Introduction
Hypertension (HTN) and dyslipidemia are established risk factors for cardiovascular disease [1–3]. Current guidelines highlight the importance of optimal blood pressure (BP) and low-density lipoprotein cholesterol (LDL-C) concentration in reducing the risk of cardiovascular disease [4,5]. Although HTN is also a known risk factor for the development and progression of chronic kidney disease (CKD), the relationship between dyslipidemia and the risk of incident CKD or progression of CKD remains controversial [6–8]. Recent studies have shown that the implementation of strict control measures for both BP and LDL-C can exert a beneficial influence on the progression of CKD [9]. However, whether this synergic management is also effective in preventing the development of CKD is unclear.
Polygenic risk scores (PRSs) are sums of genetic variants of an individual, reflecting a person’s lifetime exposure to a specific trait. Unlike prospective studies on BP or cholesterol concentrations and chronic non-communicable diseases, which typically span 4 to 5 years, using the PRS offers the advantage of examining lifetime exposure to the trait of interest [10,11]. Interestingly, a recent study revealed that patients with a genetically lower BP and lower LDL-C concentration experienced fewer cardiovascular events [12]. Despite the shared risk factors between CKD and cardiovascular diseases, the potential association of genetic variants related to combined exposure to a higher BP and a higher LDL-C concentration with the risk of CKD has not been explored. Therefore, in this study, we aimed to investigate such a potential association by using clinical and genetic information from the UK Biobank cohort.
Methods
Data source and study population
UK Biobank was a large prospective cohort study of more than 500,000 participants, aged 40 to 69 years, at 22 assessment centers in England, Wales, and Scotland from 2006 to 2010. The aim of the cohort study was to investigate the association of exposures and genetic risk factors with various chronic diseases by collecting a wide range of phenotypic and genotypic data. A detailed description was previously provided [13]. At baseline, self-reported questionnaires regarding sociodemographic, physical, lifestyle, and medical conditions were completed by patients. They also provided biospecimen samples, and anthropometric measurements were made. All participants provided written informed consent, and the study protocol was approved by the Northwest Multi-center Ethics Committee (IRAS project: ID299116). All procedures followed were in accordance with institutional guidelines. The study protocol and baseline characteristics are publicly accessible on the study website. The data are available to all researchers upon request (www.ukbiobank.ac.uk).
Among the 486,133 participants with PRS values in the cohort, patients with underlying CKD were excluded defined as those with (i) an estimated glomerular filtration rate (eGFR) of <60 mL/min/1.73 m2, or (ii) a urinary albumin-creatinine ratio (UACR) of >30 mg/g at enrollment, or (iii) CKD or end-stage kidney disease (ESKD) diagnosis according to International Classification of Diseases, 10th Revision (ICD-10) codes (n = 96,225). We further excluded participants with a history of malignancy (n = 33,918), non-European descent (n = 20,329), and incomplete measurements of covariates (n = 89,768) (Fig. 1).
Genotyping and polygenic risk scores
PRSs are weighted sums of individual risk alleles derived from genome-wide association studies (GWASs), representing an individual’s genetic predisposition to a specific trait [11]. Genotyping of single-nucleotide polymorphisms, imputation, and quality control were carried out by the UK Biobank as previously described, and details are described in the Supplementary Methods (available online) [14]. In 2022, the UK Biobank released a set of PRSs for 53 diseases and quantitative traits, which are available online [15]. UK Biobank released two sets of PRSs. The standard PRS was calculated for all UK Biobank individuals from multiple external GWAS sources, and the enhanced PRS was calculated for a testing group of 104,231 individuals from within the UK Biobank by training on the UK Biobank subgroup and external data. In this study, we used the standard PRSs for HTN and LDL-C.
Main exposures
The primary exposure of interest was the PRSs for HTN (PRS HTN) and LDL-C (PRS LDL-C). The individual association of each PRS with the risk of CKD was assessed using continuous modeling. In addition, to evaluate the combined association of the genetic predisposition to HTN and LDL-C, participants were divided into four groups: 1) the ‘reference group,’ with both PRS <the median, 2) the ‘high LDL-C-risk group,’ those with PRS LDL-C ≥the median and PRS HTN <the median, 3) the ‘high HTN-risk group,’ those with PRS LDL-C <the median and PRS HTN ≥the median, and 4) the ‘combined high-risk group,’ those with both PRSs ≥the median.
Study outcome
The primary outcome was incident CKD, which was identified according to ICD-10 codes in any primary-care data, hospital-inpatient data, and death-registry records, or according to Office of Population Censuses and Surveys Classification of Interventions and Procedures version 4 (OPCS-4) codes in hospital-inpatient data. The UK Biobank also included general practice data for about one-third of the enrolled participants. Thus, we created an additional subcohort by using eGFR and albuminuria data. In this subcohort, CKD was strictly defined as an eGFR <60 mL/min/1.73 m2 or a UACR >30 mg/g on two or more occasions at least 90 days apart.
Statistical analysis
Baseline characteristics are presented as numbers and percentages for categorical variables, means with standard deviations (SDs) for normally distributed variables, or medians with interquartile ranges for variables with a skewed distribution. Cause-specific hazard models were used to examine the risk of incident CKD for an individual’s PRSs in total cohort and subcohorts categorized according to PRS. The occurrence of death before the development of CKD was considered a competing risk of incident CKD [16]. Three models were used, with sequential adjustment for confounding factors. Model 1 contained no adjustments. Model 2 was adjusted for age, sex, body mass index (BMI), socioeconomic status, Townsend deprivation index score, alcohol use, smoking status, handgrip strength, salt addition to food, and comorbidities (diabetes mellitus [DM], cardiovascular disease, chronic pulmonary disease, and liver disease). Lastly, model 3 was further adjusted for eGFR, triglyceride concentration, high-density lipoprotein cholesterol (HDL-C) concentration, and use of medications, such as BP- and lipid-lowering drugs. The results are presented as hazard ratios (HRs) and 95% confidence intervals (CIs). Subgroup analyses were conducted to examine the effect modification of key characteristics, such as sex, age, DM, smoking status, BMI, and use of BP- or lipid-lowering medications. The proportionality assumption of the Cox model was ascertained by cumulative incident function and Schoenfeld residuals [17]. Cumulative incident CKD events were derived using the cumulative incidence function for a competing risk, and the difference between curves was analyzed using the Gray test [18].
For sensitivity analyses, the risk of incident CKD according to PRS was assessed in the subcohorts of patients utilizing eGFR and albuminuria data. An additional sensitivity analysis was performed to adjust for physical activity and dietary factors by using metabolic equivalents, total sodium intake, and total energy intake. We further conducted multiple imputations by chain equation with missing covariates [19]. All analyses were performed using Stata (version 17; StataCorp LLC) or R (ver. 4.3.2; R Foundation for Statistical Computing) software. A two-tailed p-value less than 0.05 was considered statistically significant.
Results
Baseline characteristics
The baseline characteristics of the participants stratified according to the median PRSs are described in Table 1. Overall, the median values of PRS LDL-C and PRS HTN were –0.05 and –0.07, respectively (Supplementary Fig. 1, available online). Among the 245,893 patients, the mean age of the patients was 55.7 years, and 49.8% were male. The mean baseline eGFR was 95.9 ± 11.6 mL/min/1.73 m2, and the mean BMI was 27.3 ± 4.6 kg/m2. Overall, 9,406 (3.8%) had a history of DM and 12,038 (4.9%) had a history of cardiovascular disease. Compared with the reference group, the combined high-risk group exhibited a higher mean systolic BP (SBP; 141.1 ± 19.1 mmHg vs. 136.0 ± 18.5 mmHg), LDL-C concentration (145.4 ± 34.1 mg/dL vs. 131.5 ± 30.1 mg/dL), and HTN (28.6% vs. 16.2%). In this group, a larger proportion of individuals were treated with lipid-lowering (21.8% vs. 12.4%) and antihypertensive (19.8% vs. 10.0%) medications compared to the reference group. No clinically relevant differences were observed in other baseline variables among the four groups (Table 1). Baseline SBP and LDL-C values were positively correlated to PRS HTN (γ = 0.17) and PRS LDL-C (γ = 0.28), respectively (Supplementary Fig. 2, available online).
Relationship between polygenic risk scores and incident chronic kidney disease
During a median follow-up of 13.7 years, 7,771 participants (3.2%) experienced CKD events, with an overall incidence rate of 2.38 per 1,000 person-years. There were 1,722 (2.8%), 1,778 (2.9%), 2,165 (3.5%), and 2,106 events (3.5%) of incident CKD, and the corresponding incidence rates were 2.12, 2.15, 2.63, and 2.61 per 1,000 person-years in the reference group, high LDL-C-risk group, high HTN-risk group, and combined high-risk group, respectively. In the subcohort with strictly defined CKD using general practice data, 7,626 cases (7.3%) of incident CKD occurred during a median follow-up of 13.6 years, with an incidence of 5.47 cases per 1,000 person-years. There were 1,552 (6.0%), 1,604 (6.1%), 2,269 (8.6%), and 2,201 cases (8.5%) of incident CKD in the four groups, respectively, with incidence rates of 4.49, 4.55, 6.44, and 6.39 per 1,000 person-years (Table 2, Supplementary Fig 3, available online). Independent associations of PRS HTN and PRS LDL-C with incident CKD were analyzed using a competing-risk model (Table 3). After full adjustment for confounding factors, a higher PRS HTN was associated with a significantly higher risk of incident CKD (HR per 1–SD higher PRS, 1.07; 95% CI, 1.05–1.10). However, the PRS LDL-C was not significantly associated with the risk of incident CKD (HR per 1–SD higher PRS, 1.00; 95% CI, 0.98–1.03).
To examine whether a stronger genetic predisposition for LDL-C and HTN synergically contributes to the risk of CKD, we analyzed the combined association of PRSs for HTN and LDL-C with the risk of CKD (Table 4, Fig. 2). Compared with the reference group, the high LDL-C risk group did not have an increased risk of incident CKD (HR, 1.03; 95% CI, 0.97–1.10). However, the high HTN-risk group was associated with a 13% increased risk of CKD (HR, 1.13; 95% CI, 1.06–1.20). Notably, the combined high-risk group did not exhibit a further increased risk of CKD compared with those with a higher PRS HTN alone.
The significant association observed between PRS HTN and CKD risk suggests that genetic predisposition to HTN may play a more direct role in kidney function deterioration compared to LDL-C. It is also important to note that the lack of association between PRS LDL-C and CKD does not rule out LDL-C’s contribution to CKD risk. Instead, it indicates that the genetic predisposition to LDL-C may have limited predictive value for CKD. Our findings showed no significant synergistic effect, as individuals with high PRSs for both HTN and LDL-C did not exhibit a greater CKD risk than expected from PRS HTN alone. This highlights that the two genetic predispositions likely act through independent pathways without interaction. From a clinical perspective, these findings underscore the importance of HTN management for CKD prevention, particularly in populations with a genetic predisposition to HTN, while the direct focus on LDL-C levels may be less impactful in this regard.
Sensitivity analysis
Similar results were observed in the subcohort derived from general practice data. The adjusted HRs per 1–SD higher PRS HTN and PRS LDL-C were 1.21 (95% CI, 1.18–1.24) and 1.02 (95% CI, 1.00–1.05), respectively (Table 3). In the combined analysis, the corresponding HRs were 1.05 (95% CI, 0.98–1.12), 1.39 (95% CI, 1.31–1.49), and 1.45 (95% CI, 1.36–1.55) for the high LDL-C-risk group, the high HTN-risk group, and the combined high-risk group, compared with the reference group (Table 4). However, there was no notable synergistic increase in CKD risk when comparing the high HTN-risk group with the combined high-risk group (Supplementary Table 1, available online). Additional sensitivity analyses, in which we adjusted for total sodium intake, total energy intake, and physical activity, and analysis with multiple imputation datasets yielded similar results (Supplementary Tables 2 and 3, available online).
Our sensitivity analysis suggests a consistent and significant association between higher PRS HTN and CKD risk underscoring the pivotal role of genetic predisposition to HTN in CKD development. However, the predisposition to higher LDL-C levels had minimal impact on CKD development.
Subgroup analysis
We also examined whether potentially modifiable factors influenced the association of PRSs HTN and LDL-C with incident CKD. The p for interaction in each case was >0.05, suggesting that these relationships were not influenced by sex, age, DM, cardiovascular disease, smoking status, BMI, or medication use (Fig. 3).
Discussion
In this cohort study, we examined whether individual and combined genetic predispositions to HTN and a high LDL-C concentration contributed to the development of incident CKD. We found that a higher PRS for HTN was significantly associated with the risk of CKD, whereas the PRS for a high LDL-C concentration was not. In the combined analysis, a PRS LDL-C <the median and for PRS HTN ≥the median was significantly associated with a 13% higher risk of CKD. However, the association of a PRS LDL-C ≥the median and that for PRS HTN <the median with the risk of CKD was not significant. The risk of CKD was not further increased when both PRSs were elevated.
Recently, there have been a number of studies using PRSs. One study used the PRS for CKD and stratified it into low, intermediate, and high PRS-CKD. The results showed that PRS for CKD was associated with incident CKD, and further presented low PRS group had a higher risk of triglyceride-related incident CKD [20]. Another study examined the PRS HTN and PRS LDL-C in the context of achieving BP and LDL-C targets in individuals receiving treatment and their association with major adverse cardiovascular events (MACE). This study found that a higher PRS HTN was associated with poorer BP control and a higher risk of MACE, while PRS LDL-C was associated with uncontrolled hypercholesterolemia but not with the risk of MACE [21]. The study emphasizes the roles of PRS in clinical practice from identifying patients who may need more intensive intervention. In our study, significant association between higher PRS HTN and an increased risk of CKD highlights the critical role of genetic predisposition to HTN in CKD development. This suggests that individuals with a high genetic risk for HTN may benefit from earlier and more aggressive BP management to mitigate their CKD risk. However, the absence of a significant association between PRS LDL-C and CKD risk indicates that genetic predisposition to higher LDL cholesterol levels may not directly contribute to CKD development in the general population. This finding suggests that while LDL cholesterol remains an important cardiovascular risk factor, its role in CKD pathophysiology appears more limited. Integrating PRS into routine clinical assessments could help stratify patients based on their CKD risk, enabling more personalized prevention and monitoring strategies.
In line with our study, previous studies have provided limited evidence that link the LDL-C concentration to CKD. Interventions with statins to reduce LDL-C concentrations failed to demonstrate significant effects on kidney outcomes [7,8,22]. In addition, Mendelian randomization studies investigating genetic factors associated with the LDL-C concentration did not establish causality between the LDL-C concentration and CKD [23,24]. Recently, tubular and glomerular damage caused by lipid accumulation has been proposed as a contributor to the pathogenesis of CKD [25]. Moreover, investigations have suggested that this mechanism operates independently of circulating lipid concentrations, instead being driven by lipid dysmetabolism due to insulin resistance or inflammation [26,27]. Rather than focusing solely on circulating LDL-C concentrations, the multifactorial pathogenesis of CKD, including impaired lipid metabolism, glomerular damage due to reactive oxygen species, and LDL-C-induced activation of the renin-angiotensin system may account for the possible link between the LDL-C concentration and CKD [28,29]. Thus, CKD is likely intricately related to lipid dysmetabolism and lipid-associated inflammation rather than to the LDL-C concentration alone.
High BP is a well-known risk factor for CKD, and recent guidelines of the KDIGO (Kidney Disease: Improving Global Outcomes) emphasize the importance of strict BP control [30]. The association between BP and CKD has been extensively investigated via observational and genetic studies [31–34]. In one study, the data of more than 50,000 participants in multiethnic cohorts and biobanks were pooled for the development of a PRS HTN. In that study, a higher PRS HTN was predictive of the development of coronary artery disease, ischemic stroke, type 2 DM, and even CKD [32]. Another study, involving 310,000 individuals in the United Kingdom, identified a significant association between genetically predicted BP and CKD by using SBP and diastolic BP loci. Additionally, in that study, a 5-mmHg higher SBP was associated with a 37% higher incidence of CKD [33]. Furthermore, a trans-ethnic Mendelian randomization analysis of the causal relationship between cardiometabolic factors and CKD revealed that genetically predicted BMI, HTN, SBP, and HDL-C concentration were causally associated with the risk of CKD [31]. Our study corroborates this evidence, revealing a significant association of PRS HTN with incident CKD. A 1–SD higher PRS HTN and a PRS HTN ≥the median was associated with a 7% and a 13% higher risk of CKD, respectively.
The question arises whether the genetic risk of a high LDL-C concentration jointly contributes to the risk of CKD alongside the genetic risk of HTN, even though the genetic risk of a high LDL-C concentration alone was not significantly associated with the risk of CKD. To this end, we analyzed the combined association of PRSs for these two risk factors. However, we observed no synergic association between elevated PRSs for a high LDL-C concentration and HTN. In a subcohort analysis, using general practice data of the eGFR and UACR during follow-up, a further 10% higher risk of CKD was observed when both PRSs were elevated compared with a higher PRS HTN alone, although this effect was not statistically significant. We also investigated whether these two genetic risk factors could play a role in specific risk groups such as patients with DM, cardiovascular disease, or obesity. However, no significant effect modification was observed in the subgroup analysis. Despite the lack of a synergic association in our study, previous studies have suggested that multifactorial interventions for both BP and lipid control can improve kidney outcomes. In a recent Japanese trial conducted in 2,540 patients with type 2 DM, a multistep approach was used, including intensive glycemic control (target hemoglobin A1c, <6.2%), BP control (target BP, <120/75 mmHg), and lipid control (target LDL-C concentration, <80 mg/dL). This approach resulted in a 32% reduction in the incidence of diabetic kidney disease compared to the less-intensive targets of hemoglobin A1c <6.9%, BP <130/80 mmHg, and LDL-C concentration <120 mg/dL [35]. In addition, in our group, based on a prospective CKD cohort, a 52% lower risk of CKD progression was observed in patients with an SBP <120 mmHg and LDL-C concentration <70 mg/dL compared with those with an SBP <140 mmHg and LDL-C concentration <100 mg/dL [9]. These results suggest that genetic risks related to BP and LDL-C are not modifiable and that additional synergic contributions to the risk of CKD are unlikely. However, HTN and hyperlipidemia acquired during a person’s lifespan may exacerbate the risk of CKD, but their combined contribution can be mitigated via pharmaceutical interventions and lifestyle modifications.
The main strength of this study lies in the assessment of the risk of CKD based on lifetime exposure to risk factors in individuals by using the PRS, which provides genetic information that is less susceptible to confounding variables than other information. Leveraging comprehensive genetic data, we conducted in-depth analyses to explore the association of genetic predispositions to HTN and a high LDL-C concentration with the risk of CKD in a large UK cohort. However, several limitations should be discussed. First, although we analyzed the risk of CKD based on lifetime exposure to risk factors derived from individual genetic information in this study, interventions to treat risk factors might have impacted the occurrence of CKD. To address this concern, we examined the effect modification of BP- and lipid-lowering drugs in subgroup analyses, which did not change the results of the study. Second, the PRS is an indication of the observed association of a particular trait; thus, it does not directly establish causation. Third, the lack of a synergic association between the genetic predisposition of the two risk factors observed in our study may be attributed to the relatively good health and low burden of comorbidities in the UK Biobank cohort. Fourth, pleiotropy, where a single genetic variant influences multiple traits, could affect our findings if genetic variants are included in the PRS HTN or LDL-C independently associated with kidney function. However, the impact of such pleiotropic effects is expected to be minimal, since the PRS was constructed using large, well-established GWAS and the baseline eGFR was adjusted in the analysis. Fifth, comparing traits with differently powered PRSs need careful interpretation. Further validations in independent cohorts are warranted. Lastly, the PRS used in this study was derived from individuals of European descent, potentially limiting its generalizability to the broader global population.
In conclusion, our analysis of data from the UK Biobank study revealed that a higher PRS HTN was associated with a significantly higher risk of CKD. However, we did not discover either an independent or synergic association between a higher PRS LDL-C and incident CKD, even in the presence of a higher genetic risk of HTN. Further investigations are warranted to delineate the complex interplay between the PRSs for HTN and LDL-C and the risk of incident CKD.
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