Unraveling the hidden link: neuroticism as a risk factor for upper urinary tract calculi—insights from Mendelian randomization analysis

Introduction

Upper urinary tract calculi (UUTC) and loss of follow-up are identified as the predominant risk factors for the development of renal insufficiency and the leading non-neoplastic cause of nephrectomy (1). A higher percentage of patients with urolithiasis suffer from psychological distress, including depression and anxiety (2). Depression is linked to an increased risk of developing kidney stones (3). Depression and anxiety are both marked by heightened neuroticism (4). Furthermore, personality traits, among which neuroticism is included, predict the occurrence of depression over the course of a person’s life (4). Research has identified a correlation between neuroticism and various urological disorders, including urinary incontinence, complicated urinary tract infections and cystitis (5-8). However, there is limited research on the relationship between UUTC and neuroticism. Therefore, it is crucial to investigate whether neuroticism can serve as a predictive factor for UUTC.

Conventional observational studies are susceptible to confounding variables, reversed causality, and various biases, leading to unreliable establishment of causal relationships. Mendelian randomization (MR) relies on the random assortment of alleles during the formation of gametes (9,10). As a result, genetic variations are spread throughout the population, typically unaffected by behavioral and environmental factors that may obscure the epidemiological connection between hypothetical risk factors and diseases. (9,10). In specific cases, this can offer research designs that closely mimic randomized comparisons (9,10).

Consequently, our objective was to examine the possible causal relationship between neuroticism and UUTC through MR analysis. We present this article in accordance with the STROBE-MR reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-24-379/rc).

Methods

Study design

We performed two-sample MR studies analysis to explore the causal relationship between neuroticism, as the exposure, and UTCC, as the outcome. Subsequently, we incorporated 11 neuroticism-related phenotypes for a subgroup analysis, which included irritable mood, feeling lonely, feeling miserable, experiencing mood swings, feeling guilty, worry too long after an embarrassing experience, feeling fed-up, feeling nervous, feeling worry, feeling hurt and feeling tense. The choice of SNPs [single nucleotide polymorphisms, refer to variations in a DNA sequence where a single nucleotide (A, T, C, or G) is altered, and this variation occurs in at least 1% of the population. SNPs are the most common type of genetic variation in the human genome]. as instrumental variables (IVs) followed three assumptions (11): (I) SNPs demonstrated strong correlations with neuroticism and 11 associated phenotypes; (II) SNPs should not be linked to any confounding factors [including body mass index (BMI), defined by calculating the ratio of weight to height, and diabetes]; and (III) there should be no direct genetic link between the SNPs and UUTC. Additionally, we conducted a multivariate MR analysis on phenotypes from the results of the two-sample analysis of 11 neuroticism-related phenotypes, which were significant, consistent in the direction of test methods, and free from pleiotropy, to explore the independent risk factors for UUTC. Additionally, we opted to use Neale Lab’s neuroticism score (ukb-a-230) for verification queue of the exposure (Figure 1).

Figure 1 Three hypotheses for a two-sample Mendelian randomization study and multivariate MR analysis include psychological traits that show no pleiotropy, have IVW P values below 0.05, and display consistent effects across all three MR methods. SNP, single nucleotide polymorphism; MR, Mendelian randomization; UUTC, upper urinary tract calculi; MVMR, multivariate Mendelian randomization; IVW, inverse variance weighted.

Data source

We obtained genome-wide association study (GWAS) (a research method used to identify genetic variants associated with specific traits or diseases) data for neuroticism, neuroticism score and 11 associated traits, as well as diabetes and BMI, from the website: https://gwas.mrcieu.ac.uk/. For GWAS data on UUTC, we sourced it from the Finnish database version 10 (12) (Table 1). Given that our exposure and outcome data come from cohort studies conducted in different countries, there is no overlap in the sample sizes between the two. For each exposure, outcome, and other relevant variables, the methods of assessment and diagnostic criteria for diseases can be found in the references in Table 1. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Instrument variable selection

The criteria for selecting IVs to assess the impact of exposures on outcome in our study were as follows: (I) SNPs associated with exposures that met the locus-wide significance threshold (P<5.0×10⁻⁸) were deemed potential IVs. (II) To counteract the influence of linkage disequilibrium (LD), we used the clumping method to identify independent SNPs, requiring an r²<0.001 and a minimum distance of more than 10,000 kb. (III) We systematically excluded intermediate allele palindromic SNPs from our analysis. (IV) We searched for SNPs related to diabetes and BMI on the LDlink (https://ldlink.nci.nih.gov/?tab=ldtrait) website and removed them. (V) The F-statistic was calculated as F = [R² × (n − k − 1)]/[k × (1 − R²)], with R² = 2 × (1 − MAF) × MAF × (beta/SD)². Here, n represents the sample size of the exposure GWAS study, k was the number of SNPs, MAF was the minor allele frequency (similar to effect-allele frequency in R² calculation), beta was the effect size of the SNP on exposure, and SD was the standard deviation (17). An F-value above 10 was commonly considered as a sign of strong IVs.

Statistical analysis

In our two-sample MR analysis, we took advantage of inverse variance weighted (IVW) (18,19), MR-Egger (20,21), and weighted median (22) methods. We chose the IVW method as our main analytical approach because of its strength in establishing causal effects.

We performed sensitivity analyses to evaluate the heterogeneity and pleiotropy, ensuring the robustness of our results from the two-sample MR analysis. Cochran’s Q test was utilized to detect heterogeneity among the SNPs, with a P value below 0.05 indicating the presence of heterogeneity (23,24). The MR-Egger intercept (20) and the MR-pleiotropy residual sum and outlier (PRESSO) method (25) were used to detect pleiotropy. P value below 0.05 was considered indicative of pleiotropy. We also employed MR-PRESSO to identify any outliers. When pleiotropy was not present, the IVW analysis was considered unbiased. To explore the independent influence of neuroticism phenotypes on UUTC, we conducted a multivariate MR analysis on phenotypes that demonstrated unbiased results in the IVW, with IVW P values below 0.05 and consistent directions across all three MR analysis methods (26,27).

For our MR analysis and follow-up sensitivity evaluations, we utilized Rstudio (version 4.3.2) in conjunction with the TwoSampleMR R package (version 0.5.10).

Results

The reported results are based on the IVs after removing outliers. MR-PRESSO identified outliers only in the phenotypes neuroticism score, Feeling nervous, Feeling worry, and Feeling tense (Table S1).

Causal impact of neuroticism on UUTC

The findings from IVW [odds ratio (OR) =1.15, 95% confidence interval (CI): 1.02 to 1.29, P=0.03] and weighted median (OR =1.22, 95% CI: 1.03 to 1.44, P=0.02) analyses suggested that neuroticism was a significant factor in UUTC, with a statistically significant association (P<0.05). MR-Egger estimate also displayed their positive effect on the UUTC. Verification queue similarly confirmed that the neuroticism score was a risk factor for UUTC (IVW, OR =1.11, 95% CI: 1.03 to 1.19, P=0.007). The sensitivity analysis showed that the Q values for the Cochran’s Q test of the IVW and MR-Egger methods were both 53.40, with P values of 0.46 and 0.42. These findings suggested no presence of heterogeneity. The MR-Egger intercept (P=0.94) does not support the presence of pleiotropy (Figure 2). Furthermore, the MR-PRESSO analysis demonstrates a global test P value of 0.50, indicating the absence of pleiotropic effects (Table 2).

Figure 2 Results of the two-sample MR analysis and sensitivity analysis. GWAS ID, genome-wide association study identifier; IVW, inverse variance weighted; MR, Mendelian randomization; nsnp, number of single nucleotide polymorphisms; P, the P values of the IVW, MR-Egger, and weighted median methods; OR, odds ratio; CI, confidence interval; Q, Q-value of the Q test; Q_df, Q statistic degrees of freedom; Q_P, Q statistic P value; Int.P, the P value of intercept.

Causal impact of 11 neuroticism-related phenotypes on UUTC

Following two-sample MR analysis of 11 neuroticism-related traits and UUTC, we identified 5 phenotypes that met the criteria for unbiased results in the IVW and consistent directions across all three MR analysis methods. All results are available in Table S2. All MR-PRESSO results are in Table S3.

The IVW results reveal significant positive associations with UUTC for experiencing mood swings (OR =1.41, 95% CI: 1.01 to 1.97, P=0.047), feeling lonely (OR =6.03, 95% CI: 2.12 to 17.10, P<0.001), and feeling worry (OR =1.58, 95% CI: 1.04 to 2.38, P=0.03), all with P values below 0.05. Feeling miserable showed a negative correlation with UUTC across three MR analysis methods, but the results are not statistically significant (IVW, P=0.44; MR-Egger, P=0.74; weighted median, P=0.36). Three MR analysis methods suggested a positive correlation between worry too long after an embarrassing experience and UUTC, though the findings lack statistical significance (IVW, P=0.35; MR-Egger, P=0.24; weighted median, P=0.25). The Q-test and MR-Egger results for the five aforementioned phenotypes revealed no evidence of heterogeneity or pleiotropy (Figure 2). The MR-PRESSO test results also do not support pleiotropy (Table 2).

Multivariate MR analysis

In our study, we conducted a multivariable MR analysis on the three significant phenotypes identified in section 3.2—experiencing mood swings, feeling lonely, and feeling worry—along with diabetes and BMI, to determine which of these behaviors independently contributed to the risk of UUTC. We found that the phenotype “feeling lonely” did not yield any SNPs; hence, we conducted a multivariable analysis on “experiencing mood swings”, “feeling worry”, diabetes, and BMI. Experiencing mood swings independently contributed (OR =1.75, 95% CI: 1.06 to 2.89, P=0.03) to the risk of UUTC (Figure 3).

Figure 3 Results of the multivariable MR analysis. GWAS ID, genome-wide association study identifier; nsnp, number of single nucleotide polymorphisms; OR, odds ratio; CI, confidence interval.

Discussion

In the two-sample MR study, we examined the association between neuroticism, including 11 related personality traits, and UUTC. Our findings indicated that neuroticism, along with experiencing mood swings, feeling lonely, and feeling worry, act as risk factors for UUTC. Verification queue also supports that neuroticism score is a risk factor for UUTC. Sensitivity analyses showed no evidence of heterogeneity or pleiotropy. To investigate if experiencing mood swings, feeling lonely, and feeling worry are independent risk factors for UUTC, we performed a multivariable MR analysis while also considering diabetes and BMI. The results of the multivariable analysis indicated that experiencing mood swings is an independent risk factor for UUTC.

Hormonal and metabolism imbalances may be the primary causes through which neuroticism leads to UUTC. Studies have confirmed that individuals demonstrating high levels of neuroticism tend to have higher levels of cortisol (28,29). Cortisol may increase urinary calcium concentration (30), potentially through the inhibition of calcium reabsorption in the renal tubules (31). The risk of stone formation significantly increases with an elevated level of urinary calcium (32). According to research on high cortisol levels, even healthy children with higher cortisol levels within the normal range may have negative effects on the kidney’s citrate excretion (33). In adult males, low citrate in urine may commonly trigger the formation of calcium kidney stones (34). Citrate seems to prevent the crucial process of crystal agglomeration, where separate calcium oxalate crystals join to create a stone (35). Elevated neuroticism is linked to decreased vitamin D levels (36). Having high serum vitamin D levels may be connected to a lower risk of stone formation or recurrence (37). A potential mechanism for the heightened risk of UUTC in individuals with neuroticism could be disturbances in calcium-phosphate metabolism, which are secondary to their reduced vitamin D levels. Regarding the variations in cortisol and vitamin D levels across different seasons and among individuals with different neurotic traits, our study did not include specific analyses in this area due to a lack of corresponding stratified data. Cortisol levels may be influenced by circadian rhythms and stress factors, while vitamin D levels are related to sunlight exposure and exhibit seasonal changes. Future research could consider: (I) investigating the relationships between traits such as “experiencing mood swings” with physiological indicators (such as cortisol and vitamin D levels); (II) examining the impact of seasonal factors on these physiological indicators and their expression in populations with high neuroticism. This will help deepen our understanding of the pathways through which neuroticism affects the risk of UUTC, providing a basis for clinical interventions. In individuals with neuroticism, a heightened inflammatory state could potentially serve as an underlying contributor to the development of UUTC. Neuroticism in young adults is linked to higher levels of interleukin-6 (IL-6) levels (38). Oncostatin M, a member of the IL-6 family, may promote the formation of kidney crystal deposits by directly affecting renal tubular epithelial cells and renal fibroblasts to produce crystal-binding molecules and inflammatory cytokines (39). Unhealthy lifestyle choices could potentially serve as another significant factor. There is a positive correlation between sedentary behavior and neuroticism levels (40,41). Sedentary behavior is a risk factor for kidney stones (42). Neurotic personality traits are associated with the occurrence of cystitis, urinary leakage, and difficulty in urination (8). Although there is no direct evidence indicating that individuals with neurotic traits reduce fluid intake, these patients may decrease their water consumption to reduce urination frequency due to fear of the aforementioned lower urinary tract symptoms. Therefore, reduced fluid intake may play a mediating role between neuroticism and UUTC. Future research should further explore the impact of neuroticism on fluid intake behavior and whether this is a pathway through which neuroticism increases the risk of UUTC.

In our research, MR analysis emerges as a crucial technique, providing a robust framework for assessing causality and addressing the limitations inherent in traditional observational studies. By employing genetic variants as IVs, we significantly mitigated the risk of confounding factors and reverse causality, thereby strengthening the validity of the causal link between neuroticism and 11 related psychological traits and UUTC. Nevertheless, it is essential to acknowledge certain limitations. Primarily, our dependence on summary-level data from large-scale GWASs may introduce biases and inherit limitations from the original studies. Additionally, our findings are based on European populations, which limits their generalizability to other ethnic groups. Future research should aim to include diverse ethnic cohorts to validate our results. Lastly, due to the lack of specific databases, we were unable to conduct subgroup analyses on neuroticism and the composition of stones. Furthermore, considering the significant role of gender in the incidence of UUTC (43), future research should delve into whether gender differences modulate or influence the relationship between neuroticism and UUTC. This may involve stratified analyses to assess whether there are differences in the strength and patterns of this association among men and women, thereby providing more targeted prevention and intervention strategies. Given that our study utilized public databases, we did not find GWAS data on neuroticism and UUTC stratified by gender in known public databases. In the future, we will closely monitor updates to these databases to further explore the impact of gender stratification on the relationship between neuroticism and UUTC.

Given that UUTC are the primary non-neoplastic cause of nephrectomy (1), early identification and preventive measures in at-risk groups are crucial. This strategy can significantly alleviate patient suffering and offer economic benefits to healthcare systems. Our study results indicated that neuroticism is a risk factor for UUTC, with experiencing mood swings identified as an independent risk factor for UUTC. This underscores the importance of closely monitoring nephrolithiasis in neurotic individuals and contributes to our comprehension of the brain-kidney axis.

Conclusions

Our MR analysis has identified neuroticism as a risk factor for UUTC, with experiencing mood swings identified as an independent risk factor for UUTC, thereby deepening our understanding of the brain-kidney axis. The specific mechanisms underlying this association warrant further investigation. These findings underscore the importance of monitoring urinary stones in neurotic individuals.

Acknowledgments

We express our gratitude to the Medical Research Council Integrative Epidemiology Unit (MRC IEU) consortium, at the University of Bristol for developing the IEU open GWAS project and for extracting the GWAS data. We also wish to acknowledge the contribution of the participants and investigators of the FinnGen study.

Funding: This work was financially supported by Natural Science Foundation of Liaoning Province (contract No. 2022-MS-047).

Footnote

Reporting Checklist: The authors have completed the STROBE-MR reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-24-379/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-24-379/prf

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-24-379/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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