The prevalence of MAFLD and its association with atrial fibrillation in a nationwide health check-up population in China.

Background and aims: The epidemiological characteristics of MAFLD and its relationship with atrial fibrillation (AF) are limited in China. Therefore, we explored the epidemiological characteristics of MAFLD from adults along with the association of MAFLD and 12-ECG diagnosed AF in a nationwide population from health check-up centers. Methods: This observational study used cross-sectional and longitudinal studies with 2,083,984 subjects from 2009 to 2017. Age-, sex-, and regional-standardized prevalence of MAFLD was estimated. Latent class analysis (LCA) was used to identify subclusters of MAFLD. Multivariable logistic regression and mixed-effects Cox regression models were used to analyze the relationship between MAFLD and AF. Results: The prevalence of MAFLD increased from 22.75% to 35.58% during the study period, with higher rates in males and populations with high BMI or resided in northern regions. The MAFLD population was clustered into three classes with different metabolic features by LCA. Notably, a high proportion of MAFLD patients in all clusters had overweight and prediabetes or diabetes. The MAFLD was significantly associated with a higher risk of AF in the cross-sectional study and in the longitudinal study. In addition, the coexistence of prediabetes or diabetes had the largest impact on subsequent AF. Conclusion: Our findings suggested a high prevalence of MAFLD and a high prevalence of other metabolic diseases in the MAFLD population, particularly overweight and glucose dysregulation. Moreover, MAFLD was associated with a significantly higher risk for existing and subsequent subclinical AF in the Chinese population.

Application of informatics in cancer research and clinical practice: Opportunities and challenges.

Cancer informatics has significantly progressed in the big data era. We summarize the application of informatics approaches to the cancer domain from both the informatics perspective (e.g., data management and data science) and the clinical perspective (e.g., cancer screening, risk assessment, diagnosis, treatment, and prognosis). We discuss various informatics methods and tools that are widely applied in cancer research and practices, such as cancer databases, data standards, terminologies, high-throughput omics data mining, machine-learning algorithms, artificial intelligence imaging, and intelligent radiation. We also address the informatics challenges within the cancer field that pursue better treatment decisions and patient outcomes, and focus on how informatics can provide opportunities for cancer research and practices. Finally, we conclude that the interdisciplinary nature of cancer informatics and collaborations are major drivers for future research and applications in clinical practices. It is hoped that this review is instrumental for cancer researchers and clinicians with its informatics-specific insights.

COVID-19 infection may cause ketosis and ketoacidosis.

The present study included 658 hospitalized patients with confirmed COVID-19. Forty-two (6.4%) out of 658 patients presented with ketosis on admission with no obvious fever or diarrhoea. They had a median (interquartile range [IQR]) age of 47.0 (38.0-70.3) years, and 16 (38.1%) were men. Patients with ketosis were younger (median age 47.0 vs. 58.0 years; P = 0.003) and had a greater prevalence of fatigue (31.0% vs. 10.6%; P < 0.001), diabetes (35.7% vs. 18.5%; P = 0.007) and digestive disorders (31.0% vs. 12.0%; P < 0.001). They had a longer median (IQR) length of hospital stay (19.0 [12.8-33.3] vs. 16.0 [10.0-24.0] days; P < 0.001) and a higher mortality rate (21.4% vs. 8.9%; P = 0.017). Three (20.0%) out of the 15 patients with diabetic ketosis developed acidosis, five patients (26.7%) with diabetic ketosis died, and one of these (25.0%) presented with acidosis. Two (7.4%) and four (14.3%) of the 27 non-diabetic ketotic patients developed severe acidosis and died, respectively, and one (25.0%) of these presented with acidosis. This suggests that COVID-19 infection caused ketosis or ketoacidosis, and induced diabetic ketoacidosis for those with diabetes. Ketosis increased the length of hospital stay and mortality. Meanwhile, diabetes increased the length of hospital stay for patients with ketosis but had no effect on their mortality.