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Artificial intelligence (AI) technology leads to new insights into the manipulation of quantum systems in the Noisy Intermediate-Scale Quantum (NISQ) era. Classical agent-based artificial intelligence algorithms provide a framework for the design or control of quantum systems. Traditional reinforcement learning methods are designed for the Markov Decision Process (MDP) and, hence, have difficulty in dealing with partially observable or quantum observable decision processes. Due to the difficulty of building or inferring a model of a specified quantum system, a model-free-based control approach is more practical and feasible than its counterpart of a model-based approach. In this work, we apply a model-free deep recurrent Q-network (DRQN) reinforcement learning method for qubit-based quantum circuit architecture design problems. This paper is the first attempt to solve the quantum circuit design problem from the recurrent reinforcement learning algorithm, while using discrete policy. Simulation results suggest that our long short-term memory (LSTM)-based DRQN method is able to learn quantum circuits for entangled Bell–Greenberger–Horne–Zeilinger (Bell–GHZ) states. However, since we also observe unstable learning curves in experiments, suggesting that the DRQN could be a promising method for AI-based quantum circuit design application, more investigation on the stability issue would be required.

In this work, a novel device concept for improving the efficiency and field operation of conventional InAs/GaAs quantum dot (QD) based intermediate band solar cell (IBSC) with the feature of current constraint (CC-QD-IBSC) was proposed and evaluated from both theoretical simulations and experimental demonstrations. The advantages of the proposed device concept including increasing efficiency, obtaining higher suitability for operation under concentrated photovoltaic (CPV) condition and more robust efficiency stability against intermediate band fluctuation were rigorously identified using the detailed balance theorem by comparing with conventional InAs/GaAs QD-IBSC. Furthermore, a hybrid molecular beam epitaxy (MBE) and metal organic chemical vapor deposition (MOCVD) shuttle growth of the proposed CC-QD-IBSC were adopted in this work for better preserving QD induced intermediate band feature and overcoming practical growth difficulties. By introducing a InGaP top cell for constraining the current of InAs/GaAs QD-IBSC, the short circuit current density of CC-QD-IBSC decreased to 13.0mA/cm2 under 1 sun illumination, a near 50 % reduction compared to the conventional InAs/GaAs QD-IBSC. On the contrary, by well adjusting the regrowth interface between MBE and MOCVD, we managed to reach the highest conversion efficiency of 22.8 % measured at an UL certified laboratory under light concentration ratio of 93 suns for the proposed CC-QD-IBSC with well-functioning QD and IB feature. It is our belief that our results pave the road for developing techniques geared towards higher efficiency and more practical field applications.

The hot-carrier effect and hot-carrier dynamics in GaAs solar cell device performance were investigated. Hot-carrier solar cells based on the conventional operation principle were simulated based on the detailed balance thermodynamic model and the hydrodynamic energy transportation model. A quasi-equivalence between these two models was demonstrated for the first time. In the simulation, a specially designed GaAs solar cell was used, and an increase in the open-circuit voltage was observed by increasing the hot-carrier energy relaxation time. A detailed analysis was presented regarding the spatial distribution of hot-carrier temperature and its interplay with the electric field and three hot-carrier recombination processes: Auger, Shockley–Read–Hall, and radiative recombinations.

Recent experimental results suggest that higher mobility of perovskite-type ligand passivated PbS quantum dots (QDs) could be useful for efficient solar cell applications. However, theoretical understanding of the mechanism through first principal modeling is still lacking. In this study, electronic-, optical-, and temperature-dependent carrier mobility for perovskite ligand passivated PbS QD array is calculated by using the first-principles density functional theory (DFT) combined with the nonequilibrium Green’s function (NEGF) technique and a molecular dynamics (MD)-Landauer approach. It is found that formamidinium (FA)-liganded QDs have higher mobility and enhanced optical absorption comparing to that of Cl-liganded QDs. The difference could be understood through the intermediate band featured electronic structure.

Here, we report a case study on inverse design of quantum dot optical spectra using a deep reinforcement learning algorithm for the desired target optical property of semiconductor CdxSeyTex−y quantum dots. Machine learning models were trained to predict the optical absorption and emission spectra by using the training dataset by time dependent density functional theory simulation. We show that the trained deep deterministic policy gradient inverse design agent can infer the molecular structure with an accuracy of less than 1 Å at a fixed computational time of milliseconds and up to 100–1000 times faster than the conventional heuristic particle swam optimization method. Most of the effective inverse design problems based on the surrogate machine learning and reinforcement learning model have been focused on the field of nano-photonics. Few attempts have been made in the field of quantum optical system in a similar manner. For the first time, our results, to our knowledge, provide concrete evidence that for computationally challenging tasks, a well-trained deep reinforcement learning agent can replace the existing quantum simulation and heuristics optimization tool, enabling fast and scalable simulations of the optical property of nanometer sized semiconductor quantum dots.

We present a numerical study using a self-consistent drift-diffusion approach to characterize the effect of light trapping on the quantum efficiency (QE) of a thin-film quantum dot intermediate band solar cell (QD-IBSC). In the concept of the IBSCs, the photons with energies lower than the bandgap of the host material can be absorbed in QDs due to the presence of the IB, leading to high photocurrent and conversion efficiency. However, in general, the quality of the QD structure is degraded due to the accumulated strain when the number of QD layers stacked vertically is increased, which is required to increase light absorption. In the present work, we adopted a Fabry-Pérot (FP) resonance structure to achieve high absorption with fewer QD layers. We demonstrate numerically, in a InAs/GaAs-based QDSC structure, that an increase in the QE by 8 times at maximum is achievable by tuning the position of the QD layers inserted.

Quantum computing has the potential to outperform classical computers and is expected to play an active role in various fields. In quantum machine learning, a quantum computer has been found useful for enhanced feature representation and high-dimensional state or function approximation. Quantum–classical hybrid algorithms have been proposed in recent years for this purpose under the noisy intermediate-scale quantum computer (NISQ) environment. Under this scheme, the role played by the classical computer is the parameter tuning, parameter optimization, and parameter update for the quantum circuit. In this paper, we propose a gradient descent-based backpropagation algorithm that can efficiently calculate the gradient in parameter optimization and update the parameter for quantum circuit learning, which outperforms the current parameter search algorithms in terms of computing speed while presenting the same or even higher test accuracy. Meanwhile, the proposed theoretical scheme was successfully implemented on the 20-qubit quantum computer of IBM Q, ibmq_johannesburg. The experimental results reveal that the gate error, especially the CNOT gate error, strongly affects the derived gradient accuracy. The regression accuracy performed on the IBM Q becomes lower with the increase in the number of measurement shot times due to the accumulated gate noise error.

Intermediate band solar cell is a type of photovoltaic cell which includes additional narrow band states which allow absorption of low energy below-bandgap photons that might otherwise be transmitted from the host material. Here, the authors report a type of ratchet intermediate band solar cell prepared by doping GaAs with erbium and investigate the underlying energy transfer mechanisms.

We report a hybrid quantum-classical simulation approach for simulating the optical phase transition observed experimentally in the ultrahigh-density type-II InAs quantum dot array. A hybrid simulation scheme, which contains stochastic gradient Langevin dynamics (a well-known Bayesian machine learning algorithm for big data) along with adiabatic quantum annealing, is developed to reproduce the experimentally observed phase transition. By implementing the simulation scheme into a quantum circuit, we successfully verified the phase transition observed in the experiment. Our work demonstrates for the first time the feasibility of hybridizing quantum computation with classical Langevin dynamics for the analysis of carrier dynamics and quantum phase transition of the quantum dot.

Quantifying the model complexity of quantum circuits provides a guide to avoid overfitting in quantum machine learning. Previously we established a Vapnik–Chervonenkis (VC) dimension upper bound for ‘encoding-first’ quantum circuits, where the input layer is the first layer of the circuit. In this work, we prove a general VC dimension upper bound for quantum circuit learning including ‘data re-uploading’ circuits, where the input gates can be single qubit rotations anywhere in the circuit. A linear lower bound is also constructed. The properties of the bounds and approximation-estimation trade-off considerations are discussed.

Quantum computing is suggested as a new tool to deal with large data set for machine learning applications. However, many quantum algorithms are too expensive to fit into the small-scale quantum hardware available today and the loading of big classical data into small quantum memory is still an unsolved obstacle. These difficulties lead to the study of quantum-inspired techniques using classical computation. In this work, we propose a new classification method based on support vectors from a DBSCAN–Deutsch–Jozsa ranking and an Ising prediction model. The proposed algorithm has an advantage over standard classical SVM in the scaling with respect to the number of training data at the training phase. The method can be executed in a pure classical computer and can be accelerated in a hybrid quantum–classical computing environment. We demonstrate the applicability of the proposed algorithm with simulations and theory.

Background & Aims: This was a randomized, double-blind, placebo-controlled study to assess the effects of pemvidutide, a glucagon-like peptide-1 (GLP-1)/glucagon dual receptor agonist, on liver fat content (LFC) in individuals with metabolic dysfunction-associated steatotic liver disease (MASLD)., Methods: Patients with a BMI ≥28.0 kg/m 2 and LFC ≥10% by magnetic resonance imaging-proton density fat fraction were randomized 1:1:1:1 to pemvidutide at 1.2 mg, 1.8 mg, or 2.4 mg, or placebo administered subcutaneously once weekly for 12 weeks. Participants were stratified according to a diagnosis of type 2 diabetes mellitus. The primary efficacy endpoint was relative reduction (%) from baseline in LFC after 12 weeks of treatment., Results: Ninety-four patients were randomized and dosed. Median baseline BMI and LFC across the study population were 36.2 kg/m 2 and 20.6%; 29% of patients had type 2 diabetes mellitus. At week 12, relative reductions in LFC from baseline were 46.6% (95% CI -63.7 to -29.6), 68.5% (95% CI -84.4 to -52.5), and 57.1% (95% CI -76.1 to -38.1) for the pemvidutide 1.2 mg, 1.8 mg, and 2.4 mg groups, respectively, vs. 4.4% (95% CI -20.2 to 11.3) for the placebo group (p <0.001 vs. placebo, all treatment groups), with 94.4% and 72.2% of patients achieving 30% and 50% reductions in LFC and 55.6% achieving normalization (≤5% LFC) at the 1.8 mg dose. Maximal responses for weight loss (-4.3%; p <0.001), alanine aminotransferase (-13.8 IU/L; p = 0.029), and corrected cT1 (-75.9 ms; p = 0.002) were all observed at the 1.8 mg dose. Pemvidutide was well-tolerated at all doses with no severe or serious adverse events., Conclusions: In patients with MASLD, weekly pemvidutide treatment yielded significant reductions in LFC, markers of hepatic inflammation, and body weight compared to placebo., Impact and Implications: Metabolic dysfunction-associated steatotic liver disease, and its progressive form steatohepatitis, are strongly associated with overweight/obesity and it is believed that the excess liver fat associated with obesity is an important driver of these diseases. Glucagon-like peptide-1 receptor (GLP-1R) agonists elicit weight loss through centrally and peripherally mediated effects on appetite. Unlike GLP-1R agonists, glucagon receptor agonists act directly on the liver to stimulate fatty acid oxidation and inhibit lipogenesis, potentially providing a more potent mechanism for liver fat content reduction than weight loss alone. This study demonstrated the ability of once-weekly treatment with pemvidutide, a dual GLP-1R/glucagon receptor agonist, to significantly reduce liver fat content, hepatic inflammatory activity, and body weight, suggesting that pemvidutide may be an effective treatment for both metabolic dysfunction-associated steatohepatitis and obesity., Clinical Trial Number: NCT05006885., (Copyright © 2024 Altimmune Inc. Published by Elsevier B.V. All rights reserved.)

Background: Effect of intensive lifestyle intervention (ILI) on A1C in participants with diabetes is underestimated. A1C improvement is presumed to be dependent on the amount of weight loss. Here, we evaluate the magnitude of A1C change in relation to baseline A1C and the amount of weight loss in participants with diabetes who underwent ILI over 13 years in real-world clinical practice., Methods: A total of 590 participants with diabetes were enrolled in the Weight Achievement and Intensive Treatment (Why WAIT) program, a 12-week multidisciplinary ILI program designed for real-world clinical practice between September 2005 and May 2018. We stratified participants based on baseline A1C into three groups: group A: A1C ≥ 9%, group B: A1C 8 to <9%, and group C: A1C ≥6.5% to <8%., Results: After 12-weeks of intervention, body weight decreased in all groups, and pairwise comparisons of A1C changes showed that: group A had 1.3% greater A1C reduction than group B (p = 0.0001) and 2% greater than group C (p = 0.0001), while group B had 0.7% greater A1C reduction than group C (p = 0.0001)., Conclusion: We conclude that ILI may decrease A1C by up to 2.5% in participants with diabetes. At similar magnitude of weight loss, A1C reduction was more prominent in participants with higher baseline A1C. This may be valuable for clinicians to set a realistic expectation of A1C change in response to ILI., (© 2023 The Authors. Journal of Diabetes published by Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd.)

Introduction: Intensive lifestyle intervention (ILI) has significantly reduced incidence of diabetes and improved many cardiovascular disease risk factors. We evaluated long-term effects of ILI on cardiometabolic risk factors, and microvascular and macrovascular complications among patients with diabetes in real-world clinical practice., Research Design and Methods: We evaluated 129 patients with diabetes and obesity enrolled in a 12-week translational model of ILI. At 1 year, we divided participants into group A, who maintained <7% weight loss (n=61, 47.7%), and group B, who maintained ≥7% weight loss (n=67, 52.3%). We continued to follow them for 10 years., Results: The total cohort lost an average of 10.8±4.6 kg (-9.7%) at 12 weeks and maintained an average weight loss of 7.7±10 kg (-6.9%) at 10 years. Group A maintained 4.3±9.5 kg (-4.3%) and group B maintained 10.8±9.3 kg (-9.3%) of weight loss at 10 years (p<0.001 between groups). In group A, A1c decreased from 7.5±1.3% to 6.7±0.9% at 12 weeks but rebounded to 7.7±1.4% at 1 year and 8.0±1.9% at 10 years. In group B, A1c decreased from 7.4±1.2% to 6.4±0.9% at 12 weeks then increased to 6.8±1.2% at 1 year and 7.3±1.5% at 10 years (p<0.05 between groups). Maintenance of ≥7% weight loss at 1 year was associated with a 68% lower risk of developing nephropathy for up to 10 years compared with maintenance of <7% weight loss (adjusted HR for group B: 0.32, 95% CI 0.11, 0.9, p=0.007)., Conclusions: Weight reduction in patients with diabetes can be maintained for up to 10 years in real-world clinical practice. Sustained weight loss is associated with significantly lower A1c at 10 years and improvement in lipid profile. Maintenance of ≥7% weight loss at 1 year is associated with decreased incidence of diabetic nephropathy at 10 years., Competing Interests: Competing interests: OH receives research support from the National Dairy Council, Eli Lilly and Company, and Novo Nordisk; consults for Merck, Sanofi-Aventis and Abbott Nutrition; is on the advisory board of AstraZeneca; and is a shareholder of Healthimation. None of these entities supported this research in part or total., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: The prevalence of non-alcoholic fatty liver disease (NAFLD) is increasing among patients with type 1 diabetes (T1D) paralleling the increasing prevalence of obesity among this population. However, little is known about the impact of intensive lifestyle intervention (ILI) on NAFLD in patients with T1D., Methods: Using Hepatic Steatosis Index (HSI), a noninvasive surrogate predictor of NAFLD, we retrospectively evaluated 88 adult patients with T1D and obesity after one year of participating in a 12-week ILI program in real-world clinical practice. Using the NAFLD guidelines of the American Association for the Study of Liver Diseases (AASLD), we excluded 11 participants. We matched the remaining ILI cohort (age 43 ± 12 years, females 65%, diabetes duration 22 ± 9 years, A1C 8.2 ± 0.9%, body weight 101 ± 17 kg, BMI 35.3 ± 4.9 kg/m 2 ) in 1:1 ratio with a similar cohort of patients with T1D and obesity who received standard diabetes care (SC) at the same practice and during the same period. Matching criteria included: sex, age, BMI, A1C and duration of T1D. HSI [8 + ALT/AST + BMI (+ 2 if female, + 2 if T2D)] was calculated at baseline and after 12 months of intervention., Results: At baseline, HSI was similar between the two cohorts (46.2 ± 6.1 in the ILI cohort and 44.9 ± 5.7 in the SC cohort). After 12 months, the ILI group lost an average of 5.6 ± 2.7 kg (5.8%, p < 0.05) while the SC group maintained their baseline body weight (p < 0.001 between groups). HSI decreased significantly from baseline in the ILI group (-2.7 ± 1.1, p = 0.01), but did not change in the SC group (0.6 ± 0.9, p = 0.53, p < 0.001 between groups). Percentage of patients with high likelihood of NAFLD diagnosis decreased from 100% at baseline to 88.3% in the ILI group, and was 10.4% less compared to SC (p < 0.01). Total daily insulin dose decreased in the ILI cohort compared to the SC cohort (-6.1 ± 4.2 versus 1.34 ± 4.3 units/day, p < 0.01)., Conclusions: Twelve weeks of ILI improved HSI and decreased total daily insulin requirements in patients with T1D and obesity at one year. Short-term ILI should be implemented in the management of NAFLD for obese patients with type 1 diabetes., (© 2023. The Author(s).)

Background: Intensive lifestyle intervention (ILI) is essential for diabetes management. The Weight Achievement and Intensive Treatment (Why WAIT) program is a 12-week multidisciplinary weight management program that has been implemented in real-world clinical practice since 2005 and has shown long-term maintenance of weight reduction for 5 and 10 years. During the COVID-19 pandemic, the program went virtual using telemedicine and mobile health applications., Aims: This retrospective pilot study aims to evaluate the effectiveness of a virtual model of an already established and successful in-person program for diabetes and weight management since 2005., Methods: We evaluated 38 patients with diabetes and obesity enrolled in the Why WAIT program between February 2019 and December 2020. Sixteen participants were enrolled in virtual program (VP) and were compared with 22 participants who completed the latest two physical programs (PPs) before COVID-19. We evaluated changes in body weight, A1C, blood pressure (BP), and lipid profile after 12 weeks of ILI., Results: Body weight decreased by -7.4 ± 3.6 kg from baseline in VP compared with -6.8 ± 3.5 kg in PP ( p  = 0.6 between groups). A1C decreased by -1.03% ± 1.1% from baseline in VP, and by -1.0% ± 1.2% in PP ( p  = 0.9 between groups). BP, lipid profile, and all other parameters improved in both groups with no significant difference between them., Conclusion: Virtual multidisciplinary ILI is as effective as the in-person intervention program in improving body weight, A1C, BP, and lipid profile, and in reducing the number of anti-hyperglycemic medications. Results from our study suggest that scaling the Why WAIT program in a virtual format to a larger population of patients with diabetes and obesity is feasible and is potentially as successful as the in-person program., Competing Interests: Conflict of interest statement: The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: M.A.-B., C.L.K., J.I.S., M.S., A.P.-S., J.B., C.D., H.G., J.-A.R., and S.D. have nothing to disclose. S.T. reports consulting for Research America; shareholder of Amarin corp. O.H. is consultant to Abbott Nutrition, Sanofi Aventis, AstraZeneca; his employer Joslin Diabetes Center receives research grants from Novo-Nordisk, Eli-Lilly, Gilead, and National Dairy Council. On scientific advisory board of Twin and L-Neutra and stock holder of Healthimation., (© The Author(s), 2022.)

Objective: Limited literature has examined the epidemiology of non-alcoholic fatty liver disease (NAFLD) and fibrosis among young adults in Egypt, a country with one of the highest obesity rates globally. We assessed the prevalence of steatosis and fibrosis among college students in Egypt., Design: In this cross-sectional study, we recruited students unaware of having fatty liver via a call-for-participation at a private university in the Dakahlia governorate of Egypt. Primary outcomes were the prevalence of steatosis as determined by the controlled attenuation parameter component of transient elastography and fibrosis as determined by the liver stiffness measurement component of transient elastography. Secondary outcomes were clinical parameters and socioeconomic factors associated with the presence and severity of steatosis and fibrosis., Results: Of 132 participants evaluated for the study, 120 (91%) were included (median (IQR) age, 20 (19-21) years; 65 (54.2%) female). A total of 38 participants (31.6%) had steatosis, among whom 22 (57.9%) had S3 (severe) steatosis. There was a higher risk for steatosis in persons with overweight (adjusted OR 9.67, 95% CI (2.94 to 31.7, p<0.0001) and obesity (adjusted OR 13.87, 95% CI 4.41 to 43.6, p<0.0001) compared with lean persons. Moreover, higher level of parental education was associated with progressing steatosis stages (S1-S3). Six (5%) participants had transient elastography values equivalent to F2-F3 fibrosis (four with F2 fibrosis (≥7.9 kPa), and two with F3 fibrosis (≥8.8 kPa))., Conclusion: In this cohort of college students in Egypt, around 1 in 3 had steatosis, and 1 in 20 had moderate-to-advanced fibrosis, an established risk factor for hepatic and extrahepatic morbidity and mortality. These data underscore the urgency to address the silent epidemic of NAFLD among young adults in the Middle East-North Africa region., Competing Interests: Competing interests: ST reports consulting for Research America and stock ownership in Amarin corp. outside the submitted work. NA is on advisory board/review panel for 89Bio, Echosens, Fibronostics, Gilead, Intercept, Novo Nordisk, Perspectum, Pfizer and Zydus, received grant/research support from 89Bio, Akero, Bristol Myers Squibb, Galectin, Genentech, Gilead, Intercept, Ionis, Madrigal, Metacrine, NGM Bio, Novo Nordisk, Pfizer, Viking and Zydus, and is speaker for AbbVie, Alexion, Echosens, Gilead, and Intercept. OH reports consultation to Abbott Nutrition, Merck Serono and Sanofi Aventis, received research support from Novo-Nordisk, Eli Lilly, National Dairy Council, and Gilead, and is on the advisory board of L-Nutra and Twin. He is also shareholder of Heathimation Inc. MA, AH, MA-B, HG, AE and EAE have no disclosures relevant to this work., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Most nutrition studies looking at the association of food with cardiometabolic markers rely on food frequency questionnaires, which are prone to recall bias. Pentadecanoic acid, heptadecanoic acid and trans-palmitoleic acid are fatty acids that are not synthesized endogenously but are obtained from the diet, particularly dairy, making them reasonable biomarkers of dairy consumption. We investigated the association of dairy fatty acid biomarkers with glycated hemoglobin (HbA1c) and cardiovascular risk factors in type 2 diabetes (T2D). In a clinical trial, 111 participants with T2D (age 58.5 ± 8.9 years, HbA1c 8.09 ± 0.96%) were randomized into three groups: a control group that maintained baseline dairy intake, a low-fat (LF) group that incorporated ≥3 servings/day of LF dairy and a high-fat (HF) group that incorporated ≥3 servings/day of HF dairy. We compared the fatty acids (FA) composition between the three groups at 24 weeks. Pentadecanoic acid and trans-palmitoleic acid increased in the HF group by 14.1% ± 5.4% and 17.5% ± 5.1%, respectively, but not in the control and LF groups ( p = 0.0474 and p = 0.0025 for group-by-time interaction, respectively). Those increases were positively associated with changes in total cholesterol, very-low-density lipoprotein cholesterol VLDL-C and triglycerides but were not associated with changes in HbA1c from baseline to 24 weeks. These results suggest that the intervention was successful and that participants were likely compliant, which supports the validity of the main trial.

Background: Dietary Guidelines for Americans recommend the consumption of 3 servings/d of low-fat/nonfat dairy. The effects of higher dairy consumption and its fat content are unknown in patients with type 2 diabetes., Objective: Evaluate the impact of higher consumption of high- compared with low-fat dairy on glycated hemoglobin (HbA1c), body weight, and cardiovascular disease risk factors in patients with type 2 diabetes., Methods: We enrolled 111 subjects with type 2 diabetes (aged 58.5 ± 8.9 y, 47% females, diabetes duration 13.2 ± 8.3 y, HbA1c 8.09 ± 0.96%) who consumed <3 servings of dairy/d. We randomly assigned them into 3 groups: control group maintained baseline dairy intake, low-fat (LF) group incorporated ≥3 servings/d of LF dairy, and the high-fat (HF) group incorporated ≥3 servings/d of HF dairy. We evaluated HbA1c, body weight, BMI, body composition parameters, blood pressure (BP), lipid parameters, homeostatic model assessment of insulin resistance (HOMA-IR), and total energy and macronutrient intake at baseline, and after 12 and 24 wk., Results: At 24 wk, percent energy from saturated fat increased from baseline in the HF group by 3.6%, (95% CI: 2.2, 5.1) and decreased in the LF group by -1.9% (95% CI: -3.3, -0.4). The LF group increased their percent energy from protein by 4.5% (95% CI: 2.6, 6.4), whereas the HF group decreased their percent energy from carbohydrates by -3.4% (95% CI: -0.2, -6.7). There were no differences in the mean changes in HbA1c, body weight, BMI, body composition or lipid parameters, or BP between the 3 groups at 24 wk., Conclusion: In patients with type 2 diabetes, increased dairy consumption to ≥3 servings/d compared with <3 servings/d, irrespective of its fat content, while maintaining energy intake has no effect on HbA1c, body weight, body composition, lipid profile, or BP. This trial was registered at clinicaltrials.gov as NCT02895867., (Copyright © The Author(s) on behalf of the American Society for Nutrition 2020.)

Background: Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide. The increasing prevalence of NAFLD mirrors that of obesity and type 2 diabetes over the last two decades., Main: In a two-way pathophysiologic relationship, NAFLD increases the risk of developing type 2 diabetes, while the latter promotes the progression of simple fatty liver to a more advanced form called nonalcoholic steatohepatitis (NASH). NASH increases the risk of cirrhosis and hepatocellular carcinoma (HCC), which may require liver transplantation. With the absence of FDA-approved medications for NAFLD treatment, lifestyle intervention remains the only therapy. Lately, extensive research efforts have been aimed at modifying NASH fibrosis and developing noninvasive screening methods., Conclusion: We highlight the pathophysiologic relationships between NAFLD and type 2 diabetes, discuss disease recognition, models of care, and current and emerging therapies for NASH treatment., Competing Interests: Competing interestsS.T. has nothing to disclose. N.A. advises, is on the speakers’ bureau for, and received grants from Gilead and Intercept. He advises and received grants from Allergan. He received grants from GENFIT, Madrigal, and Galmed. O.H. reports consultation to Abbott Nutrition, Gilead Inc. and Merck Sorono, grants from National Dairy Council, and own shares in Heathimation Inc. outside the submitted work., (© The Author(s) 2020.)

Dairy products are integral parts of healthy diets; however, their association with cardiometabolic (CM) health among patients with type 2 diabetes (T2D) undergoing weight management is debated. We examined the relationship between dairy consumption and CM biomarkers in 45 subjects with T2D and obesity (mean age 56 ± 9 yrs, 40% female) enrolled in a 12-week intensive multidisciplinary weight management (IMWM) program. After the IMWM program (intervention phase), subjects were followed for 12 weeks (maintenance phase). We stratified subjects based on initial average dairy consumption into infrequent (IFR), less-frequent (LFR), and frequent (FR) consumers. Outcomes were assessed at baseline, 12, and 24 weeks. There were no differences between tertiles at baseline except for higher total energy intake among FR compared with IFR. HbA1c changes showed no association with dairy consumption at 12 or 24 weeks. FR Females achieved greater weight loss at 12 weeks compared with IFR peers (-4.5 kg; 95%CI: -5.5, -3.5). There was a trend towards lower HDL-C with increasing dairy consumption during the intervention phase. In subjects with T2D and overweight or obesity, dairy consumption during weight management is not associated with HbA1c changes but with lower HDL-C and with higher magnitude of weight loss among females.

Objectives: Diabetes-specific nutritional formulas (DSNFs) are frequently used by patients with type 2 diabetes (T2D) as part of nutrition therapy to improve glycemic control and reduce body weight. However, their effects on hunger and satiety hormones when compared to an isocaloric standardized breakfast are not fully understood. This study aims to evaluate the postprandial effects of two DSNFs-Glucerna (GL) and Ultra Glucose Control (UGC)-versus oatmeal on selected satiety and hunger hormones., Method: After an overnight fast, 22 patients with T2D (mean age 62.3 ± 6.8 years, A1C 6.8 ± 0.7%, body weight 97.4 ± 21.3 kg, and BMI 33.2 ± 5.9 kg/m²) were given 200 kcal of each meal on three separate days. Blood samples for amylin, cholecystokinin (CCK), ghrelin, glucagon, leptin, and peptide-YY (PYY) were collected at baseline and 30, 60, 90, 120, 180, and 240 min after the start of each meal. Incremental area under the curve (iAUC 0-240 ) for each hormone was calculated., Results: iAUC 0-240 for glucagon and PYY were significantly higher after GL and UGC than after oatmeal (p < 0.001 for both). No difference was observed between the three meals on postprandial amylin, CCK, ghrelin, and leptin hormones., Conclusions: Intake of DSNFs significantly increases secretion of PYY and glucagon, two important satiety hormones. While subjective satiety was not directly evaluated, the increased effect on satiety hormones may partially explain the mechanism of body weight loss associated with DSNF use.

Objective: We evaluated the relationship between frequency of self-monitoring of blood glucose (SMBG) and body weight, A1C, and cardiovascular risk factors in patients with type 2 diabetes (T2D) and obesity enrolled in a 12-week intensive multidisciplinary weight management (IMWM) program., Research Design and Methods: We conducted a retrospective analysis of 42 patients who electronically uploaded their SMBG data over 12 weeks of an IMWM program and divided them into tertiles based on their average frequency of SMBG per day. Mean (range) SMBG frequencies were 2.3 (1.1-2.9) times/day, 3.4 (3-3.9) times/day, and 5 (4-7.7) times/day in the lowest, middle, and highest tertiles, respectively. Anthropometric and metabolic parameters were measured at baseline and after 12 weeks of intervention., Results: Participants in the highest tertile achieved a median change (IQR) in body weight of -10.4 kg (-7.6 to -14.4 kg) compared with -8.3 kg (-5.2 to -12.2 kg), and -6.9 kg (-4.2 to -8.9 kg) in the middle and lowest tertiles, respectively (p=0.018 for trend). Participants in the highest tertile had a median change (IQR) in A1C of -1.25% (-0.6 to -3.1%) compared with -0.8% (-0.3% to -2%) and -0.5% (-0.2% to -1.2%) in the middle and lowest tertiles, respectively (p=0.048 for trend). The association between change in body weight and SMBG frequency remained significant after adjusting for age, sex, baseline body mass index, diabetes duration, and use of insulin therapy., Conclusions: Increased frequency of SMBG during IMWM is associated with significantly better weight loss and improvement of A1C in patients with T2D and obesity. These findings may suggest future clinical recommendations aimed at increasing SMBG frequency to achieve the most favorable outcomes., Competing Interests: Competing interests: OH receives research support from the National Dairy Council; consults for Merck, Sanofi-Aventis and Abbott Nutrition; on the advisory board of Astra Zeneca and is a shareholder of Healthimation. ST is shareholder of Amarin. None of these entities supported this research in part or total.

Aims: Recent studies report that approximately 50% of patients with type 1 diabetes (T1D) are overweight or obese. This work studies the effects of intensive multidisciplinary weight management (IMWM) in patients with T1D and obesity., Methods: We retrospectively evaluated 68 patients with T1D and obesity who enrolled in a 12-week IMWM program (IMWM cohort: mean age, 42 ± 11 years; HbA1c, 8.3% ± 1.0%; body weight, 104.3 ± 18.2 kg; BMI, 36.2 ± 4.9 kg/m 2 ). We matched them 1:1 with a similar cohort of patients receiving standard care (SC cohort: mean age, 42 ± 12 years; HbA1c, 8.3% ± 1.0%; body weight, 102.4 ± 17.9 kg; BMI, 36.1 ± 4.7 kg/m 2 ). Data were collected at baseline and at 12 months., Results: Participants in the IMWM cohort had a body weight change of -6.6 ± 1.8 kg or -6.4% ± 1.6% of their initial body weight, while participants in the SC cohort had no change (P < 0.01 for group*time interaction). Participants in the IMWM cohort had a change in HbA1c of -0.4% ± 0.1% from baseline (P < 0.01), while participants in the SC cohort had no change. There was no difference in glycaemic control between cohorts at 12 months. Total daily insulin dose changed by -5.9 ± 1.8 units/d from baseline in the IMWM cohort while there was no change in the SC cohort (P < 0.01 for group*time interaction)., Conclusions: In comparison to standard care, patients with T1D and obesity who participated in an IMWM programme achieved significant weight loss and significant reduction in daily insulin dose at 1 year. Weight reduction was associated with improvements in glycaemic control compared to baseline., (© 2018 John Wiley & Sons Ltd.)

Purpose of Review: The prevalence of combined obesity and diabetes has increased dramatically in the last few decades. Although medical and surgical weight management are variably effective in addressing this epidemic, it is essential to parallel these strategies with a hypocaloric diet comprising the appropriate macronutrient composition to induce weight loss, enhance glycemic control, and improve cardiovascular risk factors. This review reports the current evidence of the role of carbohydrates and fat-based diets for weight management in patients with combined type 2 diabetes (T2D) and obesity., Recent Findings: Low-carbohydrate diets were shown to decrease postprandial glucose levels whereas high-carbohydrate, low-fat diets are considered cardio-protective. A diet with an optimal macronutrient composition remains uncertain for patients with combined T2D and obesity. Further research is still needed to define the best dietary composition that achieves the maximum benefits on weight management, glycemic control, and cardiovascular risk factors.