Biomedical subjects

Philip Van Damme

Publications and source records attributed to Philip Van Damme.

3 recordsLinked to original sources

PubMed2026

Progress towards a biotypic biomarker profile for amyotrophic lateral sclerosis-frontotemporal spectrum disorders.

Determining the optimal timing of disease-modifying therapies for neurodegenerative disorders will necessitate identification of when the underlying pathobiological process becomes active, well in advance of the point at which clinical manifestions appear. Phenoconversion, the emergence of clinically manifest syndomes, may be preceded by years to decades of silent pathobiological activity that can only be mapped by an array of biomarkers. ALS and FTD, traditionally identified as distinct clinical syndromes, are increasingly recognized to exist along a spectrum of clinical syndromes with shared genetic risk and shared underlying pathology. This clinicopathological spectrum is underpinned by cytoplasmic aggregation of TAR DNA-binding protein 43 (TDP-43) as the common neuropathological hallmark. In contrast, the majority of neuropathologically-defined frontotemporal lobar degeneration (FTLD) is associated with alterations in either TDP-43 metabolism (FTLD-TDP) or of the microtubule associated protein tau (FTLD-tau), with a smaller percentage associated with either autosomal dominant genetic mutations or impairments in the ubiquitin proteasome system. As the field of neurodegenerative disorders increasingly shifts towards the frameworks of a pathobiological definition of disease, there is a growing imperative to develop biomarkers that reflect the varied pathobiologies that underly these disorders, and to determine the sensitivity of such biomarkers to detect the presence of these pathobiologies before phenoconversion. To that end, an international workshop was convened in London, Canada in 2025 to review the evidence for existing or evolving biomarkers suitable for (1) the detection of either ALS or FTD pathobiology prior to phenoconversion and/or (2) predict phenoconversion in at risk individuals. Such biomarkers might be conceptualized as "biotypic biomarkers", capturing their ability to describe an underlying pathophysiology whilst being agnostic to the emergent clinical manifestations. Whereas no single biotypic marker is yet able to predict the emergence of ALS, FTD or their intersection, a multimodal approach to developing a biotypic biomarker profile holds promise for the detection of relevant pathobiological processes. The strength of such an approach would be augmented by also addressing issues of resiliency/susceptibility both in terms of genetic risk susceptibility profiles and developing sensitive biomarkers of genomic and cellular aging. By including such nontraditional markers of disease, a more robust picture of not only the degenerative process but also of those factors that might potentially mitigate or drive a heightened probability of disease can be derived.

cryptic exons↗

PubMed2026

Digenic inheritance of mutations in SPG7 and AFG3L2 causes motor neuron and cerebellar disorders.

BACKGROUND: Biallelic SPG7 mutations cause one of the most common forms of hereditary spastic paraplegia (HSP). Several reports have suggested that heterozygous SPG7 variants may also play a role in HSP, but also in amyotrophic lateral sclerosis (ALS). However, it remains controversial whether heterozygous SPG7 mutations are pathogenic on their own, or if other mechanisms are at play. We recently provided evidence for non-Mendelian inheritance in spastic paraplegia 7 (SPG7), as heterozygous carriers of SPG7 mutations often also carried mutations in other disease-related genes, including AFG3L2, more frequently than expected by chance. Given that SPG7 and AFG3L2 encode interacting subunits of the mitochondrial m-AAA protease complex, we hypothesized that combined heterozygous mutations in these genes may act synergistically to disrupt mitochondrial function and contribute to disease. In this study, we aimed to examine whether digenic heterozygous mutations in SPG7 and AFG3L2 can lead to a spectrum of neurodegenerative disorders. METHODS: We first analyzed genome and exome sequencing data of 6644 unrelated individuals including 4817 motor neuron disorder (MND) and ataxia patients and 1827 controls. We next analyzed an additional 18,748 exome data from rare disease cohorts to further examine the occurrence of variants in SPG7 and AFG3L2. RESULTS: Among the first 4817 MND and ataxia patients, we identified a total of 6 patients, 4 of whom were unrelated, who carried potentially pathogenic variants in both SPG7 and AFG3L2, in contrast to none in 1827 unrelated controls. Further analysis of the 18,748 additional patients with rare disease, as well as a comprehensive literature review, identified 6 more patients, 5 of whom were unrelated, who had digenic mutations in SPG7 and AFG3L2. In the two families we identified, digenic mutations in SPG7 and AFG3L2 perfectly segregated with the disease. The 12 patients reported here exhibited predominant signs of motor neuron and cerebellar involvement. CONCLUSIONS: Our findings demonstrate that digenic inheritance of concurrent heterozygous mutations in SPG7 and AFG3L2 may cause motor neuron and cerebellar disorders. Screening of the entire SPG7 and AFG3L2 genes in genetically undiagnosed cases of MND and spastic ataxia may help to increase the diagnostic yield.

Humans↗

PubMed2025

Support vector machine classification of 18F-FDG PET scans across subtypes of amyotrophic lateral sclerosis.

PURPOSE: While 18F-FDG PET imaging has demonstrated diagnostic value in people with Amyotrophic Lateral Sclerosis (PwALS) and group-level differences were identified between different disease subtypes (e.g., genetic and clinical variants), refining and validating a machine-learning-based subject-level diagnostic algorithm may improve the general applicability and reliability of 18F-FDG PET as a diagnostic tool in ALS. In this study, we employed support vector machines (SVM) to further explore the diagnostic potential of 18F-FDG PET in ALS, alongside its ability to classify between different genetic subtypes or clinical phenotypes. METHODS: 18F-FDG PET data of 36 healthy volunteers (HV), 25 people with ALS-mimicking diseases (Mimics), and 167 PwALS, grouped by genetic status (e.g., sporadic (sALS) or carrying a C9orf72 hexanucleotide repeat expansion (ALSC9orf72RE) and onset (bulbar or spinal) type, acquired with Biograph 'TruePoint' PET/CT scanner, were included in the study (Dataset 1). A second dataset of 183 PwALS and 31 Mimics acquired with Biograph 'HiRez' scanner was included as an independent cross-validation set (Dataset 2). PET images were spatially normalised to MNI space to fit linear SVMs with cross-validation. Only age-matched groups were considered to eliminate age-related effects. RESULTS: For Dataset 1, the linear SVM resulted in an average accuracy of 0.86 for the classification of ALS vs. HV, 0.53 for ALS vs. Mimics, 0.83 for ALSC9orf72RE vs. sALS, and 0.58 for bulbar vs. spinal onset. These findings were corroborated with Dataset2, with an accuracy of up to 0.76 for ALSC9orf72RE vs. sALS, and 0.59 for bulbar vs. spinal. CONCLUSION: 18F-FDG brain PET imaging, combined with SVM and age-matching, can distinguish between ALSC9orf72RE and sALS with good accuracy, but lacks sufficient discriminative power to differentiate between ALS and Mimics and between different sites of onset.

Humans↗