Skip to main content
Download PDF

The association between dietary insulin index and load with mental health

BMC Psychology volume 10, Article number: 218 (2022) Cite this article

Abstract

Background

Depression, anxiety, and stress are common mental problems. The aim of this cross-sectional study was to investigate the association between two indexes that measure postprandial insulin response to different food, dietary insulin index (DII) and insulin load (DIL), with psychological disorders.

Method

Participants (n = 10,000) aged 20–69 were randomly selected from 200 clusters in Yazd from the recruitment phase of the Yazd Health Study. The dietary intake of participants was collected by a reliable and validated food frequency questionnaire (FFQ) consisting of 178 food items. DII and DIL were calculated from the FFQ data using previously published reference values. To assess psychological disorders an Iranian validated short version of a self-reported questionnaire (Depression Anxiety Stress Scales 21 [DASS21]) was used.

Results

No significant association was observed between DIL and DII with odds of depression or anxiety using crude or adjusted models. However, individuals in the highest quartiles of DIL had the lowest odds of stress (OR: 0.69; 95% CI 0.48–1.01, P-trend = 0.047). This association remained significant after adjustment for potential confounders in model II including marital status, smoking, education, job status, salt intake, and multi-vitamin supplement use (OR: 0.38; 95% CI 0.16–0.91, P-trend = 0.039) and the third and final model which is further adjusted for BMI (OR: 0.39; 95% CI 0.16–0.91, P-trend = 0.041).

Conclusion

Overall, consumption of foods with higher DII as well as DIL were associated with lower stress scores; however, no significant relationship was observed between DII or DIL with respective depression or anxiety scores.

Peer Review reports

Introduction

Depression, anxiety, and stress are major mental health concerns in most populations. Recent reports of the global prevalence of depression, anxiety, and distress show 31.4%, 31.9% and 41.1% respectively, which is much higher than normal levels due to the COVID-19 pandemic [1]. Poor mental health imposes considerable costs on international health systems. A wealth of evidence shows psychological distress is associated with disability, early mortality, and increased onset of chronic disease such as metabolic syndrome, diabetes, cardiovascular disease, and Alzheimer’s disease [2,3,4,5].

The prevalence of depression, anxiety, and stress is much higher in diabetic patients compared to those without, and the role of insulin resistance and hyperinsulinemia in the progression of mental disease has been well-established [6,7,8].

Diet has a considerable effect on serum insulin levels and a high glycemic index (GI) and high glycemic load (GL) diet may be related to the higher risk of depression [9]. GI and GL indicate the ability of foods to raise blood glucose levels compared to glucose or white bread [10]. While high serum glucose levels correlates with insulin levels, foods containing certain amino acids and fatty acids can induce insulin secretion with a dampened effect on glucose levels [11,12,13]. An insulin index (II) has been suggested to estimate postprandial insulin levels in response to isoenergetic amounts of different foods [14]. The dietary insulin index (DII) and dietary insulin load (DIL) can be calculated by considering the DII of each food by its energy content and the frequency of consumption in the diet [15]. Because the DII indicates insulin responses directly it is arguably a better predictor of disease related to insulin resistance compared to GI.

Some previous studies have shown that higher DII and DIL may be associated with increased risk of obesity and insulin resistance [16, 17]. The negative effects of obesity on psychological disorders have been observed in several studies [18, 19]. Mechanisms include hypertrophy of adipose tissue which is accompanied by an inflammatory response that can lead to neuroinflammation [20, 21].

To our knowledge, recently, only one study investigated the relationship between DII and DIL with psychological disorders in the 3172 Iranian adults. The results of this study indicated that higher DII and DIL were related with greater odds of depression in women. However no significant association was observed between DII, DIL and anxiety [22]. Considering the role of hyperinsulinemia in psychological disorders and the necessity of more studies with larger sample sizes, we investigated this hypothesis in 7384 Iranian adults.

Materials and methods

Study population

In the current study, we used data from a Yazd Health Study (YaHS) conducted from September 2014 to December 2015 in Yazd, in central Iran. YaHS is a prospective cohort study, which includes n = 10,000 participants aged 20–69 y who were randomly selected for the recruitment phase from 200 clusters in Yazd Greater Area. The profile and details of this study were published elsewhere [23]. Written informed consent was obtained from all participants. The research was approved by the Ethics Committee of Shahid Sadoughi University of Medical Sciences, Yazd, Iran (Ethic code: 931188). Data about current and history of chronic diseases, smoking status, and socio-demographic characteristics including age, gender, marriage status and education level were obtained by interview and standard questionnaires. Participants were excluded on the following: under or over estimation of energy intake (total daily energy intake < 800 or > 6500 kcal/day), pregnancy, following a special diet and taking antidepressants. After exclusion, n = 7574 participants participated in the study.

Dietary assessment

Dietary intake of study participants was collected by a reliable and validated food frequency questionnaire (FFQ) consisting of 178 food items designed for an Iranian population [24]. FFQs were completed during face-to-face interviews and reported dietary intakes in household measures were converted to grams and inputted into the Nutritionist IV software (First Databank Inc., Hearst Corp., San Bruno, CA, USA). To calculate daily nutrient intake values for each participant, the US Department of Agriculture’s (USDA) national nutrient databank was used [25].

Dietary insulin index and load calculation

Briefly, the food insulin index (FII) refers to the incremental insulin area under the curve over a 2 h period after consumption of a 1000-kJ (239 kcal) portion of the test food divided by the area under the curve after ingestion of a 1000-kJ portion of the reference food. The insulin indices for 61 food items were obtained from previous publications [26,27,28]. For the remaining 71 food items that were not available in the published food lists, the FII of similar food items was used by the correlation between their energy, fiber, carbohydrate, protein, and fat content.

To determine the DIL, we first calculated the insulin load of each food with the following formula:

Insulin load of food = Insulin index of food × energy content of food (kcal/d)

Secondly, by summing up the insulin load of each food, the DIL was obtained. We calculated the average DII for all foods by dividing DIL by total dietary intake of energy.

Anthropometric measurements

Body weight was measured using a portable digital scale analyser with an accuracy of 0.1 kg. The participants stood in the middle of the scale, wearing the minimum possible clothing. Height was also measured in a standing position, while barefoot with the head placed in the Frankfurt position. Body Mass Index (BMI) was calculated, body weight (kg)/ height (m2). All anthropometric measurements were performed before starting the interview, again after having completed one-third of the questionnaire and for a final time after completing two-thirds of the questions in the questionnaire.

Psychological health assessment

An Iranian validated short version of the self-report questionnaire for depression, anxiety and stress (DASS 21) consisting of seven items per subscale was used [29]. The individuals read each statement and recorded their reply according to a 4-point Likert scale ranging from 0 (Does not apply to me at all) to 3 (Applies to me very much or most of the time). The scores were summed for items of each scale. As the long form of DASS has 42 items, we multiplied the final score of each scale by two. The individuals were considered to have depression, anxiety, and stress if they obtained total scores of ≥ 10, ≥ 8 and ≥ 15, respectively [30].

Statistical analysis

The normality of data was assessed using the Kolmogorov–Smirnov test. Continuous and categorical variables were compared across quartiles of DIL and DII using analysis of variance (ANOVA) and chi-square tests respectively. post-hoc analysis was performed using post-hoc Bonferroni tests. Logistic regression was applied to evaluate the relationship between DII and DIL with psychological disorders in crude and adjusted models. In Model I basic information were adjusted. In Model II, socioeconomic status and behavioural risk factors were additionally controlled. More adjustment was performed for chronic condition. Model I was adjusted for age, gender, and total energy intake. In model II, marital status, smoking, education level, employment status, salt intake and multi-vitamin use were additionally adjusted. The final model included the addition of BMI. These confounding variables were included in the model because previous studies showed associations with the psychological disorders. P-values < 0.05 were considered statistically significant. To analyze the data, the statistical Package for Social Sciences (SPSS) (version 23.0, SPSS Inc., Chicago, Illinois, USA) was used.

Results

The general characteristics of study participants across quartiles of DIL and DII are indicated in Table 1 and Additional file 1: Table S1. The final sample size included in our analyses was n = 7574 participants. The prevalence of psychological disorders did not significantly differ between quartiles of DIL and DII, except for anxiety prevalence, which was greater in the highest quartile of DII. Significant differences were observed for BMI, age, marital status, gender, employment status, education level and multi-vitamin supplement use across quartiles of DIL. These significant differences were only obtained for employment status, education level, and multi-vitamin supplement use among quartiles of DII. The distribution of smoking status did not differ throughout quartiles of DII and DIL. In addition, post-hoc analysis was performed by Bonferroni post-hoc test. Significant differences among the quartiles of DIL and DII are shown with different letters “a” or “b”.

Table 1 General characteristics of study participants across quartiles of DIL and DII
Full size table

Dietary nutrients and energy adjusted food groups are shown in Table 2 and Additional file 2: Table S2. The one-way ANOVA test followed by Bonferroni post-hoc analysis revealed significant differences between all dietary nutrients and food groups including, energy intake; percentage energy from protein, carbohydrate, and fat intake; cholesterol; saturated fatty acid; vitamin E; vitamin C; folic acid; magnesium; fruits; vegetables; red meat; fish, dairy; whole grains; refined grains; sugars; salt; legumes and nuts. Significant differences among the quartiles of DGI and DGL are shown with different letters “a” or “b”.

Table 2 Dietary intakes of study participants across quartiles of dietary insulin index and load
Full size table

The associations between DIL and DII with the prevalence of depression, anxiety, and stress in crude and adjusted models are presented in Table 3. There were no significant associations between DIL and DII with odds of depression or anxiety in crude and adjusted models. However, individuals in the highest quartiles of DIL had the lowest odds of stress (OR: 0.69; 95% CI 0.48–1.01, P-trend = 0.047). This association remained significant after adjustment for potential confounding variables in model II (OR: 0.38; 95% CI 0.16–0.91, P-trend = 0.039) and model III (OR: 0.39; 95% CI 0.16–0.91, P-trend = 0.041).

Table 3 Multivariable-adjusted ORs (and 95% CIs) for depression, anxiety and stress across quartiles of dietary insulin index and load
Full size table

Discussion

We aimed to cross-sectionally investigate the relationship between DII and DIL with indices of mental health. The results showed no significant relation between DIL and DII with odds of depression or anxiety in crude and adjusted models; however, higher DIL was associated with lower odds of stress.

To our knowledge there is only one previous study that investigated the association between DII and DIL and psychological disorders. Anjom-Shoae et al. [22], conducted a cross-sectional study in Isfahan, Iran, that also found no association between DIL and DII with odds of anxiety or depression in the whole population. Although, in contrast to our results, they found that women in the higher quartiles of DII/DIL had higher depression scores. This was not a strong finding as regression analysis of depression as a continuous variable showed no significant linear association between DIL and DII and depression in both sexes. This disagreement of results might also be due to the use of different questionnaires for measuring dietary intake and psychological disorders. In Anjom-Shoae et al. study dietary intake were assessed via a 106-item semi-quantitative FFQ (DS-FFQ) and anxiety and depression were measured by the Iranian validated version of Hospital Anxiety and Depression Scale, while in this study 178-items FFQ and Dass 21 questionnaires were used for assessing dietary intake and psychological disorders respectively. It should be noted that our larger sample size (n = 7384 compared to 3172) is probably more representative for the general population.

Obesity has negative effects on mental health through different mechanisms. First, releasing proinflammatory cytokines such as Tumour Necrosis Factor alpha (TNF-α) and interleukin-6 (IL-6) from hypertrophic adipose tissue, is accompanied by an inflammatory response [20]. Consequently, inflammatory cytokines increase in the circulation and even reach the central nervous system; here they interfere with the hypothalamus–pituitary-adrenal (HPA) axis leading to neuroinflammation [21]. Furthermore, indoleamine 2,3-dioxygenase (IDO) is an enzyme which prevents production of serotonin by catabolizing tryptophan into kynurenine [31]. Increased activity of IDO and reduction in tryptophan serum levels have been reported in obese patients [32]. At least, two studies have investigated the association between DII and DIL and obesity. Adherence to a diet with a high DIL was associated with greater odds of general obesity in women in one study [17] but not supported in another [33]. The BMI for participants of this study did not differ among different quartiles of DII and DIL, which can partly explain the null findings between DII and DIL and psychological disorders.

Although data on DII and DIL are limited, several studies assessed the relationship between GI and GL with psychological disorders [34,35,36,37]. A systematic review and meta-analysis showed no significant association between either dietary GI or GL and odds of depression in cross-sectional studies, which partially agrees with our data as most of the food with higher GI and GL also have higher DII and DIL respectively [14]. However, a significant positive relation was observed between dietary GI and depression in clinical trials and cohort studies [38].

Confirming the findings of a previous study [22], our results revealed no significant link between DII or DIL and anxiety. However, we found an inverse association between DIL and stress score. Only one previous study investigated this hypothesis, which reported no significant relationship between DII or DIL and psychological distress [22]. As mentioned above, using different questionnaires and our use of a larger sample size may explain this inconsistency. Moreover, another study showed that higher dietary GL is associated with a lower odds ratio of psychological distress [34]. It is well stablished that foods with higher GL give rise to more insulin secretion and thus a higher IL. Insulin release following consumption of carbohydrate-rich foods leads to elevated plasma tryptophan levels and is associated with increased synthesis of serotonin, a neurotransmitter involved in mental function [39]. Furthermore, stress is controlled by the HPA axis. It has been shown that consumption of sugar-rich foods reduces the feeling of stress by decreasing the HPA axis [40, 41]. The participants of the highest quartiles of DIL of this study consumed more sugars, which might lead them to experience lower levels of stress. However, controlling stress in this way is temporary, and regular consumption of these foods can lead to obesity [41]. In our analysis, the relationship between DIL and stress remained after adjustment for BMI; however, stress correlates with visceral obesity [42]. Further research with a nuanced approach to body composition measurement is warranted.

The present study has several limitations. First, we cannot infer causality due to the cross-sectional design of the study. Second, no biochemical indices were measured in our study, which limits the detection of participants with chronic disease. Third, the II of some food items were not available in the food lists of the previous studies thus, we used the similar food items as substitutes. On the other hand, our studies’ strengths included a large sample size and controlled for potential confounders such as supplements and dietary intake.

In conclusion, consumption of foods with higher II as well as IL was associated with lower stress; however, no significant relation was observed between II or IL and risk of depression and anxiety. Further studies with longitudinal design are needed to confirm these results.

Availability of data and materials

The data and materials of the current study are available from the corresponding author on reasonable request.

References

  1. Wu T, Jia X, Shi H, Niu J, Yin X, Xie J, et al. Prevalence of mental health problems during the COVID-19 pandemic: a systematic review and meta-analysis. J Affect Disord. 2021;281:91–8.

    Article  Google Scholar 

  2. Kessler RC. The costs of depression. Psychiatr Clin North Am. 2012;35(1):1–14.

    Article  Google Scholar 

  3. Rotella F, Mannucci E. Depression as a risk factor for diabetes: a meta-analysis of longitudinal studies. J Clin Psychiatry. 2013;74(1):31–7.

    Article  Google Scholar 

  4. Lasserre AM, Strippoli MF, Glaus J, Gholam-Rezaee M, Vandeleur CL, Castelao E, et al. Prospective associations of depression subtypes with cardio-metabolic risk factors in the general population. Mol Psychiatry. 2017;22(7):1026–34.

    Article  Google Scholar 

  5. Sáiz-Vázquez O, Gracia-García P, Ubillos-Landa S. Depression as a risk factor for alzheimer’s disease: a systematic review of longitudinal Meta-analyses. J Clin Med. 2021. https://doi.org/10.3390/jcm10091809.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Kan C, Silva N, Golden SH, Rajala U, Timonen M, Stahl D, et al. A systematic review and meta-analysis of the association between depression and insulin resistance. Diabetes Care. 2013;36(2):480–9.

    Article  Google Scholar 

  7. Lee JH, Park SK, Ryoo JH, Oh CM, Mansur RB, Alfonsi JE, et al. The association between insulin resistance and depression in the Korean general population. J Affect Disord. 2017;208:553–9.

    Article  Google Scholar 

  8. Huybrechts I, De Vriendt T, Breidenassel C, Rogiers J, Vanaelst B, Cuenca-García M, et al. Mechanisms of stress, energy homeostasis and insulin resistance in European adolescents–the HELENA study. Nutr Metab Cardiovasc Dis. 2014;24(10):1082–9.

    Article  Google Scholar 

  9. Rahimlou M, Morshedzadeh N, Karimi S, Jafarirad S. Association between dietary glycemic index and glycemic load with depression: a systematic review. Eur J Nutr. 2018;57(7):2333–40.

    Article  Google Scholar 

  10. Jenkins DJ, Wolever TM, Taylor RH, Barker H, Fielden H, Baldwin JM, et al. Glycemic index of foods: a physiological basis for carbohydrate exchange. Am J Clin Nutr. 1981;34(3):362–6.

    Article  Google Scholar 

  11. Moghaddam E, Vogt JA, Wolever TM. The effects of fat and protein on glycemic responses in nondiabetic humans vary with waist circumference, fasting plasma insulin, and dietary fiber intake. J Nutr. 2006;136(10):2506–11.

    Article  Google Scholar 

  12. van Loon LJ, Saris WH, Verhagen H, Wagenmakers AJ. Plasma insulin responses after ingestion of different amino acid or protein mixtures with carbohydrate. Am J Clin Nutr. 2000;72(1):96–105.

    Article  Google Scholar 

  13. Beysen C, Karpe F, Fielding BA, Clark A, Levy JC, Frayn KN. Interaction between specific fatty acids, GLP-1 and insulin secretion in humans. Diabetologia. 2002;45(11):1533–41.

    Article  Google Scholar 

  14. Holt SH, Miller JC, Petocz P. An insulin index of foods: the insulin demand generated by 1000-kJ portions of common foods. Am J Clin Nutr. 1997;66(5):1264–76.

    Article  Google Scholar 

  15. Nimptsch K, Brand-Miller JC, Franz M, Sampson L, Willett WC, Giovannucci E. Dietary insulin index and insulin load in relation to biomarkers of glycemic control, plasma lipids, and inflammation markers. Am J Clin Nutr. 2011;94(1):182–90.

    Article  Google Scholar 

  16. Mirmiran P, Esfandiari S, Bahadoran Z, Tohidi M, Azizi F. Dietary insulin load and insulin index are associated with the risk of insulin resistance: a prospective approach in tehran lipid and glucose study. J Diabetes Metab Disord. 2016. https://doi.org/10.1186/s40200-016-0247-5.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Anjom-Shoae J, Keshteli AH, Sadeghi O, Pouraram H, Afshar H, Esmaillzadeh A, et al. Association between dietary insulin index and load with obesity in adults. Eur J Nutr. 2020;59(4):1563–75.

    Article  Google Scholar 

  18. Mannan M, Mamun A, Doi S, Clavarino A. Prospective associations between depression and obesity for adolescent males and females- a systematic review and meta-analysis of longitudinal studies. PLoS ONE. 2016;11(6): e0157240.

    Article  Google Scholar 

  19. Quek YH, Tam WWS, Zhang MWB, Ho RCM. Exploring the association between childhood and adolescent obesity and depression: a meta-analysis. Obes Rev off J Int Assoc Study Obes. 2017;18(7):742–54.

    Article  Google Scholar 

  20. Shoelson SE, Lee J, Goldfine AB. Inflammation and insulin resistance. J Clin Investig. 2006;116(7):1793–801.

    Article  Google Scholar 

  21. Martins LB, Monteze NM, Calarge C, Ferreira AVM, Teixeira AL. Pathways linking obesity to neuropsychiatric disorders. Nutrition. 2019;66:16–21.

    Article  Google Scholar 

  22. Anjom-Shoae J, Hassanzadeh Keshteli A, Afshar H, Esmaillzadeh A, Adibi P. Association between dietary insulin index and load and psychological disorders. Br J Nutr. 2020;123(2):161–71.

    Article  Google Scholar 

  23. Mirzaei M, Salehi-Abargouei A, Mirzaei M, Mohsenpour MA. Cohort Profile: The Yazd Health Study (YaHS): a population-based study of adults aged 20–70 years (study design and baseline population data). Int J Epidemiol. 2018;47(3):697–8.

    Article  Google Scholar 

  24. 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 

  25. Available from: https://fdc.nal.usda.gov/.

  26. Bao J, De Jong V, Atkinson F, Petocz P, Brand-Miller JC. Food insulin index: physiologic basis for predicting insulin demand evoked by composite meals. Am J Clin Nutr. 2009;90(4):986–92.

    Article  Google Scholar 

  27. Bell K, Gray R, Munns D, Petocz P, Howard G, Colagiuri S, et al. Estimating insulin demand for protein-containing foods using the food insulin index. Eur J Clin Nutr. 2014;68(9):1055–9.

    Article  Google Scholar 

  28. Holt S, Miller J, Petocz P. An insulin index of foods: the insulin demand generated by 1000-kJ portions of common foods. Am J Clin Nutr. 1997;66(5):1264–76.

    Article  Google Scholar 

  29. Sahebi A, Asghari MJ, Salari RS. Validation of depression anxiety and stress scale (DASS-21) for an Iranian population. 2005.

  30. Maharaj S, Lees T, Lal S. Prevalence and risk factors of depression, anxiety, and stress in a cohort of Australian nurses. Int J Environ Res Public Health. 2019;16(1):61.

    Article  Google Scholar 

  31. Castanon N, Luheshi G, Layé S. Role of neuroinflammation in the emotional and cognitive alterations displayed by animal models of obesity. Front Neurosci. 2015;9:229.

    Article  Google Scholar 

  32. Favennec M, Hennart B, Caiazzo R, Leloire A, Yengo L, Verbanck M, et al. The kynurenine pathway is activated in human obesity and shifted toward kynurenine monooxygenase activation. Obesity. 2015;23(10):2066–74.

    Article  Google Scholar 

  33. Ghorbaninejad P, Imani H, Sheikhhossein F. Higher dietary insulin load and index are not associated with the risk of metabolic syndrome and obesity in Iranian adults. Int J Clin Pract. 2021;75(7):e14229.

    Article  Google Scholar 

  34. Haghighatdoost F, Azadbakht L, Keshteli AH, Feinle-Bisset C, Daghaghzadeh H, Afshar H, et al. Glycemic index, glycemic load, and common psychological disorders. Am J Clin Nutr. 2016;103(1):201–9.

    Article  Google Scholar 

  35. Minobe N, Murakami K, Kobayashi S, Suga H, Sasaki S. Higher dietary glycemic index, but not glycemic load, is associated with a lower prevalence of depressive symptoms in a cross-sectional study of young and middle-aged Japanese women. Eur J Nutr. 2018;57(6):2261–73.

    Article  Google Scholar 

  36. Aparicio A, Robles F, López-Sobaler AM, Ortega RM. Dietary glycaemic load and odds of depression in a group of institutionalized elderly people without antidepressant treatment. Eur J Nutr. 2013;52(3):1059–66.

    Article  Google Scholar 

  37. Mwamburi DM, Liebson E, Folstein M, Bungay K, Tucker KL, Qiu WQ. Depression and glycemic intake in the homebound elderly. J Affect Disord. 2011;132(1–2):94–8.

    Article  Google Scholar 

  38. Salari-Moghaddam A, Saneei P, Larijani B, Esmaillzadeh A. Glycemic index, glycemic load, and depression: a systematic review and meta-analysis. Eur J Clin Nutr. 2019;73(3):356–65.

    Article  Google Scholar 

  39. Markus CR. Effects of carbohydrates on brain tryptophan availability and stress performance. Biol Psychol. 2007;76(1–2):83–90.

    Article  Google Scholar 

  40. Ulrich-Lai YM, Ostrander MM, Herman JP. HPA axis dampening by limited sucrose intake: reward frequency vs. caloric consumption. Physiol Behav. 2011;103(1):104–10.

    Article  Google Scholar 

  41. Adam TC, Epel ES. Stress, eating and the reward system. Physiol Behav. 2007;91(4):449–58.

    Article  Google Scholar 

  42. Tenk J, Mátrai P, Hegyi P, Rostás I, Garami A, Szabó I, et al. Perceived stress correlates with visceral obesity and lipid parameters of the metabolic syndrome: a systematic review and meta-analysis. Psychoneuroendocrinology. 2018;95:63–73.

    Article  Google Scholar 

Download references

Acknowledgements

We thank Yazd people who participated in YaHS.

Funding

This study is supported by Yazd Shahid Sadoughi University of Medical Sciences.

Author information

Author notes

  1. Mina Darand and Ali Amirinejad: Equally as first author

Authors and Affiliations

  1. Department of Clinical Nutrition, School of Nutrition and Food Science, Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

    Mina Darand

  2. Nutrition and Food Security Research Center, Shahid Sadoughi University of Medical Sciences, Shohadaye Gomnam BLD. ALEM Square, Yazd, Iran

    Ali Amirinejad, Amin Salehi-Abargouei & Sayyed Saeid Khayyatzadeh

  3. Department of Nutrition, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

    Ali Amirinejad, Amin Salehi-Abargouei & Sayyed Saeid Khayyatzadeh

  4. Research Institute of Sport and Exercise Science, Liverpool John Moores University, Liverpool, UK

    Ian G. Davies

  5. Yazd Cardiovascular Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

    Masoud Mirzaei

  6. Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, UK

    Mohsen Mazidi

Authors
  1. Mina Darand
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Amirinejad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amin Salehi-Abargouei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ian G. Davies
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Masoud Mirzaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mohsen Mazidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sayyed Saeid Khayyatzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.K, Ma.M, MD and A.A: designed and conducted the study; M. D and MoM: analysed the data; A.S and AA: wrote the manuscript S.K, IGD, and Mo.M: critically revised the manuscript; S.K and Ma.M: supervised the study. The final version of the manuscript was approved by all authors.

Corresponding author

Correspondence to Sayyed Saeid Khayyatzadeh.

Ethics declarations

Ethical approval and consent to participate

We confirm that all methods were carried out in accordance with Shahid Sadoughi University of Medical Sciences guidelines and regulations. Written informed consent was taken from all participants. The research was approved by the Ethics Committee of Shahid Sadoughi University of Medical Sciences, Yazd, Iran (Ethic code: 931188).

Consent for publication

Not applicable.

Competing interests

There is no conflict of interest to declare.

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

. Table S1. General characteristics of study participants [Mean ± SD or % (n) & OR (CI)] for presence of psychological disturbances.

Additional file 2

. Table S2. Additional details about Dietary intakes of study participants across quartiles of dietary insulin index and load.

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

Darand, M., Amirinejad, A., Salehi-Abargouei, A. et al. The association between dietary insulin index and load with mental health. BMC Psychol 10, 218 (2022). https://doi.org/10.1186/s40359-022-00925-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40359-022-00925-2

Keywords

BMC Psychology

ISSN: 2050-7283

Contact us