The association between dietary acid load index with tumor stage and size, histological grade, Ki-67 marker, and the odds of breast cancer: A case-control study based on hospital evidence

doi:10.21203/rs.3.rs-4873761/v1

Download PDF

Research Article

The association between dietary acid load index with tumor stage and size, histological grade, Ki-67 marker, and the odds of breast cancer: A case-control study based on hospital evidence

https://doi.org/10.21203/rs.3.rs-4873761/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Introduction:

Several observational and ecological studies have found a positive association between dietary acid load (DAL) and increased risk of chronic diseases. There is limited evidence suggesting that an acidic environment may promote the growth of certain cancer cells and tumors. Therefore, this study aims to determine the association between the DAL and Pathobiological markers of BC and the risk of BC in Iranian women.

Methods and Materials:

In this hospital-based case-control study, 155 female BC patients confirmed by histopathology by a physician (cases) and 155 controls apparently healthy women (controls) were selected using convenience sampling. The DAL was measured based on a 168-item Food Frequency Questionnaire (FFQ). The odds of BC (95% CI) according to potential renal acid load (PRAL), net endogenous acid production (NEAP), and net acid excretion (NAE) quartiles compared to the reference (OR = 1) were measured using logistic regression, with adjustment of confounding.

Results

Using linear regression, no significant association was observed between the PRAL, NEAP, and NAE with pathobiological markers of breast cancer such as tumor size and Ki-67 marker (p > 0.05). Using multivariate logistic regression models, the Odds ratios (95%CI) of BC according to the quartile of the PRAL, NEAP, and NAE showed that All indices did not predict the risk of BC before and after adjustment (p > 0.05).

Conclusions

According to findings, DAL did not predict the risk of breast cancer. High-quality prospective studies are necessary to further specify the relationship between DAL and the risk of BC among the Iranian population.

Breast cancer

Dietary acid load

net endogenous acid production

net acid excretion

potential renal acid load

Risk factors such as obesity, alcohol consumption, oral contraceptive use, hormone replacement therapy, smoking, and dietary patterns are associated with an increased risk of breast cancer (BC)[1–4]. Previous studies have examined dietary patterns as a modifiable factor in relation to the risk of BC. Studies have shown an inverse relationship between whole grains, legumes, fruits, and vegetables and the risk of BC[5, 6]. Furthermore, a positive correlation has been observed between the consumption of red meat, processed meats, and animal fat and the risk of developing BC[7, 8]. Proposed mechanisms for explaining the relationship between diet or dietary factors and the risk of developing breast cancer are extensive[8]. It is hypothesized that the dietary acid load (DAL) can influence the acid-base balance in the body[9]. DAL consists of two components, namely the potential renal acid load (PRAL) and the net endogenous acid production (NEAP). PRAL is measured based on the intake of five essential nutrients, including protein, potassium, phosphorus, magnesium, and calcium, and NEAP is estimated based on the protein to potassium ratio in the diet[10, 11]. In recent years, the impact of the Western diet on health and disease, particularly its effect on the body's acid-base balance, has received significant attention. Studies indicate that the Western acidogenic diet is associated with an increase in body hydrogen ion load[12]. An imbalance in body acidity can lead to the occurrence of metabolic acidosis, resulting in an increase in glucocorticoids levels. This can lead to changes in appetite, bone damage, insulin resistance, visceral obesity, dyslipidemia, hypertension, and muscle proteolysis in the body[13–16]. Several observational and ecological studies have found a positive association between DAL and increased risk of chronic diseases, including diabetes, insulin resistance, non-alcoholic fatty liver disease, hypertension, and cardiometabolic factors[17–21]. There is limited evidence suggesting that an acidic environment may promote the growth of certain cancer cells and tumors[22]. Chronic acidosis increases the risk of BC by increasing the insulin-like growth factor 1 (IGF-1)[23]. Limited information is available regarding the association of an acidic diet with the risk of developing BC and the available studies also have conflicting results[24, 25]. Therefore, considering the discrepancy in results and the fact that people in the Middle East consume fewer fruits and vegetables and higher amounts of refined grains, including white rice and bread[26], conducting further studies to demonstrate the association of DAL with BC within the framework of a case-control study among Iranian adult women appears necessary. Limited studies have been conducted in this field in Iran, and these studies have not found any association between DAL and the risk of BC[25, 27]. Pathobiological markers of BC such as tumor size and Ki-67 marker as well as net acid excretion (NAE) have also not been examined in these studies. Considering that environmental conditions, genetics, and dietary patterns vary in different regions, the effects of dietary patterns and nutritional indicators may differ in various populations. Therefore, this study aims to determine the association between the DAL and Pathobiological markers of BC and the risk of BC in Iranian women.

Study design and participants

In this hospital-based case-control study, 155 female BC patients confirmed by histopathology by a physician (newly diagnosed without receiving specific treatment and without changes in diet) were selected using convenience sampling. BC disease was confirmed by histopathology by a physician [28]. Based on the inclusion criteria, the patient group was recruited from Javad Al-Aemeh Clinic in Kerman City, Iran from September 2021 to March 2023. The control group consisted of 155 apparently healthy women who met the criteria and were selected from the same clinic. The control group was determined based on self-reported questionnaires and individuals' medical history. The control group was selected from a wide spectrum of non-neoplastic diseases, including orthopedic, eye, nose, ear, or skin disorders. Age (± 5) was matched between cases and control participants.

The inclusion criteria for participation in the study for cases were as follows: females within the age range of 18–70 years, confirmation BC by histopathology, willingness to cooperate, and individuals newly diagnosed without receiving specific treatment at the time of recruitment. The inclusion criteria for the control group in the study were as follows: females aged between 18–70 years, no history of BC or any other type of cancer, and Willingness to participate. The exclusion criteria for both groups (non-entry and withdrawal from the study), were as follows: individuals with cardiovascular, renal, hepatic, autoimmune diseases, and diabetes, individuals following specific diets such as vegetarianism, etc., pregnancy and lactation, psychological and social disorders or trauma, lack of willingness to complete questionnaires., and use of anti-inflammatory drugs and antioxidant supplements in the past six months. The research protocol was in accordance with the guidelines of the Declaration of Helsinki. The Ethics Committee in Research of Sirjan School of Medical Sciences approved the study protocol (Ethical code: IR.SIRUMS.REC.1402.011). The informed written consent form was completed for all subjects at the beginning of the study.

Calculation of sample size

Using (r + 1/r) SD² (z _β+z _α/2)²/ d² formula, according to Heidari et al.'s study[27] and considering the PRAL score as the main variable, the sample size was calculated as 155 subjects for each case and control group, resulting in a total of 310 subjects.

Measurements

Two questionnaires including demographic and physical activity questionnaires were applied. The demographic questionnaire involved the subjects’ sociodemographic information such as age, education status, status of employment, the status of marital, pregnancy history, breastfeeding history, duration of breastfeeding, age of first menstruation, number of abortions, consumption of alcoholic beverages, smoking, use of contraceptive pills, family history of cancer, menopause status. To evaluate physical activity (PA) the validated short form of the International Physical Activity Questionnaire (IPAQ) was used, and the results were reported as met-min/week [29].

In the current study, weight was measured using a digital scale made in Japan with an accuracy of 0.1 Kg, without shoes and with the least possible clothing. Height was estimated to the nearest 0.5 cm with a tape measure in a standing position, with shoulders in a normal alignment without shoes. BMI was calculated using the formula (weight in kilograms/height in meters squared)[30, 31]. A trained expert performed all measurements.

The Ki-67 marker was determined by immunohistochemistry (IHC). The ultrasound and a pathology test were applied to measure tumor size and grade of tumor, respectively.

The DAL was measured based on a 168-item Food Frequency Questionnaire (FFQ) that was fulfilled for each participant. FFQ was applied to collect participant food intake data over the past year [32]. Using the Iranian Home Scale Guide the reported values for each food item were converted to grams per day. Consequently, the determination of the amounts of energy, macronutrients, and micronutrients consumed by each participant was done with Nutritionist IV (NUT4) software. Acquired data by this software were essential for the calculation of PRAL, NEAP, and NAE.

PRAL estimates the specific intestinal absorption of protein, calcium, phosphate, potassium, and magnesium, in which negative values indicate alkali-forming potential and positive values indicate acid-forming potential[33].

PRAL, NEAP, and NAE were calculated with the following formula:

PRAL (mEq/d) = (protein [g/d] × 0.49) (P [mg/d] × 0.37) - (K [mg/d] × 0.021) - (Ca [mg/d] ×0.013) - (Mg [mg/d] ×0.026)[33]

Only two nutrients including protein and potassium intake are required for the calculation of NEAP.

NEAP (mEq/day) = 54.5 × [protein intake (g/d) /K intake (mEq/d)] − 10.2[34]

NAE was computed using the scores of PRAL, Organic acids (OA), and Body surface area (BSA).

OA (mEq/day) = BSA (m²) × 41 (mEq/day per 1:73m²)[35]

Body surface area (m²)[36] = 0.007184 × height (cm) ^0.725 × weight (kg) ^0.425

NAE (mEq/day) = PRAL + OA[33]

Statistical analysis

In this study, the normal distribution of the data was evaluated using the Kolmogorov-Smirnov statistical test. Dependent variables were divided into quartiles based on of PRAL, NEAP, and NAE, and the differences between the quartiles were evaluated using one-way analysis of variance (ANOVA) for quantitative variables (with LSD post hoc) and the chi-square test for categorical variables. The comparison of quantitative results between case and control groups was performed using the independent samples t-test. Similarly, the chi-square test was used to compare qualitative variables between the case and control groups. Linear regression, along with modeling by adjusting for confounding factors (for energy, age, PA, BMI, marital status, employment status, education, age of onset of menstruation, status of menopause, pregnancy history, number of pregnancies, number of children, number of abortions, history of breastfeeding, duration of breastfeeding, history of taking contraceptive pills, family history of cancer, smoking status, alcoholic beverages status, tumor stage, histological grade, metastasis status, estrogen receptor (ER) expression, progesterone receptor (PR) expression, and human epidermal growth factor receptor 2 (HER2) expression) was used to determine the relationship between the PRAL, NEAP, and NAE (independent variable) and tumor size and Ki-67 marker (dependent variables). The Correlation between the quartiles of PRAL, NEAP, and NAE indices with tumor stage and histological grade in the case group was assessed using the Pearson correlation test. Additionally, the odds of BC (95% CI) according to PRAL, NEAP, and NAE quartiles compared to the reference (OR = 1) were measured using logistic regression, with adjustment of confounding factors (energy, age, PA, BMI, marital status, employment status, education, age of onset of menstruation, status of menopause, pregnancy history, number of pregnancies, number of children, number of abortion, history of breastfeeding, duration of breastfeeding, history of taking contraceptive pills, family history of cancer, smoking status, and alcoholic beverages status.). In this study, a p-trend was calculated for each model to compare the ORs associated with quartiles in each model. SPSS software version 23 (IBM SPSS Statistics, Armonk, USA) was used for data analysis. A p-value of less than 0.05 was considered statistically significant.

The characteristics of two compared groups, the case and control groups, are presented in Table 1. As observed, the case group had a significantly higher mean BMI than the control group (p = 0.04). Furthermore, the two groups differed significantly in terms of educational status, marital status, history of pregnancy and breastfeeding, duration of breastfeeding, use of contraceptive pills, family history of cancer, and menopausal status (p for all < 0.001).

Table 1

Comparison of characteristics between two groups of case and control
Variable	Case group (N = 155)	Control group (N = 155)	P – value*
PRAL score	-1.29 ± 16.88	-0.75 ± 22.57	0.81
NEAP score	40.66 ± 10.84	42.51 ± 10.35	0.12
NAE score	10.29 ± 16.94	10.76 ± 23.09	0.83
Age (years)	46.70 ± 10.79	46.73 ± 11.25	0.97
Weight (kg)	69.88 ± 11.31	68.85 ± 12.67	0.45
BMI (kg/m²)	26.88 ± 4.08	25.92 ± 4.34	0.04
P.A (met-min/week)	812.77 ± 780.30	777.39 ± 769.04	0.68
Energy (kcal)	2365.19 ± 675.33	2283.27 ± 692.500	0.29
Protein intake (g)	84.80 ± 29.51	82.64 ± 23.70	0.48
Phosphorus (mg)	1461.29 ± 502.40	1438.30 ± 436.99	0.66
Magnesium (mg)	347.75 ± 125.38	355.00 ± 130.98	0.61
Calcium (mg)	931.46 ± 418.99	887.25 ± 332.56	0.30
Potassium (mg)	3608.05 ± 1193.01	3509.94 ± 1639.50	0.54
Education status, n (%)			< 0.001^a
Illiterate	17 (11.0)	2 (1.3)
Elementary	16 (10.3)	5 (3.2)
Middle school	18 (11.6)	17 (11.0)
High school and diploma	55 (35.5)	57 (36.8)
University	49 (31.6)	74 (47.7)
Status of employment, n (%)			0.38 ^a
Employed	36 (23.2)	45 (29.0)
Retired	5 (3.2)	7 (4.5)
Housewife	114 (73.5)	103 (66.5)
Status of marital, n (%)			< 0.001^a
Single	9 (5.8)	36 (23.2)
Married	122 (78.7)	103 (66.5)
Widow	18 (11.6)	4 (2.6)
Divorced	6 (3.9)	12 (7.7)
Pregnancy history, n (%)			< 0.001^a
Yes	143 (92.3)	108 (69.7)
No	12 (7.7)	47 (30.3)
Breastfeeding history, n (%)			< 0.001^a
Yes	136 (87.7)	102 (65.8)
No	19 (12.3)	53 (34.2)
Duration of breastfeeding (years)	4.31 ± 3.25	2.33 ± 2.67	< 0.001
Age of first menstruation (years)	13.75 ± 1.34	13.49 ± 1.54	0.11
Number of abortions, n (%)			0.06^a
0	101 (65.2)	124 (80.0)
1	33 (21.3)	18 (11.6)
2	16 (10.3)	9 (5.8)
3	4 (2.6)	3 (1.9)
5	1 (0.6)	1 (0.6)
Smoking, n (%)			0.20^a
Yes	15 (9.7)	9 (5.8)
No	140 (90.3)	146 (94.2)
Consumption of alcoholic beverages, n (%)			0.12^a
Consumer	8 (5.2)	3 (1.9)
Non-consumer	147 (94.8)	152 (98.1)
Use of contraceptive pills, n (%)			< 0.001^a
Yes	50 (32.3)	24 (15.5)
No	105 (67.7)	131 (84.5)
Family history of cancer, n (%)			< 0.001^a
Yes	41 (26.5)	10 (6.5)
No	114 (73.5)	145 (93.5)
Menopause status, n (%)			< 0.001^a
Post-menopause	53 (34.2)	25 (16.1)
Pre-menopause	102 (65.8)	130 (83.9)
Data are means ± SD for quantitative variables and frequency (percent) for qualitative variables.
*. From Independent t-test for quantitative variables, a. Chi-square for qualitative variables.
Abbreviation: PRAL: potential renal acid load, NEAP: net endogenous acid production, NAE: net acid excretion, BMI; body mass index, and PA: physical activity.

The mean age, BMI, levels of physical activity, and dietary intake of all participants according to quartiles of PRAL, NEAP, and NAE are shown in Table 2. Regarding PRAL, significant differences were observed among the quartiles in terms of energy intake (p = 0.004), protein (p < 0.001), P (p = 0.03), Mg (p < 0.001), and K (p < 0.001). As shown in Table 2, according to the NEAP, compared to individuals in quartile 4, those in quartile 1 had significantly higher intake of Mg (p = 0.004), Ca (p < 0.001), and K (p < 0.001). Moreover, according to NAE, compared to individuals in quartile 4, those in quartile 1 had significantly lower values for BMI (p = 0.04) and higher intake of energy (p = 0.002), protein (p < 0.001), P (p = 0.004), Mg (p < 0.001), Ca (p = 0.01), and K (p < 0.001).

Table 2

Characteristic of study participants according to quartiles of PRAL, NEAP, and NAE
	Q1 (N = 77)	Q2 (N = 78)	Q3 (N = 78)	Q4 (N = 77)	Total (N = 310)	P – value*
PRAL score	-21.80 ± 27.15^a,b,c	-2.86 ± 2.28^d,e	4.30 ± 2.06^f	16.22 ± 9.57	-1.02 ± 19.90	< 0.001
Age (years)	44.87 ± 11.64	48.05 ± 11.12	47.26 ± 9.71	46.56 ± 11.20	46.69 ± 10.95	0.31
BMI (kg/m²)	25.85 ± 4.46	26.11 ± 4.03	27.35 ± 4.27	26.28 ± 4.08	26.40 ± 4.23	0.12
P.A (met-min/week)	652.36 ± 668.70	851.87 ± 826.09	857.25 ± 710.89	817.29 ± 868.58	795.08 ± 773.64	0.30
Energy (kcal)	2554.83 ± 752.70^a,b,c	2180.33 ± 589.77	2258.46 ± 680.18	2306.02 ± 659.63	2324.23 ± 684.09	0.004
Protein intake (g)	84.82 ± 27.39^a,c	75.42 ± 21.59^e	80.52 ± 23.40^f	94.29 ± 30.59	83.73 ± 26.74	< 0.001
Phosphorus (mg)	1534.96 ± 512.04^a	1346.17 ± 394.94^e	1405.57 ± 429.18	1514.40 ± 517.21	1449.80 ± 470.21	0.03
Magnesium (mg)	399.87 ± 151.13^a,b,c	328.83 ± 107.36	330.69 ± 113.57	346.66 ± 125.21	351.37 ± 128.05	0.001
Calcium (mg)	1002.46 ± 422.21^a,c	857.41 ± 300.15	897.50 ± 355.39	880.89 ± 414.50	909.36 ± 378.29	0.08
Potassium (mg)	4606.59 ± 1990.10^a,b,c	3330.26 ± 921.16	3185.62 ± 965.17	3121.33 ± 1036.22	3559.00 ± 1432.26	< 0.001
NEAP score	29.39 ± 5.19^a,b,c	37.81 ± 1.59^d,e	43.84 ± 1.83^f	55.34 ± 7.99	41.59 ± 10.62	< 0.001
Age (years)	45.17 ± 11.77	47.26 ± 10.94	47.44 ± 9.93	46.88 ± 11.14	46.69 ± 10.95	0.55
BMI (kg/m²)	26.16 ± 4.67	26.20 ± 3.93	27.18 ± 4.20	26.05 ± 4.09	26.40 ± 4.23	0.31
P.A (met-min/week)	673.48 ± 690.69	874.81 ± 836.79	788.99 ± 678.65	842.07 ± 869.80	795.08 ± 773.64	0.39
Energy (kcal)	2453.97 ± 781.46	2293.92 ± 603.66	2328.50 ± 671.10	2220.87 ± 661.58	2324.23 ± 684.09	0.19
Protein intake (g)	79.74 ± 27.08	80.65 ± 23.12	84.64 ± 25.01	89.91 ± 30.56	83.73 ± 26.74	0.07
Phosphorus (mg)	1480.80 ± 510.92	1457.15 ± 419.08	1465.13 ± 498.18	1395.81 ± 451.99	1449.80 ± 470.21	0.69
Magnesium (mg)	386.13 ± 154.17^c	358.52 ± 112.99^e	348.53 ± 129.69	312.26 ± 100.45	351.37 ± 128.05	0.004
Calcium (mg)	972.88 ± 410.77	928.59 ± 319.98	924.50 ± 409.27	811.01 ± 353.77	909.36 ± 378.29	0.05
Potassium (mg)	4403.81 ± 2057.70^a,b,c	3567.16 ± 1004.17^e	3336.00 ± 1002.67	2931.80 ± 945.20	3559.00 ± 1432.26	< 0.001
NAE score	-10.55 ± 27.65	8.34 ± 2.54	16.24 ± 2.12	28.03 ± 9.37	10.52 ± 20.22	< 0.001
Age (years)	45.01 ± 11.87	47.32 ± 10.68	47.72 ± 10.35	46.69 ± 10.87	46.69 ± 10.95	0.43
BMI (kg/m²)	25.46 ± 4.38^b,c	25.90 ± 4.17^e	26.81 ± 3.95	27.42 ± 4.24	26.40 ± 4.23	0.01
P.A (met-min/week)	665.53 ± 684.40	882.15 ± 869.21	808.16 ± 647.88	823.18 ± 865.18	795.08 ± 773.64	0.35
Energy (kcal)	2562.34 ± 756.68	2160.92 ± 591.69	2261.76 ± 678.30	2314.83 ± 648.38	2324.23 ± 684.09	0.002
Protein intake (g)	85.61 ± 27.19^a	74.52 ± 21.89^e	81.19 ± 23.35^f	93.74 ± 30.53	83.73 ± 26.74	< 0.001
Phosphorus (mg)	1552.14 ± 507.84^a,b	1316.38 ± 384.69^e	1402.15 ± 428.22	1530.86 ± 517.17	1449.80 ± 470.21	0.004
Magnesium (mg)	403.31 ± 150.81^a,b,c	318.73 ± 92.52	336.64 ± 124.50	347.42 ± 124.44	351.37 ± 128.05	< 0.001
Calcium (mg)	1016.73 ± 417.19^a,b	833.32 ± 293.52	873.80 ± 339.92	915.02 ± 429.53	909.36 ± 378.29	0.01
Potassium (mg)	4634.15 ± 1977.99^a,b,c	3281.88 ± 918.69	3199.01 ± 964.76	3129.23 ± 1029.49	3559.00 ± 1432.26	< 0.001
Data are means ± SD for quantitative variables and frequency (percent) for qualitative variables.
*. From ANOVA for quantitative variables, a. Chi-square for qualitative variables.
Post hoc (LSD) according to the following pattern:
a Significant difference between 1 compared to 2.
b Significant difference between 1 compared to 3.
c Significant difference between 1 compared to 4.
d Significant difference between 2 compared to 3.
e Significant difference between 2 compared to 4.
f Significant difference between 3 compared to 4.
Abbreviation: PRAL: potential renal acid load, NEAP: net endogenous acid production, NAE: net acid excretion, BMI; body mass index, and PA: physical activity.

The disease-related characteristics across quartiles of the PRAL, NEAP, and NAE in the case group are presented in Table 3. Regarding PRAL and NAE, significant differences were not observed among the quartiles in any characteristics (p > 0.05). According to the NEAP, significant differences were observed among the quartiles regarding the Ki-67 marker (p = 0.002) and tumor stage (p = 0.04).

Table 3

The characteristics at baseline across quartile of the PRAL, NEAP, and NAE in the case group
	Q1 (N = 44)	Q2 (N = 37)	Q3 (N = 36)	Q4 (N = 38)	Total (N = 155)	P – value*
PRAL score	-20.84 ± 12.62^a,b,c	-2.64 ± 2.42^d,e	4.49 ± 2.06^f	17.15 ± 12.36	-1.29 ± 16.88	< 0.001
Ki-67 marker (%)	38.25 ± 24.80	28.81 ± 17.03	30.98 ± 18.20	37.42 ± 20.87	34.10 ± 20.88	0.11
Tumor size (cm)	2.55 ± 1.90	3.72 ± 2.90	3.10 ± 1.82	3.34 ± 3.16	3.15 ± 2.51	0.20
Tumor stage, n (%)						0.23
Stage 1	3 (6.8)	2 (5.4)	3 (8.3)	2 (5.3)	10 (6.5)
Stage 2	32 (72.2)	28 (75.7)	20 (55.6)	20 (52.6)	100 (64.5)
Stage 3	9 (20.5)	7 (18.9)	13 (36.1)	16 (42.1)	45 (29.0)
Histological grade, n (%)						0.44
Grade 1	3 (6.8)	3 (8.1)	4 (11.1)	0 (0.0)	10 (6.5)
Grade 2	27 (61.4)	26 (70.3)	20 (55.6)	25 (65.8)	98 (63.2)
Grade 3	14 (31.8)	8 (21.6)	12 (33.3)	13 (34.2)	47 (30.3)
Metastasis status, n (%)						0.17
Yes	3 (6.8)	0 (0.0)	1 (2.8)	4 (10.5)	8 (5.2)
No	41 (93.2)	37 (100)	35 (97.2)	34 (89.5)	147 (94.8)
NEAP score	28.64 ± 5.24^a,b,c	37.71 ± 1.66^d,e	44.15 ± 1.77^f	55.29 ± 8.77	40.66 ± 10.84	< 0.001
Ki-67 marker (%)	40.52 ± 24.69^a,b	27.02 ± 15.51^e	28.50 ± 16.87^f	40.36 ± 21.35	34.10 ± 20.88	0.002
Tumor size (cm)	2.74 ± 1.89	3.15 ± 2.57	3.17 ± 2.37	3.69 ± 3.26	3.15 ± 2.51	0.44
Tumor stage, n (%)						0.04
Stage 1	4 (9.1)	2 (5.7)	3 (7.0)	1 (3.0)	10 (6.5)
Stage 2	34 (77.3)	25 (71.4)	25 (58.1)	16 (48.5)	100 (64.5)
Stage 3	6 (13.6)	8 (22.9)	15 (34.9)	16 (48.5)	45 (29.0)
Histological grade, n (%)						0.14
Grade 1	2 (4.5)	4 (11.4)	4 (9.3)	0 (6.5)	10 (6.5)
Grade 2	26 (59.1)	26 (74.3)	26 (60.5)	20 (60.6)	98 (63.2)
Grade 3	16 (36.4)	5 (14.3)	13 (30.2)	13 (39.4)	47 (30.3)
Metastasis status, n (%)						0.10
Yes	3 (6.8)	1 (2.9)	0 (0.0)	4 (12.1)	8 (5.2)
No	41 (93.2)	34 (97.1)	43 (100)	29 (87.9)	147 (94.8)
NAE score	-9.79 ± 12.69^a,b,c	8.37 ± 2.42^d,e	16.28 ± 2.05^f	28.23 ± 11.81	10.29 ± 16.94	< 0.001
Ki-67 marker (%)	37.86 ± 24.77	29.05 ± 17.49	34.58 ± 18.32	34.18 ± 21.12	34.10 ± 20.88	0.32
Tumor size (cm)	2.47 ± 1.83	3.77 ± 2.91	3.54 ± 3.01	2.97 ± 2.15	3.15 ± 2.51	0.09
Tumor stage, n (%)						0.20
Stage 1	3 (7.0)	2 (5.6)	2 (5.6)	3 (7.5)	10 (6.5)
Stage 2	32 (74.4)	27 (75.0)	19 (52.8)	22 (55.0)	100 (64.5)
Stage 3	8 (18.6)	7 (19.4)	15 (41.7)	15 (37.5)	45 (29.0)
Histological grade, n (%)						0.89
Grade 1	3 (7.0)	3 (8.3)	3 (8.3)	1 (2.5)	10 (6.5)
Grade 2	26 (60.5)	24 (66.7)	21 (58.3)	27 (67.5)	98 (63.2)
Grade 3	14 (32.6)	9 (25.0)	12 (33.3)	12 (30.0)	47 (30.3)
Metastasis status, n (%)						0.43
Yes	3 (7.0)	0 (0.0)	2 (5.6)	3 (7.5)	8 (5.2)
No	40 (93.0)	36 (100.0)	34 (94.4)	37 (92.5)	147 (94.8)
Data are means ± SD for quantitative variables and frequency (percent) for qualitative variables.
*. From ANOVA for quantitative variables, a. Chi-square for qualitative variables.
Post hoc (LSD) according to the following pattern:
a Significant difference between 1 compared to 2.
b Significant difference between 1 compared to 3.
c Significant difference between 1 compared to 4.
d Significant difference between 2 compared to 3.
e Significant difference between 2 compared to 4.
f Significant difference between 3 compared to 4.
Abbreviation: PRAL: potential renal acid load, NEAP: net endogenous acid production, NAE: net acid excretion, BMI; body mass index, PA: physical activity.

The correlation between the quartiles of PRAL, NEAP, and NAE indices with tumor stage and histological grade in the case group is shown in Table 4. PRAL and NAE did not correlate with tumor stage and histological grade before (Spearman test) and after the adjustment (the Partial test; with correction for energy, age, PA, BMI, marital status, employment status, education, age of onset of menstruation, status of menopause, pregnancy history, number of pregnancies, number of children, number of abortions, history of breastfeeding, duration of breastfeeding, history of taking contraceptive pills, family history of cancer, smoking status, alcoholic beverages status, tumor size, ER expression, PR expression, and HER2 expression) (p > 0.05). However, NEAP presented a significant correlation with the tumor stage before and after the adjustment (Model 1: p = 0.001; Model 2: p = 0.007).

Table 4

Correlation between the quartiles of PRAL, NEAP, and NAE indices with tumor stage and histological grade in the case group
Variable	Histological grade		Tumor stage
Variable	Correlation coefficient	P-value	Correlation coefficient	P-value
PRAL
Model1^a	0.06	0.44	0.17	0.02
Model2^b	0.07	0.37	0.13	0.13
NEAP
Model1^a	0.05	0.51	0.27	0.001
Model2^b	0.06	0.43	0.23	0.007
NAE
Model1^a	0.01	0.81	0.16	0.03
Model2^b	0.06	0.45	0.13	0.12
P < 0.05 was considered as significant. R was considered as the correlation coefficient (0–1).
a. Correlation and significance with the Spearman test (without adjustment).
b. Correlation and significance with the Partial test (with correction for energy, age, PA, BMI, marital status, employment status, education, age of onset of menstruation, status of menopause, pregnancy history, number of pregnancies, number of children, number of abortions, history of breastfeeding, duration of breastfeeding, history of taking contraceptive pills, family history of cancer, smoking status, alcoholic beverages status, tumor size, ER expression, PR expression, and HER2 expression.).
Abbreviation: PRAL: potential renal acid load, NEAP: net endogenous acid production, NAE: net acid excretion, PA: physical activity, BMI: body mass index, ER: estrogen, PR: progesterone, HER2: human epidermal growth factor receptor2.

As presented in Table 5, in all models (Model 1, linear regression analysis without adjustment; b. Model 2, linear regression analysis with adjustment for energy; c. Model 3, linear regression analysis with correction for energy, age, PA, BMI, marital status, employment status, education, age of onset of menstruation, status of menopause, pregnancy history, number of pregnancies, number of children, number of abortions, history of breastfeeding, duration of breastfeeding, history of taking contraceptive pills, family history of cancer, smoking status, alcoholic beverages status, tumor stage, histological grade, metastasis status, ER expression, PR expression, and HER2 expression.), no significant association was observed between the PRAL, NEAP, and NAE (independent variables) with pathobiological markers of breast cancer such as tumor size and Ki-67 marker(p > 0.05).

Table 5

The association between the PRAL, NEAP, and NAE (independent variables) with pathobiological markers of breast cancer such as tumor size and Ki-67 marker (dependent variables)
Variable	B (Unstandardized)	SE	P – value*
PRAL and tumor size
Model 1^a	0.01	0.01	0.24
Model 2^b	0.01	0.01	0.24
Model 3^c	0.01	0.01	0.35
NEAP and tumor size
Model 1^a	0.03	0.01	0.10
Model 2^b	0.03	0.01	0.10
Model 3^c	0.02	0.02	0.21
NAE and tumor size
Model 1^a	0.01	0.01	0.33
Model 2^b	0.01	0.01	0.33
Model 3^c	0.01	0.01	0.41
PRAL and Ki-67
Model 1^a	-0.13	0.09	0.16
Model 2^b	-0.14	0.10	0.15
Model 3^c	-0.12	0.10	0.22
NEAP and Ki-67
Model 1^a	-0.21	0.15	0.17
Model 2^b	-0.22	0.15	0.14
Model 3^c	-0.18	0.15	0.24
NAE and Ki-67
Model 1^a	-0.14	0.09	0.16
Model 2^b	-0.14	0.10	0.14
Model 3^c	-0.12	0.10	0.20
*.P < 0.05 was considered as significant. a. Model 1, linear regression analysis without adjustment; b. Model 2, linear regression analysis with adjustment for energy; c. Model 3, linear regression analysis with correction for energy, age, PA, BMI, marital status, employment status, education, age of onset of menstruation, status of menopause, pregnancy history, number of pregnancies, number of children, number of abortions, history of breastfeeding, duration of breastfeeding, history of taking contraceptive pills, family history of cancer, smoking status, alcoholic beverages status, tumor stage, histological grade, metastasis status, ER expression, PR expression, and HER2 expression.
Abbreviation: PRAL: potential renal acid load, NEAP: net endogenous acid production, NAE: net acid excretion, PA: physical activity, BMI: body mass index, ER: estrogen receptor, PR: progesterone receptor, HER2: human epidermal growth factor receptor

Odds ratios (95%CI) of breast cancer (dependent variables) according to the quartile of the PRAL, NEAP, and NAE (independent variables) are shown in Table 6. All indices did not predict the risk of breast cancer before and after adjustment (p > 0.05).

Table 6

Odds ratios (95%CI) of breast cancer (dependent variables) according to quartile of the PRAL, NEAP, and NAE (independent variables)
DAI	Quartile 1 Or (ref)	Quartile 2 Or (95%CI )	Quartile 3 Or (95%CI )	Quartile 4 Or (95%CI )	*P- trend
PRAL
Model 1^a	1	1.36 (0.72–2.58)	0.92 (0.49–1.73)	0.88 (0.46–1.65)	0.52
Model 2^b	1	1.32 (0.69–2.51)	0.94 (0.50–1.77)	0.88 (0.47–1.66)	0.62
Model 3^c	1	1.15 (0.52–2.53)	0.59 (0.26–1.30)	0.66 (0.29–1.49)	0.32
NEAP
Model 1^a	1	1.77 (0.93–3.36)	1.08 (0.57–2.04)	1.63 (0.86–3.09)	0.19
Model 2^b	1	1.72 (0.90–3.27)	1.07 (0.56–2.02)	1.61 (0.85–3.05)	0.22
Model 3^c	1	1.34 (0.60–2.96)	0.68 (0.30–1.52)	1.30 (0.59–2.85)	0.30
NAE
Model 1^a	1	1.17 (0.62–2.20)	0.79 (0.42–1.49)	0.79 (0.42–1.49)	0.55
Model 2^b	1	1.13 (0.59–2.14)	0.80 (0.43–1.52)	0.79 (0.42–1.50)	0.66
Model 3^c	1	1.09 (0.49–2.44)	0.46 (0.20–1.05)	0.66 (0.30–1.47)	0.15
* P < 0.05 statistically significant by Multivariable Logistic Regression. a. Model 1: unadjusted; b. Model 2: adjusted for energy; c. Model 3: adjustment for energy, age, PA, BMI, marital status, employment status, education, age of onset of menstruation, status of menopause, pregnancy history, number of pregnancies, number of children, number of abortion, history of breastfeeding, duration of breastfeeding, history of taking contraceptive pills, family history of cancer, smoking status, and alcoholic beverages status.
Abbreviation: PRAL: potential renal acid load, NEAP: net endogenous acid production, NAE: net acid excretion, PA: physical activity, BMI: body mass index.

The present case-control study examined the association of the DAL index with tumor size and the odds of breast cancer among Iranian women. We found that the DAL index was not significantly associated with the tumor size and did not predict the risk of breast cancer in Iranian women.

It has been suggested that diet-related acid load may be associated with an increased risk of cancer, considering that an imbalance in acid-base homeostasis can impact cellular and molecular activities that promote carcinogenesis or tumor progression[15, 22]. Despite this, limited epidemiological studies are available regarding the association of DAL with the risk of various cancers, especially for BC. In line with our study, Safabakhsh et al. also did not find any significant association between DAL and the odds of BC in Iranian women. The type of study (case-control) and sample size of that study are similar to the present study (150 cases and 150 controls)[25]. Moreover, in another case-control study with a different sample size (case/control: 265/134) in the Iranian population no significant association was observed between DAL and the incidence of BC[27]. In general, no significant association has been observed in studies conducted on the relationship between the DAL and BC in Iran and Asia. However, a nationwide large prospective cohort study with a 6-year follow-up period on 43,570 American and Puerto Rican women demonstrated that higher DAL (evaluated by PRAL) may be a risk factor for hormone receptor-negative BC. They suggested that plant-based diets, rich in fruits and vegetables, may have a protective effect, and high PRAL scores, which are positively associated with the consumption of meat and other acid-producing foods, may be considered a risk factor for invasive BC[37]. Another cohort study with a median of 17 years of follow-up, using data from the Long Island Breast Cancer Study Project (LIBCSP), found that DAL may increase the risk of all-cause mortality following BC among women with high serum levels of polychlorinated biphenyl (PCB)[24]. PCBs are associated with nephropathy[38] and it is assumed that they increase the risk of BC mortality[39, 40].

Wu et al. investigated the association between DAL (evaluated with PRAL and NEA) and smoking with total mortality, BC-specific mortality, and BC recurrence in BC survivors in a cohort study in China. After an average of 7.3 years of follow-up, there were 295 total deaths, 249 specific BC deaths, and 490 BC recurrences. DAL was associated with an increased risk of BC recurrence among women with > 0 pack-years of smoking, but not among women with = 0 pack-years. The authors concluded that the increase in DAL and the intensity of smoking in the past were independently and jointly associated with an increase in total mortality and BC-specific mortality[41]. The association of DAL with other cancers has been investigated, and in a cohort study, no significant association was found between DAL (evaluated by NEA) and the risk of bladder cancer[42]. Moreover, Akter et al. studied the association of DAL (evaluated by PRAL and NEAP) with total and cause-specific mortality among Japanese adults. No significant association was observed between DAL and cancer mortality and also a high DAL score was related to higher odds of total mortality and cardiovascular disease-specific mortality, mainly from ischemic heart disease[43]. Two systematic reviews and meta-analyses of observational studies by wang et al.[44] and Keramati et al.[45] suggested that high DAL may increase the risk of cancer. Contradictory results regarding the association between DAL and BC risk in conducted studies may be attributed to several factors such as the different mean of PRAL and NEAP in studies, different study design, race of study population, and sample size.

The particular mechanism of the correlation between high DAL and cancer risk has not been determined. However, several potential hormonal and non-hormonal pathways have been identified to determine how diet-induced acidosis in the long-term affects carcinogenesis. The circulating level of insulin-like growth factor (IGF-1) may be elevated by an acidosis diet. Higher levels of IGF-1 via stimulation of some signaling pathways (such as PI3K/Akt and MAPK) is associated with metastasis, angiogenesis, and tumorigenesis[23, 46, 47]. However, limited studies have evaluated the association of DAL with the concentration of IGF-1. In a study by Lim et al., the interaction of DAL with IGF-1 was not observed[48]. Cortisol production may be stimulated by an acidogenic diet. Several animal and human studies have reported an association between metabolic acidosis and increased plasma cortisol levels[49–52]. High concentrations of cortisol can progress breast cancer via several pathways such as immune system suppression, apoptosis suppression, and elevating DNA injury[53]. Moreover, an acidosis diet may develop carcinogenesis by increasing insulin resistance. Both hyperinsulinemia and high IGF-1 inhibit the hepatic production of sex hormone-binding globulin and stimulate ovarian production of sex steroids, which induce cellular proliferation and suppress apoptosis. Also, insulin resistance increases the production of reactive oxygen species which leads to DNA damage[54]. One of the pathways through which the ketogenic diet can induce carcinogenesis is the inhibition of adiponectin production. A low level of adiponectin leads to elevated concentrations of insulin and IGF-1 which in turn increase carcinogenesis[55].

Like all case-control studies, recurrent bias is a limitation of the present study. We applied FFQ in this study which has inevitable errors including Berkson's bias, social desirability, recall, and response errors. One of the strengths of this study is the investigation of pathobiological markers of BC such as tumor size and Ki-67 marker and the evaluation of the NAE. Most previous studies have only examined the risk of cancer, and the NEA has not been investigated in the majority of previous studies. Furthermore, using multivariable logistic regression in 3 different models, provided a robust platform to interrogate the incumbent data.

Ethics approval and consent to participate

The research protocol was in accordance with the guidelines of the Declaration of Helsinki. The Ethics Committee in Research of Sirjan School of Medical Sciences approved the study protocol (Ethical code: IR.SIRUMS.REC.1402.011). The informed written consent form was completed for all subjects at the beginning of the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Funding

This work has been financially supported by the Student Research Committee of Sirjan School of Medical Sciences.

Author Contribution

AKh: collection supervision, data entry, SARN: data curation, validation, AR: data curation, validation, SH: analysis, and interpretation, HB: collection supervision, data entry, review, and editing, VA: collection supervision, manuscript writing, revision, and submission approval. A Kh: all steps of research. All authors reviewed the manuscript. The final version of the manuscript was approved by all authors.

Acknowledgments

The authors extend their appreciation to Javad Al-Aemeh Clinic in Kerman City, Iran.

Data availability

All data and materials are fully presented in the manuscript.

Iyengar NM, Arthur R, Manson JE, Chlebowski RT, Kroenke CH, Peterson L, et al. Association of body fat and risk of breast cancer in postmenopausal women with normal body mass index: a secondary analysis of a randomized clinical trial and observational study. JAMA oncology. 2019;5(2):155-63.
LoConte NK, Brewster AM, Kaur JS, Merrill JK, Alberg AJ. Alcohol and cancer: a statement of the American Society of Clinical Oncology. Journal of Clinical Oncology. 2018;36(1):83-93.
Brinton LA, Brogan DR, Coates RJ, Swanson CA, Potischman N, Stanford JL. Breast cancer risk among women under 55 years of age by joint effects of usage of oral contraceptives and hormone replacement therapy. Menopause. 1998;5(3):145-51.
Gram IT, Park S-Y, Maskarinec G, Wilkens LR, Haiman CA, Le Marchand L. Smoking and breast cancer risk by race/ethnicity and oestrogen and progesterone receptor status: the Multiethnic Cohort (MEC) study. International journal of epidemiology. 2019;48(2):501-11.
Farvid MS, Chen WY, Rosner BA, Tamimi RM, Willett WC, Eliassen AH. Fruit and vegetable consumption and breast cancer incidence: Repeated measures over 30 years of follow‐up. International journal of cancer. 2019;144(7):1496-510.
Mourouti N, Papavagelis C, Plytzanopoulou P, Kontogianni M, Vassilakou T, Malamos N, et al. Dietary patterns and breast cancer: a case–control study in women. European journal of nutrition. 2015;54:609-17.
Kotepui M. Diet and risk of breast cancer. Contemporary Oncology/Współczesna Onkologia. 2016;20(1):13-9.
Farvid MS, Stern MC, Norat T, Sasazuki S, Vineis P, Weijenberg MP, et al. Consumption of red and processed meat and breast cancer incidence: A systematic review and meta‐analysis of prospective studies. International journal of cancer. 2018;143(11):2787-99.
Remer T, editor ACID‐BASE IN RENAL FAILURE: influence of diet on acid‐base balance. Seminars in dialysis; 2000: Wiley Online Library.
Remer T, Dimitriou T, Manz F. Dietary potential renal acid load and renal net acid excretion in healthy, free-living children and adolescents. The American journal of clinical nutrition. 2003;77(5):1255-60.
Frassetto LA, Lanham-New SA, Macdonald HM, Remer T, Sebastian A, Tucker KL, et al. Standardizing Terminology for Estimating the Diet-Dependent Net Acid Load to the Metabolic System1. The Journal of nutrition. 2007;137(6):1491-2.
Williams RS, Heilbronn LK, Chen DL, Coster AC, Greenfield JR, Samocha-Bonet D. Dietary acid load, metabolic acidosis and insulin resistance–Lessons from cross-sectional and overfeeding studies in humans. Clinical Nutrition. 2016;35(5):1084-90.
Haghighatdoost F, Najafabadi MM, Bellissimo N, Azadbakht L. Association of dietary acid load with cardiovascular disease risk factors in patients with diabetic nephropathy. Nutrition. 2015;31(5):697-702.
Williams RS, Kozan P, Samocha-Bonet D. The role of dietary acid load and mild metabolic acidosis in insulin resistance in humans. Biochimie. 2016;124:171-7.
Robey IF. Examining the relationship between diet-induced acidosis and cancer. Nutrition & metabolism. 2012;9:1-11.
Krupp D, Esche J, Mensink GBM, Klenow S, Thamm M, Remer T. Dietary acid load and potassium intake associate with blood pressure and hypertension prevalence in a representative sample of the German adult population. Nutrients. 2018;10(1):103.
Engberink MF, Bakker SJ, Brink EJ, van Baak MA, van Rooij FJ, Hofman A, et al. Dietary acid load and risk of hypertension: the Rotterdam Study. The American journal of clinical nutrition. 2012;95(6):1438-44.
Krupp D, Johner SA, Kalhoff H, Buyken AE, Remer T. Long-Term Dietary Potential Renal Acid Load During Adolescence Is Prospectively Associated with Indices of Nonalcoholic Fatty Liver Disease in Young Women3. The Journal of nutrition. 2012;142(2):313-9.
Zhang L, Curhan GC, Forman JP. Diet-dependent net acid load and risk of incident hypertension in United States women. Hypertension. 2009;54(4):751-5.
Fagherazzi G, Vilier A, Bonnet F, Lajous M, Balkau B, Boutron-Ruault M-C, et al. Dietary acid load and risk of type 2 diabetes: the E3N-EPIC cohort study. Diabetologia. 2014;57:313-20.
Murakami K, Sasaki S, Takahashi Y, Uenishi K. Association between dietary acid–base load and cardiometabolic risk factors in young Japanese women. British journal of nutrition. 2008;100(3):642-51.
Fenton TR, Huang T. Systematic review of the association between dietary acid load, alkaline water and cancer. BMJ open. 2016;6(6):e010438.
Murphy N, Knuppel A, Papadimitriou N, Martin RM, Tsilidis K, Smith-Byrne K, et al. Insulin-like growth factor-1, insulin-like growth factor-binding protein-3, and breast cancer risk: observational and Mendelian randomization analyses with∼ 430 000 women. Annals of Oncology. 2020;31(5):641-9.
Chronister BN, Wu T, Santella RM, Neugut AI, Wolff MS, Chen J, et al. Dietary acid load, serum polychlorinated biphenyl levels, and mortality following breast cancer in the Long Island breast cancer study project. International Journal of Environmental Research and Public Health. 2021;19(1):374.
Safabakhsh M, Imani H, Yaseri M, Omranipour R, Shab‐Bidar S. Higher dietary acid load is not associated with risk of breast cancer in Iranian women. Cancer Reports. 2020;3(2):e1212.
Esmaillzadeh A, Boroujeni HK, Azadbakht L. Consumption of energy-dense diets in relation to cardiometabolic abnormalities among Iranian women. Public health nutrition. 2012;15(5):868-75.
Heidari Z, Pashayee-Khamene F, Jalali S, Karandish M, Mohammadi S, Rashidkhani B. Diet-dependent acid load and the risk of breast cancer: A case–control study. Clinical Nutrition ESPEN. 2023;55:97-102.
Jalali S, Shivappa N, Hébert JR, Heidari Z, Hekmatdoost A, Rashidkhani B. Dietary inflammatory index and odds of breast cancer in a case-control study from Iran. Nutrition and cancer. 2018;70(7):1034-42.
Aghamohammadi V, Salmani R, Ivanbagha R, Effati daryani F, Nasiri K. Footbath as a safe, simple, and non‐pharmacological method to improve sleep quality of menopausal women. Research in nursing & health. 2020;43(6):621-8.
Pourghassem Gargari B, Aliasgharzadeh A. Effect of folic acid supplementation on indices of glycemic control, insulin resistance and lipid profile in patients with type 2 diabetes mellitus. Iranian Journal of Endocrinology and Metabolism. 2011;13(4):354-60.
Haidari F, Aghamohammadi V, Mohammadshahi M, Ahmadi-Angali K, Asghari-Jafarabadi M. Whey protein supplementation reducing fasting levels of anandamide and 2-AG without weight loss in pre-menopausal women with obesity on a weight-loss diet. Trials. 2020;21(1):1-10.
Mirmiran P, Esfahani FH, Mehrabi Y, Hedayati M, Azizi F. Reliability and relative validity of an FFQ for nutrients in the Tehran lipid and glucose study. Public health nutrition. 2010;13(5):654-62.
Remer T, Manz F. Potential renal acid load of foods and its influence on urine pH. Journal of the American Dietetic Association. 1995;95(7):791-7.
Frassetto LA, Todd KM, Morris Jr RC, Sebastian A. Estimation of net endogenous noncarbonic acid production in humans from diet potassium and protein contents. The American journal of clinical nutrition. 1998;68(3):576-83.
Doustmohammadian A, Nouri Saeidlou S, Esfandyari S, Gholizadeh E, Maadi M, Motamed N, et al. Dietary acid load (DAL), glycated hemoglobin A1c (HbA1c), and metabolic syndrome (MeS) mediate the association of the adherence to the dietary approaches to stopping hypertension (DASH) and mediterranean diet (MeD) with nonalcoholic fatty liver disease. Frontiers in Nutrition. 2022;9:921415.
Verbraecken J, Van de Heyning P, De Backer W, Van Gaal L. Body surface area in normal-weight, overweight, and obese adults. A comparison study. Metabolism. 2006;55(4):515-24.
Park YMM, Steck SE, Fung TT, Merchant AT, Elizabeth Hodgson M, Keller JA, et al. Higher diet‐dependent acid load is associated with risk of breast cancer: findings from the sister study. International journal of cancer. 2019;144(8):1834-43.
Kataria A, Trasande L, Trachtman H. The effects of environmental chemicals on renal function. Nature Reviews Nephrology. 2015;11(10):610-25.
Parada Jr H, Sun X, Tse C-K, Engel LS, Hoh E, Olshan AF, et al. Plasma levels of polychlorinated biphenyls (PCBs) and breast cancer mortality: The Carolina Breast Cancer Study. International journal of hygiene and environmental health. 2020;227:113522.
Parada Jr H, Wolff MS, Engel LS, Eng SM, Khankari NK, Neugut AI, et al. Polychlorinated biphenyls and their association with survival following breast cancer. European Journal of Cancer. 2016;56:21-30.
Wu T, Hsu F-C, Pierce JP. Increased acid-producing diet and past smoking intensity are associated with worse prognoses among breast cancer survivors: a prospective cohort study. Journal of clinical medicine. 2020;9(6):1817.
Wright ME, Michaud DS, Pietinen P, Taylor PR, Virtamo J, Albanes D. Estimated urine pH and bladder cancer risk in a cohort of male smokers (Finland). Cancer Causes & Control. 2005;16(9):1117-23.
Akter S, Nanri A, Mizoue T, Noda M, Sawada N, Sasazuki S, et al. Dietary acid load and mortality among Japanese men and women: the Japan Public Health Center–based Prospective Study. The American journal of clinical nutrition. 2017;106(1):146-54.
Wang R, Wen Z-Y, Liu F-H, Wei Y-F, Xu H-L, Sun M-L, et al. Association between dietary acid load and cancer risk and prognosis: An updated systematic review and meta-analysis of observational studies. Frontiers in Nutrition. 2022;9:891936.
Keramati M, Kheirouri S, Musazadeh V, Alizadeh M. Association of high dietary acid load with the risk of cancer: a systematic review and meta-analysis of observational studies. Frontiers in Nutrition. 2022;9:816797.
Watts EL, Fensom GK, Smith Byrne K, Perez‐Cornago A, Allen NE, Knuppel A, et al. Circulating insulin‐like growth factor‐I, total and free testosterone concentrations and prostate cancer risk in 200 000 men in UK Biobank. International journal of cancer. 2021;148(9):2274-88.
Hua H, Kong Q, Yin J, Zhang J, Jiang Y. Insulin-like growth factor receptor signaling in tumorigenesis and drug resistance: a challenge for cancer therapy. Journal of hematology & oncology. 2020;13:1-17.
Lim SY, Chan YM, Ramachandran V, Shariff ZM, Chin YS, Arumugam M. Dietary acid load and its interaction with IGF1 (rs35767 and rs7136446) and IL6 (rs1800796) polymorphisms on metabolic traits among postmenopausal women. Nutrients. 2021;13(7):2161.
Lemmens SG, Born JM, Martens EA, Martens MJ, Westerterp-Plantenga MS. Influence of consumption of a high-protein vs. high-carbohydrate meal on the physiological cortisol and psychological mood response in men and women. PloS one. 2011;6(2):e16826.
Esche J, Shi L, Sánchez-Guijo A, Hartmann MF, Wudy SA, Remer T. Higher diet-dependent renal acid load associates with higher glucocorticoid secretion and potentially bioactive free glucocorticoids in healthy children. Kidney international. 2016;90(2):325-33.
Buehlmeier J, Remer T, Frings-Meuthen P, Maser-Gluth C, Heer M. Glucocorticoid activity and metabolism with NaCl-induced low-grade metabolic acidosis and oral alkalization: results of two randomized controlled trials. Endocrine. 2016;52:139-47.
Perez GO, Oster JR, Katz FH, Vaamonde CA. The effect of acute metabolic acidosis on plasma cortisol, renin activity and aldosterone. Hormone Research in Paediatrics. 1979;11(1):12-21.
Al Sorkhy M, Fahl Z, Ritchie J. Cortisol and Breast Cancer: A review of clinical and molecular evidence. Annals of Cancer Research and Therapy. 2018;26(1):19-25.
Arcidiacono B, Iiritano S, Nocera A, Possidente K, Nevolo MT, Ventura V, et al. Insulin resistance and cancer risk: an overview of the pathogenetic mechanisms. Journal of Diabetes Research. 2012;2012(1):789174.
Kelesidis I, Kelesidis T, Mantzoros C. Adiponectin and cancer: a systematic review. British journal of cancer. 2006;94(9):1221-5.

No competing interests reported.

Download PDF

Editorial decision: Revision requested
08 Aug, 2024
Editor assigned by journal
07 Aug, 2024
Submission checks completed at journal
07 Aug, 2024
First submitted to journal
07 Aug, 2024

You are reading this latest preprint version

The association between dietary acid load index with tumor stage and size, histological grade, Ki-67 marker, and the odds of breast cancer: A case-control study based on hospital evidence

Status:

Version 1

Abstract

Introduction:

Methods and Materials:

Results

Conclusions

Introduction

Materials and methods

Study design and participants

Calculation of sample size

Measurements

Statistical analysis

Results

Discussion

Conclusions

Declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Funding

Author Contribution

Acknowledgments

Data availability

References

Additional Declarations

Status:

Version 1