Abstract
Background
Emerging evidence indicates that dietary acid load (DAL) may influence metabolic health, including renal and hepatic functions. However, studies explicitly examining the relationship between DAL and biomarkers of renal and hepatic functions among morbidly obese individuals undergoing bariatric surgery are scarce. This study aimed to investigate associations between dietary acid load indices, Potential Renal Acid Load (PRAL), Net Endogenous Acid Production (NEAP), and DAL and renal (serum creatinine, eGFR) and hepatic biomarkers (ALT, AST, ALP) in this population.
Methods
In this cross-sectional study, we assessed the dietary intake of 369 morbidly obese patients awaiting bariatric surgery, with a mean age of 39.4 years (± 10.0) and a mean BMI of 45.5 kg/m² (± 6.1). A validated food frequency questionnaire was used for this assessment. We calculated dietary acid load indices and examined their associations with renal and hepatic biomarkers. Multiple linear regression models were employed for analysis, adjusting for age, sex, BMI, education, physical activity, smoking, type 2 diabetes, hypertension, triglycerides, HDL-C, total energy intake, saturated fat, and dietary fiber intake.
Results
Higher dietary acid load scores (PRAL, NEAP, DAL) were consistently associated with increased serum creatinine (P = 0.01). No significant associations were observed with eGFR. Elevated dietary acid load indices were also significantly correlated with higher ALT (P < 0.05) and AST (P = 0.01) levels, suggesting hepatocellular dysfunction. No significant associations were found between ALP and dietary acid load indices.
Conclusion
Higher dietary acid load is associated with impaired renal and hepatocellular biomarkers among morbidly obese individuals preparing for bariatric surgery. Reducing the dietary acid load may be an effective nutritional strategy to optimize preoperative renal and hepatic health. Prospective intervention studies are warranted to confirm these associations and elucidate the underlying mechanisms.
Supplementary Information
The online version contains supplementary material available at 10.1186/s40795-025-01216-w.
Keywords: Dietary acid load, Renal biomarker, Hepatic biomarker, Bariatric surgery candidates
Introduction
Obesity has become a critical global health challenge, with its prevalence rising relentlessly over recent decades [1]. Morbid obesity which is clinically defined as a body mass index (BMI) ≥ 40 kg/m² or ≥ 35 kg/m² with obesity-related comorbidities, is associated with severe health consequences, including an elevated risk of cardiovascular disease (CVD), type 2 diabetes mellitus (T2DM), dyslipidemia, non-alcoholic fatty liver disease (NAFLD), and chronic kidney disease (CKD) [2],. These comorbidities contribute not only to elevated morbidity and mortality but also impose significant economic burdens on healthcare systems worldwide [3]. While bariatric surgery remains the most effective long-term intervention for sustained weight loss and metabolic improvement [4], emerging evidence underscores the importance of preoperative nutritional status in optimizing perioperative outcomes, reducing surgical complications, and enhancing long-term metabolic benefits [5, 6].
A growing body of research implicates chronic low-grade metabolic acidosis driven by dietary factors in the pathogenesis of obesity-related metabolic disorders, including T2DM, CVD, NAFLD, and CKD [7–11]. Western dietary patterns, characterized by excessive consumption of animal proteins, processed foods, and refined grains alongside insufficient intake of fruits and vegetables, are strongly linked to systemic acid-base imbalances [12–14]. Such diets may exacerbate metabolic acidosis, as evidenced by reduced urinary pH and increased calcium excretion in obese individuals [15]. Chronic acidogenic diets are hypothesized to promote adipogenesis and metabolic dysfunction through mechanisms involving organic acid accumulation and altered fatty acid oxidation [7, 16]. Conversely, diets rich in alkaline-forming plant foods may mitigate acidosis, potentially improving metabolic health and weight regulation [16, 17]. Notably, interindividual variations in endogenous acid production can differ more than tenfold based on dietary intake alone [12].
Dietary acid load (DAL), quantified via validated indices such as potential renal acid load (PRAL) and net endogenous acid production (NEAP), provides a robust measure of acidogenic dietary patterns. These indices, calculated from intakes of protein, potassium, calcium, phosphorus, and magnesium correlate strongly with urinary acid excretion and serve as reliable biomarkers of systemic acid-base status [18–20]. PRAL and NEAP have been extensively validated through direct correlation with urinary acid-base excretion measured over 24 h, thus establishing their reliability as indicators of diet-induced acid-base imbalance [19, 21]. Despite compelling associations between DAL and metabolic dysfunction, limited research has explored its specific effects on renal and hepatic biomarkers in morbidly obese patients undergoing bariatric surgery. Addressing this gap could inform preoperative dietary strategies to improve organ function and postoperative outcomes.
This study aims to investigate the association between DAL indices (PRAL and NEAP) and biomarkers of renal function (serum creatinine, estimated glomerular filtration rate [eGFR]) and hepatic function (alanine aminotransferase [ALT], aspartate aminotransferase [AST], alkaline phosphatase [ALP]) in morbidly obese candidates for bariatric surgery. We hypothesize that elevated DAL is associated with adverse renal and hepatic biomarker profiles and that dietary interventions to reduce acid load may optimize preoperative health in this population.
Methods
Study design and participants
This cross-sectional study was conducted from September 2023 to September 2024 at the obesity clinic of Hazrat Rasul Akram Hospital. Participants were recruited via convenience sampling from adults referred for bariatric surgery evaluation. The minimum sample size was determined using G*Power software (version 3.1.9.7) based on the following parameters: α = 0.05, power (1-β) = 0.80, effect size = 1.56 (derived from a prior study reporting a mean DAL of 37.12 ± 15.12 in a comparable population) [11]. This calculation yielded a required sample size of 358 participants.
Inclusion and exclusion criteria
Eligible participants met the following criteria: (1) Aged 18–65 years. (2) Absence of diagnosed renal, hepatic (e.g., Wilson’s disease, autoimmune hepatitis, viral hepatitis, alcoholic fatty liver disease), or malignant conditions, confirmed through clinical evaluation and medical history review. (3) Non-pregnant and non-lactating status at recruitment.
Exclusion criteria included: (1) Incomplete dietary or descriptive questionnaires (< 90% completion). (2) Missing critical biochemical or anthropometric data.3) Reported energy intake exceeding ± 3 standard deviations from the sample mean, indicating implausible dietary reporting.
Ethical considerations
The study protocol adhered to the ethical guidelines of the Declaration of Helsinki and was approved by the Ethics Committee of the Islamic Azad University, Science and Research Branch (Approval ID: IR.IAU.SRB.REC.1403.544). Written informed consent was obtained from all participants before enrollment.
Non-dietary measurement
Anthropometric measurements were conducted by trained nutritionists following standardized protocols. Body weight was measured to the nearest 0.1 kg using a calibrated digital scale (Seca, Hamburg, Germany) with participants wearing minimal clothing. Height was recorded barefoot to the nearest 0.5 cm using a wall-mounted stadiometer. Waist circumference was measured at the midpoint between the lower rib margin and the iliac crest with a non-stretchable tape. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m²).
Blood pressure measurements were obtained using a mercury sphygmomanometer calibrated according to National Institute of Standards and Technology (NIST) guidelines. After a 5-minute rest, systolic and diastolic blood pressure (SBP/DBP) were measured twice on the right arm at 15-minute intervals, with the average of the two readings recorded.
Socioeconomic status (SES) was assessed using self-reported data on the ownership of nine household assets, including a home, car, dishwasher, washing machine, LCD television, refrigerator, woven carpet, laptop computer, and microwave. Participants possessing fewer than three of these items were categorized as having low SES, those with four to six items as moderate SES, and those with more than seven items as high SES [11, 22].
Fasting blood samples were collected between 7:00–9:00 AM after a 12–14-hour overnight fast. Samples were centrifuged at 3000 rpm for 10 min within 30–45 min of collection to separate serum. Biochemical analyses were performed immediately using an automated analyzer (Selectra E, Vital Scientific, Netherlands) under standardized laboratory conditions. Fasting blood glucose (FBG) was quantified via the glucose oxidase method. Serum triglycerides (TG), total cholesterol (TC), and high-density lipoprotein cholesterol (HDL-C) were enzymatically measured using glycerol phosphate oxidase-peroxidase and cholesterol esterase/oxidase assays, respectively. Low-density lipoprotein cholesterol (LDL-C) was calculated using the Friedewald equation (applicable for TG < 400 mg/dL) [23]. ALT, AST, and ALP were assessed via kinetic and colorimetric methods (Pars Azmun kits, Tehran, Iran). Serum creatinine was measured using the modified Jaffe method, and the estimated glomerular filtration rate (eGFR) was calculated via the Modification of Diet in Renal Disease (MDRD) equation [24]. Internal quality controls were implemented for analytical accuracy.
Dietary assessment and acid load calculation
Dietary intake was evaluated using a validated 147-item semi-quantitative food frequency questionnaire (FFQ) [25, 26]. Dietary acid load indices were computed as follows:
PRAL (mEq/day) = (0.4888 × protein [g/day]) + (0.0366 × phosphorus [mg/day]) − (0.0205 × potassium [mg/day]) − (0.0125 × calcium [mg/day]) − (0.0263 × magnesium [mg/day]) [18, 20].
NEAP (mEq/day) = [54.5 × (protein [g/day]/potassium [mEq/day])] − 10.2 [21].
DAL (mEq/day) = [(body surface area [BSA; m²] × 41)/1.73 m²] + PRAL, where BSA was calculated using the Du Bois formula (Du Bois formula: [0.007184 × height0.725 × weight0.425]) [20, 27, 28].
Statistical analysis
Baseline characteristics and dietary intakes of participants were summarized according to quartiles DAL using means ± standard deviations for normally distributed continuous variables, medians (interquartile ranges) for skewed variables, and frequencies (percentages) for categorical variables. Trends across quartiles were evaluated using linear regression for continuous variables and the chi-square test for categorical variables.
Multiple linear regression models evaluated associations between acid load indices (PRAL, NEAP, DAL), as a continuous variable, and renal/hepatic biomarkers. Covariates were selected based on a combination of biological plausibility, empirical associations observed in prior epidemiological studies, and established roles as potential confounders in the relationship between dietary acid load and metabolic outcomes [7, 11, 14, 29]. Three models were constructed:
Model 1: Unadjusted.
Model 2: Adjusted for age and sex (with the exception of eGFR, for which age and sex were not included since they are integral components of its calculation to avoid redundancy).
Model 3: Further adjusted for WC, educational level, SES, PA, total cholesterol, total energy intake, saturated fatty acid intake, and total sugar intake (for eGFR, Model 3 excluded age and sex but retained the other covariates).
Statistical significance was set at p < 0.05. Analyses were performed using SPSS v26 (IBM Corp., Armonk, NY).
Results
Of 376 initially enrolled participants, two were omitted due to incomplete dietary data and five due to extreme dietary reporting (over-/under-reporting), resulting in a final analytical sample of 369. The participants had a mean age of 39.4 years (± 10.0) and a mean BMI of 45.5 (± 6.1), with 12.7% of participants being male.
Participant characteristics
The demographic, anthropometric, lifestyle, and clinical characteristics of the study participants according to quartiles of DAL are presented in Table 1. No significant differences were observed in age, sex distribution, or most anthropometric indices across quartiles, although a modest increasing trend in height was noted. The proportion of participants reporting no physical activity was highest in Q2 (25.8%) but decreased consistently thereafter, reaching 10.0% in Q4 (p-trend = 0.001). Other lifestyle factors, including smoking status, educational attainment, and socioeconomic status, showed no significant trends across quartiles (all p-values < 0.05).
Table 1.
Lifestyle and characteristics of participants according to the quartile of dietary acid loada
| Q1 (n = 92) | Q2 (n = 93) | Q3 (n = 94) | Q4 (n = 90) | P-valueb | |
|---|---|---|---|---|---|
| Age (year) | 40.5 ± 9.6 | 39.8 ± 9.2 | 38.8 ± 10.9 | 38.3 ± 10.3 | 0.109 |
| Men (%) | 11 (12.0) | 11 (11.8) | 11 (11.7) | 14 (15.6) | 0.837 |
| Weight (kg) | 121.5 ± 19.6 | 120.0 ± 22.3 | 122.3 ± 19.3 | 122.9 ± 21.9 | 0.506 |
| Height (cm) | 162.0 ± 7.8 | 162.9 ± 8.2 | 163.7 ± 8.7 | 164.6 ± 8.8 | 0.028 |
| Body mass index (Kg/m2) | 46.1 ± 5.8 | 45.0 ± 6.3 | 45.5 ± 5.6 | 45.5 ± 6.6 | 0.415 |
| Waist circumference (cm) | 117.8 ± 11.2 | 115.0 ± 12.8 | 117.7 ± 12.5 | 118.3 ± 14.3 | 0.482 |
| Current smoker (%) | 23 (25.0) | 28 (30.1) | 22 (23.4) | 22 (24.4) | 0.729 |
| Academic education (%) | 22 (23.9) | 28 (30.1) | 27 (28.7) | 22 (24.4) | 0.879 |
| Economic Status | |||||
| Low | 13 (14.1) | 15 (16.1) | 13 (13.8) | 10 (11.1) | 0.928 |
| Moderate | 74 (80.4) | 75 (80.6) | 78 (83.0) | 77 (85.6) | |
| High | 5 (5.4) | 3 (3.2) | 3 (3.2) | 3 (3.3) | |
| Physical activity level | |||||
| No Physical activity | 17 (18.5) | 24 (25.8) | 16 (17.0) | 9 (10.0) | 0.001 |
| Low | 53 (57.6) | 53 (57.0) | 67 (71.3) | 74 (82.2) | |
| Moderate | 19 (20.7) | 13 (14.0) | 5 (5.3) | 5 (5.6) | |
| High | 3 (3.3) | 3 (3.2) | 6 (6.4) | 2 (2.2) | |
| Type 2 diabetes mellitus (%) | 9 (9.8) | 2 (2.2) | 10 (10.6) | 8 (8.9) | 0.121 |
| Hypertension (%) | 24 (26.1) | 12 (12.9) | 16 (17.0) | 14 (15.6) | 0.104 |
| SBP (mmHg) | 124 ± 23 | 122 ± 16 | 123 ± 17 | 124 ± 17 | 0.947 |
| DBP (mmHg) | 85 ± 15 | 82 ± 16 | 87 ± 11 | 88 ± 10 | 0.106 |
| FBS (mg/dl) | 104 ± 25 | 104 ± 26 | 108 ± 24 | 107 ± 29 | 0.329 |
| TG (mg/dl) | 152 ± 77 | 163 ± 98 | 150 ± 72 | 140 ± 50 | 0.161 |
| Chol (mg/dl) | 184 ± 34 | 191 ± 40 | 190 ± 36 | 196 ± 35 | 0.04 |
| HDL (mg/dl) | 45 ± 9 | 45 ± 13 | 44 ± 9 | 44 ± 8 | 0.709 |
| LDL (mg/dl) | 114 ± 26 | 118 ± 32 | 118 ± 32 | 121 ± 25 | 0.178 |
| ALT (U/L) | 25 ± 13 | 29 ± 18 | 32 ± 16 | 36 ± 17 | 0.002 |
| AST (U/L) | 23 ± 10 | 27 ± 18 | 29 ± 18 | 30 ± 11 | 0.002 |
| ALP (U/L) | 188 ± 53 | 205 ± 55 | 204 ± 66 | 198 ± 60 | 0.255 |
| Creatinine (mg/dl) | 0.88 ± 0.15 | 0.89 ± 0.12 | 0.92 ± 0.14 | 0.95 ± 0.12 | 0.004 |
| eGFR (ml/min/1.73m2) | 113 ± 27 | 110 ± 31 | 113 ± 31 | 107 ± 27 | 0.355 |
Abbreviations: FBS Fasting blood sugar, TG Triglyceride, HDL High-density lipoprotein, LDL Low-density lipoprotein, ALT Alanine aminotransferase, AST Aspartate aminotransferase, ALP Alkaline phosphatase, eGFR Estimated glomerular filtration rate
a Data are presented as mean ± standard deviation for continuous variables and number (percent) for non-continuous variables
b P-values were derived from the linear regression and Chi-square analyses to test the trend of continuous and categorical variables, respectively, according to quartiles of dietary acid load
Significant p-values are highlighted in bold
Biochemical and clinical measures demonstrated several significant trends across DAL quartiles. TC increased across quartiles (p = 0.040). Both ALT and AST showed significant positive trends (p = 0.002 for both), while ALP did not exhibit a significant difference across quartiles (p = 0.255). Regarding renal markers, serum creatinine increased significantly with DAL (p = 0.004), whereas eGFR showed a non-significant decreasing trend (p = 0.355).
Dietary intake across quartiles of DAL
Dietary intake according to quartiles of DAL is summarized in Table 2. Total energy intake showed fluctuations across quartiles, decreasing from Q1 (2960 ± 1071 kcal) to Q2 (2646 ± 1105 kcal) and Q3 (2561 ± 852 kcal), before rising again in Q4 (3024 ± 1045 kcal). This non-linear pattern was not statistically significant (p-value = 0.859). The macronutrient distribution remained relatively stable, with no significant trends for protein, carbohydrate, or fat expressed as a percentage of total energy intake (all p-values > 0.05).
Table 2.
Dietary intakes of participants according to the quartile of dietary acid loada
| Q1 | Q2 | Q3 | Q4 | P-valueb | |
|---|---|---|---|---|---|
| Total energy intake (kcal) | 2960 ± 1071 | 2646 ± 1105 | 2561 ± 852 | 3024 ± 1045 | 0.859 |
| Protein (% of energy) | 14.4 ± 2.4 | 14.2 ± 2.4 | 13.8 ± 2.1 | 14.3 ± 2.1 | 0.368 |
| Carbohydrate (% of energy) | 59.9 ± 4.9 | 57.7 ± 6.3 | 57.9 ± 5.9 | 58.3 ± 6.2 | 0.732 |
| Fat (% of energy) | 28.9 ± 5.0 | 30.7 ± 6.0 | 29.8 ± 6.6 | 28.9 ± 5.5 | 0.099 |
| Whole grain (serving/day) | 4.2 ± 3.2 | 4.1 ± 4.8 | 4.6 ± 4.3 | 8.3 ± 6.0 | < 0.001 |
| Refined grain (serving/day) | 6.2 ± 3.7 | 6.1 ± 4.1 | 7.8 ± 5.3 | 7.5 ± 5.4 | < 0.001 |
| Red and processed meat (serving/day) | 1.3 ± 0.9 | 1.2 ± 0.9 | 1.3 ± 1.1 | 1.4 ± 1.3 | 0.436 |
| Poultry (serving/day) | 1.4 ± 1.1 | 1.3 ± 1.0 | 1.2 ± 1.2 | 1.7 ± 1.2 | 0.124 |
| Egg (serving/day) | 0.76 (0.50–1.20) | 0.76 (0.50–1.27) | 0.76 (0.50–1.01) | 0.76 (0.50–1.27) | 0.102 |
| Fish (serving/day) | 0.22 (0.11–0.45) | 0.21 (0.10–0.47) | 0.18 (0.06–0.31) | 0.21 (0.10–0.43) | 0.276 |
| Dairy (serving/day) | 1.8 ± 1.2 | 1.7 ± 1.2 | 1.4 ± 1.0 | 1.5 ± 1.0 | 0.016 |
| Legumes (serving/day) | 0.57 ± 0.46 | 0.39 ± 0.29 | 0.39 ± 0.28 | 0.33 ± 0.32 | < 0.001 |
| Nuts (serving/day) | 0.55 ± 0.62 | 0.49 ± 0.73 | 0.33 ± 0.49 | 0.26 ± 0.32 | < 0.001 |
| Fruits (serving/day) | 4.6 ± 2.8 | 3.4 ± 2.1 | 2.2 ± 1.3 | 1.9 ± 1.1 | < 0.001 |
| Vegetables (serving/day) | 6.9 ± 3.5 | 4.2 ± 2.2 | 3.2 ± 1.3 | 2.5 ± 1.3 | < 0.001 |
| Total sugar (serving/day) | 6.7 (2.7–11.2) | 5.9 (3.2–10.7) | 6.1 (2.5–10.8) | 5.8 (2.7–13.9) | 0.648 |
| SFA (% of energy) | 8.77 ± 2.23 | 9.59 ± 2.54 | 9.64 ± 2.21 | 9.62 ± 2.66 | 0.023 |
| Calcium (mg per 1000 kcal) | 662.8 ± 222.0 | 548.1 ± 177.1 | 484.0 ± 123.7 | 417.5 ± 111.3 | < 0.001 |
| Magnesium (mg per 1000 kcal) | 209.5 ± 38.9 | 183.2 ± 36.7 | 166.8 ± 37.4 | 174.0 ± 39.8 | < 0.001 |
| Potassium (mg per 1000 kcal) | 2273.5 ± 493.1 | 1808.1 ± 362.5 | 1453.6 ± 248.4 | 1237.1 ± 202.3 | < 0.001 |
| Total fiber intake (gram per 1000 kcal) | 23.2 ± 7.6 | 19.4 ± 6.4 | 20.7 ± 10.0 | 17.4 ± 8.5 | < 0.001 |
Abbreviations: SFA Saturated fatty acid
a Results are reported as mean ± standard deviation or median (quartiles 25–75)
b P-values were derived from linear regression analyses to test the trend of continuous variables, according to quartiles of dietary acid load.
Distinct trends were observed for food group consumption. Whole grain and refined grain intakes both demonstrated significant increasing trends (p-value < 0.001). Intake of red and processed meats, poultry, eggs, and fish showed no significant trends (all p-values > 0.05). Dairy intake decreased significantly across quartiles (p = 0.016). Plant-based food groups, including legumes, nuts, fruits, and vegetables, all demonstrated significant inverse trends with DAL (all p-values < 0.05).
Nutrient density analyses showed that saturated fatty acid intake increased significantly across DAL quartiles (p-value = 0.023). Intakes of calcium, magnesium, potassium, and dietary fiber declined significantly with increasing DAL (all p-values < 0.05). Total sugar intake showed no significant trend (p = 0.648).
Association between DAL and liver and renal biomarkers
The associations between DAL and hepatic and renal biomarkers are presented in Table 3. In the crude model (Model 1), higher DAL was positively associated with ALT (B = 0.108, SE = 0.043, pp-value = 0.012) and AST (B = 0.079, SE = 0.027, p-value = 0.004). These associations remained statistically significant after adjustment for age and sex in Model 2, and for AST after full adjustment in Model 3 (B = 0.057, SE = 0.027, p-value = 0.034). The association with ALT was attenuated and became non-significant in Model 3 (p-value = 0.065). No significant associations were observed between DAL and ALT in any model.
Table 3.
The beta coefficient of the association between dietary acid load (mEq/day) and liver enzymes and kidney function biomarkersa
| B | SE | P-value | |
|---|---|---|---|
| Alanine Aminotransferase | |||
| Model 1 | 0.108 | 0.043 | 0.012 |
| Model 2 | 0.088 | 0.041 | 0.033 |
| Model 3 | 0.079 | 0.042 | 0.065 |
| Aspartate Aminotransferase | |||
| Model 1 | 0.079 | 0.027 | 0.004 |
| Model 2 | 0.068 | 0.026 | 0.011 |
| Model 3 | 0.057 | 0.027 | 0.034 |
| Alkaline phosphatase | |||
| Model 1 | 0.181 | 0.119 | 0.129 |
| Model 2 | 0.187 | 0.119 | 0.119 |
| Model 3 | 0.160 | 0.121 | 0.188 |
| Creatinine | |||
| Model 1 | 0.001 | 0.000 | 0.005 |
| Model 2 | 0.001 | 0.000 | 0.006 |
| Model 3 | 0.001 | 0.000 | 0.012 |
| Estimated glomerular filtration rate | |||
| Model 1 | −0.035 | 0.061 | 0.567 |
| Model 3 | −0.044 | 0.052 | 0.401 |
aResults are obtain from linear regression analysis
Model 1: Crude
Model 2: Adjusted for age and sex
Model 3: Additionally adjusted for waist circumference, educational level, economic status, physical activity, total cholesterol, total energy intake, saturated fatty acid intake, and total sugar intake (for eGFR, Model 3 excluded age and sex but retained the other covariates)
Significant p-values are highlighted in bold
For renal function parameters, DAL was positively associated with serum creatinine in the crude model (B = 0.001, SE = 0.000, p-value = 0.005), and the association persisted after full adjustment in Model 3 (B = 0.001, SE = 0.000, p-value = 0.012). In contrast, no significant associations were observed between DAL and eGFR. The crude model (Model 1: B = − 0.035, SE = 0.061, p = 0.567) and the fully adjusted model (Model 3: B = − 0.044, SE = 0.052, p = 0.401) both yielded non-significant results. The associations between PRAL and NEAP with hepatic and renal biomarkers are presented in Supplementary Tables 1 and 2, respectively. For PRAL (Supplementary Table 1), higher PRAL was positively ALT and AST in crude models (Model 1) and following adjustment for age and sex (Model 2). In the fully adjusted model (Model 3), the association with AST remained statistically significant (B = 0.073, SE = 0.027, p-value = 0.008), whereas the association with ALT was attenuated and no longer reached statistical significance (p-value = 0.065). No significant associations were observed between PRAL and ALP across models. For renal outcomes, PRAL was positively associated with serum creatinine in all models (Model 3: B = 0.001, SE = 0.000, p = 0.013). However, no significant associations were observed between PRAL and eGFR in either the crude or adjusted models.
For NEAP (Supplementary Table 2), higher NEAP was consistently associated with elevated ALT and AST levels across all models. In fully adjusted models, associations remained statistically significant for ALT (B = 0.250, SE = 0.106, p = 0.019) and AST (B = 0.177, SE = 0.067, p = 0.008). NEAP was positively associated with serum creatinine in all models, with statistical significance persisting after full adjustment (B = 0.002, SE = 0.001, p = 0.019). No significant associations were observed between NEAP and eGFR in either crude or adjusted models.
Discussion
This study investigated the associations between dietary acid load indices and renal/hepatic biomarkers among morbidly obese patients scheduled for bariatric surgery. Our findings revealed significant positive correlations between higher dietary acid load and markers of renal dysfunction (elevated serum creatinine) and hepatic impairment (increased ALT and AST). In contrast, no significant associations were observed with eGFR or alkaline phosphatase.
In the present study, increasing DAL quartiles were not associated with significant differences in mean eGFR. Consistently, in multivariable linear regression models treating DAL as a continuous variable, no significant associations were observed between DAL and eGFR. By contrast, DAL was positively associated with serum creatinine (β = 0.001, p = 0.012) after full adjustment, a finding that was corroborated in analyses using PRAL and NEAP as alternative acid load indices. Taken together, these results suggest that higher dietary acid load may be more sensitively reflected by elevations in serum creatinine than by eGFR. Nevertheless, residual confounding cannot be excluded, as unmeasured nephrotoxic exposures (such as certain medications, dietary supplements, or environmental factors) may influence the observed relationship. Our results corroborate the meta-analysis by Mofrad et al. [8]. which demonstrated that elevated dietary acid load is significantly associated with an increased risk of chronic kidney disease (pooled risk ratio = 1.31; 95% CI: 1.06–1.62; p = 0.011) across nine observational studies. The authors further reported that higher DAL was associated with a significant reduction in urine pH (mean difference = − 0.47; 95% CI: − 0.85 to − 0.08; p = 0.017), supporting the hypothesis that chronic renal acid stress and reduced urinary buffering capacity may mediate the progression of kidney dysfunction. Similarly, Bahadoran et al. [30], demonstrated that higher PRAL was significantly associated with increased serum creatinine (β = 0.142, p < 0.001), and a higher protein-to-potassium ratio was also positively correlated with serum creatinine (β = 0.070, p < 0.01). These findings collectively position DAL as a modifiable risk factor, particularly in high-risk populations such as morbidly obese individuals awaiting bariatric surgery, who may already exhibit subclinical renal compromise.
Our findings of elevated ALT and AST levels associated with higher DAL suggest a plausible link between acidogenic diets and hepatocellular dysfunction in obese populations at risk of hepatic complications. However, current evidence reveals inconsistent and nonlinear relationships, underscoring the complexity of this association. For example, Emamat et al. observed a U-shaped relationship between dietary acid load and the risk of non-alcoholic fatty liver disease (NAFLD) in a case–control study. Participants in the third PRAL quintile, reflecting a near-neutral dietary acid–base status, had 54% lower odds of NAFLD compared with those in the lowest quintile (OR = 0.46; 95% CI: 0.24–0.89; p = 0.021). In contrast, the odds of NAFLD in the highest PRAL quintile were not significantly different from those in the lowest quintile (OR = 0.90; 95% CI: 0.41–1.57), suggesting that extreme acidic or alkaline dietary patterns may carry similar risks, and that a neutral acid–base status may be most favorable [10]. Similarly, Krupp et al. demonstrated that elevated dietary acid load during adolescence was positively associated with hepatic biomarkers in adulthood, particularly among females. Dietary PRAL during puberty was significantly associated with alanine aminotransferase (ALT) (β = 0.16, p = 0.01), hepatic steatosis index (HSI) (β = 0.00, p = 0.004), and fatty liver index (FLI) (β = 0.37, p = 0.01) in adulthood. Females in the highest tertile of adolescent PRAL had 3.5-, 4.4-, and 4.5-fold higher ALT, HSI, and FLI values, respectively, compared with those in the lowest tertile. These findings suggest possible gender-specific vulnerabilities and highlight the long-term metabolic consequences of acid–base imbalance, supporting the view that the relationship between dietary acid load and hepatic pathophysiology is shaped by complex interactions involving diet, sex, and genetic predisposition rather than a simple linear causal pathway [31].
Interestingly, although both ALT and AST levels increased progressively across DAL quartiles, they remained within normal clinical ranges across all groups, reflecting subclinical hepatocellular changes rather than overt liver damage. In contrast, ALP levels appeared moderately elevated across all quartiles but showed no significant trend with DAL. ALP is a non-specific enzyme produced not only by the liver but also by bone, intestine, and other tissues [32]. In the context of morbid obesity, persistently elevated ALP levels may reflect increased bone turnover, low-grade inflammation, vitamin D deficiency, or metabolic bone disease, all of which are more prevalent in individuals with severe obesity [33–36]. Given the lack of association between ALP and DAL in our regression models, these elevations are unlikely to reflect a diet-induced acid–base disturbance and may instead arise from non-hepatic, obesity-related factors. These findings underscore the complexity of interpreting ALP in cross-sectional studies and highlight the importance of considering extrahepatic sources when evaluating liver enzymes in obese populations.
The controversial and inconclusive nature of existing evidence highlights the need for rigorous investigations to clarify dietary acid load’s role in hepatic health. Prospective longitudinal studies with repeated dietary assessments, comprehensive metabolic profiling (e.g., biomarkers of hepatocyte injury, fibrosis, steatosis, cholestasis), and stratification by gender or genetic risk could elucidate nonlinear relationships and threshold effects. Additionally, randomized controlled trials testing dietary interventions to modulate acid load (e.g., increasing alkaline-forming foods) may establish causality and identify optimal acid-base ranges for hepatic health. Such research could inform tailored nutritional strategies for high-risk populations, including bariatric surgery candidates, to mitigate hepatic complications.
The associations between elevated dietary acid load and impaired renal/hepatic biomarkers may arise through multiple biologically plausible pathways: Primarily, diets high in acidogenic components (e.g., animal proteins, processed foods, refined grains) and low in alkaline-forming foods (e.g., fruits, vegetables) induce chronic low-grade metabolic acidosis [7, 14]. Persistent acid stress overwhelms renal compensatory mechanisms (e.g., bicarbonate regeneration, acid excretion), leading to sustained acidosis, reduced urine pH, and elevated urinary calcium excretion. These effects exacerbate renal injury, reduced eGFR, and impaired buffering capacity [8, 30]. Second, Chronic acidosis activates the renin-angiotensin-aldosterone system (RAAS) and elevates glucocorticoids (e.g., cortisol), disrupting insulin signaling pathways [29, 30, 37]. This cascade promotes insulin resistance, adipocyte dysfunction, and systemic inflammation, key contributors to hepatic lipid accumulation and oxidative stress. Also, acidosis impairs hepatic mitochondrial efficiency, reducing fatty acid oxidation and enhancing de novo lipogenesis. These alterations drive hepatic steatosis, inflammation, and elevated ALT/AST levels, key characteristics of obesity-related NAFLD [10, 31]. Additionally, acidogenic diets correlate with dyslipidemia, central obesity, and impaired glucose metabolism, components of metabolic syndrome that synergistically worsen hepatic and renal outcomes [7, 14, 30]. These pathways collectively link dietary acid load to the progression of renal and hepatic dysfunction, underscoring the need for dietary strategies that restore acid-base balance.
For morbidly obese patients awaiting bariatric surgery, dietary interventions targeting acid load reduction, such as increased consumption of alkaline-forming foods (e.g., fruits, vegetables) and reduced intake of acidogenic components (e.g., processed meats, refined grains), may serve as a strategic preoperative measure. Optimizing acid-base balance could attenuate metabolic syndrome severity, improve hepatic/renal biomarkers, and enhance postoperative outcomes. Future research should prioritize randomized trials to validate these interventions and define optimal dietary thresholds for this high-risk population.
While our study identified significant associations between dietary acid load and hepatocellular injury markers (ALT/AST), no statistically significant relationship emerged with alkaline phosphatase, an enzyme broadly expressed in tissues mediating biliary health, cholestasis, and bone metabolism. This divergence likely reflects ALP’s distinct physiological roles. Unlike ALT/AST, which are specific to hepatocellular injury, ALP activity is influenced by biliary obstruction, bone turnover, or hepatic infiltration processes less directly linked to acid-base disturbances. Chronic dietary acidity may primarily target pathways driving hepatocellular inflammation and steatosis, sparing the biliary or osteogenic pathways regulating ALP [32]. Existing literature provides limited insight into dietary acid load’s impact on ALP, as most studies focus on ALT/AST or composite NAFLD scores. The enzyme’s tissue-specific expression and multifunctional roles complicate interpretation, necessitating targeted investigations. Studies integrating ALP, gamma-glutamyl transferase (GGT), and bile acid profiles could clarify the tissue-specific effects of acidogenic diets. Controlled trials should explore how acid-base balance modulates bone metabolism (e.g., vitamin D, calcium homeostasis) and its secondary effects on ALP. Elucidate whether ALP’s insensitivity reflects compensatory mechanisms (e.g., bone-derived ALP buffering acidosis) or distinct pathogenic drivers.
These findings should be interpreted with caution in view of several contextual considerations. Foremost, the cross-sectional design of this study precludes establishing temporality or causality between DAL and the observed renal and hepatic biomarkers. The associations we observed may reflect pre-existing metabolic alterations that influence dietary behaviors, rather than effects of DAL itself. Additionally, the U-shaped distribution of several baseline characteristics across DAL quartiles suggests heterogeneity in dietary behaviors at both extremes. The lowest and highest DAL groups may include individuals intentionally modifying their diets for health reasons—such as adopting a plant-based low-DAL diet or a low-carbohydrate/high-protein high-DAL regimen—as well as individuals with persistently poor dietary quality at either extreme. Finally, given that all participants were candidates for bariatric surgery, recent dietary changes are likely. Some may have altered their diets voluntarily or in response to medical advice in preparation for surgery, while others may have remained on habitual diets. These factors, combined with the highly selected surgical population, complicate the interpretation of DAL–biomarker associations and reinforce the need for longitudinal and interventional research to clarify causality and better characterize the role of DAL in this clinical context.
In synthesizing the implications of our findings, it is essential to consider the broader scientific context regarding DAL [38]. DAL has emerged as an important nutritional construct with potential relevance across multiple domains of metabolic health [38]. Its public health importance is amplified by the predominance of Western dietary patterns—characterized by high intakes of animal protein, processed foods, and sodium—which typically result in elevated DAL [39]. While transient elevations in DAL can be effectively buffered, sustained high intake may contribute to low-grade metabolic acidosis, a state hypothesized to exacerbate cardiometabolic risk and potentially other non-communicable diseases [38]. Nonetheless, the majority of existing evidence is observational, and DAL often serves as a proxy for overall dietary quality rather than a direct causal factor [38]. The complexity of these relationships is further underscored by emerging data on the gut microbiota as a mediator of diet–host metabolic interactions, an area still insufficiently explored. Currently, interventional evidence remains limited, with only two ongoing registered clinical trials investigating the role of DAL in chronic kidney disease [38]. Longitudinal cohort studies and well-designed dietary interventions are therefore warranted to elucidate the independent metabolic effects of DAL and to refine nutritional strategies, particularly in high-risk populations such as bariatric surgery candidates.
Strengths and limitations
This study possesses several methodological and clinical strengths. The use of PRAL, NEAP, and DAL as multidimensional acid load assessments enhances robustness. Additionally, with high clinical relevance, the focus on bariatric candidates addresses a critical gap in preoperative metabolic optimization. Another notable strength includes the methodological rigor as standardized FFQ, controlled biochemical assays, and rigorous confounder adjustment strengthen validity.
However, this study is subject to several limitations. Firstly, given the cross-sectional nature of this study, causal relationships between dietary acid load and hepatic or renal biomarkers cannot be established. As such, the findings should not be interpreted as evidence for direct dietary recommendations but rather as hypothesis-generating observations that warrant confirmation in prospective and interventional studies. Soundly, the present was conducted in a highly selected group of individuals awaiting bariatric surgery. This population differs substantially from the general population in terms of obesity severity, metabolic status, and dietary behaviours. Such selection may introduce bias, limiting the external validity of the findings. Consequently, the associations between DAL and hepatic and renal biomarkers should be interpreted within the context of this specific clinical population, and confirmation in more diverse cohorts is warranted. In addition, dietary intake was assessed using a validated semi-quantitative FFQ. While this method is widely used in nutritional epidemiology, it is inherently prone to recall bias, response bias, and systematic misreporting. Participants may overestimate or underestimate the frequency and portion size of certain foods, particularly socially desirable or undesirable items. Estimation errors in portion sizes, seasonal variation in food availability, and memory limitations over the recall period may further contribute to measurement error. Although such misclassification is expected to be largely non-differential with respect to renal and hepatic biomarkers, it could attenuate the magnitude of true associations. These limitations should be considered when interpreting the results and highlight the importance of confirming our findings using more objective dietary assessment methods. Detailed data on alcohol consumption were not available due to cultural and religious restrictions in the study population. Consequently, potential residual confounding from alcohol intake cannot be excluded and should be considered when interpreting the findings. Future studies should integrate serum nutrients and urinary biomarkers (e.g., net acid excretion). Thirdly, the study’s population is homogeneous, and there is an overrepresentation of female participants (87.3%) and single-center recruitment limit generalizability. Lastly, despite rigorous statistical adjustments for numerous confounding factors, the possibility of residual or unmeasured confounding, such as genetic, epigenetic, or gut microbiota influences was not assessed. Future studies with broader population diversity, longitudinal designs, and objective dietary and biochemical markers are necessary to overcome these limitations.
Conclusion
This study highlights dietary acid load as a modifiable risk factor for renal and hepatic dysfunction in morbidly obese populations. While mechanisms involve acidosis-driven endocrine, inflammatory, and mitochondrial pathways, nonlinear associations (e.g., U-shaped NAFLD risk) and biomarker-specific findings (e.g., ALP insensitivity) underscore the complexity of these relationships. Addressing these gaps through targeted dietary interventions and mechanistic research could redefine preoperative care paradigms, offering a practical avenue to improve metabolic resilience in bariatric patients.
Supplementary Information
Acknowledgements
The authors express their appreciation to the participants of the study for their enthusiastic support and to the staff of the involved hospitals for their valuable help.
Authors' contributions
Overall, SAK and HA, supervised the project and approved the final version of the manuscript to be submitted. AN designed the research; AN analyzed and interpreted the data; YHK drafted the initial manuscript; and FSH critically revised the manuscript. All authors approved the final version of the manuscript submitted for publication.
Funding
No financial support was provided in any way for this research.
Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
The study involving humans was approved by the Ethics Committee at Islamic Azad University, Science and Research Branch (Approval ID: IR.IAU.SRB.REC.1403.544). The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and local institutional requirements. Written informed consent for participation was obtained from all participants prior to enrollment.
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.
Yasamin Hojabr Kalali, Seyed Ali Keshavarz and Hastimansooreh Ansar contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.