Faculty Bibliography

Exposure to toxic heavy metals has been associated with the development of attention-deficit/hyperactivity disorder (ADHD). However, fewer studies have examined the associations between abnormal levels of essential trace metals and ADHD, and none have done so using saliva. We investigated whether salivary metals were associated with ADHD in adolescents aged 12 from the Family Life Project (FLP) using a nested case-control study design that included 110 adolescents who met diagnostic criteria for inattentive (ADHD-I), hyperactive-impulsive (ADHD-H), or combined type ADHD (ADHD-C) (cases) and 173 children who did not (controls). We used inductively coupled plasma optical emission spectrophotometry to measure chromium, copper, manganese, and zinc in saliva samples. We employed logistic regression models to examine associations between quartile levels of individual metals and ADHD outcomes by subtype. Salivary copper levels were significantly associated with increased odds of any ADHD diagnosis (OR = 3.31, 95% CI: 1.08-10.12; p = 0.04) and with increased odds of ADHD-C diagnosis (OR = 8.44, 95% CI: 1.58-45.12; p = 0.01). Salivary zinc levels were significantly associated with increased odds of ADHD-C diagnosis (OR = 4.06, 95% CI: 1.21-13.69; p = 0.02). Salivary manganese levels were also significantly associated with increased odds of ADHD-C diagnosis (OR = 5.43, 95% CI: 1.08-27.27, p = 0.04). This is the first study using saliva to assess metal exposure and provide a potential link between salivary levels of copper, manganese, and zinc and ADHD diagnoses in adolescents. Public health interventions focused on metal exposures might reduce ADHD incidence in low-income, minority communities.

BACKGROUND/UNASSIGNED:Preterm birth (birth at <37 completed weeks gestation) is a significant public heatlh concern worldwide. Important health, and developmental consequences of preterm birth include altered temperament development, with greater dysregulation and distress proneness. AIMS/UNASSIGNED:The present study leveraged advanced quantitative techniques, namely machine learning approaches, to discern the contribution of narrowly defined and broadband temperament dimensions to birth status classification (full-term vs. preterm). Along with contributing to the literature addressing temperament of infants born preterm, the present study serves as a methodological demonstration of these innovative statistical techniques. STUDY DESIGN/UNASSIGNED:= 402) born at term, with data combined across investigations to perform classification analyses. SUBJECTS/UNASSIGNED:Participants included infants born preterm and term-born comparison children, either matched on chronological age or age adjusted for prematurity. OUTCOME MEASURES/UNASSIGNED:Infant Behavior Questionnaire-Revised Very Short Form (IBQ-R VSF) was completed by mothers, with factor and item-level data considered herein. RESULTS AND CONCLUSIONS/UNASSIGNED:Accuracy estimates were generally similar regardless of the comparison groups. Results indicated a slightly higher accuracy and efficiency for IBQR-VSF item-based models vs. factor-level models. Divergent patterns of feature importance (i.e., the extent to which a factor/item contributed to classification) were observed for the two comparison groups (chronological age vs. adjusted age) using factor-level scores; however, itemized models indicated that the two most critical items were associated with effortful control and negative emotionality regardless of comparison group.

PURPOSE OF REVIEW: Evidence suggests neurotoxicity of endocrine disrupting chemicals (EDCs) during sensitive periods of development. We present an overview of pediatric population neuroimaging studies that examined brain influences of EDC exposure during prenatal period and childhood. RECENT FINDINGS: We found 46 studies that used magnetic resonance imaging (MRI) to examine brain influences of EDCs. These studies showed associations of prenatal exposure to phthalates, organophosphate pesticides (OPs), polyaromatic hydrocarbons and persistent organic pollutants with global and regional brain structural alterations. Few studies suggested alteration in functional MRI associated with prenatal OP exposure. However, studies on other groups of EDCs, such as bisphenols, and those that examined childhood exposure were less conclusive. These findings underscore the potential profound and lasting effects of prenatal EDC exposure on brain development, emphasizing the need for better regulation and strategies to reduce exposure and mitigate impacts. More studies are needed to examine the influence of postnatal exposure to EDC on brain imaging.

BACKGROUND AND OBJECTIVE/OBJECTIVE:The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) recommendations standardise the reporting of prostate magnetic resonance imaging (MRI) in patients on active surveillance (AS) for prostate cancer. An international consensus group recently updated these recommendations and identified the areas of uncertainty. METHODS:A panel of 38 experts used the formal RAND/UCLA Appropriateness Method consensus methodology. Panellists scored 193 statements using a 1-9 agreement scale, where 9 means full agreement. A summary of agreement, uncertainty, or disagreement (derived from the group median score) and consensus (determined using the Interpercentile Range Adjusted for Symmetry method) was calculated for each statement and presented for discussion before individual rescoring. KEY FINDINGS AND LIMITATIONS/UNASSIGNED:Participants agreed that MRI scans must meet a minimum image quality standard (median 9) or be given a score of 'X' for insufficient quality. The current scan should be compared with both baseline and previous scans (median 9), with the PRECISE score being the maximum from any lesion (median 8). PRECISE 3 (stable MRI) was subdivided into 3-V (visible) and 3-NonV (nonvisible) disease (median 9). Prostate Imaging Reporting and Data System/Likert ≥3 lesions should be measured on T2-weighted imaging, using other sequences to aid in the identification (median 8), and whenever possible, reported pictorially (diagrams, screenshots, or contours; median 9). There was no consensus on how to measure tumour size. More research is needed to determine a significant size increase (median 9). PRECISE 5 was clarified as progression to stage ≥T3a (median 9). CONCLUSIONS AND CLINICAL IMPLICATIONS/CONCLUSIONS:The updated PRECISE recommendations reflect expert consensus opinion on minimal standards and reporting criteria for prostate MRI in AS.

INTRODUCTION/BACKGROUND:Plasma proteomic analyses of unique brain atrophy patterns may illuminate peripheral drivers of neurodegeneration and identify novel biomarkers for predicting clinically relevant outcomes. METHODS:We identified proteomic signatures associated with machine learning-derived aging- and Alzheimer's disease (AD) -related brain atrophy patterns in the Baltimore Longitudinal Study of Aging (n = 815). Using data from five cohorts, we examined whether candidate proteins were associated with AD endophenotypes and long-term dementia risk. RESULTS:Plasma proteins associated with distinct patterns of age- and AD-related atrophy were also associated with plasma/cerebrospinal fluid (CSF) AD biomarkers, cognition, AD risk, as well as mid-life (20-year) and late-life (8-year) dementia risk. EFEMP1 and CXCL12 showed the most consistent associations across cohorts and were mechanistically implicated as determinants of brain structure using genetic methods, including Mendelian randomization. DISCUSSION/CONCLUSIONS:Our findings reveal plasma proteomic signatures of unique aging- and AD-related brain atrophy patterns and implicate EFEMP1 and CXCL12 as important molecular drivers of neurodegeneration. HIGHLIGHTS/CONCLUSIONS:Plasma proteomic signatures are associated with unique patterns of brain atrophy. Brain atrophy-related proteins predict clinically relevant outcomes across cohorts. Genetic variation underlying plasma EFEMP1 and CXCL12 influences brain structure. EFEMP1 and CXCL12 may be important molecular drivers of neurodegeneration.

Health care delivery is undergoing an accelerated period of digital transformation, spurred in part by the COVID-19 pandemic and the use of "virtual-first" care delivery models such as telemedicine. Medical education has responded to this shift with calls for improved digital health training, but there is as yet no universal understanding of the needed competencies, domains, and best practices for teaching these skills. In this paper, we argue that a "digital determinants of health" (DDoH) framework for understanding the intersections of health outcomes, technology, and training is critical to the development of comprehensive digital health competencies in medical education. Much like current social determinants of health models, the DDoH framework can be integrated into undergraduate, graduate, and professional education to guide training interventions as well as competency development and evaluation. We provide possible approaches to integrating this framework into training programs and explore priorities for future research in digitally-competent medical education.

BACKGROUND:New York City (NYC) was the first COVID-19 epicenter in the United States and home to one of the country's largest contact tracing programs, NYC Test & Trace (T2). Understanding points of attrition along the stages of program implementation and follow-up can inform contact tracing efforts for future epidemics or pandemics. The objective of this study was to evaluate the completeness and timeliness of T2 case and contact notification and monitoring using a "cascade of care" approach. METHODS:This cross-sectional study included all SARS-CoV-2 cases and contacts reported to T2 from May 31, 2020 to January 1, 2022. Attrition along the "cascade of care" was defined as: (1) attempted, (2) reached, (3) completed intake (main outcome), (4) eligible for monitoring, and (5) successfully monitored. Timeliness was assessed: (1) by median days from a case's date of testing until their positive result was reported to T2, (2) from result until the case was notified by T2, and (3) from a case report of a contact until notification of the contact. RESULTS:A total of 1.45 million cases and 1.38 million contacts were reported to T2 during this period. For cases, attrition occurred evenly across the first three cascade steps (~-12%) and did not change substantially until the Omicron wave in December 2021. During the Omicron wave, the proportion of cases attempted dropped precipitously. For contacts, the largest attrition occurred between attempting and reaching (-27%), and attrition rose with each COVID-19 wave as contact volumes increased. Attempts to reach contacts discontinued entirely during the Omicron wave. Overall, 67% of cases and 49% of contacts completed intake interviews (79% and 57% prior to Omicron). T2 was timely, with a median of 1 day to receive lab results, 2 days to notify cases, and < 1 day to notify contacts. CONCLUSIONS:T2 provided a large volume of NYC residents with timely notification and monitoring. Engagement in the program was lower for contacts than cases, with the largest gap coming from inability to reach individuals during call attempts. To strengthen future test-and-trace efforts, strategies are needed to encourage acceptance of local contact tracer outreach attempts.

OBJECTIVE:To understand whether and how primary care providers and staff elicit patients' past experiences of healthcare discrimination when providing care. DATA SOURCES/STUDY SETTING/METHODS:Twenty qualitative semi-structured interviews were conducted with healthcare staff in primary care roles to inform future interventions to integrate data about past experiences of healthcare discrimination into clinical care. STUDY DESIGN/METHODS:Qualitative study. DATA COLLECTION/EXTRACTION METHODS/METHODS:Data were collected via semi-structured qualitative interviews between December 2018 and January 2019, with health care staff in primary care roles at a hospital-based clinic within an urban safety-net health system that serves a patient population with significant racial, ethnic, and linguistic diversity. PRINCIPAL FINDINGS/RESULTS:Providers did not routinely, or in a structured way, elicit information about past experiences of healthcare discrimination. Some providers believed that information about healthcare discrimination experiences could allow them to be more aware of and responsive to their patients' needs and to establish more trusting relationships. Others did not deem it appropriate or useful to elicit such information and were concerned about challenges in collecting and effectively using such data. CONCLUSIONS:While providers see value in eliciting past experiences of discrimination, directly and systematically discussing such experiences with patients during a primary care encounter is challenging for them. Collecting this information in primary care settings will likely require implementation of multilevel systematic data collection strategies. Findings presented here can help identify clinic-level opportunities to do so.

The kidney tubules constitute two-thirds of the cells of the kidney and account for the majority of the organ's metabolic energy expenditure. Acute tubular injury (ATI) is observed across various types of kidney diseases and may significantly contribute to progression to kidney failure. Non-invasive biomarkers of ATI may allow for early detection and drug development. Using the SomaScan proteomics platform on 434 patients with biopsy-confirmed kidney disease, we here identify plasma biomarkers associated with ATI severity. We employ regional transcriptomics and proteomics, single-cell RNA sequencing, and pathway analysis to explore biomarker protein and gene expression and enriched biological pathways. Additionally, we examine ATI biomarker associations with acute kidney injury (AKI) in the Kidney Precision Medicine Project (KPMP) (n = 44), the Atherosclerosis Risk in Communities (ARIC) study (n = 4610), and the COVID-19 Host Response and Clinical Outcomes (CHROME) study (n = 268). Our findings indicate 156 plasma proteins significantly linked to ATI with osteopontin, macrophage mannose receptor 1, and tenascin C showing the strongest associations. Pathway analysis highlight immune regulation and organelle stress responses in ATI pathogenesis.