Background: Few studies have explored the impact of perchlorate, nitrate, and thiocyanate (PNT) on kidney function. This study aimed to evaluate the association of urinary levels of PNT with renal function as well as the prevalence of chronic kidney disease (CKD) among the general population in the United States. Methods: This analysis included data from 13,373 adults (≥20 years) from the National Health and Nutrition Examination Survey 2005 to 2016. We used multivariable linear and logistic regression, to explore the associations of urinary PNT with kidney function. Restricted cubic splines were used to assess the potentially non-linear relationships between PNT exposure and outcomes. Results: After traditional creatinine adjustment, perchlorate (P-traditional) was positively associated with estimated glomerular filtration rate (eGFR) (adjusted β: 2.75; 95% confidence interval [CI]: 2.25 to 3.26; P < 0.001), and negatively associated with urinary albumin-to-creatinine ratio (ACR) (adjusted β: −0.05; 95% CI: −0.07 to −0.02; P = 0.001) in adjusted models. After both traditional and covariate-adjusted creatinine adjustment, urinary nitrate and thiocyanate were positively associated with eGFR (all P values <0.05), and negatively associated with ACR (all P values <0.05); higher nitrate or thiocyanate was associated with a lower risk of CKD (all P values <0.001). Moreover, there were L-shaped non-linear associations between nitrate, thiocyanate, and outcomes. In the adjusted models, for quartiles of PNT, statistically significant dose–response associations were observed in most relationships. Most results were consistent in the stratified and sensitivity analyses. Conclusions: Exposures to PNT might be associated with kidney function, indicating a potential beneficial effect of environmental PNT exposure (especially nitrate and thiocyanate) on the human kidney.

In this study, we explored the genomic and immune cell infiltration profiles among four distinct Hepatocellular carcinoma (HCC) types. This study included 100 patients (all tumors and adjacent liver tissues received WES sequencing) with HCC from the West China Hospital (WCH) and patients were divided into WCH-HBV-HCC group and WCH-NonHBV-HCC group. Additionally, this study included 106 HBV-related HCC (TCGA-HBV-HCC) and 69 alcoholic HCC (TCGA-Alcol-HCC) patients from the TCGA. We analyzed the high-frequency gene mutation, copy number variation (CNV), mutation spectrum, signatures and immune cell infiltration of these four groups. This study showed significant differences in gene mutation and CNV level among four HCC groups. Compared to genomic level, there is no significant difference between TCGA-HBV-HCC and TCGA-Alcol-HCC groups in fractions of tumor-infiltrating immune cells. According to the status of immune cell infiltration, patients were classified into immune-HIGH, immune-MIX and immune-LOW group, respectively. In the WCH-HBV-HCC and TCGA-HBV-HCC groups, more patients in the Immune-LOW group had TP53 mutation. Except for TP53, neither the other gene mutation nor tumor mutation burden was found to be associated with immune cell infiltration in the WCH-HBV-HCC, TCGA-HBV-HCC and TCGA-Alcol-HCC groups. In the CNV level, we found that samples with low immune infiltrate had higher number of deleted or amplified genes in the TCGA-HBV-HCC and TCGA-Alcol-HCC groups. We found comprehensive genomic heterogeneity among four HCC groups. The total gene CNV level, not the mutational burden of HCC, is associated with immune cell infiltration in HCC. TP53 mutation may injury the immune response of the HBV-related HCC.

The aim of this study was to evaluate the association between lifestyle profile and disease incidence/mortality in patients with non-alcoholic fatty liver disease (NAFLD). Lifestyle profiles ascertainment was based on the latent profile analysis. The associations of lifestyle profile and outcomes were analyzed by multivariate logistic or Cox regressions. Four lifestyle profiles (profile 1 and 2 for male, profile 3 and 4 for female) were established for all participants. Compared to profile 1, profile 2 (P = 0.042) and profile 3 (P = 0.013) had lower incidence for NAFLD. In contrast, profile 4 showed similar NAFLD prevalence compared to profile 1 (P = 0.756). Individuals with NAFLD within profile 3 had the best long-term survival, and the HR was 0.55 (95% CI 0.40–0.76) for all-cause mortality (compared to profile 1). Profile 4 (P = 0.098) and profile 2 (P = 0.546) had similar all-cause survival compared to profile 1. We explored the associations of healthy lifestyle score with mortality and incidence of NAFLD stratified by lifestyle profiles. We observed that with the increase of healthy lifestyle score, participants within profile 2 did not display lower NAFLD incidence and better long-term survival in NAFLD cases. In this study, lifestyle profiles were constructed in NHANES participants. The distinct lifestyle profiles may help optimize decision-making regarding lifestyle management in preventing NAFLD development, as well as selection of a more personalized approach for improving NAFLD survival.