Weight loss and bone mineral density in obese adults: a longitudinal analysis of the influence of very low energy diets

Abstract Background The long-term effect of weight reduction on skeletal health is not well understood. The purpose of this study was to examine the impact of an intensive medical weight loss intervention using very low energy diet (VLED) (~ 800 cal/day) that result in significant changes in body weight, on total body bone mineral density (BMD) over 2 years. Methods We examined the impact of VLED-induced weight loss on BMD and FFM (Fat-free Mass) after 3–6 months and again while in weight maintenance at 2 years in 49 subjects. The effects of absolute and relative rate of weight reduction assessed by change in weight in kilograms were assessed using general linear modeling, with baseline BMD (or FFM) as a covariate, and age, sex and changes in body weight as primary model predictors. Results At the end of 2 years, the average weight loss was greater for men (weight: 23.51 ± 12.5 kg) than women (weight: 16.8 ± 19.2 kg) and BMD loss was greater among women (0.03 ± 0.04 g/cm2 vs 0.01 ± 0.04 g/cm2) (all p < 0.05). After adjusting for baseline BMD, age, and sex, there was a small but significant association between total weight loss and 2-year BMD (β = − 0.001 g/cm2; p = 0.01). Similarly, there was a significant independent association between total weight loss and 2-year FFM (β = − 116.5 g; p < 0.01). Conclusions Despite significant weight loss with VLED, there was only a small loss is BMD.https://deepblue.lib.umich.edu/bitstream/2027.42/144519/1/40842_2018_Article_63.pd

A Survey on EEG Data Analysis Software

Electroencephalography (EEG) is a mechanism to understand the brain’s functioning by analyzing brain electrical signals. More recently, it has been more commonly used in studies that are focused on the causation and effect of dementia. More tools are now available to gather EEG data. This brings about the challenge of understanding brain signals, which involves signal processing. Professionals with an electrical engineering background are very comfortable analyzing EEG data. Still, scientists in computer science and related fields need a source that can identify all the tools available and the process of analyzing the data. This paper deals specifically with the existing EEG data analysis tools and the processes involved in analyzing the EEG data using these tools. Furthermore, the paper goes in-depth into identifying the tools and the mechanisms of data processing techniques. In addition, it lists a set of definitions required for a better understanding of EEG data analysis, which can be challenging. The purpose of this paper is to serve as a reference for not only scientists that are new to EEG data analysis but also seasoned scientists that are looking for a specific data component in EEG and can go straight to the section of the paper that deals with the tool that they are using

When blood is not an option: Optimal bloodless management of severe anemia in pregnancy

Standard treatment for severe anemia in pregnancy is allogeneic blood transfusion, but this is not acceptable to all patients. Options for alternative anemia treatment are available. In this case report, a 32-year-old G2P1 woman who was a Jehovah’s Witness presented at 27 weeks gestation with dyspnea, palpitations, and severe anemia (hemoglobin 2.8 g/dL) related to chronic rectal bleeding. She declined blood transfusion. An anemia management protocol (high-dose erythropoietin-stimulating agent, iron, vitamin D, vitamin C, folate, vitamin B12) rapidly increased endogenous erythropoiesis. After 12 days, hemoglobin increased to 8 g/dL. A bovine hemoglobin-based oxygen carrier was available for acute bleeding but was not used. This case highlights that early initiation of multimodal therapy can adequately increase endogenous erythropoiesis to treat life-threatening anemia in antepartum patients who do not accept blood transfusion.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/175104/1/jog15384_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/175104/2/jog15384.pd

A first‐year leadership programme for medical students

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152529/1/tct13005.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152529/2/tct13005_am.pd

"Factors Associated with Participant Retention in a Clinical, Intensive, Behavioral Weight Management Program"

Background: We sought to identify factors associated with participant retention in a 2-year, physician-lead, multidisciplinary, clinical weight management program that employs meal replacements to produce weight loss and intensive behavioral interventions and financial incentives for weight loss maintenance. We studied 270 participants enrolled in 2010 and 2011. Sociodemographic factors, health insurance, distance traveled, body mass index, comorbidities, health-related quality-of-life, and depression were explored as potential predictors of retention. Results: Mean age was 49 ± 8 years and BMI was 41 ± 5 kg/m2. Retention was excellent at 3 months (90%) and 6 months (83%). Attrition was greatest after participants were transitioned to regular foodstuffs and fell to 67% at 12 months and 51% at 2 years. Weight decreased by 15 ± 12 kg and BMI decreased by 5.1 ± 4.0 kg/m2 in 2-year completers. Older age, lower baseline BMI, and financial incentives for program participation were independently associated with retention. Fewer depressive symptoms at baseline were associated with retention. Conclusions: This multidisciplinary, clinical, weight management program demonstrated high retention and excellent outcomes. Older age at baseline, less extreme obesity, and financial incentives were associated with program retention.http://deepblue.lib.umich.edu/bitstream/2027.42/175160/2/Factors associated with participant retention in a clinical, intensive, behavioral weight management program.pdfPublished versio