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Article

Interaction between the PNPLA3 Gene and Nutritional Factors on NAFLD Development: The Korean Genome and Epidemiology Study

by
Sooyeon Oh
1,
Jooho Lee
2,*,
Sukyung Chun
2,
Ja-Eun Choi
3,
Mi Na Kim
2,
Young Eun Chon
2,
Yeonjung Ha
2,
Seong-Gyu Hwang
2,
Sang-Woon Choi
1 and
Kyung-Won Hong
3,*
1
Chaum Life Center, CHA University School of Medicine, Seoul 06062, Republic of Korea
2
Department of Gastroenterology, CHA Bundang Medical Center, CHA University School of Medicine, Seongnam 13496, Republic of Korea
3
Healthcare R&D Division, Theragen Bio Co., Ltd., Suwon 16229, Republic of Korea
*
Authors to whom correspondence should be addressed.
Nutrients 2023, 15(1), 152; https://doi.org/10.3390/nu15010152
Submission received: 27 November 2022 / Revised: 18 December 2022 / Accepted: 21 December 2022 / Published: 28 December 2022
(This article belongs to the Special Issue Nutrition and Gene Interaction)
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Abstract

:
Genetic and nutritional factors contribute to the development of non-alcoholic fatty liver disease (NAFLD); however, gene–diet interactions in NAFLD development are poorly understood. In this case–control study, a large dataset from the Korean Genome and Epidemiology Study cohort (n = 72,299) comprising genomic data, medical records, social history, and dietary data was used. We investigated the interactions between the PNPLA3 rs738409 genotype and nutritional factors and their possible effect on the risk of NAFLD development in 2950 patients with NAFLD and 12,907 controls. In the PNPLA3 risk allele group, high protein, fat, sodium, phosphorus, niacin, and vitamin B6 intakes were associated with a decreased risk of NAFLD. In the non-risk allele group, only high fat intake was associated with a decreased risk of NAFLD. Among these nutrients, high sodium intake had a significant protective interaction with the PNPLA3 genotype against NAFLD (p = 0.002). Among salty foods, only kimchi had a significant protective effect against the PNPLA3 genotype (p = 0.012). Thus, the PNPLA3 genotype is differentially associated with nutritional factors. In particular, it interacts with kimchi, a fermented vegetable dish. Therefore, fermented vegetables may serve as a tailored therapeutic food for people with the PNPLA3 risk allele.

1. Introduction

Non-alcoholic fatty liver disease (NAFLD) is one of the most common liver diseases worldwide, and its prevalence continues to increase [1]. As a spectrum of liver disease that begins with simple steatosis, NAFLD is considered a hepatic manifestation of metabolic syndrome. Owing to an increase in obesity, the total number of NAFLD cases is projected to increase by 0–30% between 2016–2030 [2]. This is a public health concern because NAFLD can progress to non-alcoholic steatohepatitis, cirrhosis, and ultimately hepatocellular carcinoma (HCC). Statistics have shown that the incidence of HCC associated with NAFLD is increasing [3]. If this trend persists, NAFLD will become the principal cause of liver transplantation and liver-related mortality in the coming decades [4].
Traditionally, a sedentary lifestyle with a high calorie intake but low energy output has been considered the major cause of hepatic fat accumulation. Recent investigations have provided new insights into the pathogenesis of NAFLD. Genome-wide association studies (GWAS) have discovered that some single nucleotide polymorphisms (SNPs) increase the risk of NAFLD, including patatin-like phospholipase domain-containing 3 (PNPLA3), transmembrane 6 superfamily member 2 (TM6SF2), sorting and assembly machinery component 50 (SAMM-50), farnesyl diphosphate farnesyl transferase I (FDFT1), collagen type XIII alpha 1 (COL13A1), neurocan (NCAN), glucokinase regulatory protein (GCKR), membrane-bound O-acyltransferase domain-containing protein 7 (MBOAT7), apolipoprotein C3 (APOC3), sterol regulatory element binding transcription factor 2 (SREBF2) rs133291, membrane-bound O-acyltransferase domain-containing 7 transmembrane channel-like 4 (MBOAT7-TMC4) rs641738, 17β-hydroxysteroid dehydrogenase type 13 (HSD17B13), and serpin family member 1 (SERPINA1) [5,6,7,8,9]. Among these, the association between PNPLA3 and NAFLD risk is the most robustly observed [6]. The PNPLA3 rs738409 C > G SNP, which leads to the replacement of isoleucine with methionine at position 148 (I148M), contributes to the development of NAFLD by promoting triglyceride synthesis and accumulation in hepatocytes [10]. The effect of this SNP was even observed in subjects with “lean NAFLD”, a condition where non-obese individuals are predisposed to develop NAFLD [11].In these circumstances, where genetic factors directly affect the pathogenesis but no specific drug therapy exists, the role of diet becomes crucial. Thus far, only a few studies have suggested the role of gene–diet interactions in NAFLD development. One such study discovered that a high intake of carbohydrates, isoflavones, and methionine increased the risk of hepatic fibrosis in NAFLD in a PNPLA3 genotype-dependent manner [12]. Another study showed that the minor allele of the haplotype in the 22q13 loci increased the risk of NAFLD via interaction with a high carbohydrate intake, including high consumption of noodles and meat [13]. Therefore, certain dietary patterns should be avoided by individuals with risk genotypes, and certain dietary patterns could potentially attenuate the genetic influence. These findings justify the idea of precision nutrition, a diet tailored to one’s genetic make-up [10,12,13,14,15]. In addition, dietary factors modulate the crosstalk between the gut microbiome and the liver through portal circulation [16,17]. Therefore, diet can also affect the gut-liver axis that plays a critical role in the development of NAFLD.In Korea, the prevalence of NAFLD (32.87–42.9%) exceeded the global average (25.24%) [9,18]. It is expected to increase in the future owing to a Westernized diet, lack of exercise, and increase in obesity and type 2 diabetes [9]. The Korean diet is based on steamed rice served with small side dishes; these side dishes are prepared mainly with vegetables, and less frequently with meat, poultry, or fish. Fermented vegetables such as kimchi are also used quite frequently. Furthermore, the traditional Korean diet uses whole mixed grains and beans; thus, it is known to be healthy, having a low glycemic index, low cholesterol, and high fiber content [19]. However, these traditional benefits have been overshadowed by an excessive consumption of refined rice, along with an increased consumption of Western foods [20].
In this study, we hypothesized that a distinctive gene–diet interaction may exist between PNPLA3 and NAFLD. We planned to test this interaction in the Korean population. The majority of Koreans belong to one ethnicity, called han minjok [21], which results in a genetic and dietary homogeneity that is beneficial for genetic and dietary studies. We used a large Korean cohort (Korean Genome and Epidemiology Study [KoGES] cohort) dataset obtained from the Korea Biobank Project (KBP) database.

2. Materials and Methods

2.1. Study Population and the Definition of NAFLD

In this case–control study, we received a dataset from the KoGES City cohort (n = 72,299) from KBP [22] that was collected from January 2004 to December 2012. The dataset included genomic data, medical records, social history, and dietary data. Patients with NAFLD were screened for the case group if they had a medical record of fatty liver disease but had neither viral hepatitis nor alcohol consumption exceeding 210 g or more per week for men and 140 g or more per week for women. Subjects without NAFLD or other underlying diseases were screened for the control group. Subjects missing medical records of fatty liver disease were excluded.

2.2. Dietary Assessment

For each nutrient, a daily intake either above or below the recommended daily value was defined as high intake or low intake, respectively. The recommended daily values used in this study are presented in Supplementary Table S1. The median value adjusted by the total energy was taken as the cut-off, and a higher intake above the median was defined as a high intake value.
For the dietary assessment, a semi-quantitative food-frequency questionnaire (FFQ) [23,24,25] comprising 103 items was developed for the KoGES data [26]. Participants reported the frequency and number of foods consumed over the past year using the FFQ. The intake of a food item three times or more per week was defined as high intake and less than three times a week as low intake. The KoGES data also provided information on the intake of 23 nutrients.

2.3. Genome-Wide Genotyping

The genotype data were provided by the Center for Genome Science, Korea National Institute of Health and were processed using the Korea Biobank Array (Affymetrix, Santa Clara, CA, USA). The experimental results of the Korea Biobank Array were filtered using the following quality control criteria: call rate > 97%, minor allele frequency > 1%, and Hardy–Weinberg equilibrium, p < 1 × 10−5. After quality control filtering, the experimental phenotypes were used to analyze the genotype datasets from the 1000 Genome Phase 1 and 2 Asian panels. GWAS identified 7,975,321 SNPs on chromosomes 1–22. The SNP genotype (PNPLA3 rs738409) of participants was extracted from the Korea Biobank array (referred to as KoreanChip), which was optimized for the Korean population to demonstrate the findings of blood biochemical traits through GWAS [27].

2.4. Statistical Analysis

We performed GWAS in our KoGES City cohort (n = 72,299) to identify the genetic indicators of NAFLD. The genetic risk of the PNPLA3 rs738409 SNP for NAFLD development was tested using additive, dominant, and recessive models. We compared the baseline characteristics between participants with and without NAFLD using the Student’s t-test for continuous variables and Pearson’s chi-squared test for categorical variables. Furthermore, the association between the PNPLA3 rs738409 SNP and NAFLD was assessed using Pearson’s chi-squared test and logistic regression analysis. The association was adjusted for age, sex, body mass index, smoking status, and alcohol intake. The influence of dietary factors on the PNPLA3 rs738409 SNP risk allele and non-risk allele groups was investigated, as was the interaction between dietary factors and the PNPLA3 rs738409 SNP. All genetic association tests were conducted using PLINK version 1.9 (https://www.cog-ge-nom-ics.org/plink) [28], and the phenotype characteristics were analyzed using the SPSS (IBM SPSS Statistics Inc., New York, NY, USA) [29] and R statistical software (v4.1.2; R Core Team) [30]. Differences were considered statistically significant at two-sided p values < 0.05.

3. Results

In the KoGES City cohort (n = 72,299), the PNPLA3 rs738409 G allele frequency was 0.41. Among the subjects (n = 72,299), those with missing data (n = 10,676) were excluded. In a review of medical records, subjects with fatty liver disease but without viral hepatitis were screened (n = 3568). Among them, participants with alcohol consumption exceeding the predefined criteria were excluded (n = 618). Among the controls (n = 58,055), subjects with underlying diseases were excluded (n = 45,148). Additionally, we excluded subjects with missing nutrient intake data in both the control group (n = 105) and in patients with NAFLD (n = 27). Finally, 2950 patients with NAFLD and 12,907 healthy controls were included in the analyses (Figure 1). The baseline characteristics of the patients with NAFLD and healthy controls are summarized in Table 1. The NAFLD group exhibited more elements of metabolic syndrome, such as higher blood pressure, higher serum levels of glucose and cholesterol, and a higher waist circumference. In contrast, there were more current drinkers and smokers in the control group (both p < 0.001) than in the NAFLD group.
The frequency of the PNPLA3 rs738409 G allele was 0.43 in the total included population, while it was 0.47 and 0.42 in the NAFLD and control groups, respectively. In our study, the PNPLA3 risk allele group (rs738409, GG + GC) had a higher proportion of NAFLD cases than the non-risk allele group (rs738409, CC) (19.9% [2110/10,616] vs. 16.0% [840/5241]; Table 1). The PNPLA3 rs738409 G allele showed a significant association with NAFLD in all additive, dominant, and recessive model analyses (odds ratio [OR] of the additive model = 1.22; 95% confidence interval, 1.15–1.30; p = 1.96 × 10−10) (Supplementary Table S2). After adjusting for age, sex, alcohol intake, and smoking, the PNPLA3 rs738409 G allele remained an independent risk factor for NAFLD (adjusted OR = 1.22; p = 1.75 × 10−10) (Table 2).

3.1. Association between Nutrients and NAFLD in the Total Population Subsection

In the macronutrient-consumption assessments, the healthy control group contained a significantly larger proportion of subjects with high protein (p < 0.001) and fat (p < 0.001) intakes (Supplementary Table S3) compared to the NAFLD group. The proportion of subjects with a high carbohydrate intake was larger in the NAFLD group than in the control group, but this difference was not statistically significant (p = 0.067) (Supplementary Table S3).
In the mineral- and vitamin-consumption assessments, a significantly larger proportion of subjects in the healthy control group had higher intakes of sodium (p = 0.003), phosphorus (p < 0.001), zinc (p = 0.04), vitamin B1 (p < 0.001), vitamin B2 (p < 0.047), niacin (p < 0.001), and vitamin B6 (p < 0.001) than those in the NAFLD group (Supplementary Table S3).
Subsequently, the proportions of NAFLD cases were compared between the high- and low-nutrient-intake groups (Table 3). High intakes of protein (OR = 0.765, p < 0.0001), fat (OR = 0.616, p < 0.0001), sodium (OR = 0.884, p < 0.0001), phosphorus (OR = 0.850, p < 0.0001), zinc (OR = 0.919, p = 0.043), vitamin B1 (OR = 0.771, p < 0.0001), vitamin B2 (OR = 0.874, p = 0.044), niacin (OR = 0.746, p < 0.0001), and vitamin B6 (OR =0.842, p < 0.0001) significantly decreased the risk of NAFLD.

3.2. Association between Nutrients and NAFLD According to the PNPLA3 Genotype Figures, Tables, and Schemes

To investigate the differential association between nutrient intake and NAFLD according to the PNPLA3 genotype, the population was divided into two groups (Table 4). In the PNPLA3 risk allele group (rs738409, GG + GC), high intakes of protein (OR 0.821, p = 0.001), fat (OR 0.755, p < 0.0001), sodium (OR 0.771, p < 0.0001), phosphorus (OR 0.851, p = 0.009), niacin (OR 0.800, p < 0.0001), vitamin B6 (OR 0.823, p = 0.001), and ash (OR 0.833, p = 0.017) were associated with a decreased risk of NAFLD (Table 4). In the non-risk allele group (rs738409, CC), a high fat intake was associated with a decreased risk of NAFLD (OR 0.794, p = 0.009), but other nutrients did not show a significant association (Table 4).
Subsequently, the interaction between nutrients and the PNPLA3 genotype was analyzed. Among the nutrients investigated, only a high sodium intake had a statistically significant protective interaction with the PNPLA3 genotype against NAFLD development (interaction p = 0.002) (Table 4). The data are described in more detail in Supplementary Tables S4 and S5 and Figure 2. In these univariate analyses, a high intake of sodium exhibited a significant protective effect against NAFLD in the PNPLA3 risk group (p < 0.0001) (Supplementary Table S4), while no such effect was observed in the PNPLA3 non-risk group (p = 0.851) (Supplementary Table S5).

3.3. Protective Foods against NAFLD in the PNPLA3 Risk Allele Group

To determine which food the sodium originated from, the association between food-intake frequency and NAFLD was investigated in the PNPLA3 genotype groups (Table 5). In the PNPLA3 risk allele group (rs738409, GG + GC), white rice, baechu kimchi, leaf mustard or scallion kimchi, green pepper, orange, coffee, sugar (for tea/coffee), and cream (for tea/coffee) were protective against NAFLD (all p < 0.05), while multigrain rice, yogurt, nuts, pickled radish, vegetable salad, and fried food increased the risk of NAFLD development (all p < 0.05). In the PNPLA3 non-risk allele group (rs738409, CC), white rice, baechu kimchi, green pepper, orange, strawberry, pear, coffee, sugar (for tea/coffee), and cream (for tea/coffee) were protective (all p < 0.05), whereas multigrain rice, yogurt, green tea, and fried food increased the NAFLD risk (all p < 0.05).
Among the foods studied, baechu kimchi had a statistically significant protective interaction with the PNPLA3 genotype against NAFLD development (p = 0.012) (Table 5 and Figure 2). This interaction between baechu kimchi and the PNPLA3 genotype was very similar to the interaction between sodium and the PNPLA3 genotype. Based on these observations, we concluded that the protective effect of a high sodium intake was probably due to the intake of baechu kimchi.

4. Discussion

Using the KoGES database, we discovered that nutrients and foods differentially affect the risk of NAFLD depending on the PNPLA3 genotype, and an interaction exists between nutrients or foods and the PNPLA3 genotype in the risk of NAFLD. A high fat intake decreased the risk of NAFLD, regardless of the PNPLA3 genotype. High intakes of protein, phosphorus, sodium, niacin, vitamin B6, and carotene were associated with a decreased risk of NAFLD in the PNPLA3 risk allele group. Among these protective nutrients, only sodium showed a significant interaction with the PNPLA3 genotype. To identify the source of the sodium, the association between food intake frequency and the PNPLA3 genotype was investigated. Only baechu kimchi, a salty food, had a significant protective interaction with the PNPLA3 genotype with respect to high sodium intake: a high intake of baechu kimchi significantly decreased the risk of NAFLD, with a greater magnitude in the PNPLA3 risk allele group than in the PNPLA3 non-risk allele group. Therefore, the preventive effect of a high sodium intake in the PNPLA3 risk allele group appears to be derived from high intake of baechu kimchi.
In the current study, a high sodium intake was inversely associated with NAFLD. This observation was a conundrum. It is possible that a high-sodium diet may indeed have a preventive effect on NAFLD. A recent animal study demonstrated that mice fed a high-sodium and high-fat diet developed less hepatic steatosis, metabolic syndrome, and insulin resistance compared with those fed a normal or low-sodium and high-fat diet [31]. The reduced steatosis was associated with lower serum aldosterone levels and downregulation of hepatic mineralocorticoid receptors; thus, decreased activation of hepatic mineralocorticoids may have resulted in beneficial downstream inhibition of lipogenesis [31]. However, many studies have reported the opposite: a high-salt diet has been linked to increased glucocorticoid production, insulin resistance, metabolic syndrome, and NAFLD development [32,33,34]. To draw a reasonable explanation for our results, we searched for food items that correlated with a high sodium intake. Among the salty foods we investigated, salted seafood, cheese, and pickled radish increased the risk of NAFLD. Kimchi alone exerted a protective effect. Thus, we concluded that the major source of dietary sodium in this study was kimchi [35], and that the high sodium intake may have been a confounding factor.
Kimchi, a representative Korean food, is a salted and fermented vegetable usually prepared from winter cabbage [36]. The health benefits of kimchi have been widely reported [36]. Among these is an improvement in metabolic markers such as blood glucose level, cholesterol level, insulin resistance, body weight, and body mass index, which have been tested in both animals [37,38,39] and humans [40,41,42]. Furthermore, kimchi exerts a beneficial effect against hepatic damage by reducing hepatic lipid synthesis and inflammatory cytokines [39]. These beneficial effects of kimchi on metabolic markers result from their modulation of gut microbiota [43,44,45,46]. Tests performed on probiotics extracted from kimchi were found to have the same beneficial effects [45,47,48,49].
The key factor linking kimchi and its protective effect against NAFLD may be the short-chain fatty acids (SCFAs) produced by the gut microbiome [50]. Among SCFAs, butyrate is known to reduce obesity and related metabolic complications, including NAFLD [50,51]. An animal study demonstrated that mice fed sodium butyrate and a high-fat diet had significantly decreased hepatic steatosis and decreased hepatic triglyceride and cholesterol levels compared with mice fed only a high-fat diet [52]. Another animal study demonstrated that SCFAs (acetate, propionate, and butyrate) ameliorate methionine- and choline-deficient diet-induced hepatic steatosis and inflammation [53]. A human study tested the gut microbiome and found fewer butyrate-producing bacteria in patients with NAFLD [54]. These studies indicated that SCFAs, including butyrate, exert a preventive effect against NAFLD. Probiotics (such as Lactobacillus casei) combined with plant extracts reduced the markers of NAFLD and increased the concentration of butyric acid in a mouse model [55]. As a fermented vegetable, kimchi simultaneously possesses the characteristics of both probiotics and prebiotics. Thus, it enhances gut microbial diversity [43] and, in turn, has a beneficial effect on NAFLD. In particular, the protective effect of kimchi on PNPLA3 risk genotypes indicates that fermented vegetables, such as kimchi, may be chosen as tailored foods for those affected.
In the present study, a high fat and protein intake had a preventive effect against NAFLD. A high-fat and low-carbohydrate diet, often known as a ketogenic diet, is a popular weight-reduction method. Many clinical trials have demonstrated that a ketogenic diet is effective in improving NAFLD [56,57,58,59,60,61,62]. A ketogenic diet decreases intrahepatic insulin resistance and, thus, serum insulin concentration [56,57,58,59,60,61,62]. This, in turn, increases the net hydrolysis of intrahepatic triglycerides, thus improving hepatic steatosis, inflammation, and fibrosis [56,57,58,59,60,61,62]. Similarly, a low-carbohydrate, high-protein diet also has metabolic benefits with regard to NAFLD. A human study demonstrated that an isocaloric low-carbohydrate diet with a high protein content decreased hepatic de novo lipogenesis, increased serum β-hydroxybutyrate concentrations (reflecting increased mitochondrial β-oxidation), and increased folate-producing Streptococcus and serum folate concentrations [63]. Furthermore, transcriptomic analysis revealed downregulation of the fatty acid synthesis pathway and upregulation of the folate-mediated one-carbon metabolism and fatty acid oxidation pathways [63]. Our study demonstrated that a high fat intake produces a protective effect regardless of the PNPLA3 genotype, whereas a high intake of protein is more beneficial in the PNPLA3 risk allele group. Nevertheless, caution should be exercised, as a ketogenic diet can elevate liver enzymes and worsen lipid profiles in patients with NAFLD [64,65]. Additionally, this diet seems less effective in premenopausal women [66]; its recommendation should therefore be carefully considered for these patients.
PNPLA3 encodes a calcium-independent triacylglycerol lipase that contains a phospholipase domain. Upon liver injury, PNPLA3 expression is induced in hepatic stellate cells (HSCs) [67]. In the presence of PNPLA3 I148M, HSCs become more proinflammatory and profibrogenic and produce less retinol, which potentially mediates the transition of HSCs into a myofibroblast-like phenotype [67,68]. Our findings suggest that a high retinol intake reduces the risk of NAFLD and may be relevant in this regard. Whether retinol intake prevents NAFLD in PNPLA3 risk genotypes warrants further investigation.
This study had several limitations. First, there may have been a recall bias in collecting data on food intake habits because of the self-reporting nature of the food-frequency questionnaire. Second, the identification of fatty liver depended solely on the medical records of the subjects. The nationwide database did not include the results of imaging studies such as sonography, computed tomography, or magnetic resonance imaging. Thus, a detailed evaluation of NAFLD severity was not possible. Third, the present study could not count the various beneficial nutrients such as antioxidants, polyunsaturated fatty acids, or amino acids. As these beneficial nutrients may directly attenuate the oxidative stress that results in hepatic fibrosis [14,69], future studies need to consider these detailed aspects of nutrition.
In the present study, the associations and interactions between NAFLD, the PNPLA3 genotype, and nutritional and dietary factors were investigated. The genetic risk of NAFLD conferred by PNPLA3 in the Korean population was verified. In addition, we discovered some nutritional and dietary factors that can significantly influence the development of NAFLD, and these factors may interact with the PNPLA3 genotype. Both increasing the proportion of fat and protein relative to that of carbohydrate and consuming fermented vegetables may help reduce NAFLD, regardless of the PNPLA3 genotype. In PNPLA3 risk genotypes, the role of a high-protein diet appears to be more important, as does meeting the daily requirement of vitamins and minerals. Furthermore, kimchi, a fermented vegetable, may induce an added protective effect in PNPLA3 risk genotypes because of its gene–diet interaction. These results suggest that planning a therapeutic diet with high protein and fermented vegetables may be a valid strategy for those with PNPLA3 risk genotypes and NAFLD, and warrants future interventional studies. Furthermore, in the near future, studies are needed on the mechanisms of interactions between various beneficial nutrients, PNPLA3, and NAFLD development.

5. Conclusions

The PNPLA3 genotype is differentially associated with nutritional and dietary factors. In particular, it interacts with kimchi, a fermented vegetable. This indicates that tailored nutritional therapy based on an individual’s genetic background may be a good strategy to prevent NAFLD, and fermented vegetables may serve as a therapeutic food for those with the PNPLA3 risk allele.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/nu15010152/s1. Supplementary Table S1. Recommended daily intake of each nutrient according to the 2020 Korean Dietary Reference Intakes; Supplementary Table S2. Genetic risk of the PNPLA3 rs738409 G allele for non-alcoholic fatty liver disease (NAFLD) occurrence in genome-wide association studies (GWAS) analyses; Supplementary Table S3. Proportion of patients with a *high intake of each nutrient; Supplementary Table S4. Non-alcoholic fatty liver disease (NAFLD) case frequencies by nutrient-consumption level in the *PNPLA3 risk group (rs738409, GG + GC) (n = 10,530) determined by univariate analyses; Supplementary Table S5. Non-alcoholic fatty liver disease (NAFLD) case frequencies by nutrient-consumption level in the *PNPLA3 non-risk group (rs738409, CC) (n = 5195) determined by univariate analyses.

Author Contributions

Conceptualization, K.-W.H. and J.L.; methodology, K.-W.H., J.L. and S.-W.C.; software, S.C. and J.-E.C.; validation, M.N.K., Y.E.C., Y.H. and S.-G.H.; formal analysis, S.C. and J.-E.C.; investigation, J.L. and S.O.; resources, K.-W.H. and J.L.; data curation, K.-W.H.; writing—original draft preparation, S.O.; writing—review and editing, M.N.K., Y.E.C., Y.H., S.-G.H., J.L. and S.-W.C.; visualization, J.L. and S.O.; supervision, S.-W.C.; project administration, J.L.; funding acquisition, K.-W.H. and J.L. All authors have read and agreed to the published version of the manuscript.

Funding

This study was conducted using bioresources from the National Biobank of Korea, the Centers for Disease Control and Prevention, Republic of Korea (2019-059). This work was supported by the Technology Innovation Program (2002781, A Platform for Prediction and Management of Health Risk Based on Personal Big Data and Lifelogging), funded by the Ministry of Trade, Industry and Energy (Republic of Korea). This study was also supported by the Research Supporting Program of the Korean Association for the Study of the Liver and the Korean Liver Foundation (2020-260).

Institutional Review Board Statement

This study was conducted in accordance with the principles described in the Declaration of Helsinki. The institutional review board of the Theragen Etex Bio Institute approved this study (internal review board no. 700062-20190819-GP-006-02).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Younossi, Z.; Anstee, Q.M.; Marietti, M.; Hardy, T.; Henry, L.; Eslam, M.; George, J.; Bugianesi, E. Global burden of NAFLD and NASH: Trends, predictions, risk factors and prevention. Nat. Rev. Gastroenterol. Hepatol. 2018, 15, 11–20. [Google Scholar] [CrossRef] [PubMed]
  2. Estes, C.; Anstee, Q.M.; Arias-Loste, M.T.; Bantel, H.; Bellentani, S.; Caballeria, J.; Colombo, M.; Craxi, A.; Crespo, J.; Day, C.P.; et al. Modeling NAFLD disease burden in China, France, Germany, Italy, Japan, Spain, United Kingdom, and United States for the period 2016–2030. J. Hepatol. 2018, 69, 896–904. [Google Scholar] [CrossRef] [PubMed]
  3. Dyson, J.; Jaques, B.; Chattopadyhay, D.; Lochan, R.; Graham, J.; Das, D.; Aslam, T.; Patanwala, I.; Gaggar, S.; Cole, M. Hepatocellular cancer: The impact of obesity, type 2 diabetes and a multidisciplinary team. J. Hepatol. 2014, 60, 110–117. [Google Scholar] [CrossRef] [PubMed]
  4. Anstee, Q.M.; Targher, G.; Day, C.P. Progression of NAFLD to diabetes mellitus, cardiovascular disease or cirrhosis. Nat. Rev. Gastroenterol. Hepatol. 2013, 10, 330–344. [Google Scholar] [CrossRef]
  5. Tong, M.; Wang, F. APOC3rs2854116, PNPLA3rs738409, and TM6SF2rs58542926 polymorphisms might influence predisposition of NAFLD: A meta-analysis. IUBMB Life 2020, 72, 1757–1764. [Google Scholar] [CrossRef]
  6. Lorenz, B.; Georg, S.; Hannes, O.; Stephan, Z.; Michael, S.; Leonora, D.; David, N.; Alexandra, F.; Felix, S.; Christian, D.; et al. PNPLA3 is the dominant SNP linked to liver disease severity at time of first referral to a tertiary center. Dig. Liver Dis. 2021, 54, 84–90. [Google Scholar] [CrossRef]
  7. Carlsson, B.; Lindén, D.; Brolén, G.; Liljeblad, M.; Bjursell, M.; Romeo, S.; Loomba, R. Review article: The emerging role of genetics in precision medicine for patients with non-alcoholic steatohepatitis. Aliment. Pharmacol. Ther. 2020, 51, 1305–1320. [Google Scholar] [CrossRef]
  8. Gao, F.; Zheng, K.I.; Chen, S.-D.; Lee, D.H.; Wu, X.-X.; Wang, X.-D.; Targher, G.; Byrne, C.D.; Chen, Y.-P.; Kim, W.; et al. Individualized Polygenic Risk Score Identifies NASH in the Eastern Asia Region: A Derivation and Validation Study. Clin. Transl. Gastroenterol. 2021, 12, e00321. [Google Scholar] [CrossRef]
  9. Huh, Y.; Cho, Y.J.; Nam, G.E. Recent Epidemiology and Risk Factors of Nonalcoholic Fatty Liver Disease. J. Obes. Metab. Syndr. 2022, 31, 17–27. [Google Scholar] [CrossRef]
  10. Mullins, V.A.; Bresette, W.; Johnstone, L.; Hallmark, B.; Chilton, F.H. Genomics in Personalized Nutrition: Can You “Eat for Your Genes”? Nutrients 2020, 12, 3118. [Google Scholar] [CrossRef]
  11. Vilarinho, S.; Ajmera, V.; Zheng, M.; Loomba, R. Emerging Role of Genomic Analysis in Clinical Evaluation of Lean Individuals With NAFLD. Hepatology 2021, 74, 2241–2250. [Google Scholar] [CrossRef] [PubMed]
  12. Vilar-Gomez, E.; Pirola, C.J.; Sookoian, S.; Wilson, L.A.; Belt, P.; Liang, T.; Liu, W.; Chalasani, N. Impact of the Association Between PNPLA3 Genetic Variation and Dietary Intake on the Risk of Significant Fibrosis in Patients With NAFLD. Am. J. Gastroenterol. 2021, 116, 994–1006. [Google Scholar] [CrossRef] [PubMed]
  13. Park, S.; Kang, S. High carbohydrate and noodle/meat-rich dietary patterns interact with the minor haplotype in the 22q13 loci to increase its association with non-alcoholic fatty liver disease risk in Koreans. Nutr. Res. 2020, 82, 88–98. [Google Scholar] [CrossRef] [PubMed]
  14. Hernandez-Rodas, M.C.; Valenzuela, R.; Videla, L.A. Relevant Aspects of Nutritional and Dietary Interventions in Non-Alcoholic Fatty Liver Disease. Int. J. Mol. Sci. 2015, 16, 25168–25198. [Google Scholar] [CrossRef] [Green Version]
  15. Kalafati, I.P.; Dimitriou, M.; Borsa, D.; Vlachogiannakos, J.; Revenas, K.; Kokkinos, A.; Ladas, S.D.; Dedoussis, G.V. Fish intake interacts with TM6SF2 gene variant to affect NAFLD risk: Results of a case-control study. Eur. J. Nutr. 2019, 58, 1463–1473. [Google Scholar] [CrossRef]
  16. Fianchi, F.; Liguori, A.; Gasbarrini, A.; Grieco, A.; Miele, L. Nonalcoholic Fatty Liver Disease (NAFLD) as Model of Gut-Liver Axis Interaction: From Pathophysiology to Potential Target of Treatment for Personalized Therapy. Int. J. Mol. Sci. 2021, 22, 6485. [Google Scholar] [CrossRef]
  17. Kong, L.; Lu, Y.; Zhang, S.; Nan, Y.; Qiao, L. Role of nutrition, gene polymorphism, and gut microbiota in non-alcoholic fatty liver disease. Discov. Med. 2017, 24, 95–106. [Google Scholar]
  18. Lee, H.W.; Kim, B.K.; Kim, S.U.; Park, J.Y.; Kim, D.Y.; Ahn, S.H.; Kim, K.J.; Han, K.H. Prevalence and Predictors of Significant Fibrosis Among Subjects with Transient Elastography-Defined Nonalcoholic Fatty Liver Disease. Dig. Dis. Sci. 2017, 62, 2150–2158. [Google Scholar] [CrossRef]
  19. Kim, M.J.; Park, S.; Yang, H.J.; Shin, P.-K.; Hur, H.J.; Park, S.-J.; Lee, K.-H.; Hong, M.; Kim, J.H.; Choi, S.-W.; et al. Alleviation of Dyslipidemia via a Traditional Balanced Korean Diet Represented by a Low Glycemic and Low Cholesterol Diet in Obese Women in a Randomized Controlled Trial. Nutrients 2022, 14, 235. [Google Scholar] [CrossRef]
  20. Lim, M.Y.; Hong, S.; Bang, S.J.; Chung, W.H.; Shin, J.H.; Kim, J.H.; Nam, Y.D. Gut Microbiome Structure and Association with Host Factors in a Korean Population. mSystems 2021, 6, e0017921. [Google Scholar] [CrossRef]
  21. Wang, C.C.; Yeh, H.Y.; Popov, A.N.; Zhang, H.Q.; Matsumura, H.; Sirak, K.; Cheronet, O.; Kovalev, A.; Rohland, N.; Kim, A.M.; et al. Genomic insights into the formation of human populations in East Asia. Nature 2021, 591, 413–419. [Google Scholar] [CrossRef] [PubMed]
  22. Kim, Y.; Han, B.G. Cohort Profile: The Korean Genome and Epidemiology Study (KoGES) Consortium. Int. J. Epidemiol. 2017, 46, 1350. [Google Scholar] [CrossRef] [PubMed]
  23. Kwon, Y.J.; Kim, J.O.; Park, J.M.; Choi, J.E.; Park, D.H.; Song, Y.; Kim, S.J.; Lee, J.W.; Hong, K.W. Identification of Genetic Factors Underlying the Association between Sodium Intake Habits and Hypertension Risk. Nutrients 2020, 12, 2580. [Google Scholar] [CrossRef] [PubMed]
  24. Kwon, Y.J.; Park, D.H.; Choi, J.E.; Lee, D.; Hong, K.W.; Lee, J.W. Identification of the interactions between specific genetic polymorphisms and nutrient intake associated with general and abdominal obesity in middle-aged adults. Clin. Nutr. 2022, 41, 543–551. [Google Scholar] [CrossRef] [PubMed]
  25. Lee, S.B.; Choi, J.E.; Park, B.; Cha, M.Y.; Hong, K.W.; Jung, D.H. Dyslipidaemia-Genotype Interactions with Nutrient Intake and Cerebro-Cardiovascular Disease. Biomedicines 2022, 10, 1615. [Google Scholar] [CrossRef]
  26. Ahn, Y.; Kwon, E.; Shim, J.E.; Park, M.K.; Joo, Y.; Kimm, K.; Park, C.; Kim, D.H. Validation and reproducibility of food frequency questionnaire for Korean genome epidemiologic study. Eur. J. Clin. Nutr. 2007, 61, 1435–1441. [Google Scholar] [CrossRef] [PubMed]
  27. Moon, S.; Kim, Y.J.; Han, S.; Hwang, M.Y.; Shin, D.M.; Park, M.Y.; Lu, Y.; Yoon, K.; Jang, H.M.; Kim, Y.K.; et al. The Korea Biobank Array: Design and Identification of Coding Variants Associated with Blood Biochemical Traits. Sci. Rep. 2019, 9, 1382. [Google Scholar] [CrossRef] [Green Version]
  28. Purcell, S.; Neale, B.; Todd-Brown, K.; Thomas, L.; Ferreira, M.A.; Bender, D.; Maller, J.; Sklar, P.; de Bakker, P.I.; Daly, M.J.; et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 2007, 81, 559–575. [Google Scholar] [CrossRef] [Green Version]
  29. Gray, C.D.; Kinnear, P.R. IBM SPSS Statistics 19 Made Simple; Psychology Press: New York, NY, USA, 2012. [Google Scholar]
  30. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2021. [Google Scholar]
  31. Cabrera, D.; Rao, I.; Raasch, F.; Solis, N.; Pizarro, M.; Freire, M.; Sáenz De Urturi, D.; Ramírez, C.A.; Triantafilo, N.; León, J.; et al. Mineralocorticoid receptor modulation by dietary sodium influences NAFLD development in mice. Ann. Hepatol. 2021, 24, 100357. [Google Scholar] [CrossRef]
  32. Baudrand, R.; Campino, C.; Carvajal, C.A.; Olivieri, O.; Guidi, G.; Faccini, G.; Vohringer, P.A.; Cerda, J.; Owen, G.; Kalergis, A.M.; et al. High sodium intake is associated with increased glucocorticoid production, insulin resistance and metabolic syndrome. Clin. Endocrinol. 2014, 80, 677–684. [Google Scholar] [CrossRef]
  33. van den Berg, E.H.; Gruppen, E.G.; Blokzijl, H.; Bakker, S.J.L.; Dullaart, R.P.F. Higher Sodium Intake Assessed by 24 Hour Urinary Sodium Excretion Is Associated with Non-Alcoholic Fatty Liver Disease: The PREVEND Cohort Study. J. Clin. Med. 2019, 8, 2157. [Google Scholar] [CrossRef] [PubMed]
  34. Kim, M.J.; Lee, K.J. Analysis of the dietary factors associated with suspected pediatric nonalcoholic fatty liver disease and potential liver fibrosis: Korean National Health and Nutrition Examination Survey 2014-2017. BMC Pediatr. 2020, 20, 121. [Google Scholar] [CrossRef] [PubMed]
  35. Jeong, Y.; Kim, E.S.; Lee, J.; Kim, Y. Trends in sodium intake and major contributing food groups and dishes in Korea: The Korea National Health and Nutrition Examination Survey 2013–2017. Nutr. Res. Pract. 2021, 15, 382–395. [Google Scholar] [CrossRef]
  36. Park, K.-Y.; Jeong, J.-K.; Lee, Y.-E.; Daily, J.W., III. Health benefits of kimchi (Korean fermented vegetables) as a probiotic food. J. Med. Food 2014, 17, 6–20. [Google Scholar] [CrossRef] [PubMed]
  37. Islam, M.S.; Choi, H. Antidiabetic effect of Korean traditional Baechu (Chinese cabbage) kimchi in a type 2 diabetes model of rats. J. Med. Food 2009, 12, 292–297. [Google Scholar] [CrossRef]
  38. Park, J.A.; Tirupathi Pichiah, P.B.; Yu, J.J.; Oh, S.H.; Daily, J.W., 3rd; Cha, Y.S. Anti-obesity effect of kimchi fermented with Weissella koreensis OK1-6 as starter in high-fat diet-induced obese C57BL/6J mice. J. Appl. Microbiol. 2012, 113, 1507–1516. [Google Scholar] [CrossRef]
  39. Woo, M.; Kim, M.; Noh, J.S.; Park, C.H.; Song, Y.O. Preventative activity of kimchi on high cholesterol diet-induced hepatic damage through regulation of lipid metabolism in LDL receptor knockout mice. Food Sci. Biotechnol. 2018, 27, 211–218. [Google Scholar] [CrossRef]
  40. An, S.Y.; Lee, M.S.; Jeon, J.Y.; Ha, E.S.; Kim, T.H.; Yoon, J.Y.; Ok, C.O.; Lee, H.K.; Hwang, W.S.; Choe, S.J.; et al. Beneficial effects of fresh and fermented kimchi in prediabetic individuals. Ann. Nutr. Metab. 2013, 63, 111–119. [Google Scholar] [CrossRef]
  41. Choi, I.H.; Noh, J.S.; Han, J.S.; Kim, H.J.; Han, E.S.; Song, Y.O. Kimchi, a fermented vegetable, improves serum lipid profiles in healthy young adults: Randomized clinical trial. J. Med. Food 2013, 16, 223–229. [Google Scholar] [CrossRef] [Green Version]
  42. Kim, E.K.; An, S.Y.; Lee, M.S.; Kim, T.H.; Lee, H.K.; Hwang, W.S.; Choe, S.J.; Kim, T.Y.; Han, S.J.; Kim, H.J.; et al. Fermented kimchi reduces body weight and improves metabolic parameters in overweight and obese patients. Nutr. Res. 2011, 31, 436–443. [Google Scholar] [CrossRef]
  43. Park, J.M.; Lee, W.H.; Seo, H.; Oh, J.Y.; Lee, D.Y.; Kim, S.J.; Hahm, K.B. Fecal microbiota changes with fermented kimchi intake regulated either formation or advancement of colon adenoma. J. Clin. Biochem. Nutr. 2021, 68, 139–148. [Google Scholar] [CrossRef] [PubMed]
  44. Han, Y.M.; Kang, E.A.; Park, J.M.; Oh, J.Y.; Lee, D.Y.; Choi, S.H.; Hahm, K.B. Dietary intake of fermented kimchi prevented colitis-associated cancer. J. Clin. Biochem. Nutr. 2020, 67, 263–273. [Google Scholar] [CrossRef] [PubMed]
  45. Park, S.E.; Kwon, S.J.; Cho, K.M.; Seo, S.H.; Kim, E.J.; Unno, T.; Bok, S.H.; Park, D.H.; Son, H.S. Intervention with kimchi microbial community ameliorates obesity by regulating gut microbiota. J. Microbiol. 2020, 58, 859–867. [Google Scholar] [CrossRef]
  46. Lee, S.H.; Whon, T.W.; Roh, S.W.; Jeon, C.O. Unraveling microbial fermentation features in kimchi: From classical to meta-omics approaches. Appl. Microbiol. Biotechnol. 2020, 104, 7731–7744. [Google Scholar] [CrossRef] [PubMed]
  47. Oh, M.R.; Jang, H.Y.; Lee, S.Y.; Jung, S.J.; Chae, S.W.; Lee, S.O.; Park, B.H. Lactobacillus plantarum HAC01 Supplementation Improves Glycemic Control in Prediabetic Subjects: A Randomized, Double-Blind, Placebo-Controlled Trial. Nutrients 2021, 13, 2337. [Google Scholar] [CrossRef]
  48. Youn, H.S.; Kim, J.H.; Lee, J.S.; Yoon, Y.Y.; Choi, S.J.; Lee, J.Y.; Kim, W.; Hwang, K.W. Lactobacillus plantarum Reduces Low-Grade Inflammation and Glucose Levels in a Mouse Model of Chronic Stress and Diabetes. Infect. Immun. 2021, 89, e0061520. [Google Scholar] [CrossRef] [PubMed]
  49. Park, J.E.; Oh, S.H.; Cha, Y.S. Lactobacillus Brevis OPK-3 from Kimchi Prevents Obesity and Modulates the Expression of Adipogenic and Pro-Inflammatory Genes in Adipose Tissue of Diet-Induced Obese Mice. Nutrients 2020, 12, 604. [Google Scholar] [CrossRef] [Green Version]
  50. Coppola, S.; Avagliano, C.; Calignano, A.; Berni Canani, R. The Protective Role of Butyrate against Obesity and Obesity-Related Diseases. Molecules 2021, 26, 682. [Google Scholar] [CrossRef]
  51. Brahe, L.K.; Astrup, A.; Larsen, L.H. Is butyrate the link between diet, intestinal microbiota and obesity-related metabolic diseases? Obes. Rev. 2013, 14, 950–959. [Google Scholar] [CrossRef]
  52. Zhao, Z.H.; Wang, Z.X.; Zhou, D.; Han, Y.; Ma, F.; Hu, Z.; Xin, F.Z.; Liu, X.L.; Ren, T.Y.; Zhang, F.; et al. Sodium Butyrate Supplementation Inhibits Hepatic Steatosis by Stimulating Liver Kinase B1 and Insulin-Induced Gene. Cell. Mol. Gastroenterol. Hepatol. 2021, 12, 857–871. [Google Scholar] [CrossRef]
  53. Deng, M.; Qu, F.; Chen, L.; Liu, C.; Zhang, M.; Ren, F.; Guo, H.; Zhang, H.; Ge, S.; Wu, C.; et al. SCFAs alleviated steatosis and inflammation in mice with NASH induced by MCD. J. Endocrinol. 2020, 245, 425–437. [Google Scholar] [CrossRef]
  54. Oh, J.H.; Lee, J.H.; Cho, M.S.; Kim, H.; Chun, J.; Lee, J.H.; Yoon, Y.; Kang, W. Characterization of Gut Microbiome in Korean Patients with Metabolic Associated Fatty Liver Disease. Nutrients 2021, 13, 1013. [Google Scholar] [CrossRef] [PubMed]
  55. Zhang, Z.; Zhou, H.; Guan, M.; Zhou, X.; Liang, X.; Lv, Y.; Bai, L.; Zhang, J.; Gong, P.; Liu, T.; et al. Lactobacillus casei YRL577 combined with plant extracts reduce markers of non-alcoholic fatty liver disease in mice. Br. J. Nutr. 2021, 125, 1081–1091. [Google Scholar] [CrossRef]
  56. Parra-Vargas, M.; Rodriguez-Echevarria, R.; Jimenez-Chillaron, J.C. Nutritional Approaches for the Management of Nonalcoholic Fatty Liver Disease: An Evidence-Based Review. Nutreints 2020, 12, 3860. [Google Scholar] [CrossRef]
  57. Tendler, D.; Lin, S.; Yancy, W.S.; Mavropoulos, J.; Sylvestre, P.; Rockey, D.C.; Westman, E.C. The Effect of a Low-Carbohydrate, Ketogenic Diet on Nonalcoholic Fatty Liver Disease: A Pilot Study. Dig. Dis. Sci. 2007, 52, 589–593. [Google Scholar] [CrossRef] [PubMed]
  58. Bueno, N.B.; de Melo, I.S.; de Oliveira, S.L.; da Rocha Ataide, T. Very-low-carbohydrate ketogenic diet v. low-fat diet for long-term weight loss: A meta-analysis of randomised controlled trials. Br. J. Nutr. 2013, 110, 1178–1187. [Google Scholar] [CrossRef] [Green Version]
  59. Cunha, G.M.; Guzman, G.; Correa De Mello, L.L.; Trein, B.; Spina, L.; Bussade, I.; Marques Prata, J.; Sajoux, I.; Countinho, W. Efficacy of a 2-Month Very Low-Calorie Ketogenic Diet (VLCKD) Compared to a Standard Low-Calorie Diet in Reducing Visceral and Liver Fat Accumulation in Patients With Obesity. Front. Endocrinol. 2020, 11, 607. [Google Scholar] [CrossRef]
  60. Watanabe, M.; Risi, R.; Camajani, E.; Contini, S.; Persichetti, A.; Tuccinardi, D.; Ernesti, I.; Mariani, S.; Lubrano, C.; Genco, A.; et al. Baseline HOMA IR and Circulating FGF21 Levels Predict NAFLD Improvement in Patients Undergoing a Low Carbohydrate Dietary Intervention for Weight Loss: A Prospective Observational Pilot Study. Nutrients 2020, 12, 2141. [Google Scholar] [CrossRef]
  61. Pérez-Guisado, J.; Muñoz-Serrano, A. The effect of the Spanish Ketogenic Mediterranean Diet on nonalcoholic fatty liver disease: A pilot study. J. Med. Food 2011, 14, 677–680. [Google Scholar] [CrossRef]
  62. Luukkonen, P.K.; Dufour, S.; Lyu, K.; Zhang, X.M.; Hakkarainen, A.; Lehtimäki, T.E.; Cline, G.W.; Petersen, K.F.; Shulman, G.I.; Yki-Järvinen, H. Effect of a ketogenic diet on hepatic steatosis and hepatic mitochondrial metabolism in nonalcoholic fatty liver disease. Proc. Natl. Acad. Sci. USA 2020, 117, 7347–7354. [Google Scholar] [CrossRef] [Green Version]
  63. Mardinoglu, A.; Wu, H.; Bjornson, E.; Zhang, C.; Hakkarainen, A.; Räsänen, S.M.; Lee, S.; Mancina, R.M.; Bergentall, M.; Pietiläinen, K.H.; et al. An Integrated Understanding of the Rapid Metabolic Benefits of a Carbohydrate-Restricted Diet on Hepatic Steatosis in Humans. Cell Metab. 2018, 27, 559–571.e555. [Google Scholar] [CrossRef] [PubMed]
  64. Anekwe, C.V.; Chandrasekaran, P.; Stanford, F.C. Ketogenic Diet-induced Elevated Cholesterol, Elevated Liver Enzymes and Potential Non-alcoholic Fatty Liver Disease. Cureus 2020, 12, e6605. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Watanabe, M.; Tuccinardi, D.; Ernesti, I.; Basciani, S.; Mariani, S.; Genco, A.; Manfrini, S.; Lubrano, C.; Gnessi, L. Scientific evidence underlying contraindications to the ketogenic diet: An update. Obes. Rev. 2020, 21, e13053. [Google Scholar] [CrossRef] [PubMed]
  66. D’Abbondanza, M.; Ministrini, S.; Pucci, G.; Nulli Migliola, E.; Martorelli, E.E.; Gandolfo, V.; Siepi, D.; Lupattelli, G.; Vaudo, G. Very Low-Carbohydrate Ketogenic Diet for the Treatment of Severe Obesity and Associated Non-Alcoholic Fatty Liver Disease: The Role of Sex Differences. Nutrients 2020, 12, 2748. [Google Scholar] [CrossRef]
  67. Bruschi, F.V.; Claudel, T.; Tardelli, M.; Caligiuri, A.; Stulnig, T.M.; Marra, F.; Trauner, M. The PNPLA3 I148M variant modulates the fibrogenic phenotype of human hepatic stellate cells. Hepatology 2017, 65, 1875–1890. [Google Scholar] [CrossRef] [Green Version]
  68. Hellemans, K.; Verbuyst, P.; Quartier, E.; Schuit, F.; Rombouts, K.; Chandraratna, R.A.; Schuppan, D.; Geerts, A. Differential modulation of rat hepatic stellate phenotype by natural and synthetic retinoids. Hepatology 2004, 39, 97–108. [Google Scholar] [CrossRef]
  69. Videla, L.A.; Valenzuela, R. Perspectives in liver redox imbalance: Toxicological and pharmacological aspects underlying iron overloading, nonalcoholic fatty liver disease, and thyroid hormone action. Biofactors 2022, 48, 400–415. [Google Scholar] [CrossRef]
Nutrients 15 00152 g001 550
Figure 1. Study population using KoGES database. Abbreviations: KoGES, Korean Genome and Epidemiology Study; NAFLD, non-alcoholic fatty liver disease.
Figure 1. Study population using KoGES database. Abbreviations: KoGES, Korean Genome and Epidemiology Study; NAFLD, non-alcoholic fatty liver disease.
Nutrients 15 00152 g001
Nutrients 15 00152 g002 550
Figure 2. Impact of dietary sodium (A) and kimchi (B) intake on the genetic susceptibility of NAFLD. Abbreviations: NAFLD, non-alcoholic fatty liver disease; PNPLA3, patatin-like phospholipase domain-containing 3.
Figure 2. Impact of dietary sodium (A) and kimchi (B) intake on the genetic susceptibility of NAFLD. Abbreviations: NAFLD, non-alcoholic fatty liver disease; PNPLA3, patatin-like phospholipase domain-containing 3.
Nutrients 15 00152 g002
Table
Table 1. Baseline characteristics of the study population.
Table 1. Baseline characteristics of the study population.
CharacteristicsTotal Population
(n = 15,725)		NAFLD
(n = 2923)		Control
(n = 12,802)		p-Value
Age (years)49.84 (±7.82)55.77 (±7.58)48.48 (±7.22)0.01
Male (%)7108 (45.20)1481 (50.68)5630 (43.98)<0.001
Female (%)8617 (54.80)1442 (49.32)7172 (56.02)
Glucose (mg/dL)92.09 (±15.03)100.67 (±23.30)90.12 (±11.51)<0.001
Total cholesterol (mg/dL)193.17 (±33.09)197.11 (±37.00)192.27 (±32.06)<0.001
HDL cholesterol (mg/dL)53.73 (±12.98)48.49 (±11.37)54.93 (±13.03)<0.001
Triglyceride (mg/dL)114.78 (±75.87)153.08 (±100.12)106.02 (±66.08)<0.001
Waist circumference (cm)80.26 (±8.55)85.9 (±7.91)78.97 (±8.16)0.01
Systolic blood pressure (mmHg)119.98 (±14.03)125.68 (±14.46)118.68 (±13.60)<0.001
Diastolic blood pressure (mmHg)74.84 (±9.58)77.47 (±9.50)74.24 (±9.50)<0.001
Body mass index (kg/m2)23.67 (±2.82)25.53 (±2.88)23.24 (±2.62)0.01
Alcohol intake <0.001
Non-drinker (n (%))6823 (43.50)1368 (46.92)5455 (42.70)
Ex-drinker (n (%))579 (3.70)206 (7.08)373 (2.95)
Current drinker (n (%))8275 (52.80)1340 (46.00)6935 (54.35)
Smoking <0.001
Non-smoker (n (%))10,590 (67.5)1850 (63.39)8740 (68.43)
Ex-smoker (n (%))2862 (18.3)703 (24.13)2159 (16.90)
Current smoker (n (%))2238 (14.3)364 (12.47)1874 (14.67)
PNPLA3 rs738409 genotype
GG (n (%))2911 (18.4)652 (22.2)2259 (17.5)<0.001
GC (n (%))7705 (48.6)1458 (48.4)6247 (48.4)
CC (n (%))5241 (33.0)840 (28.4)4401 (34.1)
Values are mean (± standard deviation) or number (%). Abbreviations: n, number; NAFLD, non-alcoholic fatty liver disease; PNPLA3, patatin-like phospholipase domain-containing 3.
Table
Table 2. Genetic risk of PNPLA3 rs738409 G allele on non-alcoholic fatty liver disease (NAFLD) occurrence in multivariable logistic regression analyses.
Table 2. Genetic risk of PNPLA3 rs738409 G allele on non-alcoholic fatty liver disease (NAFLD) occurrence in multivariable logistic regression analyses.
NAFLD (n)Control (n)Proportion of NAFLD (%)Additive Model
OR (95% CI)p-Value *
PNPLA3 rs738409 allele
C allele837438016.041
G allele2100845219.901.22 (1.15~1.30)1.75 × 10−10
Sex
Male1485565220.811
Female1452718016.820.80 (0.75–0.85)4.79 × 10−11
Age
<49 years old
>=49 years old
521
2416
7430
5402
6.55
30.9		1
4.68 (4.28–5.11)		5.8 × 10−280
Alcohol
Non-drinker1379547920.111
Ex-drinker20837835.491.76 (1.56–1.98)1.77 × 10−16
Current drinker1350697516.220.81 (0.75–0.86)7.26 × 10−10
Smoking
Non-smoker1863878117.51
Ex-smoker708216924.611.40 (1.30–1.52)2.9 × 10−17
Current smoker366188216.280.93 (0.84–1.03)0.177
Abbreviations: NAFLD, non-alcoholic fatty liver disease; 95% CI, 95% confidence interval; OR, odds ratio; PNPLA3, patatin-like phospholipase domain-containing 3. * The p-values were calculated with R Statistical Software (v 4.1.2; R Core Team 2021).
Table
Table 3. Comparison of the non-alcoholic fatty liver disease (NAFLD) case proportions between several nutrients’ high-intake and low-intake groups among the * total participants (n = 15,725), as determined by univariate analyses.
Table 3. Comparison of the non-alcoholic fatty liver disease (NAFLD) case proportions between several nutrients’ high-intake and low-intake groups among the * total participants (n = 15,725), as determined by univariate analyses.
NutrientHigh-Intake Group **Low-Intake Group **OR (95% CI)p-Value
NAFLD (n)Control (n)Proportion of NAFLD (%)NAFLD (n)Control (n)Proportion of NAFLD (%)
Energy786382317.05%2137897919.22%0.864 (0.789–0.945)0.001
Carbohydrate235410,11318.88%569268917.46%1.10 (0.994–1.217)0.064
Protein1396697116.68%1527583120.75%0.765 (0.706–0.829)<0.0001
Fat1294720915.22%1629559322.56%0.616 (0.568–0.668)<0.0001
Sodium1678773217.83%1245507019.71%0.884 (0.815–0.959)<0.0001
Potassium285128118.20%263811,52118.63%0.972 (0.849–1.112)0.677
Calcium353150818.97%257011,29418.54%1.029 (0.909–1.164)0.654
Phosphorus2021928217.88%902352020.40%0.850 (0.778–0.927)<0.0001
Iron342146018.98%258111,34218.54%1.029 (0.908–1.167)0.651
Zinc1190547517.85%1733732719.13%0.919 (0.847–0.997)0.043
Vitamin A493216018.58%243010,64218.59%1.00 (0.898–1.113)0.994
Carotene1547702618.05%1376577619.24%0.924 (0.853–1.002)0.055
Vitamin B1383209415.46%254010,70819.17%0.771 (0.686–0.867)<0.0001
Vitamin B2302149116.84%262111,31118.81%0.874 (0.767–0.996)0.044
Niacin1052550416.05%1871729820.41%0.746 (0.686–0.81)<0.0001
Vitamin B61553734417.46%1370545820.06%0.842 (0.777–0.913)<0.0001
Folate20982020.31%271411,98218.47%1.125 (0.961–1.317)0.142
Vitamin C1265558518.47%1658721718.68%0.986 (0.909–1.069)0.732
Vitamin E395187017.44%252810,93218.78%0.913 (0.813–1.027)0.129
Ash491217118.44%243210,63118.62%0.989 (0.888–1.101)0.835
Cholesterol352171517.03%257111,08718.82%0.885 (0.783–1.0)0.051
Abbreviations: NAFLD, non-alcoholic fatty liver disease; OR, odds ratio. * The proportion of NAFLD patients in the total population was 18.59% (2923/15,725). ** For each nutrient, daily intake above the recommended daily value was defined as high intake and below that as low intake. The recommended daily values used in this study are presented in Supplementary Table S1.
Table
Table 4. Association and interaction between nutrients and the PNPLA3 genotype for the risk of non-alcoholic fatty liver disease (NAFLD) development. Multivariate logistic regression analyses adjusted for age, sex, and smoking were applied.
Table 4. Association and interaction between nutrients and the PNPLA3 genotype for the risk of non-alcoholic fatty liver disease (NAFLD) development. Multivariate logistic regression analyses adjusted for age, sex, and smoking were applied.
Foodrs738409 GG + GC (n = 10,616)rs738409 CC (n = 5241)Interaction p
Proportion of NAFLD (%)OR (95% CI)pProportion of NAFLD (%)OR (95% CI)p
High IntakeLow IntakeHigh IntakeLow Intake
Energy1820.60.946 (0.835–1.071)0.38115.116.40.943 (0.778–1.141)0.5510.931
Carbohydrate20.318.31.037 (0.901–1.194)0.6171615.80.94 (0.757–1.171)0.5770.545
Protein17.722.30.821 (0.734–0.918)0.00114.817.50.863 (0.727–1.026)0.0940.381
Fat16.124.20.755 (0.675–0.845)<0.000113.419.10.794 (0.667–0.945)0.0090.154
Sodium18.521.90.771 (0.689–0.863)<0.000116.515.21.017 (0.853–1.214)0.8510.002
Potassium19.819.90.991 (0.821–1.191)0.92215.216.10.901 (0.678–1.186)0.4660.508
Calcium20.219.80.997 (0.837–1.183)0.97216.615.90.954 (0.736–1.226)0.7150.626
Phosphorus18.922.30.851 (0.755–0.96)0.00915.916.40.971 (0.801–1.181)0.770.11
Iron19.419.90.98 (0.822–1.164)0.81718.115.71.226 (0.948–1.576)0.1150.284
Zinc1920.50.948 (0.847–1.061)0.35415.516.40.917 (0.771–1.091)0.3310.758
Vitamin A19.3200.905 (0.779–1.05)0.1917.215.71.075 (0.861–1.337)0.5170.208
Vitamin B116.120.60.881 (0.748–1.033)0.12214.116.40.889 (0.697–1.128)0.3390.801
Vitamin B218.220.10.959 (0.8–1.145)0.64914.316.20.819 (0.621–1.07)0.150.696
Niacin16.921.90.8 (0.713–0.897)<0.000114.317.20.843 (0.706–1.005)0.0570.275
Vitamin B618.521.60.823 (0.736–0.92)0.00115.316.90.865 (0.728–1.028)0.0990.596
Folate20.419.80.961 (0.765–1.2)0.7320.215.71.325 (0.968–1.796)0.0740.255
Vitamin C19.8200.97 (0.867–1.085)0.59415.816.11.016 (0.854–1.208)0.8590.862
Vitamin E18.620.11.016 (0.864–1.191)0.84915.116.20.985 (0.77–1.251)0.90.869
Ash19.1200.833 (0.716–0.967)0.01717.215.71.032 (0.826–1.283)0.7820.449
Abbreviations: n, number; NAFLD, non-alcoholic fatty liver disease; 95% CI, 95% confidence interval; OR, odds ratio; PNPLA3, patatin-like phospholipase domain-containing 3.
Table
Table 5. Association and interaction between foods and the PNPLA3 genotype for the risk of non-alcoholic fatty liver disease (NAFLD) development. Multivariate logistic regression analyses adjusted for age, sex, and smoking were applied.
Table 5. Association and interaction between foods and the PNPLA3 genotype for the risk of non-alcoholic fatty liver disease (NAFLD) development. Multivariate logistic regression analyses adjusted for age, sex, and smoking were applied.
Foodrs738409 GG + GC (n = 10,616)rs738409 CC (n = 5241)Interaction p
Proportion of NAFLD (%)OR (95% CI)pProportion of NAFLD (%)OR (95% CI)p
High IntakeLow IntakeHigh IntakeLow Intake
Protective Foods
White rice15.0721.130.785 (0.68–0.9)0.00110.6917.30.674 (0.53–0.85)0.0010.689
Baechu kimchi19.0327.330.689 (0.57–0.83)<0.00115.7915.40.745 (0.63–0.87)<0.0010.012
Leaf mustard or scallion kimchi18.8919.870.758 (0.62–0.94)0.00715.85160.871 (0.71–0.99)0.3690.474
Green pepper19.7120.290.849 (0.73–0.99)0.03415.116.10.842 (0.74–0.96)0.0080.643
Orange17.6520.440.792 (0.66–0.95)0.01115.8416.30.841 (0.72–0.98)0.0230.187
Strawberry19.5520.30.871 (0.75–1.02)0.07713.7416.70.861 (0.76–0.98)0.0250.795
Pear19.0520.250.868 (0.41–0.67)0.0813.5316.40.861 (0.47–0.71)0.030.873
Coffee17.6226.250.743 (0.66–0.84)<0.00114.0921.90.741 (0.67–0.82)<0.0010.487
Sugar for tea or coffee17.6422.680.751 (0.67–0.84)<0.00113.9119.10.745 (0.68–0.82)<0.0010.757
Cream for tea or coffee17.3322.350.753 (0.68–0.84)<0.00113.818.10.761 (0.7–0.83)<0.0010.28
Risk Foods
Multi grain rice 21.8917.791.173 (1.06–1.3)0.00318.1113.611.289 (1.09–1.52)0.0020.58
Yoghurt23.5519.491.159 (1–1.34)0.04519.0415.31.147 (1.02–1.29)0.0250.59
Nuts (peanut, almond, pine nut)26.5419.341.284 (1.02–1.61)0.03223.0215.441.297 (0.93–1.82)0.1310.703
Pickled radish21.6519.841.282 (1.03–1.59)0.02417.4915.791.193 (0.85–1.67)0.3050.748
Vegetable salad23.6519.841.289 (1–1.66)0.04613.6416.141.134 (0.91–1.41)0.2540.038
Green tea19.5619.891.135 (0.99–1.3)0.06517.1515.591.149 (1.03–1.28)0.0140.554
Fried food17.7120.611.232 (1.10–1.38)<0.00114.3416.541.254 (1.05–1.503)0.0140.745
Abbreviations: n, number; NAFLD, non-alcoholic fatty liver disease; 95% CI, 95% confidence interval; OR, odds ratio; PNPLA3, patatin-like phospholipase domain-containing 3.
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Oh, S.; Lee, J.; Chun, S.; Choi, J.-E.; Kim, M.N.; Chon, Y.E.; Ha, Y.; Hwang, S.-G.; Choi, S.-W.; Hong, K.-W. Interaction between the PNPLA3 Gene and Nutritional Factors on NAFLD Development: The Korean Genome and Epidemiology Study. Nutrients 2023, 15, 152. https://doi.org/10.3390/nu15010152

AMA Style

Oh S, Lee J, Chun S, Choi J-E, Kim MN, Chon YE, Ha Y, Hwang S-G, Choi S-W, Hong K-W. Interaction between the PNPLA3 Gene and Nutritional Factors on NAFLD Development: The Korean Genome and Epidemiology Study. Nutrients. 2023; 15(1):152. https://doi.org/10.3390/nu15010152

Chicago/Turabian Style

Oh, Sooyeon, Jooho Lee, Sukyung Chun, Ja-Eun Choi, Mi Na Kim, Young Eun Chon, Yeonjung Ha, Seong-Gyu Hwang, Sang-Woon Choi, and Kyung-Won Hong. 2023. "Interaction between the PNPLA3 Gene and Nutritional Factors on NAFLD Development: The Korean Genome and Epidemiology Study" Nutrients 15, no. 1: 152. https://doi.org/10.3390/nu15010152

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