CoChat
Sign Up
Cited by 45Diabetes, Cardiovascular Risks, and LipoproteinsBody Composition Measurement TechniquesIndigenous Health and Education

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

Open access
Ramírez-Vélez, Robinson, Pérez-Sousa, Miguel Ángel, Izquierdo, Mikel, Cano-Gutierrez, Carlos A., González-Jiménez, Emilio, Schmidt-RioValle, Jacqueline, González-Ruíz, Katherine, Correa-Rodríguez, María·2019·Nutrients
DOI 10.3390/nu11081701PMC PMC6723899
Open this paper in CoChat — free accountOpen PDF

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-off points 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-off points 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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/nu11081701

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Ramírez-Vélez et al. reported that BRI demonstrated a higher AUROC than BMI in Colombian adults aged 65 years and older.

    “Ramírez-Vélez et al. [20] reported that BRI demonstrated a higher AUROC than BMI in Colombian adults aged 65 years and older, suggesting its suitability for screening metabolic diseases in older adults.”

    From The discriminatory ability of the body roundness index and body mass index for metabolic diseases in Korean adults: a comparative study (2025) · cites this paper as [20]

    The Results (§3.3; page unavailable) report higher AUCs for BRI than BMI in both men (0.77 vs 0.76) and women (0.77 vs 0.71) when identifying high cardiometabolic risk. But the Methods (§2.1; page unavailable) describe a Colombian study population aged 60 years and older, not exclusively 65 years and older. The Conclusions (§5; page unavailable) propose BRI as a screening tool for high cardiometabolic risk, rather than for metabolic diseases generally. (LLM, conf=0.90)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 39

  1. [1]Mente A. Yusuf S. Islam S. McQueen M.J. Tanomsup S. Onen C.L. Rangarajan S. Gerstein H.C. Anand S.S. INTERHEART Investigators Metabolic Syndrome and Risk of Acute Myocardial Infarction J. Am. Coll. Cardiol. 2010 55 2390 2398 10.1016/j.jacc.2009.12.053 20488312 · DOI
  2. [2]Chien K.-L. Hsu H.-C. Sung F.-C. Su T.-C. Chen M.-F. Lee Y.-T. Metabolic syndrome as a risk factor for coronary heart disease and stroke: An 11-year prospective cohort in Taiwan community Atherosclerosis 2007 194 214 221 10.1016/j.atherosclerosis.2006.07.033 16979176 · DOI
  3. [3]Boden-Albala B. Sacco R.L. Lee H.-S. Grahame-Clarke C. Rundek T. Elkind M.V. Wright C. Giardina E.-G.V. DiTullio M.R. Homma S. Metabolic Syndrome and Ischemic Stroke Risk Stroke 2008 39 30 35 10.1161/STROKEAHA.107.496588 18063821 PMC2677015 · DOI
  4. [4]Márquez-Sandoval F. Macedo-Ojeda G. Viramontes-Hörner D. Fernández Ballart J. Salas Salvadó J. Vizmanos B. The prevalence of metabolic syndrome in Latin America: A systematic review Public Health Nutr. 2011 14 1702 1713 10.1017/S1368980010003320 21486521 · DOI
  5. [5]DeBoer M.D. Gurka M.J. Woo J.G. Morrison J.A. Severity of the metabolic syndrome as a predictor of type 2 diabetes between childhood and adulthood: The Princeton Lipid Research Cohort Study Diabetologia 2015 58 2745 2752 10.1007/s00125-015-3759-5 26380985 PMC4734129 · DOI
  6. [6]Alberti K.G.M.M. Eckel R.H. Grundy S.M. Zimmet P.Z. Cleeman J.I. Donato K.A. Fruchart J.-C. James W.P.T. Loria C.M. Smith S.C. Harmonizing the Metabolic Syndrome Circulation 2009 120 1640 1645 10.1161/CIRCULATIONAHA.109.192644 19805654 · DOI · Paper page
  7. [7]Correa-Rodríguez M. Ramírez-Vélez R. Correa-Bautista J. Castellanos-Vega R. Arias-Coronel F. González-Ruíz K. Alejandro Carrillo H. Schmidt-RioValle J. González-Jiménez E. Association of Muscular Fitness and Body Fatness with Cardiometabolic Risk Factors: The FUPRECOL Study Nutrients 2018 10 1742 10.3390/nu10111742 PMC6266433 30424569 · DOI
  8. [8]Ramírez-Vélez R. Correa-Bautista J. Carrillo H. González-Jiménez E. Schmidt-RioValle J. Correa-Rodríguez M. García-Hermoso A. González-Ruíz K. Tri-Ponderal Mass Index vs. Fat Mass/Height3 as a Screening Tool for Metabolic Syndrome Prediction in Colombian Children and Young People Nutrients 2018 10 412 10.3390/nu10040412 PMC5946197 29584641 · DOI
  9. [9]Ramírez-Vélez R. Correa-Bautista J. González-Ruíz K. Tordecilla-Sanders A. García-Hermoso A. Schmidt-RioValle J. González-Jiménez E. The Role of Body Adiposity Index in Determining Body Fat Percentage in Colombian Adults with Overweight or Obesity Int. J. Environ. Res. Public Health 2017 14 1093 10.3390/ijerph14101093 28934175 PMC5664594 · DOI
  10. [10]Knowles K.M. Paiva L.L. Sanchez S.E. Revilla L. Lopez T. Yasuda M.B. Yanez N.D. Gelaye B. Williams M.A. Waist Circumference, Body Mass Index, and Other Measures of Adiposity in Predicting Cardiovascular Disease Risk Factors among Peruvian Adults Int. J. Hypertens. 2011 2011 10.4061/2011/931402 PMC3034939 21331161 · DOI
  11. [11]Browning L.M. Hsieh S.D. Ashwell M. A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0·5 could be a suitable global boundary value Nutr. Res. Rev. 2010 23 247 269 10.1017/S0954422410000144 20819243 · DOI · Paper page
  12. [12]Dobbelsteyn C. Joffres M. MacLean D. Flowerdew G. A comparative evaluation of waist circumference, waist-to-hip ratio and body mass index as indicators of cardiovascular risk factors. The Canadian Heart Health Surveys Int. J. Obes. 2001 25 652 661 10.1038/sj.ijo.0801582 11360147 · DOI
  13. [13]Krakauer N.Y. Krakauer J.C. A New Body Shape Index Predicts Mortality Hazard Independently of Body Mass Index PLoS ONE 2012 7 e39504 10.1371/journal.pone.0039504 22815707 PMC3399847 · DOI
  14. [14]Mantzoros C. Evagelopoulou K. Georgiadis E. Katsilambros N. Conicity Index as a Predictor of Blood Pressure Levels, Insulin and Triglyceride Concentrations of Healthy Premenopausal Women Horm. Metab. Res. 1996 28 32 34 10.1055/s-2007-979126 8820992 · DOI
  15. [15]Thomas D.M. Bredlau C. Bosy-Westphal A. Mueller M. Shen W. Gallagher D. Maeda Y. McDougall A. Peterson C.M. Ravussin E. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model Obesity 2013 21 2264 2271 10.1002/oby.20408 23519954 PMC3692604 · DOI · Paper page
  16. [16]Li G. Wu H. Wu X. Cao Z. Tu Y. Ma Y. Li B. Peng Q. Cheng J. Wu B. The feasibility of two anthropometric indices to identify metabolic syndrome, insulin resistance and inflammatory factors in obese and overweight adults Nutrition 2019 57 194 201 10.1016/j.nut.2018.05.004 30176572 · DOI
  17. [17]Li G. Yao T. Wu X.-W. Cao Z. Tu Y.-C. Ma Y. Li B.-N. Peng Q.-Y. Wu B. Hou J. Novel and traditional anthropometric indices for identifying arterial stiffness in overweight and obese adults Clin. Nutr. 2019 10.1016/j.clnu.2019.03.029 31056289 · DOI
  18. [18]Stefanescu A. Revilla L. Lopez T. Sanchez S.E. Williams M.A. Gelaye B. Using A Body Shape Index (ABSI) and Body Roundness Index (BRI) to predict risk of metabolic syndrome in Peruvian adults J. Int. Med. Res. 2019 10.1177/0300060519848854 PMC7140225 31144540 · DOI · Paper page
  19. [19]Maessen M.F.H. Eijsvogels T.M.H. Verheggen R.J.H.M. Hopman M.T.E. Verbeek A.L.M. de Vegt F. Entering a New Era of Body Indices: The Feasibility of a Body Shape Index and Body Roundness Index to Identify Cardiovascular Health Status PLoS ONE 2014 9 e107212 10.1371/journal.pone.0107212 25229394 PMC4167703 · DOI · Paper page
  20. [20]Krakauer N.Y. Krakauer J.C. Untangling Waist Circumference and Hip Circumference from Body Mass Index with a Body Shape Index, Hip Index, and Anthropometric Risk Indicator Metab. Syndr. Relat. Disord. 2018 16 160 165 10.1089/met.2017.0166 29649376 · DOI
  21. [21]Popkin B.M. Reardon T. Obesity and the food system transformation in Latin America Obes. Rev. 2018 19 1028 1064 10.1111/obr.12694 29691969 PMC6103889 · DOI · Paper page
  22. [22]Valdez R. A simple model-based index of abdominal adiposity J. Clin. Epidemiol. 1991 44 955 956 10.1016/0895-4356(91)90059-I 1890438 · DOI
  23. [23]Aronow W.S. Banach M. Ten most important things to learn from the ACCF/AHA 2011 expert consensus document on hypertension in the elderly Blood Press. 2012 21 3 5 10.3109/08037051.2011.615902 21991999 · DOI
  24. [24]Ramírez-Vélez R. Correa-Bautista J.E. García-Hermoso A. Cano C.A. Izquierdo M. Reference values for handgrip strength and their association with intrinsic capacity domains among older adults J. Cachexia Sarcopenia Muscle 2019 10 278 286 10.1002/jcsm.12373 30843369 PMC6463468 · DOI · Paper page
  25. [25]Bewick V. Cheek L. Ball J. Statistics review 13: Receiver operating characteristic curves Crit. Care 2004 8 508 512 10.1186/cc3000 15566624 PMC1065080 · DOI
  26. [26]DeLong E.R. DeLong D.M. Clarke-Pearson D.L. Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach Biometrics 1988 44 837 845 10.2307/2531595 3203132 · DOI · Paper page
  27. [27]Kutner M.H. Nachtsheim C. Neter J. Applied Linear Regression Models McGraw-Hill/Irwin New York, NY, USA 2004 0073014664
  28. [28]Tune J.D. Goodwill A.G. Sassoon D.J. Mather K.J. Cardiovascular consequences of metabolic syndrome Transl. Res. 2017 183 57 70 10.1016/j.trsl.2017.01.001 28130064 PMC5393930 · DOI
  29. [29]Hamer M. Stamatakis E. Metabolically healthy obesity and risk of all-cause and cardiovascular disease mortality J. Clin. Endocrinol. Metab. 2012 97 2482 2488 10.1210/jc.2011-3475 22508708 PMC3387408 · DOI
  30. [30]Davila E.P. Quintero M.A. Orrego M.L. Ford E.S. Walke H. Arenas M.M. Pratt M. Prevalence and risk factors for metabolic syndrome in Medellin and surrounding municipalities, Colombia, 2008–2010 Prev. Med. 2013 56 30 34 10.1016/j.ypmed.2012.10.027 23142534 · DOI
  31. [31]Escobedo J. Schargrodsky H. Champagne B. Silva H. Boissonnet C.P. Vinueza R. Torres M. Hernandez R. Wilson E. Prevalence of the Metabolic Syndrome in Latin America and its association with sub-clinical carotid atherosclerosis: The CARMELA cross sectional study Cardiovasc. Diabetol. 2009 8 52 10.1186/1475-2840-8-52 19781089 PMC2760519 · DOI
  32. [33]Chang Y. Guo X. Chen Y. Guo L. Li Z. Yu S. Yang H. Sun Y. A body shape index and body roundness index: Two new body indices to identify diabetes mellitus among rural populations in northeast China BMC Public Health 2015 15 794 10.1186/s12889-015-2150-2 26286520 PMC4544789 · DOI · Paper page
  33. [34]Wang H. Liu A. Zhao T. Gong X. Pang T. Zhou Y. Xiao Y. Yan Y. Fan C. Teng W. Comparison of anthropometric indices for predicting the risk of metabolic syndrome and its components in Chinese adults: A prospective, longitudinal study BMJ Open 2017 7 e016062 10.1136/bmjopen-2017-016062 28928179 PMC5623484 · DOI
  34. [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 10.1155/2018/8370304 30515323 PMC6236774 · DOI
  35. [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 10.1111/j.1467-789X.2011.00952.x 22106927 · DOI · Paper page
  36. [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 10.1017/S1368980016003281 27989263 PMC10261557 · DOI
  37. [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 10.1016/j.nut.2013.10.021 24800666 · DOI · Paper page
  38. [39]Perez-Sousa M.A. Venegas-Sanabria L.C. Chavarro-Carvajal D.A. Cano-Gutierrez C.A. Izquierdo M. Correa-Bautista J.E. Ramírez-Vélez R. Gait speed as a mediator of the effect of sarcopenia on dependency in activities of daily living J. Cachexia Sarcopenia Muscle 2019 10.1002/jcsm.12444 31066999 PMC6818451 · DOI
  39. [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 10.1155/2016/7910205 PMC5124445 27956896 · DOI

Cited by

Sorted by citations

Authors
Ramírez-Vélez, R., Pérez-Sousa, M. Á., Izquierdo, M., Cano-Gutierrez, C. A., González-Jiménez, E., Schmidt-RioValle, J., González-Ruíz, K., & Correa-Rodríguez, M.
Affiliations
  • 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
  • Universidad de Huelva (ES)
  • Hospital Universitario San Ignacio – Aging Institute, Pontificia Universidad Javeriana, Bogotá 110111, Colombia
  • Department of Nursing, Faculty of Health Sciences, University of Granada, Av. Ilustración, 60, 18016 Granada, Spain
  • Grupo de Ejercicio Físico y Deportes, Vicerrectoría de Investigaciones, Facultad de Salud, Universidad Manuela Beltrán, Bogotá 110231, DC, Colombia
ORCID
Venue
Nutrients
Publisher
MDPI AG
Published
2019-07-24
Volume
11
Issue
8
Pages
1701
ISSN
2072-6643 (ISSN-L)
DOI
10.3390/nu11081701
PMC
PMC6723899
OpenAlex
W2963430434
Cited by
45
References
1
Citations by year
2026: 7 · 2025: 6 · 2024: 6 · 2023: 6 · 2022: 3 · 2021: 11 · 2020: 5 · 2019: 1
FWCI
4.06
Type
journal article
Language
en
Update notices
correction (2019-07-24)
Open access
Licence
Locations
  • Nutrients (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Academica-e (Universidad Pública de Navarra) (submittedVersion, cc-by)
  • Repositorio Institucional (Pontificia Universidad Javeriana) (submittedVersion, cc-by-nc)
  • Repositorio Institucional de la Universidad de Huelva (Universidad de Huelva) (submittedVersion)
  • Institutional Repository of the University of Granada (University of Granada) (submittedVersion, cc-by)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Hispana (submittedVersion, cc-by-nc-nd)
  • Repositorio Institucional de la Universidad de Huelva (Universidad de Huelva) (submittedVersion, cc-by-nc-nd)
Funders
Departamento Administrativo de Ciencia, Tecnología e Innovación (ID 2013, no. 764)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
  • Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
  • Health Sciences › Health Professions › General Health Professions › Indigenous Health and Education
Keywords
cardiometabolic risk, anthropometric indices, body roundness index, waist-to-height ratio, body shape index, conicity index, body mass index, metabolic syndrome, older adults, obesity, screening tool, sex differences
MeSH terms
Age Factors, Aged, Anthropometry, Colombia, Cross-Sectional Studies, Female, Humans, Male, Middle Aged, Obesity, Predictive Value of Tests, Risk Factors, Sex Factors, Reproducibility of Results, Body Mass Index, Prevalence, Risk Assessment, Metabolic Syndrome, Waist-Height Ratio

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review