Faculty Bibliography

Importance:Interindividual variability in postprandial glycemic response (PPGR) to the same foods may explain why low glycemic index or load and low-carbohydrate diet interventions have mixed weight loss outcomes. A precision nutrition approach that estimates personalized PPGR to specific foods may be more efficacious for weight loss. Objective:To compare a standardized low-fat vs a personalized diet regarding percentage of weight loss in adults with abnormal glucose metabolism and obesity. Design, Setting, and Participants:The Personal Diet Study was a single-center, population-based, 6-month randomized clinical trial with measurements at baseline (0 months) and 3 and 6 months conducted from February 12, 2018, to October 28, 2021. A total of 269 adults aged 18 to 80 years with a body mass index (calculated as weight in kilograms divided by height in meters squared) ranging from 27 to 50 and a hemoglobin A1c level ranging from 5.7% to 8.0% were recruited. Individuals were excluded if receiving medications other than metformin or with evidence of kidney disease, assessed as an estimated glomerular filtration rate of less than 60 mL/min/1.73 m2 using the Chronic Kidney Disease Epidemiology Collaboration equation, to avoid recruiting patients with advanced type 2 diabetes. Interventions:Participants were randomized to either a low-fat diet (<25% of energy intake; standardized group) or a personalized diet that estimates PPGR to foods using a machine learning algorithm (personalized group). Participants in both groups received a total of 14 behavioral counseling sessions and self-monitored dietary intake. In addition, the participants in the personalized group received color-coded meal scores on estimated PPGR delivered via a mobile app. Main Outcomes and Measures:The primary outcome was the percentage of weight loss from baseline to 6 months. Secondary outcomes included changes in body composition (fat mass, fat-free mass, and percentage of body weight), resting energy expenditure, and adaptive thermogenesis. Data were collected at baseline and 3 and 6 months. Analysis was based on intention to treat using linear mixed modeling. Results:Of a total of 204 adults randomized, 199 (102 in the personalized group vs 97 in the standardized group) contributed data (mean [SD] age, 58 [11] years; 133 women [66.8%]; mean [SD] body mass index, 33.9 [4.8]). Weight change at 6 months was -4.31% (95% CI, -5.37% to -3.24%) for the standardized group and -3.26% (95% CI, -4.25% to -2.26%) for the personalized group, which was not significantly different (difference between groups, 1.05% [95% CI, -0.40% to 2.50%]; P = .16). There were no between-group differences in body composition and adaptive thermogenesis; however, the change in resting energy expenditure was significantly greater in the standardized group from 0 to 6 months (difference between groups, 92.3 [95% CI, 0.9-183.8] kcal/d; P = .05). Conclusions and Relevance:A personalized diet targeting a reduction in PPGR did not result in greater weight loss compared with a low-fat diet at 6 months. Future studies should assess methods of increasing dietary self-monitoring adherence and intervention exposure. Trial Registration:ClinicalTrials.gov Identifier: NCT03336411.

Structural racism represents a key determinant of the racial health disparities that has characterized the U.S. population throughout its existence. While this reality has recently begun to gain increasing acknowledgment and acceptance within the health sciences, there are still considerable challenges related to defining the concept of structural racism and operationalizing it in empirical study. In this paper, building on the existing evidence base, we propose a comprehensive framework that centers structural racism in terms of its historical roots and continued manifestation in most domains of society, and offer solutions for the study of this phenomenon and the pathways that connect it to population-level health disparities. We showcase our framework by applying it to the known link between spatial and racialized clustering of incarceration - a previously cited representation of structural racism - and disparities in adverse birth outcomes. Through this process we hypothesize pathways that focus on social cohesion and community-level chronic stress, community crime and police victimization, as well as infrastructural community disinvestment. First, we contextualize these mechanisms within the relevant extant literature. Then, we make recommendations for future empirical pathway analyses. Finally, we identify key areas for policy, community, and individual-level interventions that target the impact of concentrated incarceration on birth outcomes among Black people in the U.S.

Extensive research shows that residential segregation has severe health consequences for racial and ethnic minorities. Most research to date has operationalized segregation in terms of either poverty or race/ethnicity rather than a synergy of these factors. A novel version of the Index of Concentration at the Extremes (ICERace-Income) specifically assesses racialized economic segregation in terms of spatial concentrations of racial and economic privilege (e.g., wealthy white people) versus disadvantage (e.g., poor Black people) within a given area. This multidimensional measure advances a more comprehensive understanding of residential segregation and its consequences for racial and ethnic minorities. The aim of this paper is to critically review the evidence on the association between ICERace-Income and health outcomes. We implemented the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a rigorous search of academic databases for papers linking ICERace-Income with health. Twenty articles were included in the review. Studies focused on the association of ICERace-Income with adverse birth outcomes, cancer, premature and all-cause mortality, and communicable diseases. Most of the evidence indicates a strong association between ICERace-Income and each health outcome, underscoring income as a key mechanism by which segregation produces health inequality along racial and ethnic lines. Two of the reviewed studies examined racial disparities in comorbidities and health care access as potential explanatory factors underlying this relationship. We discuss our findings in the context of the extant literature on segregation and health and propose new directions for future research and applications of the ICERace-Income measure.

Nonadherence with positive airway pressure (PAP) therapy impedes the effectiveness of treatment and increases risk of mortality. Disparities in PAP adherence as a function of socioeconomic status (SES) are not well understood. A literature search identified 16 original publications meeting inclusion criteria that described effects of SES factors on objective PAP adherence; 69% of these articles found evidence of lower adherence as a function of SES. This integrative review provides a structured summary of the findings, highlights factors that may contribute to disparities among adult PAP users, and identifies future directions to improve equity in the management of OSA.

OBJECTIVES/OBJECTIVE:The objective of the present study is to identify which underlying beliefs about the impact of sleep on health may motivate change in sleep behavior. DESIGN/METHODS:A cross-sectional study conducted between 2012 and 2014. SETTING/METHODS:Data were from the Sleep and Healthy Activity, Diet, Environment, and Socialization (SHADES) study conducted in Philadelphia, PA, and its surrounding regions. PARTICIPANTS/METHODS:Participants consisted of N = 1007 community-dwelling adults age 22-60. MEASUREMENTS/METHODS:Respondents indicated behaviors they could improve on to facilitate sleep and their corresponding readiness to change. They were also asked items from the Sleep Practices and Attitudes Questionnaire (SPAQ) regarding the degree to which they agree with whether "not getting enough sleep" can impact a variety of health factors. RESULTS:In adjusted analyses, stage of change was associated with degree of agreement that insufficient sleep can cause sleepiness (odds ratio [OR] = 1.17, P = .035), weight gain (OR = 1.20, P < .0005), heart disease (OR = 1.21, P = .001), cholesterol (OR = 1.13, P = .047), hypertension (OR = 1.16, P = .014), moodiness (OR = 1.42, P < .0005), decreased energy (OR = 1.30, P = .002), absenteeism (OR = 1.13, P = .007), decreased performance (OR = 1.20, P = .003), concentration/memory problems (OR = 1.23, P = .004), diabetes (OR = 1.14, P = .042), and feeling tired (OR = 1.39, P < .0005). When sleep duration was added to the model, significant associations remained for all except cholesterol. When accounting for insomnia, significant associations were maintained for only weight, moodiness, performance, diabetes, and tiredness. CONCLUSIONS:Degree of belief that insufficient sleep can cause outcomes such as moodiness, occupational problems, and health problems may impact whether an individual is contemplating/attempting to change their sleep-related behaviors. Targeting these key messages about the associations between sleep health with moodiness and weight gain in informational material may enhance education/outreach efforts aimed at adults.

The consideration of sleep and circadian rhythms in the context of health is a relatively recent development in the history of the field of behavioral medicine. This special issue of the International Journal of Behavioral Medicine recognizes that sleep and circadian rhythms are fundamental to appreciating physiological, psychological, social, and environmental factors in the health and well-being of the population. The articles included in this issue draw attention to the breadth and saliency of sleep as a marker of health status and as a target of behavioral intervention to promote health. Such research highlights the diversity of participants, research methods, and clinical significance of translational sleep science allowing us to recognize the role of sleep in the context of health in new ways. These studies also illustrate progress in integrating theory, employing prospective and longitudinal designs and multimodal and integrative assessments. This introduction to the special issue concludes by discussing challenges and opportunities in the field of behavioral sleep medicine, including those posed by the coronavirus disease 2019 (COVID-19) pandemic and the need to more effectively provide sleep disorder treatment among underserved populations.

INTRODUCTION/BACKGROUND:Obstructive sleep apnea (OSA) is associated with Alzheimer's disease (AD) biomarkers in cognitively normal (CN) and mild cognitive impaired (MCI) participants. However, independent and combined effects of OSA, amyloid beta (Aβ) and tau-accumulation on AD time-dependent progression risk is unclear. METHODS:Study participants grouped by biomarker profile, as described by the A/T/N scheme, where "A" refers to aggregated Aβ, "T" aggregated tau, and "N" to neurodegeneration, included 258 CN (OSA-positive [OSA+] [A+TN+ n = 10, A+/TN- n = 6, A-/TN+ n = 10, A-/TN- n = 6 and OSA-negative [OSA-] [A+TN+ n = 84, A+/TN- n = 11, A-/TN+ n = 96, A-/TN- n = 36]) and 785 MCI (OSA+ [A+TN+ n = 35, A+/TN- n = 15, A-/TN+ n = 25, A-/TN- n = 16] and OSA- [A+TN+ n = 388, A+/TN- n = 28, A-/TN+ n = 164, A-/TN- n = 114]) older-adults from the Alzheimer's Disease Neuroimaging Initiative cohort. Cox proportional hazards regression models estimated the relative hazard of progression from CN-to-MCI and MCI-to-AD, among baseline OSA CN and MCI patients, respectively. Multi-level logistic mixed-effects models with random intercept and slope investigated the synergistic associations of self-reported OSA, Aβ, and tau burden with prospective cognitive decline. RESULTS:Independent of TN-status (CN and MCI), OSA+/Aβ+ participants were approximately two to four times more likely to progress to MCI/AD (P < .001) and progressed 6 to 18 months earlier (P < .001), compared to other participants combined (ie, OSA+/Aβ-, OSA-/Aβ+, and OSA-/Aβ-). Notably, OSA+/Aβ- versus OSA-/Aβ- (CN and MCI) and OSA+/TN- versus OSA-/TN- (CN) participants showed no difference in the risk and time-to-MCI/AD progression. Mixed effects models demonstrated OSA synergism with Aβ (CN and MCI [β = 1.13, 95% confidence interval (CI), 0.74 to 1.52, and β = 1.18, 95%CI, 0.82 to 1.54]) respectively, and with tau (MCI [β = 1.31, 95% CI, 0.87 to 1.47]), P < .001 for all. DISCUSSION/CONCLUSIONS:OSA acts in synergism with Aβ and with tau, and all three acting together result in synergistic neurodegenerative mechanisms especially as Aβ and tau accumulation becomes increasingly abnormal, thus leading to shorter progression time to MCI/AD in CN and MCI-OSA patients, respectively.