Association and predictive value of geriatric nutritional risk index, body composition, or bone mineral density in haemodialysis patients

Association Between TCBI (Triglycerides, Total Cholesterol, and Body Weight Index) and Stroke-Associated Pneumonia in Acute Ischemic Stroke Patients

Purpose

Stroke-associated pneumonia (SAP) usually complicates stroke and is linked to adverse prognoses. Triglycerides, total cholesterol, and body weight index (TCBI) is a new and simple calculated nutrition index. This study seeks to investigate the association between TCBI and SAP incidence, along with its predictive value.

Patients and Methods

Nine hundred and sixty-two patients with acute ischemic stroke were divided into SAP group and Non-SAP group. The TCBI was divided into three layers: T1, TCBI < 948.33; T2, TCBI 948.33-1647.15; T3, TCBI > 1647.15. Binary Logistic regression analysis was used to determine the relationship between TCBI levels and the incidence of SAP. Furthermore, restricted cubic splines (RCS) analysis was utilized to evaluate the influence of TCBI on the risk of SAP.

Results

TCBI in the SAP group was markedly lower compared to that in the Non-SAP group (P < 0.001). The Logistic regression model revealed that, using T3 layer as the reference, T1 layer had the highest risk for SAP prevalence (OR = 2.962, 95% CI: 1.600-5.485, P = 0.001), with confounding factors being controlled. The RCS model found that TCBI had a linear relationship with SAP (P for nonlinear = 0.490, P for overall = 0.004). Moreover, incorporating TCBI into the A2DS2 (Age, atrial fibrillation, dysphagia, sex, and severity) model substantially enhanced the initial model's predictive accuracy.

Conclusion

Low TCBI was associated with a higher risk of SAP. In clinical practice, TCBI has shown predictive value for SAP, contributing to early intervention and treatment of SAP.

Geriatric nutritional risk index as a predictor for fragility fracture risk in elderly with type 2 diabetes mellitus: A 9-year ambispective longitudinal cohort study

BACKGROUND & AIMS

The elderly are prone to fragility fractures, especially those suffering from type 2 diabetes mellitus (T2DM) combined with osteoporosis. Although studies have confirmed the association between GNRI and the prevalence of osteoporosis, the relationship between GNRI and fragility fracture risk and the individualized 10-year probability of osteoporotic fragility fractures estimated by FRAX remains unclear. This study aims to delve into the association between the GNRI and a fragility fracture and the 10-year probability of hip fracture (HF) and major osteoporotic fracture (MOF) evaluated by FRAX in elderly with T2DM.

METHODS

A total of 580 patients with T2DM aged ≥60 were recruited in the study from 2014 to 2023. This research is an ambispective longitudinal cohort study. All participants were followed up every 6 months for 9 years with a median of 3.8 years through outpatient services, medical records, and home fixed-line telephone interviews. According to the tertiles of GNRI, all subjects were divided into three groups: low-level (59.72-94.56, n = 194), moderate-level (94.56-100.22, n = 193), and high-level (100.22-116.45, n = 193). The relationship between GNRI and a fragility fracture and the 10-year probability of HF and MOF calculated by FRAX was assessed by receiver operating characteristic (ROC) analysis, Spearman correlation analyses, restricted cubic spline (RCS) analyses, multivariable Cox regression analyses, stratified analyses, and Kaplan-Meier survival analysis.

RESULTS

Of 580 participants, 102 experienced fragile fracture events (17.59%). ROC analysis demonstrated that the optimal GNRI cut-off value was 98.58 with a sensitivity of 75.49% and a specificity of 47.49%, respectively. Spearman partial correlation analyses revealed that GNRI was positively related to 25-hydroxy vitamin D [25-(OH) D] (r = 0.165, P < 0.001) and bone mineral density (BMD) [lumbar spine (LS), r = 0.088, P = 0.034; femoral neck (FN), r = 0.167, P < 0.001; total hip (TH), r = 0.171, P < 0.001]; negatively correlated with MOF (r = -0.105, P = 0.012) and HF (r = -0.154, P < 0.001). RCS analyses showed that GNRI was inversely S-shaped dose-dependent with a fragility fracture event (P < 0.001) and was Z-shaped with the 10-year MOF (P = 0.03) and HF (P = 0.01) risk assessed by FRAX, respectively. Multivariate Cox regression analysis demonstrated that compared with high-level GNRI, moderate-level [hazard ratio (HR) = 1.950; 95% confidence interval (CI) = 1.076-3.535; P = 0.028] and low-level (HR = 2.538; 95% CI = 1.378-4.672; P = 0.003) had an increased risk of fragility fracture. Stratified analysis exhibited that GNRI was negatively correlated with the risk of fragility fracture, which the stratification factors presented in the forest plot were not confounding factors and did not affect the prediction effect of GNRI on the fragility fracture events in this overall cohort population (P for interaction > 0.05), despite elderly females aged ≥70, with body mass index (BMI) ≥24, hypertension, and with or without anemia (all P < 0.05). Kaplan-Meier survival analysis identified that the lower-level GNRI group had a higher cumulative incidence of fragility fractures (log-rank, all P < 0.001).

CONCLUSION

This study confirms for the first time that GNRI is negatively related to a fragility fracture and the 10-year probability of osteoporotic fragility fractures assessed by FRAX in an inverse S-shaped and Z-shaped dose-dependent pattern in elderly with T2DM, respectively. GNRI may serve as a valuable predictor for fragility fracture risk in elderly with T2DM. Therefore, in routine clinical practice, paying attention to the nutritional status of the elderly with T2DM and giving appropriate dietary guidance may help prevent a fragility fracture event.

Role of composite objective nutritional indexes in patients with chronic kidney disease

Malnutrition persists as one of the most severe symptoms in patients with chronic kidney disease (CKD) globally. It is a critical risk factor for cardiovascular and all-cause mortality in patients with CKD. Readily available objective indicators are used to calculate composite objective nutritional assessment indexes, including the geriatric nutritional risk index, prognostic nutritional index, and controlling nutritional status score. These indexes offer a straightforward and effective method for evaluating nutritional status and predicting clinical outcomes in patients with CKD. This review presents supporting evidence on the significance of composite nutritional indexes.

A simply calculated nutritional index provides clinical implications in patients undergoing transcatheter aortic valve replacement

BACKGROUND

Malnutrition is associated with adverse outcomes in patients with aortic stenosis. The Triglycerides × Total Cholesterol × Body Weight Index (TCBI) is a simple scoring model to evaluate the status of nutrition. However, the prognostic relevance of this index in patients undergoing transcatheter aortic valve replacement (TAVR) is unknown. This study aimed to evaluate the association of the TCBI with clinical outcomes in patients undergoing TAVR.

METHODS

A total of 1377 patients undergoing TAVR were evaluated in this study. The TCBI was calculated by the formula; triglyceride (mg/dL) × total cholesterol (mg/dL) × body weight (kg)/1000. The primary outcome was all-cause mortality within 3 years.

RESULTS

Patients with a low TCBI, based on a cut-off value of 985.3, were more likely to have elevated right atrial pressure (p = 0.04), elevated right ventricular pressure (p < 0.01), right ventricular systolic dysfunction (p < 0.01), tricuspid regurgitation ≥ moderate (p < 0.01). Patients with a low TCBI had a higher cumulative 3-year all-cause (42.3% vs. 31.6%, p < 0.01; adjusted HR 1.36, 95% CI 1.05-1.77, p = 0.02) and non-cardiovascular mortality (15.5% vs. 9.1%, p < 0.01; adjusted HR 1.95, 95% CI 1.22-3.13, p < 0.01) compared to those with a high TCBI. Adding a low TCBI to EuroSCORE II improved the predictive value for 3-year all-cause mortality (net reclassification improvement, 0.179, p < 0.01; integrated discrimination improvement, 0.005, p = 0.01).

CONCLUSION

Patients with a low TCBI were more likely to have right-sided heart overload and exhibited an increased risk of 3-year mortality. The TCBI may provide additional information for risk stratification in patients undergoing TAVR.

Combined evaluation of Geriatric nutritional risk index and Neutrophil to lymphocyte ratio for predicting all-cause and cardiovascular mortality in hemodialysis patients

OBJECTIVE

Malnutrition, accompanied by an inflammatory profile, is a risk factor for poor prognosis in hemodialysis patients. The purpose of this study was to investigate the predictive value of NLR combined with GNRI for all-cause and cardiovascular mortality in hemodialysis patients.

METHODS

A total of 240 maintenance hemodialysis (MHD) patients in hemodialysis centers were enrolled in this retrospective study. The influencing factors of all-cause death in hemodialysis patients were analyzed by COX regression. The cut-off values of GNRI and NLR for predicting mortality in enrolled MHD patients were 89.01 and 4, respectively. Based on these cut-off values, the patients were divided into four groups: G1: high GNRI (≥ 89.01) + high NLR (≥ 4) group; G2: high GNRI (≥ 89.01) + low NLR (<4) group, G3: low GNRI (< 89.01) + high NLR (≥4) group; G4: low GNRI (< 89.01) + low NLR (<4).

RESULTS

During the follow-up period (average: 58 months), the all-cause mortality was 20.83%(50/240) and the cardiovascular mortality was 12.08%(29/240). Both NLR and GNRI were independent risk factors for the prognosis of MHD patients (P<0.05). Survival analysis showed that patients with low GNRI had a lower survival rate than those with high GNRI, whereas patients with high NLR had a lower survival rate than those with low NLR. Kaplan-Meier curve for all-cause mortality revealed that compared to G1, G2, and G4, G3 had the lowest survival rate, while G2 had the highest survival rate among all groups (P < 0.05). Kaplan-Meier curve for cardiovascular mortality showed that G3 had lower survival than G1, G2, and G4 (P < 0.001).

CONCLUSIONS

Our study demonstrates that bothGNRI and NLR are associated with all-cause mortality and cardiovascular mortality in MHD patients. Combining these two factorsmay contribute to a prognostic evaluation for MHD patients.

Bone Mineral Density and All-Cause Mortality in Patients with Nondialysis Chronic Kidney Disease: Results from KNOW-CKD Study

Despite the clear association between low BMD and all-cause mortality in the general population, the association has not been validated in patients with nondialysis CKD. To investigate the association of low BMD with all-cause mortality in this population, a total of 2089 patients with nondialysis CKD at stages 1 to predialysis 5 were categorized into normal BMD (T-score ≥ -1.0), osteopenia (-2.5 < T-score < -1.0), and osteoporosis (T-score ≤ - 2.5) by the BMD at femoral neck. The study outcome was all-cause mortality. Kaplan-Meier curve depicted a significantly increased number of all-cause death events in the subjects with osteopenia or osteoporosis during the follow-up period compared with subjects with normal BMD. Cox regression models demonstrated that osteoporosis, but not osteopenia, was significantly associated with an increased risk of all-cause mortality (adjusted hazard ratio 2.963, 95% confidence interval 1.655 to 5.307). Smoothing curve fitting model visualized a clear inverse correlation between BMD T-score and the risk of all-cause mortality. Even after recategorizing the subjects by BMD T-scores at total hip or lumbar spine, the result was similar to the primary analyses. Subgroup analyses revealed that the association was not significantly modified by clinical contexts, such as age, gender, body mass index, estimated glomerular filtration rate, and albuminuria. In conclusion, low BMD is associated with an increased risk of all-cause mortality in patients with nondialysis CKD. This emphasizes that the routine measurement of BMD by DXA may confer an additional benefit beyond the prediction of fracture risk in this population.

Bone Mineral Density Is Inversely Associated With Mortality in Chronic Kidney Disease Patients: A Meta-Analysis

Low bone mineral density (BMD) is suggested to be associated with increased mortality in the general health population, but the relationship in chronic kidney disease (CKD) patients is still unclear. We performed a meta-analysis to investigate the association of BMD in different sites with risk of all-cause mortality in CKD patients. We searched PubMed, EMBASE, and Web of Science to identify eligible cohort studies that evaluated the association between BMD at different sites and risk of all-cause mortality in CKD patients. Twelve cohort studies were identified, which included 2828 CKD patients and 1052 deaths. Compared with normal/high level of total body BMD, lower total body BMD was associated with 25% higher risk of all-cause mortality. The pooled relative risk (RR) was 1.25 (95% confidence interval [CI] 1.09, 1.42) with little heterogeneity across studies. Regarding BMD measured at different sites, the risk of all-cause mortality was highest for lower BMD at hip/femoral neck (pooled RR = 1.69; 95% CI 1.20, 2.40). The pooled RRs were 1.26 (95% CI 1.04, 1.53) and 1.17 (95% CI 1.00, 1.37) for lower BMD at arm and spine, respectively. Similarly, the risk of death for per SD decrease in BMD was also higher at hip/femoral neck (pooled RR = 1.43, 95% CI 1.15, 1.77) compared with arm (pooled RR = 1.03, 95% CI 1.00, 1.06) and spine (pooled RR = 1.17, 95% CI 0.98, 1.39). In conclusion, lower BMD values at hip, arm, spine, as well as the whole body are associated with increased risk of all-cause mortality in CKD patients. The excess risk is highest for patients with lower BMD at hip/femoral neck, suggesting BMD measured at hip region may be the best indicator of mortality risk in CKD patients. © 2022 American Society for Bone and Mineral Research (ASBMR).

An effective model for predicting serum albumin level in hemodialysis patients

Patients on hemodialysis (HD) are known to be at an increased risk of mortality. Hypoalbuminemia is one of the most important risk factors of death in HD patients, and is an independent risk factor for all-cause mortality that is associated with cardiac death, infection, and Protein-Energy Wasting (PEW). It is a clinical challenge to elevate serum albumin level. In addition, predicting trends in serum albumin level is effective for personalized treatment of hypoalbuminemia. In this study, we analyzed a total of 3069 records collected from 314 HD patients using a machine learning method that is based on an improved binary mutant quantum grey wolf optimizer (MQGWO) combined with Fuzzy K-Nearest Neighbor (FKNN). The performance of the proposed MQGWO method was evaluated using a series of experiments including global optimization experiments, feature selection experiments on open data sets, and prediction experiments on an HD dataset. The experimental results showed that the most critical relevant indicators such as age, presence or absence of diabetes, dialysis vintage, and baseline albumin can be identified by feature selection. Remarkably, the accuracy and the specificity of the method were 98.39% and 96.77%, respectively, demonstrating that this model has great potential to be used for detecting serum albumin level trends in HD patients.

Association of geriatric nutritional risk index with the risk of osteoporosis in the elderly population in the NHANES

BACKGROUND

Osteoporosis is common in the elderly, and malnutrition is considered a major risk factor for osteoporosis. This study investigated the relationship between the Geriatric Nutrition Risk Index (GNRI) and osteoporosis based on a large cross-sectional study of the National Health and Nutrition Examination Survey (NHANES).

METHODS

We included 7405 older adults from NHANES (2005 to 2018) and divided them into the High-GNRI and Low-GNRI groups based on GNRI levels to compare the prevalence of osteoporosis among the two groups. A multi-factor logistic regression analysis was used to determine whether GNRI was an independent risk factor for osteoporosis. Spearman's rank correlation coefficient was computed to investigate the linear relationship between geriatric nutritional risk index (GNRI) and bone mineral density (BMD) T-score. Finally, a generalized additive model (GAM) revealed whether there was a non-linear relationship between GNRI and osteoporosis.

RESULTS

The prevalence of osteoporosis was higher in the Low-GNRI group than those in the High-GNRI group (12.2% vs. 8.2%; P = 0.001). Similarly, the femoral neck BMD T-scores (-1.09 ± 1.42 vs. -0.91 ± 1.31; P = 0.003). However, there was no significant difference between Low-GNRI group and High-GNRI group in lumbar BMD T-scores (1.700 ± 1.69 vs 1.85 ± 1.72; P>0.05). The multi-factor logistic regression analysis identified low GNRI as an independent risk factor for osteoporosis (OR: 1.544; 95% CI: 1.179-2.022; P < 0.001). Besides, GNRI showed a positive linear correlation (P < 0.001) with femoral neck BMD T-scores in older adults, with a progressive trend towards higher BMD as GNRI increased. By contrast, there was no linear correlation between GNRI and lumbar BMD T-score (P = 0.978). Lastly, the dose response curve revealed the non-linear negative correlation between GNRI and the risk of osteoporosis in the elderly (non-linear P < 0.001). With the increase of GNRI, the risk of osteoporosis gradually decreased, especially when GNRI was greater than 100, the downward trend was more significant.

CONCLUSION

GNRI is an independent risk factor for osteoporosis in the elderly and is negatively and non-linearly associated with the risk of osteoporosis in the elderly population.

Geriatric nutritional risk index in prediction of muscular strength of elderly patients undergoing hemodialysis

PURPOSE

Geriatric nutritional risk index (GNRI) is one of the new tools to determine nutritional status in the elderly. This study assessed the association between GNRI and muscular strength through handgrip strength (HGS) in patients undergoing hemodialysis.

METHODS

This cross-sectional analytical study assessed 110 hemodialysis patients at Guilan, North of Iran, (mean age of 70.3 ± 6.93), 57 men and 53 women through simple random sampling. Demographic characteristics, GNRI, and HGS of patients were determined. Data were analyzed using descriptive and inferential statistics, including independent t test, AVOVA, Pearson correlation, and linear multiple regression tests.

RESULTS

The mean values of the GNRI and HGS were 93.90 ± 11.06 and 14.82 ± 3.72, respectively. Finally, it was identified that there is a direct and significant association between GNRI and HGS (p = 0.001, r = 0.734). Linear multiple regression showed that GNRI is an independent predictor of HGS (Adj.R² = 0.67, β_GNRI = 8.13).

CONCLUSION

GNRI can be used as a predictor of muscular strength in hemodialysis patients.