Cardiorenal Outcomes in Patients with IgAN: The FinnGen Study
Bibliographic record
Abstract
Background: Long-term cardiorenal outcomes of Immunoglobulin A Nephropathy (IgAN) are incompletely understood. We assessed kidney function and cardiorenal events in patients with IgAN in the FinnGen study, which covers approximately 10% of the Finnish population (N=520210). Methods: Patients with IgAN were identified using Finnish ICD10 diagnosis codes. The outcomes of interest were annual estimated glomerular filtration rate (eGFR) decline, start of kidney replacement therapy (KRT), and major adverse cardiovascular events (MACE). Out of 889 patients with IgAN, 265 were selected based on available data for eGFR. We compared outcomes in patients with IgAN (cases) vs. those with diabetes (controls), being the most common group requiring KRT in Finland. Cases (n=265) and controls (n=1060) were matched by eGFR, age, sex, and birth year (ratio 1:4). Multivariable-adjusted Cox regression and mixed-effects models were used to compare the groups. Results: Annual eGFR decline was more rapid in patients with IgAN (-2.26; 95% Confidence Interval [95% CI] -2.33, -2.18 ml/min/1.73 m2 per year) compared to those with diabetes (-1.28; 95% CI -1.43, -1.12 ml/min/1.73 m2 per year). The cumulative incidence of KRT and MACE in patiens with IgAN and diabetes is shown in Figure 1. The risk for KRT was higher in individuals with IgAN compared to those with diabetes (Hazard Ratio [HR] 4.63; 95% CI 3.49, 6.16). However, the risk for MACE was lower in patients with IgAN (HR 0.61; 95% CI 0.47, 0.88) compared to patients with diabetes. Conclusion: In this large register study, we demonstrated that patients with IgAN had a faster decline in eGFR and greater risk for KRT compared to patients with diabetes. In contrast, risk for MACE was lower in IgAN compared to diabetes in long-term follow-up. Funding: Private Foundation SupportFigure 1: Cumulative incidence of major adverse cardiovascular events (MACE) and kidney replacement therapy (KRT) from diagnosis in patients with IgA nephropathy and diabetes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".