Objective:To explore the correlation between serum ferritin (SF) and risk of lean non-alcoholic fatty liver disease (NAFLD), so as to provide the basis for the prevention and treatment of lean NAFLD.Methods:A total of 7 187 people without NAFLD at baseline who took at least 2 physical examinations in the Health Management Center of the Second Hospital of Dalian Medical University from January 2014 to December 2023 and met the selection criteria were selected as the research subjects, and all the subjects had no NAFLD at baseline. Subjects were divided into four groups according to baseline SF quartiles: 1 797 cases in the first quartile ( Q1) group, 1 797 cases in the second quartile ( Q2) group, 1 797 cases in the third quartile ( Q3) group, and 1 796 cases in the fourth quartile ( Q4) group. The incidence of lean NAFLD in each group were observed. Kaplan-Meier curve was plotted to calculate the cumulative incidence of lean NAFLD which compared by log-rank test. Cox proportional hazard regression model was used to analyze the correlation between SF and new-onset lean NAFLD, Q1, Q2, Q3 and Q4 of SF were taken as continuous variables into the model for trend test.The stability of the results was verified by two item sensitivity analyses. Time-dependent receiver operating characteristic curve (ROC) was plotted to evaluate the predictive value of SF for the onset of lean NAFLD. Results:The cumulative follow-up were 25 076 person-years. There were 230 new cases of lean NAFLD, and the incidence density was 9.172/1 000 person-years. The incidence densities of lean NAFLD in Q1, Q2, Q3 and Q4 groups were 6.915/1 000 person-years, 8.552/1 000 person-years, 9.641/1 000 person-years, 12.003/1 000 person-years, respectively. Kaplan-Meier curve indicated that the incidence of lean NAFLD was increased with the increment of SF, and the difference was statistically significant (log-rank test, χ2=9.92, P=0.019). Cox proportional hazard regression model results showed that the risk of developing lean NAFLD in Q4 group increased by 72.8% ( HR=1.728, 95% confidence interval (95% CI): 1.059 to 2.820) compared with Q1 group. Trend analysis revealed that the risk of lean NAFLD increased by 18.9% for each one-quartile increase of SF( HR=1.189, 95% CI: 1.012 to 1.396). Two sensitivity analyses indicated that the risk of NAFLD in Q4 group was 1.795 times ( HR=1.795, 95% CI: 1.083 to 2.975) or 1.654 times ( HR=1.654, 95% CI: 1.022 to 2.678) higher than that in Q1 group. The area under the curve (95% CI) of SF for predicting the incidence of lean NAFLD at 2-, 3-, 7- and 8-year follow-up based on time-dependent ROC were 0.645 (0.593 to 0.698), 0.652 (0.603 to 0.700), 0.605 (0.539 to 0.672) and 0.716 (0.597 to 0.836), respectively. Conclusion:SF is an independent risk factor for lean NAFLD and has predictive value for the new-onset of lean NAFLD.

Objective:To investigate the effect of changes in metabolic syndrome status and persistence on carotid plaque risk.Methods:This retrospective cohort study analyzed individuals who underwent routine health check-ups at the health management center of the Second Affiliated Hospital of Dalian Medical University from 2014 to 2023. Participants with at least three carotid ultrasound records meeting the inclusion criteria were classified into 4 groups based on changes in metabolic status: persistently metabolic health, transitioning from metabolic health to unhealth, transitioning from metabolic unhealth to health, and persistently metabolic unhealth. The cumulative incidence of carotid plaque in these groups was compared. A Cox proportional risk model was used to evaluate the relationship between changes in metabolic syndrome status, the number of metabolic syndrome components, and the risk of carotid plaque development. Restricted cubic spline analysis was applied to explore the association between changes in individual metabolic syndrome components and carotid plaque risk.Results:Compared to the persistently metabolic health group, the persistent unhealth group had the highest risk of developing carotid plaque( HR=1.35, 95% CI 1.05-1.74, P=0.021), followed by those who transitioned from metabolic health to unhealth and those who improved from metabolic unhealth to health. Furthermore, the risk of carotid plaque increased progressively with the number of metabolic syndrome components. Restricted cubic spline analysis revealed a nonlinear relationship between fasting blood glucose change and carotid plaque risk, while systolic blood pressure, diastolic blood pressure, waist circumference, triglycerides, and high-density lipoprotein-cholesterol showed a linear dose-response relationship with carotid plaque. Conclusions:The change of metabolic syndrome is associated with the risk of developing carotid plaque, and maintaining metabolic health, recovering from metabolic syndrome, or minimizing the number of metabolic syndrome components may be effective strategies to prevent carotid plaque formation.

Objective:To explore the relationship between thyroid hormone sensitivity and metabolic dysfunction-associated steatotic liver disease(MASLD) in a population with normal thyroid function, with a particular focus on sex-specific differences.Methods:This retrospective study included 41 355 euthyroid cases who underwent routine health examinations at the Health Management Centre of the Second Affiliated Hospital of Dalian Medical University from January 2014 to December 2023 were included. The free triiodothyronine(FT 3) to free thyroxine(FT 4) ratio(FT 3/FT 4) was calculated in order to reflect the peripheral sensitivity of the thyroid gland. Similarly, thyroid feedback quantile-based index(TFQI), thyrotrophic thyroxine resistance index(TT 4RI), and the FT 3-based TFQI-derived index(TFQI-FT 3) were calculated in order to reflect the central sensitivity of the thyroid gland. A Logistic regression was employed to analyse the effect of sex-specific thyroid hormone sensitivity indices on the prevalence of MASLD. The restricted cubic spline was used to analyse the non-linear relationship between the thyroid sensitivity hormone indices and MASLD. Furthermore, the correlation between the thyroid hormone sensitivity indices and MASLD in different subgroups was also analysed. Results:The prevalence of MASLD in the study population was 28.8%. After adjusting the model for confounders, the risk of MASLD increased by 7%, 3%, 10%, and 5% for each standard deviation increase in FT 3/FT 4, TFQI, TFQI-FT 3, and TT 4RI in the total population, respectively. The risk of MASLD increased by 6% and 5% for each standard deviation increase in FT 3/FT 4 and TFQI-FT 3 in men, respectively. For each standard deviation increase in FT 3/FT 4, TFQI, TFQI-FT 3, and TT 4RI in women, the risk of MASLD increased by 6%, 5%, 11%, and 5%, respectively. Higher FT 3/FT 4 and TFQI-FT 3 were positively associated with the risk of developing MASLD in men, and higher FT 3/FT 4, TFQI, TFQI-FT 3, and TT 4RI were positively associated with the risk of developing MASLD in women. There was a non-linear, inverted U-shaped relationship between TFQI and risk of MASLD in women. Subgroup analyses showed positive associations between FT 3/FT 4, TFQI, TFQI-FT 3, and MASLD. Conclusions:The thyroid hormone sensitivity indices may provide a basis for clinical prevention and management of MASLD in individuals with normal thyroid function. Additionally, FT 3/FT 4 and TFQI-FT 3 may indicate the risk of MASLD in the general population, while TFQI and TT 4RI are more suitable for assessing the risk of MASLD in women.

Objective:To understand the knowledge, attitudes, and behaviors of residents in areas endemic for severe fever with thrombocytopenia syndrome (SFTS) and the factors influencing these aspects, to provide a scientific basis for the prevention and control of SFTS among residents in endemic areas.Methods:A multi-stage sampling method was used to select subjects for a questionnaire survey, and the collected data were analyzed and compared.Results:The awareness rate of SFTS-related knowledge among residents in the endemic areas of Qingdao was 14.38%, while the awareness rate of tick-related knowledge was 69.78%. The mean score for protective attitudes among residents in the endemic areas was 24.50±4.09, and the mean score for protective behaviors was 11.20±2.76. Among residents of different ages, occupations, and educational levels, comparisons of SFTS-related knowledge awareness rates ( χ2=293.553, P<0.001; χ2=495.216, P<0.001; χ2=677.272, P<0.001), tick-related knowledge awareness rates ( χ2=29.986, P<0.001; χ2=23.239, P=0.002; χ2=41.003, P<0.001), protective attitude scores ( H=52.094, P<0.001; H=33.446, P<0.001; H=81.715, P<0.001), and protective behavior scores ( H=52.344, P<0.001; H=28.465, P<0.001; H=67.061, P<0.001) showed statistically significant differences. Comparisons of these same factors among residents of different genders showed no statistically significant differences ( χ2=1.159, P=0.282; χ2=0.298, P=0.585; t=-0.808, P=0.419; t=-1.229, P=0.219). Comparisons of tick-related knowledge awareness rates among residents with different frequencies of outdoor work showed no statistically significant differences ( χ2=5.367, P=0.147); however, comparisons of SFTS-related knowledge awareness rates, protective attitude scores, and protective behavior scores among residents with different frequencies of outdoor work showed statistically significant differences ( χ2=117.010, P<0.001; F=4.127, P=0.006; H=56.280, P<0.001). Multivariate linear regression analysis revealed that older age, lower educational level, and less frequent outdoor work were associated with lower protective behavior scores ( t=-3.479, P<0.001; t=4.861, P<0.001; t=-7.753, P<0.001). Conclusions:Residents in the epidemic areas of SFTS have a low level of knowledge, attitude and practice. Factors influencing protective behaviors include age, occupation, educational level, and frequency of outdoor work. Farmers are a group that requires particular attention.

Objective:To investigate the effect of changes in metabolic syndrome status and persistence on carotid plaque risk.Methods:This retrospective cohort study analyzed individuals who underwent routine health check-ups at the health management center of the Second Affiliated Hospital of Dalian Medical University from 2014 to 2023. Participants with at least three carotid ultrasound records meeting the inclusion criteria were classified into 4 groups based on changes in metabolic status: persistently metabolic health, transitioning from metabolic health to unhealth, transitioning from metabolic unhealth to health, and persistently metabolic unhealth. The cumulative incidence of carotid plaque in these groups was compared. A Cox proportional risk model was used to evaluate the relationship between changes in metabolic syndrome status, the number of metabolic syndrome components, and the risk of carotid plaque development. Restricted cubic spline analysis was applied to explore the association between changes in individual metabolic syndrome components and carotid plaque risk.Results:Compared to the persistently metabolic health group, the persistent unhealth group had the highest risk of developing carotid plaque( HR=1.35, 95% CI 1.05-1.74, P=0.021), followed by those who transitioned from metabolic health to unhealth and those who improved from metabolic unhealth to health. Furthermore, the risk of carotid plaque increased progressively with the number of metabolic syndrome components. Restricted cubic spline analysis revealed a nonlinear relationship between fasting blood glucose change and carotid plaque risk, while systolic blood pressure, diastolic blood pressure, waist circumference, triglycerides, and high-density lipoprotein-cholesterol showed a linear dose-response relationship with carotid plaque. Conclusions:The change of metabolic syndrome is associated with the risk of developing carotid plaque, and maintaining metabolic health, recovering from metabolic syndrome, or minimizing the number of metabolic syndrome components may be effective strategies to prevent carotid plaque formation.

Objective:To explore the relationship between thyroid hormone sensitivity and metabolic dysfunction-associated steatotic liver disease(MASLD) in a population with normal thyroid function, with a particular focus on sex-specific differences.Methods:This retrospective study included 41 355 euthyroid cases who underwent routine health examinations at the Health Management Centre of the Second Affiliated Hospital of Dalian Medical University from January 2014 to December 2023 were included. The free triiodothyronine(FT 3) to free thyroxine(FT 4) ratio(FT 3/FT 4) was calculated in order to reflect the peripheral sensitivity of the thyroid gland. Similarly, thyroid feedback quantile-based index(TFQI), thyrotrophic thyroxine resistance index(TT 4RI), and the FT 3-based TFQI-derived index(TFQI-FT 3) were calculated in order to reflect the central sensitivity of the thyroid gland. A Logistic regression was employed to analyse the effect of sex-specific thyroid hormone sensitivity indices on the prevalence of MASLD. The restricted cubic spline was used to analyse the non-linear relationship between the thyroid sensitivity hormone indices and MASLD. Furthermore, the correlation between the thyroid hormone sensitivity indices and MASLD in different subgroups was also analysed. Results:The prevalence of MASLD in the study population was 28.8%. After adjusting the model for confounders, the risk of MASLD increased by 7%, 3%, 10%, and 5% for each standard deviation increase in FT 3/FT 4, TFQI, TFQI-FT 3, and TT 4RI in the total population, respectively. The risk of MASLD increased by 6% and 5% for each standard deviation increase in FT 3/FT 4 and TFQI-FT 3 in men, respectively. For each standard deviation increase in FT 3/FT 4, TFQI, TFQI-FT 3, and TT 4RI in women, the risk of MASLD increased by 6%, 5%, 11%, and 5%, respectively. Higher FT 3/FT 4 and TFQI-FT 3 were positively associated with the risk of developing MASLD in men, and higher FT 3/FT 4, TFQI, TFQI-FT 3, and TT 4RI were positively associated with the risk of developing MASLD in women. There was a non-linear, inverted U-shaped relationship between TFQI and risk of MASLD in women. Subgroup analyses showed positive associations between FT 3/FT 4, TFQI, TFQI-FT 3, and MASLD. Conclusions:The thyroid hormone sensitivity indices may provide a basis for clinical prevention and management of MASLD in individuals with normal thyroid function. Additionally, FT 3/FT 4 and TFQI-FT 3 may indicate the risk of MASLD in the general population, while TFQI and TT 4RI are more suitable for assessing the risk of MASLD in women.

Objective:To understand the knowledge, attitudes, and behaviors of residents in areas endemic for severe fever with thrombocytopenia syndrome (SFTS) and the factors influencing these aspects, to provide a scientific basis for the prevention and control of SFTS among residents in endemic areas.Methods:A multi-stage sampling method was used to select subjects for a questionnaire survey, and the collected data were analyzed and compared.Results:The awareness rate of SFTS-related knowledge among residents in the endemic areas of Qingdao was 14.38%, while the awareness rate of tick-related knowledge was 69.78%. The mean score for protective attitudes among residents in the endemic areas was 24.50±4.09, and the mean score for protective behaviors was 11.20±2.76. Among residents of different ages, occupations, and educational levels, comparisons of SFTS-related knowledge awareness rates ( χ2=293.553, P<0.001; χ2=495.216, P<0.001; χ2=677.272, P<0.001), tick-related knowledge awareness rates ( χ2=29.986, P<0.001; χ2=23.239, P=0.002; χ2=41.003, P<0.001), protective attitude scores ( H=52.094, P<0.001; H=33.446, P<0.001; H=81.715, P<0.001), and protective behavior scores ( H=52.344, P<0.001; H=28.465, P<0.001; H=67.061, P<0.001) showed statistically significant differences. Comparisons of these same factors among residents of different genders showed no statistically significant differences ( χ2=1.159, P=0.282; χ2=0.298, P=0.585; t=-0.808, P=0.419; t=-1.229, P=0.219). Comparisons of tick-related knowledge awareness rates among residents with different frequencies of outdoor work showed no statistically significant differences ( χ2=5.367, P=0.147); however, comparisons of SFTS-related knowledge awareness rates, protective attitude scores, and protective behavior scores among residents with different frequencies of outdoor work showed statistically significant differences ( χ2=117.010, P<0.001; F=4.127, P=0.006; H=56.280, P<0.001). Multivariate linear regression analysis revealed that older age, lower educational level, and less frequent outdoor work were associated with lower protective behavior scores ( t=-3.479, P<0.001; t=4.861, P<0.001; t=-7.753, P<0.001). Conclusions:Residents in the epidemic areas of SFTS have a low level of knowledge, attitude and practice. Factors influencing protective behaviors include age, occupation, educational level, and frequency of outdoor work. Farmers are a group that requires particular attention.

Objective:To explore the correlation between serum ferritin (SF) and risk of lean non-alcoholic fatty liver disease (NAFLD), so as to provide the basis for the prevention and treatment of lean NAFLD.Methods:A total of 7 187 people without NAFLD at baseline who took at least 2 physical examinations in the Health Management Center of the Second Hospital of Dalian Medical University from January 2014 to December 2023 and met the selection criteria were selected as the research subjects, and all the subjects had no NAFLD at baseline. Subjects were divided into four groups according to baseline SF quartiles: 1 797 cases in the first quartile ( Q1) group, 1 797 cases in the second quartile ( Q2) group, 1 797 cases in the third quartile ( Q3) group, and 1 796 cases in the fourth quartile ( Q4) group. The incidence of lean NAFLD in each group were observed. Kaplan-Meier curve was plotted to calculate the cumulative incidence of lean NAFLD which compared by log-rank test. Cox proportional hazard regression model was used to analyze the correlation between SF and new-onset lean NAFLD, Q1, Q2, Q3 and Q4 of SF were taken as continuous variables into the model for trend test.The stability of the results was verified by two item sensitivity analyses. Time-dependent receiver operating characteristic curve (ROC) was plotted to evaluate the predictive value of SF for the onset of lean NAFLD. Results:The cumulative follow-up were 25 076 person-years. There were 230 new cases of lean NAFLD, and the incidence density was 9.172/1 000 person-years. The incidence densities of lean NAFLD in Q1, Q2, Q3 and Q4 groups were 6.915/1 000 person-years, 8.552/1 000 person-years, 9.641/1 000 person-years, 12.003/1 000 person-years, respectively. Kaplan-Meier curve indicated that the incidence of lean NAFLD was increased with the increment of SF, and the difference was statistically significant (log-rank test, χ2=9.92, P=0.019). Cox proportional hazard regression model results showed that the risk of developing lean NAFLD in Q4 group increased by 72.8% ( HR=1.728, 95% confidence interval (95% CI): 1.059 to 2.820) compared with Q1 group. Trend analysis revealed that the risk of lean NAFLD increased by 18.9% for each one-quartile increase of SF( HR=1.189, 95% CI: 1.012 to 1.396). Two sensitivity analyses indicated that the risk of NAFLD in Q4 group was 1.795 times ( HR=1.795, 95% CI: 1.083 to 2.975) or 1.654 times ( HR=1.654, 95% CI: 1.022 to 2.678) higher than that in Q1 group. The area under the curve (95% CI) of SF for predicting the incidence of lean NAFLD at 2-, 3-, 7- and 8-year follow-up based on time-dependent ROC were 0.645 (0.593 to 0.698), 0.652 (0.603 to 0.700), 0.605 (0.539 to 0.672) and 0.716 (0.597 to 0.836), respectively. Conclusion:SF is an independent risk factor for lean NAFLD and has predictive value for the new-onset of lean NAFLD.

Objective:To investigate the relationship between different obesity metabolic phenotypes and the incidence of new carotid artery plaque.Methods:The present study is a retrospective cohort study, collecting individuals from the Health Management Center of the Second Affiliated Hospital of Dalian Medical University who had two or more cervical vascular color ultrasound examinations and met the inclusion criteria from 2014 to 2022, and collected their baseline clinical data. According to whether the subjects were obese and had metabolic syndrome, they were divided into metabolically healthy non-obese group, metabolically unhealthy non-obese group, metabolically healthy obese group, and metabolically unhealthy obese group. The first physical examination time of the subjects was taken as the starting point of follow-up, and cervical vascular color ultrasound was performed during the follow-up physical examination, with the outcome event being carotid artery plaque. Kaplan-Meier survival curve analysis was used to analyze the cumulative incidence of carotid artery plaques in the four groups and log-rank test was performed, and a multifactorial Cox proportional hazards model was used to analyze the relationship between different obesity metabolic phenotypes and the risk of carotid artery plaque incidence.Results:A total of 4 890 subjects were enrolled, aged (45.4±9.6) years, and 2 754 (56.3%) males. The follow-up time was 1.14(0.93, 2.20) years. Compared with the other 3 obesity metabolic phenotypes, the incidence of carotid plaques in the metabolically unhealthy obesity group was the highest (15.4% (286/1 861)). Kaplan-Meier survival curve analysis showed that the cumulative incidence of carotid plaques in metabolically unhealthy obese subjects was about 2.962 times that of metabolically healthy non-obese subjects (log-rank P<0.001). Multivariate Cox regression results showed that the risk of carotid plaque in metabolically unhealthy obese subjects was 1.650 times that of metabolically healthy non-obese subjects (95% CI: 1.203-2.264, P=0.002). Conclusion:Metabolically unhealthy obesity phenotype is an independent risk factor for carotid plaque.

Objective:To study the correlation between different body weight metabolic phenotypes and their changes and new-onset hyperuricemia in physical examination population.Methods:This study was a retrospective cohort study. A total of 31 956 people who underwent routine physical examination and met the inclusion and exclusion criteria at the Health Management Center of the Second Affiliated Hospital of Dalian Medical University from January 1, 2014 to August 31, 2022 were selected as the study subjects to establish a dynamic physical examination cohort. The end point of follow-up was new-onset hyperuricemia or the end of follow-up period. Cox regression stepwise fitting model was used to analyze the risk of different body weight metabolic phenotypes and hyperuricemia, and stratified analysis was performed for gender. According to body weight metabolic phenotype, the subjects were divided into normal metabolism and normal weight(NMNW) group, normal metabolism and obesity (NMO) group, abnormal metabolism and normal weight (AMNW) group and abnormal metabolism and obesity (AMO) group. The risk of hyperuricemia was calculated according to the changes of body weight metabolic phenotype during the follow-up period. In the sensitivity analysis, the robustness of the results was verified by changing the diagnostic criteria for hyperuricemia, removing patients with hyperuricemia at the first year of follow-up, and removing subjects aged ≥65 years.Results:Compared with the NMNW group, the risk of hyperuricemia in the NMO group, AMNW group and AMO group increased by 78.9%, 61.3%, 115.4%, respectively ( χ2=272.88, 128.15, 496.12, all P<0.001). Patients who were initially classified as NMNW at baseline, if transitioned to NMO or AMO by the follow-up endpoint, their risk of hyperuricemia increased by 122.5% ( χ2=8.01, P<0.05) and 137.4% ( χ2=15.99, P<0.001), respectively. When the baseline AMNW group changed to AMO, the risk of hyperuricemia was increased by 119.2% ( χ2=6.63, P<0.05). For patients with AMO as baseline, if they turned into NMNW and AMNW at the end of follow-up, their risk of hyperuricemia would decrease by 58.3% ( χ2=43.67, P<0.001) and 27.2% ( χ2=16.07, P<0.001). Patients with a baseline of NMO who transitioned to NMNW and AMNW at the follow-up endpoint had their risk of developing hyperuricemia decreased by 36.7% ( χ2=25.35, P<0.001) and 30.9% ( χ2=9.70, P<0.05), respectively. Conclusions:The transition from metabolic health and non-overweight obesity to metabolic abnormalities and overweight obesity is associated with an increased risk of hyperuricemia, and improvements in metabolic health or weight are associated with a decreased risk of hyperuricemia.