Does Laminopathy Include a Kidney Phenotype?
Bibliographic record
Abstract
Background: Rare pathogenic variants in LMNA cause laminopathy phenotypes including muscular dystrophy, progeria, familial partial lipodystrophy, and cardiomyopathy. A kidney phenotype is not frequently associated with laminopathy. Methods: We employed three approaches: 1) systematic search of the literature for reports of kidney disease associated with laminopathy, 2) an evaluation of kidney-related traits in a cohort of 29 patients with familial partial lipodystrophy, and 3) a phenome-wide association study searching for traits associated with rare LMNA variants in the United Kingdom Biobank (UKBB). We employed gene-based burden testing to aggregate the effects of rare LMNA variants in UKBB exome sequencing data (n = 373,812). Rare missense and loss-of-function LMNA variants were weighted using AlphaMissense. Variant location relative to a well studied nuclear localization signal was also evaluated. Logistic regression and Cox proportional hazards models were adjusted for age, sex, ancestry, and comorbidities. Results: Ten patients with LMNA-associated kidney disease were found in the literature: 7 with focal segmental glomerulosclerosis, 2 with mesangioproliferative glomerulonephritis, and 1 without an established histological diagnosis. Among 29 patients with partial lipodystrophy (caused by LMNA R482Q and R482W), all had normal kidney function.Among 3,245 (0.87%) UKBB participants with a rare LMNA variant, there was an increased risk of AKI (hazard risk=1.55, 95% confidence interval (CI)=1.08-2.21, P=0.017) and albuminuria (odds ratio=3.58, 95% CI=1.55-8.27, P=0.003). Association with kidney traits was driven by variants upstream of the nuclear localization signal, an area classically associated with cardiomyopathy, in contrast to downstream variants associated with lipodystrophy. Conclusion: Rare pathogenic LMNA variants—particularly those upstream of the nuclear localization signal —are associated with AKI and proteinuria.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".