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Eliminating barriers to personalized medicine: learning from neurofibromatosis type 1.

ABSTRACT: With the emergence of high-throughput discovery platforms, robust preclinical small-animal models, and efficient clinical trial pipelines, it is becoming possible to envision a time when the treatment of human neurologic diseases will become personalized. The emergence of precision medicine will require the identification of subgroups of patients most likely to respond to specific biologically based therapies. This stratification only becomes possible when the determinants that contribute to disease heterogeneity become more fully elucidated. This review discusses the defining factors that underlie disease heterogeneity relevant to the potential for individualized brain tumor (optic pathway glioma) treatments arising in the common single-gene cancer predisposition syndrome, neurofibromatosis type 1 (NF1). In this regard, NF1 is posited as a model genetic condition to establish a workable paradigm for actualizing precision therapeutics for other neurologic disorders.

SUBMITTER: Gutmann DH

PROVIDER: S-EPMC4132567 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Eliminating barriers to personalized medicine: learning from neurofibromatosis type 1.

Gutmann David H DH

Neurology 20140627 5

With the emergence of high-throughput discovery platforms, robust preclinical small-animal models, and efficient clinical trial pipelines, it is becoming possible to envision a time when the treatment of human neurologic diseases will become personalized. The emergence of precision medicine will require the identification of subgroups of patients most likely to respond to specific biologically based therapies. This stratification only becomes possible when the determinants that contribute to dis ...[more]

PMID: 24975854

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Project description:BackgroundCognitive difficulties are the most common neurological complications in neurofibromatosis type 1 (NF1) patients. Recent animal models proposed increased GABA-mediated inhibition as one underlying mechanism directly affecting the induction of long-term potentiation (LTP) and learning. In most adult NF1 patients, apparent cognitive and attentional deficits, tumors affecting the nervous system and other confounding factors for neuroscientific studies are difficult to control for. Here we used a highly specific group of adult NF1 patients without cognitive or nervous system impairments. Such selected NF1 patients allowed us to address the following open questions: Is the learning process of acquiring a challenging motor skill impaired in NF1 patients? And is such an impairment in relation to differences in intracortical inhibition?MethodsWe used an established non-invasive, double-pulse transcranial magnetic stimulation (dp-TMS) paradigm to assess practice-related modulation of intracortical inhibition, possibly mediated by gamma-minobutyric acid (GABA)ergic-neurotransmission. This was done during an extended learning paradigm in a group of NF1 patients without any neuropsychological deficits, functioning normally in daily life and compared them to healthy age-matched controls.FindingsNF1 patients experienced substantial decline in motor skill acquisition (F = 9.2, p = 0.008) over five-consecutives training days mediated through a selective reduction in the early acquisition (online) and the consolidation (offline) phase. Furthermore, there was a consistent decrease in task-related intracortical inhibition as a function of the magnitude of learning (T = 2.8, p = 0.014), especially evident after the early acquisition phase.InterpretationsCollectively, the present results provide evidence that learning of a motor skill is impaired even in clinically intact NF1 patients based, at least partially, on a GABAergic-cortical dysfunctioning as suggested in previous animal work.

Project description:AimsSarcopenia is characterized by a gradual loss of skeletal muscle mass and strength with increased adverse outcomes. Recently, large-scale epidemiological studies have demonstrated a relationship between several chronic disorders and ocular pathological conditions using an oculomics approach. We hypothesized that sarcopenia can be predicted through eye examinations, without invasive tests or radiologic evaluations in the context of predictive, preventive, and personalized medicine (PPPM/3PM).MethodsWe analyzed data from the Korean National Health and Nutrition Examination Survey (KNHANES). The training set (80%, randomly selected from 2008 to 2010) data were used to construct the machine learning models. Internal (20%, randomly selected from 2008 to 2010) and external (from the KNHANES 2011) validation sets were used to assess the ability to predict sarcopenia. We included 8092 participants in the final dataset. Machine learning models (XGBoost) were trained on ophthalmological examinations and demographic factors to detect sarcopenia.ResultsIn the exploratory analysis, decreased levator function (odds ratio [OR], 1.41; P value <0.001), cataracts (OR, 1.31; P value = 0.013), and age-related macular degeneration (OR, 1.38; P value = 0.026) were associated with an increased risk of sarcopenia in men. In women, an increased risk of sarcopenia was associated with blepharoptosis (OR, 1.23; P value = 0.038) and cataracts (OR, 1.29; P value = 0.010). The XGBoost technique showed areas under the receiver operating characteristic curves (AUCs) of 0.746 and 0.762 in men and women, respectively. The external validation achieved AUCs of 0.751 and 0.785 for men and women, respectively. For practical and fast hands-on experience with the predictive model for practitioners who may be willing to test the whole idea of sarcopenia prediction based on oculomics data, we developed a simple web-based calculator application (https://knhanesoculomics.github.io/sarcopenia) to predict the risk of sarcopenia and facilitate screening, based on the model established in this study.ConclusionSarcopenia is treatable before the vicious cycle of sarcopenia-related deterioration begins. Therefore, early identification of individuals at a high risk of sarcopenia is essential in the context of PPPM. Our oculomics-based approach provides an effective strategy for sarcopenia prediction. The proposed method shows promise in significantly increasing the number of patients diagnosed with sarcopenia, potentially facilitating earlier intervention. Through patient oculometric monitoring, various pathological factors related to sarcopenia can be simultaneously analyzed, and doctors can provide personalized medical services according to each cause. Further studies are needed to confirm whether such a prediction algorithm can be used in real-world clinical settings to improve the diagnosis of sarcopenia.Supplementary informationThe online version contains supplementary material available at 10.1007/s13167-022-00292-3.

Dataset Information

Eliminating barriers to personalized medicine: learning from neurofibromatosis type 1.

Publications

Eliminating barriers to personalized medicine: learning from neurofibromatosis type 1.

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