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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: Step count is an intuitive measure of physical activity frequently quantified in health-related studies; however, accurate step counting is difficult in the free-living environment, with error routinely above 20% in wrist-worn devices against camera-annotated ground truth. This study aimed to describe the development and validation of step count derived from a wrist-worn accelerometer and assess its association with cardiovascular and all-cause mortality in a large prospective cohort., Methods: We developed and externally validated a self-supervised machine learning step detection model, trained on an open-source and step-annotated free-living dataset. Thirty-nine individuals will free-living ground-truth annotated step counts were used for model development. An open-source dataset with 30 individuals was used for external validation. Epidemiological analysis was performed using 75,263 UK Biobank participants without prevalent cardiovascular disease (CVD) or cancer. Cox regression was used to test the association of daily step count with fatal CVD and all-cause mortality after adjustment for potential confounders., Results: The algorithm substantially outperformed reference models (free-living mean absolute percent error of 12.5% vs 65%-231%). Our data indicate an inverse dose-response association, where taking 6430-8277 daily steps was associated with 37% (25%-48%) and 28% (20%-35%) lower risk of fatal CVD and all-cause mortality up to 7 yr later, compared with those taking fewer steps each day., Conclusions: We have developed an open and transparent method that markedly improves the measurement of steps in large-scale wrist-worn accelerometer datasets. The application of this method demonstrated expected associations with CVD and all-cause mortality, indicating excellent face validity. This reinforces public health messaging for increasing physical activity and can help lay the groundwork for the inclusion of target step counts in future public health guidelines., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American College of Sports Medicine.)

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.

Food intake monitoring can play an important role in the prevention of malnutrition in the aging population, but traditional tools may not be adequate for use in this target group. These tools typically involve the use of questionnaires or food diaries that require manual data entry. Due to their time-consuming nature, they are often incomplete, contain mistakes, or not used at all. An alternative to self-reporting tools, in the form of a plate system that automatically measures the consumed food during the meal, is presented in this paper. Furthermore, the system can estimate the location where each bite was taken on the plate. The system is compatible with an off-the-shelf plate that is mounted on top of a base station. Weight sensors are integrated in the base, allowing for easy removal and cleaning of the plate. Localization of bites is done by looking at the movement of the center of mass during eating. When used with a compartmentalized plate, the amount of consumed food per compartment can be measured. With prior knowledge of the type of food in each compartment, this can give an indication of calories and nutritional intake. We present a bite detection algorithm using a random forest decision tree classifier. Data from 24 aging adults (ages 52-95) eating a single meal with chopsticks was used to train and evaluate the model. Out of a total of 836 true annotated bites, the algorithm detected 602 with a precision and recall of 0.78 and 0.76, respectively. By summing the weights of detected bites from each compartment, the algorithm was able to estimate the amount of food taken per compartment with an average error of (8 ±8)% of the portion size.

In this work, a novel plate system that can detect weight and location of individual bites during meals is presented. The system consists of a base station with sensors and a detachable off-the-shelf polymer plate with three com- partments. By combining data from multiple weight sensors, the weight of individual bites can be accurately measured and localized on the plate to determine the compartment from which they were taken. With prior knowledge of the weight of the food in each compartment at the start of the meal, the system can estimate the nutritional value of the consumed food. In a test conducted in a controlled home environment, the system was able to measure the weight of consumed food in each compartment with a maximum relative error of 1.4%. The goal of the system is to replace traditional monitoring tools and to automatically monitor the amount of consumption.

Fall incidents remain an important health hazard for older adults. Fall detection systems can reduce the consequences of a fall incident by insuring that timely aid is given. Currently fall detection algorithms however suffer a reduction in accuracy when introduced in real-life situations. In this paper a late fusion technique is proposed that will improve the accuracy of existing fall detection systems. It combines the confidence levels of different single camera fall detection systems. Four different aggregation methods are compared to each other based on the Area Under the Curve (AUC) of precision-recall curves. Calculating the median of the confidence levels of five cameras an increase of 218% in the AUC of the precision-recall-curves is achieved compared to the AUC of the single camera fall detector. These results show that significant improvements can be made to the accuracy of single camera fall detectors in a relatively easy way.

This paper describes a method of detecting an elderly person's chewing motion using a glasses mounted accelerometer. A real-life dataset was collected from 13 elderly adults, aged 65 or older, during meal times in a care facility. A supervised classifier is used to automatically distinguish between epochs of chewing and non-chewing activity. Results are compared to a lab dataset of 5 young to middle-aged adults captured in previous work. K-Nearest Neighbor, Random Forest and Support Vector Machine classifiers are evaluated. All are able to achieve similar performance, with the Support Vector Machine performing the best with an F1-score of 0.73.

Background: Acarbose, an alpha-glucosidase inhibitor, unexpectedly reduced the incidence of hypertension and cardiovascular endpoints in the STOP-NIDDM study. Based on the growing evidence of a link between vasoregulatory peptides and metabolic traits, we hypothesized that changes of the Glycemic Index by acarbose may modulate vasoregulatory peptide levels via regulation of postprandial metabolism., Methods: Subjects with type 2 diabetes and with metabolic syndrome were treated with acarbose (12 weeks, 300mg/d) in a double-blind, placebo-controlled, cross-over intervention. Changes in fasting and postprandial levels of midregional pro-atrial natriuretic peptide (MR-proANP), C-terminal pro-endothelin-1 (CT-proET-1) and midregional pro-adrenomedullin (MR-proADM), WNT1 Inducible Signaling Pathway Protein 1 (WISP1) as well as fasting and postprandial glucose/insulin levels in the liquid meal test were assessed., Results: Acarbose strongly decreased postprandial insulin concentrations in subjects with metabolic syndrome (P=0.004), and postprandial glucose excursions in both groups. Postprandial MR-proANP and CT-proET-1 levels increased after acarbose treatment (P<0.01 and P<0.05, respectively) in subjects with metabolic syndrome only. No effect of acarbose treatment on MR-prADM was observed in both groups. All three peptides were correlated with each over, but neither with insulin sensitivity in euglycemic clamps, nor with adiponectin levels. WISP1 decreased after acarbose treatment in subjects with metabolic syndrome., Conclusions: Plasma MR- proANP and CT-proET-1 concentrations, but not MR-prADM concentrations, were affected by treatment with acarbose over 12 weeks. Our findings provide new possible mechanisms of acarbose action in diabetes and metabolic syndrome.

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.

The alpha-glucosidase inhibitor acarbose is administered to control blood glucose levels in type 2 diabetic patients and, in several countries, in those with impaired glucose tolerance. The efficacy and safety of the drug has been well established in these patient populations. Acarbose shows no weakening of efficacy in long-term diabetes treatment, reduces the development of type 2 diabetes in those with impaired glucose tolerance, and also appears to reduce the risk of cardiovascular disease. The underlying mechanisms of its effect on the risk of developing macrovascular complications have still to be elucidated. The mode of action of acarbose, which precedes all other metabolic processes involved in blood glucose regulation, inhibits high increases in postprandial blood glucose. Due to this early mode of action, acarbose also modifies insulin and proinsulin secretion which are both involved in ss-cell dysfunction and insulin resistance and may be independent risk factors for cardiovascular mortality. Based on the literature available the present state of knowledge on insulin and proinsulin as risk factors for cardiovascular mortality is reviewed as well as the effect of acarbose on the regulation of the ss-cells as monotherapy and in combination regimens. Possible associated interactions with the cardiovascular system are identified.

This 24-months, placebo-controlled, double-blind, randomised, group comparison study investigated the effect of acarbose vs placebo for improving metabolic control in patients with Type 2 diabetes under dietary training insufficiently controlled by diet alone. Patients randomised to acarbose had their dose increased in a stepwise manner to week 5. From week 5 onwards, they received 100 mg three times daily. This incremental dosing scheme was matched in the placebo group. All patients received specialist, intensive, continuous dietary training and counselling throughout the 2 yr of the study. Of the 74 patients randomised, 60 were included in the per-protocol analysis (28 receiving acarbose; 32 receiving placebo). HbA1c was the primary target variable. Per-protocol analysis found that, after 24 months, the mean difference in HbA1c relative to baseline value was -1.71+/-1.6% in the acarbose group and -0.82+/-1.1% in the placebo group. End-point values were 6.85+/-1.7% in the acarbose group and 7.41+/-1.1% in the placebo group. This difference between acarbose and placebo was statistically significant (p=0.02). Patients were defined as responders if they did not require additional treatment with an antidiabetic agent during the study. The responder rate under acarbose therapy was 89%, compared with 47% for placebo (p=0.0005). Acarbose-treated responders improved their HbA1c level to 6.45+/-0.82% after 24 months. The efficacy of acarbose was consistent throughout the study; decreasing efficacy was not evident. The results demonstrate the efficacy of acarbose for improving metabolic control in patients with Type 2 diabetes, even when such patients receive good dietary treatment and counselling.