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26 December 2024: Clinical Research  

Predictive Power of Anthropometric Measures and Indices in Assessing Nutrition-Related Risk Using the Geriatric Nutritional Risk Index in Elderly Patients: A Cross-Sectional Study

Justyna Nowak ORCID logo1ABCDEFG*, Marzena Jabczyk ORCID logo2E, Michał Skrzypek ORCID logo3C, Katarzyna Brukało ORCID logo4F, Bartosz Hudzik ORCID logo56C, Barbara Zubelewicz-Szkodzinska ORCID logo27E

DOI: 10.12659/MSM.946316

Med Sci Monit 2024; 30:e946316

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Abstract

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BACKGROUND: Effective assessment and diagnosis using simple nutritional screening tools are crucial for identifying malnutrition in older adults. The aim of the study was to evaluate how effectively different anthropometric parameters, indices, and body composition metrics can assess nutrition-related risks, using the Geriatric Nutritional Risk Index (GNRI) in a cohort of 185 patients >60 years.

MATERIAL AND METHODS: This study included 185 patients over 60 years old. Anthropometric measurements, indices, and body composition were examined. Nutritional status based on GNRI was categorized as major risk (1.1%), moderate risk (9.7%), low risk (15.1%), and no risk (74.1%).

RESULTS: The strongest correlations with the GNRI were observed for body mass index (BMI) (ρ=0.8628) and body fat in kilograms (ρ=0.8269), P<0.001. A unit increase in BMI decreased the odds of being in the risk group by 52.1% (OR 0.479; 95% CI 0.377-0.609; P<0.001). ROC analysis showed BMI ≤25.0 had the highest predictive value (AUC 0.93, 95% CI 0.89-0.97) in assessing nutrition-related risk in the elderly. Body fat (AUC 0.89, 95% CI 0.85-0.94), abdominal volume index (AUC 0.86, 95% CI 0.80-0.91), hip circumference (AUC 0.85, 95% CI 0.79-0.91), and waist circumference (AUC 0.85, 95% CI 0.80-0.91) also demonstrated significant predictive power (P<0.001).

CONCLUSIONS: Our study underscores the importance of using BMI and other related anthropometric measures and indices as part of routine assessments to identify and manage nutrition-related risks among elderly individuals in hospitals, care facilities, and dietetic clinics, particularly in situations where standardized tools for assessing malnutrition are not available or are impossible to use.

Keywords: Body Composition, Body Mass Index, Body Weight, Geriatrics, Nutrition Assessment

Introduction

Aging involves changes in physiological, pathological, social, and psychological functions, which lead to impaired body functions and an increased susceptibility to mortality. As a population, older adults are more prone to age-related disease, functional impairment and physical inability that can interfere with the maintenance of a good nutritional status. On the other hand, decreased food intake because of troubles with digestion and absorption of food lowers the intake of nutrient-rich foods, due to oral health, inability to chew, mouth dryness, decreased appetite, and low physical activity—the risk factors for malnutrition. That is why elderly individuals are particularly at risk for protein and micronutrient deficiency. Changes in function along with age-related malnutrition increase the risk of developing a number of age- and deficiency-related diseases, with anemia, cardiovascular disease, and diabetes [1]. Additionally, frailty syndrome and osteoporosis lead to falls and fractures [1].

Based on published data, 3% of older adults in Europe are facing a high risk of malnutrition, and an additional 48.4% are at some level of malnutrition risk, encompassing moderate and high risks combined. The prevalence of high malnutrition risk differs across various settings among older adults in Europe, with rates reaching 28.0% in hospitals, 17.5% in residential care facilities, and 8.5% in the community setting [1–3]. An increased risk of malnutrition between 2% up to 16% has been observed in groups of older adults, with a nutritional deficiency in protein and calories [4]. If mineral and vitamin deficiencies are included in this estimate, malnutrition in persons over the age of 65 years may be as high as 35% [5]. A careful assessment of nutritional status is pivotal for successful diagnosis of malnutrition in the elderly, and for the development of appropriate and comprehensive treatment plans [6–10].

There are many nutritional screening tools for assessing subjective nutritional status, with some aimed at the general population and others aimed at specific groups [6]. Various tools, such as the Nutritional Risk Screening 2002, Malnutrition Universal Screening Tool, Short Nutritional Assessment Questionnaire, Mini Nutritional Assessment, Mini Nutritional Assessment-Short Form (SF), Simplified Nutritional Appetite Questionnaire, and Geriatric Nutritional Risk Index (GNRI), provide rapid nutritional risk assessments. More complex diagnostic approaches, such as the Subjective Global Assessment, Patient-Generated Subjective Global Assessment (for patients with cancer), Nutrition Risk in the Critically Ill (NUTRIC score), and Academy of Nutrition and Dietetics-American Society for Parenteral and Enteral Nutrition Indicators, combine multiple measures and criteria to diagnose malnutrition, with variations affecting prevalence estimates [7–10].

In 2019, four major international clinical nutrition societies introduced the Global Leadership Initiative on Malnutrition criteria to unify diagnostic tools for malnutrition globally [7–9]. The European Society for Clinical Nutrition and Metabolism (ESPEN) recommends the Mini Nutritional Assessment-SF, Malnutrition Universal Screening Tool, and Nutritional Risk Screening 2002 as screening tools. The Mini Nutritional Assessment-SF and GNRI were specifically developed for screening elderly populations [9,10].

The GNRI was first described by Bouillanne et al in 2005 as a tool for identifying medical patients at risk of malnutrition-related morbidity and mortality. The GNRI description includes albumin levels, body mass, and ideal body mass, calculated using the Lorenz formula [11].

Although the GNRI is not a direct measure of malnutrition, it serves as a “nutrition-related” risk index because GNRI reflects complications related to nutritional status, such as bedsores and infections [11].

Compared with other nutritional screening tools, anthropometric measurements are simple, noninvasive, and time-efficient for assessing nutritional status in health facilities and community-based screenings [12]. Common indicators for assessing nutritional status among the elderly include involuntary weight loss, body mass index (BMI), and measurements of calf circumference, mid-arm circumference, thigh circumference, hip circumference, waist circumference, and waist-hip ratio (WHR) [13]. Bioimpedance analysis is also a simple, inexpensive, and noninvasive method for estimating body composition. It provides information on total body fat, muscle mass, lean mass, and body water when adjusted for age, sex, and race. The results of bioimpedance analysis can be useful in assessing nutritional status [10].

Therefore, the objective of this study was to evaluate how effectively different anthropometric parameters, anthropometric indices, and body composition metrics can assess nutrition-related risks, using the GNRI in a cohort of 185 individuals >60 years of age.

Material and Methods

ETHICS APPROVAL:

The study was approved by the Bioethics Board (KNW/0022/KB1/53/14) at the Medical University of Silesia, and it conformed to the principles of the Declaration of Helsinki. Informed consent for participation in the study was obtained from the participants. The presented data do not contain any information that allows the participants to be identified.

STUDY PARTICIPANTS:

To the study included 185 patients hospitalized at the Geriatric Department who met the following inclusion criteria: age over 60 years, consent to participate in the study, lack of immobilization, and no mental or cognitive impairments.

The exclusion criteria were as follows: severe hepatic disease, such as liver cirrhosis, chronic kidney disease stage 4 or higher (based on estimated glomerular filtration rate [eGFR]< 30 mL/min/1.73 m2), cancer (under treatment, diagnosis of malignancies), presence of signs or symptoms of fluid retention upon clinical examination at the index date, having an implanted cardiac pacemaker, mobility impairment, conditions that can affect anthropometric measurements (eg, diseases that could affect the intake and absorption of nutrients, such as celiac disease, inflammatory bowel diseases), and lack of patient consent to participate.

LABORATORY MEASURES:

The study was conducted in 2 phases: a structured medical interview and a physical examination. The medical assessment included gathering information on age, sex, and medical history and measuring anthropometric and biochemical parameters. All biochemical tests were performed in the morning after an overnight fast. Measurements included fasting blood glucose (mmol/L), total cholesterol (mmol/L), high-density lipoprotein (HDL) cholesterol (mmol/L), low-density lipoprotein cholesterol (mmol/L), non-HDL cholesterol (mmol/L), triglycerides (mmol/L), C-reactive protein, eGFR (mL/min/1.73 m2), serum creatinine (μmol/L), aspartate aminotransferase (U/L), alanine aminotransferase (U/L), gamma-glutamyl transferase (U/L), albumin (g/L), vitamin D (nmol/L), and thyroid-stimulating hormone (uIU/mL).

ANTHROPOMETRIC MEASUREMENTS:

Baseline nutritional assessments were performed for all patients in the morning, while the patients were fasting, without shoes, and wearing light outer clothing. Standard methods and a validated scale were used for the assessments.

Body weight was measured using the Tanita BC 420 S MA scale (with a medical certificate MDD 93/42 EEC), with a precision of 0.01 kg.

Height was measured using the Tanita HR 100 stadiometer, with a precision of 0.05 cm. Waist circumference, hip circumference, mid-arm circumference, and calf circumference were measured using the Seca 203 measuring tape, with a precision of 1 mm.

BODY COMPOSITION ANALYSES:

The body composition analyses of the patients were conducted using the Tanita BC 420 S MA Body Composition Analyzer. For each patient, the percentage of body fat, body fat in kilograms, muscle mass, free fat mass, and visceral fat rating were measured.

ANTHROPOMETRIC INDICES:

BMI was calculated using the formula: weight (kg)/height (m2)[14].

Body adiposity index (BAI) was calculated according to formula: BAI=hipcicrumference [cm]height [m]1.5-18[15].

WHR was calculated as: WHR=waist circumference (cm)/hip circumference (cm)[16].

Waist-height ratio (WHtR) was calculated as: waist circumference (cm)/height (cm)[16].

Visceral adiposity index (VAI) was calculated as:

Body roundness index (BRI) was calculated as: BRI=365.2-365.5×√(1-(((waist circumference/2π)2)/[(0.5×height)]2)[18].

A body shape index (ABSI) was calculated as: ABSI=waist circumference(m)/([BMI]2/3)×(height [m])1/2)][18].

Abdominal volume index (AVI) was calculated as: AVI=[2×(waist circumference in cm)2+0.7×(waist circumference in cm-hip circumference in cm)2]/1000[19].

NUTRITION-RELATED RISK INDEX:

To assess malnutrition-related risk of mortality and morbidity, the GNRI was calculated [20]. GNRI=(1.489×albumin [g/L])+(41.7×[weight (kg)/ideal weight (kg)]).

Ideal weight was calculated from the Lorenz equation as: height (cm)-100-(height [cm]-150)/4 for men;height (cm)-100-(height [cm]-150)/2.5 for women (Table 1).

STATISTICAL ANALYSIS:

The data were analyzed using SAS software, version 9.4 (SAS Institute Inc, Cary, NC, USA). To evaluate the normality of the distribution of the analyzed variables, the Shapiro-Wilk test was used. The variables were expressed as mean±SD for normally distributed data and as median with lower and upper quartiles otherwise. Normally distributed data were compared using the t test, while non-normally distributed data were compared using the Mann-Whitney U test. The chi-squared test was used to compare categorical variables, and the Fisher’s exact test was used in the case of small expected values. The Pearson correlation coefficient (for normally distributed data) and the Spearman rank correlation coefficient (for non-normally distributed data) were used to assess the relationships between variables. The area under the receiver operating characteristic curve (AUC) was used to evaluate the predictive ability of anthropometric measurements for nutrition-related risk (GNRI), and the Youden index was used to assess the cutoff point. Moreover, sensitivity, specificity, negative predictive value, and positive predictive value were calculated. Odds ratios (ORs) and their 95% confidence intervals (CIs) for the risk of malnutrition associated with each anthropometric index were calculated using logistic regression. The aim of the ROC analysis was to assess the diagnostic accuracy (AUC) and cut-off points of selected anthropometric parameters and indices for detecting nutrition-related risks using the GNRI, whereas the logistic regression odds ratio presents a measure of association between these indices and the GNRI. A probability level of P<0.05 was considered statistically significant.

Results

CHARACTERISTICS OF STUDY GROUP:

Nutrition-related risk based on the GNRI index was observed among 25.9% (n=48) of patients. In this group, 15.1% (n=28) had low risk, 9.7% (n=18) had moderate risk, and only 1.1% (n=2) had major risk (Table 1).

Most patients in the study group were women 69.7% (n=129). There were no statistical differences between most of the clinical and laboratory parameters (Table 2). Patients in the nutrition-related risk group were significantly older (82 years; 77.5–85) than patients in the no nutrition-related risk group (79 years; 74–83), P=0.0031.

ANTHROPOMETRIC PARAMETERS, INDICES, AND BODY COMPOSITION IN THE STUDIED GROUPS:

The nutrition-related risk group exhibited significantly lower weight (54.4 kg vs 71.7 kg; P<0.0001), hip circumference (89.3 cm vs 103 cm; P<0.0001), and waist circumference (95.5 cm vs 106 cm; P<0.0001) than the no-risk group. Furthermore, substantial differences were observed in additional anthropometric and body composition measures, including mid-arm circumference, calf circumference, BMI, WHR, WHtR, BRI, ABSI, AVI, and body fat percentage, all of which were markedly lower in the risk group (P<0.0001; Table 3).

CORRELATIONS BETWEEN ANTHROPOMETRIC PARAMETERS AND INDICES, BODY COMPOSITION, AND THE GNRI:

The strongest correlations between the analyzed anthropometric measures and the GNRI index were observed for BMI and body fat in kilograms, with correlation coefficients of ρ=0.8628 for BMI and ρ=0.8269 for body fat (P<0.001).

Additionally, weight in kilograms (ρ=0.7834), waist circumference in centimeters (ρ=0.7698), the AVI (ρ=0.7337), hip circumference in centimeters (ρ=0.7288), and mid-arm circumference (ρ=0.7135) demonstrated strong positive correlations with the GNRI, P<0.001. Other correlations between the analyzed anthropometric measures and the GNRI are detailed in Table 4.

ODDS RATIOS FOR PREDICTING NUTRITION-RELATED RISK IN THE ELDERLY BASED ON VARIOUS ANTHROPOMETRIC INDICES:

Table 5 presents the odds ratios for predicting nutrition-related risk among the elderly based on various anthropometric indices (univariable and adjusted for age and sex). A higher BMI was associated with a substantially reduced risk of nutrition-related issues. For each unit increase in BMI, the odds of being in the nutrition-related risk group decreased by about 52.1% (OR 0.479; 95% CI 0.377–0.609; P<0.001).

A higher WHtR was associated with a significantly lower risk of nutrition-related issues. An increase in WHtR by 0.1 unit was associated with a reduction in the odds of being in the nutrition-related risk group by approximately 83.7% (OR 0.163; 95% CI 0.086–0.311; P<0.001).

A higher ABSI was associated with a significantly increased risk of nutrition-related issues. For each 0.01-unit increase in ABSI, the odds of being in the nutrition-related risk group increased by approximately 152.5% (OR=2.525; 95% CI 1.348–4.729; P=0.0038; Table 5).

IDENTIFYING THRESHOLD VALUES FOR DETECTING NUTRITION-RELATED RISK USING RECEIVER OPERATING CHARACTERISTIC (ROC) CURVES BASED ON GNRI ACROSS VARIOUS ANTHROPOMETRIC MEASURES AND INDICES:

As shown in Table 6, the most statistically significant finding was the BMI parameter. The BMI cut-off value of ≤25.0 had the highest area under the curve (AUC), of 0.93 (95% CI 0.89–0.97). This indicated excellent discrimination ability, meaning BMI was the most effective measure among those listed for distinguishing between individuals with and without nutrition-related risk. With a sensitivity of 0.89 and specificity of 0.88, the BMI cut-off demonstrated both high true positive and true negative rates. This means that BMI is highly accurate in identifying individuals at risk and those not at risk of nutrition-related issues. The positive predictive value was 0.72 and the negative predictive value was 0.96. These values suggest that BMI not only predicted the presence of risk with good accuracy but also confirmed the absence of risk effectively.

Additionally, ROC analysis demonstrated a high predictive value for body fat in kg (AUC 0.89, 95% CI: 0.85–0.94), AVI (AUC 0.86, 95% CI: 0.80–0.91), hip circumference (AUC 0.85, 95% CI: 0.79–0.91), waist circumference (AUC 0.85, 95% CI: 0.80–0.91), and mid-arm circumference (AUC 0.84, 95% CI: 0.78–0.91) in identifying nutrition-related risk based on the GNRI (Table 6, Figure 1A–1H). All parameters demonstrated statistically significant predictive power, with P values <0.001, highlighting their relevance in assessing nutrition-related risk in the elderly. Among these measures, BMI showed the highest accuracy, with balanced sensitivity and specificity, making it the most effective single measure for identifying nutritional risk using the GNRI.

Discussion

STUDY LIMITATIONS:

In our study, sex differences were not considered in the statistical analyses due to the small number of male participants. Future research should address this limitation by expanding the sample size and conducting analyses stratified by sex.

Conclusions

The results of our study underscore the importance of using BMI and other related anthropometric measures and indices as part of routine assessments to identify and manage nutrition-related risks among elderly individuals in hospitals, care facilities, and dietetic clinics, particularly in situations where standardized tools for assessing malnutrition are not available or are impossible to use.

The high correlation and predictive power of these measures with the GNRI confirmed their utility in clinical settings.

Future research should be continued to explore and validate described parameters across diverse elderly populations to further enhance the accuracy and applicability of nutritional risk assessments.

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Tables

Table 1. Cut-off values of the Geriatric Nutritional Risk Index (GNRI) and the proportion of nutritional status among the study group.Table 2. Baseline clinical and laboratory characteristics (N=185).Table 3. Anthropometric and body composition measurement (N=185).Table 4. Correlation between anthropometric parameters and indices and Geriatric Nutritional Risk Index (GNRI) (n=185).Table 5. Odds ratios (OR), with 95% confidence intervals, in predicting of nutrition-related risk among elderly for various anthropometric indices, univariable and adjusted for age and sex.Table 6. Receiver operating characteristic (ROC) curves were used to identify the threshold values for detecting nutrition-related risk based on Geriatric Nutritional Risk Index (GNRI) across various anthropometric measures and indices.Table 1. Cut-off values of the Geriatric Nutritional Risk Index (GNRI) and the proportion of nutritional status among the study group.Table 2. Baseline clinical and laboratory characteristics (N=185).Table 3. Anthropometric and body composition measurement (N=185).Table 4. Correlation between anthropometric parameters and indices and Geriatric Nutritional Risk Index (GNRI) (n=185).Table 5. Odds ratios (OR), with 95% confidence intervals, in predicting of nutrition-related risk among elderly for various anthropometric indices, univariable and adjusted for age and sex.Table 6. Receiver operating characteristic (ROC) curves were used to identify the threshold values for detecting nutrition-related risk based on Geriatric Nutritional Risk Index (GNRI) across various anthropometric measures and indices.

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750