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Christoph Rosak,1 Gabriele Mertes21Specialist for Internal Medicine, Endocrinology and Diabetes, Sachsenhausen Hospital, Frankfurt/Main, Germany; 2Retired, Mülheim, GermanyAbstract: The alpha-glucosidase inhibitor acarbose has been used for more than 20 years in the management of hyperglycemia. Owing to its unique mode of action in the gastrointestinal tract, its properties are very different from other antidiabetic medications. Patients on long-term treatment to control a chronic disease are not only interested in good treatment efficacy, but are also even more interested in the safety and side effects of their medications. Significant aspects of acarbose predominantly regarding safety and tolerability in the management of type 2 diabetes and prediabetes are reviewed. It is concluded that acarbose is a convenient long-term treatment option, with benefits for both type 2 diabetics and patients in a prediabetic state.Keywords: acarbose, prediabetes, type 2 diabetes, patient considerations, side effects

Objective: To investigate the effect of meal-related self-monitoring of blood glucose on glycemic control and well-being in non-insulin-treated type 2 diabetic patients.Research Design and Methods: This 6-month study, which included 6 months of follow-up, adopted a prospective, multicenter, randomized controlled design. Subjects were randomized to two groups: one group used a blood glucose-monitoring device, kept a blood glucose/eating diary, and received standardized counseling; the control group received nonstandardized counseling on diet and lifestyle. The primary efficacy parameter was the change in HbA(1c). Secondary efficacy variables included changes in body weight, lipids, and microalbumin and changes in treatment satisfaction and well-being.Results: In the per-protocol analysis, the use of a self-monitoring blood glucose device significantly reduced HbA(1c) levels by 1.0 +/- 1.08% compared with 0.54 +/- 1.41% for the control group (P = 0.0086); subgroup analysis showed three types of responders. Body weight, total cholesterol, and microalbumin improved when using a glucometer, but there was no statistically significant difference between the two groups. Treatment satisfaction increased in both groups to a similar extent (P = 0.9). Self-monitoring resulted in a marked improvement of general well-being with significant improvements in the subitems depression (P = 0.032) and lack of well-being (P = 0.02).Conclusions: Meal-related self-monitoring of blood glucose within a structured counseling program improved glycemic control in the majority of non-insulin-treated type 2 diabetic patients in this study. The finding of three types of responders will be important for future planning of counseling and educational interventions. [ABSTRACT FROM AUTHOR]

Purpose: Understanding variation in multimorbidity across sociodemographics and social drivers of health is critical to reducing health inequities., Methods: From the multi-state OCHIN network of community-based health centers (CBHCs), we identified a cross-sectional cohort of adult (> 25 years old) patients who had a visit between 2019-2021. We used generalized linear models to examine the relationship between the Multimorbidity Weighted Index (MWI) and sociodemographics and social drivers of health (Area Deprivation Index [ADI] and social risks [e.g., food insecurity]). Each model included an interaction term between the primary predictor and age to examine if certain groups had a higher MWI at younger ages., Results: Among 642,730 patients, 28.2% were Hispanic/Latino, 42.8% were male, and the median age was 48. The median MWI was 2.05 (IQR: 0.34, 4.87) and was higher for adults over the age of 40 and American Indians and Alaska Natives. The regression model revealed a higher MWI at younger ages for patients living in areas of higher deprivation. Additionally, patients with social risks had a higher MWI (3.16; IQR: 1.33, 6.65) than those without (2.13; IQR: 0.34, 4.89) and the interaction between age and social risk suggested a higher MWI at younger ages., Conclusions: Greater multimorbidity at younger ages and among those with social risks and living in areas of deprivation shows possible mechanisms for the premature aging and disability often seen in community-based health centers and highlights the need for comprehensive approaches to improving the health of vulnerable populations., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

Digital measures of health status captured during daily life could greatly augment current in-clinic assessments for rheumatoid arthritis (RA), to enable better assessment of disease progression and impact. This work presents results from weaRAble-PRO, a 14-day observational study, which aimed to investigate how digital health technologies (DHT), such as smartphones and wearables, could augment patient reported outcomes (PRO) to determine RA status and severity in a study of 30 moderate-to-severe RA patients, compared to 30 matched healthy controls (HC). Sensor-based measures of health status, mobility, dexterity, fatigue, and other RA specific symptoms were extracted from daily iPhone guided tests (GT), as well as actigraphy and heart rate sensor data, which was passively recorded from patients' Apple smartwatch continuously over the study duration. We subsequently developed a machine learning (ML) framework to distinguish RA status and to estimate RA severity. It was found that daily wearable sensor-outcomes robustly distinguished RA from HC participants (F1, 0.807). Furthermore, by day 7 of the study (half-way), a sufficient volume of data had been collected to reliably capture the characteristics of RA participants. In addition, we observed that the detection of RA severity levels could be improved by augmenting standard patient reported outcomes with sensor-based features (F1, 0.833) in comparison to using PRO assessments alone (F1, 0.759), and that the combination of modalities could reliability measure continuous RA severity, as determined by the clinician-assessed RAPID-3 score at baseline (r 2 , 0.692; RMSE, 1.33). The ability to measure the impact of the disease during daily life-through objective and remote digital outcomes-paves the way forward to enable the development of more patient-centric and personalised measurements for use in RA clinical trials., (© 2024. The Author(s).)

Objective: Heart failure, respiratory failure and kidney failure are three severe organ failures (OF) that have high mortalities and are most prevalent in intensive care units. The objective of this work is to offer insights into OF clustering from the aspects of graph neural networks and diagnosis history., Methods: This paper proposes a neural network-based pipeline to cluster three types of organ failure patients by incorporating embedding pre-train using an ontology graph of the International Classification of Diseases (ICD) codes. We employ an autoencoder-based deep clustering architecture jointly trained with a K-means loss, and a non-linear dimension reduction is performed to obtain patient clusters on the MIMIC-III dataset., Results: The clustering pipeline shows superior performance on a public-domain image dataset. On the MIMIC-III dataset, it discovers two distinct clusters that exhibit different comorbidity spectra which can be related to the severity of diseases. The proposed pipeline is compared with several other clustering models and shows superiority., Conclusion: Our proposed pipeline gives stable clusters, however, they do not correspond to the type of OF which indicates these OF share significant hidden characteristics in diagnosis. These clusters can be used to signal possible complications and severity of illness and aid personalised treatment., Significance: We are the first to apply an unsupervised approach to offer insights from a biomedical engineering perspective on these three types of organ failure, and publish the pre-trained embeddings for future transfer learning.

Fall incidents are an important health hazard for older adults. Automatic fall detection systems can reduce the consequences of a fall incident by assuring that timely aid is given. The development of these systems is therefore getting a lot of research attention. Real-life data which can help evaluate the results of this research is however sparse. Moreover, research groups that have this type of data are not at liberty to share it. Most research groups thus use simulated datasets. These simulation datasets, however, often do not incorporate the challenges the fall detection system will face when implemented in real-life. In this Letter, a more realistic simulation dataset is presented to fill this gap between real-life data and currently available datasets. It was recorded while re-enacting real-life falls recorded during previous studies. It incorporates the challenges faced by fall detection algorithms in real life. A fall detection algorithm from Debard et al. was evaluated on this dataset. This evaluation showed that the dataset possesses extra challenges compared with other publicly available datasets. In this Letter, the dataset is discussed as well as the results of this preliminary evaluation of the fall detection algorithm. The dataset can be downloaded from www.kuleuven.be/advise/datasets.