Association between aspartate aminotransferase to alanine aminotransferase ratio and reversion to normoglycemia in people with impaired fasting glucose: a 5-year retrospective cohort study 

Association between aspartate aminotransferase to alanine aminotransferase ratio and reversion to normoglycemia in people with impaired fasting glucose: a 5-year retrospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association between aspartate aminotransferase to alanine aminotransferase ratio and reversion to normoglycemia in people with impaired fasting glucose: a 5-year retrospective cohort study Kebao Zhang, Lidan Chen, Zhe Deng, Rong Rong, Lifen Xu, Liting Xu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4945577/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Studies showed that AST/ALT ratio was related to pre-diabetes, diabetes and diabetes complications. However, there is poor evidence proved that the AST/ALT ratio was correlated with blood glucose reversion in impaired fasting glucose patients. In our study, we analyzed the relationship between AST/ALT ratio and blood glucose reversal in a large group of Chinese people with impaired fasting blood glucose. Methods Participants were recruited from Rich Healthcare Group physical examinations in 2010 to 2016. Among all the participants, 11121 Chinese adults were enrolled in our study. Cox proportional-hazards regression was used to identify the association between the AST/ALT ratio and blood glucose reversal to normoglycemia in individuals with impaired fasting glucose. Generalized additive model (GAM) and smooth curve fitting were used to identified nonlinear relationship between AST/ALT ratio and blood glucose reversion. In addition, sensitive analyses and subgroup analysis were used to test the reliability of our study. Result AST/ALT ratio was independently related to the blood glucose reversal in prediabetic populations of Chinese adults (HR = 1.187, 95% CI 1.073–1.313, P = 0.00087). Nonlinear relationship has been discovered between AST/ALT ratio and reversion to normoglycemia. On the left side of the inflection point, AST/ALT ratio was negatively related to the blood glucose reversal in populations with impaired fasting glucose(HR:0.563, 95%CI: 0.404–0.784, P = 0.0007), while on the right side of the inflection point, the relationship was positive (HR:1.281 95%CI: 1.153–1.424, P < 0.0001 ). Sensitivity analysis, competing risks multivariate Cox regression and subgroup analysis also confirmed our study results. Conclusion Our study revealed that AST/ALT ratio was independently related with reversion to normoglycemia in prediabetic Chinese people. The relationship between AST/ALT ratio and reversion to normoglycemia from IFG was non-linear. When AST/ALT ratio > 1.05, a significant positive relationship between AST/ALT ratio and reversion to normoglycemia was identified. Health sciences/Health care/Disease prevention Health sciences/Health care/Public health AST/ALT ratio Impaired fasting glucose Non-linear relationship reversion to normoglycemia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Diabetes has been a major health concern globally. According to Global burden of disease, the burden of diabetes mellitus (DM) is rising globally. Over one million deaths per year is attributed to DM independently, which make it the ninth leading cause of death [1] . Before DM, there is a state known as impaired glucose regulation (also known as prediabetes), which is characterized by increased glycated hemoglobin (A1C), impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or a combination of IFG and IGT [2] . According to American Diabetes Association (ADA) updated in 2023, prediabetes is defined as fasting plasma glucose (FPG) of 5.6-6.9mmol/L, impaired fasting glucose (2-h plasma glucose of 7.8-11.0mmol/L) or A1C of 5.7–6.4% [3] . It is noted that prediabetic state is a risk factor for DM. 5–10% of patients with prediabetic state converted to DM each year. Additionally, prediabetes itself is a risk factor for developing cardiovascular diseases such as coronary artery disease and diastolic heart failure [4] . Nevertheless, prediabetic patients can also converted back to normoglycemia [2] . Previous study had found that even transient reversal to normal glucose level from prediabetes can reduce the future development of DM, so it is worthwhile to study which factors can predict the reversal of prediabetes to normoglycemia[ 5 ]. Aspartate transaminase (AST) and alanine transaminase (ALT) are biomarkers that represent liver function. It is found that AST/ALT ratio can not only predict the likelihood of developing non-alcoholic fatty liver disease (NAFLD), but also can use to predict the risk for developing DM [6, 7] . A study conducted in Iran found that ALT/AST ratio is a risk factor for prediabetes development. Regrettablely, the relevance between AST/ALT ratio and reversal of prediabetes to normoglycemia is hardly understood. Previous studies have found that reversion to normal glucose level from prediabetes can be contributed to age, sex, race, obesity, lifestyle, baseline A1C, baseline fasting glucose level, pharmacological approach, etc [8–10] . Several studies had ellucidated that AST/ALT ratio is associated with insulin resistance [11, 12] , which can partially explain the relationship between AST/ALT ratio and DM. We propose the hypothesis that AST/ALT ratio is positively associated with reversion to normoglycemia from prediabetes. Methods Study design This study was a secondary retrospective study, which aimed to identify the association between the AST/ALT ratio and the reversion of IFG to normoglycemia. In this study, we regarded the baseline AST/ALT ratio as the independent variable and glucose reversion from IFG to normoglycemia during follow-up as the outcome variable . Data source The original data was downloaded freely from the study: Association of body mass index and age with the risk of DM in Chinese adults: a population-based cohort study.,which was uploaded by Chen et al into the DATADRYAD database ( www.datadryad.org ). Dryad Digital Repository. https://datadryad.org/stash/dataset/doi:10 . 5061%2Fdryad.ft8750v)[ 13 ]. According to the service terms stipulated by the Dryad database, researchers were allowed to use the dataset for non-commercial purposes, whether it was to share, modify, mix or creat derivative works based on the dataset, as long as they trust the author and the source of the data.[ 14 ] Study population The Rich Healthcare Group recruited participants continuously from 32 locations in 11 cities in China (Hefei,Changzhou, Nantong, Suzhou, Shenzhen, Nanjing, Guangzhou,Shanghai, Chengdu, Wuhan and Beijing ) to minimize selection bias. All procedures involved human subjects have been approved by the Clinical Research Ethics Committee of the Rich Healthcare Group. In addition, this study follows the principles outlined in the Helsinki Declaration and used untraceable codes to encode the identity information of participants in order to protect their privacy. Due to retrospective design and anonymity of data, the institutional review committee gave up the necessity of informed consent from participants[ 14 , 15 ] In the original study, 685,277 Chinese participants were enrolled and completed at least a second visits from 2010 to 2016, and 211,833 individuals (95710 female and 116123 male) were eventually involved in the raw analysis according to the following exclusion criteria: (1)missing data on weight, height,gender,fasting plasma glucose (n = 135317) ; (2) extreme BMI values ( 55kg/m2) (n = 152); (3) Participants follow-up < 2 years(n = 324233); (4) participants who were diagnosed with diabetes at baseline (4115 diagnosed by a fasting plasma glucose ≥ 7.0mmol/L and 2997 participants diagnosed by self-report); (5) participants with undefined diabetes status at follow-up (n = 6630)[ 14 ].In our study, we also excluded: (6)initial fasting plasma glucose6.9 (n = 229); (8) No available final fasting plasma glucose(n = 6); (9)missing data of ALT(n = 234); (10) missing data of AST(n = 14537); (11)outliers of AST/ALT (means plus three standard deviation)(n = 120)[ 16 ]. Finally, a total of 11121 participants (7472 male and3649 female) were included in our analysis. Figure 1 showed in detail of participants’ selection process . Variables aspartate aminotransferase to alanine aminotransferase ratio The baseline AST/ALT ratio was regarded as a continuous variable. The AST/ALT ratio was calculated by divide aspartate aminotransferase (AST, measured in U/L) by alanine aminotransferase(ALT, measured in U/L). Impaired fasting glucose The definition of impaired fasting blood glucose was the patient's FPG value between 5.6 to 6.9 mmol/L Outcome measures No self-report diabetes events and FPG levels below 5.6 mmol/L during follow-up were defined as successful glucose reversal[ 17 , 18 ]. Covariates According to the clinical experiences and previous research, some variables were recognized as high risk factors for diabetes, pre diabetes or diabetes complications[ 14 , 19 – 23 ]. So, we selected the covariates included in this study were as follow: (1) continuous variables: blood urea nitrogen (BUN), age, BMI, diastolic blood pressure (DBP), cholesterol, serum creatinine (Scr), low-density lipid cholesterol (LDL-c), Triglyceride (TG), systolic blood pressure (SBP), Fasting gblood glucose (FPG) and high-density lipid cholesterol (HDL-c). (2) categorical variables: family history of diabetes, smoking and drinking status as well as gender. Data collection The height, weight, and blood pressure of the participants were measured by professional staff. To ensure the accuracy of the data, participants were required to wear lightweight clothing and without shoes before measuring height and weight. Blood pressure was measured by standard mercury sphygmomanometer. Before measuring it, participants were required to stay in lying position quietly for 5–10 minutes. BMI was calculated by divide weight (kilograms) by the square of height (meters), with the height measurement accuracy of 0.1 centimeters and the weight measurement accuracy of 0.1 kilogram. family history of diabetes, lifestyle, personal medical history and demographic characters were collected by a survey questionnaire. Clinical variables, such as BUN, FPG, ALT, HDL-C, TC, LDL-C, TG, AST and Scr were obtained by means of autoanalyzer (Beckman 5800) after fasting for at least 10 hours[ 14 ]. Missing data processing Some variables had missing data i n this study, for example, 5 participants (0.045%) lacked DBP and SBP data, 88 individuals (0.79%) missed cholesterol and triglyceride data, 520 participants (4.68%) lost SCR data. At the same time, some variables missed quite a proportion of data. Just as LDL-C, HDL-C, drinking status, and smoking status missed data for 31.99%, 36.80%, 69.65% and 69.65% respectively. We adopted the method of multiple imputation of chain equations to handle missing data in order to maximize the use of participants’ data[ 24 ]. Ultimately, the variables contained in our article included Triglyceride;age; AST; DBP; FPG; HDL-c;gender; BMI; SBP; smoking and drinking status; LDL-c; ALT; BUN; SCR and family history of diabetes. We handled missing data based on the assumption of random missing data (MAR)[ 25 ] Statistical analysis We classified the participants into quartiles according to the AST/ALT ratio: Q1 < 0.81; 0.81 ≤ Q2 < 1.08; 1.08 ≤ Q3 < 1.40; 1.40 ≤ Q4. Continuous variables can be devided into normal distributed values and skewed values, which are described by mean ± standard deviation (SD) and median. Categorical values were described by frequencies and percentages. To evaluate the differences between the AST/ALT ratio groups. One-way ANOVA tests were used to analyze normal distributed values while chi square tests and Kruskal-Wallis H tests were used to analyze categorical values and skewed values respectively. Kaplan Meier method was employed to calculate survival estimates and time-to-event variables. Additionally, we also used the log-rank test to compare the likelihood of returning from IFG to normoglycemia among the AST/ALT ratio groups . The variance inflation factor (VIF) was used to evaluate the likelihood of covariate collinearity, calculated as VIF = 1/(1-R2). R came from the linear regression equation[ 26 ]. The studied variable was treated as the dependent variable, and other variables were regarded as independent variables in each regression analysis. As the result, if a variable’s VIF exceeded 5, it indicated collinearity between the variables, which meant that we should exclude this variable when analyzing multiple regression models. As was shown in Table S1 , the VIF of cholesterol was 6, We excluded it in the analysis of the multiple regression model.(Attachment 1: Table S1 ). We constructed three different models by using multivariate Cox proportional hazards regression analysis and univariate analysis to find out the relationship between the AST/ALT ratio and the probability of reverting to normoglycemia from IFG. Model I did not adjust for any covariates; Model II only adjusted for demographic characteristics such as smoking and drinking status, SBP, gender, DBP, BMI, family history of diabetes, age. Model III adjusted for all covariates which were listed in Table 1 , including SCR, smoking status, age, SBP, HDL-c, gender, triglyceride, drinking status, BMI, FPG, LDL-c, DBP, family history of diabetes. The article providesd hazard ratios (HR) and its corresponding 95% confidence intervals (CIs). We adjusted for confounding variables in this article was based on clinical knowledge and published reports [ 21 , 22 , 27 ]. According to the commonality screening results, there was no collinearity issue between all variables (Additional file 1: Table S1 ). Table 1 The Baseline Characteristics of Participants AST/ALT Q1( 1.40) P Participants 2780 2780 2747 2814 Age(years) 44.28 ± 11.16 49.68 ± 12.84 52.22 ± 14.24 53.40 ± 15.52 < 0.001 BMI(kg/m2) 26.49 ± 3.15 25.33 ± 3.13 24.33 ± 3.10 23.26 ± 3.08 < 0.001 SBP(mmHg) 128.42 ± 16.01 127.58 ± 17.27 127.75 ± 18.43 126.82 ± 18.91 0.009 DBP(mmHg) 80.35 ± 10.71 78.92 ± 10.88 78.03 ± 11.17 76.55 ± 11.43 < 0.001 FPG(mmol/L) 5.97 ± 0.32 5.96 ± 0.32 5.95 ± 0.31 5.92 ± 0.30 < 0.001 Cholesterol(mmol/L) 5.10 ± 0.96 4.97 ± 0.93 4.96 ± 0.94 4.92 ± 0.95 < 0.001 Triglyceride(mmol/L) 1.80 (1.22–2.69) 1.56 (1.05–2.30) 1.30 (0.90–1.90) 1.08 (0.77–1.59) < 0.001 HDL-c(mmol/L) 1.27 ± 0.28 1.32 ± 0.33 1.36 ± 0.29 1.43 ± 0.29 < 0.001 LDL-c(mmol/L) 2.97 ± 0.73 2.88 ± 0.69 2.88 ± 0.70 2.83 ± 0.70 < 0.001 ALT(U/L) 42.00 (32.40-60.12) 25.40 (21.00–31.00) 19.00 (15.60–22.50) 13.10 (11.00–16.00) < 0.001 AST(U/L) 31.37 ± 14.88 25.64 ± 9.98 24.20 ± 8.50 24.14 ± 10.44 < 0.001 AST/ALT 0.65 ± 0.11 0.94 ± 0.08 1.22 ± 0.09 1.73 ± 0.28 < 0.001 BUN(mmol/L) 5.07 ± 1.19 5.12 ± 1.22 5.05 ± 1.25 5.02 ± 1.39 0.030 Scr(umol/L) 77.14 ± 14.17 75.48 ± 15.79 73.07 ± 16.12 70.71 ± 17.40 < 0.001 Gender < 0.001 Male 2407 (86.58%) 2068 (74.39%) 1679 (61.12%) 1318 (46.84%) Female 373 (13.42%) 712 (25.61%) 1068 (38.88%) 1496 (53.16%) Smoking Status < 0.001 Current smoker 542 (19.50%) 449 (16.15%) 336 (12.23%) 296 (10.52%) Ever smoker 119 (4.28%) 109 (3.92%) 86 (3.13%) 64 (2.27%) Never smoker 2119 (76.22%) 2222 (79.93%) 2325 (84.64%) 2454 (87.21%) Drinking Status < 0.001 Current drinker 133 (4.78%) 145 (5.22%) 116 (4.22%) 87 (3.09%) Ever drinker 640 (23.02%) 549 (19.75%) 425 (15.47%) 337 (11.98%) Never drinker 2007 (72.19%) 2086 (75.04%) 2206 (80.31%) 2390 (84.93%) Family history of diabetes 0.010 No 2692 (96.83%) 2718 (97.77%) 2696 (98.14%) 2752 (97.80%) Yes 88 (3.17%) 62 (2.23%) 51 (1.86%) 62 (2.20%) Values are n (%), mean ± SD or medians (quartiles) BMI body mass index; LDL-c low-density lipoprotein cholesterol; FPG fasting plasma glucose;AST aspartate aminotransferase;DBP diastolic blood pressure; Scr serum creatinine; SBP systolic blood pressure; BUN blood urea nitrogen; ALT alanine aminotransferase; HDL-c high-density lipoprotein cholesterol; AST/ALT aspartate aminotransferase/alanine aminotransferaseratio We used a Cox proportional hazards regression model with smooth curve fitting and cubic spline function for analysis to investigate whether there was the potential non-linear correlation between the AST/ALT ratio and reversion to normoglycemia in participants with IFG. This statistical method enabled us to handle any non-linear problems present in the data. When nonlinearity was detected between data, we used recursive algorithms to identify inflection points. Then, we employed two-stage Cox proportional risk regression model on both sides of the inflection point. and we also conducted a log likelihood ratio test to determine the most appropriate model for evaluating the relationship between AST/ALT ratio and reversion to normoglycemia[ 28 ]. Considering that people diagnosed as diabetes during the follow-up were unlikely to return to normal blood glucose levels, it was necessary to exclude these population from affecting the probability of reversing blood glucose in IFG patients[ 29 , 30 ].Therefore, we use the method described by Fine and Gray to achieve multivariate Cox regression of competitive risk[ 30 , 31 ]. Developing to diabetes was considered to be a competitive risk of a return to normal glycemic events in this approach. We conducted a stratified Cox proportional risk regression model based on family history of diabetes, TG, gender, age, BMI, DBP, drinking status, SBP, smoking status and FPG to perform subgroup analyses. Firstly, we established categorical variables by using clinically significant critical points, such as DBP (≥ 90 ,<90 mmHg), BMI (≥ 28, ≥ 24 to 28, ≥ 18.5 to < 24, < 18.5 kg/m2), TG (≥ 1.7, < 1.7 mmol/L), age (≥ 45, < 45 years), SBP (≥ 140, < 140 mmHg), and FPG (≥ 6.1, < 6.1 mmol/L)[ 32 – 35 ]. Secondly, adjusted each stratification based on all other factors (Scr, TG, age, BUN, ALT, family history of diabetes, DBP, AST, gender, FPG, BMI, SBP, smoking and drinking status, LDL-c), including the stratification factors themselves itself. Finally, the models with and without interaction terms was compared to evaluate interactions by conducting a likelihood ratio test[ 36 , 37 ]. In addition, we conducted a range of sensitivity analyses to determine the reliability of our results. Firstly, we divided the AST/ALT ratio into quartiles, then converted the ratio into a continuous variable and evaluated the P-value of the trend. Which also helped us identify the potential non-linear relationships. As was known to us all, people with smoking consumption, alcohol consumption or family history of diabetes were closely related to an increased risk of developing diabetes [ 38 , 39 ]. Therefore, we excluded individuals with with smoking consumption, alcohol consumption or family history of diabetes when we explored the association between AST/ALT ratio and reversion to normoglycemia in people with IFG in our additional sensitivity analyses. Furthermore, due to missing data on smoking and alcohol consumption in variables exceeding 70%, we eliminated smoking and drinking status as covariates in the multivariate model, because this might not have any impact to model adjustment. In addition, to ensure the consistency of the research results, we also adopted the Generalized Additive Model (GAM) to incorporate continuous covariates as curves into Model IV[ 40 ]. Moreover, we also calculated the E-values to evaluate the potential impact of the association between AST/ALT ratio and reversion to normoglycemia[ 41 ] This method provided more evidence to prove the reliability of our results. Data analysis was conducted through two statistical software packages named The R Foundation ( http://www.R-project.org ) and EmpowerStats (X&Y Solutions, Inc, Boston, MA, http://www.empowerstats.com ). All statistical tests were conducted by a double-sided test, and the result with significance levels of p < 0.05, defined as statistically significant. Results Baseline characteristics of participants Table 1 showed the baseline characteristics of the participants involved in the study. A total amout of 11121 participants were included with 67.2% being male. The mean age of the population was 49.9 ± 14.0 years. The baseline AST/ALT ratio had an average of 1.1 ± 0.4. In the population, 41.4% IFG patients reverse to normoglycemia during the average follow-up years (3 years). We stratified AST/ALT ratio into four groups: Q1 < 0.81; 0.81 ≤ Q2 < 1.08; 1.08 ≤ Q3 < 1.40; 1.40 ≤ Q4). All covariates presented in Table 1 have statistical significance between the different quartiles of the AST/ALT ratio (P values < 0.05). Between Q4 (≥ 1.4) and Q1 (< 0.81) ,significant increment in age, HDL-c, AST/ALT, female, never smoker were discovered. However, there were opposite trends for BMI, cholesterol, Triglyceride, ALT, SCR, male, current smoker, ever smoker and ever drinker among the covariates. Figure 2 illustrated the distribution of the AST/ALT ratio levels, which showed a normal distribution with a range of values from 0.29 to 2.65, and a mean value of 1.14. Reversion from IFG to normoglycemia In our study,4602 individuals reverse from IGF to nomoglycemia with a total cumulative rate of 13.8/100 person years. Differences in cumulative rate of reversion to normoglycemia were discovered between the four quatile groups, ranging from 12.8 to 16.4/100 person years. The reversal rates in Q1-Q4 were as follow: 38.2(36.4–40.0), 38.5(36.7–40.3), 40.2(38.3–42.0), and 48.5(46.7–50.4). It was notable that participants in higher quatile groups received higher rates of reversion to normoglycemia (Table 2 , Fig. 3 , P < 0.0001 for trend). In Fig. 4 , We stratifed the population according to different sex and age. In the same age group, we found that female has higher reversion rates than male. As age increases, the reversion rate for both men and women decreased. Table 2 The rate of reversion to normoglycemia in people with IFG AST/ALT ratio Participants(n) Reversion events(n) Reversal rate (95% CI)(%) Per 100 person year Total 11121 4602 41.4(40.5–42.3) 13.8 Q1( 1.40) 2814 1366 48.5(46.7–50.4) 16.4 P for trend < 0.001 AST/ALT ratio aspartate aminotransferase/alanine aminotransferase ratio; CI confidence interval Univariate analyses using Cox proportional-hazards regression model In Table 3 , we used univariate analyses to unfold the relationship between reversion rate and several variables. In the table we found that reversion rate was not associated with BUN, ever drinkers, ever smokers, and people who had family history of diabetes (P > 0.05). We also discovered that reversion rate is positively correlate with female, AST/ALT ratio, HDL-c, as well as people who never smoked or drank, and negatively correlate with older age, male, higher DBP, BMI, FPG, cholesterol, SBP, triglyceride, AST and LDL-c. Table 3 Factors influencing reversion to normoglycemia among participants with IFG analyzed by univariate Cox proportional hazards regression Variable Statistics HR (95% CI) P value Age(years) 49.90 ± 13.99 0.97 (0.97, 0.98) <0.0001 Gender Male 7472 (67.19%) Ref Female 3649 (32.81%) 1.18 (1.11, 1.25) <0.0001 BMI(kg/m2) 24.85 ± 3.34 0.94 (0.93, 0.95) < 0.0001 SBP(mmHg) 127.64 ± 17.70 0.99 (0.99, 0.99) < 0.0001 DBP(mmHg) 78.46 ± 11.14 0.98 (0.98, 0.99) < 0.0001 FPG(mmol/L) 5.95 ± 0.32 0.18 (0.16, 0.20) < 0.0001 Cholesterol(mmol/L) 4.99 ± 0.95 0.89 (0.86, 0.92) < 0.0001 Triglyceride(mmol/L) 1.78 ± 1.40 0.89 (0.86, 0.91) < 0.0001 HDL-c(mmol/L) 1.34 ± 0.30 1.94 (1.80, 2.10) < 0.0001 LDL-c(mmol/L) 2.89 ± 0.71 0.93 (0.89, 0.97) 0.0004 ALT(U/L) 28.15 ± 22.27 1.00 (0.99, 1.00) < 0.0001 AST(U/L) 26.34 ± 11.59 0.99 (0.99, 0.99) < 0.0001 AST/ALT ratio 1.14 ± 0.43 1.32 (1.23, 1.41) < 0.0001 BUN(mmol/L) 5.06 ± 1.27 0.98 (0.96, 1.01) 0.1336 Scr(umol/L) 74.09 ± 16.10 1.00 (1.00, 1.00) 0.2087 Smoking Status Current smoker 1623 (14.59%) Ref Ever smoker 378 (3.40%) 1.10 (0.93, 1.31) 0.2721 Never smoker 9120 (82.01%) 1.19 (1.10, 1.30) < 0.0001 Drinking Status Current drinker 481 (4.33%) Ref Ever drinker 1951 (17.54%) 1.12 (0.95, 1.32) 0.1813 Never drinker 8689 (78.13%) 1.35 (1.16, 1.57) 0.0001 Family history of diabetes No 10858 (97.64%) Ref Yes 263 (2.36%) 0.96 (0.80, 1.16) 0.6803 BMI body mass index, FPG fasting plasma glucose, LDL-c low-density lipoprotein cholesterol, DBP diastolic blood pressure, ALT alanine aminotransferase, SBP systolic blood pressure, AST aspartate aminotransferase, non-HDL-c non-high-density lipoprotein cholesterol, BUN blood urea nitrogen, HDL-c high-density lipoprotein cholesterol, Scr serum creatinine, AST/ALT ratio aspartate aminotransferase/alanine aminotransferase ratio, HR Hazard ratios, CI confidence interval, Ref reference In Fig. 5 , we used the Kaplan-Meier curve to revealed the likelihood of reversion to normoglycemia from IFG according to different AST/ALT ratio groups. There was a significant statistical effect on the AST/ALT ratio groups and the probability of reverting to normoglycemia (log-rank test, p < 0.001). Participants with higher AST/ALT ratios had higher chances converting to normoglycemia from IFG . Multivariate analyses using the Cox proportional-hazards regression model We employed Cox proportional-hazards regression model to illustrate the relationship between AST/ALT ratio and the likelihood of reverse to normoglycemia (Table 4 ). A significant positive correlation between AST/ALT ratio and reversion to normoglycemia was discovered in model I. With every 1 unit increase in the AST/ALT ratio, the chances for blood glucose reverse back to nomoglycemia increaseed by 32% (HR = 1.32, 95% CI 1.23–1.41). In model II, we adjusted for only demographic variables and also showed similar results (HR = 1.33, 95% CI 1.23–1.43). In Model III (fully-adjusted model), the positive correlation between AST/ALT ratio and reversion to normoglycemia still can be found (HR = 1.19, 95% CI 1.07–1.31). All the results were statistically significant.(p < 0.05) Table 4 Relationship between AST/ALT ratio and reversion to normoglycemia in people with IFG in different models Exposure Model I (HR, 95% CI, P) Model II (HR, 95% CI, P) Model III (HR, 95% CI, P) Model IV (HR, 95% CI, P) AST/ALT ratio 1.32(1.23, 1.41) < 0.00001 1.33 (1.23, 1.43) < 0.00001 1.19(1.07, 1.31) 0.0009 2.74 (1.87, 4.01) < 0.00001 AST/ALT ratio Q1 Ref Ref Ref Ref Q2 0.98 (0.90, 1.06) 0.5825 1.03 (0.95, 1.13) 0.4736 0.91 (0.82, 1.01) 0.0746 0.87 (0.76, 0.99) 0.0315 Q3 0.98 (0.90, 1.07) 0.6776 1.04 (0.95, 1.13) 0.4547 0.88(0.78, 0.99) 0.0271 0.77 (0.64, 0.93) 0.0062 Q4 1.30 (1.20, 1.41) < 0.00001 1.322(1.20, 1.45) < 0.00001 1.05 (0.92, 1.20) 0.4494 0.92 (0.71, 1.18) 0.4916 P for trend <0.00001 < 0.00001 0.1123 0.43 Model I: we did not adjust other covariates Model II: we adjust gender, age, BMI, SBP, DBP, family history of diabetes, smoking and drinking status Model III: we adjust age; gender; BMI; SBP; DBP; FPG; Triglyceride; HDL-c; LDL-c; ALT; AST; BUN; SCR; family history of diabetes, smoking and drinking status Model IV: we adjusted age(smooth); gender; BMI(smooth); SBP(smooth); DBP(smooth); FPG(smooth);Triglyceride(smooth); HDL-c(smooth); LDL-c(smooth); ALT(smooth); AST(smooth); BUN(smooth); CCR(smooth); family history of diabetes, smoking and drinking status HR Hazard ratios, CI confidence, Ref reference The results of competing risks multivariate Cox proportional-hazards regression Table 5 showed the competitive analysis results from IFG to the incidence of diabetes. In Model I (unadjusted model), we discovered that as AST/ALT ratio increased, the incidence of reverting to normoglycemia also increased (SHR = 1.32, 95%CI 1.23–1.41). In Model II ,we adjusted only demographic variables. Similarly, the correlation between AST/ALT ratio and glucose reversion rate still remain positive (SHR: 1.33, 95% CI 1.23–1.43). In model III (fully adjusted model), the relationship still remain positive (SHR = 1.19, 95% CI 1.07–1.31). All results are statistically significant. (p < 0.05). Table 5 Relationship between AST/ALT ratio and reversion to normoglycemia in people with IFG in different models with competing risk of progression to diabetes Exposure Model I (SHR, 95% CI, P) Model II (SHR, 95% CI, P) Model III (SHR, 95% CI, P) AST/ALT ratio 1.32(1.23, 1.41) < 0.00001 1.33 (1.23, 1.43) < 0.0001 1.19 (1.07, 1.31) 0.0009 AST/ALT ratio Q1 Ref Ref Ref Q2 0.98 (0.90, 1.06) 0.5825 1.03 (0.95, 1.13)0.4736 0.91 (0.82, 1.01) 0.0746 Q3 0.98 (0.90, 1.07) 0.6776 1.04 (0.95, 1.13) 0.4547 0.88 (0.78, 0.99) 0.0271 Q4 1.30(1.20, 1.41) < 0.00001 1.32 (1.20, 1.45) < 0.0001 1.05 (0.92, 1.19) 0.4494 P for trend <0.00001 < 0.0001 0.1122 Model I: we did not adjust other covariates Model II: we adjust gender, age, BMI, SBP, DBP, family history of diabetes, smoking and drinking status Model III: we adjust age; gender; BMI; SBP; DBP; FPG; triglyceride; HDL-c; LDL-c; ALT; AST; BUN; SCR; family history of diabetes, smoking and drinking status Sensitivity analysis Several sensitivity analyses were conducted in order to prove the reliability of our findings. Firstly, we treated the AST/ALT ratio as a continuous variable. Secondly, we partitioned the AST/ALT ratio into quartiles with equidistant patterns of effect sizes for each group, then, We imported them into the model. Finally, the results indicated that in the unadjusted and minimally adjusted models (Model I and Model II), the p-value of the trend was consistent with the results when the AST/ALT ratio was used as a continuous variable (shown in Table 4 , 5 ). We utilized a Generalized Additive Model (GAM) to incorporate the continuity covariate as a curve in the equation (shown as Model IV in Table 4 ). The finding prompted that AST/ALT ratio was positively related to the possibility of reverting to normoglycemia. indicating an HR of 2.74 (95% CI 1.87–4.01, P < 0.00001). Additionally, in Table 6 , in order to exclude the effect of smoking or drinking habits or participants with diabetes family history, some supplementary sensitivity analyses were conducted to enhance our findings. For the reason that missing data on smoking and alcohol consumption almost reach 70%, we had to exclude these variables as covariates in some sensitivity analyses. However, although they were not included, these findings kept consistent with our results above. The participants with no drinking status (Table 6 , model a) showed a positive result between AST/ALT ratio and the possibiity of reverting to normoglycemia (HR = 1.190, 95% CI 1.066–1.327). Participants with no smoking status (Table 6 , model a) also represented the similar outcome (HR = 1.164, 95% CI 1.044–1.299 ). After excluding participants with family history of diabetes (Table 6 , model c), the result still remain positive. Table 6 Relationship between AST/ALT ratio and the probability of reverting from IFG to normoglycemia in different sensitivity analyses Exposure Model a (HR, 95% CI, P) Model b(HR, 95% CI, P) Model c (HR, 95% CI, P) Model d (HR, 95% CI, P) AST/ALT ratio 1.19 (1.07, 1.33) 0.0019 1.16 (1.04, 1.30) 0.0064 1.20 (1.08, 1.33) 0.0005 1.19 (1.08, 1.32) 0.0007 AST/ALT ratio Q1 Ref Ref Ref Ref Q2 0.91 (0.81, 1.02) 0.1138 0.92(0.82, 1.03) 0.1489 0.92 (0.83, 1.02) 0.0959 0.91 (0.83, 1.01) 0.0816 Q3 0.87 (0.76, 0.99) 0.0324 0.884 (0.776, 1.007) 0.0629 0.89 (0.7919, 1.00) 0.0497 0.88 (0.79, 0.99) 0.0333 Q4 1.03(0.89, 1.19) 0.6949 1.03 (0.89, 1.19) 0.7152 1.07 (0.94, 1.21) 0.3422 1.06 (0.93, 1.20) 0.3990 P for trend 0.2243 0.2563 0.0717 0.0913 Model a was a sensitivity analysis performed on never drinker participants (N = 8689). We adjusted age; gender; BMI; SBP; DBP; FPG;triglyceride; HDL-c; LDL-c; ALT; AST; BUN; SCR;smoking status and family history of diabetes, Model b was a sensitivity analysis performed on never smoker participants (N = 9120). We adjusted gender; BMI; age; SBP; DBP; FPG; triglyceride; HDL-c; LDL-c;; ALT; AST; BUN;SCR;;drinking status and family history of diabetes, Model c was sensitivity analysis in participants without family history of diabetes (N = 10858). We adjusted age; BMI; SBP; gender; DBP; FPG; triglyceride; HDL-c; LDL-c; ALT; AST; BUN; SCR Model d was sensitivity analysis in participants without adjusting smoking and drinking status (N = 11121). We adjusted age; BMI; SBP; gender; DBP; FPG;triglyceride; HDL-c; LDL-c; ALT; AST; BUN; SCR and family history of diabetes HR Hazard ratios, CI confidence, Ref reference Non-linearity relationship between AST/ALT ratio and incidence of glucose reversion To further investigate the relationship between AST/ALT ratio and incidence of glucose reversion, we applied Cox proportional hazards regression model and cubic spline functions (Fig. 6 ). We found that relationship between AST/ALT ratio and glucose reversion rate is non-linear. In order to find the best fit, we further applied binary two-stage Cox proportional hazards regression model and logarithmic likelihood ratio test (Table 7 ). We determined the inflection point as 1.05. For AST/ALT ratio 1.05, AST/ALT ratio is positively associated with incidence of glucose reversion (HR = 1.28, 95% CI 1.15–1.42). Table 7 The result of the two-piecewise Cox regression model Probability of reversion to normoglycemia HR (95%CI) P value Fitting model by standard Cox regression 1.19 (1.07, 1.31) 0.0009 Fitting model by two-piecewise Cox regression Inflection point of AST/ALT ratio 1.05 ≤Inflection point 0.56 (0.40, 0.78) 0.0007 >Inflection point 1.28 (1.15, 1.42) < 0.0001 P for log-likelihood ratio test < 0.001 We adjusted gender; BMI; age; SBP; DBP; FPG; triglyceride; HDL-c; LDL-c; ALT; AST; BUN; CCR; family history of diabetes; smoking and drinking status HR, Hazard ratios; CI: confidence The results of subgroup analyses In order to investigate the association between AST/ALT ratio and the likelihood of reverting to normoglycemia in different populations, we comprehensively evaluated the interactions between various variables in prespecified subgroups and exploratory subgroups (Table 8 ). All the P value of variables greater than 0.05. No significant interaction in terms of age, gender, BMI, SBP, DBP, smoking status, drinking status as well as triglycerides was identified. Table 8 Stratified associations between AST/ALT ratio and reversion to normoglycemia in people with IFG in prespecified and exploratory subgroups Characteristic No of participants HR (95% CI) P value P for interaction Age(years) 0.256 < 45 4318 1.10 (0.96, 1.26) 0.158 ≥45 6803 1.22 (1.06, 1.39) 0.005 Gender 0.831 Male 7472 1.22 (1.06, 1.39) 0.004 Female 3649 1.25 (1.05, 1.48) 0.013 BMI(kg/m2) 0.313 <18.5 188 0.47 (0.15, 1.51) 0.204 ≥18.5,<24 4336 1.24 (1.08, 1.44) 0.003 ≥ 24,<28 4780 1.34 (1.12, 1.60) 0.002 ≥28 1817 1.17 (0.82, 1.66) 0.392 SBP(mmHg) 0.545 <140 8675 1.17 (1.05, 1.30) 0.004 ≥ 140 2446 1.26 (1.00, 1.59) 0.047 DBP(mmHg) 0.883 < 90 9484 1.18 (1.06, 1.31) 0.002 ≥ 90 1637 1.15 (0.82, 1.61) 0.417 FPG(mmol/L) 0.065 < 6.1 8121 1.18 (1.06, 1.31) 0.002 ≥ 6.1 3000 1.50 (1.18, 1.92) 0.001 Triglyceride(mmol/L) 0.105 < 1.7 6858 1.23 (1.08, 1.38) 0.001 ≥ 1.7 4263 1.01 (0.83, 1.24) 0.916 Family history of diabetes 0.992 No 10858 1.20 (1.08, 1.33) 0.0005 Yes 263 1.19 (0.59, 2.41) 0.623 Drinking status 0.825 Current drinker 481 1.27 (0.69, 2.35) 0.442 Ever drinker 1951 1.09 (0.83, 1.45) 0.537 Never drinker 8689 1.19 (1.07, 1.33) 0.002 Smoking status 0.051 Current smoker 1623 1.67 (1.22, 2.29) 0.001 Ever smoker 378 0.82 (0.44, 1.52) 0.520 Never smoker 9120 1.16 (1.04, 1.30) 0.007 Note 1 : Above model adjusted for gender; BMI; age; SBP; DBP; triglyceride; HDL-c; LDL-c; ALT, AST, BUN, SCR, family history of diabetes, smoking and drinking status Discussion In our study, we found that the relationship between the AST/ALT ratio and the likelihood of reversion to normoglycemia in individuals with IFG was significant nonlinear. Besides, a threshold effect was discovered indicating different relationship between the AST/ALT ratio and reversion to normoglycemia on both sides of the inflection point. In the retrospective cohort study, we studied the relationship between AST/ALT ratio and the likelihood of reversion to normoglycemia in patients with IGF. In our research, 41.4% of all participants with IGF return back to normoglycemia. In a prospective cohort study conducted in China, 14231 Chinese participants were recruited in this study and find out that 44.9% participants were reverse to normoglycemia from prediabetes after 2 years follow-up[ 42 ]. Similarly, in a prospective cohort study conducted in Mexico, after 2.5 years of follow-up, 22.6% of patients with IGF reverse to normoglycemia while 22.9% processed to T2DM. It is noticeable that quite amount of participants with IGF reversed to normoglycemia. Thus, it is important to find out which factors contribute to reverse IGF to normal glucose level. There have been many findings suggesting that AST/ALT ratio is related to DM and non-alcoholic fatty liver disease (NAFLD). A retrospective cohort study of 87,883 participants conducted in China found out that AST/ALT ratio was negatively associated with risk of DM and a threshold effect was discovered[ 43 ]. Similarly, in Japanese population, a negative correlationship between AST/ALT ratio and incidence of DM can also be found[ 6 ]. Besides, in a retrospective study consisting of 75,204 Chinese adults, AST/ALT ratio is negatively associated with development of prediabetes[ 44 ]. What’s more, AST/ALT is also associated with insulin resistance (IR) and DM[ 11 , 12 , 43 , 45 , 46 ]. It had been long discussed that there is close interaction among DM, NAFLD and insulin resistance (IR)[ 47 , 48 ]. As mentioned before, AST/ALT ratio is negatively associated with development of prediabetes. Considerably it is not surprise to find out that participants with higher AST/ALT ratio is more likely to reverse to normoglycemia. However, no previous study have elucidated the relationship between AST/ALT ratio and reversion to normomglycemia. To our knowledge, it is the first time that we revealed that AST/ALT ratio is positively associated with reverse to normoglycemia from prediabetic state. Furthermore, we used sensitivity analyses to further confirm the reliability of our results. The exact mechanism involved in the relationship between AST/ALT ratio and reversion to normoglycemia is not well established, however, it may be contributed to lipotoxicity and insulin resistance. Firstly, in previous studies, AST/ALT is a strong predictor for NAFLD and it is widely accepted that NAFLD is an independent risk factor for DM development. Secondly, a cross-sectional study had found out that increase in hepatic triglyceride contribute to NAFLD development as well as insulin resistance, supporting the theory of hepatic lipotoxicity[ 49 ]. Thirdly, excessive amout of fat can also accumulate in pancreas, which cause so-called non-alcoholic fatty pancreas disease (NAFPD). The inability for adipose tissue to store free fatty acid (FFA) and transferring FFA to vital organs such as liver, pancreas and muscle, causing a series of biological reaction that eventually result in insulin resistance, which is crucial for DM development[ 48 , 50 ]. Fourthly, an animal study had found out that cyclic fatty acid monomers not only induce hepatic steatosis, but is also associated with increment in AST/ALT level[ 51 ]. Thus we hypothesized that in a state that adipose tissues fail to store FFA, excessive amount of fat accumulate in the liver and pancreas, which induce inflammation and oxidative stress that cause increase in AST/ALT ratio and development of insulin resistance. Our study has several strength that worth mentioning. Firstly, it’s the first study to investigate the relationship between AST/ALT ratio and reverse from prediabetic state to normal glucose level in Chinese population. Secondly, using Cox regression model, we discovered non-linear relationship between AST/ALT ratio and incidence of glucose reversion, which are important findings of this study. Thirdly, to deal with missing data, we adopted multiple imputation approach to minimize bias while maximizing statistical power. Fourthly, we conducted a series of sensitive analyses to confirm the reliability of our findings. There are some possible limitations of our study. Firstly, participants in our study were all Chinese, more investigation are needed to test the reliability of our results in other genetic backgrounds. Secondly, IGF is only one of the diagnostic criteria for prediabetes. However measuring 2-h glucose and glycated hemoglobin is challenging in such study cohort. Thirdly, our study was based on a secondary analysis of published data, variables that are not included in this data such as waist circumference cannot be adjusted. Fourthly, our study is a retrospective study, we can only identified the association between AST/ALT ratio and incidence of recurrence from prediabetes rather than a causal relationship. Fifthly, this study only analyze the AST/ALT ratio at the baseline. We stress this limitation in future studies or collaborate with other researchers to further investigate changes in AST/ALT ratio over time and the incidence of reversion from prediabetes. Conclusion Our research confirmed that there was a significant relationship between AST/ALT ratio and the possibiity of reverting to normoglycemia in individuals with IFG. The relationship between them was non-linear and had identifiable threshold effects. And their relationship was different before and after the inflection point. Before the inflection point, AST/ALT ratio was significant negatively correlated with blood glucose reversal in patients with IFG, and after the inflection point, the relationship between the two was positively correlated. These findings provided valuable insights for individuals with different AST/ALT ratio states to reverse from IFG to normal blood glucose. When the AST/ALT ratio was greater than 1.05, the likelihood of achieving blood glucose reversal increased as the AST/ALT ratio increasing. So, the AST/ALT ratio was best to be controlled above 1.05 from a clinical treatment perspective. Abbreviations AST/ALT ratio aspartate aminotransferase to alanine aminotransferase ratio SBP Systolic blood pressure Scr Serum creatinine T2DM Type 2 diabetes mellitus HbA1c Glycated hemoglobin A1c HDL-c High-density lipoprotein cholesterol VIF Variance inflation factor LDL-c Low-density lipid cholesterol BUN Blood urea nitrogen ALT Alanine aminotransferase DBP Diastolic blood pressure TG Triglyceride CI Confidence intervals IFG Impaired fasting glucose HR Hazard ratios BMI Body mass index FPG Fasting plasma glucose GAM Generalized additive models Ref Reference MAR Missing-at-random TC Total cholesterol SD Standard deviation NAFLD Nonalcoholic fatty liver disease AST Aspartate aminotransferase IDF International diabetes federation Declarations Acknowledgements The data in our study was from the article titled“Association of body mass index and age with incident diabetes in Chinese adults: a population-based cohort study,”which was conducted by Chen Y, Zhang XP, Yuan J, et al. and published in BMJ Open in 2018 (Sep 28;8(9):e021768. https://doi.org/https://doi.org/10.1136/bmjopen-2018-021768). This study was a secondary analysis, when conducting this secondary analysis, the authors of this study were grateful to all authors who participated in the original publication. Authors’ contributions Haofei Hu confirmed the research design and revise the final document. Kebao Zhang contributed to the discussion of the article. Lidan Chen and Zhe Deng downloaded data from public databases and performed data analysis; Rong rong and Lifen Xu contributed to data cleansing. Shuting Zeng and Liting Xu contributed to figure processing. Funding The Shenzhen Health Commission Discipline Construction Capacity Enhancement Project (SZXJ2017031) and the Shenzhen Key Medical Discipline Construction Fund (SZXK009) supported the development of this study. Availability of data and materials The data can be downloaded although the ‘DATADRYAD’ database (https://datadryad.org/stash) Ethics approval and consent to participate The Rich Healthcare Group review committee reviewed and approved these studies which involved participants. Due to the observational nature of the study and the fact that this information was collected retrospectively and anonymous, the Rich Healthcare Group Review Committee waived the requirement for informed consent. [30, 31]. Consent for publication Not applicable. Competing interests None References Al Kaabi, J. et al. Epidemiology of Type 2 Diabetes – Global Burden of Disease and Forecasted Trends . J. Epidemiol. Global Health , 10 (1). (2019). Tabák, A. G. et al. Prediabetes: a high-risk state for diabetes development. Lancet . 379 (9833), 2279–2290 (2012). ElSayed, N. A. et al. 2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes—2023. Diabetes Care . 46 (Supplement_1), S19–S40 (2023). Zand, A. & Patham, I. K. Prediabetes: Why Should We Care? Methodist Debakey Cardiovasc. J. 14 (4), 289–297 (2018 Oct-Dec). Perreault, L. et al. Effect of regression from prediabetes to normal glucose regulation on long-term reduction in diabetes risk: results from the Diabetes Prevention Program Outcomes Study. Lancet . 379 (9833), 2243–2251 (2012). Chen, L. et al. Association Between Aspartate Aminotransferase to Alanine Aminotransferase Ratio and Incidence of Type 2 Diabetes Mellitus in the Japanese Population: A Secondary Analysis of a Retrospective Cohort Study Volume 14p. 4483–4495 (Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy,, 2021). Zou, Y. et al. Association between the alanine aminotransferase/aspartate aminotransferase ratio and new-onset non-alcoholic fatty liver disease in a nonobese Chinese population: a population-based longitudinal study . Lipids Health Dis. , 19 (1). (2020). Galaviz, K. I. et al. Interventions for Reversing Prediabetes: A Systematic Review and Meta-Analysis. Am. J. Prev. Med. 62 (4), 614–625 (2022). Perreault, L. et al. Regression From Prediabetes to Normal Glucose Regulation and Prevalence of Microvascular Disease in the Diabetes Prevention Program Outcomes Study (DPPOS). Diabetes Care . 42 (9), 1809–1815 (2019). Perreault, L. et al. Regression From Pre-Diabetes to Normal Glucose Regulation in the Diabetes Prevention Program. Diabetes Care . 32 (9), 1583–1588 (2009). Kawamoto, R., Kusunoki, K. K., Tabara, T., Abe, Y. & Miki, M. Alanine aminotransferase/aspartate aminotransferase ratio is the best surrogate marker for insulin resistance in non-obese Japanese adults. Cardiovasc. Diabetol. 1 , 11:117 (2012 Oct). Zhao, L. et al. Serum alanine aminotransferase/aspartate aminotransferase ratio is one of the best markers of insulin resistance in the Chinese population . Nutr. Metabolism , 14 (1). (2017). Seo, M. H. et al. WY, Association of lipid and lipoprotein profiles with future development of type 2 diabetes in nondiabetic Korean subjects: a 4-year retrospective, longitudinal study . J. Clin. Endocrinol. Metab. 2011 Dec. 96 (12): p. E2050-E2054. Chen, Y. & Yuan, Z. X. Association of body mass index and age with incident diabetes in Chinese adults: a population-based cohort study. BMJ Open. 8 (9), e021768 (2018). Geleris, J. & Platt, S. Y. Observational Study of Hydroxychloroquine in Hospitalized Patients with Covid-19. N Engl. J. Med. 382 (25), 2411–2418 (2020). Zhang, N. & Zhang, H. X. Non-high-density lipoprotein cholesterol: High-density lipoprotein cholesterol ratio is an independent risk factor for diabetes mellitus: Results from a population-based cohort study. J. Diabetes . 10 (9), 708–714 (2018). American Diabetes. Association Professional Practice Committee. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2022. Diabetes Care . 45 (Suppl 1), S17–S38 (2022). Lazo-Porras, M. et al. Regression from prediabetes to normal glucose levels is more frequent than progression towards diabetes: The CRONICAS Cohort Study 163 (Diabetes Research and Clinical Practice, 2020). Peng, J. et al. Association between dyslipidemia and risk of type 2 diabetes mellitus in middle-aged and older Chinese adults: a secondary analysis of a nationwide cohort. BMJ Open. 11 (5), e042821 (2021). Yang, T. et al. Correlation between the triglyceride-to-high-density lipoprotein cholesterol ratio and other unconventional lipid parameters with the risk of prediabetes and Type 2 diabetes in patients with coronary heart disease: a RCSCD-TCM study in China. Cardiovasc. Diabetol. 21 (1), 93 (2022). Greene, M. W. et al. Utility of Fatty Liver Index to predict reversion to normoglycemia in people with prediabetes . Plos One , 16 (4). (2021). Han, Y., Huang, H. H. & Liu, Z. Association between body mass index and reversion to normoglycemia from impaired fasting glucose among Chinese adults: a 5-year cohort study. Front. Endocrinol. (Lausanne) . 14 , 1111791 (2023). Hwang, Y. C. et al. Factors associated with regression from prediabetes to normal glucose tolerance in a Korean general population: A community-based 10‐year prospective cohort study. Diabet. Med. 35 (11), 1544–1551 (2018). Groenwold, R. H., Donders, W. I., Carpenter, A. R., Altman, J. R. & Moons, D. G. Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis. CMAJ . 184 (11), 1265–1269 (2012). White, I. R. & Wood, R. P. Multiple imputation using chained equations: Issues and guidance for practice. Stat. Med. 30 (4), 377–399 (2011). Y, W. Collinearity diagnosis for a relative risk regression analysis: an application to assessment of diet-cancer relationship in epidemiological studies. Stat. Med. 11 (10), 1273–1287 (1992). Sheng, G., Kuang, L. D., Zhong, M., Zhang, Y. & Zou, S. Utility of Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio in Evaluating Incident Diabetes Risk. Diabetes Metab. Syndr. Obes. 15 , 1677–1686 (2022). Rothenbacher, D. et al. BiomarCaRE consortium. Contribution of cystatin C- and creatinine-based definitions of chronic kidney disease to cardiovascular risk assessment in 20 population-based and 3 disease cohorts: the BiomarCaRE project. BMC Med. 18 (1), 300 (2020). Noordzij, M., van Stralen, L. K., Zoccali, K. J., Dekker, C. & Jager, F. W. When do we need competing risks methods for survival analysis in nephrology? Nephrol. Dial Transpl. 28 (11), 2670–2677 (2013). Basak, R. & Chen, M. H. Understanding Competing Risks. Int. J. Radiat. Oncol. Biol. Phys. 110 (3), 636–640 (2021). Solbak, N. M., Akawung, A. R. A., Lo Siou, A. K., Kirkpatrick, G. & Robson, S. I. Strategies to Address Misestimation of Energy Intake Based on Self-Report Dietary Consumption in Examining Associations Between Dietary Patterns and Cancer Risk. Nutrients . 11 (11), 2614 (2019). . . Chen, Z. et al. Association of Triglyceride to high-density lipoprotein cholesterol ratio and incident of diabetes mellitus: a secondary retrospective analysis based on a Chinese cohort study. Lipids Health Dis. 19 (1), 33 (2020). Pop-Busui, R. et al. Effects of triple antioxidant therapy on measures of cardiovascular autonomic neuropathy and on myocardial blood flow in type 1 diabetes: a randomised controlled trial. Diabetologia . 56 (8), 1835–1844 (2013). Mullee, A. et al. N, Association Between Soft Drink Consumption and Mortality in 10 European Countries 179p. 1479–1490 (JAMA Intern Med, 2019). 11. Keidel, D. et al. N, The Role of Socioeconomic Status in the Association of Lung Function and Air Pollution-A Pooled Analysis of Three Adult ESCAPE Cohorts. Int J Environ Res Public Health, 16(11): p. 1901. (2019). Sun, Y., Yuan, N. W., Chi, X. & Xu, H. Prevalence, treatment, control of type 2 diabetes and the risk factors among elderly people in Shenzhen: results from the urban Chinese population. BMC Public. Health . 20 (1), 998 (2020). Schleger, F. et al. Family History of Diabetes Is Associated With Delayed Fetal Postprandial Brain Activity. Front. Endocrinol. (Lausanne) . 9 , 673 (2018). Zhu, F. et al. Elevated blood mercury level has a non-linear association with infertility in U.S. women: Data from the NHANES 2013–2016. Reprod. Toxicol. 91 , 53–58 (2020). Haneuse, S. & Arterburn, V. T. Using the E-Value to Assess the Potential Effect of Unmeasured Confounding in Observational Studies. JAMA . 321 (6), 602–603 (2019). Liu, X. et al. Reversion From Pre–Diabetes Mellitus to Normoglycemia and Risk of Cardiovascular Disease and All-Cause Mortality in a Chinese Population: A Prospective Cohort Study . J. Am. Heart Association , 10 (3). (2021). Xie, W. et al. Low aspartate aminotransferase/alanine aminotransferase (DeRitis) ratio assists in predicting diabetes in Chinese population . Front. Public. Health , 10. (2022). Cao, C. et al. Nonlinear relationship between aspartate aminotransferase to alanine aminotransferase ratio and the risk of prediabetes: A retrospective study based on chinese adults . Front. Endocrinol. , 13. (2022). . An, R. et al. AST-to-ALT ratio in the first trimester and the risk of gestational diabetes mellitus . Front. Endocrinol. , 13. (2022). Tanase, D. M. et al. The Intricate Relationship between Type 2 Diabetes Mellitus (T2DM), Insulin Resistance (IR), and Nonalcoholic Fatty Liver Disease (NAFLD). Journal of Diabetes Research, 2020: pp. 1–16. (2020). Chen, Z. et al. A vicious circle between insulin resistance and inflammation in nonalcoholic fatty liver disease . Lipids Health Dis. , 16 (1). (2017). Bril, F. et al. Metabolic and histological implications of intrahepatic triglyceride content in nonalcoholic fatty liver disease. Hepatology . 65 (4), 1132–1144 (2017). Filippatos, T. D. et al. Nonalcoholic Fatty Pancreas Disease: Role in Metabolic Syndrome, Prediabetes, Diabetes and Atherosclerosis. Dig. Dis. Sci. 67 (1), 26–41 (2021). Mboma, J. et al. Liver and plasma lipid changes induced by cyclic fatty acid monomers from heated vegetable oil in the rat 6p. 2092–2103 (Food Science & Nutrition, 2018). 8. Additional Declarations No competing interests reported. Supplementary Files 2Supplementalfile.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4945577","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":354925809,"identity":"a76fefb0-77f9-4e25-9229-806ad9e915ae","order_by":0,"name":"Kebao Zhang","email":"","orcid":"","institution":"Department of Emergency, The Eighth Affiliated Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Kebao","middleName":"","lastName":"Zhang","suffix":""},{"id":354925810,"identity":"4c078e44-0720-4f32-8504-45cb7d5b7e4c","order_by":1,"name":"Lidan Chen","email":"","orcid":"","institution":"Shenzhen Nanshan Medical Group Headquarter, Shenzhen 518000, Guangdong Province","correspondingAuthor":false,"prefix":"","firstName":"Lidan","middleName":"","lastName":"Chen","suffix":""},{"id":354925811,"identity":"9dc40a36-cbc5-45ed-82ca-d5bf1fad130b","order_by":2,"name":"Zhe Deng","email":"","orcid":"","institution":"Department of Emergency, Shenzhen Second People’s Hospital, Shenzhen 518000, Guangdong Province","correspondingAuthor":false,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Deng","suffix":""},{"id":354925812,"identity":"f03af896-8cfd-4461-8364-09dd99cdbb82","order_by":3,"name":"Rong Rong","email":"","orcid":"","institution":"Shenzhen Nanshan Medical Group Headquarter, Shenzhen 518000, Guangdong Province","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Rong","suffix":""},{"id":354925813,"identity":"f0d690db-b7ce-4ec3-96f1-517ad7523629","order_by":4,"name":"Lifen Xu","email":"","orcid":"","institution":"Shenzhen Nanshan Medical Group Headquarter, Shenzhen 518000, Guangdong Province","correspondingAuthor":false,"prefix":"","firstName":"Lifen","middleName":"","lastName":"Xu","suffix":""},{"id":354925814,"identity":"9971354b-9070-40ed-9faa-0fd7b4da741a","order_by":5,"name":"Liting Xu","email":"","orcid":"","institution":"Shenzhen Nanshan Medical Group Headquarter, Shenzhen 518000, Guangdong Province","correspondingAuthor":false,"prefix":"","firstName":"Liting","middleName":"","lastName":"Xu","suffix":""},{"id":354925817,"identity":"d3e685b4-be5a-408c-a18b-8e98aac6894f","order_by":6,"name":"Shuting Zeng","email":"","orcid":"","institution":"Shenzhen Nanshan Medical Group Headquarter, Shenzhen 518000, Guangdong Province","correspondingAuthor":false,"prefix":"","firstName":"Shuting","middleName":"","lastName":"Zeng","suffix":""},{"id":354925819,"identity":"c633c896-7c5a-47b4-bb30-e1130a79345e","order_by":7,"name":"Haofei Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACAwYeIGnDwMPG39j44IOBjRyRWtIYePglDjcbzihIMyZaC4NkQ3qbNM+Hw4kEtZhL5B788CHBRsbgwME2aRsD5gQG9sNHN+DTYjkjL1lyRkIaj8HhxmbrHAO2PAaetLQbeB12I8dAmvfHYR6gLY23cwx4ihkkeMwIaTH+zZPwH6glsUHawkAisYEILWbSPAkHeCQbEpukGQwMiNBy5o2Z5YyEZGAgH2w27DFIMGYj6JfjOcY3PiTY2bPxtz988OPPfzl+9sPH8GrBBGykKR8Fo2AUjIJRgA0AAJ0LSuOxXiHxAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Nephrology, Shenzhen Second People’s Hospital, Shenzhen 518000, Guangdong Province","correspondingAuthor":true,"prefix":"","firstName":"Haofei","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2024-08-20 14:01:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4945577/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4945577/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66652785,"identity":"0dd12bfb-7bb7-460f-bc20-7afd3c9cc2f2","added_by":"auto","created_at":"2024-10-15 07:51:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":587234,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of study participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 displays the participant selection process. Initially, a total of 211,833 participants were evaluated for eligibility in the original study. After excluding 200712 individuals, the final analysis consisted of 11121 subjects in the current investigation.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4945577/v1/3eab42e92e75c8081d731443.jpg"},{"id":66651903,"identity":"eca5cc36-c891-4193-be29-fd2b512bfdf8","added_by":"auto","created_at":"2024-10-15 07:43:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":194241,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of non-HDL-c/HDL-c ratio\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2 shows a normal distribution of the AST/ALT ratio, ranging from from 0.29 to 2.65, and a mean value of 1.14.\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4945577/v1/3c2a7e584bb1d19e772885a5.jpg"},{"id":66652786,"identity":"fff26dd5-4938-4bc9-94c5-4243a2ad3fc2","added_by":"auto","created_at":"2024-10-15 07:51:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":130535,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe rate of reversion to normoglycemia in people with pre-diabetes stratified by the quartiles of AST/ALT ratio.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3 presents that participants with higher AST/ALT ratio. showed higher rates of reversal to normoglycemia (P\u0026lt;0.0001 for trend).\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4945577/v1/e50eed00df871ffba9e5adff.jpg"},{"id":66651904,"identity":"f6a7662d-c5c8-44f1-b74a-05b585a5ea51","added_by":"auto","created_at":"2024-10-15 07:43:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":264386,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe rate of reversion to normoglycemia in patients with pre-diabetes of age stratification by 10 intervals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs was shown in Figure 4, the women participants with pre-diabetes showed a higher rate of reversion to normoglycemia than men, regardless of their age group. What ‘s more, the reversal rate in both men and women decreased with increasing age.\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4945577/v1/3ad7c1fd6fe8679efa4063ca.jpg"},{"id":66651902,"identity":"6a914618-067d-4b45-a048-aef64e9ab446","added_by":"auto","created_at":"2024-10-15 07:43:08","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":267492,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curves for the probability of reversion to normoglycemia from pre-diabetes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 5 illustrates the Kaplan Meier curve, which was classified according to the quartile of AST/ALT ratio to show the possibility of recovering from pre diabetes to normoglycemia. The results showed that individuals with pre-diabetes in the highest AST/ALT ratio have the highest chance of transitioning from pre-diabetes to normoglycemia.\u003c/p\u003e","description":"","filename":"15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4945577/v1/5fb3f93db8996b7d4aed4fea.jpg"},{"id":66651906,"identity":"28155b36-2111-4b21-a479-3efccf1603f7","added_by":"auto","created_at":"2024-10-15 07:43:08","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":249079,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe non-linear relationship between the AST/ALT ratio and reversion to normoglycemia in patients with pre-diabetes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn Figure 6, we employed a Cox proportional hazards regression model with cubic spline functions to investigate the association between the AST/ALT ratio and the probability of reversion from pre-diabetes to normoglycemia. The findings revealed a non-linear relationship between the AST/ALT ratio and this probability, with an inflection point observed at 1.05.\u003c/p\u003e","description":"","filename":"16.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4945577/v1/cfae42b71c25a733cb83fdb5.jpg"},{"id":70348288,"identity":"e1fcbb1d-ed69-4976-87e2-17c5877120e1","added_by":"auto","created_at":"2024-12-02 11:31:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3082178,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4945577/v1/5690da0b-595e-49bd-817e-a3485ee89eba.pdf"},{"id":66651907,"identity":"6c4aeeaa-a670-4520-946b-3a1a9b524c37","added_by":"auto","created_at":"2024-10-15 07:43:08","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":21528,"visible":true,"origin":"","legend":"","description":"","filename":"2Supplementalfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-4945577/v1/8bc14193d8b6641780742fb0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between aspartate aminotransferase to alanine aminotransferase ratio and reversion to normoglycemia in people with impaired fasting glucose: a 5-year retrospective cohort study ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetes has been a major health concern globally. According to Global burden of disease, the burden of diabetes mellitus (DM) is rising globally. Over one million deaths per year is attributed to DM independently, which make it the ninth leading cause of death\u003csup\u003e[1]\u003c/sup\u003e. Before DM, there is a state known as impaired glucose regulation (also known as prediabetes), which is characterized by increased glycated hemoglobin (A1C), impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or a combination of IFG and IGT\u003csup\u003e[2]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAccording to American Diabetes Association (ADA) updated in 2023, prediabetes is defined as fasting plasma glucose (FPG) of 5.6-6.9mmol/L, impaired fasting glucose (2-h plasma glucose of 7.8-11.0mmol/L) or A1C of 5.7\u0026ndash;6.4%\u003csup\u003e[3]\u003c/sup\u003e. It is noted that prediabetic state is a risk factor for DM. 5\u0026ndash;10% of patients with prediabetic state converted to DM each year. Additionally, prediabetes itself is a risk factor for developing cardiovascular diseases such as coronary artery disease and diastolic heart failure\u003csup\u003e[4]\u003c/sup\u003e. Nevertheless, prediabetic patients can also converted back to normoglycemia \u003csup\u003e[2]\u003c/sup\u003e. Previous study had found that even transient reversal to normal glucose level from prediabetes can reduce the future development of DM, so it is worthwhile to study which factors can predict the reversal of prediabetes to normoglycemia[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAspartate transaminase (AST) and alanine transaminase (ALT) are biomarkers that represent liver function. It is found that AST/ALT ratio can not only predict the likelihood of developing non-alcoholic fatty liver disease (NAFLD), but also can use to predict the risk for developing DM\u003csup\u003e[6, 7]\u003c/sup\u003e. A study conducted in Iran found that ALT/AST ratio is a risk factor for prediabetes development. Regrettablely, the relevance between AST/ALT ratio and reversal of prediabetes to normoglycemia is hardly understood. Previous studies have found that reversion to normal glucose level from prediabetes can be contributed to age, sex, race, obesity, lifestyle, baseline A1C, baseline fasting glucose level, pharmacological approach, etc\u003csup\u003e[8\u0026ndash;10]\u003c/sup\u003e. Several studies had ellucidated that AST/ALT ratio is associated with insulin resistance\u003csup\u003e[11, 12]\u003c/sup\u003e, which can partially explain the relationship between AST/ALT ratio and DM. We propose the hypothesis that AST/ALT ratio is positively associated with reversion to normoglycemia from prediabetes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis study was a secondary retrospective study, which aimed to identify the association between the AST/ALT ratio and the reversion of IFG to normoglycemia. In this study, we regarded the baseline AST/ALT ratio as the independent variable and glucose reversion from IFG to normoglycemia during follow-up as the outcome variable .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eThe original data was downloaded freely from the study: Association of body mass index and age with the risk of DM in Chinese adults: a population-based cohort study.,which was uploaded by Chen et al into the DATADRYAD database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.datadryad.org\" target=\"_blank\"\u003ewww.datadryad.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.datadryad.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Dryad Digital Repository. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://datadryad.org/stash/dataset/doi:10\u003c/span\u003e\u003cspan address=\"https://datadryad.org/stash/dataset/doi:10\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 5061%2Fdryad.ft8750v)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. According to the service terms stipulated by the Dryad database, researchers were allowed to use the dataset for non-commercial purposes, whether it was to share, modify, mix or creat derivative works based on the dataset, as long as they trust the author and the source of the data.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003e The Rich Healthcare Group recruited participants continuously from 32 locations in 11 cities in China (Hefei,Changzhou, Nantong, Suzhou, Shenzhen, Nanjing, Guangzhou,Shanghai, Chengdu, Wuhan and Beijing ) to minimize selection bias. All procedures involved human subjects have been approved by the Clinical Research Ethics Committee of the Rich Healthcare Group. In addition, this study follows the principles outlined in the Helsinki Declaration and used untraceable codes to encode the identity information of participants in order to protect their privacy. Due to retrospective design and anonymity of data, the institutional review committee gave up the necessity of informed consent from participants[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn the original study, 685,277 Chinese participants were enrolled and completed at least a second visits from 2010 to 2016, and 211,833 individuals (95710 female and 116123 male) were eventually involved in the raw analysis according to the following exclusion criteria: (1)missing data on weight, height,gender,fasting plasma glucose (n\u0026thinsp;=\u0026thinsp;135317) ; (2) extreme BMI values (\u0026lt;\u0026thinsp;15kg/m2 or \u0026gt;\u0026thinsp;55kg/m2) (n\u0026thinsp;=\u0026thinsp;152); (3) Participants follow-up \u0026lt;\u0026thinsp;2 years(n\u0026thinsp;=\u0026thinsp;324233); (4) participants who were diagnosed with diabetes at baseline (4115 diagnosed by a fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0mmol/L and 2997 participants diagnosed by self-report); (5) participants with undefined diabetes status at follow-up (n\u0026thinsp;=\u0026thinsp;6630)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].In our study, we also excluded: (6)initial fasting plasma glucose\u0026lt;5.6 (n\u0026thinsp;=\u0026thinsp;185586); (7) initial fasting plasma glucose\u0026gt;6.9 (n\u0026thinsp;=\u0026thinsp;229); (8) No available final fasting plasma glucose(n\u0026thinsp;=\u0026thinsp;6); (9)missing data of ALT(n\u0026thinsp;=\u0026thinsp;234); (10) missing data of AST(n\u0026thinsp;=\u0026thinsp;14537); (11)outliers of AST/ALT (\u0026lt; means minus three standard deviation (SD)or \u0026gt;means plus three standard deviation)(n\u0026thinsp;=\u0026thinsp;120)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Finally, a total of 11121 participants (7472 male and3649 female) were included in our analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed in detail of participants\u0026rsquo; selection process .\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eVariables\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003easpartate aminotransferase to alanine aminotransferase ratio\u003c/h2\u003e \u003cp\u003eThe baseline AST/ALT ratio was regarded as a continuous variable. The AST/ALT ratio was calculated by divide aspartate aminotransferase (AST, measured in U/L) by alanine aminotransferase(ALT, measured in U/L).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImpaired fasting glucose\u003c/h2\u003e \u003cp\u003eThe definition of impaired fasting blood glucose was the patient's FPG value between 5.6 to 6.9 mmol/L\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eOutcome measures\u003c/h2\u003e \u003cp\u003eNo self-report diabetes events and FPG levels below 5.6 mmol/L during follow-up were defined as successful glucose reversal[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eAccording to the clinical experiences and previous research, some variables were recognized as high risk factors for diabetes, pre diabetes or diabetes complications[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20 CR21 CR22\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. So, we selected the covariates included in this study were as follow: (1) continuous variables: blood urea nitrogen (BUN), age, BMI, diastolic blood pressure (DBP), cholesterol, serum creatinine (Scr), low-density lipid cholesterol (LDL-c), Triglyceride (TG), systolic blood pressure (SBP), Fasting gblood glucose (FPG) and high-density lipid cholesterol (HDL-c). (2) categorical variables: family history of diabetes, smoking and drinking status as well as gender.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe height, weight, and blood pressure of the participants were measured by professional staff. To ensure the accuracy of the data, participants were required to wear lightweight clothing and without shoes before measuring height and weight. Blood pressure was measured by standard mercury sphygmomanometer. Before measuring it, participants were required to stay in lying position quietly for 5\u0026ndash;10 minutes. BMI was calculated by divide weight (kilograms) by the square of height (meters), with the height measurement accuracy of 0.1 centimeters and the weight measurement accuracy of 0.1 kilogram. family history of diabetes, lifestyle, personal medical history and demographic characters were collected by a survey questionnaire. Clinical variables, such as BUN, FPG, ALT, HDL-C, TC, LDL-C, TG, AST and Scr were obtained by means of autoanalyzer (Beckman 5800) after fasting for at least 10 hours[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMissing data processing\u003c/h2\u003e \u003cp\u003eSome variables had missing data \u003cb\u003ei\u003c/b\u003en this study, for example, 5 participants (0.045%) lacked DBP and SBP data, 88 individuals (0.79%) missed cholesterol and triglyceride data, 520 participants (4.68%) lost SCR data. At the same time, some variables missed quite a proportion of data. Just as LDL-C, HDL-C, drinking status, and smoking status missed data for 31.99%, 36.80%, 69.65% and 69.65% respectively. We adopted the method of multiple imputation of chain equations to handle missing data in order to maximize the use of participants\u0026rsquo; data[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Ultimately, the variables contained in our article included Triglyceride;age; AST; DBP; FPG; HDL-c;gender; BMI; SBP; smoking and drinking status; LDL-c; ALT; BUN; SCR and family history of diabetes. We handled missing data based on the assumption of random missing data (MAR)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe classified the participants into quartiles according to the AST/ALT ratio: Q1\u0026thinsp;\u0026lt;\u0026thinsp;0.81; 0.81\u0026thinsp;\u0026le;\u0026thinsp;Q2\u0026thinsp;\u0026lt;\u0026thinsp;1.08; 1.08\u0026thinsp;\u0026le;\u0026thinsp;Q3\u0026thinsp;\u0026lt;\u0026thinsp;1.40; 1.40\u0026thinsp;\u0026le;\u0026thinsp;Q4. Continuous variables can be devided into normal distributed values and skewed values, which are described by mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and median. Categorical values were described by frequencies and percentages. To evaluate the differences between the AST/ALT ratio groups. One-way ANOVA tests were used to analyze normal distributed values while chi square tests and Kruskal-Wallis H tests were used to analyze categorical values and skewed values respectively. Kaplan Meier method was employed to calculate survival estimates and time-to-event variables. Additionally, we also used the log-rank test to compare the likelihood of returning from IFG to normoglycemia among the AST/ALT ratio groups .\u003c/p\u003e \u003cp\u003eThe variance inflation factor (VIF) was used to evaluate the likelihood of covariate collinearity, calculated as VIF\u0026thinsp;=\u0026thinsp;1/(1-R2). R came from the linear regression equation[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The studied variable was treated as the dependent variable, and other variables were regarded as independent variables in each regression analysis. As the result, if a variable\u0026rsquo;s VIF exceeded 5, it indicated collinearity between the variables, which meant that we should exclude this variable when analyzing multiple regression models. As was shown in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, the VIF of cholesterol was 6, We excluded it in the analysis of the multiple regression model.(Attachment 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe constructed three different models by using multivariate Cox proportional hazards regression analysis and univariate analysis to find out the relationship between the AST/ALT ratio and the probability of reverting to normoglycemia from IFG. Model I did not adjust for any covariates; Model II only adjusted for demographic characteristics such as smoking and drinking status, SBP, gender, DBP, BMI, family history of diabetes, age. Model III adjusted for all covariates which were listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, including SCR, smoking status, age, SBP, HDL-c, gender, triglyceride, drinking status, BMI, FPG, LDL-c, DBP, family history of diabetes. The article providesd hazard ratios (HR) and its corresponding 95% confidence intervals (CIs). We adjusted for confounding variables in this article was based on clinical knowledge and published reports [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. According to the commonality screening results, there was no collinearity issue between all variables (Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Baseline Characteristics of Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST/ALT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1(\u0026lt;\u0026thinsp;0.81)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2(0.81\u0026ndash;1.08)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3(1.08\u0026ndash;1.40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4(\u0026gt;\u0026thinsp;1.40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.28\u0026thinsp;\u0026plusmn;\u0026thinsp;11.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.68\u0026thinsp;\u0026plusmn;\u0026thinsp;12.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.22\u0026thinsp;\u0026plusmn;\u0026thinsp;14.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.40\u0026thinsp;\u0026plusmn;\u0026thinsp;15.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.49\u0026thinsp;\u0026plusmn;\u0026thinsp;3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.26\u0026thinsp;\u0026plusmn;\u0026thinsp;3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128.42\u0026thinsp;\u0026plusmn;\u0026thinsp;16.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127.58\u0026thinsp;\u0026plusmn;\u0026thinsp;17.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127.75\u0026thinsp;\u0026plusmn;\u0026thinsp;18.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126.82\u0026thinsp;\u0026plusmn;\u0026thinsp;18.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.35\u0026thinsp;\u0026plusmn;\u0026thinsp;10.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.92\u0026thinsp;\u0026plusmn;\u0026thinsp;10.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.03\u0026thinsp;\u0026plusmn;\u0026thinsp;11.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.55\u0026thinsp;\u0026plusmn;\u0026thinsp;11.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.80 (1.22\u0026ndash;2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.56 (1.05\u0026ndash;2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30 (0.90\u0026ndash;1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08 (0.77\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-c(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.00 (32.40-60.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.40 (21.00\u0026ndash;31.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.00 (15.60\u0026ndash;22.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.10 (11.00\u0026ndash;16.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.37\u0026thinsp;\u0026plusmn;\u0026thinsp;14.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.64\u0026thinsp;\u0026plusmn;\u0026thinsp;9.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.20\u0026thinsp;\u0026plusmn;\u0026thinsp;8.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.14\u0026thinsp;\u0026plusmn;\u0026thinsp;10.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST/ALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr(umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.14\u0026thinsp;\u0026plusmn;\u0026thinsp;14.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.48\u0026thinsp;\u0026plusmn;\u0026thinsp;15.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.07\u0026thinsp;\u0026plusmn;\u0026thinsp;16.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.71\u0026thinsp;\u0026plusmn;\u0026thinsp;17.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2407 (86.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2068 (74.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1679 (61.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1318 (46.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e373 (13.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e712 (25.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1068 (38.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1496 (53.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e542 (19.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e449 (16.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e336 (12.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e296 (10.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119 (4.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (3.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (3.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64 (2.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2119 (76.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2222 (79.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2325 (84.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2454 (87.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133 (4.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145 (5.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116 (4.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87 (3.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e640 (23.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e549 (19.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e425 (15.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e337 (11.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2007 (72.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2086 (75.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2206 (80.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2390 (84.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2692 (96.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2718 (97.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2696 (98.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2752 (97.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (3.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (2.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (1.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62 (2.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eValues are n (%), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or medians (quartiles)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBMI body mass index; LDL-c low-density lipoprotein cholesterol; FPG fasting plasma glucose;AST aspartate aminotransferase;DBP diastolic blood pressure; Scr serum creatinine; SBP systolic blood pressure; BUN blood urea nitrogen; ALT alanine aminotransferase; HDL-c high-density lipoprotein cholesterol; AST/ALT aspartate aminotransferase/alanine aminotransferaseratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e We used a Cox proportional hazards regression model with smooth curve fitting and cubic spline function for analysis to investigate whether there was the potential non-linear correlation between the AST/ALT ratio and reversion to normoglycemia in participants with IFG. This statistical method enabled us to handle any non-linear problems present in the data. When nonlinearity was detected between data, we used recursive algorithms to identify inflection points. Then, we employed two-stage Cox proportional risk regression model on both sides of the inflection point. and we also conducted a log likelihood ratio test to determine the most appropriate model for evaluating the relationship between AST/ALT ratio and reversion to normoglycemia[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsidering that people diagnosed as diabetes during the follow-up were unlikely to return to normal blood glucose levels, it was necessary to exclude these population from affecting the probability of reversing blood glucose in IFG patients[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].Therefore, we use the method described by Fine and Gray to achieve multivariate Cox regression of competitive risk[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Developing to diabetes was considered to be a competitive risk of a return to normal glycemic events in this approach.\u003c/p\u003e \u003cp\u003eWe conducted a stratified Cox proportional risk regression model based on family history of diabetes, TG, gender, age, BMI, DBP, drinking status, SBP, smoking status and FPG to perform subgroup analyses. Firstly, we established categorical variables by using clinically significant critical points, such as DBP (\u0026ge;\u0026thinsp;90 ,\u0026lt;90 mmHg), BMI (\u0026ge;\u0026thinsp;28, \u0026ge;\u0026thinsp;24 to 28, \u0026ge;\u0026thinsp;18.5 to \u0026lt;\u0026thinsp;24, \u0026lt;\u0026thinsp;18.5 kg/m2), TG (\u0026ge;\u0026thinsp;1.7, \u0026lt;\u0026thinsp;1.7 mmol/L), age (\u0026ge;\u0026thinsp;45, \u0026lt;\u0026thinsp;45 years), SBP (\u0026ge;\u0026thinsp;140, \u0026lt;\u0026thinsp;140 mmHg), and FPG (\u0026ge;\u0026thinsp;6.1, \u0026lt;\u0026thinsp;6.1 mmol/L)[\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Secondly, adjusted each stratification based on all other factors (Scr, TG, age, BUN, ALT, family history of diabetes, DBP, AST, gender, FPG, BMI, SBP, smoking and drinking status, LDL-c), including the stratification factors themselves itself. Finally, the models with and without interaction terms was compared to evaluate interactions by conducting a likelihood ratio test[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, we conducted a range of sensitivity analyses to determine the reliability of our results. Firstly, we divided the AST/ALT ratio into quartiles, then converted the ratio into a continuous variable and evaluated the P-value of the trend. Which also helped us identify the potential non-linear relationships. As was known to us all, people with smoking consumption, alcohol consumption or family history of diabetes were closely related to an increased risk of developing diabetes [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Therefore, we excluded individuals with with smoking consumption, alcohol consumption or family history of diabetes when we explored the association between AST/ALT ratio and reversion to normoglycemia in people with IFG in our additional sensitivity analyses. Furthermore, due to missing data on smoking and alcohol consumption in variables exceeding 70%, we eliminated smoking and drinking status as covariates in the multivariate model, because this might not have any impact to model adjustment. In addition, to ensure the consistency of the research results, we also adopted the Generalized Additive Model (GAM) to incorporate continuous covariates as curves into Model IV[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Moreover, we also calculated the E-values to evaluate the potential impact of the association between AST/ALT ratio and reversion to normoglycemia[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] This method provided more evidence to prove the reliability of our results.\u003c/p\u003e \u003cp\u003eData analysis was conducted through two statistical software packages named The R Foundation (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and EmpowerStats (X\u0026amp;Y Solutions, Inc, Boston, MA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.empowerstats.com\u003c/span\u003e\u003cspan address=\"http://www.empowerstats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All statistical tests were conducted by a double-sided test, and the result with significance levels of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, defined as statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of participants\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed the baseline characteristics of the participants involved in the study. A total amout of 11121 participants were included with 67.2% being male. The mean age of the population was 49.9\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0 years. The baseline AST/ALT ratio had an average of 1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4. In the population, 41.4% IFG patients reverse to normoglycemia during the average follow-up years (3 years). We stratified AST/ALT ratio into four groups: Q1\u0026thinsp;\u0026lt;\u0026thinsp;0.81; 0.81\u0026thinsp;\u0026le;\u0026thinsp;Q2\u0026thinsp;\u0026lt;\u0026thinsp;1.08; 1.08\u0026thinsp;\u0026le;\u0026thinsp;Q3\u0026thinsp;\u0026lt;\u0026thinsp;1.40; 1.40\u0026thinsp;\u0026le;\u0026thinsp;Q4). All covariates presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e have statistical significance between the different quartiles of the AST/ALT ratio (P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Between Q4 (\u0026ge;\u0026thinsp;1.4) and Q1 (\u0026lt;\u0026thinsp;0.81) ,significant increment in age, HDL-c, AST/ALT, female, never smoker were discovered. However, there were opposite trends for BMI, cholesterol, Triglyceride, ALT, SCR, male, current smoker, ever smoker and ever drinker among the covariates. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrated the distribution of the AST/ALT ratio levels, which showed a normal distribution with a range of values from 0.29 to 2.65, and a mean value of 1.14.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eReversion from IFG to normoglycemia\u003c/h2\u003e \u003cp\u003eIn our study,4602 individuals reverse from IGF to nomoglycemia with a total cumulative rate of 13.8/100 person years. Differences in cumulative rate of reversion to normoglycemia were discovered between the four quatile groups, ranging from 12.8 to 16.4/100 person years. The reversal rates in Q1-Q4 were as follow: 38.2(36.4\u0026ndash;40.0), 38.5(36.7\u0026ndash;40.3), 40.2(38.3\u0026ndash;42.0), and 48.5(46.7\u0026ndash;50.4). It was notable that participants in higher quatile groups received higher rates of reversion to normoglycemia (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 for trend). In Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003e, We stratifed the population according to different sex and age. In the same age group, we found that female has higher reversion rates than male. As age increases, the reversion rate for both men and women decreased.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe rate of reversion to normoglycemia in people with IFG\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST/ALT ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParticipants(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReversion events(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReversal rate (95% CI)(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePer 100 person year\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.4(40.5\u0026ndash;42.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1(\u0026lt;\u0026thinsp;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.2(36.4\u0026ndash;40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2(0.81\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.5(36.7\u0026ndash;40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3(1.08\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.2(38.3\u0026ndash;42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4(\u0026gt;\u0026thinsp;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.5(46.7\u0026ndash;50.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAST/ALT ratio aspartate aminotransferase/alanine aminotransferase ratio; CI confidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate analyses using Cox proportional-hazards regression model\u003c/h2\u003e \u003cp\u003eIn Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we used univariate analyses to unfold the relationship between reversion rate and several variables. In the table we found that reversion rate was not associated with BUN, ever drinkers, ever smokers, and people who had family history of diabetes (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). We also discovered that reversion rate is positively correlate with female, AST/ALT ratio, HDL-c, as well as people who never smoked or drank, and negatively correlate with older age, male, higher DBP, BMI, FPG, cholesterol, SBP, triglyceride, AST and LDL-c.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors influencing reversion to normoglycemia among participants with IFG analyzed by univariate Cox proportional hazards regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.90\u0026thinsp;\u0026plusmn;\u0026thinsp;13.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97 (0.97, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7472 (67.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3649 (32.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18 (1.11, 1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.85\u0026thinsp;\u0026plusmn;\u0026thinsp;3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.93, 0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127.64\u0026thinsp;\u0026plusmn;\u0026thinsp;17.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99 (0.99, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.46\u0026thinsp;\u0026plusmn;\u0026thinsp;11.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.98, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18 (0.16, 0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89 (0.86, 0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89 (0.86, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-c(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.94 (1.80, 2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93 (0.89, 0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.15\u0026thinsp;\u0026plusmn;\u0026thinsp;22.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.99, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.34\u0026thinsp;\u0026plusmn;\u0026thinsp;11.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99 (0.99, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST/ALT ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32 (1.23, 1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.96, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1336\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr(umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.09\u0026thinsp;\u0026plusmn;\u0026thinsp;16.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (1.00, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1623 (14.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e378 (3.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10 (0.93, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2721\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9120 (82.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19 (1.10, 1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e481 (4.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1951 (17.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12 (0.95, 1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8689 (78.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35 (1.16, 1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10858 (97.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e263 (2.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96 (0.80, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6803\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBMI body mass index, FPG fasting plasma glucose, LDL-c low-density lipoprotein cholesterol, DBP diastolic blood pressure, ALT alanine aminotransferase, SBP systolic blood pressure, AST aspartate aminotransferase, non-HDL-c non-high-density lipoprotein cholesterol, BUN blood urea nitrogen, HDL-c high-density lipoprotein cholesterol, Scr serum creatinine, AST/ALT ratio aspartate aminotransferase/alanine aminotransferase ratio, HR Hazard ratios, CI confidence interval, Ref reference\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e5\u003c/span\u003e, we used the Kaplan-Meier curve to revealed the likelihood of reversion to normoglycemia from IFG according to different AST/ALT ratio groups. There was a significant statistical effect on the AST/ALT ratio groups and the probability of reverting to normoglycemia (log-rank test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Participants with higher AST/ALT ratios had higher chances converting to normoglycemia from IFG .\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate analyses using the Cox proportional-hazards regression model\u003c/h2\u003e \u003cp\u003eWe employed Cox proportional-hazards regression model to illustrate the relationship between AST/ALT ratio and the likelihood of reverse to normoglycemia (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A significant positive correlation between AST/ALT ratio and reversion to normoglycemia was discovered in model I. With every 1 unit increase in the AST/ALT ratio, the chances for blood glucose reverse back to nomoglycemia increaseed by 32% (HR\u0026thinsp;=\u0026thinsp;1.32, 95% CI 1.23\u0026ndash;1.41). In model II, we adjusted for only demographic variables and also showed similar results (HR\u0026thinsp;=\u0026thinsp;1.33, 95% CI 1.23\u0026ndash;1.43). In Model III (fully-adjusted model), the positive correlation between AST/ALT ratio and reversion to normoglycemia still can be found (HR\u0026thinsp;=\u0026thinsp;1.19, 95% CI 1.07\u0026ndash;1.31). All the results were statistically significant.(p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between AST/ALT ratio and reversion to normoglycemia in people with IFG in different models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel I (HR, 95% CI, P)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel II (HR, 95% CI, P)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel III (HR, 95% CI, P)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel IV (HR, 95% CI, P)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST/ALT ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32(1.23, 1.41)\u0026thinsp;\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33 (1.23, 1.43)\u0026thinsp;\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19(1.07, 1.31) 0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2.74 (1.87, 4.01)\u0026thinsp;\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST/ALT ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.90, 1.06) 0.5825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (0.95, 1.13) 0.4736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91 (0.82, 1.01) 0.0746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.87 (0.76, 0.99) 0.0315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.90, 1.07) 0.6776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04 (0.95, 1.13) 0.4547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88(0.78, 0.99) 0.0271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.77 (0.64, 0.93) 0.0062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30 (1.20, 1.41)\u0026thinsp;\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.322(1.20, 1.45)\u0026thinsp;\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05 (0.92, 1.20) 0.4494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.92 (0.71, 1.18) 0.4916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;0.00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel I: we did not adjust other covariates\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel II: we adjust gender, age, BMI, SBP, DBP, family history of diabetes, smoking and drinking status\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel III: we adjust age; gender; BMI; SBP; DBP; FPG; Triglyceride; HDL-c; LDL-c; ALT; AST; BUN; SCR; family history of diabetes, smoking and drinking status\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eModel IV: we adjusted age(smooth); gender; BMI(smooth); SBP(smooth); DBP(smooth); FPG(smooth);Triglyceride(smooth); HDL-c(smooth); LDL-c(smooth); ALT(smooth); AST(smooth); BUN(smooth); CCR(smooth); family history of diabetes, smoking and drinking status\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eHR Hazard ratios, CI confidence, Ref reference\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eThe results of competing risks multivariate Cox proportional-hazards regression\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e showed the competitive analysis results from IFG to the incidence of diabetes. In Model I (unadjusted model), we discovered that as AST/ALT ratio increased, the incidence of reverting to normoglycemia also increased (SHR\u0026thinsp;=\u0026thinsp;1.32, 95%CI 1.23\u0026ndash;1.41). In Model II ,we adjusted only demographic variables. Similarly, the correlation between AST/ALT ratio and glucose reversion rate still remain positive (SHR: 1.33, 95% CI 1.23\u0026ndash;1.43). In model III (fully adjusted model), the relationship still remain positive (SHR\u0026thinsp;=\u0026thinsp;1.19, 95% CI 1.07\u0026ndash;1.31). All results are statistically significant. (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between AST/ALT ratio and reversion to normoglycemia in people with IFG in different models with competing risk of progression to diabetes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel I (SHR, 95% CI, P)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel II (SHR, 95% CI, P)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel III (SHR, 95% CI, P)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST/ALT ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.32(1.23, 1.41)\u0026thinsp;\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33 (1.23, 1.43)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19 (1.07, 1.31) 0.0009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST/ALT ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.90, 1.06) 0.5825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (0.95, 1.13)0.4736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91 (0.82, 1.01) 0.0746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.90, 1.07) 0.6776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04 (0.95, 1.13) 0.4547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88 (0.78, 0.99) 0.0271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.30(1.20, 1.41)\u0026thinsp;\u0026lt;\u0026thinsp;0.00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32 (1.20, 1.45)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05 (0.92, 1.19) 0.4494\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;0.00001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel I: we did not adjust other covariates\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel II: we adjust gender, age, BMI, SBP, DBP, family history of diabetes, smoking and drinking status\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel III: we adjust age; gender; BMI; SBP; DBP; FPG; triglyceride; HDL-c; LDL-c; ALT; AST; BUN; SCR; family history of diabetes, smoking and drinking status\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eSeveral sensitivity analyses were conducted in order to prove the reliability of our findings. Firstly, we treated the AST/ALT ratio as a continuous variable. Secondly, we partitioned the AST/ALT ratio into quartiles with equidistant patterns of effect sizes for each group, then, We imported them into the model. Finally, the results indicated that in the unadjusted and minimally adjusted models (Model I and Model II), the p-value of the trend was consistent with the results when the AST/ALT ratio was used as a continuous variable (shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe utilized a Generalized Additive Model (GAM) to incorporate the continuity covariate as a curve in the equation (shown as Model IV in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e ). The finding prompted that AST/ALT ratio was positively related to the possibility of reverting to normoglycemia. indicating an HR of 2.74 (95% CI 1.87\u0026ndash;4.01, P\u0026thinsp;\u0026lt;\u0026thinsp;0.00001).\u003c/p\u003e \u003cp\u003eAdditionally, in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, in order to exclude the effect of smoking or drinking habits or participants with diabetes family history, some supplementary sensitivity analyses were conducted to enhance our findings. For the reason that missing data on smoking and alcohol consumption almost reach 70%, we had to exclude these variables as covariates in some sensitivity analyses. However, although they were not included, these findings kept consistent with our results above. The participants with no drinking status (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, model a) showed a positive result between AST/ALT ratio and the possibiity of reverting to normoglycemia (HR\u0026thinsp;=\u0026thinsp;1.190, 95% CI 1.066\u0026ndash;1.327). Participants with no smoking status (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, model a) also represented the similar outcome (HR\u0026thinsp;=\u0026thinsp;1.164, 95% CI 1.044\u0026ndash;1.299 ). After excluding participants with family history of diabetes (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, model c), the result still remain positive.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between AST/ALT ratio and the probability of reverting from IFG to normoglycemia in different sensitivity analyses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel a (HR, 95% CI, P)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel b(HR, 95% CI, P)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel c (HR, 95% CI, P)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel d (HR, 95% CI, P)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST/ALT ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19 (1.07, 1.33) 0.0019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16 (1.04, 1.30) 0.0064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20 (1.08, 1.33) 0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19 (1.08, 1.32) 0.0007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST/ALT ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.81, 1.02) 0.1138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92(0.82, 1.03) 0.1489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92 (0.83, 1.02) 0.0959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91 (0.83, 1.01) 0.0816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.76, 0.99) 0.0324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.884 (0.776, 1.007) 0.0629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89 (0.7919, 1.00) 0.0497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88 (0.79, 0.99) 0.0333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03(0.89, 1.19) 0.6949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03 (0.89, 1.19) 0.7152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 (0.94, 1.21) 0.3422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.06 (0.93, 1.20) 0.3990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel a was a sensitivity analysis performed on never drinker participants (N\u0026thinsp;=\u0026thinsp;8689). We adjusted age; gender; BMI; SBP; DBP; FPG;triglyceride; HDL-c; LDL-c; ALT; AST; BUN; SCR;smoking status and family history of diabetes,\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel b was a sensitivity analysis performed on never smoker participants (N\u0026thinsp;=\u0026thinsp;9120). We adjusted gender; BMI; age; SBP; DBP; FPG; triglyceride; HDL-c; LDL-c;; ALT; AST; BUN;SCR;;drinking status and family history of diabetes,\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel c was sensitivity analysis in participants without family history of diabetes (N\u0026thinsp;=\u0026thinsp;10858). We adjusted age; BMI; SBP; gender; DBP; FPG; triglyceride; HDL-c; LDL-c; ALT; AST; BUN; SCR\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel d was sensitivity analysis in participants without adjusting smoking and drinking status (N\u0026thinsp;=\u0026thinsp;11121). We adjusted age; BMI; SBP; gender; DBP; FPG;triglyceride; HDL-c; LDL-c; ALT; AST; BUN; SCR and family history of diabetes\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eHR Hazard ratios, CI confidence, Ref reference\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eNon-linearity relationship between AST/ALT ratio and incidence of glucose reversion\u003c/h2\u003e \u003cp\u003eTo further investigate the relationship between AST/ALT ratio and incidence of glucose reversion, we applied Cox proportional hazards regression model and cubic spline functions (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003e). We found that relationship between AST/ALT ratio and glucose reversion rate is non-linear. In order to find the best fit, we further applied binary two-stage Cox proportional hazards regression model and logarithmic likelihood ratio test (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). We determined the inflection point as 1.05. For AST/ALT ratio\u0026thinsp;\u0026lt;\u0026thinsp;1.05, the relationship between AST/ALT ratio and incidence of glucose reversion was negative (HR\u0026thinsp;=\u0026thinsp;0.56, 95% CI 0.40\u0026ndash;0.78). However, when AST/ALT ratio\u0026thinsp;\u0026gt;\u0026thinsp;1.05, AST/ALT ratio is positively associated with incidence of glucose reversion (HR\u0026thinsp;=\u0026thinsp;1.28, 95% CI 1.15\u0026ndash;1.42).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe result of the two-piecewise Cox regression model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProbability of reversion to\u003c/p\u003e \u003cp\u003enormoglycemia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFitting model by standard Cox regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19 (1.07, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFitting model by two-piecewise Cox regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point of AST/ALT ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;Inflection point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56 (0.40, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;Inflection point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28 (1.15, 1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for log-likelihood ratio test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eWe adjusted gender; BMI; age; SBP; DBP; FPG; triglyceride; HDL-c; LDL-c; ALT; AST; BUN; CCR; family history of diabetes; smoking and drinking status\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eHR, Hazard ratios; CI: confidence\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eThe results of subgroup analyses\u003c/h2\u003e \u003cp\u003eIn order to investigate the association between AST/ALT ratio and the likelihood of reverting to normoglycemia in different populations, we comprehensively evaluated the interactions between various variables in prespecified subgroups and exploratory subgroups (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). All the P value of variables greater than 0.05. No significant interaction in terms of age, gender, BMI, SBP, DBP, smoking status, drinking status as well as triglycerides was identified.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStratified associations between AST/ALT ratio and reversion to normoglycemia in people with IFG in prespecified and exploratory subgroups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo of participants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP for interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10 (0.96, 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22 (1.06, 1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22 (1.06, 1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25 (1.05, 1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47 (0.15, 1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;18.5,\u0026lt;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24 (1.08, 1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;24,\u0026lt;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34 (1.12, 1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17 (0.82, 1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17 (1.05, 1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26 (1.00, 1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18 (1.06, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15 (0.82, 1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18 (1.06, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50 (1.18, 1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23 (1.08, 1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 (0.83, 1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20 (1.08, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19 (0.59, 2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27 (0.69, 2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09 (0.83, 1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever drinker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19 (1.07, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.67 (1.22, 2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82 (0.44, 1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16 (1.04, 1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote \u003cspan refid=\"FPar5\" class=\"InternalRef\"\u003e1\u003c/span\u003e: Above model adjusted for gender; BMI; age; SBP; DBP; triglyceride; HDL-c; LDL-c; ALT, AST, BUN, SCR, family history of diabetes, smoking and drinking status\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our study, we found that the relationship between the AST/ALT ratio and the likelihood of reversion to normoglycemia in individuals with IFG was significant nonlinear. Besides, a threshold effect was discovered indicating different relationship between the AST/ALT ratio and reversion to normoglycemia on both sides of the inflection point.\u003c/p\u003e \u003cp\u003eIn the retrospective cohort study, we studied the relationship between AST/ALT ratio and the likelihood of reversion to normoglycemia in patients with IGF. In our research, 41.4% of all participants with IGF return back to normoglycemia. In a prospective cohort study conducted in China, 14231 Chinese participants were recruited in this study and find out that 44.9% participants were reverse to normoglycemia from prediabetes after 2 years follow-up[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Similarly, in a prospective cohort study conducted in Mexico, after 2.5 years of follow-up, 22.6% of patients with IGF reverse to normoglycemia while 22.9% processed to T2DM. It is noticeable that quite amount of participants with IGF reversed to normoglycemia. Thus, it is important to find out which factors contribute to reverse IGF to normal glucose level.\u003c/p\u003e \u003cp\u003eThere have been many findings suggesting that AST/ALT ratio is related to DM and non-alcoholic fatty liver disease (NAFLD). A retrospective cohort study of 87,883 participants conducted in China found out that AST/ALT ratio was negatively associated with risk of DM and a threshold effect was discovered[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Similarly, in Japanese population, a negative correlationship between AST/ALT ratio and incidence of DM can also be found[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Besides, in a retrospective study consisting of 75,204 Chinese adults, AST/ALT ratio is negatively associated with development of prediabetes[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. What\u0026rsquo;s more, AST/ALT is also associated with insulin resistance (IR) and DM[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt had been long discussed that there is close interaction among DM, NAFLD and insulin resistance (IR)[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. As mentioned before, AST/ALT ratio is negatively associated with development of prediabetes. Considerably it is not surprise to find out that participants with higher AST/ALT ratio is more likely to reverse to normoglycemia. However, no previous study have elucidated the relationship between AST/ALT ratio and reversion to normomglycemia. To our knowledge, it is the first time that we revealed that AST/ALT ratio is positively associated with reverse to normoglycemia from prediabetic state. Furthermore, we used sensitivity analyses to further confirm the reliability of our results.\u003c/p\u003e \u003cp\u003eThe exact mechanism involved in the relationship between AST/ALT ratio and reversion to normoglycemia is not well established, however, it may be contributed to lipotoxicity and insulin resistance. Firstly, in previous studies, AST/ALT is a strong predictor for NAFLD and it is widely accepted that NAFLD is an independent risk factor for DM development. Secondly, a cross-sectional study had found out that increase in hepatic triglyceride contribute to NAFLD development as well as insulin resistance, supporting the theory of hepatic lipotoxicity[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Thirdly, excessive amout of fat can also accumulate in pancreas, which cause so-called non-alcoholic fatty pancreas disease (NAFPD). The inability for adipose tissue to store free fatty acid (FFA) and transferring FFA to vital organs such as liver, pancreas and muscle, causing a series of biological reaction that eventually result in insulin resistance, which is crucial for DM development[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Fourthly, an animal study had found out that cyclic fatty acid monomers not only induce hepatic steatosis, but is also associated with increment in AST/ALT level[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Thus we hypothesized that in a state that adipose tissues fail to store FFA, excessive amount of fat accumulate in the liver and pancreas, which induce inflammation and oxidative stress that cause increase in AST/ALT ratio and development of insulin resistance.\u003c/p\u003e \u003cp\u003eOur study has several strength that worth mentioning. Firstly, it\u0026rsquo;s the first study to investigate the relationship between AST/ALT ratio and reverse from prediabetic state to normal glucose level in Chinese population. Secondly, using Cox regression model, we discovered non-linear relationship between AST/ALT ratio and incidence of glucose reversion, which are important findings of this study. Thirdly, to deal with missing data, we adopted multiple imputation approach to minimize bias while maximizing statistical power. Fourthly, we conducted a series of sensitive analyses to confirm the reliability of our findings.\u003c/p\u003e \u003cp\u003eThere are some possible limitations of our study. Firstly, participants in our study were all Chinese, more investigation are needed to test the reliability of our results in other genetic backgrounds. Secondly, IGF is only one of the diagnostic criteria for prediabetes. However measuring 2-h glucose and glycated hemoglobin is challenging in such study cohort. Thirdly, our study was based on a secondary analysis of published data, variables that are not included in this data such as waist circumference cannot be adjusted. Fourthly, our study is a retrospective study, we can only identified the association between AST/ALT ratio and incidence of recurrence from prediabetes rather than a causal relationship. Fifthly, this study only analyze the AST/ALT ratio at the baseline. We stress this limitation in future studies or collaborate with other researchers to further investigate changes in AST/ALT ratio over time and the incidence of reversion from prediabetes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur research confirmed that there was a significant relationship between AST/ALT ratio and the possibiity of reverting to normoglycemia in individuals with IFG. The relationship between them was non-linear and had identifiable threshold effects. And their relationship was different before and after the inflection point. Before the inflection point, AST/ALT ratio was significant negatively correlated with blood glucose reversal in patients with IFG, and after the inflection point, the relationship between the two was positively correlated. These findings provided valuable insights for individuals with different AST/ALT ratio states to reverse from IFG to normal blood glucose. When the AST/ALT ratio was greater than 1.05, the likelihood of achieving blood glucose reversal increased as the AST/ALT ratio increasing. So, the AST/ALT ratio was best to be controlled above 1.05 from a clinical treatment perspective.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAST/ALT ratio \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;aspartate aminotransferase to alanine aminotransferase ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSBP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Systolic blood pressure\u003c/p\u003e\n\u003cp\u003eScr \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Serum creatinine\u003c/p\u003e\n\u003cp\u003eT2DM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Type 2 diabetes mellitus\u003c/p\u003e\n\u003cp\u003eHbA1c \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Glycated hemoglobin A1c\u003c/p\u003e\n\u003cp\u003eHDL-c \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;High-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eVIF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Variance inflation factor\u003c/p\u003e\n\u003cp\u003eLDL-c \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Low-density lipid cholesterol\u003c/p\u003e\n\u003cp\u003eBUN \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Blood urea nitrogen\u003c/p\u003e\n\u003cp\u003eALT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Alanine aminotransferase\u003c/p\u003e\n\u003cp\u003eDBP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Diastolic blood pressure\u003c/p\u003e\n\u003cp\u003eTG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Triglyceride\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Confidence intervals\u003c/p\u003e\n\u003cp\u003eIFG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Impaired fasting glucose\u003c/p\u003e\n\u003cp\u003eHR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Hazard ratios\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Body mass index\u003c/p\u003e\n\u003cp\u003eFPG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Fasting plasma glucose\u003c/p\u003e\n\u003cp\u003eGAM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Generalized additive models\u003c/p\u003e\n\u003cp\u003eRef \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Reference\u003c/p\u003e\n\u003cp\u003eMAR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Missing-at-random\u003c/p\u003e\n\u003cp\u003eTC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Total cholesterol\u003c/p\u003e\n\u003cp\u003eSD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Standard deviation\u003c/p\u003e\n\u003cp\u003eNAFLD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Nonalcoholic fatty liver disease\u003c/p\u003e\n\u003cp\u003eAST \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Aspartate aminotransferase\u003c/p\u003e\n\u003cp\u003eIDF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;International diabetes federation\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data in our study was from the article titled\u0026ldquo;Association of body mass index and age with incident diabetes in Chinese adults: a population-based cohort study,\u0026rdquo;which was conducted by Chen Y, Zhang XP, Yuan J, et al. and published in BMJ Open in 2018 (Sep 28;8(9):e021768. https://doi.org/https://doi.org/10.1136/bmjopen-2018-021768). This study was a secondary analysis, when conducting this secondary analysis, the authors of this study were grateful to all authors who participated in the original publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHaofei Hu confirmed the research design and revise the final document. Kebao Zhang contributed to the discussion of the article.\u0026nbsp;Lidan Chen and Zhe Deng downloaded data from public databases and performed data analysis; Rong rong and Lifen Xu contributed to data cleansing. Shuting Zeng and Liting Xu contributed to figure processing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Shenzhen Health Commission Discipline Construction Capacity Enhancement Project (SZXJ2017031) and the Shenzhen Key Medical Discipline Construction Fund (SZXK009) supported the development of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data can be downloaded although the \u0026lsquo;DATADRYAD\u0026rsquo; database (https://datadryad.org/stash)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Rich Healthcare Group review committee reviewed and approved these studies which \u0026nbsp;involved participants. Due to the observational nature of the study and the fact that this information was collected retrospectively and anonymous, the Rich Healthcare Group Review Committee waived the requirement for informed consent. [30, 31].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl Kaabi, J. et al. \u003cem\u003eEpidemiology of Type 2 Diabetes \u0026ndash; Global Burden of Disease and Forecasted Trends\u003c/em\u003e. \u003cem\u003eJ. Epidemiol. Global Health\u003c/em\u003e, \u003cb\u003e10\u003c/b\u003e(1). (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTab\u0026aacute;k, A. G. et al. Prediabetes: a high-risk state for diabetes development. \u003cem\u003eLancet\u003c/em\u003e. \u003cb\u003e379\u003c/b\u003e (9833), 2279\u0026ndash;2290 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElSayed, N. A. et al. 2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes\u0026mdash;2023. \u003cem\u003eDiabetes Care\u003c/em\u003e. \u003cb\u003e46\u003c/b\u003e (Supplement_1), S19\u0026ndash;S40 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZand, A. \u0026amp; Patham, I. K. Prediabetes: Why Should We Care? \u003cem\u003eMethodist Debakey Cardiovasc. J.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (4), 289\u0026ndash;297 (2018 Oct-Dec).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerreault, L. et al. Effect of regression from prediabetes to normal glucose regulation on long-term reduction in diabetes risk: results from the Diabetes Prevention Program Outcomes Study. \u003cem\u003eLancet\u003c/em\u003e. \u003cb\u003e379\u003c/b\u003e (9833), 2243\u0026ndash;2251 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, L. et al. \u003cem\u003eAssociation Between Aspartate Aminotransferase to Alanine Aminotransferase Ratio and Incidence of Type 2 Diabetes Mellitus in the Japanese Population: A Secondary Analysis of a Retrospective Cohort Study\u003c/em\u003eVolume 14p. 4483\u0026ndash;4495 (Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy,, 2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou, Y. et al. \u003cem\u003eAssociation between the alanine aminotransferase/aspartate aminotransferase ratio and new-onset non-alcoholic fatty liver disease in a nonobese Chinese population: a population-based longitudinal study\u003c/em\u003e. \u003cem\u003eLipids Health Dis.\u003c/em\u003e, \u003cb\u003e19\u003c/b\u003e(1). (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalaviz, K. I. et al. Interventions for Reversing Prediabetes: A Systematic Review and Meta-Analysis. \u003cem\u003eAm. J. Prev. Med.\u003c/em\u003e \u003cb\u003e62\u003c/b\u003e (4), 614\u0026ndash;625 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerreault, L. et al. Regression From Prediabetes to Normal Glucose Regulation and Prevalence of Microvascular Disease in the Diabetes Prevention Program Outcomes Study (DPPOS). \u003cem\u003eDiabetes Care\u003c/em\u003e. \u003cb\u003e42\u003c/b\u003e (9), 1809\u0026ndash;1815 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerreault, L. et al. Regression From Pre-Diabetes to Normal Glucose Regulation in the Diabetes Prevention Program. \u003cem\u003eDiabetes Care\u003c/em\u003e. \u003cb\u003e32\u003c/b\u003e (9), 1583\u0026ndash;1588 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKawamoto, R., Kusunoki, K. K., Tabara, T., Abe, Y. \u0026amp; Miki, M. Alanine aminotransferase/aspartate aminotransferase ratio is the best surrogate marker for insulin resistance in non-obese Japanese adults. \u003cem\u003eCardiovasc. Diabetol.\u003c/em\u003e \u003cb\u003e1\u003c/b\u003e, 11:117 (2012 Oct).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, L. et al. \u003cem\u003eSerum alanine aminotransferase/aspartate aminotransferase ratio is one of the best markers of insulin resistance in the Chinese population\u003c/em\u003e. \u003cem\u003eNutr. Metabolism\u003c/em\u003e, \u003cb\u003e14\u003c/b\u003e(1). (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeo, M. H. et al. WY, \u003cem\u003eAssociation of lipid and lipoprotein profiles with future development of type 2 diabetes in nondiabetic Korean subjects: a 4-year retrospective, longitudinal study\u003c/em\u003e. \u003cem\u003eJ. Clin. Endocrinol. Metab. 2011 Dec.\u003c/em\u003e \u003cb\u003e96\u003c/b\u003e(12): p. E2050-E2054.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, Y. \u0026amp; Yuan, Z. X. Association of body mass index and age with incident diabetes in Chinese adults: a population-based cohort study. \u003cem\u003eBMJ Open.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (9), e021768 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeleris, J. \u0026amp; Platt, S. Y. Observational Study of Hydroxychloroquine in Hospitalized Patients with Covid-19. \u003cem\u003eN Engl. J. Med.\u003c/em\u003e \u003cb\u003e382\u003c/b\u003e (25), 2411\u0026ndash;2418 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, N. \u0026amp; Zhang, H. X. Non-high-density lipoprotein cholesterol: High-density lipoprotein cholesterol ratio is an independent risk factor for diabetes mellitus: Results from a population-based cohort study. \u003cem\u003eJ. Diabetes\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e (9), 708\u0026ndash;714 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerican Diabetes. Association Professional Practice Committee. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2022. \u003cem\u003eDiabetes Care\u003c/em\u003e. \u003cb\u003e45\u003c/b\u003e (Suppl 1), S17\u0026ndash;S38 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLazo-Porras, M. et al. \u003cem\u003eRegression from prediabetes to normal glucose levels is more frequent than progression towards diabetes: The CRONICAS Cohort Study\u003c/em\u003e163 (Diabetes Research and Clinical Practice, 2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng, J. et al. Association between dyslipidemia and risk of type 2 diabetes mellitus in middle-aged and older Chinese adults: a secondary analysis of a nationwide cohort. \u003cem\u003eBMJ Open.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (5), e042821 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, T. et al. Correlation between the triglyceride-to-high-density lipoprotein cholesterol ratio and other unconventional lipid parameters with the risk of prediabetes and Type 2 diabetes in patients with coronary heart disease: a RCSCD-TCM study in China. \u003cem\u003eCardiovasc. Diabetol.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e (1), 93 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreene, M. W. et al. \u003cem\u003eUtility of Fatty Liver Index to predict reversion to normoglycemia in people with prediabetes\u003c/em\u003e. \u003cem\u003ePlos One\u003c/em\u003e, \u003cb\u003e16\u003c/b\u003e(4). (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan, Y., Huang, H. H. \u0026amp; Liu, Z. Association between body mass index and reversion to normoglycemia from impaired fasting glucose among Chinese adults: a 5-year cohort study. \u003cem\u003eFront. Endocrinol. (Lausanne)\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e, 1111791 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHwang, Y. C. et al. Factors associated with regression from prediabetes to normal glucose tolerance in a Korean general population: A community-based 10‐year prospective cohort study. \u003cem\u003eDiabet. Med.\u003c/em\u003e \u003cb\u003e35\u003c/b\u003e (11), 1544\u0026ndash;1551 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGroenwold, R. H., Donders, W. I., Carpenter, A. R., Altman, J. R. \u0026amp; Moons, D. G. Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis. \u003cem\u003eCMAJ\u003c/em\u003e. \u003cb\u003e184\u003c/b\u003e (11), 1265\u0026ndash;1269 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhite, I. R. \u0026amp; Wood, R. P. Multiple imputation using chained equations: Issues and guidance for practice. \u003cem\u003eStat. Med.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e (4), 377\u0026ndash;399 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY, W. Collinearity diagnosis for a relative risk regression analysis: an application to assessment of diet-cancer relationship in epidemiological studies. \u003cem\u003eStat. Med.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (10), 1273\u0026ndash;1287 (1992).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheng, G., Kuang, L. D., Zhong, M., Zhang, Y. \u0026amp; Zou, S. Utility of Non-High-Density Lipoprotein Cholesterol to High-Density Lipoprotein Cholesterol Ratio in Evaluating Incident Diabetes Risk. \u003cem\u003eDiabetes Metab. Syndr. Obes.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 1677\u0026ndash;1686 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRothenbacher, D. et al. BiomarCaRE consortium. Contribution of cystatin C- and creatinine-based definitions of chronic kidney disease to cardiovascular risk assessment in 20 population-based and 3 disease cohorts: the BiomarCaRE project. \u003cem\u003eBMC Med.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e (1), 300 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoordzij, M., van Stralen, L. K., Zoccali, K. J., Dekker, C. \u0026amp; Jager, F. W. When do we need competing risks methods for survival analysis in nephrology? \u003cem\u003eNephrol. Dial Transpl.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (11), 2670\u0026ndash;2677 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasak, R. \u0026amp; Chen, M. H. Understanding Competing Risks. \u003cem\u003eInt. J. Radiat. Oncol. Biol. Phys.\u003c/em\u003e \u003cb\u003e110\u003c/b\u003e (3), 636\u0026ndash;640 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSolbak, N. M., Akawung, A. R. A., Lo Siou, A. K., Kirkpatrick, G. \u0026amp; Robson, S. I. Strategies to Address Misestimation of Energy Intake Based on Self-Report Dietary Consumption in Examining Associations Between Dietary Patterns and Cancer Risk. \u003cem\u003eNutrients\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e (11), 2614 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003e\u0026lt;43 Dipeptidyl-peptidase (DPP)-4 inhibitors and glucagon-like peptide.pdf\u0026gt;.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003e\u0026lt;45 PROMETHEUS an observational,.pdf\u0026gt;.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, Z. et al. Association of Triglyceride to high-density lipoprotein cholesterol ratio and incident of diabetes mellitus: a secondary retrospective analysis based on a Chinese cohort study. \u003cem\u003eLipids Health Dis.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e (1), 33 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePop-Busui, R. et al. Effects of triple antioxidant therapy on measures of cardiovascular autonomic neuropathy and on myocardial blood flow in type 1 diabetes: a randomised controlled trial. \u003cem\u003eDiabetologia\u003c/em\u003e. \u003cb\u003e56\u003c/b\u003e (8), 1835\u0026ndash;1844 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMullee, A. et al. \u003cem\u003eN, Association Between Soft Drink Consumption and Mortality in 10 European Countries\u003c/em\u003e179p. 1479\u0026ndash;1490 (JAMA Intern Med, 2019). 11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeidel, D. et al. N, \u003cem\u003eThe Role of Socioeconomic Status in the Association of Lung Function and Air Pollution-A Pooled Analysis of Three Adult ESCAPE Cohorts.\u003c/em\u003e Int J Environ Res Public Health, 16(11): p. 1901. (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun, Y., Yuan, N. W., Chi, X. \u0026amp; Xu, H. Prevalence, treatment, control of type 2 diabetes and the risk factors among elderly people in Shenzhen: results from the urban Chinese population. \u003cem\u003eBMC Public. Health\u003c/em\u003e. \u003cb\u003e20\u003c/b\u003e (1), 998 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchleger, F. et al. Family History of Diabetes Is Associated With Delayed Fetal Postprandial Brain Activity. \u003cem\u003eFront. Endocrinol. (Lausanne)\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e, 673 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, F. et al. Elevated blood mercury level has a non-linear association with infertility in U.S. women: Data from the NHANES 2013\u0026ndash;2016. \u003cem\u003eReprod. Toxicol.\u003c/em\u003e \u003cb\u003e91\u003c/b\u003e, 53\u0026ndash;58 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaneuse, S. \u0026amp; Arterburn, V. T. Using the E-Value to Assess the Potential Effect of Unmeasured Confounding in Observational Studies. \u003cem\u003eJAMA\u003c/em\u003e. \u003cb\u003e321\u003c/b\u003e (6), 602\u0026ndash;603 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, X. et al. \u003cem\u003eReversion From Pre\u0026ndash;Diabetes Mellitus to Normoglycemia and Risk of Cardiovascular Disease and All-Cause Mortality in a Chinese Population: A Prospective Cohort Study\u003c/em\u003e. \u003cem\u003eJ. Am. Heart Association\u003c/em\u003e, \u003cb\u003e10\u003c/b\u003e(3). (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie, W. et al. \u003cem\u003eLow aspartate aminotransferase/alanine aminotransferase (DeRitis) ratio assists in predicting diabetes in Chinese population\u003c/em\u003e. \u003cem\u003eFront. Public. Health\u003c/em\u003e, 10. (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao, C. et al. \u003cem\u003eNonlinear relationship between aspartate aminotransferase to alanine aminotransferase ratio and the risk of prediabetes: A retrospective study based on chinese adults\u003c/em\u003e. \u003cem\u003eFront. Endocrinol.\u003c/em\u003e, 13. (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAn, R. et al. \u003cem\u003eAST-to-ALT ratio in the first trimester and the risk of gestational diabetes mellitus\u003c/em\u003e. \u003cem\u003eFront. Endocrinol.\u003c/em\u003e, 13. (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanase, D. M. et al. \u003cem\u003eThe Intricate Relationship between Type 2 Diabetes Mellitus (T2DM), Insulin Resistance (IR), and Nonalcoholic Fatty Liver Disease (NAFLD).\u003c/em\u003e Journal of Diabetes Research, 2020: pp. 1\u0026ndash;16. (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, Z. et al. \u003cem\u003eA vicious circle between insulin resistance and inflammation in nonalcoholic fatty liver disease\u003c/em\u003e. \u003cem\u003eLipids Health Dis.\u003c/em\u003e, \u003cb\u003e16\u003c/b\u003e(1). (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBril, F. et al. Metabolic and histological implications of intrahepatic triglyceride content in nonalcoholic fatty liver disease. \u003cem\u003eHepatology\u003c/em\u003e. \u003cb\u003e65\u003c/b\u003e (4), 1132\u0026ndash;1144 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFilippatos, T. D. et al. Nonalcoholic Fatty Pancreas Disease: Role in Metabolic Syndrome, Prediabetes, Diabetes and Atherosclerosis. \u003cem\u003eDig. Dis. Sci.\u003c/em\u003e \u003cb\u003e67\u003c/b\u003e (1), 26\u0026ndash;41 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMboma, J. et al. \u003cem\u003eLiver and plasma lipid changes induced by cyclic fatty acid monomers from heated vegetable oil in the rat\u003c/em\u003e6p. 2092\u0026ndash;2103 (Food Science \u0026amp; Nutrition, 2018). 8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"AST/ALT ratio, Impaired fasting glucose, Non-linear relationship, reversion to normoglycemia","lastPublishedDoi":"10.21203/rs.3.rs-4945577/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4945577/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eStudies showed that AST/ALT ratio was related to pre-diabetes, diabetes and diabetes complications. However, there is poor evidence proved that the AST/ALT ratio was correlated with blood glucose reversion in impaired fasting glucose patients. In our study, we analyzed the relationship between AST/ALT ratio and blood glucose reversal in a large group of Chinese people with impaired fasting blood glucose.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eParticipants were recruited from Rich Healthcare Group physical examinations in 2010 to 2016. Among all the participants, 11121 Chinese adults were enrolled in our study. Cox proportional-hazards regression was used to identify the association between the AST/ALT ratio and blood glucose reversal to normoglycemia in individuals with impaired fasting glucose. Generalized additive model (GAM) and smooth curve fitting were used to identified nonlinear relationship between AST/ALT ratio and blood glucose reversion. In addition, sensitive analyses and subgroup analysis were used to test the reliability of our study.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eAST/ALT ratio was independently related to the blood glucose reversal in prediabetic populations of Chinese adults (HR\u0026thinsp;=\u0026thinsp;1.187, 95% CI 1.073\u0026ndash;1.313, P\u0026thinsp;=\u0026thinsp;0.00087). Nonlinear relationship has been discovered between AST/ALT ratio and reversion to normoglycemia. On the left side of the inflection point, AST/ALT ratio was negatively related to the blood glucose reversal in populations with impaired fasting glucose(HR:0.563, 95%CI: 0.404\u0026ndash;0.784, P\u0026thinsp;=\u0026thinsp;0.0007), while on the right side of the inflection point, the relationship was positive (HR:1.281 95%CI: 1.153\u0026ndash;1.424, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 ). Sensitivity analysis, competing risks multivariate Cox regression and subgroup analysis also confirmed our study results.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study revealed that AST/ALT ratio was independently related with reversion to normoglycemia in prediabetic Chinese people. The relationship between AST/ALT ratio and reversion to normoglycemia from IFG was non-linear. When AST/ALT ratio\u0026thinsp;\u0026gt;\u0026thinsp;1.05, a significant positive relationship between AST/ALT ratio and reversion to normoglycemia was identified.\u003c/p\u003e","manuscriptTitle":"Association between aspartate aminotransferase to alanine aminotransferase ratio and reversion to normoglycemia in people with impaired fasting glucose: a 5-year retrospective cohort study ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-15 07:43:04","doi":"10.21203/rs.3.rs-4945577/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"21c0f276-aedc-44ac-8719-d4755429559e","owner":[],"postedDate":"October 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":37726896,"name":"Health sciences/Health care/Disease prevention"},{"id":37726897,"name":"Health sciences/Health care/Public health"}],"tags":[],"updatedAt":"2024-12-02T11:23:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-15 07:43:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4945577","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4945577","identity":"rs-4945577","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}