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Novel and traditional anthropometric indices to identify metabolic syndrome and metabolically healthy obesity in obese women

Year 2025, Volume: 6 Issue: 2, 91 - 97, 23.03.2025
https://doi.org/10.47582/jompac.1640684

Abstract

Aims: Traditional anthropometric indices may be inadequate for distinguishing obese individuals with low metabolic risk or those who are metabolically healthy. Therefore, newer, innovative indices may offer improved diagnostic accuracy. Current study aims to evaluate effectiveness of both traditional and novel anthropometric indices in identifying metabolic syndrome (MetS) and assessing metabolic risk factors such serum uric acid (SUA) and atherogenic index of plasma (AIP).
Methods: This was a retrospective study involving data of 292 obese women. The patients were separated into groups according to presence of MetS and their SUA and AIP levels. Predictive power was estimated using receiver operating characteristic curves, by comparing the area under the curve (AUC).
Results: Our results showed that all novel indices except the weight-adjusted waist index (WWI) had potential utility in diagnosing MetS. The lipid accumulation product (LAP) index had the highest AUC for MetS diagnosis, with a value of 0.832 (95% CI: 0.783–0.880). The abdominal volume index (AVI) and waist-to-height ratio (WHtR) showed the highest sensitivity (82.3%), while the waist-triglyceride index (WTI) had the highest specificity (89%).
Conclusion: Notably, both the visceral adiposity index (VAI) and LAP index achieved specificity and sensitivity values exceeding 70% and can be used in MetS screening of obese women. In contrast, the WWI was found to be statistically insufficient for defining MetS and distinguishing between SUA and AIP groups.

References

  • Bray GA, Kim KK, Wilding JPH. Obesity: a chronic relapsing progressive disease process. A position statement of the World Obesity Federation. Obesity Reviews. 2017;18(7):715-723. doi:10.1111/OBR.12551
  • Phillips CM. Metabolically healthy obesity: definitions, determinants and clinical implications. Rev Endocr Metab Disord. 2013;14(3):219-227. doi:10.1007/S11154-013-9252-X/TABLES/1
  • Tsatsoulis A, Paschou SA. Metabolically healthy obesity: criteria, epidemiology, controversies, and consequences. Curr Obes Rep. 2020; 9(2):109-120. doi:10.1007/S13679-020-00375-0/FIGURES/1
  • Blüher M. Metabolically healthy obesity. Endocr Rev. 2020;3(21):1-16. doi:10.1210/endrev/bnaa004
  • Elagizi A, Kachur S, Lavie CJ, et al. An overview and update on obesity and the obesity paradox in cardiovascular diseases. Prog Cardiovasc Dis. 2018;61(2):142-150. doi:10.1016/J.PCAD.2018.07.003
  • Suliga E, Ciesla E, Głuszek-Osuch M, Rogula T, Głuszek S, Kozieł D. The usefulness of anthropometric indices to identify the risk of metabolic syndrome. Nutrients. 2019;11(11):2598-2612. doi:10.3390/nu11112598
  • Wu L, Zhu W, Qiao Q, Huang L, Li Y, Chen L. Novel and traditional anthropometric indices for identifying metabolic syndrome in non-overweight/obese adults. Nutr Metab (Lond). 2021;18(1):3. doi:10.1186/s12986-020-00536-x
  • Wang H, Liu A, Zhao T, et al. 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(9): e016062. doi:10.1136/bmjopen-2017-016062
  • Adejumo EN, Adejumo AO, Azenabor A, et al. Anthropometric parameter that best predict metabolic syndrome in South west Nigeria. Diabetes Metab Syndr. 2019;13(1):48-54. doi:10.1016/J.DSX.2018.08.009
  • Ozturk EE, Yildiz H. Evaluation of different anthropometric indices for predicting metabolic syndrome. Eur Rev Med Pharmacol Sci. 2022; 26(22):8317-8325. doi:10.26355/eurrev_202211_30364
  • Li G, Wu HK, Wu XW, et al. 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. doi: 10.1016/J.NUT.2018.05.004
  • Rasaei N, Mirzababaei A, Arghavani H, et al. A comparison of the sensitivity and specificity of anthropometric measurements to predict unhealthy metabolic phenotype in overweight and obese women. Diabetes Metab Syndr. 2018;12(6):1147-1153. doi:10.1016/J.DSX.2018.06. 023
  • Zaki M, El-Bassyouni H, El-Gammal M, Kamal S. Indicators of the metabolic syndrome in obese adolescents. Arch Med Sci. 2015;11(1):92-98. doi:10.5114/aoms.2015.49214
  • Shi J, Chen Z, Zhang Y. Associations between body fat anthropometric indices and mortality among individuals with metabolic syndrome. Lipids Health Dis. 2024;23(1):318. doi:10.1186/S12944-024-02272-0/TABLES/4
  • Saito Y, Tanaka A, Node K, Kobayashi Y. Uric acid and cardiovascular disease: a clinical review. J Cardiol. 2021;78(1):51-57. doi:10.1016/J.JJCC. 2020.12.013
  • Dobiášová M. Atherogenic index of plasma [Log(triglycerides/HDL-cholesterol)]: theoretical and practical implications. Clin Chem. 2004; 50(7):1113-1115. doi:10.1373/CLINCHEM.2004.033175
  • Surrati AMQ, Mohammedsaeed W, Alfadhli EM. Anthropometrics and plasma atherogenic index in Saudi women Madinah KSA. Pak J Med Sci. 2024;40(3, Part-II):364-370. doi:10.12669/PJMS.40.3.8318
  • Akbarirad M, Darroudi S, Farsi Farima, et al. Investigation of the relationship between atherogenic index, anthropometric characteristics, and 10-year risk of metabolic syndrome: a population-based study. Ir J Med Sci. 2024;193(6):2705-2711. doi:10.1007/s11845-024-03791-6
  • Hamzeh B, Pasdar Y, Mirzaei N, et al. Visceral Adiposity Index and atherogenic index of plasma as useful predictors of risk of cardiovascular diseases: evidence from a cohort study in Iran. Lipids Health Dis. 2021; 20(1):82. doi:10.1186/s12944-021-01505-w
  • Lin SD, Tsai DH, Hsu SR. Association between serum uric acid level and components of the metabolic syndrome. J Chin Med Assoc. 2006;69(11): 512-516. doi:10.1016/S1726-4901(09)70320-X
  • Kanbay M, Jensen T, Solak Y, et al. Uric acid in metabolic syndrome: from an innocent bystander to a central player. Eur J Intern Med. 2016; 29:3-8. doi:10.1016/J.EJIM.2015.11.026
  • Sagun G, Oguz A, Karagoz E, Filizer AT, Tamer G, Mesci B. Application of alternative anthropometric measurements to predict metabolic syndrome. Clinics. 2014;69(5):347-353. doi:10.6061/CLINICS/2014(05)09
  • Akbas EM, Timuroglu A, Ozcicek A, et al. Association of uric acid, atherogenic index of plasma and albuminuria in diabetes mellitus. Int J Clin Exp Med. 2014;7(12):5737-5743.
  • Hongwei L, Zhenhai S, Wei J, et al. The effects and predictive values of novel anthropometric parameters on uric acid levels and hyperuricemia in adults. Sci Rep. 2025;15(1):956-966. doi:10.1038/s41598-024-84617-4
  • Chen D, Lu C, Chen K, et al. Association between anthropometric indices and hyperuricemia: a nationwide study in China. Clin Rheumatol. 2024; 43(3):907-920. doi:10.1007/S10067-024-06884-W/TABLES/5
  • Tu CM, Wei TE, Tseng GS, Chen CC, Liu CW. Serum uric acid is associated with incident metabolic syndrome independent of body shape index and body roundness index in healthy individuals. Nutr Metab Cardiovasc Dis. 2021;31(11):3142-3151. doi:10.1016/J.NUMECD.2021.07. 008
  • Luo Y, Hao J, He X, et al. Association between triglyceride-glucose index and serum uric acid levels: a biochemical study on anthropometry in non-obese type 2 diabetes mellitus patients. Diabetes Metab Syndr Obes. 2022;15:3447-3458. doi:10.2147/DMSO.S387961

Obez kadınlarda metaboli̇k sendrom ve metaboli̇k olarak sağlıklı obezi̇teyi̇ beli̇rlemek içi̇n yeni̇ ve geleneksel antropometri̇k endeksler

Year 2025, Volume: 6 Issue: 2, 91 - 97, 23.03.2025
https://doi.org/10.47582/jompac.1640684

Abstract

Amaç: Geleneksel antropometrik indeksler, düşük metabolik riske sahip obez bireyleri veya metabolik olarak sağlıklı olanları ayırt etmek için yetersiz olabilir. Bu nedenle, daha yeni, yenilikçi indeksler daha iyi tanısal doğruluk sunabilir. Bu çalışmanın amacı, metabolik sendromu (MetS) tanımlamada ve Serum Ürik Asit (SUA) ve Aterojenik Plazma İndeksi (AIP) gibi metabolik risk faktörlerini değerlendirmede hem geleneksel hem de yeni antropometrik indekslerin etkinliğini değerlendirmektir.
Yöntemler: Bu çalışma 292 obez kadının verilerini içeren retrospektif bir çalışmadır. Hastalar MetS varlığına ve serum ürik asit ve AIP düzeylerine göre gruplara ayrıldı. Tahmin gücü, eğri altındaki alan (AUC) karşılaştırılarak alıcı işletim karakteristik eğrileri kullanılarak tahmin edildi.
Sonuçlar: Sonuçlarımız, Ağırlığa Göre Ayarlanmış Bel İndeksi (WWI) hariç tüm yeni indekslerin MetS teşhisinde potansiyel faydaya sahip olduğunu göstermiştir. Lipid Birikim Ürünü (LAP) endeksi 0,832 (%95 GA: 0,783-0,880) değeri ile MetS tanısı için en yüksek eğri altında kalan alana (AUC) sahipti. Abdominal Hacim Endeksi (AVI) ve Bel-Boy Oranı (WHtR) en yüksek duyarlılığı (%82,3) gösterirken, Bel-Trigliserit Endeksi (WTI) en yüksek özgüllüğe (%89) sahipti.
Sonuçlar: Hem Visseral Adipozite İndeksi (VAI) hem de LAP indeksi %70'in üzerinde özgüllük ve duyarlılık değerlerine ulaşmıştır ve obez kadınların metabolik sendrom taramasında kullanılabilir. Buna karşılık, WWI MetS'yi tanımlamada ve SUA ile AIP gruplarını ayırt etmede istatistiksel olarak yetersiz bulunmuştur.

References

  • Bray GA, Kim KK, Wilding JPH. Obesity: a chronic relapsing progressive disease process. A position statement of the World Obesity Federation. Obesity Reviews. 2017;18(7):715-723. doi:10.1111/OBR.12551
  • Phillips CM. Metabolically healthy obesity: definitions, determinants and clinical implications. Rev Endocr Metab Disord. 2013;14(3):219-227. doi:10.1007/S11154-013-9252-X/TABLES/1
  • Tsatsoulis A, Paschou SA. Metabolically healthy obesity: criteria, epidemiology, controversies, and consequences. Curr Obes Rep. 2020; 9(2):109-120. doi:10.1007/S13679-020-00375-0/FIGURES/1
  • Blüher M. Metabolically healthy obesity. Endocr Rev. 2020;3(21):1-16. doi:10.1210/endrev/bnaa004
  • Elagizi A, Kachur S, Lavie CJ, et al. An overview and update on obesity and the obesity paradox in cardiovascular diseases. Prog Cardiovasc Dis. 2018;61(2):142-150. doi:10.1016/J.PCAD.2018.07.003
  • Suliga E, Ciesla E, Głuszek-Osuch M, Rogula T, Głuszek S, Kozieł D. The usefulness of anthropometric indices to identify the risk of metabolic syndrome. Nutrients. 2019;11(11):2598-2612. doi:10.3390/nu11112598
  • Wu L, Zhu W, Qiao Q, Huang L, Li Y, Chen L. Novel and traditional anthropometric indices for identifying metabolic syndrome in non-overweight/obese adults. Nutr Metab (Lond). 2021;18(1):3. doi:10.1186/s12986-020-00536-x
  • Wang H, Liu A, Zhao T, et al. 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(9): e016062. doi:10.1136/bmjopen-2017-016062
  • Adejumo EN, Adejumo AO, Azenabor A, et al. Anthropometric parameter that best predict metabolic syndrome in South west Nigeria. Diabetes Metab Syndr. 2019;13(1):48-54. doi:10.1016/J.DSX.2018.08.009
  • Ozturk EE, Yildiz H. Evaluation of different anthropometric indices for predicting metabolic syndrome. Eur Rev Med Pharmacol Sci. 2022; 26(22):8317-8325. doi:10.26355/eurrev_202211_30364
  • Li G, Wu HK, Wu XW, et al. 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. doi: 10.1016/J.NUT.2018.05.004
  • Rasaei N, Mirzababaei A, Arghavani H, et al. A comparison of the sensitivity and specificity of anthropometric measurements to predict unhealthy metabolic phenotype in overweight and obese women. Diabetes Metab Syndr. 2018;12(6):1147-1153. doi:10.1016/J.DSX.2018.06. 023
  • Zaki M, El-Bassyouni H, El-Gammal M, Kamal S. Indicators of the metabolic syndrome in obese adolescents. Arch Med Sci. 2015;11(1):92-98. doi:10.5114/aoms.2015.49214
  • Shi J, Chen Z, Zhang Y. Associations between body fat anthropometric indices and mortality among individuals with metabolic syndrome. Lipids Health Dis. 2024;23(1):318. doi:10.1186/S12944-024-02272-0/TABLES/4
  • Saito Y, Tanaka A, Node K, Kobayashi Y. Uric acid and cardiovascular disease: a clinical review. J Cardiol. 2021;78(1):51-57. doi:10.1016/J.JJCC. 2020.12.013
  • Dobiášová M. Atherogenic index of plasma [Log(triglycerides/HDL-cholesterol)]: theoretical and practical implications. Clin Chem. 2004; 50(7):1113-1115. doi:10.1373/CLINCHEM.2004.033175
  • Surrati AMQ, Mohammedsaeed W, Alfadhli EM. Anthropometrics and plasma atherogenic index in Saudi women Madinah KSA. Pak J Med Sci. 2024;40(3, Part-II):364-370. doi:10.12669/PJMS.40.3.8318
  • Akbarirad M, Darroudi S, Farsi Farima, et al. Investigation of the relationship between atherogenic index, anthropometric characteristics, and 10-year risk of metabolic syndrome: a population-based study. Ir J Med Sci. 2024;193(6):2705-2711. doi:10.1007/s11845-024-03791-6
  • Hamzeh B, Pasdar Y, Mirzaei N, et al. Visceral Adiposity Index and atherogenic index of plasma as useful predictors of risk of cardiovascular diseases: evidence from a cohort study in Iran. Lipids Health Dis. 2021; 20(1):82. doi:10.1186/s12944-021-01505-w
  • Lin SD, Tsai DH, Hsu SR. Association between serum uric acid level and components of the metabolic syndrome. J Chin Med Assoc. 2006;69(11): 512-516. doi:10.1016/S1726-4901(09)70320-X
  • Kanbay M, Jensen T, Solak Y, et al. Uric acid in metabolic syndrome: from an innocent bystander to a central player. Eur J Intern Med. 2016; 29:3-8. doi:10.1016/J.EJIM.2015.11.026
  • Sagun G, Oguz A, Karagoz E, Filizer AT, Tamer G, Mesci B. Application of alternative anthropometric measurements to predict metabolic syndrome. Clinics. 2014;69(5):347-353. doi:10.6061/CLINICS/2014(05)09
  • Akbas EM, Timuroglu A, Ozcicek A, et al. Association of uric acid, atherogenic index of plasma and albuminuria in diabetes mellitus. Int J Clin Exp Med. 2014;7(12):5737-5743.
  • Hongwei L, Zhenhai S, Wei J, et al. The effects and predictive values of novel anthropometric parameters on uric acid levels and hyperuricemia in adults. Sci Rep. 2025;15(1):956-966. doi:10.1038/s41598-024-84617-4
  • Chen D, Lu C, Chen K, et al. Association between anthropometric indices and hyperuricemia: a nationwide study in China. Clin Rheumatol. 2024; 43(3):907-920. doi:10.1007/S10067-024-06884-W/TABLES/5
  • Tu CM, Wei TE, Tseng GS, Chen CC, Liu CW. Serum uric acid is associated with incident metabolic syndrome independent of body shape index and body roundness index in healthy individuals. Nutr Metab Cardiovasc Dis. 2021;31(11):3142-3151. doi:10.1016/J.NUMECD.2021.07. 008
  • Luo Y, Hao J, He X, et al. Association between triglyceride-glucose index and serum uric acid levels: a biochemical study on anthropometry in non-obese type 2 diabetes mellitus patients. Diabetes Metab Syndr Obes. 2022;15:3447-3458. doi:10.2147/DMSO.S387961
There are 27 citations in total.

Details

Primary Language English
Subjects Endocrinology, Metabolic Medicine, Medical Biochemistry and Metabolomics (Other)
Journal Section Research Articles [en] Araştırma Makaleleri [tr]
Authors

Nergis Akbaş 0000-0002-7236-1767

Arzu Uzun 0000-0002-1505-2060

Publication Date March 23, 2025
Submission Date February 15, 2025
Acceptance Date March 5, 2025
Published in Issue Year 2025 Volume: 6 Issue: 2

Cite

AMA Akbaş N, Uzun A. Novel and traditional anthropometric indices to identify metabolic syndrome and metabolically healthy obesity in obese women. J Med Palliat Care / JOMPAC / jompac. March 2025;6(2):91-97. doi:10.47582/jompac.1640684

TR DİZİN ULAKBİM and International Indexes (1d)

Interuniversity Board (UAK) Equivalency: Article published in Ulakbim TR Index journal [10 POINTS], and Article published in other (excuding 1a, b, c) international indexed journal (1d) [5 POINTS]



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