Long-term risk for end-stage kidney disease and death in a large population-based cohort

Final Stage of Chronic Kidney Disease with Conservative Kidney Management or Renal Replacement Therapy: A Primary-Care Population Study

BACKGROUND

Studies focus on the incidence and risk factors (RFs) associated with reaching the final stage of chronic kidney disease (CKD-G5) and receiving kidney replacement therapy (KRT). Analysis of those related to reaching CKD-G5 while receiving conservative kidney management (CKM) has been neglected.

METHODS

Retrospective cohort study analysing electronic health records of individuals aged ≥ 50 with eGFR < 60 mL/min/m². Cumulative incidence rates of CKD-G5, with and without KRT, were calculated. Multinomial regression models determined odds ratios (ORs) for CKD-G5 progression with KRT, CKM, or death.

RESULTS

Among 332,164 patients, the cumulative incidence of CKD-G5 was 2.79 cases per 100 person-years. The rates were 1.92 for CKD-G5 with KRT and 0.87 for CKD-G5 with CKM. Low eGFR and albuminuria were the primary RFs. Male gender and uncontrolled blood pressure had a greater impact on KRT (OR = 2.63 CI, 1.63) than on CKD-G5 with CKM (OR = 1.45 CI, 1.31). Increasing age and rurality reduced the probability of KRT but increased the probability of CKD-G5 with CKM. Higher incomes decreased the likelihood of developing CKD-G5 with and without KRT (OR = 0.49 CI).

CONCLUSION

One-third of CKD-G5 cases receive CKM. Those are typically older, female, rural residents with lower incomes and with lesser proteinuria or cardiovascular RF. The likelihood of receiving KRT is influenced by location and socioeconomic disparities.

The Association of Excess Body Weight with Risk of ESKD Is Mediated Through Insulin Resistance, Hypertension, and Hyperuricemia

BACKGROUND

Insulin resistance, hypertension, hyperuricemia, and hypercholesterolemia are hypothesized to be important intermediates in the relationship between excess body weight and CKD risk. However, the magnitude of the total effect of excess body weight on ESKD mediated through these four pathways remains to be quantified.

METHODS

We applied a model for analysis of correlated mediators to population-based data from 100,269 Austrian individuals (mean age 46.4 years). Association of body mass index (BMI) was coalesced with ESKD risk into direct association. Indirect associations were mediated through the triglyceride-glucose (TyG) index (as an indicator of insulin resistance), mean arterial pressure (MAP), uric acid (UA), and total cholesterol (TC).

RESULTS

Mean follow-up was 23.1 years with 463 (0.5%) incident ESKD cases. An unhealthy metabolic profile (prevalence 32.4%) was associated with a markedly increased ESKD risk (multivariably adjusted hazard ratio (aHR), 3.57; 95% CI, 2.89 to 4.40), independent of BMI. A 5-kg/m2 higher BMI was associated with a 57% increased ESKD risk (aHRtotal association, 1.57; 1.38 to 1.77). Of this association, 99% (76% to 140%) arose from all mediators jointly; 33% (22% to 49%) through TyG index; 34% (24% to 50%) through MAP; 30% (21% to 45%) through UA; and 2% (-1% to 4%) through TC. The remaining direct association was nonsignificant (aHRdirect association, 1.01; 0.88 to 1.14).

CONCLUSIONS

TyG index, MAP, and UA, but not TC, mediate the association of BMI with ESKD in middle-aged adults. Our findings highlight that in addition to weight reduction, the control of metabolic risk factors might be essential in mitigating the adverse effects of BMI on kidney function.

Machine Learning for Renal Pathologies: An Updated Survey

Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available. Based on the study conducted, it is possible to see a growing trend and interest in the use of machine learning applications in nephrology, becoming an additional tool for physicians, which can enable them to make more accurate and faster diagnoses, although there remains a major limitation given the difficulty in creating public databases that can be used by the scientific community to corroborate and eventually make a positive contribution in this area.

The Triglyceride-Glucose Index and Obesity-Related Risk of End-Stage Kidney Disease in Austrian Adults

IMPORTANCE

It is unknown whether the triglyceride-glucose (TyG) index as a measure of insulin resistance is associated with the risk of developing end-stage kidney disease (ESKD). Because individuals who are overweight or obese often develop insulin resistance, mediation of the association between body mass index (BMI) and ESKD risk through the TyG index seems plausible but has not been investigated.

OBJECTIVE

To evaluate whether the TyG index is associated with ESKD risk and, if so, to what extent the TyG index mediates the association between BMI and ESKD.

DESIGN, SETTING, AND PARTICIPANTS

A total of 176 420 individuals were recruited during routine health examinations to participate in the Austrian Vorarlberg Health Monitoring and Promotion Program (VHM&PP), a prospective, population-based cohort study with participant enrollment between January 1, 1988, and June 30, 2005, and a mean follow-up of 22.7 years. Data analysis was conducted from March 1, 2020, to September 30, 2020.

EXPOSURES

Body mass index and the logarithmized product of fasting triglyceride and glucose concentrations (TyG index), as determined during the baseline health examination.

MAIN OUTCOMES AND MEASURES

End-stage kidney disease, as indicated by initiation of kidney replacement therapy, either dialysis or kidney transplantation.

RESULTS

Of the 176 420 participants, 94 885 were women (53.8%); mean (SD) age was 42.5 (15.4) years. During a mean (SD) follow-up of 22.7 (6.9) years, 454 (0.3%) participants developed ESKD and 35 234 (20.0%) died. In multivariable-adjusted Cox proportional hazards models, the TyG index was significantly associated with the risk of ESKD, both with (hazard ratio [HR] per 1-SD increase, 1.68; 95% CI, 1.56-1.82) and without (HR per 1-SD increase, 1.79; 95% CI, 1.66-1.93) the inclusion of BMI as a covariate. Mediation analysis using a newly proposed 2-stage regression method for survival data showed that a 5-point increase in BMI increased the risk of ESKD by 58% (HR [total association], 1.58; 95% CI, 1.43-1.75), and that 41.7% of the total association (95% CI, 31.6%-51.8%) was mediated through the TyG index (HR [indirect association], 1.21; 95% CI, 1.18-1.25).

CONCLUSIONS AND RELEVANCE

This study found that the TyG index appeared to be associated with ESKD risk and mediates nearly half of the total association between BMI and ESKD in the general population. Public health efforts aiming at the reduction of body weight might decrease the kidney sequelae of insulin resistance and the burden of ESKD.

A five-year longitudinal study of the relation between end-stage kidney disease as the outcomes

Background

Patients with end-stage kidney disease (ESKD) are required to undergo consecutive time-based blood and biochemical tests to determine the progression of the disease according to changes in their blood and biochemical data. This study employed a random intercept model to investigate whether time-based blood and biochemical data present any notable clinical meaning that can be used to track disease progression.

Methods

This study conducted a retrospective analysis on the dialytic data of 148 patients with ESKD, who received hemodialysis between January 2005 and December 2015. The patients were all at least 20 years old, and the data used included patient demographic information and results for at least 60 blood and biochemical tests. A random intercept model was used to analyze the relationships among blood and biochemical test results, explanatory variables of patient comorbidities, and time.

Results

The age range of patients was between 33 and 98 years, with an average of 66.1 years and those over 65 years old comprising 51.3% (n = 76) of the total. Furthermore, hypertension was found to be the most common comorbidity among patients (87.2%, n = 129), followed by anemia (48.6%, n = 72), diabetes (47.3%, n = 70), dyslipidemia (19.6%, n = 29), and peptic ulcer (19.6%, n = 29). Coronary atherosclerotic heart disease is a comorbidity that can serve as a strong and independent marker for prognosis in patients with ESKD. Serum creatinine level can serve as an alternative indicator because patients with ESKD and comorbid diabetes may exhibit increased creatinine levels.

Conclusions

The results of a parameter estimation for longitudinal data analysis suggested that comorbidity and time were critical variables influencing blood and biochemical test results. Furthermore, WBC and HBC, HCT, albumin, protein, and creatinine levels were recognized as variables of critical significance. The results obtained in this study indicate that multimorbidity increases the treatment burden on patients, leading to polypharmacy. For this reason, comprehensive care and treatment of ESKD cannot rely solely on data from one single time point; instead, longitudinal analysis and other data that can affect patient prognosis must also be considered.