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Background: Dysphagia has been reported as an adverse event for patients receiving rivastigmine for Alzheimer’s disease (AD) treatment. Objective: The purpose of this study was to determine the association between dysphagia and the usage of rivastigmine by using the pharmacovigilance data from the FDA Adverse Event Reporting System (FAERS). Methods: The risk of dysphagia in patients who took rivastigmine was compared with those of patients who took other medications. In addition, this study sought to determine if the dysphagia risk was influenced by sex, age, dosage, and medication routes of administration. Results: When compared to patients prescribed donepezil, galantamine, or memantine, individuals prescribed rivastigmine were almost twice as likely to report dysphagia as an adverse event. The dysphagia risk in individuals prescribed rivastigmine is comparable to individuals prescribed penicillamine but significantly higher than clozapine, drugs of which have been previously shown to be associated with elevated dysphagia likelihood. Individuals older than 80 were 122% more likely to report having dysphagia after being prescribed rivastigmine than patients that were 50–70 years of age. Oral administration of rivastigmine was associated with approximately 2 times greater likelihood of reporting dysphagia relative to users of the transdermal patch. In addition, dysphagia showed higher association with pneumonia than other commonly reported adverse events. Conclusion: Patients prescribed rivastigmine were at greater risk of reporting dysphagia as an adverse event than patients prescribed many other medicines. This increase in dysphagia occurrence may be attributed to the dual inhibition of both acetylcholinesterase and butyrylcholinesterase.

Objective. Previous studies have shown that some metabolic risk factors are related to nonalcoholic fatty liver disease (NAFLD). This retrospective study was performed to investigate the associations between physical examinations and blood biochemistry parameters and NAFLD status and to identify possible risk factors of NAFLD. Methods. Study participants underwent general physical examinations, blood biochemistry, and abdominal ultrasound evaluations. In addition, data regarding sex, age, ethnicity, medical history, and alcohol consumption of participants were recorded. Among the study participants (N=1994), 57.8% were male, 41.2% over the age of 50, and 52.6% with BMI≥24. 986 patients had NAFLD and 1008 had no NAFLD. We used effect size analysis and logistic regression to determine which physical examinations and blood biochemistry parameters were significant for the association between these parameters and NAFLD status. Results. Both the effect size and logistic regression indicated that BMI, diastolic blood pressure (DBP), triglycerides (TG), and serum uric acid (SUA) show a significant association with NAFLD. Females are overall at a higher risk of NAFLD, but factors such as high BMI, DBP, TG, and SUA increase the associated risk for both sexes. Compared with males, females have a higher risk of NAFLD given that they are over 50, overweight and obese (BMI at or over 24), or have high SUA. In terms of age, people older than 50 with high SUA, and people younger than 50 with high DBP and low-density lipoprotein cholesterol (LDL-C) all increase the risk of NAFLD. For BMI, high DBP and low high-density lipoprotein cholesterol (HDL-C) are risk factors for NAFLD in overweight and obese people (BMI at or over 24), whereas in normal weight and underweight people (BMI under 24), elevated LDL-C increases the risk of NAFLD. Conclusions. Our results revealed sex, age, and BMI modulate the association of physical examinations and blood biochemistry parameters and NAFLD, which may facilitate the development of personalized early warning and prevention strategies of NAFLD for at-risk populations.

BACKGROUND We should pay more attention to the long-term monitoring and early warning of type 2 diabetes and its complications. The traditional blood glucose tests are traumatic and cannot effectively monitor the development of diabetic complications. The development of mobile health is changing rapidly. Therefore, we are interested in developing a new noninvasive, economical, and instant-result method to accurately diagnose and monitor type 2 diabetes and its complications. OBJECTIVE We aimed to determine whether type 2 diabetes and its complications, including hypertension and hyperlipidemia, could be diagnosed and monitored by using pulse wave. METHODS We collected the pulse wave parameters from 50 healthy people, 139 diabetic patients without hypertension and hyperlipidemia, 133 diabetic patients with hypertension, 70 diabetic patients with hyperlipidemia, and 75 diabetic patients with hypertension and hyperlipidemia. The pulse wave parameters showing significant differences among these groups were identified. Various machine learning models such as linear discriminant analysis, support vector machines (SVMs), and random forests were applied to classify the control group, diabetic patients, and diabetic patients with complications. RESULTS There were significant differences in several pulse wave parameters among the 5 groups. The parameters height of tidal wave (h3), time distance between the start point of pulse wave and dominant wave (t1), and width of percussion wave in its one-third height position (W) increase and the height of dicrotic wave (h5) decreases when people develop diabetes. The parameters height of dominant wave (h1), h3, and height of dicrotic notch (h4) are found to be higher in diabetic patients with hypertension, whereas h5 is lower in diabetic patients with hyperlipidemia. For detecting diabetes, the method with the highest out-of-sample prediction accuracy is SVM with polynomial kernel. The algorithm can detect diabetes with 96.35% accuracy. However, all the algorithms have a low accuracy when predicting diabetic patients with hypertension and hyperlipidemia (below 70%). CONCLUSIONS The results demonstrated that the noninvasive and convenient pulse-taking diagnosis described in this paper has the potential to become a low-cost and accurate method to monitor the development of diabetes. We are collecting more data to improve the accuracy for detecting hypertension and hyperlipidemia among diabetic patients. Mobile devices such as sport bands, smart watches, and other diagnostic tools are being developed based on the pulse wave method to improve the diagnosis and monitoring of diabetes, hypertension, and hyperlipidemia.

BackgroundWe should pay more attention to the long-term monitoring and early warning of type 2 diabetes and its complications. The traditional blood glucose tests are traumatic and cannot effectively monitor the development of diabetic complications. The development of mobile health is changing rapidly. Therefore, we are interested in developing a new noninvasive, economical, and instant-result method to accurately diagnose and monitor type 2 diabetes and its complications. ObjectiveWe aimed to determine whether type 2 diabetes and its complications, including hypertension and hyperlipidemia, could be diagnosed and monitored by using pulse wave. MethodsWe collected the pulse wave parameters from 50 healthy people, 139 diabetic patients without hypertension and hyperlipidemia, 133 diabetic patients with hypertension, 70 diabetic patients with hyperlipidemia, and 75 diabetic patients with hypertension and hyperlipidemia. The pulse wave parameters showing significant differences among these groups were identified. Various machine learning models such as linear discriminant analysis, support vector machines (SVMs), and random forests were applied to classify the control group, diabetic patients, and diabetic patients with complications. ResultsThere were significant differences in several pulse wave parameters among the 5 groups. The parameters height of tidal wave (h3), time distance between the start point of pulse wave and dominant wave (t1), and width of percussion wave in its one-third height position (W) increase and the height of dicrotic wave (h5) decreases when people develop diabetes. The parameters height of dominant wave (h1), h3, and height of dicrotic notch (h4) are found to be higher in diabetic patients with hypertension, whereas h5 is lower in diabetic patients with hyperlipidemia. For detecting diabetes, the method with the highest out-of-sample prediction accuracy is SVM with polynomial kernel. The algorithm can detect diabetes with 96.35% accuracy. However, all the algorithms have a low accuracy when predicting diabetic patients with hypertension and hyperlipidemia (below 70%). ConclusionsThe results demonstrated that the noninvasive and convenient pulse-taking diagnosis described in this paper has the potential to become a low-cost and accurate method to monitor the development of diabetes. We are collecting more data to improve the accuracy for detecting hypertension and hyperlipidemia among diabetic patients. Mobile devices such as sport bands, smart watches, and other diagnostic tools are being developed based on the pulse wave method to improve the diagnosis and monitoring of diabetes, hypertension, and hyperlipidemia.