Age-Related Changes in Lipid and Glucose Levels Associated with Drug Use and Mortality: An Observational Study

Oral Corticosteroids for Skin Disease in the Older Population: Minimizing Potential Adverse Effects

Corticosteroids play a crucial role as anti-inflammatory and immunomodulatory agents in dermatology and other medical specialties; however, their therapeutic benefits are accompanied by significant risks, especially in older adults. This review examines the broad spectrum of adverse effects (AEs) associated with oral corticosteroid therapy and offers strategies to prevent, monitor, and manage these issues effectively in older adults. AEs associated with systemic corticosteroids include immune suppression, gastrointestinal problems, hyperglycemia, insulin resistance, weight gain, cardiovascular complications, ocular issues, osteoporosis, osteonecrosis, muscle weakness, collagen impairment, psychiatric symptoms, and adrenal suppression. To minimize these AEs, tailored dosing and duration, frequent monitoring, and additional preventative measures can be employed to optimize corticosteroid treatment. By customizing management plans to the specific needs and risk factors associated with each patient, clinicians can promote the safe and effective use of oral corticosteroids, ultimately improving outcomes and quality of life in patients with inflammatory dermatologic disorders.

An exploration on the machine-learning-based stroke prediction model

Introduction

With the rapid development of artificial intelligence technology, machine learning algorithms have been widely applied at various stages of stroke diagnosis, treatment, and prognosis, demonstrating significant potential. A correlation between stroke and cytokine levels in the human body has recently been reported. Our study aimed to establish machine-learning models based on cytokine features to enhance the decision-making capabilities of clinical physicians.

Methods

This study recruited 2346 stroke patients and 2128 healthy control subjects from Chongqing University Central Hospital. A predictive model was established through clinical experiments and collection of clinical laboratory tests and demographic variables at admission. Three classification algorithms, namely Random Forest, Gradient Boosting, and Support Vector Machine, were employed. The models were evaluated using methods such as ROC curves, AUC values, and calibration curves.

Results

Through univariate feature selection, we selected 14 features and constructed three machine-learning models: Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Machine (GBM). Our results indicated that in the training set, the RF model outperformed the GBM and SVM models in terms of both the AUC value and sensitivity. We ranked the features using the RF algorithm, and the results showed that IL-6, IL-5, IL-10, and IL-2 had high importance scores and ranked at the top. In the test set, the stroke model demonstrated a good generalization ability, as evidenced by the ROC curve, confusion matrix, and calibration curve, confirming its reliability as a predictive model for stroke.

Discussion

We focused on utilizing cytokines as features to establish stroke prediction models. Analyses of the ROC curve, confusion matrix, and calibration curve of the test set demonstrated that our models exhibited a strong generalization ability, which could be applied in stroke prediction.

eHealth profile of patients with diabetes

Background

Digital health technology can be useful to improve the health of patients with diabetes and to support patient-centered care and self-management. In this cross-sectional study, we described the eHealth profile of patients with diabetes, based on their use of digital health technology, and its association with sociodemographic characteristics.

Methods

We used data from the "Qualité Diabète Valais" cohort study, conducted in one region of Switzerland (Canton Valais) since 2019. Participants with type 1 or type 2 diabetes completed questionnaires on sociodemographic characteristics and on the use of digital health technology. We defined eHealth profiles based on three features, i.e., ownership or use of (1) internet-connected devices (smartphone, tablet, or computer), (2) mHealth applications, and (3) connected health tools (activity sensor, smart weight scale, or connected blood glucose meter). We assessed the association between sociodemographic characteristics and participants' eHealth profiles using stratified analyses and logistic regression models.

Results

Some 398 participants (38% women) with a mean age of 65 years (min: 25, max: 92) were included. The vast majority (94%) were Swiss citizens or bi-national and 68% were economically inactive; 14% had a primary level education, 51% a secondary level, and 32% a tertiary level. Some 75% of participants had type 2 diabetes. Some 90% of the participants owned internet-connected devices, 43% used mHealth applications, and 44% owned a connected health tool. Older age and a lower educational level were associated with lower odds of all features of the eHealth profile. To a lesser extent, having type 2 diabetes or not being a Swiss citizen were also associated with a lower use of digital health technology. There was no association with sex.

Conclusion

While most participants owned internet-connected devices, only about half of them used mHealth applications or owned connected health tools. Older participants and those with a lower educational level were less likely to use digital health technology. eHealth implementation strategies need to consider these sociodemographic patterns among patients with diabetes.

Profiling of patients with type 2 diabetes based on medication adherence data

Introduction

Type 2 diabetes mellitus (T2DM) is a complex, chronic disease affecting multiple organs with varying symptoms and comorbidities. Profiling patients helps identify those with unfavorable disease progression, allowing for tailored therapy and addressing special needs. This study aims to uncover different T2DM profiles based on medication intake records and laboratory measurements, with a focus on how individuals with diabetes move through disease phases.

Methods

We use medical records from databases of the last 20 years from the Department of Endocrinology and Diabetology of the University Medical Center in Maribor. Using the standard ATC medication classification system, we created a patient-specific drug profile, created using advanced natural language processing methods combined with data mining and hierarchical clustering.

Results

Our results show a well-structured profile distribution characterizing different age groups of individuals with diabetes. Interestingly, only two main profiles characterize the early 40-50 age group, and the same is true for the last 80+ age group. One of these profiles includes individuals with diabetes with very low use of various medications, while the other profile includes individuals with diabetes with much higher use. The number in both groups is reciprocal. Conversely, the middle-aged groups are characterized by several distinct profiles with a wide range of medications that are associated with the distinct concomitant complications of T2DM. It is intuitive that the number of profiles increases in the later age groups, but it is not obvious why it is reduced later in the 80+ age group. In this context, further studies are needed to evaluate the contributions of a range of factors, such as drug development, drug adoption, and the impact of mortality associated with all T2DM-related diseases, which characterize these middle-aged groups, particularly those aged 55-75.

Conclusion

Our approach aligns with existing studies and can be widely implemented without complex or expensive analyses. Treatment and drug use data are readily available in healthcare facilities worldwide, allowing for profiling insights into individuals with diabetes. Integrating data from other departments, such as cardiology and renal disease, may provide a more sophisticated understanding of T2DM patient profiles.

The Aging Process: A Metabolomics Perspective

Aging process is characterized by a progressive decline of several organic, physiological, and metabolic functions whose precise mechanism remains unclear. Metabolomics allows the identification of several metabolites and may contribute to clarifying the aging-regulated metabolic pathways. We aimed to investigate aging-related serum metabolic changes using a metabolomics approach. Fasting blood serum samples from 138 apparently healthy individuals (20−70 years old, 56% men) were analyzed by Proton Nuclear Magnetic Resonance spectroscopy (1H NMR) and Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS), and for clinical markers. Associations of the metabolic profile with age were explored via Correlations (r); Metabolite Set Enrichment Analysis; Multiple Linear Regression; and Aging Metabolism Breakpoint. The age increase was positively correlated (0.212 ≤ r ≤ 0.370, p < 0.05) with the clinical markers (total cholesterol, HDL, LDL, VLDL, triacylglyceride, and glucose levels); negatively correlated (−0.285 ≤ r ≤ −0.214, p < 0.05) with tryptophan, 3-hydroxyisobutyrate, asparagine, isoleucine, leucine, and valine levels, but positively (0.237 ≤ r ≤ 0.269, p < 0.05) with aspartate and ornithine levels. These metabolites resulted in three enriched pathways: valine, leucine, and isoleucine degradation, urea cycle, and ammonia recycling. Additionally, serum metabolic levels of 3-hydroxyisobutyrate, isoleucine, aspartate, and ornithine explained 27.3% of the age variation, with the aging metabolism breakpoint occurring after the third decade of life. These results indicate that the aging process is potentially associated with reduced serum branched-chain amino acid levels (especially after the third decade of life) and progressively increased levels of serum metabolites indicative of the urea cycle.

Lipotoxicity in a Vicious Cycle of Pancreatic Beta Cell Exhaustion

Hyperlipidemia is a common metabolic disorder in modern society and may precede hyperglycemia and diabetes by several years. Exactly how disorders of lipid and glucose metabolism are related is still a mystery in many respects. We analyze the effects of hyperlipidemia, particularly free fatty acids, on pancreatic beta cells and insulin secretion. We have developed a computational model to quantitatively estimate the effects of specific metabolic pathways on insulin secretion and to assess the effects of short- and long-term exposure of beta cells to elevated concentrations of free fatty acids. We show that the major trigger for insulin secretion is the anaplerotic pathway via the phosphoenolpyruvate cycle, which is affected by free fatty acids via uncoupling protein 2 and proton leak and is particularly destructive in long-term chronic exposure to free fatty acids, leading to increased insulin secretion at low blood glucose and inadequate insulin secretion at high blood glucose. This results in beta cells remaining highly active in the “resting” state at low glucose and being unable to respond to anaplerotic signals at high pyruvate levels, as is the case with high blood glucose. The observed fatty-acid-induced disruption of anaplerotic pathways makes sense in the context of the physiological role of insulin as one of the major anabolic hormones.