Dysregulated biodynamics in metabolic attractor systems precede the emergence of amyotrophic lateral sclerosis

Abstract

Evolutionarily conserved mechanisms maintain homeostasis of essential elements, and are believed to be highly time-variant. However, current approaches measure elemental biomarkers at a few discrete time-points, ignoring complex higher-order dynamical features. To study dynamical properties of elemental homeostasis, we apply laser ablation inductivelycoupled plasma mass spectrometry (LA-ICP-MS) to tooth samples to generate 500 temporally sequential measurements of elemental concentrations from birth to 10 years. We applied dynamical system and Information Theory-based analyses to reveal the longestknown attractor system in mammalian biology underlying the metabolism of nutrient elements, and identify distinct and consistent transitions between stable and unstable states throughout development. Extending these dynamical features to disease prediction, we find that attractor topography of nutrient metabolism is altered in amyotrophic lateral sclerosis (ALS), as early as childhood, suggesting these pathways are involved in disease risk. Mechanistic analysis was undertaken in a transgenic mouse model of ALS, where we find similar marked disruptions in elemental attractor systems as in humans. Our results demonstrate the application of a phenomological analysis of dynamical systems underlying elemental metabolism, and emphasize the utility of these measures in characterizing risk of disease.

Elemental dynamics in hair accurately predict future autism spectrum disorder diagnosis: an international multi-center study

“…Second, unlike related signal decomposition techniques, such as Fourier analysis or Wavelet transformations, the application of RQA is robust in the presence of noise, applicable in comparatively short time series (relative to Fourier/Wavelet), and robust against non-stationarity in the data 48 . Third, prior studies which have applied RQA/CRQA to the longitudinal analysis of essential and non-essential elements, as are utilized in this device, have shown, as noted, that RQA yields robustly generalizable measures of elemental metabolism; and, further, that these parameters are highly sensitive to systemic disease states, including autism spectrum disorder 49 , attention de cit hyperactivity disorder 21 , and amyotrophic lateral sclerosis 46 . Relevant to ASD, speci cally, two prior studies have utilized RQA to identify ASD-related dysregulation of elemental metabolism; and have utilized RQA-based features in the analysis of longitudinal elemental exposures to generate predictive classi ers for ASD which were highly accurate 21,49 .…”

“…In the feature engineering stage of processing, for each of 15 elemental time series measured in each hair, and for the pairwise interactions between each elemental time series, a recurrence matrix or crossrecurrence matrix, respectively, was generated to reconstruct underlying signal dynamics 44,45 . Following the approach developed in prior studies 21,22,24,46 utilizing elemental time series, the delay (τ) and embedding dimension (m) parameters involved in recurrence plot construction were determined through the minimization of mutual information and false-nearest neighbor algorithms, respectively; likewise, to facilitate cross-subject comparison, threshold functions, ϵ, were constrained to yield recurrence rates to 10%. From each recurrence or cross-recurrence matrix thereby derived from each sample, an array of quantitative metrics was calculated via RQA/CRQA; the estimation and interpretation of these features are summarized in Supplemental Table S1.…”

“…Models were then tuned with 5-fold cross validation in the training set, with 1000 iterations of modelbased optimization, per the procedure developed by Bichi et al 46 , to identify the best-performing set of hyperparameters.. Following hyperparameter tuning, the selected set of hyperparameters was used to t a model across the full set of training data.…”

Elemental Dynamics in Hair Accurately Predict Future Autism Spectrum Disorder Diagnosis: An International Multi-Center Study

“…In the feature engineering stage of processing, for each of the 15 elemental time series measured in each hair and for the pairwise interactions among the elemental time series, a recurrence matrix or a cross-recurrence matrix, respectively, was generated to reconstruct the underlying signal dynamics [ 28 , 29 ]. Following the approach developed in prior studies [ 17 , 18 , 30 , 31 ] utilizing elemental time series, the delay ( ) and embedding dimension (m) parameters involved in recurrence plot construction were determined through the minimization of mutual information and false-nearest neighbor algorithms, respectively; likewise, to facilitate cross-subject comparison, threshold functions, , were constrained to yield recurrence rates of 10%. From each recurrence or cross-recurrence matrix thereby derived from each sample, an array of quantitative metrics was calculated via RQAs/CRQAs; the estimation and interpretation of these features are summarized in Supplemental Table S1 .…”

Network Dynamics in Elemental Assimilation and Metabolism

“…We previously described the application of recurrence quantification analysis (RQA) and cross-recurrence quantification analysis (CRQA) to characterize dynamics in longitudinal tooth biomarkers in prior studies [ 8 , 9 , 10 , 19 ]. Briefly, this non-linear analytical method involves the application of Taken’s delay embedding for attractor reconstruction; a threshold function, ε, is then applied to each point in the reconstructed attractor, and the timing of the system’s reentry within this perimeter is defined as a recurrence.…”