Osteosarcopenic obesity (OSO) is described as the coexistence of 3 unfavorable conditions of body composition (low muscle mass, high body fat, and low bone mineral density [BMD]), which may result in a more aggravating condition to reduce physical mobility, and increase the risk of falls, fractures, hospitalizations, and functional disability,1–3 being observed more frequently in women.3–5
However, identification of the clinical factors of OSO, especially low BMD (osteopenia/osteoporosis), requires more accurate techniques and with a high cost, such as dual-energy x-ray absorptiometry (DXA), making it difficult to apply in epidemiological studies and public health services, particularly in developing countries.
The application of functional tests could be an alternative in the identification of OSO in older women, since they are low cost and easily accessible and applicable. Some of these tests, such as the handgrip strength test, have already been widely used to assess muscle strength and diagnose sarcopenia.6 In addition, it was observed that postmenopausal women with OSO presented higher reductions in handgrip strength and other physical abilities compared with those with other abnormalities in body composition.4
Thus, considering the complications and severity of OSO, as well as the difficulty of accessing the most accurate method (DXA) in some localities and specific situations, the importance of testing alternative identification methods is evidenced, aiming at earlier diagnosis, which will enable a reduction in the costs of medications, hospitalizations, and health care, as well as improvement in the quality of life of older women. Thus, the aim of this study was to analyze the sensitivity and specificity of functional tests in the identification of OSO, as well as other abnormalities related to low BMD in older women in a 24-month follow-up.
Study design and participant recruitment
The present study refers to a cohort performed between January 2015 and February 2017 in the city of Presidente Prudente, São Paulo State, Brazil, whose objective was to investigate the influence of physical activity on sarcopenia, sarcopenic obesity, and functional disability in older adults. Sample selection was performed by convenience sampling. Initial evaluations were carried out between January and February 2015 (baseline). Second and third evaluations were performed, respectively, in January and February 2016 (intermediate) and 2017 (final).
The minimum sample size was identified by an equation for the correlation coefficient, with a power of 80%, a 5% alpha error, and expected correlation coefficient of 0.287 between appendicular fat-free mass and physical activity.7 The equation indicated the need to evaluate at least 99 participants. Additionally, considering a conservative but possible 100% sample loss during the 24-month follow-up, the study needed to include 200 participants.
The study inclusion criteria were older adults 60 years or older living in Presidente Prudente who attended all evaluations at the laboratory. As exclusion criteria, the following factors were considered: living in a long-term institution; presence of a disease that could lead to muscle mass reduction, such as cancer, HIV/AIDS, tuberculosis, and chronic kidney disease.
Recruitment was performed at 2 moments. At the first moment, participants were invited to participate from 2 Basic Health Units indicated by the Municipal Health Department of the city of Presidente Prudente. Invitation and scheduling of evaluations were carried out when participants waited for the service or after the end of a consultation at the Basic Health Unit. Overall, 105 older adults aged 60 to 85 years were enrolled.
Subsequently, the invitation was extended to individuals from the general population. The research was disclosed in the local media and in other places of the municipality with high concentrations of older adults (health centers, social centers, and other social projects). All evaluations were scheduled by phone contact. A total of 328 calls were received from older adults interested in participating in the study. Of the 328 evaluations schevduled, 38 older adults did not attend the laboratory and 290 individuals aged 60 to 97 years were evaluated. Thus, the total initial sample (basic health units and general population) of the present study consisted of 395 older adults of both sexes.
Data collection was performed at the Center for Studies and Laboratory of Assessment and Motor Activity Prescription (CELAPAM), Department of Physical Education, FCT/UNESP.
In the following years (intermediate and final evaluations), phone calls were made to all older adults who had participated in the baseline assessments. Of the 395 older adults who started the study and presented valid evaluations, 211 (53%) finished the 24-month follow-up.
For the present study, only women were selected, due to the greater prevalence of OSO observed in women.3–5 Thus, the final sample for the present study was composed of 152 older women (Figure).
Diagnosis of abnormalities of body composition: Osteosarcopenic obesity, osteopenic obesity, osteosarcopenia, or osteoporosis
For the diagnosis of abnormalities in body composition, the presence of the following clinical factors was considered:
Osteosarcopenic obesity: (i) low muscle mass; (ii) high body fat; and (iii) low BMD.
Osteopenic obesity: (i) high body fat and (ii) low BMD.
Osteosarcopenia: (i) low muscle mass and (ii) low BMD.
Osteoporosis: (i) low BMD.
Estimation of body composition was performed using DXA equipment, Lunar brand model DPX-MD, software 4.7, which uses the model of 3 compartments (lean soft tissue, fat mass, and bone mineral). This technique enables estimation of components of total body composition and per body segment.
Appendicular lean soft tissue was measured for the diagnosis of sarcopenia, and skeletal muscle mass was estimated using the Kim et al8 equation, adjusted by height squared to create the skeletal muscle index (SMI). Low muscle mass was considered based on the lower 20th percentile, which in the present sample corresponded to an SMI below 6.09 kg/m2.
High fat was defined as values above 40% total fat mass, measured by DXA.1
DXA was also used to analyze the BMD at specific sites (total proximal femur and lumbar spine) to identify the presence of osteopenia or osteoporosis, with examinations performed following the manufacturer's recommendations.
The presence of osteopenia or osteoporosis was identified as T score values between −1.0 and −2.5 for osteopenia, and lower than −2.5 for osteoporosis.9
Handgrip strength was measured using a Camry digital dynamometer, model EH101 (Guangdong, China). The test was performed in duplicate, with the individuals sitting in a chair without arm support, with the shoulder slightly adducted, the elbow of the dominant arm flexed at 90°, and the forearm and wrist in neutral position. The older adults were instructed to press the dynamometer as strongly as possible, twice, with intervals of 1 minute between each attempt.10 The highest handgrip strength value was recorded.
The 4-m walking test was used to evaluate the gait speed of older adults. Older adults were advised to walk in a natural way, as if walking indoors, and the lower time obtained between the 2 walks was recorded and scored according to the respective time.11
To evaluate the strength/power of lower limbs, the chair stand test was applied. The subject kept their arms crossed over their chest and, at the evaluator's signal, got up from and sat back on the chair as fast as possible 5 times without pausing. In the event of a participant failing to perform the task described in less than 60 seconds, the test was discontinued.11
Timed Up and Go
To evaluate gait speed and dynamic balance, the Timed Up and Go test was applied, which consists of getting up from a chair, walking a distance of 3 m, turning around, and returning. The test was started with the individual correctly sitting on a stable chair, with their arms on the support (hips and backs completely leaning against the seat); the individual was allowed to use the arms ofthe chair to move from the sitting position to the standing position and vice versa.
At a sign from the evaluator (who started the stopwatch concomitantly), the subject got up, walked (at their usual pace) to the demarcation, walked around it, returned, and sat down on the chair again (the stopwatch was stopped at the time the subject was sitting correctly, with arms on the chair, at the end of the test).
For this test, any apparatus for walking (walking stick, walker, etc) could be used, and the subject could stop and rest as frequently as necessary during the test. However, they could not be helped by another person or sit down during the course.12
Body mass and stature were measured using an electronic scale Filizola Antropométrica (São Paulo, Brazil) and a fixed stadiometer Sanny, standard model (São Bernardo do Campo, São Paulo, Brazil), respectively. Body mass index—weight (kg)/height2 (m)—was calculated.
Information regarding the variables sex (male or female), age (age groups: 60-69 years and ≥70 years), and ethnicity (White, Brown/Black, and Asian) was obtained through interviews.
The comparison of mean and standard deviation values of the descriptive variables (baseline) according to the condition for abnormalities in body composition (final moment) was performed by 1-way analysis of variance followed by the Tukey post hoc test.
The mean value of each functional test (baseline) for each group of abnormalities of body composition (final moment) of the study was used as a reference and, posteriorly, ROC (receiver operating characteristic) curve analysis was used to identify the values of sensitivity and specificity. The area under the curve (AUC) of the cutoff points of functional tests was utilized to identify abnormalities in body composition (OSO, osteopenic obesity, osteosarcopenia, or osteoporosis). Cox proportional hazard regression analysis was used to analyze the relationship between functional tests and the risk for abnormalities in body composition adjusted for sex and age at baseline. The statistical treatment was performed using SPSS software (SPSS Inc, Chicago, Illinois), version 22.0, and the level of significance was set at 5%.
At the final moment of the study, 16%, 43%, 7%, and 10% of older women presented osteopenia/osteoporosis, osteopenic obesity, osteosarcopenia, and OSO, respectively. There were no differences between the age groups (60-69 years and ≥70 years) for the abnormalities (P > .05).
Significant differences were observed between the groups of older women with different abnormalities in body composition (final moment) for the descriptive variables (baseline): weight, body mass index, total body fat, total fat-free mass, SMI, femur BMD and T score, and spine BMD and T score; P ≤ .001 (Table 1).
TABLE 1 -
Mean and Standard Deviation Values for the Participants' Characteristics at Baseline, According to the Presence of Abnormalities at the Final Moment of the Study
||Othersa (n = 35)
||Osteoporosisb (n = 25)
||Osteopenic Obesity (n = 65)
||Osteosarcopenia (n = 11)
||OSO (n = 16)
||67.7 ± 5.8
||70.7 ± 6.8
||69.5 ± 6.8
||71.3 ± 8.2
||68.4 ± 5.8
||79.5 ± 13.5(a)d
||58.9 ± 7.5(b)
||70.9 ± 9.5(c)
||48.3 ± 5.2(d)
||59.6 ± 5.9(b)
||155.5 ± 6.7
||155.0 ± 7.3
||153.9 ± 5.7
||152.8 ± 5.6
||153.4 ± 8.3
||33.0 ± 5.6(a)
||24.5 ± 2.4(b,d)
||29.9 ± 3.8(c)
||20.7 ± 1.7(b,d)
||25.3 ± 2.0(b)
|Total body fat, %c
||46.8 ± 5.2(a)
||35.4 ± 4.5(b)
||45.7 ± 4.4(a)
||31.3 ± 4.1(b)
||45.7 ± 2.7(a)
|Total fat-free mass, kgc
||38.6 ± 6.3(a)
||35.2 ± 4.0(b)
||35.3 ± 3.4(b)
||30.3 ± 2.1(c)
||29.4 ± 2.5(c)
||7.4 ± 1.0(a)
||6.9 ± 0.6(a)
||7.1 ± 0.7(a)
||5.6 ± 0.3(b)
||5.5 ± 0.4(b)
|Femur BMD, g/cm2c
||1.0 ± 0.1(a)
||0.8 ± 0.1(b,c)
||0.9 ± 0.1(c)
||0.7 ± 0.1(b)
||0.8 ± 0.1(b,c)
|Femur T scorec
||0.3 ± 0.8(a)
||−1.4 ± 0.7(b,c)
||−2.1 ± 0.5(b)
||−1.4 ± 0.8(b,c)
|Spine BMD, g/cm2c
||1.3 ± 0.1(a)
||0.9 ± 0.1(b)
||1.0 ± 0.1(b)
||0.9 ± 0.2(b)
||1.0 ± 0.1(b)
|Spine T scorec
||0.7 ± 1.2(a)
||−2.3 ± 0.9(b)
||−1.6 ± 1.1(b)
||−2.5 ± 1.4(b)
||−1.5 ± 1.0(b)
Abbreviations: BMD, bone mineral density; BMI, body mass index; OSO, osteosarcopenic obesity; SMI, skeletal muscle index.
aNormal, sarcopenia, obesity, and sarcopenic obesity.
bOsteopenia or osteoporosis.
cP ≤ .001.
dDifferent italicized letters (a,b,c,d) indicate statistical difference.
The sensitivity and specificity as well as the accuracy of the functional test cutoffs to identify abnormalities of body composition are described in Table 2. It was observed that the handgrip strength test showed better sensitivity and specificity to identify OSO in older women.
TABLE 2 -
Sensitivity and Specificity of Functional Tests to Identify Abnormalities in Body Composition Related to Low Bone Mineral Density in Older Women
||AUC (95% CI)
Handgrip strength, kg
Gait speed, m/s
Chair stand, s
Handgrip strength, kg
Gait speed, m/s
Chair stand, s
Handgrip strength, kg
Gait speed, m/s
Chair stand, s
Handgrip strength, kg
Gait speed, m/s
Chair stand, s
Abbreviations: AUC, area under the curve; CI, confidence interval; SENS, sensitivity; SPEC, specificity; TUG, Timed Up and Go.
aOsteopenia or osteoporosis.
bP ≤ .05.
The larger the AUC, the greater the discriminatory power of functional tests to identify abnormalities of body composition. The confidence interval determines whether the predictive capacity of the functional indicator does not occur randomly. The lower limit of the confidence interval must not be less than 0.50.13 An AUC above 0.70 was considered satisfactory.13
Sensitivity refers to the proportion of true positives among all cases, and in this study it means the ability of functional tests to indicate abnormalities of body composition (OSO, osteopenic obesity, osteosarcopenia, or osteoporosis) among all those with such abnormalities. Specificity refers to the proportion of true negatives among those who do not present the studied event. In the case of this study, it means the ability of functional tests to rule out abnormalities of body composition (OSO, osteopenic obesity, osteosarcopenia, or osteoporosis) when they are missing.
High-specificity diagnostic tests can avoid unnecessary interventions and have less physical, financial, and emotional repercussions due to an incorrect diagnosis of abnormalities of body composition. On the other hand, the low sensitivity of a diagnostic test may make it an inadequate tool for tracking abnormalities of body in older adults.
In Table 3, it was observed that the older women with low performance in the handgrip strength test (≤19.60 kg)had a higher risk of OSO, independent of age.
TABLE 3 -
Risk of Osteosarcopenic Obesity in Older Women Who Presented Low Performance in the Functional Tests According to the Respective Cutoff Points
|HR (95% CI)a
||HR (95% CI)b
|Handgrip strength, kg
|Gait speed, m/s
|Chair stand, s
Abbreviations: CI, confidence interval; HR, hazard ratio; TUG, Timed Up and Go.
bModel adjusted for age.
The findings of the present study indicate that the use of the handgrip strength test may be an alternative for the screening of OSO in older women. Among the 4 tests analyzed, the cutoff point (19.60 kg) of the handgrip strength test presented satisfactory results for sensitivity and specificity, as well as AUC in the older women with OSO, with a higher value for specificity, which is important to avoid unnecessary interventions in the case of incorrect diagnosis.
There is some international consensus indicating the handgrip strength test in the diagnosis of sarcopenia in older adults and the European Working Group on Sarcopenia in Older People (EWGSOP), for example, suggesting cutoff points of 30 kg and 20 kg for men and women, respectively6; the value for women is close to that found in the present study (19.60 kg) for identification of OSO.
Considering that the occurrence of OSO involves unfavorable alterations in 3 components of body composition (muscle mass, body fat, and bone mass), that muscle mass is one of the main mediators of abnormalities such as obesity1,6 and osteoporosis,1,14 and that muscle strength is also directly related to the reduction in this tissue,15,16 it is acceptable that in assessing muscle strength, individuals with impairment in the 3 components of body composition (OSO) can be identified. In addition, previous studies have pointed to the relationship of muscle strength with the 3 clinical factors of OSO, low muscle mass,15,16 and BMD15,17 as well as high body fat15 separately.
Another important point is that studies have shown that the OSO population presents low muscle strength when compared with other groups with other body composition abnormalities. The study by Ilich et al4 evaluated postmenopausal women with abnormalities in body composition linked to excess body fat, and observed that women with OSO presented lower results in the handgrip strength test compared to those with other abnormalities, especially those with obesity alone.
Studies related to OSO are still emerging and there is as yet no defined consensus on the diagnostic criteria and their respective cutoff points. In a recent study, which related the prevalence of OSO with the fragility and physical function of older women, the authors used the criteria of the Foundation for the National Institutes of Health to screen for sarcopenia. These criteria use the muscle mass index adjusted by body mass index and muscle strength, as the authors consider this to be more appropriate for the older adult population, thus suggesting the need to include muscle strength in the identification of OSO.3
In this sense, the results of the present study, which indicated that older women with handgrip strength values lower than 19.60 kg have a higher risk of OSO, reinforce the fact that the handgrip strength test can be considered a good measure for screening OSO. New investigations are suggested to include this variable in the diagnosis.
Other tests involving physical mobility and strength/power of lower limbs, and their relationships with OSO, as well as other abnormalities in body composition related to low BMD, were also investigated in the present study. It was observed that although obesity is one of the clinical factors of OSO and is related to reduction in physical mobility,15,18 it was not possible to observe significant results for the tests (gait speed, chair stand, and Timed Up and Go) in the identification of OSO, and the same occurred for osteopenic obesity.
Regarding muscle mass, the results observed in the present study are in agreement with those in the literature, which emphasize that the coexistence of low muscle mass with other risk factors (high body fat and low bone mass) are more detrimental to functionality, increasing the risk of disability in older adults.3,4
As a caveat, it is worth mentioning the reduced sample size of the present study. However, as a strong aspect, we highlight the longitudinal design (24 months).
The application of the handgrip strength test, a simple and low-cost test, could be an alternative instrument for identification of OSO in older women, to be used in health services and field research.
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