Genetic landscape of primary mitochondrial diseases in children and adults using molecular genetics and genomic investigations of mitochondrial and nuclear genome
Bibliographic record
Abstract
BACKGROUND: Primary mitochondrial diseases (PMD) are one of the most common metabolic genetic disorders. They are due to pathogenic variants in the mitochondrial genome (mtDNA) or nuclear genome (nDNA) that impair mitochondrial function and/or structure. We hypothesize that there is overlap between PMD and other genetic diseases that are mimicking PMD. For this reason, we performed a retrospective cohort study. METHODS: All individuals with suspected PMD that underwent molecular genetic and genomic investigations were included. Individuals were grouped for comparison: (1) individuals with mtDNA-PMD; (2) individuals with nDNA-PMD; (3) individuals with other genetic diseases mimicking PMD (non-PMD); (4) individuals without a confirmed genetic diagnosis. RESULTS: 297 individuals fulfilled inclusion criteria. The diagnostic yield of molecular genetics and genomic investigations was 31.3%, including 37% for clinical exome sequencing and 15.8% for mitochondrial genome sequencing. We identified 71 individuals with PMD (mtDNA n = 41, nDNA n = 30) and 22 individuals with non-PMD. Adults had higher percentage of mtDNA-PMD compared to children (p-value = 0.00123). There is a statistically significant phenotypic difference between children and adults with PMD. CONCLUSION: We report a large cohort of individuals with PMD and the diagnostic yield of urine mitochondrial genome sequencing (16.1%). We think liver phenotype might be progressive and should be studied further in PMD. We showed a relationship between non-PMD genes and their indirect effects on mitochondrial machinery. Differentiation of PMD from non-PMD can be achieved using specific phenotypes as there was a statistically significant difference for muscular, cardiac, and ophthalmologic phenotypes, seizures, hearing loss, peripheral neuropathy in PMD group compared to non-PMD group.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".