Assessment of Obesity Parameters in Terms of Metabolic Age above and below Chronological Age in Adults
Chronologic age (CA) of individuals is closely related to obesity and generally affects the magnitude of obesity parameters. On the other hand, close association between basal metabolic rate (BMR) and metabolic age (MA) is also a matter of concern. It is suggested that MA higher than CA is the indicator of the need to improve the metabolic rate. In this study, the aim was to assess some commonly used obesity parameters, such as obesity degree, visceral adiposity, BMR, BMR-to-weight ratio, in several groups with varying differences between MA and CA values. The study comprises adults, whose ages vary between 18 and 79 years. Four groups were constituted. Group 1, 2, 3 and 4 were composed of 55, 33, 76 and 47 adults, respectively. The individuals exhibiting -1, 0 and +1 for their MA-CA values were involved in Group 1, which was considered as the control group. Those, whose MA-CA values varying between -5 and -10 participated in Group 2. Those, whose MAs above their real ages were divided into two groups [Group 3 (MA-CA; from +5 to + 10) and Group 4 (MA-CA; from +11 to + 12)]. Body mass index (BMI) values were calculated. TANITA body composition monitor using bioelectrical impedance analysis technology was used to obtain values for obesity degree, visceral adiposity, BMR and BMR-to-weight ratio. The compiled data were evaluated statistically using a statistical package program; SPSS. Mean ± SD values were determined. Correlation analyses were performed. The statistical significance degree was accepted as p < 0.05. The increase in BMR was positively correlated with obesity degree. MAs and CAs of the groups were 39.9 ± 16.8 vs 39.9 ± 16.7 years for Group 1, 45.0 ± 15.3 vs 51.4 ± 15.7 years for Group 2, 47.2 ± 12.7 vs 40.0 ± 12.7 years for Group 3, and 53.6 ± 14.8 vs 42 ± 14.8 years for Group 4. BMI values of the groups were 24.3 ± 3.6 kg/m2, 23.2 ± 1.7 kg/m2, 30.3 ± 3.8 kg/m2, and 40.1 ± 5.1 kg/m2 for Group 1, 2, 3 and 4, respectively. Values obtained for BMR were 1599 ± 328 kcal in Group 1, 1463 ± 198 kcal in Group 2, 1652 ± 350 kcal in Group 3, and 1890 ± 360 kcal in Group 4. A correlation was observed between BMR and MA-CA values in Group 1. No correlation was detected in other groups. On the other hand, statistically significant correlations between MA-CA values and obesity degree, BMI as well as BMR/weight were found in Group 3 and in Group 4. It was concluded that upon consideration of these findings in terms of MA-CA values, BMR-to-weight ratio was found to be much more useful indicator of the severe increase in obesity development than BMR. Also, the lack of associations between MA and BMR as well as BMR-to-weight ratio emphasize the importance of consideration of MA-CA values rather than MA.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3669299Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 741
 J. A. Batsis, and A. B. Zagaria, “Addressing obesity in aging patients,” Med. Clin. North Am., vol. 102, no. 1, pp. 65-85, Jan. 2018.
 M. M. Donma and O. Donma, “Evaluation of obesity degree from the points of view of chronological as well as metabolic ages,” NKU Med. J., vol. 7, no. 1, pp. 8-12, 2019.
 S. Chung, “Body mass index and body composition scaling to height in children and adolescents,” Ann. Pediatr. Endocrinol. Metab., vol. 20, no. 3, pp. 125-129, 2015.
 F. Q. Nuttal, “Body mass index. Obesity, BMI and health: A critical review,” Nutr. Today, vol. 50, no. 3, pp. 117-128, 2015.
 P. L. Wander, T. Hayashi, K. K. Sato, S. Uehara, Y. Hikita, D. L. Leonetti, et al., “Design and validation of a novel estimator of visceral adipose tissue area and comparison to existing adiposity surrogates,” J. Diabetes Complicat., vol. 32, no. 11, pp. 1062-1067, 2018.
 M. Siervo, J. Lara, C. Celis-Morales, M. Vacca, C. Oggioni, A. Battezzati, et al., “Age-related changes in basal substrate oxidation and visceral adiposity and their association with metabolic syndrome,” Eur. J. Nutr., vol. 55, no. 4, pp. 1755-1767, 2016.
 P. Anthanont, and M. D. Jensen, “Does basal metabolic rate predict weight gain?,” Am. J. Clin. Nutr., vol. 104, pp. 959-963, 2016.
 S. Lazzer, A. Patrizi, A. De Col, A. Saezza, and A. Sartorio, “Prediction of basal metabolic rate in obese children and adolescents considering pubertal stages and anthropometric characteristics or body composition,” Eur. J. Clin. Nutr., vol. 68, pp. 695-699, 2014.
 O. Donma, and M. M. Donma, “ Evaluation of the weight-based and fat-based indices in relation to basal metabolic rate-to-weight ratio,” Int. J. Med. Health Sci., vol. 13, no. 5, pp. 214 – 218, May 2019.
 W. L. Ripka, J. D. Modesto, L. Ulbricht, and P. M. Gewehr, “Obesity impact evaluated from fat percentage in bone mineral density of male adolescents,” Plos One, vol. 11, no. 9, pp. e0163470, 2016.
 J. P. Després, “Body fat distribution and risk of cardiovascular diseases. An update,” Circulation, vol. 126, no. 10, pp. 1301-1313, 2012.
 J. C. Garcia-Rubira, F. J. Cano-Garcia, B. Bullon, T. Seoane, P. V. Villar, M. D. Cordero, et al., “Body fat and metabolic age as indicators of inflammation and cardiovascular risk,” Eur. J. Preventive Cardiol., vol. 25, no. 3, pp. 233-234, 2018.
 J. Hertel, N. Fiedrich, K. Wittfeld, M. Pietzner, K. Budde, S. V. Auwera, et al., “Measuring biological age via metabonomics: The metabolic age score,” J. Proteome Res., vol. 15, no. 2, pp. 400-410, 2016.
 F. Alvear, R. Gomez-Campos, C. Urra-Albornoz, J. Pacheco-Carrillo, M. A. Cossio-Bolanos, “Predictors of indicators of body adiposity by chronological and biological age in children and adolescents residing in southern Chile,” Rev. Esp. Nutr. Hum. Diet, vol. 21, no. 4, pp. 360-368, 2017.
 https://tanita.eu /help-guides/products-manuals/ Accessed on 01/ 12/ 2019.
 A. Pereira Da Silva, A. Matos, A. Valente, A. Gil, I. Alonso, R. Ribeiro, et al., “Body composition assessment and nutritional status evaluation in men and women Portuguese centenarians,” J. Nutr. Health Aging, vol. 20, no. 3, pp. 256-266, 2016.
 G. Paolisso, M. Barbieri, M. Bonafe, and C. Franceschi, “Metabolic age modeling: the lesson from centenarians,” Eur. J. Clin. Invest., vol.30, no.10, pp.888-894, 2000.
 B. Calyniuk, E. Grochowska-Niedworok, M. Muc-Wierzgon, E. Nowakowska-Zajdel, and M. Osowski, “The effectiveness of the low energy diet in overweight and obese adults,” Rocz Panstw Zakl Hig., vol. 67, no. 2, pp. 137-146, 2016.
 P. J. Brown, M. M. Wall, C. Chen, M. E. Levine, K. Yaffe, S. P. Roose, “Biological age, not chronological age, is associated with late-life depression,” J. Gerontol. A. Biol. Sci. Med. Sci., vol. 73, no. 10, pp. 1370-1376, Sep. 2018.
 R. L. Worrall. “A measure of metabolic age,” Med. J. Aust., vol.42, no.18, pp.259-261, Feb. 1955.
 M. Moulis and C. Vindis, “Autophagy in metabolic age-related human diseases,” Cells, vol.7, no. 10, 149, 18 pages, 2018.
 J. Wilczynski, A. Pedrycz, D. Mucha, T. Ambrozy, and D. Mucha, “Body posture, postural stability and metabolic age in patients with Parkinson’s disease,” Bio. Med. Res. Int., vol.2017, Article ID: 3975417, 9 pages, 2017.
 C. Andersen, A. Sloan, L. Dupree, and B. Walker, “Younger relative metabolic age is associated with a more favorable body composition and plant-based dietary pattern., Curr. Dev. Nutr., vol. 3, no. suppl 1, pp. P21-038-19, Jun. 2019.