24-Hour Urinary Sodium Excretion Association with Cardiovascular Events: A Systematic Review and Dose-Response Meta-Analysis

ZHAO Dan LI Hua Min LI Chao Xiu ZHOU Bo

ZHAO Dan, LI Hua Min, LI Chao Xiu, ZHOU Bo. 24-Hour Urinary Sodium Excretion Association with Cardiovascular Events: A Systematic Review and Dose-Response Meta-Analysis[J]. Biomedical and Environmental Sciences, 2022, 35(10): 921-930. doi: 10.3967/bes2022.119
Citation: ZHAO Dan, LI Hua Min, LI Chao Xiu, ZHOU Bo. 24-Hour Urinary Sodium Excretion Association with Cardiovascular Events: A Systematic Review and Dose-Response Meta-Analysis[J]. Biomedical and Environmental Sciences, 2022, 35(10): 921-930. doi: 10.3967/bes2022.119

doi: 10.3967/bes2022.119

24-Hour Urinary Sodium Excretion Association with Cardiovascular Events: A Systematic Review and Dose-Response Meta-Analysis

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    Author Bio:

    ZHAO Dan, female, born in 1997, Master Candidate, majoring in public health

    Corresponding author: ZHOU Bo, Associate Professor, Post-doctor, Tel: 86-24-83282840, E-mail: zhoubo@cmu.edu.cn
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  • S2.  (A) Funnel plots for publication bias in the low-level groups. (B) Funnel plots for publication bias in the high-level groups

    Figure  1.  Flow chart of the literature search for studies.

    Figure  2.  (A) Forest Plot of relative risks (RRs) and 95% Confidence Intervals (CIs) for the association between low-level sodium excretion (< 4 g/d) and the risk of CV events. (B) RRs and 95% CIs for the association between high-level sodium excretion (≥ 4 g/d) and the risk of CV events.

    Figure  3.  Non-linear dose-response association between 24-h Sodium excretion and the risk of CV events. Dashed lines indicated 95% confidence intervals. Reference standard was 1.265 g/24 h.

    Figure  4.  Linear dose-response association between 24-h Sodium excretion and the risk of CV events. Dashed lines indicated 95% confidence intervals.

    S1.  Plot for sensitivity analysis in the nine studies

    S1.   PRISMA 2020 checklist

    Section and topicItemChecklist itemLocation
    where item
    is reported
    TITLE
     Title1Identify the report as a systematic review.2
    ABSTRACT
     Abstract2See the PRISMA 2020 for Abstracts checklist.2
    INTRODUCTION
     Rationale3Describe the rationale for the review in the context of existing knowledge.2
     Objectives4Provide an explicit statement of the objective(s) or question(s) the review addresses.3
    METHODS
     Eligibility criteria5Specify the inclusion and exclusion criteria for the review and how studies were grouped for the syntheses.3
     Information sources6Specify all databases, registers, websites, organisations, reference lists and other sources searched or consulted to identify studies. Specify the date when each source was last searched or consulted.3
     Search strategy7Present the full search strategies for all databases, registers and websites, including any filters and limits used.3
     Selection process8Specify the methods used to decide whether a study met the inclusion criteria of the review, including how many reviewers screened each record and each report retrieved, whether they worked independently, and if applicable, details of automation tools used in the process.3
     Data collection process9Specify the methods used to collect data from reports, including how many reviewers collected data from each report, whether they worked independently, any processes for obtaining or confirming data from study investigators, and if applicable, details of automation tools used in the process.3
     Data items10aList and define all outcomes for which data were sought. Specify whether all results that were compatible with each outcome domain in each study were sought (e.g. for all measures, time points, analyses), and if not, the methods used to decide which results to collect.3
    10bList and define all other variables for which data were sought (e.g. participant and intervention characteristics, funding sources). Describe any assumptions made about any missing or unclear information.3
    Study risk of bias assessment11Specify the methods used to assess risk of bias in the included studies, including details of the tool(s) used, how many reviewers assessed each study and whether they worked independently, and if applicable, details of automation tools used in the process.4
     Effect measures12Specify for each outcome the effect measure(s) (e.g. risk ratio, mean difference) used in the synthesis or presentation of results.4
     Synthesis methods13aDescribe the processes used to decide which studies were eligible for each synthesis (e.g. tabulating the study intervention characteristics and comparing against the planned groups for each synthesis (item #5)).
    13bDescribe any methods required to prepare the data for presentation or synthesis, such as handling of missing summary statistics, or data conversions.4
    13cDescribe any methods used to tabulate or visually display results of individual studies and syntheses.4
    13dDescribe any methods used to synthesize results and provide a rationale for the choice(s). If meta-analysis was performed, describe the model(s), method(s) to identify the presence and extent of statistical heterogeneity, and software package(s) used.4
    13eDescribe any methods used to explore possible causes of heterogeneity among study results (e.g. subgroup analysis, meta-regression).4
    13fDescribe any sensitivity analyses conducted to assess robustness of the synthesized results.4
    Reporting bias assessment14Describe any methods used to assess risk of bias due to missing results in a synthesis (arising from reporting biases).4
     Certainty assessment15Describe any methods used to assess certainty (or confidence) in the body of evidence for an outcome.4
    RESULTS
     Study selection16aDescribe the results of the search and selection process, from the number of records identified in the search to the number of studies included in the review, ideally using a flow diagram.5
    16bCite studies that might appear to meet the inclusion criteria, but which were excluded, and explain why they were excluded.
     Study characteristics17Cite each included study and present its characteristics.7–8
     Risk of bias in studies18Present assessments of risk of bias for each included study.5
    Results of individual studies19For all outcomes, present, for each study: (a) summary statistics for each group (where appropriate) and (b) an effect estimate and its precision (e.g. confidence/credible interval), ideally using structured tables or plots.9, 11
     Results of syntheses20aFor each synthesis, briefly summarise the characteristics and risk of bias among contributing studies.5
    20bPresent results of all statistical syntheses conducted. If meta-analysis was done, present for each the summary estimate and its precision (e.g. confidence/credible interval) and measures of statistical heterogeneity. If comparing groups, describe the direction of the effect.9
    20cPresent results of all investigations of possible causes of heterogeneity among study results.9
    20dPresent results of all sensitivity analyses conducted to assess the robustness of the synthesized results.11
     Reporting biases21Present assessments of risk of bias due to missing results (arising from reporting biases) for each synthesis assessed.12
    Certainty of evidence22Present assessments of certainty (or confidence) in the body of evidence for each outcome assessed.11
    DISCUSSION
     Discussion23aProvide a general interpretation of the results in the context of other evidence.12
    23bDiscuss any limitations of the evidence included in the review.13
    23cDiscuss any limitations of the review processes used.13
    23dDiscuss implications of the results for practice, policy, and future research.13
    OTHER INFORMATION
     Registration and protocol24aProvide registration information for the review, including register name and registration number, or state that the review was not registered.
    24bIndicate where the review protocol can be accessed, or state that a protocol was not prepared.
    24cDescribe and explain any amendments to information provided at registration or in the protocol.
     Support25Describe sources of financial or non-financial support for the review, and the role of the funders or sponsors in the review.
     Competing interests26Declare any competing interests of review authors.
    Availability of data, code and other materials27Report which of the following are publicly available and where they can be found: template data collection forms; data extracted from included studies; data used for all analyses; analytic code; any other materials used in the review.
      Note. From: Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71.For more information, visit: http://www.prisma-statement.org/
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    Table  1.   Characteristics of the included studies in this Meta-Analysis

    Author, yearCountryFollow-up (years)Subjects/
    events (n)
    Age (y)GenderMethods
    of Na
    measurement
    Study populationOutcome definition
    Martin J. O’Donnell, 2011[9]40 countries4.728,880/
    4,729
    66.52 ±
    7.22
    men/
    women
    collected morning fasting urine samplesparticipants in ONTARGET and the TRANSCEND trialscomposite outcome (CV mortality, myocardial infarction, stroke, and hospitalization for CHF)
    Roman Pfister, 2013[32]the United Kingdom12.919,857/
    1,210
    58.0 ±
    9.2
    men/
    women
    collected casual urine specimengeneral populationheart failure
    Michel M. Joosten, 2013[31]the
    Netherlands
    10.5 (IQR: 9.9−10.8)7,543/
    452
    28−75men/
    women
    collected 24-h urine samplesgeneral populationCHD was defined as incident cardiac morbidity and mortality (including myocardial infarction, acute and subacute ischemic heart disease, and coronary artery bypass grafting or percutaneous transluminal coronary angioplasty)
    Nancy R. Cook, 2014[10]the United States52,312/
    193
    31−50men/
    women
    collected 24-h urine samplesgeneral populationCVD or CVD death, including myocardial infarction, stroke, coronary artery bypass graft, percutaneous transluminal coronary angioplasty, or death from cardiovascular disease
    Pamela Singer, 2014[12]the United States18.63,505/
    399
    52.4 ±
    9.9
    men/
    women
    collected 24-h urine samplesparticipants were individuals in a union-sponsored, worksite hypertension program in New York City between 1978 and 1999coronary artery disease, including MI, ischemic heart disease, heart failure, and hypertensive heart disease
    Martin O’Donnell, 2019[14]628 urban
    and rural communities
    in low, middle,
    and high
    income countries
    8.2103,200/
    7,884
    35−70men/
    women
    collected morning fasting midstream urine samplegeneral populationthe primary composite outcome was all caused mortality or myocardial infarction or stroke or heart failure
    Matti A. Vuori, 2020[33]Finland144,517/
    424
    45.4 ±
    11.4
    men/
    women
    collected 24-h urine samplesgeneral populationCVD was defined as the onset of CHD, stroke, or heart failure
    Wuopio J,
    2020[35]
    the United Kingdom8.2 ± 1.0215,535/
    3,751
    40−69mencollected midstream spot urine samplesgeneral populationatrial fibrillation or atrial flutter
    257,545/
    2,221
    women
    Yi-Jie Wang, 2021[34]China19.1 (IQR: 7.4−21.4)2,112/
    279
    54 ±
    12
    men/
    women
    recorded the sleep time and calculated the 24-h urine amount from sleep time and morning voiding urinegeneral populationIncident CVD events, including CHD and stroke
      Note. 24 h UNaE: 24 h urinary sodium excretion; CV: cardiovascular; CHD: coronary heart disease; CVD: cardiovascular disease; CHF: congestive heart failure; ONTARGET: Ongoing Telmisartan Alone and in combination with Ramipril Global Endpoint Trial; TRANSCEND: Telmisartan Randomized Assessment Study in ACE Intolerant Subjects with Cardiovascular Disease.
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    Table  2.   Subgroup analysis of different levels of sodium excretion and the risk of CV events

    SubgroupsNo. of studiesLow levelHigh level
    Pooled RR (95% CI)P valueI² (%)P value for
    heterogeneity
    Pooled RR (95% CI) P value I² (%)P value for
    heterogeneity
    Follow-up duration         
     < 10 years40.910
    (0.866, 0.958)
    0.0003 < 0.010.96110.926
    (0.895, 0.957)
    < 0.0001 0.010.2904
     > 10 years50.947
    (0.821, 1.093)
    0.4575 54.770.04811.093
    (0.898, 1.329)
    0.3759 68.490.0037
    Sample size         
     < 10,00051.027
    (0.912, 1.156)
    0.6632 < 0.010.97891.228
    (1.070, 1.410)
    0.0035 < 0.010.5886
     > 10,00040.886
    (0.836, 0.940)
    0.0001 27.480.19300.918
    (0.889, 0.948)
    < 0.0001 0.020.6090
    Conducted in general population
     Yes70.912
    (0.843, 0.986)
    0.0207 42.630.16541.049
    (0.902, 1.220)
    0.5358 91.080.0021
     No20.932
    (0.834, 1.042)
    0.2162 < 0.010.47450.943
    (0.861, 1.033)
    0.2062 < 0.010.7316
    Method of urine collection      
    Collected 24-h urine samples41.042
    (0.911, 1.192)
    0.5489 < 0.010.97211.190
    (1.023, 1.385)
    0.0242 < 0.010.6025
    Collected spot urine samples50.892
    (0.847, 0.939)
    < 0.0001 12.980.26700.922
    (0.893, 0.952)
    < 0.0001 < 0.010.0744
    Study quality         
     ≤ 730.918
    (0.852, 0.989)
    0.0241 0.010.72240.928
    (0.887, 0.971)
    0.0012 < 0.010.8695
     > 760.925
    (0.826, 1.035)
    0.1743 55.120.10341.094
    (0.909, 1.316)
    0.3439 84.390.0010
    Adjusted blood pressure or hypertension      
     Yes40.883
    (0.815, 0.958)
    0.0026 42.650.16380.921
    (0.885, 0.959)
    0.0001 < 0.010.0365
     No50.967
    (0.876, 1.068)
    0.5129 14.680.57581.084
    (0.917, 1.281)
    0.3444 59.570.0211
    Adjusted potassium excretion      
     Yes40.960
    (0.873, 1.056)
    0.4035 < 0.010.66941.047
    (0.879, 1.247)
    0.6076 40.920.1626
     No50.892
    (0.829, 0.960)
    0.0023 36.530.15340.999
    (0.855, 1.168)
    0.9923 92.710.0057
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  • 收稿日期:  2021-12-28
  • 录用日期:  2022-04-22
  • 刊出日期:  2022-10-20

24-Hour Urinary Sodium Excretion Association with Cardiovascular Events: A Systematic Review and Dose-Response Meta-Analysis

doi: 10.3967/bes2022.119
    作者简介:

    ZHAO Dan, female, born in 1997, Master Candidate, majoring in public health

    通讯作者: ZHOU Bo, Associate Professor, Post-doctor, Tel: 86-24-83282840, E-mail: zhoubo@cmu.edu.cn

English Abstract

ZHAO Dan, LI Hua Min, LI Chao Xiu, ZHOU Bo. 24-Hour Urinary Sodium Excretion Association with Cardiovascular Events: A Systematic Review and Dose-Response Meta-Analysis[J]. Biomedical and Environmental Sciences, 2022, 35(10): 921-930. doi: 10.3967/bes2022.119
Citation: ZHAO Dan, LI Hua Min, LI Chao Xiu, ZHOU Bo. 24-Hour Urinary Sodium Excretion Association with Cardiovascular Events: A Systematic Review and Dose-Response Meta-Analysis[J]. Biomedical and Environmental Sciences, 2022, 35(10): 921-930. doi: 10.3967/bes2022.119
    • According to the World Health Statistics 2021, the disease burden has shifted to non-communicable diseases (NCDS) worldwide[1]. Among the four major NCDS, cardiovascular (CV) events’ prevalence has contributed most to the associated mortality. Since 2000, CV events have increased by a quarter, reaching approximately 17.9 million in 2019[1]. Moreover, CV events have become a major public health problem due to their burden on health and economic status on society. High blood pressure is one of the leading risk factors for CV events[2, 3]. Several studies suggest that dietary sodium intake is closely related to blood pressure levels, and excessive sodium intake increases the risk for hypertension[4-7]. Therefore, the risk of CV events might be affected by sodium intake by altering the blood pressure levels.

      The relationship between sodium intake and risk of CV events is not yet confirmed. Many studies were conducted to assess the association between sodium intake and the risk of CV events [8-14]. However, their conclusions were inconsistent. Some studies reported that the relationship between sodium intake and CV events was J-shaped or U-shaped, i.e., with the lower and higher sodium intake increases the risk of CV events [9, 10, 14]. Besides, some studies demonstrated that the sodium intakes were positively related to the risk of CV events [8, 13]. In contrast, other studies suggested that the association between sodium intake and CV risk was not statistically significant [11, 12]. Therefore, further investigation is required to understand the relationship between sodium intake and CV events.

      Accurate assessment of dietary sodium intake is one of the major methodological challenges in dietary sodium research. Self-reported measurement methods of dietary intake, such as using food frequency questionnaire (FFQ) or single 24-h dietary recall, limited by errors in self-report and inaccuracies in food composition databases. The TRUE Consortium recommend against using self-reported methods for assessing dietary intake due to the inaccuracy of this method [15]. In healthy people, about 93 percent of sodium intake is excreted in the urine [16]. Twenty-four hour urinary excretion is considered as the gold standard method for assessing sodium intake [17]. To clarify the relationship between sodium intake and CV risk, we used 24-hour sodium excretion as a replacement for dietary intake, and conducted a systematic review and dose-response meta-analysis of published studies evaluating the association between 24-hour sodium excretion and CV risk. Furthermore, we discussed the limitations of current studies and provided hints for further studies.

    • This meta-analysis was performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement (Supplementary Table S1 available in www.besjournal.com) [18].

      Table S1.  PRISMA 2020 checklist

      Section and topicItemChecklist itemLocation
      where item
      is reported
      TITLE
       Title1Identify the report as a systematic review.2
      ABSTRACT
       Abstract2See the PRISMA 2020 for Abstracts checklist.2
      INTRODUCTION
       Rationale3Describe the rationale for the review in the context of existing knowledge.2
       Objectives4Provide an explicit statement of the objective(s) or question(s) the review addresses.3
      METHODS
       Eligibility criteria5Specify the inclusion and exclusion criteria for the review and how studies were grouped for the syntheses.3
       Information sources6Specify all databases, registers, websites, organisations, reference lists and other sources searched or consulted to identify studies. Specify the date when each source was last searched or consulted.3
       Search strategy7Present the full search strategies for all databases, registers and websites, including any filters and limits used.3
       Selection process8Specify the methods used to decide whether a study met the inclusion criteria of the review, including how many reviewers screened each record and each report retrieved, whether they worked independently, and if applicable, details of automation tools used in the process.3
       Data collection process9Specify the methods used to collect data from reports, including how many reviewers collected data from each report, whether they worked independently, any processes for obtaining or confirming data from study investigators, and if applicable, details of automation tools used in the process.3
       Data items10aList and define all outcomes for which data were sought. Specify whether all results that were compatible with each outcome domain in each study were sought (e.g. for all measures, time points, analyses), and if not, the methods used to decide which results to collect.3
      10bList and define all other variables for which data were sought (e.g. participant and intervention characteristics, funding sources). Describe any assumptions made about any missing or unclear information.3
      Study risk of bias assessment11Specify the methods used to assess risk of bias in the included studies, including details of the tool(s) used, how many reviewers assessed each study and whether they worked independently, and if applicable, details of automation tools used in the process.4
       Effect measures12Specify for each outcome the effect measure(s) (e.g. risk ratio, mean difference) used in the synthesis or presentation of results.4
       Synthesis methods13aDescribe the processes used to decide which studies were eligible for each synthesis (e.g. tabulating the study intervention characteristics and comparing against the planned groups for each synthesis (item #5)).
      13bDescribe any methods required to prepare the data for presentation or synthesis, such as handling of missing summary statistics, or data conversions.4
      13cDescribe any methods used to tabulate or visually display results of individual studies and syntheses.4
      13dDescribe any methods used to synthesize results and provide a rationale for the choice(s). If meta-analysis was performed, describe the model(s), method(s) to identify the presence and extent of statistical heterogeneity, and software package(s) used.4
      13eDescribe any methods used to explore possible causes of heterogeneity among study results (e.g. subgroup analysis, meta-regression).4
      13fDescribe any sensitivity analyses conducted to assess robustness of the synthesized results.4
      Reporting bias assessment14Describe any methods used to assess risk of bias due to missing results in a synthesis (arising from reporting biases).4
       Certainty assessment15Describe any methods used to assess certainty (or confidence) in the body of evidence for an outcome.4
      RESULTS
       Study selection16aDescribe the results of the search and selection process, from the number of records identified in the search to the number of studies included in the review, ideally using a flow diagram.5
      16bCite studies that might appear to meet the inclusion criteria, but which were excluded, and explain why they were excluded.
       Study characteristics17Cite each included study and present its characteristics.7–8
       Risk of bias in studies18Present assessments of risk of bias for each included study.5
      Results of individual studies19For all outcomes, present, for each study: (a) summary statistics for each group (where appropriate) and (b) an effect estimate and its precision (e.g. confidence/credible interval), ideally using structured tables or plots.9, 11
       Results of syntheses20aFor each synthesis, briefly summarise the characteristics and risk of bias among contributing studies.5
      20bPresent results of all statistical syntheses conducted. If meta-analysis was done, present for each the summary estimate and its precision (e.g. confidence/credible interval) and measures of statistical heterogeneity. If comparing groups, describe the direction of the effect.9
      20cPresent results of all investigations of possible causes of heterogeneity among study results.9
      20dPresent results of all sensitivity analyses conducted to assess the robustness of the synthesized results.11
       Reporting biases21Present assessments of risk of bias due to missing results (arising from reporting biases) for each synthesis assessed.12
      Certainty of evidence22Present assessments of certainty (or confidence) in the body of evidence for each outcome assessed.11
      DISCUSSION
       Discussion23aProvide a general interpretation of the results in the context of other evidence.12
      23bDiscuss any limitations of the evidence included in the review.13
      23cDiscuss any limitations of the review processes used.13
      23dDiscuss implications of the results for practice, policy, and future research.13
      OTHER INFORMATION
       Registration and protocol24aProvide registration information for the review, including register name and registration number, or state that the review was not registered.
      24bIndicate where the review protocol can be accessed, or state that a protocol was not prepared.
      24cDescribe and explain any amendments to information provided at registration or in the protocol.
       Support25Describe sources of financial or non-financial support for the review, and the role of the funders or sponsors in the review.
       Competing interests26Declare any competing interests of review authors.
      Availability of data, code and other materials27Report which of the following are publicly available and where they can be found: template data collection forms; data extracted from included studies; data used for all analyses; analytic code; any other materials used in the review.
        Note. From: Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71.For more information, visit: http://www.prisma-statement.org/
    • A systematic search was done on the scientific databases, including ISI Web of Science, Embase, PubMed, and the Cochrane Library, for extracting the relevant articles published from the inception to June 7, 2021. To explore the impact of 24-h urinary sodium excretion on CV events, we used a combination of keywords related to CV events and urinary sodium. Keywords associated with dietary sodium and potassium intake were also searched to avoid missing out on the studies that met our subject’s criteria. In addition, the reference list was manually searched for the included studies. Detailed strategies for searching are shown in Supplementary Materials (available in www.besjournal.com).

      Figure S2.  (A) Funnel plots for publication bias in the low-level groups. (B) Funnel plots for publication bias in the high-level groups

    • Two authors independently reviewed the studies, and any conflicts were resolved with the common consensus. First, the selection was conducted based on the title and abstract. Then the full texts of studies were reviewed for conforming our subject. Inclusion criteria: 1) full-texts in English; 2) cohort studies; 3) studies assessing the relationship of 24-h urinary sodium excretion with CV risk; 4) studies assessing 24-h urinary sodium excretion as exposure in at least three categories; 5) studies with the endpoints of either combined or single CV events (incidence and mortality); 6) studies providing adjusted effect estimates and 95% confidence intervals (CIs).

      Exclusion criteria: 1) non-English articles; 2) cross-sectional studies; 3) animal studies; 4) studies including kidney diseases patients; 5) studies with un-adjusted effect estimates. In addition, if multiple studies were from the same population, we included the longest follow-up study.

    • The following dataset was retrieved independently by the two authors: the first author’s name, publication year, study region, study design, follow-up time, number of participants and cases, participant’s gender, age at baseline, method of measuring 24-h urinary sodium excretion, assessment of exposure, outcome definition, covariates adjustment in the multivariate model, the indicator of effect estimates and other relevant characteristics. If the included studies reported models with different covariates adjustment, the meta-analysis selected the model with the most adjusted covariates.

    • The Newcastle-Ottawa Quality Assessment Scale (NOS) was employed to estimate studies quality. The highest score was 9, and the lowest score was 0 for any study. Moreover, the studies that scored more than 7 were considered high quality.

    • The relative risks (RRs) with 95% CIs were used to calculate the effect size in studies, and then they were converted to natural logarithms to normalize their distribution. The hazard ratios (HRs) were considered equal to the RRs. If the reference category was not the lowest, we took the lowest category as the reference and then recalculated the effect estimates [19].

      The association between 24-h urinary sodium excretion and the risk of CV events was evaluated via dividing sodium excretion into relatively low-level excretion and high-level excretion. Though the World Health Organization (WHO) recommended a sodium intake of < 2 g/d [20] and the Institute of Medicine (IOM) defined a tolerable upper sodium intake level of 2.3 g/d [21], few people reached the recommended level. Global mean sodium intake was 3.95 g/d in 2010 [22], and the mean sodium excretion ranged from 3 to 4.9 g/d in the studies that included in this meta-analysis. Therefore, the cut-off point of 4 g was used in our meta-analysis, low-level excretion was defined as < 4 g and high-level excretion ≥ 4 g in our study. In one study, dose groups with medians higher than the cut-off point were classified as high-level, otherwise low-level. If studies reported sodium excretion in moles, it was converted into grams (1 mol = 23 g). In one study, the fixed-effects model combined the RRs of different dose groups. The random-effects model summarizes the total RRs between the included studies [23]. Sensitivity analysis was employed by removing one study to assess the potential effect of a single study on the pooled RR. Some studies included heart failure as an outcome, sensitivity analysis was conducted by excluding these studies. The study by Ma et al. was excluded due to duplication with our included population cohort, sensitivity analysis was also conducted by including this study [24]. The heterogeneity between the included studies was computed adopting Cochran’s Q test and I2 statistic. Studies with values of I2 statistics less than 30% were considered to have no heterogeneity, whereas studies with more than 75% had notable heterogeneity or otherwise showed moderate heterogeneity [25]. To explain the observed heterogeneity, we performed subgroup analysis based on the follow-up duration, sample size, population information, method of urine collection, study quality, and adjusted confounders such as blood pressure or hypertension, potassium excretion. The funnel plot, Egger’s test [26], and Begg’s test [27] were used to estimate the publication bias.

      Then the dose-response meta-analysis was performed to explore the possible dose-response association, using the method proposed by Greenland, Longnecker, and Orsini [28, 29]. The method requires the number of cases and non-cases, or person-years in categories of urinary sodium excretion and the reported mean for every category of exposures. If the mean for each category of exposures was not reported, it was replaced with the median; otherwise, the midpoint in the exposure category was used. For the open category (highest and lowest), 20% high or low was calculated from the nearest cut-off point. Next, the non-linear relationship of urinary sodium excretion and CV events risk was estimated using the restricted cubic splines model with three knots at fixed percentiles (5%, 50%, and 95%)[30]. In contrast, the generalized least-squares regression was used to fit the model [29]. Finally, we pooled the effect estimates using the two-stage approach's random-effects models. Whenever the second spline coefficient was equal to zero, the non-linear P-value for the dose-response meta-analysis was computed. The Stata 14.0 and Stata 16.0 software (StataCorp, College Station, Texas 77845 USA) were used for the statistical analysis.

    • A total of 9,495 records were searched by the online databases and the references of the relevant studies. After excluding the duplicates and examining studies by titles and abstracts, 90 articles were included for assessing the full text. According to the inclusion and exclusion criteria, finally, 9 studies were eventually included. Selection details are described in Figure 1.

      Figure 1.  Flow chart of the literature search for studies.

    • The characteristics of the 9 included studies are shown in Table 1 [9, 10, 12, 14, 31-35]. Further information (the categories of urinary sodium excretion in original studies, the original effect size and transformed effect size, and covariates/factors adjusted in multivariate model) is shown in Supplementary Table S2 available in www.besjournal.com. The studies included in the meta-analysis were published between 2011 and 2021. A total of 645,006 participants with a median follow-up period of 4.7 to 19.1 years were recruited in our study. Two studies were carried out in the United States [10, 12], two in the United Kingdom [32, 35], one in the Netherlands [31], one in Finland [33], one in China [34], and two multi-centric studies with recruited subjects from several countries [9, 14]. For urinary sodium excretion assessment, 4 studies collected the 24-h urine samples [10, 12, 31, 33], 2 collected the morning fasting urine samples [9, 14], 2 collected the spot urine samples [32, 35], whereas the 1 collected the overnight urine [34]. The mean score of included studies for NOS was 8 (Supplementary Table S3 available in www.besjournal.com). Moreover, most studies results were greater than 7 [10, 31-35]. One in nine studies reported data on men and women, respectively [35]. The covariates adjusted in most multivariate models were the baseline age, body mass index (BMI), ethnicity/race, sex, alcohol intake, smoking, blood pressure or hypertension, and blood fat level. The outcome definition of studies included total CVD, CVD death, heart failure, coronary heart disease, all caused mortality, atrial fibrillation, and atrial flutter. The daily urinary sodium excretion ranged from 1.265 to 9.4 g. Besides, all studies reported HR. Three studies reported a J-shaped relationship between calculated sodium excretion and risk of CV events [9, 14, 33], 2 studies reported a U-shaped [32, 35], and 2 studies reported a linear association [10, 34]. On the contrary, 2 studies suggested no significant effect of sodium excretion upon CV events [12, 31].

      Table 1.  Characteristics of the included studies in this Meta-Analysis

      Author, yearCountryFollow-up (years)Subjects/
      events (n)
      Age (y)GenderMethods
      of Na
      measurement
      Study populationOutcome definition
      Martin J. O’Donnell, 2011[9]40 countries4.728,880/
      4,729
      66.52 ±
      7.22
      men/
      women
      collected morning fasting urine samplesparticipants in ONTARGET and the TRANSCEND trialscomposite outcome (CV mortality, myocardial infarction, stroke, and hospitalization for CHF)
      Roman Pfister, 2013[32]the United Kingdom12.919,857/
      1,210
      58.0 ±
      9.2
      men/
      women
      collected casual urine specimengeneral populationheart failure
      Michel M. Joosten, 2013[31]the
      Netherlands
      10.5 (IQR: 9.9−10.8)7,543/
      452
      28−75men/
      women
      collected 24-h urine samplesgeneral populationCHD was defined as incident cardiac morbidity and mortality (including myocardial infarction, acute and subacute ischemic heart disease, and coronary artery bypass grafting or percutaneous transluminal coronary angioplasty)
      Nancy R. Cook, 2014[10]the United States52,312/
      193
      31−50men/
      women
      collected 24-h urine samplesgeneral populationCVD or CVD death, including myocardial infarction, stroke, coronary artery bypass graft, percutaneous transluminal coronary angioplasty, or death from cardiovascular disease
      Pamela Singer, 2014[12]the United States18.63,505/
      399
      52.4 ±
      9.9
      men/
      women
      collected 24-h urine samplesparticipants were individuals in a union-sponsored, worksite hypertension program in New York City between 1978 and 1999coronary artery disease, including MI, ischemic heart disease, heart failure, and hypertensive heart disease
      Martin O’Donnell, 2019[14]628 urban
      and rural communities
      in low, middle,
      and high
      income countries
      8.2103,200/
      7,884
      35−70men/
      women
      collected morning fasting midstream urine samplegeneral populationthe primary composite outcome was all caused mortality or myocardial infarction or stroke or heart failure
      Matti A. Vuori, 2020[33]Finland144,517/
      424
      45.4 ±
      11.4
      men/
      women
      collected 24-h urine samplesgeneral populationCVD was defined as the onset of CHD, stroke, or heart failure
      Wuopio J,
      2020[35]
      the United Kingdom8.2 ± 1.0215,535/
      3,751
      40−69mencollected midstream spot urine samplesgeneral populationatrial fibrillation or atrial flutter
      257,545/
      2,221
      women
      Yi-Jie Wang, 2021[34]China19.1 (IQR: 7.4−21.4)2,112/
      279
      54 ±
      12
      men/
      women
      recorded the sleep time and calculated the 24-h urine amount from sleep time and morning voiding urinegeneral populationIncident CVD events, including CHD and stroke
        Note. 24 h UNaE: 24 h urinary sodium excretion; CV: cardiovascular; CHD: coronary heart disease; CVD: cardiovascular disease; CHF: congestive heart failure; ONTARGET: Ongoing Telmisartan Alone and in combination with Ramipril Global Endpoint Trial; TRANSCEND: Telmisartan Randomized Assessment Study in ACE Intolerant Subjects with Cardiovascular Disease.
    • The association between the high and the low levels of sodium excretion with the risk of CV events are shown in Figure 2. The total RR was 0.91 (95% CI: 0.86, 0.96; I2 = 16.82%) and 1.01 (95% CI: 0.91, 1.12; I2 = 84.25%) for the association between low-level sodium excretion (vs. the lowest of 1.265 g) and the high-level sodium excretion (vs. the lowest of 1.265 g) with the risk of CV events, respectively. However, significant heterogeneity between the studies was found (P = 0.01) in the high-level group. Therefore, subgroup analyses were carried out based on the follow-up duration, sample size, population information, method of urine collection, study quality, and adjusted confounders such as blood pressure or hypertension, potassium excretion (Table 2). Further, the subgroup of sample size and method of urine collection could explain the part of the heterogeneity.

      Figure 2.  (A) Forest Plot of relative risks (RRs) and 95% Confidence Intervals (CIs) for the association between low-level sodium excretion (< 4 g/d) and the risk of CV events. (B) RRs and 95% CIs for the association between high-level sodium excretion (≥ 4 g/d) and the risk of CV events.

      Table 2.  Subgroup analysis of different levels of sodium excretion and the risk of CV events

      SubgroupsNo. of studiesLow levelHigh level
      Pooled RR (95% CI)P valueI² (%)P value for
      heterogeneity
      Pooled RR (95% CI) P value I² (%)P value for
      heterogeneity
      Follow-up duration         
       < 10 years40.910
      (0.866, 0.958)
      0.0003 < 0.010.96110.926
      (0.895, 0.957)
      < 0.0001 0.010.2904
       > 10 years50.947
      (0.821, 1.093)
      0.4575 54.770.04811.093
      (0.898, 1.329)
      0.3759 68.490.0037
      Sample size         
       < 10,00051.027
      (0.912, 1.156)
      0.6632 < 0.010.97891.228
      (1.070, 1.410)
      0.0035 < 0.010.5886
       > 10,00040.886
      (0.836, 0.940)
      0.0001 27.480.19300.918
      (0.889, 0.948)
      < 0.0001 0.020.6090
      Conducted in general population
       Yes70.912
      (0.843, 0.986)
      0.0207 42.630.16541.049
      (0.902, 1.220)
      0.5358 91.080.0021
       No20.932
      (0.834, 1.042)
      0.2162 < 0.010.47450.943
      (0.861, 1.033)
      0.2062 < 0.010.7316
      Method of urine collection      
      Collected 24-h urine samples41.042
      (0.911, 1.192)
      0.5489 < 0.010.97211.190
      (1.023, 1.385)
      0.0242 < 0.010.6025
      Collected spot urine samples50.892
      (0.847, 0.939)
      < 0.0001 12.980.26700.922
      (0.893, 0.952)
      < 0.0001 < 0.010.0744
      Study quality         
       ≤ 730.918
      (0.852, 0.989)
      0.0241 0.010.72240.928
      (0.887, 0.971)
      0.0012 < 0.010.8695
       > 760.925
      (0.826, 1.035)
      0.1743 55.120.10341.094
      (0.909, 1.316)
      0.3439 84.390.0010
      Adjusted blood pressure or hypertension      
       Yes40.883
      (0.815, 0.958)
      0.0026 42.650.16380.921
      (0.885, 0.959)
      0.0001 < 0.010.0365
       No50.967
      (0.876, 1.068)
      0.5129 14.680.57581.084
      (0.917, 1.281)
      0.3444 59.570.0211
      Adjusted potassium excretion      
       Yes40.960
      (0.873, 1.056)
      0.4035 < 0.010.66941.047
      (0.879, 1.247)
      0.6076 40.920.1626
       No50.892
      (0.829, 0.960)
      0.0023 36.530.15340.999
      (0.855, 1.168)
      0.9923 92.710.0057
    • Based on the included studies, the dose-response meta-analysis was carried out. A significant non-linear relationship was found between sodium excretion and the risk of CV events (Pnon-linearity < 0.001) (Figure 3). In addition, the Wald test’s result showed that the two slopes in the non-linear model were significantly different (P < 0.001). We also excluded 5 studies using spot urine samples and performed a dose-response meta-analysis with the remaining 4 studies. The results showed that the sodium excretion and risk of CV events were associated linearly (P = 0.02) (Figure 4). The RR in the model showed that every 1 g increase in sodium excretion was associated with an increase in the risk of CV events up to 4% (RR: 1.04; 95% CI: 1.01, 1.07).

      Figure 3.  Non-linear dose-response association between 24-h Sodium excretion and the risk of CV events. Dashed lines indicated 95% confidence intervals. Reference standard was 1.265 g/24 h.

      Figure 4.  Linear dose-response association between 24-h Sodium excretion and the risk of CV events. Dashed lines indicated 95% confidence intervals.

      None of the studies significantly affected the combined RR for sensitivity analysis in the low-level and high-level groups (Supplementary Figure S1 available in www.besjournal.com). In studies without an outcome of heart failure [10, 31, 34, 35], the combined RR for sensitivity analysis was 0.94 (95% CI: 0.86, 1.02) in the low-level group and 1.17 (95% CI: 0.91, 1.50) in the high-level group. When included in the study by Ma et al.[24], the combined RR was 0.95 (95% CI: 0.88, 1.04) in the low-level group and 1.07 (95% CI: 0.94, 1.23) in the high-level group.

      Figure S1.  Plot for sensitivity analysis in the nine studies

    • The results of the funnel plots did not exclude any possible publication bias for the low-level and high-level groups (Supplementary Figure S2 available in www.besjournal.com). Neither the Egger’s nor Begg’s test detected any evidence of publication bias for the low-level group (P-value for Egger: 0.132; P-value for Begg: 0.252). The Egger’s test showed possible publication bias for the high-level group (P-value for Egger: 0.0006; P-value for Begg: 0.076).

    • This current study included 645,006 participants; 21,542 had CV events. In the low-level group, the relationship between sodium excretion and the risk of CV events was statistically significant (P < 0.001). However, the relationship between sodium excretion and the risk of CV events was not statistically significant in the high-level group (P = 0.885). A significant non-linear relationship was observed between sodium excretion and CV risk (Pnon-linearity < 0.001). Moreover, the result of the dose-response meta-analysis with the 4 studies collecting 24-h urine samples showed that the sodium excretion and CV events risk were associated linearly. It is indicated that increased sodium excretion might be a risk factor for the CV events.

      This meta-analysis showed significant heterogeneity in the high-level sodium excretion (I2 = 84.25%). In the subgroup analysis of the method of urine collection, the pooled RR of studies with 24-h urine collection was 1.190 (1.023, 1.385), suggesting the statistically significant relationship of urinary sodium excretion with the risk of CV events. However, the result of studies using collected spot urine samples was converse. This might be due to the error in 24-hour sodium excretion computed by collecting spot urine samples. Then the sodium excretion calculated by spot urine samples was not accurate and thus might have affected the authenticity of the final results.

      Our study observed a J-shaped relationship between sodium intake (measured by urinary sodium biomarker) and CV events. The non-linear dose-response connection was also found in another meta-analysis [36]. In addition, the proof of a U-shaped association shown in a previous meta-analysis indicated that both low sodium and high sodium intakes were connected to the higher risk of CV events [37]. The RR in our linear model showed that every 1 g increase in sodium intake was associated with an increase in the risk of CV events up to 4%. Likewise, a recent meta-analysis showed that the sodium intake and risk of CV events were associated linearly. And that meta-analysis showed that every 1 g increase in sodium intake was associated with an increase in CV events risk up to 6% [38]. The reasons for the different results may be: 1) the methods of sodium measurement were different (urinary sodium biomarker or self-reported dietary intake measurement); 2) different definitions of study outcomes; 3) the ranges of sodium dose and cut-off points were different in the included studies.

      Sodium is an essential nutrient component in our diet for maintaining the proper blood volume and blood pressure. The increase in sodium concentration influences the role of the renin-angiotensin-aldosterone system and elevates the heart burden [39, 40]. In addition, blunt renal salt excretion enlarges the extracellular fluid volume and increases blood pressure, manifested as the salt sensitivity of blood pressure [41]. Moreover, a high-sodium diet is related to myocardial contractility and changes in the proteins associated with calcium homeostasis [42]. Therefore, dietary sodium intake might influence the risk of CV events.

      According to the linear dose-response meta-analysis conducted with studies collecting 24-h urine samples, increased sodium excretion was a risk factor for the CV events. We recommend that sodium intake be adapted based on individual risk factors. The public should be educated about the dangers of excessive sodium intake. Moreover, no convincing explanation exists for the mechanism of lower sodium intake related to a higher risk of CV events. Therefore, a non-pharmacological measure of reducing sodium intake is a cost-effective option for preventing CV events. There are also some studies examined the association between 24-h urinary potassium excretion and CV events [9, 14, 43, 44]. The results of these studies were similar, that is, higher potassium excretion was associated with a lower risk of CV events. It is indicated that salt substitute with lower sodium and higher potassium may reduce the risk of CV events effectively.

      In epidemiological studies without a urinary sodium biomarker, self-reported dietary intake measurement methods were considered significant bias [45]. The formulas for estimating the sodium intake from spot urine contain certain factors (e.g., sex, age, and creatinine concentration) strongly associated with CV events [46, 47]. Although 24-hour urinary excretion is considered the gold standard method for assessing sodium intake [17], a single measurement is not sufficient for providing a reliable estimation of long-term average sodium intake. We could not modify a single measurement's mean estimate of regression dilution bias [48]. Individuals at high risk of CV events might consciously consume less sodium, making the association between sodium intake and disease unreliable. Furthermore, non-adjustment of total energy intake can also produce errors because the total energy intake is highly correlated with sodium intake [49]. Moreover, almost all previous articles are observational studies, whereas randomized controlled trials (RCTs) are lacking, which are required to confirm the existing conclusions. To standardize the data reporting, we recommend (i) using multiple inconsecutive 24-hour urine collections; (ii) selecting subjects without high risk of CV events; (iii) adjusting sodium intake for the total energy intake.

      This study had the following strengths. First, different meta-analysis approaches were employed to clarify the relationship between the 24-hour sodium excretion and CV events risk. Second, the total sample size of the meta-analysis was large (n = 645,006), and multiple confounding factors that might affect the results were adjusted in the included studies to make the results more reliable. Third, we plotted a dose-response relationship graph, with the lowest point of the curve providing a threshold dose for sodium excretion that increases the risk of CV events. Finally, in all studies, the dose-response analysis combined with different dose groups of sodium excretion included a wider range of sodium excretion than a single study, making the meta-analysis results more comprehensive and accurate.

      However, the study had some limitations. First, multiple confounding factors adjusted in different included studies were not identical, which might have affected the authenticity of the results. Second, this meta-analysis assumed that the heterogeneity among the studies was significant due to the differences in study quality, sample size, follow-up duration, and division of sodium excretion dose groups. Third, several studies did not use the lowest dose group as the reference to calculate the effect estimates. Although we recalculated the effect estimates, it inevitably caused some errors in the recalculated 95% CIs. Finally, only four studies collected 24-hour urine samples to measure sodium excretion. The remaining five used samples of spot urine, which might have led to the inaccurate calculations of 24-hour sodium excretion.

    • To conclude, this meta-analysis showed a significant relationship between the 24-h sodium excretion and the risk of CV events. In studies collecting 24-h urine samples, a linear relationship was observed between sodium excretion and CV events. However, further relevant studies are needed to validate our conclusions further.

    • ZHAO Dan analyzed the data and wrote the manuscript. LI Hua Min and LI Chao Xiu designed the tables and figures. ZHOU Bo proposed the idea for the study and supervised the whole study.

参考文献 (49)
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