Abstract
Objective
The rapid increase in the global elderly population poses significant mental health challenges. Dietary factors, particularly dietary acid load, are increasingly recognized as influential factors in mental health and psychological well-being. This study aims to elucidate the associations of dietary acid load with psychological disorders, sleep quality, and mood among Iranian Older Adults.
Method
This study included a randomly selected sample of 398 elderly individuals. Their dietary habits were examined using a validated food frequency questionnaire (FFQ). To estimate dietary acid load, three well-established indices were employed: potential renal acid load (PRAL), net endogenous acid production (NEAP), and dietary acid load (DAL). Mental health status was evaluated by applying the Iranian version of the Depression, Anxiety, and Stress Scale (DASS-21), which has been validated for accuracy. Furthermore, participants’ sleep quality and mood were assessed through the Pittsburgh Sleep Quality Index (PSQI) and the Profile of Mood States (POMS), respectively.
Result
In fully adjusted models controlling for demographic, dietary, and lifestyle variables (e.g., age, BMI, socioeconomic status, caffeine intake), individuals in the highest tertile of the DAL index exhibited greater levels of stress than those in the lowest tertile (OR: 3.06, 95% CI = 1.43–6.57, p = 0.003). Elevated PRAL values were also linked to increased depressive symptoms (OR: 2.18, 95% CI = 1.04–4.58, p = 0.032). Higher levels of all three dietary acid load indices were significantly associated with poorer sleep quality (p < 0.05). However, no statistically significant associations were identified between dietary acid load and either anxiety or mood status (p > 0.05).
Conclusion
Elderly individuals with elevated PRAL and DAL values showed increased symptoms of depression and stress, respectively. Additionally, higher dietary acid load was associated with poorer sleep quality. Prospective studies and clinical trials are needed to validate these relationships and to better understand the underlying mechanisms.
Keywords: Older adults, PRAL, DAL, NEAP, Stress, Anxiety, Depression, Mood, Sleep quality, Dietary acid load, Psychological disorders
Introduction
The global population of older adults has increased significantly in recent decades, presenting major health and social challenges. While this demographic shift began in higher-income nations, low- and middle-income countries have experienced the most notable changes in population aging in recent decades. Projections indicate that by 2050, nearly two-thirds of individuals aged 60 and older will reside in these regions [1]. Older adults face numerous physical health challenges; however, psychological disorders are among the most significant factors affecting their quality of life [2]. Depression, for instance, has a reported prevalence of 10–25% among individuals aged 65 and older, with its incidence notably increasing after the age of 81 [3]. Sleep quality also deteriorates significantly with age, with nearly 50% of older adults experiencing poor sleep quality [4]. In addition, mood disorders in late life are linked to severe negative outcomes, including medical comorbidities, cognitive decline, a heightened risk of dementia, increased suicide rates, and overall mortality [5, 6]. The increasing rate of population aging in Iran, accompanied by a high prevalence of mental health problems among older adults in recent years, has highlighted the need for greater attention to the psychological well-being of this vulnerable population [7, 8]. Among the various factors influencing psychological well-being in older adults, diet and appropriate food choices are fundamental in averting these physiological challenges [9]. Certain dietary patterns, such as the Western dietary pattern, are closely linked with an increased risk of depression and anxiety. This pattern is defined through excessive consumption of red meat, eggs, and processed foods [10, 11]. Conversely, plant-based diet and related food groups, including fruits, vegetables, and nuts, are known to lower the risk of Psychological disorders [12]. These nutrient-rich foods play an essential role in enhancing mental health and improving sleep quality [13–15].
One key indicator of dietary health is dietary acid load, which tends to be lower among individuals consuming higher amounts of vegetables, fruits, nuts, and legumes [16]. Epidemiological studies have explored the link between dietary acid load with psychological disorders, as well as sleep quality. The majority of research has highlighted a significant association, demonstrating that increased dietary acid load is strongly linked to higher rates of depression, anxiety, and poorer sleep quality. However, in certain cases, no significant relationship has been observed [17–20].
Notably, no study to date has specifically investigated the association of dietary acid load and mood, nor has the association been examined within elderly populations. In the present study, we also applied all three major dietary acid load indices (PRAL, NEAP, and DAL) to allow for a more comprehensive evaluation of these associations. Therefore, the present cross-sectional study aims to address this gap by evaluating the association between dietary acid load and Psychological disorders, sleep quality, and mood among older adults.
Method
Study population and design
Between October 2022 and May 2023, we carried out a cross-sectional investigation involving 398 older adults living in the Southern region of Tehran. Using simple random sampling, participants were enlisted from TUMS-affiliated healthcare centers to guarantee an unbiased and representative sample. A list of eligible older adults was obtained from the electronic health records of each center, and participants were randomly selected using computer-generated random numbers. Eligible individuals were ≥ 60 years old, free of chronic diseases and medications, since both factors can affect psychological assessments. Those who altered their diet for weight or disease management in the past six months were not included. The study protocol received ethical approval from the Research Ethics Committee of Tehran University of Medical Sciences. All procedures adhered to the Declaration of Helsinki. Written informed consent was obtained before participation.
Sample size calculation
To determine the required sample size for this study, abdominal obesity was identified as the key dependent variable, as it was anticipated to demand the largest number of participants among the outcomes assessed [21]. The sample size was calculated using the method for comparing two proportions, as recommended by Fleiss in Statistical Methods for Rates and Proportions [22]. This calculation indicated that 396 participants were needed. To ensure adequate sample coverage and accommodate potential non-responses or exclusions, a total of 400 individuals were recruited from the target population.
Assessment of dietary intake
Dietary intake was assessed via a validated and reliable semi-quantitative Food Frequency Questionnaire (FFQ) consisting of 168 food items [23, 24]. Data collection was performed through face-to-face interviews administered by trained interviewers to capture information on the frequency and portion size of foods consumed over the previous year. The FFQ provided a comprehensive list of food items alongside predetermined serving sizes, enabling participants to report their consumption frequency as daily, weekly, monthly, or annual intakes. The reported consumption frequencies were standardized to reflect daily intakes, and food amounts were converted into grams per day using household measurement guidelines [25]. Nutrient analysis (including estimations of total energy and other dietary components) was performed using Nutritionist 4 software (First Databank, Hearst Corp., San Bruno, CA, USA), which was customized for Iranian foods.
Assessment of dietary acid load
Dietary acid load represents the net acid production in the body, which results from the metabolic processing of consumed foods. It reflects the dietary balance between acid-producing and base-producing foods [16]. Nutrient intake data used to calculate PRAL, NEAP, and DAL were obtained from dietary analysis using Nutritionist IV software. To achieve a more comprehensive evaluation, dietary acid load was assessed using three widely recognized indices.
-
Potential renal acid load (PRAL) [26, 27]
PRAL (mEq/day) = 0.4888×protein intake (g/day) + 0.0366×phosphorus (mg/day) − 0.0205×potassium (mg/day) − 0.0263×magnesium (mg/day) − 0.0125×calcium (mg/day).
-
Net endogenous acid production (NEAP) [28]
NEAP (mEq/d) = [54.5 × protein intake (g/d) ÷ potassium intake (mEq/d)] −10.2.
-
Dietary acid load (DAL) combining PRAL and body surface area (BSA) [27, 29]
DAL (mEq/ day) = [PRAL + (BSA [m2] × 41 [mEq/ day]/ 1.73 m2]
where BSA is calculated as:
BSA = 0.024265×Height (cm)0.3964×Weight (kg)0.5378.
PRAL, NEAP, and DAL have been validated as effective measures for estimating dietary acid-base load. Their validity was assessed by comparing these scores to the 24-hour urinary acid load in healthy adults [27, 28].
Assessment of psychological fisorders: stress, anxiety, and depression
Psychological disorders were evaluated using the Iranian validated version of the Depression Anxiety.
Stress Scale (DASS-21), a widely recognized 21-item self-report instrument [30, 31]. This tool comprises three subscales designed to independently measure symptoms of depression, anxiety, and stress. Each item is rated on a four-point Likert scale ranging from 0 (never) to 3 (almost always), reflecting the frequency of symptoms experienced over the past week. Subscale scores are calculated by summing the responses to the relevant items, yielding a total score ranging from 0 to 21 for each domain. Based on standard cut-off points, depression is considered within the normal range at scores of 0–9, while scores above 9 suggest increasing levels of depressive symptoms. For anxiety, scores of 0–7 are interpreted as normal, with higher scores indicating elevated anxiety. Stress scores of 0–14 are deemed normal, whereas scores exceeding 14 are indicative of heightened stress levels [32, 33].
Sleep quality assessment
We evaluated participants’ sleep quality using the Pittsburgh Sleep Quality Index (PSQI), a self-administered instrument consisting of 19 items grouped into seven domains: perceived sleep quality, sleep latency, total sleep time, habitual sleep efficiency, nighttime disturbances, use of sleep medication, and daytime dysfunction. Each domain is rated on a 0–3 scale, producing a composite PSQI score from 0 to 21; higher scores denote poorer overall sleep. In line with established conventions, a global PSQI score of 5 or above was used to distinguish poor sleepers from good sleepers. The PSQI’s reliability and validity have been well established across diverse populations, including Iranian adults [34, 35].
Mood assessment
Participants’ mood states were evaluated using the long-form version of the Profile of Mood States (POMS), a widely recognized psychological assessment tool frequently employed in cognitive and behavioral research [36]. The Persian version of the questionnaire has demonstrated strong psychometric properties, including acceptable validity and reliability in Iranian populations [37]. This 65-item instrument requires respondents to indicate the extent to which they experienced various mood-related symptoms at the time of completing the survey, using a 5-point Likert scale ranging from 1 (not at all) to 5 (extremely).
The POMS measures six core mood dimensions: Tension–Anxiety, Depression–Dejection, Anger–Hostility, Fatigue–Inertia, Confusion–Bewilderment, and Vigor–Activity. To derive a Total Mood Disturbance (TMD) score, the scores of the five negative subscales are summed, and the Vigor score is subtracted from this total, with higher TMD scores reflecting greater emotional distress. In this study, mood scores were classified into two groups: above average (> 29.04) and below average (< 29.04). Scores below the cutoff (< 29.04) reflected a more favorable mood, whereas higher scores (> 29.04) indicated greater mood disturbance.
Anthropometric assessment
Anthropometric measurements were obtained in accordance with the standardized protocol outlined by the World Health Organization (WHO), and all assessments were performed by a trained assistant to ensure accuracy and reliability. Body weight was measured to the nearest 0.1 kg using a calibrated digital scale (Seca 725 GmbH & Co., Hamburg, Germany), with participants wearing light clothing and no shoes. Height was recorded to the nearest 0.5 cm using a non-stretchable tape, with participants standing barefoot against a flat surface in a neutral position, with their shoulders relaxed. Body mass index (BMI) was then calculated by dividing the participants’ weight (kilograms) by the square of their height (meters) [38].
Assessment of other variables
Trained interviewers gathered participants’ personal data via standardized, in-person interviews. Collected sociodemographic variables included age, sex, marital status (married vs. single), educational attainment (less than high school vs. high school or higher), employment (employed, unemployed, retired), household size, and housing tenure (owner vs. renter). We also inquired about ownership of key household items (e.g., private car, microwave, washing machine, dishwasher, laptop, internet access). Monthly income was grouped into three categories (≤ 20 million Rials, 20–60 million Rials, ≥ 60 million Rials), and total years of schooling were noted. For statistical analyses, socioeconomic status (SES) was operationalized as a composite index based on educational attainment, household income, and ownership of selected household assets. Participants were categorized into tertiles of SES groups (low, medium, and high(according to their total score. Smoking habits were classified as current smokers or non-smokers, and regular use of dietary supplements was recorded.
Physical activity levels were evaluated via the International Physical Activity Questionnaire (IPAQ), a globally validated instrument with confirmed reliability in Iran. This tool includes seven questions covering physical activities over the past week. Respondents reported the frequency and duration of vigorous and moderate activities, walking, and time spent sitting. Duration was recorded in minutes to calculate MET (Metabolic Equivalent of Task) values. Total weekly physical activity was determined by summing MET-minutes across all activity types [39, 40].
Statistical analysis
Participants were classified into tertiles based on the sample-specific distributions of NEAP, PRAL, and DAL. Baseline characteristics were compared across tertiles using one-way ANOVA for continuous variables (mean ± SD) and chi-square tests for categorical variables (percentage). Dietary intake differences were evaluated using ANCOVA, adjusting for total energy intake (except when analyzing energy intake itself). The distribution of all continuous variables was assessed for normality using the Kolmogorov-Smirnov test. Skewed variables were not transformed prior to analysis. Binary logistic regression was used to examine associations between NEAP, PRAL, and DAL and psychological disorders, sleep quality, and TMD. Participants were categorized based on predefined cut-off scores that were assigned to each outcome variable. Three models were applied: the crude model)an unadjusted model(, Model 1 (a model controlling for total energy intake), and Model 2 (fully adjusted model that additionally accounted for age, sex, BMI, education, marital status, socioeconomic status, physical activity, smoking, supplement use, dietary fiber, omega-3, and caffeine intake). Dietary fiber, omega-3 fatty acids, and caffeine intake were included as confounders because evidence suggests that each may influence both dietary acid load and psychological outcomes, including sleep and mood [41–44]. Adjusting for these variables aimed to minimize potential confounding and better clarify the independent relationships under investigation All analyses were conducted in SPSS for Windows, version 26.0 (version 26.0, IBM Corp., Armonk, NY, USA), and statistical significance was defined as a p-value below 0.05.
Result
Table 1 shows the baseline features of all 398 participants categorized by PRAL, NEAP, and DAL tertiles. Individuals in the top NEAP tertile had a significantly higher proportion of men (p = 0.007). participants in the uppermost DAL tertile exhibited greater body weight (p < 0.001), a larger share of males (p < 0.001), and lower rates of supplement use (p = 0.016). No other demographic or lifestyle variables differed significantly between the extreme tertiles of PRAL, NEAP, and DAL.
Table 1.
General characteristics of participants across tertiles of PRAL, NEAP, and DAL among Iranian older adults
| PRAL | NEAP | DAL | |||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| variables |
T1
(n = 132) |
T2
(n = 134) |
T3
(n = 132) |
P-value 1 |
T1
(n = 132) |
T2
(n = 133) |
T3
(n = 133) |
P-value 1 |
T1
(n = 132) |
T2
(n = 133) |
T3
(n = 133) |
P-value 1 | |||||||||||||
| Age (years) | 63.87 ± 3.96 | 62.98 ± 3.31 | 63.24 ± 3.68 | 0.123 | 63.85 ± 3.50 | 63.20 ± 3.89 | 63.03 ± 3.60 | 0.162 | 63.92 ± 4.08 | 63.37 ± 3.44 | 62.83 ± 3.39 | 0.056 | |||||||||||||
| BMI (kg/m2) | 28.85 ± 4.37 | 28.78 ± 5.05 | 28.96 ± 5.05 | 0.951 | 28.87 ± 4.86 | 28.62 ± 4.78 | 29.10 ± 4.85 | 0.722 | 28.12 ± 4.68 | 29.11 ± 4.87 | 29.34 ± 4.85 | 0.087 | |||||||||||||
| Weight |
75.27± 12.15 |
75.76± 13.26 |
75.72± 10.81 |
0.933 |
74.26± 12.63 |
75.91± 12.53 |
76.57± 11.01 |
0.277 |
70.81± 11.77 |
75.76± 11.45 |
80.30 ± 1 1.07 |
< 0.001 | |||||||||||||
| Gender (N, %) | 0.150 | 0.007 | < 0.001 | ||||||||||||||||||||||
| male | 57 (42.9) | 71 (53) | 71 (53.4) | 52 (39.1) | 70 (52.2) | 77 (57.9) | 45 (34.1) | 65 (48.9) | 88 (66.2) | ||||||||||||||||
| Supplement intake | 0.342 | 0.071 | 0.016 | ||||||||||||||||||||||
| NO | 117 (88) | 120 (89.6) | 123 (93.2) | 115 (86.5) | 119 (89.5) | 126 (94.7) | 115 (87.1) | 116 (87.2) | 128 (96.2) | ||||||||||||||||
|
Physical activity (MET-h/week) |
878.81 ± 885.83 | 844.20 ± 1123.50 | 1068.10 ± 1218.35 | 0.272 |
813.52± 872.08 |
952.40 ± 1137.33 | 1024.40 ± 1549.14 | 0.358 | 922.51 ± 927.07 | 988.74 ± 1671.70 | 891.12± 907.96 | 0.802 | |||||||||||||
| Education level (N, %) | 0.250 | 0.139 | 0.252 | ||||||||||||||||||||||
| Illiterate | 12 (9) | 11 (8.2) | 20 (15.2) | 9 (6.8) | 12 (9) | 22 (16.5) | 12 (9.1) | 13 (9.8) | 18 (13.5) | ||||||||||||||||
| Elementary/high school | 59 (44.4) | 75 (56) | 60 (45.5) | 69 (51.9) | 60 (45.1) | 65 (48.9) | 60 (45.5) | 75 (56.4) | 58 (43.6) | ||||||||||||||||
| High school./diploma | 47 (35.3) | 36 (26.9) | 40 (30.3) | 40 (30.1) | 47 (35.3) | 36 (27.1) | 45 (34.1) | 37 (27.8) | 41 (30.8) | ||||||||||||||||
| University | 15 (11.3) | 12 (9) | 12 (9.1) | 15 (11.3) | 14 (10.5) | 10 (7.5) | 15 (11.4) | 8 (6) | 16 (12) | ||||||||||||||||
| Marital status (N, %) | 0.872 | 0.941 | 0.732 | ||||||||||||||||||||||
| Married | 105 (78.9) | 108 (80.6) | 103(78) | 104 (78.2) | 106 (79.7) | 106 (79.7) | 102 (77.3) | 105 (78.9) | 108 (81.2) | ||||||||||||||||
| Socioeconimic status (N, %) | 0.225 | 0.107 | 0.844 | ||||||||||||||||||||||
| Low | 30 (22.7) | 34 (25.4) | 42 (31.8) | 34 (25.8) | 28 (21.1) | 44 (33.1) | 31 (23.7) | 40 (30.1) | 35 (26.3) | ||||||||||||||||
| Medium | 51 (38.6) | 61 (45.5) | 52 (39.4) | 50 (37.9) | 58 (43.6) | 56 (42.1) | 56 (42.7) | 52 (39.1) | 55 (41.4) | ||||||||||||||||
| high | 51 (38.6) | 39 (29.1) | 38 (28.8) | 48 (36.4) | 47 (35.3) | 33 (24.8) | 44 (33.6) | 41 (30.8) | 43 (32.3) | ||||||||||||||||
| Smoking status (N, %) | 0.504 | 0.471 | 0.300 | ||||||||||||||||||||||
| nonsmoker | 108 (81.2) | 102 (76.1) | 107 (81.1) | 110 (82.7) | 105 (78.9) | 102 (76.7) | 111 (84.1) | 103 (77.4) | 103 (77.4) | ||||||||||||||||
Values in bold indicate statistically significant differences between tertiles (p < 0.05)
1 The Chi-square test was used for qualitative variables, and the analysis of variance (ANOVA) test was used for continuous variables
Values are mean ± SD or percentages
BMI: body mass index, PRAL: potential renal acid load, NEAP: net endogenous acid production, DAL: dietary acid load
Table 3 summarizes the energy-adjusted dietary intake for participants grouped by PRAL, NEAP, and DAL tertiles. Participants in the highest PRAL tertile consumed more protein (p < 0.001), grains (p < 0.001), eggs (p = 0.033), and meat (p = 0.001), whereas those in the lowest tertile had higher intakes of fiber (p < 0.001), fruits (p < 0.001), vegetables (p < 0.001), legumes (p = 0.039), nuts (p = 0.044), caffeine (p < 0.001), iron (p = 0.002), magnesium (p < 0.001), and potassium(p < 0.001). Similarly, individuals in the highest NEAP tertile consumed more protein (p < 0.001), grains (p = < 0.001), eggs (p = 0.014), and meat (p < 0.001), while those in the lowest tertile had greater intakes of MUFA (p < 0.001), SFA (p < 0.001), PUFA (p < 0.001), omega-3 (p < 0.001), fiber (p < 0.001), fruits (p < 0.001), vegetables (p = < 0.001), legumes (p = 0.002), nuts (p < 0.001), caffeine (p < 0.001), zinc (p = 0.005), magnesium (p < 0.001), and potassium (p < 0.001). For DAL, participants in the highest tertile consumed more protein (p < 0.001), grains (p < 0.001), meat (p < 0.001), and eggs (p = 0.002), whereas those in the lowest tertile reported higher intakes of energy (p < 0.001), fiber (p < 0.001), fruits (p < 0.001), vegetables (p < 0.001), legumes (p = 0.027), nuts (p = 0.009), caffeine (p < 0.001), iron (p = 0.010), magnesium (p < 0.001), and potassium (p < 0.001).
Table 2.
Table 2. Energy-adjusted dietary intakes across tertiles of PRAL, NEAP, and DAL among Iranian older adults*
| PRAL | NEAP | DAL | ||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nutrients and food groups |
T1 (n = 132) |
T2 (n = 134) |
T3 (n = 134) |
P-value£ |
T1 (n = 132) |
T2 (n = 133) |
T3 (n = 133) |
P-value£ |
T1 (n = 132) |
T2 (n = 133) |
T3 (n = 133) |
P-value£ | ||||||||||||
|
Energy (kcal) |
2258.38± 58.05 |
2148.86± 57.83 |
2193.71± 58.05 |
0.406 |
2196.61± 57.96 |
2131.75± 57.74 |
2272.73± 57.96 |
0.227 |
2344.91± 56.42 |
1968.71± 56.21 |
2297.04± 56.21 |
< 0.001 | ||||||||||||
|
Carbohydrate (g/d) |
352.649± 3.27 |
344.09± 3.26 |
349.04± 3.27 |
0.179 |
345.40± 3.26 |
345.65± 3.25 |
354.72± 3.27 |
0.072 |
352.13± 3.31 |
343.93± 3.34 |
351.30± 3.28 |
0.148 | ||||||||||||
|
Protein (g/d) |
66.15± 0.97 |
66.34± 0.97 |
72.09± 0.97 |
< 0.001 |
63.48± 0.96 |
69.71± 0.96 |
71.36± 0.96 |
< 0.001 |
64.87± 0.97 |
67.33± 0.98 |
72.56± 0.97 |
< 0.001 | ||||||||||||
|
Fat (g/d) |
65.33± 1.48 |
67.44± 1.48 |
63.01± 1.48 |
0.107 |
69.88± 1.44 |
66.84± 1.43 |
59.06± 1.44 |
< 0.001 |
66.58± 1.50 |
67.12± 1.51 |
62.46± 1.49 |
0.055 | ||||||||||||
|
MUFA (g/d) |
20.53± 0.59 |
21.76± 0.59 |
20.12± 0.59 |
0.122 |
22.28± 0.58 |
21.57± 0.57 |
18.56± 0.58 |
< 0.001 |
21.00± 0.60 |
21.79± 0.60 |
16.77± 0.59 |
0.057 | ||||||||||||
|
PUFA (g/d) |
16.50± 0.48 |
17.15± 0.48 |
15.84± 0.48 |
0.155 |
17.75± 0.47 |
16.98± 0.47 |
14.76± 0.47 |
< 0.001 |
17.09± 0.49 |
16.82± 0.49 |
15.69± 0.48 |
0.096 | ||||||||||||
|
SFA (g/d) |
21.09± 0.61 |
21.44± 0.60 |
20.04± 0.60 |
0.233 |
22.52± 0.59 |
21.07± 0.59 |
18.98± 0.59 |
< 0.001 |
21.32± 0.61 |
21.21± 0.62 |
20.14± 0.61 |
0.318 | ||||||||||||
|
EPA (g/d) |
0.009± 0.001 |
0.008± 0.001 |
0.008± 0.001 |
0.403 |
0.009± 0.001 |
0.009± 0.001 |
0.007± 0.001 |
0.226 |
0.008± 0.001 |
0.009± 0.001 |
0.007± 0.001 |
0.638 | ||||||||||||
|
DHA (g/d) |
0.029± 0.003 |
0.025± 0.003 |
0.026± 0.003 |
0.601 |
0.027± 0.003 |
0.028± 0.003 |
0.024± 0.003 |
0.411 |
0.026± 0.003 |
0.028± 0.003 |
0.026± 0.003 |
0.840 | ||||||||||||
|
Omega-3 (g/d) |
0.391± 0.02 |
0.437± 0.02 |
0.372± 0.02 |
0.038 |
0.454± 0.02 |
0.412± 0.02 |
0.334± 0.02 |
< 0.001 |
0.421± 0.02 |
0.423± 0.02 |
0.360± 0.02 |
0.024 | ||||||||||||
|
Total fiber (g/d) |
21.87± 0.36 |
17.62± 0.36 |
16.04± 0.36 |
< 0.001 |
21.54± 0.37 |
18.10± 0.37 |
15.89± 0.37 |
< 0.001 |
21.69± 0.38 |
17.55± 0.38 |
16.40± 0.37 |
< 0.001 | ||||||||||||
|
Grains (g/d) |
365.08± 10.14 |
407.96± 10.10 |
478.43± 10.12 |
< 0.001 |
340.91± 9.44 |
415.73± 9.72 |
494.78± 9.46 |
< 0.001 |
361.97± 10.12 |
406.47± 10.22 |
484.20± 10.04 |
< 0.001 | ||||||||||||
|
Fruits (g/d) |
430.25± 11.40 |
319.12± 11.36 |
246.05± 11.39 |
< 0.001 |
409.03± 11.88 |
336.45± 11.85 |
249.80± 11.90 |
< 0.001 |
417.25± 12.15 |
313.16± 12.27 |
267.57± 12.05 |
< 0.001 | ||||||||||||
|
Vegetables (g/d) |
430.68± 12.12 |
290.74± 12.07 |
249.41± 12.10 |
< 0.001 |
422.81± 12.22 |
306.66± 12.19 |
241.24± 12.24 |
< 0.001 |
429.20± 12.35 |
291.86± 12.48 |
251.20± 12.25 |
< 0.001 | ||||||||||||
|
Legums (g/d) |
22.40± 1.05 |
20.80± 1.05 |
18.61± 1.05 |
0.039 |
23.43± 1.04 |
20.25± 1.04 |
18.12± 1.04 |
0.002 |
23.01± 1.06 |
19.58± 1.07 |
19.41± 1.05 |
0.027 | ||||||||||||
|
Nuts (g/d) |
13.57± 1.38 |
11.87± 1.37 |
8.75± 1.38 |
0.044 |
15.76± 1.35 |
11.90± 1.35 |
6.52± 1.35 |
< 0.001 |
13.66± 1.39 |
12.67± 1.41 |
8.01± 1.38 |
0.009 | ||||||||||||
|
Dairy (g/d) |
303.17± 17.28 |
282.99± 17.21 |
259.25± 17.26 |
0.199 |
287.05± 17.10 |
318.78± 17.06 |
239.30± 17.13 |
0.005 |
294.33± 17.55 |
278.74± 17.72 |
273.59± 17.41 |
0.683 | ||||||||||||
|
Eggs (g/d) |
25.32± 2.04 |
28.68± 2.03 |
32.87± 2.04 |
0.033 |
24.22± 2.03 |
30.30± 2.03 |
32.34± 2.04 |
0.014 |
24.85± 2.05 |
27.28± 2.07 |
34.74± 2.03 |
0.002 | ||||||||||||
|
Meat (g/d) |
49.35± 2.60 |
56.13± 2.59 |
63.55± 2.59 |
0.001 |
48.79± 2.59 |
56.49± 2.58 |
63.75± 2.59 |
< 0.001 |
48.28± 2.61 |
56.58± 2.64 |
64.48± 2.59 |
< 0.001 | ||||||||||||
|
Caffeine (mg/d) |
279.42± 11.60 |
213.99± 11.55 |
167.87± 11.58 |
< 0.001 |
268.82± 11.81 |
214.59± 11.79 |
177.87± 11.83 |
< 0.001 |
267.87± 11.86 |
226.23± 12.00 |
168.03± 11.79 |
< 0.001 | ||||||||||||
|
Iron (mg/d) |
16.29± 0.24 |
15.13± 0.23 |
15.54± 0.24 |
0.002 |
16.11± 0.24 |
15.37± 0.24 |
15.47± 0.24 |
0.059 |
16.26± 0.24 |
15.32± 0.24 |
15.43± 0.24 |
0.010 | ||||||||||||
|
Zinc (mg/d) |
7.57± 0.15 |
7.15± 0.15 |
7.21± 0.15 |
0.106 |
7.53± 0.15 |
7.49± 0.15 |
6.91± 0.15 |
0.005 |
7.46± 0.15 |
7.20± 0.15 |
7.30± 0.15 |
0.467 | ||||||||||||
|
Calcium (mg/d) |
836.34± 23.57 |
767.36± 23.48 |
768.15± 23.54 |
0.061 |
801.72± 23.44 |
833.82± 23.39 |
735.81± 23.49 |
0.011 |
819.52± 23.96 |
772.56± 24.19 |
780.90± 23.76 |
0.341 | ||||||||||||
|
Magnesium (mg/d) |
282.31± 4.12 |
233.24± 4.11 |
209.74± 4.12 |
< 0.001 |
280.54± 4.01 |
242.81± 4.00 |
201.86± 4.02 |
< 0.001 |
278.12± 4.34 |
235.64± 4.38 |
212.74± 4.30 |
0.000 | ||||||||||||
|
Potassium (mEq/d) |
98.42± 1.43 |
76.79± 1.42 |
65.35± 1.43 |
< 0.001 |
96.97± 1.43 |
80.10± 1.43 |
63.46± 1.43 |
< 0.001 |
96.81± 1.54 |
77.17± 1.56 |
67.01± 1.53 |
0.000 | ||||||||||||
|
Sodium (mg/d) |
4941.54± 244.61 |
5037.60± 243.63 |
5370.29± 244.30 |
0.429 |
5120.99± 244.24 |
4876.29± 243.75 |
5353.37± 244.72 |
0.387 |
5012.23± 247.62 |
5003.47± 250.07 |
5362.37± 245.60 |
0.502 | ||||||||||||
Values in bold indicate statistically significant differences between tertiles (p < 0.05)
PRAL: potential renal acid load; NEAP, net endogenous acid production; DAL: dietary acid load; SFA: saturated fatty acid; MUFA: monounsaturated fatty acids; PUFA: polyunsaturated fatty acid; EPA: Eicosapentaenoic acid; DHA: Docosahexaenoic acid
*Values are MEAN ± SE
£ All p-values resulted from analysis of ANCOVA, except for energy. P-values for energy resulted from ANOVA
Table 3.
Odds ratio and 95% confidence interval for psychological disorder, quality of sleep, and mood by tertiles of dietary acid load among Iranian older adults
| PRAL | NEAP | DAL | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variable | T1 (n = 132) |
T2 (n = 134) |
T3 (n = 134) |
P-trend* | T1 (n = 132) |
T2 (n = 133) |
T3 (n = 133) |
P-trend* | T1 (n = 132) |
T2 (n = 133) |
T3 (n = 133) |
P-trend* |
| Stress | ||||||||||||
| crudea | 1 |
1.09 (0.66–1.79) |
1.85 (1.13–3.03) |
0.013 | 1 |
1.02 (0.62–1.68) |
1.79 (1.10–2.93) |
0.019 | 1 |
1.24 (0.75–2.03) |
1.73 (1.06–2.83) |
0.029 |
| Model I | 1 |
1.04 (0.63–1.71) |
1.83 (1.13-3.00) |
0.016 | 1 |
0.99 (0.60–1.64) |
1.91 (1.16–3.14) |
0.011 | 1 |
1.03 (0.62–1.73) |
1.73 (1.05–2.84) |
0.031 |
| Model II | 1 |
1.00 (0.54–1.87) |
1.89 (0.93–3.83) |
0.058 | 1 |
1.020 (0.55–1.88) |
1.80 (0.86–3.74) |
0.100 | 1 |
1.30 (0.68–2.48) |
3.06 (1.43–6.57) |
0.003 |
| anxiety | ||||||||||||
| crudea | 1 |
1.38 (0.85–2.24) |
1.24 (0.76–2.02) |
0.389 | 1 |
1.08 (0.67–1.76) |
1.31 (0.81–2.13) |
0.268 | 1 |
1.61 (0.99–2.61) |
1.02 (0.62–1.66) |
0.946 |
| Model I | 1 |
1.36 (0.83–2.21) |
1.23 (0.76-2.00) |
0.411 | 1 |
1.07 (0.66–1.74) |
1.33 (0.82–2.17) |
0.244 | 1 |
1.54 (0.94–2.54) |
1.01 (0.62–1.65) |
0.971 |
| Model II | 1 |
1.13 (0.63–2.02) |
0.97 (0.50–1.91) |
0.904 | 1 |
1.14 (0.64–2.03) |
1.47 (0.73–2.95) |
0.278 | 1 |
1.52 (0.83–2.78) |
1.14 (0.56–2.31) |
0.793 |
| Depression | ||||||||||||
| crudea | 1 |
1.39 (0.81–2.39) |
2.59 (1.63–4.38) |
< 0.001 | 1 |
0.88 (0.52–1.52) |
2.06 (1.24–3.42) |
0.004 | 1 |
1.42 (0.94–2.40) |
1.84 (1.09–3.09) |
0.022 |
| Model I | 1 |
1.37 (0.80–2.36) |
2.58 (1.52–4.35) |
< 0.001 | 1 |
0.87 (0.51–1.50) |
2.10 (1.26–3.50) |
0.004 | 1 |
1.34 (0.78–2.30) |
1.83 (1.09–3.07) |
0.023 |
| Model II | 1 |
1.29 (0.67–2.47) |
2.18 (1.04–4.58) |
0.032 | 1 |
0.71 (0.37–1.34) |
1.24 (0.60–2.57) |
0.460 | 1 |
1.40 (0.72–2.69) |
1.94 (0.90–4.20) |
0.089 |
| Poor Sleep | ||||||||||||
| crudea | 1 |
1.00 (0.59–1.70) |
1.43 (0.82–2.51) |
0.211 | 1 |
1 (0.59–1.70) |
1.63 (0.93–2.86) |
0.096 | 1 |
1.16 (0.68–1.96) |
1.85 (1.05–3.26) |
0.034 |
| Model I | 1 |
0.95 (0.56–1.63) |
1.39 (0.79–2.45) |
0.255 | 1 |
0.97 (0.57–1.65) |
1.68 (0.95–2.97) |
0.079 | 1 |
0.95 (0.54–1.65) |
1.81 (1.02–3.22) |
0.044 |
| Model II | 1 |
1.22 (0.65–2.30) |
2.09 (1.01–4.36) |
0.045 | 1 |
1.29 (0.69–2.40) |
2.55 (1.19–5.48) |
0.016 | 1 |
1.59 (0.82–3.10) |
4.76 (2.14–10.57) |
< 0.001 |
| Low Mood | ||||||||||||
| crudea | 1 |
1.09 (0.66–1.79) |
2.09 (1.28–3.42) |
0.003 | 1 |
1.12 (0.68–1.84) |
2.03 (1.24–3.31) |
0.005 | 1 |
1.35 (0.83–2.21) |
1.48 (0.91–2.42) |
0.119 |
| Model I | 1 |
1.06 (0.64–1.74) |
2.07 (1.26–3.38) |
0.004 | 1 |
1.10 (0.67–1.82) |
2.10 (1.28–3.45) |
0.003 | 1 |
1.22 (0.74–2.02) |
1.47 (0.90–2.40) |
0.128 |
| Model II | 1 |
0.74 (0.40–1.36) |
1.31 (0.65–2.65) |
0.352 | 1 |
1.02 (0.56–1.87) |
1.61 (0.78–3.31) |
0.175 | 1 |
1.09 (0.59–2.04) |
1.41 (0.68–2.96) |
0.347 |
Values in bold indicate statistically significant differences between tertiles (p < 0.05)
PRAL: potential renal acid load; NEAP, net endogenous acid production; DAL: dietary acid load
*calculated by logistic regression
a unadjusted model
Model I: adjusted for energy
Model II: adjusted for energy, age, socioeconomic status, physical activity, supplement intake, marital status, BMI, dietary fiber, omega3, smoking status, sex, education, and caffeine
Odds ratios (ORs) and 95% confidence intervals (CIs) across each tertile of NEAP, PRAL, and DAL are summarized in Table 3. In both the unadjusted model and Model I, participants in the highest tertiles of PRAL(ORcrude: 1.85, 95% CI = 1.13–3.03, p = 0.013, ORmodel I: 1.83, 95% CI = 1.13-3.00, p = 0.016), NEAP (ORcrude: 1.79, 95% CI = 1.10–2.93, p = 0.0193, ORmodel I: 1.91, 95% CI = 1.16–3.14, p = 0.011), and DAL (ORcrude: 1.73, 95% CI = 1.06–2.83, p = 0.029, ORmodel I: 1.73, 95% CI = 1.05–2.84, p = 0.031) xhibited significantly greater odds of elevated stress than those in the lowest tertiles. When additional covariates were included in Model II, the associations for PRAL and NEAP were attenuated and no longer significant, whereas the top DAL tertile retained a strong link with stress(ORmodel II: 3.06, 95% CI = 1.43–6.57, p = 0.003). However, no statistically significant association between any dietary acid load index and anxiety was observed in either unadjusted or adjusted analyses.
In both the unadjusted analysis and Model I, elevated PRAL(ORcrude: 2.59, 95% CI = 1.63–4.38, p < 0.001, ORmodel I: 2.58, 95% CI = 1.63–4.38, p < 0.001), NEAP (ORcrude: 2.06, 95% CI = 1.24–3.42, p = 0.004, ORmodel I: 2.10, 95% CI = 1.26–3.50, p = 0.004), and DAL (ORcrude: 1.84, 95% CI = 1.09–3.09, p = 0.022, ORmodel I: 1.83, 95% CI = 1.09–3.07, p = 0.023) were linked to greater odds of depression. After full adjustment in Model II, only PRAL remained significantly associated with depression (ORmodel II: 2.18, 95% CI = 1.04–4.58, p = 0.032). Further analysis regarding the connection between dietary acid load and sleep quality indicated that, after controlling for multiple confounding variables in Model II, higher scores in PRAL (OR: 2.09, 95% CI = 1.01–4.36, p = 0.045), NEAP (OR: 2.55, 95% CI = 1.19–5.48, p = 0.016), and DAL (OR: 4.76, 95% CI = 2.14–10.57, p < 0.001) were strongly correlated with poor sleep quality. These results highlight the potential negative impact of a higher dietary acid load on sleep health.
Initial analysis of the association between dietary acid load and mood using the crude and Model I frameworks demonstrated that participants in the highest tertiles of PRAL(ORcrude: 2.09, 95% CI = 1.28–3.42, p = 0.003, ORmodel I: 2.07, 95% CI = 1.26–3.38, p = 0.004) and NEAP (ORcrude: 2.03, 95% CI = 1.24–3.31, p = 0.005, ORmodel I: 2.10, 95% CI = 1.28–3.45, p = 0.003) were significantly more likely to experience lower mood compared to those in the lowest tertiles. Nonetheless, these associations did not remain significant after full adjustment in Model II. Furthermore, no significant correlation was found between DAL and mood in any of the models.
Discussion
Our cross-sectional analysis showed that participants with elevated PRAL values experienced notably more depressive symptoms alongside poorer sleep quality. We likewise found that higher PRAL scores corresponded to increased stress and diminished mood, yet these relationships lost significance once all confounding factors were accounted for. Similarly, higher NEAP scores were linked to poorer sleep quality, and initial associations with higher stress, greater depressive symptoms, and lower mood were no longer significant following comprehensive adjustment in model 2. Additionally, higher DAL scores were directly associated with greater stress and poorer sleep quality, while the initial association with depressive symptoms was attenuated after adjustment for confounding variables. To date, no research has thoroughly explored how overall dietary acid load, quantified by PRAL, NEAP, and DAL, relates to depression, anxiety, stress, sleep quality, and mood in an elderly population.
The existing body of literature presents a diverse range of findings regarding the relationship between dietary acid load and mental health outcomes. While few studies have failed to demonstrate a significant relationship between dietary acid load and disorders such as depression and anxiety [45–47], the majority have indicated that increased dietary acid load is positively correlated with a higher risk of mental health issues [17–20, 46]. In a cross-sectional investigation, Bahari and colleagues assessed adults aged 35 to 65 to determine how dietary acid load relates to both depression and anxiety. They estimated participants’ dietary acid load using two indices, PRAL and DAL. The results indicated that PRAL did not significantly correlate with depression or anxiety, nor did DAL show any link with depression. Nonetheless, they reported a significant positive association between DAL scores and anxiety levels [47]. In contrast, another Iranian cross-sectional study found that higher PRAL, NEAP, and DAL values each showed a significant positive correlation with both depression and stress [19]. The discrepancy between their findings and ours may be attributed to differences in adjustments for caffeine intake.
Caffeine, a widely consumed neuroactive compound, is known to influence mental health and cognitive function [48] and In contrast, previous studies, such as the cross-sectional analysis by Bahari et al., did not adjust for caffeine intake, despite evidence suggesting that caffeine can influence psychological disorders, sleep quality, and mood, as well as being associated with dietary acid load [44, 49, 50]. Consequently, caffeine could act as a confounding variable in the relationship between dietary acid load and psychological outcomes, and the lack of adjustment for this factor in previous analyses may explain some of the observed differences in findings. Our study’s initial models (crude and Model 1) revealed significant associations between the NEAP and DAL indices and depression. However, after full adjustment (including caffeine), these associations attenuated and lost statistical significance, with only PRAL maintaining a direct relationship with depressive symptoms. These discrepancies may, at least in part, be attributed to differences in statistical control for confounders, particularly caffeine, which itself may mediate or mask diet-related effects on mental health. A long-term cohort study on breast cancer survivors with over seven years of follow-up found no significant association between NEAP and depression, while a direct relationship was observed between PRAL and depressive symptoms [46] These findings are consistent with our results, reinforcing the potential role of PRAL as a relevant dietary acid load index with depression. Considering that most studies, including ours, reveal significant associations between specific dietary acid load indicators and at least one Psychological disorder, understanding the mechanisms by which lower acid load influences mental health becomes critically important.
A possible mechanism is that diets with higher acid loads are often rich in meat, processed foods, and low-fiber products [16, 51], which cause systemic inflammation by elevating inflammatory markers such as interleukin-6 (IL-6), and C-reactive protein (CRP) and tumor necrosis factor-alpha (TNF-α([52, 53]. This inflammation may compromise the blood-brain barrier, allowing peripheral immune cells and cytokines to infiltrate the brain and activate microglia and other glial cells. The resulting neuroinflammation can induce neurotoxicity, dysregulate neurotransmitter systems, and disrupt neural circuits responsible for mood regulation, all of which are linked to the onset of depression and stress [54–56].In line with previous research [17, 20], our study found that a higher dietary acid load was associated with decreased sleep quality, which may, in turn, be related to increased systemic inflammation [57]. Inflammatory cytokines such as TNF-α, which are elevated during systemic inflammation, can interfere with brain regions involved in sleep regulation. These molecules disrupt the architecture of non-rapid eye movement (NREM) and rapid eye movement (REM) sleep, particularly by reducing the duration of restorative REM sleep, ultimately leading to decreased overall sleep quality [58]. Additionally, elevated dietary acid load may contribute to depression and disrupted sleep quality through its association with heightened cortisol levels [59–62]. Cortisol, a key stress hormone, is known to influence serotonin (5-HT) function in the brain, an essential neurotransmitter for mood regulation. Impairments in serotonin activity are strongly linked to both the onset and severity of depressive disorders [63, 64]. Additionally, elevated cortisol levels can stimulate inflammatory pathways, potentially exacerbating depressive symptoms [65]. The impact of cortisol on sleep is also notable; it follows a circadian rhythm, decreasing during the evening and peaking before morning waking. Deviations in this rhythmic cycle, such as persistently high cortisol levels at night or reduced fluctuations, are often correlated with poorer perceived sleep quality, particularly in older populations [66]. Our findings did not reveal any significant association between dietary acid load and anxiety and mood. Whereas earlier studies have identified a direct link between dietary acid load and stress [17–19], to our knowledge, no study has specifically explored this relationship in relation to mood. One possible explanation for this discrepancy is the inclusion of caffeine intake as a confounding variable in our analysis, as caffeine consumption can influence mental health outcomes. Notably, prior studies did not adjust for caffeine as a potential confounder in examining the relationship between dietary acid load and anxiety [17, 19], which may explain discrepancies in findings. In our analysis, significant associations between PRAL and NEAP and mood were observed in the crude and Model 1 analyses; however, these associations lost significance after full adjustment, likely reflecting the impact of caffeine included in our fully adjusted model due to its known effects on psychological outcomes. Additionally, differences in participants’ ages between our study and others may partly account for the variation in findings.
As far as we are aware, no previous research has investigated the association between dietary acid load and mood while simultaneously employing three distinct indices for its quantification. Additionally, A validated FFQ was used to evaluate participants’ dietary intake, ensuring reliable calculations of acid load. Another notable strength of the study is the exclusion of participants with chronic diseases or medication use, as these factors could potentially influence the outcomes. Despite the study’s novelty and strengths, several limitations must be noted. The cross-sectional design precludes conclusions about causality. The reliance on participant memory in the FFQ assessment introduces the possibility of recall bias, particularly among older adults due to age-related declines in memory. Although extensive covariate adjustment was performed, the possibility of residual confounding due to unmeasured or unknown factors cannot be entirely excluded. Furthermore, as the sample was restricted to older adults from Tehran and individuals with chronic diseases were excluded, the generalizability of these findings to other populations is limited. Considering the limitations of this study, advancing our understanding of the link between dietary acid load and different aspects of mental health will require further high-quality prospective and interventional investigations.
Conclusion
This study indicates that a higher DAL score is associated with a greater risk of stress, while a higher PRAL score is linked to an increased risk of depression. Furthermore, all indices of dietary acid load consistently demonstrated that greater acid load was linked with poorer sleep quality. Considering that dietary acid load is a modifiable nutritional factor associated with mental health and sleep quality in older adults, the present findings highlight the potential for including dietary acid load in clinical and dietary strategies. Therefore, clinicians and nutritionists may consider dietary acid load when developing individualized interventions to support psychological and sleep health in this population.
Acknowledgements
We sincerely appreciate the valuable participation and cooperation of all the elderly individuals who took part in this study.
Author contributions
LA and MMM conceived and designed the study.PNH and HA contributed to data collection. MMM and NAH prepared the initial draft of the manuscript. HA conducted the data analysis and contributed to the interpretation of the results. LA and MMM critically revised the manuscript for important intellectual content. LA provided overall supervision of the project. All authors reviewed and approved the final version of the manuscript.
Funding
This research was supported by Tehran University of Medical Sciences, Tehran, Iran (Grant No. 1404-1-212-90485).
Data availability
The data supporting the findings of this study are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
The study protocol received ethical approval from the Research Ethics Committee of Tehran University of Medical Sciences. All procedures adhered to the Declaration of Helsinki. Written informed consent was obtained before participation.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Mohammadmatin Mahjourian and Hanieh Abbasi contributed equally as frist authors.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data supporting the findings of this study are available from the corresponding author upon reasonable request.