Next Article in Journal
Influence of Gluten-Free Diet on Gut Microbiota Composition in Patients with Coeliac Disease: A Systematic Review
Previous Article in Journal
The Alleviation of Gut Microbiota-Induced Depression and Colitis in Mice by Anti-Inflammatory Probiotics NK151, NK173, and NK175
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 14
Issue 10
10.3390/nu14102082
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Association between Vitamin Intake and Chronic Kidney Disease According to a Variant Located Upstream of the PTGS1 Gene: A Cross-Sectional Analysis of Shika Study

by
Kim-Oanh Pham
1,2,*,
Akinori Hara
2,
Hiromasa Tsujiguchi
2,
Keita Suzuki
2,
Fumihiko Suzuki
2,3,
Sakae Miyagi
4,
Takayuki Kannon
5,
Takehiro Sato
5,
Kazuyoshi Hosomichi
5,
Hirohito Tsuboi
6,
Thao Thi Thu Nguyen
7,
Yukari Shimizu
8,
Yasuhiro Kambayashi
9,
Masaharu Nakamura
2,
Chie Takazawa
2,
Haruki Nakamura
2,
Toshio Hamagishi
2,
Aki Shibata
2,
Tadashi Konoshita
10,
Atsushi Tajima
5 and
Hiroyuki Nakamura
2add Show full author list remove Hide full author list
1
Information Management Department, Asia Center for Air Pollution Research, Niigata City 950-2144, Japan
2
Department of Hygiene and Public Health, Faculty of Medicine, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa City 920-8640, Japan
3
Community Medicine Support Dentistry, Faculty of Dentist, Ohu University Hospital, Koriyama 963-8611, Japan
4
Innovative Clinical Research Center, Takaramachi Campus, Kanazawa University, Kanazawa City 920-8640, Japan
5
Department of Bioinformatics and Genomics, Faculty of Medicine, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa City 920-8640, Japan
6
Institute of Medical, Pharmaceutical and Health Sciences, Kakuma Campus, Kanazawa University, Kanazawa City 920-1192, Japan
7
Department of Epidemiology, Faculty of Public Health, Haiphong University of Medicine and Pharmacy, Hai Phong 180000, Vietnam
8
Department of Nursing, Faculty of Health Sciences, Komatsu University, Komatsu City 923-0961, Japan
9
Department of Public Health, Faculty of Veterinary Medicine, Okayama University of Science, Imabari 794-8555, Japan
10
Third Department of Internal Medicine, Faculty of Medical Sciences, University of Fukui, Tsuruga 914-0055, Japan
*
Author to whom correspondence should be addressed.
Nutrients 2022, 14(10), 2082; https://doi.org/10.3390/nu14102082
Submission received: 31 March 2022 / Revised: 2 May 2022 / Accepted: 14 May 2022 / Published: 16 May 2022
(This article belongs to the Section Micronutrients and Human Health)
Browse Figures
Versions Notes

Abstract

:
Chronic kidney disease (CKD) patients have been advised to take vitamins; however, the effects have been controversial. The individual differences in developing CKD might involve genetic variants of inflammation, including variant rs883484 located upstream of the prostaglandin-endoperoxide synthase 1 (PTGS1) gene. We aimed to identify whether the 12 dietary vitamin intake interacts with genotypes of the rs883484 on developing CKD. The population-based, cross-sectional study had 684 Japanese participants (≥40 years old). The study used a validated, brief, self-administered diet history questionnaire to estimate the intake of the dietary vitamins. CKD was defined as estimated glomerular filtration < 60 mL/min/1.73 m2. The study participants had an average age of 62.1 ± 10.8 years with 15.4% minor homozygotes of rs883484, and 114 subjects had CKD. In the fully adjusted model, the higher intake of vitamins, namely niacin (odds ratio (OR) = 0.74, 95% confidence interval (CI): 0.57–0.96, p = 0.024), α-tocopherol (OR = 0.49, 95% CI: 0.26–0.95, p = 0.034), and vitamin C (OR = 0.97, 95% CI: 0.95–1.00, p = 0.037), was independently associated with lower CKD tendency in the minor homozygotes of rs883484. The results suggested the importance of dietary vitamin intake in the prevention of CKD in middle-aged to older-aged Japanese with minor homozygous of rs883484 gene variant.

1. Introduction

Chronic kidney disease (CKD) is a global burden, with approximately 10% of adults affected worldwide [1]. The disease is a progressive chronic condition, which can escalate to severe damage to organs, necessitating either dialysis or organ transplant to preserve life. Significantly, the aging population, such as the Japanese with degrading renal functions, is vulnerable to CKD [2]. Recently, precision nutrition has helped design beneficial prevention approaches for individuals based on their genetic profile [3,4]. Vitamin and genetic factors are both known to contribute to susceptibility to CKD [5,6]; however, there is not much information about the interaction between vitamin intake and gene variants on CKD development.
PTGS1 (prostaglandin-endoperoxide synthase 1) gene, involved in the inflammatory response, expresses the enzyme catalyzing the conversion of arachidonic acid to prostaglandin (PG). It encodes cyclooxygenase 1 (COX-1) and peroxidase proteins and regulates angiogenesis in endothelial cells. PTGS1 is one of the genes regulating blood pressure and arachidonic acid metabolism; it is one of the potential gatekeeper genes in programmed hypertension [7]. Thus, most of the studies about the expression of the PTSG1 gene are on cardiovascular diseases [8,9].
A series of studies in the Japanese population identified rs883484 located upstream of the PTGS1 gene as one of the genetic variants associated with CKD in individuals with metabolic syndrome [10], hypertension [11], and the general population [12]. The frequency of rs883484 polymorphism (CC:CT:TT) was 38:48:14 in general Japanese (> 60 years) [12] and 68:28:3 in Sweden males (>50 years old) [8]. The results suggested that PTGS1 might also be susceptible to CKD among the Japanese.
The dietary vitamins among CKD patients are essential in controlling the progression rate of kidney failure, reducing uremic toxicity and proteinuria, and lowering the risk of kidney disease-related secondary complications [13,14]. Vitamins (water-soluble and fat-soluble vitamins) are recommended for CKD patients to control oxidant and inflammation levels [14]. CKD patients are inclined to vitamin deficiency due to gastrointestinal symptoms, dietary restriction, multi pharmacological prescription, or dialysis [15]. Moreover, the risk of vitamin malnutrition rises with age [16]. Even though the vitamins are good for CKD patients, the results are conflicting [17], in which genetic factors might be involved. Therefore, the relationship between vitamins and gene variants on CKD needed investigation.
Disease prevention is an important strategy to control the burden of CKD, and the identification of genetic and nutritional factors is crucial for predicting risk and plausible intervention. The interaction between vitamin intake and genetic factors underlying the predisposition of CKD in individuals has remained unknown. Thus, the present study was to determine the effect of vitamin intake on CKD according to the rs883484 variant located upstream of the PTGS1 gene in a middle-aged to older-aged population in Shika Town, Japan.

2. Materials and Methods

2.1. Study Design and Population

This cross-sectional study utilized the data from Shika Cohort Study conducted from 2013 to 2019 in Shika Town, a rural area in Japan. Besides obtaining information from the self-administered questionnaires, genotypes of genome-wide single-nucleotide polymorphisms (SNPs) and daily salt intake were acquired from the comprehensive medical check-ups.
The number of eligible participants determined the sample size. There were 1335 voluntary participants from 40 years old with medical records (Figure 1). Excluded were individuals who disagreed with genome analysis or one of a pair of genetic relatives (n = 434), those without dietary intake information (n = 196) and daily salt intake (n = 13), and those with daily energy intake of <600 kcal/day or >4000 kcal/day (n = 8); thus, the present study included 684 participants.

2.2. Genotyping of rs883484

Genomic DNA was extracted from blood samples utilizing the QIAamp DNA Blood Maxi Kit (Qiagen, Hilden, Germany) corresponding to the manufacturer’s instructions or entrusting to a specialized company in clinical laboratory testing (SRL, Inc., Tokyo, Japan). Genome-wide SNP genotyping was executed using the Japonica Array v2 [18] (TOSHIBA Co., Ltd., Tokyo, Japan). Quality control (QC) procedures for the obtained genome-wide SNP genotype data have been described elsewhere [19]. In brief, SNP genotype data and participants were filtered according to gender identity between karyotypes and the questionnaire, call rates, the Hardy–Weinberg equilibrium test, inbreeding coefficient, cryptic relatedness, and population structure. Genotypes of rs883484 for 901 unrelated subjects (based on genome-wide π ^ values) passing the QC were extracted from the array data. In the QC step, the call rate for the SNP was 100%, and a departure from the Hardy–Weinberg equilibrium was not observed (p = 0.9082).

2.3. Assessment of Chronic Kidney Disease

To evaluate the kidney function, estimated glomerular filtration rate (eGFR) was calculated following the simplified equation proposed by Japanese Society of Nephrology and described in the Modification of Diet in Renal Disease Study: eGFR (mL/min/1.73 m2) = 194 × [age (years)]−0.287 × [serum creatinine (mg/dL)]−1.094 × [0.739 if female] [20]. The enzymatic method was used to quantify the serum creatinine concentrations. In this study, the criteria to define CKD was that the eGFR is less than 60 mL/min/1.73 m2 [21]. The eGFR is an estimated value that might not reflect the exact GFR, and the cutoff point to determine CKD might cause bias in the analysis.

2.4. Vitamin Intake Assessment

The intake of water-soluble vitamins (vitamin C, B1, B2, niacin, pantothenic acid, B6, folate, and B12) and fat-soluble vitamins (α-tocopherol, vitamin D, K, and retinol) was evaluated by the Japanese version of the brief self-administered dietary history questionnaire (BDHQ). These vitamins are recommended for CKD patients to improve their renal condition. BDHQ assembles information on the average intake of 58 types of food and beverage during the previous month to estimate the habitual intake of nutrients. The vitamin intake was estimated using a computer algorithm for the BDHQ based on the Standard Tables of Food Composition in Japan [22]. To avoid bias from excessive intake of nutritional supplements, mineral or vitamin intake from supplements was not included in the calculation. The Japanese version of BDHQ has been validated in previous studies [22,23]. The nutritional intake adjusted for energy using the density method was performed instead of the crude values of the dietary record.

2.5. Other Variables

Blood pressure (BP) was measured twice consecutively at rest in a sitting position, and averages were used as BP data. Hypertension was set as systolic blood pressure (SBP) of ≥140 mm Hg diastolic blood pressure (DBP) of ≥90 mm Hg. Glycated hemoglobin (HbA1c) and fasting plasma glucose (FPG) were measured during the medical examination. Body mass index (BMI) was calculated from weight and height in the follow-up appointments and expressed as kg/m2. Diabetes was described as HbA1c of ≥6.5% or FPG ≥ 126 mg/dL, which is the cutoff value used by the Japan Society to diagnose diabetes mellitus, or previously diagnosed as diabetes [24]. The daily salt intake was estimated from the daily urinary sodium excretion by the Japanese Society of Hypertension Guidelines 2014 [25].
Age (set at the time of measurement), sex, smoking status, alcohol consumption, and exercise frequency were evaluated using self-administered questionnaires. Habitual alcohol consumption was determined as drinking more than one glass of sake (22 g ethanol) per day three times a week or more [26]. The decent frequency of exercise was estimated as follows: participants exercised more than 30 min at least twice a week during the preceding year or performed tasks such as walking, cleaning, and carrying baggage for more than one hour per day [26]. The categorical variables were presented as binary: sex (0 = woman, 1 = man), hypertension prevalence (0 = no hypertension, 1 = hypertension), diabetes prevalence (0 = no diabetes, 1 = diabetes), smoking status (0 = non-smoker or ex-smoker, 1 = current smoker), drinking habit (0 = non-drinker, 1 = drinker), and exercise frequency (0 = non-exercise, 1 = frequent exercise).

2.6. Statistical Analysis

Descriptive characteristics at baseline were compared according to the CKD status and rs883484 genotypes. The CKD status is based on the presence or absence of eGFR < 60 mL/min/1.73 m2 (0 = non-CKD, 1 = CKD). The genotypes were divided into major homozygous and heterozygous (CC + CT) and minor homozygous (TT). The independent-sample Student’s t-test was used to compare the continuous data between subjects with different CKD statuses and rs883484 genotypes, and the chi-square test compared categorical data. The interaction between vitamin intake and rs883484 genotypes on CKD was performed using a two-way Analysis of Covariance (ANCOVA). A multiple logistic regression analysis was utilized to examine the association between vitamin intake and CKD status according to the rs883484 genotypes after adjustments for the following independent factors: age, sex, BMI, hypertension, diabetes, drinking habits, exercise habits, smoking habits, and the daily salt intake.
All statistical analyses in the present study used the IBM SPSS Statistics version 25.0 (IBM Corp., Tokyo, Japan). Odds ratios (OR), p-values, and 95% confidence intervals (CI) were calculated. The results with a two-sided p-value < 0.05 were considered to be significant.

3. Results

3.1. Participant Characteristics According to CKD Status

The studied participants were 684 after the screening process, which is shown in Figure 1. The sample size produced the effect size of 0.11 with ten covariates in two groups, with an 80% power and 5% significance. Participant characteristics, lifestyle habits, frequency distributions of rs883484 genotypes, and nutrient intake are summarized according to CKD status in Table 1. Among 684 participants (62.1 (SD, 10.8) years old), 54.8% (n = 375) were women, and 114 (16.6%) had CKD. The minor homozygous of rs883484 was 15.4%, and the major homozygous and heterozygous group was 84.6% of the studied population. Age (p = 0.000), BMI (p = 0.023), the prevalence of hypertensive subjects (p = 0.009), and SBP (p = 0.001) were significantly higher in the CKD group than those in the non-CKD group. There were more females (p = 0.018) and current smokers (p = 0.002) and higher daily salt intake (p = 0.001) in the non-CKD group than in the CKD group, while the vitamin C intake (p = 0.037) was significantly lower in the non-CKD group than that in the CKD group. The ratio of frequently exercising people (p = 0.088) in the CKD group was borderline significantly higher than that in the non-CKD group. Diabetes individuals and lipid profiles (triglyceride and total cholesterol) were not significantly different between groups. Except for vitamin C, there was no significant difference in the consumption of vitamins intake between groups.

3.2. Participant Characteristics According to rs883484 Genotypes and CKD Status

In Table 2, there were two groups according to gene rs883484 genotypes: the major homozygous and heterozygous (CC + CT) group versus the minor homozygous (TT) group, which were further divided by CKD status. Overall, 16.9% and 15.2% of participants had CKD in CC + CT and TT groups, respectively. Age significantly affected CKD in both genotype groups. In both genotype groups, the daily salt intake was lower in the CKD group than in the non-CKD group: p = 0.003 (CC + CT group) and p = 0.060 (TT group). In the minor homozygous group, the intake of the vitamins was lower in the CKD group, with significant differences in niacin (p = 0.027) and α-tocopherol (p < 0.050). Diabetes preferences and lipid profiles were not significantly different between groups.
In CC + CT group, age (p < 0.001), BMI (p = 0.002), SBP (p < 0.001), and prevalence of hypertension (p = 0.006) was significantly higher in CKD group. There were more females (p = 0.009) and current smokers (p < 0.001) in the non-CKD group than in the CKD group. In contrast to the TT group, there was a significant difference in vitamin C intake (p = 0.012) and a marginally significant difference in α-tocopherol intake (p = 0.067) between the groups: the CKD group consumed more vitamin C and α-tocopherol than the non-CKD group. There was a significant interaction between CKD groups and rs883484 genotypes on the intake of vitamins (p = 0.028 for vitamin C, p = 0.041 for vitamin B1, p = 0.046 for niacin, p = 0.005 for α-tocopherol) (Table 3). Niacin (p = 0.03) and α-tocopherol (p = 0.01) showed significant results, while vitamin B1, B6, B12, and vitamin C had borderline significance on Bonferroni analysis (Figure 2). In the CC + CT group, the intake of vitamins did not affect the CKD, whereas in the TT group, the higher intake of vitamins resulted in lower CKD outcomes. The result suggests a relationship between the intake of vitamins and CKD according to the gene variant. From descriptive results in Table 1 and Table 2, age, sex, BMI, hypertension, smoking, and daily salt intake strongly influenced CKD outcomes. Diabetes, drinking habits, and exercise were strong covariates [1,3,4,26]. All 684 subjects had no missing data in the confounder and main effect variables.

3.3. Relationship between Antioxidant Vitamins Intake and CKD According to Gene Variants

The effect of the consumption of antioxidant vitamins on CKD stratified by rs883484 genotypes is presented in Table 4. The fully adjusted model included age, sex, BMI, hypertension, diabetes, and lifestyle habits, including drinking, smoking, exercise, and daily salt intake. The results showed that high consumption of niacin (OR = 0.739, 95% CI: 0.569–0.961, p = 0.024), α-tocopherol (OR = 0.492, 95% CI: 0.256–0.947, p = 0.034), vitamin C (OR = 0.971, 95% CI: 0.945–0.998, p = 0.037), folate (OR = 0.987, 95% CI: 0.997–1.003, p = 0.063), vitamin B6 (OR = 0.037, 95% CI: 0.001–1.131, p = 0.059), pantothenic acid (OR = 0.407, 95% CI: 0.153–1.078, p = 0.070), and vitamin B1 (OR = 0.003, 95% CI: 0.000–1.773, p = 0.075) inversely correlated with CKD in TT group after adjustment. In the CC + CT group, the association between vitamin intake and CKD was insignificant after adjustment.

4. Discussion

The present study discovered that a higher intake of water-soluble vitamins and fat-soluble vitamins, especially niacin, vitamin C, and α-tocopherol, was independently associated with lower CKD tendency in the Japanese population minor homozygote of rs883484 located upstream of PTGS1 gene. In contrast, the participants with the C allele showed no significant effect of vitamins on CKD status. In addition, the association between the intake of fat-soluble vitamins (except for α-tocopherol), vitamin B2, and vitamin B12 and kidney function was not observed in both groups of rs883484 genotypes. Unexpectedly, the salt intake in the CKD group was lower than that in the non-CKD group. Hence, the effect of elevated blood pressure by consuming salt on CKD might be excluded.
The present results for vitamins intake are consistent with previous studies indicating the protective effect of dietary vitamins intake and CKD. Vitamin C, niacin, and α-tocopherol significantly affected CKD status. Moreover, the lower intake of vitamins B1, B6, pantothenic acid, and folate were borderline significantly associated with higher CKD prevalence. Niacin (nicotinic acid, vitamin B3) is the oldest drug to treat dyslipidemia [27,28] and hyperphosphatemia [29]. Niacin reduces serum phosphorus levels and oxidative stress, including inflammation and endothelial dysfunction, and improves the CKD outcome [30]. Vitamin C is vital for many biological functions and attenuates reactive oxygen species, and renal oxidative damage preserves hydroxylase and monooxygenase enzymes and endothelium and vascular function in renal injury patients [31]. However, vitamin C deficiency is common among CKD patients due to dialytic vitamin C clearance, high vitamin C food restriction, and escalated vitamin C catabolism in vivo from inflammation [14,15]. α-Tocopherol, which belonged to the vitamin E group, was inversely associated with CKD in middle-aged and older-aged Japanese women [26]. The intake of vitamins C and E alone or in combination lessened kidney function damage, renal injury, and arterial pressure in rats with salt-sensitive hypertension [32]. Even though vitamins have been reported to be protective against CKD because of their antioxidant and anti-inflammatory [14], the results were not statistically significant [27].
In the CC + CT group, the intake of vitamins did not alter the outcome of CKD, while in the TT group, it drastically changed the CKD status. Yoshida et al. showed that the rs883484 (C/T) located upstream of the PTGS1 gene was associated with CKD in the general population [12] and individuals with metabolic syndrome [10] as well as hypertension [11] patients. The SNP is located in the 5′ flanking region [8], which contains the promoter and may contain enhancers or other protein binding sites. Polymorphisms in this region can induce changes in the regulation of transcription. Helmersson et al. displayed that the TT genotype of rs883484 was associated with the increased formation of PGF2α, a product of COX [8]. PTGS1 gene encodes COX-1, which synthesizes PG and thromboxane (Tx), which leads to inflammatory damage to the kidney [33]; thus, the COX pathway might be the mechanism for developing CKD in the case of TT genotype for rs883484. The previous findings suggested that the minor homozygote (TT genotype) of rs883484 increased the PTGS1 gene expression and the amount of COX-1, which worsened renal function. COX-1 is the primary target of nonsteroidal anti-inflammatory drugs (NSAIDs). Inhibition of PGs by pharmacological medicine enhances immunocompetence in animals and humans. Likewise, the supplement of vitamins has a similar effect on PGs. Vitamins C and E inhibited PGE2 production by human gingival fibroblasts and SCC-25 oral squamous carcinoma cells [34]. Plasma PGE2 concentrations decreased in men and women after 14 days of using vitamin C-rich vegetable soup [35]. Niacin regulates PGF2α [36], PGE2 [37], and PGD2 [38] through COX-1 and COX-2. Because of the enhanced expression of COX-1, individuals with TT genotype of rs883484 might become more sensitive to the antioxidant and anti-inflammatory effects of vitamins. Thus, the TT genotype of rs883484 seemed to be more susceptible to dietary intake of vitamins against kidney failure.
This study has several limitations. First, this cross-sectional study cannot establish a causal relationship, which means we cannot determine that the dietary intake of antioxidant vitamins can prevent CKD development in the general population with minor homozygotes of rs883484. Second, the estimated GFR was used instead of directly measured GFR to define CKD. Third, the self-administered dietary history questionnaire (BDHQ) cannot accurately determine the nutrition intake due to a limited number of food and beverage items. Moreover, there is a lack of objective markers of daily vitamin intake and energy intake.

5. Conclusions

In conclusion, TT allele carriers of the rs883484 variant, occupying 15.4% of the studied population, had a high risk of CKD. However, dietary vitamin intake is inversely associated with CKD in minor homozygotes of rs883484. A diet rich in vitamins would benefit those with a minor homozygous rs883484 variant. Major homozygote and heterozygote of rs883484 did not respond to vitamin intake to protect against CKD. The current study was conducted on the middle-aged to older-aged Japanese general population; therefore, its finding has practical meaning to generalize. Further primary and longitudinal studies using objective markers of vitamin intake are needed to examine the underlying mechanism preventing CKD development.

Author Contributions

Conceptualization, K.-O.P., A.H., A.T. and H.N. (Hiroyuki Nakamura); methodology, K.-O.P., A.H., H.T. (Hiromasa Tsujiguchi), T.K. (Takayuki Kannon), T.S., K.H. and H.N. (Hiroyuki Nakamura); formal analysis, K.-O.P.; investigation, K.-O.P., A.H. and K.S.; resources, H.N. (Hiroyuki Nakamura); data curation, T.K. (Takayuki Kannon), H.T. (Hiromasa Tsujiguchi), and C.T.; writing—original draft preparation, K.-O.P.; writing—review and editing, K.-O.P., A.H., H.T. (Hiromasa Tsujiguchi), K.S., F.S., S.M., T.S., H.T. (Hirohito Tsuboi), T.T.T.N., Y.S., Y.K., M.N., C.T., H.N. (Haruki Nakamura), T.H., A.S., T.K. (Tadashi Konoshita), A.T. and H.N. (Hiroyuki Nakamura); visualization: K.-O.P.; supervision, A.H., A.T. and H.N. (Hiroyuki Nakamura); project administration, H.T. (Hiromasa Tsujiguchi); funding acquisition, H.N. (Hiroyuki Nakamura). All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by a Grant-in-Aid for Scientific Research (B) by the Ministry of Education, Culture, Sport, Science, and Technology (MEXT), number 19H03882.

Institutional Review Board Statement

The study procedure followed the Declaration of Helsinki and was approved by the Committees on the Ethics of Human Research of Kanazawa University (No. 1491).

Informed Consent Statement

All participants signed the written informed consent.

Data Availability Statement

The data that support the present study’s findings are available from the corresponding author upon reasonable request.

Acknowledgments

We want to thank the Health and Welfare Center staff of Shika Town and the Department of Environmental and Preventive Medicine staff, Institute of Medical, Pharmaceutical and Health Sciences, Faculty of Medicine, Kanazawa University.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kalantar-Zadeh, K.; Jafar, T.H.; Nitsch, D.; Neuen, B.L.; Perkovic, V. Chronic Kidney Disease. Lancet 2021, 398, 786–802. [Google Scholar] [CrossRef]
  2. O’Sullivan, E.D.; Hughes, J.; Ferenbach, D.A. Renal Aging: Causes and Consequences. J. Am. Soc. Nephrol. 2017, 28, 407–420. [Google Scholar] [CrossRef] [Green Version]
  3. 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]
  4. Peña-Romero, A.C.; Navas-Carrillo, D.; Marín, F.; Orenes-Piñero, E. The Future of Nutrition: Nutrigenomics and Nutrigenetics in Obesity and Cardiovascular Diseases. Crit. Rev. Food. Sci. Nutr. 2018, 58, 3030–3041. [Google Scholar] [CrossRef]
  5. Wuttke, M.; Li, Y.; Li, M.; Sieber, K.B.; Feitosa, M.F.; Gorski, M.; Tin, A.; Wang, L.; Chu, A.Y.; Hoppmann, A.; et al. A Catalog of Genetic Loci Associated with Kidney Function from Analyses of a Million Individuals. Nat. Genet. 2019, 51, 957–972. [Google Scholar] [CrossRef] [Green Version]
  6. Shi, S.; Xue, F. Current Antioxidant Treatments in Organ Transplantation. Oxid. Med. Cell. Longev. 2016, 2016, 8678510. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Tain, Y.L.; Huang, L.T.; Chan, J.Y.H.; Lee, C. Te Transcriptome Analysis in Rat Kidneys: Importance of Genes Involved in Programmed Hypertension. Int. J. Mol. Sci. 2015, 16, 4744–4758. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Helmersson, J.; Ärnlöv, J.; Axelsson, T.; Basu, S. A Polymorphism in the Cyclooxygenase 1 Gene Is Associated with Decreased Inflammatory Prostaglandin F2α Formation and Lower Risk of Cardiovascular Disease. Prostaglandins Leukot. Essent. Fat. Acids 2009, 80, 51–56. [Google Scholar] [CrossRef] [PubMed]
  9. Lee, C.R.; North, K.E.; Bray, M.S.; Couper, D.J.; Heiss, G.; Zeldin, D.C. Cyclooxygenase Polymorphisms and Risk of Cardiovascular Events: The Atherosclerosis Risk in Communities (ARIC) Study. Clin. Pharmacol. Ther 2008, 83, 52–60. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  10. Yoshida, T.; Kato, K.; Fujimaki, T.; Yokoi, K.; Oguri, M.; Watanabe, S.; Metoki, N.; Yoshida, H.; Satoh, K.; Aoyagi, Y.; et al. Association of a Polymorphism of the Apolipoprotein E Gene with Chronic Kidney Disease in Japanese Individuals with Metabolic Syndrome. Genomics 2009, 93, 221–226. [Google Scholar] [CrossRef] [Green Version]
  11. Yoshida, T.; Kato, K.; Yokoi, K.; Watanabe, S.; Metoki, N.; Satoh, K.; Aoyagi, Y.; Nishigaki, Y.; Nozawa, Y.; Yamada, Y. Association of Candidate Gene Polymorphisms with Chronic Kidney Disease in Japanese Individuals with Hypertension. Hypertens. Res. 2009, 32, 411–418. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Yoshida, T.; Kato, K.; Fujimaki, T.; Yokoi, K.; Oguri, M.; Watanabe, S.; Metoki, N.; Yoshida, H.; Satoh, K.; Aoyagi, Y.; et al. Association of Genetic Variants with Chronic Kidney Disease in Japanese Individuals. Clin. J. Am. Soc. Nephrol. 2009, 4, 883–890. [Google Scholar] [CrossRef] [PubMed]
  13. Zha, Y.; Qian, Q. Protein Nutrition and Malnutrition in CKD and ESRD. Nutrients 2017, 9, 208. [Google Scholar] [CrossRef] [PubMed]
  14. Borran, M.; Dashti-Khavidaki, S.; Alamdari, A.; Naderi, N. Vitamin C and Kidney Transplantation: Nutritional Status, Potential Efficacy, Safety, and Interactions. Clin. Nutr. ESPEN 2021, 41, 1–9. [Google Scholar] [CrossRef] [PubMed]
  15. Raimann, J.G.; Levin, N.W.; Craig, R.G.; Sirover, W.; Kotanko, P.; Handelman, G. Is Vitamin C Intake Too Low in Dialysis Patients? Semin. Dial. 2013, 26, 1–5. [Google Scholar] [CrossRef] [PubMed]
  16. Jankowska, M.; Rutkowski, B.; Dębska-Ślizień, A. Vitamins and Microelement Bioavailability in Different Stages of Chronic Kidney Disease. Nutrients 2017, 9, 282. [Google Scholar] [CrossRef] [Green Version]
  17. MacCubbin, D.; Tipping, D.; Kuznetsova, O.; Hanlon, W.A.; Bostom, A.G. Hypophosphatemic Effect of Niacin in Patients without Renal Failure: A Randomized Trial. Clin. J. Am. Soc. Nephrol. 2010, 5, 582–589. [Google Scholar] [CrossRef] [Green Version]
  18. Kawai, Y.; Mimori, T.; Kojima, K.; Nariai, N.; Danjoh, I.; Saito, R.; Yasuda, J.; Yamamoto, M.; Nagasaki, M. Japonica Array: Improved Genotype Imputation by Designing a Population-Specific SNP Array with 1070 Japanese Individuals. J. Hum. Genet. 2015, 60, 581–587. [Google Scholar] [CrossRef] [Green Version]
  19. Nomura, A.; Sato, T.; Tada, H.; Kannon, T.; Hosomichi, K.; Tsujiguchi, H.; Nakamura, H.; Takamura, M.; Tajima, A.; Kawashiri, M. Polygenic Risk Scores for Low-Density Lipoprotein Cholesterol and Familial Hypercholesterolemia. J. Hum. Genet. 2021, 66, 1079–1087. [Google Scholar] [CrossRef]
  20. Matsuo, S.; Imai, E.; Horio, M.; Yasuda, Y.; Tomita, K.; Nitta, K.; Yamagata, K.; Tomino, Y.; Yokoyama, H.; Hishida, A. Revised Equations for Estimated GFR From Serum Creatinine in Japan. Am. J. Kidney Dis. 2009, 53, 982–992. [Google Scholar] [CrossRef]
  21. Levin, A.; Bilous, R.; Coresh, J. Chapter 1: Definition and Classification of CKD. Kidney Int. Suppl. 2013, 3, 19–62. [Google Scholar]
  22. Kobayashi, S.; Yuan, X.; Sasaki, S.; Osawa, Y.; Hirata, T.; Abe, Y.; Takayama, M.; Arai, Y.; Masui, Y.; Ishizaki, T. Relative Validity of Brief-Type Self-Administered Diet History Questionnaire among Very Old Japanese Aged 80 Years or Older. Public Health Nutr. 2019, 22, 212–222. [Google Scholar] [CrossRef] [PubMed]
  23. Kobayashi, S.; Honda, S.; Murakami, K.; Sasaki, S.; Okubo, H.; Hirota, N.; Notsu, A.; Fukui, M.; Date, C. Both Comprehensive and Brief Self-Administered Diet History Questionnaires Satisfactorily Rank Nutrient Intakes in Japanese Adults. J. Epidemiol. 2012, 22, 151–159. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Araki, E.; Goto, A.; Kondo, T.; Noda, M.; Noto, H.; Origasa, H.; Osawa, H.; Taguchi, A.; Tanizawa, Y.; Tobe, K.; et al. Japanese Clinical Practice Guideline for Diabetes 2019. Diabetol. Int. 2020, 11, 165–223. [Google Scholar] [CrossRef] [PubMed]
  25. Society, T.J.; All, H.; Observational, S.R. Chapter 4. Lifestyle Modifications. Hypertens. Res. 2014, 37, 286–290. [Google Scholar] [CrossRef]
  26. Hara, A.; Tsujiguchi, H.; Suzuki, K.; Suzuki, F.; Kasahara, T.; Oanh, P.K.; Miyagi, S.; Kannon, T.; Tajima, A.; Wada, T.; et al. Gender Difference in the Association of Dietary Intake of Antioxidant Vitamins with Kidney Function in Middle-Aged and Elderly Japanese. J. Nutr. Sci. 2021, 10, 1–7. [Google Scholar] [CrossRef]
  27. Ahmed, M.H. Niacin as Potential Treatment for Dyslipidemia and Hyperphosphatemia Associated with Chronic Renal Failure: The Need for Clinical Trials. Ren. Fail. 2010, 32, 642–646. [Google Scholar] [CrossRef]
  28. Cho, K.; Kim, H.; Kamanna, V.S.; Vaziri, N.D. Niacin Improves Renal Lipid Metabolism and Slows Progression in Chronic Kidney Disease. Biochim. Biophys. Acta Gen. Subj. 2010, 1800, 6–15. [Google Scholar] [CrossRef]
  29. Ginsberg, C.; Lx, J.H. Nicotinamide and Phosphate Homeostasis in Chronic Kidney Disease. Cur. Opin. Nephrol. Hypertens. 2016, 25, 285–291. [Google Scholar] [CrossRef]
  30. Streja, E.; Kovesdy, C.P.; Streja, D.A.; Moradi, H.; Kalantar-Zadeh, K.; Kashyap, M.L. Niacin and Progression of CKD. Am. J. Kidney Dis. 2015, 65, 785–798. [Google Scholar] [CrossRef] [Green Version]
  31. Dennis, J.M.; Witting, P.K. Protective Role for Antioxidants in Acute Kidney Disease. Nutrients 2017, 9, 718. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Tian, N.; Thrasher, K.D.; Gundy, P.D.; Hughson, M.D.; Manning, R.D. Antioxidant Treatment Prevents Renal Damage and Dysfunction and Reduces Arterial Pressure in Salt-Sensitive Hypertension. Hypertension 2005, 45, 934–939. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Wang, T.; Fu, X.; Chen, Q.; Patra, J.K.; Wang, D.; Wang, Z.; Gai, Z. Arachidonic Acid Metabolism and Kidney Inflammation. Int. J. Mol. Sci. 2019, 20, 3683. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. ElAttar, T.M.A.; Lin, H.S. Effect of Vitamin C and Vitamin E on Prostaglandin Synthesis by Fibroblasts and Squamous Carcinoma Cells. Prostaglandins Leukot. Essent. Fat. Acids 1992, 47, 253–257. [Google Scholar] [CrossRef]
  35. Sánchez-Moreno, C.; Cano, M.P.; De Ancos, B.; Plaza, L.; Olmedilla, B.; Granado, F.; Martín, A. Consumption of High-Pressurized Vegetable Soup Increases Plasma Vitamin C and Decreases Oxidative Stress and Inflammatory Biomarkers in Healthy Humans. J. Nutr. 2004, 134, 3021–3025. [Google Scholar] [CrossRef] [Green Version]
  36. Fujimori, K.; Amano, F. Niacin Promotes Adipogenesis by Reducing Production of Anti-Adipogenic PGF2α through Suppression of C/EBPβ-Activated COX-2 Expression. Prostaglandins Other Lipid Mediat. 2011, 94, 96–103. [Google Scholar] [CrossRef]
  37. Sugita, K.; Ikenouchi-Sugita, A.; Nakayama, Y.; Yoshioka, H.; Nomura, T.; Sakabe, J.I.; Nakahigashi, K.; Kuroda, E.; Uematsu, S.; Nakamura, J.; et al. Prostaglandin e 2 is Critical for the Development of Niacin-Deficiency- Induced Photosensitivity via ROS Production. Sci. Rep. 2013, 3, 2973. [Google Scholar] [CrossRef]
  38. Song, W.L.; Stubbe, J.; Ricciotti, E.; Alamuddin, N.; Ibrahim, S.; Crichton, I.; Prempeh, M.; Lawson, J.A.; Wilensky, R.L.; Rasmussen, L.M.; et al. Niacin and Biosynthesis of PGD 2 by Platelet COX-1 in Mice and Humans. J. Clin. Investig. 2012, 122, 1459–1468. [Google Scholar] [CrossRef] [Green Version]
Nutrients 14 02082 g001 550
Figure 1. Flow chart showing the study enrolment procedure.
Figure 1. Flow chart showing the study enrolment procedure.
Nutrients 14 02082 g001
Nutrients 14 02082 g002 550
Figure 2. Interaction between vitamin intake and rs883484 genotype in the recessive model on CKD by two-way ANCOVA with adjustment for age, sex, BMI, hypertension, diabetes, smoking status, drinking, and exercise habits. p-Values were from the Bonferroni test.
Figure 2. Interaction between vitamin intake and rs883484 genotype in the recessive model on CKD by two-way ANCOVA with adjustment for age, sex, BMI, hypertension, diabetes, smoking status, drinking, and exercise habits. p-Values were from the Bonferroni test.
Nutrients 14 02082 g002
Table
Table 1. Participant characteristics in different CKD statuses.
Table 1. Participant characteristics in different CKD statuses.
Total (n = 684)non-CKD (n = 570)CKD (n = 114)p-Value *6
Mean (SD)Mean (SD)Mean (SD)
Characteristics
Age (years)62.1 (10.8)60.7 (10.5)68.9 (9.6)0.000
Female, n (%)375 (54.8)324 (56.8)51 (44.7)0.018
BMI *1 (kg/m2)23.2 (3.2)23.1 (3.1)23.9 (3.8)0.023
SBP *2 (mmHg)138.7 (19.4)137.5 (19.1)144.4 (20.0)0.001
DBP *3 (mmHg)80.1 (11.4)80.1 (11.2)80.2 (12.2)0.900
Hypertension, n (%)320 (46.8)254 (44.6)66 (57.9)0.009
HbA1c *4 (%)5.9 (0.7)5.9 (0.7)6.0 (0.5)0.406
FPG *5 (mg/dL)96.5 (18.0)96.3 (18.2)97.6 (16.9)0.526
Diabetes, n (%)95 (13.9)76 (13.3)19 (16.7)0.347
Triglyceride (mg/dL)118.6 (85.9)117.4 (87.1)124.6 (79.9)0.411
Total cholesterol (mg/dL)215.0 (34.5)215.7 (34.9)211.5 (32.1)0.240
Lifestyle habit
Current smoker, n (%)130 (19.0)120 (21.1)10 (8.8)0.002
Current drinker, n (%)331 (48.4)279 (48.9)52 (45.6)0.516
Frequent exercise, n (%)407 (40.5)331 (58.1)76 (66.7)0.088
Genotypes of rs883484 0.669
TT, n (%)105 (15.4)89 (15.6)16 (14.0)
CC + CT, n (%)579 (84.6)481 (84.4)98 (86.0)
Nutrition intake
Vitamin B1 (mg/1000 kcal)0.4 (0.1)0.4 (0.1)0.4 (0.1)0.352
Vitamin B2 (mg/1000 kcal)0.7 (0.2)0.7 (0.2)0.7 (0.2)0.251
Niacin (mg/1000 kcal)9.7 (2.8)9.7 (2.8)9.4 (3.2)0.285
Vitamin B6 (mg/1000 kcal)0.7 (0.2)0.7 (0.2)0.7 (0.2)0.674
Vitamin B12 (μg/1000 kcal)5.8 (3.1)5.9 (3.1)5.7 (3.1)0.693
Folate (μg/1000 kcal)178.2 (75.7)176.9 (76.3)184.7 (72.8)0.313
Pantothenic acid (mg/1000 kcal)3.4 (0.8)3.4 (0.8)3.5 (0.8)0.569
Vitamin C (mg/1000 kcal)61.1 (31.6)59.9 (30.7)67.4 (35.6)0.037
Retinol (μg/1000 kcal)210.9 (400.4)213.4 (434.2)198.8 (139.5)0.722
Vitamin D (μg/1000 kcal)8.4 (5.1)8.5 (5.1)8.2 (5.3)0.649
α-tocopherol (mg/1000 kcal)3.9 (1.1)3.9 (1.1)4.0 (1.2)0.361
Vitamin K (μg/1000 kcal)164.8 (92.8)165.1 (93.5)163.2 (89.9)0.838
Daily salt intake (g/day)9.5 (2.4)9.7 (2.4)8.8 (2.4)0.001
*1 Body mass index; *2 Systolic blood pressure; *3 Diastolic blood pressure; *4 Glycated hemoglobin. *5 Fasting plasma glucose. *6 t-tests for continuous variables and chi-square tests for categorical variables; p-values of <0.05 are shown in bold. SD, standard deviation.
Table
Table 2. Participant characteristics in different rs883484 genotypes and CKD statuses.
Table 2. Participant characteristics in different rs883484 genotypes and CKD statuses.
CC + CT (n = 579) TT (n = 105)
non-CKD (n = 481)CKD (n = 98)p-Value *non-CKD (n = 89)CKD (n = 16)p-Value
Mean (n)SD (%)Mean (n)SD (%)Mean (n)SD (%)Mean (n)SD (%)
Characteristics
Age (years)60.610.469.09.80.00061.310.968.08.30.020
Sex, n (%) Female27156.34141.80.0095359.61062.50.825
BMI (kg/m2)23.13.124.23.60.00222.82.922.34.50.637
SBP (mmHg)137.718.7145.319.80.000136.621.2138.621.30.722
DBP (mmHg)80.211.380.112.40.98579.610.980.711.60.708
Hypertension21745.15960.20.00637.041.67.043.80.871
HbA1c (%)5.90.76.00.60.4715.90.65.90.40.685
FPG (mg/dL)96.518.398.217.30.41595.817.893.013.50.611
Diabetes6513.51616.30.4641112.4318.80.489
Triglyceride (mg/dL)117.791.1125.683.30.425115.761.6118.656.40.863
Total cholesterol (mg/dL)215.735.3211.933.50.339215.632.7208.822.10.421
Lifestyle habit
Current smoker, n (%)105.021.86.06.10.00015.016.94.021.10.436
Current drinker, n (%)236.049.147.048.00.84243.048.35.04.80.207
Frequent exercise, n (%)280.058.267.068.40.06251.042.79.056.30.938
Nutrition intake
Vitamin B1 (mg/1000 kcal)0.40.10.40.10.1240.40.10.40.10.255
Vitamin B2 (mg/1000 kcal)0.70.20.70.20.2070.70.20.70.30.902
Niacin (mg/1000 kcal)9.72.89.63.20.7509.72.68.12.80.027
Vitamin B6 (mg/1000 kcal)0.70.20.70.20.3080.70.20.60.20.191
Vitamin B12 (μg/1000 kcal)5.93.15.93.20.9355.83.04.72.30.179
Folate (μg/1000 kcal)175.277.6187.674.90.147186.068.5167.156.80.302
Pantothenic acid (mg/1000 kcal)3.40.83.50.80.3793.40.83.30.90.499
Vitamin C (mg/1000 kcal)58.530.268.737.30.01267.132.458.921.60.214
Retinol (μg/1000 kcal)217.3468.8209.6145.40.872192.2140.7132.366.40.099
Vitamin D (μg/1000 kcal)8.55.18.45.40.9238.55.27.14.80.332
α-tocopherol (mg/1000 kcal)3.81.14.11.20.0674.11.13.51.4<0.05
Vitamin K (μg/1000 kcal)165.095.0165.592.40.966165.785.6149.273.40.471
Daily salt intake (g/day)9.62.58.82.50.0039.92.18.82.10.060
* t-tests for continuous variables and chi-square tests for categorical variables; p-values of <0.05 are shown in bold.
Table
Table 3. Main effect of CKD status and rs883484 genotypes and their interaction on vitamin intakes.
Table 3. Main effect of CKD status and rs883484 genotypes and their interaction on vitamin intakes.
p-Value for CKD Status *p-Value for Genotypes *p-Value for Interaction *
Vitamin B10.2380.5450.041
Vitamin B20.6580.3260.435
Niacin0.0390.0370.046
Vitamin B60.0920.1520.085
Vitamin B120.0540.1660.256
Folate0.1740.4860.133
Pantothenic acid0.2290.1030.252
Vitamin C0.1920.6200.028
Retinol0.5510.3380.591
Vitamin D0.0380.4130.504
α-tocopherol0.0730.1330.005
Vitamin K0.0970.4750.569
* Two-way Analysis of Covariance (ANCOVA) was performed with adjustment for age, sex, body mass index, hypertension, diabetes, smoking status, drinking, and exercise habits. p-Values of <0.05 are shown in bold.
Table
Table 4. Association between antioxidant vitamins intake and CKD according to rs883484 genotypes.
Table 4. Association between antioxidant vitamins intake and CKD according to rs883484 genotypes.
CC + CTp-ValueTTp-Value
OR95% CIOR95% CI
Model 1Water-soluble vitamins
Vitamin B17.6680.743–79.1060.0870.0050.000–1.3840.065
Vitamin B21.5220.509–4.5470.4520.0810.003–2.4970.151
Niacin1.0080.930–1.0930.8400.7440.582–0.9510.018
Vitamin B61.3440.404–4.4760.6300.0370.002–0.8450.039
Vitamin B120.9720.899–1.0520.4880.8070.641–1.0170.069
Folate1.0010.998–1.0040.6130.9880.975–1.0000.046
Pantothenic acid1.0730.789–1.4590.6520.4180.174–1.0030.051
Vitamin C1.0060.998–1.0130.1340.9750.975–0.9510.042
Fat-soluble vitamins
Retinol1.0000.999–1.0010.9550.9910.982–1.0000.059
Vitamin D0.970.924–1.0180.2150.9060.789–1.0400.159
α-tocopherol1.220.983–1.5140.0710.4950.272–0.9000.021
Vitamin K0.9990.999–0.9960.3720.9930.985–1.0010.098
Model 2Water-soluble vitamins
Vitamin B15.5570.493–62.6090.1650.0060.000–2.2130.089
Vitamin B21.4470.458–4.5730.5290.0920.003–2.9170.176
Niacin1.0140.934–1.1010.7370.7640.592–0.9860.039
Vitamin B61.2650.368–4.3510.7090.0550.002–1.3730.077
Vitamin B120.980.905–1.0620.6210.8310.831–1.0590.135
Folate1.0000.997–1.0040.8340.9880.976–1.0010.073
Pantothenic acid1.0320.748–1.4230.8500.4350.176–1.0790.073
Vitamin C1.0040.996–1.0120.3230.9730.947–0.9990.046
Fat-soluble vitamins
Retinol1.0000.999–1.0010.9110.9920.983–1.0010.097
Vitamin D0.9710.925–1.0190.2310.9250.803–1.0660.283
α-tocopherol1.1790.946–1.4700.1440.5120.276–0.9520.034
Vitamin K0.9980.996–1.0010.2580.9940.985–1.0020.147
Model 3Water-soluble vitamins
Vitamin B14.4450.378–52.3170.2360.0030.000–1.7730.075
Vitamin B21.1870.369–3.8240.7730.0670.002–2.5820.147
Niacin1.0060.926–1.0940.8800.7390.569–0.9610.024
Vitamin B61.1990.343–4.1860.7770.0370.001–1.1310.059
Vitamin B120.9780.902–1.0600.5890.8230.633–1.0690.144
Folate1.0000.974–1.0010.9600.9870.997–1.0030.063
Pantothenic acid0.9810.707–1.3610.9090.4070.153–1.0780.070
Vitamin C1.0030.995–1.0110.4280.9710.945–0.9980.037
Fat-soluble vitamins
Retinol1.0000.999–1.0010.8800.9920.982–1.0020.126
Vitamin D0.9720.925–1.0210.2530.9240.792–1.0790.317
α-tocopherol1.1640.929–1.4580.1870.4920.256–0.9470.034
Vitamin K0.9990.996–1.0010.2710.9940.985–1.0030.181
OR, odds ratios; CI, confidence interval. p-values of <0.05 are shown in bold. Model 1: age, sex, body mass index (BMI), hypertension, and diabetes; Model 2: age, sex, BMI, hypertension, diabetes, exercise, smoking, and drinking; Model 3: age, sex, BMI, hypertension, diabetes, exercise, smoking, drinking, and salt intake.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Pham, K.-O.; Hara, A.; Tsujiguchi, H.; Suzuki, K.; Suzuki, F.; Miyagi, S.; Kannon, T.; Sato, T.; Hosomichi, K.; Tsuboi, H.; et al. Association between Vitamin Intake and Chronic Kidney Disease According to a Variant Located Upstream of the PTGS1 Gene: A Cross-Sectional Analysis of Shika Study. Nutrients 2022, 14, 2082. https://doi.org/10.3390/nu14102082

AMA Style

Pham K-O, Hara A, Tsujiguchi H, Suzuki K, Suzuki F, Miyagi S, Kannon T, Sato T, Hosomichi K, Tsuboi H, et al. Association between Vitamin Intake and Chronic Kidney Disease According to a Variant Located Upstream of the PTGS1 Gene: A Cross-Sectional Analysis of Shika Study. Nutrients. 2022; 14(10):2082. https://doi.org/10.3390/nu14102082

Chicago/Turabian Style

Pham, Kim-Oanh, Akinori Hara, Hiromasa Tsujiguchi, Keita Suzuki, Fumihiko Suzuki, Sakae Miyagi, Takayuki Kannon, Takehiro Sato, Kazuyoshi Hosomichi, Hirohito Tsuboi, and et al. 2022. "Association between Vitamin Intake and Chronic Kidney Disease According to a Variant Located Upstream of the PTGS1 Gene: A Cross-Sectional Analysis of Shika Study" Nutrients 14, no. 10: 2082. https://doi.org/10.3390/nu14102082

APA Style

Pham, K. -O., Hara, A., Tsujiguchi, H., Suzuki, K., Suzuki, F., Miyagi, S., Kannon, T., Sato, T., Hosomichi, K., Tsuboi, H., Nguyen, T. T. T., Shimizu, Y., Kambayashi, Y., Nakamura, M., Takazawa, C., Nakamura, H., Hamagishi, T., Shibata, A., Konoshita, T., ... Nakamura, H. (2022). Association between Vitamin Intake and Chronic Kidney Disease According to a Variant Located Upstream of the PTGS1 Gene: A Cross-Sectional Analysis of Shika Study. Nutrients, 14(10), 2082. https://doi.org/10.3390/nu14102082

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop