Identification of interactions between genetic risk scores and dietary patterns for personalized prevention of kidney dysfunction in a population-based cohort

CKD affects millions globally and is associated with substantial morbidity and mortality, including cardiovascular complications. Early detection of kidney dysfunction (declining eGFR) is critical. Traditional risk factors include hypertension, diabetes, and obesity, and growing evidence implicates genetic susceptibility, including polygenic influences identified through GWAS. Genetic risk scores (GRSs) can quantify cumulative genetic predisposition. Diet is a modifiable determinant of CKD, and dietary pattern analysis captures synergistic effects of foods and nutrients better than single-nutrient approaches. However, gene–diet interactions influencing kidney dysfunction remain insufficiently elucidated. This study aimed to identify genetic risk factors, compute a GRS for kidney dysfunction, extract major dietary patterns, and evaluate interactions between GRS and dietary patterns on kidney dysfunction prevalence in a large Korean cohort.

Prior studies highlight that dietary patterns influence CKD risk: healthier patterns (e.g., high in fruits, vegetables, whole grains, low-fat dairy) are associated with lower CKD risk, whereas Western-type patterns are associated with higher risk. Nutrient components such as high sodium, high animal protein, and high phosphorus have been linked to adverse kidney outcomes, while higher fiber intake is associated with reduced inflammation and mortality in CKD. GWASs have identified numerous loci associated with eGFR and CKD across populations and enable construction of GRSs that capture polygenic risk. Gene–diet interaction research in other phenotypes (e.g., obesity) shows that dietary habits can modify genetic risk expression, underscoring the potential of personalized nutrition. Large sample sizes are typically required to detect interactions, motivating analyses in population-based cohorts.

Study design and population: Cross-sectional analysis of baseline data from the Ansan and Ansung cohorts of the Korean Genome and Epidemiology Study (KoGES; recruitment 2001–2002). From 10,030 adults, exclusions were applied for missing dietary data (n=697), missing serum creatinine or outliers (n=2), history or treatment of CKD (n=262), implausible energy intake (<500 or >5000 kcal/day) or BMI > 50 kg/m² (n=373), and missing genotype data (n=466), yielding 8,230 participants aged 40–69 years (47.9% men, 52.1% women). Ethics approvals and informed consent were obtained. Definition of kidney dysfunction: eGFR calculated using the CKD-EPI equation with sex-specific creatinine thresholds; kidney dysfunction defined as eGFR < 90 mL/min/1.73 m² (capturing early-stage CKD, including stage 2). Serum creatinine measured colorimetrically (Hitachi Automatic Analyzer 7600). Genotyping and imputation: DNA from peripheral leukocytes genotyped on Affymetrix Genome-Wide Human SNP Array 5.0. Quality control filtered markers by call rate, MAF, and Hardy–Weinberg equilibrium per prior criteria. Imputation performed with IMPUTE using HapMap JPT+CHB (release 22), NCBI build 35/dbSNP build 126. After excluding SNPs with MAF < 0.01 and missing rate > 0.05, 1,590,162 SNPs (observed + imputed) were analyzed. GWAS and GRS: GWAS for kidney dysfunction conducted using GCTA generalized linear models, adjusting for sex (fixed effect), age, BMI, HbA1c, and blood pressure. Genome-wide significance threshold P < 5×10⁻⁸. LD clumping and causal region analysis performed using CAVIAR; SNP positions annotated via Ensembl 54. Ninety-four SNPs most strongly associated with eGFR were retained. A weighted GRS was computed as the sum of risk alleles multiplied by SNP-specific beta coefficients (effect sizes relate to increasing GFR). Participants were categorized as low-GR (GRS > 0) and high-GR (GRS ≤ 0). Covariates: Demographics (site, income), lifestyle (alcohol, smoking), physical activity (IPAQ categories: low, moderate, high), education, energy intake, BMI. Dietary assessment and pattern derivation: A validated 103-item food-frequency questionnaire assessed usual intake. Principal component analysis with varimax rotation was used; initial 22 factors (eigenvalues ≥1.4) were examined, then factors with substantive explained variance and loadings ≥0.3 were retained, yielding three patterns: prudent; flour-based and animal food; white rice. Factor scores were divided into tertiles (T1–T3). Nutrient intakes were summarized by tertiles (supplementary tables). Statistical analysis: SAS 9.4 used. Descriptive statistics for categorical and continuous variables. Multivariable-adjusted logistic regression estimated odds ratios (ORs) and 95% CIs for kidney dysfunction across dietary pattern tertiles within GRS strata. Two models: Model 1 adjusted for sex and age; Model 2 adjusted for sex, age, BMI, energy intake, household income, alcohol, smoking, education, and physical activity. Trend tests and interaction p-values were computed. Joint-effects models combined GRS category and dietary tertiles.

- GWAS and GRS: Of 1,590,162 SNPs, 94 SNPs were identified as most strongly associated with eGFR and used to compute GRS. Notable loci included rs17071575 (ADAMTS9; beta −7.3) and rs12242220 (WDFY4; beta −7.6). High-GR participants (GRS ≤ 0) had lower mean eGFR than low-GR participants (P < 0.0001). - Dietary patterns: Three patterns were extracted: (1) Prudent (vegetables, kimchi, fermented paste, sauces/seasonings, mushrooms), (2) Flour-based and animal food (wheat flour/bread, eggs, fish, shellfish), (3) White rice (positive loading for white rice, negative for whole grains). - Flour-based and animal food pattern: Higher adherence associated with higher kidney dysfunction prevalence in both GRS strata. Model 1 p-trend < 0.0001 in both low- and high-GR groups. In Model 2, p-trend = 0.0050 (low-GR) and 0.0065 (high-GR). T3 vs T1 ORs (Model 2): 1.306 (1.068–1.598) in low-GR; 1.283 (1.061–1.552) in high-GR. - Prudent pattern: In high-GR, higher adherence was associated with lower prevalence in Model 1 (p-trend = 0.0332), but this attenuated and was not significant in Model 2 (p-trend = 0.2152). In low-GR, trends toward lower prevalence were not significant (Model 2 p-trend = 0.0840). - White rice pattern: In high-GR, higher adherence was associated with lower prevalence in Model 1 (T3 vs T1 OR 0.720 [0.620–0.837], p-trend < 0.0001), but not significant after full adjustment (Model 2 p-trend = 0.0775). In low-GR, trends were not significant (Model 2 p-trend = 0.0894). - Joint effects: Using low-GR with low dietary score as reference, the highest prevalence was seen in high-GR with high flour-based and animal food pattern scores (OR 2.061, 95% CI 1.719–2.471). For the prudent pattern, low-GR with high adherence showed the lowest prevalence (OR 0.899, 95% CI 0.758–1.065). For the white rice pattern, the highest prevalence was seen in high-GR with low pattern scores (OR 1.828, 95% CI 1.560–2.142).

The study demonstrates that both genetic predisposition and dietary behavior contribute to early kidney dysfunction risk and that their interaction influences prevalence. A polygenic architecture underlies kidney function, with multiple loci—including ADAMTS9 and WDFY4—implicated in renal physiology and related pathways. Among dietary patterns, a flour-based and animal food pattern consistently increased kidney dysfunction prevalence irrespective of genetic risk, even after multivariable adjustment, highlighting the adverse role of refined grains, animal-derived foods, and potentially higher sodium/protein loads. Prudent dietary behavior showed suggestive protective associations—particularly among high-GR individuals before full adjustment—while the white rice pattern showed inverse associations in minimally adjusted models that attenuated after controlling for confounders. Joint-effect analyses further underscore that individuals with higher genetic risk adhering to unfavorable dietary patterns had the highest odds of dysfunction, supporting the rationale for GR-informed dietary guidance. These findings address the research question by showing that genetic risk profiles can inform which dietary patterns may most influence kidney dysfunction risk, supporting personalized nutrition strategies for prevention in at-risk groups.

Dietary patterns interact with genetic susceptibility to shape kidney dysfunction risk. A flour-based and animal food pattern was associated with higher prevalence across genetic risk strata, while healthier dietary behaviors showed suggestive protective effects. Integrating genetic risk profiles into dietary counseling may enhance prevention and management of kidney dysfunction. Future research should replicate these findings in diverse populations and further elucidate mechanistic gene–diet pathways to refine personalized dietary recommendations.

- Study population drawn from a specific Korean cohort, which may limit generalizability to other ethnic or demographic groups. - Dietary intake assessed via self-reported FFQ, introducing potential recall/reporting bias and measurement error. - Dietary patterns may change over time; the analysis reflects a single time point (baseline snapshot).