Abstract
Objective
The objective of this study was to identify the nutritional status of hospitalized elderly and verify if calf circumference can be a tool to monitor nutritional status in this population.
Methods
A total of 170 inpatients (79 men and 91 women) aged more than 60 years were assessed. Anthropometric and dietary assessments were done according to standard procedures. The software STATISTICA 6.0 was used for the statistical analysis. The confidence interval was set at 95% and significance level at 5% (p<0.05).
Results
Body mass index assessment revealed a high rate of underweight patients (45.3%), and arm circumference and triceps skinfold revealed a high prevalence of depletion. Males had more lean mass according to the mid-arm muscle circumference (p=0.017) and mid-arm muscle area (p=0.01), and females presented higher triceps skinfold values (p<0.001). There was a positive correlation between calf circumference and Body Mass Index (p<0.001), arm circumference (p=0.001), triceps skinfold (p=0.001), mid-arm muscle circumference (p=0.001), and mid-arm muscle area (p=0.001).
Conclusion
This study found a positive correlation between calf circumference and nutritional status of assessed patients indicating that this measurement can be used as a complementary tool for monitoring the nutritional status of elderly inpatients.
Key words: Nutrition assessment, aged, anthropometry, nutritional status
Introduction
Age-related health problems in developing countries tend to be the same as those seen in developed countries (chronic diseases that require ongoing and expensive care). They are further aggravated by the presence of problems such as malnutrition and infectious diseases. The elderly tend to require health services more often and hospitalization rates for this group are higher than those observed for other age groups and they also require longer length of stay (1).
In recent years, studies have demonstrated the importance of keeping patients at an adequate nutritional and metabolic status. Healthy aging improves not only quality of life during old age but also reduces healthcare costs. One of the ways to achieve healthy aging is to eat a varied diet since nutrient deficiencies prevent good health. Nutrient deficiencies can be mitigated by nutritional interventions that introduce a balanced diet (2).
The main causes for malnutrition among the elderly are low food intake, changes in nutrient requirements, malabsorption, abnormal bacterial flora, drug interactions, alcoholism, increased catabolism, reduced nutrient reserves and less conversion of vitamins to their active forms (3).
Different studies have suggested that malnutrition is an important antecedent of morbidities and death among the elderly. It is currently admitted that malnutrition influences medical-surgical treatment negatively, increasing the risk of developing severe complications while in the hospital and after discharge. Malnourished elderly are more prone to infections, osteoporosis, fractures, respiratory and cardiac problems as well as mortality associated with the severity of nutritional deficiencies. Furthermore, malnutrition is associated with longer hospital stays, forcing the health care services to spend much more (2).
Diagnosing the nutritional status of the elderly and identifying the factors that contribute to such diagnosis are essential, yet complex processes. This complexity is due to a number of physiological, pathological, financial, and lifestyle changes that occur as individuals age (4).
Anthropometry has been shown to be an important indicator of the nutritional status of the elderly. Yet, changes that occur with age may compromise the accuracy and precision of anthropometric diagnosis if specific steps are not taken to neutralize or mitigate the effect of these changes on the assessment (5). Anthropometric measurements used to assess the elderly are usually easy to obtain and measure and are non-invasive and costless ( 6., 7., 8., 9., 10., 11.). The main measurements are weight, height, girths, and skinfolds (12). Arm circumference can be efficiently used to classify the nutritional risk and/or status of an individual and calf circumference has been referenced as a sensitive indicator of lean mass loss among the elderly (14).
The objective of this study was to identify the nutritional status of elderly inpatients and verify if there is an association between calf circumference and other anthropometric indicators used in this population.
Research methods and procedures
The patients included in this paper are participants in the Study “Nutritional Diagnosis and Intervention of Inpatients and
Outpatients” of a hospital located in the city of Campinas, SP. Campinas is a Brazilian city in the state of Sao Paulo, located roughly 90 km to the northwest of the state’s capital city. The estimated population of Campinas in 2006 was 1,059,420 inhabitants. The metropolitan area consists of 19 municipalities and an estimated population of 3.2 million inhabitants (6.75% of the state’s population). Currently, this area contributes about one-third of the industrial production of the state and its Human Development Index (HDI) was 0.852 in the year 2000. The population studied in this project is a representative sample of typical Campinas municipality.
The patients included in the study were aged 60 or more years and whose medical records contained all anthropometric measurements. Hence, of the 2239 patients assessed from February 2006 to December 2007, 170 (79 men and 91 women) aged from 60 to 92 years were included. This study was approved by the local research ethics committee. All participants signed a free and informed consent form.
Anthropometric assessment was conducted by trained staff using standard procedures (14). The anthropometric variables were: current weight (CW), height (H), wrist girth (WG), calf circumference (CC), arm circumference (AC), and triceps skinfold (TS).
These measurements allowed the following to be calculated: body mass index (BMI), mid-arm muscle circumference (MAMC), and mid-arm muscle area (MAMA). The instruments used were an electronic scale, brand Marte®, model PP180, with 180 Kg capacity and 0.1 Kg accuracy; a Lange Skinfold Caliper adipometer, brand TBW®, with a 0-60 millimeter (mm) scale and ±1.0 mm accuracy; a non-stretchable metric tape, brand TBW®, measuring 150 centimeters (cm) and 0.1cm accuracy; and a triangle.
The anthropometric measurements were classified according to the percentiles proposed by FRISANCHO (1990) (15) and BURR & PHILLIPS (1984) (16). Lean mass (AC, MAMC, MAMA) was considered depleted where the percentiles were equal to or below 10. Triceps skinfold values ≤ P15 were considered depleted and > P95 were considered excessive in terms of fat.
The body mass index (BMI) was calculated by dividing the weight in kilograms by the square of the height in meters (weight/height2), and classified according to the criteria proposed by LIPSCHITZ (1994) (17).
Dietary intake was assessed using a 24-hour recall. For the sake of accuracy, participants were asked what foods they ate and at what times, how the foods were prepared, amounts in portions, and product brands. The questionnaire also included other questions such as the amount of oil, sugar, and salt used monthly; amount of liquids ingested daily; number of people per household; and use of dietary supplements. The dietary data obtained in cooking units were converted to grams and milliliters in order to analyze nutrient intake. The centesimal composition of the foods listed in the 24-hour recalls was calculated by the software NutWin® (2002) (18), version 1.5.
The foods that were not listed in the said software were added from food composition tables and labels ( 19., 20., 21.). The macronutrient percentages of the diet were compared with the dietary recommendations proposed by the Food and Nutrition Board (2002) (22).
Statistical analyses were done using the software STATISTICA 6.0, StatSoft, USA, 2001. The variables were compared with the t-test (independent variables) and ANOVA (three or more groups). Pearson’s correlation coefficient was used to verify the correlation between calf circumference and the remaining anthropometric variables. The confidence interval was set at 95% and the significance level at 5%.
Results
A total of 170 inpatients participated in this study where 46.5% were males (n=79) and 53.5% were females (n=91). Mean age was 72.5 ± 8.6 years with no statistical difference between genders, and length of hospital stay was around 10 days. Males had more lean mass according to MAMC (p=0.017), MAMA (p=0.01), and CC (p=0.013), and females had higher TSF values (p<0.001). The remaining characteristics of the assessed population according to gender are listed in Table 1.
Table 1.
Characteristics of the studied population according to gender
| All (n=170) | Men (n=79) | Women (n=91) | P value | |
|---|---|---|---|---|
| Age (years) | 72.5 ± 8.7 | 71.2 ±8.3 | 73.6 ± 8.9 | 0.077 |
| DIH (days) | 10.5 ±9.1 | 9.2 ± 6.9 | 11.4 ± 10.5 | 0.175 |
| BMI (Kg/m2) | 23.8 ±6.3 | 23.2 ±5.7 | 24.4 ±6.8 | 0.220 |
| CC (cm) | 31.5 ± 5.1 | 32.6 ±5.7 | 30.6 ± 4.3 | 0.013 |
| AC (cm) | 27.8 ±4.9 | 27.6 ±4.5 | 28.0 ± 5.3 | 0.652 |
| TSF (cm) | 15.9 ±7.8 | 13.5 ±6.6 | 18.1 ± 8.2 | <0.001 |
| MAMC (cm) | 22.7 ±3.5 | 23.4 ± 3.7 | 22.1 ± 3.2 | 0.017 |
| MAMA (cm2) | 42.1 ± 12.6 | 44.7 ± 13.2 | 39.8 ±11.6 | 0.010 |
| Linfocitos (mm3) | 1669.3 ±934.0 | 1544.2 ±937.1 | 1770.1 ± 926.8 | 0.204 |
| TEV (Kcal) | 1313.1 ±902.8 | 1381.8 ± 1154.1 | 1258.2 ± 638.6 | 0.419 |
| PROT (g/Kg) | 9.27 ±28.1 | 11.07 ±30.69 | 7.84 ± 26.07 | 0.498 |
| PROT (%) | 26.5 ±23.7 | 28.9 ± 25.5 | 24.5 ±22.1 | 0.263 |
| CHO (%) | 56.6 ± 17.1 | 57.1 ± 17.8 | 56.2 ±16.7 | 0.775 |
| LIP (%) | 33.5 ±22.2 | 35.0 ± 24 | 32.3 ±20.8 | 0.464 |
Values in mean ± SD. DIH: Days in hospital; BMI: Body Mass Index; WG: wrist girth; CC: Calf circumference; AC: Arm circumference; TSF: Triceps skinfold; MAMC: Mid-arm muscle circumference; MAMA: Mid-arm muscle area; TEV: Total energy value; PROT: Proteins; CHO: Carbohydrates; LIP: Lipids.
Figure 1 illustrates the classification of the anthropometric measurements (BMI, AC, MAMC, MAMA and TSF) obtained from these inpatients. Note that 45.3% of the patients were underweight according to their BMI and that 17.1%, 20.6% and 27.1% of the patients presented values below normal for AC, MAMC and MAMA respectively. Also, 20.6% of the patients were considered depleted according to their TSF values. BMI revealed that almost half of the patients were underweight (45.3%) and almost a quarter were overweight (24.7%).
Figure 1.
Classification of the anthropometric variables of the studied population (n=170) BMI: Body Mass Index; AC: Arm circumference; TSF: Triceps skinfold; MAMC: Mid-arm muscle circumference; MAMA: Mid-arm muscle area.
Regarding the association between CC and the other anthropometric and body composition variables (Figure 2), there is a good positive correlation between CC and the following: BMI (r=0.48; p<0.001), AC (r=0.53; p=0.001), TSF (r=0.36; p=0.001), MAMC (r=0.47; p=0.001) and MAMA (r=0.48; p=0.001).
Figure 2.
Pearson’s linear correlation between calf circumference and the following body composition variables: BMI, TFS, WC, AC, MAMC, MAMA BMI: Body Mass Index; WG: wrist girth; CC: Calf circumference; AC: Arm circumference; TSF: Triceps skinfold; MAMC: Mid-arm muscle circumference; MAMA: Mid-arm muscle area.
Table 2 shows the distribution of population according to the 10 and 90 percentiles and according to age stratified into decades. Values that reflect amount of lean mass (AC, MAMC and MAMA) fell as age advanced. Dietary assessment showed that energy and protein intakes also decreased with age.
Table 2.
Characteristics of the studied population according to the 10 and 90 percentiles and age group (years)
| 10 Percentile | 90 Percentile | CI 95% | 60-69 | Age group (decades) |
<90 | ||
|---|---|---|---|---|---|---|---|
| 70-79 | 80-89 | ||||||
| DIH | 3 | 21 | 9.1 -11.9 | 9.0 ± 7.0a | 10.1 ± 5.0a.b | 15.3 ± 9.9b.c | 7.7 ± 2.7c |
| Weight (Kg) | 41.2 | 78.6 | 57.6 - 62.4 | 63.7 ± 15.4a | 59.0 ± 11.7a | 56.1 ± 11.1b | 41.1 ± 13.1ab |
| Height (m) | 1.46 | 1.70 | 1.57 - 1.60 | 1.61 ±0.10a | 1.57±0.07a.b | 1.58 ± 0.08b | 1.39 ± 0.33 |
| BMI (Kg/m2) | 16.5 | 31.0 | 22.8 - 24.7 | 24.5 ± 5.8a | 24.3 ± 5.2b | 22.6 ± 4.4c | 16.6 ± 5.0a.b.c |
| WG (cm) | 14.0 | 19.0 | 15.7 - 16.6 | 16.7 ± 3.0a | 16.2 ± 1.9b | 14.9 ± 2.0a.b | 13.9 ±3.4 |
| CC (cm) | 26.0 | 37.2 | 30.7 - 32.3 | 32.0 ± 4.7 | 31.5 ±3.4 | 30.9 ± 3.7 | 26.3 ± 6.1 |
| AC (cm) | 22.0 | 34.2 | 27.1 - 28.6 | 28.8 ± 5.2 | 27.9 ± 3.8a | 26.3 ± 3.0b | 21.1 ±6.2a.b |
| TSF (cm) | 6.0 | 29.0 | 14.8 - 17.2 | 16.4 ± 8.3a | 16.4 ± 6.3 | 15.2 ±6.1 | 11.3 ±4.8a |
| MAMC (cm) | 18.7 | 27.2 | 22.2 - 23.2 | 23.5 ± 3.7a | 22.8 ± 2.7b | 21.5±2.3a.b | 17.7 ± 4.8a.b |
| MAMA (cm2) | 27.9 | 58.7 | 40.2 -44.0 | 45.0 ± 13.0a | 42.0 ± 9.9b | 37.5 ± 8.1a.b | 29.2 ± 11.2a.b |
| Lymphocytes (mm3) | 590.0 | 3094.0 | 1494.4 - 1844.2 | 1851.2 ± 1052.7a | 1637.9 ± 679.0a | 1496.1 ± 583.8a | 921.0 ±339.4a |
| TEV (Kcal) | 455.7 | 2041.0 | 1163.3 - 1462.8 | 1634.5 ± 1122.5a | 1207.6 ± 428.5a | 1143.8 ±300.5a | 1072.3 ± 300.6a |
| PROT (g/Kg) | 0.5 | 2.9 | 4.6 - 13.9 | 1.3 ± 1.0a | 1.0±0.5a | 1.0±0.3a | 0.8 ± 0.3a |
| PROT(%) | 13.5 | 30.8 | 22.5 - 30.4 | 20.7 ± 7.9 | 19.5 ± 5.5 | 18.8 ±3.1 | 14.7 ± 7.3 |
| CHO (%) | 42.6 | 72.0 | 53.7 - 59.4 | 53.1 ± 16.0 | 50.0 ± 9.9 | 53.9 ± 7.0 | 48.2 ± 13.1 |
| LIP (%) | 16.3 | 43.9 | 29.8 - 37.2 | 27.3 ± 10.4 | 27.5 ± 6.9 | 26.9 ± 5.0 | 27.7 ± 8.5 |
Values in mean ± SD. DIH: Days in hospital; BMI: Body Mass Index; WG: wrist girth; CC: Calf circumference; AC: Arm circumference; TSF: Triceps skinfold; MAMC: Mid-arm muscle circumference; MAMA: Mid-arm muscle area; TEV: Total energy value; PROT: Proteins; CHO: Carbohydrates; LIP: Lipids. The letters a,b,c,d express the difference between the groups where p<0.05
Discussion
Many individuals assessed in this study were nutritionally debilitated or depleted and showed loss of muscle mass as revealed by their anthropometric data. Malnutrition among elderly inpatients is very common and depending on the group of patients studied, its prevalence may be as high as 60% (23). The elderly lose muscle mass progressively. This loss is caused by many factors such as physical inactivity and a reduction of food intake which result in less energy expenditure, and consequently, a lower energy requirement (4). Males had more muscle mass than females, corroborating published data (24).
Although many patients were classified as underweight according to their BMI, 24.7% were overweight, reflecting the impact of the nutritional transition occurring in Brazil. At the same time that malnutrition rates fall, there is a noticeable increase in the rates of overweight and obesity in the Brazilian population. Projections of studies done in the last three decades indicate a clearly endemic behavior of the problem (25).
Calf circumference was positively associated with anthropometric variables that assess muscle mass and fat tissue. Khadivzadeh (2002) (13) found a strong positive correlation between arm and calf circumferences and BMI and body weight in 2000 healthy Iranian women aged from 15 to 49 years, suggesting the use of arm and calf circumferences as indicators of nutritional risk.
BMI and recent weight loss are important instruments (26). However, it is not always possible to obtain this information as the patient may be bedridden and thus unable to be weighed. In these cases, taking circumferences and skinfold measurements allow a nutritional diagnosis to be made (27). Calf circumference is considered a sensitive indicator of muscle changes among the elderly and should be used to monitor these changes. It indicates changes of fat-free mass that occur with age and reduced physical activity (4). The subject study found a positive correlation between calf circumference and the nutritional status of the assessed patients, indicating that this measurement can be used as a complementary indicator when to monitor the nutritional status of the elderly.
Recorded lymphocyte values indicate mild immunological malnutrition, which is associated with the results obtained by the anthropometric variables assessed. However it is important to interpret these tests correctly because although they detect nutritional problems early, biochemical indicators can be influenced by diseases, drugs, or stress; factors that are frequently found among the elderly.
Generally, as age increased, length of hospital stay also increased and values of anthropometric and body composition indicators decreased. Studies show that height also diminishes with age. This decrease begins at around age 40 years and becomes more intense as the individual grows older, corroborating the data obtained in this study. Possible reasons for this decrease in height include flattening of the vertebrae, narrowing of the intervertebral discs, kyphosis, scoliosis, arching of the lower limbs and/or flattening of the plantar arch (4). Regarding weight, studies show that men gain weight until age 65 years and women until age 75 years after which they start to lose weight. The main causes of this weight fluctuation include loss of body water and loss of visceral weight, along with reduction of muscle tissue (11). These changes in muscle tissue occur mainly as a result of reduced physical activity and basal metabolic rate. Body fat in the elderly increases and the distribution of body fat changes, with more fat being stored in the abdominal region and less stored in the limbs (28).
This study also showed that energy and protein intake among the elderly decline as age increases. In the oldest age group, protein intake was below the recommended level for elderly individuals (1.0g/Kg/day) (22), which may be considered an additional risk for malnutrition in this older population (29). It is important to emphasize that although the mean macronutrient intakes are within the Dietary Recommended Intake (DRI) values and the sample size is adequate, analyzing the dietary pattern of this population with one 24-hour recall is questionable.
Conclusion
Many of the patients assessed in this study were underweight and nutritionally depleted. Calf circumference positively and significantly correlated with the nutritional status of the patient, showing that this measurement can be used as a complementary indicator to monitor the nutritional status of the elderly. Assessment and monitoring of the nutritional status of the elderly should be routine practice in healthcare services since they provide an early nutritional diagnosis allowing intervention to be made and monitoring the efficacy of this intervention.
Financial disclosure
None of the authors had any financial interest or support for this paper.
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