Statistics Korea with our dataset using each individual’s distinctive identifier (17). Mortality information had been obtained till December 2009. Statistical analyses All analyses have been performed employing SPSS (SPSS version 18.0, Chicago, IL, USA). Information are presented as the mean ?regular deviation for continuous variables and as proportions for categorical variables. Variations in continuous variables have been anahttp://dx.doi.org/10.3346/jkms.2013.28.7.lyzed by one-way evaluation of variance (ANOVA), and variations in categorical variables had been analyzed by chi-square tests. Co-variate ANOVA (ANCOVA) was applied for adjusting independent factors associated to BP for example age, eGFR, BMI, total serum cholesterol, protein, calcium, phosphorus, glucose, potassium, higher density lipoprotein cholesterol (HDL cholesterol), and alkaline phosphatase (ALP). Statistical significance was deemed to be indicated when P 0.05. To detect intra-group differences in BP among the 4 sodium groups, we used Bonferroni’s correction (P 0.05/9 = 0.006 or P 0.05/4 = 0.013). The group with pNa 138-140 mM/L was designated the reference group. These analyses were adjusted for gender, age, eGFR, BMI, calcium, total serum cholesterol, phosphorus, fasting glucose, potassium, ALP, HDL cholesterol, and metabolic syndrome. We compared the cumulative incidence of all-cause mortality among participants, and categorized them into 4 groups in line with the pNa levels, via a log-rank test.4CzIPN Data Sheet Cox’s hazard proportional analysis was applied to estimate the hazard ratios (HRs) for all-cause mortality. Ethics statement This study protocol was reviewed and authorized by the institutional evaluation board on the Seoul National University Bundang Hospital (IRB quantity: H-1003-095-104). Informed consent was waived by the board.RESULTSCharacteristics of participants We excluded 12,703 with a history of anti-hypertensive medication, ten,486 diabetics, and 8,209 with an eGFR 60 mL/min/ 1.(R)-(Piperidin-3-yl)methanol Order 73 m2 of your all participants. Also, we excluded three,640 participants with pNa 138 mM/L; the remaining 97,009 participants have been enrolled in the study. Of the 97,009 participants, 52.7 were male, median age was 49 yr, 17.9 had metabolic syndrome, and 0.6 had CAD. We divided the 97,009 participants into 4 groups in accordance with pNa levels and designated group 1 because the reference group. Of the four sodium groups, age (P 0.001), gender, BMI (P 0.001), BUN (P 0.001), creatinine (P 0.001), eGFR (P 0.001), hemoglobin (P 0.001), potassium (P 0.001), calcium (P 0.001), phosphorus (P 0.001), protein (P 0.001), ALP (P 0.001), total serum cholesterol (P 0.PMID:33534892 001), HDL cholesterol (P 0.001), glucose (P = 0.002), HbA1c (P 0.001), urine protein 1+ by dipstick, CAD, number of metabolic syndrome components ALT (P = 0.007), had been drastically different, as determined by ANOVA or chi-square tests (Table 1). BP in line with pNa levels All individuals showed optimistic correlations involving pNa and SBP, DBP, or PP (P for trend 0.001). Fig. 1A shows the connection in between SBP and pNa levels. In participants with pNahttp://jkms.orgOh SW, et al. ?Mortality Risks and Plasma Sodium LevelsTable 1. Baseline characteristics of participants stratified by plasma sodium Plasma sodium (mM/L) Parameters Na (mM/L) Age (yr) Guys ( ) BMI (m2/kg) BUN (mg/dL) Creatinine (mg/dL) eGFR (mL/min/1.73 m2) CRP (mg/dL), ESR (mm/hr) Hemoglobin (g/dL) Potassium (mM/L) Calcium (mg/dL) Phosphorus (mg/dL) Protein (g/dL) ALT (U/L)* AST (U/L)* ALP (U/L) GT (U/L)* Cholesterol (mg/dL).