Performance of Anthropometric Indicators in the Prediction of Metabolic Syndrome in the Elderly

Abstract

Introduction:

Studies have demonstrated the relevance of anthropometric indicators in the prediction of metabolic syndrome (MetS). However, researches involving older people are still scarce. Therefore, the objective was to describe the frequency of MetS and to determine the performance of anthropometric indicators as predictors of MetS in the total sample, in men and women.

Methods:

Cross-sectional study involving 479 elderly individuals attended in primary health care. The revised National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATPIII) guidelines were used for the MetS diagnosis. The anthropometric indicators evaluated were neck circumference, sagittal abdominal diameter (SAD), SAD/height, sagittal index, and conicity index (C-Index). The predictive performance of the MetS anthropometric indicators was determined using a receiver operating characteristic (ROC) curve. A cutoff point >0.700 was used to evaluate diagnostic performance.

Results:

The frequency of MetS was 60.5%. The anthropometric indicators demonstrating adequate performance were in total sample: SAD/height (auROC = 0.810), SAD (auROC = 0.777), and C-Index (auROC = 0.706); in women: SAD (auROC = 0.820), SAD/height (auROC = 0.810), neck circumference (auROC = 0.782), and C-Index (auROC = 0.727); in men: SAD/height (auROC = 0.768), SAD (auROC = 0.760), and C-Index (auROC = 0.724).

Conclusions:

A high frequency of MetS was observed. Of the five anthropometric indicators investigated, three presented good performance in the total sample (SAD, SAD/height, and C-Index), four in women (SAD, SAD/height, neck circumference, and C-Index), and three in men (SAD, SAD/height, and C-Index). The anthropometric indicators, SAD, SAD/height, and C-Index, proved to perform adequately in all the three segments investigated.

Introduction

Population aging is a global reality.¹ Morphophysiological alterations and an increased burden of chronic noncommunicable diseases occur with the aging process,² particularly of cardiovascular diseases, which are the main causes of morbidity and mortality worldwide.³

In this context, metabolic syndrome (MetS) is highlighted by a set of risk factors that contribute to the development of cardiovascular diseases and diabetes mellitus, including abdominal obesity, high blood pressure, glucose intolerance, insulin resistance, high triglycerides, and low concentrations of high-density lipoprotein cholesterol (HDL-cholesterol).^4,5

MetS is more prevalent in the elderly population^6

–9 due to the morphophysiological changes that occur with the aging process, favoring the appearance of those alterations that constitute MetS.¹⁰ The increase in morbidity and mortality linked to this important public health problem necessitates its screening, with economical and easy-to-apply criteria that are applicable to all populations,¹¹ especially administered in an outpatient follow-up setting by nonspecialists.

The use of anthropometric indicators may assist in MetS prediction due to their operational simplicity, as well as their association with metabolic risk factors.¹² However, no consensus in the scientific literature exists regarding the best indicator capable of predicting MetS in the elderly population, given the physiological alterations inherent in the aging process and the different cutoff points, many of which are specific for adults, and with different diagnostic criteria.¹³ In addition to this deficiency, the fact remains that the majority of studies investigating the association of anthropometric indicators with MetS in the elderly have used classic indicators, such as body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR).^12,14

–17

Rosemberg,¹⁴ in his master's degree dissertation, described the performance of four anthropometric measures of obesity and central obesity (WC, WHR, waist-to-height ratio (WHtR), and BMI) in elderly participants of the study “Epidemiological and Clinical Study of the Elderly Attended by the Family Health Strategy of the Municipality of Porto Alegre (EMI-SUS).” The researcher concluded that all the investigated measures were adequate and further proposed cutoff points for the elderly people assisted in primary health care.¹⁴ Studies involving alternative indicators of obesity and central obesity, such as sagittal abdominal diameter (SAD),^18,19 sagittal abdominal diameter/height ratio (SAD/height),^20,21 neck circumference,^22,23 sagittal index,^24,25 and conicity index (C-Index),^26,27 are still incipient.

Given this scenario, the objectives of this study were (A) to describe the frequency of MetS and its components in the elderly attended in a primary health care setting and (B) to determine the performance of anthropometric indicators in predicting MetS in the elderly in general, and by gender.

Methods

Study design

This is a cross-sectional study.

Population and sample

The sample consisted of 479 elderly individuals, ages 60 or older, enrolled in the Family Health Strategy of Porto Alegre, Rio Grande do Sul, Brazil, and all of whom were evaluated by an interdisciplinary team of the EMI-SUS project, in the period from March 2011 to December 2012. Details of the EMI-SUS research methods applied are described in Gomes et al.²⁸ All elderly individuals not meeting the research variables registered in the EMI-SUS database were excluded from the present study.

The sample size was determined taking into account sensitivity and specificity, considering for both calculations a confidence interval (CI) for standard normal distribution of Z = 1.96, accuracy of 0.05, and diagnosis prevalence of 30% in the target population. In addition, sensitivity of 95% and specificity of 80% were considered for calculating sample size, resulting in a required N of 243 and 351 participants, respectively.

MetS diagnosis

The National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATPIII) guidelines, revised by the American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement (AHA/NHLBI),²⁹ were used for the diagnosis of MetS, considering the presence of three or more of the following components: increased WC (≥102 cm for men and ≥88 cm for women); increased fasting glucose (≥100 mg/dL or undergoing treatment with oral hypoglycemic drugs and/or insulin); high triglycerides (≥150 mg/dL or undergoing drug treatment); decreased HDL-cholesterol (<40 mg/dL for men and <50 mg/dL for women, or undergoing drug treatment); and high blood pressure (≥130 mmHg or ≥85 mmHg, or undergoing drug treatment).²⁹

WC was measured using an inelastic tape, with the measurement being recorded at the midpoint between the costal border and the iliac crest, with the elderly person in the orthostatic position.³⁰

Serum levels of glucose, total cholesterol, HDL-cholesterol, and triglycerides were obtained utilizing the enzymatic colorimetric method (GOD-Trinder), using Labtest^® kits.

Blood pressure was measured on the nondominant arm, using a calibrated mercury sphygmomanometer and, where necessary, a cuff suitable for the obese. The measurement was taken with the individual sitting, having first emptied their bladder and being at rest for at least 5 min. Blood pressure measurement was repeated after 30 min, with the mean figure of the two readings being considered.

Predictor variables

The predictive anthropometric indicators used in this study were neck circumference, SAD, SAD/height ratio, sagittal index, and C-Index.

Trained nutritionists, certified by the International Society for the Advancement of Kinanthropometry (ISAK), recorded the anthropometric measurements.

Neck circumference was measured perpendicular to the long axis of the neck, just above the laryngeal prominence, at its smallest circumference.³¹

Sagittal abdominal diameter (SAD) was recorded in centimeters using a Holtain-Kahn abdominal caliper with calibration rod, and measured as the distance between the surface of the back and the top of the abdomen, with the elderly person lying on the examination table in a supine position, with knees flexed at 45° and soles of the feet flat to the table. The measurement was taken at the midpoint between the upper border of the iliac crest and the costal border.³²

SAD/height ratio was calculated by dividing the mean SAD (cm) by the estimated height (cm), which was determined by the Chumlea equation³³ using the measurement of knee height.

Sagittal index was obtained by dividing the mean SAD (cm) by the thigh circumference, which was measured in centimeters at the meso-femoral point.³⁴

C-Index was calculated using weight, estimated height (previously described), and WC, in accordance with the following equation:^35,36 \documentclass{aastex}\usepackage{amsbsy}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{bm}\usepackage{mathrsfs}\usepackage{pifont}\usepackage{stmaryrd}\usepackage{textcomp}\usepackage{portland, xspace}\usepackage{amsmath, amsxtra}\usepackage{upgreek}\pagestyle{empty}\DeclareMathSizes{10}{9}{7}{6}\begin{document} \begin{align*}{\rm{C - Index = }}{{{\rm{waist circumference (m)}}} \over {{\rm{0}}{\rm{.109}}\sqrt {{{{\rm{weight (kg)}}} \over {{\rm{height (m)}}}}} }}\end{align*} \end{document}

The weight measurement (kg), used for calculating the C-Index, was obtained using a Filizola^® platform scale, with a capacity of up to 200 kg. The elderly individual was situated in the center of the platform in an orthostatic position, wearing light clothing, with bare feet, and arms relaxed and extended down the length of their body, palms facing the thighs.^31,37 The WC of each individual was recorded using an inelastic tape measure, while standing. The measurement was obtained at the midpoint between the last rib and the iliac crest. The reading was taken at the moment of normal expiration, with the abdomen relaxed.³⁰

Statistical analysis

Qualitative variables are presented using absolute and relative frequency and quantitative variables as mean and standard deviation. The Kolmogorov–Smirnov test was applied to analyze the normality of the quantitative data, and Student's t-test for comparison between the measures.

The predictive power of the MetS anthropometric indicators and the cutoff points were determined using a receiver operating characteristic (ROC) curve, with a 95% CI. An area under the curve (auROC) greater than 70% was considered to be adequate for predicting with acceptable accuracy.³⁸ The cutoff points for each anthropometric indicator were identified using the Youden index.³⁹ Sensitivity (Se), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV) were calculated for each indicator.

Statistical analysis was performed using SPSS, version 21.0, and a P < 0.05 significance level was adopted.

Ethical aspects

The EMI-SUS research study was approved by the Research Ethics Committees of both the Pontifical Catholic University of Rio Grande do Sul (PUCRS) (registration n°. CEP-10/04967) and the Municipal Health Secretariat of Porto Alegre (protocol n°. 001.021434.10.7). The present study was conducted in accordance with the ethical principles set out in Resolution 466/12 and approved by the Scientific Commission of the Institute of Geriatrics and Gerontology, PUCRS. All research participants signed an informed consent form.

Results

The sample consisted of 479 elderly individuals, with a mean age (range) of 68.1 ± 6.8 (60–100) years, the majority being female (63.5%). The frequency of MetS in the elderly was 60.5%, who most frequently presented three diagnostic components of MetS (28.4%). The most commonly found component of MetS was high blood pressure (69.5%), with the least frequent being decreased HDL-cholesterol (34.9%) (Table 1).

Table 1.

Sample Characteristics and Prevalence of Metabolic Syndrome and Its Components in the Elderly Attended in Primary Health Care

Variables	Sample total N (%)
Gender
Female	304 (63.5)
Male	175 (36.5)
Age group
60–69 years	312 (65.1)
70–79 years	134 (28.0)
80 years or older	33 (6.9)
Metabolic syndrome
Yes	290 (60.5)
No	189 (39.5)
Metabolic syndrome components
Increased waist circumference	284 (59.3)
Elevated glycemia	312 (65.1)
Hypertriglyceridemia	185 (38.6)
Decreased HDL-cholesterol	167 (34.9)
High blood pressure	333 (69.5)
Number of metabolic syndrome components
0 component	12 (2.5)
1 component	65 (13.6)
2 components	112 (23.4)
3 components	136 (28.4)
4 components	107 (22.3)
5 components	47 (9.8)

HDL-cholesterol, high-density lipoprotein cholesterol.

All the anthropometric variables studied presented significant differences when participants with and without MetS were compared, with the mean values being higher in individuals with MetS (Table 2).

Table 2.

Distribution of the Investigated Anthropometric Indicators, According to the Presence or Absence of Metabolic Syndrome in the Sample as a Whole, and Considering Women and Men

	Metabolic syndrome
Anthropometric indicators	No (N = 189) Mean ± SD	Yes (N = 290) Mean ± SD	Mean difference (95% CI)	P
Neck circumference (cm)
Total sample	35.91 ± 3.39	37.26 ± 3.06	−1.35 (−1.93 to −0.76)	<0.001
Women	33.54 ± 2.18	36.15 ± 2.45	−2.61 (−3.19 to −2.03)	<0.001
Men	38.34 ± 2.59	40.13 ± 2.60	−1.79 (−2.57 to −1.01)	<0.001
Sagittal abdominal diameter (cm)
Total sample	22.51 ± 2.96	25.53 ± 2.87	−3.02 (−3.57 to −2.48)	<0.001
Women	21.72 ± 3.03	25.39 ± 2.85	−3.67(−4.39 to −2.95)	<0.001
Men	23.30 ± 2.67	25.91 ± 2.92	−2.60 (−3.45 to −1.75)	<0.001
Sagittal abdominal diameter/height
Total sample	0.14 ± 0.02	0.16 ± 0.02	−0.02 (−0.02 to −0.018)	<0.001
Women	0.14 ± 0.02	0.16 ± 0.02	−0.02 (−0.03 to −0.02)	<0.001
Men	0.14 ± 0.01	0.16 ± 0.02	−0.02 (−0.02 to −0.01)	<0.001
Sagittal index
Total sample	0.49 ± 0.04	0.51 ± 0.05	−0.02 (−0.03 to −0.02)	<0.001
Women	0.47 ± 0.04	0.50 ± 0.05	−0.03 (−0.05 to −0.02)	<0.001
Men	0.50 ± 0.05	0.53 ± 0.05	−0.03 (−0.04 to −0.01)	0.001
C-Index
Total sample	1.55 ± 0.09	1.62 ± 0.08	−0.07 (−0.08 to −0.05)	<0.001
Women	1.54 ± 0.10	1.62 ± 0.09	−0.08 (−0.10 to −0.05)	<0.001
Men	1.57 ± 0.07	1.63 ± 0.07	−0.06 (−0.08 to −0.04)	<0.001

P, Student's t-test.

C-Index, conicity index; CI, confidence interval; SD, standard deviation.

The performance of anthropometric indicators in predicting MetS in the elderly is presented in Table 3. The cutoff points, area under the curve, sensitivity, specificity, PPV, and NPV are described for each anthropometric indicator in the total sample, and by gender. The anthropometric indicators that performed well in predicting MetS in the total sample were SAD, SAD/height, and C-Index. Considering gender, SAD, SAD/height, neck circumference, and C-Index performed well for the women, and SAD, SAD/height, and C-Index for the men.

Table 3.

Performance of Anthropometric Indicators in Predicting Metabolic Syndrome in the Elderly

				Metabolic syndrome
Anthropometric indicators	Youden index cutoff point	Area under the curve (95% CI)	P	Total N (%)	No N (%)	Yes N (%)	Se	Sp	PPV	NPV
Neck circumference
Total sample	≥34.10	0.611 (0.559–0.664)	<0.001	367 (76.8)	122 (64.9)	245 (84.5)	0.844	0.351	0.668	0.595
Women	≥34.09	0.782 (0.727–0.838)	<0.001	197 (64.8)	33 (34.7)	164 (78.5)	0.785	0.652	0.832	0.579
Men	≥38.59	0.695 (0.617–0.773)	<0.001	97 (55.7)	38 (40.9)	59 (72.8)	0.728	0.591	0.608	0.714
Sagittal abdominal diameter
Total sample	≥24.65	0.777 (0.734–0.820)	<0.001	210 (45.6)	37 (19.9)	173 (62.9)	0.629	0.801	0.824	0.594
Women	≥23.34	0.820 (0.766–0.875)	<0.001	172 (59.1)	20 (21.5)	152 (76.8)	0.768	0.785	0.883	0.613
Men	≥25.04	0.760 (0.685–0.835)	<0.001	68 (40.0)	17 (18.3)	51 (66.2)	0.662	0.817	0.750	0.745
Sagittal abdominal diameter/height
Total sample	≥0.14	0.810 (0.771–0.850)	<0.001	254 (55.7)	49 (26.6)	205 (75.4)	0.754	0.734	0.807	0.668
Women	≥0.13	0.810 (0.755–0.865)	<0.001	193 (66.3)	32 (34.4)	161 (81.3)	0.813	0.656	0.834	0.622
Men	≥0.14	0.768 (0.695–0.841)	<0.001	63 (37.1)	16 (17.2)	47 (61.0)	0.610	0.828	0.746	0.720
Sagittal index
Total sample	≥0.49	0.638 (0.587–0.689)	<0.001	229 (100.0)	66 (100.0)	163 (100.0)	1.000	0.000	0.712	0.000
Women	≥0.49	0.692 (0.631–0.754)	<0.001	128 (44.0)	20 (21.5)	108 (54.5)	0.545	0.785	0.844	0.448
Men	≥0.48	0.649 (0.566–0.731)	0.001	103 (60.6)	45 (48.4)	58 (75.3)	0.753	0.516	0.563	0.716
C-Index
Total sample	≥1.57	0.706 (0.660–0.753)	<0.001	277 (58.3)	75 (39.9)	202 (70.4)	0.704	0.601	0.729	0.571
Women	≥1.56	0.727 (0.666–0.788)	<0.001	144 (47.5)	28 (29.5)	116 (55.8)	0.558	0.705	0.806	0.421
Men	≥1.61	0.724 (0.649–0.799)	<0.001	57 (32.9)	18 (19.4)	39 (48.8)	0.486	0.806	0.684	0.647

C-Index, conicity index; CI, confidence interval; Se, sensitivity; Sp, specificity; PPV, positive predictive value; NPV, negative predictive value.

Discussion

The present study aimed to describe the frequency of MetS and its components, and to determine the performance of anthropometric indicators (neck circumference, SAD, SAD/height, sagittal index, and C-Index) in predicting MetS in the elderly in general, and by gender, attended in primary health care (EMI-SUS study participants). It is important to emphasize that primary care is the first level of health care in Brazil⁴⁰ and that the search for low-cost and easy-to-use indicators is of fundamental importance.⁴¹

The principle findings of the present study were a high prevalence of MetS in the evaluated elderly and confirmation that of the five anthropometric indicators analyzed, three (SAD, SAD/height, and C-Index) presented an adequate performance in predicting MetS in the three segments investigated (total sample, men, and women). It is well documented in the literature that progressive changes in total and regional fat distribution occur in the aging process, with a preferential increase in abdominal fat, particularly visceral fat, and a decrease in lower body subcutaneous fat.⁴² These study results are supported by the fact that SAD, SAD/height, and C-Index are indicators of central adiposity,⁴³ with these being an important etiological factor in the cluster of conditions that make up MetS in the elderly.⁴⁴

The frequency of MetS found in our study (60.5%) is higher than another Brazilian study conducted by Rigo et al.,⁴⁵ which was 53.4%. This variation may be explained by the fact that the sample in the present study consisted of the elderly accompanied in a primary care setting, whereas the other study enrolled elderly community-dwelling people. It is known that in Brazilian primary care, the high frequency of diabetic and hypertensive individuals is so concerning that the Ministry of Health instituted the HiperDia Program in 2002 to serve the population in the outpatient network of the Unified Health System (SUS).⁴⁶ In researches conducted by Rocha et al.¹⁰ and Vieira et al.,⁴⁷ the frequencies of MetS were similar to those found in our study (58.0% and 58.6%, respectively). Both studies used the same diagnostic criteria and the samples were composed of the elderly in primary care, confirming our findings. The frequencies of MetS in international researches were lower than the present study (34.5%, 35.3%),^48,49 which can be explained by the difference in profile of the population (ethnicity) and/or inclusion criteria applied in the studies.

Researches examining the prevalence of MetS have presented differing findings, due, in part, to the profile of the population evaluated⁴⁵ and also as there is no consensus on its definition and the use of different cutoff points for components, causing repercussions in clinical practice and health policies.⁵⁰ It should be noted that the identification of MetS in primary care is essential for early intervention, regarding the individual risk factors that compose this syndrome, in addition to preventing the adverse complications of this condition, such as cardiovascular events.⁴⁴

A research involving elderly individuals enrolled in the Elderly Health Program reported a high prevalence of these MetS components, with 63.9% having high blood pressure and 52.5% with altered fasting blood glucose levels.⁵¹ The management of these two diseases in primary care is important, as hypertension and diabetes mellitus can have negative consequences for elderly health.^52
–54 Therefore, public health programs such as HiperDia are of prime importance because they accompany hypertensive and diabetic patients to reduce the morbimortality impact associated with these chronic diseases.⁴⁶

The anthropometric indicator mean values were all observed to be statistically higher in the elderly with MetS. Other studies evaluating anthropometric indicators as predictors of MetS have also found mean values to be higher in syndromic individuals.^15,55,56

Among the anthropometric indicators evaluated, SAD/height presented a good predictive performance for MetS in the total sample, and for both gender groups. No other studies in the literature were found to have evaluated the predictive power of this indicator for MetS, however, this measure is of potential interest as it is shown to be a strong predictor of cardiovascular disease risk in the elderly, as do SAD and WHR.²⁰ It is important to note that elderly people suffer height shrinkage over time, which is a result of the morphological changes that occur during the aging process, such as reduction of the plantar arch, increased curvature of the spine, and flattening of intervertebral discs.⁵⁷ Consequently, we use knee height measurement to estimate body height,³³ as this seems to be subject to less change with the aging process.⁵⁸ In this context, our research group (GERICEN) has published a previous study showing its applicability in estimating height for calculating BMI in the elderly.⁵⁹

In a study by Risérus et al.,¹⁹ SAD was shown to be a good anthropometric tool to identify “metabolically obese” men (auROC = 0.80; 95% CI: 0.77–0.82).¹⁹ In another study conducted with Brazilian adults, SAD demonstrated good sensitivity (0.96 and 0.86) and specificity (0.85 and 0.84) in the identification of abdominal obesity, especially in women.⁶⁰ The value of auROC was higher (men 0.99; women 0.93) due to the fact that SAD was used in the identification of abdominal obesity, and in the present study it was used in the prediction of MetS. SAD is correlated with abdominal adipose tissue and has been proposed as an alternative body size to WC.⁶¹

Considering the C-Index, research by Motamed et al.⁶² evaluating six obesity indexes (BMI, WC, WHR, WHtR, abdominal volume index, and C-Index), among the best discriminators in the diagnosis of MetS in adult and elderly populations, demonstrated the C-Index to present a good performance for women (auROC = 0.748; 95% CI: 0.727–0.768), but not for men (auROC = 0.670; 95% CI: 0.650–0.690).⁶² The results for women showed similarities to our findings. On the contrary, Oliveira et al. (2017)⁴⁴ encountered an auROC of 0.752 (95% CI: 0.788–0.897) in the elderly. This variation may be due to the population being composed of elderly people from long-term care institutions, as well as not using the same diagnostic criteria for MetS.

Some studies have evaluated the predictive value of BMI, WC, and WHR^15,16 for MetS, however, the results are conflicting,¹⁷ showing lower auROC values in comparison with the anthropometric indicators that performed well in the present study.

Surprisingly, the neck circumference did not present a good predictive performance in both the total and male samples in the present study. However, some studies have shown this indicator to be a good tool in the identification of MetS.^23,63,64 These results can be explained as they relate to differing age groups and ethnicities.

The sagittal index was also seen to not present a good predictive performance. Research using anthropometric indicators for MetS prediction in hemodialysis patients found similar values to the present study (auROC = 0.634; 95% CI: 0.46–0.80).²⁴ Although it has been proposed as an alternative to estimating the distribution of body fat and prediction of morbidities, studies that evaluate the sagittal index as a predictor for MetS are still in the early stages, especially in relation to the elderly population. Therefore, further research is needed to better clarify these results.

It is important to note that due to the very large patient demand in primary care, associated with a reduced number of professionals in these services, the time allotted for consultations is often reduced.⁶⁵ This necessitates the adoption of simple, economic, and user-friendly methods in this context to assist in the early diagnosis of MetS in this underserved population, increasing access to basic health care.

Consequently, simple anthropometric measurements, such as SAD, SAD/height, and C-Index, that require no sophisticated equipment (tape measure, weight scale, and stadiometer) can be used in primary care attention as tools for the identification of this condition.

It is important to list some positive aspects and limitations of this study.

The relevance of this study is highlighted, as research evaluating anthropometric indicators and morbidities involving the elderly population in particular is in the early stages. Furthermore, and to the best of our knowledge, the anthropometric indicators used have not been described in other studies of the Brazilian elderly population in primary care. In addition, there are few Brazilian researches evaluating the frequency of MetS using the revised NCEP-ATPIII guidelines in the elderly population attended in primary health care.

As a limiting factor, the cross-sectional study design can be noted, which avoids the possibility of establishing the cause and effect relationship. The conduct of longitudinal studies is suggested to confirm the findings of this research, as well as the extension of this study to different scenarios of health care.

Conclusion

The present study results demonstrated a high frequency of MetS in the elderly attended in a primary health care setting, with the most frequent components of MetS being high blood pressure and elevated glycemia levels. Of the five anthropometric indicators investigated, three (SAD, SAD/height, and C-Index) were observed to present a good performance in the total sample, four (SAD, SAD/height, neck circumference, and C-Index) performed well in the female group, and three (SAD, SAD/height, and C-Index) in the male group. In other words, the three anthropometric indicators of SAD, SAD/height, and C-Index proved to perform adequately in all three segments. Therefore, for practical purposes, we recommend the use of these indicators in the clinical evaluation of the elderly attended in primary health care.

Footnotes

Acknowledgments

The authors thank the researchers from the EMI-SUS study, especially Dr. Irenio Gomes (study coordinator) and the nutritionists Dr. Vera Elizabeth Closs, Dr. Laura Schlatter Rosemberg, and Betina Ettrich (for collecting and recording anthropometric data). They also thank the Research Support Foundation of Rio Grande do Sul for the financial support (FAPERGS - process number 09/0075-7 and 09/0055-0).

This study was financed, in part, by the Coordination for the Improvement of Higher Education Personnel—Brazil (CAPES), under finance code 001 (Jamile Ceolin's scholarship) and PNPD/CAPES 2785/09-9.

Author Disclosure Statement

The authors declare no conflict of interest.

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