Skip to main content

The association of dietary patterns with endocannabinoids levels in overweight and obese women



Higher levels of anandamide (AEA) and 2-arachidonoylglycerol (2-AG), the main arachidonic acid-derived endocannabinoids, are frequently reported in overweight and obese individuals. Recently, endocannabinoids have become a research interest in obesity area regarding their role in food intake. The relationship between dietary patterns and endocannabinoids is poorly understood; therefore, this study evaluated the association of the dietary patterns with AEA and 2-AG levels in overweight and obese women.


In this cross sectional study, 183 overweight and obese females from Tabriz, Iran who aged between 19 and 50 years old and with mean BMI = 32.44 ± 3.79 kg/m2 were interviewed. The AEA and 2-AG levels were measured, and the dietary patterns were assessed using food frequency questionnaire. To extract the dietary patterns, factor analysis was applied. The association between AEA and 2-AG levels and dietary patterns was analyzed by linear regression.


Three major dietary patterns including “Western”, “healthy”, and “traditional” were extracted. After adjusting for age, physical activity, BMI, waist circumference, and fat mass, higher levels of AEA and 2-AG were observed in participants who were in the highest quintile of the Western pattern (P <  0.05). Also, in both unadjusted and adjusted models, significantly lower levels of AEA and 2-AG were detected in the women of the highest quintile of the healthy pattern (P <  0.01). Moreover, there was no significant association between “traditional” pattern and AEA and 2- AG levels in both unadjusted and adjusted models (P > 0.05).


In regard with the lower levels of endocannabinoids in healthy dietary pattern, adherence to healthy pattern might have promising results in regulating endocannabinoids levels.


Overweight/obesity is one of the serious public health issues in developing countries such as Iran, and according to data about 22.5% of women and 10.5% of men are obese in Iran [1,2,3]. Scientific evidence suggests that chronic consumption of foods which contain large amounts of sugars and fats (i.e., the Western diet) is one of the main obesity drivers [4]. The worldwide impact of overweight/obesity and its complications indicate an urgent need to distinguish the important molecular mechanisms and metabolic targets implicated in energy balance [5]. In the past 15 years, the endocannabinoid system (ECS) has appeared as a lipid signaling system involved in the energy balance regulation, as it has control on every aspect of calorie regulation [6]. This system consists of endogenous ligands N-arachidonoyl-ethanolamide (anandamide), 2-arachidonoyl glycerol (2 AG), the cannabinoid 1 and 2 receptors, and enzymes responsible for the biosynthesis and degradation of ligands [7]. The endogenous ligands are lipid derivatives of a ω 6-polyunsaturated fatty acid, arachidonic acid (ARA), with multiple functions [8]. A growing body of evidence suggests that, overstimulation of ECS can lead to obesity and also to obesity- associated disorders and higher levels of ARA-derived AEA and 2-AG are frequently observed in the overweight and obese individuals [9, 10]. Since dietary intake of fatty acids is the main source of the endogenous cannabinoids biosynthesis in mammals, changes in nutritional status might affect the levels of EC [11, 12].

Nutrition transition, and specifically acquisition of a Western diet (large amounts of red meats, fast foods and snacks), is one of the factors that may help explaining the changes in the diet as well as obesity [8]. For example, in a study by Hall and colleagues, the consumption of ultra-processed foods led to greater energy intake and weight gain compared to unprocessed diets [13]. Furthermore, in Western diet (high-fat, high-sucrose)-induced obese rodents, both AEA and 2-AG levels increased, which was found to drive overeating [4]. Also, after oral exposure to dietary fats, the eCB levels elevated in the rodents’ small intestinal epithelium which in turn prompts food consumption, as CB1 receptors blockade pharmacologically in the small intestine suppressed food intake exactly before sham-feeding [14, 15]. Researchers have recently focused on dietary patterns for assessing the relationships between diet and diseases [16, 17]. It is an approach with more precise diet assessment, which provides more detailed data than analyzing one nutrient or food, as they are usually consumed with each other [18]. Although the potential roles of endocannabinoids and their respond to energy balance have been recognized highly, the important effect of different kinds of diets on endocannabinoids levels are largely unknown yet [11]. To the best of our knowledge, no study has assessed the association between overall dietary patterns and endocannabinoids levels in the overweight/ obese females. This study was conducted to examine the association of the dietary patterns with AEA and 2-AG levels in overweight and obese women.

Material and methods

Study participants

This cross-sectional study was carried out on 183 overweight and obese women who lived in Tabriz during October 2017 to February 2018 (Fig. 1). Premenopausal women aged between 19 and 50 years old and BMI between 25 to 40 kg/m2 were recruited through announcements and flyer distribution in health care centers (Table 1). The subjects free of any chronic diseases such as diabetes, kidney and liver disease were included in the study. Additionally, pregnancy or lactating, consumption of any medicine affecting appetite such as antidepressant drugs as well as steroids, significant weight loss during last 3 months were also the exclusion criteria. The International Physical Activity Questionnaire (IPAQ) [19] was applied to evaluate the individuals’ physical activity and the results were presented as “low”, “moderate”, and “high”. The study protocol was approved by the Ethics Committee of Tabriz University of Medical Science (IR.TBZMED.REC.1396.620). A comprehensible consent form was signed by each participant.

Fig. 1
figure 1

Flowchart of study design

Table 1 Participants’ demographic, anthropometric, and laboratory data

Anthropometric and biochemical measurements

Weight of each participant was determined in fasting state wearing light clothes and no shoes with a precision of 0.1 kg. Height was measured by a stadiometer in standing barefoot position. Body mass index was calculated as weight (kg)/(height (cm)2). Also, fat mass was determined by bioelectrical impedance analysis (BC-418MA, Tanita, Japan).

Whole blood samples were collected at baseline, after a 14 h fasting and the serum and plasma samples were separated from whole blood by centrifugation at 1500 g at 4 °C for 10 min and were frozen immediately at − 80 °C until assay. Plasma samples were processed no later than 10 min. Analysis of AEA and 2-AG levels was carried out using human enzyme-linked immunosorbent assay (ELISA) kits (Hangzhou Eastbiopharm Co. Ltd., Hangzhou, Zhejiang, China) [20, 21].

Dietary assessment

Participants’ dietary intake was determined applying a valid and reliable semi-quantitative food frequency questionnaire (FFQ) containing 147 food items (with standard portion sizes) consumed frequently by Iranians [22,23,24]. The questionnaire was completed via direct interview by trained dietitian. The women were asked to report the frequency of consumption of every food item on a daily, weekly, monthly or yearly basis. Afterward, the stated frequency for each food object was converted into a daily intake. By utilizing household measures, the serving sizes of the consumed meals were transformed into grams. Regarding the likeness of nutrient profile, each of the 147 food items were allocated to one of the 32 specified food groups (Table 2). Standard methods were applied by trained researchers for measurements of the anthropometric indices.

Table 2 Food groups used for dietary pattern analysis

Statistical analysis

Data were analyzed using SPSS software (SPSS Inc., Chicago, IL, version 20). The 147 food items in the FFQ were changed into daily consumption frequencies and were then categorized into 32 food groups, with regards to their consumption frequencies and nutritional characteristics (Table 2). Factor analysis and principal component analysis were applied to detect the major eating patterns [16]. The food groups that had communalities < 0.3 were excluded. Eigenvalue > 1.5, Scree plot and natural interpretation were the criteria for retention of the factors [25]. The extracted factors (dietary patterns) were named concerning the food groups that had high positive loading, were comparable to the healthy and Western dietary patterns, and also based on the existing literatures. To compute the factor score, a weighted mean of the items related to each pattern was used. For this, every item was multiplied by its corresponding loading in factor analysis. After that, the weighted mean was divided by the sum over the loadings which is named the factor score [26]. Every participant received a factor score for each identified pattern. Participants were categorized by quintile of the dietary pattern scores.

The association between nutritional patterns and AEA, 2-AG was analyzed using linear regression models in different models (Model1: unadjusted, Model2: adjusted for BMI, WC and fat mass). The statistical significance was considered at the P <  0.05 level.


The participants’ demographic, anthropometric, and laboratory data is presented in Table 1. The participants’ mean age and mean BMI were 34.2 ± 8.22 years old, and 32.44 ± 3.79 kg/m2 respectively. The 55.2% of the individuals had low levels of activity, 38.8% were moderately active and the rest of them were highly active (6%). The AEA and 2-AG levels were 4.54 ± 1.22 ng/mL and 5.42 ± 1.50 ng/mL respectively.

By the use of factor analysis, 3 major dietary patterns were extracted which were labeled as following: the healthy dietary pattern (high in other vegetables, Cruciferous vegetables, tomato, yellow vegetables, low fat dairy, green leafy vegetable, and red meat), the Western dietary pattern (high in processed meat, organ meat, pizza, processed meat, coffee, sweets, soft drinks and French fries), and the traditional dietary pattern (tea, fish, poultry, and sugar) [27] (Table 3). Totally these three factors explained 25.47% of the whole variance. The Kaiser- Mayer- Olkin value for the items was 0.63 and the Bartlett’s Test of Sphericity was significant.

Table 3 Factor loading matrix for the three major dietary patternsa in overweight and obese women

Linear regression analysis was applied to assess the association of AEA and 2-AG across quintile of the dietary patterns (Table 4) (Suppl. Materials 1., 2.). In the case of “Western” dietary pattern, in unadjusted model, there was no significant relationship between “Western” dietary pattern and 2-AG (P = 0.09). However, regarding AEA, those in the highest quintile of this pattern had significantly higher levels of AEA (P <  0.01) in comparison to those in the lowest quintile. Additionally, after controlling age, physical activity, BMI, waist circumference, and fat mass, the P value decreased and the relation between “Western” dietary pattern and 2-AG became significant (P <  0.05) and significantly higher levels of 2-AG were observed in those who were in the highest quintile of this pattern. Concerning the “healthy” dietary pattern, in unadjusted and adjusted models, women in the highest quintile of this pattern had significantly lower AEA and 2-AG levels (P <  0.01) compared to those in the lowest quintile. Also, there was no significant association between “traditional” pattern and AEA and 2- AG levels in both in unadjusted and adjusted models (P > 0.05).

Table 4 Linear regression analysis of the association between AEA and 2-AG with dietary patterns quintiles


It has been proposed that, diets that are high in fat have the potential of modulating endocannabinoids levels irrespective of their FA composition and exposure to dietary fats increases the eCB production in the rodents [14, 15, 27, 28]. It appears that complete investigation of the dietary patterns can be helpful in understanding the association between diet and AEA, and 2-AG levels.

To the best of our knowledge, this study was the first study, which evaluated the association of dietary patterns with endocannabinoids levels in the overweight/obese women.

In the current study, three major dietary patterns were identified in the overweight/obese women: “Western”, “healthy”, and traditional dietary patterns. In a study by Esmaillzadeh et al., three major dietary patterns including healthy, Western, and Iranian dietary pattern were also reported in obese women [29]. Also, in nurses with premenstrual syndrome and females with metabolic syndrome, three major dietary patterns including “Western”, “healthy”, and “traditional” patterns were extracted [30, 31].

Concerning the AEA and 2-AG levels, present results are in apparent contrast with those published previously which might be due to the differences in subjects’ demographic characteristics, intervention and sample pre-treatment [32,33,34,35].

In the present study, high adherence to Western dietary pattern resulted in significantly higher levels of AEA and 2-AG, compared to high adherence to healthy dietary pattern. The positive association between the Western pattern and endocannabinoids levels could be due to the food groups components found in this dietary pattern. In this pattern, organ meat, processed meat, pizza, French fries, and soft drinks were dominant. There is notable shift to Western dietary pattern consumption, greatly loaded in red meats, fast foods, and soft drinks in developing countries such as Iran [36]. Moreover, the prevalence of high fat diets (~ 40% of energy) is globally rising due to their palatability and also the fats low cost [37, 38].

ECS are lipid mediators and their biosynthesis can be modified directly by dietary fat intake [4, 39,40,41]. In animals, diets that are high in fat prompt binge eating behaviors [39] and lead to significantly elevated levels of AEA, 2-AG [40, 42], and intestinal motility [43], probably increasing stimulation of the cannabinoid receptor. Also, high fat diets caused an increase in the FA synthesis, which was partially triggered by chronic CB1Footnote 1 activation and subsequent induction of the expression of the lipogenic transcription factor sterol regulatory element-binding protein-1c (SREBP-1c), and greater production of acetyl coenzyme-A carboxylase-1, and fatty acid synthase production [42]. As a result, the fatty acid biosynthetic pathway might be indicated as a common molecular target for the central appetitive and peripheral metabolic effects of endocannabinoids. Furthermore, a decrease in MGLFootnote 2 and FAAHFootnote 3 activities and an increase in NAPE-PLDFootnote 4 action have been found to cause an elevation in AEA levels in response to high fat diets in animals [44]. However, human studies about the endocannabinoid system modulation by dietary intake are very limited. In a study by Gatta-Cherifi et al., meal containing 45% energy from carbohydrate 35% lipids, and 20% protein was tested in obese and healthy subjects. They reported an increase in fasting AEA and 2-AG levels showing the chronic overstimulation of cannabinoid receptor [45].

Additionally, a high dietary intake of linoleic acid (ω-6) can raise the arachidonic acid synthesis triggering the EC production [46]. Food processing always comprises the use of a variety of vegetable oils. The addition of vegetable oils that contain a relatively high amount of ω − 6 fatty acids contributes to an excess ratio of ω − 6 to ω − 3. Surplus intakes omega-6 vegetable oils are associated with reduction of EPA/DHA incorporation into cellular membranes, increasing the AEA and 2-AG production [46, 47]. Elevated levels of 2-AG in the whole brain and in the plasma of adults and developing animals were observed in the rats deficient in ω-3; whereas, supplementation with ω-3 seems to decrease the AEA level [48, 49]. Furthermore, Alvheim et al., showed that, in a diet with 60% of energy from lipids, rising energy from linoleic acid from 1 to 8% led to an elevation in AA in the red blood cells and liver, and also a subsequent 3-fold increase in both AEA and 2-AG [50].

At the end, it is noteworthy to point out that the higher levels of AEA and 2-AG levels in Western dietary pattern might lead to further pathological conditions as ECS dysregulation has been correlated with the development of glucose intolerance, dyslipidemia, and obesity; phenomena that are often accompanied by a myriad of neuroendocrine changes which may play a causative role in ECS dysregulation determination [51,52,53,54,55,56].

Study strengths and limitations

Researchers have largely focused on the macronutrient portions; whereas, the association of dietary patterns with AEA and 2-AG levels was evaluated here, which can be regarded as the main strength point of the present study. However, the presented findings should also be interpreted in light of some limitations as follows: the cross-sectional design, which made it impossible to demonstrate the causality of the interactions. A FFQ with standard portion sizes was applied to estimate the food intakes, in which the measurement error (such as over -reporting or under-reporting of food intakes purposely or unintentionally) might not be precluded and might contain inaccuracies. Also, evaluation of the association of Iranian major dietary patterns with endocannabinoids levels might prevent the generalizability of data. In present study only premenopausal women were included and since menstrual cycle can affect the AEA and 2-AG levels, this issue therefore should be taken into consideration in further studies.


In conclusion, three major dietary patterns were extracted in this study and the Western dietary pattern was associated with increased levels of endocannabinoids, while the healthy dietary pattern was associated with decreased AEA and 2-AG levels. Consequently, adherence to healthy dietary pattern might have promising results in regulating endocannabinoids levels. However, more longitudinal studies with dietary behaviors evaluation are required to confirm the preliminary results.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.


  1. Cannabinoid receptor type 1

  2. Monoacylglycerol lipase

  3. Fatty acid amide hydrolase

  4. N-acyl phosphatidylethanolamine phospholipase D



Body mass index


Endocannabinoid system




2-arachidonoyl glycerol


food frequency questionnaire


Monoacylglycerol lipase


Fatty acid amide hydrolase


N-acyl phosphatidylethanolamine phospholipase D


  1. Hu L, Huang X, You C, Li J, Hong K, Li P, et al. Prevalence of overweight, obesity, abdominal obesity and obesity-related risk factors in southern China. PloS One. 2017;12(9):e0183934–e.

    PubMed  PubMed Central  Google Scholar 

  2. Saghafi-Asl M, Aliasgharzadeh S, Asghari-Jafarabadi M. Factors influencing weight management behavior among college students: An application of the Health Belief Model. Plose One. 2020;15(2):e0228058.

    CAS  Google Scholar 

  3. Ayatollahi S, Ghoreshizadeh Z. Prevalence of obesity and overweight among adults in Iran. Obes Rev. 2010;11(5):335–7.

    CAS  PubMed  Google Scholar 

  4. Argueta DA, DiPatrizio NV. Peripheral endocannabinoid signaling controls hyperphagia in western diet-induced obesity. Physiol Behav. 2017;171:32–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Ghanemi A, Yoshioka M, St-Amand J. Broken Energy Homeostasis and Obesity Pathogenesis: The Surrounding Concepts. J Clin Med. 2018;7(11):453.

    CAS  PubMed Central  Google Scholar 

  6. Gatta-Cherifi B, Cota D. New insights on the role of the endocannabinoid system in the regulation of energy balance. Int J Obes (Lond). 2016;40(2):210–9.

    CAS  Google Scholar 

  7. Collu R, Scherma M, Piscitelli F, Giunti E, Satta V, Castelli MP, et al. Impaired brain endocannabinoid tone in the activity-based model of anorexia nervosa. Int J Eat Disord. 2019;52(11):1251–62.

    PubMed  Google Scholar 

  8. Freitas HR, Isaac AR, Malcher-Lopes R, Diaz BL, Trevenzoli IH, De Melo Reis RA. Polyunsaturated fatty acids and endocannabinoids in health and disease. Nutr Neurosci. 2018;21(10):695–714.

    CAS  PubMed  Google Scholar 

  9. Little TJ, Cvijanovic N, DiPatrizio NV, Argueta DA, Rayner CK, Feinle-Bisset C, Young RL. Plasma endocannabinoid levels in lean, overweight, and obese humans: relationships to intestinal permeability markers, inflammation, and incretin secretion. Am J Physiol Endocrinol Metab. 2018;315(4):E489–95.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Côté M, Matias I, Lemieux I, Petrosino S, Alméras N, Després JP, Di Marzo V. Circulating endocannabinoid levels, abdominal adiposity and related cardiometabolic risk factors in obese men. Int J Obes. 2007;31:692–9.

    Google Scholar 

  11. Naughton SS, Mathai ML, Hryciw DH, McAinch AJ. Fatty Acid Modulation of the Endocannabinoid System and the Effect on Food Intake and Metabolism. Int J Endocrinol. 2013;2013:11.

    Google Scholar 

  12. Watkins BA, Kim J. The endocannabinoid system: directing eating behavior and macronutrient metabolism. Front Psychol. 2015;5:1506.

    PubMed  PubMed Central  Google Scholar 

  13. Hall KD, Ayuketah A, Brychta R, Cai H, Cassimatis T, Chen KY, et al. Ultra-processed diets cause excess calorie intake and weight gain: an inpatient randomized controlled trial of ad libitum food intake. Cell Metab. 2019;30(1):67–77.

    CAS  PubMed  Google Scholar 

  14. DiPatrizio NV, Astarita G, Schwartz G, Li X, Piomelli D. Endocannabinoid signal in the gut controls dietary fat intake. Proc Natl Acad Sci USA. 2011;108:12,904–8.

    CAS  Google Scholar 

  15. DiPatrizio NV, Joslin A, Jung KM, Piomelli D. Endocannabinoid signaling in the gut mediates preference for dietary unsaturated fats. FASEB J. 2013;27:2513–20.

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  17. Kant AK. Dietary patterns and health outcomes. J Am Diet Assoc. 2004;104(4):615–35.

    PubMed  Google Scholar 

  18. Shim J-S, Oh K, Kim HC. Dietary assessment methods in epidemiologic studies. Epidemiol Health. 2014;36:e2014009–e.

    PubMed  PubMed Central  Google Scholar 

  19. Moghaddam MB, Aghdam FB, Jafarabadi MA, Allahverdipour H, Nikookheslat SD, Safarpour S. The Iranian Version of International Physical Activity Questionnaire (IPAQ) in Iran: content and construct validity, factor structure, internal consistency and stability. World Appl Sci J. 2012;18(8):1073–80.

    Google Scholar 

  20. Haidari F, Aghamohammadi V, Mohammadshahi M, Ahmadi-Angali K. Effect of whey protein supplementation on levels of endocannabinoids and some of metabolic risk factors in obese women on a weight-loss diet: a study protocol for a randomized controlled trial. Nutr J. 2017;16:70.

    PubMed  PubMed Central  Google Scholar 

  21. Kheirouri S, Alizadeh M, Jafari-Vayeghan H, Darabi M, Gholmohammadi A, Saleh-Ghadimi S. Effect of flaxseed oil supplementation on the erythrocyte membrane fatty acid composition and endocannabinoid system modulation in patients with coronary artery disease: a double- blind randomized controlled trial. Genes Nutr. 2020;15:9.

    PubMed  PubMed Central  Google Scholar 

  22. Mirmiran P, Esfahani FH, Mehrabi Y, Hedayati M, Azizi F. Reliability and relative validity of an FFQ for nutrients in the Tehran lipid and glucose study. Public Health Nutr. 2010;13(5):654–62.

    PubMed  Google Scholar 

  23. Esfahani FH, Asghari G, Mirmiran P, Azizi F. Reproducibility and relative validity of food group intake in a food frequency questionnaire developed for the Tehran Lipid and Glucose Study. J Epidemiol. 2010;20(2):150–8.

    PubMed  Google Scholar 

  24. Asghari G, Rezazadeh A, Hosseini-Esfahani F, Mehrabi Y, Mirmiran P, Azizi F. Reliability, comparative validity and stability of dietary patterns derived from an FFQ in the Tehran Lipid and Glucose Study. Br J Nutr. 2012;108(6):1109–17.

    CAS  PubMed  Google Scholar 

  25. Kim JO, Mueller CW, Mueller JOKCW, Collection BLJF. SAGE. factor analysis: statistical methods and Practical Issues. Beverly Hills: SAGE Publications; 1978.

    Google Scholar 

  26. Jafari-Vayghan H, Tarighat-Esfanjani A, Asghari Jafarabadi M, Ebrahimi-Mameghani M, Saleh Ghadimi S, Lalezadeh Z. Association between dietary patterns and serum leptin-to-adiponectin ratio in apparently healthy adults. J Am Coll Nutr. 2015;34(1):49–55.

    CAS  PubMed  Google Scholar 

  27. Diep TA, Madsen AN, Holst B, Kristiansen MM, Wellner N, Hansen SH, et al. Dietary fat decreases intestinal levels of the anorectic lipids through a fat sensor. The FASEB J. 2011;25(2):765–74.

    CAS  PubMed  Google Scholar 

  28. Di Marzo V, Capasso R, Matias I, Aviello G, Petrosino S, Borrelli F, et al. The role of endocannabinoids in the regulation of gastric emptying: alterations in mice fed a high-fat diet. Br J Pharmacol. 2008;153(6):1272–80.

    PubMed  PubMed Central  Google Scholar 

  29. Esmaillzadeh A, Azadbakht L. Major dietary patterns in relation to general obesity and central adiposity among Iranian women. J Nutr. 2008;138(2):358–63.

    CAS  PubMed  Google Scholar 

  30. Esmaillzadeh A, Kimiagar M, Mehrabi Y, Azadbakht L, Hu FB, Willett WC. Dietary patterns, insulin resistance, and prevalence of the metabolic syndrome in women. Am J Clin Nutr. 2007;85(3):910–8.

    CAS  PubMed  Google Scholar 

  31. Farasati N, Siassi F, Koohdani F, Qorbani M, Abashzadeh K, Sotoudeh G. Western dietary pattern is related to premenstrual syndrome: a case-control study. Br J Nutr. 2015;114(12):2016–21.

    CAS  PubMed  Google Scholar 

  32. Quercioli A, Montecucco F, Pataky Z, Thomas A, Ambrosio G, Staub C, et al. Improvement in coronary circulatory function in morbidly obese individuals after gastric bypass-induced weight loss: relation to alterations in endocannabinoids and adipocytokines. Eur Heart J. 2013;34(27):2063–73.

    PubMed  Google Scholar 

  33. Zoon HFA, de Bruijn SEM, Smeets PAM, de Graaf C, Janssen IMC, Schijns W, et al. Altered Neural Responsivity to Food Cues in Relation to Food Preferences, but Not Appetite-Related Hormone Concentrations After RYGB-surgery. Behav Brain Res. 2018;1(353):194–202.

    Google Scholar 

  34. Hauer D, Schelling G, Gola H, Campolongo P, Morath J, Roozendaal B, et al. Plasma Concentrations of Endocannabinoids and Related Primary Fatty Acid Amides in Patients With Post-Traumatic Stress Disorder. PLoS One. 2013;8(5):e62741.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Martins CJ, Genelhu V, Pimentel MM, Celoria BM, Mangia RF, Aveta T, et al. Circulating Endocannabinoids and the Polymorphism 385C > A in Fatty Acid Amide Hydrolase (FAAH) Gene May Identify the Obesity Phenotype Related to Cardiometabolic Risk: A Study Conducted in a Brazilian Population of Complex Interethnic Admixture. PLoS One. 2015;10(11):e0142728.

    PubMed  PubMed Central  Google Scholar 

  36. Zaribaf F, Mohammadifard N, Sarrafzadegan N, Karimi G, Gholampour A, Azadbakht L. Dietary patterns in relation to lipid profiles among Iranian adults. J Cardiovasc Thorac Res. 2019;11(1):19–27.

    PubMed  PubMed Central  Google Scholar 

  37. Drewnowski A, Popkin BM. The nutrition transition: new trends in the global diet. Nutr Rev. 1997;55(2):31–43.

    CAS  PubMed  Google Scholar 

  38. Drewnowski A, Shrager EE, Lipsky C, Stellar E, Greenwood MR. Sugar and fat: sensory and hedonic evaluation of liquid and solid foods. Physiol Behav. 1989;45(1):177–83.

    CAS  PubMed  Google Scholar 

  39. Higuchi S, Irie K, Yamaguchi R, Katsuki M, Araki M, Ohji M, et al. Hypothalamic 2-arachidonoylglycerol regulates multistage process of high-fat diet preferences. PloS one. 2012;7(6):e38609.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Izzo AA, Piscitelli F, Capasso R, Aviello G, Romano B, Borrelli F, et al. Peripheral endocannabinoid dysregulation in obesity: relation to intestinal motility and energy processing induced by food deprivation and re-feeding. Br J Pharmacol. 2009;158(2):451–61.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Argueta DA, Perez PA, Makriyannis A, DiPatrizio NV. Cannabinoid CB1 Receptors Inhibit Gut-Brain Satiation Signaling in Diet-Induced Obesity. Front Physiol. 2019;10:704.

    PubMed  PubMed Central  Google Scholar 

  42. Osei-Hyiaman D, DePetrillo M, Pacher P, Liu J, Radaeva S, Bátkai S, et al. Endocannabinoid activation at hepatic CB1 receptors stimulates fatty acid synthesis and contributes to diet-induced obesity. J Clin Invest. 2005;115(5):1298–305.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Piscitelli F, Carta G, Bisogno T, Murru E, Cordeddu L, Berge K, et al. Effect of dietary krill oil supplementation on the endocannabinoidome of metabolically relevant tissues from high-fat-fed mice. Nutr Metab. 2011;8(1):51.

    CAS  Google Scholar 

  44. Aviello G, Matias I, Capasso R, Petrosino S, Borrelli F, Orlando P, et al. Inhibitory effect of the anorexic compound oleoylethanolamide on gastric emptying in control and overweight mice. J Mol Med. 2008;86(4):413–22.

    CAS  PubMed  Google Scholar 

  45. Gatta-Cherifi B, Matias I, Vallée M, Tabarin A, Marsicano G, Piazza PV, et al. Simultaneous postprandial deregulation of the orexigenic endocannabinoid anandamide and the anorexigenic peptide YY in obesity. Int J Obes. 2011;36(6):880–5.

    Google Scholar 

  46. Bosma-den Boer MM, van Wetten ML, Pruimboom L. Chronic inflammatory diseases are stimulated by current lifestyle: how diet, stress levels and medication prevent our body from recovering. Nutr Metab. 2012;9(1):32.

    Google Scholar 

  47. DiNicolantonio JJ, OKeefe J. Dietary fats, blood pressure and artery health. Open Heart. 2019;6:e001035.

    PubMed  PubMed Central  Google Scholar 

  48. Wood JT, Williams JS, Pandarinathan L, Janero DR, Lammi-Keefe CJ, Makriyannis A. Dietary docosahexaenoic acid supplementation alters select physiological endocannabinoid-system metabolites in brain and plasma. J Lipid Res. 2010;51(6):1416–23.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Watanabe S, Doshi M, Hamazaki T. n-3 Polyunsaturated fatty acid (PUFA) deficiency elevates and n-3 PUFA enrichment reduces brain 2-arachidonoylglycerol level in mice. Prostaglandins Leukot Essent Fatty Acids. 2003;69(1):51–9.

    CAS  PubMed  Google Scholar 

  50. Alvheim AR, Malde MK, Osei-Hyiaman D, Lin YH, Pawlosky RJ, Madsen L, et al. Dietary linoleic acid elevates endogenous 2-AG and anandamide and induces obesity. Obesity. 2012;20(10):1984–94.

    CAS  PubMed  Google Scholar 

  51. Mazier W, Saucisse N, Gatta-Cherifi B, Cota D. The Endocannabinoid System: Pivotal Orchestrator of Obesity and Metabolic Disease. Trends Endocrinol Metab. 2015;26(10):524–37.

    CAS  PubMed  Google Scholar 

  52. Hanlon EC, Leproult R, Stuhr K, Doncheck E, de Wit H, Hillard CJ, et al. Circadian misalignment of the 24-h profile of endocannabinoid 2-arachidonoylglycerol in healthy obese adults. J Clin Endocrinol Metab. 2020;105(3):792–802.

    Google Scholar 

  53. Engeli S, Lehmann AC, Kaminski J, Haas V, Janke J, Zoerner AA, et al. Influence of dietary fat intake on the endocannabinoid system in lean and obese subjects. Obesity. 2014;22(5):E70–6.

    CAS  PubMed  Google Scholar 

  54. Bennetzen MF, Wellner N, Ahmed SS, Ahmed SM, Diep TA, Hansen HS, et al. Investigations of the human endocannabinoid system in two subcutaneous adipose tissue depots in lean subjects and in obese subjects before and after weight loss. Int J Obes. 2011;35(11):1377–84.

    CAS  Google Scholar 

  55. Pastor A, Fernández-Aranda F, Fitó M, JiménezMurcia S, Botella C, Fernández-Real JM, et al. A lower olfactory capacity is related to higher circulating concentrations of endocannabinoid 2-arachidonoylglycerol and higher body mass index in women. PloS one. 2016;11(2):e0148734.

    PubMed  PubMed Central  Google Scholar 

  56. Lotfi Yagin N, Aliasgharzadeh S, Alizadeh M, Aliasgari F, Mahdavi R. The association of circulating endocannabinoids with appetite regulatory substances in obese women. Obes Res Clin Pract. 2020.

Download references


The authors wish to thank the participants for their cooperation, time and patience and Tabriz University of Medical Sciences for the financial support. The results of this paper are from Neda Lotfi’s Ph.D. thesis.


This study was funded Tabriz University of Medical Sciences.

Author information

Authors and Affiliations



NLY and RM was the major contributor of the manuscript, designed the research project and agreed for all aspects of the work and wrote the manuscript. SA and SH collected and interpreted the data and performed the statistical analysis. FA made substantial contributions to the data interpretation. All authors read and approved the final version of manuscript.

Corresponding author

Correspondence to Reza Mahdavi.

Ethics declarations

Ethics approval and consent to participate

All participants provided written informed consent prior to commencing the study. The study and subsequent analysis were approved by the Ethics Committee of Tabriz University of Medical Sciences.

Consent for publication

Not applicable.

Competing interests

The authors declare that there is no conflict of interest.

Additional information

Publisher’s Note

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

Supplementary information

Additional file 1: Figure S1.

The association between Western Pattern Score and AEA. Figure S2. The association between Healthy Pattern Score and AEA. Figure S3. The association between Traditional Pattern Score and AEA. Figure S4. The association between Western Pattern Score and 2-AG. Figure S5. The association between Healthy Pattern Score and 2-AG. Figure S6. The association between Traditional Pattern Score and 2-AG.

Additional file 2: Figure S1.

Linear regression analysis graph of measured and whole model predicted AEA value. (Healthy Dietary Pattern). Figure S2. Linear regression analysis graph of measured and whole model predicted AEA value (Western Dietary Pattern). Figure S3. Linear regression analysis graph of measured and whole model predicted AEA value (Traditional Dietary Pattern). Figure S4. Linear regression analysis graph of measured and whole model predicted 2-AG value (Healthy Dietary Pattern). Figure S5. Linear regression analysis graph of measured and whole model predicted 2-AG value (Western Dietary Pattern). Figure S6. Linear regression analysis graph of measured and whole model predicted 2-AG value (Traditional Dietary Pattern).

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

Yagin, N.L., Hajjarzadeh, S., Aliasgharzadeh, S. et al. The association of dietary patterns with endocannabinoids levels in overweight and obese women. Lipids Health Dis 19, 161 (2020).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: