BACKGROUND: The incidence and mortality rates of lung cancer remain on the rise, creating an urgent need for screening among high-risk populations and early prevention. This study aims to explore the association and interaction between multidimensional lifestyle, socioeconomic status, and the incidence of lung cancer, and to provide scientific evidence for screening high-risk populations and preventing lung cancer. METHODS: Healthy lifestyle score was constructed using information on smoking, alcohol consumption, exercise, diet and sleep obtained through a questionnaire survey. Socioeconomic status was evaluated based on information on education, employment, and family income, and genetic testing data were used to assess the risk of genetic variation. A proportional hazards assumption test was conducted, and the Cox proportional hazards model was applied to analyze the associations between healthy lifestyle scores, socioeconomic status, and lung cancer, as well as the interactions among various factors, after adjusting for the risk of genetic variation, age, gender, diabetes, hypertension and the living environment score. RESULTS: A total of 245,538 samples that entered the cohort from March, 2006 to October, 2010 were included and followed up until December 31, 2022. The participants were divided into the case group (n=1472) and the control group (n=244,066). The analysis results showed that after adjusting for covariates, there was still an association between the healthy lifestyle score, socioeconomic status, and the incidence of lung cancer: compared with participants with a high healthy lifestyle score, the risk of lung cancer in participants with medium and low healthy lifestyle scores was significantly increased, with hazard ratios (HR) of 2.12 (95%CI: 1.86-2.41) and 3.36 (95%CI: 2.82-3.99) respectively; compared with participants with high socioeconomic status, the risk of lung cancer in participants with medium and low socioeconomic status was significantly increased, with HR of 1.29 (95%CI: 1.13-1.48) and 1.67 (95%CI: 1.46-1.90) respectively. Moreover, there were interactions between smoking status and socioeconomic status (Pfor interaction=0.05), as well as the other four lifestyle factors (Pfor interaction=0.02). CONCLUSIONS This study identified the association between multidimensional lifestyle factors and socioeconomic status with the incidence of lung cancer, as well as interactions between smoking and socioeconomic status and four other lifestyle factors, providing a scientific basis for screening and prevention in high-risk populations for lung cancer.
Humans ; Lung Neoplasms/epidemiology* ; Male ; Female ; Middle Aged ; Incidence ; Life Style ; Social Class ; Aged ; Adult ; Risk Factors

ObjectiveTo investigate the pathogenic spectrum and epidemiological characteristics of diarrheal disease in Fengxian District of Shanghai, and to provide scientific basis for the prevention and control of diarrheal diseases. MethodsBasic information of the initial adult cases visited diarrheal disease surveillance sentinel hospital in Fengxian District, Shanghai, was collected from August 2013 to 2023, and fecal samples were collected at 1∶5 sampling intervals to isolate and identify 5 kinds of diarrheagenic Escherichia coli (DEC), Salmonella (SAL), Vibrio parahaemolyticus, Campylobacter, Vibrio cholerae, Shigella and Yersinia enterocolitica (YE). Simultaneously, nucleic acid detection was performed for 3 kinds of rotavirus, 2 kinds of norovirus, intestinal adenovirus, astrovirus and sapovirus. ResultsA total of 1 861 cases of newly diagnosed diarrheal disease were reported, with the peak in July to August. Additionally, 704 surveillance samples were detected, with a total positive detection rate of 50.57%. The detection rates of bacterial, viral and mixed infection were 25.14%, 21.02% and 4.40%, respectively. Among the pathogens detected, DEC accounted for the highest (17.61%, 124/704), followed by norovirus (16.48%, 116/704), rotavirus (6.39%, 45/704), SAL (5.97%, 42/704) and Campylobacter (3.84%, 27/704). DEC detected were mainly enteroaggregative Escherichia coli and enterotoxigenic Escherichia coli, with no detection of Vibrio cholerae, Shigella and YE. The highest total pathogen detection rate was observed from June to September, and the detection peaks of norovirus were from March to June and from October to December, whereas that of DEC was from June to October. The detection rate of rotavirus peaked from January to February, but which was not detected between 2020‒2023. The SAL positive rate peak was in September, whereas that of Campylobacter was from July to September. ConclusionThe main pathogens detected in Fengxian District from 2013‒2019 are DEC, norovirus, rotavirus, SAL and Campylobacter. Different pathogens have different detection peaks, with bacteria predominating in summer and viruses in winter and spring. Prevention and control measures should be carried out according to the epidemiological characteristics of different seasons.

This study aimed to comprehensively examine the association of gallstones, cholecystectomy, and cancer risk. Multivariable logistic regressions were performed to estimate the observational associations of gallstones and cholecystectomy with cancer risk, using data from a nationwide cohort involving 239 799 participants. General and gender-specific two-sample Mendelian randomization (MR) analysis was further conducted to assess the causalities of the observed associations. Observationally, a history of gallstones without cholecystectomy was associated with a high risk of stomach cancer (adjusted odds ratio (aOR)=2.54, 95% confidence interval (CI) 1.50-4.28), liver and bile duct cancer (aOR=2.46, 95% CI 1.17-5.16), kidney cancer (aOR=2.04, 95% CI 1.05-3.94), and bladder cancer (aOR=2.23, 95% CI 1.01-5.13) in the general population, as well as cervical cancer (aOR=1.69, 95% CI 1.12-2.56) in women. Moreover, cholecystectomy was associated with high odds of stomach cancer (aOR=2.41, 95% CI 1.29-4.49), colorectal cancer (aOR=1.83, 95% CI 1.18-2.85), and cancer of liver and bile duct (aOR=2.58, 95% CI 1.11-6.02). MR analysis only supported the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer. This study added evidence to the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer, highlighting the importance of cancer screening in individuals with gallstones.
Humans ; Mendelian Randomization Analysis ; Gallstones/complications* ; Female ; Male ; Cholecystectomy/statistics & numerical data* ; Middle Aged ; Risk Factors ; Aged ; Adult ; Neoplasms/etiology* ; Stomach Neoplasms/epidemiology*

ObjectiveTo understand the epidemiological distribution and gene evolutionary variation of influenza A (H3N2) viruses in Fengxian District, Shanghai, in the surveillance year of 2023, and to provide a reference basis for influenza prevention and control. MethodsThe prevalence of influenza virus in Fengxian District in the 2023 influenza surveillance year (April 2023‒March 2024) was analyzed. The hemagglutinin (HA) gene, neuraminidase (NA) gene, and amino acid sequences of 75 strains of H3N2 influenza viruses were compared with the vaccine reference strain for similarity matching and phylogenetic evolutionary analysis, in addition to an analysis of gene characterization and variation. ResultsIn Fengxian District, there was a mixed epidemic of H3N2 and H1N1 in the spring of 2023, with H3N2 being the predominant subtype in the second half of the year, and Victoria B becoming the predominant subtype in the spring of 2024. A total of 75 influenza strains of H3N2 with HA and NA genes were distributed in the 3C.2a1b.2a.2a.2a.3a.1 and B.4 branches, with overall similarity to the reference strain of the 2024 vaccine higher than that of the reference strain of the 2022 and 2023 vaccine. Compared with the 2023 vaccine reference strain, three antigenic sites and one receptor binding site were changed in HA, with three glycosylation sites reduced and two glycosylation sites added; where as in NA seven antigenic sites and the 222nd resistance site changed with two glycosylation sites reduced. ConclusionThe risk of antigenic variation and drug resistance of H3N2 in this region is high, and it is necessary to strengthen the publicity and education on the 2024 influenza vaccine and long-term monitoring of influenza virus prevalence and variation levels.

ObjectiveTo investigate the features of serum HBV RNA in different stages of the natural history of chronic hepatitis B virus （HBV） infection without antiviral treatment， as well as its correlation with serum HBV DNA and HBsAg. MethodsA total of 306 treatment-naïve patients with chronic HBV infection who attended Department of Infections Diseases and Hepatoloty， the Second Hospital of Shandong University from January 2023 to June 2024 were divided into six groups based on the different stages of natural history， i.e.， HBeAg-positive chronic HBV infection group with 29 patients， HBeAg-positive chronic hepatitis B （CHB） group with 107 patients， HBeAg-negative chronic HBV infection group with 18 patients， HBeAg-negative CHB group with 60 patients， HBeAg-positive indeterminate-phase chronic HBV infection group with 7 patients， and HBeAg-negative indeterminate-phase chronic HBV infection group with 85 patients. Real-time isothermal RNA amplification was used to measure serum high-sensitivity HBV RNA. The Kruskal-Wallis H test was used for comparison between multiple groups of continuous data， while the Mann-Whitney U test was used for comparison between two groups. The Spearman method was used to investigate the correlation of HBV RNA with HBV DNA and HBsAg. ResultsThe HBeAg-positive chronic HBV infection group showed the highest level of serum HBV RNA ［7.5 （7.4‍ ‍—‍ ‍7.9） log₁₀ copies/mL］， followed by the HBeAg-positive CHB group ［7.4 （6.4‍ ‍—‍ ‍7.9） log₁₀ copies/mL］， the HBeAg-negative CHB group ［4.5 （3.0‍ ‍—‍ ‍5.7） log₁₀ copies/mL］， and the HBeAg-negative chronic HBV infection group ［1.0 （1.0‍ ‍—‍ ‍2.0） log₁₀ copies/mL］； the HBeAg-positive indeterminate-phase chronic HBV infection group had a serum HBV RNA level of 3.9 （3.7‍ ‍—‍ ‍5.7） log₁₀ copies/mL， and the HBeAg-negative indeterminate-phase chronic HBV infection group had a serum HBV RNA level of 2.0 （1.0‍ ‍—‍ ‍3.0） log₁₀ copies/mL； there was a significant difference in serum HBV RNA level between the six groups （H=830.770， P<0.001）. There was a significant difference in HBV RNA level between the HBeAg-positive chronic HBV infection group and all the other groups except the HBeAg-positive CHB group （all P<0.001）. In the 306 patients with HBV infection， HBV RNA was strongly correlated with HBV DNA （r=0.92， P<0.001） and was moderately correlated with HBsAg （r=0.67， P<0.001）. The correlation between serum HBV RNA and HBsAg in HBeAg-positive patients （r=0.61， P<0.001） was stronger than that in HBeAg-negative patients （r=0.31， P<0.001）. For the patients with HBeAg-positive chronic HBV infection， the male patients with ALT>30 U/L and the female patients with ALT>19 U/L had a significantly lower serum HBV RNA level than the male patients with ALT≤30 U/L and the female patients with ALT≤19 U/L （P<0.001）， and there was no significant difference in serum HBV RNA level between the latter group of patients and the HBeAg-positive CHB group （P>0.05）. ConclusionIn patients with chronic HBV infection who do not receive antiviral therapy， there is a difference in serum HBV RNA level in different stages of natural history， and serum HBV RNA level has the strongest correlation with HBV DNA and a relatively weak correlation with HBsAg. In patients with HBeAg-positive chronic HBV infection， serum HBV RNA level in male patients with ALT>30 U/L and female patients with ALT>19 U/L are in the transition stage between HBeAg-positive chronic HBV infection and HBeAg-positive CHB.

Objectives:To analyze whether there is a causal relationship between plasma proteins and the risk of coronary atherosclerosis(CAS)based on a two-sample Mendelian randomization(MR)analysis and to identify potential therapeutic targets for CAS.Methods:Single nucleotide polymorphisms(SNP)associated with plasma proteins from the UK Biobank Pharmacoproteomics Program(UKB-PPP)database were used as instrumental variables and outcome data were obtained from genome-wide association study databases.The Wald ratio method and inverse variance weighted(IVW)method in two-sample MR were employed as the primary approaches to assess the causal relationship between plasma proteins and CAS.Colocalization analysis was conducted as a sensitivity analysis to ensure the robustness of the MR findings.Results:A total of 132 plasma proteins were found to have causal associations with an increased risk of CAS.Colocalization analysis revealed that 12 plasma proteins shared genetic variants with CAS.Among them,Proprotein convertase subtilise/kexin type 9(PCSK9)(OR=1.23,95%CI:1.15-1.32,P<0.01)and neurocan(NCAN)(OR=1.35,95%CI:1.21-1.52,P<0.01)exhibited posterior probability of hypothesis4(PPH4)values<0.80 in the colocalization analysis,indicating strong support for colocalization and suggesting their potential as primary plasma protein drug targets for CAS.Conclusions:PCSK9 is associated with an increased risk of CAS and is confirmed as a therapeutic target for CAS.NCAN emerges as another potential therapeutic target for CAS.

Objective:To construct a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury (TBI) and evaluate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 1 120 TBI patients admitted to Changde Hospital Affiliated to Xiangya Medical College of Central South University from May 2019 to December 2024. The patients were divided into the training set ( n=784) and verification set ( n=336) at a ratio of 7∶3. Based on the Glasgow outcome scale-extended (GOS-E) at discharge, the training set was stratified into favorable prognosis group ( n=335, GOS-E 5-8 points) and poor prognosis group ( n=449, GOS-E 1-4 points). The two groups in the training set were compared in terms of general baseline indicators, TBI-related clinical indicators, and admission laboratory blood test results. Univariate analysis and Lasso regression analysis were employed to screen risk factors associated with postoperative poor in-hospital prognosis in TBI patients. Multivariate Logistic regression analysis was used to determine independent risk factors and construct a regression equation. The regression equation was presented using R language to create a visual nomogram for predicting postoperative poor in-hospital prognosis in TBI patients. In both the training set and verification set, the predictive performance of the model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC), plotting calibration curves, and performing decision curve analysis (DCA). Results:The results of the univariate analysis indicated that the age, Charlson complication index (CCI), time from trauma to admission, time from trauma to operation, cause of injury, abbreviated injury scale (AIS) (head and neck), injury severity score (ISS), admission Glasgow coma scale (GCS), admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraventricular hemorrhage, subarachnoid hemorrhage, decompressive craniotomy, intraoperative blood loss, intraoperative blood transfusion, traumatic cerebral infarction, postoperative delayed bleeding, epilepsy seizures, as well as the following admission tested results including red blood cell count, white blood cell count, platelet count, neutrophil percentage, percentage of lymphocytes, albumin, total bilirubin, urea nitrogen, thrombin time (TT), prothrombin time (PT), international standardized ratio (INR), glutamic aminotransferase, alanine aminotransferase, creatinine, and blood glucose were statistically different between the two groups in the training set ( P<0.05). Lasso regression analysis suggested 14 risk factors of age, CCI, cause of injury, head and neck AIS, ISS, admission GCS, admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraoperative blood loss, admission platelet count, admission albumin, admission blood glucose for postoperative poor in-hospital prognosis. The results of the multivariate Logistic regression analysis showed that age ( OR=1.02, 95% CI 1.00, 1.03, P<0.01), CCI ( OR=1.46, 95% CI 1.02, 2.09, P<0.05), head and neck AIS ( OR=1.43, 95% CI 1.11, 1.85, P<0.01), ISS ( OR=2.16, 95% CI 1.39, 3.35, P<0.01), admission GCS ( OR=1.59, 95% CI 1.19, 2.13, P<0.01), intracerebral hematoma ( OR=4.41, 95% CI 2.15, 9.44, P<0.01), intraoperative blood loss ( OR=1.05, 95% CI 1.00, 1.09, P<0.05), admission platelet count ( OR=0.98, 95% CI 0.97, 0.99, P<0.01), admission blood glucose ( OR=1.08, 95% CI 1.02, 1.15, P<0.05) could be the main risk factors to construct a prediction model for postoperative poor in-hospital prognosis in TBI patients. Meanwhile, a regression equation was constructed: Logit[ P/(1- P)]=-2.4+ 0.02×"age"+0.38×"CCI"+0.36×"head and neck AIS"+0.77×"ISS"+0.47×"admission GCS"+1.48×"intracerebral hematoma"+0.05×intraoperative blood loss-0.02×admission platelet count+0.08×admission blood glucose. In the training set, the predictive model for poor postoperative in-hospital prognosis in TBI patients achieved an AUC of 0.87 (95% CI 0.84, 0.89), with a Youden′s index of 0.57, sensitivity of 73.70%, and specificity of 83.00%. In the verification set, the model showed an AUC of 0.80 (95% CI 0.76, 0.85), with a Youden′s index of 0.63, sensitivity of 65.20%, and specificity of 77.90%. In the training set, the Brier score for the calibration curve was 0.14 (95% CI 0.13, 0.16). In the verification set, the Brier score for the calibration curve was 0.18 (95% CI 0.15, 0.20). The DCA diagram indicated that the nomogram prediction model provided high clinical net benefit for predicting postoperative poor in-hospital prognosis in TBI patients. Conclusion:The prediction model for postoperative poor in-hospital prognosis in TBI patients, constructed based on age, CCI, head and neck AIS, ISS, admission GCS, intracerebral hematoma, intraoperative blood loss, admission platelet count, and admission blood glucose, exhibits good predictive performance.

Objective:To construct a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury (TBI) and evaluate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 1 120 TBI patients admitted to Changde Hospital Affiliated to Xiangya Medical College of Central South University from May 2019 to December 2024. The patients were divided into the training set ( n=784) and verification set ( n=336) at a ratio of 7∶3. Based on the Glasgow outcome scale-extended (GOS-E) at discharge, the training set was stratified into favorable prognosis group ( n=335, GOS-E 5-8 points) and poor prognosis group ( n=449, GOS-E 1-4 points). The two groups in the training set were compared in terms of general baseline indicators, TBI-related clinical indicators, and admission laboratory blood test results. Univariate analysis and Lasso regression analysis were employed to screen risk factors associated with postoperative poor in-hospital prognosis in TBI patients. Multivariate Logistic regression analysis was used to determine independent risk factors and construct a regression equation. The regression equation was presented using R language to create a visual nomogram for predicting postoperative poor in-hospital prognosis in TBI patients. In both the training set and verification set, the predictive performance of the model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC), plotting calibration curves, and performing decision curve analysis (DCA). Results:The results of the univariate analysis indicated that the age, Charlson complication index (CCI), time from trauma to admission, time from trauma to operation, cause of injury, abbreviated injury scale (AIS) (head and neck), injury severity score (ISS), admission Glasgow coma scale (GCS), admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraventricular hemorrhage, subarachnoid hemorrhage, decompressive craniotomy, intraoperative blood loss, intraoperative blood transfusion, traumatic cerebral infarction, postoperative delayed bleeding, epilepsy seizures, as well as the following admission tested results including red blood cell count, white blood cell count, platelet count, neutrophil percentage, percentage of lymphocytes, albumin, total bilirubin, urea nitrogen, thrombin time (TT), prothrombin time (PT), international standardized ratio (INR), glutamic aminotransferase, alanine aminotransferase, creatinine, and blood glucose were statistically different between the two groups in the training set ( P<0.05). Lasso regression analysis suggested 14 risk factors of age, CCI, cause of injury, head and neck AIS, ISS, admission GCS, admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraoperative blood loss, admission platelet count, admission albumin, admission blood glucose for postoperative poor in-hospital prognosis. The results of the multivariate Logistic regression analysis showed that age ( OR=1.02, 95% CI 1.00, 1.03, P<0.01), CCI ( OR=1.46, 95% CI 1.02, 2.09, P<0.05), head and neck AIS ( OR=1.43, 95% CI 1.11, 1.85, P<0.01), ISS ( OR=2.16, 95% CI 1.39, 3.35, P<0.01), admission GCS ( OR=1.59, 95% CI 1.19, 2.13, P<0.01), intracerebral hematoma ( OR=4.41, 95% CI 2.15, 9.44, P<0.01), intraoperative blood loss ( OR=1.05, 95% CI 1.00, 1.09, P<0.05), admission platelet count ( OR=0.98, 95% CI 0.97, 0.99, P<0.01), admission blood glucose ( OR=1.08, 95% CI 1.02, 1.15, P<0.05) could be the main risk factors to construct a prediction model for postoperative poor in-hospital prognosis in TBI patients. Meanwhile, a regression equation was constructed: Logit[ P/(1- P)]=-2.4+ 0.02×"age"+0.38×"CCI"+0.36×"head and neck AIS"+0.77×"ISS"+0.47×"admission GCS"+1.48×"intracerebral hematoma"+0.05×intraoperative blood loss-0.02×admission platelet count+0.08×admission blood glucose. In the training set, the predictive model for poor postoperative in-hospital prognosis in TBI patients achieved an AUC of 0.87 (95% CI 0.84, 0.89), with a Youden′s index of 0.57, sensitivity of 73.70%, and specificity of 83.00%. In the verification set, the model showed an AUC of 0.80 (95% CI 0.76, 0.85), with a Youden′s index of 0.63, sensitivity of 65.20%, and specificity of 77.90%. In the training set, the Brier score for the calibration curve was 0.14 (95% CI 0.13, 0.16). In the verification set, the Brier score for the calibration curve was 0.18 (95% CI 0.15, 0.20). The DCA diagram indicated that the nomogram prediction model provided high clinical net benefit for predicting postoperative poor in-hospital prognosis in TBI patients. Conclusion:The prediction model for postoperative poor in-hospital prognosis in TBI patients, constructed based on age, CCI, head and neck AIS, ISS, admission GCS, intracerebral hematoma, intraoperative blood loss, admission platelet count, and admission blood glucose, exhibits good predictive performance.

Objectives:To analyze whether there is a causal relationship between plasma proteins and the risk of coronary atherosclerosis(CAS)based on a two-sample Mendelian randomization(MR)analysis and to identify potential therapeutic targets for CAS.Methods:Single nucleotide polymorphisms(SNP)associated with plasma proteins from the UK Biobank Pharmacoproteomics Program(UKB-PPP)database were used as instrumental variables and outcome data were obtained from genome-wide association study databases.The Wald ratio method and inverse variance weighted(IVW)method in two-sample MR were employed as the primary approaches to assess the causal relationship between plasma proteins and CAS.Colocalization analysis was conducted as a sensitivity analysis to ensure the robustness of the MR findings.Results:A total of 132 plasma proteins were found to have causal associations with an increased risk of CAS.Colocalization analysis revealed that 12 plasma proteins shared genetic variants with CAS.Among them,Proprotein convertase subtilise/kexin type 9(PCSK9)(OR=1.23,95%CI:1.15-1.32,P<0.01)and neurocan(NCAN)(OR=1.35,95%CI:1.21-1.52,P<0.01)exhibited posterior probability of hypothesis4(PPH4)values<0.80 in the colocalization analysis,indicating strong support for colocalization and suggesting their potential as primary plasma protein drug targets for CAS.Conclusions:PCSK9 is associated with an increased risk of CAS and is confirmed as a therapeutic target for CAS.NCAN emerges as another potential therapeutic target for CAS.

Background/Aims: Low educational attainment is a well-established risk factor for nonalcoholic fatty liver disease (NAFLD) in developed areas. However, the association between educational attainment and the risk of NAFLD is less clear in China. Methods: A cross-sectional study including over 200,000 Chinese adults across mainland China was conducted. Information on education level and lifestyle factors were obtained through standard questionnaires, while NAFLD and advanced fibrosis were diagnosed using validated formulas. Outcomes included the risk of NAFLD in the general population and high probability of fibrosis among patients with NAFLD. Logistic regression analysis was employed to estimate the risk of NAFLD and fibrosis across education levels. A causal mediation model was used to explore the potential mediators. Results: Comparing with those receiving primary school education, the multi-adjusted odds ratios (95% confidence intervals) for NAFLD were 1.28 (1.16 to 1.41) for men and 0.94 (0.89 to 0.99) for women with college education after accounting for body mass index. When considering waist circumference, the odds ratios (95% CIs) were 0.94 (0.86 to 1.04) for men and 0.88 (0.80 to 0.97) for women, respectively. The proportions mediated by general and central obesity were 51.00% and 68.04% for men, while for women the proportions were 48.58% and 32.58%, respectively. Furthermore, NAFLD patients with lower educational attainment showed an incremental increased risk of advanced fibrosis in both genders. Conclusions In China, a low education level was associated with a higher risk of prevalent NAFLD in women, as well as high probability of fibrosis in both genders.