Changes in basal metabolic rate associated with depressive symptoms in Peruvian adults

Authors

Keywords:

basal metabolism, depression, logistic models, mental health, odds ratio

Abstract

Introduction: Depression affects mental and organic homeostasis, leading to alterations in energy metabolism.

Objective: To determine the association between basal metabolic rate and depressive symptoms in Peruvian adults.

Methods: An analytical and cross-sectional study based on the 2022 Family Demographic and Health Survey. The study population consisted of 31,681 adults. Variables included the presence of depression, basal metabolic rate, and sex. Educational level, weight, and height were included for multivariate adjustment. The Student t-test was used for independent samples, and the odds ratio was adjusted using binary logistic regression.

Results: Low basal metabolic rate was higher in groups with moderate (women= 70.30%; men= 71.90%), moderately severe (women= 73.10%; men= 75.30%), and severe (women= 71.40%; men= 70%) depression. The average basal metabolic rate in women with depression was 1206 kilocalories/24 hours, while in women without depression it was 1295 kilocalories/24 hours (p< 0.001). In men with depression, the average basal metabolic rate was 1501 kilocalories/24 hours, while in men without depression it was 1598 kilocalories/24 hours (p< 0.001). In multivariate analysis, women with depressive symptoms had a Basal Metabolic Rate < 1400 kilocalories/24 hours 1.20 times more often than women without depressive symptoms (Odds Ratio= 1.195; 95% Confidence Interval= 1.121-1.197); p< 0.001). In men, the Basal Metabolic Rate < 1600 kilocalories/24 hours was 1.32 times more often (Odds Ratio= 1.319; 95% Confidence Interval= 1.278-1.357); p < 0.001).

Conclusions: Depressive symptoms are associated with alterations in the basal metabolic rate in Peruvian adults.

Downloads

Download data is not yet available.

References

1. Cavieres Á, López-Silva P. La depresión como enfermedad: en defensa del modelo biomédico en psiquiatría [Internet]. Rev Med Chil. 2021; 149(2):274-80. DOI: 10.4067/s0034-98872021000200274

2. Christensen MC, Wong CMJ, Baune BT. Symptoms of major depressive disorder and their impact on psychosocial functioning in the different phases of the disease: Do the perspectives of patients and healthcare providers differ? [Internet]. Front Psychiatry. 2020; 11:280. DOI: 10.3389/fpsyt.2020.00280

3. Smarr KL, Keefer AL. Measures of depression and depressive symptoms: Beck depression inventory-II (BDI-II), center for epidemiologic studies depression scale (CES-D), geriatric depression scale (GDS), hospital anxiety and depression scale (HADS), and patient health questionnaire-9 (PHQ-9) [Internet]. Arthritis Care Res (Hoboken). 2011; 63 Suppl 11(S11):S454-66. DOI: 10.1002/acr.20556

4. Dean J, Keshavan M. The neurobiology of depression: An integrated view [Internet]. Asian J Psychiatr. 2017; 27:101-11. DOI: 10.1016/j.ajp.2017.01.025

5. Chodavadia P, Teo I, Poremski D, Fung DSS, Finkelstein EA. Prevalence and economic burden of depression and anxiety symptoms among Singaporean adults: results from a 2022 web panel [Internet]. BMC Psychiatry. 2023; 23(1):104. DOI: 10.1186/s12888-023-04581-7

6. Kupcova I, Danisovic L, Klein M, Harsanyi S. Effects of the COVID-19 pandemic on mental health, anxiety, and depression [Internet]. BMC Psychol. 2023 [acceso: 16/09/2023]; 11(1):108-9. DOI: 10.1186/s40359-023-01130-5

7. COVID-19 Mental Disorders Collaborators. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic [Internet]. Lancet. 2021 [acceso: 16/09/2023]; 398(10312):1700-12. Disponible en: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(21)02143-7/fulltext

8. Cazal M, Bouzas Marins JC, Natali AJ, Vallejo Soto DF, Sillero-Quintana M. Efecto del ejercicio físico en la tasa metabólica en reposo: aplicación en el control de la obesidad [Internet]. Rev Andal Med Deport. 2019 [acceso: 16/09/2023]; (Avance Online):272-7. Disponible en: https://dialnet.unirioja.es/servlet/articulo?codigo=7159407

9. Liu T, Xu Y, Yi C-X, Tong Q, Cai D. The hypothalamus for whole-body physiology: from metabolism to aging [Internet]. Protein Cell. 2022; 13(6):394-421. DOI: 10.1007/s13238-021-00834-x

10. Simon JJ, Stopyra MA, Mönning E, Sailer S, Lavandier N, Kihm LP, et al. Neuroimaging of hypothalamic mechanisms related to glucose metabolism in anorexia nervosa and obesity [Internet]. J Clin Invest. 2020 [acceso: 16/09/2023]; 130(8):4094-103. Disponible en: https://www.jci.org/articles/view/136782

11. Espinoza García AS, Martínez Moreno AG, Reyes Castillo Z. Papel de la grelina y la leptina en el comportamiento alimentario: evidencias genéticas y moleculares [Internet]. Endocrinol Diabetes Nutr (Engl ). 2021 [acceso: 16/09/2023]; 68(9):654-63. Disponible en: https://www.sciencedirect.com/science/article/pii/S2530016421000471

12. Izquierdo AG, Crujeiras AB, Casanueva FF, Carreira MC. Leptin, obesity, and Leptin resistance: Where are we 25 years later? [Internet]. Nutrients. 2019 [acceso: 16/09/2023]; 11(11):2704. Disponible en: https://www.mdpi.com/2072-6643/11/11/2704

13. Zimmerman CA, Leib DE, Knight ZA. Neural circuits underlying thirst and fluid homeostasis [Internet]. Nat Rev Neurosci. 2017 [acceso: 16/09/2023]; 18(8):459-69. Disponible en: https://www.nature.com/articles/nrn.2017.71

14. Lee DH, Keum N, Hu FB, Orav EJ, Rimm EB, Willett WC, et al. Predicted lean body mass, fat mass, and all cause and cause specific mortality in men: prospective US cohort study [Internet]. BMJ. 2018 [acceso: 16/09/2023]; 362:k2575. Disponible en: https://www.bmj.com/content/362/bmj.k2575

15. Miller T, Mull S, Aragon AA, Krieger J, Schoenfeld BJ. Resistance training combined with diet decreases body fat while preserving lean mass independent of resting metabolic rate: A randomized trial [Internet]. Int J Sport Nutr Exerc Metab. 2018 [acceso: 16/09/2023]; 28(1):46-54. Disponible en: https://journals.humankinetics.com/view/journals/ijsnem/28/1/article-p46.xml

16. Olejnícková J, Forejt M, Cermáková E, Hudcová L. Factors influencing basal metabolism of Czechs of working age from South Moravia [Internet]. Cent Eur J Public Health. 2019 [acceso: 16/09/2023]; 27(2):135-40. Disponible en: https://cejph.szu.cz/pdfs/cjp/2019/02/09.pdf

17. Malik VS, Willet WC, Hu FB. Nearly a decade on - trends, risk factors and policy implications in global obesity [Internet]. Nat Rev Endocrinol. 2020 [acceso: 16/09/2023]; 16(11):615-6. Disponible en: https://www.nature.com/articles/s41574-020-00411-y

18. Villena Chávez JE. Prevalencia de sobrepeso y obesidad en el Perú [Internet]. Rev Peru Ginecol Obstet. 2018 [acceso: 16/09/2023]; 63(4):593-8. Disponible en: http://www.scielo.org.pe/scielo.php?script=sci_arttext&pid=S2304-51322017000400012

19. Al-Khatib Y, Akhtar MA, Kanawati MA, Mucheke R, Mahfouz M, Al-Nufoury M. Depression and metabolic syndrome: A narrative review [Internet]. Cureus. 2022; 14(2):e22153. DOI: 10.7759/cureus.22153

20. Ballestar-Tarín ML, Ibáñez-del Valle V, Mafla-España MA, Navarro-Martínez R, Cauli O. Salivary brain-derived neurotrophic factor and cortisol associated with psychological alterations in university students [Internet]. Diagnostics (Basel). 2024; 14(4):447. DOI: 10.3390/diagnostics14040447

21. Taylor VH, MacQueen GM. The role of adipokines in understanding the associations between obesity and depression [Internet]. J Obes. 2010; 2010:1-6. DOI: 10.1155/2010/748048

22. Blasco BV, García-Jiménez J, Bodoano I, Gutiérrez-Rojas L. Obesity and depression: Its prevalence and influence as a prognostic factor: A systematic review [Internet]. Psychiatry Investig. 2020; 17(8):715-24. DOI: 10.30773/pi.2020.0099

23. Luppino FS, de Wit LM, Bouvy PF, Stijnen T, Cuijpers P, Penninx BWJH, et al. Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies: A systematic review and meta-analysis of longitudinal studies [Internet]. Arch Gen Psychiatry. 2010 [acceso: 16/09/2023]; 67(3):220-9. Disponible en: https://jamanetwork.com/journals/jamapsychiatry/fullarticle/210608

24. Martina Chávez M, Amemiya Hoshi I, Suguimoto Watanabe SP, Arroyo Aguilar RS, Zeladita-Huaman JA, Castillo Parra H. Depresión en adultos mayores en el Perú: distribución geoespacial y factores asociados según ENDES 2018 - 2020 [Internet]. An Fac Med (Lima Peru : 1990). 2022 [acceso: 16/09/2023]; 83(3):180-7. Disponible en: https://www.scielo.org.pe/scielo.php?script=sci_arttext&pid=S1025-55832022000300180

25. Huarcaya-Victoria J, De-Lama-Morán R, Quiros M, Bazán J, López K, Lora D. Propiedades psicométricas del Patient Health Questionnaire (PHQ-9) en estudiantes de medicina en Lima, Perú [Internet]. Rev Neuropsiquiatr. 2020 [acceso: 16/09/2023]; 83(2):72-8. Disponible en: https://www.scielo.org.pe/scielo.php?script=sci_arttext&pid=S0034-85972020000200072

26. Levis B, Benedetti A, Thombs BD, DEPRESsion Screening Data (DEPRESSD) Collaboration. Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: individual participant data meta-analysis [Internet]. BMJ. 2019 [acceso: 16/09/2023]; 365:l1476. Disponible en: https://pubmed.ncbi.nlm.nih.gov/30967483/

27. Dajpratham P, Pukrittayakamee P, Atsariyasing W, Wannarit K, Boonhong J, Pongpirul K. The validity and reliability of the PHQ-9 in screening for post-stroke depression [Internet]. BMC Psychiatry. 2020 [acceso: 16/09/2023]; 20(1):291. Disponible en: https://pubmed.ncbi.nlm.nih.gov/32517743/

28. Bendavid I, Lobo DN, Barazzoni R, Cederholm T, Coëffier M, de van der Schueren M, et al. The centenary of the Harris-Benedict equations: How to assess energy requirements best? Recommendations from the ESPEN expert group [Internet]. Clin Nutr. 2021 [acceso: 16/09/2023]; 40(3):690-701. Disponible en: https://www.sciencedirect.com/science/article/pii/S0261561420-306166

29. Bi X, Forde CG, Goh AT, Henry CJ. Basal metabolic rate and body composition predict habitual food and macronutrient intakes: Gender differences [Internet]. Nutrients. 2019;11(11):2653. DOI: 10.3390/nu11112653

30. Navarrete Mejia PJ, Parodi García JF, Vega García E, Pareja Cruz A, Benites Azabache JC, et al. Factores asociados al sedentarismo en jóvenes estudiantes de educación superior. Perú, 2017 [Internet]. Horiz méd. 2019 [acceso: 16/09/2023]; 19(1):46-52. Disponible en: https://www.scielo.org.pe/scielo.php?script=sci_arttext&pid=S1727-558X2019000100008

31. Fernández-García JC, Cardona F, Tinahones FJ. Inflamación, obesidad y dieta mediterránea {Internet]. Rev Endocrinol Nutr. 2010[acceso: 13/05/2025];18(2):65-71. Disponible en: https://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1665-11462010000200002

32. Cazal MDM, Bouzas Marins JC, Natali AJ, Vallejo Soto DF, Sillero-Quintana M. Efecto del ejercicio físico en la tasa metabólica en reposo: aplicación en el control de la obesidad {Internet]. Rev Andal Med Deport. 2019[acceso: 13/05/2025];12(3):272-7. Disponible en: http://dx.doi.org/10.33155/j.ramd.2017.10.004

33. Ortega FB, Ruiz JR, Labayen I, Lavie CJ, Blair SN. The Fat but Fit paradox: what we know and don't know about it {Internet]. Br J Sports Med. 2018[acceso: 13/05/2025];52(3):151-3. Disponible en: http://dx.doi.org/10.1136/bjsports-2016-097400

34.. Cortés Romero CE, Escobar Noriega A, Cebada Ruiz J, Soto Rodríguez G, Bilbao Reboredo T, Vélez Pliego M. Estrés y cortisol: implicaciones en la ingesta de alimento [Internet]. Rev cuba investig bioméd. 2018 [acceso: 16/09/2023]; 37(3):1-15. Disponible en: https://scielo.sld.cu/scielo.php?pid=S0864-03002018000300013&script=sci_arttext

35. Stetler C, Miller GE. Depression and hypothalamic-pituitary-adrenal activation: a quantitative summary of four decades of research. Psychosom Med [Internet]. 2011;73(2):114-26. Disponible en: http://dx.doi.org/10.1097/PSY.0b013e31820ad12b

36. Paddon-Jones D, Sheffield-Moore M, Creson DL, Sanford AP, Wolf SE, Wolfe RR, et al. Hypercortisolemia alters muscle protein anabolism following ingestion of essential amino acids [Internet]. Am J Physiol Endocrinol Metab. 2003[acceso: 13/05/2025];284(5):E946-53. Disponible en: http://dx.doi.org/10.1152/ajpendo.00397.2002

37. Romero Romero EE, Young J, Salado-Castillo R. Fisiología del estrés y su integración al sistema nervioso y endocrino [Internet]. Rev Med Cient. 2020 [acceso: 16/09/2023]; 32:61-70. Disponible en: https://revistamedicocientifica.org/index.php/rmc/article/view/535

38. Fernandes M, Mutch D, Leri F. The relationship between fatty acids and different depression-related brain regions, and their potential role as biomarkers of response to antidepressants [Internet]. Nutrients. 2017; 9(3):298. DOI: 10.3390/nu9030298

39. Zhang L, Sun H, Liu Z, Yang J, Liu Y. Association between dietary sugar intake and depression in US adults: a cross-sectional study using data from the National Health and Nutrition Examination Survey 2011-2018 [Internet]. BMC Psychiatry. 2024; 24(1)-20. DOI: 10.1186/s12888-024-05531-7

Published

2025-05-22

How to Cite

1.
Guevara Tirado A. Changes in basal metabolic rate associated with depressive symptoms in Peruvian adults. Rev Cubana Med Milit [Internet]. 2025 May 22 [cited 2025 May 23];54(2):e025075289. Available from: https://revmedmilitar.sld.cu/index.php/mil/article/view/75289