Integrated molecular and clinical profiling of primary mitochondrial oxidative phosphorylation disorders in an Indian cohort: Insights from genetics, neuroimaging, and machine learning.

Primary mitochondrial disorders are clinically and genetically heterogeneous and remain underdiagnosed in resource-limited settings. We performed a retrospective observational study (March 2016- January 2024) at a tertiary neurology center in Eastern India to characterize the clinical, biochemical, neuroimaging, electrophysiological, and molecular features of suspected mitochondrial disease and to explore interpretable machine-learning approaches for syndromic stratification. Forty-eight patients from 42 unrelated families were classified as mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS; n = 17), chronic progressive external ophthalmoplegia (CPEO; n = 14), Leber hereditary optic neuropathy (LHON; n = 10), or Leigh syndrome (n = 7). Mean age at presentation was 23.9 years (range: 9 months-60 years), with a slight male predominance. Neuroimaging was abnormal in 23/48 (47.9%) and showed syndrome-concordant patterns, including stroke-like cortical lesions in MELAS and symmetric basal ganglia involvement in Leigh syndrome; brain magnetic resonance imaging was typically normal in CPEO. Elevated blood and/or cerebrospinal fluid lactate was common, and electroencephalographic abnormalities were concentrated in MELAS and Leigh syndrome. Targeted molecular testing in a subset identified pathogenic mtDNA variants consistent with phenotype, including MT-TL1 variants in MELAS, m.11778G>A in MT-ND4 in LHON, and m.8993T>G in MT-ATP6 in Leigh syndrome; no mtDNA deletions were detected in tested CPEO cases. Decision tree and random forest models highlighted clinically intuitive discriminators (e.g., visual loss, external ophthalmoplegia/ptosis, and seizure phenotype), supporting their potential role as transparent triage tools for targeted molecular evaluation. This cohort provides the first detailed characterization of mitochondrial syndromes in Eastern India and supports a pragmatic diagnostic framework integrating bedside phenotyping, targeted assays, and interpretable machine learning.