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

Geriatric nutritional risk index and mortality from all-cause, cancer, and non-cancer in US cancer survivors: NHANES 2001-2018

Background

Malnutrition is strongly correlated with worsened treatment outcomes, reduced standard of living, and heightened mortality rates among individuals with cancer. Our research explores how the Geriatric Nutritional Risk Index (GNRI), a measure of nutritional status, relates to all-cause mortality, cancer-specific, and non-cancer mortality among middle-aged and older adult cancer patients.

Methods

We enrolled 3,253 participants aged 40 and above who were diagnosed with cancer. The data was obtained from the National Health and Nutrition Examination Survey (NHANES) dataset covering the period from 2001 to 2018, with a median follow-up duration of 83 months. According to the GNRI levels, patients in the study were classified into two distinct groups: the group with a low GNRI (<98) and the group with a high GNRI (≥ 98). We conducted a Kaplan-Meier survival analysis to assess how survival rates vary with different nutritional conditions. Multivariable Cox regression analyses were performed to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause mortality, as well as cancer-specific and non-cancer-related mortality. Restricted cubic spline (RCS) analyses and subgroup evaluations were performed to augment the robustness and validity of our findings.

Results

A total of 1,171 deaths were documented, with 383 attributed to cancer, and 788 from other causes. After adjusting for potential confounders, the analysis demonstrated that, within a specified range, an elevation in the GNRI is inversely associated with mortality from all causes, cancer-specific, and non-cancer causes. Moreover, Kaplan-Meier survival curves for all-cause, cancer-specific, and non-cancer mortality distinctly showed a more pronounced decrease in survival rates among individuals in the low GNRI group (<98). Notably, the restricted cubic spline regression model (RCS) revealed statistically significant non-linear associations between GNRI scores and mortality rates. The P-values were ≤0.001 for both all-cause and non-cancer mortality, and 0.024 for cancer-specific mortality.

Conclusion

Our study conclusively demonstrated a robust correlation between GNRI scores and mortality rates among cancer patients, encompassing all-cause mortality as well as specific mortality related to both cancerous and non-cancerous causes. The GNRI may be a valuable prognostic tool for predicting cancer mortality outcomes, offering insights that may inform nutritional management and influence the clinical treatment strategies for cancer survivors.

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.

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.

Associations of postoperative outcomes with geriatric nutritional risk index after conventional and robotic-assisted total knee arthroplasty: a randomized controlled trial

BACKGROUND

The association between postoperative outcomes of robotic-assisted total knee arthroplasty (RA-TKA) and nutrition status among elderly adults remained unclear. The authors aimed to evaluate these associations and provide a nutrition status reference for the surgical technique selection of TKA.

METHODS

In the present study, the authors used data from a multicenter, prospective, randomized controlled project, which recruited patients underwent TKA therapy. A total of 88 elderly adults (age ≥65 years old) were included in this study. Their preoperative and postoperative demographic data and radiographic parameters were collected. Clinical outcomes, including postoperative hip-knee-ankle (HKA) angle deviation, knee society score (KSS), 10 cm visual analog scale, and so on, were observed and compared between the RA-TKA group and the conventional TKA group. Logistic regression was performed to adjust several covariates. In addition, according to the results of restricted cubic splines analyses, all participants were categorized into two groups with GNRI≤100 and GNRI >100 for further subgroup analyses.

RESULTS

Our results showed despite having a lower postoperative HKA angle deviation, the RA-TKA group had a similar postoperative KSS score compared with the conventional TKA group in elderly adults. Among elderly patients with GNRI>100, RA-TKA group achieved significantly more accurate alignment (HKA deviation, P =0.039), but did not obtain more advanced postoperative KSS scores because of the compensatory effect of good nutrition status. However, among elderly patients with GNRI≤100, RA-TKA group had significantly higher postoperative KSS scores compared to the conventional TKA group ( P =0.025) and this association were not altered after adjustment for other covariates.

CONCLUSION

Considering the clinical outcomes of conventional TKA may be more susceptible to the impact of nutrition status, elderly patients with GNRI≤100 seem to be an applicable population for RA-TKA, which is more stable and would gain significantly more clinical benefits compared with conventional TKA.

Development and Validation of a Nomogram Model for Individualizing the Risk of Osteopenia in Abdominal Obesity

OBJECTIVE

This study was aimed to create and validate a risk prediction model for the incidence of osteopenia in individuals with abdominal obesity.

METHODS

Survey data from the National Health and Nutrition Examination Survey (NHANES) database for the years 2013-2014 and 2017-2018 was selected and included those with waist circumferences ≥102 m in men and ≥88 cm in women, which were defined as abdominal obesity. A multifactor logistic regression model was constructed using LASSO regression analysis to identify the best predictor variables, followed by the creation of a nomogram model. The model was then verified and evaluated using the consistency index (C-index), area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA). Results Screening based on LASSO regression analysis revealed that sex, age, race, body mass index (BMI), alkaline phosphatase (ALP) and Triglycerides (TG) were significant predictors of osteopenia development in individuals with abdominal obesity (P < 0.05). These six variables were included in the nomogram. In the training and validation sets, the C indices were 0.714 (95 % CI: 0.689-0.738) and 0.701 (95 % CI: 0.662-0.739), respectively, with corresponding AUCs of 0.714 and 0.701. The nomogram model exhibited good consistency with actual observations, as demonstrated by the calibration curve. The DCA nomogram showed that early intervention for at-risk populations has a net positive impact.

CONCLUSION

Sex, age, race, BMI, ALP and TG are predictive factors for osteopenia in individuals with abdominal obesity. The constructed nomogram model can be utilized to predict the clinical risk of osteopenia in the population with abdominal obesity.

Preoperative nutritional optimization for adult spinal deformity: Review

PURPOSE

The main objective of this review article is to examine the role that nutrition has on adult spinal deformity. The information presented in this review aims to provide spine surgeons with a broad overview of screening, assessment, and interventional strategies that may be used for presurgical nutritional optimization.

METHODS

A comprehensive literature review utilizing three biomedical databases was performed to generate articles of interest. Published articles related to nutrition, adult spinal deformity, spine surgery and orthopaedics were reviewed for the composition of this article. Nutrition may play a role in optimizing postoperative outcomes following adult spinal deformity surgeries, such as limiting delirium, length of stay, blood transfusion, and other medical complications. The use of screening tools, such as the PNI and CONUT score can assess preoperative nutritional status and may provide some utility in evaluating nutrition status in patients undergoing deformity surgery. Balancing both macronutrients and micronutrients, notably, carbohydrates, protein, albumin, and vitamin D can play a role in preoperative optimization.

CONCLUSION

Adult spinal deformity patients are at an increased risk for malnutrition. These patients should be assessed for nutrition status to prime them for surgery, minimize complications, and maximize their outcomes. However, further studies are needed to determine how nutrition ultimately affects adult spinal deformity patients in the postoperative period and to establish specific nutritional recommendations for this unique population.

Association between geriatric nutritional risk index and depression prevalence in the elderly population in NHANES

BACKGROUND

The prevalence of depression is increasing in the elderly population, and growing evidence suggests that malnutrition impacts mental health. Despites, research on the factors that predict depression is limited.

METHODS

We included 2946 elderly individuals from National Health and Nutrition Examination Survey (NHANES) spanning the years 2011 through 2014. Depressive symptoms were assessed using the PHQ-9 scale. Multinomial logistic regression was performed to evaluate the independent association between Geriatric Nutritional Risk Index (GNRI) and depression prevalence and scores. Subgroup analysis was conducted to explore potential factors influencing the negative correlation between GNRI and depression. Restricted cubic spline graph was employed to examine the presence of a non-linear relationship between GNRI and depression.

RESULTS

The depression group had a significantly lower GNRI than the non-depression group, and multivariate logistic regression showed that GNRI was a significant predictor of depression (P < 0.001). Subgroup analysis revealed that certain demographic characteristics were associated with a lower incidence of depression in individuals affected by GNRIs. These characteristics included being female (P < 0.0001), non-Hispanic black (P = 0.0003), having a moderate BMI (P = 0.0005), having a college or associates (AA) degree (P = 0.0003), being married (P = 0.0001), having a PIR between 1.50 and 3.49 (P = 0.0002), being a former smoker (P = 0.0002), and having no history of cardiovascular disease (P < 0.0001), hypertension (P < 0.0001), and diabetes (P = 0.0027). Additionally, a non-linear negative correlation (non-linear P < 0.01) was found between GNRI and depression prevalence, with a threshold identified at GNRI = 104.17814.

CONCLUSION

The GNRI demonstrates efficacy as a reliable indicator for forecasting depression in the elderly population. It exhibits a negative nonlinear correlation with the prevalence of depression among geriatric individuals.

Effect of immunology biomarkers associated with hip fracture and fracture risk in older adults

Osteoporosis is a skeletal disease that can increase the risk of fractures, leading to adverse health and socioeconomic consequences. However, current clinical methods have limitations in accurately estimating fracture risk, particularly in older adults. Thus, new technologies are necessary to improve the accuracy of fracture risk estimation. In this observational study, we aimed to explore the association between serum cytokines and hip fracture status in older adults, and their associations with fracture risk using the FRAX reference tool. We investigated the use of a proximity extension assay (PEA) with Olink. We compared the characteristics of the population, functional status and detailed body composition (determined using densitometry) between groups. We enrolled 40 participants, including 20 with hip fracture and 20 without fracture, and studied 46 cytokines in their serum. After conducting a score plot and two unpaired t-tests using the Benjamini-Hochberg method, we found that Interleukin 6 (IL-6), Lymphotoxin-alpha (LT-α), Fms-related tyrosine kinase 3 ligand (FLT3LG), Colony stimulating factor 1 (CSF1), and Chemokine (C-C motif) ligand 7 (CCL7) were significantly different between fracture and non-fracture patients (p < 0.05). IL-6 had a moderate correlation with FRAX (R2 = 0.409, p < 0.001), while CSF1 and CCL7 had weak correlations with FRAX. LT-α and FLT3LG exhibited a negative correlation with the risk of fracture. Our results suggest that targeted proteomic tools have the capability to identify differentially regulated proteins and may serve as potential markers for estimating fracture risk. However, longitudinal studies will be necessary to validate these results and determine the temporal patterns of changes in cytokine profiles.

Osteoporosis Feature Selection and Risk Prediction Model by Machine Learning Using a Cross-Sectional Database

BACKGROUND

The purpose of this study was to verify the accuracy and validity of using machine learning (ML) to select risk factors, to discriminate differences in feature selection by ML between men and women, and to develop predictive models for patients with osteoporosis in a big database.

METHODS

The data on 968 observed features from a total of 3,484 the Korea National Health and Nutrition Examination Survey participants were collected. To find preliminary features that were well-related to osteoporosis, logistic regression, random forest, gradient boosting, adaptive boosting, and support vector machine were used.

RESULTS

In osteoporosis feature selection by 5 ML models in this study, the most selected variables as risk factors in men and women were body mass index, monthly alcohol consumption, and dietary surveys. However, differences between men and women in osteoporosis feature selection by ML models were age, smoking, and blood glucose level. The receiver operating characteristic (ROC) analysis revealed that the area under the ROC curve for each ML model was not significantly different for either gender.

CONCLUSIONS

ML performed a feature selection of osteoporosis, considering hidden differences between men and women. The present study considers the preprocessing of input data and the feature selection process as well as the ML technique to be important factors for the accuracy of the osteoporosis prediction model.

Geriatric nutrition risk index in the prediction of all-cause and cardiovascular mortality in elderly hypertensive population: NHANES 1999-2016

Background

Hypertension is a major risk factor for the global burden of disease, and nutrition is associated with an increased risk of mortality from multiple diseases. Few studies have explored the association of nutritional risk with all-cause mortality and cardiovascular mortality in hypertension, and our study aims to fill this knowledge gap.

Method

We included data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2016 on a total of 10,037 elderly patients with hypertension. The nutritional status was evaluated using the Geriatric Nutrition Risk Index (GNRI). Kaplan-Meier survival analysis was performed to analyze the survival rates of different nutritional risk groups. COX proportional risk regression models were used to analyze the predictive effect of GNRI on all-cause mortality and cardiovascular mortality in hypertensive patients. Restricted cubic splines (RCS) were used to explore the nonlinear relationship between GNRI and mortality.

Result

The mean age of the hypertensive patients was 70.7 years. A total of 4255 (42.3%) all-cause mortality and 1207 (17.2%) cardiovascular mortality occurred during a median follow-up period of 106 months. Kaplan-Meier showed a more significant reduction in survival for the moderate to severe malnutrition risk of GNRI. The adjusted COX proportional hazards model showed that the hazard ratios for all-cause mortality and cardiovascular mortality in the moderate to severe malnutrition risk group for GNRI were 2.112 (95% CI, 1.377,3.240) and 2.604 (95% CI, 1.603,4.229), respectively. The RCS showed that increased GNRI was associated with a reduced risk of all-cause mortality and cardiovascular mortality risk reduction.

Conclusion

Malnutrition exposure assessed by GNRI effectively predicts the risk of all-cause mortality and cardiovascular mortality in the elderly with hypertension.

Geriatric Nutrition Risk Index as a predictor of cardiovascular and all-cause mortality in older Americans with diabetes

BACKGROUND AND AIMS

Few studies have examined the relationship between malnutrition, as defined by the Geriatric Nutrition Risk Index (GNRI), and all-cause mortality and cardiovascular mortality events, particularly in persons with diabetes. The study aimed at the association between GNRI and all-cause mortality and cardiovascular mortality in older Americans with diabetes.

METHODS

Data from this retrospective study were obtained from the National Health and Nutrition Examination (NHANES) 1999-2016. Using data from The NHANES Public-Use Linked Mortality Files to assess all-cause mortality (ACM) and cardiovascular mortality (CVM). After excluding participants younger than 60 years and without diabetes, and with missing follow-up data, 4400 cases were left in this study. Persons with diabetes were divided by GNRI into 3 groups: GNRI ≥ 98; 92 ≤ GNRI < 98; and GNRI < 92; (No; Low; Moderate/Severe (M/S) group). We used Cox proportional hazard regression model to explore the predictive role of GNRI on ACM and CVM in elderly persons with diabetes. Restricted cubic splines to investigate the existence of a dose-response linear relationship between them.

RESULT

During a median follow-up period of 89 months, a total of 538 (12.23%) cardiovascular deaths occurred and 1890 (42.95%) all-cause deaths occurred. Multifactorial COX regression analysis showed all-cause mortality (hazard ratio [HR]: 2.58, 95% CI: 1.672-3.994, p < 0.001) and cardiovascular mortality (HR: 2.29, 95% CI: 1.063-4.936, p = 0.034) associated with M/S group risk of malnutrition in GNRI compared to no group. A negative association between GNRI and all-cause mortality was observed across gender and ethnicity. However, the same negative association between GNRI and cardiovascular mortality was observed only for males (HR:0.94, 95% CI:0.905-0.974, p < 0.001) and other races (HR:0.92, 95% CI:0.861-0.976, p = 0.007). And there was no significant correlation between low malnutrition and cardiovascular mortality (p = 0.076). Restricted cubic splines showed a nonlinear relationship between GNRI and all-cause mortality and cardiovascular mortality (non-linear p < 0.001, non-linear p = 0.019).

CONCLUSIONS

Lower GNRI levels are associated with mortality in older patients with diabetes. GNRI may be a predictor of all-cause mortality and cardiovascular mortality risk in older patients with diabetes.