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Purpose: Recent studies have focused on whether kidney injury molecule-1 (KIM-1) might serve as a marker of acute kidney tubular injury. Our study analyzed the levels of KIM-1 in the healthy population of different ages to explore the correlation between KIM-1 and age. Moreover, we constructed a model to predict kidney age. Methods: A cross-sectional study was conducted by Huashan Hospital, Shanghai, China, between April 2020 and December 2020. KIM-1 and other kidney biomarkers were measured in 176 healthy individuals ranging from 26 to 91 years old. Statistical correlated analyses for urinary KIM-1, creatinine (uCREA), potassium (K), sodium (Na) and chlorine (Cl), plasmic renin, angiotensin-2 (AngII) and aldosterone (ALD), and serum microalbuminuria (MALB), ß2-microglobulin (B2MG), cystatin C (CYSC), urea nitrogen (BUN), creatinine (CREA), and glucose (GLU) were performed to assess the correlation between age and kidney biomarkers. All variables were selected as independent variables for the prediction of age by multiple linear regression. Results: KIM-1 positively correlated with age in kidney healthy people (r = 0.41, p < 0.05), whether among females (r = 0.51, p < 0.05) or males (r = 0.27, p < 0.05). It was much related to K (r = 0.34), B2MG (r = 0.28), and CL (r = 0.23). The predicted model was constructed with eGFR, Cl, ALD, CYSC, KIM-1, BUN, GLU and AngII, reaching an adjusted R2 of 69.5% and a standard error of the estimated 7.84 years. Conclusion: The level of urinary KIM-1 increases with age in healthy people. The model constructed by KIM-1 and the other 7 biomarkers can predict kidney age in healthy people.
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BACKGROUND: DNAs released from tumor cells into blood (circulating tumor DNAs, ctDNAs) carry tumor-specific genomic aberrations, providing a non-invasive means for cancer detection. In this study, we aimed to leverage somatic copy number aberration (SCNA) in ctDNA to develop assays to detect early-stage HCCs. METHODS: We conducted low-depth whole-genome sequencing (WGS) to profile SCNAs in 384 plasma samples of hepatitis B virus (HBV)-related HCC and cancer-free HBV patients, using one discovery and two validation cohorts. To fully capture the robust signals of WGS data from the complete genome, we developed a machine learning-based statistical model that is focused on detection accuracy in early-stage HCC. FINDINGS: We built the model using a discovery cohort of 209 patients, achieving an overall area under curve (AUC) of 0.893, with 0.874 for early-stage (Barcelona clinical liver cancer [BCLC] stage 0-A) and 0.933 for advanced-stage (BCLC stage B-D). The performance of the model was then assessed in two validation cohorts (76 and 99 patients) that only consisted of patients with stage 0-A HCC. Our model exhibited a robust predictive performance, with an AUC of 0.920 and 0.812 for the two validation cohorts. Further analyses showed the impact of tumor sample heterogeneity in model training on detecting early-stage tumors, and a refined model addressing the heterogeneity in the discovery cohort significantly increased model performance in validation. INTERPRETATION: We developed an SCNA-based, machine learning-driven model in the non-invasive detection of early-stage HCC in HBV patients and demonstrated its performance through strict independent validations.