Chung-Ze WuDr. - AOCE 2026

Education: School of Medicine, National Defense Medical Center Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University Current Position: Deputy Director, Department of Medical Administration, Taipei Medical University-Shuang Ho Hospital, Ministry of Health and Welfare, Taiwan, R.O.C. Director, Department of Outpatient Service, Taipei Medical University-Shuang Ho Hospital, Ministry of Health and Welfare, Taiwan, R.O.C. Attending Physician, Division of Endocrinology and Metabolism, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, Ministry of Health and Welfare, Taiwan, R.O.C. Member of the 16th Executive Board, the Diabetes Association of the Republic of China (Taiwan) Academic Position: Associate Professor, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan, R.O.C.

21 MARCH

Time	Session

13:30 15:00	[Symposium] Diabetes Mellitus (3) MASLD and Dementia Correlate with Diabetic Management Chung-Ze WuTaiwan Moderator Jenny GuntonAustralia Moderator Closing the Type 2 Diabetes Gap in Cardiovascular and Renal DiseasePeople with type 2 diabetes die, on average, 6-7 years earlier. This is mostly due to excess cardiovascular events. This presentation will discuss options for lowering cardiovascular and renal risk in people with type 2 diabetes.Managing Hyperglycaemia in Patients Receiving Immune Checkpoint InhibitorsIt is estimated that >20% of people treated with Immune Checkpoint Inhibitors (ICI) for their cancer will experience new or worsening hyperglycaemia. This presentation will discuss the differential diagnoses for the cause of hyperglycaemia in people treated with ICI and treatment strategies Mechanistic Insights into the Gut–Liver–Brain Axis in MASLD: Metabolic Crosstalk and Neuroinflammation Lee-Ling LimMalaysia Speaker Mechanistic Insights into the Gut–Liver–Brain Axis in MASLD: Metabolic Crosstalk and NeuroinflammationThe gut–liver–brain axis plays an important role in the pathogenesis and progression of metabolic dysfunction-associated steatotic liver disease (MASLD). Disruptions in gut microbiota, increased intestinal permeability, and microbial metabolites drive hepatic lipotoxicity and systemic inflammation. These hepatic signals, together with other metabolic dysfunctions, worsen neuroinflammatory responses and metabolic dysregulation. This lecture will discuss mechanistic links across the axis, and understanding these interconnected mechanisms offers opportunities to refine risk stratification and develop targeted interventions that address MASLD as a multisystem disease.Early-Onset Diabetes: Expanding the Spectrum of ComplicationsEarly-onset diabetes is increasing globally and is characterized by an accelerated trajectory of metabolic dysfunction. People diagnosed at a younger age experience a longer lifetime exposure to hyperglycaemia, adiposopathy, and inflammation, leading to an expanded spectrum of complications. Emerging evidence highlights earlier onset of kidney disease, heart failure, fatty liver disease, cognitive decline, and mental health disorders in this high-risk population. This lecture will synthesize current epidemiology, mechanistic insights, and evolving phenotypes, underscoring the urgent need for precision prevention, aggressive risk-factor modification, and integrated care models to reduce premature morbidity and mortality. MASLD and Cognitive Impairment Correlate with Diabetic Management Noriko Satoh-AsaharaJapan Speaker MASLD and Cognitive Impairment Correlate with Diabetic ManagementIn recent years, the coexistence of metabolic dysfunction–associated steatotic liver disease (MASLD) and cognitive decline in patients with diabetes has attracted growing attention. These conditions are not merely concurrent comorbidities but share common pathophysiological mechanisms involving insulin resistance, chronic inflammation, and gut dysbiosis. Using a large health checkup database, we reported that a body weight gain of more than 10 kg since the age of 20 is a significant risk factor for the development of MASLD (Nutrients, 2025). Moreover, we found that subsequent weight reduction markedly attenuated this risk, emphasizing the importance of appropriate weight management. In our multicenter diabetic cohort studies of the National Hospital Organization (JOMS/J-DOS2), we reported that circulating soluble TREM2 (sTREM2) —a receptor specifically expressed in monocytes and microglia—was significantly associated with cognitive decline in patients with diabetes, suggesting its potential as a predictive biomarker for dementia (Diabetes Metab, 2019; Front Endocrinol, 2022). Furthermore, our network meta-analysis in patients with type 2 diabetes revealed that SGLT2 inhibitors, GLP-1 receptor agonists, and thiazolidinediones may reduce the risk of cognitive impairment (Diabetes Obes Metab, 2025). Novel antidiabetic agents, particularly GLP-1 receptor agonists, have been shown to improve hepatic function and preserve cognitive performance. Collectively, these findings suggest that optimized diabetic management may hold the key to preventing both MASLD and dementia. In this presentation, I would like to summarize recent evidence and discuss optimal therapeutic strategies for MASLD and cognitive impairment in patients with diabetes. Diabetes Mellitus-Dementia Correlate with Diabetic Management Chaur-Jong HuTaiwan Speaker Diabetes Mellitus-Dementia Correlate with Diabetic ManagementDiabetes mellitus (DM) is a major metabolic disorder that substantially increases the risk of cognitive decline and dementia, including Alzheimer’s disease (AD) and vascular dementia. Growing evidence indicates that chronic hyperglycemia, insulin resistance, vascular injury, oxidative stress, and neuroinflammation are key mechanisms linking DM to neurodegeneration. Insulin resistance in the brain disrupts neuronal glucose utilization, enhances tau phosphorylation, and accelerates amyloid-β accumulation, while advanced glycation end-products (AGEs) and diabetes-related microvascular dysfunction further exacerbate neuronal injury. Effective diabetic management plays a critical role in mitigating dementia risk. Antidiabetic agents such as metformin, thiazolidinediones, and particularly glucagon-like peptide-1 receptor agonists (GLP-1RAs) have demonstrated neuroprotective effects beyond glycemic control. GLP-1RAs improve insulin signaling, reduce neuroinflammation, enhance mitochondrial function, promote autophagy, and inhibit apoptosis, leading to preserved cognitive functions in preclinical models. Clinical studies show that GLP-1RAs may improve specific cognitive domains in patients with type 2 DM and reduce the incidence of cognitive impairment. However, the recent phase 3 trials, Eoke and Evoke+ failed to show the beneficial effects on AD. Overall, the strong interplay between DM and dementia highlights the necessity of optimal glycemic control and strategic use of antidiabetic therapies with neuroprotective potential. Integrating metabolic management into dementia prevention frameworks may offer an effective approach to reducing the global burden of cognitive disorders. 201DE

Time

Session

13:30

15:00

[Symposium] Diabetes Mellitus (3)

MASLD and Dementia Correlate with Diabetic Management

Chung-Ze WuTaiwan Moderator

Jenny GuntonAustralia Moderator Closing the Type 2 Diabetes Gap in Cardiovascular and Renal DiseasePeople with type 2 diabetes die, on average, 6-7 years earlier. This is mostly due to excess cardiovascular events. This presentation will discuss options for lowering cardiovascular and renal risk in people with type 2 diabetes.Managing Hyperglycaemia in Patients Receiving Immune Checkpoint InhibitorsIt is estimated that >20% of people treated with Immune Checkpoint Inhibitors (ICI) for their cancer will experience new or worsening hyperglycaemia. This presentation will discuss the differential diagnoses for the cause of hyperglycaemia in people treated with ICI and treatment strategies

Mechanistic Insights into the Gut–Liver–Brain Axis in MASLD: Metabolic Crosstalk and Neuroinflammation

Lee-Ling LimMalaysia Speaker Mechanistic Insights into the Gut–Liver–Brain Axis in MASLD: Metabolic Crosstalk and NeuroinflammationThe gut–liver–brain axis plays an important role in the pathogenesis and progression of metabolic dysfunction-associated steatotic liver disease (MASLD). Disruptions in gut microbiota, increased intestinal permeability, and microbial metabolites drive hepatic lipotoxicity and systemic inflammation. These hepatic signals, together with other metabolic dysfunctions, worsen neuroinflammatory responses and metabolic dysregulation. This lecture will discuss mechanistic links across the axis, and understanding these interconnected mechanisms offers opportunities to refine risk stratification and develop targeted interventions that address MASLD as a multisystem disease.Early-Onset Diabetes: Expanding the Spectrum of ComplicationsEarly-onset diabetes is increasing globally and is characterized by an accelerated trajectory of metabolic dysfunction. People diagnosed at a younger age experience a longer lifetime exposure to hyperglycaemia, adiposopathy, and inflammation, leading to an expanded spectrum of complications. Emerging evidence highlights earlier onset of kidney disease, heart failure, fatty liver disease, cognitive decline, and mental health disorders in this high-risk population. This lecture will synthesize current epidemiology, mechanistic insights, and evolving phenotypes, underscoring the urgent need for precision prevention, aggressive risk-factor modification, and integrated care models to reduce premature morbidity and mortality.
MASLD and Cognitive Impairment Correlate with Diabetic Management

Noriko Satoh-AsaharaJapan Speaker MASLD and Cognitive Impairment Correlate with Diabetic ManagementIn recent years, the coexistence of metabolic dysfunction–associated steatotic liver disease (MASLD) and cognitive decline in patients with diabetes has attracted growing attention. These conditions are not merely concurrent comorbidities but share common pathophysiological mechanisms involving insulin resistance, chronic inflammation, and gut dysbiosis. Using a large health checkup database, we reported that a body weight gain of more than 10 kg since the age of 20 is a significant risk factor for the development of MASLD (Nutrients, 2025). Moreover, we found that subsequent weight reduction markedly attenuated this risk, emphasizing the importance of appropriate weight management. In our multicenter diabetic cohort studies of the National Hospital Organization (JOMS/J-DOS2), we reported that circulating soluble TREM2 (sTREM2) —a receptor specifically expressed in monocytes and microglia—was significantly associated with cognitive decline in patients with diabetes, suggesting its potential as a predictive biomarker for dementia (Diabetes Metab, 2019; Front Endocrinol, 2022). Furthermore, our network meta-analysis in patients with type 2 diabetes revealed that SGLT2 inhibitors, GLP-1 receptor agonists, and thiazolidinediones may reduce the risk of cognitive impairment (Diabetes Obes Metab, 2025). Novel antidiabetic agents, particularly GLP-1 receptor agonists, have been shown to improve hepatic function and preserve cognitive performance. Collectively, these findings suggest that optimized diabetic management may hold the key to preventing both MASLD and dementia. In this presentation, I would like to summarize recent evidence and discuss optimal therapeutic strategies for MASLD and cognitive impairment in patients with diabetes.
Diabetes Mellitus-Dementia Correlate with Diabetic Management

Chaur-Jong HuTaiwan Speaker Diabetes Mellitus-Dementia Correlate with Diabetic ManagementDiabetes mellitus (DM) is a major metabolic disorder that substantially increases the risk of cognitive decline and dementia, including Alzheimer’s disease (AD) and vascular dementia. Growing evidence indicates that chronic hyperglycemia, insulin resistance, vascular injury, oxidative stress, and neuroinflammation are key mechanisms linking DM to neurodegeneration. Insulin resistance in the brain disrupts neuronal glucose utilization, enhances tau phosphorylation, and accelerates amyloid-β accumulation, while advanced glycation end-products (AGEs) and diabetes-related microvascular dysfunction further exacerbate neuronal injury. Effective diabetic management plays a critical role in mitigating dementia risk. Antidiabetic agents such as metformin, thiazolidinediones, and particularly glucagon-like peptide-1 receptor agonists (GLP-1RAs) have demonstrated neuroprotective effects beyond glycemic control. GLP-1RAs improve insulin signaling, reduce neuroinflammation, enhance mitochondrial function, promote autophagy, and inhibit apoptosis, leading to preserved cognitive functions in preclinical models. Clinical studies show that GLP-1RAs may improve specific cognitive domains in patients with type 2 DM and reduce the incidence of cognitive impairment. However, the recent phase 3 trials, Eoke and Evoke+ failed to show the beneficial effects on AD. Overall, the strong interplay between DM and dementia highlights the necessity of optimal glycemic control and strategic use of antidiabetic therapies with neuroprotective potential. Integrating metabolic management into dementia prevention frameworks may offer an effective approach to reducing the global burden of cognitive disorders.

201DE

22 MARCH

Time	Session

14:00 15:30	[DAROC Symposium 6] Artificial Intelligence Chung-Ze WuTaiwan Moderator Feng-Chih KuoTaiwan Moderator (TBC) Hsiu-Hsi ChenTaiwan Speaker AI-Assisted Discovery of Omic Signature in Diabetic Kidney Disease I-Wen WuTaiwan Speaker AI-Assisted Discovery of Omic Signature in Diabetic Kidney DiseaseDiabetic kidney disease (DKD) is a leading cause of chronic kidney disease and end-stage renal disease, posing significant global health and economic burdens. Traditional management of DKD relies on standardized approaches, which often fail to account for the complexity of individual patient profiles. Precision medicine leverages individualized patient data—spanning genetic, proteomic, metabolic, and clinical information—to optimize diagnosis, risk assessment, and therapeutic interventions. However, translating this paradigm into clinical practice presents significant challenges and opportunities. This presentation focuses on the practical aspects of integrating precision medicine into DKD management. Key themes include the role of genetic and epigenetic biomarkers in risk stratification, the integration of multi-omics data with machine learning for predictive modeling and the design of personalized treatment regimens using tools such as pharmacogenomics. By examining real-world implementation strategies and overcoming barriers, this presentation aims to guide healthcare providers, researchers, and policymakers toward a sustainable and patient-centered precision medicine framework for DKD. Applications of Machine Learning in Medical Research Dee PeiTaiwan Speaker The roles of Machine Learning in Medical ResearchArtificial intelligence (AI) is fundamentally transforming clinical research by democratizing access to advanced analytical tools and establishing new standards for scientific publication. This presentation outlines a comprehensive framework for integrating machine learning (ML) methodologies into clinical studies—from raw data preparation to manuscript submission—while addressing critical challenges in model development, validation, and interpretation. We emphasize that financial barriers to sophisticated analysis have largely dissolved with the advent of open-source AI platforms, enabling researchers to move beyond legacy statistical software toward reproducible, transparent ML pipelines. The workflow begins with strategic data preparation, including appropriate imputation techniques (k-NN, MissForest, MICE) and feature standardization. For binary classification tasks—common in clinical prediction—It is advocate a rigorous protocol encompassing stratified cross-validation, hyperparameter tuning via nested CV, and explicit overfitting controls (regularization, feature limitation to ≥10 events per variable). Model selection should prioritize algorithms matching the clinical question: logistic regression for interpretability, random forests for robust baselines, and gradient boosting for maximal performance—while acknowledging trade-offs in complexity and calibration risk. Evaluation must extend beyond conventional ROC-AUC to include precision-recall curves (especially for imbalanced data), calibration assessment (slope ~1, intercept ~0), Brier score, and decision curve analysis for clinical utility. SHAP values provide essential interpretability for "black-box" models, translating complex predictions into clinically actionable insights. Crucially, it is stressed that accuracy alone is misleading in medical contexts; minimizing false negatives often carries greater clinical consequence than overall accuracy. Reproducibility demands fixed random seeds, complete pipeline documentation, and packaging preprocessing steps with final models for deployment. As journals increasingly expect ML-enhanced analyses, studies relying solely on traditional statistics face diminished publication prospects. It is concluded that AI integration is no longer optional but essential for contemporary clinical research seeking impact, rigor, and real-world applicability in an era where algorithmic insight complements—not replaces—clinical expertise. 3F Banquet Hall

Time

Session

14:00

15:30

[DAROC Symposium 6] Artificial Intelligence

Chung-Ze WuTaiwan Moderator

Feng-Chih KuoTaiwan Moderator

(TBC)

Hsiu-Hsi ChenTaiwan Speaker
AI-Assisted Discovery of Omic Signature in Diabetic Kidney Disease

I-Wen WuTaiwan Speaker AI-Assisted Discovery of Omic Signature in Diabetic Kidney DiseaseDiabetic kidney disease (DKD) is a leading cause of chronic kidney disease and end-stage renal disease, posing significant global health and economic burdens. Traditional management of DKD relies on standardized approaches, which often fail to account for the complexity of individual patient profiles. Precision medicine leverages individualized patient data—spanning genetic, proteomic, metabolic, and clinical information—to optimize diagnosis, risk assessment, and therapeutic interventions. However, translating this paradigm into clinical practice presents significant challenges and opportunities. This presentation focuses on the practical aspects of integrating precision medicine into DKD management. Key themes include the role of genetic and epigenetic biomarkers in risk stratification, the integration of multi-omics data with machine learning for predictive modeling and the design of personalized treatment regimens using tools such as pharmacogenomics. By examining real-world implementation strategies and overcoming barriers, this presentation aims to guide healthcare providers, researchers, and policymakers toward a sustainable and patient-centered precision medicine framework for DKD.
Applications of Machine Learning in Medical Research

Dee PeiTaiwan Speaker The roles of Machine Learning in Medical ResearchArtificial intelligence (AI) is fundamentally transforming clinical research by democratizing access to advanced analytical tools and establishing new standards for scientific publication. This presentation outlines a comprehensive framework for integrating machine learning (ML) methodologies into clinical studies—from raw data preparation to manuscript submission—while addressing critical challenges in model development, validation, and interpretation. We emphasize that financial barriers to sophisticated analysis have largely dissolved with the advent of open-source AI platforms, enabling researchers to move beyond legacy statistical software toward reproducible, transparent ML pipelines. The workflow begins with strategic data preparation, including appropriate imputation techniques (k-NN, MissForest, MICE) and feature standardization. For binary classification tasks—common in clinical prediction—It is advocate a rigorous protocol encompassing stratified cross-validation, hyperparameter tuning via nested CV, and explicit overfitting controls (regularization, feature limitation to ≥10 events per variable). Model selection should prioritize algorithms matching the clinical question: logistic regression for interpretability, random forests for robust baselines, and gradient boosting for maximal performance—while acknowledging trade-offs in complexity and calibration risk. Evaluation must extend beyond conventional ROC-AUC to include precision-recall curves (especially for imbalanced data), calibration assessment (slope ~1, intercept ~0), Brier score, and decision curve analysis for clinical utility. SHAP values provide essential interpretability for "black-box" models, translating complex predictions into clinically actionable insights. Crucially, it is stressed that accuracy alone is misleading in medical contexts; minimizing false negatives often carries greater clinical consequence than overall accuracy. Reproducibility demands fixed random seeds, complete pipeline documentation, and packaging preprocessing steps with final models for deployment. As journals increasingly expect ML-enhanced analyses, studies relying solely on traditional statistics face diminished publication prospects. It is concluded that AI integration is no longer optional but essential for contemporary clinical research seeking impact, rigor, and real-world applicability in an era where algorithmic insight complements—not replaces—clinical expertise.

3F Banquet Hall

Chung-Ze WuDr. Taiwan

21 MARCH

22 MARCH