Genetic regulation of serum IgA levels and susceptibility to common immune, infectious, kidney, and cardio-metabolic traits

Liu, Lili; Khan, Atlas; Sánchez, Elena; Li, Yifu; Steers, Nicholas J.; Balderes, Olivia; Zhang, Junying; Krithivasan, Priya; LeDesma, Robert A.; Fischman, Clara; Hebbring, Scott J.; Harley, John B.; Moncrieffe, Halima; Kottyan, Leah C.; Namjou, Bahram; Walunas, Theresa L.; Knevel, Rachel; Raychaudhuri, Soumya; Karlson, Elizabeth W.; Denny, Joshua C.; Stanaway, Ian B.; Crosslin, David R.; Rauen, Thomas; Floege, Jürgen; Eitner, Frank; Moldoveanu, Zina; Reily, Colin; Knoppová, Barbora; Hall, Stacy; Sheff, Justin T.; Julian, Bruce A.; Wyatt, Robert; Suzuki, Hitoshi; Xie, Jingyuan; Chen, Nan; Zhou, Xu‐jie; Zhang, Hong; Hammarström, Lennart; Viktorin, Alexander; Magnusson, Patrik K. E.; Shang, Ning; Hripcsak, George; Weng, Chunhua; Rundek, Tatjana; Elkind, Mitchell S.V.; Oelsner, Elizabeth C.; Barr, R. Graham; Ionita‐Laza, Iuliana; Novák, Jan; Gharavi, Ali G.; Kiryluk, Krzysztof

doi:10.1038/s41467-022-34456-6

Cited by 21 publications

(11 citation statements)

References 97 publications

(120 reference statements)

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“…The unadjusted Cox regression analysis suggested that sex, systolic arterial pressure, diastolic arterial pressure, proteinuria, eGFR, uric acid, triglycerides, and tubular atrophy/interstitial fibrosis (T) were risk factors for developing ESKD (Table 2). A study supported elevated serum IgA as a causal factor in IgA nephropathy through Mendelian randomization (38). Some studies have suggested the association between the poor prognosis of renal disease and dyslipidemia.…”

Section: Performance Of the 5-year Eskd Prediction Modelmentioning

confidence: 98%

Machine learning in predicting T-score in the Oxford classification system of IgA nephropathy

Zhang

Weng

et al. 2023

Front. Immunol.

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BackgroundImmunoglobulin A nephropathy (IgAN) is one of the leading causes of end-stage kidney disease (ESKD). Many studies have shown the significance of pathological manifestations in predicting the outcome of patients with IgAN, especially T-score of Oxford classification. Evaluating prognosis may be hampered in patients without renal biopsy.MethodsA baseline dataset of 690 patients with IgAN and an independent follow-up dataset of 1,168 patients were used as training and testing sets to develop the pathology T-score prediction (Tpre) model based on the stacking algorithm, respectively. The 5-year ESKD prediction models using clinical variables (base model), clinical variables and real pathological T-score (base model plus Tbio), and clinical variables and Tpre (base model plus Tpre) were developed separately in 1,168 patients with regular follow-up to evaluate whether Tpre could assist in predicting ESKD. In addition, an external validation set consisting of 355 patients was used to evaluate the performance of the 5-year ESKD prediction model using Tpre.ResultsThe features selected by AUCRF for the Tpre model included age, systolic arterial pressure, diastolic arterial pressure, proteinuria, eGFR, serum IgA, and uric acid. The AUC of the Tpre was 0.82 (95% CI: 0.80–0.85) in an independent testing set. For the 5-year ESKD prediction model, the AUC of the base model was 0.86 (95% CI: 0.75–0.97). When the Tbio was added to the base model, there was an increase in AUC [from 0.86 (95% CI: 0.75–0.97) to 0.92 (95% CI: 0.85–0.98); P = 0.03]. There was no difference in AUC between the base model plus Tpre and the base model plus Tbio [0.90 (95% CI: 0.82–0.99) vs. 0.92 (95% CI: 0.85–0.98), P = 0.52]. The AUC of the 5-year ESKD prediction model using Tpre was 0.93 (95% CI: 0.87–0.99) in the external validation set.ConclusionA pathology T-score prediction (Tpre) model using routine clinical characteristics was constructed, which could predict the pathological severity and assist clinicians to predict the prognosis of IgAN patients lacking kidney pathology scores.

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Section: Performance Of the 5-year Eskd Prediction Modelmentioning

confidence: 98%

Machine learning in predicting T-score in the Oxford classification system of IgA nephropathy

Zhang

Weng

et al. 2023

Front. Immunol.

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“…In contrast, the disease is rare in Africa, many Asian and South American countries, in indigenous Australian Aborigines, and is uncommon in African Americans (28, 29,[31][32][33][34][35][36][37][38][39][40]188). The rarity of IgAN in African Americans is remarkable in the light of the findings of a recent GWAS study that found African ancestry consistently associated with higher serum IgA levels and greater frequency of IgAincreasing alleles compared to other ancestries and that a high serum IgA level was correlated with IgAN (189). However, this study did not test for an association of ancestry with serum levels of Gd-IgA1, a small fraction of serum total IgA and the autoantigen for development of IgAN, or examine the potential influence of environmental factors.…”

Section: Potential Role Of Ebv Infection In the Geographic Distributi...mentioning

confidence: 99%

IgA Nephropathy: Pleiotropic impact of Epstein-Barr virus infection on immunopathogenesis and racial incidence of the disease

Městecký

Julian²,

Raška³

2023

Front. Immunol.

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IgA nephropathy (IgAN) is an autoimmune disease in which poorly galactosylated IgA1 is the antigen recognized by naturally occurring anti-glycan antibodies, leading to formation of nephritogenic circulating immune complexes. Incidence of IgAN displays geographical and racial disparity: common in Europe, North America, Australia, and east Asia, uncommon in African Americans, many Asian and South American countries, Australian Aborigines, and rare in central Africa. In analyses of sera and cells from White IgAN patients, healthy controls, and African Americans, IgAN patients exhibited substantial enrichment for IgA-expressing B cells infected with Epstein-Barr virus (EBV), leading to enhanced production of poorly galactosylated IgA1. Disparities in incidence of IgAN may reflect a previously disregarded difference in the maturation of the IgA system as related to the timing of EBV infection. Compared with populations with higher incidences of IgAN, African Americans, African Blacks, and Australian Aborigines are more frequently infected with EBV during the first 1-2 years of life at the time of naturally occurring IgA deficiency when IgA cells are less numerous than in late childhood or adolescence. Therefore, in very young children EBV enters “non-IgA” cells. Ensuing immune responses prevent infection of IgA B cells during later exposure to EBV at older ages. Our data implicate EBV-infected cells as the source of poorly galactosylated IgA1 in circulating immune complexes and glomerular deposits in patients with IgAN. Thus, temporal differences in EBV primo-infection as related to naturally delayed maturation of the IgA system may contribute to geographic and racial variations in incidence of IgAN.

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“…The correlation between local helminth diversity and the geographical prevalence of IgAN explain the higher genetic risk of IgAN in East Asia and favour the role of impaired intestinal immunity in the pathogenesis (Kiryluk et al 2014 ). In the recent genome-wide association study Liu et al ( 2022 ) described 20 significant loci, including 11 novel loci which were significantly associated with IgA serum levels and correlated positively with IgA nephropathy. Identification of novel genetic biomarkers of IgAN can also be achieved with the use of machine learning and statistic-based bioinformatic models.…”

Section: Omicsmentioning

confidence: 99%

Omics are Getting Us Closer to Understanding IgA Nephropathy

Mucha

Pac

Pączek

2023

Arch. Immunol. Ther. Exp.

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During the last decade, thanks to omics technologies, new light has been shed on the pathogenesis of many diseases. Genomics, epigenomics, transcriptomics, and proteomics have helped to provide a better understanding of the origin and heterogeneity of several diseases. However, the risk factors for most autoimmune diseases remain unknown. The successes and pitfalls of omics have also been observed in nephrology, including immunoglobulin A nephropathy (IgAN), the most common form of glomerulonephritis and a principal cause of end-stage renal disease worldwide. Unfortunately, the immense progress in basic research has not yet been followed by the satisfactory development of a targeted treatment. Although, most omics studies describe changes in the immune system, there is still insufficient data to apply their results in the constantly evolving multi-hit pathogenesis model and thus do to provide a complete picture of the disease. Here, we describe recent findings regarding the pathophysiology of IgAN and link omics studies with immune system dysregulation. This review provides insights into specific IgAN markers, which may lead to the identification of potential targets for personalised treatment in the future.

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Genetic regulation of serum IgA levels and susceptibility to common immune, infectious, kidney, and cardio-metabolic traits

Cited by 21 publications

References 97 publications

Machine learning in predicting T-score in the Oxford classification system of IgA nephropathy

Machine learning in predicting T-score in the Oxford classification system of IgA nephropathy

IgA Nephropathy: Pleiotropic impact of Epstein-Barr virus infection on immunopathogenesis and racial incidence of the disease

Omics are Getting Us Closer to Understanding IgA Nephropathy

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