Geriatric population had been increasing worldwide. Decades ago, Boss
and Seegmiller, (1981) had conducted an intensive review in which the main
conclusion was that the aging process affects all organs and human body systems.
Immune function and digestive tract motility were considered probably the most
affected and those which influence more significantly life quality and could
promote specific disorders in elderly (Boss and Seegmiller,
1981; Russell, 1992). Psychological disorders had
also been considered a matter of concern (Etemadi and Ahmadi,
2009) and could be possible factors affecting food intake.
Nutritional status could be specially affected by several morphologic, metabolic
and behavioural changes frequent in elderly. Studies revealed that these changes
could affect not only the nutritional status but also the body composition and
anthropometric measurements which were considered important health indicators
and used as main parameters in malnutrition screening tools (Harris
et al., 2008; Perissinotto et al., 2002;
Vellas et al., 1999).
Studies pointed out several changes in body composition and anthropometric
measurements. Loss of muscle mass had been frequently found and could be an
important cause of disability (Doherty, 2003; Wells
and Dumbrell, 2006). Elmadfa and Meyer (2008) had
reported increased body fat levels and Hughes et al.
(2004) had referred to a redistribution of those body fat deposits. These
changes were defined as sarcopenic obesity, which could be associated with the
high prevalence of several metabolic disorders in elderly (Zamboni
et al., 2008).
Obesity and overweight have a complex aetiology, resulting from a bilateral
interaction between caloric consumption and insufficient energy output (Afridi
and Khan, 2004). Despite the clear influence of diet in body composition
and several anthropometric measurements, the direct influence of specific foods
and/or nutrients had not been free of some controversies.
Some studies had associated body composition with energy, lipid, protein and
carbohydrate ingestion (Periwal and Chow, 2006; Randi
et al., 2007; Slattery et al., 1992;
Trichopoulou et al., 2002). Most studies indicated
a positive association between body mass index and protein intake (Periwal
and Chow, 2006; Randi et al., 2007; Slattery
et al., 1992; Trichopoulou et al., 2002)
but not with carbohydrate intake (Ahluwalia et al.,
2009; Randi et al., 2007; Slattery
et al., 1992) which were not associated with waist circumference
neither with waist-to-hip ratio (Ahluwalia et al.,
2009; Slattery et al., 1992).
Some data (Sharkey et al., 2002) also revealed
a lack of association between body mass index and other nutrients such as calcium,
magnesium, folic acid, zinc and D, B12, B6, C and E vitamins in 60 or more years
old people. In fact, overweight could not despise frequent micronutrient deficiencies
in this age. A study conducted in 2003 revealed a considerable deficiency in
magnesium, copper and zinc (Slowinska and Wadolowska, 2003).
Antioxidant consumption had also been considered a special matter of concern
considering their benefits in neurodegenerative diseases prevention (Saeed
et al., 2005).
It is important to consider that most of these results are based on 24 h recalls
and food frequency questionnaires which had been lacking of some accuracy because
it depends on the individual memory and could not consider an accurate assessment
of food quantities (Sun et al., 2010).
Considering this, the present study intended to establish a clearer association between body composition and other anthropometric measurements with food consumption and specific nutrient intake.
MATERIALS AND METHODS
Sample and ethical approval: The present study was conducted from January
to March 2011 and included a sample of 34 elderly people 65 or more years old
that were attending a long-term care institution in Sardoal, a Portuguese village.
Study design and procedures were conducted after the ethical commission approval according to Portuguese legislation and the Declaration of Helsinki from World Medical Association. Data were obtained under informed consent.
Anthropometric measurements: Height was measured using a stadiometer with 1 mm precision and a maximum height of 2.10 m with the individuals on their foot with no shoes, equally distributed weight between feet, heels together and natural pending arms aside of the body with open hands with palms close to lateral thigh region and head positioned according to Frankforts plane.
Individuals were weighted minimally dressed in a portable digital scale with a 150 kg maximum capacity and +/100 g error margin.
Circumferences were measured with a flexible non-elastic tape while the individuals stood feet together with warms resting by their sides. Waist circumference was measured in the horizontal plane between the lowest rib end and the iliac crest and brachial circumference was measured by encircling the arm midway between elbow and shoulder.
Skinfold measurement was done using Slim Guide calliper with 1 mm error margin,
the skinfold measures taken were tricep, bicep, subescapular and suprailiac
according to standard procedures (WHO, 1995). All these
anthropometric evaluation procedures were conducted separately and there time
in all the individuals and the average value was taken.
Body mass index and body fat mass: BMI was calculated by the ratio between
weight and square height (WHO, 1995) while Body fat mass
was calculated through Brozek formula (Brozek et al.,
Food intake assessment: Food intake was accurately measured. All foods and beverages ingested during a period of 7 days were weighted and registered in the moment and the daily nutritional analysis was done using the portuguese food composition table.
Statistical analysis: All statistical tests were conducted in Statistical Package for Social Sciences (SPSS) version 17.0 for Windows.
Additionally to average, standard deviation and frequency analysis, anthropometric measurements, body composition and food intake data were grouped by sex. To evaluate if there was a significant difference, the parametric test t-student or the non parametric Mann-Whitney tests were conducted.
To evaluate the association between the dependent variables like weight, waist and muscular circumference, body mass index and body fat mass, Pearson correlation test was conducted.
Considering the probable strong associations between these variables, they were grouped in smaller variables using a principal components analysis converting these and allowing a more direct association between the several anthropometric results and the food intake data. All statistical tests were conducted with a significance level of 5%.
Sample general characteristics: Of the individuals present in the sample,
18 were male and 16 female. The overall mean age was 81.1±7.2 years and
these distribution is presented in Table 1.
Anthropometric measurements: Weight, height and BMI: There were found significant differences (p<0.05) among the several anthropometric measurements between sexes except BMI, as presented in Table 2.
Women had an average weight recorded of 60.0±7.8 kg while the average weight in male was 77.8±12.4 kg.
It is important to note that half the individuals were obese with BMI above 30 kg m-2, these were specially men (32.4%) while the 8.8% of the sample with normal weight were female like presented in Table 3.
|| Sample distribution in age groups
|| General anthropometric measures analysis in the sample in
|| Waist circumference distribution
|| Body fat mass distribution in the sample
Anthropometric measurements: Waist circumference and body fat mass: From Table 4 data it could be especially important to note that among both genres, more than half of the individuals had shown high waist circumference measures which represent an high metabolic risk factor.
|| Average energy and macronutrient intake in both sexes
|| Recommended energy and macronutrient intakes for older persons
|a: Lipid intake should represent 20-35% of the total energy
|| Comparison between intakes and recommended values considering
dietary reference intakes for adults of 70 or more year old
|| Components from principal components analysis and respective
Additionally, like presented in Table 5, almost all the individuals had also shown high body fat mass indexes which was not significantly different among sexes.
Food intake assessment: Results from food assessment revealed little differences when compared to dietary recommended intakes. Men ingested in average 1851±279 kcal and women calculus had reported an average of 1527±215 kcal. In Table 6 these data could also be compared with recommended dietary intakes for this age (Table 7). There was a significant difference between male and female average caloric consumption (p<0.05).
Despite energy intakes were significantly lower (p<0.05) than the dietary reference intakes for Older People, macronutrient intake had remained within the recommended values. In fact, values reported for average protein and carbohydrate intake were higher than the reference values. Considering that there was not defined any dietary reference intake for lipids, only the recommended proportion in energy intake (20-35%), values were within this interval but could be slightly higher in order to reach the recommended caloric ingestion values. The comparison between guidelines and reported values had been resumed in Table 8.
Principal component analysis: Through principal component analysis two main components were defined: Component 1: Anthropometry and Component 2: Body fat mass. Principal components definition and relative weights of each variable are presented in Table 9.
Component 1 explains 72,07% of the total variance while Component 2 had been responsible for 19,30% of the total variance, both represent 91,37% of total variance.
Anthropometric measurements and food intake: After principal component analysis had been done, the multiple linear regression analysis had shown that only lipid ingestion was a significant predictable factor for Anthropometry.
The resulting final adjusted model was (F(1.32) = 10.4; p = 0.003; R2a
Anthropometry = 1.930+0,35xLipid intake/day
Thus, a higher lipid intake had been associated with Anthropometry variable increases with possibly higher muscular and waist circumference, BMI and weight.
However, the same analysis did any significant model for Body fat mass (p>0.05) and food intake variables.
Anthropometric data from the chosen sample had revealed a high frequency of
excessive weight and higher waist circumferences than recommended, these features
had been proven to be independently associated with hypertension in elderly
(Latiffah and Hanachi, 2008).
Results from the present study had also shown a strong association between
lipid intake and anthropometric measures like body mass index, muscular and
waist circumferences but not with body fat mass which was in accordance with
experimental data obtained by Vahdatpour et al. (2008).
Anthropometric measures in these individuals had shown similarities with previous
studies about nutritional status of elderly populations (Odenigbo
et al., 2010). In the present study energy intakes were lower than
recommended for this age but the excessive weight observed could also be due
to insufficient energy expenditure, quite frequent in elderly and possible associated
with increased risk of cardiovascular risk factors like dyslipidemia (Gharouni
et al., 2008).
The association between BMI and specific macronutrient intake was only found
on studies with diverse age groups (Randi et al.,
2007; Trichopoulou et al., 2002) and non
elderly people (Ahluwalia et al., 2009).
Most studies had used only BMI as a variable which could be an important limitation
considering that there had been reported significant height declines during
aging. Factors like vertebra compression, vertebral disc morphologic changes
and muscular tone loss were the main reasons for height decline (Corish
and Kennedy, 2003). Perissinotto et al. (2002)
reported an average height decrease of 2 to 5 cm each 10 years of age. Considering
these factors, age had been considered an important co variation factor in BMI
(Aslam et al., 2010).
Odenigbo et al. (2011) had considered waist
circumference as a best indicator for obesity than BMI in adults. Previously
Halkjaer et al. (2006) results had shown that
only protein had a significant effect in waist circumference.
Body fat mass and waist circumference had been considered better indicators
for cardiovascular disease risk (Lee et al., 2008).
In the present study both these variables were separated by the principal component
analysis procedure. While waist circumference was considered in Component 1
that was associated with lipid intake but fat mass, included in Component 2,
did not show a strong association.
Dietary fat had become a complex paradox in human nutrition. Despite being
energy dense, previous studies revealed a weak or inexistent association between
fat intake and body weight gain (Lee et al., 2008)
while excessive energy intake itself, from fat or other macronutrient, had shown
to be determinant for weight gain and could probably explain the increasing
prevalence of obesity (Swinburn et al., 2009).
In the present study, energy intake was lower than the recommended for this
age group and lipid intake was found to be within the desired range which means
there was not an excessive fat consumption.
The present study had shown that excessive weight and high body fat mass had started to affect seriously elderly people.
There was found a significant association between lipid intake and anthropometric measures but had not seem to contribute do body fat mass. Dietary lipids have a crucial role in adult nutrition, including in elderly people despite the lower energy expenditures. In one hand, fat intake should accomplish the recommended guidelines in order to satisfy the essential fatty acids needs in this population, on the other hand should not be excessively consumed in order to maintain a balanced energy intake and prevent weight gain.
Considering the possible lower food intake and the high risk of malnutrition, fat could also help to accomplish the needed and recommended energy intakes in elderly people and by this way prevent unwanted weight-loss.
Further studies should be conducted in order to evaluate the role of the different fat types and specific food group consumption on body composition.