Proteomic insights into the pathophysiology of hypertension-associated albuminuria: Pilot study in a South African cohort.

BACKGROUND: Hypertension is an important public health priority with a high prevalence in Africa. It is also an independent risk factor for kidney outcomes. We aimed to identify potential proteins and pathways involved in hypertension-associated albuminuria by assessing urinary proteomic profiles in black South African participants with combined hypertension and albuminuria compared to those who have neither condition. METHODS: The study included 24 South African cases with both hypertension and albuminuria and 49 control participants who had neither condition. Protein was extracted from urine samples and analysed using ultra-high-performance liquid chromatography coupled with mass spectrometry. Data were generated using data-independent acquisition (DIA) and processed using Spectronaut™ 15. Statistical and functional data annotation were performed on Perseus and Cytoscape to identify and annotate differentially abundant proteins. Machine learning was applied to the dataset using the OmicLearn platform. RESULTS: Overall, a mean of 1,225 and 915 proteins were quantified in the control and case groups, respectively. Three hundred and thirty-two differentially abundant proteins were constructed into a network. Pathways associated with these differentially abundant proteins included the immune system (q-value [false discovery rate] = 1.4 × 10- 45), innate immune system (q = 1.1 × 10- 32), extracellular matrix (ECM) organisation (q = 0.03) and activation of matrix metalloproteinases (q = 0.04). Proteins with high disease scores (76-100% confidence) for both hypertension and chronic kidney disease included angiotensinogen (AGT), albumin (ALB), apolipoprotein L1 (APOL1), and uromodulin (UMOD). A machine learning approach was able to identify a set of 20 proteins, differentiating between cases and controls. CONCLUSIONS: The urinary proteomic data combined with the machine learning approach was able to classify disease status and identify proteins and pathways associated with hypertension-associated albuminuria.

Genetic association and transferability for urinary albumin-creatinine ratio as a marker of kidney disease in four Sub-Saharan African populations and non-continental individuals of African ancestry.

Background: Genome-wide association studies (GWAS) have predominantly focused on populations of European and Asian ancestry, limiting our understanding of genetic factors influencing kidney disease in Sub-Saharan African (SSA) populations. This study presents the largest GWAS for urinary albumin-to-creatinine ratio (UACR) in SSA individuals, including 8,970 participants living in different African regions and an additional 9,705 non-resident individuals of African ancestry from the UK Biobank and African American cohorts. Methods: Urine biomarkers and genotype data were obtained from two SSA cohorts (AWI-Gen and ARK), and two non-resident African-ancestry studies (UK Biobank and CKD-Gen Consortium). Association testing and meta-analyses were conducted, with subsequent fine-mapping, conditional analyses, and replication studies. Polygenic scores (PGS) were assessed for transferability across populations. Results: Two genome-wide significant (P < 5 × 10-8) UACR-associated loci were identified, one in the BMP6 region on chromosome 6, in the meta-analysis of resident African individuals, and another in the HBB region on chromosome 11 in the meta-analysis of non-resident SSA individuals, as well as the combined meta-analysis of all studies. Replication of previous significant results confirmed associations in known UACR-associated regions, including THB53, GATM, and ARL15. PGS estimated using previous studies from European ancestry, African ancestry, and multi-ancestry cohorts exhibited limited transferability of PGS across populations, with less than 1% of observed variance explained. Conclusion: This study contributes novel insights into the genetic architecture of kidney disease in SSA populations, emphasizing the need for conducting genetic research in diverse cohorts. The identified loci provide a foundation for future investigations into the genetic susceptibility to chronic kidney disease in underrepresented African populations Additionally, there is a need to develop integrated scores using multi-omics data and risk factors specific to the African context to improve the accuracy of predicting disease outcomes.

Apolipoprotein L1 High-Risk Genotypes and Albuminuria in Sub-Saharan African Populations.

BACKGROUND AND OBJECTIVES: Recessive inheritance of African-specific APOL1 kidney risk variants is associated with higher risk of nondiabetic kidney disease, progression to kidney failure, and early-onset albuminuria that precedes eGFR decline. The effect of APOL1 risk variants on kidney disease in continental Africans is understudied. Objectives of this study were to determine APOL1 risk allele prevalence and associations between APOL1 genotypes and kidney disease in West, East, and South Africa. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: This cross-sectional population-based study in four African countries included 10,769 participants largely aged 40-60 years with sociodemographic and health information, anthropometry data, and blood and urine tests for biomarkers of kidney disease. APOL1 risk alleles were imputed from the H3Africa genotyping array, APOL1 risk allele and genotype frequencies were determined, and genetic associations were assessed for kidney disease. Kidney disease was defined as the presence of eGFR <60 ml/min per 1.73 m2, albuminuria, or a composite end point including eGFR <60 ml/min per 1.73 m2 and/or albuminuria. RESULTS: High G1 allele frequencies occurred in South and West Africa (approximately 7%-13%). G2 allele frequencies were highest in South Africa (15%-24%), followed by West Africa (9%-12%). Associations between APOL1 risk variants and albuminuria were significant for recessive (odds ratio, 1.63; 95% confidence interval, 1.25 to 2.12) and additive (odds ratio, 1.39; 95% confidence interval, 1.09 to 1.76) models. Associations were stronger for APOL1 G1/G1 genotypes versus G0/G0 (odds ratio, 3.87; 95% confidence interval, 2.16 to 6.93) compared with either G2/G2 (odds ratio, 1.65; 95% confidence interval, 1.09 to 2.51) or G1/G2 (odds ratio, 1.24; 95% confidence interval, 0.83 to 1.87). No association between APOL1 risk variants and eGFR <60 ml/min per 1.73 m2 was observed. CONCLUSIONS: APOL1 G1 and G2 alleles and high-risk genotype frequencies differed between and within West and South Africa and were almost absent from East Africa. APOL1 risk variants were associated with albuminuria but not eGFR <60 ml/min per 1.73 m2. There may be differential effects of homozygous G1 and G2 genotypes on albuminuria that require further investigation. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_05_16_CJN14321121.mp3.

The Use of 'Omics for Diagnosing and Predicting Progression of Chronic Kidney Disease: A Scoping Review.

Globally, chronic kidney disease (CKD) contributes substantial morbidity and mortality. Recently, various 'omics platforms have provided insight into the molecular basis of kidney dysfunction. This scoping review is a synthesis of the current literature on the use of different 'omics platforms to identify biomarkers that could be used to detect early-stage CKD, predict disease progression, and identify pathways leading to CKD. This review includes 123 articles published from January 2007 to May 2021, following a structured selection process. The most common type of 'omic platform was proteomics, appearing in 55 of the studies and two of these included a metabolomics component. Most studies (n = 91) reported on CKD associated with diabetes mellitus. Thirteen studies that provided information on the biomarkers associated with CKD and explored potential pathways involved in CKD are discussed. The biomarkers that are associated with risk or early detection of CKD are SNPs in the MYH9/APOL1 and UMOD genes, the proteomic CKD273 biomarker panel and metabolite pantothenic acid. Pantothenic acid and the CKD273 biomarker panel were also involved in predicting CKD progression. Retinoic acid pathway genes, UMOD, and pantothenic acid provided insight into potential pathways leading to CKD. The biomarkers were mainly used to detect CKD and predict progression in high-income, European ancestry populations, highlighting the need for representative 'omics research in other populations with disparate socio-economic strata, including Africans, since disease etiologies may differ across ethnic groups. To assess the transferability of findings, it is essential to do research in diverse populations.

The podocin V260E mutation predicts steroid resistant nephrotic syndrome in black South African children with focal segmental glomerulosclerosis.

In black African children with focal segmental glomerulosclerosis (FSGS) there are high rates of steroid resistance. The aim was to determine genetic associations with apolipoprotein L1 (APOL1) renal risk variants and podocin (NPHS2) variants in 30 unrelated black South African children with FSGS. Three APOL1 variants were genotyped and the exons of the NPHS2 gene sequenced in the cases and controls. APOL1 risk alleles show a modest association with steroid sensitive nephrotic syndrome (SSNS) and steroid resistant nephrotic syndrome (SRNS). The NPHS2 V260E variant was present in SRNS cases (V/V = 5; V/E = 4; E/E = 11), and was absent in SSNS cases. Haplotype analysis suggests a single mutation origin for V260E and it was associated with a decline in kidney function over a 60-month period (p = 0.026). The V260E variant is a good predictor of autosomal recessive SRNS in black South African children and could provide useful information in a clinical setting.