@Article{info:doi/10.2196/68519,
author="Peroutka, Markie
and Rivero Covelo, Ignacio",
title="Effects of Ventral Pallidum--Nucleus Accumbens Shell Neural Pathway Modulation on Sucrose Consumption and Motivation in Female Rats: Chemogenetic Manipulation",
journal="JMIRx Bio",
year="2025",
month="Mar",
day="8",
volume="3",
pages="e68519",
keywords="ventral pallidum",
keywords="nucleus accumbens shell",
keywords="chemogenetics",
keywords="sucrose",
keywords="feeding behavior",
keywords="food motivation",
keywords="palatable food",
keywords="DREADD",
keywords="designer receptors exclusively activated by designer drugs",
abstract="Background: The neural control of food intake involves interactions between homeostatic and nonhomeostatic systems. The nucleus accumbens shell (AcbSh) and ventral pallidum (VP) play key roles in regulating ingestive behavior and project to each other. Previous studies have shown that these projections influence food consumption, with sex differences reported in the modulation of sucrose intake by VP projections. Objective: This study aimed to investigate the effects of chemogenetic activation or inhibition of projections from the VP to the AcbSh on sucrose consumption and the motivation to work for sucrose in female rats. Methods: Chemogenetic tools (DREADD [designer receptors exclusively activated by designer drugs]) were used to selectively activate or inhibit VP projections to the AcbSh in female Sprague-Dawley rats (Gi [inhibitory G protein] DREADD: n=11; Gq [excitatory G protein] DREADD: n=10; and no DREADD: n=12). Rats were trained on a progressive ratio operant task to assess motivation to work for sucrose. Additionally, free-access sucrose consumption tests were conducted using a 20\% sucrose solution. The effects of chemogenetic modulation were analyzed using two-way ANOVA. Results: Chemogenetic manipulation of VP projections to the AcbSh did not significantly affect the motivation to work for sucrose in the progressive ratio task (F2,31=1.780; P=.18). However, a significant interaction between DREADD type and drug administration was observed in the sucrose consumption test. Activation of the VP-AcbSh projection (using Gq DREADD) decreased sucrose intake, while inhibition (using Gi DREADD) increased sucrose intake (F2,31=18.891; P=.001). No significant changes in sucrose consumption were observed in the control group without DREADD expression (P=.50). Conclusions: This study shows that projections from the VP to the AcbSh modulate sucrose intake but do not affect the motivation to work for sucrose. Chemogenetic activation reduced sucrose consumption, while inhibition increased it, suggesting that distinct neural circuits within the VP-AcbSh pathway may differentially regulate feeding behaviors. These findings highlight the role of this pathway in the consumption of palatable foods and indicate that future research should consider factors such as sex, food macronutrient composition, and specific neural subpopulations to better understand their role in feeding behavior. ",
doi="10.2196/68519",
url="https://bio.jmirx.org/2025/1/e68519"
}


@Article{info:doi/10.2196/44384,
author="Degen, Isabella
and Robson Brown, Kate
and Reeve, J. Henry W.
and Abdallah, S. Zahraa",
title="Beyond Expected Patterns in Insulin Needs of People With Type 1 Diabetes: Temporal Analysis of Automated Insulin Delivery Data",
journal="JMIRx Med",
year="2024",
month="Nov",
day="27",
volume="5",
pages="e44384",
keywords="multivariate time series",
keywords="k-means",
keywords="clustering",
keywords="machine learning",
keywords="temporal patterns",
keywords="data-driven",
keywords="OpenAPS",
keywords="open dataset",
keywords="type 1 diabetes",
keywords="insulin needs",
abstract="Background: Type 1 diabetes (T1D) is a chronic condition in which the body produces too little insulin, a hormone needed to regulate blood glucose. Various factors such as carbohydrates, exercise, and hormones impact insulin needs. Beyond carbohydrates, most factors remain underexplored. Regulating insulin is a complex control task that can go wrong and cause blood glucose levels to fall outside a range that protects people from adverse health effects. Automated insulin delivery (AID) has been shown to maintain blood glucose levels within a narrow range. Beyond clinical outcomes, data from AID systems are little researched; such systems can provide data-driven insights to improve the understanding and treatment of T1D. Objective: The aim is to discover unexpected temporal patterns in insulin needs and to analyze how frequently these occur. Unexpected patterns are situations where increased insulin does not result in lower glucose or where increased carbohydrate intake does not raise glucose levels. Such situations suggest that factors beyond carbohydrates influence insulin needs. Methods: We analyzed time series data on insulin on board (IOB), carbohydrates on board (COB), and interstitial glucose (IG) from 29 participants using the OpenAPS AID system. Pattern frequency in hours, days (grouped via k-means clustering), weekdays, and months were determined by comparing the 95\% CI of the mean differences between temporal units. Associations between pattern frequency and demographic variables were examined. Significant differences in IOB, COB, and IG across temporal dichotomies were assessed using Mann-Whitney U tests. Effect sizes and Euclidean distances between variables were calculated. Finally, the forecastability of IOB, COB, and IG for the clustered days was analyzed using Granger causality. Results: On average, 13.5 participants had unexpected patterns and 9.9 had expected patterns. The patterns were more pronounced (d>0.94) when comparing hours of the day and similar days than when comparing days of the week or months (0.3<d<0.52). Notably, 11 participants exhibited a higher IG overnight despite concurrently higher IOB (10/11). Additionally, 17 participants experienced an increase in IG after COB decreased after meals. The significant associations between pattern frequency and demographics were moderate (0.31?$\tau$?0.48). Between clusters, mean IOB (P=.03, d=0.7) and IG (P=.02, d=0.67) differed significantly, but COB did not (P=.08, d=0.55). IOB and IG were most similar (mean distance 5.08, SD 2.25), while COB and IG were most different (mean distance 11.43, SD 2.6), suggesting that AID attempts to counteract both observed and unobserved factors that impact IG. Conclusions: Our study shows that unexpected patterns in the insulin needs of people with T1D are as common as expected patterns. Unexpected patterns cannot be explained by carbohydrates alone. Our results highlight the complexity of glucose regulation and emphasize the need for personalized treatment approaches. Further research is needed to identify and quantify the factors that cause these patterns. ",
doi="10.2196/44384",
url="https://xmed.jmir.org/2024/1/e44384"
}


@Article{info:doi/10.2196/56993,
author="Oyebola, Kolapo
and Ligali, Funmilayo
and Owoloye, Afolabi
and Erinwusi, Blessing
and Alo, Yetunde
and Musa, Z. Adesola
and Aina, Oluwagbemiga
and Salako, Babatunde",
title="Machine Learning--Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals",
journal="JMIRx Med",
year="2024",
month="Sep",
day="11",
volume="5",
pages="e56993",
keywords="hyperglycemia",
keywords="diabetes",
keywords="machine learning",
keywords="hypertension",
keywords="random forest",
abstract="Background: Noncommunicable diseases continue to pose a substantial health challenge globally, with hyperglycemia serving as a prominent indicator of diabetes. Objective: This study employed machine learning algorithms to predict hyperglycemia in a cohort of individuals who were asymptomatic and unraveled crucial predictors contributing to early risk identification. Methods: This dataset included an extensive array of clinical and demographic data obtained from 195 adults who were asymptomatic and residing in a suburban community in Nigeria. The study conducted a thorough comparison of multiple machine learning algorithms to ascertain the most effective model for predicting hyperglycemia. Moreover, we explored feature importance to pinpoint correlates of high blood glucose levels within the cohort. Results: Elevated blood pressure and prehypertension were recorded in 8 (4.1\%) and 18 (9.2\%) of the 195 participants, respectively. A total of 41 (21\%) participants presented with hypertension, of which 34 (83\%) were female. However, sex adjustment showed that 34 of 118 (28.8\%) female participants and 7 of 77 (9\%) male participants had hypertension. Age-based analysis revealed an inverse relationship between normotension and age (r=?0.88; P=.02). Conversely, hypertension increased with age (r=0.53; P=.27), peaking between 50?59 years. Of the 195 participants, isolated systolic hypertension and isolated diastolic hypertension were recorded in 16 (8.2\%) and 15 (7.7\%) participants, respectively, with female participants recording a higher prevalence of isolated systolic hypertension (11/16, 69\%) and male participants reporting a higher prevalence of isolated diastolic hypertension (11/15, 73\%). Following class rebalancing, the random forest classifier gave the best performance (accuracy score 0.89; receiver operating characteristic--area under the curve score 0.89; F1-score 0.89) of the 26 model classifiers. The feature selection model identified uric acid and age as important variables associated with hyperglycemia. Conclusions: The random forest classifier identified significant clinical correlates associated with hyperglycemia, offering valuable insights for the early detection of diabetes and informing the design and deployment of therapeutic interventions. However, to achieve a more comprehensive understanding of each feature's contribution to blood glucose levels, modeling additional relevant clinical features in larger datasets could be beneficial. ",
doi="10.2196/56993",
url="https://xmed.jmir.org/2024/1/e56993"
}


@Article{info:doi/10.2196/56405,
author="Onishi, Tatsuki",
title="Incidence of Postoperative Diabetes Mellitus After Roux-en-Y Reconstruction for Gastric Cancer: Retrospective Single-Center Cohort Study",
journal="JMIRx Med",
year="2024",
month="Aug",
day="14",
volume="5",
pages="e56405",
keywords="diabetes mellitus",
keywords="gastrectomy",
keywords="gastric cancer surgery",
keywords="glucose metabolism",
keywords="postoperative diabetes onset",
keywords="surgery outcomes",
abstract="Background: Sleeve gastrectomy is an effective surgical option for morbid obesity, and it improves glucose homeostasis. In patients with gastric cancer and type 2 diabetes mellitus (DM), gastrectomy, including total gastrectomy, is beneficial for glycemic control. Objective: This study aims to clarify the effects of gastrectomy and different reconstructive techniques on the incidence of postoperative DM in patients with gastric cancer. Methods: This retrospective, single-center, cohort study included 715 patients without DM who underwent total gastrectomy at the Tokyo Metropolitan Bokutoh Hospital between August 2005 and March 2019. Patients underwent reconstruction by Roux-en-Y (RY) gastric bypass or other surgical techniques (OT), with DM onset determined by hemoglobin A1c levels or medical records. Analyses included 2-sample, 2-tailed t tests; $\chi$2 tests; and the Kaplan-Meier method with log-rank tests to compare the onset curves between the RY and OT groups, along with additional curves stratified by sex. A Swimmer plot for censoring and new-onset DM was implemented. Results: Stratified data analysis compared the RY and OT reconstruction methods. The hazard ratio was 1.52 (95\% CI 1.06-2.18; P=.02), which indicated a statistically significant difference in the incidence of new-onset diabetes between the RY and OT groups in patients with gastric cancer. The hazard ratio after propensity score matching was 1.42 (95\% CI 1.09-1.86; P=.009). Conclusions: This first-of-its-kind study provides insight into how different methods of gastric reconstruction affect postoperative diabetes. The results suggest significant differences in new-onset DM after surgery based on the reconstruction method. This research highlights the need for careful surgical planning to consider potential postoperative DM, particularly in patients with a family history of DM. Future studies should investigate the role of gut microbiota and other reconstructive techniques, such as laparoscopic jejunal interposition, in developing postoperative DM. ",
doi="10.2196/56405",
url="https://xmed.jmir.org/2024/1/e56405"
}


@Article{info:doi/10.2196/55976,
author="Makunts, Tigran
and Joulfayan, Haroutyun
and Abagyan, Ruben",
title="Thyroid Hyperplasia and Neoplasm Adverse Events Associated With Glucagon-Like Peptide-1 Receptor Agonists in the Food and Drug Administration Adverse Event Reporting System: Retrospective Analysis",
journal="JMIRx Med",
year="2024",
month="May",
day="1",
volume="5",
pages="e55976",
keywords="GLP-1",
keywords="FDA",
keywords="averse event reporting",
keywords="cancer",
keywords="oncology",
keywords="neoplasm",
keywords="drugs",
keywords="pharmacy",
keywords="pharmacology",
keywords="pharmaceutics",
keywords="medication",
keywords="medications",
keywords="glucagon-like peptide-1",
keywords="Food and Drug Administration",
keywords="weight loss",
keywords="diabetes",
keywords="obesity",
keywords="thyroid hyperplasia",
keywords="FAERS",
keywords="FDA Adverse Event Reporting System",
abstract="Background: Glucagon-like peptide-1 (GLP-1) receptor agonists (RAs) are one of the most commonly used drugs for type 2 diabetes mellitus. Clinical guidelines recommend GLP-1 RAs as an adjunct to diabetes therapy in patients with chronic kidney disease, presence or risk of atherosclerotic cardiovascular disease, and obesity. The weight loss observed in clinical trials has been explored further in healthy individuals, putting GLP-1 RAs on track to be the next weight loss treatment. Objective: Although the adverse event profile is relatively safe, most GLP-1 RAs come with a labeled boxed warning for the risk of thyroid cancers, based on animal models and some postmarketing case reports in humans. Considering the increasing popularity of this drug class and its expansion into a new popular indication, a further review of the most recent postmarketing safety data was warranted to quantify thyroid hyperplasia and neoplasm instances. Methods: GLP-1 RA patient reports from the US Food and Drug Administration (FDA) Adverse Event Reporting System database were analyzed using reporting odds ratios and 95\% CIs. Results: In this study, we analyzed over 18 million reports from the US FDA Adverse Event Reporting System and provided evidence of significantly increased propensity for thyroid hyperplasias and neoplasms in patients taking GLP-1 RA monotherapy when compared to patients taking sodium-glucose cotransporter-2 (SGLT-2) inhibitor monotherapy. Conclusions: GLP-1 RAs, regardless of indication, are associated with an over 10-fold increase in thyroid neoplasm and hyperplasia adverse event reporting when compared to SGLT-2 inhibitors. ",
doi="10.2196/55976",
url="https://xmed.jmir.org/2024/1/e55976"
}


@Article{info:doi/10.2196/22912,
author="Ronaldson, Amy
and Freestone, Mark
and Zhang, Haoyuan
and Marsh, William
and Bhui, Kamaldeep",
title="Using Structural Equation Modelling in Routine Clinical Data on Diabetes and Depression: Observational Cohort Study",
journal="JMIRx Med",
year="2022",
month="Apr",
day="27",
volume="3",
number="2",
pages="e22912",
keywords="depression",
keywords="diabetes",
keywords="electronic health records",
keywords="acute care",
keywords="PLS-SEM",
keywords="path analysis",
keywords="equation modelling",
keywords="accident",
keywords="emergency care",
keywords="emergency",
keywords="structural equation modelling",
keywords="clinical data",
abstract="Background: Large data sets comprising routine clinical data are becoming increasingly available for use in health research. These data sets contain many clinical variables that might not lend themselves to use in research. Structural equation modelling (SEM) is a statistical technique that might allow for the creation of ``research-friendly'' clinical constructs from these routine clinical variables and therefore could be an appropriate analytic method to apply more widely to routine clinical data. Objective: SEM was applied to a large data set of routine clinical data developed in East London to model well-established clinical associations. Depression is common among patients with type 2 diabetes, and is associated with poor diabetic control, increased diabetic complications, increased health service utilization, and increased health care costs. Evidence from trial data suggests that integrating psychological treatment into diabetes care can improve health status and reduce costs. Attempting to model these known associations using SEM will test the utility of this technique in routine clinical data sets. Methods: Data were cleaned extensively prior to analysis. SEM was used to investigate associations between depression, diabetic control, diabetic care, mental health treatment, and Accident \& Emergency (A\&E) use in patients with type 2 diabetes. The creation of the latent variables and the direction of association between latent variables in the model was based upon established clinical knowledge. Results: The results provided partial support for the application of SEM to routine clinical data. Overall, 19\% (3106/16,353) of patients with type 2 diabetes had received a diagnosis of depression. In line with known clinical associations, depression was associated with worse diabetic control ($\beta$=.034, P<.001) and increased A\&E use ($\beta$=.071, P<.001). However, contrary to expectation, worse diabetic control was associated with lower A\&E use ($\beta$=--.055, P<.001) and receipt of mental health treatment did not impact upon diabetic control (P=.39). Receipt of diabetes care was associated with better diabetic control ($\beta$=--.072, P<.001), having depression ($\beta$=.018, P=.007), and receiving mental health treatment ($\beta$=.046, P<.001), which might suggest that comprehensive integrated care packages are being delivered in East London. Conclusions: Some established clinical associations were successfully modelled in a sample of patients with type 2 diabetes in a way that made clinical sense, providing partial evidence for the utility of SEM in routine clinical data. Several issues relating to data quality emerged. Data improvement would have likely enhanced the utility of SEM in this data set. ",
doi="10.2196/22912",
url="https://med.jmirx.org/2022/2/e22912",
url="http://www.ncbi.nlm.nih.gov/pubmed/37725546"
}