Association of Dietary Acid Load and Depression among Iranian Women: a case-control study

doi:10.21203/rs.3.rs-3338756/v1

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Association of Dietary Acid Load and Depression among Iranian Women: a case-control study

https://doi.org/10.21203/rs.3.rs-3338756/v1

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Background

The present study aimed to assess the association between Dietary Acid Load and Depression in Iranian women.

Material and methods

This case-control study was conducted among 110 depressed women and 220 control subjects from two psychiatric clinics in Tehran, Iran. A validated semi-quantitative food frequency questionnaire consisting of 168 items was used to assess food consumption in the previous 12 months. The dietary acid load was calculated using the relevant data with 2 validated methods: PRAL and NEAP. The association between depression and PRAL and NEAP was investigated using multivariate logistic regression in crude and adjusted models.

Results

The findings of the present study showed participants in the highest quartile of PRAL and NEAP had a 4.11- and 4.43-times higher risk of depression than the participants in the lowest quartile, respectively (Odds Ratio (OR)_PRAL: 3.52; 95% (CI):1.64 to 7.54, p for trend: <0.001) and (Odds Ratio (OR)_NEAP: 4.07; 95% (CI):1.81 to 8.89, p for trend: <0.001).

Conclusion

there is a significant inverse association between dietary acid load scores (PRAL and NEAP) and the odds of depression in women. However more studies are needed to support these findings in the future.

Health sciences/Diseases/Neurological disorders

Health sciences/Risk factors

dietary acid load

potential renal acid load

net endogenous acid production

psychological disorder

depression

case-control study

Psychological disorders, especially depression, have grown alarmingly worldwide in recent years (1). About4.4% (5.1% female, 3.6% male) of the world's population and 21% of the Iranian adult population are struggling with depressive disorders(1, 2).

Different factors, such as genetics, and environmental and social factors, can play a role in mental disorders development(3–5). In addition, recent studies indicate the key role of nutrition and diet in preventing or reducing the incidence of these disorders (6–8). Focusing on dietary approaches rather than a particular food can yield more reliable results because of the synergistic effects and collective impact of foods on each other (9). Dietary acid-base load is also a holistic indicator for assessing the alkaline properties and acidity of an individual’s diet(10). Net endogenous acid production (NEAP) and potential renal acid load (PRAL) are valid and simple methods for estimating the dietary acid load from dietary composition data(11, 12).

Recent studies have shown that foods with a high acid load, such as foods of animal origin, and food patterns rich in fruits and vegetables, such as Mediterranean or vegetarian-based diets that have a high alkaline load, are related to the increase and decrease of mental disorders such as depression, respectively(13–15). However, some studies did not observe any significant relationships(16).

Regarding the etiology of mental illness, the findings show that a high dietary acid load can stimulate the secretion of glucocorticoids, such as cortisol, and affect the mental state of the individual(17, 18). Glucocorticoids can cause physiological and functional changes in parts of the limbic system (hippocampus, and amygdala) thereby causing fundamental changes in emotions and behaviors(19, 20).

some researchers have linked the different results of the protective or causative role of diet in mental disorders to the acidic load of the diet(21). However, as far as we know, because of the disagreements mentioned in studies linking acidic and alkaloid foods to depression and anxiety, more studies are needed to reach definitive conclusions in this regard. In today's societies, especially in West Asian and North African countries, where consumption of alkaline foods such as fruits and vegetables are very low, while psychological disorders are very common, more reliable results can be obtained (2, 22). In addition, limited studies with conflicting results in examining the association between dietary acid load and depression led us to conduct a closer look at this issue.

Study population

This case-control study was conducted among 110 depressed women and 220 control subjects from two psychiatric clinics in Tehran, Iran. Major depressive disorders were diagnosed based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) (23), standardized in Iran (24) by psychiatrists. The control group had no major depression disorder, according to the Beck Depression Inventory questionnaire (BDI-II), (25) standardized in Iran. (26). Both groups should not have a history of depression in the past year. The matching factors between case and control groups were sex, age, and residential area. Each patient was matched with two control subjects within 10- year age range. The inclusion criteria were female sex, age range of 18– 65 years, suffering from major depressive disorder, and living in Tehran. The exclusion criteria are noted below. Presence of other cognitive disorders or psychotic sickness diagnosed by a psychiatrist, receiving any anti-depression medications or treatments, suffering from any hormonal disorders, suffering from chronic diseases such as diabetes, heart disease, cancer and etc, history of any trauma in the past 3 months that lead to unconsciousness and hospitalization, Suffering from chronic and infectious diseases in the last 2 weeks, alcohol and drugs use at the study time or in the past 3 months, BMI ≥ 40 kg/m², pregnancy and lactation at the study time or in the past year, having any type of special diet in the past 2 months and in the past year and reluctance to participate in the study. The sample size was computed as 110 women with depression and 220 control subjects, using the software open Epi, with two-sided confidence (27, 28). A written informed consent form was completed by the subjects. This study has been approved by the Ethics Committee of Tehran University of Medical Sciences.

Assessment of dietary intake

A validated semi-quantitative food frequency questionnaire consisting of 168 items was used to assess food consumption in the previous 12 months (29). Data from the questionnaire was converted into grams per day. The conversion of food portion sizes to grams was conducted via household measures (30). The registered foods were assessed using the upgraded Nutritionist IV software (First Databank, San Bruno, CA, USA) specifically designed for Iranian foods. The FFQs were administered by an expert dietitian.

Calculation of Dietary Acid Load Indices

The dietary acid load was calculated using the relevant data with 2 validated methods: PRAL and NEAP. PRAL was calculated using the method explained by Remer and Manz (12):

PRAL (mEq/day) = (0.4888 × protein intake [g/day]) + (0.0366 × phosphorus [mg/day]) − (0.0205 × potassium [mg/day]) − (0.0125 × calcium [mg/day]) − (0.0263 × magnesium [mg/day]). Moreover, NEAP was computed by the algorithm illustrated by Frassetto et al.(31) as follows: NEAP (mEq/day) = (54.5 × protein intake [g/day]/potassium intake [mEq/day]) − 10.2.

Assessment of other variables

General information and data on drug use were collected through a general questionnaire. For all participants weight, and height were measured, and BMI was calculated. y, standardized in Iran (32). A categorized physical activity questionnaire, based on the metabolic equivalent hours per day (MET-h/day), was applied. This questionnaire includes nine levels of activity, from rest and sleep (MET = 0.9) to severe activity (MET ≥ 6). The validity and reliability of this questionnaire have been affirmed by Kelishadi et al. in Iran(33).

Statistical methods

Qualitative and quantitative variables were compared between the case and control groups using a chi-square test and independent t-test, respectively.

Also, the comparison of quantitative variables and dietary intakes of the participants across quartiles of PRAL was made using a one-way ANOVA test. The association between depression and PRAL and NEAP was investigated using multivariate logistic regression in crude and adjusted models. The model I was adjusted for energy intake. In model II, physical activity, history of depression, smoking status, periods of unemployment, job, and BMI were additionally adjusted. Data were analyzed using the Statistical Package for Social Sciences (SPSS, version 26.0). P values < 0.05 were considered statistically significant.

General characteristics of the study population

General characteristics of women with and without depression and also across PRAL quartiles are presented in the Table 1. As shown in this table, physical activity, history of depression, periods of unemployment and job were significantly different between the case and control group (P < 0.05). As the PRAL increased, the number of smokers and people with a history of depression increased (P < 0.05).

Table 1

Characteristics of study participants according to the patients and controls and also quartiles of the PRAL score
Variables	Health status			PRAL quartiles
Variables	Control	Case	P^a	Q1	Q2	Q3	Q4	P^b
BMI (kg/m2)	26.43 ± 5.51	26.45 ± 5.13	0.98	27.25 ± 5.72	27.14 ± 6.02	25.69 ± 4.71	25.63 ± 4.79	0.08
Physical activity(MET-h/week)	38.58 ± 4.93	36.94 ± 5.45	0.006	38.75 ± 4.38	37.72 ± 5.57	38.81 ± 6.02	38.88 ± 4.28	0.05
Years of education	12.20 ± 4.56	11.24 ± 4.27	0.070	12.16 ± 4.32	11.37 ± 4.61	11.95 ± 4.68	12.07 ± 4.34	0.67
History of depression, yes (n)	22(10)	33(30)	< 0.001	22(10)	33(30)	22(10)	33(30)	< 0.001
Marital status
Single	67(30.3)	32(29.1)	0.24	21(26.5)	20(24.1)	26(31.3)	32(36.6)	0.45
Married	143(64.7)	67(60.9)		56(68.3)	58(69.9)	51(61.4)	45(54.2)
Divorced, widowed, living apart from spouse	11(5)	11(10)		5(6.1)	5(6)	6(7.2)	6(7.2)
Smoking status n(%)
Non smoker	209(94.6)	98(89.9)	0.11	80(76.6)	79(95.2)	75(90.4)	73(89)	< 0.001
smoker	12(5.4)	11(10.1)	0.11	2(2.4)	4(4.8)	8(9.6)	9(11)	< 0.001
Periods of unemployment
House worker	98(44.3)	65(59.1)	0.007	44(53.7)	46(55.4	39(47)	34(41)	0.23
≤ 6 months in the last 5 years	104(47.1)	32(29.1)		6(7.3)	9(10.8)	5(6)	12(14.5)
More than 6 months in the last 5 years	19(8.6)	13(11.8)		32(39)	28(33.7)	39(47)	37(44.6)
Job
Employee	42(19.4)	12(11.3)	0.014	18(22)	13(16.3)	11(13.6)	12(15.2)	0.10
Self-employed	32(14.8)	16(15.1)		8(9.8)	12(15)	11(13.6)	17(21.5)
Students	38(17.6)	9(8.5)		7(8.5)	8(10)	17(21)	15(19)
House worker & retired	104(48.1)	69(65.1)		49(59.8)	47(58.8)	42(51.9)	35(44.3)
BMI: Body mass index As characteristics are very similar according to the NEAP score, baseline characteristics are only presented according to the PRAL score quartiles ^a Obtained from Chi-squared test and independent t-test for categorical and continuous variables, respectively ^b Chi-squared test for qualitative variables and ANOVA test for quantitative variables Continuous and categorical data are presented as mean ± SD and frequency (percentage). P < 0.05 was considered as statistically significant

Dietary intakes of participants

Table 2 presented the energy and dietary intakes of study participants based on PRAL quartiles. The individuals in the highest PRAL quartiles had significantly greater intakes of energy, protein, cholesterol, saturated fatty acid, monounsaturated fatty acid, polyunsaturated fatty acid, magnesium, zinc, phosphorus (p < 0.05), but significantly lower consumption of carbohydrates, fiber, calcium, potassium, vitamin A, vitamin C, vitamin B6 and B12 (p < 0.05). Intake of fat (P = 0.36), sodium (P = 0.13), and vitamin B12 (P = 0.37) was not significantly different between PRAL quartiles. Based on food group intake, participants with a higher score for the PRAL had a significantly lower intake of fruits, vegetables (p < 0.001) whereas, they had a hieghr intake of red meat (p = 0.008) and refined grains (p < 0.001). There was no significant difference between the intake of fish (P = 0.23), dairy (P = 0.08), whole grain products (P = 0.13), legumes and nuts (P = 0.15), sugars(P = 0.65), sugar-sweetened beverages (P = 0.28), and tea and coffee (P = 0.28), across PRAL quartiles.

Table 2

Dietary intakes of participants according to the PRAL quartiles
Variables	Q1(N = 82)	Q2(N = 83)	Q3(N = 83)	Q4(N = 83)	P^*
PRAL (mEq/day)	-49.73 ± 23.53	-19.45 ± 3.79	-7.49 ± 4.60	19.95 ± 25.29	< 0.001
NEAP (mEq/day)	22.30 ± 5.15	29.70 ± 3.89	36.58 ± 3.44	51.20 ± 10.34	< 0.001
Nutrients
Energy intake(kcal)	2872.6 ± 1209.5	2411.8 ± 843.5	2530 ± 953.6	3060.6 ± 1216.5	< 0.001
Carbohydrate(gr)	63.42 ± 0.08	61.57 ± 0.06	61.42 ± 0.07	58.85 ± 0.09	0.003
Protein(gr)	13.70 ± 0.02	13.97 ± 0.02	14.35 ± 0.02	16.13 ± 0.09	0.01
Fat(gr)	25.86 ± 0.07	26.87 ± 0.06	28.08 ± 0.07	27.78 ± 0.09	0.36
Fibre (g/d)	38.42 ± 14.72	24.98 ± 7.86	23.87 ± 8.14	26.02 ± 21.83	< 0.001
Cholesterol (mg/d)	281.22 ± 171.87	258.84 ± 140.79	247.23 ± 116.19	327.09 ± 283.77	< 0.001
SFA (mg/d)	24.48 ± 13.42	21.53 ± 8.97	22.04 ± 12.27	27.91 ± 17.57	0.009
MUFA (mg/d)	26.46 ± 16.19	23.49 ± 12.08	23.97 ± 13.51	29.56 ± 16.43	0.031
PUFA (mg/d)	21.97 ± 17.89	18.81 ± 10.84	18.97 ± 11.24	24.64 ± 15.54	0.024
Zinc(mg)	13.22 ± 5.39	11.24 ± 4.26	11.80 ± 4.23	16.10 ± 8.36	< 0.001
Phosphorus (mg/day)	1774.75 ± 734.91	1498.32 ± 561.61	1559.73 ± 549.76	2050.12 ± 1008.65	< 0.001
Potassium (mg/day)	6317.21 ± 2521.29	4374.48 ± 1343.74	4041.48 ± 1349.23	4084.45 ± 1608.45	< 0.001
Calcium (mg/day)	1286.86 ± 463.54	1051.15 ± 390.96	1054.02 ± 435.91	1156.88 ± 483.75	0.002
Magnesium (mg/day)	508.32 ± 218.18	397.07 ± 137.06	403.56 ± 143.23	515.09 ± 364.49	< 0.001
Sodium (mg/day)	3927.35 ± 1909.09	3801.80 ± 2013.87	3725.79 ± 2141.43	4444.20 ± 2542.79	0.13
Vitamin A (RAE/d)	2758.91 ± 2105.37	1523.93 ± 771.45	1536.64 ± 911.56	1349.35 ± 767.60	< 0.001
Vitamin C (mg/d)	388.59 ± 196.43	218.65 ± 76.35	188.94 ± 91.10	146.90 ± 78.15	< 0.001
Vitamin B6 (mg/d)	3.17 ± 2.02	2.16 ± 0.76	2.15 ± 0.75	2.42 ± 1.20	< 0.001
Folate (µg/d)	710.06 ± 290.59	549.04 ± 204.30	559.02 ± 222.31	654.40 ± 307.98	< 0.001
Vitamin B12 (mg/d)	8.57 ± 39.17	3.91 ± 3.18	3.80 ± 2.47	5.26 ± 5.29	0.37
Food groups (g/1000Kcal)
Fruits	944.69 ± 448.73	520.66 ± 202.64	478.13 ± 258.63	362.74 ± 209.21	< 0.001
Vegetables	537.38 ± 259.33	368.82 ± 166.08	313.59 ± 152.43	231.08 ± 128.01	< 0.001
Red meat	24.66 ± 72.22	19.79 ± 17.54	25.17 ± 17.88	39.72 ± 65.03	0.008
Fish	8.45 ± 8.68	6.65 ± 9.70	10.13 ± 16.36	10.56 ± 17.10	0.23
Dairy	492.98 ± 249.22	408.29 ± 226.20	406.67 ± 297.79	405.43 ± 261.12	0.08
Legumes and nuts	81.17 ± 57.78	67.73 ± 49.17	65.94 ± 52.73	29.78 ± 25.53a	0.15
Whole grains	70.11 ± 97.49	64.05 ± 70.97	78.86 ± 107.67	102.34 ± 154.29	0.13
Refined grains	362.15 ± 28.76	380.67 ± 222.67	446.84 ± 244.37	608.40 ± 322.87	< 0.001
Sugars	22.45 ± 66.25b	23.32 ± 19.02	22.36 ± 20.79	19.35 ± 15.72	0.65
Sugar-sweetened beverages (ml/day)	37.47 ± 61.09	66.46 ± 129.02	56.41 ± 120.89	62.16 ± 86.94	0.28
Tea and coffee	859.19 ± 510.70	887.20 ± 498	730.91 ± 598.87	656.59 ± 482.61	0.28
PRAL: Potential renal acid load; NEAP: net endogenous acid production As characteristics are very similar according to the NEAP score, baseline characteristics are only presented according to the PRAL score quartiles ^* ANOVA test was used.

Association between PRAL and NEAP and odds of depression

Crude and multivariate-adjusted odds ratios for the association of depression and PRAL and NEAP are shown in Table 3. In the crude model, participants in the highest quartile of PRAL and NEAP had a 4.11- and 4.43-times higher risk of depression than the participants in the lowest quartile, respectively. This association remained significant after adjustment for energy intake, physical activity, history of depression, smoking status, periods of unemployment, job and BMI (Odds Ratio (OR)_PRAL: 3.52; 95% (CI):1.64 to 7.54, p for trend: <0.001) and (Odds Ratio (OR)_NEAP: 4.07; 95% (CI):1.81 to 8.89, p for trend: <0.001).

Table 3

The OR (95%CI) of depression across quartiles of PRAL and NEAP
Dietary acid load score	Cases (N)	OR (95% CI)
PRAL (mEq/day)		Crude OR (CI 95%)	Model I OR (CI 95%)	Model II OR (CI 95%)
<-27.2	27	1	1	1
-27.2 to -13.6	20	1.21 (0.58–2.52)	1.31 (0.62–2.77)	0.95 (0.42–2.14)
-13.5 to 1.61	30	2.16 (1.07–4.34)	2.30 (1.13–4.67)	2.59 (1.19–5.60)
≥ 2.21	43	4.11 (2.07–8.16)	4.03 (2.02–8.03)	3.52 (1.64–7.54)
P-trend		< 0.001	< 0.001	< 0.001
NEAP (mEq/day)
< 27.2	16	1	1	1
27.2 to 27.9	20	1.31 (0.62–2.75)	1.33 (0.63–2.81)	1.14 (0.51–2.55)
34.4 to 37.7	31	2.45 (1.21–4.97)	2.38 (1.17–4.84)	2.12 (0.96–4.66)
≥ 42.9	43	4.43 (2.21–8.88)	4.23 (2.10–8.52)	4.07 (1.81–8.89)
P-trend		< 0.001	< 0.001	< 0.001
PRAL: Potential renal acid load; NEAP: Net endogenous acid production Model I: adjusted for energy intake Model II: Model I + adjusted for physical activity, history of depression, smoking status, periods of unemployment, job and BMI Logistic regression was used.

Our findings showed that higher dietary acid load indices (PRAL and NEAP) are associated with higher odds of depression in women. Furthermore, we observed that participants in the highest quartile of dietary acid load compared to the lowest quartile, had a significantly higher intake of red meat and refined grains. Also, fruit and vegetable consumption were significantly lower in the highest quartile of dietary acid load versus the lowest quartile.

Few studies have investigated the relationship between dietary acid load and depression. A cross-sectional study on women in the age range of 20–50, showed that individuals who had higher dietary acid load scores had greater odds for stress, anxiety, and depression compared to lower scores (21). Results from Ausimmune longitudinal study (AusLong study) with a 10-year follow-up indicated that a higher dietary acid load in multiple sclerosis patients increased depression levels but not anxiety (34). Wu et al. in a longitudinal study among breast cancer survivors demonstrated that women in the highest quartile of PRAL had a greater risk of depression versus women in the lowest quartile (35). Findings from a study on 4378 persons (1909 male and 2469 female) reported that subjects in the highest category of the dietary acid load had a higher risk of anxiety and depression compared to those in the lowest (36). However, some other research did not show a significant relationship between acidic foods intake and the risk of depression (37). The results from a cohort study demonstrated that adherence to a high-protein diet might be a protective factor against depression in men while having a detrimental role in women (38). It was might that these disagreement results related to differences in the sources of consumed proteins. It is suggested that a higher intake of animal proteins, contrary to herbal sources of proteins, is a main cause of psychological disorders, like depression (39).

Our results demonstrated that women in the highest quartile of dietary acid load versus the lowest quartile, had a significantly higher intake of red meat and refined grains. In a cross-sectional study on 3172 adults (18–55 years old), women in the highest quartile of refined grains intake compared with the first quartile, had higher odds of anxiety and depression (40). In a study by Mofrad et al. women in the highest quartile of red meat consumption compared with those in the lowest quartile had a highest prevalence of depressive symptoms, anxiety and stress (41). In contrast, the results of a study published in 2021, showed no any association between red meat consumption and mental disorders in women (42). Moreover, Chi et al. in a prospective study, did not find any relationship between red meat and poultry consumption with the risk of depression in postmenopausal women (43). Also, we observed that fruit and vegetable consumption were significantly lower in the highest quartile of dietary acid load compared to the lowest quartile. Findings from the Taiwan Longitudinal Survey on Aging (TLSA) showed that fruit and vegetable consumption combined with physical activity was inversely related to the risk of depression (44). Grases et al. in an observational retrospective study indicated, no consumption of legumes and low intake of fruit and vegetable, were significantly correlated with depression (45). Results of ALSWH study (Australian Longitudinal Study on Women’s Health) by 15 years follow up showed that a higher consumption of fruit (≥ 4 servings) and vegetable (≥ 5 servings) was associated with lower odds of depression symptoms (46). The Australian Diabetes, Obesity and Lifestyle Study (AusDiab study) after 12 years follow up, demonstrated that a diet rich in fruits and vegetables, especially yellow/orange/red and leafy green vegetables, lowered the odds of depressive symptoms (47). Moreover, other cross-sectional studies on Finnish population and on university students from 16 countries found no association between fruit and vegetable intake and depression (48, 49). Different findings might be due to diversity in the study population, using different measurement tools for the evaluation of psychopathy, and don’t adjust some special dietary intakes (such as fiber and omega-3) as confounders (50, 51).

Several pathways have been suggested to describe the positive association between dietary acid load and psychopathy: First, there is an acid-sensing ion channel-1a (ASIC1a) expresses in the nervous system, especially in the amygdala, which has a central role in the regulation of mood (52, 53). Overexpression of ASIC1a in mice caused the fear condition, while ASIC1a inhibition had anti-anxiety and anti-depressant effects (54). Therefore, a high dietary acid load produces high PH might motivate ASIC1a and result in psychological disorders. Second, metabolic acidosis can elevate the secretion of cortisol (55, 56). A large number of researches supported a direct relationship between cortisol and psychological disorders (57–59). In addition, metabolic acidosis can increase the inflammatory parameters level (60), so oxidative stress might result in higher risk of psychological disorders (61).

Consumption of refined grains augments the risk of inflammation, as a risk factor for psychopathy (62). Moreover, refined grains lead to postprandial hyperglycemia and hyperinsulinemia (63, 64) and this might reduce blood glucose concentrations to a level that is a threshold for the release of autonomic hormones such as cortisol, adrenaline, growth hormone and glucagon (65). Red meat contains a high amount of dietary fat which has contributed to causing depression in animals. In one study that was conducted on rats, those who had a high-fat diet were more likely to have depression (66). Arachidonic acid (AA) which is abundant in red meat, produces the inflammatory mediators named eicosanoids. In contrast, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are known due to their anti-inflammatory effects. Therefore, the level of inflammation ascertains through the balance between AA and DHA or EPA. High consumption of red meat by changing the balance toward AA results in inflammation and finally depression (67). In addition, animal protein consumption caused an increase in some gut microbiota like Bacteroides entero, which are deemed to have a role in developing depression via the link between the gut and brain (68).

The existing findings are focused on the particular nutrients within fruit and vegetables, which affect psychological health (69). B vitamins have effects on single-carbon metabolism and synthesis of neurotransmitters, including serotonin, catecholamines and other monoamine neurotransmitters (70, 71). Moreover, the vital role of B vitamins in brain function is ascertained via a number of neuropsychiatric signs related to deficiencies in each of the B vitamins, like folate and vitamin B12 (8, 71). A recent investigation showed that the association between dietary patterns and depression is mediated by folate and vitamin B12 serum concentrations. This study reported that an increase in serum folate and vitamin B12 in healthy dietary pattern and a decrease in these two vitamins in unhealthy dietary pattern were associated with depression (72). Also, fruits and vegetables are rich in antioxidants components and have a promising relationship with psychological well-being (73). They lead to decrease oxidative stress and inflammation, potentially via the activation of transcription factors, including nuclear factor erythroid 2-related factor 2 (Nrf2) or nuclear factor κB (NFκB) (74).

Although this was the first case-control study that examined the relation of dietary acid load scores (PRAL and NEAP) among women and results were adjusted for energy intake, physical activity, history of depression, smoking status, periods of unemployment, job and BMI; however, this study had some limitations. First, due to our study only included women, our results cannot be generalized to both genders. Furthermore, the use of the FFQ questionnaire for participants’dietary assessment is inevitably subject to error. Due to the subjective nature of this method, over-or under-reporting of food intake cannot be predicted, therefore more detailed studies are required.

Our results revealed that there is a significant inverse association between dietary acid load scores (PRAL and NEAP) and the odds of depression in women. However more studies are needed to support these findings in the future.

Conflict of interest:

None

Authorship

MD and MH designed the study.MD, MGH, VA and MH contributed in statistical analysis, data interpretation and manuscript drafting. The final version of manuscript for submission was approved by all authors for submission.

Acknowledgments:

The authors thank the subjects who participated in the study.

Financial support

None

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Association of Dietary Acid Load and Depression among Iranian Women: a case-control study

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Abstract

INTRODUCTION

MATERIAL AND METHODS

Study population

Assessment of dietary intake

Calculation of Dietary Acid Load Indices

Assessment of other variables

Statistical methods

RESULTS

General characteristics of the study population

Association between PRAL and NEAP and odds of depression

DISCUSSION

CONCLUSION

Declarations

Conflict of interest:

Acknowledgments:

References

Additional Declarations

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