Ocena współzależnośc7i między składem ciała a wskaźnikiem wagowo-wzrostowym – BMI

ORYGINALNY ARTYKUŁ

Ocena współzależności między składem ciała a wskaźnikiem wagowo-wzrostowym – BMI

Katarzyna Banach 1 , Paweł Glibowski 1 , Paulina Skorek 1

1. Department of Biotechnology, Microbiology and Human Nutrition, Faculty of Food Science and Biotechnology, University of Life Sciences in Lublin

Opublikowany: 2019-11-05
DOI: 10.5604/01.3001.0013.5564
GICID: 01.3001.0013.5564
Dostępne wersje językowe: pl en
Wydanie: Postepy Hig Med Dosw 2019; 73 : 572-580

 

Abstrakt

Aim: Body composition, especially the mass of adipose tissue, affects the risk of developing the metabolic and cardiovascular diseases as well as some cancers. The aim of this study was to determine the relationship between the body composition of adults and their Body Mass Index. Material/Methods: The study involved 120 subjects (69 women and 51 men) aged 19 – 66 (30.55 ± 10.41). The recruited subjects were assigned to three subgroups: with normal body weight, overweight and obesity, depending on the BMI value. There were 40 subjects in each subgroup, including 23 women and 17 men. Besides the measurements of height and body mass, the SECA mBCA515 analyser was applied for the body composition analysis using the bioelectric impedance method. Results: A significant association was found between the BMI index and fat mass, lean mass and muscle mass, both in the whole group and after taking into account the sex. The correlation coefficient R range was from –0.88 to 0.97. The incidence of obesity in the studied group according to body fat content criteria (>25% for men and >30% for women) was 57%, while according to BMI criteria –33%. BMI cut-off points for obesity were 27.8, 26.4 and 26.4 kg/m2 for men, women and for all, respectively. Conclusions: In epidemiological studies, to identify obese people, body fat should also be taken into account in addition to BMI. If obesity is understood as excess fat, and not excess weight, the cut-off points for BMI-based obesity should be lowered.

Przypisy

  • 1. Andreoli A., Garaci F., Cafarelli F.P., Guglielmi G.: Body compositionin clinical practice. Eur. J. Radiol., 2016; 85: 1461–1468
    Google Scholar
  • 2. Babai M.A., Arasteh P., Hadibarhaghtalab M., Naghizadeh M,M.,Salehi A., Askari A., Homayounfar R.: Defining a BMI cut-off pointfor the Iranian population: The Shiraz Heart Study. PLoS One, 2016;11: e0160639
    Google Scholar
  • 3. Blaak E.: Gender differences in fat metabolism. Curr. Opin. Clin.Nutr. Metab. Care, 2001; 4: 499–502
    Google Scholar
  • 4. Böhm A., Heitmann B.L.: The use of bioelectrical impedance analysisfor body composition in epidemiological studies. Eur. J. Clin.Nutr., 2013; 67: S79–S85
    Google Scholar
  • 5. Bradley A.P.: The use of the area under the ROC curve in theevaluation of machine learning algorithms. Pattern Recognition,1997; 30: 1145–1159
    Google Scholar
  • 6. Chuang H.H., Li W.C., Sheu B.F., Liao S.C., Chen J.Y., Chang K.C., TsaiY.W.: Correlation between body composition and risk factors for cardiovasculardisease and metabolic syndrome. Biofactors, 2012; 38: 284–291
    Google Scholar
  • 7. De Lorenzo A., Deurenberg P., Pietrantuono M., Di Daniele N., CervelliV., Andreoli A.: How fat is obese? Acta Diabetol., 2003; 40: S254–S257
    Google Scholar
  • 8. DeNicola E., Aburizaiza O.S., Siddique A., Khwaja H., CarpenterD.O.: Obesity and public health in the Kingdom of Saudi Arabia. Rev.Environ. Health, 2015; 30: 191–205
    Google Scholar
  • 9. Drewnowski A.: Obesity and the food environment: Dietary energydensity and diet costs. Am. J. Prev. Med., 2004; 27: 154–162
    Google Scholar
  • 10. Fedewa M.V., Nickerson B.S., Esco M.R.: Associations of bodyadiposity index, waist circumference, and body mass index in youngadults. Clin. Nutr., 2019; 38: 715–720
    Google Scholar
  • 11. Fisher J.O., Liu Y., Birch L.L., Rolls B.J.: Effects of portion sizeand energy density on young children’s intake at a meal. Am. J. Clin.Nutr., 2007; 86: 174–179
    Google Scholar
  • 12. Franco L.P., Morais C.C., Cominetti C.: Normal-weight obesitysyndrome: diagnosis, prevalence, and clinical implications. Nutr.Rev., 2016; 74: 558–570
    Google Scholar
  • 13. Fuller N.J., Jebb S.A., Laskey M.A., Coward W.A., Elia M.: Four–component model for the assessment of body composition in humans:comparison with alternative methods, and evaluation of thedensity and hydration of fat-free mass. Clin. Sci., 1992; 82: 687–693
    Google Scholar
  • 14. Gallagher D., Heymsfield S.B., Heo M., Jebb S.A., MurgatroydP.R., Sakamoto Y.: Healthy percentage body fat ranges: an approachfor developing guidelines based on body mass index. Am. J. Clin.Nutr., 2000; 72: 694-701
    Google Scholar
  • 15. Gogga P., Karbowska J., Meissner W., Kochan Z.: Rola leptyny wregulacji metabolizmu lipidów i węglowodanów. Postępy Hig. Med.Dośw., 2011; 65: 255–262
    Google Scholar
  • 16. Gómez-Ambrosi J., Silva C., Galofré J.C., Escalada J., Santos S.,Millán D., Vila N., Ibañez P., Gil M.J., Valentí V., Rotellar F., RamírezB., Salvador J., Frühbeck G.: Body mass index classification missessubjects with increased cardiometabolic risk factors related to elevatedadiposity. Int. J. Obes., 2012; 36: 286–294
    Google Scholar
  • 17. González Jiménez E.: Body composition: Assessment and clinicalvalue. Endocrinol. Nutr., 2013; 60: 69–75
    Google Scholar
  • 18. Habib S.S.: Body mass index and body fat percentage in assessmentof obesity prevalence in Saudi adults. Biomed. Environ. Sci.,2013; 26: 94–99
    Google Scholar
  • 19. Hung S.P., Chen C.Y., Guo F.R., Chang C.I., Jan C.F.: Combine bodymass index and body fat percentage measures to improve the accuracyof obesity screening in young adults. Obes. Res. Clin. Pract.,2017; 11: 11–18
    Google Scholar
  • 20. Kupusinac A., Stokić E., Sukić E., Rankov O., Katić A.: What kindof relationship is between body mass index and body fat percentage?J. Med. Syst., 2017; 41: 5
    Google Scholar
  • 21. Lee K., Lee S., Kim Y.J., Kim Y.J.: Waist circumference, dual-energyX-ray absortiometrically measured abdominal adiposity and computedtomographically derived intra-abdominal fat area on detectingmetabolic risk factors in obese women. Nutrition, 2008; 24: 625–631
    Google Scholar
  • 22. Lukaski H.C.: Methods for the assessment of human body composition:traditional and new. Am. J. Clin. Nutr., 1987; 46: 537–556
    Google Scholar
  • 23. Lwin R., Darnell B., Oster R., Lawrence J., Foster J., Azziz R., GowerB.A.: Effect of oral estrogen on substrate utilization in postmenopausalwomen. Fertil. Steril., 2008; 90: 1275–1278
    Google Scholar
  • 24. Major–Gołuch A., Miazgowski T., Krzyżanowska-ŚwiniarskaB., Safronow K., Hajduk A.: Porównanie pomiarów masy tłuszczu umłodych zdrowych kobiet z prawidłową masą ciała za pomocą impedancjibioelektrycznej i densytometrii. Endokr. Otył. Zab. Przem.Mat., 2010; 6: 189–195
    Google Scholar
  • 25. Małecka-Massalska T., Popiołek J., Teter M., Homa-Mlak I., DecM., Makarewicz A., Karakuła-Juchnowicz H.: Wykorzystanie kątafazowego do oceny stanu odżywienia pacjentów z jadłowstrętempsychicznym. Psychiatr. Pol., 2017; 51: 1121–1131
    Google Scholar
  • 26. Marra M., Sammarco R., De Filippo E., Caldara A., Speranza E.,Scalfi L., Contaldo F., Pasanisi F.: Prediction of body composition inanorexia nervosa: Results from a retrospective study. Clin. Nutr.,2018; 37: 1670–1674
    Google Scholar
  • 27. Memish Z.A., El Bcheraoui C., Tuffaha M., Robinson M., Daoud F.,Jaber S., Mikhitarian S., Al Saeedi M., AlMazroa M.A., Mokdad A.H.,Al Rabeeah A.A.: Obesity and associated factors – Kingdom of SaudiArabia, 2013. Prev. Chronic. Dis., 2014; 11: E174
    Google Scholar
  • 28. Miazgowski T., Kucharski R., Sołtysiak M., Taszarek A., MiazgowskiB., Widecka K.: Visceral fat reference values derived from healthyEuropean men and women aged 20–30 years using GE Healthcaredual-energy x-ray absorptiometry. PLoS One, 2017; 12: e0180614
    Google Scholar
  • 29. Moradi-Lakeh M., El-Bcheraoui C., Tuffaha M., Daoud F., Al SaeediM., Basulaiman M., Memish Z.A., Al Mazroa M.A., Al RabeeahA.A., Mokdad A.H.: The health of Saudi youths: current challengesand future opportunities. BMC Fam. Pract., 2016; 17: 26
    Google Scholar
  • 30. Palmer B.F., Clegg D.J.: The sexual dimorphism of obesity. Mol.Cell Endocrinol., 2015; 402: 113–119
    Google Scholar
  • 31. Peine S., Knabe S., Carrero I., Brundert M., Wilhelm J., Ewert A.,Denzer U., Jensen B., Lilburn P.: Generation of normal ranges for measuresof body composition in adults based on bioelectrical impedanceanalysis using the seca mBCA. Int. J. Body Compos. Res., 2013; 11: 67–76
    Google Scholar
  • 32. Reiss J., Iglseder B., Kreutzer M., Weilbuchner I., TreschnitzerW., Kässmann H., Pirich C., Reiter R.: Case finding for sarcopeniain geriatric inpatients: performance of bioimpedance analysis incomparison to dual X-ray absorptiometry. BMC Geriatr., 2016; 16: 52
    Google Scholar
  • 33. Romero-Corral A., Somers V.K., Sierra-Johnson J., Thomas R.J., Collazo-Clavell M.L., Korinek J., Allison T.G., Batsis J.A., Sert-Kuniyoshi F.H.,Lopez-Jimenez F.: Accuracy of body mass index in diagnosing obesity inthe adult general population. Int. J. Obes., 2008; 32: 959–966
    Google Scholar
  • 34. Son J.W., Lee S.S., Kim S.R., Yoo S.J., Cha B.Y., Son H.Y., Cho N.H..:Low muscle mass and risk of type 2 diabetes in middle-aged and olderadults: findings from the KoGES. Diabetologia, 2017; 60: 865–872
    Google Scholar
  • 35. Stanowisko Polskiego Towarzystwa Dietetyki – Standardy leczeniadietetycznego otyłości prostej u osób dorosłych. Dietetyka, 2015; 8
    Google Scholar
  • 36. Swinburn B.A., Sacks G., Hall K.D., McPherson K., Finegood D.T.,Moodie M.L., Gortmaker S.L.: The global obesity pandemic: shapedby global drivers and local environments. Lancet, 2011; 378: 804–814
    Google Scholar
  • 37. Wang H., Chen Y.E., Eitzman D.T.: Imaging body fat: techniquesand cardiometabolic implications. Arterioscler. Thromb. Vasc. Biol.,2014; 34: 2217–2223
    Google Scholar
  • 38. WHO expert consultation: Appropriate body-mass index forAsian populations and its implications for policy and interventionstrategies. Lancet, 2004; 363: 157–163
    Google Scholar
  • 39. World Health Organization (WHO): Diet, nutrition and the preventionof chronic diseases. Report of a Joint WHO/FAO Expert Consultation.WHO Technical Report Series, 2003; 916, Geneva, Switzerland
    Google Scholar
  • 40. World Health Organization (WHO): Obesity and overweight,Fact sheets. http://www.who.int/mediacentre/factsheets/fs311/en/ (04.03.2019)
    Google Scholar
  • 41. World Health Organization (WHO): Obesity: Preventing andmanaging the global epidemic. Report on a WHO Consultation onObesity., 3–5 June 1997, Geneva, Switzerland
    Google Scholar
  • 42. World Health Organization (WHO): Obesity: preventing andmanaging the global epidemic. Report of a WHO Consultation. WHOTechnical Report Series, 2000; 894, Geneva, Switzerland
    Google Scholar
  • 43. World Health Organization (WHO): Physical status: the use andinterpretation of anthropometry. Report of a WHO Expert Committee.WHO Technical Report Series, 1995; 854, Geneva, Switzerland
    Google Scholar
  • 44. Zahorska-Markiewicz B., Podolec P., Kopeć G., Drygas W., Godycki-Ćwirko M., Opala G., Kozek E., Zdrojewski T.,Pająk A., Undas A.,Małecki M., Czarnecka D., Naruszewicz M., Stańczyk J., Sieradzki J.:Polish Forum for Prevention Guidelines on overweight and obesity.Kardiol. Pol., 2008; 66: 594–596
    Google Scholar

Pełna treść artykułu

Skip to content