INTRODUCTION

The world's rate of population aging is increasing due to the simultaneous decrease in birth rates as well as the increased mortality rates, leading to the phenomenon of demographic burden. The World Health Organization (WHO) declared the issue of aging to be a priority and stated the need for multisectoral action plans in this regard [1]. Approximately one-fifth of the world population is predicted to be over 65 years old by 2030 [2]. Nutrition is an important factor affecting the course of physical and cognitive functions in aging [3]. In addition, a fair number of conditions, such as comorbidities, depression, dementia, disability, drug use, taste disorders, and dysphagia, are all associated with malnutrition [4]. Physiological and psychological responses to food choices and preferences appear to alter during aging. That said, little is yet known about the mechanisms of appetite control changes throughout life [5].   That said, it is clear that malnutrition is a common clinical condition in the elderly. Treatment of the existing diseases will get further complicated if malnutrition is not diagnosed and treated. In addition, malnutrition increases morbidity and mortality as it will cause an increase in patient-related complications. It is also worth noting herein that inadequate food consumption in the elderly living in hospitals or nursing homes, usually due to lack of appetite, has increased malnutrition prevalence [6].

Although the prevalence of malnutrition in the elderly varies depending on the characteristics of the population and the defined criteria, it was mainly determined as 5-10%, 30-60%, and 15-65% in the elderly living at home, nursing homes, and in hospitals respectively [6-9]. In a comprehensive study conducted with nursing home residents aged ≥65 years between 2007 and 2018 participating in a 6-month nutritionDay-project, it was unearthed that 10.5% of 11,923 non-malnourished residents developed malnutrition within these 6 months [10]. Diagnosis of malnutrition can be difficult due to some physiological changes that occur in the natural course of the aging process. Therefore, it is important to perform nutritional screening as part of regular checkups [11].   In fact, regular evaluation of nutritional status is of prime importance in the elderly group with a high risk of malnutrition [1]. Attention should be paid to high-risk groups with inadequate nutritional intake, low body mass index (BMI), severe cognitive impairment, immobility, and advanced age [10].

The path to be followed in the detection and treatment of malnutrition in the elderly, therefore, should follow the following route: screening, detection, intervention, monitoring, and evaluation, respectively. Malnutrition screening is a simple, quick, and general procedure to detect the risk of major nutritional problems at an earlier time. Numerous screening tests such as Nutritional Risk Screening-2002 (NRS-2002), Short Nutrition Assessment Questionnaire (SNAQ), Seniors in the Community: Risk Evaluation for Eating and Nutrition (SCREEN II), Malnutrition Universal Screening Tool (MUST), Malnutrition Screening Tool (MST), Subjective Global Assessment (SGA), Mini Nutritional Assessment (MNA), Mini Nutritional Assessment-Short Form (MNA-SF), and Geriatric Nutritional Risk Index (GNRI) have hitherto been developed to evaluate malnutrition and/or risk in the geriatric age group. SGA, NRS-2002, and MNA are frequently used in clinical evaluations of malnutrition in the elderly [12]. Apparently, these malnutrition screening tools were used to create significant differences in the prevalence of malnutrition [13].

Failure to identify the effectiveness of malnutrition screening procedures, despite the increase in the prevalence of malnutrition and its negative effects on health, inescapably causes delays both in diagnosis and treatment [14]. This study intends to determine the nutritional status of the elderly by using anthropometric measurements, hand grip strength, and different screening tools (MNA, MUST, NSI, and GNRI) and to evaluate the compatibility of these data with each other.

MATERIALS AND METHODS

This research was designed as a cross-sectional, descriptive, and quantitative study. Data were collected from a nursing home in Ankara, the capital of Turkey, after obtaining the necessary permissions. The inclusion criteria were being over 65 years old, living in a nursing home for at least 6 months, and having serum albumin levels checked within the last three weeks of the relevant period. The exclusion criteria were determined as having a disease-causing cognitive impairment such as Alzheimer's or dementia, having a severe hearing problem, and being bedridden. The study was carried out voluntarily, and a total of 88 individuals who agreed to participate, met the inclusion criteria, and signed the informed consent form were included in the research.

A face-to-face investigation technique for the questionnaire, a patient file review, and anthropometric measurements were utilized to collect the data. The questionnaire included questions about the participating individuals' demographic information and general health status. The albumin values of the participants were obtained by examining the tests performed in the last three weeks of the said period from the health records. MNA, MUST, NSI and GNRI screening tests were applied to all the participants, and hand grip strength measurements and anthropometric measurements were also included.

Ethical considerations

The ethical approval was obtained from the related University Ethics Committee with the decision numbered 60 and dated 12/24/2021. The study was planned and completed in compliance with the Declaration of Helsinki.

Determination of nutritional status

Malnutrition screening tools, which are MNA, MUST, NSI, and GNRI, used in the recent study are suitable and validated to be resorted to for a study with Turkish older adults. All four screening tools were applied by a trained dietitian, as described in the references [15-18].    

Mini nutritional assessment (MNA)

The Mini Nutritional Assessment is a single, rapid assessment of the nutritional status of elderly patients in outpatient clinics, hospitals, and nursing homes. After calculating the total score, the subjects were classified into three categories: (I) <17 as those with protein-calorie malnutrition, (II) 17–23.5 as those carrying the risk of malnutrition, and (III) ≥24 as those with the adequate nutritional status [19].  

Malnutrition universal screening tool (MUST)

The malnutrition Universal Screening Tool is a simple and quick method for a comprehensive nutritional assessment. Patients were classified as having normal nutrition with scores 0-1 and at risk of malnutrition with scores ≥2 [20].  

The nutrition screening initiative (NSI)

The Nutrition Screening Initiative form was developed by the American Academy of Family Medicine, the American Diet Academy, and the National Aging Council to assess the nutritional status and risk factors for the elderly [21]. A total score between 0-2 points reflects a low risk for nutritional evaluation, which necessitates reevaluation after 6 months; 3-5 points reflect a moderate risk, which calls for reevaluation after 3 months; ≥6 points reflect high risk.

Geriatric nutritional risk index (GNRI)

The Geriatric Nutrition Risk Index was defined by Boulline et al. for use in the elderly living at home or in a nursing home [22]. The albumin value used in the calculation was taken from the results of the analysis made within the last 3 months of the health records of the elderly. For GNRI, the patients who scored <82 were classified as severe risk, 82 to <92 were classified as those carrying moderate risk, the ones scoring 92-98 were classified as possessing a mild risk, and those scoring >98 were classified as having no malnutrition risk.

Anthropometric measurements

The researchers performed anthropometric measurements following the techniques found in the literature. The body- weight was measured using the BC-532 TANITA. Height, waist circumference (WC), hip circumference (HC), mid-upper arm circumference (MUAC), calf circumference (CC), ulna length, and arm span measurements in the elderly were carried out by the researchers with a non-stretchable measuring tape [23]. Body Mass Index (BMI) was calculated with the formula of body weight (kg)/height (m²). As is well known, with aging, muscle mass decreases, and the visceral adipose tissue in the trunk and abdomen increases. This is also related to losing subcutaneous lean tissue in the arms and legs. This situation reduces the validity of BMI in the evaluation of nutritional status. A modified BMI below 23 kg/m² is accepted as an indicator of malnutrition [24]. Classification of BMI was considered in the following way: <23.0 kg/m² as underweight; 24.0-26.9 kg/m² as normal; >27 kg/m² as overweight [25]. Hand grip strength was measured with a hand dynamometer. The participant sat on a chair upright and was requested to squeeze the dynamometer three times with each (right and left) of the hands. The mean value of three measurements was reported accordingly [26].

Physical activity level (PAL)

The sleep duration of the participants, the activities they did at night (e.g., visiting the toilet, changing clothes, praying, and the like.), and the activities they did in their private areas (rooms) were questioned, the physical activities in the common areas were observed and recorded as 15 minute-periods, the 24-hour physical activity levels were determined, and the PAL value was calculated [27]. 

Statistical analysis

Quantitative data are expressed as percentage, mean, and standard deviation. For two-group comparisons, the t-test was referred to for the normally distributed data in independent groups, the Mann Whitney-U test was made use of for the data that was not normally distributed, ANOVA was used for the normally distributed data in comparisons of more than two groups, and Kruskal Wallis test was preferred for data without distribution. The Spearman Correlation Coefficient was employed in cases of two continuous variables. SPSS 21 (SPSS Inc., Chicago, Ill, USA) program was run in the analysis of the data; p <0.05 was taken as the level of significance. A linear regression model was deployed to estimate the dependent variable of the MNA score and to analyze the influencing factors. First off, the independent variables were analyzed using univariate regression. Then, the model was created using the enter method in multiple regression analysis.

RESULTS AND DISCUSSION

A total of 88 individuals, 60 male (68.8%) and 28 (31.8%) female, were included in the study. The length of stay in the nursing home was 47.13±59.61 months. The mean age of women (81.18±6.99 years) is higher than men (74.92±7.97 years) (p=0.01).   Most of the elderly (n=79, 89.8%) have been diagnosed at least with one chronic disease, 33.0% have chewing and swallowing problems, and 4.0% have appetite problems. It was understood by the participants that systolic and diastolic blood pressure did not differ according to the gender. Whilst the number of snacks was higher in women (p=0.014), the number of main meals did not differ according to gender (p>0.05) (Table 1).

Table 1. Nutritional habits, demographics, and other characteristics of the participants

Abbreviations: SD, standard deviation; BMI, body mass index. Descriptive statistics are expressed as frequency(percentage) or mean (standard deviation).

* p < 0.05 obtained from t-test, Mann–Whitney U, and chi-square test.

While the waist, hip, mid-upper arm, and calf circumferences and the BMI of the participants did not differ according to gender, a statistically significant difference was found between other anthropometric and hand grip strength measurement values. Significantly higher MNA, GNRI, and NSI scores were found in males (p=0.044; p=0.014; p=0.044, respectively) (Table 2).

Table 2. The anthropometric measurements of the participants and the results of different screening tools

Abbreviations: SD, standard deviation; BMI, body mass index; MNA, Mini Nutritional Assessment; GNRI, Geriatric Nutritional Risk Index; NSI, Nutritional Risk Screening.Descriptive statistics are expressed as minimum, maximum, and mean (standard deviation). * p < 0.05, **p<0.001 obtained from t-test and Mann–Whitney U.

A negative correlation was located between NSI and GNRI, which was not statistically significant in terms of the score (p=0.248 r=-0.124). A negative correlation between the MNA and NSI scores (p<0.001, r=-0.419) and a positive relationship between the GNRI and MNA scores (p<0.00.1 r=0.424) was determined. A statistically significant positive correlation between right-hand grip strength and GNRI score (p=0.04 r=0.223), MNA score (p=0.003 r= 0.317), and negative correlation with NSI score (p=0.004 r=-0.310) were determined. A statistically significant positive correlation between left-hand grip strength and GNRI score (p=0.76 r=0.193), MNA score (p=0.005 r= 0.302), and negative correlation with NSI score (p=0.020 r=-0.252) were identified. Whereas there existed a moderately significant relationship between MNA score and albumin, PAL (p<0.001), a lower statistically significant relationship was observed between BMI (p=0.032), MUAC (p=0.003), and calf circumference (p=0.009). There recognized a very high significant correlation between GNRI score and albumin (p<0.001) and a low statistically significant correlation was worked out between GNRI and PAL (p=0.004), waist-hip ratio (p=0.015). A low level of statistically significant correlation was deduced between NSI score and waist height (p=0.040) and PAL (p=0.001) (Table 3).

Table 3. Correlation between MNA, GNRI and NSI scores and parameters

Abbreviations: PAL, Physical Activity Level; BMI, body mass index. * p < 0.05, **p<0.001 obtained from Spearman Correlation

The participants were analyzed and classified according to the scores out of MUST, NSI, MNA, and GNRI tools (Table 4). The majority of the participants had no nutritional problems and were with low-to-no malnutrition risk. While NSI results differed according to gender (p=0.020), no difference between genders was perceived in other screening tools implemented (Table 4).

Table 4. Distribution of participants according to screening tools

Abbreviations: MUST, Malnutrition Screening Test; NSI, Nutritional Risk Screening; MNA, Mini Nutritional Assessment; GNRI, Geriatric Nutritional Risk Index. χ²: The chi-square test of independence. Descriptive statistics are expressed as frequency (percentage). * p < 0.05 obtained from Fisher–Freeman–Halton test.

The effect of parameters on the NSI and GNRI groups of each estimate is given in Table 5. An increase in the NSI score by 1 point will increase the probability of being in the "risk group of malnutrition" according to the "no malnutrition" category by 1,732 times and the probability of being grouped under the "malnutrition" category according to the "no malnutrition" category by 2.751 times. Increasing the GNRI score by 1 point will decrease the probability of being in the "risk group of malnutrition" by 0.794 times according to the "no malnutrition" category and will decline 0.969 times the probability of being grouped under the "malnutrition" category according to the "no malnutrition" category.

Table 5. Model parameter estimators

Abbreviations: NSI, Nutritional Risk Screening; MNA, Mini Nutritional Assessment; GNRI, Geriatric Nutritional Risk Index.CI, confidence interval; OR, odds ratio, Overall significance of model *p< 0.05, **p< 0.001

When the predictive variables are considered individually in univariate linear regression analysis, it is clear that a 1-point boost in the GNRI score corresponds to a 0.33 increase in the MNA score, a 1-point increase in the NSI score leads to a 1.163-decrease in the MNA score, 1-point increase in the right-hand grip strength score results in a 0.080 increase in the MNA score, a 1-point increase in left- hand grip strength score results in a 0.067 increase in the MNA score.

The coefficient of determination (R²) of the model was found to be 0.631 (Table 6).

Table 6. Univariate Linear and Multiple Linear Regression between GNRI, NSI scores, hand grip strength, gender, age

Abbreviations: CI, confidence interval; OR, odds ratio, NSI, Nutritional Risk Screening; GNRI, Geriatric Nutritional Risk Index; HGS; hand grip strength. ** Overall significance of model p < 0.001

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