Skip to main content
Download PDF

Plant and animal protein intake and its association with depression, anxiety, and stress among Iranian women

BMC Public Health volume 23, Article number: 161 (2023) Cite this article

Abstract

Background

Mental disorders are conditions that affect the usual function of the brain, causing a huge burden on societies. The causes are often unclear, but previous research has pointed out, as is the case with many other diseases, that nutrition could have a major role in it. Amino acids, the building blocks of proteins, are the main precursor of neurotransmitters (the chemical messengers in the brain) malfunction of which is heavily associated with a wide range of brain disorders.

Methods

We assumed different sources of dietary protein could have different impacts on mental well-being. Hence, we decided to collect the nutritional data (with a validated and reliable semi-quantitative food-frequency questionnaire) from a sample of 489 Iranian women and investigate the association between animal and plant protein sources and the risk of depression, anxiety, and stress. Symptoms of these mental disorders were assessed using a validated Depression, Anxiety, and Stress Scales (DASS) questionnaire with 21 items.

Results

After multivariable adjustment, it was shown that women in the highest tertile of animal protein intake were more likely to show symptoms of depression (OR: 2.63; 95% CI: 1.45, 4.71; P = 0.001), anxiety (OR: 1.83; 95% CI: 1.04, 3.22; P = 0.03), and stress (OR: 3.66; 95% CI: 2.06, 6.50; p < 0.001). While no significant association was seen between plant protein and any of the studied mental disorders.

Conclusion

Overall, our findings suggest that a diet high in animal protein could predispose individuals to mental illnesses.

Peer Review reports

Introduction

A mental disorder is defined as a mental pattern in which the normal personal function of an individual is disrupted or impaired. Affecting millions of people, they account for a significant proportion of the global disease burden [1]. they consist of several complications, among which depression, anxiety, and distress are the most common. It is estimated that 4.7 and 7.3% of the global population suffer from depression and anxiety, respectively [2]. Reports from Iran suggest that about 21% of Iranian adults show depressive and anxiety symptoms [3]. Also, psychological disorders are on the rise, particularly among women, and reports suggest they are twice as likely as men to be afflicted with these complications [4]. The outcomes of psychological disorders vary from poor occupational, academic, and social status to being prone to developing some chronic severe complications, including cardiovascular disease, diabetes, and cancer [5,6,7,8]. Hence, finding the best approach to control and manage these diseases is of utmost importance. Though the exact mechanism behind mental illnesses is still not fully understood, genetic and environmental factors seem to play a critical role [9, 10]. Identifying disease-modifying risk factors could be a practical approach to preventing and managing these diseases.

Evidence reveals the role of diet in the onset, severity, and duration of mental disorders [11]. Healthy eating habits have been promoted to help prevent or even treat some of the most notorious mental disorders [12]. One of the crucial components of a healthy diet is protein. It can be provided from both plant and animal food sources. Plant sources were formerly regarded as incomplete proteins. However, new studies have revealed that prudent vegetarian diets could adequately supply all essential amino acids [13]. Yet, the absorption of which is still considered lower than that of animal sources [14]. Amino acids (mostly tryptophan, phenylalanine, and tyrosine) are suggested to have significant roles in mental health as they help build neurotransmitters [11]. Neurotransmitters, such as dopamine and serotonin, are the chemicals that allow brain cells to communicate with each other, the malfunction of which has been strongly associated with various brain disorders [15]. A study showed that brain concentration of the mentioned amino acids was significantly lower in patients showing depressive behavior compared to healthy individuals [16]. Low levels of serotonin as a result of a tryptophan-depleted diet resulted in poor memory and depressed mood [17, 18].

In the present study, we aspire to find out which source of protein, animal or plant, could result in better mental outcomes. To the best of our knowledge, no study has ever investigated this matter before. Hence, we decided to conduct this cross-sectional observational study to examine the association between different protein sources and the risk of psychiatric disorders in women.

Method

Study design and participants

Subjects of the present study were recruited from 10 health centers affiliated with the Tehran University of Medical Sciences. The sample size was determined employing the following formula: N= [(Z1-α/2)2 P(1-P)]/d2. Using P = 29, d = 4.06, and α = 0.05 [19]. Our study population consisted of healthy women who were in the age range of 20–50 years old and of Iranian descent. Women who were pregnant and lactating, premenopausal, or suffered from chronic diseases such as diabetes, cardiovascular disease, cancer, kidney or liver disorders, were diagnosed with a mental illness or took drugs affecting mental status were excluded. Overall, 489 individuals were included in the study. Written informed consent was signed by each participant. This study was confirmed by the research council of the School of Nutritional Sciences and Dietetics (number: 9611323008), TUMS.

Dietary assessment

Dietary intake was evaluated using a validated semi-quantitative food frequency questionnaire (FFQ) containing 168 food items [20]. Participants were aided by trained dietitians in completing all forms and questionnaires. Participants were asked to report the frequency of each food during the past year on a daily, weekly, monthly, or annual basis. The animal protein category was defined as the sum of meat (beef, lamb), poultry, fish, egg, and dairy. The plant protein category consisted of whole grains, refined grains, legumes, nuts and seeds, fruits, and vegetables. The amount of each food was converted to grams using household measures. Each food item was coded, and nutrients were calculated using the NUTRITIONIST IV software for Iranian foods (version 7.0; N-Squared Computing, Salem, OR, USA).

Dietary inflammatory index (DII)

FFQ-derived dietary data were used to calculate DII scores for all participants. Shivappa et al. [21] developed this index based on 45 food and nutrients that had been assumed to be associated with one or more of the pro-inflammatory (Interleukin-1β, Interleukin-6, Tumor Necrosis Factor-α, or CRP) or anti-inflammatory biomarkers (Interleukin-4 and Interleukin-10). Then, a score was given to each food parameter based on whether it favors the odds of inflammation by shifting towards inflammatory markers (+ 1) or reduces inflammation by doing otherwise (-1), or did not produce any significant change in the inflammatory markers (0). In the current study, we calculated DII scores based on 30 food parameters (some of the nutrients listed in the above study were not available in our database) which are as follows: energy, carbohydrate, protein, total fat, monounsaturated fat, polyunsaturated fat, saturated fat, omega-3, omega-6 fatty acids, cholesterol, fiber, thiamin, riboflavin, niacin, vitamin B6, folic acid, vitamin B12, iron, magnesium, selenium, zinc, β carotene, vitamin A, C, D, E and tea, onion, caffeine, and garlic.

Assessment of psychological profile

The psychological profile was assessed using a questionnaire of depression, anxiety, and stress scale (DASS-21), the reliability of which was previously confirmed [22]. Each of the three DASS subscales consists of 7 questions, and the answers to which contained four options and were given a score of 0 (never applied to me) to 3 (applied to me very much or most of the time). The final score was obtained by totalizing the scores of each of the three sub-scales multiplied by two. The results were interpreted as ‘normal,’ ‘mild,’ ‘moderate,’ ‘severe,’ to ‘extremely severe’ for each subscale. However, for statistical analysis, subjects were classified into two categories normal and abnormal.

Anthropometric measurements and physical activity

The Height was measured to the nearest 0.5 cm with a tape measure while the subjects were in a standing position, with their shoulders in a normal alignment and shoes removed. The weight was measured by a digital scale (SECA, Hamburg, Germany. With an accuracy of 0.1 kg) while the subjects were barefoot and wearing a minimum of clothes. For the waist circumference (WC), the narrowest abdominal circumference between the iliac crest and the rib cage was measured. Body mass index was calculated by dividing the weight (kg) by height squared (m2). The amount of physical activity was recorded and presented in metabolic equivalents × h/d (Met.h/d). Activity level was ranked into four categories (light, moderate, strong, and intense). Participants’ physical activity level was calculated as Met.h/d [23].

General information

General information, including age, marital status, smoking status, socioeconomic status (SES), chronic diseases (diabetes, cardiovascular disease, cancer, kidney or liver disorders), family history of chronic diseases, medication and supplement use, and menopausal status was obtained. SES score was evaluated as an index of socioeconomic status regarding the family situation (being head of the family, self-care, or under supervision), frequency of travel within the country and abroad, welfare status, occupational status, the head of the family’s occupational status, education (≤ Diploma > Diploma), the head of the family’s education, and family size (≤ 4, > 4 people).

Statistical analysis

General characteristics across tertiles of animal and plant protein intake were expressed as means ± SDs for continuous variables and numbers and percentages for categorical variables. To examine the differences across tertiles, we used ANOVA for continuous variables and a Chi-square test for categorical variables. Dietary intakes of study participants across animal and plant protein tertiles were compared using ANCOVA. All values were adjusted for energy intake. We used binary logistic regressions to estimate ORs and 95% CIs for psychological profiles across plant and animal protein tertiles in crude and multivariable-adjusted models. In these analyses, age and total energy intake, SES (low, medium, and high), marital status (married, single), physical activity, supplement use (yes/no), drug use (yes/no), family history of chronic disease (yes/no), sleep time, out of home time, body size image (normal, abnormal), were controlled in the adjusted model according to the earlier data conducted on the matter. To pin down the results solely to the effects of protein, we made a further adjustment for DII. All statistical analyses were done using the Statistical Package for Social Sciences (version 21; SPSS Inc.). P < 0.05 was considered to be statistically significant.

Results

The general characteristics of the study population across tertiles of plant and animal proteins are shown in Table 1. The total mean and standard deviation (SD) of age, weight, BMI, and physical activity of participants was 31.82 (7.68), 64.41 (12.00), and 24.45 (4.51), 39.88 (6.76), respectively. BMI and weight have shown significant differences among tertiles of plant and animal proteins. Also, participants with the highest adherence to plant protein were more physically active than those with low adherence. The frequency and percentage of participants with depression, anxiety, and psychological distress were 175 (35.8%), 280 (57.3%), and 204 (41.7%), respectively.

Table 1 General characteristics of participants across the tertiles of Animal protein and Plant protein
Full size table

Dietary intakes of the study population among tertiles of plant and animal proteins are presented in Table 2. Participants in the highest tertile of plant protein had a higher intake of whole grains, vegetables, energy, protein, carbohydrate, total fiber, vitamin A, thiamine, vitamin C, calcium, magnesium, potassium, zinc, and iron and lower intakes of total fat and vitamin B12 compared with those in the lowest tertile. We did a further investigation to see if the tertiles overlap. Frequency analysis showed that only 5.5% of the participants were in the highest tertile of both animal and plant proteins which we assume could hardly affect the result of this study.

Table 2 Dietary intakes of study participants across the tertiles of Animal protein and Plant proteins
Full size table

Participants in the top tertile of animal protein consumed more dairy, meats, energy, protein, total fat, vitamin A, vitamin B12, calcium, magnesium, potassium, zinc, and iron and consumed a lower amount of fruits and dietary fiber. Moreover, there was no significant difference in the consumption of Vitamin B6, fruits, dairy, legumes, and nuts across tertiles of plant protein and intakes of carbohydrate, Thiamine, Vitamin B6, Vitamin C, whole grains, vegetables, legumes, and nuts among animal protein tertiles.

Multivariable-adjusted odds ratio (OR) and 95% Confidence intervals (CIs) for psychological profiles across tertiles of plant and animal proteins are presented in Table 3. In the crude model, a higher score of animal protein was directly related to the risk of depression (OR = 1.82; 95% CI: 1.15, 2.90; P = 0.03), anxiety (OR = 1.70; 95% CI: 1.08, 2.67; P = 0.04), and psychological distress (OR = 2.72; 95% CI: 1.71, 4.32; P = < 0.001). After controlling for potential confounders comprising age, energy intake, physical activity, number of deliveries, socioeconomic status, supplemented use, marital status, educational level, BMI, and DII, the association between depression (OR: 2.63; 95% CI: 1.45, 4.71; P = 0.001,), anxiety (OR: 1.83; 95% CI: 1.04, 3.22; P = 0.03), and psychological distress (OR: 3.66; 95% CI: 2.06, 6.50; p < 0.001) across highest vs. lowest animal protein tertiles remained significant. However, in comparison of top to bottom tertiles of plant protein, there were no significant association between depression (OR = 0.87; 95% CI: 0.55, 1.37; P = 0.21), anxiety (OR = 1.26; 95% CI: 0.80, 1.97; P = 0.47), and psychological distress (OR = 0.88; 95% CI: 0.56, 1.39; P = 0.34) with plant protein tertiles in the crude model, and the associations remained insignificant even after adjusting for confounding factors in the fully-adjusted model.

Table 3 Crude and multivariable-adjusted odds ratios (95% CIs) for depression, anxiety, and stress across tertiles of plant and animal proteins
Full size table

Discussion

The present study suggests that poultry and dairy products are the most important contributors to animal protein intake in a representative sample of the Iranian population. While rice and legumes were the most important contributors to plant protein intake.

Our findings indicated that a higher animal protein intake is associated with an increased risk of depression, anxiety, and stress in adult women. However, there was no significant association between a high plant protein intake and the mentioned mental disorders.

Mainstream medicine views mental disorders as a result of neurochemical imbalances, for instance, depression is often viewed as a serotonin imbalance, and new anti-depressants are prescribed to target the serotonin network [24]. Another primary neurotransmitter is GABA, a lack of which has been linked to anxiety. Thus many drugs that counter anxiety do so by stimulating GABA release [25].

Nutrition can play a vital role in the pathophysiology and management of psychiatric disorders by affecting the regulation of neurotransmitters. Certain amino acids (especially tryptophan, tyrosine, and phenylalanine) found in high-quality protein sources are known to be the main precursors of these neurotransmitters [26]. It was also found that the rate of brain serotonin synthesis depends on the concentrations of tryptophan in the brain [27]. Rosier et al. revealed that a dietary intervention with low levels of phenylalanine and tyrosine would cause a rapid lowering of mood in patients who recovered from depression [28].

In this study, we found that consuming more animal protein is associated with an increased risk of psychiatric disorders. There is some evidence that could justify our findings. Tryptophan is the primary precursor of serotonin [29]. To enter the brain, a carrier protein must transport tryptophan through the blood-brain barrier. However, tryptophan is in constant competition with six other amino acids (isoleucine, leucine, phenylalanine, tyrosine, and valine) to bind to the carrier [30]. Consuming rich protein sources provides the body with the mentioned amino acids in abundance, making it more arduous for tryptophan to pass through the barrier. As a result, serotonin production might be reduced. Moreover, in a clinical trial study on 18 individuals who were divided into two groups, it was revealed that the group who consumed plant-based meals during the test had higher brain tryptophan and tyrosine levels than those who consumed meals high in animal sources [31].

Another explanation may involve the metabolism of homocysteine. Homocysteine is a byproduct of animal protein as it is converted from methionine, an amino acid found abundantly in red meat. Homocysteine’s serum level further increases if folate is not adequate in the body, which is common among women [32]. Higher homocysteine levels are strongly associated with major psychiatric disorders [33]. It is suggested that elevated homocysteine levels could cause cerebral vascular disease and neurotransmitter deficiency [34].

It should be noted that several other factors could also promote diet-induced damage to mental health, including oxidative stress, insulin resistance, and inflammation [35]. They could be the inevitable outcomes of a long-term high intake of animal products that contain nutrients such as saturated fatty acid, arachidonic acid, heme iron, and cholesterol, which are known to cause inflammation [36]. Systemic inflammation could significantly affect the brain by actively transporting cytokines through the brain and disrupting neurotransmitters’ metabolism [37]. Furthermore, excessive consumption of red meat was shown to alter gut microbiota composition [38], generating bioactive metabolites that could cause neuroinflammation through the gut-brain-axis [39]. Hence, it may not be plausible to attribute the results of the study solely to the proteins, although we did try to neutralize other nutrients’ effects by controlling for DII, which is a dietary index developed to measure the potential impact of a diet on the inflammatory status of an individual [21].

Our findings were in general agreement with previous studies investigating the matter. A meta-analysis of 8 observational studies showed that meat consumption could be associated with a slightly higher risk of depression [40]. In a cross-sectional study conducted on 892 Asian residents of the United States, a vegetarian diet which was characterized by no intake of meat, poultry, and fish was found to be inversely associated with the prevalence of depression [41]. furthermore, a cohort study conducted on 3502 participants found that the Mediterranean diet, which is rich in plant-based foods and low in red meat, had an inverse relation with depression [42]. The same conclusion was drawn in another large cohort study where the relationship between the dietary approach to hypertension (DASH) diet and depression was assessed [43]. A Japanese study found that plant protein was associated with decreased prevalence of depressive symptoms [44]. A clinical study by Beezhold et al. concluded that restricting animal-based foods such as meat, fish, and poultry improved short-term mood [45].

On the other hand, Li et al. reported that protein intake from milk and milk products was inversely associated with depressive symptoms [46]. They suggested that a-lactalbumin, a whey-derived protein that is a rich source of tryptophan, could exert beneficial effects on mood and cognition.

The present study could further expand our knowledge regarding the association of the protein with mental well-being. Still, some limitations should be noted. First, the recall bias in reporting dietary intake has probably affected the results. The cross-sectional nature of our study was another limitation, as it prevented us from inferring causality. The study was performed only on females aged 20–50 years, which affects the generalizability to the larger population. Also, due to the different influence of gonadal steroids on mood [47], we could have gotten better insight into the variable of sex if men had also been present. Furthermore, it has been reported that the menstrual cycle could affect depressive symptoms, which were not regarded in our study [48]. Also, the DASS-21 is a self-reported scale based on a dimensional rather than a categorical conception of mental disorder. This scale is used to measure the severity of symptoms of anxiety, stress, and depression and is helpful for screening, not for diagnosis.

In conclusion, we found that high adherence to animal protein is associated with an elevated risk of psychiatric disorders. Future longitudinal studies are required to further our understanding of the effect of different protein sources on mental health.

Data availability

The data supporting this study’s findings are available upon reasonable request and with permission of Dr. Leila Azadbakht. However, restrictions apply to the availability of these data as they contain the personal information of the participants. So.

Abbreviations

DASS-21:

Depression, Anxiety and Stress Scale

BCAA:

Branched-Chain amino acids

BMI:

Body Mass Index

FFQ:

Food frequency questionnaire

WC:

Wait Circumference

CVD:

Cardiovascular Disease

SFA:

Saturated Fatty Acids

HDL:

High-density Lipoprotein

LDL:

Low-density Lipoproteins

OR:

Odds Ratio

CI:

Confidence interval

References

  1. Hesdorffer DC. Comorbidity between neurological illness and psychiatric disorders. CNS Spectr. 2016;21(3):230–8.

    Article  Google Scholar 

  2. Ferrari A, Somerville A, Baxter A, Norman R, Patten S, Vos T, et al. Global variation in the prevalence and incidence of major depressive disorder: a systematic review of the epidemiological literature. Psychol Med. 2013;43(3):471–81.

    Article  CAS  Google Scholar 

  3. Noorbala AA, Yazdi SB, Yasamy M, Mohammad K. Mental health survey of the adult population in Iran. Br J Psychiatry. 2004;184(1):70–3.

    Article  CAS  Google Scholar 

  4. Srivastava K. Women and mental health: psychosocial perspective. Industrial psychiatry journal. 2012;21(1):1.

    Article  Google Scholar 

  5. Penninx BW, Guralnik JM, Havlik RJ, Pahor M, Ferrucci L, Cerhan JR, et al. Chronically depressed mood and cancer risk in older persons. J Natl Cancer Inst. 1998;90(24):1888–93.

    Article  CAS  Google Scholar 

  6. Markowitz S, Friedman MA, Arent SM. Understanding the relation between obesity and depression: causal mechanisms and implications for treatment. Clin Psychol Sci Pract. 2008;15(1):1–20.

    Article  Google Scholar 

  7. Van der Kooy K, van Hout H, Marwijk H, Marten H, Stehouwer C, Beekman A. Depression and the risk for cardiovascular diseases: systematic review and meta analysis. Int J Geriatric Psychiatry: J psychiatry late life allied Sci. 2007;22(7):613–26.

    Article  Google Scholar 

  8. Mezuk B, Eaton WW, Albrecht S, Golden SH. Depression and type 2 diabetes over the lifespan: a meta-analysis. Diabetes Care. 2008;31(12):2383–90.

    Article  Google Scholar 

  9. Flint J, Kendler Kenneth S. The Genetics of Major Depression. Neuron. 2014;81(3):484–503.

    Article  CAS  Google Scholar 

  10. Kwong ASF, López-López JA, Hammerton G, Manley D, Timpson NJ, Leckie G, et al. Genetic and environmental risk factors Associated with Trajectories of Depression symptoms from adolescence to Young Adulthood. JAMA Netw Open. 2019;2(6):e196587–e.

    Article  Google Scholar 

  11. Rao TSS, Asha MR, Ramesh BN, Rao KSJ. Understanding nutrition, depression and mental illnesses. Indian J psychiatry. 2008;50(2):77–82.

    Article  Google Scholar 

  12. Ljungberg T, Bondza E, Lethin C. Evidence of the importance of Dietary Habits regarding depressive symptoms and Depression. Int J Environ Res Public Health. 2020;17(5):1616.

    Article  CAS  Google Scholar 

  13. Melina V, Craig W, Levin S. Position of the Academy of Nutrition and Dietetics: vegetarian diets. J Acad Nutr Dietetics. 2016;116(12):1970–80.

    Article  Google Scholar 

  14. Berrazaga I, Micard V, Gueugneau M, Walrand S. The role of the Anabolic Properties of Plant- versus animal-based protein sources in supporting muscle Mass maintenance: a critical review. Nutrients. 2019;11(8):1825.

    Article  CAS  Google Scholar 

  15. Jacobs BL, Van Praag H, Gage F. Adult brain neurogenesis and psychiatry: a novel theory of depression. Mol Psychiatry. 2000;5(3):262–9.

    Article  CAS  Google Scholar 

  16. Kofler M, Schiefecker AJ, Gaasch M, Sperner-Unterweger B, Fuchs D, Beer R, et al. A reduced concentration of brain interstitial amino acids is associated with depression in subarachnoid hemorrhage patients. Sci Rep. 2019;9(1):2811.

    Article  Google Scholar 

  17. Biskup CS, Sánchez CL, Arrant A, Van Swearingen AE, Kuhn C, Zepf FD. Effects of acute tryptophan depletion on brain serotonin function and concentrations of dopamine and norepinephrine in C57BL/6J and BALB/cJ mice. PLoS ONE. 2012;7(5):e35916.

    Article  CAS  Google Scholar 

  18. Riedel WJ, Klaassen T, Deutz NE, van Someren A, van Praag HM. Tryptophan depletion in normal volunteers produces selective impairment in memory consolidation. Psychopharmacology. 1999;141(4):362–9.

    Article  CAS  Google Scholar 

  19. Valipour G, Esmaillzadeh A, Azadbakht L, Afshar H, Hassanzadeh A, Adibi P. Adherence to the DASH diet in relation to psychological profile of iranian adults. Eur J Nutr. 2017;56(1):309–20.

    Article  CAS  Google Scholar 

  20. 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.

    Article  Google Scholar 

  21. Shivappa N, Steck SE, Hurley TG, Hussey JR, Hébert JR. Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr. 2014;17(8):1689–96.

    Article  Google Scholar 

  22. SAMANI S, JOUKAR B. A study on the reliability and validity of the short form of the depression anxiety stress scale (DASS-21). 2007.

  23. Ainsworth BE, Haskell WL, Whitt MC, Irwin ML, Swartz AM, Strath SJ, et al. Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc. 2000;32(9):S498–S504.

    Article  CAS  Google Scholar 

  24. Schloss P, Williams DC. The serotonin transporter: a primary target for antidepressant drugs. J Psychopharmacol. 1998;12(2):115–21.

    Article  CAS  Google Scholar 

  25. Mohler H. The GABA system in anxiety and depression and its therapeutic potential. Neuropharmacology. 2012;62(1):42–53.

    Article  Google Scholar 

  26. Fernstrom JD. Dietary amino acids and brain function. J Am Diet Assoc. 1994;94(1):71–7.

    Article  CAS  Google Scholar 

  27. Fernstrom JD. Effects on the diet on brain neurotransmitters. Metab Clin Exp. 1977;26(2):207–23.

    Article  CAS  Google Scholar 

  28. Roiser JP, Levy J, Fromm SJ, Goldman D, Hodgkinson CA, Hasler G, et al. Serotonin transporter genotype differentially modulates neural responses to emotional words following tryptophan depletion in patients recovered from depression and healthy volunteers. J Psychopharmacol. 2012;26(11):1434–42.

    Article  Google Scholar 

  29. Wurtman RJ, Hefti F, Melamed E. Precursor control of neurotransmitter synthesis. Pharmacol Rev. 1980;32(4):315–35.

    CAS  Google Scholar 

  30. Fernstrom JD, Larin F, Wurtman RJ. Correlation between brain tryptophan and plasma neutral amino acid levels following food consumption in rats. Life Sci. 1973;13(5):517–24.

    Article  CAS  Google Scholar 

  31. Wurtman RJ, Wurtman JJ, Regan MM, McDermott JM, Tsay RH, Breu JJ. Effects of normal meals rich in carbohydrates or proteins on plasma tryptophan and tyrosine ratios. Am J Clin Nutr. 2003;77(1):128–32.

    Article  CAS  Google Scholar 

  32. Jacques PF, Bostom AG, Wilson PW, Rich S, Rosenberg IH, Selhub J. Determinants of plasma total homocysteine concentration in the Framingham offspring cohort. Am J Clin Nutr. 2001;73(3):613–21.

    Article  CAS  Google Scholar 

  33. Yu J, Xue R, Wang Q, Yu H, Liu X. The Effects of Plasma Homocysteine Level on the Risk of Three Major Psychiatric Disorders: A Mendelian Randomization Study.Frontiers in Psychiatry. 2022;13.

  34. Folstein M, Liu T, Peter I, Buell J, Arsenault L, Scott T, et al. The homocysteine hypothesis of depression. Am J Psychiatry. 2007;164(6):861–7.

    Article  Google Scholar 

  35. Sarris J, Logan AC, Akbaraly TN, Amminger GP, Balanzá-Martínez V, Freeman MP, et al. Nutritional medicine as mainstream in psychiatry. The Lancet Psychiatry. 2015;2(3):271–4.

    Article  Google Scholar 

  36. Kiecolt-Glaser JK. Stress, food, and inflammation: psychoneuroimmunology and nutrition at the cutting edge. Psychosom Med. 2010;72(4):365–9.

    Article  CAS  Google Scholar 

  37. Adjibade M, Andreeva VA, Lemogne C, Touvier M, Shivappa N, Hébert JR, et al. The inflammatory potential of the Diet is Associated with depressive symptoms in different subgroups of the General Population. J Nutr. 2017;147(5):879–87.

    Article  CAS  Google Scholar 

  38. Mendelsohn AR, Larrick JW. Dietary modification of the microbiome affects risk for cardiovascular disease. Rejuven Res. 2013;16(3):241–4.

    Article  CAS  Google Scholar 

  39. Simkin DR. Microbiome and mental health, specifically as it relates to adolescents. Curr Psychiatry Rep. 2019;21(9):1–12.

    Article  Google Scholar 

  40. Zhang Y, Yang Y, Xie M-S, Ding X, Li H, Liu Z-C, et al. Is meat consumption associated with depression? A meta-analysis of observational studies. BMC Psychiatry. 2017;17(1):409.

    Article  Google Scholar 

  41. Jin Y, Kandula NR, Kanaya AM, Talegawkar SA. Vegetarian diet is inversely associated with prevalence of depression in middle-older aged South Asians in the United States.Ethnicity & health. 2019:1–8.

  42. Skarupski KA, Tangney CC, Li H, Evans DA, Morris MC. Mediterranean diet and depressive symptoms among older adults over time. J Nutr Health Aging. 2013;17(5):441–5.

    Article  CAS  Google Scholar 

  43. Perez-Cornago A, Sanchez-Villegas A, Bes-Rastrollo M, Gea A, Molero P, Lahortiga-Ramos F, et al. Relationship between adherence to Dietary Approaches to stop hypertension (DASH) diet indices and incidence of depression during up to 8 years of follow-up. Public Health Nutr. 2017;20(13):2383–92.

    Article  Google Scholar 

  44. Nanri A, Eguchi M, Kuwahara K, Kochi T, Kurotani K, Ito R, et al. Macronutrient intake and depressive symptoms among japanese male workers: the Furukawa Nutrition and Health Study. Psychiatry Res. 2014;220(1):263–8.

    Article  CAS  Google Scholar 

  45. Beezhold BL, Johnston CS. Restriction of meat, fish, and poultry in omnivores improves mood: a pilot randomized controlled trial. Nutr J. 2012;11:9.

    Article  CAS  Google Scholar 

  46. Li Y, Zhang C, Li S, Zhang D. Association between dietary protein intake and the risk of depressive symptoms in adults. Br J Nutr. 2020;123(11):1290–301.

    Article  CAS  Google Scholar 

  47. Rubinow DR, Schmidt PJ. Gonadal steroids, brain, and behavior: role of context. Dialogues Clin Neurosci. 2002;4(2):123–37.

    Article  Google Scholar 

  48. Mahon JN, Rohan KJ, Nillni YI, Zvolensky MJ. The role of perceived control over anxiety in prospective symptom reports across the menstrual cycle. Arch Women Ment Health. 2015;18(2):239–46.

    Article  Google Scholar 

Download references

Acknowledgements

This study was extracted from an MSc dissertation that was approved by the Tehran University of Medical Sciences (no. 9611323008).

Funding

The Iranian Ministry of Health and Medical Education has contributed to the funding of this study, Grant no 9611323008.

Author information

Authors and Affiliations

  1. Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, P. O. Box: 1416643931, Tehran, Iran

    Ali Sheikhi, Fereydoun Siassi, Abolghassem Djazayery & Leila Azadbakht

  2. Department of Clinical Psychology, University of Tehran, Tehran, Iran

    Bijan Guilani

  3. Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, IR, Iran

    Leila Azadbakht

Authors
  1. Ali Sheikhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fereydoun Siassi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abolghassem Djazayery
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bijan Guilani
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Leila Azadbakht
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

LA designed the study protocol. ASh gathered data, wrote the manuscript, and conducted the analysis. FS, AD, BG, and LA supervised the process.

Corresponding author

Correspondence to Leila Azadbakht.

Ethics declarations

Ethics approval and consent to participate

This project was approved by the research council (research project number: 9611323008) and the ethics committee (research ethics number: IR.TUMS.VCR.REC.1398.492) of the Tehran university of medical sciences. Participants were fully informed about the study objectives and methods and were assured of the confidentiality of their information. A written informed consent form was signed by all participants. All methods were carried out in accordance with relevant guidelines and regulations.

Consent publication

Not Applicable.

Competing interests

None of the authors had any conflict of interest.

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 http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) 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

Sheikhi, A., Siassi, F., Djazayery, A. et al. Plant and animal protein intake and its association with depression, anxiety, and stress among Iranian women. BMC Public Health 23, 161 (2023). https://doi.org/10.1186/s12889-023-15100-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-023-15100-4

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us