Background: Targeting the gut microbiota may become a new therapeutic to prevent and delay the progression of chronic kidney disease (CKD). Nonetheless, the causal relationship between specific intestinal flora and CKD is still unclear. Materials and Method: To identify genetically predicted microbiota, we used summary data from genome-wide association studies on gut microbiota in 18340 participants from 24 cohorts. Furthermore, we genetically predicted the causal relationship between 211 gut microbiotas and six phenotypes (outcomes) (CKD, estimated glomerular filtration rate (eGFR), urine albumin to creatinine ratio (UACR), dialysis, rapid progress to CKD, and rapid decline of eGFR). Four Mendelian randomization (MR) methods, including inverse variance weighted (IVW), MR-Egger, weighted median, and weighted mode were used to investigate the casual relationship between gut microbiotas and various outcomes. The result of IVW was deemed as the primary result. Then, Cochrane’s Q test, MR-Egger, and MR-PRESSO Global test were used to detect heterogeneity and pleiotropy. The leave-one method was used for testing the stability of MR results and Bonferroni-corrected was used to test the strength of the causal relationship between exposure and outcome. Results: Through the MR analysis of 211 microbiotas and six clinical phenotypes, a total of 36 intestinal microflora were found to be associated with various outcomes. Among them, Class Bacteroidia (=−0.005, 95% CI: −0.001 to −0.008, p = 0.002) has a strong causality with lower eGFR after the Bonferroni-corrected test, whereas phylum Actinobacteria (OR = 1.0009, 95%CI: 1.0003–1.0015, p = 0.0024) has a strong causal relationship with dialysis. The Cochrane’s Q test reveals that there is no significant heterogeneity between various single nucleotide polymorphisms. In addition, no significant level of pleiotropy was found according to MR-Egger and MR-PRESSO Global tests. Conclusions: Through the two-sample MR analysis, we identified the specific intestinal flora that has a causal relationship with the incidence and progression of CKD at the level of gene prediction, which may provide helpful biomarkers for early disease diagnosis and potential therapeutic targets for CKD.

Background: Although recent research suggests that alterations in gut microbiota and metabolites play a critical role in the pathophysiology of immunoglobulin A nephropathy (IgAN), the causal relationship between specific intestinal flora and metabolites and the risk of IgAN remains unclear. Method: This study employed Mendelian randomization (MR) to investigate the causal association between gut microbiota and IgAN. To explore potential associations between gut microbiota and various outcomes, four MR methods were applied: inverse variance weighted (IVW), MR-Egger, weighted median, and weighted mode. If the results of the four methods are inconclusive, we prefer the IVW as the primary outcome. Additionally, MR-Egger, MR-PRESSO-Global, and Cochrane’s Q tests were used to detect heterogeneity and pleiotropy. The stability of MR findings was assessed using the leave-one-out approach, and the strength of the causal relationship between exposure and outcome was tested using Bonferroni correction. Additional clinical samples were utilized to validate the results of Mendelian randomization, and the outcomes were visualized through an ROC curve, confusion matrix, and correlation analysis. Result: This study examined a total of 15 metabolites and 211 microorganisms. Among them, eight bacteria and one metabolite were found to be associated with the risk of IgAN (p < 0.05). The Bonferroni-corrected test reveals that only Class. Actinobacteria (OR: 1.20, 95% CI: 1.07–1.36, p = 0.0029) have a significant causal relationship with IgAN. According to Cochrane’s Q test, there is no substantial heterogeneity across different single-nucleotide polymorphisms (p > 0.05). Furthermore, MR-Egger and MR-PRESSO-Global tests (p > 0.05) showed no evidence of pleiotropy. No reverse causal association was found between the risk of IgAN and microbiota or metabolites (p > 0.05). Clinical specimens demonstrated the effectiveness and accuracy of Actinobacteria in distinguishing IgAN patients from those with other glomerular diseases (AUC = 0.9, 95% CI: 0.78–1.00). Additionally, our correlation analysis revealed a potential association between Actinobacteria abundance and increased albuminuria (r = 0.85) and poorer prognosis in IgAN patients (p = 0.01). Conclusion: Through MR analysis, we established a causal link between Actinobacteria and the incidence of IgAN. Moreover, clinical validation using fecal samples indicated that Actinobacteria might be associated with the onset and poorer prognosis of IgAN. This finding could provide valuable biomarkers for early, noninvasive detection of the disease and potential therapeutic targets in IgAN.

Objectives: Previous studies have shown that there may be a positive correlation between serum uric acid levels and hyperthyroidism. However, the relationship between thyroid function and serum uric acid in healthy people is unclear. This study analyzed the relationship between impaired thyroid hormone sensitivity and serum uric acid levels, and presented them in quantitative form. Research design and methods: This is a cross-sectional study of 4460 adults (male: 2300; female: 2160) who participated in the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2010. Parameters representing central sensitivity to thyroid hormones were calculated as: thyroid feedback quantile-based index (TFQI_FT4), thyroid stimulating hormone index (TSHI), and total thyroxine (T4) resistance index (TT4RI); Peripheral sensitivity to thyroid hormone was evaluated by FT3/FT4 ratio. In addition, we have innovated total triiodothyronine (T3) resistance index (TT3RI) and TFQI_FT3 indexes based on FT3 and TSH. Multiple linear regression models were used to evaluate the correlation between thyroid resistance index and serum uric acid, and the results were presented graphically as smooth curve fittings. Results: Higher levels of serum uric acid were associated with decreased sensitivity to thyroid hormones in euthyroid individuals. In conjunction with an increase in the thyroid hormone sensitivity index value, uric acid levels gradually increased as well. Furthermore, we found a segmented relationship between TT3RI and serum uric acid changes. The saturation and threshold analyses indicated that 18.85 was the turning point (logarithmic likelihood ratio test = 0.036). When TT3RI < 18.85, the relationship between serum uric acid and TT3RI was not significant [β(95% CI) 0.47 (− 0.05, 1.00), P = 0.077], but when TT3RI > 18.85, there was a significant rise in serum uric acid with an increase in TT3RI [β(95% CI) 3.94 (0.94, 6.95), P = 0.010]. A further finding of the interaction test was that impaired thyroid hormone sensitivity and uric acid changes vary among different age groups and BMI levels. Conclusions: Decreased sensitivity to thyroid hormones was associated with high levels of serum uric acid in people with normal thyroid function. The interaction test shows that different age groups and BMI groups impact the association between impaired thyroid hormone sensitivity and serum uric acid. Furthermore, smooth curve fitting revealed a segmental relationship between TT3RI and serum uric acid levels.