Skip to main content
NCBI home page
Medicine logoLink to Medicine
. 2024 Nov 15;103(46):e40497. doi: 10.1097/MD.0000000000040497

Causal relationship of salt intake with osteoarthritis: A Mendelian randomization analysis

Chengrui Yang a, Tieqiang Wang b, Chunzhi Zhao a, Jiawei Lu a, Runbin Shen b, Guoliang Li b, Jianyong Zhao a,b,*
PMCID: PMC11575978  PMID: 39560570

Abstract

Recent studies have demonstrated a correlation between salt intake (SI) and various diseases. However, it remains uncertain whether the relationship between SI (including salt added to food and sodium levels in urine) and benign osteoarthritis is causal. To investigate this, we conducted a 2-sample Mendelian randomization (MR) analysis to estimate the causal impact of SI on osteoarthritis (OA). A genome-wide association study of salt added to food and sodium in urine was used as the exposure, while hip osteoarthritis, knee osteoarthritis, and rheumatoid arthritis were defined as the outcomes. Inverse variance weighting (IVW) was used to calculate causal estimates, and sensitivity analyses were performed using methods including weighted mode, weighted median, MR-Egger, and Bayesian weighted MR. All statistical analyses were conducted using R software. Our results, primarily based on the IVW method, support the existence of a causal relationship between salt added to food and knee osteoarthritis (KOA). Specifically, salt added to food was associated with a decreased risk of KOA (OR = 1.248, P = .024, 95% CI: 1.030–1.512). This study is the first MR investigation exploring the causal relationship between salt added to food and KOA, potentially providing new insights and a theoretical basis for the prevention and treatment of KOA in the future.

Keywords: Mendelian randomization, osteoarthritis, salt

1. Introduction

Osteoarthritis (OA) is a major global cause of pain, functional impairment, and disability.[1] Hip osteoarthritis (HOA) and knee osteoarthritis (KOA) are among the leading causes, ranking 38th in Disability Adjusted Life Years.[2] Currently, treatment primarily focuses on pain management, with patients consistently prioritizing pain management as their primary concern.[3] However, approximately half of the nonsurgical treatment patients are unable to manage pain effectively.[4] Despite the heavy burden of the disease, there are currently no approved disease-modifying drugs to improve OA. Attention to prevention appears particularly crucial; however, a Korean cohort study suggested that increased salt intake (SI) may exacerbate the occurrence of OA.[5] Research now suggests that in the United States, over 40% of diverse racial/ethnic groups report habitually adding salt at the table[6] Preferences or habits may prompt individuals to add salt liberally during cooking or at the table,[7] which could be a primary mechanism for long-term high-SI.[8] A recent systematic review reported that the average daily SI for adults globally ranges from 5.2 to 15.5 g/d,[9] surpassing the limits recommended by the World Health Organization(WHO)[10] and current dietary guidelines.[11] Adding salt to the table may increase SI and remains a significant contributor to total SI, accounting for approximately 15% to 20%.[12] Furthermore, there is limited research on the relationship between salt intake and arthritis, particularly regarding the addition of salt to the table, making the connection between table salt and arthritis incidence unclear. However, research on the connection between SI and OA is limited, and the causal relationship remains uncertain. Therefore, it is necessary to establish a clear causal link between SI and OA.

The Mendelian randomization (MR) approach allows for inferring causal associations between exposures and subsequent outcomes.[13,14] This method uses genes as instrumental variables (IVs), which are less prone to confounding influences, as they rely on the random assortment of genetic variation at conception.[15] The objective of this study was to explore the potential causal relationship between SI and OA using 2-sample MR analysis. Through this investigation, we aimed to uncover new insights into the role of SI in the development of OA and identify potential pathways for prevention and treatment. To our knowledge, this is the first application of MR to examine the pathogenic impact of SI on the development of OA.

Based on the above, we undertook this MR analysis with the aim of elucidating the potential causal relationship between SI with osteoarthritis.

2. Study design

We employed a 2-sample MR approach to systematically investigate the causal relationships between SI and the risks of OA and RA. To ensure the reliability of the MR results, we adhered to 3 core assumptions: genetic instruments must be directly related to metabolite exposure; genetic instruments should be independent of the outcomes (OA and RA) and free from any known or unknown confounding factors. The effects of these instruments on the outcomes must be mediated solely through the target metabolite (Fig. 1).[16]

Figure 1.

Figure 1.

Open in a new tab

The schematic illustration of the causal relationship between SI and KOA and RA through MR analyses. (A) Principles of MR; (B) the flowchart of the MR analysis. BWMR = Bayesian weighting Mendelian randomization, HOA = hip osteoarthritis, IVs = instrument variables, IVW = inverse variance weighted, KOA = knee osteoarthritis, MR = Mendelian randomization, RA = rheumatoid arthritis, SI = salt intake, SNPs = single nucleotide polymorphisms, WM = weight median.

3. Data sources

Data related to SI were obtained from the IEU Open GWAS project (https://gwas.mrcieu.ac.uk/). Salt added to food (ukb-b-8121) was obtained from the study by UKBiobank with 9,851,867 SNPs and a sample size of 462,630 individuals. Sodium in urine was obtained from the study by Mbatchou et al[16] with 10,783,698 SNPs, and a sample size of 396,020 individuals. GWAS data on KOA and HOA were sourced from the IEU Open GWAS project website (https://gwas.mrcieu.ac.uk/) and accessed on May 28, 2024. All the data used in this study originated from a European population. Specifically, the dataset for Keen Osteoarthritis (ebi-a-GCST005813)[16] consisted of 15,708,690 SNPs and included a sample size of 22,347 individuals, and hip osteoarthritis (ebi-a-GCST007091)[16] consisted of 29,771,219 SNPs and included a sample size of 393,873 individuals. Rheumatoid arthritis (ebi-a-GCST90018910)[17] consisted of 24,175,266 SNPs and included a sample size of 417,256 individuals. It is important to note that this study involved a reanalysis of preexisting publicly available data, thus obviating the need for additional ethical approval.

4. Selection of instrumental variables

To ensure the validity and accuracy of the findings regarding the relationship between SI and OA risk, we implemented the following measures:

Because SI is not independent, we chose a locus-specific significance threshold (P < 5 × 10−8, r2 < 0.001, 10,000 kb), which has been widely used in previous MR studies.[18]

To assess the strength of our selected SNPs as IVs, we calculated the F statistic and the proportion of variance explained (R2) for each IV in relation to the exposure trait. A threshold F statistic > 10 was considered indicative of robust IVs.[19] (Table S1, Supplemental Digital Content, http://links.lww.com/MD/N941).

5. Statistical analysis

Statistical analyses were conducted using R version 4.2.3, and MR analyses were performed using the MR package in R software.

In our study, the inverse variance weighted (IVW) method was the primary approach for assessing causal relationships between exposures and outcomes. This method assumes no horizontal pleiotropy across all IVs, thereby allowing precise estimation of causality.[20] In addition, we performed secondary analyses to assess the robustness of our findings. The MR-Egger method provides unbiased causal estimates in the presence of horizontal pleiotropy, with its intercept used to detect such pleiotropy.[21] When at least 50% of the IVs were valid, the weighted median (WM) method was used to obtain robust causal estimates.[22] Results were considered more robust if the P-value was <.05 in 2 or more MR methods.[23] Cochran Q test was performed to assess heterogeneity among the available data to ensure that the obtained causal estimates were not influenced by individual SNPs. We conducted a leave-one-out analysis by systematically removing each SNP and assessing any changes in the previously established causal relationships.[24] Additionally, we performed Bayesian weighted Mendelian randomization (BWMR)[25] to enhance the reliability of the results. Subsequently, scatter and funnel plots were utilized to visually represent the relationships and interactions between each genetic instrument. All MR analyses were performed using the “TwoSampleMR” package (Version 4.2.3). Importantly, it is noteworthy that this R package can automatically align the exposure and outcome datasets using effect allele frequencies, ensuring that the impact of the SNP on the exposure and the impact of the SNP on the outcome correspond to the same allele SNPs.[26]

6. Reverse Mendelian randomization

To explore whether the outcomes studied had an impact on SI, we conducted reverse MR analysis. In this reverse analysis, we used SNPs selected from the data on OA and their subtypes as IVs, with the chosen SI as the outcome. The purpose of this study was to investigate whether the previously established relationship between outcomes and SI was bidirectional.

7. Results

7.1. Selection of instrumental variables

After undergoing a rigorous selection process, we conducted MR analysis to examine the relationship between SI, OA and RA. To ensure the robustness of our findings, we only included SI with a minimum of 3 SNPs. The F-statistics for the SNPs involved were >10, indicating that our results are less likely to be affected by weak instrumental variable bias. Detailed information about the IVs is provided in Table S2, Supplemental Digital Content, http://links.lww.com/MD/N942).

Causal estimation of the SI for OA and RA. We estimated the causal relationship between SI and OA and identified a total of 1 suggestive association (P < .05) (Fig. 2). We found that genetically predicted salt added to food was associated with a high risk of KOA (OR = 1.248, 95% CI: 1.030–1.513, P = .023) in the IVW method. Urine sodium was not found to be associated with OA or RA. Salt added to the food was not found to be associated with HOA and RA.

Figure 2.

Figure 2.

Open in a new tab

MR results and forest plot of SI with a causal relationship to KOA. CI = confidence interval, OR = odds ratio.

No evidence of pleiotropy was found in the strong causal relationships described above (Table S3, Supplemental Digital Content, http://links.lww.com/MD/N943). However, we did find that the heterogeneity test showed P < .05. The main results of the IVW method used in this study may provide reliable causal effects with low heterogeneity. Sensitivity analyses indicated that no single SNP was driving the associations, demonstrating the consistency and stability of the results (Fig. 3; Table S4, Supplemental Digital Content, http://links.lww.com/MD/N944). Additionally, BWMR = 0.018 < 0.05 indicates the robustness of the findings.

Figure 3.

Figure 3.

Open in a new tab

Mendelian randomization results of salt added to food and knee osteoarthritis (KOA): (A) Scatter plot of genetic correlations between salt added to food and KOA using different MR methods. The slopes of the lines represent the causal effect of each method; (B) Forest plot of the causal effects of dietary salt-related SNPs on KOA. The red and black dots/bars indicate the causal estimates of Salt added to food on the risk of KOA patients. (C) Leave-one-out analysis forest plot of the SNPs related to Salt added to food and KOA.

8. Reverse Mendelian randomization

To validate whether the observed SI was influenced by KOA risk, we conducted a reverse MR analysis with KOA as the exposure and salt added to the food as the outcome. The results showed no evidence of reverse causation (with P > .05 (Table 5, Supplemental Digital Content, http://links.lww.com/MD/N945.

9. Discussion

We confirmed a causal relationship between sodium intake and knee osteoarthritis, supported by sensitivity analyses that underscore the robustness of this association. To the best of our knowledge, this is the first MR study to systematically evaluate the causal effect of sodium on context of osteoarthritis. Our findings provide new insights into the role of gene-environment interactions in the pathogenesis of osteoarthritis and offer additional evidence to support initiatives aimed at reducing sodium intake. This research also opens new avenues for mitigating complications associated with knee osteoarthritis.[27,28]

We identified a potential causal link between salt added to food and increased risk of knee osteoarthritis. Notably, urinary sodium levels were not correlated with OA or RA. Furthermore, no association was found between added salt in food and HOA or RA.

Considering the significant impact of sodium intake on health, the WHO and current international dietary guidelines recommend that SI for the general population should be limited to <5 g/d [10] or 6 g/d,[11] and high sodium intake has long been a serious and important public health issue owing to its association with various diseases, including hypertension, cardiovascular disease, obesity, and osteoporosis.[29–31] However, a causal relationship between SI and arthritis remains unclear. In a recent cross-sectional study from the US NHANES 2011 to 2016, Matsunaga et al reported no significant association between OA and high sodium intake.[32] In contrast, our study presents different findings. A recent animal experiment indicated that mice on a high-salt diet exhibited increased levels of NaCl in the synovium, and NaCl exacerbates arthritis in mice through Th17 cell induction.[33] Another recent cross-sectional study found that the prevalence of KOA was significantly higher in subjects with high sodium intake (≥5 g/d).[5] In summary, increased SI in food may exacerbate knee osteoarthritis. Our results emphasize the positive correlation between the added salt in food and the incidence of KOA, which is consistent with our findings.

Knee joint pain is considered a key predictive factor for disease progression and the occurrence of KOA.[34] However, animal experiments have shown that a high-salt diet leads to decreased pain threshold in response to mechanical stimulation in mice, with CCR2-mediated inflammatory responses playing a crucial role in regulating salt-induced pain sensitivity.[35] Further research has indicated that Ccr2-deficient mice exhibit reduced damage associated with KOA.[36] Additionally, SI increases the expression of CCR2 in monocytes and circulating, MCP-1 levels in the human body.[37] Moreover, in human OA synovium, the abundance of CCR2 + cells is associated with cartilage erosion in OA.[38]

Although OA has traditionally been classified as a noninflammatory disease caused by cartilage wear, it is now believed to involve multiple inflammatory pathways that lead to chronic low-grade inflammation. Joint inflammation can trigger cellular senescence, increase the sensitivity of nociceptive neurons, and exacerbate osteochondral pathology.[39] A meta-analysis showed that serum C-reactive protein (CRP) levels are significantly elevated and closely associated with pain and physical function decline in patients with OA.[40] One study indicated a significant association between urinary sodium excretion and serum CRP level.[41] Increasing evidence suggests that a high-salt diet drives inflammatory responses through direct and indirect effects on the immune cells. Specifically, a high-salt diet increases pro-inflammatory cytokines in macrophages and T cells while reducing anti-inflammatory cytokines.[42] It also induces the production of inflammatory adipokines in adipocytes.[43] Therefore, the current findings may be related to the pro-inflammatory effects induced by high-SI, which is consistent with the conclusions of this study.

However, regardless of whether in developed regions[6,12,44] or in low- and middle-income countries[38,45–47] the habit of adding salt to the table is widespread. This habit poses a barrier to the implementation of salt reduction efforts as the public lacks awareness of the health benefits of not adding salt at the table. Therefore, raising public awareness and implementing salt reduction interventions is necessary. Our research findings reveal for the first time a causal relationship between SI and the risk of osteoarthritis, as evidenced by MR and reverse MR analyses. We found a positive association between salt added to food and the risk of KOA, thus providing evidence for its prevention and treatment. Although SI is important for the general population, controlling the habit of adding salt to food may be crucial in preventing knee osteoarthritis. Globally, limiting the habit of adding salt to the table, even in moderate amounts, would bring significant benefits.[48]

Finally, the study methodology itself may have influenced the findings. Although the design of MR aims to reduce the influence of confounding variables, uncontrolled confounders may still exist, including undetected genetic variations. This study has several strengths. First, a notable advantage of our study is the extensive coverage of genetic variables, allowing us to analyze the relationship between SI and various types of arthritis (OA, RA), specifically covering salt added to food and sodium in the urine. Furthermore, by leveraging the MR design, our study largely avoided the issues of reverse causality and residual confounding. Extensive sensitivity analyses excluded the possibility of pleiotropy and ensured the credibility of our inferences regarding the causal relationship between SI and the risk of arthritis (OA and RA).

Despite providing important insights, our study had several limitations that warrant further investigation. First, sample size may have affected the generalizability of our findings. Although our sample was derived from multiple cohorts, a selection bias may still exist. The selection of genetic variants in the sample may not be comprehensive enough, potentially affecting the external validity of the results. Additionally, although the MR design aims to mitigate the effects of confounding variables, unmeasured confounding factors may still exist, including undetected genetic variants which could influence our conclusions. Furthermore, recent literature suggests that certain food additives, such as krill oil, may alleviate osteoarthritis symptoms.[49,50]

A significant strength of our study is its extensive coverage of genetic variables, allowing us to analyze the relationship between sodium intake and various types of arthritis (OA and RA), particularly focusing on added salt in food and sodium in urine. However, we only used GWAS data associated with added salt in food and sodium in urine; therefore, the results may not be exhaustive. Second, this study did not exclude SNPs related to confounding factors; however, efforts were made to minimize the potential confounding effects on the results. Third, some reports from the UK Biobank utilized predictive equations to estimate 24-hour urinary sodium as a surrogate measure for sodium intake.[31,51] Fourth, our heterogeneity test yielded a P-value < .05, indicating heterogeneity in our findings, which may have resulted from differences between the cohorts. Our analysis did not indicate horizontal pleiotropy and we conducted individual exclusion analyses to ensure the robustness of the results. However, our study found no correlation between urinary sodium and osteoarthritis, and the inconsistency in results may primarily be attributed to differences in sodium measurement methods. Future research should involve larger sample sizes and prospective studies, employing more IVs to analyze the causal relationship between sodium intake and arthritis and to further confirm the causality between sodium intake and arthritis.

10. Conclusions

This is the first MR study to explore the causalities between specific salts added to food and KOA, which may provide new ideas and a theoretical basis for the prevention and treatment of KOA in the future.

Author contributions

Conceptualization: Chengrui Yang, Tieqiang Wang.

Data curation: Chengrui Yang, Tieqiang Wang.

Formal analysis: Jiawei Lu.

Funding acquisition: Chengrui Yang.

Investigation: Runbin Shen.

Methodology: Tieqiang Wang.

Project administration: Tieqiang Wang, Jianyong Zhao.

Resources: Chengrui Yang.

Software: Chengrui Yang.

Supervision: Tieqiang Wang.

Validation: Tieqiang Wang, Chunzhi Zhao.

Visualization: Tieqiang Wang.

Writing – original draft: Chengrui Yang.

Writing – review & editing: Chengrui Yang, Guoliang Li, Jianyong Zhao.

Supplementary Material

medi-103-e40497-s001.xlsx (31.5KB, xlsx)
Open in a new tab
medi-103-e40497-s002.xlsx (14.2KB, xlsx)
Open in a new tab
medi-103-e40497-s003.xlsx (10KB, xlsx)
Open in a new tab
medi-103-e40497-s004.xlsx (10.3KB, xlsx)
Open in a new tab
medi-103-e40497-s005.xlsx (10.3KB, xlsx)
Open in a new tab

Abbreviations:

BWMR
Bayesian weighted Mendelian randomization
GWAS
genome-wide association study
HOA
hip osteoarthritis
IVs
instrumental variables
IVW
inverse variance weighted
KOA
knee osteoarthritis
MR
Mendelian randomization
OA
osteoarthritis
SI
salt intake
WHO
World Health Organization
WM
weighted median

This study was supported by publicly available genome-wide association studies (GWAS), including the GWAS Catalog. This study was funded by the clinical research on the use of Jiangu Capsule for treating qi stagnation and blood stasis type knee osteoarthritis, project number 2021340.

Here, our study was based on large-scale GWAS datasets, and not individual-level data. Hence, ethical approval was not required.

The authors declare that this research was conducted in the absence of any commercial or financial relationships that could be construed as potential competing interests.

All data generated or analyzed during this study are included in this published article [and its supplementary information files]. The datasets for the genome-wide association study (GWAS) summary statistics can be found in the GWAS catalog (https://gwas.mrcieu.ac.uk/, accessed on September 01, 2023).

Supplemental Digital Content is available for this article.

How to cite this article: Yang C, Wang T, Zhao C, Lu J, Shen R, Li G, Zhao J. Causal relationship of salt intake with osteoarthritis: A Mendelian randomization analysis. Medicine 2024;103:46(e40497).

CY and TW contributed to this article equally.

References

  • [1].Murray CJL, Vos T, Lozano R, et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380:2197–223. [DOI] [PubMed] [Google Scholar]
  • [2].Cross M, Smith E, Hoy D, et al. The global burden of hip and knee osteoarthritis: estimates from the global burden of disease 2010 study. Ann Rheum Dis. 2014;73:1323–30. [DOI] [PubMed] [Google Scholar]
  • [3].Tallon D, Chard J, Dieppe P. Exploring the priorities of patients with osteoarthritis of the knee. Arthritis Care Res. 2000;13:312–9. [DOI] [PubMed] [Google Scholar]
  • [4].Conaghan PG, Peloso PM, Everett SV, et al. Inadequate pain relief and large functional loss among patients with knee osteoarthritis: evidence from a prospective multinational longitudinal study of osteoarthritis real-world therapies. Rheumatology (Oxford, England). 2015;54:270–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [5].Ha Y-J, Ji E, Lee JH, et al. High Estimated 24-Hour Urinary Sodium excretion is related to symptomatic knee osteoarthritis: a nationwide cross-sectional population-based study. J Nutr Health Aging. 2022;26:581–9. [DOI] [PubMed] [Google Scholar]
  • [6].Firestone MJ, Beasley JM, Kwon SC, Ahn J, Trinh-Shevrin C, Yi SS. Asian American dietary sources of sodium and salt behaviors compared with other racial/ethnic groups, NHANES, 2011-2012. Ethnicity Dis. 2017;27:241–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [7].Mittelmark MB, Sternberg B. Assessment of salt use at the table: comparison of observed and reported behavior. Am J Public Health. 1985;75:1215–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [8].Cornélio ME, Gallani MCBJ, Godin G, et al. Behavioural determinants of salt consumption among hypertensive individuals. J Hum Nutr Diet. 2012;25:334–44. [DOI] [PubMed] [Google Scholar]
  • [9].Bhat S, Marklund M, Henry ME, et al. A systematic review of the sources of dietary salt around the world. Adv Nutr (Bethesda, Md.). 2020;11:677–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [10].WHO, I., Guideline: Sodium Intake for Adults and Children. Geneva, Switzerland: World Health Organization; 2012. [PubMed] [Google Scholar]
  • [11].Rong S, Liao Y, Zhou J, Yang W, Yang Y. Comparison of dietary guidelines among 96 countries worldwide. Trends Food Sci Technol. 2021;109:219–29. [Google Scholar]
  • [12].Sutherland J, Edwards P, Shankar B, Dangour AD. Fewer adults add salt at the table after initiation of a national salt campaign in the UK: a repeated cross-sectional analysis. Br J Nutr. 2013;110:552–8. [DOI] [PubMed] [Google Scholar]
  • [13].Tin A, Köttgen A. Mendelian randomization analysis as a tool to gain insights into causes of diseases: a primer. J Am Soc Nephrol. 2021;32:2400–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [14].Lv B-M, Quan Y, Zhang H-Y. Causal inference in microbiome medicine: principles and applications. Trends Microbiol. 2021;29:736–46. [DOI] [PubMed] [Google Scholar]
  • [15].Emdin CA, Khera AV, Kathiresan S. Mendelian randomization. JAMA. 2017;318:1925–6. [DOI] [PubMed] [Google Scholar]
  • [16].Bowden J, Holmes MV. Meta-analysis and Mendelian randomization: a review. Res Synth Methods. 2019;10:486–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [17].Sakaue S, Kanai M, Tanigawa Y, et al. ; FinnGen. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet. 2021;53:1415–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [18].Bouras E, Karhunen V, Gill D, et al. Circulating inflammatory cytokines and risk of five cancers: a Mendelian randomization analysis. BMC Med. 2022;20:1–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [19].Pierce BL, Ahsan H, VanderWeele TJ. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol. 2011;40:740–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [20].Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [21].Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [22].Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40:304–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [23].Jin Q, Ren F, Dai D, Sun N, Qian Y, Song P. The causality between intestinal flora and allergic diseases: insights from a bi-directional two-sample Mendelian randomization analysis. Front Immunol. 2023;14:1121273. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [24].Gronau QF, Wagenmakers E-J. Limitations of Bayesian leave-one-out cross-validation for model selection. Comput Brain Behav. 2019;2:1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [25].Zhao J, Ming J, Hu X, Chen G, Liu J, Yang C. Bayesian weighted Mendelian randomization for causal inference based on summary statistics. Bioinformatics. 2020;36:1501–8. [DOI] [PubMed] [Google Scholar]
  • [26].Cohen JF, Chalumeau M, Cohen R, Korevaar DA, Khoshnood B, Bossuyt PMM. Cochran’s Q test was useful to assess heterogeneity in likelihood ratios in studies of diagnostic accuracy. J Clin Epidemiol. 2015;68:299–306. [DOI] [PubMed] [Google Scholar]
  • [27].Wang H, Liu J-F, Wang F, et al. A comparison of the clinical efficacy of total hip arthroplasty via direct anterior approach and posterior approach: a meta-analysis. Medicine (Baltimore). 2024;103:e39237. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [28].Meng J, Tang H, Xiao Y, et al. Long-term effects of exercise therapy versus arthroscopic partial meniscectomy for degenerative meniscal tear: a meta-analysis of randomized controlled trials. Asian J Surg. 2024;47:2566–73. [DOI] [PubMed] [Google Scholar]
  • [29].Cook NR, Appel LJ, Whelton PK. Sodium intake and all-cause mortality over 20 years in the trials of hypertension prevention. J Am Coll Cardiol. 2016;68:1609–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [30].Milajerdi A, Djafarian K, Shab-Bidar S. Dose–response association of dietary sodium intake with all-cause and cardiovascular mortality: a systematic review and meta-analysis of prospective studies. Public Health Nutr. 2019;22:295–306. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [31].Elliott P, Muller DC, Schneider-Luftman D, et al. Estimated 24-hour urinary sodium excretion and incident cardiovascular disease and mortality among 398 628 individuals in UK Biobank. Hypertension. 2020;76:683–91. [DOI] [PubMed] [Google Scholar]
  • [32].Matsunaga M, Lim E, Davis J, Chen JJ. Dietary quality associated with self-reported diabetes, osteoarthritis, and rheumatoid arthritis among younger and older US adults: a cross-sectional study using NHANES 2011–2016. Nutrients. 2021;13:545. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [33].Jung SM, Kim Y, Kim J, et al. Sodium chloride aggravates arthritis via Th17 polarization. Yonsei Med J. 2019;60:88–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [34].Wang Y, Teichtahl AJ, Abram F, et al. Knee pain as a predictor of structural progression over 4 years: data from the osteoarthritis initiative, a prospective cohort study. Arthritis Res Ther. 2018;20:1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [35].Fan A, Oladiran O, Shi XQ, Zhang J. High-salt diet decreases mechanical thresholds in mice that is mediated by a CCR2-dependent mechanism. J Neuroinflammation. 2020;17:1–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [36].Raghu H, Lepus CM, Wang Q, et al. CCL2/CCR2, but not CCL5/CCR5, mediates monocyte recruitment, inflammation and cartilage destruction in osteoarthritis. Ann Rheum Dis. 2017;76:914–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [37].Wenstedt EF, Verberk SG, Kroon J, et al. Salt increases monocyte CCR2 expression and inflammatory responses in humans. JCI insight. 2019;4:e130508. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [38].Webster J, Su’a SAF, Ieremia M, et al. Salt intakes, knowledge, and behavior in Samoa: monitoring salt-consumption patterns through the World Health Organization’s Surveillance of Noncommunicable Disease Risk Factors (STEPS). J Clin Hypertens (Greenwich). 2016;18:884–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [39].Millerand M, Berenbaum F, Jacques C. Danger signals and inflammaging in osteoarthritis. Clin Exp Rheumatol. 2019;37(Suppl 120):48–56. [PubMed] [Google Scholar]
  • [40].Jin X, Beguerie JR, Zhang W, et al. Circulating C reactive protein in osteoarthritis: a systematic review and meta-analysis. Ann Rheum Dis. 2015;74:703–10. [DOI] [PubMed] [Google Scholar]
  • [41].Yilmaz R, Akoglu H, Altun B, Yildirim T, Arici M, Erdem Y. Dietary salt intake is related to inflammation and albuminuria in primary hypertensive patients. Eur J Clin Nutr. 2012;66:1214–8. [DOI] [PubMed] [Google Scholar]
  • [42].Dahan S, Segal Y, Shoenfeld Y. Dietary factors in rheumatic autoimmune diseases: a recipe for therapy? Nat Rev Rheumatol. 2017;13:348–58. [DOI] [PubMed] [Google Scholar]
  • [43].Lee M, Sorn SR, Lee Y, Kang I. Salt induces adipogenesis/lipogenesis and inflammatory adipocytokines secretion in adipocytes. Int J Mol Sci. 2019;20:160. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [44].Millett C, Laverty AA, Stylianou N, Bibbins-Domingo K, Pape UJ. Impacts of a national strategy to reduce population salt intake in England: serial cross sectional study. PLoS One. 2012;7:e29836. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [45].Bockarie T, Odland ML, Wurie H, et al. Prevalence and socio-demographic associations of diet and physical activity risk-factors for cardiovascular disease in Bo, Sierra Leone. BMC Public Health. 2021;21:1530. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [46].Reyhani P, Azabdaftari F, Ebrahimi-Mamagani M, Asghari-Jafarabadi M, Shokrvash B. The predictors of high dietary salt intake among hypertensive patients in Iran. Int J Hypertens. 2020;2020:6748696. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [47].Menyanu E, Charlton KE, Ware LJ, Russell J, Biritwum R, Kowal P. Salt use behaviours of Ghanaians and South Africans: a comparative study of knowledge, attitudes and practices. Nutrients. 2017;9:939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [48].He FJ, Tan M, Ma Y, MacGregor GA. Salt reduction to prevent hypertension and cardiovascular disease: JACC state-of-the-art review. J Am Coll Cardiol. 2020;75:632–47. [DOI] [PubMed] [Google Scholar]
  • [49].Meng J, Liu P, Gao S. Treatment of knee OA with krill oil. JAMA. 2024;332:1484. [DOI] [PubMed] [Google Scholar]
  • [50].Meng J, Li X, Liu W, et al. The role of vitamin D in the prevention and treatment of SARS-CoV-2 infection: a meta-analysis of randomized controlled trials. Clin Nutr (Edinburgh, Scotland). 2023;42:2198–206. [DOI] [PubMed] [Google Scholar]
  • [51].Welsh C, et al. Urinary sodium excretion, blood pressure, and risk of future cardiovascular disease and mortality in subjects without prior cardiovascular disease. Hypertension. 2019;73:1202–9. [DOI] [PubMed] [Google Scholar]

Articles from Medicine are provided here courtesy of Wolters Kluwer Health

RESOURCES