The study of relationship between nutritional behaviors and metabolic indices: A systematic review
Sarah Nouriyengejeh1, Bahare Seyedhoseini2, Parastou Kordestani-Moghadam3, Ata Pourabbasi2
1 Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
2 Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
3 Social Determinants of Health Research Center, Lorestan University of Medical Sciences, Khorramabad, Iran
|Date of Submission||14-Jan-2020|
|Date of Acceptance||20-Jun-2020|
|Date of Web Publication||30-Oct-2020|
Dr. Ata Pourabbasi
Ground Floor, EMRI Central Building, Al-E-Ahmad Hyw., Tehran
Source of Support: None, Conflict of Interest: None
Metabolic indices are the wide range of characteristic factors, which can be changed during several medical conditions such as metabolic syndrome. Nutrition and related behaviors are one of the main aspects of human lifestyle which recent investigations have recognized their roles in the development of metabolic disorders. According to the spread of risky nutritional habits/behaviors due to the changes in lifestyle, and its importance in the prevalence of metabolic disorders, the authors attempted to summarize these evidences in a systematic review. The present study is a systematic review that encompasses those studies investigating the association between metabolic indices and nutritional/dietary behaviors published in two international databases in recent 11 years. Twenty-nine related articles were considered and their data were extracted. The relation between food choices and metabolic indices is more frequent in studies. While, inhibition and abstinent and eating together were two behavioral sets with the smallest share of research. Anthropometric indices have the highest rate in the evaluations. Finding the links between nutritional behavior and metabolic indices will be the key point in selecting the different types of interventions. These results will guide therapists to the accurate recognition of metabolic effects in targeting behavior for their intervention.
Keywords: Behavior, feeding behavior, metabolism, nutrition assessment
|How to cite this article:|
Nouriyengejeh S, Seyedhoseini B, Kordestani-Moghadam P, Pourabbasi A. The study of relationship between nutritional behaviors and metabolic indices: A systematic review. Adv Biomed Res 2020;9:66
|How to cite this URL:|
Nouriyengejeh S, Seyedhoseini B, Kordestani-Moghadam P, Pourabbasi A. The study of relationship between nutritional behaviors and metabolic indices: A systematic review. Adv Biomed Res [serial online] 2020 [cited 2021 Oct 20];9:66. Available from: https://www.advbiores.net/text.asp?2020/9/1/66/299501
| Introduction|| |
Metabolic indices are the wide range of characteristic factors, which can be changed during several medical conditions. Metabolic syndrome (MetS) as the main metabolic disorder with impaired metabolic indices is a set of signs and symptoms, including abdominal obesity, glucose intolerance, high blood pressure, and dyslipidemia, in which the insulin resistance is the most common pathophysiologic characteristic. In addition, MetS is one of the most important diseases with metabolic changes and the high proportion of research work on it. More than 1 per 3 American adults involve in MetS. The prevalence of MetS among Middle East countries is reported up to 63%, according to some national surveys.,, Regarding these studies, MetS is also correlated with the risk of other diseases, such as type II diabetes and cardiovascular diseases.,,
Recent investigations have recognized the role of lifestyle in the development of chronic diseases such as diabetes and MetS. Nutrition and related behaviors are one of the main aspects of human lifestyle whose effects on metabolic indices have been shown in studies.,,,, For instance, some researches have demonstrated that a healthy diet is associated with a decline in the prevalence of MetS.,,,, Furthermore, the effect of emotional eating disorders on the weight control and its significant role in the development of MetS has been proven.,,, In fact, the “eating until feeling full” and “fast eating” are two abnormal habits, which are in relation with high blood pressure, impaired lipid profiles, and fatty liver. As well as, evidences on behaviors such as the type of food and the number of daily meals, especially breakfast, demonstrate their association with metabolic indices.,
A closer look on the studies conducted so far reveals that nutritional issues and their metabolic correlates include the wide range of topics, such as nutritional habits, eating patterns, and food content; among them, the nutritional habits – metabolic axis – is the point of interest in recent years.,,,
According to the spread of risky nutritional habits/behaviors due to the changes in lifestyle, and its importance in changing metabolic indices and consequently the prevalence of metabolic disorders, the authors attempted to summarize these evidences by designing and running a systematic review to provide a general overview in this regard. Regarding the fact that the authors could not find the comprehensive research in this field, it seems that the current study could gather the results of existing research and show a future horizon for the next studies.
| Materials and Methods|| |
The present study is a systematic review that encompasses those studies investigating the association between metabolic indices and nutritional/dietary behaviors as the following.
Two valid databases, PubMed and Scopus, were searched using key words including Dietary, Eating, Nutrition, Habit, Behavior, and a combination of them to identify studies conducted until September 2019. The articles were limited to those human studies published in English since 2008. It should be noted that only original studies were included in the current research.
After reading the titles, the articles were categorized as relevant and nonrelevant by two researchers, according to study objectives. The relevant ones were read in their full text in order to data extraction.
Followed by determining the relevant studies in terms of titles and abstracts, the researchers used the STROBE checklist (i.e., strengthening the reporting of observational studies in epidemiology) which is a standard checklist to evaluate the selected papers. Articles given at least score 40 points according to the checklist questions were entered into the research.
All articles were further evaluated in terms of the behaviors and metabolic indices. All data including title, year of publication, samples, measurements, measurement tools, and main findings of the selected papers were extracted and categorized in the form of a table.
| Results|| |
Totally, the 11,174 articles were found in initial search. Nearly 4511 articles were duplicates, and 6627 articles served as irrelevant after the evaluation. Finally, 34 related articles were considered and their data were extracted. A summary of the data of these papers is summarized in [Figure 1]. The five full texts were not available, so E-mails were sent to their authors to request the full text. Four authors did not respond after 2 weeks, but because of the lack of papers in this area, we tried to extract data from the abstracts in their full capacity.
|Figure 1: The flowchart shows the process of searching and selecting articles for the review|
Click here to view
In the end, in all of these 34 remaining studies, 47 behavioral codes and 83 metabolic indices were measured in participants. The data extracted from these articles are shown in [Table 1].
|Table 1: Data extracted from selected articles, including: authors, year of publication, title, study participants, measurements, tools, and main findings|
Click here to view
Behavioral codes extracting from the studies were classified and identified into eight categories by an expert panel including food choice, drinking habits, set meals, calorie intake, mindful eating, inhibition and abstinence, eating together, and food safety [Table 2]. Furthermore, the metabolic indices were classified into eight groups including protein and amino acid, glycemic profile, lipid profile, vital signs, anthropometric indices, hormones, diseases, and others by the same experts. These categorizations, mentioned in [Table 2], could help a better understanding of research trends on behavior-metabolic relations.
|Table 2: Subcategorized nutritional behaviors based on expert panel discussion|
Click here to view
As shown in [Table 3], the relation between food choices and metabolic indices is more frequent in studies. While, inhibition and abstinent and eating together were two behavioral sets with the smallest share of research. Anthropometric indices have the highest rate in the evaluations, namely 11%–100% of studies assessed at least one anthropometric index. Food choice as one of the behavioral categories, with the highest relative frequency, gets 26% of anthropometric indices.
|Table 3: The absolute and relative frequency of metabolic indices measured in dietary/nutritional behavior categories|
Click here to view
| Discussion|| |
In this study, the authors investigated all the 10-year relevant original articles in the field of nutrition/dietary behavior-metabolic axis. The literature overview shows that the majority of the researchers have focused on the nutritional contents and its other aspects such as nutritional/dietary behaviors, which can affect metabolic status, have been less considered.
Nutritional behaviors more relevant to the type of food choice behaviors such as eating fast food, cooking with available ingredients, meat-only diet, the consumption of crustaceans, and the family of Lobsters and crabs were placed in this category.
Several studies have focused on these behaviors and their metabolic effects. In some studies, healthy food choice was associated with a reduction in the risk of developing metabolic diseases and normal body mass index (BMI).,,, In a study by Ahn et al., the consumption of rice among 26,006 Korean volunteers was examined, and the results showed that rice consumption with green vegetables, especially in postmenopausal women, has a role in reducing the risk of developing MetS. However, there are some controversies in these relations. In a study done by Bloomer et al. on the Daniel's diet (rich in whole vegetables and fruits), no statistically significant reduction was shown on the oxidative stress.
The behaviors included in drinking category are nonalcoholic and alcoholic beverage intake, milk consumption, etc., These behaviors have been studied in five researches; withal mostly, their impact on lipid and glycemic profile was assessed.
For instance, Korean researchers conduct an investigation on adult women and found that the high levels of soft drink consumption can be important for the risk of Met. In another study conducted by Al-Haifi et al., the association of sweet and nonalcoholic beverages with BMI was examined, and the findings shows that controlling this behavioral pattern has a more effective role on BMI than physical activity.
The set of nutritional behaviors included the hours during a day spent on eating, the number of meals, eating breakfast or not, and so on, which have been considered as set meals. These behaviors and their impact on 34 metabolic indices related to protein and amino acid have been studied so far. Most of these researches show the positive effect of recommended proper set meals (e. g., eating all three daily meals, especially breakfast) on metabolic indices. Eating breakfast is one of the most effective behaviors, and there are several works in this issue.,,,, This behavior has a significant effect on the reduction of BMI and the risk of developing MetS. Furthermore, avoidance of eating breakfast, which increases insulin resistance, can also increase hunger and reduce the feeling of satiety., Thomas et al. detected that a short-term change in set meal habits would have a negative effect on metabolic indices. Beside, Alexandrove et al. led an investigation on 10–17 youths, and their work showed that eating breakfast (as a primer meal) could prevent obesity. In another study, it is found that eating habits (such as skipping or eating breakfast) have a greater impact on changes in body mass in contrast with physical activity.
The majority of the investigations focus on calorie intake. This behavior category consists of the total sugar, carbohydrate and fat consumption, and related topics. Studies in this area have found that controlling the input calorie can help to reduce harmful metabolic parameters.,, In these studies, the change in nutritional behaviors for the control of calorie intake would help to improve overall health. It also plays an important role in the regulation of intestinal microbes, which is theoretically related to the probability of developing future chronic diseases.
Mindful eating behavior is only addressed by three studies. This category involves fast eating, eating consciously (avoid doing something else while eating and being fully focused on eating) and eating emotionally. These studies have suggested that eating consciously as a behavior helps to reduce abdominal fat and metabolic risk factors as well as a great influence on the individual weight gain.,,
Food safety is another set of nutritional behaviors which only one research runs with this concept. This set of behaviors includes avoiding the hot food and the school cafeteria. The results showed a significant effect of these behaviors on the reduction of metabolic risk factors.
Inhibition and abstinent behaviors include habits that help the individual control their appetite and behaviors that somehow play a role in inhibitory functions. Hunger, dietary restraint, eating until feeling full, and external based (responding to exogenous stimuli), such as the smell and appearance of food, are among those behaviors that fall into this set. In studies that examined these behaviors, it has been observed that adopting a proper pattern of inhibition and abstinent has a significant effect on the reduction of the risk of metabolic diseases and their risk factors.,,
Eating together consists of several behaviors such as eating with friends, eating with family, sharing food, and eating in parties. Nevertheless, there are few evidences in this set of behaviors. In a study conducted by Choi et al., 29 participants with more than one metabolic risk factor were dining with others, and the participants were found to have a significant reduction in weight, wrist size, and BMI during 16 weeks. However, other interventions have been designed in addition to eating together in their study.,
It could be concluded that most of the studies have focused on investigating the association between food choices and anthropometric indices, and the least studies have been done on the relationship between the concentration of nutritional hormones and behaviors such as drinking and eating habits. Although it was expected that the association of calorie intake with all metabolic indices has been checked out, only half of the studies examined this nutritional behavior. The authors could not find more related literatures considering the associations of metabolic diseases and “making safe food choices” as well as “eating together” behaviors, and the association of inhibition and abstinent eating behaviors has been investigated in few studies. Furthermore, there is a dearth in research on glycemic, lipid, and amino acid profiles, and behaviors such as eating together and eating safe food (for example, refusing to consume hot foods) are among the areas that have been less explored by researchers.
| Conclusion|| |
Assessing the relation between nutritional behavior/eating habits and metabolic indices leads to new search fields in behavioral interventions. The essential goal in these interventions is to promote metabolic status and decrease metabolic disorder incidences. Accordingly, finding the links between nutritional behavior and metabolic indices will be the key point in selecting the different types of interventions. The results of these studies will guide therapists to the accurate recognition of metabolic effects in targeting behavior for their intervention. In addition, these results will be a proper field for boosting metabolic health.
Furthermore, detecting the relations between nutritional behaviors and metabolic indices will be a vital point for policymaking and designing social interventions. Finding these relations could prioritize the selected behaviors for interventions in population level. As may be expected, the selected behaviors for population-wide interventions should have the maximum effect on metabolic indices. In addition, the result will help to find the effective behaviors in this regard.
The authors acknowledge Mrs. Ghobadi and other staff of Endocrinology and Metabolism Research Institute for their nice cooperation in this project.
Financial support and sponsorship
The study was supported by Endocrinology and Metabolism Research Institute (grant no. 1396-02-98-2186), Tehran University of Medical Science.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Sherling DH, Perumareddi P, Hennekens CH. Metabolic syndrome. J Cardiovasc Pharmacol Ther 2017;22:365-7.
Ansarimoghaddam A, Adineh HA, Zareban I, Iranpour S, HosseinZadeh A, Kh F. Prevalence of metabolic syndrome in Middle-East countries: Meta-analysis of cross-sectional studies. Diabetes Metab Syndr 2018;12:195-201.
Al Suwaidi J, Zubaid M, El-Menyar AA, Singh R, Rashed W, Ridha M, et al
. Prevalence of the metabolic syndrome in patients with acute coronary syndrome in six middle eastern countries. J Clin Hypertens (Greenwich) 2010;12:890-9.
Ashraf H, Rashidi A, Noshad S, Khalilzadeh O, Esteghamati A. Epidemiology and risk factors of the cardiometabolic syndrome in the Middle East. Expert Rev Cardiovasc Ther 2011;9:309-20.
Pérez-Martínez P, Mikhailidis DP, Athyros VG, Bullo M, Couture P, Covas MI, et al
. Lifestyle recommendations for the prevention and management of metabolic syndrome: An international panel recommendation. Nutr Rev 2017;75:307-26.
Ahn Y, Park SJ, Kwack HK, Kim MK, Ko KP, Kim SS. Rice-eating pattern and the risk of metabolic syndrome especially waist circumference in Korean Genome and Epidemiology Study (KoGES). BMC Public Health 2013;13:61.
Al-Daghri NM, Khan N, Alkharfy KM, Al-Attas OS, Alokail MS, Alfawaz HA, et al
. Selected dietary nutrients and the prevalence of metabolic syndrome in adult males and females in Saudi Arabia: A pilot study. Nutrients 2013;5:4587-604.
Alexandrov AA, Poryadina GI, Kotova MB, Ivanova EI. The specificity of schoolchildren's eating habits in Moscow and Murmansk. Voprosy Pitaniia 2014;83:67-74.
Al-Haifi AR, Al-Fayez MA, Al-Athari BI, Al-Ajmi FA, Allafi AR, Al-Hazzaa HM, et al
. Relative contribution of physical activity, sedentary behaviors, and dietary habits to the prevalence of obesity among Kuwaiti adolescents. Food Nutr Bull 2013;34:6-13.
Atkins JL, Whincup PH, Morris RW, Lennon LT, Papacosta O, Wannamethee SG. Dietary patterns and the risk of CVD and all-cause mortality in older British men. Br J Nutr 2016;116:1246-55.
Barbaresko J, Siegert S, Koch M, Aits I, Lieb W, Nikolaus S, et al
. Comparison of two exploratory dietary patterns in association with the metabolic syndrome in a Northern German population. Br J Nutr 2014;112:1364-72.
DiBello, Julia R, Stephen T. McGarvey, Peter Kraft, Robert Goldberg, Hannia Campos, et al.
“Dietary patterns are associated with metabolic syndrome in adult Samoans.” The Journal of nutrition 139. 2009;10:1933-43.
Kruger R, De Bray JG, Beck KL, Conlon CA, Stonehouse W. Exploring the relationship between body composition and eating behavior using the three factor eating questionnaire (TFEQ) in young New Zealand Women. Nutrients 2016;8: (386-397).
Cardi V, Leppanen J, Treasure J. The effects of negative and positive mood induction on eating behaviour: A meta-analysis of laboratory studies in the healthy population and eating and weight disorders. Neurosci Biobehav Rev 2015;57:299-309.
van Strien T, Konttinen H, Homberg JR, Engels RC, Winkens LH. Emotional eating as a mediator between depression and weight gain. Appetite 2016;100:216-24.
Vartanian LR, Porter AM. Weight stigma and eating behavior: A review of the literature. Appetite 2016;102:3-14.
Hsieh SD, Muto T, Murase T, Tsuji H, Arase Y. Eating until feeling full and rapid eating both increase metabolic risk factors in Japanese men and women. Public Health Nutr. 2011;14:1266-9.
Thomas EA, Higgins J, Bessesen DH, McNair B, Cornier MA. Usual breakfast eating habits affect response to breakfast skipping in overweight women. Obesity (Silver Spring) 2015;23:750-9.
Almoosawi S, Prynne CJ, Hardy R, Stephen AM. Time-of-day and nutrient composition of eating occasions: Prospective association with the metabolic syndrome in the 1946 British birth cohort. Int J Obes (Lond) 2013;37:725-31.
Anderson AL, Harris TB, Tylavsky FA, Perry SE, Houston DK, Hue TF, et al
. Dietary patterns and survival of older adults. J Am Diet Assoc 2011;111:84-91.
Mohammadi H, Karimifar M, Heidari Z, Zare M, Amani R. The effects of wheat germ supplementation on metabolic profile in patients with type 2 diabetes mellitus: A randomized, double-blind, placebo-controlled trial. Phytother Res 2020;34:879-85.
Alhakbany MA, Alzamil HA, Alabdullatif WA, Aldekhyyel SN, Alsuhaibani MN, Al-Hazzaa HM. Lifestyle habits in relation to overweight and obesity among Saudi women attending health science Colleges. J Epidemiol Glob Health 2018;8:13-9.
Almanza-Aguilera E, Urpi-Sarda M, Llorach R, Vázquez-Fresno R, Garcia-Aloy M, Carmona F, et al
. Microbial metabolites are associated with a high adherence to a Mediterranean dietary pattern using a 1H-NMR-based untargeted metabolomics approach. J Nutr Biochem 2017;48:36-43.
Angoorani P, Ejtahed HS, Mirmiran P, Mirzaei S, Azizi F. Dietary consumption of advanced glycation end products and risk of metabolic syndrome. Int J Food Sci Nutr 2016;67:170-6.
Bajaber AS, Abdelkarem HM, El-Mommten AM. Dietary approach and its relationship with metabolic syndrome components. Int J Pharm Technol Res 2016;9:237-46.
Bajerska J, Woźniewicz M, Suwalska A, Jeszka J. Eating patterns are associated with cognitive function in the elderly at risk of metabolic syndrome from rural areas. Eur Rev Med Pharmacol Sci 2014;18:3234-45.
Bean MK, Mazzeo SE, Stern M, Evans RK, Bryan D, Ning Y, et al
. Six-month dietary changes in ethnically diverse, obese adolescents participating in a multidisciplinary weight management program. Clin Pediatr (Phila) 2011;50:408-16.
Bloomer RJ, Trepanowski JF, Kabir MM, Alleman RJ Jr., Dessoulavy ME. Impact of short-term dietary modification on postprandial oxidative stress. Nutr J 2012;11:16.
Burkert NT, Freidl W, Großschädel F, Muckenhuber J, Stronegger WJ, Rásky E. Nutrition and health:Different forms of diet and their relationship with various health parameters among Austrian adults. Wien Klin Wochenschr 2014;126:113-8.
Castro MA, Baltar VT, Marchioni DM, Fisberg RM. Examining associations between dietary patterns and metabolic CVD risk factors: A novel use of structural equation modelling. Br J Nutr 2016;115:1586-97.
Chan R, Chan D, Lau W, Lo D, Li L, Woo J. A cross-sectional study to examine the association between dietary patterns and risk of overweight and obesity in Hong Kong Chinese adolescents aged 10-12 years. J Am Coll Nutr 2014;33:450-8.
Chang AR, Grams ME. Serum phosphorus and mortality in the Third National Health and Nutrition Examination Survey (NHANES III): Effect modification by fasting. Am J Kidney Dis 2014;64:567-73.
Choi J, Se-Young O, Lee D, Tak S, Hong M, Park SM, et al
. Characteristics of diet patterns in metabolically obese, normal weight adults (Korean National Health and Nutrition Examination Survey III, 2005). Nutr Metab Cardiovasc Dis 2012;22:567-74.
Choi Y, Lee MJ, Kang HC, Lee MS, Yoon S. Development and application of a web-based nutritional management program to improve dietary behaviors for the prevention of metabolic syndrome. Comput Inform Nurs 2014;32:232-41.
Chung S, Ha K, Lee HS, Kim CI, Joung H, Paik HY, et al
. Soft drink consumption is positively associated with metabolic syndrome risk factors only in Korean women: Data from the 2007-2011 Korea National Health and Nutrition Examination Survey. Metabolism 2015;64:1477-84.
Daubenmier J, Kristeller J, Hecht FM, Maninger N, Kuwata M, Jhaveri K, et al
. Mindfulness intervention for stress eating to reduce cortisol and abdominal fat among overweight and obese women: An exploratory randomized controlled study. J Obes 2011;2011:651936.
Kant AK, Leitzmann MF, Park Y, Hollenbeck A, Schatzkin A. Patterns of recommended dietary behaviors predict subsequent risk of mortality in a large cohort of men and women in the United States. J Nutr 2009;139:1374-80.
Kim CK, Kim HJ, Chung HK, Shin D. Eating alone is differentially associated with the risk of metabolic syndrome in korean men and women. Int J Environ Res Public Health 2018;15: (1020-1034).
Miguet M, Masurier J, Chaput JP, Pereira B, Lambert C, Dâmaso AR, et al
. Cognitive restriction accentuates the increased energy intake response to a 10-month multidisciplinary weight loss program in adolescents with obesity. Appetite 2019;134:125-34.
Shin A, Lim SY, Sung J, Shin HR, Kim J. Dietary intake, eating habits, and metabolic syndrome in Korean men. J Am Diet Assoc 2009;109:633-40.
Sierra-Johnson J, Undén AL, Linestrand M, Rosell M, Sjogren P, Kolak M, et al
. Eating meals irregularly: A novel environmental risk factor for the metabolic syndrome. Obesity (Silver Spring) 2008;16:1302-7.
Son H, Kim H. Influence of living arrangements and eating behavior on the risk of metabolic syndrome: A national cross-sectional study in South Korea. Int J Environ Res Public Health 2019;16: (919-929).
Tao L, Yang K, Huang F, Liu X, Li X, Luo Y, et al
. Association between self-reported eating speed and metabolic syndrome in a Beijing adult population: A cross-sectional study. BMC Public Health 2018;18:855.
[Table 1], [Table 2], [Table 3]
|This article has been cited by|
||Assessing Commensality in Research
| ||Henrik Scander,Agneta Yngve,Maria Lennernäs Wiklund |
| ||International Journal of Environmental Research and Public Health. 2021; 18(5): 2632 |
|[Pubmed] | [DOI]|
||Immune Influencers in Action: Metabolites and Enzymes of the Tryptophan-Kynurenine Metabolic Pathway
| ||Masaru Tanaka,Fanni Tóth,Helga Polyák,Ágnes Szabó,Yvette Mándi,László Vécsei |
| ||Biomedicines. 2021; 9(7): 734 |
|[Pubmed] | [DOI]|
||The Project Collection Food, Nutrition and Health, with a Focus on Eating Together
| ||Agneta Yngve,Nicklas Neuman,Irja Haapala,Henrik Scander |
| ||International Journal of Environmental Research and Public Health. 2021; 18(4): 1572 |
|[Pubmed] | [DOI]|
||Assessing Time of Eating in Commensality Research
| ||Henrik Scander,Maria Lennernäs Wiklund,Agneta Yngve |
| ||International Journal of Environmental Research and Public Health. 2021; 18(6): 2941 |
|[Pubmed] | [DOI]|