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Genome-wide association analysis of cystatin c and creatinine kidney function in Chinese women

BMC Medical Genomics volume 17, Article number: 272 (2024) Cite this article

Abstract

Background

With increasing incidence and treatment costs, chronic kidney disease (CKD) has become an important public health problem in China, especially in females. However, the genetic determinants are very limited. The estimated glomerular filtration rate (eGFR) based on creatinine is commonly used as a measure of renal function but can be easily affected by other factors. In contrast, eGFR based on both creatinine and cystatin C (eGFRcr-cys) improved the diagnostic accuracy of CKD. To our knowledge, no genome-wide association analysis of eGFRcr-cys has been conducted in the Chinese population.

Methods

By conducting a Genome-Wide association study(GWAS), a method used to identify associations between genetic regions (genomes) and traits/diseases, we examined the relationship between genetic factors and eGFRcr-cys in Chinese women, with 1983 participants and 3,838,121 variants included in the final analysis.

Result

One significant locus (20p11.21) was identified in the Chinese female population, which has been reported to be associated with eGFR based on cystatin C (eGFRcys) in the European population. More importantly, we found two new suggestive loci (1p31.1 and 11q24.2), which have not yet been reported. A total of three single nucleotide polymorphisms were identified as the most important variants in these regions, including rs2405367 (CST3), rs66588571(KRT8P21), and rs626995 (OR8B2).

Conclusion

We identified 3 loci 20p11.21, 1p31.1, and 11q24.2 to be significantly associated with eGFRcr-cys. These findings and subsequent functional analysis describe new biological clues related to renal function in Chinese women and provide new ideas for the diagnosis and treatment development of CKD.

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Background

Chronic kidney disease (CKD) has become an important public health concern in the world with its increasing incidence rate [1]. The global prevalence of CKD was approximately 9.1%, with nearly 700 million patients, one-third of whom were in China and India [2, 3]. In 2022, a meta-analysis on the prevalence of CKD in Asia revealed that China had the highest number of adult CKD patients in Asia and faced a huge burden of CKD [4]. According to the prevalence report of CKD, significant disparities exist in the likelihood of developing CKD between genders. As reported by the 2017 Global Burden of Disease (GBD) research, women are 1.29 times more likely than males to have CKD worldwide in terms of age-standardized prevalence [2]. Meanwhile, a 2022 meta-analysis on sex differences in the prevalence of CKD in Asia revealed that the overall prevalence of CKD in women was 1.07 times higher than that in men in the Asian region. The analysis also noted that the results were driven by the considerable size of China’s population [5]. Genetic factors related to kidney function have been extensively studied in European populations [6,7,8,9], but the impact of genetic variations may vary among different populations [10], highlighting the need for genetic locus research related to kidney function in different populations, especially in China [11].

The estimated glomerular filtration rate (eGFR) calculated by serum creatinine (SCr) is often used to assess renal function, but this method has some drawbacks. It can easily lead to the overdiagnosis of CKD and be affected by the patient’s characteristics such as age and sex [12]. Cystatin C (Cys-C), which is less affected by the above characteristics, can be used as another marker of glomerular function [13]. Cys-C is particularly suitable for detecting mild renal impairment as well as rapid changes in eGFR [14,15,16]. Recently, research has tended to use the eGFR calculated based on the combination of SCr and Cys-C (eGFRcr-cys), which could improve the diagnostic and staging accuracy of CKD [17,18,19,20]. However, no GWAS of eGFRcr-cys has been conducted in Asians to date. In this study, we first conducted a genome-wide association study (GWAS) to identify genetic variants that are associated with renal function using eGFRcr-cys in Chinese women.

Materials and methods

Study participants

This study is based on the Jurong Women Health Study in China. The Jurong Woman Health Study is a prospective study of chronic disease prevalence and risk factors conducted by the Center for Disease Control of Jurong City in 2015 [21]. The participants were volunteers from various communities in Jurong City, with the informed consent of all participants. The study was performed in accordance with the Declaration of Helsinki and has been approved by the Institutional Review Board of Nanjing Medical University (2015077).

The survey information for the Jurong Cohort study included questionnaires and anthropometric and blood tests, which included demographic information (age and sex) and other clinical information (Body Mass Index, history of hypertension and diabetes) of the participants.

Genotyping, quality control filtering, and genotype imputation

Genomic DNA was extracted from the blood samples taken and genome-wide genotyping was performed using Infinium Asian Screening Array (ASA) Bead Chip. Single nucleotide polymorphisms (SNPs) with the following conditions were excluded, genotype call rate < 0.95, MAF < 0.01, and Hardy-Weinberg balance accurate test P < 1 × 10− 6. The remaining SNPs after quality control were pre-phased using SHAPEIT2 [22], followed by IMPUTE2 [23] for genotype imputation on the basis of a reference panel from 1000 Genome Project phase 3 East Asian (1000GP3-EAS) population. Variants with MAF < 0.05 were eliminated after genotype imputation, leaving 3,838,121 SNPs for the final analysis.

For sample quality control, individuals with the following conditions were excluded, participants with related individuals (pihat(Proportion IBD, i.e. P(IBD = 2) + 0.5*P(IBD = 1)) > 0.1875), high missing genotype rate (≥ 0.02), elevated heterozygosity rates deviating from the mean ± three SDs or inconsistent gender data. One participant who could not obtain SCr and Cys-C data was excluded, and 1983 participants were finally included (Fig. 1).

Fig. 1
figure 1

Detailed flowchart of the study analyses. EAS, East Asian; SNPs, single nucleotide polymorphisms; QC, quality control; MAF, minor allele frequency; eQTL, expression quantitative trait locus;

Full size image

Calculation of eGFR

Using the Chinese eGFR equation proposed by Ma Yingchun et al. [24], eGFR for each subject was calculated based on Scr, Cys-C, age, and sex: eGFR (mL/min/1.73m2) = 169 × Pcr-0.608 × cysC-0.63 × Age-0.157 (Female × 0.83). When the calculated eGFR was < 15 ml/min/1.73 m2 or > 200 ml/min/1.73 m2, it was set to 15 ml/min/1.73 m2 and 200 ml/min/1.73 m2, respectively, to avoid excessive influence of outliers. The natural logarithm transformation is further carried out.

Association analysis and identification of lead SNPs

Using PLINK1.9 [25], an additive genetic model is assumed to fit the linear regression model to the natural logarithm of the eGFR. Adjustments were made for covariates including age, hypertension, diabetes, and the first five principal components. In the analysis, the genome-wide significance level was set to P < 5 × 10− 8, and the genome-wide suggestion level was set to P < 10− 5. The comprehensive platform FUMA [26] was used to cluster SNPs, identify independent genomic loci, and annotate SNPs. The LD structure was calculated using 1000G Phase 3 EAS as the reference panel, and the variations that were significant (P < 10− 5) and independent from each other (r2 threshold < 0.6) were defined as independent significant SNPs. If the LD blocks of independent significant SNPs are less than 250 kb apart from each other, they are combined into one genomic locus. When independent significant SNPs are independent of each other (r2 < 0.1), they are defined as independent lead SNPs (Fig. 1).

Functional annotation and mapping

Based on the Ensembl gene (Build 85), ANNOVAR [27] is used to annotate the functional consequences of all independent significant SNPs and SNPs that are in LD with the independent significant SNPs. Functionally annotated SNPs are subsequently mapped to genes by (1) positional mapping, (2) expression quantitative trait locus (eQTL) mapping, and (3) chromatin interaction mapping. At the same time, gene analysis was performed using the MAGMA tool [28], which used the SNP-wise model and was corrected by the Bonferroni method. SNPs were mapped to 16,117 protein-coding genes, so the significance threshold of P-value is was defined at P = 0.05/16,117 = 3.10 × 10− 6.

At the same time, FUMA associated independent significant SNPs and related SNPs with the National Human Genome Research Institute (NHGRI) GWAS Catalog [29] to gain insight into the associations of SNPs with various phenotypes at previously reported risk loci.

Expression quantitative trait loci

We examined publicly available expression quantitative trait loci association data from various sources, including GTEx [30], NephQTL [31], and the Human Kidney eQTL Atlas [32], to determine whether our identified SNPs were associated with renal function.

Gene set enrichment and tissue expression analysis

FUMA’s GENE2FUNC [26] function was used for gene set enrichment. The GENE2FUNC function uses candidate genes mapped from identified SNPs for gene set enrichment analysis. Tissue expression analysis was performed using the MAGMA tool [28], in which gene expression data sets were obtained from GTEx v8 [33] which included 53 tissue types.

Result

Findings from genome-wide association study

A total of 1983 participants’ data were included in this study for analysis. Baseline characteristics of the participants are shown in Table 1. We used eGFRcr-cys as a continuous dependent variable for quantitative trait locus analysis. The Q-Q plot showed no significant genomic bloat (λGC = 1.03) in association results (Additional file 1: Fig.S1.). The Manhattan plot shows that one locus achieved genome-wide significance and three achieved suggestive significance (Fig. 2).

Table 1 Characteristics of participants
Full size table
Fig. 2
figure 2

Manhattan plots of eGFR in a Chinese population revealed one genome-wide and two suggestive significant loci. The upper red line corresponds to a genome-wide significance threshold of 5 × 10− 8, while the lower blue line corresponds to a suggestive association threshold of 10− 5

Full size image

Our study revealed a total of four independent significant SNPs associated with eGFRcr-cys in the Chinese female population (Table 2, Additional file 1: Fig.S2.). We found a locus was significantly associated with eGFRcr-cys on chromosome 20 (20p11.21), which was led by rs2405367 (P = 6.26E-09), and rs2897119 was an independent significant SNP (P = 3.93E-06). Both the SNPs were located in the intergenic region near the CST3 gene.

Table 2 Four independent significant SNPs associated with eGFR identified in our study
Full size table

In addition, we found two suggestive loci associated with eGFRcr-cys on chromosome 1(1p31.1) and chromosome 11(11q24.2), whose lead SNPs were rs66588571 (P = 3.14E-06) and rs626995 (P = 3.33E-06), respectively. They were located in intergenic regions near the KRT8P21 and OR8B2 genes, respectively.

Functional annotation and mapping

Using the FUMA platform, 8, 10, and 74 genes were mapped based on positional, eQTL, and chromatin interaction information of SNPs, respectively (Additional file 2: Table S1-S3.). We combined the genes mapped in these three ways, with each gene value counted only once, and identified a total of 79 candidate genes (Additional file 2: Table S4.). In the gene analysis performed by MAGMA, only CST3 and CST9 were significant (P < 3.10 × 10− 6).

According to the GWAS Catalog, we identified other SNPs within ± 1 MB of the Lead SNP rs2405367 that were reported to be associated with Cystatin-C levels. Moreover, some SNPs within ± 1 MB of rs66588571 have been reported to be associated with triglyceride levels (Additional file 2: Table S5.).

Expression quantitative trait loci

After querying kidney eQTL data from several sources, we found that the Lead SNP rs2405367 at 20p11.21 was associated with higher expression of CST9 in renal tubules and lower expression of CST3 and RP11-218C14.8 in glomeruli. An independent significant SNP rs2897119 in the same region was also associated with lower expression of CST3 in the glomeruli (Additional file 2: Table S6.).

Gene sets enrichment and tissue expression analyses

Gene sets enrichment analysis showed that candidate genes were significantly associated with several biological pathways. These include olfactory signaling pathways, olfactory transduction, reducing the activity of a cysteine-type endopeptidase, modulating the activity of a peptidase, body mass index, resistin levels, and other pathways (Additional file 1: Fig. S3, Additional file 2: Table S7.). The MAGMA was used to analyze the expression of 53 tissue types (Additional file 1: Fig. S4, Additional file 2: Table S8.), and it was found that the expression of non-specific tissues was higher than that of other tissues, and reached a significant level (P > 0.05/53).

Discussion

leveraging data of 1983 women from China, we conducted this GWAS to identify renal susceptibility loci in the Chinese female population by including both Scr and Cys-C as markers of renal function decline. In this study, we identified three genetic loci related to renal function in the Chinese female population, including 20p11.21(rs2405367), 1p31.1 (rs66588571), and 11q24.2(rs626995), among which rs2405367 has been found to be associated with eGFRcys [6] in the European population. Nevertheless, the other two Lead SNPs we identified (rs66588571 and rs626995) did not have a statistically significant association with renal function in the European population, suggesting that distinct genetic mechanisms might exist between different races [6, 7, 34].

In most geographic regions for which data are available, the prevalence of stage 3–5 CKD is higher in women than in men [35]. Complications during pregnancy and perinatal periods are risk factors for developing CKD, such as thrombotic microangiopathies [36] and preeclampsia [37], with a significant risk ratio of 1.82 for CKD and 3.01 for renal failure in women with a history of preeclampsia [38]. Studies have also demonstrated that women with CKD are less likely than men to be cognizant of their condition, to undergo screening for and diagnosis of CKD, and to have less access to treatment [39]. Gender differences in the risk factors, prevalence, and diagnosis and treatment of CKD illustrate the importance of conducting GWAS studies on renal function in women. Our study identified specific genetic loci related to the kidney and revealed the possible pathway of its influence on renal function, which has important significance for early screening, diagnosis, and treatment of kidney diseases in Chinese women.

First, we found the Lead SNP rs2405367 and the independent significant SNP rs2897119 in 20p11.21, both of which are in the intergenic region near the CST3 gene. Through the query of kidney eQTL data, we also found that these two SNPS affect the expression of CST3 and CST9 in kidney tissues. Studies have found that CST3 gene mutations may be associated with Alzheimer’s disease [40, 41], age-related macular degeneration [42], and cardiovascular disease. A study by Ding et al. [43] assessing whether CST3 mutations are associated with large atherosclerotic stroke (LAAS) and prognosis found that it may affect serum cystatin C concentration independent of renal function. Meanwhile, another report of disease-causing genes in high-risk heart failure patients found that the CST3 gene was highly expressed in both men and women compared to the control group [44].

The CST3 gene encodes cystatin C protein, which is a cysteine protease inhibitor. Some studies have observed the expression of CST3 in mouse kidney tissues and found that plasma concentration of cystatin C and its mRNA level in the kidney are related to genetic variation, and are co-regulated by immune gene transcription [45]. It has also been found that cystatin C can antagonize TGF-β receptors, thus inactivating fibroblasts, and is a potential biological drug for the treatment of renal fibrosis [46]. More studies are needed to determine whether CST3 gene polymorphisms have an effect on kidney function.

Secondly, we found the lead SNP rs66588571 in 1p31.1, which mapped genes including CRYZ, TYW3, etc. It has been observed that CRYZ is highly expressed in the renal collecting ducts [47]. CRYZ and TYW3 were also found to be related to resistin levels [48]. Our study also enriched pathways associated with resistin levels and body mass index, which have been consistent with previous studies. Resistin is a cysteine-rich cytokine secreted by fat cells [49]. The study found that circulating resistin levels were significantly elevated in patients with renal impairment [50,51,52,53]. Studies have shown that the increase of resistin can induce the secretion of inflammatory cytokines such as tumor necrosis factor α (TNF-α) [54], and resistin can significantly increase the expression of endothelin (ET)-1 [55, 56] and monocyte chemotactic protein (MCP)-1(a mediator of inflammation) [55], the overexpression of these substances will cause inflammation, lead to glomerular sclerosis and renal tubulointerstitial fibrosis, thus affecting kidney function. The lead SNP we identified in 11q24.2 has not been previously identified; however, whether this SNP is only associated with renal function in female individuals is unknown and more research is needed.

Finally, we found the lead SNP rs626995 at 11q24.2, which is located near OR8B2. In the pathway analysis conducted by GENE2FUNC, we found that the OR8B2 gene is related to olfactory signaling. Olfactory receptors (OR) are chemical sensors responsible for the sense of smell, and the functional expression of OR in kidney tissue has been demonstrated by current studies [57,58,59,60]. Pluznick JL et al. [57] demonstrated for the first time that OR and OR signal transduction have important contributions to renal homeostasis. It has been found that OR is expressed in MD cells to control renin secretion and GFR [61], and there is also evidence that kidney OR has physiological functions such as blood pressure regulation [62] and maintenance of glucose homeostasis [63]. The study further discovered that OR was correlated with sex disparities in blood pressure. Male mice with OR gene knockout (KO) exhibited reduced renin expression and diastolic blood pressure, whereas female KO mice demonstrated enhanced vasoconstriction and elevated blood pressure [64]. In addition to the known role of OR in the normal physiology of the kidney, we have also found from previous studies that OR is significantly associated with the progression of renal fibrosis [65]. The study by Zhang et al. [66]also found that OR may be a potential target of calcium stones, and OR inhibitors may provide a new way to prevent and treat renal calcium stones.

The advantage of this study lies in the fact that we are the first GWAS to include both creatinine and cystatin to calculate eGFR in a Chinese female population, and we identified three significant loci (20p11.21, 1p31.1, and 11q24.2). Additionally, eQTL, pathway analysis, and tissue expression analysis were employed to validate and explore how the identified SNPs may impact kidney function. However, there are some limitations in our study. Firstly, the sample size is relatively small, with merely 1,985 participants included in the final study. The eGFR phenotype is complex and can be influenced by multiple factors, thus a larger sample size is requisite for the study of this phenotype. Secondly, the participants were solely recruited from one cohort in southern China, which may not be representative. Finally, as no GWAS studies of eGFR involving cystatin have been conducted in China or even other parts of Asia, it is challenging to compare our results with those of other Asian populations. To shed further light on the genetic basis of renal function in China, a higher sample size and more diversified GWASs are required, including cystatin C for renal function.

Conclusions

This is the first that GWAS based on eGFRcr-cys has been reported in a Chinese female population. Despite the relatively small sample size, we discovered novel kidney-related genetic loci. Pathway analysis of the detected sites revealed that resistin and olfactory signals were associated with renal function. These results offer novel biological hints about renal function in Chinese women and offer fresh perspectives on the advancement of CKD diagnosis and treatment.

Data availability

Summary statistics for this study are available in the NODE ( https://www.biosino.org/node ) with the accession number OEZ00017578 or through the URL: https://www.biosino.org/node/analysis/detail/OEZ00017578.

Abbreviations

CKD:

Chronic kidney disease

GBD:

Global Burden of Disease

eGFR:

The estimated glomerular filtration rate

SCr:

Serum creatinine

Cys-C:

Cystatin C

eGFRcr-cys:

the eGFR calculated based on the combination of SCr and Cys-C

GWAS:

Genome-wide association study

ASA:

Asian Screening Array

SNPs:

Single nucleotide polymorphisms

1000GP3-EAS:

1000 Genome Project Phase 3 East Asian

NHGRI:

The National Human Genome Research Institute

eQTL:

expression quantitative trait locus

OR:

Olfactory receptors

KO:

gene knockout

References

  1. Jager KJ, Kovesdy C, Langham R, Rosenberg M, Jha V, Zoccali C. A single number for advocacy and communication-worldwide more than 850 million individuals have kidney diseases. Nephrol Dial Transpl. 2019;34:1803–5.

    Article  Google Scholar 

  2. Global regional. National burden of chronic kidney disease, 1990–2017: a systematic analysis for the global burden of Disease Study 2017. Lancet. 2020;395:709–33.

    Article  Google Scholar 

  3. Global regional. National incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the global burden of Disease Study 2017. Lancet. 2018;392:1789–858.

    Article  Google Scholar 

  4. Liyanage T, Toyama T, Hockham C, Ninomiya T, Perkovic V, Woodward M, et al. Prevalence of chronic kidney disease in Asia: a systematic review and analysis. BMJ Glob Health. 2022;7:e007525.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Hockham C, Bao L, Tiku A, Badve SV, Bello AK, Jardine MJ, et al. Sex differences in chronic kidney disease prevalence in Asia: a systematic review and meta-analysis. Clin Kidney J. 2022;15:1144–51.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Stanzick KJ, Li Y, Schlosser P, Gorski M, Wuttke M, Thomas LF, et al. Discovery and prioritization of variants and genes for kidney function in > 1.2 million individuals. Nat Commun. 2021;12:4350.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Pattaro C, Teumer A, Gorski M, Chu AY, Li M, Mijatovic V, et al. Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat Commun. 2016;7:10023.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Wuttke M, Li Y, Li M, Sieber KB, Feitosa MF, Gorski M, et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat Genet. 2019;51:957–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Morris AP, Le TH, Wu H, Akbarov A, van der Most PJ, Hemani G, et al. Trans-ethnic kidney function association study reveals putative causal genes and effects on kidney-specific disease aetiologies. Nat Commun. 2019;10:29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Peterson RE, Kuchenbaecker K, Walters RK, Chen C-Y, Popejoy AB, Periyasamy S, et al. Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. Cell. 2019;179:589.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Limou S, Vince N, Parsa A. Lessons from CKD-Related Genetic Association studies–moving Forward. Clin J Am Soc Nephrol. 2018;13:140–52.

    Article  PubMed  Google Scholar 

  12. Ferguson TW, Komenda P, Tangri N. Cystatin C as a biomarker for estimating glomerular filtration rate. Curr Opin Nephrol Hypertens. 2015;24:295–300.

    Article  CAS  PubMed  Google Scholar 

  13. Levey AS, Fan L, Eckfeldt JH, Inker LA. Cystatin C for glomerular filtration rate estimation: coming of age. Clin Chem. 2014;60:916–9.

    Article  CAS  PubMed  Google Scholar 

  14. Chantrel F, Agin A, Offner M, Koehl C, Moulin B, Hannedouche T. Comparison of cystatin C versus creatinine for detection of mild renal failure. Clin Nephrol. 2000;54:374–81.

    CAS  PubMed  Google Scholar 

  15. Dharnidharka VR, Kwon C, Stevens G. Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis. Am J Kidney Dis. 2002;40:221–6.

    Article  CAS  PubMed  Google Scholar 

  16. Herget-Rosenthal S, Marggraf G, Hüsing J, Göring F, Pietruck F, Janssen O, et al. Early detection of acute renal failure by serum cystatin C. Kidney Int. 2004;66:1115–22.

    Article  CAS  PubMed  Google Scholar 

  17. Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012;367:20–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Inker LA, Eneanya ND, Coresh J, Tighiouart H, Wang D, Sang Y, et al. New Creatinine- and cystatin C-Based equations to Estimate GFR without Race. N Engl J Med. 2021;385:1737–49.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Xia F, Hao W, Liang J, Zhao Z, Wu Y, Yu F, et al. Comparison of estimated glomerular filtration rate equations based on serum creatinine-, cystatin C- and creatinine-cystatin C in elderly Chinese patients. Int Urol Nephrol. 2023;55:943–52.

    Article  CAS  PubMed  Google Scholar 

  20. Fu EL, Levey AS, Coresh J, Elinder C-G, Rotmans JI, Dekker FW, et al. Accuracy of GFR estimating equations in patients with discordances between Creatinine and Cystatin C-Based estimations. J Am Soc Nephrol. 2023;34:1241–51.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Cheng Y, Liu M, Liu Y, Xu H, Chen X, Zheng H, et al. Chronic kidney disease: prevalence and association with handgrip strength in a cross-sectional study. BMC Nephrol. 2021;22:246.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. O’Connell J, Gurdasani D, Delaneau O, Pirastu N, Ulivi S, Cocca M, et al. A general approach for haplotype phasing across the full spectrum of relatedness. PLoS Genet. 2014;10:e1004234.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5:e1000529.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Ma Y-C, Zuo L, Chen J-H, Luo Q, Yu X-Q, Li Y, et al. Improved GFR estimation by combined creatinine and cystatin C measurements. Kidney Int. 2007;72:1535–42.

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Watanabe K, Taskesen E, van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun. 2017;8:1826.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38:e164.

    Article  PubMed  PubMed Central  Google Scholar 

  28. de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015;11:e1004219.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, et al. The NHGRI GWAS catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014;42:1001–6. Database issue:D.

    Article  Google Scholar 

  30. GTEx Consortium. Human genomics. The genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348:648–60.

    Article  PubMed Central  Google Scholar 

  31. Gillies CE, Putler R, Menon R, Otto E, Yasutake K, Nair V, et al. An eQTL Landscape of kidney tissue in human nephrotic syndrome. Am J Hum Genet. 2018;103:232–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Qiu C, Huang S, Park J, Park Y, Ko Y-A, Seasock MJ, et al. Renal compartment-specific genetic variation analyses identify new pathways in chronic kidney disease. Nat Med. 2018;24:1721–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science. 2020;369:1318–30.

    Article  Google Scholar 

  34. Gorski M, van der Most PJ, Teumer A, Chu AY, Li M, Mijatovic V, et al. 1000 genomes-based meta-analysis identifies 10 novel loci for kidney function. Sci Rep. 2017;7:45040.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Carrero JJ, Hecking M, Chesnaye NC, Jager KJ. Sex and gender disparities in the epidemiology and outcomes of chronic kidney disease. Nat Rev Nephrol. 2018;14:151–64.

    Article  PubMed  Google Scholar 

  36. Ganesan C, Maynard SE. Acute kidney injury in pregnancy: the thrombotic microangiopathies. J Nephrol. 2011;24:554–63.

    Article  PubMed  Google Scholar 

  37. Balofsky A, Fedarau M. Renal failure in pregnancy. Crit Care Clin. 2016;32:73–83.

    Article  PubMed  Google Scholar 

  38. Ferreira RC, Fragoso MBT, Dos Santos Tenório MC, Silva JVF, Bueno NB, Goulart MOF, et al. Pre-eclampsia is associated with later kidney chronic disease and end-stage renal disease: systematic review and meta-analysis of observational studies. Pregnancy Hypertens. 2020;22:71–85.

    Article  PubMed  Google Scholar 

  39. Chesnaye NC, Carrero JJ, Hecking M, Jager KJ. Differences in the epidemiology, management and outcomes of kidney disease in men and women. Nat Rev Nephrol. 2024;20:7–20.

    Article  PubMed  Google Scholar 

  40. Hua Y, Zhao H, Lu X, Kong Y, Jin H. Meta-analysis of the cystatin C(CST3) gene G73A polymorphism and susceptibility to Alzheimer’s disease. Int J Neurosci. 2012;122:431–8.

    Article  CAS  PubMed  Google Scholar 

  41. Mi W, Pawlik M, Sastre M, Jung SS, Radvinsky DS, Klein AM, et al. Cystatin C inhibits amyloid-beta deposition in Alzheimer’s disease mouse models. Nat Genet. 2007;39:1440–2.

    Article  CAS  PubMed  Google Scholar 

  42. Zurdel J, Finckh U, Menzer G, Nitsch RM, Richard G. CST3 genotype associated with exudative age related macular degeneration. Br J Ophthalmol. 2002;86:214–9.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Ding Y, Xu Z, Pan Y, Meng X, Xiang X, Li H, et al. Association between CST3 Gene Polymorphisms and large-artery atherosclerotic stroke. Front Neurol. 2021;12:738148.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Ahmed Z, Zeeshan S, Liang BT. RNA-seq driven expression and enrichment analysis to investigate CVD genes with associated phenotypes among high-risk heart failure patients. Hum Genomics. 2021;15:67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Huda MN, VerHague M, Albright J, Smallwood T, Bell TA, Que E et al. Dissecting the Genetic Architecture of Cystatin C in Diversity Outbred mice. G3 (Bethesda). 2020;10:2529–41.

  46. Kim Y-I, Shin H-W, Chun Y-S, Park J-W. CST3 and GDF15 ameliorate renal fibrosis by inhibiting fibroblast growth and activation. Biochem Biophys Res Commun. 2018;500:288–95.

    Article  CAS  PubMed  Google Scholar 

  47. Alam G, Luan Z, Gul A, Lu H, Zhou Y, Huo X, et al. Activation of farnesoid X receptor (FXR) induces crystallin zeta expression in mouse medullary collecting duct cells. Pflugers Arch. 2020;472:1631–41.

    Article  CAS  PubMed  Google Scholar 

  48. Qi Q, Menzaghi C, Smith S, Liang L, de Rekeneire N, Garcia ME, et al. Genome-wide association analysis identifies TYW3/CRYZ and NDST4 loci associated with circulating resistin levels. Hum Mol Genet. 2012;21:4774–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Steppan CM, Bailey ST, Bhat S, Brown EJ, Banerjee RR, Wright CM, et al. The hormone resistin links obesity to diabetes. Nature. 2001;409:307–12.

    Article  CAS  PubMed  Google Scholar 

  50. Kaynar K, Kural BV, Ulusoy S, Cansiz M, Akcan B, Misir N, et al. Is there any interaction of resistin and adiponectin levels with protein-energy wasting among patients with chronic kidney disease. Hemodial Int. 2014;18:153–62.

    Article  PubMed  Google Scholar 

  51. Axelsson J, Bergsten A, Qureshi AR, Heimbürger O, Bárány P, Lönnqvist F, et al. Elevated resistin levels in chronic kidney disease are associated with decreased glomerular filtration rate and inflammation, but not with insulin resistance. Kidney Int. 2006;69:596–604.

    Article  CAS  PubMed  Google Scholar 

  52. Mantula PS, Outinen TK, Jaatinen P, Hämäläinen M, Huhtala H, Pörsti IH, et al. High plasma resistin associates with severe acute kidney injury in Puumala hantavirus infection. PLoS ONE. 2018;13:e0208017.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Yaturu S, Reddy RD, Rains J, Jain SK. Plasma and urine levels of resistin and adiponectin in chronic kidney disease. Cytokine. 2007;37:1–5.

    Article  CAS  PubMed  Google Scholar 

  54. Chen W-C, Lu Y-C, Kuo S-J, Lin C-Y, Tsai C-H, Liu S-C, et al. Resistin enhances IL-1β and TNF-α expression in human osteoarthritis synovial fibroblasts by inhibiting miR-149 expression via the MEK and ERK pathways. FASEB J. 2020;34:13671–84.

    Article  CAS  PubMed  Google Scholar 

  55. Verma S, Li S-H, Wang C-H, Fedak PWM, Li R-K, Weisel RD, et al. Resistin promotes endothelial cell activation: further evidence of adipokine-endothelial interaction. Circulation. 2003;108:736–40.

    Article  CAS  PubMed  Google Scholar 

  56. Maggio ABR, Wacker J, Montecucco F, Galan K, Pelli G, Mach F, et al. Serum resistin and inflammatory and endothelial activation markers in obese adolescents. J Pediatr. 2012;161:1022–7.

    Article  CAS  PubMed  Google Scholar 

  57. Pluznick JL, Zou D-J, Zhang X, Yan Q, Rodriguez-Gil DJ, Eisner C, et al. Functional expression of the olfactory signaling system in the kidney. Proc Natl Acad Sci U S A. 2009;106:2059–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Kalbe B, Schlimm M, Wojcik S, Philippou S, Maßberg D, Jansen F, et al. Olfactory signaling components and olfactory receptors are expressed in tubule cells of the human kidney. Arch Biochem Biophys. 2016;610:8–15.

    Article  CAS  PubMed  Google Scholar 

  59. Shepard BD. The sniffing kidney: roles for renal olfactory receptors in Health and Disease. Kidney360. 2021;2:1056–62.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Halperin Kuhns VL, Sanchez J, Sarver DC, Khalil Z, Rajkumar P, Marr KA, et al. Characterizing novel olfactory receptors expressed in the murine renal cortex. Am J Physiol Ren Physiol. 2019;317:F172–86.

    Article  Google Scholar 

  61. Shepard BD, Pluznick JL. How does your kidney smell? Emerging roles for olfactory receptors in renal function. Pediatr Nephrol. 2016;31:715–23.

    Article  PubMed  Google Scholar 

  62. Pluznick JL. Renal and cardiovascular sensory receptors and blood pressure regulation. Am J Physiol Ren Physiol. 2013;305:F439–444.

    Article  CAS  Google Scholar 

  63. Shepard BD, Cheval L, Peterlin Z, Firestein S, Koepsell H, Doucet A, et al. A renal olfactory receptor aids in kidney glucose handling. Sci Rep. 2016;6:35215.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Xu J, Choi R, Gupta K, Warren HR, Santhanam L, Pluznick JL. An evolutionarily conserved olfactory receptor is required for sex differences in blood pressure. Sci Adv. 2024;10:eadk1487.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Motahharynia A, Moein S, Kiyanpour F, Moradzadeh K, Yaqubi M, Gheisari Y. Olfactory receptors contribute to progression of kidney fibrosis. NPJ Syst Biol Appl. 2022;8:8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Zhang Q, Wei H, Huang G, Jin L. CCL7 and olfactory transduction pathway activation play an important role in the formation of CaOx and CaP kidney stones. Front Genet. 2023;14:1267545.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors thanks research participants from Jurong Woman Health Study.

Funding

The present study was funded by Chongqing Talents: Exceptional Young Talents Project (CQYC202005003), the National Natural Science Foundation of China (82373643 and 8217121613), the Outstanding Youth Science Fund of Chongqing (cstc2020jcyjjqX0014), and the Nation Key Research and Development Program of China (Grant No. 2018YFC2000703).

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Authors and Affiliations

  1. College of Public Health, Southwest Medical University, Luzhou, Sichuan, China

    Yang Cai & Xiangyu Ma

  2. Department of Epidemiology, College of Preventive Medicine, Third Military Medical University (Army Medical University), Chongqing, China

    Yang Cai, Hongyao Lv, Meng Yuan, Jiao Wang, Wenhui Wu, Xiaoyu Fang & Xiangyu Ma

  3. Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China

    Changying Chen, Jialing Mu, Fangyuan Liu, Xincheng Gu, Hankun Xie & Chong Shen

  4. Institute for the prevention and control of chronic non-communicable diseases, Center for Disease Control and Prevention of Jurong City, Jurong, China

    Yu Liu & Haifeng Xu

  5. Department of Clinical Epidemiology, Geriatric Hospital of Nanjing Medical University, Nanjing, China

    Yao Fan

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  1. Yang Cai
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Contributions

YC, CS and XYM study conceptualization and design. YC, HYL, CYC , JLM, FYL, XCG, HKX, YL, HFX and YF investigated, acquired and analyzed the data. YC, HYL, MY, JW, WHW, XYF, CS, and XYM performed statistical analysis and completed the visualization. YC, CS and XYM wrote and finalized the manuscript. CS and XYM supervised the study. All authors have read and approved the final manuscript.

Corresponding authors

Correspondence to Chong Shen or Xiangyu Ma.

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Ethics approval and consent to participate

This study has been approved by the Institutional Review Board of Nanjing Medical University (2015077). All participants have provided written informed consent at enrollment into the study.

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Not applicable.

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The authors declare no competing interests.

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Cai, Y., Lv, H., Yuan, M. et al. Genome-wide association analysis of cystatin c and creatinine kidney function in Chinese women. BMC Med Genomics 17, 272 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12920-024-02048-6

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BMC Medical Genomics

ISSN: 1755-8794

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