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 explore the correlation of cumulative atherogenic index of plasma (cumAIP) and cumulative atherosclerosis index (cumAI) with new-onset non-alcoholic fatty liver disease (NAFLD).Methods:From January 2017 to December 2023, 2 472 subjects who underwent health checkups at the Second Affiliated Hospital of Dalian Medical University for 3 consecutive years were enrolled. Triglyceride, total cholesterol, high density lipoprotein cholesterol and their measurement time intervals were used to calculate cumAIP and cumAI. The subjects were divided into Q1, Q2, Q3 and Q4 groups with the threshold values of 25th percentile, median and 75th percentile of the baseline atherogenic index of plasma (AIP) and atherosclerotic index (AI) subjects. Cox regression model was used to analyze the effects of cumAIP and cumAI on the new-onset NAFLD, restricted cubic spline was performed to analyze the nonlinear association between cumAIP and cumAI and new-onset NAFLD, and the clinical decision curve was used to compare the decision value of different indicators for NAFLD. Results:The risk of NAFLD gradually increased along with the increasing of cumAIP and cumAI. In the quartile groups of cumAIP, the incidence of Q1 to Q4 groups was 6.15%, 8.74%, 15.05%, and 25.08%, respectively. In the quartile groups of cumAI, the incidence of Q1 to Q4 groups was 5.99%, 11.17%, 15.21%, and 22.65%, respectively. After adjusting the confounding factors, the risk of new-onset NAFLD in the high-level group ( Q4) was higher than that in the low-level cumAIP group ( Q1) ( HR=3.15, 95% confidence interval (95% CI): 2.15 to 4.63, P<0.001) and the high-level cumAI group ( Q4) ( HR=2.74, 95% CI: 1.82 to 4.10, P<0.001). cumAIP and cumAI showed a significant nonlinear association with new-onset NAFLD ( χ2=119.15, 94.53; both P<0.001). The cumAIP had higher predictive value for NAFLD than the other cumulative lipid metrics and baseline AIP or AI. Conclusion:CumAIP and cumAI can be served as new predictive indicators of NAFLD, with a particular focus on the dynamic cumulative changes of AIP, which can achieve effective early screening for NAFLD.

Objective:Investigating the association of the systemic immune-inflammation index and the pan-inflammation index with incident non-alcoholic fatty liver disease (NAFLD).Methods:This retrospective cohort study included 42 891 participants who underwent at least two health examinations at the Second Affiliated Hospital of Dalian Medical University between 2014 and 2023. Based on their levels of the systemic immune inflammation index (SII) and the pan immune inflammation value (PIV), participants were respectively divided into four quartile groups (Q1 to Q4). Cox proportional hazards models were employed to analyze the association of different SII and PIV levels, as well as their quartile groups, with new onset NAFLD in the total population and across various subgroups. Restricted cubic splines were used to examine the dose response relationship between these inflammatory indices and incident NAFLD. Additionally, sensitivity analyses were conducted to confirm the robustness of the findings.Results:After adjusting for confounding factors, the natural logarithm-transformed lnSII ( HR=1.247, 95% CI: 1.184-1.314, P<0.001) and lnPIV ( HR=1.192, 95% CI: 1.148-1.238, P<0.001) were significantly positively associated with the risk of NAFLD. When the subjects were grouped by SII quartiles (Q1-Q4), compared with those in Q1, participants in Q2, Q3, and Q4 exhibited progressively higher risks of incident NAFLD:11.9% ( HR=1.119, 95% CI: 1.051-1.192, P<0.001), 17.1% ( HR=1.171, 95% CI: 1.100-1.248, P<0.001), and 29.1% ( HR=1.291, 95% CI: 1.211-1.377, P<0.001), respectively. Quartile analysis of PIV yielded similar trends: the risk of incident NAFLD increased for 10.4% ( HR=1.104, 95% CI: 1.034-1.179, P=0.003), 18.7% ( HR=1.187, 95% CI: 1.112-1.266, P<0.001), and 30.5% ( HR=1.305, 95% CI: 1.223-1.393, P<0.001) in Q2, Q3, and Q4 group respectively when compared to that in Q1 group. Subgroup analysis confirmed consistent associations of SII and PIV with elevated NAFLD risk across all subgroups. Conclusion:Elevated levels of SII and PIV are significantly associated with increased risk of 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 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 explore the correlation of cumulative atherogenic index of plasma (cumAIP) and cumulative atherosclerosis index (cumAI) with new-onset non-alcoholic fatty liver disease (NAFLD).Methods:From January 2017 to December 2023, 2 472 subjects who underwent health checkups at the Second Affiliated Hospital of Dalian Medical University for 3 consecutive years were enrolled. Triglyceride, total cholesterol, high density lipoprotein cholesterol and their measurement time intervals were used to calculate cumAIP and cumAI. The subjects were divided into Q1, Q2, Q3 and Q4 groups with the threshold values of 25th percentile, median and 75th percentile of the baseline atherogenic index of plasma (AIP) and atherosclerotic index (AI) subjects. Cox regression model was used to analyze the effects of cumAIP and cumAI on the new-onset NAFLD, restricted cubic spline was performed to analyze the nonlinear association between cumAIP and cumAI and new-onset NAFLD, and the clinical decision curve was used to compare the decision value of different indicators for NAFLD. Results:The risk of NAFLD gradually increased along with the increasing of cumAIP and cumAI. In the quartile groups of cumAIP, the incidence of Q1 to Q4 groups was 6.15%, 8.74%, 15.05%, and 25.08%, respectively. In the quartile groups of cumAI, the incidence of Q1 to Q4 groups was 5.99%, 11.17%, 15.21%, and 22.65%, respectively. After adjusting the confounding factors, the risk of new-onset NAFLD in the high-level group ( Q4) was higher than that in the low-level cumAIP group ( Q1) ( HR=3.15, 95% confidence interval (95% CI): 2.15 to 4.63, P<0.001) and the high-level cumAI group ( Q4) ( HR=2.74, 95% CI: 1.82 to 4.10, P<0.001). cumAIP and cumAI showed a significant nonlinear association with new-onset NAFLD ( χ2=119.15, 94.53; both P<0.001). The cumAIP had higher predictive value for NAFLD than the other cumulative lipid metrics and baseline AIP or AI. Conclusion:CumAIP and cumAI can be served as new predictive indicators of NAFLD, with a particular focus on the dynamic cumulative changes of AIP, which can achieve effective early screening for NAFLD.

Objective:Investigating the association of the systemic immune-inflammation index and the pan-inflammation index with incident non-alcoholic fatty liver disease (NAFLD).Methods:This retrospective cohort study included 42 891 participants who underwent at least two health examinations at the Second Affiliated Hospital of Dalian Medical University between 2014 and 2023. Based on their levels of the systemic immune inflammation index (SII) and the pan immune inflammation value (PIV), participants were respectively divided into four quartile groups (Q1 to Q4). Cox proportional hazards models were employed to analyze the association of different SII and PIV levels, as well as their quartile groups, with new onset NAFLD in the total population and across various subgroups. Restricted cubic splines were used to examine the dose response relationship between these inflammatory indices and incident NAFLD. Additionally, sensitivity analyses were conducted to confirm the robustness of the findings.Results:After adjusting for confounding factors, the natural logarithm-transformed lnSII ( HR=1.247, 95% CI: 1.184-1.314, P<0.001) and lnPIV ( HR=1.192, 95% CI: 1.148-1.238, P<0.001) were significantly positively associated with the risk of NAFLD. When the subjects were grouped by SII quartiles (Q1-Q4), compared with those in Q1, participants in Q2, Q3, and Q4 exhibited progressively higher risks of incident NAFLD:11.9% ( HR=1.119, 95% CI: 1.051-1.192, P<0.001), 17.1% ( HR=1.171, 95% CI: 1.100-1.248, P<0.001), and 29.1% ( HR=1.291, 95% CI: 1.211-1.377, P<0.001), respectively. Quartile analysis of PIV yielded similar trends: the risk of incident NAFLD increased for 10.4% ( HR=1.104, 95% CI: 1.034-1.179, P=0.003), 18.7% ( HR=1.187, 95% CI: 1.112-1.266, P<0.001), and 30.5% ( HR=1.305, 95% CI: 1.223-1.393, P<0.001) in Q2, Q3, and Q4 group respectively when compared to that in Q1 group. Subgroup analysis confirmed consistent associations of SII and PIV with elevated NAFLD risk across all subgroups. Conclusion:Elevated levels of SII and PIV are significantly associated with increased risk of 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.