Chronic kidney disease in the elderly and frail patient: perspectives with opinions and comments

Global, regional, and national burden inequality of chronic kidney disease, 1990-2021: a systematic analysis for the global burden of disease study 2021

Background

Chronic kidney disease (CKD) is a significant global health issue, often linked to diabetes, hypertension, and glomerulonephritis. However, aggregated statistics can obscure heterogeneity across subtypes, age, gender, and regions. This study aimed to analyze global CKD trends from 1990 to 2021, focusing on age, gender, socio-demographic index (SDI), and regional variations.

Methods

Data were extracted from the Global Burden of Disease (GBD) 2021 database, covering prevalence, incidence, mortality, and disability-adjusted life years (DALYs). These were presented as counts per 100,000 population and age-standardized rates, with uncertainty intervals (UIs) to highlight variability. Joinpoint regression was used to assess trends over the 30-year period.

Results

In 2021, global CKD prevalence was 359 million, with 11.13 million new cases, 1.53 million deaths, and 44.45 million DALYs-up 92, 156, 176, and 114% since 1990. While prevalence slightly declined, incidence, mortality, and DALYs increased significantly. CKD burden varied by region and age, with notable gender disparities.

Conclusion

The study highlights a dramatic rise in CKD burden linked to population growth and aging, emphasizing the need for targeted treatment and effective global healthcare policies.

Curvilinear association between Framingham Steatosis Index and chronic kidney disease: a nationwide cross-sectional study

Introduction

Fatty liver disease is potentially linked to chronic kidney disease (CKD), yet the association between the Framingham Steatosis Index (FSI) and CKD remains uncharted. Our study thoroughly investigated the correlation between FSI and CKD, aiming to elucidate the underlying links between these two conditions.

Methods

The relationship between FSI and CKD was evaluated using a weighted multivariate logistic regression model, and the curvilinear relationship between FSI and CKD was explored through smooth curve fitting. We engaged a recursive partitioning algorithm in conjunction with a two-stage linear regression model to determine the inflection point. By conducting stratified analyses, the heterogeneity within subpopulations was explored.

Results

In the fully adjusted Model 3, which accounted for all covariates, the odds ratios (ORs) (95% CI) for the association between FSI and CKD were 1.01 (0.97, 1.06), indicating no significant statistical association. Sensitivity analysis confirms the stability of the relationship between FSI and CKD. Smooth curve fitting discloses a non-linear association between FSI and CKD. The two-piecewise linear regression model, applied to explore this non-linearity, identified an inflection point at an FSI value of -3.21. Below this threshold, the OR (95% CI) was 0.25 (0.17, 0.37), signifying an inverse correlation between FSI and CKD. Above the inflection point, the OR (95% CI) was 1.19 (1.13, 1.25), suggesting a positive correlation. In the stratified curve analysis, the results were essentially consistent with the overall findings, except for the subgroups with BMI > 30 and age > 50.

Conclusion

We found a curvilinear relationship between FSI and CKD.

Elevated TyG-BMI index predicts incidence of chronic kidney disease

Chronic kidney disease (CKD) represents a significant global public health issue, with its incidence and prevalence escalating annually. Metabolic disorders are one of the major etiological factors of CKD. This study investigates the relationship between the emerging metabolic index triglyceride-glucose body mass index (TyG-BMI) and the onset of CKD. Our study enrolled 3,485 healthy participants (1,576 men and 1,909 women), with a follow-up period of 3 years. The primary outcome was the emergence of CKD, defined by an eGFR less than 60 mL/(min × 1.73 m2) or the onset of proteinuria. To examine the TyG-BMI and CKD onset relationship, we used univariate and multivariate logistic regression analyses, stratified analyses, and receiver operating characteristic (ROC) curves. After a three-year follow-up, CKD developed in 2% (n = 70) of the participants. Subjects were divided into three equal groups based on their TyG-BMI values, from lowest to highest. After adjusting for potential confounders, the highest TyG-BMI group exhibited a multifactor-adjusted odds ratio (OR) of 4.24 (95% CI 1.30-13.78, P = 0.016) compared to the lowest group. Stratified analyses revealed that the association between TyG-BMI and CKD onset was stronger among females, individuals younger than 60 years, and those with a BMI ≥ 24 kg/m2. Furthermore, TYG-BMI was effective in predicting the incidence of CKD. Our findings indicate that TyG-BMI is an independent risk factor for the onset of CKD and that assessment of TyG-BMI may be useful for the early identification of individuals at high risk for CKD.

The error of estimated GFR in predialysis care

The error of estimated glomerular filtration rate (eGFR) and its consequences in predialysis are unknown. In this prospective multicentre study, 315 predialysis patients underwent measured GFR (mGFR) by the clearance of iohexol and eGFR by 52 formulas. Agreement between eGFR and mGFR was evaluated by concordance correlation coefficient (CCC), total deviation index (TDI) and coverage probability (CP). In a sub-analysis we assessed the impact of eGFR error on decision-making as (i) initiating dialysis, (ii) preparation for renal replacement therapy (RRT) and (iii) continuing clinical follow-up. For this sub-analysis, patients who started RRT due to clinical indications (uremia, fluid overload, etc.) were excluded. eGFR had scarce precision and accuracy in reflecting mGFR (average CCC 0.6, TDI 70% and cp 22%) both in creatinine- and cystatin-based formulas. Variations -larger than 10 ml/min- between mGFR and eGFR were frequent. The error of formulas would have suggested (a) premature preparation for RTT in 14% of stable patients evaluated by mGFR; (b) to continue clinical follow-up in 59% of subjects with indication for RTT preparation due to low GFRm and (c) to delay dialysis in all asymptomatic patients (n = 6) in whom RRT was indicated based on very low mGFR. The error of formulas in predialysis was frequent and large and may have consequences in clinical care.

Development and Validation of a Risk Prediction Model for Frailty in Patients with Chronic Diseases

The occurrence rate of frailty is high among patients with chronic diseases. However, the assessment of frailty among these patients is still far from being a routine part of clinical practice. The aim of this study is to develop a validated predictive model for assessing frailty risk in patients with chronic illnesses. This study recruited 543 patients with chronic diseases, and 237 were included in the development and validation of the predictive model. A total of 57 frailty related indicators were analyzed, encompassing sociodemographic variables, health status, physical measurements, nutritional assessment, physical activity levels, and blood biomarkers. There were 100 cases (42.2%) presenting frailty symptoms. Multivariate logistic regression analysis revealed that gender, age, chronic diseases, Mini Nutritional Assessment score, and Clinical Frailty Scale score were predictive factors for frailty in chronic disease patients. Utilizing these factors, a nomogram model demonstrated good consistency and accuracy. The AUC values for the predictive model and validation set were 0.946 and 0.945, respectively. Calibration curves, ROC, and DCA indicated the nomogram had favorable predictive performance. Altogether, the comprehensive nomogram developed here is a promising and convenient tool for assessing frailty risk in patients with chronic diseases, aiding clinical practitioners in screening high-risk populations.