Skip to main content Accessibility help
×
Home
Hostname: page-component-cf9d5c678-5wlnc Total loading time: 0.67 Render date: 2021-07-31T13:36:33.972Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "metricsAbstractViews": false, "figures": true, "newCiteModal": false, "newCitedByModal": true, "newEcommerce": true, "newUsageEvents": true }

Association of dietary acid load and plant-based diet index with sleep, stress, anxiety and depression in diabetic women

Published online by Cambridge University Press:  06 December 2019

Elnaz Daneshzad
Affiliation:
Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran
Seyed-Ali Keshavarz
Affiliation:
Department of Clinical Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran
Mostafa Qorbani
Affiliation:
Non-communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran Chronic Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
Bagher Larijani
Affiliation:
Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
Nick Bellissimo
Affiliation:
School of Nutrition, Ryerson University, Toronto, Canada
Leila Azadbakht
Affiliation:
Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
Corresponding
E-mail address:
Rights & Permissions[Opens in a new window]

Abstract

Diabetes is a common chronic disease with various complications. The present study was conducted to determine the association of plant-based diet index (PDI) and dietary acid load (DAL) with sleep status as well as mental health in type 2 diabetic women. In this cross-sectional study, a validated FFQ was used to assess dietary intakes of 230 diabetic patients. We created a whole PDI, healthful PDI (hPDI) and unhealthful PDI (uPDI). DAL was calculated based on potential renal acid load and net endogenous acid production method. The Pittsburgh Sleep Quality Index and twenty-one-item Depression, Anxiety and Stress Scale were used to assess sleep and mental health disorders, respectively. Participants in the top group of uPDI had greater risk of poor sleep (OR 6·47, 95 % CI 2·75, 15·24). However, patients who were in the top group of hPDI had a lower risk of sleep problems (OR 0·28, 95 % CI 0·13, 0·62). Participants in the top group of uPDI had greater risk of depression, anxiety and stress (OR 9·35, 95 % CI 3·96, 22·07; OR 4·74, 95 % CI 2·28, 9·85; OR 4·24, 95 % CI 2·14, 8·38, respectively). In conclusion, participants with higher DAL scores and patients who adhered to animal-based diets rather than plant-based diets were more likely to be poor sleepers and have mental health disorders.

Type
Full Papers
Copyright
© The Authors 2019

Sleep disturbances and psychological disorders including stress, anxiety, aggression and depression are common complications in diabetic patients. Diabetic patients have poor sleep quality, with poorer sleep efficiency and sleep disorders compared with non-diabetics(Reference Sridhar and Madhu1Reference Budhiraja, Roth and Hudgel3). Moreover, anxiety and depression are more prevalent in diabetic patients than individuals with normal glucose tolerance(Reference Shinkov, Borissova and Kovatcheva4). Approximately one-third of type 2 diabetic patients have sub-threshold depression(Reference Bot, Pouwer and Ormel5) and over 40 % present with minor or major depression with anxiety disorders(Reference Golden, Shah and Naqibuddin6).

Diet is an important and modifiable environmental factor, which can affect glycaemic control as well as psychological disorders and sleep disturbances. For example, low glycaemic index foods which contain high dietary fibre are associated with lower blood glucose(Reference Valachovicova, Krajcovicova-Kudlackova and Blazicek7). Moreover, Zn, Mg and B-vitamins, which are abundant in vegetables, are associated with decreased risk of depression(Reference Murakami, Mizoue and Sasaki8,Reference Jacka, Mykletun and Berk9) . Based on previous studies, these micronutrients can also lead to an improved sleep quality(Reference Ji and Liu10,Reference Rondanelli, Opizzi and Monteferrario11) . Today, according to interaction and combination of various macro- and micronutrients in a whole diet, epidemiological studies are conducted to determine the association of dietary patterns and dietary quality indices and various diseases. Protective dietary patterns with the content of nuts, fruits and vegetables are associated with reduced risk of depression(12). Plant-based diet index (PDI) and dietary acid load (DAL) are two indices to assess whole diet quality. As nutrients which present in vegetable and fruits have indicated good effects on sleep and psychological status, the present study hypothesised that whether a whole plant diet has a reduced association with sleep disturbances and psychological disorders. Based on previous studies in diabetic patients, DAL was positively associated with type 2 diabetes risk(Reference Fagherazzi, Vilier and Bonnet13) and the metabolic syndrome(Reference Iwase, Tanaka and Kobayashi14). Also, women with higher DAL scores had increased risk of gestational diabetes mellitus(Reference Saraf-Bank, Tehrani and Haghighatdoost15). Furthermore, metabolic acidosis leads to reduced insulin secretion and induces insulin resistance(Reference Saraf-Bank, Tehrani and Haghighatdoost15).

Therefore, the aim of this cross-sectional study was to determine the association of a PDI and DAL with sleep status, anxiety, stress and depression in type 2 diabetic women.

Methods

This cross-sectional study was carried out in 230 type 2 diabetic women who were referred to diabetes research or health centres in Tehran, Iran. The sample size was determined based on the mean of psychological distress score in patients with lowest (15·98 (sd 9·72)) and highest (12·38 (sd 9·95)) adherence to the PDI, respectively: α 0·05 and β 0·2 (power 80 %). Therefore, in the present study, the sample size was determined about 115 individuals in each group(Reference Zamani, Daneshzad and Siassi16). Participants were randomly selected and provided informed written consent. Only women with type 2 diabetes without other medical complications were included in the present study. Women who had other chronic diseases such as thyroid problems, cancers, CVD and kidney disease were excluded. In addition, individuals who reported a total energy intake of <3347·2 and >17 572·8 kJ were excluded. The present study was approved by the ethical committee of Tehran University of Medical Sciences (96-03-161-36923).

Assessment of anthropometric measures

Body weight was measured in minimal clothing using a calibrated digital scale (SECA 803). Height was measured while participants were in standing position using an unstretched tape measure to the nearest to 0·1 mm. BMI was calculated as weight divided by height squared (kg/m2). Waist circumference was measured using an unstretched tape measure to the nearest to 0·1 cm at the narrowest cite of the waist in light clothing.

Assessment of dietary intake

A 168-item semi-quantitative FFQ, which was validated and reliable for the Iranian population, was used to determine past year dietary intakes(Reference Azadbakht and Esmaillzadeh17). All participants filled the amount and frequency of consumption of each food item on a daily, weekly or monthly basis during the past year. The reported portion sizes of consumed foods converted to g/d. Nutritionist IV software (version 7.0; N-Squared Computing) which was adapted for Iranian foods was used for nutrients analysis.

Plant-based diet indices

We created a whole PDI, healthful PDI (hPDI) and unhealthful PDI (uPDI) as reported in previous studies(Reference Martinez-Gonzalez, Sanchez-Tainta and Corella18,Reference Satija, Bhupathiraju and Rimm19) . Briefly, eighteen food groups are created and classified into three main categories: animal foods, and healthy and unhealthy plant foods. Healthy food groups included fruits, vegetables, whole grains, legumes, vegetable oils, nuts, tea and coffee, whereas less healthy food groups included sugar-sweetened beverages, refined grains, fruit juices, potatoes, sweets and desserts. Animal food groups included dairy products, eggs, animal fats, fish and seafood, poultry and red meat, and miscellaneous animal-based foods. Food grouping details are shown in online Supplementary Table S1. These eighteen food groups were ranked into quintiles and given scores between 1 and 10. For creating PDI, the highest decile of a food group received a score of 10 and the lowest decile received a score of 1.

Participants in the highest decile of animal food groups received a score of 1 and to the lowest decile received a score of 10. For hPDI, healthy plant food groups received a positive score, whereas scores were reversed for unhealthy plant food groups and animal food groups. For uPDI, a score of 1 was given to the lowest decile of less healthy plant food groups and 10 for the highest decile, whereas reverse scores were attributed to healthy plant and animal food groups (online Supplementary Table S1)(Reference Satija, Bhupathiraju and Rimm19). Finally, all eighteen food group scores were summed (range 18–180) to attain the indices score, which indicates that a higher intake of all three indices reflected lower animal food intake. Finally, these three indices were categorised into two groups by median-split to assess their association with dependent measures.

Assessment of dietary acid load

DAL was calculated based on potential renal acid load (PRAL)(Reference Remer and Manz20,Reference Remer, Dimitriou and Manz21) and net endogenous acid production (NEAP) method(Reference Frassetto, Todd and Morris22).

$${\rm{PRAL }}\;\left( {{\rm{mEq/d}}} \right)\, = \,\left( {{\rm{protein }}\;\left( {{\rm{g/d}}} \right)\, \times \,0\!\cdot\!49} \right)\, + \,\left( {{\rm{P }}\;\left( {{\rm{mg/d}}} \right)\, \times \,0\!\cdot\!037} \right)\,-\,\left( {{\rm{K }}\;\left( {{\rm{mg}}/{\rm{d}}} \right)\, \times \,0\!\cdot\!021} \right)\,-\,\left( {{\rm{Ca }}\;\left( {{\rm{mg}}/{\rm{d}}} \right)\, \times \,0\!\cdot\!013} \right)\,-\,\left( {{\rm{Mg }}\;\left( {{\rm{mg}}/{\rm{d}}} \right)\, \times \,0\!\cdot\!026} \right).$$
$${\rm{Estimated \ NEAP }\;}\left( {{\rm{mEq/d}}} \right)\, = \,\left( {54\!\cdot\!5\, \times \,{\rm{protein \;intake }}\;\left( {{\rm{g/d}}} \right)/{\rm{K \;intake }}\;\left( {{\rm{mEq/d}}} \right)} \right)\,-\,10\!\cdot\!2.$$

Assessment of sleep

The Pittsburgh Sleep Quality Index is a self-report sleep instrument which has been validated in several studies(Reference Akbarzadeh, Khezri and Mahmudi23Reference Farrahi Moghaddam, Nakhaee and Sheibani25). This questionnaire measures the quality and pattern of sleep over the past month and consists of nine items, differentiating from poor to good on a 0–3 scale (0, not in the past month; 1, less than once per week; 2, once or twice per week and 3, three or more times per week). These items explain sleep latency, duration and efficiency, use of sleep medication, sleep disturbances and daytime dysfunction. Pittsburgh Sleep Quality Index scores are between 0 and 21. A score of 5 and above indicates poor sleep quality.

Assessment of stress, anxiety and depression

The twenty-one-item Depression, Anxiety and Stress Scale is a self-reported questionnaire which contains twenty-one items to assess the severity of negative emotional states and symptoms of depression, anxiety and stress in the last week. These subscales include seven questions with a rating scale between 0 (never) and 3 (always). For depression, total score between 0 and 9 is considered normal, whereas scores above 9 indicate increasing severity of depression. For anxiety, a total score between 0 and 7 is normal, whereas scores above 7 indicate an increase for stress, a total score of 0–14 is normal, and greater is determined as having stress. The validity and reliability of twenty-one-item Depression, Anxiety and Stress Scale have been investigated in Iran(Reference Samani and Jokar26,Reference Elyansb27) .

Assessment of other variables

Socio-demographic information including age, education level and occupation, income, smoking habits, medical history and current medication and supplement use were assessed by the questionnaire. Physical activity (PA) levels were recorded over 7 d and expressed as metabolic equivalent h/week(Reference Ainsworth, Haskell and Whitt28). Blood pressure was measured in duplicate using a sphygmomanometer, and the mean of both measured was used as the participants blood pressure. Biochemical markers including fasting blood sugar, 2-h postprandial blood sugar, Hb A1C, total cholesterol, HDL-cholesterol, LDL-cholesterol and TAG were obtained from the participants’ medical files.

Statistical analysis

Participant characteristics were compared by ANOVA or χ 2 tests and reported as the mean values and standard deviations or percentages. Dietary intakes were reported by median-split for PDI and DAL, adjusted for energy intake using ANCOVA. Also, energy-adjusted dietary intakes in poor and good sleepers and healthy participants or participants with mental health disorders were reported using ANCOVA. The association of sleep and mental health status by media-split for PDI and DAL was determined using the χ 2 test. Moreover, having a mental health disorder and poor sleep in diabetic patients was presented in different models using binary logistic regression. In model 1, adjustment was performed for age, BMI, PA, socio-economic status, supplements consumption, vitamin D and energy intake. Further statistical control was performed in model 2 for medications, lipid profile, blood pressure, sleep duration at night and nap time. Finally, linear regression as a continuous statistical method was used to present the association between DAL score and the score of PDI with mental health disorders and having poor sleep through the fully adjusted model. SPSS version 16 was used to analyse the data. P < 0·05 was considered statistically significant.

Results

Table 1 shows the general participant characteristics. Mean age was 59·9 years. There was a significant association between both weight and BMI with uPDI (P < 0·05). A significant difference was observed in nap time and sleep duration at night and PRAL as well as uPDI (P < 0·05). There was a significant difference for SES across groups of the PDI, hPDI, uPDI and PRAL scores. Participants did not report consuming alcohol or smoking.

Consumption of carbohydrate, Na, organ meats, processed meats, high-fat dairy products, starchy vegetables and refined grains was greater in the top uPDI group. Consumption of carbohydrate, fibre, K, Fe, Mg, Cu, P, Mn, vitamins A, K, E, C, B6, and folate was lower in the top PRAL group. Table 2 presents energy-adjusted dietary intakes among PDI, NEAP and PRAL.

Table 1. Participant characteristics by median-split plant-based indices and dietary acid load (Mean values and standard deviations)

PDI, Plant-based diet index; hPDI, healthy PDI; uPDI, unhealthy PDI; NEAP, net endogenous acid production; PRAL, potential renal acid load; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; PA, physical activity; SES; socio-economic status; BGR, blood glucose reducers; BPR, blood pressure reducers; BLR, blood lipids reducers.

* Calculated by the χ 2 and t tests for qualitative and quantitative variables, respectively.

1: lower than median, 2: higher than median.

Table 2. Dietary intakes by median-split plant-based diet indices (PDI) and dietary acid load (Mean values with their standard errors)

hPDI, healthy PDI; uPDI, unhealthy PDI; NEAP, net endogenous acid production; PRAL, potential renal acid load; CHO, carbohydrate; RAE, retinol activity equivalents.

* Calculated by t test for energy intake and multivariate ANCOVA for other variables. All the variables, except energy, adjusted for energy intake.

1: lower than median, 2: higher than median.

Energy-adjusted dietary intakes among good and poor sleepers and patients with and without mental health disorders are described in Table 3. Protein, fibre, K, Fe, Ca, Mg, P, Zn, Cu, Mn, folate, vitamins B2, B3, B6, A and C intake and low-fat dairy products, vegetables and fruits consumption in poor sleepers and patients with mental health disorders were lower than participants without these problems (P < 0·05). Starchy vegetables, refined grains and high-fat dairy consumption were greater in participants with mental health disorders and poor sleepers (P < 0·05).

Table 3. Dietary intakes among poor and good sleepers and healthy participants or participants with mental disorders (Mean values with their standard errors)

CHO, carbohydrate.

* Calculated by t test for energy intake and multivariate ANCOVA for other variables. All the variables, except energy, adjusted for energy intake.

According to the χ 2 test, there was a significant association between sleep status and hPDI and uPDI (P < 0·0001). There was also a significant association between sleep status and NEAP and PRAL (P < 0·0001). Table 4 shows the OR and 95 % CI for having mental health disorders and poor sleep in crude model and adjusted models across DAL scores and PDI groups. Participants in the top group of uPDI, NEAP and PRAL scores were more likely to be poor sleepers in crude and adjusted models. Participants in the top group of hPDI had a 72 % decreased risk of poor sleep. Participants in the top group of hPDI had 74 and 76 % decreased risk of anxiety and stress, respectively. Participants in the top group of uPDI had more than nine, four and four times increased risk of depression, anxiety and stress, respectively. Table 5 presents the association between DAL score and the score of PDI with mental health disorders and having poor sleep using linear regression as a continuous statistical method.

Table 4. Mental disorders and having poor sleep by median-split dietary acid load and plant-based diet indices (PDI) (Odds ratios and 95 % confidence intervals)

hPDI, healthy PDI; uPDI, unhealthy PDI; NEAP, net endogenous acid production; PRAL, potential renal acid load.

* Calculated by logistic regression.

1: lower than median, 2: higher than median.

§ Model 1: All the variables adjusted for age, BMI, socio-economic status, physical activity, supplement intake, and vitamin D and energy intake.

|| Model 2: All the variables adjusted further for medications, lipid profile, blood pressure, sleep duration at night and nap time in addition to adjusted variables in model 1.

Table 5. Association between dietary acid load score and the score of plant-based diet indices (PDI) with mental disorders and having poor sleep using linear regression* (β-Coefficients and P values)

hPDI, healthy PDI; uPDI, unhealthy PDI; NEAP, net endogenous acid production; PRAL, potential renal acid load; PSQI, Pittsburgh Sleep Quality Index.

* Calculated by linear regression. Full-adjusted model: all the variables adjusted for age, BMI, socio-economic status, physical activity, supplement intake, vitamin D and energy intake, medications, lipid profile, blood pressure, sleep duration at night and nap time.

Discussion

This cross-sectional study revealed that higher DAL scores and adherence to uPDI were associated with poor sleep. Also, greater adherence to uPDI increased the risk of depression, anxiety and stress. Participants in the top group of the hPDI had decreased risk of poor sleep, depression, anxiety and stress. To the best of our knowledge, this is the first study that has assessed the association between DAL and PDI and psychological disorders and sleep status in diabetic patients.

It was reported in a prospective cohort study that DAL was associated with the development of type 2 diabetes(Reference Fagherazzi, Vilier and Bonnet13) and gestational diabetes mellitus(Reference Saraf-Bank, Tehrani and Haghighatdoost15). Also, some vegetarian diets are associated with a reduction in the incidence of diabetes(Reference Tonstad, Stewart and Oda29). However, there is no study that has assessed the association between DAL and PDI with diabetes complications. PDI emphasise greater intakes of whole grains, fruits, vegetables, nuts, vegetable oils, nuts and legumes and lower consumption of animal fats, fish, meat and animal-based foods (online Supplementary Table S1). The cumulative and synergic effects of these healthy food groups within dietary patterns can have protective effects on depressive symptoms(Reference Opie, Itsiopoulos and Parletta30). A meta-analysis of thirteen studies represented that high intakes of fruits, vegetables, nuts and whole grains were associated with a reduced odds of depression(Reference Lai, Hiles and Bisquera31). Another systematic review found that vegetarian diets have an inverse association with depressive symptoms(Reference Molendijk, Molero and Ortuno Sanchez-Pedreno32). Moreover, dietary patterns rich in fruits and vegetables, which have a high content of fibre, antioxidants and polyphenols, have been positively associated with mental health outcomes in adolescents(Reference Oddy, Robinson and Ambrosini33,Reference McMartin, Jacka and Colman34) .

One of the biological mechanisms that is involved in reducing mental disorders is a low level of inflammation status(Reference Schmitz and Ecker35). Inflammation can trigger melancholic symptoms through activation of inflammatory pathways in the brain(Reference Slavich and Irwin36). Low intake of whole grains, fruits and vegetables is associated with increased inflammatory markers(Reference Lopez-Garcia, Schulze and Fung37). Plant-based diets contain alkali-rich food groups such as whole grains, vegetables and fruits, while containing less of animal products, high-protein, and high-phosphorus foods(Reference Remer and Manz38). Plant-based diets increase bicarbonate and bicarbonate precursors, while animal products increase potential anorganic acid precursors(Reference Remer39). Hence, vegetarian diets significantly have lower DAL(Reference Deriemaeker, Aerenhouts and Hebbelinck40). Even moderate increases in DAL stimulate secretion and activity of glucocorticoids which leads to renal acid excretion(Reference Esche, Shi and Sanchez-Guijo41,Reference Buehlmeier, Remer and Frings-Meuthen42) . On the other hand, glucocorticoids modulate emotion and behaviour through changes in limbic areas of the brain(Reference Hamm, Ambuhl and Alpern43,Reference Mora, Segovia and Del Arco44) . Systematic acid–base balance modifies blood-brain turnover and glutamate turnover in the brain(Reference Ang, Hoop and Kazemi45). Glucocorticoids alter the expression and activation of vesicular proteins which are involved in glutamate neurotransmission(Reference Popoli, Yan and McEwen46). Moreover, glutamatergic neurons regulate brain activity and sleep stages(Reference Shi and Yu47).

To the best of our knowledge, there is only one publication that has assessed the association between DAL and mental disorders in, which showed that participants with higher PRAL had more hyperactivity and emotional problems(Reference Buhlmeier and Harris48). However, this study was conducted in adolescents, and psychological disorders were determined using the Strengths and Difficulties Questionnaire.

The main analysis indicates that higher adherence to hPDI is associated with lower odds of mental disorders in crude and all adjusted models. Also, higher adherence to the hPDI was associated with a 90 % lower odds of sleep disorders. Consistent with the foregoing, a recent review suggests plant-based diets may have the potential to improve overall health status through the effect on improving sleep quality(Reference St-Onge, Crawford and Aggarwal49). Cao et al. have showed that there was a significant inverse association between isoflavone intakes and sleep duration(Reference Cao, Taylor and Zhen50). Besides lower DAL and related mechanisms in plant-based diets, high amounts of antioxidants, phytochemicals, flavonoids, vitamins and minerals are related to beneficial effects on mental(Reference Bell, Lamport and Butler51) and sleep disorders through suppression of inflammation and reducing oxidative stress(Reference Hermsdorff, Zulet and Puchau52). Legumes and beans are high in tryptophan which is a precursor of melatonin and serotonin, which play a role in sleep regulation(Reference Bravo, Matito and Cubero53). Beezhold et al. reported that greater adherence to a vegetarian diet and even less animal food intake was associated with better mood(Reference Beezhold, Radnitz and Rinne54).

In the present study, outcome measures were adjusted for several confounders such as age, BMI, energy intake and PA. Although we adjusted for several known confounders, there are possible residual effects which can have effect on the outcome variables of interest. Increased body weight and BMI increase the risk of diabetes, insulin resistance and higher blood glucose concentrations. Furthermore, there is an association between obesity, sleep quality and depressive symptoms(Reference Wise55,Reference Fisher, Law and Dudeney56) . Moreover, body fatness is associated with both depression and sleep quality(Reference Haidar, de Vries and Karavetian57). A study in Swiss adolescents showed that higher levels of PA were related to more favourable sleep quality and lower insomnia scores, but participants tend to overestimate their level of PA(Reference Lang, Brand and Feldmeth58). However, PA has been shown to have favourable effects on sleep quality(Reference Dworak, Wiater and Alfer59), by modulating symptoms of anxiety, stress and depression(Reference Brand, Gerber and Beck60). Totally, PA was not significantly associated with poor sleep and psychological symptoms in the present study.

While this is the first study that has examined the association between DAL and PDI with sleep and psychological status among diabetic patients, there are several limitations. We randomly included diabetic women from different socio-economic status which could be a representative sample of diabetic women in Tehran. However, the relationship is not generalisable to other populations with different sex and health conditions. Moreover, the present study was conducted on a diabetic population from Tehran, and the generalisability of results to the other cities in Iran is uncertain. Due to the observational nature of the present study, cause and effect cannot be established. Furthermore, while adjustment for confounding was performed, there are possible residual effects which may have affected the outcome variables. In addition, laboratory markers of acid–base balance were not collected in the present study. While the limitations of FFQ have been widely reported for dietary assessment in large studies, there is limited validation data for anions. The twenty-one-item Depression, Anxiety and Stress Scale questionnaire is a self-reported scale which may lead to misclassification of participants and is therefore not suitable scale for clinical diagnosis of depression and anxiety.

In conclusion, the present study has shown that there was a positive association between psychological and sleep disorders with DAL, while plant-based diets had a protective effect. Prospective cohorts or intervention studies of vegetarian diets are needed to confirm our findings.

Acknowledgements

The present study is supported by Tehran University of Medical Sciences (grant number 96-03-161-36923). We would like to express our gratitude to Dr Nasli, secretary of the Diabetes Research Center of Tehran University of Medical Sciences, and all staff member for their help with the present study. The present study was approved by the ethical committee of Tehran University of Medical Sciences.

E. D., B. L. and L. A. designed and L. A. supervised the study. E. D. conducted the study. E. D., M. Q. and L. A. performed the statistical analyses. E. D. prepared a first draft of the manuscript and L. A., S. A. K. and B. A. finalised it. N. B. reviewed and edited the manuscript.

The authors declare that they have no conflicts of interest.

Supplementary material

For supplementary material referred to in this article, please visit https://doi.org/10.1017/S0007114519003179

References

Sridhar, GR & Madhu, K (1994) Prevalence of sleep disturbances in diabetes mellitus. Diabetes Res Clin Pract 23, 183186.Google ScholarPubMed
Luyster, FS & Dunbar-Jacob, J (2011) Sleep quality and quality of life in adults with type 2 diabetes. Diabetes Educ 37, 347355.Google ScholarPubMed
Budhiraja, R, Roth, T, Hudgel, DW, et al. (2011) Prevalence and polysomnographic correlates of insomnia co-morbid with medical disorders. Sleep 34, 859867.CrossRefGoogle Scholar
Shinkov, A, Borissova, AM, Kovatcheva, R, et al. (2018) Increased prevalence of depression and anxiety among subjects with metabolic syndrome and known type 2 diabetes mellitus – a population-based study. Postgrad Med 130, 251257.Google ScholarPubMed
Bot, M, Pouwer, F, Ormel, J, et al. (2010) Predictors of incident major depression in diabetic outpatients with subthreshold depression. Diabet Med 27, 12951301.CrossRefGoogle ScholarPubMed
Golden, SH, Shah, N, Naqibuddin, M, et al. (2017) The prevalence and specificity of depression diagnosis in a clinic-based population of adults with type 2 diabetes mellitus. Psychosomatics 58, 2837.CrossRefGoogle Scholar
Valachovicova, M, Krajcovicova-Kudlackova, M, Blazicek, P, et al. (2006) No evidence of insulin resistance in normal weight vegetarians. A case–control study. Eur J Nutr 45, 5254.CrossRefGoogle ScholarPubMed
Murakami, K, Mizoue, T, Sasaki, S, et al. (2008) Dietary intake of folate, other B vitamins, and omega-3 polyunsaturated fatty acids in relation to depressive symptoms in Japanese adults. Nutrition 24, 140147.Google ScholarPubMed
Jacka, FN, Mykletun, A, Berk, M, et al. (2011) The association between habitual diet quality and the common mental disorders in community-dwelling adults: the Hordaland Health Study. Psychosom Med 73, 483490.Google ScholarPubMed
Ji, X & Liu, J (2015) Associations between blood zinc concentrations and sleep quality in childhood: a cohort study. Nutrients 7, 56845696.CrossRefGoogle Scholar
Rondanelli, M, Opizzi, A, Monteferrario, F, et al. (2011) The effect of melatonin, magnesium, and zinc on primary insomnia in long-term care facility residents in Italy: a double-blind, placebo-controlled clinical trial. J Am Geriatr Soc 59, 8290.Google ScholarPubMed
Dietary Guidelines Advisory Committee (2015) Scientific Report of the 2015 Dietary Guidelines Advisory Committee. Washington, DC: Dietary Guidelines Advisory Committee.Google Scholar
Fagherazzi, G, Vilier, A, Bonnet, F, et al. (2014) Dietary acid load and risk of type 2 diabetes: the E3N-EPIC cohort study. Diabetologia 57, 313320.CrossRefGoogle ScholarPubMed
Iwase, H, Tanaka, M, Kobayashi, Y, et al. (2015) Lower vegetable protein intake and higher dietary acid load associated with lower carbohydrate intake are risk factors for metabolic syndrome in patients with type 2 diabetes: post-hoc analysis of a cross-sectional study. J Diabet Investig 6, 465472.CrossRefGoogle ScholarPubMed
Saraf-Bank, S, Tehrani, H, Haghighatdoost, F, et al. (2017) The acidity of early pregnancy diet and risk of gestational diabetes mellitus. Clin Nutr 37, 20542059.CrossRefGoogle ScholarPubMed
Zamani, B, Daneshzad, E, Siassi, F, et al. (2019) Association of plant-based dietary patterns with psychological profile and obesity in Iranian women. Clin Nutr (epublication ahead of print version 26 July 2019).Google ScholarPubMed
Azadbakht, L & Esmaillzadeh, A (2009) Red meat intake is associated with metabolic syndrome and the plasma C-reactive protein concentration in women. J Nutr 139, 335339.Google ScholarPubMed
Martinez-Gonzalez, MA, Sanchez-Tainta, A, Corella, D, et al. (2014) A provegetarian food pattern and reduction in total mortality in the Prevencion con Dieta Mediterranea (PREDIMED) study. Am J Clin Nutr 100, 320s328s.CrossRefGoogle ScholarPubMed
Satija, A, Bhupathiraju, SN, Rimm, EB, et al. (2016) Plant-based dietary patterns and incidence of type 2 diabetes in US men and women: results from three prospective cohort studies. PLoS Med 13, e1002039.CrossRefGoogle ScholarPubMed
Remer, T & Manz, F (1994) Estimation of the renal net acid excretion by adults consuming diets containing variable amounts of protein. Am J Clin Nutr 59, 13561361.CrossRefGoogle ScholarPubMed
Remer, T, Dimitriou, T & Manz, F (2003) Dietary potential renal acid load and renal net acid excretion in healthy, free-living children and adolescents. Am J Clin Nutr 77, 12551260.CrossRefGoogle ScholarPubMed
Frassetto, LA, Todd, KM, Morris, RC, et al. (1998) Estimation of net endogenous noncarbonic acid production in humans from diet potassium and protein contents. Am J Clin Nutr 68, 576583.CrossRefGoogle ScholarPubMed
Akbarzadeh, H, Khezri, HD, Mahmudi, G, et al. (2014) Sleep quality among Iranian nurses working in intensive care units versus general wards: a cross-sectional study. Iran J Endocrinol Metab 7, 930.Google Scholar
Emami Zeydi, A, Jannati, Y, Darvishi Khezri, H, et al. (2014) Sleep quality and its correlation with serum C-reactive protein level in hemodialysis patients. Saudi J Kidney Dis Transpl 25, 750755.Google ScholarPubMed
Farrahi Moghaddam, J, Nakhaee, N, Sheibani, V, et al. (2012) Reliability and validity of the Persian version of the Pittsburgh Sleep Quality Index (PSQI-P). Sleep Breath 16, 7982.CrossRefGoogle Scholar
Samani, S & Jokar, B (2008) Validity and reliability of the short form of Depression Anxiety Stress Scales. J Soc Sci Hum Shiraz Univ 26, 6577.Google Scholar
Elyansb, S (2010) Examine the relationship between religion restraint and death anxiety among students and seminarians of Qom city. Relig Health 3, 5568.Google Scholar
Ainsworth, BE, Haskell, WL, Whitt, MC, et al. (2000) Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exercise 32, S498S504.CrossRefGoogle ScholarPubMed
Tonstad, S, Stewart, K, Oda, K, et al. (2013) Vegetarian diets and incidence of diabetes in the Adventist Health Study-2. Nutr Metab Cardiovasc Dis 23, 292299.CrossRefGoogle ScholarPubMed
Opie, RS, Itsiopoulos, C, Parletta, N, et al. (2017) Dietary recommendations for the prevention of depression. Nutr Neurosci 20, 161171.Google ScholarPubMed
Lai, JS, Hiles, S, Bisquera, A, et al. (2014) A systematic review and meta-analysis of dietary patterns and depression in community-dwelling adults. Am J Clin Nutr 99, 181197.Google ScholarPubMed
Molendijk, M, Molero, P, Ortuno Sanchez-Pedreno, F, et al. (2018) Diet quality and depression risk: a systematic review and dose–response meta-analysis of prospective studies. J Affect Disord 226, 346–254.CrossRefGoogle ScholarPubMed
Oddy, WH, Robinson, M, Ambrosini, GL, et al. (2009) The association between dietary patterns and mental health in early adolescence. Prev Med 49, 3944.Google ScholarPubMed
McMartin, SE, Jacka, FN & Colman, I (2013) The association between fruit and vegetable consumption and mental health disorders: evidence from five waves of a national survey of Canadians. Prev Med 56, 225230.CrossRefGoogle ScholarPubMed
Schmitz, G & Ecker, J (2008) The opposing effects of n-3 and n-6 fatty acids. Prog Lipid Res 47, 147155.CrossRefGoogle ScholarPubMed
Slavich, GM & Irwin, MR (2014) From stress to inflammation and major depressive disorder: a social signal transduction theory of depression. Psychol Bull 140, 774815.CrossRefGoogle Scholar
Lopez-Garcia, E, Schulze, MB, Fung, TT, et al. (2004) Major dietary patterns are related to plasma concentrations of markers of inflammation and endothelial dysfunction. Am J Clin Nutr 80, 10291035.Google ScholarPubMed
Remer, T & Manz, F (1995) Potential renal acid load of foods and its influence on urine pH. J Am Diet Assoc 95, 791797.CrossRefGoogle ScholarPubMed
Remer, T (2001) Influence of nutrition on acid-base balance – metabolic aspects. Eur J Nutr 40, 214220.CrossRefGoogle ScholarPubMed
Deriemaeker, P, Aerenhouts, D, Hebbelinck, M, et al. (2010) Nutrient based estimation of acid-base balance in vegetarians and non-vegetarians. Plant Foods Hum Nutr 65, 7782.CrossRefGoogle ScholarPubMed
Esche, J, Shi, L, Sanchez-Guijo, A, et al. (2016) Higher diet-dependent renal acid load associates with higher glucocorticoid secretion and potentially bioactive free glucocorticoids in healthy children. Kidney Int 90, 325333.Google ScholarPubMed
Buehlmeier, J, Remer, T, Frings-Meuthen, P, et al. (2016) Glucocorticoid activity and metabolism with NaCl-induced low-grade metabolic acidosis and oral alkalization: results of two randomized controlled trials. Endocrine 52, 139147.Google ScholarPubMed
Hamm, LL, Ambuhl, PM & Alpern, RJ (1999) Role of glucocorticoids in acidosis. Am J Kidney Dis 34, 960965.CrossRefGoogle ScholarPubMed
Mora, F, Segovia, G, Del Arco, A, et al. (2012) Stress, neurotransmitters, corticosterone and body–brain integration. Brain Res 1476, 7185.CrossRefGoogle ScholarPubMed
Ang, RC, Hoop, B & Kazemi, H (1992) Brain glutamate metabolism during metabolic alkalosis and acidosis. J Appl Physiol 73, 25522558.Google ScholarPubMed
Popoli, M, Yan, Z, McEwen, BS, et al. (2011) The stressed synapse: the impact of stress and glucocorticoids on glutamate transmission. Nat Rev Neurosci 13, 2237.CrossRefGoogle ScholarPubMed
Shi, YF & Yu, YQ (2013) The roles of glutamate in sleep and wakefulness. J Zhejiang Univ Med Sci (Zhejiang da xue xue bao Yi xue ban) 42, 583590.Google ScholarPubMed
Buhlmeier, J & Harris, C (2018) Dietary acid load and mental health outcomes in children and adolescents: results from the GINIplus and LISA birth cohort studies. Nutrients 10, E582.CrossRefGoogle ScholarPubMed
St-Onge, MP, Crawford, A & Aggarwal, B (2018) Plant-based diets: reducing cardiovascular risk by improving sleep quality? Curr Sleep Med Rep 4, 7478.CrossRefGoogle ScholarPubMed
Cao, Y, Taylor, AW, Zhen, S, et al. (2017) Soy isoflavone intake and sleep parameters over 5 years among Chinese adults: longitudinal analysis from the Jiangsu nutrition study. J Acad Nutr Diet 117, 536544.e2.CrossRefGoogle ScholarPubMed
Bell, L, Lamport, DJ, Butler, LT, et al. (2015) A review of the cognitive effects observed in humans following acute supplementation with flavonoids, and their associated mechanisms of action. Nutrients 7, 1029010306.CrossRefGoogle ScholarPubMed
Hermsdorff, HH, Zulet, MA, Puchau, B, et al. (2010) Fruit and vegetable consumption and proinflammatory gene expression from peripheral blood mononuclear cells in young adults: a translational study. Nutr Metab 7, 42.CrossRefGoogle ScholarPubMed
Bravo, R, Matito, S, Cubero, J, et al. (2013) Tryptophan-enriched cereal intake improves nocturnal sleep, melatonin, serotonin, and total antioxidant capacity levels and mood in elderly humans. Age (Dordr) 35, 12771285.CrossRefGoogle ScholarPubMed
Beezhold, B, Radnitz, C, Rinne, A, et al. (2015) Vegans report less stress and anxiety than omnivores. Nutr Neurosci 18, 289296.CrossRefGoogle ScholarPubMed
Wise, J (2018) Progress on children’s mental health services is too slow, says commissioner for England. BMJ 363, k4953.Google ScholarPubMed
Fisher, E, Law, E, Dudeney, J, et al. (2019) Psychological therapies (remotely delivered) for the management of chronic and recurrent pain in children and adolescents. Cochrane Database Syst Rev, issue 4, CD011118.Google ScholarPubMed
Haidar, SA, de Vries, NK, Karavetian, M, et al. (2018) Stress, anxiety, and weight gain among university and college students: a systematic review. J Acad Nutr Diet 118, 261274.CrossRefGoogle ScholarPubMed
Lang, C, Brand, S, Feldmeth, AK, et al. (2013) Increased self-reported and objectively assessed physical activity predict sleep quality among adolescents. Physiol Behav 20, 4653.CrossRefGoogle Scholar
Dworak, M, Wiater, A, Alfer, D, et al. (2008) Increased slow wave sleep and reduced stage 2 sleep in children depending on exercise intensity. Sleep Med 9, 266272.Google ScholarPubMed
Brand, S, Gerber, M, Beck, J, et al. (2010) Exercising, sleep-EEG patterns, and psychological functioning are related among adolescents. World J Biol Psychiatry 11, 129140.CrossRefGoogle Scholar
Figure 0

Table 1. Participant characteristics by median-split plant-based indices and dietary acid load (Mean values and standard deviations)

Figure 1

Table 2. Dietary intakes by median-split plant-based diet indices (PDI) and dietary acid load (Mean values with their standard errors)

Figure 2

Table 3. Dietary intakes among poor and good sleepers and healthy participants or participants with mental disorders (Mean values with their standard errors)

Figure 3

Table 4. Mental disorders and having poor sleep by median-split dietary acid load and plant-based diet indices (PDI) (Odds ratios and 95 % confidence intervals)

Figure 4

Table 5. Association between dietary acid load score and the score of plant-based diet indices (PDI) with mental disorders and having poor sleep using linear regression* (β-Coefficients and P values)

Supplementary material: File

Daneshzad et al. Supplementary Materials

Daneshzad et al. Supplementary Materials

Download Daneshzad et al. Supplementary Materials(File)
File 17 KB
You have Access
6
Cited by

Send article to Kindle

To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle. Find out more about sending to your Kindle.

Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Association of dietary acid load and plant-based diet index with sleep, stress, anxiety and depression in diabetic women
Available formats
×

Send article to Dropbox

To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Dropbox.

Association of dietary acid load and plant-based diet index with sleep, stress, anxiety and depression in diabetic women
Available formats
×

Send article to Google Drive

To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your <service> account. Find out more about sending content to Google Drive.

Association of dietary acid load and plant-based diet index with sleep, stress, anxiety and depression in diabetic women
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *