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Cited by 40Diabetes, Cardiovascular Risks, and LipoproteinsBody Composition Measurement TechniquesLipoproteins and Cardiovascular Health

Anthropometric and metabolic indices in assessment of type and severity of dyslipidemia

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Zaid, Muhammad, Ameer, Fatima, Munir, Rimsha, Rashid, Rida, Farooq, Nimrah, Hasnain, Shahida, Zaidi, Nousheen·2017·Journal of Physiological Anthropology
DOI 10.1186/s40101-017-0134-xPMC PMC5330152
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Abstract

BACKGROUND: It has been shown that obesity is associated with increased rates of dyslipidemia. The present work revisits the association between plasma lipid levels and classical indicators of obesity including body mass index (BMI). The significance of various anthropometric/metabolic variables in clinical assessment of type and severity of dyslipidemia was also determined. Recently described body indices, a body shape index (ABSI) and body roundness index (BRI), were also assessed in this context. METHODS: For the present cross-sectional analytical study, the participants (n = 275) were recruited from the patients visiting different health camps. Participants were anthropometrically measured and interviewed, and their fasting intravenous blood was collected. Plasma lipid levels were accordingly determined. RESULTS: The values for different anthropometric parameters are significantly different between dyslipidemic and non-dyslipidemic participants. Receiver operating characteristics curve analyses revealed that all the tested variables gave the highest area under the curve (AUC) values for predicting hypertriglyceridemia in comparison to other plasma lipid abnormalities. BRI gave slightly higher AUC values in predicting different forms of dyslipidemia in comparison to BMI, whereas ABSI gave very low values. CONCLUSIONS: Several anthropometric/metabolic indices display increased predictive capabilities for detecting hypertriglyceridemia in comparison to any other form of plasma lipid disorders. The capacity of BRI to predict dyslipidemia was comparable but not superior to the classical indicators of obesity, whereas ABSI could not detect dyslipidemia.

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  • Various anthropometric measurements such as BMI, waist-to-height ratio, and waist circumference can help identify types or status of abnormal metabolism.

    “Various anthropometric measurements, such as body mass index (BMI), waist-to-height ratio (WHtR), and waist circumference (WC), can help identify types or the status of abnormal metabolism (12–14).”

    From The waist-to-height ratio is a good predictor for insulin resistance in women with polycystic ovary syndrome (2024) · cites this paper as [13]

    The Results section on predictive capabilities reports that waist circumference, waist-to-height ratio, and BMI help distinguish people with dyslipidemia from those without it; it also reports their predictive value for high triglycerides. The evidence concerns dyslipidemia and specific lipid abnormalities, not abnormal metabolism generally. (LLM, conf=0.82)

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  1. [1]Gidding SS A perspective on obesity Am J Med Sci 1995 310 Suppl 1 S68 S71 10.1097/00000441-199512000-00012 7503127 · DOI
  2. [2]Saeed AA. Anthropometric predictors of dyslipidemia among adults in Saudi Arabia. Epidemiol Biostat Public Health. 2013;10:e8733-1.
  3. [3]Bibiloni MM Salas R Pons A Tur JA Prevalence of dyslipidaemia and associated risk factors among Balearic Islands’ adolescents, a Mediterranean region Eur J Clin Nutr 2015 69 722 728 10.1038/ejcn.2014.236 25351644 · DOI
  4. [4]Mota dos Santos C Sa Silva C Cesar de Araujo E Kruze Grande de Arruda I da Silva Diniz A Coelho Cabral P Lipid and glucose profiles in outpatients and their correlation with anthropometric indices Rev Port Cardiol 2013 32 35 41 23266377
  5. [5]Ali Chehrei SS Keshteli AH Daneshmand MA Rezaei J Correlation of dyslipidemia with waist to height ratio, waist circumference, and body mass index in Iranian adults Asia Pac J Clin Nutr 2007 16 248 253 17468079
  6. [6]Bhowmik B Munir SB Diep LM Siddiquee T Habib SH Samad MA Azad Khan AK Hussain A Anthropometric indicators of obesity for identifying cardiometabolic risk factors in a rural Bangladeshi population J Diabetes Investig 2013 4 361 368 10.1111/jdi.12053 24843680 PMC4020230 · DOI
  7. [7]Organization WH Obesity: Preventing and Managing the Global Epidemic. Report of a World Health Organization consultation 2000 Geneva World Health Organization 11234459
  8. [8]Heo M Faith MS Pietrobelli A Heymsfield SB Percentage of body fat cutoffs by sex, age, and race-ethnicity in the US adult population from NHANES 1999–2004 Am J Clin Nutr 2012 95 594 602 10.3945/ajcn.111.025171 22301924 · DOI · Paper page
  9. [9]Shah NR Braverman ER Measuring adiposity in patients: the utility of body mass index (BMI), percent body fat, and leptin PLoS One 2012 7 e33308 10.1371/journal.pone.0033308 22485140 PMC3317663 · DOI · Paper page
  10. [10]Romero-Corral A Somers VK Sierra-Johnson J Thomas RJ Collazo-Clavell ML Korinek J Allison TG Batsis JA Sert-Kuniyoshi FH Lopez-Jimenez F Accuracy of body mass index in diagnosing obesity in the adult general population Int J Obes (Lond) 2008 32 959 966 10.1038/ijo.2008.11 18283284 PMC2877506 · DOI · Paper page
  11. [12]Kok P Seidell JC Meinders AE The value and limitations of the body mass index (BMI) in the assessment of the health risks of overweight and obesity Ned Tijdschr Geneeskd 2004 148 2379 2382 15615272
  12. [13]Nevill AM Stewart AD Olds T Holder R Relationship between adiposity and body size reveals limitations of BMI Am J Phys Anthropol 2006 129 151 156 10.1002/ajpa.20262 16270304 · DOI
  13. [14]Prentice AM Jebb SA Beyond body mass index Obes Rev 2001 2 141 147 10.1046/j.1467-789x.2001.00031.x 12120099 · DOI
  14. [15]Zaccagni L Barbieri D Gualdi-Russo E Body composition and physical activity in Italian university students J Transl Med 2014 12 120 10.1186/1479-5876-12-120 24885945 PMC4025557 · DOI
  15. [16]Heymsfield SB Scherzer R Pietrobelli A Lewis CE Grunfeld C Body mass index as a phenotypic expression of adiposity: quantitative contribution of muscularity in a population-based sample Int J Obes (Lond) 2009 33 1363 1373 10.1038/ijo.2009.184 19773739 PMC3156622 · DOI
  16. [17]Pouliot MC Despres JP Lemieux S Moorjani S Bouchard C Tremblay A Nadeau A Lupien PJ Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women Am J Cardiol 1994 73 460 468 10.1016/0002-9149(94)90676-9 8141087 · DOI
  17. [18]Janssen I Heymsfield SB Allison DB Kotler DP Ross R Body mass index and waist circumference independently contribute to the prediction of nonabdominal, abdominal subcutaneous, and visceral fat Am J Clin Nutr 2002 75 683 688 11916754
  18. [19]Hsieh SD Yoshinaga H Do people with similar waist circumference share similar health risks irrespective of height? Tohoku J Exp Med 1999 188 55 60 10.1620/tjem.188.55 10494900 · DOI
  19. [20]Browning LM Hsieh SD 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
  20. [21]Krakauer NY Krakauer JC 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
  21. [22]Thomas DM Bredlau C Bosy-Westphal A Mueller M Shen W Gallagher D Maeda Y McDougall A Peterson CM Ravussin E Heymsfield SB Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model Obesity (Silver Spring) 2013 21 2264 2271 10.1002/oby.20408 23519954 PMC3692604 · DOI · Paper page
  22. [23]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
  23. [24]Duncan MJ Mota J Vale S Santos MP Ribeiro JC Associations between body mass index, waist circumference and body shape index with resting blood pressure in Portuguese adolescents Ann Hum Biol 2013 40 163 167 10.3109/03014460.2012.752861 23327095 · DOI
  24. [25]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
  25. [26]He S Chen X Could the new body shape index predict the new onset of diabetes mellitus in the Chinese population? PLoS One 2013 8 e50573 10.1371/journal.pone.0050573 23382801 PMC3559745 · DOI
  26. [27]Maessen MF Eijsvogels TM Verheggen RJ Hopman MT Verbeek AL 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
  27. [28]Posadas-Sanchez R Posadas-Romero C Zamora-Gonzalez J Mendoza-Perez E Cardoso-Saldana G Yamamoto-Kimura L Lipid and lipoprotein profiles and prevalence of dyslipidemia in Mexican adolescents Metabolism 2007 56 1666 1672 10.1016/j.metabol.2007.07.009 17998019 · DOI
  28. [29]Kong C Nimmo L Elatrozy T Anyaoku V Hughes C Robinson S Richmond W Elkeles RS Smoking is associated with increased hepatic lipase activity, insulin resistance, dyslipidaemia and early atherosclerosis in type 2 diabetes Atherosclerosis 2001 156 373 378 10.1016/S0021-9150(00)00664-X 11395034 · DOI
  29. [30]Keys A Fidanza F Karvonen MJ Kimura N Taylor HL Indices of relative weight and obesity J Chronic Dis 1972 25 329 343 10.1016/0021-9681(72)90027-6 4650929 · DOI
  30. [31]Deurenberg P Weststrate JA Seidell JC Body mass index as a measure of body fatness: age- and sex-specific prediction formulas Br J Nutr 1991 65 105 114 10.1079/BJN19910073 2043597 · DOI
  31. [32]Kawamoto R Tabara Y Kohara K Miki T Kusunoki T Takayama S Abe M Katoh T Ohtsuka N Relationships between lipid profiles and metabolic syndrome, insulin resistance and serum high molecular adiponectin in Japanese community-dwelling adults Lipids Health Dis 2011 10 79 10.1186/1476-511X-10-79 21586120 PMC3118370 · DOI
  32. [33]Martin SS Blaha MJ Elshazly MB Toth PP Kwiterovich PO Blumenthal RS Jones SR Comparison of a novel method vs the Friedewald equation for estimating low-density lipoprotein cholesterol levels from the standard lipid profile JAMA 2013 310 2061 2068 10.1001/jama.2013.280532 24240933 PMC4226221 · DOI · Paper page
  33. [34]Shirasawa T Ochiai H Ohtsu T Nishimura R Morimoto A Hoshino H Tajima N Kokaze A LDL-cholesterol and body mass index among Japanese schoolchildren: a population-based cross-sectional study Lipids Health Dis 2013 12 1 6 10.1186/1476-511X-12-77 23705977 PMC3680021 · DOI
  34. [35]García OP del Rocío Arellano M de la Torre-Carbot K Rosado JL Simple anthropometric measurements to predict dyslipidemias in Mexican school-age children: a cross-sectional study Open J Prev Med 2011 1 171 181 10.4236/ojpm.2011.13023 · DOI

Cited by

Sorted by citations

Authors
Zaid, M., Ameer, F., Munir, R., Rashid, R., Farooq, N., Hasnain, S., & Zaidi, N.
Affiliations
University of the Punjab (PK)
Corresponding author
Zaidi, Nousheen
ORCID
Venue
Journal of Physiological Anthropology
Publisher
Springer Science and Business Media LLC
Published
2017-02-28
Volume
36
Issue
1
Pages
19-19
ISSN
  • 1880-6791 (ISSN-L)
  • 1880-6805
DOI
10.1186/s40101-017-0134-x
PMC
PMC5330152
OpenAlex
W2592754752
Cited by
40
References
0
Citations by year
2026: 2 · 2025: 7 · 2024: 4 · 2023: 6 · 2022: 3 · 2021: 5 · 2020: 4 · 2019: 3 · 2018: 3 · 2017: 3
FWCI
2.81
Type
journal article
Language
en
Update notices
correction (2017-11-22)
Open access
Licence
cc-by
Locations
  • Journal of PHYSIOLOGICAL ANTHROPOLOGY (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Higher Education Commission, Pakistan (2505/R&D/11-2670)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
  • Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
  • Health Sciences › Medicine › Surgery › Lipoproteins and Cardiovascular Health
Keywords
dyslipidemia, hypertriglyceridemia, body mass index, anthropometric indices, body shape index, body roundness index, plasma lipid levels, obesity, dyslipidemia severity
MeSH terms
Anthropology, Physical, Anthropometry, Body Composition, Body Weight, Cross-Sectional Studies, Humans, Lipids, ROC Curve, Dyslipidemias

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