Delineating the role of the urinary metabolome in the lithogenesis of calcium-based kidney stones

Mechanism of Nephrolithiasis: Does the Microbiome Play a Role?

Nephrolithiasis imposes a significant health care burden around the world. In the past decade, there has been considerable interest in the human microbiota in relation to the onset of nephrolithiasis. Most of the research has focused on degradation of oxalate, a known causative factor for nephrolithiasis, by bacteria in the gut. More recently, the role of antibiotic exposure and changes to short-chain fatty acids have been investigated. Studies have revealed that the urinary tract, previously thought to be sterile, harbors resident microbial communities closely associated with nephrolithiasis. In this mini-review, we evaluate potential causative roles of the microbiome in the onset of nephrolithiasis and the development of novel therapies to prevent this disease. PATIENT SUMMARY: This mini-review discusses scientific evidence on the influence of bacteria in our intestines and urinary tract on the formation of kidney stones. We discuss possible therapies targeting these bacteria that could prevent kidney stones from forming.

The Plasma Metabolome and Risk of Incident Kidney Stones

Key Points

Information on metabolomic profiles in kidney stone formers is limited. This article describes investigations of associations between plasma metabolomic profiles and the risk of incident, symptomatic kidney stones. Three novel metabolites had negative associations with kidney stones: β -cryptoxanthin and two forms of sphingomyelin.

Background

Information on metabolomic profiles in kidney stone formers is limited. To examine independent associations between plasma metabolomic profiles and the risk of incident, symptomatic kidney stones in adults, we conducted prospective nested case-control studies in two large cohorts.

Methods

We performed plasma metabolomics on 1758 participants, including 879 stone formers (346 from the Health Professionals Follow-Up Study [HPFS] cohort, 533 from the Nurses' Health Study [NHS] II cohort) and 879 non–stone formers (346 from HPFS, 533 from NHS II) matched for age, race, time of blood collection, fasting status, and (for NHS II) menopausal status and luteal day of menstrual cycle for premenopausal participants. Conditional logistic regression models were used to estimate the odds ratio (OR) of kidney stones adjusted for body mass index; hypertension; diabetes; thiazide use; and intake of potassium, animal protein, oxalate, dietary and supplemental calcium, caffeine, and alcohol. A plasma metabolite–based score was developed in each cohort in a conditional logistic regression model with a lasso penalty. The scores derived in the HPFS (“kidney stones metabolite score [KMS]_HPFS”) and the NHS II (“KMS_NHS”) were tested for their association with kidney stone risk in the other cohort.

Results

A variety of individual metabolites were associated with incident kidney stone formation at prespecified levels of metabolome-wide statistical significance. We identified three metabolites associated with kidney stones in both HPFS and NHS II cohorts: β -cryptoxanthin, sphingomyelin (d18:2/24:1, d18:1/24:2), and sphingomyelin (d18:2/24:2). The standardized KMS_HPFS yielded an OR of 1.23 (95% confidence interval, 1.05 to 1.44) for stones in the NHS II cohort. The standardized KMS_NHS was in the expected direction but did not reach statistical significance in HPFS (OR, 1.16; 95% confidence interval, 0.97 to 1.39).

Conclusions

The findings of specific metabolites associated with kidney stone status in two cohorts and a plasma metabolomic signature offer a novel approach to characterize stone formers.

Mendelian randomization study of urolithiasis: exploration of risk factors using human blood metabolites

BACKGROUND

Urolithiasis is a highly prevalent global disease closely associated with metabolic factors; however, the causal relationship between blood metabolites and urolithiasis remains poorly understood.

METHOD

In our study, we employed a bi-directional two-sample Mendelian randomization (MR) analysis to investigate the causal associations between urolithiasis and metabolites. The random-effects inverse-variance weighted (IVW) estimation method was utilized as the primary approach, complemented by several other estimators including MR-Egger, weighted median, colocalization and MR-PRESSO. Furthermore, the study included replication and meta-analysis. Finally, we conducted metabolic pathway analysis to elucidate potential metabolic pathways.

RESULTS

After conducting multiple tests for correction, glycerol might contribute to the urolithiasis and dehydroisoandrosterone sulfate (DHEA-S) might inhibit this process. Furthermore, several blood metabolites had shown potential associations with a causal relationship. Among the protective metabolites were lipids (dehydroisoandrosterone sulfate and 1-stearoylglycerol (1-monostearin)), amino acids (isobutyrylcarnitine and 2-aminobutyrate), a keto acid (acetoacetate) and a carbohydrate (mannose). The risk metabolites included lipids (1-palmitoylglycerophosphoethanolamine, glycerol and cortisone), a carbohydrate (erythronate), a peptide (pro-hydroxy-pro) and a fatty acid (eicosenoate). In reverse MR analysis, urolithiasis demonstrated a statistically significant causal relationship with butyrylcarnitine, 3-methyl-2-oxobutyrate, scyllo-inositol, leucylleucine and leucylalanine. However, it was worth noting that none of the blood metabolites exhibited statistical significance after multiple corrections. Additionally, we identified one metabolic pathway associated with urolithiasis.

CONCLUSION

The results we obtained demonstrate the causal relevance between two metabolites and urolithiasis, as well as identify one metabolic pathway potentially associated with its development. Given the high prevalence of urolithiasis, further investigations are encouraged to elucidate the mechanisms of these metabolites and explore novel therapeutic strategies.

Metabolomic profiles and pathogenesis of nephrolithiasis

PURPOSE OF REVIEW

Kidney stone disease is caused by supersaturation of urine with certain metabolites and minerals. The urine composition of stone formers has been measured to prevent stone recurrence, specifically calcium, uric acid, oxalate, ammonia, citrate. However, these minerals and metabolites have proven to be unreliable in predicting stone recurrence. Metabolomics using high throughput technologies in well defined patient cohorts can identify metabolites that may provide insight into the pathogenesis of stones as well as offer possibilities in therapeutics.

RECENT FINDINGS

Techniques including 1H-NMR, and liquid chromatography paired with tandem mass spectroscopy have identified multiple possible metabolites involved in stone formation. Compared to formers of calcium oxalate stones, healthy controls had higher levels of hippuric acid as well as metabolites involved in caffeine metabolism. Both the gut and urine microbiome may contribute to the altered metabolome of stone formers.

SUMMARY

Although metabolomics has offered several potential metabolites that may be protective against or promote stone formation, the mechanisms behind these metabolomic profiles and their clinical significance requires further investigation.

Short-term effects of ambient air pollution on emergency department visits for urolithiasis: A time-series study in Wuhan, China

Background

Previous studies have explored the correlation between short-term exposure to air pollution and urinary system diseases, but lack of evidence on the correlation between air pollution and urolithiasis.

Methods

Daily data of emergency department visits (EDVs), concentrations of six air pollutants (SO₂, NO₂, PM_2.5, PM₁₀, CO, and O₃) and meteorological variables were collected in Wuhan, China, from 2016 to 2018. And a time-series study was conducted to investigate short-term effects of air pollutants on urolithiasis EDVs. In addition, stratified analyses by season, age and gender were also conducted.

Results

A total of 7,483 urolithiasis EDVs were included during the study period. A 10-μg/m³ increase of SO₂, NO₂, PM_2.5, CO, PM₁₀, and O₃ corresponded to 15.02% (95% confidence interval [CI]: 1.69%, 30.11%), 1.96% (95% CI: 0.19%, 3.76%), 1.09% (95% CI:-0.24%, 2.43%), 0.14% (95% CI: 0.02%, 0.26%), 0.72% (95% CI: 0.02%, 1.43%), and 1.17% (95% CI: 0.40%, 1.94%) increases in daily urolithiasis EDVs. Significant positive correlations were observed between SO₂, NO₂, CO, and O₃ and urolithiasis EDVs. The correlations were mainly among females (especially PM_2.5 and CO) and younger people (especially SO₂, NO₂, and PM₁₀) but the effect of CO was more obvious in elders. Furthermore, the effects of SO₂ and CO were stronger in warm seasons, while the effects of NO₂ were stronger in cool seasons.

Conclusion

Our time-series study indicates that short-term exposure to air pollution (especially SO₂, NO₂, CO, and O₃) was positively correlated with EDVs for urolithiasis in Wuhan, China, and the effects varied by season, age and gender.

Biofilms on Indwelling Artificial Urinary Sphincter Devices Harbor Complex Microbe-Metabolite Interaction Networks and Reconstitute Differentially In Vitro by Material Type

The artificial urinary sphincter (AUS) is an effective treatment option for incontinence due to intrinsic sphincteric deficiency in the context of neurogenic lower urinary tract dysfunction, or stress urinary incontinence following radical prostatectomy. A subset of AUS devices develops infection and requires explant. We sought to characterize biofilm composition of the AUS device to inform prevention and treatment strategies. Indwelling AUS devices were swabbed for biofilm at surgical removal or revision. Samples and controls were subjected to next-generation sequencing and metabolomics. Biofilm formation of microbial strains isolated from AUS devices was reconstituted in a bioreactor mimicking subcutaneous tissue with a medical device present. Mean patient age was 73 (SD 10.2). All eighteen artificial urinary sphincter devices harbored microbial biofilms. Central genera in the overall microbe−metabolite interaction network were Staphylococcus (2620 metabolites), Escherichia/Shigella (2101), and Methylobacterium-Methylorubrum (674). An rpoB mutation associated with rifampin resistance was detected in 8 of 15 (53%) biofilms. Staphylococcus warneri formed greater biofilm on polyurethane than on any other material type (p < 0.01). The results of this investigation, wherein we comprehensively characterized the composition of AUS device biofilms, provide the framework for future identification and rational development of inhibitors and preventive strategies against device-associated infection.