Faculty Bibliography

Background/UNASSIGNED:Among adult kidney transplant (KT) recipients, the risk of post-KT adverse outcomes differs by type of induction immunosuppression. Immune response to induction differs as recipients age; yet, choice of induction is barely tailored by age likely due to a lack of evidence of the risks and benefits. Methods/UNASSIGNED:Using Scientific Registry of Transplant Recipients data, we identified 39336 first-time KT recipients (2010-2016). We estimated the length of stay (LOS), acute rejection (AR), graft failure, and death by induction type using logistic and Cox regression weighted by propensity score to adjust for confounders. We tested whether these estimates differed by age (65+ versus 18-64 y) using a Wald test. Results/UNASSIGNED: = 0.03 and 0.003) differed by recipient age. Discharge was on average 11% shorter in rATG among younger recipients (relative time = 0.89; 95% confidence interval [CI], 0.81-0.99) but not among older recipients (relative time = 1.01; 95% CI, 0.95-1.08). rATG was not associated with mortality among older (hazard ratio = 1.05; 95% CI, 0.96-1.15), but among younger recipients (hazard ratio = 0.87; 95% CI, 0.80-0.95), it was associated with reduced mortality risk. Conclusions/UNASSIGNED:rATG should be considered to prevent AR, especially among recipients with high-immunologic risk regardless of age; however, choice of induction should be tailored to reduce LOS and risk of mortality, particularly among younger recipients.

Biomarkers predict World Trade Center-Lung Injury (WTC-LI); however, there remains unaddressed multicollinearity in our serum cytokines, chemokines, and high-throughput platform datasets used to phenotype WTC-disease. To address this concern, we used automated, machine-learning, high-dimensional data pruning, and validated identified biomarkers. The parent cohort consisted of male, never-smoking firefighters with WTC-LI (FEV1, %Pred< lower limit of normal (LLN); n = 100) and controls (n = 127) and had their biomarkers assessed. Cases and controls (n = 15/group) underwent untargeted metabolomics, then feature selection performed on metabolites, cytokines, chemokines, and clinical data. Cytokines, chemokines, and clinical biomarkers were validated in the non-overlapping parent-cohort via binary logistic regression with 5-fold cross validation. Random forests of metabolites (n = 580), clinical biomarkers (n = 5), and previously assayed cytokines, chemokines (n = 106) identified that the top 5% of biomarkers important to class separation included pigment epithelium-derived factor (PEDF), macrophage derived chemokine (MDC), systolic blood pressure, macrophage inflammatory protein-4 (MIP-4), growth-regulated oncogene protein (GRO), monocyte chemoattractant protein-1 (MCP-1), apolipoprotein-AII (Apo-AII), cell membrane metabolites (sphingolipids, phospholipids), and branched-chain amino acids. Validated models via confounder-adjusted (age on 9/11, BMI, exposure, and pre-9/11 FEV1, %Pred) binary logistic regression had AUCROC [0.90(0.84-0.96)]. Decreased PEDF and MIP-4, and increased Apo-AII were associated with increased odds of WTC-LI. Increased GRO, MCP-1, and simultaneously decreased MDC were associated with decreased odds of WTC-LI. In conclusion, automated data pruning identified novel WTC-LI biomarkers; performance was validated in an independent cohort. One biomarker-PEDF, an antiangiogenic agent-is a novel, predictive biomarker of particulate-matter-related lung disease. Other biomarkers-GRO, MCP-1, MDC, MIP-4-reveal immune cell involvement in WTC-LI pathogenesis. Findings of our automated biomarker identification warrant further investigation into these potential pharmacotherapy targets.

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.

We report the largest and most diverse genetic study of type 1 diabetes (T1D) to date (61,427 participants), yielding 78 genome-wide-significant (P < 5 × 10^-8) regions, including 36 that are new. We define credible sets of T1D-associated variants and show that they are enriched in immune-cell accessible chromatin, particularly CD4⁺ effector T cells. Using chromatin-accessibility profiling of CD4⁺ T cells from 115 individuals, we map chromatin-accessibility quantitative trait loci and identify five regions where T1D risk variants co-localize with chromatin-accessibility quantitative trait loci. We highlight rs72928038 in BACH2 as a candidate causal T1D variant leading to decreased enhancer accessibility and BACH2 expression in T cells. Finally, we prioritize potential drug targets by integrating genetic evidence, functional genomic maps and immune protein-protein interactions, identifying 12 genes implicated in T1D that have been targeted in clinical trials for autoimmune diseases. These findings provide an expanded genomic landscape for T1D.

Income, education, and cumulative-risk indices likely obscure meaningful heterogeneity in the mechanisms through which poverty impacts child outcomes. This study draws from contemporary theory to specify multiple dimensions of poverty-related adversity and resources, with the aim of better capturing these nuances. Using data from the Family Life Project (N = 1,292), we leveraged moderated nonlinear factor analysis (Bauer, 2017) to establish group- and longitudinally invariant environmental measures from infancy to early adolescence. Results indicated three latent factors-material deprivation, psychosocial threat, and sociocognitive resources-were distinct from each other and from family income. Each was largely invariant across site, racial group, and development and showed convergent and discriminant relations with age-twelve criterion measures. Implications for ensuring socioculturally valid measurements of poverty are discussed.

This study examines whether changes in classroom quality predict within-child changes in achievement and behavioral problems in elementary school (ages spanning approximately 6-11 years old). Drawing on data from a longitudinal study of children in predominantly low-income, nonurban communities (n = 1,078), we relied on child fixed effects modeling, which controlled for stable factors that could bias the effects of classroom quality. In general, we found that changes in classroom quality had small and statistically nonsignificant effects on achievement and behavior. However, we found that moving into a high-quality classroom, particularly those rated as high in Classroom Organization, had positive effects on achievement and behavior for children with significant exposure to poverty in early life.

The needs of persons living with Alzheimer's disease and Alzheimer's disease-related dementia (AD/ADRD) are challenged by tremendous complexity impacting both care delivery and financing. Most persons living with dementia (PLWD) also suffer from other chronic medical or mental health conditions, which further burden quality of life and function. In addition to difficult treatment choices, optimal dementia care models likely involve people and services that are not typical pieces of the health care delivery system but are all critical partners-care partners, social workers, and community services, to name a few. More than 200 models of dementia care have demonstrated some efficacy. However, these successful interventions that might address much of the care needed by PLWD are uninsured in the United States, where insurance coverage has focused on acute care needs. This poses great difficulties for both care provision and care financing. In this article, we review these 3 key challenges: dementia care for those with chronic comorbid disease; care models that require people who are not typical providers in traditional care delivery systems; and the mandate to provide high-quality care that is currently not funded by usual health care insurance. We propose promising next steps that could substantially improve the lives of PLWD and the lives of their care partners, and highlight some of the many research questions that remain.

BACKGROUND:Wheeze and allergic sensitization are the strongest early-life predictors of childhood asthma development; the molecular origins of these early-life phenotypes are poorly understood. OBJECTIVES:We sought to identify metabolites associated with early-life wheeze, allergic sensitization, and childhood asthma. METHODS:We conducted a nested case-control study using Environmental influences on Child Health Outcomes Program cohorts for discovery and independent replication. Wheeze and allergic sensitization were defined by number of wheeze episodes and positive specific IgE at age 1 year, respectively. Asthma was defined as physician diagnosis of asthma at age 5 or 6 years. We used untargeted metabolomics, controlling for observed and latent confounding factors, to assess associations between the plasma metabolome and early-life wheeze, allergy, and childhood asthma. RESULTS:Eighteen plasma metabolites were associated with first-year wheeze in the discovery cohort (n = 338). Z,Z unconjugated bilirubin (UCB) and its related metabolites exhibited a dose-response relationship with wheeze frequency; UCB levels were 13% (β = 0.87; 95% CI, 0.74-1.02) and 22% (β = 0.78; 95% CI, 0.68-0.91) lower in children with 1 to 3 and 4+ wheeze episodes compared with those who never wheezed, respectively. UCB levels were also associated with childhood asthma (β = 0.82; 95% CI, 0.68-0.98). Similar trends were observed in 2 independent cohorts. UCB was significantly negatively correlated with eicosanoid- and oxidative stress-related metabolites. There were no significant associations between metabolites and allergic sensitization. CONCLUSIONS:We identified a novel inverse, dose-dependent association between UCB and recurrent wheeze and childhood asthma. Inflammatory lipid mediators and oxidative stress byproducts inversely correlated with UCB, suggesting that UCB modulates pathways critical to the development of early-life recurrent wheeze and childhood asthma.