Blood Glucose Data Research Articles

Evaluation of Phytochemical and Antidiabetic Potentials of Aqueous, Ethanol, and Acetone Crude Extracts of Plectranthus Neochilus Leaves on Alloxan Induced Diabetic Wistar Rats.

Diabetes mellitus is a metabolic disorder which leads to abnormally high blood glucose level (hyperglycemia). This research work is aimed at evaluating the antidiabetic potentials of aqueous, ethanol and acetone extracts of plectranthus neochilus leaves on alloxan induced diabetic wistar rats. Materials and Methods: The experimental rats were induced and made diabetic by single intra-peritoneal administration of Alloxan monohydrate at a dose rate of 150 mg/kg dissolved in 0.9% freshly prepared normal saline at pH 5.5. Sixty-five adult (male) albino rats weighing 160-200g were used for this study. The Animals were divided into 12 groups of 5 rats each namely: A: Normal control, B: Diabetic control 150kg/body weight of alloxan, C: Diabetic + 1000mg/kg body weight of Standard drug (Metformin), D: Diabetic + 250mg/kg body weight of aqueous extract, E: Diabetic + 500mg/kg body weight of aqueous extract, F: Diabetic + 1000mg/kg body weight of aqueous extract, G: Diabetic + 250mg/kg body weight ethanol extract, H: Diabetic + 500mg/kg body weight of ethanol extract, I: Diabetic + 1000mg/kg body weight of ethanol extract, J: Diabetic + 250mg/kg body weight of acetone extract, K: Diabetic + 500mg/kg body weight of acetone extract, L: Diabetic + 1000mg/kg body weight of acetone extract. The extracts were administered orally for a duration of 28days at doses of (250, 500, and 1000 mg/kg body weight). Results: Treatment with extracts significantly reduced (p<0.05) elevated blood glucose level associated with alloxan induced diabetic (27.85±0.95) rats in the aqueous extract (5.90±0.10), ethanol extract (10.15±4.35) but no significant ameliorative effect with the acetone extract (23.35±2.82) at dosage of 1000mg/kg body weight. The aqueous extract significantly reduced (p<0.05) elevated blood glucose level more when compared to the normal control (5.85±0.05) rats as when compared to the ethanol and acetone extracts at dose 1000mg/kg body weight. Conclusion: The blood glucose data obtained using Alloxan hyperglycaemic rats clearly shows that the crude aqueous extract has better anti-diabetic effect at dose of 1000mg/kg, followed by the ethanol extract and then the acetone extract in the 28 days of treatment, the ameliorative effect is also dose dependent. Thus, the leaves caused significant and consistent reductions in diabetic drug-induced hyperglycaemia in albino rats.

A Machine Learning Web App to Predict Diabetic Blood Glucose Based on a Basic Noninvasive Health Checkup, Sociodemographic Characteristics, and Dietary Information: Case Study.

Over the past few decades, diabetes has become a serious public health concern worldwide, particularly in Bangladesh. The advancement of artificial intelligence can be reaped in the prediction of blood glucose levels for better health management. However, the practical validity of machine learning (ML) techniques for predicting health parameters using data from low- and middle-income countries, such as Bangladesh, is very low. Specifically, Bangladesh lacks research using ML techniques to predict blood glucose levels based on basic noninvasive clinical measurements and dietary and sociodemographic information. To formulate strategies for public health planning and the control of diabetes, this study aimed to develop a personalized ML model that predicts the blood glucose level of urban corporate workers in Bangladesh. Based on the basic noninvasive health checkup test results, dietary information, and sociodemographic characteristics of 271 employees of the Bangladeshi Grameen Bank complex, 5 well-known ML models, namely, linear regression, boosted decision tree regression, neural network, decision forest regression, and Bayesian linear regression, were used to predict blood glucose levels. Continuous blood glucose data were used in this study to train the model, which then used the trained data to predict new blood glucose values. Boosted decision tree regression demonstrated the greatest predictive performance of all evaluated models (root mean squared error=2.30). This means that, on average, our model's predicted blood glucose level deviated from the actual blood glucose level by around 2.30 mg/dL. The mean blood glucose value of the population studied was 128.02 mg/dL (SD 56.92), indicating a borderline result for the majority of the samples (normal value: 140 mg/dL). This suggests that the individuals should be monitoring their blood glucose levels regularly. This ML-enabled web application for blood glucose prediction helps individuals to self-monitor their health condition. The application was developed with communities in remote areas of low- and middle-income countries, such as Bangladesh, in mind. These areas typically lack health facilities and have an insufficient number of qualified doctors and nurses. The web-based application is a simple, practical, and effective solution that can be adopted by the community. Use of the web application can save money on medical expenses, time, and health management expenses. The created system also aids in achieving the Sustainable Development Goals, particularly in ensuring that everyone in the community enjoys good health and well-being and lowering total morbidity and mortality.

Hubungan Pengetahuan, Sikap, Tindakan Gaya Hidup Sehat dengan Pasien Diabetes Mellitus Tipe 2 di Klinik Budhi Pratama Restu Ibu Jakarta Tahun 2022

Diabetes mellitus (DM) is one of the chronic metabolic diseases that is still a global concern today. DM prevalence in the DKI Jakarta province is the highest in Indonesia, which is 3.4% (Riskesdas, 2018). The description of knowledge, attitudes, and practices of the community towards DM is very important for research to determine programs to prevent increased morbidity and mortality due to DM. Study objective to explain the relationship between knowledge, attitudes, practices about a healthy lifestyle with blood sugar control of DM patients at the Budhi Pratama Restu Ibu Clinic, East Jakarta and the most influential dominant factors. Methods used was Cross sectional analytic descriptive research. Sample was 86 participants of Prolanis DM. Respondents were given a Knowledge, Attitude, and Practice questionnaire about a healthy lifestyle. Fasting Blood Glucose (FBG) data was taken from medical records. Data analysis with chi-square and logistic regression backward LR model. Results showed weak correlation between knowledge (p = 0.005), there is no correlation between attitudes (p = 0.107), and significant correlation between practices and blood sugar control of DM patients (p = 0.000). Practice is the dominant factor that affects the regression coefficient = 0.000. Knowledge and practices about a healthy lifestyle are associated with blood sugar control of DM patients. We suggest further research comparing Prolanis participants and non-participants.Keywords : knowledge, attitude, practices, healthy lifestyle, fasting blood glucose, diabetes mellitus

The role of continuous glucose monitoring in physical activity and nutrition management: perspectives on present and possible uses

[Purpose] Continuous glucose monitoring (CGM) is on the rise as the prevalence of obesity and diabetes increases. This review aimed to explore the use of CGM and its potential novel applications in physical activity and nutrition management.[Methods] We searched PubMed, Web of Science, and Wiley Online Library databases using the keywords ‘continuous glucose monitor,’ ‘nutrition,’ ‘physical activity,’ and ‘numerical modeling.’[Results] Continuous blood glucose measurement is useful for individuals with obesity and diabetes. Long-term blood glucose data allow for personalized planning of nutritional composition, meal timing, and physical activity type and intensity, as well as help prevent hypoglycemia and hyperglycemia. Thus, understanding the limitations of CGM is important for its effective use.[Conclusion] CGM systems are being increasingly used to monitor and identify appropriate blood glucose controlling interventions. Blood glucose level is influenced by various factors such as nutrient composition, meal timing, physical activity, circadian rhythm, and cortisol levels. Numerical modeling can be used to analyze the complex relationship between stress, sleep, nutrition, and physical activity, which affect blood glucose levels. In future, blood glucose, sleep, and stress data will be integrated to predict appropriate lifestyle levels for blood glucose management. This integrated approach improves glucose control and overall wellbeing, potentially reducing societal costs.

Investigation of The Effect of Combined Exercises on Glycemic Control in Individuals with Type 1 Diabetes

The aim of the present study was to investigate the effect of the combined physical exercise protocols on the blood glucose (BG) of children with T1D. The study was conducted over four weeks using self-controlled clinical trials. The first two weeks were control period and the last two weeks were the experimental period. A total of 6 exercise sessions were performed 3 days a week during the experimental period. The exercise program consisted of aerobic, stretching, and resistance exercises. Each exercise session was performed for 40 minutes. A total of 15 children with T1D, whose mean age was 13.3±SD, were included in the study. The blood glucose and insulin dose data of the participants were recorded in both periods through the continuous glucose measurement system and the registry. It was determined that aerobic exercises provided a significant decrease in blood glucose (p < .05). There was no statistically significant difference between the mean BG values in the control (211.7±37.3) and experimental (208.7±40.4) periods (p >0.05). On the other hand, a statistically significant decrease was found in the total daily insulin doses used by participants during the experimental (31.6±20.8 Unit) period (p < 0.001). Physical exercises had an acute positive effect on blood glucose in children with T1D. However, the effect of exercise alone is not sufficient for chronic glycemic control in T1D. For chronic glycemic control, first of all, the injected insulin doses should be carefully regulated according to carbohydrates and exercise.

Conflicts of Control: Continuous Blood Glucose Monitoring and Coordinated Caregiving for Teenagers with Type 1 Diabetes

Continuous blood glucose monitoring (CGM) is a bio-sensing technology designed to help individuals with diabetes understand and manage their blood sugar levels. Better control and management have the promise of extending and improving the quality of life with diabetes. For teenagers with Type 1 Diabetes (T1D), CGM has the potential to allow for a more collaborative T1D management and control experience by sharing real-time blood glucose data with caregivers. To understand how well CGM designs are living up to this promise and to identify potential challenges, we used the Asynchronous Remote Communities (ARC) with 16 teenagers with T1D and their informal caregivers over a 6 month period to investigate families' lived experiences with CGM and its data sharing function. We found three challenges in caregiver-teenager collaboration; lack of visibility into behaviors behind data, difficulties balancing teenage daily life with T1D control, and technical limitations of current CGM systems. We also found three issues that hinder informal caregiving coordination; unclear task assignment, difficulties in maintaining standards of care, and a lack of informal caregiver rotation. We provide a number of design implications based on these findings to better facilitate home care for teenagers' T1D.

How can tuberculosis services better support patients with a diabetes co-morbidity? A mixed methods study in the Philippines

BackgroundPeople with diabetes mellitus (DM) have an estimated two- to three-times greater risk of adverse tuberculosis (TB) treatment outcomes compared to those without DM. Blood glucose control is a primary aim of managing DM during TB treatment, yet TB programmes are not generally adapted to provide DM services. The purpose of this study was to understand perceptions and the lived experiences of diabetic patients in TB treatment in the Philippines, with a view to informing the development of disease co-management strategies.MethodsThis mixed methods study was conducted within a prospective cohort of adults newly-starting treatment for drug-sensitive and drug-resistant TB at 13 public TB clinics in three regions of the Philippines. Within the subset of 189 diabetic persons who self-reported a prior DM diagnosis, or were diagnosed by screenings conducted through the TB clinic, longitudinal blood glucose data were used to ascertain individuals’ glycaemic control (controlled or uncontrolled). Univariable logistic regression analyses exploring associations between uncontrolled glycaemia and demographic and clinical factors informed purposive sampling of 31 people to participate in semi-structured interviews. All audio-recorded data were transcribed and thematic analysis performed.ResultsParticipants — both with controlled and uncontrolled blood glucose — were knowledgeable about diabetes and its management. However, a minority of participants were aware of the impact of DM on TB treatment and outcomes. Many participants newly-diagnosed with DM at enrolment in TB treatment had not perceived any diabetic symptoms prior and would have likely not sought clinical consult otherwise. Access to free glucose-lowering medications through TB clinics was a key enabling resource. However, participants expressed fear of side effects and interrupted access to glucose-lowering medications, and a preference for phytotherapy. Many participants felt that physical and financial impacts of TB and its treatment were challenges to DM management.Conclusions and recommendationsResults of this study indicate that public TB clinics can provide diabetic patients with additional health care resources and education to address co-morbidity. TB programmes might consider identifying patients with complicated DM, and offering diabetic monitoring and management, as DM and diabetic complications may compound the burden of TB and its treatment.

Understanding temporal changes and seasonal variations in glycemic trends using wearable data.

Seasonal variations in glycemic trends remain largely unstudied despite the growing prevalence of diabetes. To address this gap, our objective is to investigate temporal changes in glycemic trends by analyzing intensively sampled blood glucose data from 137 patients (ages 2 to 76, primarily type 1 diabetes) over the course of 9 months to 4.5 years. From over 91,000 days of continuous glucose monitor data, we found that glycemic control decreases significantly around the holidays, with the largest decline observed on New Year's Day among the patients with already poor glycemic control (i.e., <55% time in the target range). We also observed seasonal variations in glycemic trends, with patients having worse glycemic control in the months of November to February (i.e., mid-fall and winter, in the United States), and better control in the months of April to August (i.e., mid-spring and summer). These insights are critical to inform targeted interventions that can improve diabetes outcomes.

Real-World Adherence and Effectiveness of Remote Patient Monitoring Among Medicaid Patients With Diabetes: Retrospective Cohort Study.

The prevalence of diabetes in the United States is high and increasing, and it is also the most expensive chronic condition in the United States. Self-monitoring of blood glucose or continuous glucose monitoring are potential solutions, but there are barriers to their use. Remote patient monitoring (RPM) with appropriate support has the potential to provide solutions. We aim to investigate the adherence of Medicaid patients with diabetes to daily RPM protocols, the relationship between adherence and changes in blood glucose levels, and the impact of daily testing time on blood glucose changes. This retrospective cohort study analyzed real-world data from an RPM company that provides services to Texas Medicaid patients with diabetes. Overall, 180 days of blood glucose data from an RPM company were collected to assess transmission rates and blood glucose changes, after the first 30 days of data were excluded due to startup effects. Patients were separated into adherent and nonadherent cohorts, where adherent patients transmitted data on at least 120 of the 150 days. z tests and t tests were performed to compare transmission rates and blood glucose changes between 2 cohorts. In addition, we analyzed blood glucose changes based on their testing time-between 1 AM and 10 AM, 10 AM and 6 PM, and 6 PM and 1 AM. Mean patient age was 70.5 (SD 11.8) years, with 66.8% (n=255) of them being female, 91.9% (n=351) urban, and 89% (n=340) from south Texas (n=382). The adherent cohort (n=186, 48.7%) had a mean transmission rate of 82.8% before the adherence call and 91.1% after. The nonadherent cohort (n=196, 51.3%) had a mean transmission rate of 45.9% before and 60.2% after. The mean blood glucose levels of the adherent cohort decreased by an average of 9 mg/dL (P=.002) over 5 months. We also found that variability of blood glucose level of the adherent cohort improved 3 mg/dL (P=.03) over the 5-month period. Both cohorts had the majority of their transmissions between 1 AM and 10 AM, with 70.5% and 53.2% for the adherent and nonadherent cohorts, respectively. The adherent cohort had decreasing mean blood glucose levels over 5 months, with the largest decrease during the 6 PM to 1 AM time period (30.9 mg/dL). Variability of blood glucose improved only for those tested from 10 AM to 6 PM, with improvements of 6.9 mg/dL (P=.02). Those in the nonadherent cohort did not report significant changes. RPM can help manage diabetes in Medicaid clients by improving adherence rates and glycemic control. Adherence calls helped improve adherence rates, but some patients still faced challenges in transmitting blood glucose levels. Nonetheless, RPM has the potential to reduce the risk of adverse outcomes associated with diabetes.

Maternal and fetal outcomes in a retrospective cohort of obstetrical patients that used an insulin pump paired with a continuous glucose monitor

Objective To assess maternal and neonatal outcomes in pregnant pregestational diabetic patients using a continuous subcutaneous insulin infusion (CSII) pump paired with a continuous glucose monitor (CGM). Methods This retrospective cohort study included 55 patients who delivered within one healthcare system from October 2019 to October 2022 with pregestational diabetes managed using CSII pumps paired with CGM. Maternal blood glucose (BG) data were analyzed for the two-week period preceding delivery. The percentage of time spent at a BG level of less than 140 mg/dL was recorded and compared between patients with and without obstetric and neonatal morbidities. Results Patients who delivered with severe preeclampsia (S. PreE) had a significantly lower mean percentage of time spent at BG < 140 mg/dL than those who did not (S. PreE 15/55, 63.1% ± 19.0 vs. 40/55, 73.6% ± 13.8; p = 0.03). Mothers who had a preterm birth (PTB) had a significantly lower mean percentage of time spent at BG < 140 mg/dL than those who delivered at term (PTB 35/55, 66.4% ± 16.4 vs 20/55, 78.3% ± 11.9; p = 0.006). The mean percentage of time spent at a BG < 140 mg/dL among mothers of neonates with respiratory distress syndrome (RDS) was significantly lower than those without RDS (RDS present 13/55, 59.7% ± 20.4 vs 42/55, 74.1% ± 12.7; p = 0.003). There was a significant correlation between a greater neonatal birth weight percentile and worse time spent at BG < 140 mg/dL (r = − 0.31; p = 0.02). No other significant differences were observed between the groups. Conclusion Improved blood glucose levels in pregestational diabetic patients using a CSII pump and CGM is associated with reduced maternal and neonatal morbidity as well as lower birth weight percentile neonates. Future studies are needed to clarify how much time each day a patient needs to spend below a given blood sugar, how long this blood glucose should be maintained, and what specific blood glucose target should be selected.

The Effect of Nigella sativa Powder on Blood Sugar and Lipid Profiles in Type 2 Diabetic Patients

Background: Diabetes is a chronic metabolic disease that impairs plasma lipid levels and prone a person to cardiovascular diseases. Since standard therapies in diabetes control fail in many patients, using herbal medicines in these patients has been considered. Objectives: The present study aimed to evaluate the effect of Nigella sativa (NS) seed powder on blood sugar and fat profile in type 2 diabetic patients. Methods: Eighty patients with type 2 diabetes were recruited and randomly assigned into control and experimental groups. The experimental group received a capsule containing Nigella sativa seed powder for two months, and the control group received a placebo. Blood glucose and lipid profile data were statistically analyzed at the end of two months. This study was conducted in diabetic clinic of Khomein City during the 2019 - 2020 year. The groups were compared using a repeated measures analysis of variance (ANOVA) test. Results: The mean age of patients was 53.82 in the experimental group and 55.12 in the control group. In comparison between the two measurements before and after the interventions, the variables glycosylated hemoglobin (HbA1c), cholesterol (Chol), and high-density lipoprotein (HDL) showed a statistically significant difference (P = 0.0001). The interactive effect between the control and intervention groups before and after the interventions was statistically significant for the postprandial glucose (PPG) variable (P = 0.035). Conclusions: The results of our study showed that the consumption of NS seed in diabetic patients could change the fat profile and reduce hemoglobin HbA1c, which indicated that Nigella sativa seed had been effective in reducing blood sugar over time.

A State-by-State online transfer learning framework with incremental clustering for blood glucose prediction

This study proposes a State-by-State online transfer learning framework for blood glucose (BG) prediction. We assume that the BG data can be divided into states, representing different states of the patient. A thoroughly reliable blood glucose prediction model should not only be able to mimic the patient's physiology, such as fasting, food intake, and exercise, but also cope with external disturbances, such as noise, exercise, unannounced meals, and stress, among other factors. The state of the fixed-length BG data is separated using a clustering method. For each state, an appropriate prediction model is trained. This framework transforms the online prediction of BG into the process of matching the prediction model online, using incremental clustering methods and online transfer learning methods to cope when there is no matching model for the new state data input. The method uses incremental clustering to accurately divide the state of patients' blood glucose time series, builds a prediction model by state, and uses online transfer learning to train a new model when data is insufficient. In conclusion, we obtained the highest prediction accuracy on the dataset using this strategy.

Simulating Realistic Continuous Glucose Monitor Time Series By Data Augmentation.

Simulated data are a powerful tool for research, enabling benchmarking of blood glucose (BG) forecasting and control algorithms. However, expert created models provide an unrealistic view of real-world performance, as they lack the features that make real data challenging, while black-box approaches such as generative adversarial networks do not enable systematic tests to diagnose model performance. To address this, we propose a method that learns missingness and error properties of continuous glucose monitor (CGM) data collected from people with type 1 diabetes (OpenAPS, OhioT1DM, RCT, and Racial-Disparity), and then augments simulated BG data with these properties. On the task of BG forecasting, we test how well our method brings performance closer to that of real CGM data compared with current simulation practices for missing data (random dropout) and error (Gaussian noise, CGM error model). Our methods had the smallest performance difference versus real data compared with random dropout and Gaussian noise when individually testing the effects of missing data and error on simulated BG in most cases. When combined, our approach was significantly better than Gaussian noise and random dropout for all data sets except OhioT1DM. Our error model significantly improved results on diverse data sets. We find a significant gap between BG forecasting performance on simulated and real data, and our method can be used to close this gap. This will enable researchers to rigorously test algorithms and provide realistic estimates of real-world performance without overfitting to real data or at the expense of data collection.

1118-P: Accuracy of Dexcom G6 Continuous Glucose Monitor in Pediatric Diabetic Ketoacidosis Admissions

Continuous glucose monitors (CGMs) are an integral part of care for youth with type 1 diabetes (T1D) though lack FDA labeling for inpatient use. While some adult data on CGM use in inpatient settings is available, pediatric data are minimal. This retrospective chart review evaluated the accuracy of Dexcom G6 CGM versus point of care (POC, Nova Biomedical StatStrip [MARD 6%])) blood glucose values from pediatric inpatient encounters. Blood glucose data, diagnosis codes, and initial labs were collected from the medical record. CGM values were obtained from Dexcom Clarity CSV files. Paired glucose values (N=1191) from 83 patients with T1D (median age 12 yrs, 54% male, 69% non-Hispanic White) were used to calculate mean absolute relative difference (MARD) and Clarke Error Grid. Data from DKA admissions (N=665) had a MARD of 11.1% with 97.8% of values within A&amp;B zones, compared to 11.4% and 98.5% for non-DKA admissions (N=526). Values from severe DKA admissions (N= 307) (pH &amp;lt;7.15 and/or bicarbonate &amp;lt;5 mmol/L) had a lower MARD compared to non-severe admissions (N=358) (8.4% vs 13.4%, p=0.01). In summary, CGM accuracy is comparable between DKA and non-DKA admissions. The accuracy of CGMs, even in severe DKA, suggests potential usability during pediatric hospital encounters. Further analysis will differentiate POC versus lab glucose and the effect of medications, including IV insulin infusions. Disclosure L.A.Waterman: None. L.Pyle: None. L.Towers: None. E.Jost: Other Relationship; Tandem Diabetes Care, Inc. A.J.Karami: None. C.Berget: Consultant; Insulet Corporation, Dexcom, Inc., Other Relationship; Tandem Diabetes Care, Inc. G.P.Forlenza: Advisory Panel; Medtronic, Consultant; Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care, Inc., Lilly Diabetes, Research Support; Medtronic, Abbott, Dexcom, Inc., Insulet Corporation, Tandem Diabetes Care, Inc. R.Wadwa: Consultant; Eli Lilly and Company, Other Relationship; Dexcom, Inc., Eli Lilly and Company, Research Support; Dexcom, Inc., Eli Lilly and Company, Beta Bionics, Inc., Tandem Diabetes Care, Inc. E.C.Cobry: None. Funding National Institutes of Health (5T32DK063687)

148-LB: Glycemic Outcomes of Telemedicine Endocrinology Care for Adults with T2D

Current specialty care for T2D via telemedicine may not be as effective for patients with clinical complexity given barriers to delivery of key care elements, such blood glucose data sharing and involvement of multidisciplinary teams. In this retrospective cohort study, adults who had a telemedicine endocrinology visit for T2D in a large health system from May-October 2020 were followed through May 2022 to assess follow-up care modality and HbA1c change. Patients were assigned to telemedicine (n=1,182, baseline HbA1c 7.6%) and in-person (n=1,049, baseline HbA1c 7.4%) cohorts based on follow-up care modality. Multivariable regression models estimated the association of baseline insulin regimen and key comorbidities (macrovascular disease and psychological conditions) with HbA1c change over 12 months in each cohort. Patients who used multiple daily injections (MDI) had less HbA1c improvement at 12 months vs those not on insulin in both cohorts, and HbA1c change was significantly worse for patients on MDI who used only telemedicine (HbA1c change telemedicine-only 0.77% higher, 95% CI 0.59-0.94; HbA1c change in-person 0.52% higher, 0.34-0.70, p&amp;lt;0.001 for both; see figure). Key comorbidities were not associated with HbA1c change in either cohort. These results indicate that patients in endocrinology care who use complex insulin regimens may need additional support to achieve HbA1c improvement, especially with telemedicine. Disclosure M. Zupa: None. S. D. Rothenberger: None. J. Y. Lin: None. A. Rosland: None. Funding National Center for Advancing Translational Sciences (KL2TR001856)

Feasibility and Preliminary Behavioral and Clinical Efficacy of a Diabetes Education Chatbot Pilot Among Adults With Type 2 Diabetes.

Diabetes self-management education and support (DSMES) improves diabetes outcomes yet remains consistently underutilized. Chatbot technology offers the potential to increase access to and engagement in DSMES. Evidence supporting the case for chatbot uptake and efficacy in people with diabetes (PWD) is needed. A diabetes education and support chatbot was deployed in a regional health care system. Adults with type 2 diabetes with an A1C of 8.0% to 8.9% and/or having recently completed a 12-week diabetes care management program were enrolled in a pilot program. Weekly chats included three elements: knowledge assessment, limited self-reporting of blood glucose data and medication taking behaviors, and education content (short videos and printable materials). A clinician facing dashboard identified need for escalation via flags based on participant responses. Data were collected to assess satisfaction, engagement, and preliminary glycemic outcomes. Over 16 months, 150 PWD (majority above 50 years of age, female, and African American) were enrolled. The unenrollment rate was 5%. Most escalation flags (N = 128) were for hypoglycemia (41%), hyperglycemia (32%), and medication issues (11%). Overall satisfaction was high for chat content, length, and frequency, and 87% reported increased self-care confidence. Enrollees completing more than one chat had a mean drop in A1C of -1.04%, whereas those completing one chat or less had a mean increase in A1C of +0.09% (P = .008). This diabetes education chatbot pilot demonstrated PWD acceptability, satisfaction, and engagement plus preliminary evidence of self-care confidence and A1C improvement. Further efforts are needed to validate these promising early findings.

The dietary inflammatory index is positively associated with insulin resistance in underweight and healthy weight adults.

The aim of this study was to explore the relationship between dietary inflammatory index (DII) and insulin resistance (IR) in underweight and healthy weight adults. This cross-sectional study involved 3205 participants from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018. All dietary data used to calculate the DII were obtained based on the average of two 24-h dietary recall interviews. Participants were divided into an anti-inflammatory diet group and a pro-inflammatory diet group based on DII<0 and DII≥0, respectively. Fasting blood glucose and fasting insulin data used to calculate IR index (HOMA-IR) were from laboratory data in the NHANES database. According to the linear regression analysis results of DII and HOMA-IR, we found that there was a positive relationship between DII and IR. A positive association between DII and HOMA-IR was seen in the following groups after stratification: by age in 20-39-year olds, by sex in males, by race in Non-Hispanic Whites, by family history of diabetes in those without a family history of diabetes, by education level in those with high school education, by smoking status in current smokers and non-smokers, by hypertension in those with hypertension, by BMI in those with a BMI of 18.5-24.99, by hypertriglyceridemia (HTG) in those without HTG, by poverty impact ratio (PIR) in those with PIR≤1.3 and>1.3, and by physical activity in those with moderate recreational activities. In conclusion, in underweight and healthy weight adults, DII was positively correlated with the risk of IR.

An early screening model of pulse detection technology for hepatic steatosis

BackgroundThe increasing prevalence of hepatic steatosis presents a considerable challenge to public health. There is a critical need for the development of novel preventive and screening strategies for this condition. This study evaluated the potential applications of wrist pulse detection technology for the early detection of liver diseases. The pulse time-domain features of a medical exam population with and without hepatic steatosis were assessed to develop a screening model for this disease. MethodsParticipants were consecutively recruited from March 2021 to March 2022 in the medical examination centers of the Yueyang Hospital of Integrated Traditional Chinese and Western Medicine and the Shanghai Municipal Hospital of Traditional Chinese Medicine. Clinical data from 255 participants, including general information (sex, age, and body mass index), and data related to glucose and blood lipids (fasting plasma glucose, triglyceride, total cholesterol, high-density lipoprotein, and low-density lipoprotein levels) were collected. Wrist pulse signals were acquired using a pulse detection device, and the pulse time-domain features, including t1, t4, t5, T, w1, w2, h2/h1, h3/h1, and h5/h1 were extracted. Participants were assigned to hepatic steatosis and non-hepatic steatosis groups according to their abdominal ultrasound examination results. Their clinical data and pulse time-domain features were compared using chi-square and parametric or non-parametric statistical methods. Three datasets were used to construct screening models for hepatic steatosis based on the random forest algorithm. The datasets for modeling were defined as Dataset 1, containing blood glucose and lipid data and general information; Dataset 2, containing time-domain features and general information; Dataset 3, containing time-domain features, blood glucose and lipid data, and general information. The evaluation metrics, accuracy, precision, recall, F1-score, and areas under the receiver operating characteristic curve (AUC) were compared for each model. ResultsThe time-domain features of the two groups differed significantly. The t1, t4, t5, T, h2/h1, h3/h1, w1, and w2 features were higher in the hepatic steatosis group than in the non-hepatic steatosis group (P < 0.05), while the h5/h1 features were lower in the hepatic steatosis group than in the non-hepatic steatosis group (P < 0.05). The screening models for hepatic steatosis based on both time-domain features and blood glucose and lipid data outperformed those based on time-domain features or blood markers alone. The accuracy, precision, recall, F1-score, and AUC of the combined model were 81.18%, 80.56%, 76.32%, 79%, and 87.79%, respectively. These proportions were 1.57%, 1.86%, 1.76%, 2%, and 3.54% higher, respectively, than those of the model based on time-domain features alone and 3.14%, 4.2%, 2.64%, 4%, and 6.47% higher, respectively, than those of the model based on blood glucose and lipid alone. ConclusionThe early screening model for hepatic steatosis using datasets that included pulse time-domain features achieved better performance. The findings suggest that pulse detection technology could be used to inform the development of a mobile medical device or remote home monitoring system to test for hepatitis steatosis.

Change in cardiovascular health among adults with current or past major depressive disorder enrolled in intensive smoking cessation treatment

BackgroundElevated depressive symptoms and cigarette smoking are independently associated with poorer cardiovascular health (CVH), but it is unknown whether their treatment can synergistically improve CVH. We sought to characterize CVH of adults with comorbid depression and smoking and examine changes in CVH associated with changes in smoking and depression. MethodsParticipants (N = 300, 55 % women) were adult smokers (≥ 1 cigarette/day) with lifetime major depressive disorder enrolled in a 12-week intervention trial targeting depression and smoking. Multiple linear regression examined prospective associations between changes in depression (Beck Depression Inventory-II), smoking (past 24-hour cigarettes or smoking abstinence), and modified CVH score (per American Heart Association, excluding smoking: diet, physical activity, body mass index, blood glucose, cholesterol, blood pressure). ResultsBaseline mean CVH score was 5.87/12 points (SD = 2.13). No participants met “ideal” on all CVH components (blood glucose: 48 %, cholesterol: 46 %, physical activity: 38 %, body mass index: 24 %, blood pressure: 22 %, diet: 3 %). CVH scores did not change from baseline to end-of-treatment (M = 0.18 points, SD = 1.36, p = .177), nor did change in depression × smoking predict change in CVH (p = .978). However, greater reductions in depression were significantly associated with greater improvements in CVH (β = −0.04, SE = 0.01, p = .015). LimitationsThis study was limited by a short follow-up period, missing blood glucose and cholesterol data, and treatment-seeking smokers. ConclusionsAdults with comorbid depression and smoking had poor CVH. Although integrated treatment for depression and smoking improved both conditions, only reductions in depression were associated with improvements in CVH. These findings have implications for integrating psychosocial treatment into CVH promotion efforts. RegistrationNCT02378714 (clinicaltrials.gov).

Application of glycemic qualification rate based on fingerstick glucose monitoring in women with gestational diabetes mellitus

Objective To explore the appropriate application of glycemic qualification rate (GQR) calculated by fingerstick blood glucose (BG) monitoring for patients with gestational diabetes mellitus (GDM) by analyzing the relationship between BG control and adverse pregnancy outcomes. Methods Fingerstick Blood Glucose data during the second and third trimester of singleton pregnant women diagnosed with GDM were collected. GQR which is defined as the percentage of fingerstick BG values reaching the targets of BG control in a period of time was calculated. Patients were divided into three groups according to tertiles (tertile 1, GQR <56.25%; tertile 2, GQR 56.25–75%; and tertile 3, GQR ≥75%). Pregnant outcomes were compared among the three groups. Univariate analysis and logistic regression were performed to analyze the potential relationship between GQR and pregnancy outcomes. Receiver operating characteristic (ROC) curves were calculated to determine the cutoff values. We also explored that whether twice or three times monitoring per day would be adequate for GQR calculation, so we brought in two or three glucose measuring times per day to explore the relationship between new GQR and adverse outcomes. Results A total of 311 patients diagnosed with GDM were analyzed. In univariate analysis, the incidences of cesarean section of tertile 1–3 groups were 61.4%, 58.7%, and 44.9%, respectively (p < .05). The incidences of neonatal hypoglycemia of tertiles 1–3 groups were 19.8%, 18.6%, and 8.7% (p < .05). The difference of composite outcomes was statistically significant (p = .001). After adjustment, the patients with worse BG control (lower GQR) had higher risk of cesarean section (tertile 1 – aOR = 2.029, 1.128–3.648), neonatal hypoglycemia (tertile 1: aOR = 2.498, 1.082–5.766) as well as composite outcomes. The ROC curve of GQR indicated the predictive value for neonatal hypoglycemia (area under the ROC curve (AUC) 0.612 (0.532–0.692)) and neonatal composite outcomes (AUC 0.593 (0.528–0.657)) with optimal cutoff values of 81.1% and 73.5%, respectively. We also explored that whether twice or three times monitoring per day would be adequate for GQR calculation. The result showed that GQR only calculated by FBG + 2hPG after lunch (2h AL) per day also had well relationship with cesarean section (tertile 1: OR = 2.412, 1.322–4.398), neonatal hypoglycemia (tertile 1: aOR = 4.497, 1.607–12.586), and neonatal composite outcomes (tertile 1: aOR = 1.959, 95% confidence interval (CI): 1.114–3.444, p = .020). Conclusions The GQR calculated by the easily applicable fingerstick BG is related to occurrence of cesarean section and neonatal hypoglycemia in GDM women. GQR ≥ 80% is recommended for better pregnancy outcomes. As for the number of points monitoring per day, GQR calculated by FBG + 2h AL was an optimal option for better pregnancy outcomes if mothers needed to simplify the process of monitoring.