The Use of the Arm Circumference as a Measure to Detect Underweight in Individuals Aged 60 Years or Older Living in Costa Rica

Circunferencia del brazo como medida para detectar bajo peso en personas de 60 años o más residentes en Costa Rica

Circunferência do braço como medida para detectar baixo peso em pessoas com 60 anos de idade ou mais que vivem na Costa Rica

Eduardo Aguilar-Fernández1*, Xinia Fernández-Rojas2

Received: Jun/27/2022 • Accepted: Mar/9/2023 • Published: Sep/1/2023

Abstract [Objective]: This investigation focuses on the association between arm circumference and body mass index, and the estimation of cutoff values of this arm measurement for identifying low weight in Costa Rican residents aged 60 years and over. [Methods]: The study included a total of 2514 persons 60 years old or older who participated in the project “Costa Rica: Study of Longevity and Healthy Aging.” The analysis included calculation of Spearman’s correlation coefficient between arm circumference and the body mass index, the area under the Receiver Operating Characteristic (ROC) curve, and sensitivity and specificity values for measurements of arm circumference corresponding to a body mass index lower than 22 kg/m2. [Results]: The Spearman’s correlation coefficient between the body mass index and the arm circumference was 0.794 (0.774 for men and 0.806 for women). The optimum cutoff point was estimated at 26.5 cm, with a Youden’s Index of 0.7256, a sensitivity of 87.79 %, and a specificity of 84.77 %. Specific cutoff points for men and women were 26.5 cm and 25.9 cm, respectively. [Conclusions]: There is a strong relationship between arm circumference and body mass index in Costa Rican residents aged 60 years and over. A first approach was established to determine an adequate cutoff point in the measurement of arm circumference that will allow the detection of persons with low weight and greater nutritional risk in this population. Keywords: Body mass index; elderly; nutritional status; sensitivity; specificity. Resumen [Objetivo]: Estudiar la asociación entre la circunferencia del brazo y el índice de masa corporal así como estimar valores de corte de esta medida del brazo para detectar bajo peso en personas residentes en Costa Rica. [Métodos]: El estudio consideró 2514 personas de 60 años o más participantes en el proyecto Costa Rica: Estudio de longevidad y envejecimiento saludable. Los análisis incluyeron el cálculo de coeficientes de correlación de Spearman entre la circunferencia del brazo y el índice de masa corporal, el área bajo la curva Característica Operativa del Receptor y valores de sensibilidad y especificidad para medidas de la circunferencia del brazo, correspondientes al índice de masa corporal menores a 22 kg/m2. [Resultados]: el coeficiente de correlación de Spearman entre el índice de masa corporal y la circunferencia del brazo fue de 0.794 (0.774 para hombres y 0.806 para mujeres). Además, el punto de corte óptimo se estimó en 26.5 cm, dado un Índice de Youden de 0.7256, una sensibilidad de 87.79% y especificidad de 84.77%. Los puntos de cortes específicos para hombres y mujeres fueron 26.5 cm y 25.9 cm, respectivamente. [Conclusiones]: existe alta relación entre la circunferencia del brazo y el índice de masa corporal en la población de 60 años o más residente en Costa Rica y se estableció un primer acercamiento hacia la determinación de un punto de corte adecuado en la medida de la circunferencia del brazo, para detectar personas con bajo peso y mayor riesgo nutricional en esta población costarricense. Palabras claves: especificidad; estado nutricional; índice de masa corporal; personas mayores; sensibilidad Resumo [Objetivo]: estudar a associação entre a circunferência do braço e o índice de massa corporal, bem como estimar os valores de corte desta medição do braço para detectar baixo peso em pessoas que vivem na Costa Rica. [Métodos]: O estudo considerou 2514 pessoas com 60 anos ou mais participando do projeto “Costa Rica: Estudo da Longevidade e do Envelhecimento Saudável”. As análises incluíram o cálculo dos coeficientes de correlação Spearman entre a circunferência do braço e o índice de massa corporal, a área sob a curva de características de operação do receptor, e os valores de sensibilidade e especificidade para medições da circunferência do braço correspondentes ao índice de massa corporal inferior a 22 kg/m2. [Resultados]: O coeficiente de correlação do Spearman entre o índice de massa corporal e a circunferência do braço foi de 0,794 (0,774 para homens e 0,806 para mulheres). Além disso, o ponto de corte ideal foi estimado em 26,5 cm, dado um índice de Youden de 0,7256, uma sensibilidade de 87,79% e uma especificidade de 84,77%. Os pontos de corte específicos para homens e mulheres eram 26,5 cm e 25,9 cm, respectivamente. [Conclusões]: existe uma alta relação entre a circunferência do braço e o índice de massa corporal na população com 60 anos ou mais que vive na Costa Rica, e foi estabelecida uma primeira abordagem para a determinação de um ponto de corte adequado na medição da circunferência do braço, para detectar pessoas com baixo peso e maior risco nutricional nesta população costarriquenha. Palavras-chave: especificidade; estado nutricional; índice de massa corporal; pessoas idosas; sensibilidade

Introduction

Anthropometric characteristics are related to nutrition, genetic composition, environmental characteristics, social and cultural conditions, lifestyle, functional status, and health (Sánchez-García et al., 2007). On the other hand, the deterioration in the nutritional status of older adults is a complex, multidimensional phenomenon. It involves physical and psychological aspects and is exacerbated by an a reduction in autonomy, loneliness, and chronic diseases, which impacts the quality of life of this population group (Chen et al., 2001).

Body mass index (BMI) has been recognized as an appropriate measure for evaluating the nutritional status of individuals (World Health Organization, 1985), as it is inexpensive, non-invasive, and does not require a high level of expertise to be collected (Nube et al., 1998). However, this tool as a measuring instrument has been associated with certain constraints. In cases where individuals have mobility impairments, it may not always be feasible to measure weight and height accurately (Goswami et al., 2018; Sultana et al., 2015). Moreover, in field settings, it is often difficult to handle the equipment needed to assess weight and height (Das et al., 2020).

Considering these limitations, the measurement of the upper arm circumference, at the midpoint, between the olecranon and the acromion (arm circumference) has been proposed as an alternative to assess nutritional and health status in older adults (Goswami et al., 2018; Selvaraj et al., 2017; Thorup et al., 2020; Wijnhoven et al., 2012). This is because changes in this circumference have been associated with variations in weight (Tsai & Chang, 2011), the measurement is considered easier to implement than the BMI, requires the use of fewer resources (Shi et al., 2020), and has acceptable sensitivity and specificity for detecting underweight (Chakraborty et al., 2011).

The purpose of the study was to study the association between arm circumference and BMI. The study also aimed to estimate the cut-off values of this arm measurement to detect underweight in people aged 60 years or older living in Costa Rica.

Methodology

Type of Study

This study is a non-experimental, cross-sectional, and correlational design.

Study Population

Costa Rica: A Study of Longevity and Healthy Aging (CRELES) is a longitudinal study that considers a representative sample of individuals living in Costa Rica, regardless of their nationality, who were born before 1946; that is, who were 60 years of age or older at the time of the first interview. CRELES was developed by the Centro Centroamericano de Población (CCP—Central America Population Center) and the Instituto de Investigaciones en Salud (INISA—Institute of Health Research) of the University of Costa Rica, in collaboration with other institutions such as the Caja Costarricense del Seguro Social (CCSS—Costa Rican Social Security Fund) and the Consejo Nacional de la Persona Adulta Mayor (CONAPAM—National Council for Older Adults), with funding from the Welcome Trust Foundation, and was approved by the Scientific Ethics Committee of the University of Costa Rica at its March 17, 2004 session (ref: VI-763-CEC-23-04), research project number 828-A2-825, which conducted about 3000 interviews, and the first round took place between November 2004 and September 2006 (Rosero-Bixby et al., 2013). This research project includes 2514 individuals aged 60 years or older who participated in the first round of interviews and provided complete information on all the variables considered in the study.

Anthropometric Measurements

Body weight was measured with shoes and any items in pockets removed, using a Life Source, M&D medical, model UC-321p scale, placed on a flat and carpetless surface. The height measurement was taken using a Seca brand stadiometer and was not conducted on individuals presenting significant spinal deformities. Finally, the upper arm circumference was measured at the midpoint between the acromion (posterior shoulder bone) and the olecranon or protuberant bone of the elbow while the individual was seated or standing (Rosero-Bixby et al., 2013). The body mass index (BMI) was measured by dividing weight (kg) by height squared (m2) and was categorized according to the established criteria for older adults. These criteria consider values below 22 kg/m2 as underweight, between 22.0 kg/m2 and 26.9 kg/m2 as normal weight, between 27.0 kg/m2 and 31.9 kg/m2 as overweight, and greater than or equal to 32 kg/m2 as obesity (Lipschitz, 1994).

Data Analysis

Descriptive statistics, such as the average and standard deviation (SD) for continuous variables and the number and percentage for categorical variables, were calculated. For the statistical analysis of the variables age, weight, height, BMI and arm circumference, the t-test was used; while for the variable BMI categories, the chi-square test was used to compare by sex. In addition, sampling weights were considered in the estimations.

Scatter plots were constructed with the estimated regression line to identify the relation between the arm circumference and the BMI. Likewise, Pearson’s correlation coefficient was estimated to assess the level of the relation. Receiver operating characteristic (ROC) curves were obtained for all participants and for women and men separately, considering a BMI < 22 kg/m2, which has been suggested as a cut-off point to indicate underweight in the older adult population (Lipschitz, 1994; Lipschitz, 1994); Spanish Society of Parental and Enteral Nutrition [SENPE], 2007; 2011). Sensitivity and specificity were estimated for the different groups of participants. The Youden index (YI) was also calculated as sensitivity + specificity - 1 to obtain an optimal cut-off point in arm circumference to identify underweight, which was established for the value of this circumference that presents the highest YI (Youden, 1950).

Statistical analyses were performed using STATA version 13.1 (StataCorp, 2013), and p-values < 0.05 were considered statistically significant.

Results

The information analyzed included 2,514 individuals, of which 1,164 were men and 1,350 were women. The average age of the population was 70.1 (69.8 for men and 70.5 for women). Weight and height were higher in men (p < 0.001, respectively), while arm circumference and BMI (p = 0.012 and p < 0.001, respectively) were higher in women. Besides, 11.1% of the population is underweight if BMI values < 22 kg/m2 are taken as a reference (Table 1).

Table 1. General Characteristics of the Study Population

Characteristics	Total	Men	Women	p
	(n = 2514)	(n = 1164)	(n = 1350)
Age (SD)	70.1 (7.85)	69.8 (7.71)	70.5 (7.97)	0.048
Weight (SD)	66.4 (13.8)	70.5 (13.1)	62.7 (13.4)	< 0.001
Height (SD)	156.4 (9.76)	163.7 (6.63)	149.7 (7.02)	< 0.001
Arm Circumference (SD)	30.1 (4.25)	29.9 (3.66)	30.4 (4.72)	0.012
BMI (SD)	27.0 (5.18)	26.1 (4.21)	27.8 (5.82)	< 0.001
Class BMI				< 0.001
< 22.0	11.1	12.1	10.2
22.0-26.9	38.6	45.0	32.7
27.0-31.9	34.6	33.0	36.2
≥ 32.0	15.7	9.9	20.9

Note: Own source of research

The scatter plot with regression adjustments between arm circumference and BMI showed a positive correlation between these indicators. The estimated regression equation shows that BMI = 0.056 + 0.893 * arm circumference (BMI = – 0.188 + 0.880 * arm circumference in men and BMI = 0.821 + 0.887 * arm circumference in women) with p < 0.001 in all cases. In addition, Spearman’s correlation coefficient of 0.794 (0.774 for men and 0.806 for women) indicates a high correlation between arm circumference and BMI (Figure 1).

Figure 1. Correlation Between the Arm Circumference and BMI

Note: Own source of research

Analysis of ROC curves generated for all individuals and for men and women separately. The area under the ROC curve showed values greater than 0.90 (0.9317 for all individuals, 0.9042 for men, and 0.9536 for women), and differences (p < 0.001) were found in the results between men and women (Figure 2).

Figure 2. ROC Curves of Arm Circumference From BMI < 22 kg/m2

Note: Own source of research

The estimated YI for different arm circumference measurements revealed an optimal cut-off point of 26.5 cm, considering a YI = 0.7256, a sensitivity of 87.79%, and a specificity of 84.77%. On the other hand, the YI by sex, estimated for arm circumference measurements, revealed an optimal cut-off point of 26.5 cm in men with YI = 0.6664, sensitivity = 82.80%, and specificity = 83.84%, and 25.9 cm in women with YI = 0.7861, sensitivity = 89.37% and specificity = 89.24% (Table 2).

Table 2. Sensitivity, Specificity, and YI for Different Values of the Arm Circumference for All Individuals and by Sex

Arm circumference	Sensitivity	Specificity	YI
All participants
24.0	0.5573	0.9708	0.5280
24.5	0.6412	0.9524	0.5936
25.0	0.7125	0.9288	0.6413
25.5	0.7837	0.9048	0.6885
26.0	0.8321	0.8859	0.7180
26.5	0.8779	0.8477	0.7256
27.0	0.9008	0.8109	0.7117
27.5	0.9262	0.7595	0.6858
28.0	0.9491	0.7176	0.6667
28.5	0.9644	0.6577	0.6221
29.0	0.9771	0.6115	0.5886
Men
24.0	0.4355	0.9765	0.4120
24.5	0.5269	0.9560	0.4829
25.0	0.6075	0.9294	0.5370
25.5	0.6935	0.9049	0.5985
26.0	0.7634	0.8793	0.6428
26.5	0.8280	0.8384	0.6664
27.0	0.8495	0.8067	0.6562
27.5	0.8871	0.7454	0.6325
28.0	0.9194	0.6912	0.6106
28.5	0.9409	0.6309	0.5717
29.0	0.9624	0.5798	0.5421
Women
24.0	0.6667	0.9658	0.6325
24.5	0.7440	0.9493	0.6932
25.0	0.8068	0.9283	0.7350
25.5	0.8647	0.9046	0.7694
25.9	0.8937	0.8924	0.7861
26.0	0.8937	0.8915	0.7852
26.5	0.9227	0.8556	0.7783
27.0	0.9469	0.8145	0.7614
27.5	0.9614	0.7717	0.7330
28.0	0.9758	0.7402	0.7160
28.5	0.9855	0.6807	0.6662
29.0	0.9903	0.6387	0.6290

Note: Own source of research

Conclusions

The study found a high and positive correlation between arm circumference and BMI. This result coincides with previous studies that have reported high correlations between arm circumference and BMI in different populations. Examples of reported values have been 0.872 (Thorup et al., 2020) and 0.780 (Benítez Brito et al., 2016) in hospitalized individuals and 0.860 in non-pregnant adult women (Kumar et al., 2019). In contrast, values of 0.760 (Goswami et al., 2018) and 0.740 (Selvaraj et al., 2017) have been reported in individuals aged 60 years and older. In addition, an area under the curve of 0.9317 in the ROC curve analysis showed that arm circumference has a high ability to detect underweight among older adults, as a value in the range of 0.9 to 1.0 considers the diagnostic test as excellent (Okeh & Okoro, 2012).

On the other hand, the YI has been proposed as a method to obtain the optimal cut-off point in arm circumference to detect underweight. However, this value may vary depending on the definition adopted and the type of population studied. Different studies have reported arm circumference values including 22.5 cm in non-pregnant adult women (Kumar et al., 2019), 22.7 cm in men and 21.9 cm in women aged 18 years and older (Das et al., 2018), 24.3 cm in men older than 18 years (Chakraborty et al., 2011) or 24.5 cm in hospitalized individuals (Thorup et al., 2020). Furthermore, Tang et al. (2020) mentioned that values ranging from 23.5 cm to 25.0 cm could be useful as an appropriate indicator for detecting underweight in adults. In older adults, suggested values are 24 cm (Selvaraj et al., 2017) or 25.2 cm (Goswami et al., 2018). However, these studies considered underweight based on BMI values < 18.5 kg/m2 following the categorization proposed by the World Health Organization (World Health Organization, 2000).

In this study, BMI values < 22 kg/m2 were used as criteria to determine underweight in the older adult population. This helped to establish that arm circumference measurements of less than 26.5 cm can be indicators of underweight in this population. A study conducted on gastrostomy-fed older adults, for whom a BMI value of 22.5 kg/m2 was used as a cut-off point, found an arm circumference measurement of 26 cm (Barosa et al., 2018). Likewise, based on the principle of considering the highest possible sensitivity for a specificity above 80% (Thorup et al., 2020), an appropriate cut-off point of arm circumference is suggested, considering an adjustment of 26.5 cm.

In the analysis for men and women, the arm circumference cut-off points of 26.5 cm and 25.9 cm , respectively, had the highest possible sensitivity when considering a specificity greater than 80%. Other studies (Ferro-Luzzi & James, 1996; Goswami et al., 2018; Sultana et al., 2015) have also reported cut-point values different for men and women for assessing nutritional status in different populations. In this regard, Ferro-Luzzi & James (1996) suggest the importance of considering the variable of sex separately when lower body weights are present since men with normal weight have substantially more muscle but less fat in the arms than women. Females lose less muscle per kilogram of weight loss than men because their fat reserves are constitutionally larger.

Concerning the nutritional status of people, it is important to mention that poor condition triggers a series of health problems that affect well-being and quality of life (Abizanda et al., 2016; Balcombe & Saweirs, 2001). Besides, underweight in the older adult population may aggravate the deterioration of health and functional status, loss of autonomy and increase the risk of disability (Zhen et al., 2018), and elevate mortality rates (Payette et al., 1999).

On the other hand, decreasing arm circumference measurement at lower values increases mortality risk for all causes of death (Chen et al., 2014; Hollander et al., 2013; Mason et al., 2008; Schaap et al., 2018; Weng et al., 2018; Wijnhoven et al., 2010; Wu et al., 2017), as well as for specific causes, such as cardiovascular disease (Chen et al., 2014), chronic obstructive pulmonary disease (Ho et al., 2016), or Alzheimer’s disease (Sousa et al., 2020). Moreover, it has been recommended that arm circumference be implemented in the design of nutritional or health assessment scales (Tsai & Chang, 2011) and be considered as a more feasible and valid anthropometric measure of poor nutritional status than body mass index given the ease of its assessment in older adults (Schaap et al., 2018; Wijnhoven et al., 2010).

Thus, the specification of appropriate cut-off points can bring significant benefits to public health programs aimed at addressing nutritional problems in the older adult population. In addition, taking into account that a BMI value < 22 kg/m2 has been recommended to indicate underweight in older adults, the results of the study can contribute to establishing an adequate cut-off point in arm circumference for the evaluation of the nutritional status of this population. This becomes relevant because of the feasibility with which it is possible to obtain this measurement in individuals, even including those with different mobility problems, who may be highly represented in this age group. It is also possible that this aids the identification of people at risk of being underweight, which can contribute to the effectiveness of action plans aimed at treating possible deficiencies in the nutritional status of these people. Thus, an early intervention can conduce to the improvement of their quality of life.

Notwithstanding the positive implications of the results, the study has limitations. First, the cross-sectional nature of the sample does not help establish causal relations between arm circumference and BMI. In addition, variables related to the actual nutrient intake and morbidity of the participants were not included.

Finally, this study concludes that there is a significant relation between arm circumference and BMI in the population aged 60 years and older living in Costa Rica. The results establish that values of 26.5 cm in men and 25.9 cm in women constitute the first approach to determine an adequate cut-off point for measuring arm circumference to detect individuals with nutritional problems in the study population from BMI values below 22 kg/m2. In such a way, a low-cost measure of this nature can be implemented in regions where resources are scarce and among individuals for whom prompt and timely treatment can bring substantial improvements in the quality of life.

Funding

Universidad Nacional, Costa Rica [National University, Costa Rica].

Acknowledgments

We are grateful to the CRELES project, a longitudinal study of the University of Costa Rica conducted by the Centro Centroamericano de Población [Central America Population Center] in collaboration with the Instituto de Investigaciones en Salud [Institute of Health Research], with the support of the Wellcome Trust Foundation. Principal investigator: Luis Rosero-Bixby. Co-investigators: Xinia Fernandez and William H. Dow. Collaborating researchers: Ericka Méndez, Guido Pinto, Hannia Campos, Kenia Barrantes, Floribeth Fallas, Gilbert Brenes, and Fernando Morales. IT and support staff: Daniel Antich, Aaron Ramirez, Jeisson Hidalgo, Juanita Araya, and Yamileth Hernandez. Field personnel: José Solano, Julio Palma, Jenny Méndez, Maritza Aráuz, Mabelyn Gómez, Marcela Rodríguez, Geovanni Salas, Jorge Vindas, and Roberto Patiño.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Author Contributions Statement

All authors acknowledge that the final version of this article has been read and approved.

The total percentage of contributions to the conceptualization, preparation, and correction of this article was as follows: E.A.F. 80% and X.F.R. 20%

Data Availability Statement

Data supporting the results of this study will be made available by the corresponding author E.A.F. upon reasonable request.

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