Skip to main content
Download PDF

Association of FHIT gene variant and salty food preference with the incidence of metabolic syndrome

Genes & Nutrition volume 20, Article number: 5 (2025) Cite this article

Abstract

Background

Taste perception plays a critical role in determining dietary choices and adherence to specific dietary patterns, which may lead to metabolic syndrome (MetS). The fragile histidine triad diadenosine triphosphatase (FHIT) gene plays a key role in cellular processes such as apoptosis and DNA repair, and thus may be an important factor in metabolic regulation. Despite this, the relationship between preference for salty foods, FHIT variants, and MetS is still largely unexplored. This study aimed to investigate the interaction between salty food preference and FHIT variation on the risk of MetS in middle-aged Korean adults.

Results

Over a mean follow-up of 10.6 years, 796 MetS cases were recorded. With regard to MetS incidence, the interaction between the FHIT rs2006807 variant and salty food preference was shown in women. Notably, women with the CA/CC genotype who preferred salty foods had a 1.42-fold higher incidence of MetS compared to those with the AA genotype who disliked salty foods (hazard ratio 1.42, 95% confidence interval 1.03–1.97).

Conclusions

This study highlights the potential impact of genetic factors and taste preferences when combined on the risk of MetS. Specifically, our findings suggest a significant genetic association between the FHIT rs2006807 variant and preference for salty foods, indicating that genetic predisposition may influence dietary choices. These results imply that personalized nutritional strategies that consider both genetic variations and individual dietary preferences may be effective in preventing MetS.

Introduction

Metabolic syndrome (MetS) is characterized by a set of conditions, including obesity, dyslipidemia, hyperglycemia, and hypertension, that increase the risk of type 2 diabetes, cardiovascular disease (CVD), and mortality rates [1, 2]. Over the past several decades, the global prevalence of MetS has significantly increased, impacting both clinical care and public health [3]. Estimates show that the worldwide prevalence of MetS among adults ranged from 12.5 to 31.4% in 2021 [4]. In most countries across the Americas (USA: 41.8% in 2018, Mexico: 49.8% in 2022), Europe and the Mediterranean (Portugal: 43.1% in 2009, Turkey: 34.6% in 2010, Croatia: 44% in 2020), Sub-Saharan Africa (Ghana: 41% in 2020, Nigeria: 23% in 2020), and the Asia-Pacific regions (Japan: 28.5% in 2023, China: 24.5% in 2018), studies report that 15–50% of the adult population is affected by MetS [2, 5,6,7,8,9,10,11]. In Korea, the prevalence of MetS has notably increased since 2001, ranging from 27.1 to 33.2%. Among Korean adults, 27.7% of those aged ≤ 30 years and 50% of those aged ≥ 65 years have MetS, with an increasing trend observed among men in their 30s and 40s and women over 70 years according to data published in 2021 [2, 3, 12]. As MetS-associated diseases persist longer in the population, disability-adjusted life years (DALYs) and the economic burden linked to MetS and related diseases are anticipated to increase markedly [2, 4]. Thus, it is critical to systematically explore and address potential risk factors to reduce and prevent the prevalence of MetS.

MetS is primarily the result of overnutrition, poor dietary choices, and a sedentary lifestyle, leading to adiposity and subsequent metabolic issues [1, 7]. Among the multifaceted factors which influence MetS, taste perception significantly determines dietary choices and thereby affects the risk of obesity and diabetes [13,14,15]. Previous studies have identified the overconsumption of salty foods as a key factor that induces metabolic disorders, including hypertension, insulin resistance, and endothelial dysfunction in both human and animal models [5, 16, 17]. Moreover, obese individuals prefer salty foods and exhibit decreased salt sensitivity [5, 18]. However, findings on the impact of a preference for salty foods on MetS and its related diseases are varied and unclear [17, 19,20,21,22,23]. Some studies suggest that greater salt taste sensitivity and preference are linked to lower odds of MetS [5, 19, 24]. Conversely, other studies indicate that a preference for salty foods is associated with higher risks of these conditions, highlighting the intricate nature of this relationship [22, 23, 25]. In contrast, a notable positive link was observed between preferences for sweet, spicy, and sour tastes, and obesity and body composition, especially in middle-aged and older adults [14, 20, 23]. Moreover, older adults with a liking for sweet and spicy foods have been found to have an increased risk of cardiometabolic diseases, whereas a preference for salt seems to indicate lower risk, suggesting a complex interplay between age, taste preferences, and disease risk [20, 26, 27]. Nevertheless, studies on the relationship between a preference for salty foods and MetS are limited and this relationship has not been comprehensively investigated [5, 23]. This gap in research underscores the need for more detailed studies to explore how these preferences develop over time and their long-term impact on health.

Genomic variants have pinpointed several susceptibility factors associated with the traits of MetS [13, 28]. Among the numerous variants identified by genome-wide association studies (GWAS) that have offered insights into the pathogenesis of MetS, the fragile histidine triad diadenosine triphosphatase (FHIT) gene has emerged as a significant variant [28, 29]. The FHIT gene plays a critical role in cellular processes such as apoptosis and DNA repair, making it a crucial factor in metabolic regulation [29, 30]. While primarily recognized as a tumor suppressor gene [30], there is growing interest in understanding how its variants might contribute to the risk of MetS. Some studies have suggested a positive association between FHIT gene variants and obesity, with certain single nucleotide polymorphisms (SNPs) contributing to an increase of approximately 0.1 units in body mass index (BMI) or 0.3 kg of weight in tall individuals [28, 31]. In addition, FHIT is also implicated in metabolic processes, including those associated with MetS components such as blood pressure and diabetes [32, 33]. However, the exact mechanisms and the strength of this association remain underexplored and require further investigation [28]. Given the various taste preferences found to be associated with MetS and its components [23, 25, 26], it is essential to assess how genetic variations and specific genotypes might interact with preference for salty taste to influence the progression of MetS. Since MetS significantly increases the risk of type 2 diabetes, CVD, and cancer [1, 3, 5], any additional insights on the contributing factors and interrelationships could help in the development of targeted interventions. Recent GWAS have identified the FHIT gene as a potential contributor to MetS. While genetic factors play a crucial role in MetS development, dietary preferences, particularly salt intake, may interact with genetic variants to influence disease risk. However, the interaction between the FHIT gene variants and salty food preference in relation to MetS remains largely unexplored. Therefore, this study aimed to investigate how the FHIT rs2006807 variant and salty food preference interact to affect MetS incidence in middle-aged and older Korean adults. Thus, this study aimed to investigate the effects of the FHIT rs2006807 variant and a preference for salty foods on the incidence of MetS in middle-aged and older Korean adults.

Methods

Study settings and participants

We utilized data from the Ansan-Ansung cohort of the Korean Genome and Epidemiology Study (KoGES). The KoGES Ansan-Ansung study included 10,030 participants aged 40–69 years. The baseline recruitment of the study was conducted between 2001 and 2002, with participants followed up every two years. The data used in this study spans from the baseline (2001–2002) to the seventh follow-up examination (2015–2016). Details on KoGES have been described elsewhere [34]. Participants with no data on the FHIT rs2006807 genotypes (n = 1,221), salty food preferences (n = 107), MetS (n = 5,558), MetS at baseline (n = 812), and potential confounders (n = 520) were excluded from the study. Therefore, the analysis as presented in Fig. 1 included 1,812 (885 men and 927 women) final study participants. The study adhered to the principles outlined in the Declaration of Helsinki and received approval from the Institutional Review Board of Inha University (IRB No. 240307–1 A). Written informed consent was obtained from all participants.

Fig. 1
figure 1

Flow diagram of the study design, including the study participants inclusion and elimination criteria

Full size image

Assessment of preference for salty foods

In the KoGES, information about participants’ dietary information and food preferences was assessed using a validated questionnaire administered by well-trained interviewers [35, 36]. The participants rated their preference for salty foods on a five-point scale, ranging from “very much like” to “very much dislike”. In this study, participants were further categorized into two groups based on their level of preference: those with an average to a strong preference for salty foods (“average,” “like,” and “very much like”) were classified as “like,” whereas those who did not prefer salty foods (“dislike” and “very much dislike”) were classified as “dislike”.

Assessment of MetS

MetS was assessed based on the criteria recommended by the National Cholesterol Education Program-Adult Treatment Panel (NCEP-ATP III) [37]. MetS is considered present if three or more of the following five criteria are met: [1] abdominal obesity (waist circumference ≥ 80 cm in women and ≥ 90 cm in men); [2] hyperglycemia (fasting blood sugar ≥ 110 mg/dL, diagnosed with diabetes or currently on diabetes medication; [3] hypertriglyceridemia (plasma triglyceride concentration ≥ 150 mg/dL); [4] low high-density lipoprotein cholesterol (HDL-C, plasma HDL-C concentration < 50 mg/dL in women; < 40 mg/dL in men; [5] elevated blood pressure (systolic blood pressure [SBP] ≥ 130 mmHg, diastolic blood pressure [DBP] ≥ 85mmHg, diagnosed with hypertension, or currently on antihypertensive medication) [38,39,40].

Genotyping and imputation

Korean genomic data were genotyped using the Affymetrix Genome-Wide Human SNP Array 5.0 (Affymetrix, Santa Clara, CA, USA). After genotyping, GWAS was performed on the entire sample to identify SNPs associated with participants’ preference for salty foods, adjusting for sex and age. To ensure the quality of the data, SNP markers that deviated from Hardy-Weinberg equilibrium with a p-value < 1 × 10− 6 were excluded from the analysis, as Hardy-Weinberg equilibrium is an important indicator of genotyping quality and population structure [41]. The FHIT rs2006807, with a p-value of 2.02 × 10− 5 was selected, as illustrated in Fig. 2. FHIT rs2006807 is located on chromosome 3, and the SNP is situated within an intron 5 of FHIT. Using regional plots, we identified 341 SNPs between 59705075 bp and 61217173 bp on chromosome 3 (Fig. 3). The FHIT rs2006807 is most statistically significant on chromosome 3, and shows a high r2 value with surrounding SNPs in the plot, indicating a strong genetic relationship. In this study, 8,809 SNPs corresponding to FHIT rs2006807 were analyzed. Furthermore, the CC genotype of rs2006807 was classified as the major genotype, while the CA and AA genotypes were categorized as minor genotypes.

Fig. 2
figure 2

Manhattan plots for the genome-wide association study (GWAS) of salty food preference

Full size image
Fig. 3
figure 3

Regional plot for single nucleotide polymorphisms (SNPs) on FHIT gene that are significantly associated with preference for salty foods

Full size image

The SNPs shown in the figure are located on chromosome 3: 59.7–61.2 Mb, with the highlighted rs2006807 representing a key SNP. The SNPs are plotted by their statistical significance (-log10 p-value), the purple diamond (rs2006807) is the most significantly associated with preference for salty foods in Korean adults.

Statistical analyses

GWAS analysis to select SNPs associated with salty food preference was performed using PLINK software (version 1.9, https://www.cog-genomics.org/plink/1.9) [42]. SNPs and genotype frequencies associated with the preference for salty foods were identified with logistic regression analysis. Three different genetic models (CA, CC, AA) were selected and further categorized into two models (CA, CC vs. AA) for the analysis. The data used for expression quantitative trait loci (eQTL) analysis were obtained via the GTEx browser (https://gtexportal.org/). Haploview was used for Manhattan plots and linkage disequilibrium (LD) block. To obtain regional association plots, the web-based program Locuszoom version 1.3 (http://csg.sph.umich.edu/locuszoom/) was used.

The baseline characteristics are presented using descriptive statistics and include frequencies, percentages, and means ± standard deviations (SDs). Differences between the groups categorized according to the MetS status and genotype were assessed using t-tests and generalized linear models for continuous variables, and Chi-square tests for categorical variables. Additionally, a multivariable Cox proportional hazards model was employed to calculate the adjusted hazard ratios (HRs) for the incidence of MetS based on the genotype and preference for salty foods. In the analysis, we incorporated several demographic and lifestyle factors as potential confounders. These include age (years), income (< 1 million won/month, 1–3 million won/month, ≥ 3 million won/month), education level (elementary or technical college/university/graduate school), residency area (Ansan, Ansung), smoking status (never, past, current), alcohol intake (never, past, current), BMI (kg/m2), physical activity (metabolic equivalent of task, MET-hours/week), and energy intake (kcal/day). In Model 1, age was adjusted, and in Model 2, age, area, BMI, smoking status, drinking status, education, income, total energy intake, and MET were adjusted. Baseline characteristic comparisons and multivariable Cox proportional hazards models were performed stratified by sex, and significance was considered to be p < 0.05. These data were analyzed using SAS version 9.4 (SAS Institute, Cary, NC, USA).

Results

General characteristics of the study participants based on the presence of MetS

The baseline characteristics of the participants according to the incidence of MetS are depicted in Table 1. The average follow-up period was 10.6 years, and 796 cases of MetS were recorded. The participants were divided into groups with MetS (n = 796) and without MetS (n = 1,016) at the end of the follow-up. There was a significant difference in the preference for salty foods between both men and women. In both groups with and without MetS, the proportion of participants who preferred salty foods was higher than the proportion who did not (p < 0.05). Women with MetS were older than women without MetS (p < 0.05). MetS components such as BMI, waist circumference, systolic and diastolic blood pressure, triglyceride levels, and fasting blood glucose levels were significantly higher in the group with MetS than in the group without MetS in both men and women, whereas HDL-cholesterol levels were significantly lower (p < 0.05). Total energy and carbohydrate intakes were higher in women with MetS than in women without MetS (p < 0.05). In both men and women, the rate of Ansan (urban) was higher than that of Ansung (rural) in both groups, with and without MetS (p < 0.05). Significant differences in education and income levels were identified only among women. In terms of education level, both groups with and without MetS had the highest elementary/technical college graduation rates. Regarding the income level, both groups with and without MetS showed the highest rate of spending 1 to 3 million won per month. There was a significant difference in smoking status among men, with the group with MetS having the highest percentage of current smokers, and the group without MetS having the highest percentage of former smokers. There was a significant difference in physical activity level only in women, and women with MetS had higher MET levels than women without MetS.

Table 1 General characteristics of the study participants based on the presence of metabolic syndrome
Full size table

General characteristics of the study participants based on FHIT variant genotypes

Table 2 shows the baseline characteristics of the study participants according to the genotypes (AA, CC, and CA) of the FHIT rs2006807 variant. In women, the rs2006807 AA and CC/CA genotypes had a higher proportion of those who preferred salty foods than those who did not. Only in men, there were significant differences in physical activity levels between thee CC/CA and AA genotypes, with men in the CC/CA genotype having higher MET values compared to the AA genotype. Other variables did not differ significantly between groups.

Table 2 General characteristics of the study participants based on the FHIT rs2006807 genotypes
Full size table

Functional annotations of FHIT variants with eQTL

This study performed eQTL analysis based on the GTEx database to determine gene expression levels according to SNP genotypes. eQTLs were uncovered for the SNP (rs2006807) in human tissues. As a result, the AA genotype of rs2006807 contributed to lower expression levels of the FHIT gene in the adrenal gland, while the CC genotype contributed to higher expression levels of the FHIT gene in the adrenal gland (p = 1.05 × 10− 6) (Fig. 4). These results demonstrate that the expression of the FHIT gene in human tissue differs depending on the genotypes of the FHIT gene.

Fig. 4
figure 4

Identification of the gene expression of the FHIT gene and SNPs in eQTL

Full size image

The expression of genotype rs2006807 in the chr3_60550687_A_C_b38 region. The gene expression of each genotype in the adrenal gland was presented using GTEx Portal and showed statistical significance (p = 1.05 × 10− 6). The white line indicates mean expression levels, and the black box indicate 25% and 75% quantiles. Data were obtained via the GTEx browser (https://gtexportal.org/).

LD block structure of SNPs in the FHIT gene

The LD blocks were analyzed for SNPs associated with the FHIT gene using the Haploview program based on Korean genomic data. The SNPs rs2006807, rs107342, and rs6762597 are associated with each other, indicating a potential linkage among these variants within the FHIT gene region (Fig. 5).

Fig. 5
figure 5

Linkage disequilibrium (LD) block structure of SNPs in the FHIT gene

Full size image

It includes the SNP (rs2006807) located within the FHIT gene, with the LD block visually represented below. The colors indicate the strength of LD between different SNPs, with red representing stronger LD. The numbers in each cell indicate that a higher value signifies a stronger association between the two SNPs. This plot was generated using Haploview software.

Association between preference for salty foods and MetS

Table 3 shows the incidence of MetS according to a preference for salty foods. A significant positive association between salty food preference and MetS was observed in both the unadjusted model for men and women and in Model 1 adjusted for age (P < 0.05). However, the significant association disappeared in Model 2, which adjusted for all demographic and health-related variables.

Table 3 Incidence of metabolic syndrome based on preference for salty foods in Korean adults
Full size table

Association between the FHIT rs2006807 genotype and MetS

Table 4 shows the incidence of MetS according to the FHIT rs2006807 genotype. In all models, there was no statistically significant difference in the incidence of MetS based on salty food preferences in both men and women.

Table 4 Incidence of metabolic syndrome based on FHIT rs2006807 genotypes in Korean adults
Full size table

Incidence of MetS according to preference for salty foods and the FHIT rs2006807 genotype

Table 5 shows the incidence of MetS according to the combination of preference for salty foods and the FHIT rs2006807 genotype. In all models, regardless of covariate adjustment, women showed significant differences in the incidence of MetS according to salty food preference and genetic variation. In model 2, which adjusted for both demographic and health-related variables, the incidence of MetS was 1.42 times higher in women with the CC/CA genotype who preferred salty foods than in women with the AA genotype who did not prefer salty foods (HR 1.42, 95% CI 1.03–1.97).

Table 5 Incidence of metabolic syndrome based on salty food preference and FHIT genotypes in Korean adults
Full size table

Supplementary Table 1 shows the incidence of MetS according to the combination of salty food preference and FHIT rs2006807 genotype in the entire population, regardless of gender. The incidence of MetS in the entire population showed significant differences according to salty food preference and genetic variation. The incidence of MetS was 1.22 times higher in the CC/CA genotype, which prefers salty food, than in the AA genotype, which does not prefer salty food (HR 1.22, 95% CI 0.98–1.51).

Discussion

In this prospective cohort study, we explored the impact of the FHIT rs2006807 gene variant and a preference for salty food on the incidence of MetS among middle-aged and older adults in Korea. FHIT rs2006807 and preference for salty food were not significantly associated with MetS, but these two variables interacted to have a significant effect. Specifically, women with the CC/CA genotype of FHIT rs2006807 and a preference for salty foods had the highest risk of MetS compared to those with the AA genotype who disliked salty food. These findings highlight the significant interplay between genetic variants, taste preference, and MetS, and underscore the importance of considering gender differences in future research and interventions related to MetS.

In this study, a preference for salty foods alone was not significantly associated with MetS. Previous studies have investigated the association between salt intake and MetS, and have emphasized the detrimental effects of consuming salt-rich foods on MetS [5, 20, 24, 25]. However, the association between MetS and taste perception or dietary choices, such as a preference for salty foods, has not been extensively studied. A cross-sectional study conducted in Croatia showed that the lower the salt perception threshold, that is, the more sensitive one is to the salty taste, the lower the risk of developing MetS. However, no significant association was found between the habit of adding salt before tasting food and perceived saltiness [5]. This indicates that an individual’s preference for salty taste and actual consumption of salty foods may be inconsistent, which may support our findings. Changes in preferences for saltiness occur as a result of adaptation to familiarity [43]. Since the saltiness that an individual prefers most in a food is the salt concentration that he or she most frequently consumes, repeated exposure to the same salty food may result in relearning new levels of appropriate saltiness as a result of familiarity [44]. Therefore, salty taste preferences may have changed over the study period and the association with actual food intake may be relative across individuals. This phenomenon may be particularly relevant in the context of Koreans, who frequently consume salted foods such as kimchi, soy sauce, and various pickled fish products [45, 46]. Additionally, the perception of saltiness can be influenced by several other characteristics, including age, smoking, and drinking habits [47].

This study indicated that there was no significant association between the FHIT rs2006807 variant alone and the incidence of MetS. To our knowledge, no study to date has directly confirmed the association between the FHIT gene variants and MetS. Previous studies have reported that FHIT variants are associated with increased risk factors for MetS, such as obesity, diabetes, and hypertension [13, 14, 28]. FHIT, a tumor suppressor gene, is involved in apoptosis regulation and cell cycle control [48]. Changes in FHIT expression promote tumor progression, and reduced expression is associated with signaling pathways in various cancers [49]. The role of the FHIT gene may overlap with the mechanisms driving the components of the MetS. Insulin resistance, chronic inflammation, and neurohormonal activation are essential factors in the progression of MetS [50], and oxidative stress contributes significantly to these factors [51, 52]. In a study that identified the role of the FHIT gene in regulating responses to oxidative and replication stress, it was demonstrated that the absence or mutation of FHIT leads to the accumulation of oxidative damage, which can further exacerbate the components of MetS [53]. However, since FHIT is primarily involved in DNA repair and response to cellular stress [54], it may not directly affect the major metabolic pathways that drive MetS compared to genetic factors that are closely related to inflammation, insulin signaling, and lipid metabolism [55].

We found that the combined effect of a preference for salty foods and the CA/CC genotype of the FHIT rs2006807 variant significantly increased the risk of MetS in women. The FHIT gene-taste preference interaction suggests that individuals genetically predisposed to metabolic disturbances due to these variants may be more susceptible to the adverse effects of high salt intake [56]. Potential mechanisms linking gene variants, preferences for salty food, and MetS may involve the regulation of sodium metabolism and inflammatory pathways [56, 57]. The interaction with the FHIT gene may influence how individuals respond to high dietary sodium intake, affecting blood pressure regulation, insulin signaling pathways, lipid metabolism, and alterations in hormone levels related to sodium balance, thereby increasing the overall risk of MetS [56, 58, 59]. The effects of the interaction that were observed only in women may be due to sex-specific factors that modulate the genetic and dietary influences on metabolic health. This gender difference may be attributed to hormonal differences and lipid profiles that affect salt sensitivity and blood pressure regulation, as well as differences in health behaviors and dietary patterns between men and women [23, 60,61,62,63]. In particular, postmenopausal women may be more susceptible to the effects of high salt intake on blood pressure and metabolic health, which could explain the stronger association observed in this group [64, 65]. In this study, eQTL analysis was conducted using the GTEx database to investigate gene expression levels, and eQTLs for SNP (rs2006807) were identified in the adrenal gland. The AA genotype of rs2006807 was associated with lower expression levels of the FHIT gene in the adrenal gland, while the CC genotype was linked to higher expression levels. Previous studies have indicated a relationship between aldosterone, a hormone produced by the adrenal cortex, and MetS. These studies suggest that individuals with MetS tend to have higher plasma aldosterone levels compared to those without the syndrome [66]. High aldosterone levels may exacerbate the risk of metabolic disorders by affecting sodium retention and blood pressure regulation, thus providing additional insights into the mechanisms linking salt intake, FHIT alterations, and MetS.

Therefore, considering both genetic and dietary factors in the prevention and management of MetS, particularly in populations with high dietary salt intake, is crucial [5, 59]. Our findings have pivotal implications for healthcare strategies and interventions aimed at tackling the prevalence of MetS. Given the positive relationships between genetic predispositions, dietary preferences, and metabolic health, tailored nutritional guidelines can be developed to mitigate the associated risks and burdens. Such approaches may enhance the effectiveness of dietary interventions aimed at reducing the incidence of MetS, particularly in genetically susceptible populations [7]. Additionally, interventional studies targeting salt intake reduction in high-risk populations are warranted to evaluate the effectiveness of these strategies in preventing and managing MetS.

This study is the first to investigate the effect of the FHIT rs2006807 variant on the development of MetS moderated by the preference for salty foods in middle-aged and older Koreans. A prospective cohort design was used to explore the causal relationship between the genetic variant of FHIT and preference for salty foods interaction and the development of MetS. In addition, many potential covariates, such as age, area, drinking habits, and smoking status, were controlled to confirm the independent effect of this interaction. Nonetheless, our study has limitations that must be acknowledged. First, the assessment of preference for salty foods may introduce potential bias, as participants may have misreported or inaccurately recalled their dietary habits. Second, the study was conducted in a specific population of middle-aged Korean adults, which may limit the generalizability of the findings to other age groups or ethnic populations. Expanding the research to include other populations and younger cohorts would help to validate these findings and enhance their applicability. Third, given that traditional Korean diets are high in salt [46], the findings may not be directly applicable to populations with different dietary habits and genetic backgrounds. Moreover, while the study focused on the preference for salty foods, it did not account for other dietary factors such as a preference for fat and sugar or the intake of foods rich in these ingredients. These dietary factors may interact in complex ways, contributing to overall cardiometabolic risks [1, 5]. Future research should consider a broader range of dietary preferences and their interactions with genetic factors in the context of MetS. Finally, although our study confirmed the association, it did not elucidate the underlying biological processes linking the FHIT variant to MetS. Further studies should explore these biological pathways, particularly in relation to salt intake, to better understand the gene-diet interactions observed in this study.

Conclusions

In conclusion, this study established a significant combined effect of the FHIT rs2006807 gene variant and a preference for salty foods on the development of MetS in middle-aged Korean women. These findings underscore the need for a deeper understanding of how genetic predispositions interact with dietary habits to influence metabolic health. Further research should explore these interactions and the underlying mechanisms of the role of the FHIT gene in MetS, as well as confirm these findings in other populations and age groups. In addition, the potential gender contribution to the association between the FHIT gene, preference for salty taste, and MetS incidence needs to be elucidated.

Data availability

The Korean Genome and Epidemiology Study (KoGES) data are available through a procedure described at https://biobank.nih.go.kr/cmm/main/mainPage.do (accessed 10 July 2024).

References

  1. Isomaa B, Almgren P, Tuomi T, Forsén B, Lahti K, Nissén M, et al. Cardiovascular morbidity and mortality associated with the metabolic syndrome. Diabetes Care. 2001;24(4):683–9.

    Article  CAS  PubMed  Google Scholar 

  2. Lobene AJ. The increasing prevalence of metabolic syndrome in Korea: A multifarious disease with a multifactorial etiology. JACC Asia. 2023;3(3):503–5.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Lee SE, Han K, Kang YM, Kim SO, Cho YK, Ko KS, et al. Trends in the prevalence of metabolic syndrome and its components in South Korea: findings from the Korean National health insurance service database (2009–2013). PLoS ONE. 2018;13(3):e0194490.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Noubiap JJ, Nansseu JR, Lontchi-Yimagou E, Nkeck JR, Nyaga UF, Ngouo AT, et al. Geographic distribution of metabolic syndrome and its components in the general adult population: A meta-analysis of global data from 28 million individuals. Diabetes Res Clin Pract. 2022;188:109924.

    Article  CAS  PubMed  Google Scholar 

  5. Veček NN, Mucalo L, Dragun R, Miličević T, Pribisalić A, Patarčić I et al. The association between salt taste perception, mediterranean diet and metabolic syndrome: A Cross-Sectional study. Nutrients. 2020;12(4).

  6. Lan Y, Mai Z, Zhou S, Liu Y, Li S, Zhao Z, et al. Prevalence of metabolic syndrome in China: an up-dated cross-sectional study. PLoS ONE. 2018;13(4):e0196012.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Iwasaki Y, Arisawa K, Katsuura-Kamano S, Uemura H, Tsukamoto M, Kadomatsu Y et al. Associations of Nutrient Patterns with the Prevalence of Metabolic Syndrome: Results from the Baseline Data of the Japan Multi-Institutional Collaborative Cohort Study. Nutrients. 2019;11(5).

  8. Liang X, Or B, Tsoi MF, Cheung CL, Cheung BMY. Prevalence of metabolic syndrome in the united States National health and nutrition examination survey 2011-18. Postgrad Med J. 2023;99(1175):985–92.

    Article  PubMed  Google Scholar 

  9. Bowo-Ngandji A, Kenmoe S, Ebogo-Belobo JT, Kenfack-Momo R, Takuissu GR, Kengne-Ndé C, et al. Prevalence of the metabolic syndrome in African populations: A systematic review and meta-analysis. PLoS ONE. 2023;18(7):e0289155.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ortiz-Rodríguez MA, Bautista-Ortiz LF, Villa AR, Antúnez-Bautista PK, Aldaz-Rodríguez MV, Estrada-Luna D, et al. Prevalence of metabolic syndrome among Mexican adults. Metab Syndr Relat Disord. 2022;20(5):264–72.

    PubMed  Google Scholar 

  11. Liu J, Liu Q, Li Z, Du J, Wang C, Gao Y, et al. Prevalence of metabolic syndrome and risk factors among Chinese adults: results from a Population-Based Study - Beijing, China, 2017–2018. China CDC Wkly. 2022;4(29):640–5.

    PubMed  PubMed Central  Google Scholar 

  12. Huh JH, Kang DR, Kim JY, Koh KK. Metabolic syndrome fact sheet 2021: executive report. Cardiometab Syndr J. 2021;1(2):125–34.

    Article  Google Scholar 

  13. Lee KW, Shin D. Interactions between bitter taste receptor gene variants and dietary intake are associated with the incidence of type 2 diabetes mellitus in Middle-Aged and older Korean adults. Int J Mol Sci. 2023;24(3).

  14. Fernández-Carrión R, Sorlí JV, Coltell O, Pascual EC, Ortega-Azorín C, Barragán R et al. Sweet taste preference: relationships with other tastes, liking for sugary foods and exploratory Genome-Wide association analysis in subjects with metabolic syndrome. Biomedicines. 2021;10(1).

  15. Lee KH, Mo J. The factors influencing meal satisfaction in older adults: A systematic review and Meta-analysis. Asian Nurs Res (Korean Soc Nurs Sci). 2019;13(3):169–76.

    PubMed  Google Scholar 

  16. Serirukchutarungsee S, Watari I, Narukawa M, Podyma-Inoue KA, Sangsuriyothai P, Ono T. Two-generation exposure to a high-fat diet induces the change of salty taste preference in rats. Sci Rep. 2023;13(1):5742.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. He FJ, Marrero NM, MacGregor GA. Salt intake is related to soft drink consumption in children and adolescents. Hypertension. 2008;51(3):629–34.

    Article  CAS  PubMed  Google Scholar 

  18. Li Q, Jin R, Yu H, Lang H, Cui Y, Xiong S, et al. Enhancement of neural salty preference in obesity. Cell Physiol Biochem. 2017;43(5):1987–2000.

    Article  CAS  PubMed  Google Scholar 

  19. Kirsten VR, Wagner MB. Salt taste sensitivity thresholds in adolescents: are there any relationships with body composition and blood pressure levels? Appetite. 2014;81:89–92.

    Article  PubMed  Google Scholar 

  20. Yuan D, Tang H, Yang P, Guo C. Taste preferences, cardiometabolic diseases and mild cognitive impairment: a prospective cohort analysis of older Chinese adults. Br J Nutr. 2024;131(6):1064–73.

    Article  CAS  PubMed  Google Scholar 

  21. Liem DG. Infants’ and Children’s Salt Taste Perception and Liking: A Review. Nutrients. 2017;9(9).

  22. Abdulai T, Runqi T, Mao Z, Oppong TB, Amponsem-Boateng C, Wang Y et al. Preference for high dietary salt intake is associated with undiagnosed type 2 diabetes: the Henan rural cohort. Front Nutr. 2020;7.

  23. Bertoli S, Laureati M, Battezzati A, Bergamaschi V, Cereda E, Spadafranca A, et al. Taste sensitivity, nutritional status and metabolic syndrome: implication in weight loss dietary interventions. World J Diabetes. 2014;5(5):717–23.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Shim E, Ryu HJ, Hwang J, Kim SY, Chung EJ. Dietary sodium intake in young Korean adults and its relationship with eating frequency and taste preference. Nutr Res Pract. 2013;7(3):192–8.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Itoh N, Tsuya A, Togashi H, Kimura H, Konta T, Nemoto K, et al. Increased salt intake is associated with diabetes and characteristic dietary habits: a community-based cross-sectional study in Japan. J Clin Biochem Nutr. 2022;71(2):143–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Lampuré A, Castetbon K, Deglaire A, Schlich P, Péneau S, Hercberg S, et al. Associations between liking for fat, sweet or salt and obesity risk in French adults: a prospective cohort study. Int J Behav Nutr Phys Act. 2016;13(1):74.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Lauer RM, Filer LJ, Reiter MA, Clarke WR. Blood pressure, salt preference, salt threshold, and relative weight. Am J Dis Child. 1976;130(5):493–7.

    CAS  PubMed  Google Scholar 

  28. Abou Ziki MD, Mani A. Metabolic syndrome: genetic insights into disease pathogenesis. Curr Opin Lipidol. 2016;27(2):162–71.

    Article  CAS  PubMed  Google Scholar 

  29. Waters CE, Saldivar JC, Hosseini SA, Huebner K. The FHIT gene product: tumor suppressor and genome caretaker. Cell Mol Life Sci. 2014;71(23):4577–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Sadangi S, Mandhane A, Panda AK, Kapusetti G, Behera SK, Pati S. Identification of rare NsSNPs in fragile histidine triad (FHIT) gene to explore its correlation with oral cancer: an in-silico approach. Results Chem. 2023;6:101072.

    Article  CAS  Google Scholar 

  31. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518(7538):197–206.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Rampersaud E, Damcott CM, Fu M, Shen H, McArdle P, Shi X, et al. Identification of novel candidate genes for type 2 diabetes from a Genome-Wide association scan in the old order Amish: evidence for replication from diabetes-Related quantitative traits and from independent populations. Diabetes. 2007;56(12):3053–62.

    Article  CAS  PubMed  Google Scholar 

  33. Dannewitz Prosseda S, Tian X, Kuramoto K, Boehm M, Sudheendra D, Miyagawa K, et al. FHIT, a novel modifier gene in pulmonary arterial hypertension. Am J Respir Crit Care Med. 2019;199(1):83–98.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Kim Y, Han BG. Cohort profile: the Korean genome and epidemiology study (KoGES) consortium. Int J Epidemiol. 2017;46(2):e20.

    Article  PubMed  Google Scholar 

  35. Ahn Y, Kwon E, Shim JE, Park MK, Joo Y, Kimm K, et al. Validation and reproducibility of food frequency questionnaire for Korean genome epidemiologic study. Eur J Clin Nutr. 2007;61(12):1435–41.

    Article  CAS  PubMed  Google Scholar 

  36. Park SJ, Lyu J, Lee K, Lee HJ, Park HY. Nutrition survey methods and food composition database update of the Korean genome and epidemiology study. Epidemiol Health. 2024;46:e2024042.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Third Report of the National Cholesterol Education Program (NCEP). Expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult treatment panel III) final report. Circulation. 2002;106(25):3143–421.

    Article  Google Scholar 

  38. Sangyeoup L, Hye Soon P, Sun Mee K, Hyuk Sang K, Dae Young K, Dae Joong K, et al. Cut-off points of waist circumference for defining abdominal obesity in the Korean population. J Obes Metab Syndr. 2006;15(1):1–9.

    Google Scholar 

  39. Choi SH, Ahn CW, Cha BS, Chung Y-S, Lee KW, Lee HC, et al. The prevalence of the metabolic syndrome in Korean adults: comparison of WHO and NCEP criteria. Yonsei Med J. 2005;46(2):198–205.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Min K-B, Min J-Y, Paek D, Cho S-I. The impact of the components of metabolic syndrome on heart rate variability: using the NCEP-ATP III and IDF definitions. Pacing Clin Electrophysiol. 2008;31(5):584–91.

    Article  PubMed  Google Scholar 

  41. Jeong SW, Chung M, Park S-J, Cho SB, Hong K-W. Genome-wide association study of metabolic syndrome in Koreans. Genomics Inf. 2014;12(4):187.

    Article  Google Scholar 

  42. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Bobowski N. Shifting human salty taste preference: potential opportunities and challenges in reducing dietary salt intake of Americans. Chemosens Percept. 2015;8:112–6.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Methven L, Langreney E, Prescott J. Changes in liking for a no added salt soup as a function of exposure. Food Qual Prefer. 2012;26(2):135–40.

    Article  Google Scholar 

  45. Jeong Y, Kim ES, 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(3):382–95.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Park HR, Jeong GO, Lee SL, Kim JY, Kang SA, Park KY, et al. Workers intake too much salt from dishes of eating out and food service cafeterias; direct chemical analysis of sodium content. Nutr Res Pract. 2009;3(4):328–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Lampuré A, Schlich P, Deglaire A, Castetbon K, Péneau S, Hercberg S, et al. Sociodemographic, psychological, and lifestyle characteristics are associated with a liking for salty and sweet tastes in French adults. J Nutr. 2015;145(3):587–94.

    Article  PubMed  Google Scholar 

  48. Sard L, Accornero P, Tornielli S, Delia D, Bunone G, Campiglio M, et al. The tumor-suppressor gene FHIT is involved in the regulation of apoptosis and in cell cycle control. Proc Natl Acad Sci. 1999;96(15):8489–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Kiss DL, Baez W, Huebner K, Bundschuh R, Schoenberg DR. Impact of FHIT loss on the translation of cancer-associated mRNAs. Mol cancer. 2017;16:1–13.

    Article  Google Scholar 

  50. Fahed G, Aoun L, Bou Zerdan M, Allam S, Bou Zerdan M, Bouferraa Y, et al. Metabolic syndrome: updates on pathophysiology and management in 2021. Int J Mol Sci. 2022;23(2):786.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Niu Z, Jiang D, Shen J, Liu W, Tan X, Cao G. Potential role of the fragile histidine triad in cancer Evo-Dev. Cancers. 2023;15(4):1144.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Rana S, Ali S, Wani HA, Mushtaq QD, Sharma S, Rehman MU. Metabolic syndrome and underlying genetic determinants-A systematic review. J Diabetes Metab Disord. 2022;21(1):1095–104.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Okumura H, Ishii H, Pichiorri F, Croce CM, Mori M, Huebner K. Fragile gene product, Fhit, in oxidative and replicative stress responses. Cancer Sci. 2009;100(7):1145–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Ishii H, Mimori K, Inoue H, Inageta T, Ishikawa K, Semba S, et al. Fhit modulates the DNA damage checkpoint response. Cancer Res. 2006;66(23):11287–92.

    Article  CAS  PubMed  Google Scholar 

  55. Stančáková A, Laakso M. Genetics of metabolic syndrome. Rev Endocr Metab Disord. 2014;15:243–52.

    Article  PubMed  Google Scholar 

  56. Mohammadifard N, Moazeni F, Azizian-Farsani F, Gharipour M, Khosravi E, Sadeghian L, et al. Genetic variation in salt taste receptors impact salt intake and blood pressure. Sci Rep. 2023;13(1):4037.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Boiko AS, Pozhidaev IV, Paderina DZ, Bocharova AV, Mednova IA, Fedorenko OY, et al. Search for possible associations of FTO gene polymorphic variants with metabolic syndrome, obesity and body mass index in schizophrenia patients. Pharmgenomics Pers Med. 2021;14:1123–31.

    PubMed  PubMed Central  Google Scholar 

  58. Baratta F, D’Erasmo L, Di Costanzo A, Umbro I, Pastori D, Angelico F et al. Metabolic syndrome but not fatty liver-Associated genetic variants correlates with glomerular renal function decline in patients with Non-Alcoholic fatty liver disease. Biomedicines. 2022;10(3).

  59. Farquhar WB, Edwards DG, Jurkovitz CT, Weintraub WS. Dietary sodium and health: more than just blood pressure. J Am Coll Cardiol. 2015;65(10):1042–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Barris CT, Faulkner JL, Belin de Chantemèle EJ. Salt sensitivity of blood pressure in women. Hypertension. 2023;80(2):268–78.

    Article  CAS  PubMed  Google Scholar 

  61. Kim GH, Lee HM. Frequent consumption of certain fast foods May be associated with an enhanced preference for salt taste. J Hum Nutr Diet. 2009;22(5):475–80.

    Article  CAS  PubMed  Google Scholar 

  62. Tian X, Xu X, Zhang K, Wang H. Gender difference of metabolic syndrome and its association with dietary diversity at different ages. Oncotarget. 2017;8(43):73568–78.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Meloni A, Cadeddu C, Cugusi L, Donataccio MP, Deidda M, Sciomer S et al. Gender differences and cardiometabolic risk: the importance of the risk factors. Int J Mol Sci. 2023;24(2).

  64. Pechère-Bertschi A, Burnier M. Female sex hormones, salt, and blood pressure regulation*. Am J Hypertens. 2004;17(10):994–1001.

    Article  PubMed  Google Scholar 

  65. Alipour P, Azizi Z, Raparelli V, Norris CM, Kautzky-Willer A, Kublickiene K, et al. Role of sex and gender-related variables in development of metabolic syndrome: A prospective cohort study. Eur J Intern Med. 2024;121:63–75.

    Article  CAS  PubMed  Google Scholar 

  66. Kidambi S, Kotchen JM, Grim CE, Raff H, Mao J, Singh RJ, et al. Association of adrenal steroids with hypertension and the metabolic syndrome in Blacks. Hypertension. 2007;49(3):704–11.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This study was conducted with bioresources from the National Biobank of Korea, the Korea Disease Control and Prevention Agency, Republic of Korea (NBK-2024-019).

Funding

This work was supported by a National Research Foundation of Korea (NRF) grant, funded by the Korean government (MSIT) (RS-2024-00340086).

Author information

Author notes

  1. Jihyun Kim and Suyeon Lee contributed equally to this work.

Authors and Affiliations

  1. Department of Food and Nutrition, Inha University, 100 Inha-ro, Michuhol-gu, Incheon, 22212, Republic of Korea

    Jihyun Kim, Suyeon Lee & Dayeon Shin

  2. Department of Ophthalmology, Korea University College of Medicine, Seoul, 02841, Republic of Korea

    Shiva Raj Acharya

Authors
  1. Jihyun Kim
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Suyeon Lee
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Shiva Raj Acharya
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Dayeon Shin
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

J.K.: conceptualization, data curation, methodology, formal analysis, writing-original draft, and writing-review and editing. S.L.: methodology, formal analysis, writing-original draft, and writing-review and editing. S.R.A.: writing-original draft. D.S.: conceptualization, data curation, funding acquisition, methodology, supervision, and writing— review and editing. All authors have read and agreed to the published final version of the manuscript.

Corresponding author

Correspondence to Dayeon Shin.

Ethics declarations

Ethical approval and consent to participate

The IRB of Inha University has approved the study (IRB No. 240307–1A), and the participants have given written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, J., Lee, S., Acharya, S.R. et al. Association of FHIT gene variant and salty food preference with the incidence of metabolic syndrome. Genes Nutr 20, 5 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12263-025-00762-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12263-025-00762-z

Keywords

Genes & Nutrition

ISSN: 1865-3499

Contact us