Journal of Gerontology & Geriatric Medicine Category: Medical Type: Research Article

Body Mass Index in the Elderly and its Correlation with Anthropometric Parameters Evaluated by Body Impedance

Alexander Morales-Eraso1*, Diana Astaiza2 and Mayra Ayala2
1 Grupo de Investigación en Gerontología y Geriatría, Universidad de Caldas, Pasto, Colombia
2 Centro de neurorehabilitación Juntos, Pasto, Colombia

*Corresponding Author(s):
Alexander Morales-Eraso
Grupo De Investigación En Gerontología Y Geriatría, Universidad De Caldas, Pasto, Colombia
Tel:+57 3155820887,
Email:alexandermoraleserazo@gmail.com

Received Date: Feb 20, 2023
Accepted Date: Mar 03, 2023
Published Date: Mar 10, 2023

Abstract

The results of the evaluation are presented to a group of older adults in whom the Body Mass Index (BMI) and anthropometric measurements determined by impedance were evaluated. 124 people aged 60 years and over were included. In the results found in this group of older adults, an adequate correlation was found between the BMI and the measurements of the waist-hip index, the percentage of body fat and the degree of obesity, all of them parameters related to the metabolic syndrome and cardiovascular mortality. These findings suggest that elevated BMI appears to be a reliable marker of metabolic risk in the study population.

Keywords

Body mass index; Cardiovascular risk; Elderly; Impedance measurement

Key messages

  • With increasing age, structural changes in body compartments occur that can modify the nutritional assessment
  • The body mass index has been a widely used measurement to estimate the amount of body fat and it retains its usefulness in the elderly
  • The anthropometric evaluation obtained by impedance measurement adequately complements the traditional nutritional evaluation in the elderly

Introduction

Due to the increase in older adults in the population, it is essential to define whether the evaluations used to assess nutritional status are applied in the same way and have the same utility in the elderly. At the level of the general structure, when aging, the human being presents a decrease in total body water, a decrease in muscle mass, an increase in fat mass [1] and loss of height due to dehydration of the intervertebral discs [2]. In relation to weight, an increase has been documented up to the age of 50, stabilization for a decade and weight loss from the age of 60 [3]. While in young adults the proportion of muscle mass represents up to 45% of body weight, in the elderly it is reduced to 27% [4]. 

Obesity, defined as the unhealthy accumulation of body fat, is a relevant medical condition as it increases the risk of metabolic syndrome, type 2 diabetes mellitus, cardiovascular disease, and increases all-cause mortality [5]. Although there is certainty of the harmful implications for health derived from obesity and metabolic syndrome; there are no standardized criteria to define metabolic health, thus making it difficult to compare between different studies [6]. 

The World Health Organization (WHO) has defined obesity as the presence of a Body Mass Index (BMI) equal to or greater than 30 kg/m2 [7]. The advantage offered by this measurement lies essentially in the ease of obtaining it; however, there is controversy regarding its usefulness to assess the elderly, since it is a generic measure used for both sexes and independent of age. On the other hand, its use in geriatric patients is still discussed, taking into account the physiological changes in this stage of life. In this sense, changes in body fat, muscle mass, water composition, and height are variables that ostensibly modify BMI parameters, which is why the evaluation of body composition has recently been defined as a better marker of health than weight or BMI isolated [8]. 

Bioelectrical Impedance Analysis is a widely used and non-invasive method that is proposed as an alternative in the assessment of body parameters without the limitations of BMI. It is plausibly proposed as a reliable and highly accurate tool, since it allows estimating body composition from body resistance to the passage of a small electrical current [9]. In this sense, we propose an investigation that aims to evaluate in a population of older adults, the correlation of BMI with other anthropometric variables obtained through direct measurement and body impedance measurement and the implications in terms of global cardiovascular risk.

Materials and Methods

A descriptive correlational study was carried out, for which the information of 124 patients older than 60 years of the Geriatric outpatient service of the “Instituto de aging de Nariño”, located in the city of Pasto (Colombia), in the period of study, was included the months of July to December 2022. 

Anthropometric and body composition variables were measured in these patients by impedancemetry in the 5 segments (Right Arm, Left Arm, Trunk, Right Leg, Left Leg) (In Body 120-DSM-BIA Segmental Multifrequency. 20kHz, 100kHz). Circumferences were taken using a tape measure, using the standard technique, and height using a wall-mounted stadiometer, both were reported in centimeters. The evaluation period was from January 1 to July 30, 2020. For the continuous variables, the means and standard deviation were calculated, and for the discrete variables, medians and ranges. The Shapiro-Wilks or Kolmogorov-Smirnov tests were used as normality tests. Subsequently, a Pearson correlation analysis was performed for the variables with normal distribution and Spearman for those that did not meet this condition. Initially, an analysis of the general population was carried out, followed by a subgroup analysis categorized by age. For the correlation analysis, the following parameters were established: high correlation: 0.70-0.99; moderate correlation: 0.50-0.69; weak correlation: 0.20-0.49) and very weak correlation: 0.09-0.19. 

The data were analyzed with the statistical program SPSS version 25. The participants signed authorization for the procedure and for the use of the data collected for research purposes.

Results

Of the 124 study participants, 96 (corresponding to 77.4%) were women. The most frequent age group was 60 to 79 years. The gender distribution in the age groups is shown in table 1. 

Age group

Gender

no

%

60-79

Man

19

22.9

Women

64

77.1

Total

83

100.0

80+

Man

9

22.0

Women

32

78.0

Total

41

100

Table 1: Sociodemographic characteristics of the study population categorized by age. 

The anthropometric parameters of the impedance measurement are shown in table 2. 

Variable

Half

Median

Standard deviation

Minimum

Maximum

Range

 
 

BMI

27.23

26.90

4,407

17

43

26

 

Age

74.68

73.50

9,261

60

96

36

 

Weight (kg)

64.95

64.70

11,907

41

106

64

 

Height (cm)

154.41

154.00

9,479

136

186

fifty

 

Waist Circumference (cm)

95.24

96.00

10,298

65

123

58

 

Hip Circumference (cm)

101.11

101.50

8,340

80

127

47

 

Waist Hip Index

.92

.93

.059

1

1

   

Body Fat Mass (kg)

25.39

25.00

7,951

10

49

39

 

Skeletal Muscle Mass (kg)

21.29

20.75

4,592

13

37

24

 

Body Fat Percentage

38.62

39.10

7,813

18

53

35

 

Basal Metabolic Rate (Cal)

1223.94

1203.00

164,706

944

1793

849

 

Degree of Obesity

126.05

124.50

20,767

79

202

123

 

Table 2: Anthropometric characteristics of the study population. 

When comparing by age groups, all anthropometric variables are higher in the 60 to 79-year-old group, in absolute values, although a difference measure was not obtained (Table 3). 

Age group

Variable

Half

Median

Standard deviation

Minimum

Maximum

Range

60-79

BMI

27.75

27.30

4,056

19

38

19

Weight (kg)

68.00

67.70

11,355

46

106

60

Height* (cm)

156.53

155.00

8,764

141

186

Four. Five

Waist Circumference (cm)

95.72

96.00

10,996

65

123

58

Hip Circumference (cm)

102.11

102.00

8,087

80

126

46

Waist Hip Index

.93

.93

.056

1

1

 

Body Fat Mass

26.59

26.30

8,141

eleven

49

38

Skeletal Muscle Mass

22.47

21.40

4,487

14

37

23

Body Fat Percentage

38.63

39.60

8,213

18

52

35

Basal Metabolic Rate

1264.01

1228.00

162,035

967

1793

826

Degree of Obesity

128.44

126.00

19,189

89

178

89

80+

BMI

26.21

25.60

4,923

17

43

26

Weight (kg)

58.93

59.20

10,722

41

84

43

Height (cm)

150.22

149.00

9,543

136

175

39

Waist Circumference (cm)

94.29

95.00

8,810

74

110

36

Hip Circumference (cm)

99.12

97.00

8,574

86

127

41

Waist Hip Index

.91

.91

.062

1

1

 

Body Fat Mass

23.03

22.40

7,077

10

44

3. 4

Skeletal Muscle Mass

18.95

18.00

3,887

13

28

fifteen

Body Fat Percentage

38.61

38.10

7,054

24

53

29

Basal Metabolic Rate

1144.78

1106.00

140,986

944

1469

525

Degree of Obesity

121.32

119.00

23,101

79

202

123

Table 3: Analysis by age subgroups of the anthropometric characteristics. 

When analyzing the correlation values in the total group, the BMI is found to have a highly positive correlation with the waist-hip ratio, body fat mass, percentage of body fat and degree of obesity. There is a low correlation, although it remains significant with skeletal muscle mass and basal metabolic rate. There is no correlation with height (Table 4). 

Variable

BMI

Pearson correlation

Sperman's correlation

t-Student

p-Value

R.

R2

p-Value

R.

R2

p-Value

Gender

           

0.017**

Age*

     

0.022

-0.205

0.042

 

Weight

0

0.742

0.551

       

Height*

     

0.053

-.174

0.030

 

Waist Circumference*

     

0

0.749

0.561

 

Hip*

     

0

0.801

0.642

 

Waist Hip Index

0

0.89

0.792

       

Body Fat Mass

0

0.917

0.841

       

Skeletal Muscle Mass*

     

0.005

0.248

0.062

 

Body Fat Percentage

0

0.753

0.567

       

Basal Metabolic Rate*

     

0.005

0.249

0.062

 

Degree of Obesity

0

0.998

0.996

       

Table 4: Correlation between BMI and anthropometric and demographic variables (Total population).

*The variable does not have a normal distribution **Equal variances are assumed (p < 0.05 Levene’s test). 

When analyzing by age groups, the values of the anthropometric variables are very similar to the total population, except that the significance of the correlation between skeletal muscle mass and basal metabolic rate is lost in the 60 to 79-year-old group (Table 5). 

Variable

Correlation with BMI

Age=60-79

Age=80+

Pearson correlation

Sperman's correlation

t-Student

Pearson correlation

Sperman's correlation

t-Student

p-Value

R.

R2

p-Value

R.

R2

p-Value

p-Value

R.

R2

p-Value

R.

R2

p-Value

Gender

           

0.025**

           

0.278**

Weight

0

0.736

0.542

       

0

0.757

0.573

       

Height*

     

0.026

-0.244

0.060

 

0.034

-0.331

0.110

       

Waist Circumference

0

0.761

0.579

       

0

0.696

0.484

       

Hip Circumference

0

0.796

0.634

             

0

0.810

0.656

 

Waist Hip Index

0

0.899

0.808

       

0

0.867

0.752

       

Body Fat Mass (kg)

0

0.921

0.848

       

0

0.933

0.870

       

Skeletal Muscle Mass (kg)*

     

0.163

0.154

0.024

       

0.03

0.339

0.115

 

Body Fat Percentage

0

0.769

0.591

       

0

0.779

0.607

       

Basal Metabolic Rate (cal)*

     

0.168

0.153

0.023

       

0.022

0.357

0.127

 

Degree of Obesity

0

0.998

0.996

       

0

0.999

0.998

       

Table 5: Correlation between BMI and anthropometric and demographic variables (Analysis of subgroups by categorized ages).

*The variable does not have a normal distribution **Equal variances are assumed (p < 0.05 Levene’s test).

Discussion

In older adults, significant changes in body composition occur, which are derived, among others, from metabolic modifications as well as imbalances between energy intake and the demands of sedentary lifestyles [10]. These changes facilitate the appearance of overweight-obesity, which is considered the most frequent nutritional disorder in older adults in Western populations [11]. Between the second and ninth decades the percentage of body fat increases from 35 to 50%. In addition, with aging there is a redistribution of fat from peripheral to central, which is more associated with cardiovascular risk and metabolic syndrome [12]. In the results found in this group of older adults, an adequate correlation was found between the BMI and the measurements of the waist-hip ratio, the percentage of body fat and the degree of obesity, all of them parameters related to metabolic syndrome and cardiovascular mortality. These findings suggest that regardless of the changes in the physiology of geriatric patients, elevated BMI appears to be a reliable marker of metabolic risk in the study population. A direct relationship between the degree of obesity, the presence of morbidity and functional deterioration related to the metabolic and mechanical detriments produced by excess fat has been evidenced, highlighting the concept of sarcopenic obesity [13,14]. In this investigation, a lower correlation of BMI with skeletal muscle mass was demonstrated, which suggests that BMI is a parameter that, as expected, does not contribute significantly to assessing sarcopenia. 

The decrease in muscle mass and the increase in body fat are important factors in the functional limitation and disability of elderly patients [15]. On the other hand, the coexistence between obesity and sarcopenia can have a deleterious potentiating effect, constituting a pathophysiological continuum that increases general morbidity and mortality, particularly in old age [13,16,17]. For this reason, the comprehensive geriatric assessment incorporates other elements such as the measurement of strength, gait speed or instruments such as the “short battery of physical performance” (SPSS), which allow assessment of sarcopenia and its functional repercussions. 

The determination by impedancemetry of anthropometric parameters, together with the functional evaluation, allow the development of rehabilitation programs that promote the gain of muscle mass and the control of adipose mass and, importantly, carry out a control evaluation to verify the achievement of the proposed goals initially. In this sense, in addition to dietary programs, there is a beneficial effect of physical conditioning programs. Thus, for example, in a study conducted by Moreno et al., in older people from 13 regions of Colombia, an association was demonstrated between physical exercise and increased muscle strength and functional capacity, as well as an inverse relationship with body fat [18]. 

The relationship between the degree of obesity and outcomes in the elderly is controversial, some mention possible benefits of a lower degree of overweight, including a reduction in the risk of fractures [19], a disputed advantage in survival, referred to by some as the “obesity paradox” or also in reference to the increase in energy reserves during prolonged periods of illness [20]. On the other hand, in healthy elderly people in the community, the behavior between BMI and risk of all causes of mortality takes the form of a “J-curve”, with its extreme low values of 18.5 or less. At the high end, the risk of death appears to be lower in older adults who are overweight (BMI 25-30) or moderately obese (BMI 30-35) [21]. 

However, there is a more or less general consensus that obesity at all ages significantly increases mortality attributable to cardiovascular disease [22]. Besides the Mortality risk in the obese older adult is more related to fat distribution than to total body fat [22], which suggests that at any given weight, the risk of death increases with increasing waist circumference, a measure that is easy to perform and that in this study was correlated with the BMI. The importance of measuring not only the BMI but also the abdominal perimeter and the waist-hip ratio is then highlighted, in the measurements carried out by nutritionists and doctors in the physical examination of geriatric patients. Impedance analysis adds valuable data to traditional anthropometric assessment, such as body fat mass, skeletal muscle mass, body fat percentage.

Conclusion

In this group of older adults, it was found that the BMI is a measure that correlates well with the anthropometric variables obtained by impedancemetry, from which it can be inferred that the BMI retains its usefulness as an estimator of nutritional status, coupled with clinical and laboratory assessment, if relevant. The early and periodic measurement of these parameters is intended to carry out multimodal interventions that allow modifying the risk.

Author’s Contributions

All authors participated in the preparation of the manuscript and in its final revision.

Conflict of Interest

None of the authors declare conflicts of interest in the development and preparation of this research.

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Citation: Morales-Eraso A, Astaiza D, Ayala M (2023) Body Mass Index in the Elderly and its Correlation with Anthropometric Parameters Evaluated by Body Impedance. J Gerontol Geriatr Med 9: 169.

Copyright: © 2023  Alexander Morales-Eraso, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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