Nidogen-1 Could Play a Role in Diabetic Kidney Disease Development: A Genome Wide Association Meta-analysis

doi:10.21203/rs.3.rs-1848223/v1

Background:

Diabetic kidney disease (DKD) affects about 40% of patients with diabetes. It is uncurable and usually lead to end-stage renal disease (ESRD). The pathogenesis of DKD is still not fully understood and the genetics of DKD have not yet been extensively studied.

In this study we investigate the genetic basis of DKD in type 2 diabetes (T2D) to provide more insights into the pathogenesis of the disease.

Results:

Using the data provided by the UK Biobank (UKBB), we performed a DKD genome-wide association study (GWAS) in 13,123 individuals with T2D as well as two creatinine estimated glomerular filtration rate (eGFR) GWA studies; one in 28,136 individuals with T2D and the other in 339,080 non-diabetic individuals. We also conducted a DKD GWAS meta-analysis combining our results with those published by the SUMMIT consortium.

We confirm two loci previously reported to be associated with chronic kidney disease (CKD) and eGFR in T2D. The UMOD-PDILT locus is associated with DKD (P = 1.08E-09) as well as creatinine eGFR in both people with T2D (P = 5.35E-15) and people without diabetes (P = 2.06E-72). The PRKAG2 locus is associated with creatinine eGFR in people with (P = 6.22E-10) and without (P = 2.61E-72) T2D. Our meta-analysis reveals a novel association between DKD and variant rs72763500 (chr1:236116561) which is a splicing quantitative trait locus (sQTL) for nidogen-1 (NID1) gene.

Conclusion:

Our data confirms two loci previously reported in association with CKD and creatinine eGFR in T2D. It also suggests that NID1, a major component of the renal tubular basement membrane, could play a role in DKD development in T2D. While our NID1 finding remains to be replicated, it is a step towards a more comprehensive understanding of DKD pathogenesis.

Diabetes causes CKD in about 40% of patients with diabetes in the US and is the leading cause of ESRD [1]. While DKD usually occurs after 10 years in T1D, it can manifest at the time of diagnosis in T2D [2]. DKD is characterized by glomerular hyperfiltration at early stages then progress to albuminuria, reduced eGFR, and subsequent ESRD [3]. These functional changes are accompanied by structural changes which include thickening of the glomerular basement membrane as well as capillary and tubular basement membranes [4].

Not all patients with diabetes develop DKD and early heritability studies showed that DKD happens in family clusters [5, 6] which indicates genetic susceptibility. While the heritability of DKD is estimated to be 35% in T1D [7], it is only 8% in T2D [8] which could be due to the highly heterogenous phenotype in T2D [9]. Although the genetics of T2D have been intensively explored by many GWA studies, the genetics of its complications have not received as much attention from researchers. In particular, the genetic of DKD is much less studied in T2D compared to DKD in T1D and most performed studies are of small sample sizes.

In 2003, the very first DKD GWAS in T2D was conducted using gene-based single nucleotide polymorphisms (SNPs) [10]. It initially identified the SLC12A3 gene as a candidate gene for DKD in a group of 188 (94 cases and 94 controls) Japanese individuals. The results were replicated in a larger group of 732 individuals (466 cases and 266 controls). In 2005, another GWAS using gene-based SNPs identified ELMO1 as a DKD associated gene in a discovery cohort of 179 (87 cases and 92 controls) and a replication group of 701 (459 cases and 242 controls) Japanese individuals [11].

In 2009, a larger GWAS including 359,193 autosomal SNPs was performed in 820 cases and 885 controls of people with T1D identified four loci associated with DKD near FRMD3, CHN2/CPVL, CARS, and MYO16/IRS2 genes [12, 13]. These findings were replicated in African American (965 cases and 1029 controls) [14], European (743 cases and 646 controls) [15], and in Japanese [16] cohorts with T2D.

In 2018, the Surrogate markers for Micro- and Macro-vascular hard endpoints for Innovative diabetes Tools (SUMMIT) consortium conducted the largest DKD GWAS [17, 8]. The study included seven phenotype and a total of 26,827 individuals with T2D (12,710 with DKD). The main DKD phenotype included 5,717 individuals (3,345 DKD cases and 2,372 controls) with T2D. The GWAS identified a novel variant near GABRR1 gene associated with microalbuminuria in T2D.

One of the core advantages of conducting GWA studies is gaining insights into the pathophysiology of human diseases. Despite the effort to uncover the genetic basis of DKD, the heterogeneous phenotype and the complexity of the disease made the GWA studies challenging and limited their replicability [9, 18]. Up to now, these factors have restricted our full understanding of the pathogenesis of DKD.

Here we conducted a GWAS for DKD in Europeans with T2D using data from the UKBB. To further explore the results of the DKD GWAS, we performed a GWAS for creatinine eGFR measurement in both people with T2D and people who have not been diagnosed with T1D or T2D. Furthermore, we used the summary statistics released by the SUMMIT consortium [17, 8] to perform a DKD meta-analysis study. While our study confirms the previously published findings in both CKD and creatinine eGFR in T2D, it also uncovers a novel association between NID1 and DKD.

DKD Phenotype Definition and Study Population:

Clinically, a patient will be diagnosed with CKD if either reduced eGFR (GFR < 60 ml/min/1.73 m2) or increased albuminuria defined by urine albumin creatinine ratio (ACR) ≥ 30 mg/g is present for more than 3 month [19].

Table 1. DKD GWAS sample characteristics.

		Control	Case	P value
		(n = 7982)	(n = 5141)	P value
Characteristics	Age (years)	73.8 ± 6.7	76.3 ± 5.6	2.20E-16
	Women (%)	37.8	37.4	0.57
	Diabetes duration (years)	14.2 ± 3.4	12.5 ± 5.4	2.20E-16
	BMI	31.8 ± 5.9	32.6 ± 5.9	1.10E-14
	HbA1C	6.7 ± 1.2	6.9 ± 1.4	3.86E-10
Biomarkers*	eGFR (mL/min/1.73 m2)	83.8 ± 10	63.8 ± 18	2.20E-16
	Serum creatinine (mg/dl)	0.79 ± 015	1.1 ± 0.6	2.20E-16
	Spot urinary creatinine (mg/dl)	113 ± 69	118 ± 73	2.98E-05
	Urinary albumin (mg/l)	14.7 (10–1160)	24 (12–6747)	2.20E-16
	Urinary sodium (mmol /l)	85 ± 45	78 ± 41	2.20E-16
	Urinary potassium (mmol/l)	66.5 ± 33	63 ± 31	6.55E-11
A total of 13123 individuals with T2D were included in the analysis. Characteristics and Biomarkers are reported as the mean ± SD except for Women (percentage) and Urinary albumin (median and IQR). * Reported biomarkers values were collected at the first assessment visit to the UKBB.

These diagnostic criteria do not differentiate DKD from CKD unrelated to T2D. Despite being rarely performed in practice, a kidney biopsy is the gold standard for determining the cause of CKD [9, 20]. These factors make it challenging to define the phenotype of DKD but considering the timing of a CKD diagnosis relative to the T2D diagnosis can help identify DKD. As described below, we adopted a CKD in T2D vs long duration T2D without CKD definition in this study leading to a diabetes duration that is significantly (P = 2.20E-16) shorter in DKD cases compared to complication-free T2D controls (Table 1).

We used the International Classification of Diseases 10th revision (ICD-10) to identify the study’s cohort from the UKBB. We selected participants with any T2D ICD-10 code (E11.0, E11.1, E11.2, E11.3, E11.4, E11.5, E11.6, E11.7, E11.8, E11.9). Out of 46,308 participants with T2D, we included 13123 (7982 controls and 5141 cases) unrelated White Europeans in our GWAS. Table 1 summarizes the characteristics of the study’s cohort.

We considered three criteria to define DKD: (1) Participants who were assigned the ICD-10 code E11.2 (Type 2 diabetes mellitus with kidney complications), (2) Participants who were assigned any CKD ICD-10 code (N18.0, N18.1, N18.2, N18.3, N18.4, N18.5, N18.8, N18.9) after being diagnosed with T2D, (3) Participants with GFR < 60 ml/min/1.73 m2 or ACR ≥ 30 mg/g after being diagnosed with T2D. Participants with T2D for more than 10 years and have not developed any diabetes related complications were included as controls.

DKD GWAS:

In the DKD GWAS, only one locus reached genome-wide significance: UMOD-PDILT (Uromodulin - Protein disulfide-isomerase-like protein of the testis) which is located on chromosome 16 (Table 2 and Fig. 1A). The same locus was previously reported to be associated with CKD and reduced renal function represented by both serum creatinine and cystatin c eGFR in diabetes [21, 22]. Genomic inflation factor was estimated based on median chi-square statistics and λ_GC was 0.99 (See QQ plot; Supplementary Fig. 1A).

Table 2. Genome-wide significant associations with eGFR, DKD, and DKD meta-analysis.


SNP	CHR			BP	Gene	A1			A2	MAF			DKD	eGFR (T2D)	eGFR (non-diabetic)	DKD Meta-analysis		DKD	DKD Meta-analysis	eGFR (T2D)	eGFR (non-diabetic)
rs12917707		16	20367690		UMOD		T	G			0.18	1.08E-09		5.35E-15	2.06E-72	4.201E-08	0.80 (0.75, 0.86)		0.7933	1.22 (0.9, 1.5)	0.77 (0.7, 0.84)
rs13333226		16	20365654		UMOD		G	A			0.19	3.09E-09		2.78E-14	1.12E-72	NS	0.81 (0.74, 0.87)		-	1.17 (0.87, 1.47)	0.77 (0.7, 0.84)
rs4293393		16	20364588		UMOD		G	A			0.18	4.84E-09		6.36E-14	4.97E-73	NS	0.81 (0.76, 0.87)		-	1.16 (0.86, 1.45)	0.77 (0.7, 0.84)
rs7185940		16	20410488		PDILT		A	G			0.28	5.3E-08		5.54E-12	1.89E-33	NS	0.84 (0.8, 0.9)		-	0.92 (0.66, 1.71)	0.44 (0.38, 0.5)
rs7192797		16	20411763		PDILT		A	G			0.28	7.5E-08		1.37E-11	1.43E-33	NS	0.85 (0.8, 0.9)		-	0.89 (0.64, 1.2)	0.44 (0.38, 0.5)
rs9926580		16	20410547		PDILT		T	C			0.28	6.6E-08		2.24E-11	4.96E-34	NS	0.85 (0.8, 0.9)		-	0.89 (0.63, 1.1)	0.45 (0.39, 0.5)
rs11864909		16	20400839		PDILT		T	C			0.29	NS		4.11E-13	8.50E-45	NS	-		-	0.95 (0.7, 1.2)	0.51 (0.45, 0.47)
rs35208507		16	20388929		PDILT		G	A			0.31	NS		7.39E-12	6.86E-40	NS	-		-	0.47 (0.63, 1.1)	0.88 (0.41, 0.52)
rs4321205		16	20375536		PDILT		T	C			0.38	NS		1.69E-10	2.01E-30	NS	-		-	0.78 (0.54, 1)	0.39 (0.33, 0.44)
rs7805747		7	151407801		PRKAG2		A	G			0.27	NS		6.22E-10	2.61E-72	NS	-		-	-0.83 (-1.09, -0.57)	-0.66 (-0.72, -0.61)
rs72763500		1	236116561		NID1		C	T				NS		NS	NS	2.47E-08	-		0.8566	-	-
CHR: Chromosome; BP: SNP position (Build 37); A1: Minor allele; A2: Reference allele; MAF: Minor allele frequency; DKD: Diabetic kidney disease; eGFR: estimated glomerular filtration rate; OR: odd ratio

eGFR GWA Studies:

To further investigate the UMOD association with renal function, we performed two eGFR GWA studies in two unrelated white European groups selected from UKBB. One in 28,136 individuals (10,988 females and 17,148 males) with T2D and another 339,080 in individuals (185,994 females and 153,086 males) who have not developed T2D or T1D. Both GWA studies were corrected for sex and age at the time of creatinine measurement.

The calculation of eGFR is detailed in the methods section. Genomic inflation factors were calculated based on median chi-square statistics and λ_GC values were 1.02, and 1.4 for eGFR in T2D GWAS, and eGFR in non-diabetic individuals respectively (See QQ plots in Supplementary Fig. 1B and 1C). We applied genomic control correction to the latter and the adjusted P values are presented in the Manhattan plot (Fig. 1C).

In the T2D eGFR GWAS, 2 loci reached genome-wide significance: UMOD-PDILT, and PPKAG2 (Table 2 and Fig. 1B). These two loci were previously identified to be associated with CKD, creatinine eGFR, and cystatin c eGFR [26]. The UMOD-PDILT locus was reported in association with creatinine eGFR in T2D [8] and both T1D and T2D combined [8, 22, 23]. The PPKAG2 loci was identified in association with creatinine eGFR in T2D [8].

The same two loci, among many, showed genome-wide significance in the non-diabetic eGFR GWAS (Table 2 and Fig. 1C). The difference in the number of the significant signals in the eGFR T2D vs non-diabetic GWA studies could be attributed to the much larger sample size of the non-diabetic GWAS.

The UMOD-PDILT loci have been reported to be associated with the rapid decline in eGFR [24] which could explain its significant association with both eGFR and DKD across all performed GWA studies.

DKD Meta-analysis:

As mentioned previously, the SUMMIT consortium released summary statistics for a meta-analysis of seven DKD phenotypes. The overall DKD was defined by microalbuminuria, late DKD (albumin excretion rate ≥ 200 µg/min), or ESRD (eGFR < 15 mL/min/1.73 m2 or kidney dialysis or transplant). The study comprised a total of 3,345 cases and 2,372 controls of European descendent. We chose to include the SUMMIT consortium summary data in our meta-analysis because it is one of the largest DKD GWA studies to date. In addition, it matches both our study’s phenotype definition and population ancestry.

The UMOD locus remained significant in the meta-analysis as in the other three GWA studies. We also identified a novel variant, rs72763500 (chr1:236116561), that have not been reported to be associated with DKD before (Table 2 and Fig. 2). This novel variant is unique to DKD and did not show a significant association with the eGFR measurement. We queried the NHGRI GWAS catalog (www.genome.gov) to investigate the association of variant rs72763500 with related phenotypes but it has not been reported by any study. The variant is reported by the Genotype-Tissue Expression (GTEx) project (https://gtexportal.org/home/snp/rs72763500) as a splicing quantitative trait locus (sQTL) of NID1 gene.

For two decades, GWA studies have contributed to our understanding of the biological and physiological aspects of many human traits and diseases. They are a powerful tool that have the potential to decipher the pathophysiology of many diseases as well as uncover new drug targets.

DKD is uncurable and usually lead to a debilitating ESRD which greatly reduces the patients’ quality of life. As we mentioned earlier, the genetics of DKD have not yet been extensively studied and, to date, the DKD GWA studies are still limited by the small sample sizes. While moderately extending the sample size of available studies, our study confirms the previously reported variants to be associated with CKD, eGFR in T2D, and reduced renal function. It also reports a novel association between NID1 gene that may be specific to DKD.

NID1 is a sulfated glycoprotein ubiquitous basement membrane protein and is involved in the glomerular basement membrane (GBM) development. We queried the Human Protein Atlas single cell RNA expression database (https://www.proteinatlas.org/ENSG00000116962-NID1/single+cell+type) for NID1 tissue enrichment. NID1 is highly expressed in mesenchymal cells in adipose tissue, breast, esophagus, endometrium, liver, skeletal, and heart muscles. In the kidney, it is more abundant in the proximal tubular cells of the nephron in comparison to the distal tubular cells and collecting duct cells. On the contrary, UMOD is more abundant in the distal tubular cells and collecting duct cells compared to the proximal tubular cells.

The GBM is mainly composed of laminin, type IV collagen, nidogens (NID1 and NID2), and heparan sulfate proteoglycan [25, 26]. NID1 binds both laminin and type IV collagen [27]. Deletion of one of the nidogen in mice does not affect the development of basement membranes but deletion of both genes leads to basement membrane abnormalities in the lungs and heart and subsequently perinatal death. The nidogen double mutant mice developed normal glomerular basement membrane but the tubular basement membrane in nidogen was sometimes absent or altered [28]. The effect of nidogen mutation on the human kidney is still largely unknown.

Our meta-analysis finding, although biologically relevant, is limited by the lack of a replication study. More studies are needed to substantiate and further investigate the role of NID1 in human DKD.

eGFR Calculation:

eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) creatinine equation:

eGFRcr = 142 x min(Scr/κ, 1)α x max(Scr/κ, 1)-1.200 x 0.9938Age x 1.012 [if female]

where: Scr = serum creatinine in mg/dL, κ = 0.7 (females) or 0.9 (males), α = -0.241 (female) or -0.302 (male), min(Scr/κ, 1) is the minimum of Scr/κ or 1.0, max(Scr/κ, 1) is the maximum of Scr/κ or 1.0, and Age (years) [29].

GWAS Quality Control:

SNPs and individuals with genotyping rate < 98% were excluded. SNPs with minor allele frequency < 5% were excluded. SNPs deviating from Hardy Weinberg equilibrium test (P value < 1e − 10 in cases and < 1e − 6 in controls) were removed. Individuals who deviate > 3 SD from the samples mean heterozygosity rate were removed. Principal component analysis (PCA) was performed to correct for population substructure. Only White European participants were selected for further analysis. The quality control steps and PCA were performed using Plink V 1.9 [30]. The PCA for the eGFR GWAS in non-diabetic individuals was performed using bigsnpr [31]. Related individuals were identified using KING [32] and individuals with kinship coefficient > = 0.17 were excluded from the analysis.

Statistical Analysis:

Genome-wide association (GWA) analyses were performed using an additive model. The DKD GWAS was corrected for age, sex, duration of diabetes, and the top 10 principal components. The same covariates were adjusted for in the eGFR GWA studies except for the duration of diabetes. The analyses were conducted using Plink V 1.9 [30].

DKD Meta-analysis:

We conducted an inverse-variance fixed effects meta-analysis including the summary statistics from our DKD GWAS and the above-mentioned SUMMIT study. The meta-analysis was performed using Plink V 1.9 [30].

CKD: Chronic kidney disease.

DKD: Diabetic kidney disease.

eGFR: Estimated glomerular filtration rate.

ESKD: End-stage kidney disease.

GTEx: Genotype-Tissue Expression.

GWA: Genome-wide association.

GWAS: Genome-wide association study.

NID1: Nidogen-1.

PCA: Principal component analysis.

SNP: Single nucleotide polymorphism.

sQTL: splicing quantitative trait locus.

SUMMIT: Surrogate markers for Micro- and Macro-vascular hard endpoints for Innovative diabetes Tools.

T1D: Type 1 diabetes.

T2D: Type 2 diabetes.

UKBB: UK Biobank.

Availability of data and materials:

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Competing interests:

We report no conflicts of interest, for all listed authors, relevant to this article.

Ethics approval and consent to participate:

Not applicable.

Consent for publication:

Not applicable.

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No competing interests reported.

NID1DKDsupplmentHG.docx

Nidogen-1 Could Play a Role in Diabetic Kidney Disease Development: A Genome Wide Association Meta-analysis

Status:

Version 1

Abstract

Figures

Background

Results

DKD Phenotype Definition and Study Population:

DKD GWAS:

eGFR GWA Studies:

DKD Meta-analysis:

Discussion

Methods

eGFR Calculation:

GWAS Quality Control:

Statistical Analysis:

DKD Meta-analysis:

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1