BACKGROUND: The effect of air pollution on annual change rates in grip strength and body composition in the elderly is unknown. OBJECTIVES: This study evaluated the effects of long-term exposure to ambient air pollution on change rates of grip strength and body composition in the elderly. METHODS: In the period 2016-2020, grip strength and body composition were assessed and measured 1-2 times per year in 395 elderly participants living in the Taipei basin. Exposure to ambient fine particulate matters (PM_2.5), nitric dioxide (NO₂), and ozone (O₃) from 2015 to 2019 was estimated using a hybrid Kriging/Land-use regression model. In addition, long-term exposure to carbon monoxide (CO) was estimated using an ordinary Kriging approach. Associations between air pollution exposures and annual changes in health outcomes were analyzed using linear mixed-effects models. RESULTS: An inter-quartile range (4.1 µg/m³) increase in long-term exposure to PM_2.5 was associated with a faster decline rate in grip strength (-0.16 kg per year) and skeletal muscle mass (-0.14 kg per year), but an increase in body fat mass (0.21 kg per year). The effect of PM_2.5 remained robust after adjustment for NO₂, O₃ and CO exposure. In subgroup analysis, the PM_2.5-related decline rate in grip strength was greater in participants with older age (>70 years) or higher protein intake, whereas in skeletal muscle mass, the decline rate was more pronounced in participants having a lower frequency of moderate or strenuous exercise. The PM_2.5-related increase rate in body fat mass was higher in participants having a lower frequency of strenuous exercise or soybean intake. CONCLUSIONS Among the elderly, long-term exposure to ambient PM_2.5 is associated with a faster decline in grip strength and skeletal muscle mass, and an increase in body fat mass. Susceptibility to PM_2.5 may be influenced by age, physical activity, and dietary protein intake; however, these modifying effects vary across different health outcomes, and further research is needed to clarify their mechanisms and consistency.
Humans ; Hand Strength ; Aged ; Male ; Female ; Environmental Exposure/adverse effects* ; Follow-Up Studies ; Taiwan ; Air Pollution/adverse effects* ; Particulate Matter/adverse effects* ; Muscle, Skeletal/drug effects* ; Air Pollutants/adverse effects* ; Ozone/adverse effects* ; Aged, 80 and over ; Adipose Tissue/drug effects* ; Body Composition/drug effects* ; Nitrogen Dioxide/adverse effects*

Objectives: Concerns about erythropoietin (EPO) therapy for anemia in patients with end-stage renal disease (ESRD) contributing to potential bone loss and increased fracture risks are growing. This study investigated the impact of EPO administration on the risk of common osteoporotic fractures in ESRD patients. Methods: This population-based retrospective cohort study compared EPO users and non-EPO users among ESRD patients undergoing hemodialysis, diagnosed with ESRD between 2000 and 2014 identified from the National Health Insurance Research Database of Taiwan. The cohorts were matched at a propensity score ratio of 1:1, resulting in equal sample sizes of 2839. Variables related to comorbidities were considered. Results: EPO users exhibited higher cumulative incidences of major osteoporotic fractures, hip fractures, spine fractures, and wrist fractures compared with the non-EPO user (all P < 0.001). In adjusted Cox regression models, higher adjusted subdistribution hazard ratios (aSHRs) were observed for major osteoporotic fractures (2.41, 95% confidence interval [CI] = 2.01–2.89), osteoporotic hip fractures (2.19, 95% CI = 1.69–2.85), spine fractures (2.50, 95% CI = 1.87–3.34), and wrist fractures (2.34, 95% CI = 1.44–3.78) in EPO users than in nonEPO users. The risk of major osteoporotic fractures significantly increased with increasing EPO doses (P for trend < 0.0001), and a similar trend was observed for the risks of osteoporotic spine and wrist fractures. Conclusions Our findings suggest that EPO treatment in patients with ESRD undergoing hemodialysis is associated with an increased risk of osteoporotic fractures.

Objectives: Concerns about erythropoietin (EPO) therapy for anemia in patients with end-stage renal disease (ESRD) contributing to potential bone loss and increased fracture risks are growing. This study investigated the impact of EPO administration on the risk of common osteoporotic fractures in ESRD patients. Methods: This population-based retrospective cohort study compared EPO users and non-EPO users among ESRD patients undergoing hemodialysis, diagnosed with ESRD between 2000 and 2014 identified from the National Health Insurance Research Database of Taiwan. The cohorts were matched at a propensity score ratio of 1:1, resulting in equal sample sizes of 2839. Variables related to comorbidities were considered. Results: EPO users exhibited higher cumulative incidences of major osteoporotic fractures, hip fractures, spine fractures, and wrist fractures compared with the non-EPO user (all P < 0.001). In adjusted Cox regression models, higher adjusted subdistribution hazard ratios (aSHRs) were observed for major osteoporotic fractures (2.41, 95% confidence interval [CI] = 2.01–2.89), osteoporotic hip fractures (2.19, 95% CI = 1.69–2.85), spine fractures (2.50, 95% CI = 1.87–3.34), and wrist fractures (2.34, 95% CI = 1.44–3.78) in EPO users than in nonEPO users. The risk of major osteoporotic fractures significantly increased with increasing EPO doses (P for trend < 0.0001), and a similar trend was observed for the risks of osteoporotic spine and wrist fractures. Conclusions Our findings suggest that EPO treatment in patients with ESRD undergoing hemodialysis is associated with an increased risk of osteoporotic fractures.

Objectives: Concerns about erythropoietin (EPO) therapy for anemia in patients with end-stage renal disease (ESRD) contributing to potential bone loss and increased fracture risks are growing. This study investigated the impact of EPO administration on the risk of common osteoporotic fractures in ESRD patients. Methods: This population-based retrospective cohort study compared EPO users and non-EPO users among ESRD patients undergoing hemodialysis, diagnosed with ESRD between 2000 and 2014 identified from the National Health Insurance Research Database of Taiwan. The cohorts were matched at a propensity score ratio of 1:1, resulting in equal sample sizes of 2839. Variables related to comorbidities were considered. Results: EPO users exhibited higher cumulative incidences of major osteoporotic fractures, hip fractures, spine fractures, and wrist fractures compared with the non-EPO user (all P < 0.001). In adjusted Cox regression models, higher adjusted subdistribution hazard ratios (aSHRs) were observed for major osteoporotic fractures (2.41, 95% confidence interval [CI] = 2.01–2.89), osteoporotic hip fractures (2.19, 95% CI = 1.69–2.85), spine fractures (2.50, 95% CI = 1.87–3.34), and wrist fractures (2.34, 95% CI = 1.44–3.78) in EPO users than in nonEPO users. The risk of major osteoporotic fractures significantly increased with increasing EPO doses (P for trend < 0.0001), and a similar trend was observed for the risks of osteoporotic spine and wrist fractures. Conclusions Our findings suggest that EPO treatment in patients with ESRD undergoing hemodialysis is associated with an increased risk of osteoporotic fractures.

Objectives: Concerns about erythropoietin (EPO) therapy for anemia in patients with end-stage renal disease (ESRD) contributing to potential bone loss and increased fracture risks are growing. This study investigated the impact of EPO administration on the risk of common osteoporotic fractures in ESRD patients. Methods: This population-based retrospective cohort study compared EPO users and non-EPO users among ESRD patients undergoing hemodialysis, diagnosed with ESRD between 2000 and 2014 identified from the National Health Insurance Research Database of Taiwan. The cohorts were matched at a propensity score ratio of 1:1, resulting in equal sample sizes of 2839. Variables related to comorbidities were considered. Results: EPO users exhibited higher cumulative incidences of major osteoporotic fractures, hip fractures, spine fractures, and wrist fractures compared with the non-EPO user (all P < 0.001). In adjusted Cox regression models, higher adjusted subdistribution hazard ratios (aSHRs) were observed for major osteoporotic fractures (2.41, 95% confidence interval [CI] = 2.01–2.89), osteoporotic hip fractures (2.19, 95% CI = 1.69–2.85), spine fractures (2.50, 95% CI = 1.87–3.34), and wrist fractures (2.34, 95% CI = 1.44–3.78) in EPO users than in nonEPO users. The risk of major osteoporotic fractures significantly increased with increasing EPO doses (P for trend < 0.0001), and a similar trend was observed for the risks of osteoporotic spine and wrist fractures. Conclusions Our findings suggest that EPO treatment in patients with ESRD undergoing hemodialysis is associated with an increased risk of osteoporotic fractures.

Objectives: Concerns about erythropoietin (EPO) therapy for anemia in patients with end-stage renal disease (ESRD) contributing to potential bone loss and increased fracture risks are growing. This study investigated the impact of EPO administration on the risk of common osteoporotic fractures in ESRD patients. Methods: This population-based retrospective cohort study compared EPO users and non-EPO users among ESRD patients undergoing hemodialysis, diagnosed with ESRD between 2000 and 2014 identified from the National Health Insurance Research Database of Taiwan. The cohorts were matched at a propensity score ratio of 1:1, resulting in equal sample sizes of 2839. Variables related to comorbidities were considered. Results: EPO users exhibited higher cumulative incidences of major osteoporotic fractures, hip fractures, spine fractures, and wrist fractures compared with the non-EPO user (all P < 0.001). In adjusted Cox regression models, higher adjusted subdistribution hazard ratios (aSHRs) were observed for major osteoporotic fractures (2.41, 95% confidence interval [CI] = 2.01–2.89), osteoporotic hip fractures (2.19, 95% CI = 1.69–2.85), spine fractures (2.50, 95% CI = 1.87–3.34), and wrist fractures (2.34, 95% CI = 1.44–3.78) in EPO users than in nonEPO users. The risk of major osteoporotic fractures significantly increased with increasing EPO doses (P for trend < 0.0001), and a similar trend was observed for the risks of osteoporotic spine and wrist fractures. Conclusions Our findings suggest that EPO treatment in patients with ESRD undergoing hemodialysis is associated with an increased risk of osteoporotic fractures.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is an increasingly common liver disease worldwide. MAFLD is diagnosed based on the presence of steatosis on images, histological findings, or serum marker levels as well as the presence of at least one of the three metabolic features: overweight/obesity, type 2 diabetes mellitus, and metabolic risk factors. MAFLD is not only a liver disease but also a factor contributing to or related to cardiovascular diseases (CVD), which is the major etiology responsible for morbidity and mortality in patients with MAFLD. Hence, understanding the association between MAFLD and CVD, surveillance and risk stratification of MAFLD in patients with CVD, and assessment of the current status of MAFLD management are urgent requirements for both hepatologists and cardiologists. This Taiwan position statement reviews the literature and provides suggestions regarding the epidemiology, etiology, risk factors, risk stratification, nonpharmacological interventions, and potential drug treatments of MAFLD, focusing on its association with CVD.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Background/Aims: The performance of machine learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups. Methods: The study retrospectively enrolled 1,411 consecutive treatment-naïve patients with the Barcelona Clinic Liver Cancer (BCLC) stage 0 to A HCC from 2012 to 2021. The patients were randomly divided into a training cohort (n=988) and validation cohort (n=423). Two risk scores (CATS-IF and CATS-INF) were developed to predict overall survival (OS) in the training cohort using the conventional methods (Cox proportional hazards model) and ML-based methods (LASSO Cox regression), respectively. They were then validated and compared in the validation cohort. Results: In the training cohort, factors for the CATS-IF score were selected by the conventional method, including age, curative treatment, single large HCC, serum creatinine and alpha-fetoprotein levels, fibrosis-4 score, lymphocyte-tomonocyte ratio, and albumin-bilirubin grade. The CATS-INF score, determined by ML-based methods, included the above factors and two additional ones (aspartate aminotransferase and prognostic nutritional index). In the validation cohort, both CATS-IF score and CATS-INF score outperformed other modern prognostic scores in predicting OS, with the CATSINF score having the lowest Akaike information criterion value. A calibration plot exhibited good correlation between predicted and observed outcomes for both scores. Conclusions Both the conventional Cox-based CATS-IF score and ML-based CATS-INF score effectively stratified patients with early-stage HCC into distinct prognostic groups, with the CATS-INF score showing slightly superior performance.

Traditional acute kidney injury (AKI) classifications, which are centered around semi-anatomical lines, can no longer capture the complexity of AKI. By employing strategies to identify predictive and prognostic enrichment targets, experts could gain a deeper comprehension of AKI’s pathophysiology, allowing for the development of treatment-specific targets and enhancing individualized care. Subphenotyping, which is enriched with AKI biomarkers, holds insights into distinct risk profiles and tailored treatment strategies that redefine AKI and contribute to improved clinical management. The utilization of biomarkers such as N-acetyl-β-D-glucosaminidase, tissue inhibitor of metalloprotease-2·insulin-like growth factor-binding protein 7, kidney injury molecule-1, and liver fatty acid-binding protein garnered significant attention as a means to predict subclinical AKI. Novel biomarkers offer promise in predicting persistent AKI, with urinary motif chemokine ligand 14 displaying significant sensitivity and specificity. Furthermore, they serve as predictive markers for weaning patients from acute dialysis and offer valuable insights into distinct AKI subgroups. The proposed management of AKI, which is encapsulated in a structured flowchart, bridges the gap between research and clinical practice. It streamlines the utilization of biomarkers and subphenotyping, promising a future in which AKI is swiftly identified and managed with unprecedented precision. Incorporating kidney biomarkers into strategies for early AKI detection and the initiation of AKI care bundles has proven to be more effective than using care bundles without these novel biomarkers. This comprehensive approach represents a significant stride toward precision medicine, enabling the identification of high-risk subphenotypes in patients with AKI.