Retinol and retinol binding protein 4 levels and COVID-19: a Mendelian randomization study

BACKGROUND

The Corona Virus Disease 2019 (COVID-19) pandemic has struck globally. Whether the related proteins of retinoic acid (RA) signaling pathway are causally associated with the risk of COVID-19 remains unestablished. We conducted a two-sample Mendelian randomization (MR) study to assess the associations of retinol, retinol binding protein 4 (RBP4), retinol dehydrogenase 16 (RDH16) and cellular retinoic acid binding protein 1 (CRABP1) with COVID-19 in European population.

METHODS

The outcome utilized the summary statistics of COVID-19 from the COVID-19 Host Genetics Initiative. The exposure data were obtained from public genome wide association study (GWAS) database. We extracted SNPs from exposure data and outcome data. The inverse variance weighted (IVW), MR-Egger and Wald ratio methods were employed to assess the causal relationship between exposure and outcome. Sensitivity analyses were performed to ensure the validity of the results.

RESULTS

The MR estimates showed that retinol was associated with lower COVID-19 susceptibility using IVW (OR: 0.69, 95% CI: 0.53-0.90, P: 0.0065), whereas the associations between retinol and COVID-19 hospitalization or severity were not significant. RBP4 was associated with lower COVID-19 susceptibility using the Wald ratio (OR: 0.83, 95% CI: 0.72-0.95, P: 0.0072). IVW analysis showed RDH16 was associated with increased COVID-19 hospitalization (OR: 1.10, 95% CI: 1.01-1.18, P: 0.0199). CRABP1 was association with lower COVID-19 susceptibility (OR: 0.95, 95% CI: 0.91-0.99, P: 0.0290) using the IVW.

CONCLUSIONS

We found evidence of possible causal association of retinol, RBP4, RDH16 and CRABP1 with the susceptibility, hospitalization and severity of COVID-19. Our study defines that retinol is significantly associated with lower COVID-19 susceptibility, which provides a reference for the prevention of COVID-19 with vitamin A supplementation.

Bibliometric Analysis of Development Trends and Research Hotspots in the Study of Data Mining in Nursing Based on CiteSpace

Backgrounds

With the advent of the big data era, hospital information systems and mobile care systems, among others, generate massive amounts of medical data. Data mining, as a powerful information processing technology, can discover non-obvious information by processing large-scale data and analyzing them in multiple dimensions. How to find the effective information hidden in the database and apply it to nursing clinical practice has received more and more attention from nursing researchers.

Aim

To look over the articles on data mining in nursing, compiled research status, identified hotspots, highlighted research trends, and offer recommendations for how data mining technology might be used in the nursing area going forward.

Methods

Data mining in nursing publications published between 2002 and 2023 were taken from the Web of Science Core Collection. CiteSpace was utilized for reviewing the number of articles, countries/regions, institutions, journals, authors, and keywords.

Results

According to the findings, the pace of data mining in nursing progress is not encouraging. Nursing data mining research is dominated by the United States and China. However, no consistent core group of writers or organizations has emerged in the field of nursing data mining. Studies on data mining in nursing have been increasingly gradually conducted in the 21st century, but the overall number is not large. Institution of Columbia University, journal of Cin-computers Informatics Nursing, author Diana J Wilkie, Muhammad Kamran Lodhi, Yingwei Yao are most influential in nursing data mining research. Nursing data mining researchers are currently focusing on electronic health records, text mining, machine learning, and natural language processing. Future research themes in data mining in nursing most include nursing informatics and clinical care quality enhancement.

Conclusion

Research data shows that data mining gives more perspectives for the growth of the nursing discipline and encourages the discipline's development, but it also introduces a slew of new issues that need researchers to address.

Post-traumatic growth among emergency nurses after COVID-19 in Shanghai, China: a qualitative study

OBJECTIVES

This study aims to investigate the post-traumatic growth of emergency nurses (ENs) in Shanghai, China, in 2022 following the COVID-19 pandemic.

DESIGN

A phenomenological qualitative research approach was employed, with 17 ENs being interviewed between July and August 2022. Data collection was conducted through semistructured, in-depth interviews, and data analysis was carried out using the Colaizzi's seven-step analysis method.

SETTING

A third-level hospital in Shanghai.

PARTICIPANTS

A total of 17 ENs were interviewed through face-to-face, semistructured, in-depth interviews.

RESULTS

Three main themes and eight subthemes were extracted from the data: (a) stress, (b) restructuring and (c) growth.

CONCLUSION

Significant stress was imposed on ENs by the Shanghai COVID-19 pandemic, but cognitive restructuring was successfully undergone by them, leading to the experience of growth. It is recommended that post-traumatic growth levels be enhanced through professional psychological counselling and tailored support measures for different stages.

Curcumin for Treating Breast Cancer: A Review of Molecular Mechanisms, Combinations with Anticancer Drugs, and Nanosystems

Breast cancer (BC) has become the fifth most prevalent cause of cancer-related morbidity, attracting significant attention from researchers due to its heightened malignancy and drug resistance. Conventional chemotherapy approaches have proven inadequate in addressing all BC subtypes, highlighting the urgent need for novel therapeutic approaches or drugs. Curcumin (CUR), a phytochemical derived from Curcuma longa (turmeric), has shown substantial potential in inhibiting BC cell migration, metastasis, and proliferation. However, the use of CUR in this context comes with challenges due to its dynamic and easily degradable nature, poor aqueous solubility, low bioavailability, rapid metabolism, and swift systemic elimination, collectively limiting its clinical applications. As such, we provide an overview of the properties, synthesis, and characterization of the hybridization of CUR and its analogue with chemo-drug building blocks. We reviewed research from the last five years on CUR's biogenesis with respect to the regulation of BC, revealing that CUR participates in arresting BC cells in the cell cycle and significantly induces apoptosis in BC cells. Information on the chemotherapeutic and antitumor mechanisms of CUR in BC, including regulation of the cell cycle, increased cell apoptosis, and inhibition of multidrug resistance (MDR), was compiled. Additionally, we provide an overview of CUR loaded into nanomaterials that are cotreated with other chemotherapeutic drugs, such as paclitaxel, thymoquinone, and tamoxifen. In this review, we discuss different types of nanoparticles that can be used for CUR delivery, such as polymeric nanoparticles, carbon nanotubes, and liposomes. By comparing the size, entrapment efficiency, drug-loading capacity, release time, biocompatibility, pharmaceutical scale, and reproducibility of various nanomaterials, we aimed to determine which formulations are better suited for loading CUR or its analogue. Ultimately, this review is expected to offer inspiring ideas, promising strategies, and potential pathways for developing advanced anti-BC strategy nanosystems in clinical practice.

Proton Therapy in Breast Cancer: A Review of Potential Approaches for Patient Selection

Proton radiotherapy may be a compelling technical option for the treatment of breast cancer due to its unique physical property known as the "Bragg peak." This feature offers distinct advantages, promising superior dose conformity within the tumor area and reduced radiation exposure to surrounding healthy tissues, enhancing the potential for better treatment outcomes. However, proton therapy is accompanied by inherent challenges, primarily higher costs and limited accessibility when compared to well-developed photon irradiation. Thus, in clinical practice, it is important for radiation oncologists to carefully select patients before recommendation of proton therapy to ensure the transformation of dosimetric benefits into tangible clinical benefits. Yet, the optimal indications for proton therapy in breast cancer patients remain uncertain. While there is no widely recognized methodology for patient selection, numerous attempts have been made in this direction. In this review, we intended to present an inspiring summarization and discussion about the current practices and exploration on the approaches of this treatment decision-making process in terms of treatment-related side-effects, tumor control, and cost-efficiency, including the normal tissue complication probability (NTCP) model, the tumor control probability (TCP) model, genomic biomarkers, cost-effectiveness analyses (CEAs), and so on. Additionally, we conducted an evaluation of the eligibility criteria in ongoing randomized controlled trials and analyzed their reference value in patient selection. We evaluated the pros and cons of various potential patient selection approaches and proposed possible directions for further optimization and exploration. In summary, while proton therapy holds significant promise in breast cancer treatment, its integration into clinical practice calls for a thoughtful, evidence-driven strategy. By continuously refining the patient selection criteria, we can harness the full potential of proton radiotherapy while ensuring maximum benefit for breast cancer patients.

Evaluation of complexity and deliverability of IMRT treatment plans for breast cancer

This study aimed to predict the outcome of patient specific quality assurance (PSQA) in IMRT for breast cancer using complexity metrics, such as MU factor, MAD, CAS, MCS. Several breast cancer plans were considered, including LBCS, RBCS, LBCM, RBCM, left breast, right breast and the whole breast for both Edge and TrueBeam LINACS. Dose verification was completed by Portal Dosimetry (PD). The receiver operating characteristic (ROC) curve was employed to determine whether the treatment plans pass or failed. The area under the curve (AUC) was used to assess the classification performance. The correlation of PSQA and complexity metrics was examined using Spearman's rank correlation coefficient (Rs). For LINACS, the most suitable complexity metric was found to be the MU factor (Edge Rs = - 0.608, p < 0.01; TrueBeam Rs = - 0.739, p < 0.01). Regarding the specific breast cancer categories, the optimal complexity metrics were as follows: MAD (AUC = 0.917) for LBCS, MCS (AUC = 0.681) for RBCS, MU factor (AUC = 0.854) for LBCM and MAD (AUC = 0.731) for RBCM. On the Edge LINAC, the preferable method for breast cancers was MCS (left breast, AUC = 0.938; right breast, AUC = 0.813), while on the TrueBeam LINAC, it became MU factor (left breast, AUC = 0.950) and MCS (right breast, AUC = 0.806), respectively. Overall, there was no universally suitable complexity metric for all types of breast cancers. The choice of complexity metric depended on different cancer types, locations and treatment LINACs. Therefore, when utilizing complexity metrics to predict PSQA outcomes in IMRT for breast cancer, it was essential to select the appropriate metric based on the specific circumstances and characteristics of the treatment.

Dosimetric rationale and preliminary experience in proton plus carbon-ion radiotherapy for esophageal carcinoma: a retrospective analysis

BACKGROUND

Concurrent chemoradiotherapy has been standard of care for unresectable esophageal carcinoma. There were no reports on proton radiotherapy (PRT) plus carbon-ion radiotherapy (CIRT) with pencil beam scanning (PBS) for esophageal carcinoma. This study evaluated the tolerability and efficiency of proton and sequential carbon-ion boost radiotherapy for esophageal carcinoma.

METHODS

From April 2017 to July 2020, 20 patients with primary esophageal carcinoma at stages II-IV were treated with PRT plus sequential CIRT with PBS. A median relative biological effectiveness-weighted PRT dose of 50 Gy in 25 fractions, and a sequential CIRT dose of 21 Gy in 7 fractions were delivered. Respiratory motion management was used if the tumor moved > 5 mm during the breathing cycle. A dosimetric comparison of photon intensity-modulated radiotherapy (IMRT), PRT, and CIRT was performed. The median times and rates of survivals were estimated using the Kaplan-Meier method. Comparison of the dose-volume parameters of the organs at risk employed the Wilcoxon matched-pairs test.

RESULTS

Twenty patients (15 men and 5 women, median age 70 years) were included in the analysis. With a median follow-up period of 25.0 months, the 2-year overall survival and progression-free survival rates were 69.2% and 57.4%, respectively. The patients tolerated radiotherapy and chemotherapy well. Grades 1, 2, 3, and 4 acute hematological toxicities were detected in 25%, 30%, 10%, and 30% of patients, respectively. Grades 3-5 acute non-hematological toxicities were not observed. Late toxicity events included grades 1, 2, and 3 in 50%, 20%, and 10% (pulmonary and esophageal toxicity in each) of patients. Grades 4-5 late toxicities were not noted. PRT or CIRT produced lower doses to organs at risk than did photon IMRT, especially the maximum dose delivered to the spinal cord and the mean doses delivered to the lungs and heart.

CONCLUSIONS

PRT plus CIRT with PBS appears to be a safe and effective treatment for esophageal carcinoma. PRT and CIRT delivered lower doses to organs at risk than did photon IMRT. Further investigation is warranted.

Identification of a Prognostic Signature for Ovarian Cancer Based on Ubiquitin-Related Genes Suggesting a Potential Role for FBXO9

BACKGROUND

Ovarian cancer (OV) is associated with high mortality and poses challenges in diagnosis and prognosis prediction. Ubiquitin-related genes (UbRGs) are involved in the initiation and progression of cancers, but have still not been utilized for diagnosis and prognosis of OV.

METHODS

K48-linked ubiquitination in ovarian tissues from our OV and control cohort was assessed using immunohistochemistry. UbRGs, including ubiquitin and ubiquitin-like regulators, were screened based on the TCGA-OV and GTEx database. Univariate Cox regression analysis identified survival-associated UbRGs. A risk model was established using the LASSO regression and multivariate Cox regression analysis. The relationship between UbRGs and immune cell infiltration, tumor mutational burden, drug sensitivity, and immune checkpoint was determined using the CIBERSORT, ESTIMATE, and Maftools algorithms, based on the Genomics of Drug Sensitivity in Cancer and TCGA-OV databases. GEPIA2.0 was used to analyze the correlation between FBXO9/UBD and DNA damage repair-related genes. Finally, FBXO9 and UBD were accessed in tissues or cells using immunohistochemistry, qPCR, and Western blot.

RESULTS

We confirmed the crucial role for ubiquitination in OV as a significant decrease of K48-linked ubiquitination was observed in primary OV lesions. We identified a prognostic signature utilizing two specific UbRGs, FBXO9 and UBD. The risk score obtained from this signature accurately predicted the overall survival of TCGA-OV training dataset and GSE32062 validation dataset. Furthermore, this risk score also showed association with immunocyte infiltration and drug sensitivity, revealing potential mechanisms for ubiquitination mediated OV risk. In addition, FBXO9, but not UBD, was found to be downregulated in OV and positively correlated with DNA damage repair pathways, suggesting FBXO9 as a potential cancer suppressor, likely via facilitating DNA damage repair.

CONCLUSIONS

We identified and validated a signature of UbRGs that accurately predicts the prognosis, offers valuable guidance for optimizing chemotherapy and targeted therapies, and suggests a potential role for FBXO9 in OV.

Exosomal Long Non-Coding Ribonucleic Acid Ribonuclease Component of Mitochondrial Ribonucleic Acid Processing Endoribonuclease Is Defined as a Potential Non-Invasive Diagnostic Biomarker for Bladder Cancer and Facilitates Tumorigenesis via the miR-206/G6PD Axis

Bladder cancer (BLCA) is one of the cancers that is highly sensitive to specific non-invasive tumor biomarkers that facilitate early diagnosis. Exosome-derived long non-coding RNAs (lncRNAs) hold promise as diagnostic biomarkers for BLCA. In this study, we employed RNA-sequencing to compare the expression patterns of lncRNAs in urine exosomes from three BLCA patients and three healthy individuals. RMRP displayed the most significant differential expression. Elevated RMRP expression levels were observed in urinary and plasma exosomes from BLCA patients compared with those from healthy individuals. RMRP exhibited significant associations with certain BLCA patient clinicopathological features, including tumor stage, poor prognosis, and tumor grade. Combined diagnosis using RMRP in urine and plasma exosomes demonstrated a superior diagnostic performance with receiver operating characteristic curve analysis. RMRP was found to be related to BLCA tumor progression and the cell migration and invasion processes via the miR-206/G6PD axis both in vitro and in vivo. Mechanistically, RMRP serves as an miR-206 sponge, as suggested by dual-luciferase reporter assays and RNA immunoprecipitation. Our study suggests that the combined diagnosis of RMRP in urinary and plasma exosomes can serve as an excellent non-invasive diagnostic biomarker for BLCA patients. Additionally, targeting the RMRP/miR-206/G6PD axis holds promise as a therapeutic strategy for BLCA.

A Novel Albumin-Related Nutrition Biomarker Predicts Breast Cancer Prognosis in Neoadjuvant Chemotherapy: A Two-Center Cohort Study

BACKGROUND

Recently, there has been a growing focus on the prognostic significance of nutrition-related biomarkers. We attempted to explore the association between a novel albumin-related nutrition marker called "lymphocyte × albumin (LA)" and disease-free survival (DFS) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC).

METHODS

In total, 711 non-metastatic breast cancer patients who underwent NAC at two medical centers were retrospectively analyzed. We performed least absolute shrinkage and selection operator (LASSO) Cox regression analysis as well as multivariate Cox regression analyses to identify the variables associated with DFS and to establish a predictive nomogram.

RESULTS

The nomogram incorporated four variables based on the multivariate analysis of DFS in the training cohort: LA, ypN stage, ypT stage, and hormone receptor status. In comparison with the traditional TNM staging system, the nomogram demonstrated superior discrimination, calibration ability, and clinical usefulness in both the training set and internal and external validation sets. Furthermore, patients stratified into different risk groups resulted in significant differences in DFS.

CONCLUSIONS

LA is an independent prognostic biomarker, and LA-based prognostic nomogram offers a more precise assessment of DFS for breast cancer patients treated with NAC, potentially serving as a valuable tool for personalized prognostic predictions.

Promotion factors of emergency nurses' post-traumatic growth during the COVID-19 pandemic in Shanghai: a qualitative study

BACKGROUND

Since March 2022, Shanghai, China, has experienced a severe wave of SARS-CoV-2 transmission caused by the Omicron variant strain. The pandemic has severely constrained the local healthcare system. After treating critically ill COVID-19 patients, emergency nurses may experience some positive changes due to new insights or gains in their work, even if they have had traumatic experiences. This study aimed to explore the promoting factors of emergency nurses' post-traumatic growth during the COVID-19 pandemic in Shanghai. We hoped to provide a new perspective and theoretical basis for intervening in and promoting the psychological rehabilitation of medical staff after traumatic circumstances.

METHODS

This study employed a qualitative design based on the phenomenological approach. 18 participants from the emergency department of a third-level class-A hospital in Shanghai who participated in treating COVID-19 patients were enrolled using purposive sampling. Data collection was through in-depth and semi-structured interviews and continued until reaching data saturation. The seven-step Colaizzi process was used for data analysis.

RESULTS

The investigation uncovered two themes and six subthemes. Internal factors contained self-affirmation, deliberate rumination, and cognitive restructuring, which constituted attitudes and behaviours that participants could subjectively determine. External factors included social support, transformational leadership, and role modelling, which constituted factors influenced by others or the environment.

CONCLUSIONS

The promoting factors of PTG of emergency nurses originated from different sources such as individuals, organizations, and society. In addition to good psychological adjustment of the individual, society, hospitals, and nursing managers should focus on establishing supportive PTG strategies. The ultimate purpose is to improve the retention rate and career growth of nurses.

Application of artificial neural network in daily prediction of bleeding in ICU patients treated with anti-thrombotic therapy

OBJECTIVES

Anti-thrombotic therapy is the basis of thrombosis prevention and treatment. Bleeding is the main adverse event of anti-thrombosis. Existing laboratory indicators cannot accurately reflect the real-time coagulation function. It is necessary to develop tools to dynamically evaluate the risk and benefits of anti-thrombosis to prescribe accurate anti-thrombotic therapy.

METHODS

The prediction model,daily prediction of bleeding risk in ICU patients treated with anti-thrombotic therapy, was built using deep learning algorithm recurrent neural networks, and the model results and performance were compared with clinicians.

RESULTS

There was no significant statistical discrepancy in the baseline. ROC curves of the four models in the validation and test set were drawn, respectively. One-layer GRU of the validation set had a larger AUC (0.9462; 95%CI, 0.9147-0.9778). Analysis was conducted in the test set, and the ROC curve showed the superiority of two layers LSTM over one-layer GRU, while the former AUC was 0.8391(95%CI, 0.7786-0.8997). One-layer GRU in the test set possessed a better specificity (sensitivity 0.5942; specificity 0.9300). The Fleiss' k of junior clinicians, senior clinicians, and machine learning classifiers is 0.0984, 0.4562, and 0.8012, respectively.

CONCLUSIONS

Recurrent neural networks were first applied for daily prediction of bleeding risk in ICU patients treated with anti-thrombotic therapy. Deep learning classifiers are more reliable and consistent than human classifiers. The machine learning classifier suggested strong reliability. The deep learning algorithm significantly outperformed human classifiers in prediction time.

A new dyslipidemia-based scoring model to predict transplant-free survival in patients with hepatitis E-triggered acute-on-chronic liver failure

BACKGROUND/AIMS

Hepatitis E virus (HEV)-triggered acute-on-chronic liver failure (ACLF) has unacceptably high short-term mortality. However, it is unclear whether the existing predictive scoring models are applicable to evaluate the prognosis of HEV-triggered ACLF.

METHODS

We screened datasets of patients with HEV-triggered ACLF from a regional tertiary hospital for infectious diseases in Shanghai, China, between January 2011 and January 2021. Clinical and laboratory parameters were recorded and compared to determine a variety of short-term mortality risk factors, which were used to develop and validate a new prognostic scoring model.

RESULTS

Out of 4952 HEV-infected patients, 817 patients with underlying chronic liver disease were enrolled in this study. Among these, 371 patients with HEV-triggered ACLF were identified and allocated to the training set (n = 254) and test set (n = 117). The analysis revealed that hepatic encephalopathy (HE), ascites, triacylglycerol and apolipoprotein A (apoA) were associated with 90-day mortality (P < 0.05). Based on these significant indicators, we designed and calculated a new prognostic score = 0.632 × (ascites: no, 1 point; mild to moderate, 2 points; severe, 3 points) + 0.865 × (HE: no, 1 point; grade 1-2, 2 points; grade 3-4, 3 points) - 0.413 × triacylglycerol (mmol/L) - 2.171 × apoA (g/L). Compared to four well-known prognostic models (MELD score, CTP score, CLIF-C OFs and CLIF-C ACLFs), the new scoring model is more accurate, with the highest auROCs of 0.878 and 0.896, respectively, to predict 28- and 90-day transplantation-free survival from HEV-triggered ACLF. When our model was compared to COSSH ACLF IIs, there was no significant difference. The test data also demonstrated good concordance.

CONCLUSIONS

This study is one of the first to address the correlation between hepatitis E and serum lipids and provides a new simple and efficient prognostic scoring model for HEV-triggered ACLF.

Protocol of the integrated boost to the dominant intraprostatic nodule in stereotactic body radiation therapy for localized prostate cancer

Aim: To explore the safety and efficacy of the integrated boost to the dominant intraprostatic nodule (DIN) based on 68Ga prostate-specific membrane antigen PET/MRI in stereotactic body radiation therapy (SBRT) for patients with localized prostate cancer. Methods: SBRT regimen is employed - namely, sequential integrated boost (SIB) to the DIN based on 68Ga prostate-specific membrane antigen PET/MRI. SIB prescription dose of 36.25 Gy in five fractions to fixed prophylactic tumoricidal region is delivered, followed by 7.25 Gy in one fraction added to the DIN every other day. The primary end point of the study will be toxicity assessed by the Common Terminology Criteria for Adverse Events 5.0 grading scale. Secondary end points include biochemical progression-free survival, local progression-free survival, distant metastasis-free survival and overall survival. Discussion: This trial is to prove the safety and efficacy of sequential integrated boost to the DIN in SBRT. Clinical Trial Registration: NCT04599699 (ClinicalTrials.gov).

Ketogenic diet therapy in children with epilepsy caused by SLC2A1 mutations: a single-center single-arm retrospective study

BACKGROUND

This retrospective study assessed the efficacy and safety of ketogenic diet therapies in children with epilepsy caused by SLC2A1 genetic mutations and glucose transporter type 1 deficiency syndrome.

METHODS

Pediatric patients with epilepsy symptoms admitted to our medical center between January 2017 and October 2021 were included if they presented with an SLC2A1 genetic mutation on whole-exome sequencing. We analyzed the patients' convulsions and treatment with antiepileptic drugs. The patients were followed up at different time periods after ketogenic diet therapies.

RESULTS

Six patients with SLC2A1 mutations were included in this study. The patients had seizures of different types and frequencies, and they took antiepileptic drugs to relieve their symptoms. They were then treated with a ketogenic diet for at least four months. We analyzed epilepsy control rates at 1, 2, 3, 6, and 12 months after ketogenic diet treatment. All patients were seizure-free within a month of receiving the diet therapy. All patients were followed up for six months, three were followed up for 12 months after the treatment, and there was no recurrence of epilepsy during this period. After antiepileptic drug withdrawal, none of the patients experienced seizure relapse when receiving ketogenic diet treatment alone. No severe adverse events occurred during the therapy.

CONCLUSIONS

Ketogenic diet therapy is very effective and safe for the treatment of epilepsy caused by SLC2A1 mutations. Therefore, patients with glucose transporter type 1 deficiency syndrome caused by SLC2A1 mutations should begin ketogenic diet treatment as soon as possible.

Influence of Ethnicity on the Accuracy of Non-Invasive Scores Predicting Non-Alcoholic Fatty Liver Disease

OBJECTIVES

Presence of non-alcoholic fatty liver disease (NAFLD) can predict risks for diabetes, cardiovascular disease and advanced liver disease in the general population. We aimed to establish a non-invasive score for prediction of NAFLD in Han Chinese, the largest ethnic group in the world, and detect whether ethnicity influences the accuracy of such a score.

METHODS

Liver fat content (LFAT) was measured by quantitative ultrasound in 3548 subjects in the Shanghai Changfeng Community and a Chinese score was created using multivariate logistic regression analyses. This new score was internally validated in Chinese and externally in Finns. Its diagnostic performance was compared to the NAFLD liver fat score, fatty liver index (FLI) and hepatic steatosis index (HSI) developed in Finns, Italians and Koreans. We also analyzed how obesity related to LFAT measured by 1H-MRS in 79 Finns and 118 Chinese with type 2 diabetes (T2D).

RESULTS

The metabolic syndrome and T2D, fasting serum insulin, body mass index (BMI) and AST/ALT ratio were independent predictors of NAFLD in Chinese. The AUROC in the Chinese validation cohort was 0.76 (0.73-0.78) and in Finns 0.73 (0.68-0.78) (p<0.0001). 43%, 27%, 32% and 42% of Chinese had NAFLD when determined by the Chinese score, NAFLD liver fat score (p<0.001 vs. Chinese score), FLI (p<0.001) and HSI (NS). For any given BMI and waist circumference, the Chinese had a markedly higher LFAT than the Finns.

CONCLUSION

The predictors of NAFLD in Han Chinese are as in Europids but the Chinese have more LFAT for any given degree of obesity than Europids. Ethnicity needs to be considered when NAFLD is predicted using risk scores.