Association between fitness, anthropometric indices and laboratory parameters in elderly women

Background : Aging causes an involution of anthropometric and health indices that can affect physical fitness. Aim: To determine the influence of anthropometric and health indices on the physical fitness of elderly women. Material and Methods: Anthropometric parameters, serum lipids, blood glucose and physical fitness evaluated using Senior Fitness Test, were assessed in 140 women aged 70 ± 5 years. The association between parameters was analyzed using Pearson’s correlation coefficient and multiple regression models. Results: In the regression models serum lipids and the suprailiac skinfold were significant predictors of the up and go test (R 2 = 0.48). HDL cholesterol and the level of physical activity were predictors of the two minutes step test (R 2 = 0.31). Serum lipids, suprailiac skinfold and age were predictors of the back-scratch test (R 2 = 0.41). Fasting blood glucose and HDL cholesterol were predictors of the chair sit and reach test (R 2 = 0.24). Serum lipids and body mass index were predictors of the arm curl test (R 2 = 0.37). Body mass index and serum lipids were predictors of the chair stand test (R 2 = 0.49). Conclusions: Anthropometric variables, serum lipid levels and blood glucose were predictors of different physical fitness parameters in these women. (Rev Med Chile 2020; 148: 1742-1749)

Ageing is related to the appearance of sarcopenia, loss of muscle strength, deterioration of flexibility, and decreased aerobic endurance, which are morphophysiological changes capable of deteriorating the physical fitness of people and directly affecting their independence and functionality 3,4 . Modifications in body composition have also been reported, which leads to greater accumulation of adipose tissue towards the abdominal region 5 . The increase in visceral fat is associated with a higher risk of metabolic diseases and mortality in older women 6 .
One way to quantify changes in body composition is by using anthropometric indices such as body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHR), and percentage of fat mass 7 . These anthropometric variables, in addition to having broad validity, have additionally been associated with loss of physical fitness and functionality 8 . Several studies point to the BMI, WC, and WHR as strong indices of loss of physical fitness; however, not all authors agree 9,10 .
Otherwise, it has been pointed out that women over 60 years of age maintain an inadequate epidemiological profile, characterized by unfavorable health indices, which become risk factors for the maintenance of functionality 11 . Recent literature shows that older women have a high incidence of dyslipidaemia and type II diabetes mellitus 12 , in addition to poor physical activity and dedicating several hours of the day to seden-tary activities [13][14][15] . These factors, as a whole, are considered risk factors for the development of chronic non-communicable diseases and the loss of physical fitness 12,14,16 .
Physical fitness corresponds to the physiological ability to perform activities of daily living with normality that depends on the compensation of skills, such as muscle strength, aerobic endurance, flexibility, agility, and dynamic balance 17 . Some health indices, including anthropometric ones, can individually modify the physical fitness of the elderly; however, to date, the works that show how these indices could be related to each other, or conjugated, are limited to the positive or negative influence of physical fitness on the elderly. Therefore, the objective of this study was to determine the influence of anthropometric and health indices on the physical fitness of elderly women.

Study design
Quantitative research of transversal design was carried out. The sample was obtained through a non-probabilistic sample for convenience, constituted by 140 older women belonging to six community centers in the city of Talca, who met the study selection criteria and were available to participate in the study. Inclusion criteria encompassed: a) seniority greater than or equal to one year in the senior center, b) female sex between the ages of 65 and 75, c) be independent. Women with a score ≥ 43 points in the Functional Examination of the Older Adults (EFAM-Chile) were considered independent and d) lipid profile and fasting glycemia tests performed in the last three months.
tional performance or were classified as dependent according to EFAM-Chile (≤ 42 points), a history of surgery in the six months prior to the study, or any chronic uncontrolled disease were excluded.
All women evaluated agreed to participate voluntarily in the study and signed an informed consent form authorizing the use of information for scientific purposes. The research protocol was approved by the Ethics Committee of the Santo Tomás University (Nº 41/2017), which verified that the procedures followed the ethical considerations of the Helsinki declaration.

Anthropometric indices
Participants were evaluated barefoot and in light clothes in a room with the necessary conditions to protect their health and privacy. Body weight was determined using Scale-tronix 5002 mechanical portable scale (Welch Allyn ® , New York, USA) (0.1 kg accuracy).Height was measured in a bipedal position using a stadiometer with a Seca 217 portable scale (Seca, Hamburg, Germany) (0.1 cm accuracy). The BMI was then calculated according to the internationally established criteria, which indicate dividing the body weight by the biped square height (kg /m 2 ) 18 .
Abdominal adiposity was determined through WC measurement using a Sanny brand tape measure (Sanny, Sao Paulo, Brazil) (0.1 cm accuracy), with the individual standing and taking as an anatomical reference the midpoint between the iliac crest and the last rib 19 . WHR was measured by dividing the WC by standing height 20 . Finally, the fat mass was obtained by measuring the bicipital, tricipital, subscapular, and suprailiac skin folds using a Lange Skinfold model C-130 caliper (Creative Health Products, Inc., Ann Arbor, USA) (0.5 mm accuracy), then calculating the percentage of fat mass using the Durnin and Womersley equation 21 . All measurements were taken by trained health professionals.

Laboratory parameters
Participants were asked for their blood glucose and lipid profile tests. The latter included total cholesterol, triglycerides (TG), high-density lipoprotein cholesterol (c-HDL), low-density lipoprotein cholesterol (c-LDL), and atherogenic index. The atherogenic index was calculated by dividing the values of high-density triglycerides/ lipoproteins (TG/c-HDL) 22 .

International Physical Activity Questionnaire
The level of physical activity was measured through the short version of the International Physical Activity Questionnaire (IPAQ). Total physical activity was expressed continuously in metabolic-energy equivalents (METs) 23,24 .

Physical fitness
The physical fitness was determined according to the protocol of evaluations of the Senior Fitness Test 25 . The order of application of the tests included in the battery were: a) Chair Stand Test to assess the strength of the lower body, counting the number of repetitions in 30 s; b) Arm Curl Test to assess the strength on the upper body, using a 3-lb (women) and 5-lb (men) dumbbell, counting the number of repetitions in 30 s; c) a Two-minute Step Test to assess aerobic fitness, registering the number of knee elevations; d) Chair sit and reach Test to assess the flexibility of the lower body, measured in cm; e) Back-scratch Test to assess the flexibility of the upper body, measured in cm; and f) Up-and-go Test to assess agility and dynamic balance, surrounding a cone at 8 feet (2.44 m) and registering the time in seconds 25 .

Statistical analysis
The Statistical Package for Social Science (SPSS) version 23.0 was used for all analyses. The variables were subjected to the Kolmogorov-Smirnov normality test and a descriptive analysis calculating the mean, standard deviation (SD), and their respective 95% confidence intervals. A multiple linear regression model was applied using stepwise method to determine the influence of anthropometric and health indices on the physical fitness of the participants. All analyses were adjusted for age and BMI. The level of statistical significance was defined as p < 0.05.

Results
The results of the anthropometric measurements are shown in Table 1. It can be seen that the average age of the participants was 69.50 ± 4.64 years. The results of the health and fitness indices are shown in Table 2.
The variables that were significant for the multiple linear regression models are observed in Table 3. For the Up-and-go Test, the variables that were significant were the total cholesterol c-HDL and suprailiac fold. This model had an explanation level of 47.7%. In the Two-minute Step Test, the model that was significant included the variables c-HDL and level of physical activity, with an explanation level of 31.0%.
In the flexibility tests, the level of explanation of the models was 41.4% and 24.1% for the Back-scratch Test and the Chair sit and reach Test, respectively. For the Back-scratch Test, the model indicated that the variables of age, total cholesterol, c-HDL, and suprailiac fold together influence the performance of the test. For its part, no significant model was found for the Chair sit and reach Test.
In the Arm Curl Test, the variables that were significant in the multiple linear regression model were the c-LDL and c-HDL, with an explanation level of 37.1%. Finally, in the Chair Stand Test, the model that was significant included the variables of body weight, BMI, c-LDL, and c-HDL, with an explanation level of 49.2%.

Discussion
The results of this study reveal that there is an influence of anthropometric and health index on the physical fitness of elderly women belonging to community centers based on models obtained through a multiple linear regression analysis. Specifically, the variables that were associated with a low dynamic balance performance and flexibility were high plasma total cholesterol and greater thickness of the suprailiac fold, adding to these a higher age in the model of the variable of flexibility. On the other hand, in the upper and lower body strength, it was found that the increase in BMI and LDL-c is associated with a lower performance of this aspect of physical fitness. In addition, it was noted that the level of physical activity is an explanatory factor of aerobic endurance. An interesting finding of this study is that c-HDL proved to be a factor that is favorably associated with the performance of all the tests evaluated in the Senior Fitness Test, being significant in all the models obtained from the aforementioned variables.
This research revealed that anthropometric Fitness and anthropometic and biochemical indices in older women -Y. Concha-Cisternas et al indices (higher BMI and greater thickness of the suprailiac fold), and health (high c-LDL and total cholesterol, in addition to low c-HDL) related to the accumulation of adiposity are key factors associated with low performance in physical fitness tests in elderly women. It has been suggested that people who are overweight and obese have limited motor performance due to the morphological changes they suffer from increased body weight, mainly due to abdominal adiposity 26,27 . These changes would cause biomechanical movement restriction that would make it difficult to carry out activities that involve changes in the position of the center of mass, for example, in the dynamic balance Up-and-go Test. A recent study indicates that adiposity would affect the performance of gross motor skills but not fine motor tasks (since it does not involve major changes in the center of mass), which would support the hypothesis of morphological restriction 28 . Likewise, it is believed that the deterioration of physical fitness caused by excess adiposity may result from the inability to keep postural stability 26 . This could be explained by the accumulation of adipose tissue in the vicinity of the joints would increase the inertia of the body segments, affecting joint stiffness and limiting the range of motion. As a result, people with excess weight may have less coordination and, consequently, greater difficulty in performing motor tasks related to physical fitness. In addition, the limitation of the range of motion due to accumulation of adipose tissue in the body areas near the joints could be the cause of the poor performance in the test of joining the hands behind the back observed in our study.
Regarding the low performance of muscle strength in people with greater adiposity, it has been proposed that the accumulation of fat mass could alter the normal mechanisms of force development, due to physiological and neuromuscular changes 29 . Some authors have argued that the myoelectric manifestations related to poor motor behavior are a response of the central nervous system to electrochemical imbalance in muscle fiber, and the reduction in the speed of propagation of intracellular action potential 30,31 . In this sense, myoelectric manifestations related to the generation of force in older adults would be enhanced or exacerbated in the presence of intramuscular and subcutaneous fat. In addition, it can also be noted that people with greater adiposity have less muscle mass; therefore, there is a reduction in muscle strength. A significant relationship between the fat mass and the expression of proinflammatory cytokines in the muscle has also been observed, which could reduce electrochemical balance and neural conductivity 32 . Likewise, it has been seen that overweight individuals have alterations in muscle activation patterns 33 . This would directly affect muscle strength due to lower efficiency in the recruitment of motor units. It is likely that in the elderly women evaluated in our study the anthropometric (higher BMI) and health variables (high c-LDL and low c-HDL) related to the accumulation of adiposity negatively influenced the performance of the strength tests of the Senior Fitness Test.
In general, the literature indicates that people with healthy behaviors, such as regular physical activity, have greater cardiorespiratory capacity and better health, and therefore lower risk of cardiovascular disease 34 . In addition, it has been described that people with high levels of physical activity have higher concentrations of c-HDL 35 . On the contrary, a high concentration of total cholesterol and c-LDL has been associated with unhealthy behaviours such as overweight, obesity, and low levels of physical activity 35,36 . Of the plasma lipids, c-HDL and TG are the most sensitive molecules to change their concentration by physical activity. The decrease in the concentration of c-HDL due to sedentary lifestyle is due, among other causes, to the decrease in activity and the amount of the enzyme that limits the catabolism of lipoproteins, specifically lipoprotein lipase, and also to the increase in activity of lipoprotein liver lipase 37 . Both changes favor the decrease in cholesterol and the increase in TG and its recapture by hepatocyte 38 . On the other hand, physical activity promotes the reverse process, since by increasing the activity and mass of lipoprotein lipase, as well as decreasing the activity of hepatic lipoprotein lipase, the increase in c-HDL is favored 39 . Other mechanisms that contribute to the increase in the concentration of c-HDL by exercise are the stimulus in the synthesis of Apo AI apoprotein (structural protein of HDL) and formation of preß1-HDL (nascent HDL), as well as the increase in the enzymatic activity of lecithin: cholesterol acyltransferase (LCAT, cholesterol esterifying protein in HDL) 37,40 . This is related to the results of our study, where it was possible to observe the joint influence of the level of physical activity and c-HDL on the performance in the 2-minute walk test of the older women evaluated. In addition, it would help to understand the influence observed in our c-HDL results on the performance of the physical tests evaluated by the Senior Fitness Test.
Among the limitations of this study is the selection of participants from a non-probabilistic sampling, which may restrict the external representativeness of the study, and although data related to physical activity levels were collected using validated instruments, they could not present the actual conditions of the participants. The cross-sectional nature of the study does not allow establishing causality in associations.

Conclusion
Anthropometric and health indices are associated together with the physical fitness of older women. This fact suggests encouraging the adequate control and management of the anthropometric and health indices related to the deterioration of physical fitness and, at the same time, encouraging actions that favor the improvement of physical capacities for the benefit of greater autonomy and functional independence in old age.