Volume 2025, Issue 1 4066662
Research Article
Open Access

The Association of Diet With High Phosphatemic Index With Odds of Nonalcoholic Fatty Liver Disease

Mitra Kazemi Jahromi

Mitra Kazemi Jahromi

Endocrinology and Metabolism Research Center , Hormozgan University of Medical Sciences , Bandar Abbas , Iran , hums.ac.ir

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Mostafa Norouzzadeh

Mostafa Norouzzadeh

Nutrition and Endocrine Research Center , Research Institute for Endocrine Disorders , Research Institute for Endocrine Sciences , Shahid Beheshti University of Medical Sciences , Tehran , Iran , sbmu.ac.ir

Department of Nutrition , School of Public Health , Iran University of Medical Sciences , Tehran , Iran , iums.ac.ir

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Farshad Teymoori

Farshad Teymoori

Department of Nutrition , School of Public Health , Iran University of Medical Sciences , Tehran , Iran , iums.ac.ir

Nutritional Sciences Research Center , Iran University of Medical Sciences , Tehran , Iran , iums.ac.ir

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Niloufar Saber

Niloufar Saber

Nutrition and Endocrine Research Center , Research Institute for Endocrine Disorders , Research Institute for Endocrine Sciences , Shahid Beheshti University of Medical Sciences , Tehran , Iran , sbmu.ac.ir

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Hamid Ahmadirad

Hamid Ahmadirad

Nutrition and Endocrine Research Center , Research Institute for Endocrine Disorders , Research Institute for Endocrine Sciences , Shahid Beheshti University of Medical Sciences , Tehran , Iran , sbmu.ac.ir

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Hossein Farhadnejad

Corresponding Author

Hossein Farhadnejad

Nutrition and Endocrine Research Center , Research Institute for Endocrine Disorders , Research Institute for Endocrine Sciences , Shahid Beheshti University of Medical Sciences , Tehran , Iran , sbmu.ac.ir

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Ammar Salehi-Sahlabadi

Ammar Salehi-Sahlabadi

Department of Biochemistry and Nutrition , School of Medicine , Shahrekord University of Medical Sciences , Shahrekord , Iran , skums.ac.ir

Department of Community Nutrition , School of Nutrition and Food Science , Isfahan University of Medical Sciences , Isfahan , Iran , mui.ac.ir

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Parvin Mirmiran

Corresponding Author

Parvin Mirmiran

Nutrition and Endocrine Research Center , Research Institute for Endocrine Disorders , Research Institute for Endocrine Sciences , Shahid Beheshti University of Medical Sciences , Tehran , Iran , sbmu.ac.ir

Department of Clinical Nutrition and Dietetics , Faculty of Nutrition and Food Technology , National Nutrition and Food Technology Research Institute , Shahid Beheshti University of Medical Sciences , Tehran , Iran , sbmu.ac.ir

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First published: 07 February 2025
Academic Editor: Jin Chai

Abstract

Aim: A high intake of dietary phosphorus with an effect on the serum phosphorus level may affect the health status and predict the occurrence of some clinical disorders. Therefore, in the current study, we aimed to determine phosphatemic index (PI) according to serum levels of phosphorus after intake of different foods and assess its association with the odds of nonalcoholic fatty liver disease (NAFLD).

Methods: The current study was conducted with case-control design on 225 newly diagnosed NAFLD cases and 450 controls aged 20–60 years. Dietary intake data were collected by a validated food frequency questionnaire. The PI was calculated as the area under the curve (AUC) of serum phosphorus after eating test food divided by the AUC for the food supply containing an equal quantity of phosphorus. Multivariable logistic regression models were used to evaluate the NAFLD odds according to tertiles of the PI score.

Results: The median (IQR) of dietary PI in participants of case and control groups was 83.9 (55.8–114.2) and 82.1 (52.6–119.6), respectively. In the age and sex-adjusted model, there was no statistically significant association between PI and the odds of NAFLD (OR = 1.47; 95% CI: 0.98–2.19, Ptrend = 0.065). However, in fully adjusted model, after controlling the effects of age, sex, waist-to-hip ratio, smoking, dietary intake of energy, dietary fiber, physical activity, and socioeconomic status, the odds of NAFLD increased across tertiles of PI (OR = 1.97; 95% CI: 1.08–3.58, Ptrend = 0.028).

Conclusions: Our results suggested that a diet with a higher PI score may contribute to an increase in the odds of NAFLD independent of common confounders.

1. Introduction

Nonalcoholic fatty liver disease (NAFLD) has emerged as a leading global health concern, with its prevalence and impact on liver health steadily increasing [13]. Projections suggest that by 2030, NAFLD may become the primary reason for liver transplantation [4]. NAFLD is characterized by the accumulation of fat in more than 5% of hepatocytes, occurring in the absence of other liver-damaging factors such as excessive alcohol consumption, viral or autoimmune hepatitis, hepatotoxic medications, or endocrine disorders [5, 6]. Recent research has revealed that NAFLD’s effects extend beyond the liver, impacting multiple organ systems throughout the body [7]. The global prevalence of NAFLD is estimated at 29.8% [8], with recent studies indicating that over 30% of the Iranian population is affected [9].

Diet, among other lifestyle factors, plays a crucial role in determining the risk of NAFLD. Its impact on liver health can be analyzed through various lenses, including dietary patterns, food groups, and specific nutrients. Phosphorus (P), a vital nutrient in our diet, is essential for numerous biological processes, such as energy metabolism, phospholipid and nucleotide synthesis, bone development, and intracellular signaling [1012]. However, excessive dietary intake of phosphorus may pose long-term health risks. Research has shown that high phosphorus consumption and elevated blood phosphorus levels, even within the normal range, are associated with increased risk of cardiovascular diseases (CVDs) [1316] and mortality [17, 18]. This association has been observed not only in the general population but also in patients with chronic kidney disease.

A study by Shin et al. [19] found that serum phosphorus levels are strongly and dose-dependently linked to the odds of NAFLD. This study suggests that serum phosphorus is directly involved in the development of NAFLD rather than just acting indirectly through metabolic abnormalities. Limited research indicates that increased circulating phosphate can raise levels of oxidative stress and cause endothelial impairment, which might explain the link to NAFLD [2023]. However, some studies present contrasting findings. They show that a high-phosphate diet (HPD) can actually reduce hepatic lipogenesis and promote fat oxidation in vertebrates [24, 25]. Recently, Narasaki et al. [26] developed a phosphatemic index (PI) based on serum levels of phosphorus after eating different foods. This index could help determine how dietary and circulating phosphorus might predict clinical disorders. In a follow-up validation study, they found that eating meals high in PI for 5 days led to higher blood levels of intact fibroblast growth factor 23 (FGF23). The rise in blood phosphorus after eating reflects not only just how much phosphorus was absorbed from food but also how the body processes it, including its distribution to tissues and removal by the kidneys. This postprandial rise can be used to calculate the dietary phosphorus load from foods or meals [26].

Given the potential significance of dietary and circulating phosphorus in predicting chronic disease risk, the current study aimed to investigate the possible association between dietary PI and odds of NAFLD in Iranian adults.

2. Materials and Methods

2.1. Study Design and Population

Our case-control study was conducted at the Metabolic Liver Disease Research Center, affiliated with the Isfahan University of Medical Sciences. The study population comprised 225 newly diagnosed NAFLD cases and 450 controls, and all aged between 20 and 60 years. NAFLD diagnosis was confirmed through liver ultrasound scans, along with the absence of alcohol consumption and other liver disease causes. The control group consisted of individuals whose liver ultrasounds revealed no signs of hepatic steatosis.

We calculated the sample size of the present study using the G power software Version 3.1.9.4. According to the previous investigation that examined the possible relationship of serum phosphorus with chronic diseases such as metabolic syndrome, cancer, and CVDs, we assumed at least 30% higher odds of NAFLD for individuals in the highest tertile of PI compared to those in the lowest one [27]. Considering the Type I error of 5% and two-tailed p value, study power of 80% (β = 0.20), the ratio of controls to cases as 2, and the odds ratio (OR) of NAFLD as 1.30 for highest vs lowest group of PI, we needed a sample of 473 participants (158 cases and 315 controls). However, we recruited 225 newly diagnosed NAFLD patients and 450 controls, aged 20 to 60 years old, to keep track of any possible dropouts.

The inclusion criteria for this study were as follows: (1) participants were not following any special diet aimed at weight loss or the management of pre-existing chronic diseases; (2) participants had not used medications known to be hepatotoxic or steatogenic; and (3) participants had no history of liver or renal diseases, CVDs, Type 2 diabetes, thyroid disorders, malignancies, or autoimmune diseases. In addition, participants were excluded if they (1) completed fewer than 35 food items in the food frequency questionnaire (FFQ) or (2) reported implausible daily energy intakes (≤ 800 kcal/day or ≥ 4500 kcal/day).

This study adhered to the ethical standards outlined in the 1964 Helsinki Declaration and its subsequent amendments. The Ethics Committee of Isfahan University of Medical Sciences approved our study protocol. Prior to enrollment, all participants provided informed written consent.

2.2. Dietary Assessment

Dietary intake data for the study population were collected using a validated semiquantitative FFQ consisting of 168 items [28]. Participants were requested to report their average consumption over the past year by selecting from various frequency categories. Standardized Iranian household measurements were used to convert portion sizes into grams [29]. Daily energy and nutrient intakes were calculated primarily using the United States Department of Agriculture (USDA) Food Composition Table (FCT), with the Iranian FCT applied for local foods not included in the USDA database [30, 31].

2.2.1. Calculation of PI

The PI was calculated as the ratio of the area under the curve (AUC) for serum phosphorus levels following the consumption of each test food to the AUC for an equivalent amount of phosphorus [26]. As defined by Narasaki et al. [26], the PI is calculated using the following method:
(1)

The AUCs, representing serum phosphorus levels for over 6 h postconsumption, were calculated by summing all segments under the curve, including areas below the fasting concentration, and dividing by the total blood collection time.

2.3. Anthropometric and Physical Activity Measurements

We collected anthropometric data using standardized procedures and equipment. Participants were weighed on digital scales to the nearest 100 g, wearing minimal clothing and no shoes. Height measurements were taken using a stadiometer to the nearest 0.5 cm, with participants standing barefoot. We calculated body mass index (BMI) as weight (kg) divided by height squared (m2). Waist circumference (WC) was measured using an unstretched tape measure, positioned horizontally between the lowest rib and the iliac crest. We took care to avoid applying pressure to the skin, ensuring accurate readings to the nearest 0.1 cm. For hip circumference (HC), we used a nonelastic tape measure placed around the widest part of the buttocks, keeping the tape parallel to the floor. As with WC, we were careful not to compress the skin during measurement. We derived the waist-to-hip ratio (WHR) by dividing the WC by the HC, both measured in centimeters.

To assess physical activity levels, we employed the International Physical Activity Questionnaire (IPAQ). Trained interviewers administered the IPAQ through face-to-face sessions with participants [32]. We quantified physical activity in metabolic equivalents per week (METs/week), following established guidelines [33, 34].

2.4. Assessment of Other Variables

Trained dietitians administered a standardized demographic questionnaire to assess variables such as age, sex, waist and HC, smoking status, education level, family size, homeownership, foreign travel, income, and socioeconomic status (SES) [35]. We categorized smoking status into two groups: “Yes” for daily, occasional, and former smokers and “No” for individuals who had never smoked. To evaluate SES, we developed a scoring system based on three key factors. The first factor considered household size, with participants receiving one point for households of four or fewer members and zero points for larger households. The second factor assessed educational attainment, awarding one point for a Bachelor’s degree or higher and zero points for education below this level. The third factor examined housing ownership, with one point given for owned property and zero points for rented accommodation. We calculated the total SES score by summing these individual scores. Participants were then classified into SES categories based on their total score. Those with a score of three were categorized as high SES, those with a score of two as moderate SES, and those with a score of zero or one as low SES.

2.5. Statistical Analysis

All statistical analyses were performed using the Statistical Package Software for Social Science (SPSS), Version 21 (SPSS Inc., Chicago, IL, USA). A p value of < 0.05 was considered statistically significant. Data normality was assessed using the Kolmogorov–Smirnov test. General characteristics and dietary intake data were presented as frequencies (%) for qualitative variables and as mean ± SD or median (25–75 IQR) for quantitative variables. Participants were classified into tertiles based on their PI score. The general and dietary variables according to tertiles of the PI score were presented, and the p values for trends in categorical and continuous variables were calculated using linear regression and chi-square tests. We used multivariable logistic regression to determine the possible association between PI score tertiles and the odds of NAFLD. The final models were adjusted for age, sex, WHR, smoking, energy intake, fiber intake, physical activity, and SES [36]. The OR and 95% confidence interval (CI) for NAFLD across the PI score tertiles were reported.

3. Results

The median (IQR) of dietary PI in the case and control groups was 83.9 (55.8–114.2) and 82.1 (52.6–119.6), respectively. Table 1 shows the characteristics of participants according to tertiles of dietary PI. Participants in the highest tertile of PI had significantly higher HC than those in the lowest tertile (p = 0.003). However, no significant differences were observed in age, sex, WC, WHR, physical activity, smoking, education level, family size, house acquisition, income, or SES across PI tertiles. Our findings in Table 1 also show that dietary intakes of energy, protein, fat, and saturated fatty acids increased significantly across PI tertiles, whereas carbohydrate, polyunsaturated fatty acids, and fiber intakes significantly decreased.

Table 1. Population characteristics based on the tertiles of the phosphatemic index.
Variables Tertiles of the phosphatemic index P-trend
T1 (n = 224) T2 (n = 225) T3 (n = 226)
Demographic data
Age (year) 37.7 ± 8.82 38.2 ± 8.84 38.3 ± 8.91 0.447
Male (%) 58.5 52.4 48.2 0.091
Waist circumference (cm) 89.7 ± 10.6 90.9 ± 10.5 91.4 ± 11.5 0.100
Hip circumference (cm) 98.3 ± 7.00 99.4 ± 8.40 100.6 ± 8.45 0.003
Waist-to-hip ratio 0.91 ± 0.07 0.91 ± 0.7 0.90 ± 0.07 0.661
Physical activity (MET/h/week) 1150 (785–1544) 1250 (852–1987) 1234 (790–1865) 0.526
Smoking (yes, %) 4.0 5.3 3.1 0.489
Education level (bachelor and higher, %) 45.5 49.8 45.1 0.550
Family size (> 4 members, %) 71.9 65.3 63.3 0.130
House acquisition (yes, %) 69.6 68.4 67.3 0.862
Foreign travel (yes, %) 15.2 14.2 18.1 0.494
Income (high, %) 10.7 12.0 11.5 0.911
Socioeconomic status (%) 0.139
 Low (%) 24.1 28.4 31.9
 Middle (%) 44.6 35.1 35.0
 High (%) 31.3 36.4 33.2
  
Dietary intake
Energy intake (kcal/d) 2026 ± 636 2198 ± 551 2429 ± 612 < 0.001
Carbohydrate (% kcal) 57.3 ± 6.99 56.7 ± 6.75 53.6 ± 6.20 < 0.001
Protein (% kcal) 12.5 ± 2.13 13.3 ± 2.37 13.8 ± 2.19 < 0.001
Fat (% kcal) 30.0 ± 7.22 29.8 ± 6.62 32.5 ± 6.40 < 0.001
Polyunsaturated fatty acids (% kcal) 6.87 ± 2.67 6.08 ± 2.03 6.34 ± 2.05 0.026
Monounsaturated fatty acids (% kcal) 10.7 ± 3.00 10.2 ± 2.58 11.0 ± 2.54 0.173
Saturated fatty acids (% kcal) 9.29 ± 2.89 10.2 ± 2.69 11.6 ± 2.81 < 0.001
Fiber (g/1000 kcal) 17.1 ± 8.16 16.2 ± 7.56 15.0 ± 5.29 0.002
  • Note: Data are presented as mean ± SD or median (25–75 interquartile range) for continuous, and percentage for categorical variables. P for trend was calculated using linear regression and the Chi-square test for continuous and categorical variables, respectively.

Table 2 presents the dietary intake of PI food sources based on the tertiles of PI. The dietary intake of fish, ham, eggs, milk, cheeses, and broccoli increased significantly with higher PI tertiles (p < 0.05). While soy intake increased across PI tertiles, this trend was not statistically significant (p = 0.191).

Table 2. Dietary intake of major phosphate food sources based on the tertiles of phosphatemic index.
Variables Tertiles of phosphatemic index P-trend
T1 (n = 224) T2 (n = 225) T3 (n = 226)
Food sources (g/d)
 Soy 0.04 (0.0–1.03) 0.06 (0.0–1.50) 0.14 (0.0–2.14) 0.191
 Fish 5.51 (3.22–9.50) 6.74 (3.59–12.6) 8.09 (4.51–16.3) < 0.001
 Ham 0.35 (0.0–2.16) 0.35 (0.0–2.16) 0.44 (0.0–2.16) 0.026
 Egg 7.62 (3.56–15.25) 12.4 (6.67–22.8) 15.2 (7.62–22.8) < 0.001
 Milk 40.8 (11.7–74.0) 74.0 (32.8–142) 230 (171–313) < 0.001
 Chesses 4.28 (1.00–12.85) 30.0 (12.85–30.0) 30.0 (30.0–30.0) < 0.001
 Broccoli 2.54 (0.25–6.20) 3.10 (0.50–6.20) 3.10 (0.63–13.2) < 0.001
  • Note: Data are presented as median (25–75 interquartile range).

Table 3 shows the ORs and 95% CI of NAFLD across tertiles of PI. In the crude model, there was a nonsignificant association between PI and the odds of NAFLD (OR = 1.45; 95% CI: 0.97–2.15, P for trend = 0.074). After adjusting for age and sex in Model 1, the association remained nonsignificant (OR = 1.47; 95% CI: 0.98–2.19, P for trend = 0.065). However, after additionally adjusting for WHR, physical activity, smoking, SES, and energy intake, individuals in the highest PI tertile showed significantly higher odds of NAFLD compared to those in the lowest tertile (OR = 1.92; 95% CI: 1.06–3.47, P for trend = 0.034). In the final model, after additional adjustment for dietary fiber intake, the association between PI and the odds of NAFLD remained statistically significant (OR = 1.97; 95% CI: 1.08–3.58, P for trend = 0.028).

Table 3. Odds ratios (ORs) and 95% confidence intervals (CIs) for NAFLD based on tertiles of dietary phosphatemic index.
OR (95% CI) of NAFLD
Tertiles of dietary phosphatemic index P for trend
T1 T2 T3
Phosphatemic index
 Median score 85 177 298
 Case/total 64/224 78/225 83/226
 Crude model 1.00 (ref) 1.32 (0.89–1.97) 1.45 (0.97–2.15) 0.074
 Model 1  1.00 (ref) 1.33 (0.89–1.99) 1.47 (0.98–2.19) 0.065
 Model 2 1.00 (ref) 1.56 (0.89–2.75) 1.92 (1.06–3.47) 0.034
 Model 3 1.00 (ref) 1.57 (0.89–2.76) 1.97 (1.08–3.58) 0.028
  • Model 1: Adjusted for age and sex.
  • Model 2: Adjusted for Model 1 and waist-to-hip ratio, smoking, physical activity, socioeconomic status, and dietary intake of energy.
  • Model 3: Adjusted for Model 2 and fiber intake.

4. Discussion

The current study is the first study that has assessed the relationship between dietary PI, determined based on major phosphorus sources (soy, fish, ham, egg, milk, cheeses, and broccoli) and the odds of NAFLD. Our findings indicate that a higher dietary PI can be associated with an increase in the odds of NAFLD.

Our findings align with the previous studies suggesting that elevated serum phosphorus levels can increase the risk of mortality and various chronic disorders, such as CVDs [13, 14, 3739]. Achinger et al. demonstrated that controlling serum phosphorus concentration in dialysis patients can reduce ventricular hypertrophy by improving vascular compliance [13]. Although this effect is more pronounced in individuals with renal disorders [37], higher serum phosphorus levels have been shown to independently increase the risk of heart failure and mortality in people with normal kidney function [14, 38, 39]. Jhuang et al. found that higher serum phosphorus levels in older adults can increase the risk of metabolic syndrome and its components [27]. In the framework of the Atherosclerosis Risk in Communities Study (ARIC), Onufrak et al. reported that serum phosphorous was positively associated with atherosclerosis risk in the general population, independent of common atherosclerosis risk factors [40]. Moreover, evidence suggests a causal relationship between serum phosphorus and prostate cancer incidence [41], indicating that long-term elevated serum phosphorus may be a risk factor for chronic diseases. The adverse health effects of high serum phosphorus levels may be explained by its positive association with inflammation, vascular dysfunction, cardiac remodeling, low-grade albuminuria, elevated serum parathyroid hormone (PTH), and high serum FGF23 [39]. A cross-sectional study also showed that higher serum phosphorus levels are dose-dependently associated with increased NAFLD risk, and the prevalence of dyslipidemia is higher in the upper quartiles of serum phosphorus [19]. In addition, higher serum FGF-23 levels have been linked to insulin resistance [27]. Jamialahmadi et al. demonstrated that high serum PTH levels provoke inflammation and are associated with liver fibrosis and steatosis [42]. Finally, serum phosphorus elevation, by affecting phospholipids metabolism, increases the synthesis of lipid particles such as cholesterol. This disturbance in glucose metabolism, along with elevated serum phosphorus, can lead to an increase in serum triglycerides [23].

Our results can be explained by the fact that serum phosphorus is regulated by intake, output (30% gastrointestinal and 70% renal), absorption, and distribution [43]. Although the majority of the population consumes more phosphorous than the recommended daily allowance, its long-term health effects are not clear. Evidence linking dietary phosphorous to clinical outcomes is limited and inconclusive [17]. In the framework of the National Health and Nutrition Examination Survey (NHANES) III cohort, Chang et al. and Mendonça et al. reported that high dietary phosphorus intake, regardless of serum phosphorus levels, may increase the risk of all-cause mortality in the healthy population [17, 37]. In contrast, some studies indicate that the effect of high serum phosphorus on all-cause mortality may be influenced by phosphorus bioavailability [17]. De Boer et al. found that each 500 mg increase in dietary phosphate intake corresponds to a 0.06 mg/dL increase in serum phosphorus levels [44]. One possible explanation for these conflicting results is the timing of measurement, as many studies have assessed the relationship between dietary phosphorus intake and fasting serum phosphorus levels [43]. Considering the diurnal changes in serum phosphorus level, which peak in the afternoon, measuring the relationship between dietary phosphorus and serum phosphorus later in the day might resolve this conflict. Available evidence suggests controlling dietary phosphorus intake, restricting protein consumption, or using phosphate binders to manage serum phosphorus levels [43]. PI, as a novel index, accounts for phosphorus bioavailability, tissue distribution, and renal excretion [26]. Thus, PI may have potential for predicting the risk of chronic disorders such as NAFLD.

NAFLD is a proinflammatory condition that begins with fat accumulation in the liver and can progress to cirrhosis; it is also considered as a manifestation of metabolic syndrome in the liver [45]. The health burden of NAFLD extends beyond the liver, as patients often face increased mortality rates due to CVDs and cancer. Armstrong et al. showed that NAFLD can be classified as an independent risk factor for extra-hepatic disorders such as CVDs [7]. Our results indicate that a higher PI, which reflects elevated postprandial serum phosphorus, is associated with an increased risk of NAFLD. These findings suggest that modifying dietary PI could help manage NAFLD risk. Some studies have examined the relationship between various components of the PI, including meat and dairy [46], and an increased risk of liver disease. Zhang et al. found that diets high in animal foods, which lead to greater intake of animal protein and heme iron, increase the risk of NAFLD in Chinese adults [47]. Part of the PI score is related to the consumption of eggs and red meat, sources of saturated fatty acids that contribute to chronic diseases. In contrast, other components of PI, such as fish, soy, and broccoli, have protective effects against these disorders due to their high nutrient content, including omega-3 fatty acids. Therefore, the results of this study highlight the importance of PI as it integrates these food groups, emphasizing dietary phosphorus intake as a risk factor for liver diseases.

There is potential for future studies to confirm these results. The current PI is based on major food sources of organic phosphorous; however, inorganic phosphorus, which is mainly found in food additives, has higher bioavailability (90%–100%) compared to organic phosphorus and should be considered [26]. Serum phosphate levels may also be influenced by age and sex, with older men typically having lower serum phosphorus levels than younger men and levels increasing after menopause [37]. In addition, factors such as estrogen use and supplementation with vitamin D or calcium may also affect serum phosphorus levels [48].

Our study has several strengths. It is the first observational investigation that assesses the association between the PI of the diet and the odds of NAFLD using a representative sample size. Moreover, we employed valid and reliable food frequency and physical activity questionnaires to collect data from the study population. However, some limitations should be noted. The case-control design of our study prevents us from establishing causality between exposures and outcomes. In addition, while we used a validated FFQ, measurement errors may still occur. At the same time, while we adjusted for multiple confounding variables in our statistical analysis, some residual confounding effects may persist due to unmeasured or unknown factors. The wide range for CIs in the present study, which has a case-control design and a smaller sample size than a cohort study, is to be expected; however, the power level of the present study was calculated using the G power software based on the study sample size, the Type I and II error of 5% and 20%, respectively, the ratio of beta to alpha equal to 4, and R2 of confounder variables included to the regression model equal to 0.596. The power of study estimated as 0.98, which indicates sufficient study power. We utilized ultrasonography to detect NAFLD in our study. While liver biopsy and MRI are considered gold standard tests for diagnosing NAFLD, ultrasonography offers a safe, cost-effective alternative with high sensitivity and specificity. It can be performed easily in outpatient settings and is effective in detecting hepatic steatosis, a key indicator of NAFLD. Thus, ultrasonography serves as a practical screening tool for the early detection of NAFLD. Moreover, our hospital-based case-control study was conducted at a single metabolic liver disease center in Iran, affiliated with the Isfahan University of Medical Sciences. This may limit the generalizability of our findings; however, the center serves as a referral clinic for patients from various parts of Isfahan city.

5. Conclusions

In conclusion, our findings suggest that a diet with higher PI score can be associated with increased odds of NAFLD. Further high-quality population-based prospective studies are required to confirm these results and elucidate the possible association of dietary and serum levels of phosphorous and NAFLD risk in other population.

Conflicts of Interest

The authors declare no conflicts of interest.

Author Contributions

M.K.J., A.S.-S., and F.T. contributed to conceptualizing and designing the current study. H.A., F.T., and H.F. analyzed and interpreted the data. M.N., N.S., M.K.J., and H.A. drafted the initial manuscript. H.F. and P.M. supervised the project. All authors read and approved the final manuscript.

M.K.J. and F.T. equally contributed to this work (equally first author).

Funding

No funding was received for this research.

Acknowledgments

We extend our gratitude to Isfahan University of Medical Sciences and Hormozgan University of Medical Sciences for their support and collaboration in this research. We also thank all participants for their contributions to this study.

    Data Availability Statement

    The datasets analyzed in the current study are available from the corresponding author on reasonable request.

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