thinsp;7.0 mmol/L or self-reported diabetes or use glucose-lowering drugs), current smoking (yes compared with no), current alcohol drinking (yes compared with no), total homocysteine (\u0026lt;\u0026thinsp;15 compared with \u0026ge;\u0026thinsp;15 umol/L), TC (median, \u0026lt; 5.78 compared with \u0026ge;\u0026thinsp;5.78 mmol/L) at baseline. Potential interactions were examined by including the interaction terms into those logistic regression models with the greatest number of confounding variables.\u003c/p\u003e\n \u003cp\u003eA 2-tailed \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to be statistically significant in all analyses. R software version 3.4.3 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.R-project.org\u003c/span\u003e\u003c/span\u003e) and Empower version 2.17.8 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.empowerstats.com\u003c/span\u003e\u003c/span\u003e, X\u0026amp;Y Solutions, Inc.) were used for all statistical analyses.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants and baseline characteristics\u003c/h2\u003e \u003cp\u003eAmong 1255 first stroke case, 1113 were ischemic stroke, 174 were hemorrhagic stroke and 5 no type stroke. The mean (SD) age at baseline was 70.75 (8.06) years and 50.52% were females. The differences of risk factors for stroke between cases and controls were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median plasma magnesium levels did not differ between stroke cases and controls (19.87 vs. 19.99 mg/L in each; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.123) in univariate analyses. Subjects in the lowest tertile of plasma magnesium, which were more likely to have higher rate of diabetes mellitus and use glucose-lowering drugs, to have higher fasting glucose concentrations, SBP, and eGFR, and to have lower triglycerides compared with subjects in the highest tertile (Supplemental table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean plasma magnesium levels in the lowest and highest tertile, were 17.95 and 21.95 mg/L, respectively.\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 by cases and control*\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1255)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1255)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.76\u0026thinsp;\u0026plusmn;\u0026thinsp;8.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.75\u0026thinsp;\u0026plusmn;\u0026thinsp;8.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e634 (50.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e634 (50.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.87\u0026thinsp;\u0026plusmn;\u0026thinsp;3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.51\u0026thinsp;\u0026plusmn;\u0026thinsp;4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003eSBP, mm Hg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149.34\u0026thinsp;\u0026plusmn;\u0026thinsp;21.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157.17\u0026thinsp;\u0026plusmn;\u0026thinsp;23.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003eDBP, mm Hg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.31\u0026thinsp;\u0026plusmn;\u0026thinsp;11.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.20\u0026thinsp;\u0026plusmn;\u0026thinsp;12.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003eSmoking, n (%)\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 \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever or former\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000 (79.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e959 (76.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003e255 (20.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e296 (23.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking, n (%)\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 \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever or former\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e938 (74.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e960 (76.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003e317 (25.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e295 (23.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabor intensity, n (%)\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 \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e907 (72.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e978 (77.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e267 (21.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e221 (17.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 ( 6.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 ( 4.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-reported Hypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e566 (45.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e746 (59.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003eSelf-reported Diabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163 (12.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e261 (20.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication use, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertensive drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e488 (38.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e664 (52.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003eGlucose-lowering drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108 ( 8.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193 (15.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003eLipoprotein-lowering drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 ( 1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 ( 1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiplatelet drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 ( 1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 ( 4.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003eLaboratory results\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003eHDL cholesterol, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003eGlucose, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.98\u0026thinsp;\u0026plusmn;\u0026thinsp;2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\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\u003eTotal homocysteine, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.47\u0026thinsp;\u0026plusmn;\u0026thinsp;6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.26\u0026thinsp;\u0026plusmn;\u0026thinsp;7.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium levels, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma calcium, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, mL \u0026middot; min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e \u0026middot; 1.73 m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.29\u0026thinsp;\u0026plusmn;\u0026thinsp;13.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.69\u0026thinsp;\u0026plusmn;\u0026thinsp;15.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: BMI\u0026thinsp;=\u0026thinsp;body mass index, SBP\u0026thinsp;=\u0026thinsp;systolic blood pressure, DBP\u0026thinsp;=\u0026thinsp;diastolic blood pressure, HDL\u0026thinsp;=\u0026thinsp;high density lipoprotein, eGFR\u0026thinsp;=\u0026thinsp;estimated glomerular filtration rate. *Data are presented as number (%) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between plasma magnesium and first stroke\u003c/h2\u003e \u003cp\u003eThe association of plasma magnesium with the risk of first stroke and first ischemic stroke remained U-shaped, with increased risk at low and high plasma magnesium levels in adjusted model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Compared to tertile 2 [T2] of plasma magnesium levels (19.14 -\u0026lt; 20.66 mg/L), the ORs and 95% confidence interval [CI] for the lowest tertile (tertile 1 [T1] plasma magnesium\u0026thinsp;\u0026lt;\u0026thinsp;19.14 mg/L) was 1.37 (95% CI: 1.10, 1.70). Likewise, the highest tertile (tertile 3 [T3] plasma magnesium\u0026thinsp;\u0026ge;\u0026thinsp;20.66 mmol/L) was also associated with an increased risk for first stroke (OR: 1.28, 95% CI: 1.03, 1.60). Similarly, in the fully adjusted model (Model 2), participants in the T1 and T3 had a higher risk of first ischemic stroke (T1: 1.36, 95% CI: 1.07, 1.72; T3: 1.31, 95% CI: 1.03, 1.67, respectively) than those in the reference tertile (T2). However, no significant association was found between either low or high tertile of plasma magnesium and risk of first hemorrhagic stroke (OR: 1.34, 95% CI: 0.70, 2.55; OR: 1.33, 95% CI: 0.72, 2.49, respectively).\u003c/p\u003e \u003cp\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\u003eRisk of stroke associated with plasma magnesium concentrations\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=\"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=\"char\" char=\".\" 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=\"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 \u003cp\u003eMagnesium, mg/L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCases/\u003c/p\u003e \u003cp\u003econtrols\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStoke\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\u003eTertile\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\u003eT 1 (\u0026lt;\u0026thinsp;19.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e456/381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.47 (1.21, 1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37 (1.10, 1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT 2 (\u0026ge;\u0026thinsp;19.14,\u0026lt;20.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e376/460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\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\u003eT 3 (\u0026ge;\u0026thinsp;20.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e423/414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26 (1.03, 1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.28 (1.03, 1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIschemic stroke\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\u003eTertile\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\u003eT 1 (\u0026lt;\u0026thinsp;19.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e404/336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49 (1.21, 1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.36 (1.07, 1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT 2 (\u0026ge;\u0026thinsp;19.14,\u0026lt;20.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e317/390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\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\u003eT 3 (\u0026ge;\u0026thinsp;20.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e358/353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26 (1.01, 1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.31 (1.03, 1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemorrhagic stroke\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\u003eTertile\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\u003eT 1 (\u0026lt;\u0026thinsp;19.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51/42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.52 (0.88, 2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.34 (0.70, 2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT 2 (\u0026ge;\u0026thinsp;19.14,\u0026lt;20.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55/69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\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\u003eT 3 (\u0026ge;\u0026thinsp;20.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65/60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37 (0.82, 2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33 (0.72, 2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 1 is conditioned on the matching factors of age, sex, and study site\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel 2 is conditioned on the matching factors of age, sex, and study site, and adjusted for BMI, smoking status, alcohol consumption, self-reported hypertension, self-reported diabetes, Labor intensity, baseline SBP, baseline DBP, baseline fasting blood glucose, total cholesterol, triglycerides, HDLC, total homocysteine, serum calcium, eGFR, antihypertensive drugs, lipoprotein-lowering drugs, antiplatelet drugs, glucose-lowering drugs.\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\u003eStratified analysis by potential effect modifiers\u003c/h2\u003e \u003cp\u003eInteraction analyses were presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. No significant effect-modifications were found for: age (\u0026lt;\u0026thinsp;70 compared with \u0026ge;\u0026thinsp;70 years; \u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;=\u0026thinsp;0.851), sex (male compared with female; \u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;=\u0026thinsp;0.643), BMI (\u0026lt;\u0026thinsp;24 compared with \u0026ge;\u0026thinsp;24; \u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;=\u0026thinsp;0.363), SBP (\u0026lt;\u0026thinsp;140 compared with \u0026ge;\u0026thinsp;140; \u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;=\u0026thinsp;0.087), fasting glucose (\u0026lt;\u0026thinsp;7.0 compared with \u0026ge;\u0026thinsp;7.0 mmol/L or self-reported diabetes or use glucose-lowering drugs; \u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;=\u0026thinsp;0.727), current alcohol drinking (yes compared with no; \u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;=\u0026thinsp;0.400), total homocysteine (\u0026lt;\u0026thinsp;15 compared with \u0026ge;\u0026thinsp;15 umol/L; \u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;=\u0026thinsp;0.863) or TC (median, \u0026lt; 5.78 compared with \u0026ge;\u0026thinsp;5.78 mmol/L; \u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;=\u0026thinsp;0.841) at baseline, on the association between plasma magnesium (T2 \u003cem\u003evs\u003c/em\u003e. T1 and T3) and the risk of first stroke.\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\u003eMultivariate odds ratios of stroke associated with low and high versus medium plasma magnesium, stratified by other risk factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSubgroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ecases/ controls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eOR (95% CI) * of Magnesium Tertiles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;19.14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;19.14, \u0026lt;\u0026thinsp;20.67\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20.67\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e588/590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.39 (1.02, 1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.31 (0.96, 1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e667/665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.28 (0.97, 1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.22 (0.93, 1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.643\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\u003e621/621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.22 (0.92, 1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.21 (0.90, 1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\u003e634/634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.45 (1.08, 1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.30 (0.98, 1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e328/404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.56 (1.06, 2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.17 (0.79, 1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e927/851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.22 (0.96, 1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.31 (1.03, 1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e313/449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.84 (1.25, 2.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.33 (0.91, 1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e942/806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.19 (0.93, 1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.24 (0.97, 1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFasting glucose, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e896/1032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.29 (1.02, 1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.22 (0.97, 1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;7.0 or history of diabetes or use glucose-lowering drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e359/223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.60 (1.05, 2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.45 (0.88, 2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecurrent smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.035\u003c/p\u003e \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\u003e959/1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.22 (0.96, 1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.32 (1.05, 1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\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\u003e296/255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.04 (1.30, 3.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.02 (0.64, 1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCurrent alcohol drinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.400\u003c/p\u003e \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\u003e960/938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.41 (1.11, 1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.23 (0.98, 1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\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\u003e295/317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.28 (0.85, 1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.46 (0.96, 2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHomocysteine, umol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e869/940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.31 (1.03, 1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.22 (0.96, 1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e386/315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.31 (0.88, 1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.36 (0.93, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCholesterol, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e641/613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.24 (0.93, 1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.25 (0.93, 1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e614/642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.42 (1.06, 1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.27 (0.95, 1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eORs of first stroke in relation to plasma concentrations of magnesium (tertiles) were calculated using multivariate logistic regression models. Each subgroup analysis adjusted, if not stratified, for age, sex, BMI, smoking status, alcohol consumption, self-reported hypertension, self-reported diabetes, Labor intensity, baseline SBP, baseline DBP, baseline fasting blood glucose, total cholesterol, triglycerides, HDLC, total homocysteine, serum calcium, eGFR, antihypertensive drugs, lipoprotein-lowering drugs, antiplatelet drugs, glucose-lowering drugs.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHowever, a stronger positive association between the lowest tertile of plasma magnesium and first stroke was found in participants with current smoking (OR: 2.04; 95% CI: 1.30, 3.20) compared to those without current smoking (OR:1.22; 95% CI: 0.96, 1.54; \u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;=\u0026thinsp;0.035).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large prospective nested case-control study we found that the association of plasma magnesium concentrations with risk of first stroke and ischemic stroke followed a U-shaped curve, both low and high levels of plasma magnesium were associated with high risk of first stroke and ischemic stroke. In addition, current smoking was a modifier for the association between low plasma magnesium and first stroke.\u003c/p\u003e \u003cp\u003ePrevious studies have reported conflicting results for the relationships between plasma magnesium and the risk of stroke. The ARIC study [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] based on 577 ischemic stroke cases with 16 years of follow-up, found that the association between plasma magnesium and ischemic stroke was eliminated after adjustment for hypertension and diabetes mellitus. The REGARDS study [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] conducted in 27,770 participants, consistently reported that plasma magnesium had no significant effect on the risk of stroke with a median follow up of 5.5 years. However, a mendelian randomization analysis in European reported that each 0.1 mmol/L increase in genetically predicted serum magnesium concentrations were 0.78 (95% CI: 0.69\u0026ndash;0.89) for all ischemic stroke [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Additionally, a nested case-control study from the Nurses\u0026rsquo; Health Study (NHS, 459 ischemic stroke cases) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], conducted in a female population, with a mean follow-up duration of 16 years, found that compared with participants in quintile 5 (0.95 -\u0026lt; 1.15 mmol/L), the lowest risk of ischemic stroke was found in quintile 4 (0.90 -\u0026lt; 0.95 mmol/L; RR: 0.75, 95% CI: 0.48\u0026ndash;1.16), but not in the lowest quintile (0.7 -\u0026lt; 0.82 mmol/L; RR: 1.34, 95% CI: 0.82\u0026ndash;2.17). It suggested that the association between plasma magnesium and risk of first stroke is not a simple, linear one. Therefore, the association between plasma magnesium and the risk of stroke remains uncertain. Moreover, few studies to date have examined the relation between plasma magnesium and the risk of first stroke [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the current study, two new insights were demonstrated. Firstly, we found that there was a significant U-shaped relation between plasma magnesium concentration and first stroke and first ischemic stroke risk in this population. Consistent with several previous studies [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the risk of first stroke significantly increased in participants with low magnesium levels. Generally, Low plasma magnesium levels was associated with higher hypertension, diabetes mellitus and atrial fibrillation risk [\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Moreover, a direct role of low magnesium has been observed in endothelial dysfunction [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], lipoprotein peroxidation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and platelet aggregation and coagulation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These were already known to be associated with stroke. Additionally, the risk of first stroke also significantly increased in participants with high magnesium levels. High magnesium concentrations can increase the risk of soft tissue calcification via parathyroid hormone inhibition and less bone turnover [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and possibly associated with old age and renal impairment [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], both of them were associated with stroke. Moreover, high plasma magnesium may altered ion channel properties, induced by an altered oxido-redox state, lead to proarrhythmic conditions with normal hearts [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Thus, high magnesium levels might be a mediator factors or directly influence stroke. However, our findings seemed to be biologically plausible, more studies are needed to verify our results and to further examine the biological mechanisms.\u003c/p\u003e \u003cp\u003eAlthough the normal reference range currently recommended for magnesium in blood serum is 0.76\u0026ndash;1.15 mmol/L in general population [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, there is no acceptable value or range for plasma manganese according to different populations and health outcomes (i.e., atrial fibrillation [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], coronary heart disease [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], type 2 diabetes [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and hospitalized patients [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]). In our study, 82.35% of participants were in the normal reference range also had a high risk of first stroke, the lowest risk of stroke was found in the second tertile of plasma magnesium, this suggests that this \u0026ldquo;medium\u0026rdquo; plasma magnesium concentration may be more suitable for Chinese middle-aged and elderly populations with regards to prevention of first stroke. Therefore, there is a future study need to confirm the association of dietary and plasma magnesium with stroke in additional large prospective studies.\u003c/p\u003e \u003cp\u003eSecondly, current smoking was a significant effect modifier: a strong association between the lowest tertile of plasma magnesium and risk of stroke was found among current smokers. China, the world's most populous nation, is also the largest consumer of cigarettes. A recent meta analysis [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] revealed that current smokers had a 92% (95% CI: 1.49\u0026ndash;2.48) increased risk of stroke. The potential mechanisms underlying a smoking \u0026times; low magnesium interaction is unknown. In our current study, a plausible biological explanation for the interaction may be due to the fact that low plasma magnesium and smoking could possibly share some cellular and molecular mechanisms for the pathogenesis of stroke. Smoking also has been proven to be related to diabetes, high blood pressure and atrial fibrillation [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], which are strong risk factors for stroke. Additionally, smoking elevates the levels of homocysteine and fibrinogen, and is associated with the development of atherosclerosis. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] Further studies are needed to verify this hypothesis.\u003c/p\u003e \u003cp\u003eThe strengths of our study include the large number of participants and objectively measured plasma magnesium levels at baseline. Our study participants with stroke were confined to newly diagnosed and plasma magnesium measured before stoke diagnosis. Our study also have several potential limitations. First, we used a single assessment of plasma magnesium at baseline to analyze the associations between plasma or dietary magnesium and stroke incidence. It was known that the levels of plasma magnesium in the body vary constantly over time. Second, given the relatively small number of hemorrhagic stroke cases, we had limited power to explore the association between plasma magnesium and the risk of first hemorrhagic stroke. Our results warrant further investigations, including large-scale cohort studies and randomized trials. Third, our study focused on a middle-aged and elderly Chinese Han ethnicity population, therefore our results may not be suitable to younger populations or other racial and ethnic groups.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBoth low and high plasma magnesium levels were positively associated with first stroke risk, taking on a U-shaped trend in the large nested case-control study in a middle-aged and elderly Chinese population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the contribution the all staff who participated in this study as well as the study participants who shared their time with us.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHB, XC and XX designed the study and directed its implementation, including quality assurance and control. WZ and ML conducted data analysis and wrote the manuscript. LL, YS, XW and BW provided scientific comments and advice. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by funding from the following: the National Key Research and Development Program (2018ZX09301034003); the 111 project from the Education Ministry of China (B18053); the National Natural Science Foundation of China (81960074, 81500233); Jiangxi Provincial Drug Administration Science and Technology Project\u0026nbsp;(2022JS41,2023JS26).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted according to the principles laid down in the Declaration of Helsinki, and\u0026nbsp;approved by the Ethics Committee of the Institute of Biomedicine, Anhui Medical University, Hefei, China. All participants signed written informed consent.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Center for Prevention and Treatment of Cardiovascular Diseases, the Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eDepartment of Cardiovascular Medicine, the Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eState Key Laboratory of Cardiovascular Disease, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100037, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u0026nbsp;\u003c/sup\u003eBeijing Advanced Innovation Center for Food Nutrition and Human Health, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100091, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u003c/sup\u003e Shenzhen Evergreen Medical Institute, Shenzhen, Guangdong 518038, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e6\u003c/sup\u003e Institute of Biomedicine, Anhui Medical University, Hefei 230032, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e7\u003c/sup\u003e National Clinical Research Study Center for Kidney Disease; the State Key Laboratory for Organ Failure Research; Renal Division, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong 510515, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e8\u003c/sup\u003e Department of Population, Family and Reproductive Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore MD21218, USA.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018;392:1736-1788. doi: 10.1016/S0140-6736(18)32203-7\u003c/li\u003e\n\u003cli\u003eZhou M, Wang H, Zeng X, Yin P, Zhu J, Chen W, et al. Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2019;394:1145-1158. doi: 10.1016/S0140-6736(19)30427-1\u003c/li\u003e\n\u003cli\u003eAdebamowo SN, Spiegelman D, Willett WC, Rexrode KM. Association between intakes of magnesium, potassium, and calcium and risk of stroke: 2 cohorts of US women and updated meta-analyses. Am J Clin Nutr 2015;101:1269-1277. doi: 10.3945/ajcn.114.100354 \u003c/li\u003e\n\u003cli\u003eLarsson SC, Orsini N, Wolk A. Dietary magnesium intake and risk of stroke: A meta-analysis of prospective studies. Am J Clin Nutr 2012;95:362-366. doi:10.3945/ajcn.111.022376 \u003c/li\u003e\n\u003cli\u003eMerrill PD, Ampah SB, He K, Rembert NJ, Brockman J, Kleindorfer D, et al. Association between trace elements in the environment and stroke risk: The reasons for geographic and racial differences in stroke (REGARDS) study. J Trace Elem Med Biol 2017;42:45-49. doi:10.1016/j.jtemb.2017.04.003\u003c/li\u003e\n\u003cli\u003eVolpe SL. Magnesium, the metabolic syndrome, insulin resistance, and type 2 diabetes mellitus. Crit Rev Food Sci Nutr 2008;48:293-300. doi:10.1080/10408390701326235\u003c/li\u003e\n\u003cli\u003eShechter M. Magnesium and cardiovascular system. Magnes Res 2010;23:60-72. doi:10.1684/mrh.2010.0202\u003c/li\u003e\n\u003cli\u003eKolte D, Vijayaraghavan K, Khera S, Sica DA, Frishman WH. Role of magnesium in cardiovascular diseases. Cardiol Rev 2014;22:182-92. doi:10.1097/CRD.0000000000000003\u003c/li\u003e\n\u003cli\u003eDel GL, Imamura F, Wu JH, de Oliveira OM, Chiuve SE, Mozaffarian D. Circulating and dietary magnesium and risk of cardiovascular disease: A systematic review and meta-analysis of prospective studies. Am J Clin Nutr 2013;98:160-173. doi:10.3945/ajcn.112.053132\u003c/li\u003e\n\u003cli\u003eJoosten MM, Gansevoort RT, Mukamal KJ, van der Harst P, Geleijnse JM, Feskens EJ, et al. Urinary and plasma magnesium and risk of ischemic heart disease. Am J Clin Nutr 2013;97:1299-1306. doi:10.3945/ajcn.112.054114\u003c/li\u003e\n\u003cli\u003eChambers EC, Heshka S, Gallagher D, Wang J, Pi-Sunyer FX, Pierson RJ. Serum magnesium and type-2 diabetes in African Americans and Hispanics: A New York cohort. J Am Coll Nutr 2006;25:509-513.doi:10.1080/07315724.2006.10719566\u003c/li\u003e\n\u003cli\u003eXu T, Chen GC, Zhai L, Ke KF. Nonlinear reduction in risk for type 2 diabetes by magnesium intake: An updated Meta-Analysis of prospective cohort studies. Biomed Environ Sci 2015;28:527-534. doi:10.3967/bes2015.075\u003c/li\u003e\n\u003cli\u003eSarrafzadegan N, Khosravi-Boroujeni H, Lotfizadeh M, Pourmogaddas A, Salehi-Abargouei A. Magnesium status and the metabolic syndrome: A systematic review and meta-analysis. Nutrition 2016;32:409-417. doi:10.1016/j.nut.2015.09.014\u003c/li\u003e\n\u003cli\u003eLarsson SC, Traylor M, Burgess S, Boncoraglio GB, Jern C, Michaelsson K, et al. Serum magnesium and calcium levels in relation to ischemic stroke: Mendelian randomization study. Neurology 2019;92:e944-950. doi:10.1212/WNL.0000000000007001\u003c/li\u003e\n\u003cli\u003eZhang X, Xia J, Del GL, Hruby A, Dai Q, Song Y. Serum magnesium concentrations and all-cause, cardiovascular, and cancer mortality among U.S. Adults: Results from the NHANES I Epidemiologic Follow-up Study. Clin Nutr 2018;37:1541-1549. doi:10.1016/j.clnu.2017.08.021\u003c/li\u003e\n\u003cli\u003eOhira T, Peacock JM, Iso H, Chambless LE, Rosamond WD, Folsom AR. Serum and dietary magnesium and risk of ischemic stroke: The Atherosclerosis Risk in Communities Study. Am J Epidemiol 2009;169:1437-1444. doi:10.1093/aje/kwp071\u003c/li\u003e\n\u003cli\u003eAdebamowo SN, Jimenez MC, Chiuve SE, Spiegelman D, Willett WC, Rexrode KM. Plasma magnesium and risk of ischemic stroke among women. Stroke 2014;45:2881-2886. doi:10.1161/STROKEAHA.114.005917\u003c/li\u003e\n\u003cli\u003eMeschia JF, Bushnell C, Boden-Albala B, Braun LT, Bravata DM, Chaturvedi S, et al. Guidelines for the primary prevention of stroke: A statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2014;45:3754-3832. doi:10.1161/STR.00 00000000000046\u003c/li\u003e\n\u003cli\u003eAltura BT, Memon ZI, Zhang A, Cheng TP, Silverman R, Cracco RQ, et al. Low levels of serum ionized magnesium are found in patients early after stroke which result in rapid elevation in cytosolic free calcium and spasm in cerebral vascular muscle cells. Neurosci Lett 1997;230:37-40. doi:10.1016/s0304-3940(97)00471-0\u003c/li\u003e\n\u003cli\u003eFeng P, Niu X, Hu J, Zhou M, Liang H, Zhang Y, et al. Relationship of serum magnesium concentration to risk of short-term outcome of acute ischemic stroke. Blood Press 2013;22:297-301. doi:10.3109/08037051.2012.759696\u003c/li\u003e\n\u003cli\u003eCojocaru IM, Cojocaru M, Burcin C, Atanasiu NA. Serum magnesium in patients with acute ischemic stroke. Rom J Intern Med 2007;45(3):269-273.\u003c/li\u003e\n\u003cli\u003eKhan AM, Lubitz SA, Sullivan LM, Sun JX, Levy D, Vasan RS, et al. Low serum magnesium and the development of atrial fibrillation in the community: The Framingham Heart Study. Circulation 2013;127:33-38. doi:10.1161/CIRCULATIONAHA.111.082511\u003c/li\u003e\n\u003cli\u003eMisialek JR, Lopez FL, Lutsey PL, Huxley RR, Peacock JM, Chen LY, et al. Serum and dietary magnesium and incidence of atrial fibrillation in whites and in African Americans--Atherosclerosis Risk in Communities (ARIC) study. Circ J 2013;77(2):323-329. doi:10.1253/circj.cj-12-0886\u003c/li\u003e\n\u003cli\u003ePalmer BF, Clegg DJ. Electrolyte and Acid-Base disturbances in patients with diabetes mellitus. N Engl J Med 2015;373:548-559. doi: 10.1056/NEJMra1503102\u003c/li\u003e\n\u003cli\u003eRamadass S, Basu S, Srinivasan AR. SERUM magnesium levels as an indicator of status of Diabetes Mellitus type 2. Diabetes Metab Syndr 2015;9:42-45. doi:10.1016/j.dsx.2014.04.024\u003c/li\u003e\n\u003cli\u003ePeacock JM, Folsom AR, Arnett DK, Eckfeldt JH, Szklo M. Relationship of serum and dietary magnesium to incident hypertension: The Atherosclerosis Risk in Communities (ARIC) Study. Ann Epidemiol 1999;9:159-165. doi:10.1016/s1047-2797(98)00040-4\u003c/li\u003e\n\u003cli\u003eMaier JA, Malpuech-Brugere C, Zimowska W, Rayssiguier Y, Mazur A. Low magnesium promotes endothelial cell dysfunction: Implications for atherosclerosis, inflammation and thrombosis. Biochim Biophys Acta 2004;1689:13-21. doi:10.1016/j.bbadis.2004.01.002\u003c/li\u003e\n\u003cli\u003eBlache D, Devaux S, Joubert O, Loreau N, Schneider M, Durand P, et al. Long-term moderate magnesium-deficient diet shows relationships between blood pressure, inflammation and oxidant stress defense in aging rats. Free Radic Biol Med 2006;41:277-284. doi:10.1016/j.freeradbiomed. 2006.04.008\u003c/li\u003e\n\u003cli\u003eKolte D, Vijayaraghavan K, Khera S, Sica DA, Frishman WH. Role of magnesium in cardiovascular diseases. Cardiol Rev 2014;22:182-192. doi:10.1097/CRD.0000000000000003\u003c/li\u003e\n\u003cli\u003eKanbay M, Goldsmith D, Uyar ME, Turgut F, Covic A. Magnesium in chronic kidney disease: Challenges and opportunities. Blood Purif 2010;29:280-292. doi:10.1159/000276665\u003c/li\u003e\n\u003cli\u003eClark BA, Brown RS. Unsuspected morbid hypermagnesemia in elderly patients. Am J Nephrol 1992;12:336-343. doi:10.1159/000168469\u003c/li\u003e\n\u003cli\u003eSantulli G, Pagano G, Sardu C, Xie W, Reiken S, D\u0026apos;Ascia SL, et al. Calcium release channel RyR2 regulates insulin release and glucose homeostasis. J Clin Invest 2015;125:1968-1978. doi: 10.1172/JCI79273\u003c/li\u003e\n\u003cli\u003eSardu C, Carreras G, Katsanos S, Kamperidis V, Pace MC, Passavanti MB, et al. Metabolic syndrome is associated with a poor outcome in patients affected by outflow tract premature ventricular contractions treated by catheter ablation. BMC Cardiovasc Disord 2014;14:176. doi: 10.1186/1471-2261-14-176\u003c/li\u003e\n\u003cli\u003eGrober U, Schmidt J, Kisters K. Magnesium in prevention and therapy. Nutrients 2015;7:8199-8226. doi: 10.3390/nu7095388\u003c/li\u003e\n\u003cli\u003eIsmail Y, Ismail AA, Ismail AA. The underestimated problem of using serum magnesium measurements to exclude magnesium deficiency in adults; A health warning is needed for \u0026quot;normal\u0026quot; results. Clin Chem Lab Med 2010;48:323-327. doi: 10.1515/CCLM.2010.077\u003c/li\u003e\n\u003cli\u003eSvagzdiene M, Sirvinskas E, Baranauskiene D, Adukauskiene D. Correlation of magnesium deficiency with C-reactive protein in elective cardiac surgery with cardiopulmonary bypass for ischemic heart disease. Medicina (Kaunas) 2015;51:100-06. doi: 10.1016/j.medici.2015.03.003\u003c/li\u003e\n\u003cli\u003eMisialek JR, Lopez FL, Lutsey PL, Huxley RR, Peacock JM, Chen LY, et al. Serum and dietary magnesium and incidence of atrial fibrillation in whites and in African Americans--Atherosclerosis Risk in Communities (ARIC) study. Circ J 2013;77:323-329. doi: 10.1253/circj.cj-12-0886\u003c/li\u003e\n\u003cli\u003eKhan AM, Lubitz SA, Sullivan LM, Sun JX, Levy D, Vasan RS, et al. Low serum magnesium and the development of atrial fibrillation in the community: The Framingham Heart Study. Circulation 2013;127:33-38. doi: 10.1161/CIRCULATIONAHA.111.082511\u003c/li\u003e\n\u003cli\u003eKieboom BC, Niemeijer MN, Leening MJ, van den Berg ME, Franco OH, Deckers JW, et al. Serum magnesium and the risk of death from coronary heart disease and sudden cardiac death. J Am Heart Assoc 2016;5:e002707. doi: 10.1161/JAHA.115.002707\u003c/li\u003e\n\u003cli\u003ePham PC, Pham PM, Pham SV, Miller JM, Pham PT. Hypomagnesemia in patients with type 2 diabetes. Clin J Am Soc Nephrol 2007;2:366-373. doi: 10.2215/CJN.02960906\u003c/li\u003e\n\u003cli\u003eCheungpasitporn W, Thongprayoon C, Qian Q. Dysmagnesemia in hospitalized patients: Prevalence and prognostic importance. Mayo Clin Proc 2015;90:1001-1010. doi: 10.1016/j.mayocp.2015.04.023\u003c/li\u003e\n\u003cli\u003ePan B, Jin X, Jun L, Qiu S, Zheng Q, Pan M. The relationship between smoking and stroke: A meta-analysis. Medicine (Baltimore) 2019;98:e14872. doi: 10.1097/MD.0000000000014872\u003c/li\u003e\n\u003cli\u003eAune D, Schlesinger S, Norat T, Riboli E. Tobacco smoking and the risk of atrial fibrillation: A systematic review and meta-analysis of prospective studies. Eur J Prev Cardiol 2018;25:1437-1451. doi: 10.1177/2047487318780435\u003c/li\u003e\n\u003cli\u003eYoon U, Kwok LL, Magkidis A. Efficacy of lifestyle interventions in reducing diabetes incidence in patients with impaired glucose tolerance: A systematic review of randomized controlled trials. Metabolism 2013;62:303-14. doi: 10.1016/j.metabol.2012.07.009\u003c/li\u003e\n\u003cli\u003eBowman TS, Gaziano JM, Buring JE, Sesso HD. A prospective study of cigarette smoking and risk of incident hypertension in women. J Am Coll Cardiol 2007;50(21):2085-2092. doi: 10.1016/j.jacc.2007.08.017\u003c/li\u003e\n\u003cli\u003ePanagiotakos DB, Pitsavos C, Chrysohoou C, Skoumas J, Masoura C, Toutouzas P, et al. Effect of exposure to secondhand smoke on markers of inflammation: The ATTICA study. Am J Med 2004;116:145-150. doi: 10.1016/j.amjmed.2003.07.019\u003c/li\u003e\n\u003cli\u003eHackshaw A, Morris JK, Boniface S, Tang JL, Milenkovic D. Low cigarette consumption and risk of coronary heart disease and stroke: Meta-analysis of 141 cohort studies in 55 study reports. BMJ 2018;360:j5855. doi: 10.1136/bmj.j5855\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":"plasma magnesium, first stroke, hypertension, U-shaped curve","lastPublishedDoi":"10.21203/rs.3.rs-4732467/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4732467/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003ePrevious studies on the association between plasma magnesium concentrations and risk of first stroke were limited. We aimed to evaluate the association between plasma magnesium concentrations and the risk of first stroke in a community based Chinese population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe study sample population was drawn from “H-type Hypertension Prevention and Control Public Service Project” in China. We conducted a nested case–control analysis and matched 1255 cases with an equal number of controls for age ± 1 years, sex and study sites. Conditional logistic regression models was used to estimate the association of plasma magnesium with the risk of stroke and stroke subtypes (ischemic stroke and hemorrhagic strokes).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Using restricted cubic splines, there was a U-shaped association of plasma magnesium concentrations with risk of first stroke and ischemic stroke. Compared with the middle tertile (19.14 -\u0026lt; 20.67 mg/L), the multivariate-adjusted ORs (95% confidence interval [CI]) of stroke and ischemic stroke in the lowest tertile plasma magnesium were 1.37 (1.10, 1.70) and 1.36 (1.07, 1.72), in the highest tertile of plasma magnesium were 1.28 (1.03, 1.60) and 1.31 (1.03, 1.67), respectively. Furthermore, a stronger positive association between low tertile of plasma magnesium and first stroke was found in participants with current smoking than without (\u003cem\u003eP\u003c/em\u003e-interaction=0.035). No significant effect modifications were observed in subgroup analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Our study indicated a U-shaped association between plasma magnesium and first stroke, \u0026nbsp;especially among current smoker.\u003c/p\u003e","manuscriptTitle":"U-shaped Association Between Plasma Magnesium and First Stroke: A Community Based Nested Case-control Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-26 12:32:30","doi":"10.21203/rs.3.rs-4732467/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":"e17387f9-9434-41cb-869b-abd6eba2ba95","owner":[],"postedDate":"August 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-21T02:38:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-26 12:32:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4732467","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4732467","identity":"rs-4732467","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}