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BackgroundLean body mass (LM) plays an important role in mobility and metabolic function. We previously identified five loci associated with LM adjusted for fat mass in kilograms. Such an adjustment may reduce the power to identify genetic signals having an association with both lean mass and fat mass.ObjectivesTo determine the impact of different fat mass adjustments on genetic architecture of LM and identify additional LM loci.MethodsWe performed genome-wide association analyses for whole-body LM (20 cohorts of European ancestry with n = 38,292) measured using dual-energy X-ray absorptiometry) or bioelectrical impedance analysis, adjusted for sex, age, age2, and height with or without fat mass adjustments (Model 1 no fat adjustment; Model 2 adjustment for fat mass as a percentage of body mass; Model 3 adjustment for fat mass in kilograms).ResultsSeven single-nucleotide polymorphisms (SNPs) in separate loci, including one novel LM locus (TNRC6B), were successfully replicated in an additional 47,227 individuals from 29 cohorts. Based on the strengths of the associations in Model 1 vs Model 3, we divided the LM loci into those with an effect on both lean mass and fat mass in the same direction and refer to those as "sumo wrestler" loci (FTO and MC4R). In contrast, loci with an impact specifically on LM were termed "body builder" loci (VCAN and ADAMTSL3). Using existing available genome-wide association study databases, LM increasing alleles of SNPs in sumo wrestler loci were associated with an adverse metabolic profile, whereas LM increasing alleles of SNPs in "body builder" loci were associated with metabolic protection.ConclusionsIn conclusion, we identified one novel LM locus (TNRC6B). Our results suggest that a genetically determined increase in lean mass might exert either harmful or protective effects on metabolic traits, depending on its relation to fat mass.

BackgroundDeveloping effective, widely useful, weight management programs is a priority in health care because obesity is a major health problem. ObjectiveThis study developed and investigated a new, comprehensive, multifactorial, daily, intensive, psychologist coaching program based on cognitive behavioral therapy (CBT) modules. The program was delivered via the digital health care mobile services Noom Coach and InBody. MethodsThis was an open-label, active-comparator, randomized controlled trial. A total of 70 female participants with BMI scores above 24 kg/m2 and no clinical problems besides obesity were randomized into experimental and control groups. The experimental (ie, digital CBT) group (n=45) was connected with a therapist intervention using a digital health care service that provided daily feedback and assignments for 8 weeks. The control group (n=25) also used the digital health care service, but practiced self-care without therapist intervention. The main outcomes of this study were measured objectively at baseline, 8 weeks, and 24 weeks and included weight (kg) as well as other body compositions. Differences between groups were evaluated using independent t tests and a per-protocol framework. ResultsMean weight loss at 8 weeks in the digital CBT group was significantly higher than in the control group (–3.1%, SD 4.5, vs –0.7%, SD 3.4, P=.04). Additionally, the proportion of subjects who attained conventional 5% weight loss from baseline in the digital CBT group was significantly higher than in the control group at 8 weeks (32% [12/38] vs 4% [1/21], P=.02) but not at 24 weeks. Mean fat mass reduction in the digital CBT group at 8 weeks was also significantly greater than in the control group (–6.3%, SD 8.8, vs –0.8%, SD 8.1, P=.02). Mean leptin and insulin resistance in the digital CBT group at 8 weeks was significantly reduced compared to the control group (–15.8%, SD 29.9, vs 7.2%, SD 35.9, P=.01; and –7.1%, SD 35.1, vs 14.4%, SD 41.2, P=.04). Emotional eating behavior (ie, mean score) measured by questionnaire (ie, the Dutch Eating Behavior Questionnaire) at 8 weeks was significantly improved compared to the control group (–2.8%, SD 34.4, vs 21.6%, SD 56.9, P=.048). Mean snack calorie intake in the digital CBT group during the intervention period was significantly lower than in the control group (135.9 kcal, SD 86.4, vs 208.2 kcal, SD 166.3, P=.02). Lastly, baseline depression, anxiety, and self-esteem levels significantly predicted long-term clinical outcomes (24 weeks), while baseline motivation significantly predicted both short-term (8 weeks) and long-term clinical outcomes. ConclusionsThese findings confirm that technology-based interventions should be multidimensional and are most effective with human feedback and support. This study is innovative in successfully developing and verifying the effects of a new CBT approach with a multidisciplinary team based on digital technologies rather than standalone technology-based interventions. Trial RegistrationClinicalTrials.gov NCT03465306; https://clinicaltrials.gov/ct2/show/NCT03465306

BackgroundEmotional eating (EE) is one of the most significant symptoms of various eating disorders. It has been difficult to collect a large amount of behavioral data on EE; therefore, only partial studies of this symptom have been conducted. To provide adequate support for online social media users with symptoms of EE, we must understand their behavior patterns to design a sophisticated personalized support system (PSS). ObjectiveThis study aimed to analyze the behavior patterns of emotional eaters as the first step to designing a personalized intervention system. MethodsThe machine learning (ML) framework and Latent Dirichlet Allocation (LDA) topic modeling tool were used to collect and analyze behavioral data on EE. Data from a subcommunity of Reddit, /r/loseit, were analyzed. This dataset included all posts and feedback from July 2014 to May 2018, comprising 185,950 posts and 3,528,107 comments. In addition, deleted and improperly collected data were eliminated. Stochastic gradient descent–based ML classifier with an accuracy of 90.64% was developed to collect refined behavioral data of online users with EE behaviors. The expert group that labeled the dataset to train the ML classifiers included a medical doctor specializing in EE diagnosis and a nutritionist with profound knowledge of EE behavior. The experts labeled 5126 posts as EE (coded as 1) or others (coded as 0). Finally, the topic modeling process was conducted with LDA. ResultsThe following 4 macroperspective topics of online EE behaviors were identified through linguistic evidence regarding each topic: addressing feelings, sharing physical changes, sharing and asking for dietary information, and sharing dietary strategies. The 5 main topics of feedback were dietary information, compliments, consolation, automatic bot feedback, and health information. The feedback topic distribution significantly differed depending on the type of EE behavior (overall P

Nicotinamide adenine dinucleotide (NAD)+ serves as a crucial coenzyme in numerous essential biological reactions, and its cellular availability relies on the activity of the nicotinamide phosphoribosyltransferase (NAMPT)-catalyzed salvage pathway. Here we show that treatment with saturated fatty acids activates the NAD+ salvage pathway in hypothalamic astrocytes. Furthermore, inhibition of this pathway mitigates hypothalamic inflammation and attenuates the development of obesity in male mice fed a high-fat diet (HFD). Mechanistically, CD38 functions downstream of the NAD+ salvage pathway in hypothalamic astrocytes burdened with excess fat. The activation of the astrocytic NAMPT–NAD+–CD38 axis in response to fat overload induces proinflammatory responses in the hypothalamus. It also leads to aberrantly activated basal Ca2+ signals and compromised Ca2+ responses to metabolic hormones such as insulin, leptin, and glucagon-like peptide 1, ultimately resulting in dysfunctional hypothalamic astrocytes. Our findings highlight the significant contribution of the hypothalamic astrocytic NAD+ salvage pathway, along with its downstream CD38, to HFD-induced obesity.The cellular levels of the critical coenzyme nicotinamide adenine dinucleotide (NAD)+ depend on its salvage pathway. Here, the authors show that the NAD+ salvage pathway in hypothalamic astrocytes is activated to promote obesity in high fat diet-fed mice. [ABSTRACT FROM AUTHOR]

We investigate the voltage control of magnetism in a van der Waals (vdW) heterostructure device consisting of two distinct vdW materials, the ferromagnetic Fe3-xGeTe2 and the ferroelectric In2Se3. It is observed that gate voltages applied to the Fe3-xGeTe2/In2Se3 heterostructure device modulate the magnetic properties of Fe3-xGeTe2 with significant decrease in coercive field for both positive and negative voltages. Raman spectroscopy on the heterostructure device shows voltage-dependent increase in the in-plane In2Se3 and Fe3-xGeTe2 lattice constants for both voltage polarities. Thus, the voltage-dependent decrease in the Fe3-xGeTe2 coercive field, regardless of the gate voltage polarity, can be attributed to the presence of in-plane tensile strain. This is supported by density functional theory calculations showing tensile-strain-induced reduction of the magnetocrystalline anisotropy, which in turn decreases the coercive field. Our results demonstrate an effective method to realize low-power voltage-controlled vdW spintronic devices utilizing the magnetoelectric effect in vdW ferromagnetic/ferroelectric heterostructures. The control of magnetism by electric field is an important goal for future development of low-power spintronics. Here, the authors demonstrate voltage control of magnetism in van der Waals ferromagnetic/ferroelectric heterostructure devices via the strain-mediated magnetoelectric effect. [ABSTRACT FROM AUTHOR]

Background and Objectives: 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) (PETFDG) image can visualize neuronal injury of the brain in Alzheimer's disease. Early-phase amyloid PET image is reported to be similar to PETFDG image. This study aimed to generate PETFDG images from 18F-florbetaben PET (PETFBB) images using a generative adversarial network (GAN) and compare the generated PETFDG (PETGE-FDG) with real PETFDG (PETRE-FDG) images using the structural similarity index measure (SSIM) and the peak signal-to-noise ratio (PSNR). Materials and Methods: Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, 110 participants with both PETFDG and PETFBB images at baseline were included. The paired PETFDG and PETFBB images included six and four subset images, respectively. Each subset image had a 5 min acquisition time. These subsets were randomly sampled and divided into 249 paired PETFDG and PETFBB subset images for the training datasets and 95 paired subset images for the validation datasets during the deep-learning process. The deep learning model used in this study is composed of a GAN with a U-Net. The differences in the SSIM and PSNR values between the PETGE-FDG and PETRE-FDG images in the cycleGAN and pix2pix models were evaluated using the independent Student's t-test. Statistical significance was set at p ≤ 0.05. Results: The participant demographics (age, sex, or diagnosis) showed no statistically significant differences between the training (82 participants) and validation (28 participants) groups. The mean SSIM between the PETGE-FDG and PETRE-FDG images was 0.768 ± 0.135 for the cycleGAN model and 0.745 ± 0.143 for the pix2pix model. The mean PSNR was 32.4 ± 9.5 and 30.7 ± 8.0. The PETGE-FDG images of the cycleGAN model showed statistically higher mean SSIM than those of the pix2pix model (p < 0.001). The mean PSNR was also higher in the PETGE-FDG images of the cycleGAN model than those of pix2pix model (p < 0.001). Conclusions: We generated PETFDG images from PETFBB images using deep learning. The cycleGAN model generated PETGE-FDG images with a higher SSIM and PSNR values than the pix2pix model. Image-to-image translation using deep learning may be useful for generating PETFDG images. These may provide additional information for the management of Alzheimer's disease without extra image acquisition and the consequent increase in radiation exposure, inconvenience, or expenses. [ABSTRACT FROM AUTHOR]