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Epigenetic machine learning: utilizing DNA methylation patterns to predict spastic cerebral palsy.

ABSTRACT: BACKGROUND:Spastic cerebral palsy (CP) is a leading cause of physical disability. Most people with spastic CP are born with it, but early diagnosis is challenging, and no current biomarker platform readily identifies affected individuals. The aim of this study was to evaluate epigenetic profiles as biomarkers for spastic CP. A novel analysis pipeline was employed to assess DNA methylation patterns between peripheral blood cells of adolescent subjects (14.9?±?0.3 years old) with spastic CP and controls at single CpG site resolution. RESULTS:Significantly hypo- and hyper-methylated CpG sites associated with spastic CP were identified. Nonmetric multidimensional scaling fully discriminated the CP group from the controls. Machine learning based classification modeling indicated a high potential for a diagnostic model, and 252 sets of 40 or fewer CpG sites achieved near-perfect accuracy within our adolescent cohorts. A pilot test on significantly younger subjects (4.0?±?1.5 years old) identified subjects with 73% accuracy. CONCLUSIONS:Adolescent patients with spastic CP can be distinguished from a non-CP cohort based on DNA methylation patterns in peripheral blood cells. A clinical diagnostic test utilizing a panel of CpG sites may be possible using a simulated classification model. A pilot validation test on patients that were more than 10 years younger than the main adolescent cohorts indicated that distinguishing methylation patterns are present earlier in life. This study is the first to report an epigenetic assay capable of distinguishing a CP cohort.

SUBMITTER: Crowgey EL

PROVIDER: S-EPMC6011336 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Epigenetic machine learning: utilizing DNA methylation patterns to predict spastic cerebral palsy.

Crowgey Erin L EL Marsh Adam G AG Robinson Karyn G KG Yeager Stephanie K SK Akins Robert E RE

BMC bioinformatics 20180621 1

<h4>Background</h4>Spastic cerebral palsy (CP) is a leading cause of physical disability. Most people with spastic CP are born with it, but early diagnosis is challenging, and no current biomarker platform readily identifies affected individuals. The aim of this study was to evaluate epigenetic profiles as biomarkers for spastic CP. A novel analysis pipeline was employed to assess DNA methylation patterns between peripheral blood cells of adolescent subjects (14.9 ± 0.3 years old) with spastic C ...[more]

PMID: 29925314

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Project description:AimTo compare anterior and posterior standing balance reactions, as measured by single-stepping thresholds, in children with and without spastic cerebral palsy (CP).MethodSeventeen ambulatory children with spastic CP (eight males, nine females) and 28 typically developing children (13 males, 15 females; age range 5-12y, mean [SD] 9y 2mo [2y 3mo]), were included in this cross-sectional, observational study. Balance reaction skill was quantified as anterior and posterior single-stepping thresholds, or the treadmill-induced perturbations that consistently elicited a step in that direction. In order to understand the underlying mechanisms of between-group differences in stepping thresholds, dynamic stability was quantified using the minimum margin of stability. Ankle muscle activation latency, magnitude, and co-contraction were assessed with surface electromyography.ResultsWe observed an age and group interaction for anterior thresholds (p=0.001, partial η2 =0.24). At older (≈11y; p<0.001, partial η2 =0.48), but not younger (≈7y; p=0.33, partial η2 =0.02) ages, typically developing children had larger anterior thresholds than those with CP. In response to near-threshold anterior perturbations, older typically developing children recovered from more instability than their peers with CP (p=0.004, partial η2 =0.18). Older children had no between-group differences in ankle muscle activity. No between-group differences were observed in posterior thresholds.InterpretationThe effects of CP on balance reactions are age- and direction-specific. Older typically developing children are more able or willing to withhold a step when unstable.What this paper addsChildren with spastic cerebral palsy have age- and direction-specific balance-reaction impairments. Lower anterior stepping thresholds were observed in older, but not younger children. Older typically developing children withheld a forward step at higher levels of instability. No between-group differences were seen in posterior stepping thresholds.

Project description:Hereditary spastic paraplegia (HSP) is a neurological, genetic disorder that predominantly presents with lower limb spasticity and muscle weakness. Pediatric pure HSP types with infancy or childhood symptom onset resemble in clinical presentation to children with bilateral spastic cerebral palsy (SCP). Hence, treatment approaches in these patient groups are analogous. Altered muscle characteristics, including reduced medial gastrocnemius (MG) muscle growth and hyperreflexia have been quantified in children with SCP, using 3D-freehand ultrasound (3DfUS) and instrumented assessments of hyperreflexia, respectively. However, these muscle data have not yet been studied in children with HSP. Therefore, we aimed to explore these MG muscle characteristics in HSP and to test the hypothesis that these data differ from those of children with SCP and typically developing (TD) children. A total of 41 children were retrospectively enrolled including (1) nine children with HSP (ages of 9-17 years with gross motor function levels I and II), (2) 17 age-and severity-matched SCP children, and (3) 15 age-matched typically developing children (TD). Clinically, children with HSP showed significantly increased presence and severity of ankle clonus compared with SCP (p = 0.009). Compared with TD, both HSP and SCP had significantly smaller MG muscle volume normalized to body mass (p ≤ 0.001). Hyperreflexia did not significantly differ between the HSP and SCP group. In addition to the observed pathological muscle activity for both the low-velocity and the change in high-velocity and low-velocity stretches in the two groups, children with HSP tended to present higher muscle activity in response to increased stretch velocity compared with those with SCP. This exploratory study is the first to reveal MG muscle volume deficits in children with HSP. Moreover, high-velocity-dependent hyperreflexia and ankle clonus is observed in children with HSP. Instrumented impairment assessments suggested similar altered MG muscle characteristics in pure HSP type with pediatric onset compared to bilateral SCP. This finding needs to be confirmed in larger sample sizes. Hence, the study results might indicate analogous treatment approaches in these two patient groups.

Project description:BackgroundCerebral palsy (CP) is the most common physical disability among children (2.5 to 3.6 cases per 1000 live births). Inadequate physical activity (PA) is a major problem effecting the health and well-being of children with CP. Practical, yet accurate measures of PA are needed to evaluate the effectiveness of surgical and therapy-based interventions to increase PA. Accelerometer-based motion sensors have become the standard for objectively measuring PA in children and adolescents; however, current methods for estimating physical activity intensity in children with CP are associated with significant error and may dramatically underestimate HPA in children with more severe mobility limitations. Machine learning (ML) models that first classify the PA type and then predict PA intensity or energy expenditure using activity specific regression equations may be more accurate than standalone regression models. However, the feasibility and validity of ML methods has not been explored in youth with CP. Therefore, the purpose of this study was to develop and test ML models for the automatic identification of PA type in ambulant children with CP.MethodsTwenty two children and adolescents (mean age: 12.8?±?2.9 y) with CP classified at GMFCS Levels I to III completed 7 activity trials while wearing an ActiGraph GT3X+ accelerometer on the hip and wrist. Trials were categorised as sedentary (SED), standing utilitarian movements (SUM), comfortable walking (CW), and brisk walking (BW). Random forest (RF), support vector machine (SVM), and binary decision tree (BDT) classifiers were trained with features extracted from the vector magnitude (VM) of the raw acceleration signal using 10?s non-overlapping windows. Performance was evaluated using leave-one-subject out cross validation.ResultsSVM (82.0-89.0%) and RF (82.6-88.8%) provided significantly better classification accuracy than BDT (76.1-86.2%). Hip (82.7-85.5%) and wrist (76.1-82.6%) classifiers exhibited comparable prediction accuracy, while the combined hip and wrist (86.2-89.0%) classifiers achieved the best overall performance. For all classifiers, recognition accuracy was excellent for SED (94.1-97.9%), good to excellent for SUM (74.0-96.6%) and brisk walking (71.5-86.0%), and modest for comfortable walking (47.6-70.4%). When comfortable and brisk walking were combined into a single walking class, recognition accuracy ranged from 90.3 to 96.5%.ConclusionsML methods provided acceptable classification accuracy for detection of a range of activities commonly performed by ambulatory children with CP. The resultant models can help clinicians more effectively monitor bouts of brisk walking in the community. The results indicate that 2-step models that first classify PA type and then predict energy expenditure using activity specific regression equations are worthy of exploration in this patient group.

Dataset Information

Epigenetic machine learning: utilizing DNA methylation patterns to predict spastic cerebral palsy.

Publications

Epigenetic machine learning: utilizing DNA methylation patterns to predict spastic cerebral palsy.

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