Geriatric Nutritional Risk Index is associated with adverse outcomes in patients with hypertension: the Fukushima Cohort study.

Malnutrition is reportedly associated with adverse clinical outcomes in various populations. However, associations between nutritional status and adverse outcomes in patients with hypertension have not been sufficiently elucidated. We therefore aimed to investigate the impact of nutritional status as evaluated by the Geriatric Nutritional Risk Index (GNRI) on adverse outcomes in patients with hypertension. We conducted a retrospective cohort study of 1588 hypertensive patients enrolled in the Fukushima Cohort Study. Participants were categorized into tertiles (T1-T3) according to GNRI at baseline. The primary endpoint of the present study was a kidney event, defined as a combination of a 50% decline in eGFR from baseline and end-stage kidney disease requiring kidney replacement therapy. Associations between GNRI and kidney events were assessed using Kaplan-Meier curves and multivariate Cox regression analyses. Median age was 64 years, 55% were men, median eGFR was 63.1 mL/min/1.73 m2, and median GNRI was 101.3. The lower GNRI group (T1) showed an increased incidence of kidney events in the Kaplan-Meier curve analysis. Compared to the highest GNRI group (T3), lower GNRI carried a higher risk of kidney events for both T2 (hazard ratio [HR] 1.38, 95% confidence interval [CI] 0.71-2.68) and T1 (HR 3.59, 95%CI 1.96-6.63). Similar relationships were observed for risks of all-cause death and cardiovascular events. Lower GNRI was associated with kidney events, all-cause death, and cardiovascular events in patients with hypertension. Nutritional status as evaluated by GNRI could offer a simple and useful predictor of adverse outcomes in this population.

Predicting CKD progression using time-series clustering and light gradient boosting machines.

Predicting the transition of kidney function in chronic kidney disease is difficult as specific symptoms are lacking and often overlooked, and progress occurs due to complicating factors. In this study, we applied time-series cluster analysis and a light gradient boosting machine to predict the trajectories of kidney function in non-dialysis dependent chronic kidney disease patients with baseline estimated glomerular filtration rate (GFR) ≥ 45 mL/min/1.73 m2. Based on 5-year changes in estimated GFR, participants were stratified into groups with similar trajectories by cluster analysis. Next, we applied the light gradient boosting machine algorithm and Shapley addictive explanation to develop a prediction model for clusters and identify important parameters for prediction. Data from 780 participants were available for analysis. Participants were classified into five classes (Class 1: n = 78, mean [± standard deviation] estimated GFR 100 ± 19.3 mL/min/1.73 m2; Class 2: n = 176, 76.0 ± 9.3 mL/min/1.73 m2; Class 3: n = 191, 59.8 ± 5.9 mL/min/1.73 m2; Class 4: n = 261, 52.7 ± 4.6 mL/min/1.73 m2; and Class 5: n = 74, 53.5 ± 12.0 mL/min/1.73 m2). Declines in estimated GFR were 8.9% in Class 1, 12.2% in Class 2, 4.9% in Class 3, 12.0% in Class 4, and 45.1% in Class 5 during the 5-year period. The accuracy of prediction was 0.675, and the top three most important Shapley addictive explanation values were 1.61 for baseline estimated GFR, 0.12 for hemoglobin, and 0.11 for body mass index. The estimated GFR transition of patients with preserved chronic kidney disease mostly depended on baseline estimated GFR, and the borderline for estimated GFR trajectory was nearly 50 mL/min/1.73 m2.