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

Water, Soil, Noise, and Light Pollution: JACC Focus Seminar, Part 2

Various forms of pollution carry a substantial burden with respect to increasing the risk of causing and exacerbating noncommunicable diseases, especially cardiovascular disease. The first part of this 2-part series on pollution and cardiovascular disease provided an overview of the impact of global warming and air pollution. This second paper provides an overview of the impact of water, soil, noise, and light pollution on the cardiovascular system. This review discusses the biological mechanisms underlying these effects and potential environmental biometrics of exposure. What is clear from both these pollution papers is that significant efforts and redoubled urgency are needed to reduce the sources of pollution in our environment, to incorporate environmental risk factors into medical education, to provide resources for research, and, ultimately, to protect those who are particularly vulnerable and susceptible.

Associations between prenatal metal and metalloid mixtures in teeth and reductions in childhood lung function

BACKGROUND

Metal(oid)s have been cross-sectionally associated with lung function outcomes in childhood but there is limited data on their combined effects starting in utero. Child sex may further modify these effects.

OBJECTIVE

Examine associations between in utero and early life exposure to metals assessed via novel dentine biomarkers and childhood lung function and explore effect modification by child sex.

METHODS

Analyses included 291 children enrolled in the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) study, a longitudinal birth cohort study in Mexico City. Weekly dentine levels of arsenic (As), cadmium (Cd), cobalt (Co), copper (Cu), manganese (Mn), nickel (Ni), and lead (Pb) were measured from 15 weeks pre-birth to 15 weeks post birth in deciduous children's teeth. Lung function was tested at ages 8-14 years and then modeled as age, height and sex adjusted z-scores. Associations were modeled using lagged weighted quantile sum (LWQS) regression to evaluate the potential for a time-varying mixture effect adjusting for maternal age and education at enrollment and exposure to environmental tobacco smoke in pregnancy. Models were also stratified by sex.

RESULTS

We identified a window of susceptibility at 12-15 weeks pre-birth in which the metal mixture was associated with lower FVC z-scores in children aged 8-14 years. Cd and Mn were the largest contributors to the mixture effect (70 %). There was also some evidence of effect modification by sex, in which the mean weights and weighted correlations over the identified window was more evident in males when compared to females. In the male stratum, Cd, Mn and additionally Pb also dominated the mixture association.

CONCLUSIONS

Prenatal metal(oid) exposure was associated with lower lung function in childhood. These findings underscore the need to consider both mixtures and windows of susceptibility to fully elucidate effects of prenatal metal(oid) exposure on childhood lung function.

Deciduous teeth from the New Hampshire birth cohort study: Early life environmental and dietary predictors of dentin elements

BACKGROUND

Sparse research exists on predictors of element concentrations measured in deciduous teeth.

OBJECTIVE

To estimate associations between maternal/child characteristics, elements measured in home tap water during pregnancy and element concentrations in the dentin of shed deciduous teeth.

METHODS

Our analysis included 152 pregnant person-infant dyads followed from the second trimester through the end of the first postnatal year from the New Hampshire Birth Cohort Study. During pregnancy and early infancy, we collected dietary and sociodemographic information via surveys, measured elements in home tap water, and later collected naturally exfoliated teeth from child participants. We measured longitudinal deposition of elements in dentin using LA-ICP-MS. Multivariable linear mixed models were used to estimate associations between predictors and dentin element concentrations.

RESULTS

We measured 12 elements in dentin including those previously reported (Ba, Mn, Pb, Sr, Zn) and less frequently reported (Al, As, Cd, Cu, Hg, Li, and W). A doubling of Pb or Sr concentrations in water was associated with higher dentin Pb or Sr respectively in prenatally formed [9% (95%CI: 3%, 15%); 3% (1%, 6%)] and postnatally formed [10% (2%, 19%); 6% (2%, 10%)] dentin. Formula feeding from birth to 6 weeks or 6 weeks to 4 months was associated with higher element concentrations in postnatal dentin within the given time period as compared to exclusive human milk feeding: Sr: 6 weeks: 61% (36%, 90%) and 4 months: 85% (54%, 121%); Ba: 6 weeks: 35% (3.3%, 77%) and 4 months: 42% (10%, 83%); and Li: 6 weeks: 61% (33%, 95%) and 4 months: 58% (31%, 90%).

SIGNIFICANCE

These findings offer insights into predictors of dentin elements and potential confounders in exposure-health outcome relationships during critical developmental periods.

Self-organized bistability on globally coupled higher-order networks

Self-organized bistability (SOB) stands as a critical behavior for the systems delicately adjusting themselves to the brink of bistability, characterized by a first-order transition. Its essence lies in the inherent ability of the system to undergo enduring shifts between the coexisting states, achieved through the self-regulation of a controlling parameter. Recently, SOB has been established in a scale-free network as a recurrent transition to a short-living state of global synchronization. Here, we embark on a theoretical exploration that extends the boundaries of the SOB concept on a higher-order network (implicitly embedded microscopically within a simplicial complex) while considering the limitations imposed by coupling constraints. By applying Ott-Antonsen dimensionality reduction in the thermodynamic limit to the higher-order network, we derive SOB requirements under coupling limits that are in good agreement with numerical simulations on systems of finite size. We use continuous synchronization diagrams and statistical data from spontaneous synchronized events to demonstrate the crucial role SOB plays in initiating and terminating temporary synchronized events. We show that under weak-coupling consumption, these spontaneous occurrences closely resemble the statistical traits of the epileptic brain functioning.

Heterogeneity of dynamic synergetic configurations of salience network in children with autism spectrum disorder

Atypical functional connectivity (FC) patterns have been identified in autism spectrum disorders (ASD), especially within salience network (SN) and between SN and default mode network (DMN) and central executive network (CEN). But whether the dynamic configuration of intra-SN and inter-SN (SN with DMN and CEN) FC in ASD is also heterogeneous remains unknown. Based on the resting-state functional magnetic resonance imaging data from 105 ASD and 102 typically-developing controls (TC), we calculated the time-varying FC of intra-SN and inter-SN (SN with DMN and CEN). Then, the joint recurrence features for the time-varying FC were calculated to assess how the SN dynamically recruits different configurations of network segregation and integration in ASD, that is, synergies, from the dynamical systems perspective. We analyzed the differences in synergetic patterns between ASD subtypes obtained by k-means clustering algorithm based on the synergy of SN and TC, and investigated the relationships between synergy of SN and severity of clinical symptoms of ASD for ASD subtypes. Two ASD subtypes were revealed, where the synergy of SN in ASD subtype 1 has lower stability and periodicity compared to the TC, and ASD subtype 2 exhibits the opposite alteration. Synergy of SN for ASD subtype 1 and 2 was found to predict the severity of communication impairments and restricted and repetitive behaviors in ASD, respectively. These results suggest the existence of subtypes with distinct patterns of the synergy of SN in ASD, and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD.

Mitochondrial and metabolic features of salugenesis and the healing cycle

Pathogenesis and salugenesis are the first and second stages of the two-stage problem of disease production and health recovery. Salugenesis is the automatic, evolutionarily conserved, ontogenetic sequence of molecular, cellular, organ system, and behavioral changes that is used by living systems to heal. It is a whole-body process that begins with mitochondria and the cell. The stages of salugenesis define a circle that is energy- and resource-consuming, genetically programmed, and environmentally responsive. Energy and metabolic resources are provided by mitochondrial and metabolic transformations that drive the cell danger response (CDR) and create the three phases of the healing cycle: Phase 1-Inflammation, Phase 2-Proliferation, and Phase 3-Differentiation. Each phase requires a different mitochondrial phenotype. Without different mitochondria there can be no healing. The rise and fall of extracellular ATP (eATP) signaling is a key driver of the mitochondrial and metabolic reprogramming required to progress through the healing cycle. Sphingolipid and cholesterol-enriched membrane lipid rafts act as rheostats for tuning cellular sensitivity to purinergic signaling. Abnormal persistence of any phase of the CDR inhibits the healing cycle, creates dysfunctional cellular mosaics, causes the symptoms of chronic disease, and accelerates the process of aging. New research reframes the rising tide of chronic disease around the world as a systems problem caused by the combined action of pathogenic triggers and anthropogenic factors that interfere with the mitochondrial functions needed for healing. Once chronic pain, disability, or disease is established, salugenesis-based therapies will start where pathogenesis-based therapies end.

Recurrence quantification analysis of periodic dynamics in the default mode network in first-episode drug-naïve schizophrenia

Abnormal functional connectivity (FC) within the default model network (DMN) in schizophrenia has been frequently reported in previous studies. However, traditional FC analysis was mostly linear correlations based, with the information on nonlinear or temporally lagged brain signals largely overlooked. Fifty-five first-episode drug-naïve schizophrenia (FES) patients and 53 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging scanning. The DMN was extracted using independent component analysis. Recurrence quantification analysis was used to measure the duration, predictability, and complexity of the periodic processes of the nonlinear DMN time series. The Mann‒Whitney U test was conducted to compare these features between FES patients and HCs. The support vector machine was applied to discriminate FES from HCs based on these features. Determinism, which means predictability of periodic process activity, between the ventromedial prefrontal cortex (vMPFC) and posterior cingulate and between the vMPFC and precuneus, was significantly decreased in FES compared with HCs. Determinism between the vMPFC and precuneus was positively correlated with category fluency scores in FES. The classifier achieved 77% accuracy. Our results suggest that synchronized periodicity among DMN brain regions is dysregulated in FES, and the periodicity in BOLD signals may be a promising indicator of brain functional connectivity.

Associations between Elemental Metabolic Dynamics and Default Mode Network Functional Connectivity Are Altered in Autism

Autism is a neurodevelopmental condition associated with atypical social communication, cognitive, and sensory faculties. Recent advances in exposure biology suggest that biomarkers of elemental uptake and metabolism measured in hair samples can yield an effective signal predictive of autism diagnosis. Here, we investigated if elemental biomarkers in hair were associated with functional connectivity in regions of the default mode network (DMN) previously linked to autism. In a study sample which included twin pairs with concordant and discordant diagnoses for autism, our analysis of hair samples and neuroimaging data supported two general findings. First, independent of autism diagnosis, we found a broad pattern of association between elemental biomarkers and functional connectivity in the DMN, which primarily involved dynamics in zinc metabolism. Second, we found that associations between the DMN and elemental biomarkers, particularly involving phosphorus, calcium, manganese, and magnesium, differed significantly in autistic participants from control participants. In sum, these findings suggest that functional dynamics in elemental metabolism relate broadly to persistent patterns of functional connectivity in the DMN, and that these associations are altered in the emergence of autism.

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

Autism spectrum disorder (ASD) is a neurodevelopmental condition diagnosed in approximately 2% of children. Reliance on the emergence of clinically observable behavioral patterns only delays the mean age of diagnosis to approximately 4 years. However, neural pathways critical to language and social functions develop during infancy, and current diagnostic protocols miss the age when therapy would be most effective. We developed non-invasive ASD biomarkers using mass spectrometry analyses of elemental metabolism in single hair strands, coupled with machine learning. We undertook a national prospective study in Japan, where hair samples were collected at 1 month and clinical diagnosis was undertaken at 4 years. Next, we analyzed a national sample of Swedish twins and, in our third study, participants from a specialist ASD center in the US. In a blinded analysis, a predictive algorithm detected ASD risk as early as 1 month with 96.4% sensitivity, 75.4% specificity, and 81.4% accuracy (n = 486; 175 cases). These findings emphasize that the dynamics in elemental metabolism are systemically dysregulated in autism, and these signatures can be detected and leveraged in hair samples to predict the emergence of ASD as early as 1 month of age.

Association Between Measures Derived From Children's Primary Exfoliated Teeth and Psychopathology Symptoms: Results From a Community-Based Study

Mental disorders are among the most disabling health conditions globally. However, there remains a lack of valid, reliable, noninvasive, and inexpensive biomarkers to identify (at an early age) people who are at the greatest risk of experiencing a future mental health condition. Exfoliated primary teeth, when used in combination with established and emerging tools (e.g., family history, imaging, genetics, epigenetics), may provide important additional insights about vulnerability to mental illness. Teeth are especially promising because they develop in parallel with the brain and maintain a permanent record of environmental insults occurring during prenatal and perinatal development. Despite their potential, few empirical studies have investigated features of exfoliated teeth in relation to mental health. Here, we used micro-CT imaging to test the hypothesis that measures derived from exfoliated primary incisors associated with psychopathology symptoms in a community-based sample of children (n = 37). We found that enamel volume (β = −0.77, 95% CI, −1.35 to −0.18, P = 0.01) had large negative associations with internalizing symptoms, and enamel mineral density (β = 0.77, 95% CI, 0.18–1.35, P = 0.01) had large positive associations with internalizing behavioral symptoms, even after stringent control for multiple testing. Pulp volume (β = −0.50, 95% CI, −0.90 to −0.09, P = 0.02) had a moderately-large negative association with externalizing behavioral symptoms, though these associations did not survive multiple testing correction. These results support the ongoing investigation of teeth as potential novel biomarkers of mental health risk.

Systems Biology to Address Unmet Medical Needs in Neurological Disorders

Neurological diseases are highly prevalent and constitute a significant cause of mortality and disability. Neurological disorders encompass a heterogeneous group of neurodegenerative conditions, broadly characterized by injury to the peripheral and/or central nervous system. Although the etiology of neurological diseases varies greatly, they share several characteristics, such as heterogeneity of clinical presentation, non-cell autonomous nature, and diversity of cellular, subcellular, and molecular pathways. Systems biology has emerged as a valuable platform for addressing the challenges of studying heterogeneous neurological diseases. Systems biology has manifold applications to address unmet medical needs for neurological illness, including integrating and correlating different large datasets covering the transcriptome, epigenome, proteome, and metabolome associated with a specific condition. This is particularly useful for disentangling the heterogeneity and complexity of neurological conditions. Hence, systems biology can help in uncovering pathophysiology to develop novel therapeutic targets and assessing the impact of known treatments on disease progression. Additionally, systems biology can identify early diagnostic biomarkers, to help diagnose neurological disease preceded by a long subclinical phase, as well as define the exposome, the collection of environmental toxicants that increase risk of certain neurological diseases. In addition to these current applications, there are numerous potential emergent uses, such as precision medicine.

Network Dynamics in Elemental Assimilation and Metabolism

Metabolism and physiology frequently follow non-linear rhythmic patterns which are reflected in concepts of homeostasis and circadian rhythms, yet few biomarkers are studied as dynamical systems. For instance, healthy human development depends on the assimilation and metabolism of essential elements, often accompanied by exposures to non-essential elements which may be toxic. In this study, we applied laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to reconstruct longitudinal exposure profiles of essential and non-essential elements throughout prenatal and early post-natal development. We applied cross-recurrence quantification analysis (CRQA) to characterize dynamics involved in elemental integration, and to construct a graph-theory based analysis of elemental metabolism. Our findings show how exposure to lead, a well-characterized toxicant, perturbs the metabolism of essential elements. In particular, our findings indicate that high levels of lead exposure dysregulate global aspects of metabolic network connectivity. For example, the magnitude of each element's degree was increased in children exposed to high lead levels. Similarly, high lead exposure yielded discrete effects on specific essential elements, particularly zinc and magnesium, which showed reduced network metrics compared to other elements. In sum, this approach presents a new, systems-based perspective on the dynamics involved in elemental metabolism during critical periods of human development.