Skip to main content

The association between Healthy Beverage Index and psychological disorders among overweight and obese women: a cross-sectional study


Background and aims

The Healthy Beverage Index (HBI) is a valuable technique to estimate the synergistic effects of overall beverage consumption. Several studies have evaluated the associations between HBI and beneficial changes in the health status. however, there is no study on the association between patterns of beverage consumption and mental health status. Therefore, this study sought to examine the association between HBI and psychological disorders among overweight and obese women.


199 overweight and obese women, between the ages of 18 and 55 y, were enrolled in this cross-sectional study in Tehran, Iran. To collect beverage dietary data, a validated semi-quantitative food-frequency questionnaire (FFQ) was used. Furthermore, the DASS-21 questionnaire was used to assess psychological profile states.


The association of total depression anxiety stress (DASS) score with healthy beverage index (HBI) tertiles in models was marginally significant (OR =: 0.78; 95% CI 0.30–2.02; P-value = 0.074; (OR = 0.77; 95% CI 0.28–2.16; P-value = 0.062), respectively. In terms of stress, anxiety, and depression, after adjusting for confounders, participants with higher HBI in the third tertile had lower odds of depression vs. the first tertile (OR = 0.99; 95% CI 0.35–2.81; P-trend = 0.040).


We demonstrate that the total DASS score was associated with HBI tertiles. We also found that participants with higher HBI had lower odds of depression. However, additional well-designed studies are needed to confirm the veracity of these findings.

Peer Review reports


According to the World Health Organization, psychological health issues are expected to become an increasingly serious risk to public health by 2030 [1]. Psychological distress such as depressionn and anxiety, is among the most prominent conditions that compose mental diseases [2]. Depression is a severe mental illness that affects around 4.4% of the world's population, which is a significant leading cause of disease burden [3]; furthermore, anxiety affects about one in 13 people of the population [4]. These psychiatric problems not only have negative occupational, academic, and social consequences, but they also create a major financial burden on the medical system [5, 6].

Psychological problems are becoming more common, especially in women, who are twice as likely as males to suffer from depression [7, 8]. Obesity and psychological illnesses have a direct association; indeed, obesity can lead to psychological illnesses such as depression and anxiety. Obesity has been shown to increase the prevalence of depression by 32.8 percent and anxiety disorder by 30.5 percent [9]. According to research carried out by Noorbala and colleagues in 2004 using the General Health Questionnaire, 21 percent of the overall population in Iran suffered from mental health problems (25.9% of women and 14.6 percent of males) [10]. In 2008, this percentage was raised to 36% in Tehran, with women showing a greater percent compared to males (2 to 1) [11].Moreover, Obesity patterns in different nations are based on gender. Women are more likely than males to be diagnosed with obesity [12, 13]. Given the relevance and high frequency of mental health disorders, particularly among women, as well as their greatest influence in generating other diseases and consequences [14], observational studies to assess the risk of psychological disorders in overweight and obese women should be developed to improve mental health. Adults who have been classified with depression or anxiety are also much more likely to be inactive and obese [15]. Both mental disorders and obesity are affected by a variety of inherited and environmental variables [16,17,18], and diet has been proposed as a key component in the development of these prevalent diseases [19,20,21].

To date, substantial research has been conducted on the relationship between individual beverage consumption and overweight/obesity, particularly sugar-sweetened beverages [22,23,24]. Milk, coffee, tea, and other unsweetened beverages have been linked to improved health, particularly cognitive function [25, 26]. Sugar-sweetened beverage (SSB) consumption, on the other hand, has been linked to weight gain and obesity [27,28,29].

Few studies, however, have investigated the general quality of daily beverage consumption in the context of evaluating all consumed beverages as a pattern studies [30,31,32,33]. There is also another study which validated HBI in Iran [34]. A Healthy Beverage Index (HBI) could be used to assess overall beverage quality and to discern whether changes in beverage consumption patterns are related to better health. As a result, the HBI has been established as a comprehensive concept in nutritional epidemiology studies to evaluate the quality of overall beverage intake and its association with health-related outcomes [30]. Eight beverage categories, total beverage energy, and fluid intake were included in this index [35]. The Beverage Guidance Panel provided the majority of HBI components; however, the recommendations were transformed in to fluid requirements as a percentage of overall fluid requirements. Furthermore, the HBI classified "caloric drinks with certain nutrients" into three categories: full fat milk, 100% fruit juice, and alcohol. The usual fluid requirement of one mL per kcal eaten was used to calculate total fluid requirements [36].Because the effect of a single beverage may be too weak to be identifiable, the cumulative effects of numerous beverages incorporated in a total dietary index may give better identification [26]. Furthermore, because the HBI can be used as a counseling tool to encourage healthy beverage selection, it may have significant ramifications for public health.

To the best of our knowledge, no study has investigated the relationship between HBI patterns and the risk of psychological disorders in overweight and obese women. Several studies have suggested that adherence to the HBI might improve the abnormal plasma lipid markers and other risk factors associated with metabolic syndrome and cardiovascular disease (CVDs) [30, 34], whilst some studies have demonstrated HBI scores are associated with beneficial changes in the health status of women. Considering some of the risk factors for psychological disorders, and the possible association between the HBI diet and these risk factors, we conducted this study to determine the association between HBI dietary patterns and the risk of psychological disorders in overweight and obese women.

Method and materials

Study population

Participants were chosen from a cross-sectional survey conducted in Tehran, Iran, in 2019. This study enrolled a total of 199 obese and overweight women between the ages of 18 and 55. Women with a history of CVDs, hypertension, type 2 diabetes (T2D), polycystic ovary syndrome (PCOS), kidney failure, stroke, thyroid disease, liver disease, cancer, inflammatory disorders, and individuals taking any therapeutic drugs, weight loss program, or supplements during the study period were excluded. Another exclusion criterion included total energy consumption of < 500 or > 3500 kcal/day. To take part in our study, all participants signed a written informed consent form prior to study commencement.

Data collection

Participants' age, marital status, and educational level were recorded. For measuring the height, we used a non-stretch tape measure, with participants in a standing position and unshod, height was measured and recorded at a precision of 0.5 cm [37]. With the individuals standing upright, NC was measured using non-stretchable plastic tape at the halfway point of the neck, between the mid-cervical spine and the mid anterior neck, to within 1 mm. Body mass index (BMI) was determined by dividing body weight by the square of body height and is represented in kilograms per square meter (kg/m2), with weight in kilograms and height in meters. A manual sphygmomanometer was used to monitor systolic and diastolic blood pressure on the left arm, while sitting, after a 5-min rest interval. The Tehran University of Medical Sciences (TUMS) Ethics Committee approved this study (IR.TUMS.MEDICINE.REC.1401.206). All methods were performed in accordance with relevant guidelines and regulations.

Physical activity assessment

Individuals' physical activity was assessed using the short-term International Physical Activity Questionnaire (IPAQ) [38]. This questionnaire calculates the physical activity of all participants during the past 7 days. The validity and reliability of IPAQ questionnaire was assessed across 12 countries. The criterion reliability of this questionnaire had the Spearman’s ρ of around 0.8. The median ρ for the validity has been reported at around 0.30, which was similar to other validation studies. IPAQ is a validated self-reported seven-item measure of physical activity that shows physical activity rate (vigorous, moderate, walking, and inactive) over the last week, and then the values were multiplied by their metabolic equivalent (MET) quantities, and the acquired numbers were summed together to calculate a MET/min/week value.

Body composition

Weight (kg), fat-free mass, bone mineral content, percent body fat (percent BF), skeletal muscle mass (SMM), soft lean mass (SLM), fat-free mass index (FFMI), intracellular water, and extracellular water were all measured using a tetrapolar bioelectrical impedance analysis (InBody 770 scanner, Seoul, Korea) [20]. Participants took off their shoes, jackets, and sweaters before standing barefoot on the balancing scale and holding the machine's handles [39].

Blood parameters

After 10–12 h night fasting, blood samples were drawn at the Nutrition and Biochemistry laboratory of the School of Nutritional Sciences and Dietetics, TUMS. Standard methods were used to collect and measure biochemical variables, including blood sugar tests (FBG and HbA1c) and lipid profiles (Triglyceride (TG) (mg/dl), high-density lipoprotein (HDL) (mg/dl), total cholesterol (TC) (mg/dl), and low-density lipoprotein (LDL) (mg/dl).

Dietary assessment

Face-to-face interviews were used to assess dietary consumption using a standardized and reliable food-frequency questionnaire (FFQ) [40]. Subjects were ask to report the frequency of each food item consumed on a daily, weekly, monthly, or yearly during the past year. This evaluation was conducted by asking participants about the occurrence of food items consumed from a prepared list of foods. Using home measures, the final portion amounts were converted to grams per day. The residual approach was then used to modify these figures for calorie intake. Dietary intakes were assessed by the Iranian Food Composition Table (FCT) and by using N4 (First Data Bank, San Bruno, CA) software to estimate energy and nutrient intakes.

HBI scoring system

Dufey and Davy [30] formulated a method for calculating the HBI. Water, unsweetened coffee and tea, low-fat milk (1.5 percent fat, fat-free, and/or soy milk), diet drinks (including non-calorically sweetened coffee and tea and other artificially sweetened beverages), 100 percent fruit juice, alcohol (including beer, wine, and liquor), full-fat milk (1.5 percent fat), and sugar-sweetened beverages (including fruit drinks, sweetened coffee and tea, and soda) were the eight categories of beverages consumed [30]. The final HBI score ranges from 0 to 100, with a higher number indicating better compliance with beverage standards [30]. The maximum final HBI score was 90, since diet drinks (with a score ranging between 0 and 5) and alcohol (with score numbers from 0 to 5) were not consumed by participants in this study.

Assessment of mental health

We assessed the mental health of participants using the 21-question version of the Depression Anxiety Stress Scales (DASS-21), which has been shown to be a valid tool for the evaluation of stress, depression, and anxiety. Each of the three DASS-21 scales contains 7 items, divided into subscales with similar content [41]. DASS-21 scores were multiplied by 2 to calculate the final score, as is reported based on guidelines. Scores ≥ 10, ≥ 8, and ≥ 15 were considered as cutoff points for having depression, anxiety, and stress, respectively [42].

Statistical analysis

SPSS v.26 software (SPSS Inc., IL, USA) was used for statistical analysis, and statistical significance was accepted at P < 0.05, while P = 0.05–0.07 was considered marginally significant in the present study. The Kolmogorov–Smirnov test was used to determine the normality of data distribution; quantitative data were reported as means and standard deviation (SD), and categorical data were reported as numbers with percentage. According to the HBI, the participants were categorized in to tertiles based on their scores, to: tertile 1 (< 63), tertile 2 (63–67), and tertile 3 (> 67), respectively. To compare quantitative and categorical variables across HBI tertiles, one-way analysis of variance (ANOVA) and chi-square (χ2) tests were performed, respectively. After controlling for confounders (age, body mass index, energy intake, physical activity), and considering BMI as a collinear variable for anthropometrics and body composition variables, dietary intakes were compared across the tertiles of HBI using analysis of covariance (ANCOVA). Binary logistic regression was used to determine whether different HBI were associated with the risk of depression, anxiety, and stress. In adjusted model 1, age, BMI, energy intake, and physical activity were controlled. In adjusted model 2, age, BMI, energy intake, physical activity, education level, job, and marital status were controlled. An odds ratio (OR) with 95% Confidence Interval (CI) was calculated.


Study population characteristics

One hundred and ninety-nine participants completed this study, where the overall prevalence of HBI tertiles was 72 (36.2%) for tertile 1, 76 (38.2%) for tertile 2, and 51 (25.6%) for tertile 3. The mean (SD) age and BMI of participants were 36.09 (8.52) years and 30.77 (4.22) kg/m2, respectively. The economic status, marriage, and employment were such that 77 (38.7%) respondents had a moderate economic status, 153 (76.9%) respondents were married, and 113 (56.8%) were employed. The majority of respondents were educated to diploma (75 (37.7%)) and bachelor or higher (98 (49.2%)) level. The mean (SD) of the DASS score, stress score, anxiety score, and depression score were 37.46 (24.56), 16.11 (10.13), 10.59 (8.24), and 10.75 (9.70), respectively.

Baseline characteristics of study particiants categorized according to the tertiles of HBI in obese and overweight women

The baseline characteristics of study participants, categorized according to the HBI tertiles, are presented in Table 1. As shown in Table 1, P-values for all variables were reported in the crude model, and after adjustment with potential confounders, including age, BMI, energy intake, and PA. In the crude model, there was a significant mean difference in terms of physical activity among the tertiles of HBI (P = 0.044), neck circumference (NC) (P = 0.021), total cholesterol (TC) (0.011), high-density lipoprotein cholesterol (HDL-c) (P = 0.019), and low-density lipoprotein cholesterol (LDL-c) (P = 0.001). After adjustment with confounders (age, BMI, energy intake, and physical activity), the mean difference of fat-free mass (FFM) (P = 0.011), bone mineral content (BMC) (P = 0.022), skeletal muscle mass (SMM) (P = 0.011), soft lean mass (SLM) (P = 0.011), fat-free mass index (FFMI) (P = 0.036), intracellular water (IW) (P = 0.037), and extracellular water (EW) (P = 0.024) became significant. In terms of physical activity, NC, TC, HDL, and LDL, after adjustment with confounders, the mean difference of HDL and LDL remained significant (P < 0.05). Following Bonferroni post-hoc testing, the significant mean difference in FFM, SLM, BMC, SMM, FFMI, IW, EW, and HDL were between T1 and T2, such that the mean difference of T1 was higher than T2 in all variables except HDL, where T2 was higher than T1. In terms of LDL, the mean difference was between T1 and T3, such that the mean difference in T3 was lower than in T1. In categorical variables, a significant mean difference among the participants was seen in terms of marital status (P = 0.009), after controlling for confounders. There was no significant difference in terms of other variables in Table 1.

Table 1 Baseline characteristics of study particiants categorized according to the tertiles of healthy beverage index in obese and overweight women (n = 199)

Dietary food intakes across HBI tertiles in obese and overweight women

Dietary intake of all participants according to HBI tertiles is presented in Table 2. Mean difference of vitamin E (P = 0.008), whole grains (P = 0.003), and fruits (P = 0.032) was statistically significant in the crude model. After controlling for energy intake, the mean difference of carbohydrate (P = 0.027), potassium (P = 0.027), and biotin (0.011) became significant (P < 0.05). In terms of vitamin E, whole grains, and fruits, the mean difference remained significant (P < 0.05).

Table 2 Dietary food intakes across healthy bevarage index tertiles in obese and overweight women (n = 199)

There was no significant difference in other variables in Table 2.

Association of psychological disorders and healthy beverage index in obese and overweight women

The associations of stress, anxiety, depression, and total DASS score with HBI are shown in Table 3. In the crude model, there was no significant relationship between depression, anxiety, stress, and total DASS score with the tertiles of HBI (P > 0.05). After adjusting for confounders in model 1 (adjusting for age, energy intake, BMI, and physical activity) and model 2 (adjusting for age, energy intake, BMI, physical activity, marital status, economic status, job, and education), the mean difference of variables remained insignificant (P > 0.05).

Table 3 Association of psychological disorders and healthy beverage index in obese and overweight women (n = 199)

Association of DASS score and it’s components with HBI tertiles in obese and overweight women

The crude and adjusted OR and 95% CI of the DASS score and its components across tertiles of HBI were shown in Table 4. In the crude model, there was no significant association between DASS score and its components with HBI tertiles (P > 0.05). After adjustment with confounders in model 1 (adjusting for age, energy intake, BMI, and physical activity) and model 2 (adjusting for age, energy intake, BMI, physical activity, marital status, economic status, job, and education), the association of total DASS score with HBI tertiles was marginally significant (OR 0.78; 95% CI 0.30–2.02; P_value = 0.074), (OR 0.77; 95% CI 0.28–2.16; P_value = 0.062), respectively. In terms of stress, anxiety, and depression, after adjusting with confounders in model 2, participants with higher HBI (third vs. first tertile) had lower odds of depression (OR 0.99; 95% CI 0.35–2.81; P_trend = 0.040).

Table 4 Association of DASS score and it’s components with healthy bevarage index tertiles in obese and overweight women (n = 199)


We investigated the association between HBI and psychological disorders among overweight and obese women. To the best of our knowledge, no studies have been conducted in this field thus far, and this study represents the first contribution to the literature in this regard. In the present study, total DASS score was associated with the second tertile of HBI; meaning that with increasing HBI score, the total DASS score decreased. We also found that participants with higher HBI had lower odds of depression. Based on our results, beverages might impact on micro and macronutrients. As explained in other studies and consistent with our results, beverages like milk can impact on macro and micronutrients (like potassium) [43, 44].

In one study, daily consumption of sugar sweetened-beverages (SSBs) contributed to manifestation of psychological disorders; however, no association was observed between consuming 100% fruit juice and psychological disorders [45]. In this study, only SSBs and 100% fruit juice were examined, and the authors did not categorize their results based on DASS scores. Consistent with our results, some studies have reported that the amount of sugar from beverages is associated with a higher incidence of depression and other psychological disorders [46]. It should be noted that 66.9% of participants were men while in our study the participants were women. It was demonstrated that overconsumption of SSBs caused dysregulation of the stress response [47]. In another study, it was demonstrated that the risk of depression in subjects consuming 3 cups of tea daily is 37% lower than in those who do not consume tea [48]. In this study, coffee consumption acted as tea and reduced depression. Numerous studies, conducted in China [49], Singapore [50], and USA [51], have shown a significant association between caffeine and caffeinated drinks consumption and depression. Indeed, a study conducted in the USA on women with a mean age of 63 demonstrated that women who drink more than four cups of caffeinated coffee had 20% lower risk of depression than women who consumed less [51]. These studies were consistent with our study. In two studies conducted in Finland [52, 53], the relationship between tea consumption and depression was weak or not significant. It seems that the difference between these weak results could be due to the difference in the gender and age of participants, as well as the different scales that were used for the evaluation of depression. A study conducted on children found that depressed children consume more caffeinated drinks than children with non-depressed symptoms [54] this study were inconsistent with our results. A case–control study found high consumption of soft drinks and industrial fruit juices was associated with an increased risk of depression. It should be noted that this study evaluated healthy and unhealthy dietary patterns and did not focus just on the beverages [55]. Another beverage that could be associated with depression is alcohol. According to the extant literature, people who drink alcohol are more vulnerable to depression, whilst people with depression are more likely to have alcohol misuse to relieve their distress [56,57,58].

The SSBs could affect mental health by their sugary components; indeed, sugar can induce chronic systematic inflammation by activating the innate immune system, thus affecting psychological disorders [59]. Animal studies have shown that sugar could increase depression incidence by activating the hypothalamic–pituitary–adrenal (HPA) axis and inducing elevation in glucocorticoids [60]. The mechanism behind the effect of coffee on the mental health is the potential stimulation of the central nervous system, enhancing dopaminergic neurotransmission [61]. One mechanism by which beverages could affect psychological disorders via carbohydrates. By modulating plasma concentrations of tryptophan and of large neutral amino acids (LNAA), carbohydrates could affect mental performance [62]. The ability of carbohydrates to increase the uptake of circulating tryptophan into the brain depends on its ability to promote the secretion of insulin [63]; thus, by consuming more carbohydrates, a greater secretion of insulin will ensue, plasma levels of LNAA will decrease, and the supply of tryptophan to the brain will increase [63]. Moreover, by consuming SSBs, and the side effects that follow, including obesity and type 2 diabetes (T2D), researchers have found a bi-directional relationship between obesity or T2D and depression [64,65,66].

Although representing the first study in this field, our study had some limitations that warrant consideration. First, due to the cross-sectional design, causality could not be inferred. For example, psychological disorders like depression may lead to a higher HBI score — an association that cannot be identified with our design. Second, the sample size used to conduct this study was small and should be up-scaled in further work. Third, we could not adjust for potential cofounders such as other nutrients or other demographic data, thus precluding a completely robust set of models. Nevertheless, despite the noted limitations, our study had numerous strengths, including; we assessed the association between HBI and psychological disorders among overweight and obese women for the first time, thereby allowing novel insight into this relationship; furthermore, we used a validated and reliable FFQ to evaluate the dietary intakes of the participants.


We found participants with higher HBI scores had lower odds of depression. Since previous studies were consistent with our results, this is important regarding the development of preventative approaches to reduce psychological disorders. Our results showed that the total DASS score was probably associated with HBI tertiles. Based on our results and future studies in this field, it has been possible to reduce psychological disorders and especially depression, by using healthy beverages. Future studies with a bigger sample size, considering men and women, are needed to confirm the veracity of these findings.

Availability of data and materials

The datasets generated and /or analyzed during the current study are not publicly available due to preserving participant anonymity but are available from the Khadijeh Mirzaei on reasonable request.



Bioelectrical impedance analyzer


Body mass index


Bone mineral content


Cardiovascular disease


Depression anxiety stress


Extracellular water


Food frequency questionnaire


Fat free mass


Fat-free mass index


Glucose oxidase-phenol 4-aminoantipyrine peroxidase


Glycerol-3-phosphate oxidase-phenol 4-aminoantipyrine peroxidase


Healthy Beverage Index


High-density lipoprotein


Cholesterol intracellular water






Large neutral amino acids


Metabolic equivalent


Neck circumference


Polycystic ovary syndrome


Physical Activity Questionnaire


Physical activity


Sugar-sweetened beverage


Skeletal muscle mass


Standard deviation


Total cholesterol




Waist circumferences


Waist to hip ratio


  1. Centers for Disease Control and Prevention. Mental Health: Data and Publications; 2018.

  2. British Psychological Society (UK); 2011. (NICE Clinical Guidelines N, COMMON MENTAL HEALTH DISORDERS.: National Collaborating Centre for Mental Health (UK). Common Mental Health Disorders: Identification and Pathways to Care. Leicester.

  3. Evans-Lacko SAGS, Al-Hamzawi A, et al. Socio-economic variations in the mental health treatment gap for people with anxiety, mood, and substance use disorders: results from the WHO World Mental Health (WMH) surveys. Psychol Med. 2018;48(9):1560–71.

    Article  CAS  PubMed  Google Scholar 

  4. Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, Abbasi-Kangevari M, Abbastabar H, Abd-Allah F, Abdelalim A. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–22.

    Article  Google Scholar 

  5. Collaborators GMD. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry. 2022.

  6. Murray CJ, Aravkin AY, Zheng P, Abbafati C, Abbas KM, Abbasi-Kangevari M, Abd-Allah F, Abdelalim A, Abdollahi M, Abdollahpour I. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1223–49.

    Article  Google Scholar 

  7. Viertiö S, Kiviruusu O, Piirtola M, Kaprio J, Korhonen T, Marttunen M, Suvisaari J. Factors contributing to psychological distress in the working population, with a special reference to gender difference. BMC Public Health. 2021;21(1):611.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Jafarirad S, Rasaie N, Darabi F. Comparison of anthropometric indices and lifestyle factors between healthy university students and those affected by premenstrual syndrome. Jundishapur Sci Med J. 2016;15(2):217–27.

    Google Scholar 

  9. Wang S, Sun Q, Zhai L, Bai Y, Wei W, Jia L. The prevalence of depression and anxiety symptoms among overweight/obese and non-overweight/non-obese children/adolescents in china: a systematic review and meta-analysis. Int J Environ Res Public Health. 2019;16:3.

    Google Scholar 

  10. Noorbala AA, Bagheri Yazdi SA, Yasamy MT, Mohammad K. Mental health survey of the adult population in Iran. Br J Psychiatry J Ment Sci. 2004;184:70–3.

    Article  CAS  Google Scholar 

  11. Sarr PT, Kasturiarachchi C, Yang H, Co CJ, Shimpo A, Fujino S, Morimata J. Investigating the motivating factors behind high delivery rates of the urban HEART Birthing Facility in San Martin de Porres, Philippines.

  12. Garawi F, Devries K, Thorogood N, Uauy R. Global differences between women and men in the prevalence of obesity: is there an association with gender inequality? Eur J Clin Nutr. 2014;68(10):1101–6.

    Article  CAS  PubMed  Google Scholar 

  13. Cooper AJ, Gupta SR, Moustafa AF, Chao AM. Sex/gender differences in obesity prevalence, comorbidities, and treatment. Curr Obes Rep. 2021;10(4):458–66.

    Article  PubMed  Google Scholar 

  14. Brown S, Inskip H, Barraclough B. Causes of the excess mortality of schizophrenia. Br J Psychiatry J Ment Sci. 2000;177:212–7.

    Article  CAS  Google Scholar 

  15. Blasco BV, García-Jiménez J, Bodoano I, Gutiérrez-Rojas L. Obesity and depression: its prevalence and influence as a prognostic factor: a systematic review. Psychiatry Investig. 2020;17(8):715–24.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Flores-Dorantes MT, Díaz-López YE, Gutiérrez-Aguilar R. Environment and gene association with obesity and their impact on neurodegenerative and neurodevelopmental diseases. Front Neurosci. 2020;14.

  17. Meng P, Ye J, Chu X, Cheng B, Cheng S, Liu L, Yang X, Liang C, Zhang F. Associations between genetic loci, environment factors and mental disorders: a genome-wide survival analysis using the UK Biobank data. Transl Psychiatry. 2022;12(1):17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Yarizadeh H, Setayesh L, Majidi N, Rasaei N, Mehranfar S, Ebrahimi R, Casazzza K, Mirzaei K. Nutrient patterns and their relation to obesity and metabolic syndrome in Iranian overweight and obese adult women. Eating Weight Disord Stud Anorexia Bulimia Obesity 2021:1–11.

  19. Lavallee KL, Zhang XC, Schneider S, Margraf J. Obesity and mental health: a longitudinal, cross-cultural examination in Germany and China. Front Psychol. 2021;12.

  20. Patsalos O, Keeler J, Schmidt U, Penninx B, Young AH, Himmerich H. Diet, obesity, and depression: a systematic review. J Person Med. 2021, 11(3).

  21. Salehi Z, Shiraseb F, Rasaei N, Mirzaei K. Association of energy adjusts nutrient-rich foods (ENRF) on mental health among obese and overweight women: a cross-sectional study; 2021.

  22. Auerbach BJ, Wolf FM, Hikida A, Vallila-Buchman P, Littman A, Thompson D, Louden D, Taber DR, Krieger J. Fruit juice and change in BMI: a meta-analysis. Pediatrics. 2017;139:4.

    Article  Google Scholar 

  23. Gui Z-H, Zhu Y-N, Cai L, Sun F-H, Ma Y-H, Jing J, Chen Y-J. Sugar-sweetened beverage consumption and risks of obesity and hypertension in Chinese children and adolescents: a national cross-sectional analysis. Nutrients. 2017;9(12):1302.

    Article  PubMed Central  Google Scholar 

  24. Mirmiran P, Yuzbashian E, Asghari G, Hosseinpour-Niazi S, Azizi F. Consumption of sugar sweetened beverage is associated with incidence of metabolic syndrome in Tehranian children and adolescents. Nutr Metab. 2015;12(1):25.

    Article  CAS  Google Scholar 

  25. Ferruzzi MG, Tanprasertsuk J, Kris-Etherton P, Weaver CM, Johnson EJ. Perspective: the role of beverages as a source of nutrients and phytonutrients. Adv Nutr. 2020;11(3):507–23.

    Article  PubMed  Google Scholar 

  26. Popkin BM, Armstrong LE, Bray GM, Caballero B, Frei B, Willett WC. A new proposed guidance system for beverage consumption in the United States. Am J Clin Nutr. 2006;83(3):529–42.

    Article  CAS  PubMed  Google Scholar 

  27. Ferretti F, Mariani M. Sugar-sweetened beverage affordability and the prevalence of overweight and obesity in a cross section of countries. Glob Health. 2019;15(1):30.

    Article  Google Scholar 

  28. Malik VS, Hu FB. Sugar-sweetened beverages and cardiometabolic health: an update of the evidence. Nutrients. 2019;11(8):1840.

    Article  CAS  PubMed Central  Google Scholar 

  29. Sigala DM, Stanhope KL. An exploration of the role of sugar-sweetened beverage in promoting obesity and health disparities. Curr Obes Rep. 2021;10(1):39–52.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Duffey KJ, Davy BM. The healthy beverage index is associated with reduced cardiometabolic risk in US adults: a preliminary analysis. J Acad Nutr Diet. 2015;115(10):1682-1689.e1682.

    Article  PubMed  Google Scholar 

  31. Parker MK, Davy BM, Hedrick VE. Preliminary Assessment of the healthy beverage index for US children and adolescents: a tool to quantify the overall beverage intake quality of 2- to 19-year olds. J Acad Nutr Diet. 2022;122(2):371-383.e376.

    Article  PubMed  Google Scholar 

  32. Hasheminejad N, Malek Mohammadi T, Mahmoodi MR, Barkam M, Shahravan A. The association between beverage consumption pattern and dental problems in Iranian adolescents: a cross sectional study. BMC Oral Health. 2020;20(1):74.

    Article  PubMed  PubMed Central  Google Scholar 

  33. LaRowe TL, Moeller SM, Adams AK. Beverage patterns, diet quality, and body mass index of US preschool and school-aged children. J Am Diet Assoc. 2007;107(7):1124–33.

    Article  CAS  PubMed  Google Scholar 

  34. Jalilpiran Y, Mozaffari H, Askari M, Jafari A, Azadbakht L. The association between Healthy Beverage Index and anthropometric measures among children: a cross-sectional study. Eating Weight Disord EWD. 2021;26(5):1437–45.

    Article  Google Scholar 

  35. Platania A, Castiglione D, Sinatra D, Urso M, Marranzano M. Fluid intake and beverage consumption description and their association with dietary vitamins and antioxidant compounds in Italian adults from the Mediterranean healthy eating, aging and lifestyles (MEAL) Study. Antioxidants (Basel). 2018;7:4.

    Google Scholar 

  36. Hedrick VE, Davy BM, Myers EA, You W, Zoellner JM. Changes in the healthy beverage index in response to an intervention targeting a reduction in sugar-sweetened beverage consumption as compared to an intervention targeting improvements in physical activity: results from the talking health trial. Nutrients. 2015;7(12):10168–78.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Norgan N: A Review of: “Anthropometric standardization reference manual”. Edited by TG LOHMAN, AF ROCHE and R. MARTORELL.(Champaign, IL.: Human Kinetics Books, 1988.)[Pp. vi+ 177.]£ 28· 00. ISBN 087322 121 4. Ergonomics 1988, 31(10):1493–1494.

  38. Hossein M, Moghaddam BS, 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; 2012.

  39. McLester CN, Nickerson BS, Kliszczewicz BM, McLester JR. Reliability and agreement of various InBody body composition analyzers as compared to dual-energy X-ray absorptiometry in healthy men and women. J Clin Densitomet. 2020;23(3):443–50.

    Article  Google Scholar 

  40. Rezazadeh A, Omidvar N, Tucker KL. Food frequency questionnaires developed and validated in Iran: a systematic review. Epidemiol Health. 2020;42: e2020015.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Henry JD, Crawford JR. The short-form version of the Depression Anxiety Stress Scales (DASS-21): construct validity and normative data in a large non-clinical sample. Br J Clin Psychol. 2005;44(2):227–39.

    Article  PubMed  Google Scholar 

  42. Brown TA, Chorpita BF, Korotitsch W, Barlow DH. Psychometric properties of the Depression Anxiety Stress Scales (DASS) in clinical samples. Behav Res Ther. 1997;35(1):79–89.

    Article  CAS  PubMed  Google Scholar 

  43. Montenegro-Bethancourt G, Vossenaar M, Doak CM, Solomons NW. Contribution of beverages to energy, macronutrient and micronutrient intake of third-and fourth-grade schoolchildren in Quetzaltenango, Guatemala. Maternal Child Nutr. 2010;6(2):174–89.

    Google Scholar 

  44. Venci B, Hodac N, Lee S-Y, Shidler M, Krikorian R. Beverage consumption patterns and micronutrient and caloric intake from beverages in older adults with mild cognitive impairment. J Nutr Gerontol Geriatr. 2015;34(4):399–409.

    Article  PubMed  Google Scholar 

  45. Freije SL, Senter CC, Avery AD, Hawes SE, Jones-Smith JC. Association between consumption of sugar-sweetened beverages and 100% fruit juice with poor mental health among US adults in 11 US States and the District of Columbia. Prev Chronic Dis. 2021;18:E51.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Knüppel A, Shipley MJ, Llewellyn CH, Brunner EJ. Sugar intake from sweet food and beverages, common mental disorder and depression: prospective findings from the Whitehall II study. Sci Rep. 2017;7(1):1–10.

    Article  CAS  Google Scholar 

  47. Harrell CS, Burgado J, Kelly SD, Johnson ZP, Neigh GN. High-fructose diet during periadolescent development increases depressive-like behavior and remodels the hypothalamic transcriptome in male rats. Psychoneuroendocrinology. 2015;62:252–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Grosso G, Micek A, Castellano S, Pajak A, Galvano F. Coffee, tea, caffeine and risk of depression: a systematic review and dose–response meta-analysis of observational studies. Mol Nutr Food Res. 2016;60(1):223–34.

    Article  CAS  PubMed  Google Scholar 

  49. Ng T-P, Aung K, Feng L, Nyunt M, Yap K. Tea consumption and physical function in older adults: a cross-sectional study. J Nutr Health Aging. 2014;18(2):161–6.

    Article  CAS  PubMed  Google Scholar 

  50. Feng L, Gwee X, Kua E-H, Ng T-P. Cognitive function and tea consumption in community dwelling older Chinese in Singapore. J Nutr Health Aging. 2010;14(6):433–8.

    Article  CAS  PubMed  Google Scholar 

  51. Lucas M, Mirzaei F, Pan A, Okereke OI, Willett WC, O’Reilly ÉJ, Koenen K, Ascherio A. Coffee, caffeine, and risk of depression among women. Arch Intern Med. 2011;171(17):1571–8.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Hintikka J, Tolmunen T, Honkalampi K, Haatainen K, Koivumaa-Honkanen H, Tanskanen A, Viinamäki H. Daily tea drinking is associated with a low level of depressive symptoms in the Finnish general population. Eur J Epidemiol. 2005;20(4):359–63.

    Article  CAS  PubMed  Google Scholar 

  53. Ruusunen A, Lehto SM, Tolmunen T, Mursu J, Kaplan GA, Voutilainen S. Coffee, tea and caffeine intake and the risk of severe depression in middle-aged Finnish men: the Kuopio Ischaemic Heart Disease Risk Factor Study. Public Health Nutr. 2010;13(8):1215–20.

    Article  PubMed  Google Scholar 

  54. Benko CR, Farias AC, Farias LG, Pereira EF, Louzada FM, Cordeiro ML. Potential link between caffeine consumption and pediatric depression: a case–control study. BMC Pediatr. 2011;11(1):1–5.

    Article  Google Scholar 

  55. Khosravi M, Sotoudeh G, Majdzadeh R, Nejati S, Darabi S, Raisi F, Esmaillzadeh A, Sorayani M. Healthy and unhealthy dietary patterns are related to depression: a case–control study. Psychiatry Investig. 2015;12(4):434.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Chou SP, Lee HK, Cho MJ, Park JI, Dawson DA, Grant BF. Alcohol use disorders, nicotine dependence, and co-occurring mood and anxiety disorders in the United States and South Korea—a cross-national comparison. Alcoholism Clin Exp Res. 2012;36(4):654–62.

    Article  Google Scholar 

  57. Teesson M, Hall W, Slade T, Mills K, Grove R, Mewton L, Baillie A, Haber P. Prevalence and correlates of DSM-IV alcohol abuse and dependence in Australia: findings of the 2007 National Survey of Mental Health and Wellbeing. Addiction. 2010;105(12):2085–94.

    Article  PubMed  Google Scholar 

  58. Xu Y, Zeng L, Zou K, Shan S, Wang X, Xiong J, Zhao L, Zhang L, Cheng G. Role of dietary factors in the prevention and treatment for depression: an umbrella review of meta-analyses of prospective studies. Transl Psychiatry. 2021;11(1):1–13.

    Article  CAS  Google Scholar 

  59. Bosma-den Boer MM, van Wetten M-L, 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):1–14.

    Article  Google Scholar 

  60. Vickers NJ. Animal communication: when i’m calling you, will you answer too? Curr Biol. 2017;27(14):R713–5.

    Article  CAS  PubMed  Google Scholar 

  61. Godos J, Pluchinotta FR, Marventano S, Buscemi S, Li Volti G, Galvano F, Grosso G. Coffee components and cardiovascular risk: beneficial and detrimental effects. Int J Food Sci Nutr. 2014;65(8):925–36.

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  63. Sanchez-Villegas A, Zazpe I, Santiago S, Perez-Cornago A, Martinez-Gonzalez MA, Lahortiga-Ramos F. Added sugars and sugar-sweetened beverage consumption, dietary carbohydrate index and depression risk in the Seguimiento Universidad de Navarra (SUN) Project. Br J Nutr. 2018;119(2):211–21.

    Article  CAS  PubMed  Google Scholar 

  64. Bruce D, Davis W, Starkstein S, Davis T. Clinical risk factors for depressive syndrome in Type 2 diabetes: the Fremantle Diabetes Study. Diabet Med. 2018;35(7):903–10.

    Article  CAS  PubMed  Google Scholar 

  65. 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  PubMed  PubMed Central  CAS  Google Scholar 

  66. Wang J, Light K, Henderson M, O’Loughlin J, Mathieu M-E, Paradis G, Gray-Donald K. Consumption of added sugars from liquid but not solid sources predicts impaired glucose homeostasis and insulin resistance among youth at risk of obesity. J Nutr. 2014;144(1):81–6.

    Article  CAS  PubMed  Google Scholar 

Download references


The authors thank the laboratory of Nutrition Sciences and Dietetics in Tehran University of Medical Sciences (TUMS). We are grateful to all of the participants for their contribution to this research. This study was approved by the Research ethics committee of the Tehran University of Medical Sciences (TUMS), Tehran, Iran with ethics number IR.TUMS.MEDICINE.REC.1401.206. All participants signed a written informed consent that was approved by this committee prior to enrollment in the study.


This study was supported by TUMS and grant ID was 99-3212-51715.

Author information

Authors and Affiliations



NR and KM designed the search; NR and KM conducted the sampling; FS performed statistical analysis; NR, RGE, FS, FA, FG, CCTC and KM wrote the paper, KM primary responsibility for final content. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Khadijeh Mirzaei.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This study was supported by grants from the Tehran University of Medical Sciences (TUMS), Tehran, Iran. Each individual was informed completely regarding the study protocol and provided a written and informed consent form before taking part in the study. literate family members of illiterate participants provided informed consent for the study and this method is approved by the Ethics Committee of Tehran University of Medical Sciences, Tehran, Iran.'

Consent for publication

All authors approved the final manuscript and consent for publication.

Competing interest

There are no competing financial interests in relation to the current study.

Additional information

Publisher's Note

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

Rights and permissions

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

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rasaei, N., Ghaffarian-Ensaf, R., Shiraseb, F. et al. The association between Healthy Beverage Index and psychological disorders among overweight and obese women: a cross-sectional study. BMC Women's Health 22, 295 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Depression
  • Anxiety
  • Stress
  • Mental health
  • Healthy beverage index
  • Obesity