Socioeconomic inequalities in molecular risk for chronic diseases observed in young adulthood.

Many common chronic diseases of aging are negatively associated with socioeconomic status (SES). This study examines whether inequalities can already be observed in the molecular underpinnings of such diseases in the 30s, before many of them become prevalent. Data come from the National Longitudinal Study of Adolescent to Adult Health (Add Health), a large, nationally representative sample of US subjects who were followed for over two decades beginning in adolescence. We now have transcriptomic data (mRNA-seq) from a random subset of 4,543 of these young adults. SES in the household-of-origin and in young adulthood were examined as covariates of a priori-defined mRNA-based disease signatures and of specific gene transcripts identified de novo. An SES composite from young adulthood predicted many disease signatures, as did income and subjective status. Analyses highlighted SES-based inequalities in immune, inflammatory, ribosomal, and metabolic pathways, several of which play central roles in senescence. Many genes are also involved in transcription, translation, and diverse signaling mechanisms. Average causal-mediated effect models suggest that body mass index plays a key role in accounting for these relationships. Overall, the results reveal inequalities in molecular risk factors for chronic diseases often decades before diagnoses and suggest future directions for social signal transduction models that trace how social circumstances regulate the human genome.

Socioeconomic Inequalities and Molecular Risk for Aging in Young Adulthood.

Diverse manifestations of biological aging often reflect disparities in socioeconomic status (SES). In this paper, we examine associations between indicators of SES and an mRNA-based aging signature during young adulthood, before clinical indications of aging are common. We use data from wave V (2016-2018) of the National Longitudinal Study of Adolescent to Adult Health, a nationally representative study of adults aged 33-43 years, with transcriptomic data from a subset of 2,491 participants. Biological aging is measured using 1) a composite transcriptomic aging signature previously identified by Peters et al.'s out-of-sample meta-analysis (Nat Commun. 2015;6:8570) and 2) 9 subsets that represent functional pathways of coexpressed genes. SES refers to income, education, occupation, subjective social status, and a composite measure combining these 4 dimensions. We examine hypothesized mechanisms through which SES could affect aging: body mass index, smoking, health insurance status, difficulty paying bills, and psychosocial stress. We find that SES-especially the composite measure and income-is associated with transcriptomic aging and immune, mitochondrial, ribosomal, lysosomal, and proteomal pathways. Counterfactual mediational models suggest that the mediators partially account for these associations. The results thus reveal that numerous biological pathways associated with aging are already linked to SES in young adulthood.

A Bayesian functional approach to test models of life course epidemiology over continuous time.

BACKGROUND: Life course epidemiology examines associations between repeated measures of risk and health outcomes across different phases of life. Empirical research, however, is often based on discrete-time models that assume that sporadic measurement occasions fully capture underlying long-term continuous processes of risk. METHODS: We propose (i) the functional relevant life course model (fRLM), which treats repeated, discrete measures of risk as unobserved continuous processes, and (ii) a testing procedure to assign probabilities that the data correspond to conceptual models of life course epidemiology (critical period, sensitive period and accumulation models). The performance of the fRLM is evaluated with simulations, and the approach is illustrated with empirical applications relating body mass index (BMI) to mRNA-seq signatures of chronic kidney disease, inflammation and breast cancer. RESULTS: Simulations reveal that fRLM identifies the correct life course model with three to five repeated assessments of risk and 400 subjects. The empirical examples reveal that chronic kidney disease reflects a critical period process and inflammation and breast cancer likely reflect sensitive period mechanisms. CONCLUSIONS: The proposed fRLM treats repeated measures of risk as continuous processes and, under realistic data scenarios, the method provides accurate probabilities that the data correspond to commonly studied models of life course epidemiology. fRLM is implemented with publicly-available software.

Residual Partial Least Squares Learning: Brain Cortical Thickness Simultaneously Predicts Eight Non-pairwise-correlated Behavioural and Disease Outcomes in Alzheimer's Disease.

Alzheimer's Disease (AD) is the leading cause of dementia. It results in cortical thickness changes and is associated with a decline in cognition and behaviour. Such decline affects multiple important day-to-day functions, including memory, language, orientation, judgment and problem-solving. Recent research has made important progress in identifying brain regions associated with single outcomes, such as individual AD status and general cognitive decline. The complex projection from multiple brain areas to multiple AD outcomes, however, remains poorly understood. This makes the assessment and especially the prediction of multiple AD outcomes - each of which may unveil an integral yet different aspect of the disease - challenging, particularly when some are not strongly correlated. Here, uniting residual learning, partial least squares (PLS), and predictive modelling, we develop an explainable, generalisable, and reproducible method called the Residual Partial Least Squares Learning (the re-PLS Learning) to (1) chart the pathways between large-scale multivariate brain cortical thickness data (inputs) and multivariate disease and behaviour data (outcomes); (2) simultaneously predict multiple, non-pairwise-correlated outcomes; (3) control for confounding variables (e.g., age and gender) affecting both inputs and outcomes and the pathways in-between; (4) perform longitudinal AD disease status classification and disease severity prediction. We evaluate the performance of the proposed method against a variety of alternatives on data from AD patients, subjects with mild cognitive impairment (MCI), and cognitively normal individuals (n=1,196) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our results unveil pockets of brain areas in the temporal, frontal, sensorimotor, and cingulate areas whose cortical thickness may be respectively associated with declines in different cognitive and behavioural subdomains in AD. Finally, we characterise re-PLS' geometric interpretation and mathematical support for delivering meaningful neurobiological insights and provide an open software package (re-PLS) available at https://github.com/thanhvd18/rePLS.

The roles, challenges, and merits of the p value.

Since the 18th century, the p value has been an important part of hypothesis-based scientific investigation. As statistical and data science engines accelerate, questions emerge: to what extent are scientific discoveries based on p values reliable and reproducible? Should one adjust the significance level or find alternatives for the p value? Inspired by these questions and everlasting attempts to address them, here, we provide a systematic examination of the p value from its roles and merits to its misuses and misinterpretations. For the latter, we summarize modest recommendations to handle them. In parallel, we present the Bayesian alternatives for seeking evidence and discuss the pooling of p values from multiple studies and datasets. Overall, we argue that the p value and hypothesis testing form a useful probabilistic decision-making mechanism, facilitating causal inference, feature selection, and predictive modeling, but that the interpretation of the p value must be contextual, considering the scientific question, experimental design, and statistical principles.

An integrative approach for the analysis of risk and health across the life course: challenges, innovations, and opportunities for life course research.

Life course epidemiology seeks to understand the intricate relationships between risk factors and health outcomes across different stages of life to inform prevention and intervention strategies to optimize health throughout the lifespan. However, extant evidence has predominantly been based on separate analyses of data from individual birth cohorts or panel studies, which may not be sufficient to unravel the complex interplay of risk and health across different contexts. We highlight the importance of a multi-study perspective that enables researchers to: (a) Compare and contrast findings from different contexts and populations, which can help identify generalizable patterns and context-specific factors; (b) Examine the robustness of associations and the potential for effect modification by factors such as age, sex, and socioeconomic status; and (c) Improve statistical power and precision by pooling data from multiple studies, thereby allowing for the investigation of rare exposures and outcomes. This integrative framework combines the advantages of multi-study data with a life course perspective to guide research in understanding life course risk and resilience on adult health outcomes by: (a) Encouraging the use of harmonized measures across studies to facilitate comparisons and synthesis of findings; (b) Promoting the adoption of advanced analytical techniques that can accommodate the complexities of multi-study, longitudinal data; and (c) Fostering collaboration between researchers, data repositories, and funding agencies to support the integration of longitudinal data from diverse sources. An integrative approach can help inform the development of individualized risk scores and personalized interventions to promote health and well-being at various life stages.

Seasonal flu vaccination, a matter of emotion? An experimental study on role of compassion, socioeconomic status and perceived threat among healthcare workers.

OBJECTIVE: This study tests the impact of threat on compassion and vaccination intention among healthcare workers (HCWs) with low and high socio-economic status (SES) in France. DESIGN: A total of 309 HCWs were analyzed (Mage=39.29, SD = 11.76). Participants with high (n = 138) or low (n = 171) SES were randomly assigned to a Threat (n = 187) versus a No-Threat (n = 122) condition through filling in MacArthur's scale. During this manipulation, participants read about an interaction involving a HCW with an SES higher than that of the participant. After filling in the MacArthur scale, all participants went through a compassion manipulation. Finally, participants read a text describing a patient's distress. MAIN OUTCOME MEASURES: The primary outcome was the vaccination intention score. The secondary outcome included the compassion score. RESULTS: The interaction of the Group X SES Subjective on compassion was not significant (p = .34, ηp2 = .003, 95%CI [-.39,.07]). The interaction of the Group X Diploma on vaccination intention with high compassion was significant (p<.001, ηp2 = .173, 95%CI [.11,1.68]). Planned comparisons revealed a significant difference in vaccination intention score between HCWs with low SES between Threat (M = 3.58, SD = 2.56) and No-Threat (M = 5.27, SD = 2.27; p=.01) conditions. CONCLUSION: Ultimately, compassion inhibited the distress elicited in the threat condition in HCWs with high compassion.