Early screening of non-alcoholic fatty liver disease (NAFLD) is critical for early diagnosis and management. The disease was renamed and its diagnostic criteria revised as metabolic-associated FLD (MAFLD) in 2020 and further updated to metabolic dysfunction-associated steatotic liver disease (MASLD) in 2023. This study evaluated the predictive performance and clinical feasibility of non-invasive diagnostic indicators across the NAFLD, MAFLD and MASLD diagnostic criteria.

Cross-sectional study.

Health Management Centre in China.

A total of 5810 participants aged ≥18 years were enrolled. Individuals with missing laboratory data, imaging results or self-reported information were excluded.

Disease-specific indicators included Fatty Liver Index (FLI), Hepatic Steatosis Index and Zhejiang University index (ZJU). Non-disease-specific indicators included lipid accumulation product (LAP), Visceral Adiposity Index and the Triglyceride and Glucose Index. Subgroup analysis was performed by gender and Body Mass Index (BMI).

The area under the receiver operating characteristic curve (AUROC) for all six non-invasive indicators exceeded 0.7. FLI showed the optimal predictive performance across the three criteria (NAFLD-AUROC: 0.802, MAFLD-AUROC: 0.847 and MASLD-AUROC: 0.811), with comparable performance observed for ZJU (0.797, 0.838 and 0.809, respectively). Pairwise z-tests demonstrated a significant difference between FLI and ZJU for MAFLD (p0.05). Subgroup analyses revealed that ZJU performed better in males (NAFLD-AUROC: 0.790, MAFLD-AUROC: 0.839 and MASLD-AUROC: 0.803), while FLI was superior in females (NAFLD-AUROC: 0.832, MAFLD-AUROC: 0.838 and MASLD-AUROC: 0.838) and in participants who were overweight (NAFLD-AUROC: 0.709, MAFLD-AUROC: 0.765 and MASLD-AUROC: 0.709). LAP exhibited the highest predictive efficacy in the normal BMI subgroup (NAFLD-AUROC: 0.758, MAFLD-AUROC: 0.804 and MASLD-AUROC: 0.796).

FLI exhibited the highest predictive efficacy across all diagnostic criteria, and ZJU showed comparable performance. Considering diagnostic accuracy and clinical practicality, ZJU is recommended as a favourable, non-invasive tool for population-based screening in the Chinese population.

To identify and explore variable groups and individual predictors of long sickness absences outside of well-known predictors such as service use and previous sickness absence using machine learning, explainable artificial intelligence methods and a submodel approach.

Retrospective study of prospectively collected registry data on sickness absences and a questionnaire used in health examinations.

Electronic medical record data of one large occupational health service provider in Finland.

11 533 employees of various occupations who, between 2011 and 2019, had at least once completed a health questionnaire that could be linked to service usage data and who had not had their initial health check within 1 year before or 3 months after completing the questionnaire.

To identify predictors of at least one long sickness absence period (≥30 days) during a 2-year follow-up.

The highest area under the receiver operating characteristic curve (AUROC) values among the submodel groups were for the sickness absence and service use submodels (0.68–0.74). The AUROC values for the submodels of sociodemographic factors, health habits or diseases data category ranged from 0.55 to 0.67 and from 0.55 to 0.67 for the submodels of questionnaire data. The AUROC value of the ensemble model that combined all submodels was 0.79 (95% CI 0.788 to 0.794).

The most important factors predicting long sickness absences based on the submodels were reported pain, number of symptoms and diseases, body mass index and short sleep duration. Additionally, several work and mental health-related variables increased the risk of long sickness absence.

Other variables besides service use and sickness absence increase the accuracy in predicting long sickness absence and providing information for planning interventions that could have a beneficial impact on work disability risk.

Obesity is a growing public health issue worldwide, and anxiety is a major psychological disorder associated with obesity. Electroacupuncture (EA) has been proven to be a feasible treatment modality for obesity and anxiety in clinical practice. However, data on the effectiveness of EA for anxiety patients with obesity are lacking. Therefore, this study aimed to evaluate the effectiveness and safety of EA for anxiety in patients with obesity and to observe the brain functional status of the patients and the intervention effects of EA on brain function by using functional MRI (fMRI).

In this randomised, blinded, sham-controlled clinical trial, 72 patients with obesity from two hospitals with anxiety will be randomly divided into EA and control groups in a 1:1 ratio by using a random number table. Patients in the EA group will receive EA treatment with penetrating needling at specific acupoints for 8 weeks. The control group will receive Park’s acupuncture with non-penetrating needling. Weight, waist, body mass index, Self-rating Anxiety Scale score, State-Trait Anxiety Inventory score and Pittsburgh Sleep Quality Index will be measured before treatment, after 8 weeks of treatment and at the 1-month follow-up evaluation. Objective metabolic parameters such as triglyceride, total cholesterol, fasting blood glucose, ghrelin, leptin, cortisol and adrenocorticotropic hormone levels will also be measured before and after the 8-week intervention. 20 patients will be randomly selected from the EA and control groups before treatment. These randomly selected patients will undergo fMRI scans before and after treatment. Regional homogeneity, amplitude of low-frequency fluctuation and resting-state functional connectivity will be evaluated to compare the dysfunctional brain regions between two groups of patients after treatment.

The study protocol has been approved by the Hospital Ethics Committee of Second Affiliated Hospital of Anhui University of Chinese Medicine (2023-zj-42). Informed consent will be obtained prior to starting study-related procedures. The results will be disseminated in peer-reviewed journals and at scientific conferences.

Chinese Clinical Trial Registry. ChiCTR2400083594, registered 29 April 2024.