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.
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References
1.
GoldaniH, AdamiFS, AntunesMT, et al.Applicatility of the visceral adiposity index (VAI) in the prediction of the components of the metabolic syndrome in elderly. Nutr Hosp, 2015; 32:1609–1615.
2.
World Health Organization. World Report on Ageing and Health. Geneva: WHO; 2014.
3.
Brazil. Ministry of Health. Department of Health Surveillance. Department of Health Situation Analysis. Strategic actions plan for coping with chronic noncommunicable diseases (DCNT) in Brazil 2011–2022. Department of Health Situation Analysis. Brasília: Ministry of Health, 2011. 148p. yl.–(Series B. Basic Health Texts).
4.
AlbertiKG, EckelRH, GrundySM, et al.Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation, 2009; 120:1640–1645.
5.
PapakonstantinouE, LambadiariV, DimitriadisG, et al.Metabolic syndrome and cardiometabolic risk factors. Curr Vasc Pharmacol, 2013; 11:858–879.
6.
ErvinRB. Prevalence of Metabolic Syndrome Among Adults 20 Years of Age and Over, by Sex, Age, Race and Ethnicity, and Body Mass Index: United States, 2003–2006. Natl Health Stat Report, 2009; 5:1–7.
7.
AkbulutG, KöksalE, BiliciS, et al.Metabolic syndrome (MS) in elderly: A cross sectional survey. Arch Gerontol Geriatr, 2011; 53:e263–e266.
8.
SumnerAD, SardiGL, ReedJF. Components of the metabolic syndrome differ between young and old adults in the US population. J Clin Hypertens (Greenwich), 2012; 14:502–506.
9.
StramagliaG, GrecoA, GuglielmiGG, et al.Echocardiography and dual-energy x-ray absorptiometry in the elderly patients with metabolic syndrome: A comparison of two different tecniques to evaluate visceral fat distribution. J Nutr Health Aging, 2010; 14:6–10.
10.
RochaFL, MeloRLP, MenezesTN. Factors associated with metabolic syndrome among the elderly in the northeast of Brazil. Rev Bras Geriatr Gerontol, 2016; 19:978–986.
11.
YangH, XinZ, FengJP, et al.Waist-to-height ratio is better than body mass index and waist circumference as a screening criterion for metabolic syndrome in Han Chinese adults. Medicine (Baltimore), 2017; 96:e8192.
12.
PaulaHAA, RibeiroRCL, RosadoLE, et al.Classic anthropometric and body composition indicators can predict risk of metabolic syndrome in the elderly. Ann Nutr Metab, 2012; 60:264–271.
13.
OliveiraCCO, CostaED, RorizAKC, et al.Predictors of Metabolic Syndrome in the elderly: a review. Int J Cardiovasc Sci, 2017; 30:343–353.
14.
RosembergLS. Determination of obesity and central obesity anthropometric parameters cut-off points for metabolic syndrome and cardiovascular risk in older people assisted in primary care. 2013. 113f. Dissertation. (Master in Biomedical Gerontology) - Institute of Geriatrics and Gerontology, PUCRS, Porto Alegre, 2013.
15.
GharipourM, SadeghiM, DianatkhahM, et al.The Cut-Off Values of Anthropometric Indices for Identifying Subjects at Risk for Metabolic Syndrome in Iranian Elderly Men. J Obes, 2014; 2014:1–6.
16.
Guasch-FerréM, BullóM, Martínez-GonzálezMÁ, et al.Waist-to-height ratio and cardiovascular risk factors in elderly individuals at high cardiovascular Risk. PLoS One, 2012; 7:e43275.
17.
ChuFL, HsuCH, JengC. Low predictability of anthropometric indicators of obesity in metabolic syndrome (MS) risks among elderly women. Arch Gerontol Geriatr, 2012; 55:718–723.
18.
ShardaM, JainP, GuptaA, et al.Correlation and Comparison of Various Anthropometric Measurements of Body Fat Distribution and Sagittal Abdominal Diameter as a Screening Tool for Cardio Metabolic Risk Factors and Ischaemic Heart Disease in Elderly Population. J Assoc Physicians India, 2015; 63:22–26.
19.
RisérusU, de FaireU, BerglundL, et al.Sagittal abdominal diameter as a screening tool in clinical research: Cut-offs for cardiometabolic risk. J Obes, 2010; Pii:757939.
20.
CarlssonAC, RisérusU, EngströmG, et al.Novel and established anthropometric measures and the prediction of incident cardiovascular disease: A cohort study. Int J Obes (Lond), 2013; 37:1579–1585.
21.
KahnHS, RissanenH, BullardKM, et al.The population distribution of the sagittal abdominal diameter (SAD) and SAD/height ratio among Finnish adults. Clin Obes, 2014; 4:333–341.
22.
LuoY, MaX, ShenY, et al.Neck circumference as an effective measure for identifying cardiometabolic syndrome: A comparison with waist circumference. Endocrine, 2017; 55:822–830.
23.
HingorjoMR, ZehraS, ImranE, et al.Neck circumference: A supplemental tool for the diagnosis of metabolic syndrome. J Pak Med Assoc, 2016; 66:1221–1226.
SmithDA, NessEM, HerbertR, et al.Abdominal diameter index: A more powerful anthropometric measure for prevalent coronary heart disease risk in adult males. Diabetes Obes Metab, 2005; 7:370–380.
26.
GadelhaAB, MyersJ, MoreiraS, et al.Comparison of adiposity indices and cut-off values in the prediction of metabolic syndrome in postmenopausal women. Diabetes Metab Syndr, 2016; 10:143–148.
27.
GowdaV, PhilipKM. Abdominal volume index and conicity index in predicting metabolic abnormalities in young women of different socioeconomic class. Int J Med Sci Public Health, 2016; 5:1452–1456.
28.
GomesI, NogueiraEL, EngroffP, et al.The multidimensional study of the elderly in the Family health strategy in Porto Alegre, Brazil (EMI-SUS). Pan Am J Aging Res, 2013; 1:20–24.
29.
GrundySM, CleemanJI, DanielsSR, et al.Diagnosis and Management of the Metabolic Syndrome: An American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation, 2005; 112:2735–2752.
30.
JanssenI, KatzmarzykPT, RossR. Body Mass Index, Waist Circumference, and Health Risk. Evidence in Support of Current National Institutes of Health Guidelines. Arch Intern Med, 2002; 162:2074–2079.
31.
National Health and Nutrition Examination Survey (NHANES) Anthropometry Procedures Manual. 2007. Accessed at <http://www.cdc.gov/nchs/data/nhanes/nhanes_07_08/manual_an.pdf.> on September10, 2018.
32.
SampaioLR, SimõesEJ, AssisAM, et al.Validity and reliability of the sagittal abdominal diameter as a predictor of visceral abdominal fat. Arq Bras Endocrinol Metabol, 2007; 51:980–986.
33.
ChumleaWC, RocheAF, SteinbaughML. Estimating stature from knee height for persons 60 to 90 years of age. J Am Geriatr Soc, 1985; 33:116–120.
34.
VasquesACJ, RosadoLEFPL, RosadoGP, et al.Predictive ability of anthropometric and body composition indicators in the identification of insulin resistance. Arq Bras Endocrinol Metab, 2009; 53:72–79.
35.
ValdezR, SeidellJC, AhnYI, et al.A new index of abdominal adiposity as an indicator of risk for cardiovascular disease. A cross population study. Int J Obes Relat Metab Disord, 1993; 17:77–82.
36.
ValdezR. A simple model-based index of abdominal adiposity. J Clin Epidemiol, 1991; 46:491–494.
37.
CoelhoMASC, AmorimRB. Nutritional Evaluation in Geriatrics. In: DuarteACG (organizer). Nutritional assessment: clinical and laboratory aspects. São Paulo: Atheneu; 2007: 155–176.
38.
WiansFH. “Clinical laboratory tests: Which, why, and what do the results mean?” LabMedicine, 2009; 40:105–113.
39.
Hajian-TilakiK. Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation. Caspian J Intern Med, 2013; 4:627–635.
40.
PaimJ, TravassosC, AlmeidaC, et al.The Brazilian health system: History, advances, and challenges. Lancet, 2011; 377:1778–1797.
41.
Brazil. Ministry of Health. Obesity. Brasília: Ministry of Health, 2006. (Notebooks for Basic Attention, No. 12) (Series A. Norms and Technical Manuals).
42.
KukJL, SaundersTJ, DavidsonLE, et al.Age-related changes in total and regional fat distribution. Ageing Res Rev, 2009; 8:339–348.
43.
VasquesAC, RosadoL, RosadoG, et al.Anthropometric indicators of insulin resistance. Arq Bras Cardiol, 2010; 95:e14–e23.
44.
OliveiraCC, RorizAK, RamosLB, et al.Indicators of Adiposity Predictors of Metabolic Syndrome in the Elderly. Ann Nutr Metab, 2017; 70:9–15.
45.
RigoJC, VieiraJL, DalacorteRR, et al.Prevalence of Metabolic Syndrome in an Elderly Community: Comparison between Three Diagnostic Methods. Arq Bras Cardiol, 2009; 93:85–91.
46.
Brazil. Ministry of Health. Ministerial order n. 371. [Internet]. Brasília; 2002. [Accessed September10, 2018]. Available from: http://bvsms.saude.gov.br/bvs/saudelegis/gm/2002/prt0371_04_03_2002_rep.html.
47.
VieiraEC, PeixotoMRG, SilveiraEA. Prevalence and factors associated with Metabolic Syndrome in elderly users of the Unified Health System. Rev Bras Epidemiol, 2014; 17:805–817.
48.
VinluanCM, ZreikatHH, LevyJR, et al.Comparison of Different Metabolic Syndrome Definitions and Risks of Incident Cardiovascular Events in the Elderly. Metabolism, 2012; 61:302–309.
49.
Van HerptTT, DehghanA, HoekMV, et al.The clinical value of metabolic syndrome and risks of cardiometabolic events and mortality in the elderly: The Rotterdam study. Cardiovasc Diabetol, 2016; 27:15–69.
50.
Brazilian Society of Cardiology. I Brazilian guideline for diagnosis and treatment of the metabolic syndrome. Arq Bras Cardiol, 2005; 84:1–28.
51.
SantosPCM, FerreiraALL, MoriRMSC. Frequency of Metabolic Syndrome in aged enrolled in the Health of the Aged Program of Municipal Unit of Belém-PA. Rasbran, 2017; 8:75–81.
52.
LionakisN, MendrinosM, SanidasS. Hypertension in the elderly. World J Cardiol, 2012; 4:135–147.
53.
ChentliF, AzzougD, MahgounS. Diabetes mellitus in elderly. Indian J Endocrinol Metab, 2015; 19:744–752.
54.
RosaCB, AgostiniJA, BianchiPD, et al.Metabolic Syndrome and nutritional status of elderly enrolled in the HiperDia system. Sci Med, 2016; 26:ID23100.
55.
Alvero-CruzJR, VázquezRF, VegaMDMG, et al.Sensitivity and specificity of abdominal adiposity with metabolic syndrome in the elderly. Rev Esp Geriatr Gerontol, 2017; 52:128–134.
56.
SagunG, OguzA, KaragozE, et al.Application of alternative anthropometric measurements to predict metabolic syndrome. Clinics, 2014; 69:347–353.
57.
LimaMFS, De OliveiraLP, CabralNLA, et al.Estimating the height of elderly nursing home residents: Which equation to use?. PLoS One, 2018; 13:e0205642.
58.
PerissinottoE, PisentC, SergiG, GrigolettoF, EnziG. Anthropometric measurements in the elderly: Age and gender differences. Br J Nutr, 2007; 87:177–186.
59.
ClossVE, FeoliAMP, SchwankeCHA. Knee-height as a reliable alternative measure for nutritional assessment in older adults. Rev Nutr, 2015; 28:475–484.
60.
PimentelGD, Portero-McLellanKC, MaestáN, et al.Accuracy of sagittal abdominal diameter as predictor of abdominal fat among Brazilian adults: A comparation with waist circumference. Nutr Hosp, 2010; 25:656–661.
61.
KahnHS, BullardKM. Indicators of abdominal size relative to height associated with sex, age, socioeconomic position and ancestry among US adults. PLoS One, 2017; 12:e0172245.
62.
MotamedN, SohrabiM, PoustchiH, et al.The six obesity indices, which one is more compatible with metabolic syndrome? A population based study. Diabetes Metab Syndr, 2017; 11:173–177.
63.
CuiT, YanBH, LiuZ, et al.Neck circumference: A valuable anthropometric measurement to detect metabolic syndrome among different age groups in China. Diabetes Metab Res Rev, 2017; 34:e2966.
64.
YanQ, SunD, LiX, et al.Neck circumference is a valuable tool for identifying metabolic syndrome and obesity in Chinese elder subjects: A community-based study. Diabetes Metab Res Rev, 2014; 30:69–76.
65.
LubenowJAM, BarrêtoAJR, NogueiraJA, et al.Access for the Elderly to Primary Health Care Services: An Integrative Review. Int Arch Med, 2016; 9:1–13.
