Multiomic profiling of new-onset kidney function decline: insights from the STANISLAS study cohort with a 20-year follow-up

Abstract

Background: Identifying the biomarkers associated with new-onset glomerular filtration rate (GFR) decrease in an initially healthy population could offer a better understanding of kidney function decline and help improving patient management.

Methods: Here we described the proteomic and transcriptomic footprints associated with new-onset kidney function decline in an initially healthy and well-characterized population with a 20-year follow-up. This study was based on 1087 individuals from the familial longitudinal Suivi Temporaire Annuel Non-Invasif de la Santé des Lorrains Assurés Sociaux (STANISLAS) cohort who attended both visit 1 (from 1993 to 1995) and visit 4 (from 2011 to 2016). New-onset kidney function decline was approached both in quantitative (GFR slope for each individual) and qualitative (defined as a decrease in GFR of >15 ml/min/1.7 m²) ways. We analysed associations of 445 proteins measured both at visit 1 and visit 4 using Olink Proseek^® panels and 119 765 genes expressions measured at visit 4 with GFR decline. Associations were assessed using multivariable models. The Bonferroni correction was applied.

Results: We found several proteins (including PLC, placental growth factor (PGF), members of the tumour necrosis factor receptor superfamily), genes (including CCL18, SESN3), and a newly discovered miRNA-mRNA pair (MIR1205-DNAJC6) to be independently associated with new-onset kidney function decline. Complex network analysis highlighted both extracellular matrix and cardiovascular remodelling (since visit 1) as well as inflammation (at visit 4) as key features of early GFR decrease.

Conclusions: These findings lay the foundation to further assess whether the proteins and genes herein identified may represent potential biomarkers or therapeutic targets to prevent renal function impairment.

Keywords: glomerular filtration rate; proteomic; transcriptomic.

Graphical Abstract

Figure 1:

Flow chart of the study.

Figure 2:

Associations between proteins levels measured at V1 and eGFR slope. (a) Forrest plot of proteins levels measured at V1 independently associated with eGFR slope. Multivariable linear regression model includes the following variables at V1: age, sex, BMI, current smoker, and eGFR. Dots represent β coefficient and standard error for each protein. P values (with Bonferroni correction) are indicated. (b) Network depiction of proteins co-expression modules that are positively correlated with eGFR slope at V1. Red dots represent proteins and blue diamonds represent biological process connecting the overrepresented proteins. NPPC, natriuretic peptide precursor C; IGFBP6, insulin-like growth factor binding protein-6; CTS3, cystatin C.

Figure 3:

Associations between proteins levels measured at V4 and eGFR slope. (a) Forrest plot of proteins levels measured at V4 independently associated with eGFR slope. Multivariable linear regression model includes the following variables at V1: age, sex, BMI, current smoker, and eGFR. Dots represent β coefficient and standard error for each protein. P values (with Bonferroni correction) are indicated. (b) Network depiction of proteins co-expression modules that are positively correlated with eGFR slope at V4. Red dots represent proteins and blue diamonds represent biological process connecting the overrepresented proteins.

Figure 4:

Associations between proteins levels measured at V4 and with ΔeGFR ≥ 15 ml/min/1.7 m². Forrest plot of proteins levels measured at V4 independently associated with eGFR slope. Multivariable linear regression model includes the following variables at V1: age, sex, BMI, current smoker, and eGFR. Dots represent β coefficient and standard error for each protein. P values (with Bonferroni correction) are indicated.

Figure 5:

Associations between genes expressions at V4 and with ΔeGFR ≥ 15 ml/min/1.7 m². (a) Volcano plot of genes differentially expressed at V4 when comparing groups of individuals with ΔeGFR < 15 ml/min/1.7 m² (n = 718) and ΔeGFR ≥ 15 mL/min/1.7 m² (n = 89). The x axis represents the mean fold change observed for each gene whereas the y axis displays the log₁₀ of the P value. Each dot represents one gene. The green dots represent downregulated genes and red dots represent upregulated genes. (b) Forrest plots of genes measured at V4 independently associated with ΔeGFR ≥ 15 ml/min/1.7 m². Multivariable logistic regression model includes the following variables at V1: age, sex, BMI, current smoker, and eGFR. Dots represent β coefficient and standard error for each gene. (c) Predicted duplex formation between miR-1205 and the 3′-UTR of the DNAJC6 mRNA by the miRDB and Targetscan algorithms.