Validation of Surrogate Anthropometric Indices in Older Adults: What Is the Best Indicator of High Cardiometabolic Risk Factor Clustering?

nutrients Article Validation of Surrogate Anthropometric Indices in Older Adults: What Is the Best Indicator of High Cardiometabolic Risk Factor Clustering? Robinson Ramírez-Vélez 1,* , Miguel Ángel Pérez-Sousa 2, Mikel Izquierdo 1,3 , Carlos A. Cano-Gutierrez 4, Emilio González-Jiménez 5, Jacqueline Schmidt-RioValle 5, Katherine González-Ruíz6and María Correa-Rodríguez 5 1 Department of Health Sciences, Public University of Navarra, Navarrabiomed-Biomedical Research Centre, IDISNA-Navarra’s Health Research Institute, C/irunlarrea 3, Complejo Hospitalario de Navarra, 31008 Pamplona, Navarra, Spain 2Faculty of Sport Sciences, University of Huelva, Avenida de las Fuerzas Armadas s/n, 21007 Huelva, Spain 3Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 28029 Madrid, Spain 4Hospital Universitario San Ignacio – Aging Institute, Pontificia Universidad Javeriana, Bogotá110111, Colombia 5Department of Nursing, Faculty of Health Sciences, University of Granada, Av. Ilustración, 60, 18016 Granada, Spain 6Grupo de Ejercicio Físico y Deportes, Vicerrectoría de Investigaciones, Facultad de Salud, Universidad Manuela Beltrán, Bogotá110231, DC, Colombia *Correspondence: [email protected]; Tel.: +34-699-993-920 Received: 5 July 2019; Accepted: 22 July 2019; Published: 24 July 2019   Abstract: The present study evaluated the ability of five obesity-related parameters, including a body shape index (ABSI), conicity index (CI), body roundness index (BRI), body mass index (BMI), and waist-to-height ratio (WtHR) for predicting increased cardiometabolic risk in a population of elderly Colombians. A cross-sectional study was conducted on 1502 participants (60.3% women, mean age 70 ± 7.6 years) and subjects’ weight, height, waist circumference, serum lipid indices, blood pressure, and fasting plasma glucose were measured. A cardiometabolic risk index (CMRI) was calculated using the participants’ systolic and diastolic blood pressure, triglycerides, high-density lipoprotein and fasting glucose levels, and waist circumference. Following the International Diabetes Federation definition, metabolic syndrome was defined as having three or more metabolic abnormalities. All surrogate anthropometric indices correlated significantly with CMRI (p<0.01). Receiver operating characteristic curve analysis of how well the anthropometric indices identified high cardiometabolic risk showed that WtHR and BRI were the most accurate indices. The best WtHR and BRI cut-offpoints in men were 0.56 (area under curve, AUC 0.77) and 4.71 (AUC 0.77), respectively. For women, the WtHR and BRI cut-offpoints were 0.63 (AUC 0.77) and 6.20 (AUC 0.77), respectively. In conclusion, BRI and WtHR have a moderate discriminating power for detecting high cardiometabolic risk in older Colombian adults, supporting the idea that both anthropometric indices are useful screening tools for use in the elderly. Keywords: anthropometric indices; diagnosis criteria; metabolic syndrome; cardiometabolic risk; elderly 1. Introduction Metabolic syndrome (MetS) is a complex cluster of cardiovascular risk factors associated with a sedentary lifestyle, poor nutrition, and consequent overweight. It is also strongly associated with Nutrients 2019,11, 1701; doi:10.3390/nu11081701 www.mdpi.com/journal/nutrients Nutrients 2019,11, 1701 2 of 16 other abnormalities linked to cardiovascular disease (CVD), including glucose intolerance (type 2 diabetes, impaired glucose tolerance, or impaired fasting glycemia), insulin resistance, abdominal obesity, dyslipidemia, and hypertension [ 1 ]. Accordingly, MetS increases the risk of developing diseases of cardiovascular origin, such as acute myocardial infarction, ischemic stroke, or coronary heart disease [ 2 ]. Indeed, the prevalence of CVD attributable to MetS is estimated at around 12–17% [ 3 ]. Several studies have examined the presence of MetS in Latin America, reporting associated factors including advanced age, having Hispanic or indigenous heritage, physical inactivity, high alcohol intake, smoking, history of hypertension or type 2 diabetes (first-degree family members), and having a low socioeconomic status (reviewed in [ 4 ]). The general prevalence of MetS in Latin-American countries has been established as 24.9% (range: 18.8–43.3%) and is slightly more frequent in women (25.3%) than in men (23.2%). The clinical utility of identifying MetS in older adults has been much debated because, among the issues raised, it has been argued that there is no consensus on the clinical criteria for screening the elderly population to identify patients likely to be characterized with MetS. In this line, several clinical criteria and cut-offpoints have been proposed. For instance, the cardiometabolic risk index (CMRI) in older adults, measured as a continuous summary score, might represent an important intermediate or preclinical outcome that can be measured prior to the onset of disease, and could provide opportunities for prevention. As a marker of cardiometabolic disease risk, the use of adult CMRI severity z-scores has been suggested as an accurate method to detect overall metabolic changes [ 5 ]. This continuous score would be more sensitive to small and large changes that do not modify the most recent Joint Interim Statement of the International Diabetes Federation (IDF) Task Force on Epidemiology and Prevention criteria [ 6 ]. Thus, an increase in cholesterol from 150 to 250 mg/dl would have no impact on the IDF score, but would be reflected as a non-trivial change in the continuous CMRI [ 7 ]. Nevertheless, there is no validated or harmonized consensus for defining CMRI in older adults, and several continuous CMRI scores have been reported in the literature, as described in previous narrative reviews. Measurements of anthropometric indices are inexpensive and non-invasive, and are easily conducted as part of normal health exams. Interestingly, anthropometric measurements such as body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WtHR) show a close correlation with MetS components and could thus be useful surrogate markers for predicting MetS [ 8 – 10 ]. That being said, there remains controversy over which anthropometric indices [ 11 ] are the most appropriate predictors of cardiometabolic disease [ 12 ]. In 2012, Krakauer and Krakauer developed “A Body Shape Index” (ABSI), based on WC adjusted for height and weight [ 13 ], and demonstrated that a high ABSI is associated with the accumulation of excess abdominal adipose tissue and seems to be a substantial risk factor for premature mortality in the general population [ 13 ]. In a similar vein, the conicity index (CI), an index of abdominal obesity, has been considered useful for detecting central obesity, and has been studied as a predictor for alterations in fasting insulin, blood pressure, and triglyceride levels [ 14 ]. Lastly, in 2013, Thomas and colleagues [ 15 ] developed the body roundness index (BRI), which combines height and WC to predict the percentage of body fat. When compared with other anthropometric indices, BRI was optimal for identifying MetS, insulin resistance, inflammatory factors [ 16 ], and arterial stiffness [ 17 ] in obese and overweight populations. However, to date, few studies have evaluated the predictive ability of BRI, ABSI, or CI compared with traditional metrics, such as BMI and WtHR, with regard to CMRI in older adults [18–20]. South America has undergone a rapid epidemiologic transition, including a non-communicable disease epidemic [ 21 ] and adverse lifestyle changes that could contribute to increase a cluster of cardiometabolic risk factors such as MetS [ 4 ]. To the best of our knowledge, the predictive power of anthropometric measurements, which can be measured easily in a routine health exam, has not been assessed in elderly Latin-American individuals with high cardiovascular risk, for whom the early detection of risk factors is essential for prevention of CVD. This is particularly true in Colombia, where anthropometric index measurements and blood collection are not usually standard in the annual health exam, and, to date, there have been few studies conducted in the general older population. Nutrients 2019,11, 1701 3 of 16 For these reasons, the aim of the present study was to evaluate the prevalence of MetS using a CMRI among older adults from Colombia, and validate the associated anthropometric surrogate markers. We also compared the predictive ability of BRI, ABSI, CI, BMI, and WtHR to determine whether there is a single best CMRI predictor. 2. Materials and Methods 2.1. Study Design and Participants The data for this secondary cross-sectional study was obtained from the 2015 Colombian Health, Well-Being and Aging Survey (SABE 2015, from the Spanish: SAlud, Bienestar and Envejecimiento, 2015), a multicenter project conducted from 2014 to 2015 by the Pan-American Health Organization and supported by the Epidemiological Office of the Ministry of Health and Social Protection of Colombia (https://www.minsalud.gov.co/). The survey is a cross-sectional tool for exploring and evaluating several aspects that intervene in the phenomenon of aging and old age in the Colombian population [ 19 ]. Details of the survey have been previously published [ 19 ]. SABE 2015 was a joint venture between the Ministry of Health and Social Protection and the Administrative Department of Science, Technology and Innovation in Colombia. The sample was regionally representative and involved self-representation in large cities, with urban-rural stratification of the sample and stage selection in accordance with the municipal map available from the Ministry of Health and Social Protection, with the following hierarchy: municipalities, urban/rural segments, homes or sidewalks, homes, and people. The study included the Colombian population ≥ 60 years old, and the indicators were disaggregated by age range, sex, ethnicity, and socioeconomic level. To calculate the original sample size, the non-institutionalized Colombian population aged ≥ 60 years was considered, and the following parameters were used: minimum estimable proportion =0.03, design effect =1.2, and Relative Standard Error =0.05 (1.2). The universe of study comprised 99% of the population residing in private homes in both urban and rural areas. A total of 23,694 surveys were conducted across the country and 6365 total population segments were investigated in 246 municipalities. As Bogot á is the capital it was independently selected, with a total of 545 urban segments and one rural segment. The average number of adults per segment was 4.2. The estimation of means or proportions was conducted to a level of precision of up to 6% of the maximum expected error, at a level of national disaggregation only. The basic procedure for the population survey was a face-to-face interview using a structured questionnaire. The interviewers visited the selected homes, carrying the appropriate identification. At each home visited, the standardized process involved the following: identifying the participants, registering the demographic data, obtaining the signed informed consent, applying the established filters and selection criteria, obtaining a signed assent form when necessary, and completion of the questionnaire by the interviewer. A total of 1502 participants from 86 municipalities were included in this analysis. The institutional review boards involved in developing the SABE 2015 study (the University of Caldas, ID protocol CBCS-021-14, and the University of Valle, ID protocol 09-014 and O11-015) reviewed and approved the study protocol. Written informed consent was obtained from each individual before inclusion and completion of the first examination. One of the authors (C.A.C.-G.) applied to the Ministry of Health and Social Protection of Colombia and obtained permission to use publicly available data for research and teaching purposes (permission and details available at https://www.minsalud.gov.co/). The study protocol for the secondary analysis was approved by the Human Subjects Committee at the Pontificia Universidad Javeriana (ID protocol 20/2017-2017/180, FM-CIE-0459-17) in accordance with the Declaration of Helsinki (World Medical Association) and Resolution 8430 from 1993, of the then Colombian Ministry of Health, on technical, scientific, and administrative standards for conducting research with humans. Nutrients 2019,11, 1701 4 of 16 2.2. Anthropometric Measurements The research teams of the coordinating centers (Caldas and Valle universities, Colombia) trained the data collection staffto carry out the face-to-face interviews and physical measurements. Anthropometric measurements included height and body weight, which were measured using a portable stadiometer (SECA 213 ® , Hamburg, Germany) and an electronic scale (Kendall graduated platform scale), respectively. BMI was estimated in kg/m 2 from the measured body weight and height. WC was measured using inextensible anthropometric tape with the subjects standing erect and relaxed, with their arms at their sides and their feet positioned close together, parallel to the floor. WtHR was calculated as the ratio of WC (cm) to height (cm). The other anthropometric indexes (BRI, ABSI, and CI) were calculated using the following formulas: BRI =364.2 − 365.5 (1 −π - 2 WC 2 (m) Height −2 (m)) 1/2 [ 15 ]; ABSI =WC (m)/(BMI 2/3 (kg/m 2 )Height 1/2 (m)) [ 13 ]; CI =0.109 −1 WC (m) (Weight (kg)/Height (m))−1/2[22]. 2.3. Serum Biochemical Examination After an overnight fast, blood was collected in the morning. Blood samples were centrifuged for 10 min at 3000 rpm, 30 min after sampling. All samples were delivered to a single central laboratory (Dinamica Laboratories, Bogot á , Colombia) for analysis within 24 h. Serum fasting glucose, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol, and triglycerides (TG) were analyzed using enzymatic colorimetric methods (Olympus AU5200, Melville, NY, USA). Low-density lipoprotein cholesterol (LDL-C) was estimated using the Friedewald equation ((LDL-C) =(Total Cholesterol) – (HDL-C) −((TG)/5)). 2.4. Blood Pressure Determination We measured systolic (SBP) and diastolic (DBP) blood pressure levels using an automatic blood pressure monitor (OMRON HEM-705, Omron Healthcare Co., Ltd., Kyoto, Japan), following the recommendations of the American College of Cardiology Foundation/American Heart Association 2011 Expert Consensus Document on Hypertension in the Elderly [ 23 ]. Values were recorded after 5 min of rest in the sitting position and three consecutive measures were obtained, waiting for at least 30 s between readings. The average of the three values for each measurement were used in the analysis. 2.5. Diagnostic Criteria of Metabolic Syndrome MetS was defined according to the most recent Joint Interim Statement of the IDF [ 6 ] by adopting the Ethnic Central and South American criteria for WC. Participants were classified as having MetS if they had at least three of following metabolic risk factors or components (MetS-components): abdominal obesity (WC ≥ 90 cm for Latin-American males and ≥ 80 cm for Latin-American females), elevated TG (fasting serum TG ≥ 150 mg/dL or taking medication for abnormal lipid levels), low HDL-C (fasting serum HDL-C <40 mg/dL in males and <50 mg/dL in females, or specific treatment for this lipid abnormality), elevated blood pressure (SBP ≥ 130 mmHg or DBP ≥ 85 mmHg or taking hypertension medication), or elevated fasting glucose (serum glucose level ≥ 100 mg/dL or taking diabetes medication). 2.6. Definition of Cardiometabolic Risk Index We calculated the CMRI as a continuous score of the MetS risk factors. The CMRI was calculated using sexand race-specific algorithms for the IDF criteria cut-offvalues, using the values of the participants’ SBP and DBP, TG, HDL-C, fasting glucose, and WC. For each of these variables, a z-score was computed as the number of standard deviation (SD) units from the sample mean after normalization of the variables, that is, z-score =((value − sample mean)/sample SD)). The HDL-C z-score was multiplied by − 1 to indicate higher cardiovascular risk with increasing value. Individuals with a Nutrients 2019,11, 1701 5 of 16 CMRI ≥1 SD above the mean were identified as having increased cardiometabolic risk, and a lower CMRI (<1 SD) being indicative of a healthier risk profile. 2.7. Co-Variables For lifestyle characteristics, personal habits regarding alcohol intake (participants were categorized as those who do not drink and those who drink less than one day per week, two to six days a week, or every day) and cigarette smoking (participants were categorized as those who do not smoke and those who have never-smoked, those who currently smoke or those who previously smoked) were recorded. A “proxy physical activity” report was conducted by the following questions: (i) “Have you regularly exercised, such as jogging or dancing, or performed rigorous physical activity at least three times a week for the past year?”; (ii) “do you walk at least three times a week between nine and 20 blocks (1.6 km) without resting?”; (iii) “do you walk at least three times a week eight blocks (0.5 km) without resting?”. Participants were considered physically active if they responded affirmatively to two of the three questions [24]. Medical information including multimorbidity, as well as chronic conditions adapted from the original SABE study, was assessed by asking the participants if they had been medically diagnosed with hypertension, type 2 diabetes mellitus, chronic obstructive pulmonary disease, CVD (heart attack, angina), stroke, cancer, arthritis, osteoporosis, or sensory impairments (vision and hearing loss). Race/ethnicity was self-reported and grouped into indigenous (people belonging to various indigenous groups such as Ika, Kankuamo, Ember á , Misak, Nasa, Wayuu, Awu á , and Mokane); black, “mulatto”, or Afro-Colombian; white; and other (mestizo, gypsy, etc.). Socioeconomic status was determined on a scale of one to six based on the housing stratum, with one representing the highest level of poverty and six the greatest wealth. This classification was developed by the National Government of Colombia and considers the physical characteristics of the dwellings as well as their surroundings. Classification into one of the six strata was taken to approximate the hierarchical socioeconomic differences from poverty to wealth. 2.8. Statistical Analysis Descriptive analyses using the mean ± SD or standard error (SE) for the continuous variables, median and interquartile range for the skewed continuous variables, and the frequency distribution of the categorical variables were used to determine the characteristics of the sample. Data normality was examined using the Kolmogorov–Smirnofftest. Significant differences between men and women were analyzed using Student’s t-test, Wilcoxon rank-sum test, or chi-square ( χ2 ) post-hoc test. To visualize the relationship between CMRI and anthropometric indices, Spearman and Pearson correlation and linear regression analysis were applied to the total sample and individual genders. The linear regression analysis was adjusted by age as a covariate. The area under receiver operating characteristic (ROC) curves was calculated to evaluate the abilities of the anthropometric indices to predict high CMRI. Cut-offpoints were proposed after calculation of Youden’s Index (sensitivity +specificity − 1) [ 25 ]. The DeLong et al. [ 26 ] non-parametric approach was used to compare the areas under the ROC curves. Since abdominal obesity is a component of CMRI, we conducted a multicollinearity test for the anthropometric indices that included WC (WtHR, CI, BRI, and ABSI), and the variance inflation factor (VIF) was calculated. Each cardiometabolic risk factor among BMI, WtHR, BRI, ABSI, and CI was determined using analysis of variance without any adjustment and then after adjusting (analysis of covariance, ANCOVA) for ethnicity, socio-economic status, smoking status, alcohol intake, physical activity “proxy”, and medical conditions (i.e., presence or absence of osteoporosis, CVD, hypertension, type 2 diabetes, cancer, or respiratory diseases) as covariates, followed by Tukey’s test. Collinearity was tested between all anthropometric indexes that included WC; a VFI >10, was interpreted as high collinearity [27]. Statistical analyses were performed using SPSS v24.0 (IBM, Armonk, NY, USA) and JASP v0.9 (JASP Team, Amsterdam, The Netherlands). Statistical significance was defined as p<0.05. Nutrients 2019,11, 1701 6 of 16 3. Results 3.1. Baseline Characteristics of the Participants The participants ´ characteristics are summarized in Table 1. Of the 1502 older adults studied, 60.3% were women, and the mean age was 70 ± 7.6 years. The prevalence of smoking (9.7%), alcohol intake (12.7%), and a physical activity proxy (17.7%) was relatively low, but significantly higher in CMRI ≥ 1 SD than in CMRI <1 SD (alcohol: 13.1% vs. 12.6%, p<0.001). The means (SD or range interquartile) of the WtHR, BMI, BRI, ABSI, and CI in the overall sample were 0.59 (0.1), 27.3 (24−30) kg/m2 , 5.2 (4.1 − 6.3), 0.081 (0.078 − 0.085), and 22.2 (20.9 − 23.8), respectively. The overall prevalence of MetS was 58.7%. Significant differences were found between the high/low CMRI status groups for almost all characteristics, with the exception of height, LDL-C and HDL-C levels. Table 1. Characteristics of study participants according to high ( ≥ 1 SD) and low (<1 SD) cardiometabolic risk index (CMRI) status among Colombian older adults. Characteristics Total Sample (n=1502) High CMRI ≥1 SD (n=397) Low CMRI <1 SD (n=1105) p-Value Sex, n(%) Men 596 (39.7) 141 (23.7) 455 (76.3) <0.001 Women 906 (60.3) 254 (28.0) 652 (72.0) <0.001 Socioeconomic status 1 456 (30.4) 121 (30.5) 335 (32.1) <0.001 2 635 (42.3) 176 (44.3) 459 (41.5) <0.001 3 375 (25.0) 98 (24.7) 277 (25.1) <0.001 4 29 (1.9) 2 (0.5) 27 (2.4) <0.001 >5 7 (0.5) 0 (0.0) 7 (0.6) N.A Ethnic group Indigenous 78 (5.2) 25 (6.3) 53 (4.8) 0.002 Black 119 (7.9) 28 (7.1) 91 (8.2) <0.001 White 396 (26.4) 106 (26.7) 290 (26.2) <0.001 Others 909 (60.5) 194 (48.9) 512 (46.3) <0.001 Smoking status, n(%) Yes 145 (9.7) 29 (7.3) 116 (10.5) <0.001 No 1357 (90.3) 368 (92.7) 989 (89.5) <0.001 Alcohol intake, n(%) Yes 191 (12.7) 52 (13.1) 139 (12.6) <0.001 No 1310 (87.2) 345 (86.9) 965 (87.3) <0.001 Physical Activity “proxy”, n(%) Physically active 266 (17.7) 70 (17.6) 196 (17.7) 0.980 Non-Physically active 1231 (82.0) 323 (81.4) 908 (82.2) <0.001 Anthropometric measures/indices Height (m) 1.55 (1.49–1.62) 1.54 (1.49–1.62) 1.55 (1.49–1.62) 0.170 Weight (kg) 64 (57–72) 71 (63–79) 62 (55–69) <0.001 Waist circumference (cm) 92 (85–100) 101 (93–107) 89 (83–97) <0.001 Body mass index (kg/m2)27 (24–30) 29.7 (26.7–33) 26.1 (23.3–29) <0.001 WtHR 0.59 (0.1) 0.64 (0.06) 0.57 (0.06) <0.001 BRI 5.2 (4.1–6.3) 6.4 (5.3–7.7) 4.8 (3.9–5.9) <0.001 ABSI (m11/6·kg −2/3)0.081 (0.078–0.085) 0.083 (0.080–0.086) 0.081 (0.077–0.084) <0.001 CI 22.2 (20.9–23.8) 21.1 (19.8–22.4) 22.6 (21.4–24.1) <0.001 Metabolic syndrome components, n(%) Prevalence of MetS 811 (58.7) 308 (77.6) 503 (45.5) <0.001 Abdominal obesity 1177 (78.4) 374 (94.2) 803 (72.7) <0.001 Hypertension 790 (52.6) 304 (76.6) 486 (44.0) <0.001 High levels of fasting glucose 465 (31.0) 220 (55.4) 245 (22.2) <0.001 High levels of triglycerides 696 (46.3) 253 (63.7) 443 (40.1) <0.001 Low levels of HDL-C 821 (54.7) 219 (55.2) 602 (54.5) 0.393 Nutrients 2019,11, 1701 7 of 16 Table 1. em Cont. Characteristics Total Sample (n=1502) High CMRI ≥1 SD (n=397) Low CMRI <1 SD (n=1105) p-Value Cardiometabolic measurements SBP (mmHg) 130 (117–145) 142 (130–163) 126 (114–140) <0.001 DBP (mmHg) 72 (65–79) 78 (72–86) 70 (64–77) <0.001 MBP (mmHg) 92 (84–101) 100 (91–111) 89 (81–97) <0.001 Total cholesterol (mg/dL) 193 (166–221) 202 (171–232) 190 (164–216) <0.001 Triglycerides (mg/dL) 144 (105–192) 174 (134–252) 134 (101–180) <0.001 LDL-C (mg/dL) 126 (102–149) 127 (103–152) 125 (102–147) 0.116 HDL-C (mg/dL) 43 (36–53) 43 (36–54) 44 (36–53) 0.740 Glucose (mg/dL) 94 (86–102) 102 (93–121) 91 (84–98) <0.001 CMRI −0.21 (−1.41–1.07) 2.00 (1.44–2.84) −0.83 (−1.83–0.05) <0.001 Self-report comorbid chronic diseases, n(%) Hypertension 826 (55.0) 249 (62.7) 577 (52.2) <0.001 Diabetes 245 (16.3) 113 (28.5) 132 (11.9) <0.001 Respiratory diseases 165 (11.0) 49 (12.3) 116 (10.5) <0.001 Cardiovascular diseases 213 (14.2) 155 (39.0) 58 (5.2) <0.001 Stroke 70 (4.7) 22 (5.5) 48 (4.3) <0.001 Osteoporosis 184 (12.3) 66 (16.6) 118 (10.7) <0.001 Cancer 80 (5.3) 56 (14.1) 24 (2.2) <0.001 Hearing loss 360 (24.1) 89 (22.4) 271 (24.5) <0.001 Vision loss 851 (56.7) 228 (57.4) 623 (56.4) <0.001 Skewed continuous variables are reported as median and interquartile range (Q3-Q1), for non-skewed continuous variables mean values (standard deviations (SD)) are given, and categorical variables are reported as numbers and percentages in brackets. Significant between-sex differences (Student’s t-test, Wilcoxon rank-sum test or χ 2). BMI: body mass index; WtHR: waist-to-height ratio; BRI: body roundness index; ABSI: a body shape index; CI: conicity index; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol; CMRI: cardiometabolic risk index. p-values marked in bold are significant. 3.2. Association between Surrogate Anthropometric Indices with CMRI Linear regression analyses of surrogate anthropometric indices and CMRI on the total sample and also stratified by sex are shown in Figure 1. Overall, we found an acceptable-to-moderate positive correlation of CMRI with WtHR (r=0.52, p<0.001), ABSI (r=0.17, p<0.001), BMI (r=0.46, p<0.001), and BRI (r=0.52, p<0.001), whereas CI was negatively correlated with CMRI (r= − 0.42, p<0.001). When analyzing by sex, the decreasing order of the correlation coefficients in men was WtHR (r=0.50, p<0.001), BRI (r=0.50, p<0.001), BMI (r=0.49, p<0.001), CI (r= − 0.46, p<0.001), and ABSI ( r=0.16 , p<0.001), while in women the decreasing order of the correlation coefficients was WtHR ( r=0.55 , p<0.001 ), BRI ( r=0.54 ,p<0.001), BMI (r=0.45, p<0.01 ), CI ( r=−0.44 ,p<0.001), and ABSI ( r=0.22 , p<0.001). Nutrients 2019,11, 1701 8 of 16 Nutrients 2019, 11, x FOR PEER REVIEW 8 of 17 Figure 1. Association between surrogate anthropometric indices and CMRI, on the total sample and stratified by sex. BMI: body mass index; WtHR: waist to height ratio; BRI: body roundness index; ABSI: a body shape index; CI: conicity index; CMRI: cardiometabolic risk index. Figure 1. Association between surrogate anthropometric indices and CMRI, on the total sample and stratified by sex. BMI: body mass index; WtHR: waist to height ratio; BRI: body roundness index; ABSI: a body shape index; CI: conicity index; CMRI: cardiometabolic risk index. Nutrients 2019,11, 1701 9 of 16 3.3. Optimal Cut-Offs for Screening for CMRI by Sex The ROC curve analyses of the diagnostic performance of BMI, WtHR, BRI, ABSI, and CI in identifying a high cardiometabolic risk are shown in Table 2and Figure 2. In men, when considering the full sample, the best cut-offvales of BMI, WtHR, BRI, ABSI, and CI for detecting high cardiometabolic risk (CMRI ≥ 1 SD) were 25.2 (area under curve, AUC 0.76, sensitivity 84.4% and specificity 54.7%), 0.56 (AUC 0.77, sensitivity 83.6% and specificity 58.9%), 4.71 (AUC 0.77, sensitivity 83.6% and specificity 58.9%), 0.083 (AUC 0.60, sensitivity 69.5% and specificity 53.6%), and 22.9 (AUC 0.75, sensitivity 72.3% and specificity 65.9%), respectively. For women, the best cut-offvalues of BMI, WtHR, BRI, ABSI, and CI for detecting high cardiometabolic risk (CMRI ≥ 1 SD) were 28.4 (AUC 0.71, sensitivity 69.5% and specificity 64.1%), 0.63 (AUC 0.77, sensitivity 64.4% and specificity 76.7%), 6.20 (AUC 0.77, sensitivity 65.2% and specificity 76.1%), 0.080 (AUC 0.62, sensitivity 68.7% and specificity 51.6%), and 21.0 (AUC 0.71, sensitivity 63.6% and specificity 70.2%), respectively. Table 2. Cut-offpoints, area under curve, sensitivity and specificity for BMI, WtHR, BRI, ABSI, and CI to detect high cardiometabolic risk (CMRI ≥1 SD) by sex. Parameters BMI WtHR BRI ABSI CI Men Women Men Women Men Women Men Women Men Women Area under curve 0.76 0.71 0.77 0.77 0.77 0.77 0.60 0.62 0.75 0.71 p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 Optimal cut-off25.2 28.4 0.56 0.63 4.71 6.20 0.083 0.080 22.9 21.0 Youden index J 0.39 0.33 0.42 0.41 0.42 0.41 0.23 0.20 0.38 0.33 Sensitivity (%) 84.4 69.5 83.6 64.4 83.6 65.2 69.5 68.7 72.3 63.6 Specificity (%) 54.7 64.1 58.9 76.7 58.9 76.1 53.6 51.6 65.9 70.2 (+) Likelihood ratio 1.83 1.93 2.00 2.70 2.04 2.74 1.50 1.42 2.12 2.14 (–) Likelihood ratio 0.29 0.48 0.28 0.47 0.28 0.46 0.57 0.60 0.42 0.52 BMI: body mass index; WtHR: waist to height ratio; BRI: body roundness index; ABSI: a body shape index; CI: conicity index. Nutrients 2019, 11, x FOR PEER REVIEW 9 of 17 3.3. Optimal Cut-Offs for Screening for CMRI by Sex The ROC curve analyses of the diagnostic performance of BMI, WtHR, BRI, ABSI, and CI in identifying a high cardiometabolic risk are shown in Table 2 and Figure 2. In men, when considering the full sample, the best cut-off vales of BMI, WtHR, BRI, ABSI, and CI for detecting high cardiometabolic risk (CMRI ≥ 1 SD) were 25.2 (area under curve, AUC 0.76, sensitivity 84.4% and specificity 54.7%), 0.56 (AUC 0.77, sensitivity 83.6% and specificity 58.9%), 4.71 (AUC 0.77, sensitivity 83.6% and specificity 58.9%), 0.083 (AUC 0.60, sensitivity 69.5% and specificity 53.6%), and 22.9 (AUC 0.75, sensitivity 72.3% and specificity 65.9%), respectively. For women, the best cut-off values of BMI, WtHR, BRI, ABSI, and CI for detecting high cardiometabolic risk (CMRI ≥ 1 SD) were 28.4 (AUC 0.71, sensitivity 69.5% and specificity 64.1%), 0.63 (AUC 0.77, sensitivity 64.4% and specificity 76.7%), 6.20 (AUC 0.77, sensitivity 65.2% and specificity 76.1%), 0.080 (AUC 0.62, sensitivity 68.7% and specificity 51.6%), and 21.0 (AUC 0.71, sensitivity 63.6% and specificity 70.2%), respectively. Table 2. Cut-off points, area under curve, sensitivity and specificity for BMI, WtHR, BRI, ABSI, and CI to detect high cardiometabolic risk (CMRI ≥ 1 SD) by sex. Parameters BMI WtHR BRI ABSI CI Men Women Men Women Men Women Men Women Men Women Area under curve 0.76 0.71 0.77 0.77 0.77 0.77 0.60 0.62 0.75 0.71 p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 Optimal cut-off 25.2 28.4 0.56 0.63 4.71 6.20 0.083 0.080 22.9 21.0 Youden index J 0.39 0.33 0.42 0.41 0.42 0.41 0.23 0.20 0.38 0.33 Sensitivity (%) 84.4 69.5 83.6 64.4 83.6 65.2 69.5 68.7 72.3 63.6 Specificity (%) 54.7 64.1 58.9 76.7 58.9 76.1 53.6 51.6 65.9 70.2 (+) Likelihood ratio 1.83 1.93 2.00 2.70 2.04 2.74 1.50 1.42 2.12 2.14 (–) Likelihood ratio 0.29 0.48 0.28 0.47 0.28 0.46 0.57 0.60 0.42 0.52 BMI: body mass index; WtHR: waist to height ratio; BRI: body roundness index; ABSI: a body shape index; CI: conicity index. Figure 2. Diagnostic performance of surrogate anthropometric indices to detect high risk of CMRI by gender. BMI: body mass index; WtHR: waist-to-height ratio; BRI: body roundness index; ABSI: a body shape index; CI: conicity index. Figure 2. Diagnostic performance of surrogate anthropometric indices to detect high risk of CMRI by gender. BMI: body mass index; WtHR: waist-to-height ratio; BRI: body roundness index; ABSI: a body shape index; CI: conicity index. The ROC curves were compared using a pairwise comparison method and the differences between the five methods are shown in Table 3. Independently of sex, the ROC-AUC of WtHR did not significantly differ from that of BRI. The results indicated that WtHR and BRI seem to provide the best Nutrients 2019,11, 1701 16 of 16 35. Amirabdollahian, F.; Haghighatdoost, F. Anthropometric Indicators of Adiposity Related to Body Weight and Body Shape as Cardiometabolic Risk Predictors in British Young Adults: Superiority of Waist-to-Height Ratio. J. Obes. 2018,2018, 8370304. [CrossRef] [PubMed] 36. Ashwell, M.; Gunn, P.; Gibson, S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: Systematic review and meta-analysis. Obes. Rev. 2012 ,13, 275–286. [CrossRef] [PubMed] 37. Liu, P.J.; Ma, F.; Lou, H.P.; Zhu, Y.N. Comparison of the ability to identify cardiometabolic risk factors between two new body indices and waist-to-height ratio among Chinese adults with normal BMI and waist circumference. Public Health Nutr. 2017,20, 984–991. [CrossRef] [PubMed] 38. Haghighatdoost, F.; Sarrafzadegan, N.; Mohammadifard, N.; Asgary, S.; Boshtam, M.; Azadbakht, L. Assessing body shape index as a risk predictor for cardiovascular diseases and metabolic syndrome among Iranian adults. Nutrition 2014,30, 636–644. [CrossRef] [PubMed] 39. Perez-Sousa, M.A.; Venegas-Sanabria, L.C.; Chavarro-Carvajal, D.A.; Cano-Gutierrez, C.A.; Izquierdo, M.; Correa-Bautista, J.E.; Ram í rez-V é lez, R. Gait speed as a mediator of the effect of sarcopenia on dependency in activities of daily living. J. Cachexia Sarcopenia Muscle 2019. [CrossRef] [PubMed] 40. Gomez, F.; Corchuelo, J.; Curcio, C.L.; Calzada, M.T.; Mendez, F. SABE Colombia: Survey on Health, Well-Being, and Aging in Colombia-Study Design and Protocol. Curr. Gerontol. Geriatr. Res. 2016 ,2016. [CrossRef] © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).