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Background and Objectives: The study aimed to evaluate the performance of a predictive model using the kidney failure risk equation (KFRE) for end-stage renal disease (ESRD) in diabetes and to investigate the impact of glomerular filtration rate (GFR) as estimated by different equations on the performance of the KFRE model in diabetes. Design, Setting, Participants, and Measurements: A total of 18,928 individuals with diabetes without ESRD history from the UK Biobank, a prospective cohort study initiated in 2006–2010, were included in this study. Modification of diet in renal disease (MDRD), chronic kidney disease epidemiology collaboration (CKD-EPI) or revised Lund–Malmö (r-LM) were used to estimate GFR in the KFRE model. Cox proportional risk regression was used to determine the correlation coefficients between each variable and ESRD risk in each model. Harrell's C-index and net reclassification improvement (NRI) index were used to evaluate the differentiation of the models. Analysis was repeated in subgroups based on albuminuria and hemoglobin A1C (HbA1c) levels. Results: Overall, 132 of the 18,928 patients developed ESRD after a median follow-up of 12 years. The Harrell's C-index based on GFR estimated by CKD-EPI, MDRD, and r-LM was 0.914 (95% CI = 0.8812–0.9459), 0.908 (95% CI = 0.8727–0.9423), and 0.917 (95% CI = 0.8837–0.9496), respectively. Subgroup analysis revealed that in diabetic patients with macroalbuminuria, the KFRE model based on GFR estimated by r-LM (KFRE-eGFRr-LM) had better differentiation compared to the KFRE model based on GFR estimated by CKD-EPI (KFRE-eGFRCKD-EPI) with a KFRE-eGFRr-LM C-index of 0.846 (95% CI = 0.797–0.894, p = 0.025), while the KFRE model based on GFR estimated by MDRD (KFRE-eGFRMDRD) showed no significant difference compared to the KFRE-eGFRCKD-EPI (KFRE-eGFRMDRD C-index of 0.837, 95% CI = 0.785–0.889, p = 0.765). Subgroup analysis of poor glycemic control (HbA1c >8.5%) demonstrated the same trend. Compared to KFRE-eGFRCKD-EPI (C-index = 0.925, 95% CI = 0.874–0.976), KFRE-eGFRr-LM had a C-index of 0.935 (95% CI = 0.888–0.982, p = 0.071), and KFRE-eGFRMDRD had a C-index of 0.925 (95% CI = 0.874–0.976, p = 0.498). Conclusions: In adults with diabetes, the r-LM equation performs better than the CKD-EPI and MDRD equations in the KFRE model for predicting ESRD, especially for those with macroalbuminuria and poor glycemic control (HbA1c >8.5%). [ABSTRACT FROM AUTHOR]

Background: Diabetic kidney disease (DKD) lacks a simple and relatively accurate predictor. The Triglyceride–Glucose (TyG) Index is a proxy of insulin resistance, but the association between the TyG Index and DKD is less certain. We investigated if the TyG Index can predict DKD onset effectively. Materials and Methods: Cross-sectional and longitudinal analyses were undertaken. In total, 1432 type-2 diabetes mellitus (T2DM) patients were included in the cross-sectional analysis. The TyG Index (calculated by ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]) was split into three tertiles. Associations of the TyG Index with microalbuminuria and estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 were calculated. Longitudinally, 424 patients without DKD at baseline were followed up for 21 (range, 12– 24) months. The main outcome was DKD prevalence as defined with eGFR < 60 mL/min/1.73 m2 or continuously increased urinary microalbuminuria: creatinine ratio (> 30 mg/mL) over 3 months. Cox regression was used to analyze the association between the TyG Index at baseline and DKD. Receiver operating characteristics curve (ROC) analysis was used to assess the sensitivity and specificity of the TyG Index in predicting DKD. Results: In cross-sectional analysis, patients with a higher TyG Index had a higher risk of microalbuminuria (OR = 2.342, 95% CI = 1.744– 3.144, p < 0.001), and eGFR < 60 mL/min/1.73 m2 (1.696, 95% CI =1.096– 2.625, p = 0.018). Longitudinally, 94 of 424 participants developed DKD. After confounder adjustment, patients in the high tertile of the TyG Index at baseline had a greater risk to developing DKD than those in the low tertile (HR = 1.727, 95% CI = 1.042– 2.863, p = 0.034). The area under the ROC curve was 0.69 (0.63– 0.76). Conclusion: The TyG Index is a potential predictor for DKD in T2DM patients. Clinical Trial: Clinical Trials identification number = NCT03692884. [ABSTRACT FROM AUTHOR]

Objectives: Aldosterone-to-renin ratio (ARR) based screening is the first step in the diagnosis of primary aldosteronism (PA). However, the guideline-recommended ARR cutoff covers a wide range, from the equivalent of 1.3 to 4.9 ng·dl -1 /mIU∙l -1 . We aimed to optimize the ARR cutoff for PA screening based on the risk of cardiovascular diseases (CVD)., Methods: Longitudinally, we included hypertensive participants from the Framingham Offspring Study (FOS) who attended the sixth examination cycle and followed up until 2014. At baseline (1995-1998), we used circulating concentrations of aldosterone and renin to calculate ARR (unit: ng·dl -1 /mIU∙l -1 ) among 1,433 subjects who were free of CVD. We used spline regression to calculate the ARR threshold based on the incident CVD. We used cross-sectional data from the Chongqing Primary Aldosteronism Study (CONPASS) to explore whether the ARR cutoff selected from FOS is applicable to PA screening., Results: In FOS, CVD risk increased with an increasing ARR until a peak of ARR 1.0, followed by a plateau in CVD risk (hazard ratio 1.49, 95%CI 1.19-1.86). In CONPASS, when compared to essential hypertension with ARR < 1.0, PA with ARR ≥ 1.0 carried a higher CVD risk (odds ratio 2.24, 95%CI 1.41-3.55), while essential hypertension with ARR ≥ 1.0 had an unchanged CVD risk (1.02, 0.62-1.68). Setting ARR cutoff at 2.4 ~ 4.9, 10% ~30% of PA subjects would be unrecognized although they carried a 2.45 ~ 2.58-fold higher CVD risk than essential hypertension., Conclusions: The CVD risk-based optimal ARR cutoff is 1.0 ng·dl -1 /mIU∙l -1 for PA screening. The current guideline-recommended ARR cutoff may miss patients with PA and high CVD risk., Clinical Trial Registration: ClinicalTrials.gov (NCT03224312).

Introduction: A large number of people around the world are exposed to the risks of passive smoking. This prospective study aimed to examine the association between secondhand smoke exposure, exposure time, and the incidence of chronic kidney disease (CKD) and determine whether this association was influenced by genetic susceptibility., Methods: The study included 214244 participants of the UK Biobank who were initially free of CKD. Cox proportional hazards model was used to estimate the associations between secondhand smoke exposure time and the risks of CKD in people who have never smoked. The genetic risk score for CKD was calculated by a weighted method. The likelihood ratio test comparing models was used to examine the cross-product term between secondhand smoke exposure and genetic susceptibility to CKD outcomes., Results: During a median of 11.9 years of follow-up, 6583 incidents of CKD were documented. Secondhand smoke exposure increased the risk of CKD (HR=1.09; 95% CI: 1.03-1.16, p<0.01), and a dose-response relationship between CKD prevalence and secondhand smoke exposure time was found (p for trend<0.01). Secondhand smoke exposure increases the risk of CKD even in people who never smoke and have a low genetic risk (HR=1.13; 95% CI: 1.02-1.26, p=0.02). There was no statistically significant interaction between secondhand smoke exposure and genetic susceptibility to CKD (p for interaction=0.80)., Conclusions: Secondhand smoke exposure is associated with higher risk of CKD, even in people with low genetic risk, and the relationship is dose dependent. These findings change the belief that people with low genetic susceptibility and without direct participation in smoking activities are not prone to CKD, emphasizing the need to avoid the harm of secondhand smoke in public places., Competing Interests: The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. The authors declare that they have no competing interests, financial or otherwise, related to the current work. All authors declare that since the initial planning of the work, support for the present work was provided by the entities as outlined in the Funding declaration., (© 2023 Lan R. et al.)

Backgrounds: Ectopic fat deposition is closely related to chronic kidney disease (CKD). Currently, there are few population studies that have been conducted to determine the relationship between renal parenchyma fat deposition and the risk of CKD among patients with type 2 diabetes mellitus (T2DM). Therefore, we employed magnetic resonance imaging (MRI) to detect renal parenchyma fat content in individuals with T2DM, expressed as renal fat fraction (FF), to explore whether renal FF is an important risk factor for CKD in patients with T2DM., Methods: In this cross-sectional study, 189 subjects with T2DM were enrolled. CKD was defined as the estimated glomerular filtration rate (eGFR)<60 mL/min/1.73m 2 . Measurement of the renal FF was performed on a 3.0-T MRI (MAGNETOM Skyra, Siemens, Erlangen, Germany). Binary logistic regression was used to determine the association between tertiles of renal FF and risk of CKD. Receiver-operator characteristic (ROC) curves were constructed to evaluate the sensitivity and specificity of renal FF in detecting CKD in T2DM patients., Results: The patients were divided into three groups according to tertiles of the renal FF level (2.498 - 7.434). As renal FF increases, patients tend to be older, and more abdominally obese, with a decreased eGFR (p<0.05). After adjustment for potential confounders, patients in the highest tertile of renal FF had a significantly increased risk of CKD than those in the lowest tertile (odds ratio (OR) = 3.98, 95% confidence interval (CI) = 1.12 - 14.09, p = 0.032), and the area under the ROC curve for this model was 0.836 (0.765-0.907)., Conclusions: The renal FF is significantly independently associated with CKD in patients with T2DM., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Shen, Xie, Chen, Mao, Qin, Lan, Yang, Hu, Li, Ye, Luo, Gong, Li, Mao and Wang.)

Background: Individual lifestyle varies in the real world, and the comparative efficacy of lifestyles to preserve renal function remains indeterminate. We aimed to systematically compare the effects of lifestyles on chronic kidney disease (CKD) incidence, and establish a lifestyle scoring system for CKD risk identification., Methods: Using the data of the UK Biobank cohort, we included 470,778 participants who were free of CKD at the baseline. We harnessed the light gradient boosting machine algorithm to rank the importance of 37 lifestyle factors (such as dietary patterns, physical activity (PA), sleep, psychological health, smoking, and alcohol) on the risk of CKD. The lifestyle score was calculated by a combination of machine learning and the Cox proportional-hazards model. A CKD event was defined as an estimated glomerular filtration rate <60 ml/min/1.73 m 2 , mortality and hospitalization due to chronic renal failure, and self-reported chronic renal failure, initiated renal replacement therapy., Results: During a median of the 11-year follow-up, 13,555 participants developed the CKD event. Bread, walking time, moderate activity, and vigorous activity ranked as the top four risk factors of CKD. A healthy lifestyle mainly consisted of whole grain bread, walking, moderate physical activity, oat cereal, and muesli, which have scored 12, 12, 10, 7, and 7, respectively. An unhealthy lifestyle mainly included white bread, tea >4 cups/day, biscuit cereal, low drink temperature, and processed meat, which have scored -12, -9, -7, -4, and -3, respectively. In restricted cubic spline regression analysis, a higher lifestyle score was associated with a lower risk of CKD event ( p for linear relation < 0.001). Compared to participants with the lifestyle score < 0, participants scoring 0-20, 20-40, 40-60, and >60 exhibited 25, 42, 55, and 70% lower risk of CKD event, respectively. The C-statistic of the age-adjusted lifestyle score for predicting CKD events was 0.710 (0.703-0.718)., Conclusion: A lifestyle scoring system for CKD prevention was established. Based on the system, individuals could flexibly choose healthy lifestyles and avoid unhealthy lifestyles to prevent CKD., (Copyright © 2022 Luo, Gong, Chen, Gao, Peng, Wang, Luo, Yang, Kang, Peng, Ma, Mei, Liu, Li, Yang, Wang and Hu.)