cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27074110.v1
title: Statin therapy and upper tract urothelial carcinoma risk in hyperlipidemic patients with chronic kidney disease and end-stage kidney disease, a population-based 17-year follow-up study

description: The upper tract urothelial carcinoma (UTUC) risk associated with statin therapy in hyperlipidemic patients with chronic kidney disease (CKD) and end-stage kidney disease (ESKD) remains obscure. This retrospective cohort study investigated the UTUC risk for hyperlipidemic patients with CKD or ESKD associated with statin therapy. From the national insurance claims data of Taiwan, we identified hyperlipidemic patients and established three pairs of statin users and non-users sub-cohorts matched by propensity scores: 401,490 pairs with normal kidney function, 37,734 pairs with CKD, and 6271 pairs with ESKD. Incidence rates and hazard ratio (HR) of UTUC were estimated, by the end of 2016, between statin and non-statin cohorts, and between hydrophilic statins users and lipophilic statins users. Time-dependent model estimated adjusted HR, and sub-distribution HR (sHR) accounting for the competing risk of deaths. The statin-users with ESKD were at increased UTUC risk (sHR 1.98; 95% confidence interval (CI), 1.28–3.06), significant for younger patients (40–64 years). The incidence was twofold greater in women than in men (31.8 versus 15.9 per 10,000 person-years). Receiving lipophilic statins was associated with increased UTUC risk in CKD and ESKD patients, while receiving hydrophilic statins was associated with increased UTUC risk in ESKD patients. Patients with ESKD receiving statin were at an increased UTUC risk, significant for younger group (&lt;65 y/o). The positive associations between UTUC and statin persisted in both genders with ESKD, and in therapy with either lipophilic statins or hydrophilic statins. Statin users with ESKD deserve attention for UTUC prevention.
DOI: 10.6084/m9.figshare.27601048
title: Lipid metabolism disorders and albuminuria risk: insights from National Health and Nutrition Examination Survey 2001–2018 and Mendelian randomization analyses

description: Previous studies have revealed an underlying connection between abnormal lipid metabolism and albuminuria. We aim to investigate the causal relationship between lipid metabolism disorders and the risk of albuminuria from both a population and genetic perspective. A cross-sectional study was conducted by using data from the National Health and Nutrition Examination Survey (NHANES) 2001-2018. Multivariable-adjusted logistic regression, subgroup analysis, interaction tests and restricted cubic spline (RCS) were employed statistically. Mendelian randomization (MR) analysis was performed to validate the causal relationship between exposure and outcome to mitigate confounding factors and reverse causation interference. After adjusting for confounders, HDL levels (1.03-2.07 nmol/L) were associated with a reduced risk of albuminuria. In contrast, elevated cholesterol levels (&gt;6.2 nmol/L) and triglyceride levels (&gt;2.3 nmol/L) were associated with an increased risk of albuminuria. Serum triglyceride concentration emerged as a potential risk factor for albuminuria. In MR analysis, a reduced risk of albuminuria was associated with serum total HDL level (IVW: OR = 0.91, 95% CI = 0.86-0.97, <i>p</i> = 0.002). In contrast, cholesterol esters in medium VLDL (IVW: OR = 1.05, 95% CI = 1.00-1.10, <i>p</i> = 0.032), chylomicrons and extremely large VLDL (IVW: OR = 1.08, 95% CI = 1.03-1.14, <i>p</i> = 0.003), and triglycerides (IVW: OR = 1.14, 95% CI = 1.09-1.19, <i>p</i> &lt; 0.001) were associated with an increased risk of albuminuria. A causal relationship exists between serum lipid metabolism disorder and albuminuria risk. Further validation of additional blood lipid metabolism biomarkers is imperative for future studies.
DOI: 10.6084/m9.figshare.27601048.v1
title: Lipid metabolism disorders and albuminuria risk: insights from National Health and Nutrition Examination Survey 2001–2018 and Mendelian randomization analyses

description: Previous studies have revealed an underlying connection between abnormal lipid metabolism and albuminuria. We aim to investigate the causal relationship between lipid metabolism disorders and the risk of albuminuria from both a population and genetic perspective. A cross-sectional study was conducted by using data from the National Health and Nutrition Examination Survey (NHANES) 2001-2018. Multivariable-adjusted logistic regression, subgroup analysis, interaction tests and restricted cubic spline (RCS) were employed statistically. Mendelian randomization (MR) analysis was performed to validate the causal relationship between exposure and outcome to mitigate confounding factors and reverse causation interference. After adjusting for confounders, HDL levels (1.03-2.07 nmol/L) were associated with a reduced risk of albuminuria. In contrast, elevated cholesterol levels (&gt;6.2 nmol/L) and triglyceride levels (&gt;2.3 nmol/L) were associated with an increased risk of albuminuria. Serum triglyceride concentration emerged as a potential risk factor for albuminuria. In MR analysis, a reduced risk of albuminuria was associated with serum total HDL level (IVW: OR = 0.91, 95% CI = 0.86-0.97, <i>p</i> = 0.002). In contrast, cholesterol esters in medium VLDL (IVW: OR = 1.05, 95% CI = 1.00-1.10, <i>p</i> = 0.032), chylomicrons and extremely large VLDL (IVW: OR = 1.08, 95% CI = 1.03-1.14, <i>p</i> = 0.003), and triglycerides (IVW: OR = 1.14, 95% CI = 1.09-1.19, <i>p</i> &lt; 0.001) were associated with an increased risk of albuminuria. A causal relationship exists between serum lipid metabolism disorder and albuminuria risk. Further validation of additional blood lipid metabolism biomarkers is imperative for future studies.
DOI: 10.6084/m9.figshare.27965169
title: Diverse regulated cell death patterns and immune traits in kidney allograft with fibrosis: a prediction of renal allograft failure based on machine learning, single-nucleus RNA sequencing and molecular docking

description: <b>Objectives: </b>Post-transplant allograft fibrosis remains a challenge in prolonging allograft survival. Regulated cell death has been widely implicated in various kidney diseases, including renal fibrosis. However, the role of different regulated cell death (RCD) pathways in post-transplant allograft fibrosis remains unclear. <b>Methods and Results: </b>Microarray transcriptome profiling and single-nuclei sequencing data of post-transplant fibrotic and normal grafts were obtained and used to identify RCD-related differentially expressed genes. The enrichment activity of nine RCD modalities in tissue and cells was examined using single-sample gene set enrichment analysis, and their relations with immune infiltration in renal allograft samples were also assessed. Parenchymal and non-parenchymal cells displayed heterogeneity in RCD activation. Additionally, cell–cell communication analysis was also conducted in fibrotic samples. Subsequently, weighted gene co-expression network analysis and seven machine learning algorithms were employed to identify RCD-related hub genes for renal fibrosis. A 9-gene signature, termed RCD risk score (RCDI), was constructed using the least absolute shrinkage and selection operator and multivariate Cox regression algorithms. This signature showed robust accuracy in predicting 1-, 2-, and 3-year allograft survival status (area under the curve for 1-, 2-, and 3-year were 0.900, 0.877, 0.858, respectively). Immune infiltration analysis showed a strong correlation with RCDI and the nine model genes. Finally, molecular docking simulation suggested rapamycin, tacrolimus and mycophenolate mofetil exhibit strong interactions with core RCD-related receptors. <b>Conclusions: </b>In summary, this study explored the activation of nine RCD pathways and their relationships with immune traits, identified potential RCD-related hub genes associated with renal fibrosis, and highlighted potential therapeutic targets for renal allograft fibrosis.
DOI: 10.6084/m9.figshare.27965169.v1
title: Diverse regulated cell death patterns and immune traits in kidney allograft with fibrosis: a prediction of renal allograft failure based on machine learning, single-nucleus RNA sequencing and molecular docking

description: <b>Objectives: </b>Post-transplant allograft fibrosis remains a challenge in prolonging allograft survival. Regulated cell death has been widely implicated in various kidney diseases, including renal fibrosis. However, the role of different regulated cell death (RCD) pathways in post-transplant allograft fibrosis remains unclear. <b>Methods and Results: </b>Microarray transcriptome profiling and single-nuclei sequencing data of post-transplant fibrotic and normal grafts were obtained and used to identify RCD-related differentially expressed genes. The enrichment activity of nine RCD modalities in tissue and cells was examined using single-sample gene set enrichment analysis, and their relations with immune infiltration in renal allograft samples were also assessed. Parenchymal and non-parenchymal cells displayed heterogeneity in RCD activation. Additionally, cell–cell communication analysis was also conducted in fibrotic samples. Subsequently, weighted gene co-expression network analysis and seven machine learning algorithms were employed to identify RCD-related hub genes for renal fibrosis. A 9-gene signature, termed RCD risk score (RCDI), was constructed using the least absolute shrinkage and selection operator and multivariate Cox regression algorithms. This signature showed robust accuracy in predicting 1-, 2-, and 3-year allograft survival status (area under the curve for 1-, 2-, and 3-year were 0.900, 0.877, 0.858, respectively). Immune infiltration analysis showed a strong correlation with RCDI and the nine model genes. Finally, molecular docking simulation suggested rapamycin, tacrolimus and mycophenolate mofetil exhibit strong interactions with core RCD-related receptors. <b>Conclusions: </b>In summary, this study explored the activation of nine RCD pathways and their relationships with immune traits, identified potential RCD-related hub genes associated with renal fibrosis, and highlighted potential therapeutic targets for renal allograft fibrosis.
DOI: 10.6084/m9.figshare.26936095
title: Late diagnosis of CKD and associated survival after initiation of renal replacement therapy in Kazakhstan: analysis of nationwide electronic healthcare registry 2014–2019

description: Chronic kidney disease (CKD) presents a significant global health challenge, often progressing to end-stage renal disease (ESRD) necessitating renal replacement therapy (RRT). Late referral (LR) to nephrologists before RRT initiation is linked with adverse outcomes. However, data on CKD diagnosis and survival post-RRT initiation in Kazakhstan remain limited. This study aims to investigate the impact of late CKD diagnosis on survival prognosis after RRT initiation. Data were acquired from the Unified National Electronic Health System (UNEHS) for CKD patients initiating RRT between 2014 and 2019. Survival post-RRT initiation was assessed using the Cox Proportional Hazards Model. Totally, 211,655 CKD patients were registered in the UNEHS databases and 9,097 (4.3%) needed RRT. The most prevalent age group among RRT patients is 45–64 years, with a higher proportion of males (56%) and Kazakh ethnicity (64%). Seventy-four percent of patients were diagnosed late. The median follow-up time was 537 (IQR: 166–1101) days. Late diagnosis correlated with worse survival (HR = 1.18, <i>p</i> &lt; 0.001). Common comorbidities among RRT patients include hypertension (47%), diabetes (21%), and cardiovascular diseases (26%). The history of transplantation significantly influenced survival. Regional disparities in survival probabilities were observed, highlighting the need for collaborative efforts in healthcare delivery. This study underscores the substantial burden of CKD in Kazakhstan, with a majority of patients diagnosed late. Early detection strategies and timely kidney transplantation emerge as crucial interventions to enhance survival outcomes.
DOI: 10.6084/m9.figshare.26936095.v1
title: Late diagnosis of CKD and associated survival after initiation of renal replacement therapy in Kazakhstan: analysis of nationwide electronic healthcare registry 2014–2019

description: Chronic kidney disease (CKD) presents a significant global health challenge, often progressing to end-stage renal disease (ESRD) necessitating renal replacement therapy (RRT). Late referral (LR) to nephrologists before RRT initiation is linked with adverse outcomes. However, data on CKD diagnosis and survival post-RRT initiation in Kazakhstan remain limited. This study aims to investigate the impact of late CKD diagnosis on survival prognosis after RRT initiation. Data were acquired from the Unified National Electronic Health System (UNEHS) for CKD patients initiating RRT between 2014 and 2019. Survival post-RRT initiation was assessed using the Cox Proportional Hazards Model. Totally, 211,655 CKD patients were registered in the UNEHS databases and 9,097 (4.3%) needed RRT. The most prevalent age group among RRT patients is 45–64 years, with a higher proportion of males (56%) and Kazakh ethnicity (64%). Seventy-four percent of patients were diagnosed late. The median follow-up time was 537 (IQR: 166–1101) days. Late diagnosis correlated with worse survival (HR = 1.18, <i>p</i> &lt; 0.001). Common comorbidities among RRT patients include hypertension (47%), diabetes (21%), and cardiovascular diseases (26%). The history of transplantation significantly influenced survival. Regional disparities in survival probabilities were observed, highlighting the need for collaborative efforts in healthcare delivery. This study underscores the substantial burden of CKD in Kazakhstan, with a majority of patients diagnosed late. Early detection strategies and timely kidney transplantation emerge as crucial interventions to enhance survival outcomes.
DOI: 10.6084/m9.figshare.26310346.v1
title: Effect of different exit-site care dressings on preventing peritoneal dialysis related infection from nontropical area: a systematic review and network meta-analysis

description: This study aims to conduct a systematic review and network meta-analysis to comprehensively evaluate the efficacy of various dressings in preventing exit-site infection (ESI) and peritonitis. We searched PubMed, Embase, Web of Science, CINAHL Plus with Full Text (EBSCO), Sino Med, Wan Fang Data, China National Knowledge Infrastructure (CNKI) from 1 January 1999 to 10 July 2023. The language restrictions were Chinese and English. Randomized controlled trials, non-randomized controlled trials, and self-controlled trials were included in this study. We used ROB 2 tool to evaluate the quality of the included literature. Two authors independently extracted the data according to the Cochrane Handbook. A Frequentist network meta-analysis was performed using Stata17.0 according to PRISAMA with a random effects model. From 2092 potentially eligible studies, thirteen studies were selected for analysis, including nine randomized controlled studies, three quasi-experimental studies and one self-controlled trial. A total of 1229 patients were included to compare five types of exit site care dressings, named disinfection dressings, antibacterial dressings, non-antibacterial occlusive dressings, sterile gauze, and no-particular dressings. The outcome of prevention ESI is antibacterial dressings (SUCRA = 97.6) &gt;non-antibacterial occlusive dressings (SUCRA = 68.3) &gt;disinfection dressings (SUCRA = 50.6) &gt;no-particular dressings (SUCRA = 23.9) &gt;sterile gauze (SUCRA = 9.5). The antibacterial dressings were more effective than sterile gauze (OR = 0.13, 95%CI 0.04∼0.44), and no-particular dressing (OR = 0.18, 95%CI 0.07∼0.50) in preventing ESI; the non-antibacterial occlusive dressings were effective than sterile gauze (OR:0.30, 95%CI 0.16∼0.57). There is no statistical significance between no-particular dressings and other types of dressings in preventing the mature ESI. There is no statistical significance in the effectiveness of five types of dressings in preventing peritonitis. The no-particular dressings maybe more cost-effective for preventing mature ESI. None of the dressings was more effective than another in preventing peritonitis. Then, none of the different types of dressing is strongly recommended for preventing ESI or peritonitis. <b>Registration</b>CRD42022366756
DOI: 10.6084/m9.figshare.26310346
title: Effect of different exit-site care dressings on preventing peritoneal dialysis related infection from nontropical area: a systematic review and network meta-analysis

description: This study aims to conduct a systematic review and network meta-analysis to comprehensively evaluate the efficacy of various dressings in preventing exit-site infection (ESI) and peritonitis. We searched PubMed, Embase, Web of Science, CINAHL Plus with Full Text (EBSCO), Sino Med, Wan Fang Data, China National Knowledge Infrastructure (CNKI) from 1 January 1999 to 10 July 2023. The language restrictions were Chinese and English. Randomized controlled trials, non-randomized controlled trials, and self-controlled trials were included in this study. We used ROB 2 tool to evaluate the quality of the included literature. Two authors independently extracted the data according to the Cochrane Handbook. A Frequentist network meta-analysis was performed using Stata17.0 according to PRISAMA with a random effects model. From 2092 potentially eligible studies, thirteen studies were selected for analysis, including nine randomized controlled studies, three quasi-experimental studies and one self-controlled trial. A total of 1229 patients were included to compare five types of exit site care dressings, named disinfection dressings, antibacterial dressings, non-antibacterial occlusive dressings, sterile gauze, and no-particular dressings. The outcome of prevention ESI is antibacterial dressings (SUCRA = 97.6) &gt;non-antibacterial occlusive dressings (SUCRA = 68.3) &gt;disinfection dressings (SUCRA = 50.6) &gt;no-particular dressings (SUCRA = 23.9) &gt;sterile gauze (SUCRA = 9.5). The antibacterial dressings were more effective than sterile gauze (OR = 0.13, 95%CI 0.04∼0.44), and no-particular dressing (OR = 0.18, 95%CI 0.07∼0.50) in preventing ESI; the non-antibacterial occlusive dressings were effective than sterile gauze (OR:0.30, 95%CI 0.16∼0.57). There is no statistical significance between no-particular dressings and other types of dressings in preventing the mature ESI. There is no statistical significance in the effectiveness of five types of dressings in preventing peritonitis. The no-particular dressings maybe more cost-effective for preventing mature ESI. None of the dressings was more effective than another in preventing peritonitis. Then, none of the different types of dressing is strongly recommended for preventing ESI or peritonitis. <b>Registration</b>CRD42022366756
DOI: 10.6084/m9.figshare.27275943
title: Multimodal ultrasound deep learning to detect fibrosis in early chronic kidney disease

description: We developed a multimodal ultrasound (US) deep learning (DL) fusion model to automatically classify early fibrosis in patients with chronic kidney disease (CKD). This prospective study included patients with CKD who underwent continuous gray-scale US, superb microvascular imaging, and strain elastography from May to November 2022. According to the pathological tubular atrophy and interstitial fibrosis score, patients were divided into minimal and mild groups (affected area ≤10% and 11 − 25% of the total cortical volume, respectively). The dataset was divided into training (70%) and test (30%) sets. A DL model combining the features of the three US modes was developed to predict early fibrosis in patients with CKD. We compared these findings with the area under the receiver operating characteristic curve (AUC) of the clinical model by analyzing the receiver operating characteristic curve in the test set. The AUC of single-mode DL based on gray-scale US, superb microvascular imaging, and strain elastography was 0.682, 0.745, and 0.648, respectively, while that of the multimodal US DL model was 0.86. The accuracy, specificity, and sensitivity of the multimodal US DL model were 0.779, 0.767, and 0.796, respectively, and the negative and positive predictive values were 0.842 and 0.706, respectively. The AUC of the multimodal US DL model was significantly better than that of the single-mode DL and clinical models. The DL algorithm developed using multimodal US images can effectively predict early fibrosis in patients with CKD with significantly greater accuracy than single-mode DL or clinical models.
DOI: 10.6084/m9.figshare.27275943.v1
title: Multimodal ultrasound deep learning to detect fibrosis in early chronic kidney disease

description: We developed a multimodal ultrasound (US) deep learning (DL) fusion model to automatically classify early fibrosis in patients with chronic kidney disease (CKD). This prospective study included patients with CKD who underwent continuous gray-scale US, superb microvascular imaging, and strain elastography from May to November 2022. According to the pathological tubular atrophy and interstitial fibrosis score, patients were divided into minimal and mild groups (affected area ≤10% and 11 − 25% of the total cortical volume, respectively). The dataset was divided into training (70%) and test (30%) sets. A DL model combining the features of the three US modes was developed to predict early fibrosis in patients with CKD. We compared these findings with the area under the receiver operating characteristic curve (AUC) of the clinical model by analyzing the receiver operating characteristic curve in the test set. The AUC of single-mode DL based on gray-scale US, superb microvascular imaging, and strain elastography was 0.682, 0.745, and 0.648, respectively, while that of the multimodal US DL model was 0.86. The accuracy, specificity, and sensitivity of the multimodal US DL model were 0.779, 0.767, and 0.796, respectively, and the negative and positive predictive values were 0.842 and 0.706, respectively. The AUC of the multimodal US DL model was significantly better than that of the single-mode DL and clinical models. The DL algorithm developed using multimodal US images can effectively predict early fibrosis in patients with CKD with significantly greater accuracy than single-mode DL or clinical models.
DOI: 10.6084/m9.figshare.26354672
title: Changes in the spectrum of biopsy-proven renal diseases over 11 years: a single-center study in China

description: There have been some shifts in the frequency and distribution of biopsy-proven renal diseases in China over recent years. The aim of the study was to investigate the changing spectrum of renal diseases from the view of kidney biopsy data in a single center of China. A total of 10,996 cases of native renal biopsies from patients aged ≥15 years old in Huashan Hospital, Fudan University, between 2008 and 2018 were analyzed retrospectively. The results showed that primary glomerular nephropathy (PGN) remained the most common biopsy-proven renal disease (69.42% of total), with IgA nephropathy (IgAN) accounting for 44.40% of PGN, membranous nephropathy (MN) for 28.55%, minimal change disease (MCD) for 13.26% and focal segmental glomerulosclerosis (FSGS) for 8.00%. During the study period, the proportion of MN in PGN appeared an increasing tendency, while that of IgAN and MCD remained stable and that of FSGS showed a decline. Secondary glomerular nephropathy (SGN) constituted 21.54% of total cases, among which the leading two diseases were lupus nephritis (LN) and Henoch-Schonlein purpura nephritis (HSN) which accounted for 41.08% and 19.11% respectively. The 11-year retrospective study revealed that PGN was the predominant histologic diagnosis among patients undergoing renal biopsy and the most frequent type of PGN remained to be IgAN, followed by MN which increased dramatically.