The public health exposome and pregnancy-related mortality in the United States: a high-dimensional computational analysis

Harville, Emily W.; Grady, Stephen K.; Ma, Li; Juarez, Patricia-Matthews; Vilda, Dovile; Wallace, Maeve

doi:10.1186/s12889-022-14397-x

Cited by 5 publications

(3 citation statements)

References 50 publications

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“…In the context of biological data, such as gene expression data, this property has already been already defined as the "propensity of a clustering algorithm to maintain output coherence over a range of settings" (49). Interestingly, this definition has been applied in the study of exposome and pregnancy-related mortality in the United States (50). In summary, we believe that ClustALL represents one of the initial necessary steps towards incorporating two necessary features into clinical stratification: parameter-based robustness and the identification of more than one stratification.…”

Section: Discussionmentioning

confidence: 99%

ClustALL: A robust clustering strategy for stratification of patients with acutely decompensated cirrhosis

Palomino-Echeverria,

Huergou,

Ortega-Legarre

et al. 2023

Preprint

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Patient heterogeneity represents a significant challenge for both individual patient management and clinical trial design, especially in the context of complex diseases. Most existing clinical classifications are based on scores built to predict patients’ outcomes. These classical methods may thus miss features that contribute to heterogeneity without necessarily translating into prognostic implications.To address patient heterogeneity at hospital admission, we developed ClustALL, a computational pipeline designed to handle common clinical data challenges such as mixed data types, missing values, and collinearity. ClustALL also facilitates the unsupervised identification of multiple and robust stratifications. We applied ClustALL to a prospective European multicentre cohort of patients with acutely decompensated cirrhosis (AD) (n=766), a highly heterogeneous disease. ClustALL identified five robust stratifications for patients with AD, using only data at hospital admission. All stratifications included markers of impaired liver function and number of organ dysfunction or failure, and most included precipitating events. When focusing on one of these stratifications, patients were categorized into three clusters characterized by typical clinical features but also having a prognostic value. Re-assessment of patient stratification during follow-up delineated patients’ outcomes, with further improvement of the prognostic value of the stratification. We validated these findings in an independent prospective multicentre cohort of patients from Latin America (n=580).In conclusion, this study developed ClustALL, a novel and robust stratification method capable of addressing challenges tied to intricate clinical data and applicable to complex diseases. By applying ClustALL to patients with AD, we identified three patient clusters, offering insights that could guide future clinical trial design.

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Section: Discussionmentioning

confidence: 99%

ClustALL: A robust clustering strategy for stratification of patients with acutely decompensated cirrhosis

Palomino-Echeverria,

Huergou,

Ortega-Legarre

et al. 2023

Preprint

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“…The aforementioned paraclique algorithm 82 is a more or less ideal method for this application, largely due to its use of modern graph theoretical techniques and its resilience to noise. In the life sciences alone, we have employed it not only in the study of the exposome, 15 but also in the study of lung cancer, 94 , 101 transcriptomics, 102 proteomics, 103 epigenetics, 104 , 105 aging, 106 diabetes, 107 allergic rhinitis, 108 obesity, 93 community-acquired pneumonia, 109 pregnancy-related mortality, 84 molecular subtyping, 110 and even in studying the effects of radiation on living organisms. 111 In brief, the paraclique algorithm interleaves extreme edge density with noise resilience and soft thresholding.…”

Section: Discussionmentioning

confidence: 99%

Seminar: Scalable Preprocessing Tools for Exposomic Data Analysis

Grady,

Dojcsak,

Harville

et al. 2023

Environ Health Perspect

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Background: The exposome serves as a popular framework in which to study exposures from chemical and nonchemical stressors across the life course and the differing roles that these exposures can play in human health. As a result, data relevant to the exposome have been used as a resource in the quest to untangle complicated health trajectories and help connect the dots from exposures to adverse outcome pathways. Objectives: The primary aim of this methods seminar is to clarify and review preprocessing techniques critical for accurate and effective external exposomic data analysis. Scalability is emphasized through an application of highly innovative combinatorial techniques coupled with more traditional statistical strategies. The Public Health Exposome is used as an archetypical model. The novelty and innovation of this seminar’s focus stem from its methodical, comprehensive treatment of preprocessing and its demonstration of the positive effects preprocessing can have on downstream analytics. Discussion: State-of-the-art technologies are described for data harmonization and to mitigate noise, which can stymie downstream interpretation, and to select key exposomic features, without which analytics may lose focus. A main task is the reduction of multicollinearity, a particularly formidable problem that frequently arises from repeated measurements of similar events taken at various times and from multiple sources. Empirical results highlight the effectiveness of a carefully planned preprocessing workflow as demonstrated in the context of more highly concentrated variable lists, improved correlational distributions, and enhanced downstream analytics for latent relationship discovery. The nascent field of exposome science can be characterized by the need to analyze and interpret a complex confluence of highly inhomogeneous spatial and temporal data, which may present formidable challenges to even the most powerful analytical tools. A systematic approach to preprocessing can therefore provide an essential first step in the application of modern computer and data science methods. https://doi.org/10.1289/EHP12901

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“…In comparison to preterm birth, associations of heat exposure with fetal growth restriction or low birth weight in human populations are less consistent (Chersich et al 2020, Syed et al 2022. Further, while evidence for heat increasing maternal mortality is limited, it has been highlighted as a potential strong predictor of pregnancyrelated mortality in the United States of America (Harville et al 2022), but further work is required to address this gap in knowledge. Associations of heat exposure with preterm birth and reduced fetal weight are further pronounced in populations already at risk of these complications including low socioeconomic groups (Son et al 2019), black and Hispanic women in the US (Carmichael et al 2014, Ngo & Horton 2016, Basu et al 2017, First Nations Australians (Wang et al 2013), and those with chronic conditions (Schifano et al 2013, Basu et al 2017.…”

Section: Heat Exposure In Pregnancy Causes Health Complications: Epid...mentioning

confidence: 99%

RISING STARS: The heat is on: how does heat exposure cause pregnancy complications?

Wyrwoll

2023

Journal of Endocrinology

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Incidence and severity of heatwaves is increasing globally with concomitant health complications. Pregnancy is a critical time in the life-course at risk of adverse health outcomes due to heat exposure. Dynamic physiological adaptations, which includes altered thermoregulatory pathways, occur in pregnancy. If heat dissipation is ineffective, maternal and neonate health outcomes can be compromised. Indeed, epidemiological studies and animal models reveal that exposure to heat in pregnancy likely elicits an array of health complications including miscarriage, congenital anomalies, low birth weight, stillbirth, and preterm birth. Despite these associations, the reasons for why these complications occur is unclear. An array of physiological and endocrine changes in response to heat exposure in pregnancy likely underpin the adverse health outcomes, but currently conclusive evidence is sparse. Accompanying these fundamental gaps in knowledge is a poor understanding of what exact climatic conditions challenge pregnant physiology. Moreover, the overlay of thermoregulatory-associated behaviours such as physical activity need to be taken in to consideration when assessing the risks for human health and identifying critical populations at-risk. While the health impacts from heat are largely preventable through strategic interventions, for the related clinical practice, public health and policy approaches to be effective, the gaps in basic science understanding urgently need to be addressed.

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The public health exposome and pregnancy-related mortality in the United States: a high-dimensional computational analysis

Cited by 5 publications

References 50 publications

ClustALL: A robust clustering strategy for stratification of patients with acutely decompensated cirrhosis

ClustALL: A robust clustering strategy for stratification of patients with acutely decompensated cirrhosis

Seminar: Scalable Preprocessing Tools for Exposomic Data Analysis

RISING STARS: The heat is on: how does heat exposure cause pregnancy complications?

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