Faculty Bibliography

Objectives/UNASSIGNED:Hemoglobin A1c (HbA1c) is associated with hypertension in prediabetes (PD) and type 2 diabetes (T2D), both of which are associated with increased cardiovascular disease (CVD) risk. Traditionally, HbA1c is the standard approach to assessing glycemic status, however, glycemic variability (GV)-the daily fluctuations in blood glucose concentrations-may be more predictive of CVD. Little is known regarding the relationship between GV and blood pressure (BP). Therefore, we examined GV and HbA1c and their associations with BP in overweight adults with PD and early stage T2D enrolled in a weight loss study. Methods/UNASSIGNED:Participants had a history of PD or T2D treated with lifestyle alone or lifestyle and metformin. Data for this report were obtained at baseline and included sociodemographics, height, weight, BP, and HbA1c. Up to two weeks of continuous glucose monitoring data were collected using the Abbott Freestyle Libre Pro, and mean amplitude of glycemic excursions (MAGE) was computed using EasyGV. Linear mixed models were used to test the associations of MAGE and HbA1c with BP, with adjustment for sociodemographic characteristics, as well as to compare MAGE on weekdays versus weekends. Repeated measures ANOVA was used to investigate the within-subject and between-subject variation of MAGE. All analyses were performed using R software. Results/UNASSIGNED:Â =Â 0.03). Conclusions/UNASSIGNED:Among overweight adults with PD and early-stage T2D, MAGE was found to be associated with SBP. This suggests that a measure of daily GV may be a good alternative measure of metabolic health outcomes in this population. Funding Sources/UNASSIGNED:This work was funded by the American Heart Association.

Objectives/UNASSIGNED:We compared self-monitoring adherence in participants randomized to two weight loss programs: a STANDARDIZED, one-size-fits-all, low-fat diet, or a diet PERSONALIZED to minimize the postprandial glycemic response. Methods/UNASSIGNED:Â =Â 20) received app feedback about intake of total calories plus a meal-specific predicted glycemic score. Total meal entries were measured at 1, 2 and 3 months. Self-monitoring adherence was defined as logging >50% of expected meals each month into the PNP app, assuming 3 meals/day. Session attendance was also measured. Repeated measures binomial logistic regression analysis was used to assess change in adherence due to treatment group, time (i.e., months), and the interaction between treatment and time, adjusting for age, gender and BMI. Results/UNASSIGNED:Â =Â 0.006). The proportion of attendance at videoconference sessions was similar between groups (STANDARDIZED: 77.1%; PERSONALIZED: 77.5%). Conclusions/UNASSIGNED:Two weight loss programs having similar calorie targets, behavioral approach, and contact schedule resulted in similar session attendance. However, adherence to self-monitoring was better when feedback was personalized. Funding Sources/UNASSIGNED:American Heart Association.

Objectives/UNASSIGNED:Standard procedures to estimate resting energy expenditure (REE) using indirect calorimetry are time-consuming, and may be unnecessary. Indeed, the guidelines recommend a pre-test resting period of 30-minutes, followed by a 5-minute stabilization period, and then waiting until the first steady state period (SS), defined as a 5-minute period with a coefficient of variance (CV) of <10% for VO2 and VCO2, to estimate REE. The aim of the study was to evaluate alternative procedures for estimating REE in adults with overweight and obesity. Methods/UNASSIGNED:estimates were compared to the other estimates of REE using paired t-tests. Results/UNASSIGNED:Â =Â 0.05). Conclusions/UNASSIGNED:and rolling averages suggest the standard criteria may be unnecessary in a group setting. Funding Sources/UNASSIGNED:American Heart Association.

Weight loss reduces the risk of type 2 diabetes mellitus (T2D) in overweight and obese individuals. Although the physiological response to food varies among individuals, standard dietary interventions use a "one-size-fits-all" approach. The Personal Diet Study aims to evaluate two dietary interventions targeting weight loss in people with prediabetes and T2D: (1) a low-fat diet, and (2) a personalized diet using a machine-learning algorithm that predicts glycemic response to meals. Changes in body weight, body composition, and resting energy expenditure will be compared over a 6-month intervention period and a subsequent 6-month observation period intended to assess maintenance effects. The behavioral intervention is delivered via mobile health technology using the Social Cognitive Theory. Here, we describe the design, interventions, and methods used.

Patients with complex chronic diseases usually must make multiple lifestyle changes to limit and manage their conditions. Numerous studies have shown that education alone is insufficient for engaging people in lifestyle behavior change, and that theory-based behavioral approaches also are necessary. However, even the most motivated individual may have difficulty with making lifestyle changes because of the information complexity associated with multiple behavior changes. The goal of the current Healthy Hearts and Kidneys study was to evaluate, different mobile health (mHealth)-delivered intervention approaches for engaging individuals with type 2 diabetes (T2D) and concurrent chronic kidney disease (CKD) in behavior changes. Participants were randomized to 1 of 4 groups, receiving: (1) a behavioral counseling, (2) technology-based self-monitoring to reduce information complexity, (3) combined behavioral counseling and technology-based self-monitoring, or (4) baseline advice. We will determine the impact of randomization assignment on weight loss success and 24-hour urinary excretion of sodium and phosphorus. With this report we describe the study design, methods, and approaches used to assure information security for this ongoing clinical trial. Clinical Trials.gov Identifier: NCT02276742.

Introduction/UNASSIGNED:Biomarkers of metabolic syndrome expressed soon after World Trade Center (WTC) exposure predict development of WTC Lung Injury (WTC-LI). The metabolome remains an untapped resource with potential to comprehensively characterise many aspects of WTC-LI. This case-control study identified a clinically relevant, robust subset of metabolic contributors of WTC-LI through comprehensive high-dimensional metabolic profiling and integration of machine learning techniques. Methods/UNASSIGNED:Never-smoking, male, WTC-exposed firefighters with normal pre-9/11 lung function were segregated by post-9/11 lung function. Cases of WTC-LI (forced expiratory volume in 1s <lower limit of normal, n=15) and controls (n=15) were identified from previous cohorts. The metabolome of serum drawn within 6 months of 9/11 was quantified. Machine learning was used for dimension reduction to identify metabolites associated with WTC-LI. Results/UNASSIGNED:580 metabolites qualified for random forests (RF) analysis to identify a refined metabolite profile that yielded maximal class separation. RF of the refined profile correctly classified subjects with a 93.3% estimated success rate. 5 clusters of metabolites emerged within the refined profile. Prominent subpathways include known mediators of lung disease such as sphingolipids (elevated in cases of WTC-LI), and branched-chain amino acids (reduced in cases of WTC-LI). Principal component analysis of the refined profile explained 68.3% of variance in five components, demonstrating class separation. Conclusion/UNASSIGNED:Analysis of the metabolome of WTC-exposed 9/11 rescue workers has identified biologically plausible pathways associated with loss of lung function. Since metabolites are proximal markers of disease processes, metabolites could capture the complexity of past exposures and better inform treatment. These pathways warrant further mechanistic research.

Background: Behavioral methods enhance the effectiveness of lifestyle interventions, but are often resource intensive. Although mobile health (mHealth) technology can help create lower input interventions, their feasibility, acceptability and efficacy have not been adequately evaluated in hemodialysis (HD) patients.
Method(s): Maintenance HD patients with persistent hyperphosphatemia (n=40) were randomized to receive: (1) educational (Edu) videos (EDU), (2) Edu + mobile selfmonitoring (SM) with MyNetDiary (MON), or (3) Edu + SM + social cognitive theory (SCT)-based behavioral counseling videos (SCT) over a 12-week period with videos for each group delivered using iPads. Serum phosphorus concentrations (sPO4) were measured at baseline, 12 and 24 weeks, and a 5-point Likert scale survey on the mHealth technology was completed at 24-weeks. Two participants in the EDU group with no follow-up sPO4 measurements were excluded; missing sPO4 measurements at 12-and 24-weeks were imputed by carrying forward the most recent sPO4 values.
Result(s): At the end of the intervention phase (12-weeks), there was a non-significant trend towards greater decreases in sPO4 in the MON (-0.5+/-1.6 mg/dL, p=0.32) and SCT (-0.3+/-2.1 mg/dL, p=0.56) groups compared to the EDU group (+0.2+/-1.4 mg/dL), but these differences had mostly disappeared by the end of the monitoring phase (24-weeks) (EDU +0.1+/-1.2 mg/dL, MON -0.1+/-1.9 mg/dL, SCT -0.1+/-2.1 mg/dL). Most participants agreed or strongly agreed that the iPads were convenient (64%), and SM helped them stay motivated (68%), take binders (61%), and limit phosphorus intake (68%). Relatively few participants reported that they agreed or strongly agreed that they sometimes "got lost" maneuvering the iPad programs (24%), felt that SM wasn't worthwhile (16%), or would have preferred face-to-face meetings offsite (4%).
Conclusion(s): Many HD patients are willing, able and report benefits of engaging in technology-supported behavioral interventions involving SM and SCT. Although these programs are easy to disseminate with limited resources once developed, any benefits for phosphorus management in HD patients may last only as long as the intervention is active