Skip to main content

The association between deficiency of nutrient intake and resting metabolic rate in overweight and obese women: a cross-sectional study



The double burden of malnutrition is an emerging public health concern nowadays which a correlation with obesity. This study aimed to examine the relationship between resting metabolic rate (RMR) and dietary intake of zinc, vitamin C, and riboflavin in overweight and obese women.


The RMR/FFM showed a significant association with riboflavin (β = 1.59; 95% CI 1.04–23.26, P = 0.04) and zinc (β = 0.78; 95% CI 1.04–4.61, P = 0.03) in the crude model. Moreover, differences in vitamin C and RMR/FFM was marginal significant (β = 0.75; 95% CI 0.95–4.77, P = 0.06). After adjusting for confounders the riboflavin association change to marginal significance (β = 1.52; 95% CI 0.91–23.04, P = 0.06). After controlling for potential confounders, the associations change between zinc and RMR/FFM (β = 0.66; 95% CI 0.78–4.86, P = 0.15) and between RMR/FFM and vitamin C (β = 0.48; 95% CI 0.66–3.96, P = 0.28). Our study showed a significant association between dietary intake of zinc, riboflavin, and vitamin C and change in RMR/FFM in overweight and obese women.


Obesity rates are growing globally [1]. More than 26% of the Iranian adult community is currently obese [2]. Females were much more affected than males [3, 4]. Obesity and its associated metabolic disorders develop when energy intake is more than energy expenditure; this can be caused by diminished physical activity, the disability of the central nervous system to down-regulate the ingestion of high-calorie foods, or appetite [5]. Although various factors contribute to the etiology of obesity, sedentary lifestyles, and unhealthy eating habits are among the principal contributors to the world obesity epidemic [6]. 60–75% of total energy expenditure is correlated with resting metabolic rate (RMR), which is an important part of daily energy consumption [7]. Weight gaining and obesity may be associated with low RMR [8].

Diet quality can be evaluated to better understand overall eating patterns [9]. Poor diet quality is a significant factor in the development of many chronic diseases, including obesity [10]. Investigations revealed, however, intake of vegetables and fruit is associated with a lower risk of obesity [11], inappropriate food behavior contributes to obesity and contributes to vitamin deficiency. Studies revealed that most vitamins are inadequate in obese individuals [12]. The double burden of malnutrition (DBM) is an emerging public health concern nowadays that happens as an inevitable consequence of nutritional transition [13]. The coexistence of overnutrition and undernutrition is often referred to as the DBM [14]. Nutrient adequacy ratio (NAR) and mean adequacy ratio (MAR), known as healthier diet quality indices. Higher scores in diet quality inversely connected with body mass index (BMI), and obesity [15]. Similarly, zinc and vitamin C are closely related to adiposity [16, 17]. In particular, some diet with a high score of diet quality like the Mediterranean dietary pattern has been reported to be inversely connected with BMI, and waist circumference [18].

To the best of our knowledge, this is the first study to investigate the relationship between deficiency of nutrient intakes such as zinc, vitamin C, and riboflavin and RMR in the adult women population. Accordingly, this study was performed to examine the NAR with RMR/FFM among a group of Iranian adult women.

Main text

Materials and methods

Study population

This cross-sectional research was performed on 293 adult women aged between 18 and 48 years who were selected by a multistage cluster random sampling method that had been referred to health centers in Tehran recruited. Participants were enrolled in the study according to inclusion and exclusion criteria. Inclusion criteria were: general health, overweight and obese women with BMI in the range of 25–40 kg/m2. The exclusion criteria were as follows: regular use of medicine, history of hypertension, cardiovascular diseases, and other chronic diseases, alcohol consumption, smoking, pregnancy, lactation period, and menopause. Furthermore, those who had been following an arbitrary special dietary regimen, and also those with any significant body weight fluctuations over the past 1 year and energy intakes lower than 800 kcal/day or higher than 4200 kcal/day were excluded.

Energy expenditure measurements

RMR was measured by indirect calorimetry (spirometer METALYZERR 3B-R3, Cortex Biophysik GmbH, Leipzig, Germany). According to the manufacturer’s instructions, gas ventilation and exchange is calibrated before each test. RMR is evaluated by measuring the amount of O2 consumed and CO2 produced. The RMR was assessed in the morning, after a comfortable night’s sleep, and following a 10–12 h fast. Participants were asked to avoid caffeine or alcohol consumption and severe exercise for a day before RMR measurements. After reclining in a steady-state and a supine position in a quiet room, the RMR was measured for 30 min. The respiratory exchange ratio and oxygen uptake (VO2) were analyzed within the middle 20 min of the resting period. Predictive RMR was determined using the Harris–Benedict equation, which considers the weight, height, and age of participants.

Body composition measurement

Body composition, including weight, BMI, fat mass, and fat-free mass (FFM) were acquired using a multi-frequency bioelectrical impedance analyzer InBody 770 scanner (Inbody Co., Seoul, Korea According to the manufacturer’s instructions, participants removed their shoes, coats, and sweaters, and stood on the balance scale in bare feet, and grasped the handles of the machine.

Biochemical assessment and hormonal assay

Metabolic health was assessed using the metabolic parameters that measured following standard chemical procedures. A 12-h fasting venous blood sample was used to measure all biochemical markers. Serum glucose was evaluated by a colorimetric method based on the GOD-PAP method. Serum insulin concentrations were analyzed by enzyme-linked immunosorbent assay (ELISA) method (Human insulin ELISA kit, Monobind Inc., Lake Forest, USA).

HOMA and QUICKI calculations

Insulin resistance was estimated by homeostasis model assessment (HOMA). The HOMA was calculated according to the following equation: HOMA = [Fasting Plasma Glucose (mmol/L) × Fasting Plasma Insulin (mIU/L)]/22.5 [19]. Insulin sensitivity quantitative insulin sensitivity check index (ISQUICKI) was assessed by: ISQUICKI = 1/[log (fasting insulin) + log (fasting glucose) [20].

Dietary intake assessment

Dietary intake data of the past year were obtained using a validated semi-quantitative food-frequency questionnaire (FFQ) [21], comprised of 168-item a trained nutritionist administered these FFQ. The FFQ consisted of a list of foods with standard serving sizes. Participants were asked to report their frequency and amount of each food item consumed during the previous year. Portion sizes of the consumed foods were converted to grams using household measurements [22]. Nutritionist IV computer software was used for the nutrient analysis of the diets. The database of this software was modified for Iranian foods.

Nutrient adequacy ratios (NAR)

For calculating the NAR, the ratio of daily individual intakes to the standard recommended amounts for the subject’s sex and age category was used. The standard recommended amounts are based on RDA (Recommended daily allowances) [23]. We calculated the NAR for three key nutrients, including zinc, vitamin C, and riboflavin according to the above-mentioned method. The prevalence of nutrient deficiency was estimated using NAR. NAR lower than one is considered as a deficiency.

Assessment of other covariates

International physical activity questionnaire (IPAQ, short form) were obtained by using an interview-based questionnaire from all participants about all the vigorous and moderate elements over the last 7 days, considering the time spent on these activities for height measurements, subjects were in a standing position without shoes, in contact with the wall with their head, shoulders, heels, and hips, and their height was recorded to the nearest 0.1 cm.

Statistical analysis

All statistical analysis was performed using the IBM SPSS software version 22.0 (SPSS, Chicago, IL, USA), and P-values less than 0.05 were considered statistically significant. The normal distribution of data was checked by the Kolmogorov–Smirnov test. An independent sample t test was used for assessed differences between groups with the low and standard intake of nutrients. RMR/FFM was analyzed after adjusting for FFM. The differences between RMR/FFM groups and dietary intake of nutrients were assessed by the binary logistic regression were performed to adjust for confounders effects such as age, energy intake, and physical activity (METs/day). Results were presented as odds ratios (ORs) and 95% confidence intervals (CIs) compared with the RMR groups.


Study population characteristics

A total of 293 healthy overweight and obese women were enrolled. The mean age, height, weight, and BMI of the study participants were 36.39 years (SD = 8.71), 161.84 cm (SD = 5.85), 80.22 kg (SD = 11.28), and 30.77 kg/m2 (SD = 3.79), respectively (Table 1) the mean body composition, RMR components, biochemical and anthropometric characteristics of subjects are shown in Table 1.

Table 1 Study of population characteristics

Participant’s characteristics between standard and deficiency of daily nutrient intakes

Dietary intake of three nutrients including riboflavin, vitamin C, and zinc were categorized based on nutrient adequacy ratios (NAR) and divided into two groups, standard and deficiency (Table 2). The RMR indicated a significant association with zinc (P = 0.001), which demonstrates people who consume higher zinc had higher RMR. Moreover, other factors like RMR/FFM (P = 0.06), V. O2 (P = 0.001), V. CO2 (P = 0.007), body fat mass (P = 0.02), FFM (P = 0.006), height (P = 0.005), and weight (P = 0.006) had a significant relationship with zinc. Besides, riboflavin had a significant association with body fat mass (P = 0.05), however, there were showed no relationship between vitamin C and participant characteristics (P > 0.05).

Table 2 Participants’ characteristics between standard and deficiency of daily nutrient intakes

Association of riboflavin, vitamin C, and zinc with RMR/FFM among obese women

Table 3 shows multivariate-adjusted models for the prevalence of higher RMR/FFM across the median dietary intake of riboflavin, vitamin C, and zinc. The RMR/FFM showed a significant association with riboflavin, vitamin C, and zinc in the crude model. For riboflavin, in the crude model before adjustment for the confounders, showed a statistically significant relationship (β = 1.59; 95% CI 1.04–23.26, P = 0.04). After controlling for the potential confounders, the association change to marginal significance (β = 1.52; 95% CI 0.91–23.04, P = 0.06). Moreover, differences in vitamin C and RMR/FFM was marginal significant (β = 0.75; 95% CI 0.95–4.77, P = 0.06). But after controlling for the potential confounders, the association disappeared (P = 0.28). Differences in zinc and RMR/FFM were also significant in the crude model (β = 0.78; 95% CI 1.04–4.61, P = 0.03), but after adjustment for the potential confounders, the association disappeared (P = 0.15).

Table 3 Association of riboflavin, vitamin C and zinc and RMR/FFM among obese women


The results of this study showed a significant association between riboflavin intake and the RMR/FFM. RMR was distinct in standard or deficiency consumption of riboflavin. There was not any significant association between vitamin C, zinc and RMR/FFM.

The findings of the current study indicate that there is no association between the amount of zinc consumed and RMR. However, other studies demonstrated that zinc has different functions in the metabolism of energy and works as a component of several enzymes crucial to the metabolism of carbohydrates, proteins, and lipids and metabolism of hormones that take part in the progress of obesity, especially insulin, and seems to be connected with the mechanisms of insulin resistance usually present among obese people [11, 2428]. Previous investigations recommend a negative association between RMR and insulin resistance [29, 30]. Subjects with obesity and impaired glucose tolerance showed higher RMR levels than those with obesity and normal glucose tolerance [31]. This discrepancy in the findings may be due to the limitations of the present study such as participants in the same-sex sample and assessing dietary intakes from FFQ. This study found no significant association between vitamin C and RMR. Some studies have shown that vitamin C administration significantly decreased RMR [33]. A probable mechanism could be due to the role of ascorbic acid in the expression of genes involved in adipogenesis, metabolism of glucocorticoids [12, 34] and inflammatory response. The result of this study is in line with previous studies which showed that the increase in blood vitamin C concentrations associated with the change in RMR/FFM [32]. This finding was consistent with a previous observation that by Selman Colin that showed the vitamin C supplementation did not affect daily energy expenditure or resting metabolism. This finding strongly recommends that antioxidant effects of the vitamin were not being compensated for by modulations in the rate of oxidative metabolism, which might affect total rates of reactive oxygen species product [35].

In this study, we also found that women who consumed higher riboflavin were more likely to have higher RMR. Considering the dietary restrictions on food intake or common dietary mistakes perceived among obese people, changes in the micronutrient intake leading to their deficiency are possible. Suitable riboflavin content is necessary to perform the effector function of macrophages with inhibition proliferation, intensification of apoptosis incidence, and also the reduction in phagocytosis efficiency [9]. Furthermore, resting reactive oxygen species production was raised while respiratory burs, a key ingredient of intracellular killing, were destroyed. Considering the significant function of adipocytes in the creation of obesity-related chronic inflammation to be justifiable to verify the influence of riboflavin deficiency on adipocytes function in the context of pro-inflammatory activation [36, 37]. That seems for future need more attention to the intake of sufficient micronutrients according to guidelines for preventing decreasing RMR so that reduce overweight and obesity. According to this study, more attention needs for riboflavin rich foods.


In conclusion, we could find a significant association between dietary intake of riboflavin, and change in RMR/FFM in overweight and obese women, however after controlling for ranges of potential confounding factors the statically meaningful for zinc and vitamin C disappear.


The major limitation of this study was the participants in the same-sex sample that it is not possible to generalize the results to men population. Because of the study type, cross-sectional study, we could not determine the causality. Another limitation for assessing dietary intakes from FFQ is misclassification. Albeit we controlled for the effect of the potential confounder by the statistical methods, because of unknown confounder cannot be excluded residual confounding will affect.

Availability of data and materials

Participants in this study did not agree to the public sharing of their data so supporting data is not available.



Resting metabolic rate


Double burden of malnutrition


Nutrient adequacy ratio


Mean adequacy ratio


Body mass index


Fat-free mass


Enzyme-linked immuno-sorbent assay


Homeostasis model assessment


Insulin sensitivity quantitative insulin sensitivity check index


Food frequency questionnaire


Recommended daily allowances


International physical activity questionnaire


Respiratory quotient


Fasting blood sugar


  1. Conklin AI, Ponce NA, Crespi CM, Frank J, Nandi A, Heymann J. Economic policy and the double burden of malnutrition: cross-national longitudinal analysis of minimum wage and women’s underweight and obesity. Public Health Nutr. 2018;21(5):940–7.

    Article  Google Scholar 

  2. WHO. Eastern Mediterranean region: framework for health information systems and core indicators for monitoring health situation and health system performance. 2017. Accessed 1 Mar 2014_ 28 Feb 2015

  3. Jafari-Adli S, Jouyandeh Z, Qorbani M, Soroush A, Larijani B, Hasani-Ranjbar S. Prevalence of obesity and overweight in adults and children in Iran; a systematic review. J Diabetes Metab Disord. 2014;13(1):1.

    Article  Google Scholar 

  4. Barak F, Falahi E, Keshteli AH, Yazdannik A, Esmaillzadeh A. Adherence to the dietary approaches to stop hypertension (DASH) diet in relation to obesity among Iranian female nurses. Public Health Nutr. 2015;18(4):705–12.

    Article  Google Scholar 

  5. Fukunaka A, Fujitani Y. Role of zinc homeostasis in the pathogenesis of diabetes and obesity. Int J Mol Sci. 2018;19(2):476.

    Article  Google Scholar 

  6. Shi Z, Makrides M, Zhou SJ. Dietary patterns and obesity in preschool children in Australia: a cross-sectional study. Asia Pac J Clin Nutr. 2018;27(2):406–12.

    CAS  PubMed  Google Scholar 

  7. Kim DK. Accuracy of predicted resting metabolic rate and relationship between resting metabolic rate and cardiorespiratory fitness in obese men. J Exerc Nutr Biochem. 2014;18(1):25.

    Article  Google Scholar 

  8. Buscemi S, Verga S, Caimi G, Cerasola G. Low relative resting metabolic rate and body weight gain in adult Caucasian Italians. Int J Obes. 2005;29(3):287–91.

    Article  CAS  Google Scholar 

  9. Ruiz LD, Zuelch ML, Dimitratos SM, Scherr RE. Adolescent obesity: diet quality, psychosocial health, and cardiometabolic risk factors. Nutrients. 2019;12(1):43.

    Article  Google Scholar 

  10. Bettermann EL, Hartman TJ, Easley KA, Ferranti EP, Jones DP, Quyyumi AA, et al. Higher mediterranean diet quality scores and lower body mass index are associated with a less-oxidized plasma glutathione and cysteine redox status in adults. J Nutr. 2018;148(2):245–53.

    Article  Google Scholar 

  11. Hosseini B, Saedisomeolia A, Allman-Farinelli M. Association between antioxidant intake/status and obesity: a systematic review of observational studies. Biol Trace Elem Res. 2017;175(2):287–97.

    Article  CAS  Google Scholar 

  12. Thomas-Valdés S, Tostes MDGV, Anunciação PC, da Silva BP, Sant’Ana HMP. Association between vitamin deficiency and metabolic disorders related to obesity. Crit Rev Food Sci Nutr. 2017;57(15):3332–43.

    Article  Google Scholar 

  13. Hasan M, Sutradhar I, Shahabuddin A, Sarker M. Double burden of malnutrition among Bangladeshi women: a literature review. Cureus. 2017.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Sekiyama M, Jiang HW, Gunawan B, Dewanti L, Honda R, Shimizu-Furusawa H, et al. Double burden of malnutrition in rural West Java: household-level analysis for father–child and mother–child pairs and the association with dietary intake. Nutrients. 2015;7(10):8376–91.

    Article  CAS  Google Scholar 

  15. Fallaize R, Livingstone KM, Celis-Morales C, Macready AL, San-Cristobal R, Navas-Carretero S, et al. Association between diet-quality scores, adiposity, total cholesterol and markers of nutritional status in European adults: findings from the Food4Me study. Nutrients. 2018;10(1):49.

    Article  Google Scholar 

  16. Hu FB. Dietary pattern analysis: a new direction in nutritional epidemiology. Curr Opin Lipidol. 2002;13(1):3–9.

    Article  CAS  Google Scholar 

  17. Md G, Pac A, KüÇüKbay F, Tayfun M, GüL A. Serum zinc, copper, and magnesium levels in obese children. Pediatr Int. 2011;39:339–41.

    Google Scholar 

  18. Bertoli S, Leone A, Vignati L, Bedogni G, Martínez-González MÁ, Bes-Rastrollo M, et al. Adherence to the Mediterranean diet is inversely associated with visceral abdominal tissue in Caucasian subjects. Clin Nutr. 2015;34(6):1266–72.

    Article  Google Scholar 

  19. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412–9.

    Article  CAS  Google Scholar 

  20. Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, et al. Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab. 2000;85(7):2402–10.

    Article  CAS  Google Scholar 

  21. Mirmiran P, Hosseini Esfahani F, Azizi F. Relative validity and reliability of the food frequency questionnaire used to assess nutrient intake: Tehran lipid and glucose study. Iran J Diabetes Lipid Disord. 2009;9(2):185–97.

    Google Scholar 

  22. Morgan KJ, Zabik ME, Stampley GL. The role of breakfast in diet adequacy of the US adult population. J Am Coll Nutr. 1986;5(6):551–63.

    Article  CAS  Google Scholar 

  23. Mahan LK, Escott-Stump S. Krause’s food nutrition and diet therapy. 10th ed. Philadelphia: WB Saunders; 2008.

    Google Scholar 

  24. Maxwell C, Volpe SL. Effect of zinc supplementation on thyroid hormone function. A case study of two college females. Ann Nutr Metab. 2007;51(2):188–94.

    Article  CAS  Google Scholar 

  25. Chen MD, Lin PY, Sheu WHH. Zinc status in plasma of obese individuals during glucose administration. Biol trace elem res. 1997;60(12):123–9.

    Article  CAS  Google Scholar 

  26. Weisstaub G, Hertrampf E, De Romana DL, Salazar G, Bugueño C, Castillo-Duran C. Plasma zinc concentration, body composition and physical activity in obese preschool children. Biol Trace Elem Res. 2007;118(2):167–74.

    Article  CAS  Google Scholar 

  27. Do Nascimento Marreiro D, Fisberg M, Cozzolino SMF. Zinc nutritional status and its relationships with hyperinsulinemia in obese children and adolescents. Biol trace elem res. 2004;100(2):137–49.

    Article  Google Scholar 

  28. Mahawar KK, Bhasker AG, Bindal V, Graham Y, Dudeja U, Lakdawala M, et al. Zinc deficiency after gastric bypass for morbid obesity: a systematic review. Obes Surg. 2017;27(2):522–9.

    Article  Google Scholar 

  29. Alawad AO, Merghani TH, Ballal MA. Resting metabolic rate in obese diabetic and obese non-diabetic subjects and its relation to glycaemic control. BMC Res Notes. 2013;6(382):1756–500.

    Google Scholar 

  30. Sun MX, Zhao S, Mao H, Wang ZJ, Zhang XY, Yi L. Increased BMR in overweight and obese patients with type 2 diabetes may result from an increased fat-free mass. J Huazhong Univ Sci Technolog Med Sci. 2016;36(1):59–63.

    Article  CAS  Google Scholar 

  31. Drabsch T, Holzapfel C, Stecher L, Petzold J, Skurk T, Hauner H. Associations between C-reactive protein, insulin sensitivity, and resting metabolic rate in adults: a mediator analysis. Front Endocrinol. 2018.

    Article  Google Scholar 

  32. Velthuis-te Wierik EJ, van Leeuwen RE, Hendriks HF, Verhagen H, Loft S, Poulsen HE, et al. Short-term moderate energy restriction does not affect indicators of oxidative stress and genotoxicity in humans. J Nutr. 1995;125(10):2631–9.

    CAS  PubMed  Google Scholar 

  33. Owu DU, Antai AB, Udofia KH, Obembe AO, Obasi KO, Eteng MU. Vitamin C improves basal metabolic rate and lipid profile in alloxan-induced diabetes mellitus in rats. J Biosci. 2006;31(5):575–9.

    Article  CAS  Google Scholar 

  34. Park B, Kim J. Oral contraceptive use, micronutrient deficiency, and obesity among premenopausal females in Korea: the necessity of dietary supplements and food intake improvement. PLoS ONE. 2016;11(6):e0158177.

    Article  Google Scholar 

  35. Ambra R, Canali R, Pastore G, Natella F. Covid-19 and diet: an evaluation of information available on internet in Italy. Acta Biomed. 2021;92(1):e2021077.

    PubMed  PubMed Central  Google Scholar 

  36. Stenzel AP, Carvalho R, Jesus P, Bull A, Pereira S, Saboya C, et al. Serum antioxidant associations with metabolic characteristics in metabolically healthy and unhealthy adolescents with severe obesity: an observational study. Nutrients. 2018;10(2):150.

    Article  Google Scholar 

  37. Mazur-Bialy AI, Pochec E. Vitamin B2 deficiency enhances the pro-inflammatory activity of adipocyte, consequences for insulin resistance and metabolic syndrome development. Life Sci. 2017;178:9–16.

    Article  CAS  Google Scholar 

Download references


We are extremely grateful to all the participants who took part in this study and the school of Nutritional and Dietetics at Tehran University of medical sciences.


This study is funded by grants from the Tehran University of Medical Sciences (TUMS) (Grant ID: 97-03-161-41155).

Author information

Authors and Affiliations



SFS, AM, AA and FS wrote the Manuscript, KM had full access to all the data in the study and was responsible for the integrity and accuracy of the data. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Khadijeh Mirzaei.

Ethics declarations

Ethics approval and consent to participate

All procedures involving human subjects were approved by the Ethics Commission of Tehran University of Medical Sciences (IR.TUMS.VCR.REC.1395.1597), and all participants signed written informed consent.

Consent for publication

This is formally to submit the article entitled “The association between deficiency of nutrient on resting metabolic rate in overweight and obese women: a cross-sectional study” prepared by the Tehran University of Medical Sciences for review and, hopefully, publication in your prestigious journal. The authors would like to advise that all authors listed have contributed to the work. All authors have agreed to submit the manuscript to Diabetology and Metabolic Syndrome. No part of the work has been published before. There is no conflict of interest in this paper.

Competing interests

All authors declared that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sajjadi, S.F., Mirzababaei, A., Abdollahi, A. et al. The association between deficiency of nutrient intake and resting metabolic rate in overweight and obese women: a cross-sectional study. BMC Res Notes 14, 179 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: