Triglyceride-glucose index is associated with the risk of impaired fasting glucose: A 5-year retrospective cohort study in Chinese elderly people

Triglyceride-glucose index is associated with the risk of impaired fasting glucose: A 5-year retrospective cohort study in Chinese elderly people | 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 Triglyceride-glucose index is associated with the risk of impaired fasting glucose: A 5-year retrospective cohort study in Chinese elderly people Jie Liu, Feng Yi, Kai Duan, Haibo Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4413051/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The relationship between the triglyceride-glucose (TyG) index and impaired fasting glucose (IFG) in elderly individuals remains uncertain. Our study aimed to explore the association between the TyG index and the risk of future IFG in this population. This retrospective cohort study included 17,746 elderly individuals over 60. In this population, Cox regression models proportional to hazards, along with smooth curve fitting and cubic spline functions, were employed to examine the association between the baseline TyG index and the risk of IFG. Subgroup analyses and sensitivity were also performed to ensure the robustness of the study findings. After adjusting for covariates, a positive relationship between the TyG index and the risk of IFG was found (HR = 1.43, 95% CI: 1.27–1.60, p < 0.0001). The likelihood of IFG rose steadily as the TyG index quartiles (from Q1 to Q4) increased, with Q4 demonstrating a 62% elevated risk compared to Q1 (adjusted HR = 1.62, 95% CI: 1.37–1.90). Additionally, we found the association between TyG index and risk of IFG was a linear. Sensitivity and subgroup analyses confirmed the stability of the results. Our study observed a linear association between the TyG index and the development of IFG in elderly Chinese individuals. Recognizing this relationship can help clinicians identify high-risk individuals and implement targeted interventions to reduce their risk of progressing to diabetes. Health sciences/Endocrinology Health sciences/Risk factors TyG index Impaired fasting glucose retrospective cohort study Chinese elder adults Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Impaired fasting glucose (IFG) is a glycemic state between normality and diabetes, defined by the American Diabetes Association (ADA) as fasting plasma glucose levels ranging from 5.6 mmol/L to 6.9 mmol/L 1 . IFG is prevalent in the elderly globally, more so than diabetes itself in this demographic 2,3 . IFG not only increases the risk of developing type 2 diabetes but is also closely associated with elevated cardiovascular disease risk 4–7 . Additionally, research suggests a potential link between IFG and increased all-cause mortality 8 . Therefore, early identification and intervention in IFG risk factors are crucial for reducing disease burden. The triglyceride-glucose (TyG) index, calculated as the product of triglyceride (TG) levels and fasting plasma glucose (FPG) 9 , serves as a convenient measure for assessing an individual’s degree of insulin resistance 10 . It has been validated across diverse clinical settings and populations, including middle-aged and elderly individuals, as well as those with conditions like obesity. The TyG index has shown robustness and broad applicability in its association with prediabetes, type 2 diabetes risk, and its predictive capability for IFG or diabetes development 11–13 . Furthermore, the TyG index has been identified as an effective marker for cardiovascular diseases 14 . Thus, assisting in the early identification and management of diabetes and associated metabolic disorders. With China's aging population rapidly increasing and projected to reach 25% by 2030 and 37.92% by 2100, the prevalence of diabetes among the elderly is becoming a significant concern 15,16 . The natural decline in metabolic functions with age, especially in pancreatic function and insulin sensitivity, increases the risk of diabetes in this demographic 17 . Studying IFG in the elderly can identify high-risk individuals early and implement measures to delay or prevent diabetes onset. Currently, there is insufficient evidence regarding the ralationship between the TyG index and risk of IFG among the elderly. Our study aims to investigate this relationship in individuals aged 60 and above, endorsing the TyG index’s clinical utility for early IFG detection, potentially influencing diabetes management and preventive healthcare in the elderly. Results Baseline characteristics of participants The baseline characteristics of the study participants were summarized as showed in Table 1. The study participants were grouped into IFG and Normal groups, with the IFG group displaying significantly higher mean age and BMI compared to the Normal group (p < 0.001). This group also had elevated SBP and DBP, as well as higher FBG levels (p < 0.001). Further differences included higher TG levels, lower HDL-c levels, and lower LDL cholesterol in the IFG group (p < 0.001). Additionally, ALT and Scr levels were significantly higher in the IFG group (p < 0.001). Sex distribution and smoking status also differed significantly between the two groups, while no significant differences were observed in drinking status and family history of diabetes. Notably, the follow-up duration was longer in the IFG group (p < 0.001). The distribution of the TyG index, as shown in Fig 1, indicated a normal distribution ranging from 6.61 to 11.05, with a mean of 8.57. Table 1 The baseline characteristics of participants. group Normal IFG P-value participants 13683 4063 Age (years) 66.87 ± 6.45 67.47 ± 6.61 <0.001 BMI (kg/m2) 23.77 ± 2.97 24.63 ± 3.06 <0.001 SBP (mmHg) 130.10 ± 18.64 134.83 ± 18.95 <0.001 DBP (mmHg) 77.97 ± 11.04 79.95 ± 11.55 <0.001 FBG at baseline (mg/dL) 87.24 ± 8.29 90.91 ± 7.22 <0.001 TG (mg/dL) 135.23 ± 81.50 145.35 ± 87.01 <0.001 TyG index 8.54 ± 0.52 8.65 ± 0.53 <0.001 ALT (U/L) 21.02 ± 13.62 22.99 ± 15.85 <0.001 AST (U/L) 25.65 ± 9.70 26.37 ± 11.66 0.008 BUN (mmol/L) 5.17 ± 1.27 5.17 ± 1.25 0.852 Scr (μmol/L) 72.56 ± 20.05 74.49 ± 16.59 <0.001 TC (mmol/L) 5.16 ± 0.94 5.16 ± 0.95 0.753 HDL-c (mmol/L) 1.39 ± 0.32 1.37 ± 0.30 0.002 LDL-c (mmol/L) 3.03 ± 0.72 2.98 ± 0.70 <0.001 Sex <0.001 Male 7374 (53.89%) 2377 (58.50%) Female 6309 (46.11%) 1686 (41.50%) Smoking status 0.039 Current smoker 947 (6.92%) 233 (5.73%) Ever smoker 111 (0.81%) 28 (0.69%) Never 2517 (18.40%) 737 (18.14%) Drinking status 0.216 Current drinker 116 (0.85%) 36 (0.89%) Ever drinker 352 (2.57%) 102 (2.51%) Never 3107 (22.71%) 860 (21.17%) Family history of diabetes 0.098 No 13584 (99.28%) 4023 (99.02%) Yes 99 (0.72%) 40 (0.98%) Follow-up (year) 3.07 ± 0.89 3.34 ± 0.91 <0.001 Continuous variables were summarized as mean (SD) or medians (quartile interval); categorical variables were displayed as percentage (%) Abbreviations: BMI, body mass index; SBP, systolic blood pressure; DBP; diastolic blood pressure; TG triglyceride; TC, total cholesterol; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; AST aspartate aminotransferase; ALT, alanine aminotransferase; BUN, blood urea nitrogen; Scr, serum creatinine; FBG, fasting plasma glucose; TyG index, triglyceride glucose index. Figure 1. Distribution of TyG index. It presented a normal distribution, ranging from 6.61 to 11.05, with a mean of 8.57. Incidence of IFG in participants Table 2 and Fig 2 describe the incidence rates of IFG. Among the participants, 4,063 (22.9%) developed IFG. Participants were divided into subgroups based on the quartiles of the TyG index. The incidence rates of IFG per 1,000 person-years were 58.89, 64.26, 79.69, and 90.33 for each TyG index quartile. The incidence rates of IFG in each TyG index quartile were as follows: Q1: 18.89%, Q2: 19.93%, Q3: 24.60%, and Q4: 28.08%. Participants with the highest TyG index (Q4) had a higher risk of developing IFG compared to those with the lowest TyG index (Q1) (trend p < 0.001). Table 2 The Incidence rate of IFG (% or Per 1000 person-year). TyG index (quartile) Participants (n) IFG (n) Incidence (95%CI) (%) Per 1000 person-year Total 17746 4063 22.90 (22.28-23.51) 73.19 Q1 4437 842 18.98 (17.82-20.13) 58.99 Q2 4436 884 19.93 (18.75-21.10) 64.26 Q3 4435 1091 24.60 (23.33-25.87) 79.69 Q4 4438 1246 28.08 (26.75-29.40) 90.33 P for trend <0.001 Figure 2. The Kaplan-Meier curve depicts the probability of IFG occurrence, stratified by the TyG index. The graph demonstrates that the likelihood of IFG occurrence gradually increases with higher TyG index values. This suggests that patients with the highest TyG index have the highest probability of experiencing IFG. Multivariable analysis using Cox proportional hazards regression model In Table 3, the relationship between the TyG index and the risk of IFG is examined using multivariable analysis with the Cox proportional hazards regression model. The results show that in the crude model, the hazard ratio for the TyG index was statistically significant. When adjusting for age and sex (Model I), the hazard ratio remained significant but slightly decreased. Further adjustments in Model II, which included various additional factors, still showed a significant association between the TyG index and the risk of IFG. Additionally, after adjustments, consistent trends were observed across all quartiles of the TyG index, further highlighting the significant relationship between the TyG index quartiles and the risk of IFG. These findings suggest that the TyG index is independently associated with an increased risk of IFG, even after accounting for potential confounding factors. This underscores the importance of considering the TyG index in assessing the risk of IFG in clinical and research settings. Table 3 Relationship between TyG index and risk of IFG in different models Exposure Crude model (HR,95%CI) P Model I(HR,95%CI) P Model II(HR,95%CI) P GAM (HR,95%CI) P TyG index 1.43 (1.35, 1.51) <0.0001 1.44 (1.36, 1.52) <0.0001 1.43 (1.27, 1.60) <0.0001 1.48 (1.32, 1.66) <0.0001 (quartile) Q1 Ref Ref Ref Ref Q2 1.18 (1.08, 1.30) 0.0005 1.18 (1.08, 1.30) 0.0005 0.99 (0.85, 1.15) 0.8550 0.99 (0.85, 1.16) 0.9454 Q3 1.48 (1.35, 1.62) <0.0001 1.49 (1.36, 1.63) <0.0001 1.35 (1.16, 1.58) 0.0001 1.38 (1.18, 1.61) <0.0001 Q4 1.65 (1.51, 1.80) <0.0001 1.66 (1.52, 1.82) <0.0001 1.62 (1.37, 1.90) <0.0001 1.71 (1.45, 2.02) <0.0001 P for trend <0.0001 <0.0001 <0.0001 <0.0001 Crude model: we did not adjust other covariates. Model I: we adjusted age, sex. Model II: we adjusted age, sex, SBP, DBP, BMI, ALT, AST, BUN, Scr, TC, LDL-C, HDL-c, family history of diabetes, drinking status, and smoking status. Model III: we adjusted age(smooth), sex, SBP (smooth), DBP (smooth), BMI (smooth), BUN (smooth), Scr (smooth), ALT (smooth), AST (smooth), TC (smooth), LDL-C(smooth), HDL-c(smooth), smoking status, drinking status, family history of diabetes. HR, Hazard ratios; CI, confidence, Ref, reference. Sensitivity analysis We conducted a series of sensitivity analyses to ensure the validity of our findings. Initially, in Model III using the generalized additive model (GAM) with additional smooth terms for various variables, we observed a HR of 1.48 (95%CI 1.32-1.66, P < 0.0001), indicating a significant association (Table 3). By excluding participants with a BMI ≥ 28 kg/m2 and adjusting for confounding factors, the results consistently showed a positive correlation between the TyG index and the risk of impaired fasting glucose (IFG) with an HR of 1.42 (95% CI: 1.26-1.61, p < 0.0001). Moreover, we conducted sensitivity analyses by excluding individuals aged ≥ 80 years, and the association between the TyG index and IFG risk remained significant (risk ratio = 1.45, 95% confidence interval: 1.29-1.64, p < 0.0001) after adjustments. Additionally, when analyzing participant’s SBP＜140mmHg, the risk ratio was 1.51 (95% confidence interval: 1.31-1.74, p < 0.0001) (Table 4). After considering all these sensitivity analyses, we can confidently conclude that our results are dependable and robust, emphasizing the consistent positive association between the TyG index and the risk of IFG. Table 4 Relationship between TyG index and the risk of IFG in different sensitivity analyses. Exposure Model I (HR,95%CI) P Model II (HR,95%CI) P Model III (HR,95%CI) P TyG index 1.42 (1.26, 1.61) <0.0001 1.45 (1.29, 1.64) <0.0001 1.51 (1.31, 1.74) <0.0001 (quartile) Q1 Ref Ref Ref Q2 0.96 (0.82, 1.13) 0.6550 1.05 (0.90, 1.24) 0.5228 1.05 (0.87, 1.27) 0.6229 Q3 1.32 (1.12, 1.55) 0.0010 1.40 (1.19, 1.65) <0.0001 1.58 (1.30, 1.92) <0.0001 Q4 1.58 (1.33, 1.88) <0.0001 1.68 (1.41, 2.00) <0.0001 1.91 (1.55, 2.35) <0.0001 P for trend <0.0001 <0.0001 <0.0001 Crude model I was a sensitivity analysis performed after excluding participants with BMI≥ 28 kg/m2. we adjusted age, sex, SBP, DBP, ALT, AST, BUN, Scr, TC, LDL-C, HDL-c, family history of diabetes, drinking status, and smoking status. Model II was a sensitivity analysis performed after excluding participants with age≥ 80 years old. We adjusted a age, sex, SBP, DBP, BMI, ALT, AST, BUN, Scr, TC, LDL-C, HDL-c, family history of diabetes, drinking status, and smoking status. Model III was a sensitivity analysis performed after excluding participants with SBP ≥ 140mmHg. We adjusted a age, sex, SBP, DBP, BMI, ALT, AST, BUN, Scr, TC, LDL-C, HDL-c, family history of diabetes, drinking status, and smoking status. HR, Hazard ratios; CI, confidence, Ref, referenc. Relationship between the TyG index and the risk of IFG In our study, we found a clear linear relationship between the TyG index and the risk of Impaired Fasting Glucose (IFG). By utilizing a Cox proportional hazards regression model with cubic spline functions, we were able to evaluate this relationship and confirmed that it is indeed linear (Fig 3). This finding suggests that as the TyG index increases, so does the risk of developing IFG. Figure 3. The association between TyG index and the risk of IFG is linear. Subgroup Analysis Results As illustrated in Table 5, a detailed subgroup analysis was conducted. Gender, age, BMI, systolic and diastolic blood pressures, smoking and drinking habits, and a family history of diabetes did not alter the association between the TyG index and the risk of IFG. Thus, no significant interactions were observed between these variables and the TyG index (all interaction P ＞ 0.05). Table 5. Effect size of TyG index on IFG in prespecified and exploratory subgroups Variable HR (95% CI) P-value P for interaction Age, yeas 0.6378 60-70 1.44 (1.26, 1.64) <0.0001 70-80 1.45 (1.16, 1.82) 0.0011 80-90 1.13 (0.77, 1.64) 0.5336 ≥90 2.11 (0.14, 30.92) 0.5780 BMI (kg/m 2 ) 0.2039 ＜18.5 0.79 (0.35, 1.75) 0.556 ≥18.5， ＜25 1.53 (1.32, 1.77) <0.0001 ≥25， ＜28 1.43 (1.19, 1.72) 0.0002 ≥28 1.20 (0.90, 1.60) 0.2047 Sex 0.4588 Male 1.38 (1.20, 1.59) <0.0001 female 1.49 (1.27, 1.76) <0.0001 SBP (mmHg) 0.0596 ＜140 1.55 (1.36, 1.78) <0.0001 ≥140 1.28 (1.08, 1.52) 0.0047 DBP (mmHg) 0.7253 ＜90 1.43 (1.27, 1.62) <0.0001 ≥90 1.37 (1.10, 1.72) 0.0057 Family history of diabetes 0.5347 Yes 1.43 (1.27, 1.60) <0.0001 No 0.74 (0.09, 6.30) 0.7869 Discussion This retrospective cohort study aims to investigate the relationship between the TyG index and the risk of IFG. We found that the risk of IFG increases with the TyG index, and a linear relationship between them was observed. The highest quartile of the TyG index has a 1.62 times higher risk of IFG compared to the lowest quartile. These results suggest that the TyG index may be an effective indicator for monitoring IFG. Globally, the incidence of prediabetes is on the rise. According to estimates by the World Health Organization, in 2021, the global diabetes population was approximately 537 million. The prevalence of diabetes in China rose significantly from 22.5 million individuals in the year 2000 to a staggering 140.9 million by 2021 18 . The number of people with prediabetes exceeds these figures. A study in the United States suggests that about one-third of adults are considered to have prediabetes 19 . The prevalence of prediabetes in Chinese adults is alarmingly high, with an estimated rate of 50.1%. the prevalence of prediabetes is higher in men, with a rate of 52.1%, compared to women at 48.1%. This suggests that one out of every two Chinese adults may be at risk of developing diabetes if proper measures are not taken 20 . Studies have indicated that the standardized incidence rate of prediabetes in the overall population of China is 62.6 cases per 1000 person-years (73.8 cases per 1000 person-years among males and 51.2 cases per 1000 person-years among females) 21 . Additionally, another cohort study in China, which included 4093 Chinese adults with a median follow-up time of 3.25 years, found that 26.2% of participants developed prediabetes 22 . IFG is a state of prediabetes, and the primary outcome variable studied here was IFG. Our study reveals that over a 5-year period, 22.9% of participants developed impaired fasting glucose (IFG), with an incidence rate of 73.19 per 1000 person-years. The differences in prediabetes incidence rates among various studies could be attributed to variations in participants’ age, follow-up duration, and ethnicity. Notably, less than 11% of individuals are aware of their condition 23 . Therefore, identifying factors leading to IFG is crucial for preventing diabetes and its complications. A cohort study in China, involving 201,298 individuals, found that a high TyG index was independently associated with the risk of developing diabetes (HR) = 3.34; 95% CI = 3.11–3.60) 24 . Another cohort study involving 4543 Chinese adults utilized logistic regression analysis adjusted for several confounders, showing that for each standard deviation increase in the TyG index, the risk of prediabetes increased by 1.38 times (95% CI = 1.28–1.48) 22 . Hence, we hypothesize that an increase in the TyG index might be related to an increased risk of IFG in the elderly. Unfortunately, reports on their relationship are scarce. Linhao Zhang et al. found a non-linear relationship between the TyG index and impaired fasting blood glucose by analyzing data from 25,159 patients 25 . Xiaoxia Li et al. demonstrated that, based on baseline data, logistic analysis showed that after multivariate adjustment, the TyG index was significantly positively correlated with IFG. However, the association was not significant after further adjustment (HR, 1.06; 95% CI, 0.58–1.96; p for trend = 0.784) 26 . This study may differ from ours because the sample size was not as large as ours and the influence of age was not analyzed separately. However, our study found a positive, linear correlation between the TyG index and the risk of IFG in the elderly. Our research adds to the existing literature, supporting the hypothesis that an elevated TyG index is associated with an increased IFG risk. Compared to other studies, in our research, the TyG index was utilized in both categorical and continuous forms to examine the correlation between TyG index and the risk of IFG. Our aim was to minimize loss of information and accurately measure the association between the two variables. Furthermore, the confounding factors adjusted in our study differ from previous ones. We adjusted for a wider variety of variables, such as age, gender, systolic blood pressure, diastolic blood pressure, body mass index, alanine aminotransferase, aspartate aminotransferase, blood urea nitrogen, serum creatinine, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, diabetes family history, alcohol consumption, and tobacco use. Sensitivity analysis confirmed that this relationship persists even after excluding participants with age≥80 years, BMI≥28kg/m2, and SBP≥140mmHg. Additionally, subgroup analyses and interaction tests on age, DBP, BMI, SBP, gender, and family history of diabetes showed no interactions, confirming the stability of the relationship between the TyG index and IFG risk. This finding provides a reference for clinical interventions to reduce the probability of IFG in the elderly by targeting the TyG index. The study has several strengths. Firstly, we have established a linear relationship between the TyG index and the risk of IFG among the elderly for the first time. In addition, the research involved a substantial group of 17,746 senior citizens and accounted for variables like BMI, age, TC, SBP, BUN, ALT, AST, DBP, Scr, LDL-C, HDL-C, gender, diabetic family history, alcohol consumption, and tobacco use to reduce possible distortions. To ensure the reliability and robustness of the results, sensitivity analysis was performed. Moreover, subgroup analysis and tests for interactions were conducted. The results indicate that the TyG index has different effects on IFG risk across various subgroups, further validating the experiment’s stability. Despite these strengths, our study has several limitations. First, the average follow-up duration of the study participants was only 5.0 years, which is relatively short. Second, participants were only followed up once, with information available only at two time points (baseline and follow-up). Third, there were no available serum insulin level data, thus the predictive value of the TyG index could not be compared. Fourth, values for impaired glucose tolerance at 2 hours or HbA1c were not collected, hence further analysis of the relationship between the TyG index and changes in glucose tolerance and HbA1c was not possible. Fifth, the study focused only on individuals aged over 60 years, and may not represent all populations; further research should expand to include various age groups. Conclusion This study revealed a linear relationship between the TyG index and the IFG in Chinese individuals aged over 60. Understanding this linear relationship can help clinicians identify high-risk elder individuals and implement focused interventions to reduce the risk of developing diabetes. Methods Study design This study utilized data from a previous retrospective cohort study conducted by Chinese researchers (Chen et al.) 27 . The target independent variable was the TyG at baseline. The outcome variable was the development from normoglycemia to IFG at follow-up. Data source Access to the original dataset was granted at no cost through the DATADRYAD platform (www.datadryad.org), courtesy of Ying Chen et al 27 . In accordance with Dryad’s usage policy, the data is available for academic and research purposes, allowing users to share, adapt, alter, and build upon the material, provided it is not for commercial use and proper attribution is given to the original authors and source. The dataset was sourced from a publicly accessible study published in 2018 titled “Association of body mass index and age with diabetes onset in Chinese adults: a population-based cohort study,” which can be found at http://dx.doi.org/10.1136/bmjopen-2018-021768. For those interested, the dataset can be retrieved from the following link: https://doi.org/10.5061/dryad.ft8750v. Ethics approval and consent to participate The prior study received ethical approval from the Rich Healthcare Group Review Board. Given that the current study involves a secondary analysis of existing data, there was no need for obtaining informed consent or additional ethical approval. All methods were performed in accordance with the relevant guidelines and regulations. Research Population The primary research included 685,277 individuals aged 20 years and above, all of whom had undergone a minimum of two health assessments. The study focused on participants who, during follow-up, had FPG levels ranging from 6.1 to 6.9 mmol/l without any prior diagnosis of diabetes. The initial selection excluded participants based on several factors: (1) lack of detailed information regarding weight, height, or gender; (2) BMI values outside the normal range (55kg/m2); (3) intervals between visits shorter than 2 years; (4) missing FPG readings; (5) individuals diagnosed with diabetes at the start or with uncertain diabetes status at the time of follow-up. Following these criteria, the study retained 211,833 participants. Further analysis led to the exclusion of an additional 194,087 participants for reasons including: 1) absence of follow-up FPG readings, 2) baseline FPG levels ≥5.6 mmol/l, 3) FPG levels >6.9 mmol/l during follow-up, 4) unclear diabetes diagnosis at follow-up, and 5) lack of triglyceride (TG) values or being less than 60 years of age. Elder people are defined as those aged over 60 years old or older 25 . Ultimately, the study included 17,746 participants. The process of selecting participants for this study is depicted in Figure 4. Figure. 4 Flowchart illustrating the selection process of study participants Data collection In this study, data collection included demographic information such as age, diastolic blood pressure (DBP), systolic blood pressure (SBP), height, and weight, from which body mass index (BMI) was calculated. To ensure consistency in data collection, staff received specialized training focusing on demographic data and key measurements, including blood pressure. Tests were uniformly conducted in a standardized laboratory environment for triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), total cholesterol (TC), blood urea nitrogen (BUN), serum creatinine (Scr), alanine aminotransferase (ALT), low-density lipoprotein cholesterol (LDL-c), and FPG. Additionally, the study collected information on the patients’ smoking and drinking histories, defining current drinking as 1, former drinking as 2, never drinking as 3, and unknown drinking status as 4. Similarly, current smoking was coded as 1, former smoking as 2, never smoking as 3, and unknown smoking status as 4. Outcome and definitions At follow-up, our focus was on identifying individuals who had an impaired fasting glucose (IFG) condition. This was determined by having fasting plasma glucose (FPG) levels fall within the range of 6.1-6.9 mmol/l, with no reported cases of new-onset diabetes. 28 . We defined “elderly patients” as aged over 60 29 . Missing data processing In this study, the number of participants with missing data was as follows: 7 (0.00%) each for DBP and SBP, 1 person (0.00%) for TC, 869 (0.69%) for ALT, 72640 (58%) for AST, 60634 (48.39%) for LDL-c, 6192 (4.9%) for Scr, 60579 (48.34%) for HDL-c, and 11452 (9.1%) for BUN. To mitigate the uncertainty caused by missing data, this study utilized multiple imputation techniques. The imputation model included ALT, LDL-c, AST, Scr, HDL-c, and BUN, with 5 iterations using linear regression. Data analysis assumed that missingness was random (MAR) 30, 31 . Statistical analysis The main variable examined in this research is the TyG index, which is defined by the equation: TyG index = ln [FPG (mg/dL) × TG (mg/dL)/2] 32 . We divided it into four quartiles and considered it as a continuous variable. We presented the mean and standard deviation for continuous variables that followed a normal distribution, while the median was reported for data that did not follow a normal distribution. For categorical variables, we presented the frequency and proportion of the data in our study. To analyze the differences between different TyG index groups, we utilized the Kruskal-Wallis H test for data that was not normally distributed, one-way analysis of variance for normally distributed data, and the chi-square test for categorical variables. We developed numerous models to evaluate the correlation between the TyG index and the risk of IFG: a baseline model without any adjustments, a simplified model adjusting for gender and age only (Model I), and a comprehensive model adjusting for multiple covariates (Model II: recording a variety of demographic and health-related variables, such as age, gender, BMI, blood pressure, liver enzymes, cholesterol levels, serum creatinine, family history of diabetes, alcohol consumption, and smoking status, in our study. From each model, we noted the effect size (hazard ratio HR) along with its 95% confidence interval (CI). After considering potential confounding variables through clinical expertise, reviewing literature, and analyzing data univariately, we incorporated a multifaceted Cox proportional hazards model. This model introduced cubic spline functions and implemented smooth curve fitting to investigate potential nonlinear links between the TyG index and IFG risk. Furthermore, applying a segmented Cox proportional hazards model aided in elucidating this nonlinear association. To validate our findings, we conducted a series of sensitivity analyses. By incorporating continuous variables into a generalized additive model (GAM) in curve form, we further confirmed the robustness of the results. Additionally, we conducted analyses using stratified Cox proportional hazards models in different subgroups (such as age, gender, blood pressure, smoking, and drinking status). Finally, we used likelihood ratio tests to examine whether there were interactions in the model, both in models including interaction terms and those without. All analyses were performed using Empower Stats (X&Y Solutions, Inc., Boston, MA, http://www.empowerstats.com), with a statistical significance level set at a two-sided P value less than 0.05. Declarations Data availability statement The dataset was sourced from a publicly accessible study published in 2018 titled “Association of body mass index and age with diabetes onset in Chinese adults: a population-based cohort study,” which can be found at http://dx.doi.org/10.1136/bmjopen-2018-021768. For those interested, the dataset can be retrieved from the following link: https://doi.org/10.5061/dryad.ft8750v. Ethics statement The studies involving human participants were reviewed and approved by The Rich Healthcare Group Review Board. The ethics committee waived the need for written informed consent. Author contributions Jie Liu, Feng Yi, and Kai Duan contributed to the study concept and design, researched, and interpreted the data, and drafted the manuscript. Haibo Liu analyzed the data and reviewed the manuscript. All authors read and approved the final manuscript. Funding Not applicable. Competing interests The authors declare no competing interests. Ethics statement The studies involving human participants were reviewed and approved by Rich Healthcare Group Review Board. The ethics committee waived the requirement of written informed consent for participation. References Standards of Medical Care in Diabetes–2010. Diabetes Care. 33 Suppl 1, S11-S61 (2010). Jorge-Galarza, E. et al. Adipose Tissue Dysfunction Increases Fatty Liver Association with Pre Diabetes and Newly Diagnosed Type 2 Diabetes Mellitus. Diabetol. Metab. Syndr. 8, 73 (2016). Ek, A. E., Rössner, S. M., Hagman, E. & Marcus, C. High Prevalence of Prediabetes in a Swedish Cohort of Severely Obese Children. Pediatr. Diabetes. 16, 117–128 (2015). Kim, M. K. et al. Cumulative Exposure to Impaired Fasting Glucose and Future Risk of Type 2 Diabetes Mellitus. Diabetes. Res. Clin. Pract. 175, 108799 (2021). Kim, J. H. & Lim, J. S. Trends of Diabetes and Prediabetes Prevalence Among Korean Adolescents From 2007 to 2018. J. Korean Med. Sci. 36, e112 (2021). Zuo, Y. et al. Association of Impaired Fasting Glucose with Cardiovascular Disease in the Absence of Risk Factor. J. Clin. Endocrinol. Metab. 107, e1710-e1718 (2022). Lind, V. et al. Impaired Fasting Glucose: A Risk Factor for Atrial Fibrillation and Heart Failure. Cardiovasc. Diabetol. 20, 227 (2021). Lee, S. H., Han, K., Kwon, H. S. & Kim, M. K. Frequency of Exposure to Impaired Fasting Glucose and Risk of Mortality and Cardiovascular Outcomes. Endocrinol. Metab. 36, 1007–1015 (2021). Unger, G., Benozzi, S. F., Perruzza, F. & Pennacchiotti, G. L. Triglycerides and Glucose Index: A Useful Indicator of Insulin Resistance. Endocrinol Nutr. 61, 533–540 (2014). Fritz, J. et al. The Triglyceride-Glucose Index as a Measure of Insulin Resistance and Risk of Obesity-Related Cancers. Int. J. Epidemiol. 49, 193–204 (2020). Darshan, A. V. et al. Comparison of Triglyceride Glucose Index and Hba1C as a Marker of Prediabetes - A Preliminary Study. Diabetes Metab. Syndr.-Clin. Res. Rev. 16, 102605 (2022). Zhang, L. & Zeng, L. Non-Linear Association of Triglyceride-Glucose Index with Prevalence of Prediabetes and Diabetes: A Cross-Sectional Study. Front. Endocrinol. 14, 1295641 (2023). Song, T. et al. Triglyceride-Glucose Index Predicts the Risk of Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis. Gynecol. Endocrinol. 38, 10–15 (2022). Tao, L. C., Xu, J. N., Wang, T. T., Hua, F. & Li, J. J. Triglyceride-Glucose Index as a Marker in Cardiovascular Diseases: Landscape and Limitations. Cardiovasc. Diabetol. 21, 68 (2022). Zhou, T., Zhang, X., Fan, S., Deng, Z. & Jiao, C. The Impact of Early Neighborhood Cohesion, and its Mechanism, On Cognitive Function in Later Life. Front. Psychiatry. 13, 848911 (2022). 12. Older Adults: Standards of Medical Care in Diabetes-2019. Diabetes Care. 42, S139-S147 (2019). de Jesús, G. J. et al. Older Subjects with Β-Cell Dysfunction Have an Accentuated Incretin Release. J. Clin. Endocrinol. Metab. 103, 2613–2619 (2018). Sun, H. et al. Erratum to "Idf Diabetes Atlas: Global, Regional and Country-Level Diabetes Prevalence Estimates for 2021 and Projections for 2045" [Diabetes Res. Clin. Pract. 183 (2022) 109119]. Diabetes. Res. Clin. Pract. 204, 110945 (2023). Ogurtsova, K. et al. Idf Diabetes Atlas: Global Estimates for the Prevalence of Diabetes for 2015 and 2040. Diabetes. Res. Clin. Pract. 128, 40–50 (2017). Xu, Y. et al. Prevalence and Control of Diabetes in Chinese Adults. Jama. 310, 948–959 (2013). 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, 1041616 (2022). Wen, J. et al. Elevated Triglyceride-Glucose (Tyg) Index Predicts Incidence of Prediabetes: A Prospective Cohort Study in China. Lipids Health Dis. 19, 226 (2020). Horstman, C., Aronne, L., Wing, R., Ryan, D. H. & Johnson, W. D. Implementing an Online Weight-Management Intervention to an Employee Population: Initial Experience with Real Appeal. Obesity. 26, 1704–1708 (2018). Li, X. et al. Association Between Triglyceride-Glucose Index and Risk of Incident Diabetes: A Secondary Analysis Based On a Chinese Cohort Study: Tyg Index and Incident Diabetes. Lipids Health Dis. 19, 236 (2020). Zhang, L. & Zeng, L. Non-Linear Association of Triglyceride-Glucose Index with Prevalence of Prediabetes and Diabetes: A Cross-Sectional Study. Front. Endocrinol. 14, 1295641 (2023). Li, X. et al. Association of Non-Insulin-Based Insulin Resistance Indices with Risk of Incident Prediabetes and Diabetes in a Chinese Rural Population: A 12-Year Prospective Study. Diabetes Metab. Syndr. Obes. 15, 3809–3819 (2022). Chen, Y. et al. Association of Body Mass Index and Age with Incident Diabetes in Chinese Adults: A Population-Based Cohort Study. Bmj Open. 8, e21768 (2018). 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2021. Diabetes Care. 44, S15-S33 (2021). Torreggiani, M. et al. Spontaneously Low Protein Intake in Elderly Ckd Patients: Myth Or Reality? Analysis of Baseline Protein Intake in a Large Cohort of Patients with Advanced Ckd. Nutrients. 13, (2021). White, I. R., Royston, P. & Wood, A. M. Multiple Imputation Using Chained Equations: Issues and Guidance for Practice. Stat. Med. 30, 377–399 (2011). Groenwold, R. H. et al. Missing Covariate Data in Clinical Research: When and When Not to Use the Missing-Indicator Method for Analysis. Can. Med. Assoc. J. 184, 1265–1269 (2012). Qin, Y. et al. A High Triglyceride-Glucose Index is Associated with Contrast-Induced Acute Kidney Injury in Chinese Patients with Type 2 Diabetes Mellitus. Front. Endocrinol. 11, 522883 (2020). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 Jun, 2024 Reviews received at journal 11 Jun, 2024 Reviewers agreed at journal 11 Jun, 2024 Reviews received at journal 27 May, 2024 Reviewers agreed at journal 27 May, 2024 Reviewers invited by journal 25 May, 2024 Editor assigned by journal 25 May, 2024 Editor invited by journal 21 May, 2024 Submission checks completed at journal 21 May, 2024 First submitted to journal 13 May, 2024 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. We do this by developing innovative software and high quality services for the global research community. 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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-4413051","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":308602830,"identity":"8edb5e04-d643-4e78-a9d4-023c69e901f3","order_by":0,"name":"Jie Liu","email":"","orcid":"","institution":"Shenzhen New Frontier United Family Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Liu","suffix":""},{"id":308602831,"identity":"3c68fa39-2929-494b-bf37-f2c3a1d9cfc0","order_by":1,"name":"Feng Yi","email":"","orcid":"","institution":"Yueyang Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Yi","suffix":""},{"id":308602832,"identity":"24222460-4a0c-40d1-994f-d30f218f6900","order_by":2,"name":"Kai Duan","email":"","orcid":"","institution":"Yueyang Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Duan","suffix":""},{"id":308602833,"identity":"e9384363-a218-48e4-a980-64b062ba3ab9","order_by":3,"name":"Haibo Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACefb+7z8/VEjI8bM3EKnFsOeAgbTEGQtjyZ4DxFpzI8FAgrelItHgRgKROhhnJCQYSDZIJDDcfLzxBkONTTRBLew8Dw4kFO6QyGOcnVZswXAsLbeBoC3tiQ0HJM9IFDNL55hJMDYcJqyF4UAyYwNvm0Rim+QZYrWcSGNmAGnpkeAhUothzxk2ZokzEsYSPEC/JBDjF3n2HjbGDxV1cvbHD2+88aHGhgiHIQEDYFCTCAwkSNUxCkbBKBgFIwMAANnTPs28ibUuAAAAAElFTkSuQmCC","orcid":"","institution":"Yueyang Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Haibo","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-05-13 11:56:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4413051/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4413051/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57866297,"identity":"c16a8cce-ba58-455a-8530-732a3ccb7ee5","added_by":"auto","created_at":"2024-06-06 15:50:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32058,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of TyG index. It presented a normal distribution, ranging from 6.61 to 11.05, with a mean of 8.57.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4413051/v1/5b3ee4072dac9d4299e35da5.png"},{"id":57867218,"identity":"2cfa864b-536a-4957-ad35-85fd1e13c39b","added_by":"auto","created_at":"2024-06-06 15:58:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":373990,"visible":true,"origin":"","legend":"\u003cp\u003eThe Kaplan-Meier curve depicts the probability of IFG occurrence, stratified by the TyG index. The graph demonstrates that the likelihood of IFG occurrence gradually increases with higher TyG index values. This suggests that patients with the highest TyG index have the highest probability of experiencing IFG.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4413051/v1/c41a601e1cbbca08e6e462a5.png"},{"id":57866299,"identity":"beec0ee8-de67-417d-93d4-e8db58266f4d","added_by":"auto","created_at":"2024-06-06 15:50:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":160470,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between TyG index and the risk of IFG is linear.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4413051/v1/a9ad57079992799e6e0ccce4.png"},{"id":57866300,"identity":"02621f71-be82-473d-9e68-04c9435f90d5","added_by":"auto","created_at":"2024-06-06 15:50:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":189548,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart illustrating the selection process of study participants\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4413051/v1/b803f6f251e165aa30f4ee6f.png"},{"id":57867702,"identity":"ae829cbf-972e-4670-a0f4-0e17dfc9b368","added_by":"auto","created_at":"2024-06-06 16:06:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1168508,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4413051/v1/bfba660c-bfb6-4e91-9ef5-bc533fbb6133.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Triglyceride-glucose index is associated with the risk of impaired fasting glucose: A 5-year retrospective cohort study in Chinese elderly people","fulltext":[{"header":"Introduction","content":"\u003cp\u003eImpaired fasting glucose (IFG) is a glycemic state between normality and diabetes, defined by the American Diabetes Association (ADA) as fasting plasma glucose levels ranging from 5.6 mmol/L to 6.9 mmol/L\u003csup\u003e1\u003c/sup\u003e. IFG is prevalent in the elderly globally, more so than diabetes itself in this demographic \u003csup\u003e2,3\u003c/sup\u003e. IFG not only increases the risk of developing type 2 diabetes but is also closely associated with elevated cardiovascular disease risk \u003csup\u003e4\u0026ndash;7\u003c/sup\u003e. Additionally, research suggests a potential link between IFG and increased all-cause mortality \u003csup\u003e8\u003c/sup\u003e. Therefore, early identification and intervention in IFG risk factors are crucial for reducing disease burden.\u003c/p\u003e \u003cp\u003eThe triglyceride-glucose (TyG) index, calculated as the product of triglyceride (TG) levels and fasting plasma glucose (FPG)\u003csup\u003e9\u003c/sup\u003e, serves as a convenient measure for assessing an individual\u0026rsquo;s degree of insulin resistance \u003csup\u003e10\u003c/sup\u003e. It has been validated across diverse clinical settings and populations, including middle-aged and elderly individuals, as well as those with conditions like obesity. The TyG index has shown robustness and broad applicability in its association with prediabetes, type 2 diabetes risk, and its predictive capability for IFG or diabetes development \u003csup\u003e11\u0026ndash;13\u003c/sup\u003e. Furthermore, the TyG index has been identified as an effective marker for cardiovascular diseases \u003csup\u003e14\u003c/sup\u003e. Thus, assisting in the early identification and management of diabetes and associated metabolic disorders.\u003c/p\u003e \u003cp\u003eWith China's aging population rapidly increasing and projected to reach 25% by 2030 and 37.92% by 2100, the prevalence of diabetes among the elderly is becoming a significant concern \u003csup\u003e15,16\u003c/sup\u003e. The natural decline in metabolic functions with age, especially in pancreatic function and insulin sensitivity, increases the risk of diabetes in this demographic \u003csup\u003e17\u003c/sup\u003e. Studying IFG in the elderly can identify high-risk individuals early and implement measures to delay or prevent diabetes onset.\u003c/p\u003e \u003cp\u003eCurrently, there is insufficient evidence regarding the ralationship between the TyG index and risk of IFG among the elderly. Our study aims to investigate this relationship in individuals aged 60 and above, endorsing the TyG index\u0026rsquo;s clinical utility for early IFG detection, potentially influencing diabetes management and preventive healthcare in the elderly.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline characteristics of participants\u003c/p\u003e\n\u003cp\u003eThe baseline characteristics of the study participants were summarized as showed in Table 1. The study participants were grouped into IFG and Normal groups, with the IFG group displaying significantly higher mean age and BMI compared to the Normal group (p \u0026lt; 0.001). This group also had elevated SBP and DBP, as well as higher FBG levels (p \u0026lt; 0.001). Further differences included higher TG levels, lower HDL-c levels, and lower LDL cholesterol in the IFG group (p \u0026lt; 0.001). Additionally, ALT and Scr levels were significantly higher in the IFG group (p \u0026lt; 0.001). Sex distribution and smoking status also differed significantly between the two groups, while no significant differences were observed in drinking status and family history of diabetes. Notably, the follow-up duration was longer in the IFG group (p \u0026lt; 0.001). The distribution of the TyG index, as shown in Fig 1, indicated a normal distribution ranging from 6.61 to 11.05, with a mean of 8.57.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 \u003c/strong\u003eThe baseline characteristics of participants.\u003c/p\u003e\n\u003ctable width=\"534\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003egroup\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003eNormal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003eIFG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eparticipants\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e13683\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e4063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eAge (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e66.87 \u0026plusmn; 6.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e67.47 \u0026plusmn; 6.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eBMI (kg/m2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e23.77 \u0026plusmn; 2.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e24.63 \u0026plusmn; 3.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eSBP (mmHg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e130.10 \u0026plusmn; 18.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e134.83 \u0026plusmn; 18.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eDBP (mmHg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e77.97 \u0026plusmn; 11.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e79.95 \u0026plusmn; 11.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eFBG at baseline (mg/dL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e87.24 \u0026plusmn; 8.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e90.91 \u0026plusmn; 7.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eTG (mg/dL)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e135.23 \u0026plusmn; 81.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e145.35 \u0026plusmn; 87.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eTyG index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e8.54 \u0026plusmn; 0.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e8.65 \u0026plusmn; 0.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eALT (U/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e21.02 \u0026plusmn; 13.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e22.99 \u0026plusmn; 15.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eAST (U/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e25.65 \u0026plusmn; 9.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e26.37 \u0026plusmn; 11.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eBUN (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e5.17 \u0026plusmn; 1.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e5.17 \u0026plusmn; 1.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e0.852\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eScr (\u0026mu;mol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e72.56 \u0026plusmn; 20.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e74.49 \u0026plusmn; 16.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eTC (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e5.16 \u0026plusmn; 0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e5.16 \u0026plusmn; 0.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e0.753\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eHDL-c (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e1.39 \u0026plusmn; 0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e1.37 \u0026plusmn; 0.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eLDL-c (mmol/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e3.03 \u0026plusmn; 0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e2.98 \u0026plusmn; 0.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e7374 (53.89%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e2377 (58.50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e6309 (46.11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e1686 (41.50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eSmoking status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e0.039\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eCurrent smoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e947 (6.92%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e233 (5.73%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eEver smoker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e111 (0.81%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e28 (0.69%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eNever\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e2517 (18.40%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e737 (18.14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eDrinking status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e0.216\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eCurrent drinker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e116 (0.85%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e36 (0.89%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eEver drinker\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e352 (2.57%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e102 (2.51%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eNever\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e3107 (22.71%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e860 (21.17%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eFamily history of diabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e0.098\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e13584 (99.28%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e4023 (99.02%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e99 (0.72%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e40 (0.98%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"120\"\u003e\n\u003cp\u003eFollow-up (year)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e3.07 \u0026plusmn; 0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"163\"\u003e\n\u003cp\u003e3.34 \u0026plusmn; 0.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"109\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eContinuous variables were summarized as mean (SD) or medians (quartile interval); categorical variables were displayed as percentage (%)\u003c/p\u003e\n\u003cp\u003eAbbreviations: BMI, body mass index; SBP, systolic blood pressure; DBP; diastolic blood pressure; TG triglyceride; TC, total cholesterol; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; AST aspartate aminotransferase; ALT, alanine aminotransferase; BUN, blood urea nitrogen; Scr, serum creatinine; FBG, fasting plasma glucose; TyG index, triglyceride glucose index.\u003c/p\u003e\n\u003cp\u003eFigure 1. Distribution of TyG index. It presented a normal distribution, ranging from 6.61 to 11.05, with a mean of 8.57.\u003c/p\u003e\n\u003cp\u003eIncidence of IFG in participants\u003c/p\u003e\n\u003cp\u003eTable 2 and Fig 2 describe the incidence rates of IFG. Among the participants, 4,063 (22.9%) developed IFG. Participants were divided into subgroups based on the quartiles of the TyG index. The incidence rates of IFG per 1,000 person-years were 58.89, 64.26, 79.69, and 90.33 for each TyG index quartile. The incidence rates of IFG in each TyG index quartile were as follows: Q1: 18.89%, Q2: 19.93%, Q3: 24.60%, and Q4: 28.08%. Participants with the highest TyG index (Q4) had a higher risk of developing IFG compared to those with the lowest TyG index (Q1) (trend p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 \u003c/strong\u003eThe Incidence rate of IFG (% or Per 1000 person-year).\u003c/p\u003e\n\u003ctable width=\"633\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003eTyG index (quartile)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"129\"\u003e\n\u003cp\u003eParticipants (n)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003eIFG (n)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003eIncidence (95%CI) (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003ePer 1000 person-year\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"129\"\u003e\n\u003cp\u003e17746\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e4063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e22.90 (22.28-23.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e73.19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003eQ1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"129\"\u003e\n\u003cp\u003e4437\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e842\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e18.98 (17.82-20.13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e58.99\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"129\"\u003e\n\u003cp\u003e4436\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e884\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e19.93 (18.75-21.10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e64.26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"129\"\u003e\n\u003cp\u003e4435\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e1091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e24.60 (23.33-25.87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e79.69\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"129\"\u003e\n\u003cp\u003e4438\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e1246\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e28.08 (26.75-29.40)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e90.33\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"124\"\u003e\n\u003cp\u003eP for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"129\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"130\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFigure 2. The Kaplan-Meier curve depicts the probability of IFG occurrence, stratified by the TyG index. The graph demonstrates that the likelihood of IFG occurrence gradually increases with higher TyG index values. This suggests that patients with the highest TyG index have the highest probability of experiencing IFG.\u003c/p\u003e\n\u003cp\u003eMultivariable analysis using Cox proportional hazards regression model\u003c/p\u003e\n\u003cp\u003eIn Table 3, the relationship between the TyG index and the risk of IFG is examined using multivariable analysis with the Cox proportional hazards regression model. The results show that in the crude model, the hazard ratio for the TyG index was statistically significant. When adjusting for age and sex (Model I), the hazard ratio remained significant but slightly decreased. Further adjustments in Model II, which included various additional factors, still showed a significant association between the TyG index and the risk of IFG. Additionally, after adjustments, consistent trends were observed across all quartiles of the TyG index, further highlighting the significant relationship between the TyG index quartiles and the risk of IFG. These findings suggest that the TyG index is independently associated with an increased risk of IFG, even after accounting for potential confounding factors. This underscores the importance of considering the TyG index in assessing the risk of IFG in clinical and research settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Relationship between TyG index and risk of IFG in different models\u003c/p\u003e\n\u003ctable width=\"596\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eExposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eCrude model (HR,95%CI) P\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eModel I(HR,95%CI) P\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eModel II(HR,95%CI) P\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eGAM (HR,95%CI) P\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eTyG index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e1.43 (1.35, 1.51) \u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e1.44 (1.36, 1.52) \u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e1.43 (1.27, 1.60) \u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e1.48 (1.32, 1.66) \u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u0026nbsp;(quartile)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; Q1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; Q2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e1.18 (1.08, 1.30) 0.0005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e1.18 (1.08, 1.30) 0.0005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e0.99 (0.85, 1.15) 0.8550\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e0.99 (0.85, 1.16) 0.9454\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; Q3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e1.48 (1.35, 1.62) \u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e1.49 (1.36, 1.63) \u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e1.35 (1.16, 1.58) 0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e1.38 (1.18, 1.61) \u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; Q4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e1.65 (1.51, 1.80) \u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e1.66 (1.52, 1.82) \u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e1.62 (1.37, 1.90) \u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e1.71 (1.45, 2.02) \u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003eP for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"119\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCrude model: we did not adjust other covariates.\u003c/p\u003e\n\u003cp\u003eModel I: we adjusted age, sex.\u003c/p\u003e\n\u003cp\u003eModel II: we adjusted age, sex, SBP, DBP, BMI, ALT, AST, BUN, Scr, TC, LDL-C, HDL-c, family history of diabetes, drinking status, and smoking status.\u003c/p\u003e\n\u003cp\u003eModel III: we adjusted age(smooth), sex, SBP (smooth), DBP (smooth), BMI (smooth), BUN (smooth), Scr (smooth), ALT (smooth), AST (smooth), TC (smooth), LDL-C(smooth), HDL-c(smooth), smoking status, drinking status, family history of diabetes. HR, Hazard ratios; CI, confidence, Ref, reference.\u003c/p\u003e\n\u003cp\u003eSensitivity analysis\u003c/p\u003e\n\u003cp\u003eWe conducted a series of sensitivity analyses to ensure the validity of our findings. Initially, in Model III using the generalized additive model (GAM) with additional smooth terms for various variables, we observed a HR of 1.48 (95%CI 1.32-1.66, P \u0026lt; 0.0001), indicating a significant association (Table 3). By excluding participants with a BMI \u0026ge; 28 kg/m2 and adjusting for confounding factors, the results consistently showed a positive correlation between the TyG index and the risk of impaired fasting glucose (IFG) with an HR of 1.42 (95% CI: 1.26-1.61, p \u0026lt; 0.0001). Moreover, we conducted sensitivity analyses by excluding individuals aged \u0026ge; 80 years, and the association between the TyG index and IFG risk remained significant (risk ratio = 1.45, 95% confidence interval: 1.29-1.64, p \u0026lt; 0.0001) after adjustments. Additionally, when analyzing participant\u0026rsquo;s SBP＜140mmHg, the risk ratio was 1.51 (95% confidence interval: 1.31-1.74, p \u0026lt; 0.0001) (Table 4). After considering all these sensitivity analyses, we can confidently conclude that our results are dependable and robust, emphasizing the consistent positive association between the TyG index and the risk of IFG.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Relationship between TyG index and the risk of IFG in different sensitivity analyses.\u003c/p\u003e\n\u003ctable width=\"593\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003eExposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003eModel I (HR,95%CI) P\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003eModel II (HR,95%CI) P\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eModel III (HR,95%CI) P\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003eTyG index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e1.42 (1.26, 1.61) \u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e1.45 (1.29, 1.64) \u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.51 (1.31, 1.74) \u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e\u0026nbsp;(quartile)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; Q1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003eRef\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; Q2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e0.96 (0.82, 1.13) 0.6550\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e1.05 (0.90, 1.24) 0.5228\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.05 (0.87, 1.27) 0.6229\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; Q3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e1.32 (1.12, 1.55) 0.0010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e1.40 (1.19, 1.65) \u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.58 (1.30, 1.92) \u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; Q4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e1.58 (1.33, 1.88) \u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e1.68 (1.41, 2.00) \u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e1.91 (1.55, 2.35) \u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"102\"\u003e\n\u003cp\u003eP for trend\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"161\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"170\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCrude model I was a sensitivity analysis performed after excluding participants with BMI\u0026ge; 28 kg/m2. we adjusted age, sex, SBP, DBP, ALT, AST, BUN, Scr, TC, LDL-C, HDL-c, family history of diabetes, drinking status, and smoking status.\u003c/p\u003e\n\u003cp\u003eModel II was a sensitivity analysis performed after excluding participants with age\u0026ge; 80 years old. We adjusted a age, sex, SBP, DBP, BMI, ALT, AST, BUN, Scr, TC, LDL-C, HDL-c, family history of diabetes, drinking status, and smoking status.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Model III was a sensitivity analysis performed after excluding participants with SBP \u0026ge; 140mmHg. We adjusted a age, sex, SBP, DBP, BMI, ALT, AST, BUN, Scr, TC, LDL-C, HDL-c, family history of diabetes, drinking status, and smoking status. HR, Hazard ratios; CI, confidence, Ref, referenc.\u003c/p\u003e\n\u003cp\u003eRelationship between the TyG index and the risk of IFG\u003c/p\u003e\n\u003cp\u003eIn our study, we found a clear linear relationship between the TyG index and the risk of Impaired Fasting Glucose (IFG). By utilizing a Cox proportional hazards regression model with cubic spline functions, we were able to evaluate this relationship and confirmed that it is indeed linear (Fig 3). This finding suggests that as the TyG index increases, so does the risk of developing IFG.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 3. The association between TyG index and the risk of IFG is linear.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSubgroup Analysis Results\u003c/p\u003e\n\u003cp\u003eAs illustrated in Table 5, a detailed subgroup analysis was conducted. Gender, age, BMI, systolic and diastolic blood pressures, smoking and drinking habits, and a family history of diabetes did not alter the association between the TyG index and the risk of IFG. Thus, no significant interactions were observed between these variables and the TyG index (all interaction P ＞ 0.05).\u003c/p\u003e\n\u003cp\u003eTable 5. Effect size of TyG index on IFG in prespecified and exploratory subgroups\u003c/p\u003e\n\u003ctable width=\"513\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003eHR (95% CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eP-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003eP for interaction\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003eAge, yeas\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.6378\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e60-70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e1.44 (1.26, 1.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e70-80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e1.45 (1.16, 1.82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.0011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e80-90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e1.13 (0.77, 1.64)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.5336\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026ge;90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e2.11 (0.14, 30.92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.5780\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003eBMI (kg/m 2 )\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.2039\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e＜18.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e0.79 (0.35, 1.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.556\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026ge;18.5， ＜25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e1.53 (1.32, 1.77)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026ge;25， ＜28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e1.43 (1.19, 1.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.0002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026ge;28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e1.20 (0.90, 1.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.2047\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.4588\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e1.38 (1.20, 1.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;female\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e1.49 (1.27, 1.76)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003eSBP (mmHg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.0596\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e＜140\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e1.55 (1.36, 1.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026ge;140\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e1.28 (1.08, 1.52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.0047\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003eDBP (mmHg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.7253\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e＜90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e1.43 (1.27, 1.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003e\u0026ge;90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e1.37 (1.10, 1.72)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.0057\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003eFamily history of diabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.5347\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e1.43 (1.27, 1.60)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"144\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e0.74 (0.09, 6.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.7869\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis retrospective cohort study aims to investigate the relationship between the TyG index and the risk of IFG. We found that the risk of IFG increases with the TyG index, and a linear relationship between them was observed. The highest quartile of the TyG index has a 1.62 times higher risk of IFG compared to the lowest quartile. These results suggest that the TyG index may be an effective indicator for monitoring IFG.\u003c/p\u003e\n\u003cp\u003eGlobally, the incidence of prediabetes is on the rise. According to estimates by the World Health Organization, in 2021, the global diabetes population was approximately 537 million. The prevalence of diabetes in China rose significantly from 22.5 million individuals in the year 2000 to a staggering 140.9 million by 2021\u003csup\u003e18\u003c/sup\u003e. The number of people with prediabetes exceeds these figures. A study in the United States suggests that about one-third of adults are considered to have prediabetes\u0026nbsp;\u003csup\u003e19\u003c/sup\u003e. The prevalence of prediabetes in Chinese adults is alarmingly high, with an estimated rate of 50.1%. the prevalence of prediabetes is higher in men, with a rate of 52.1%, compared to women at 48.1%. This suggests that one out of every two Chinese adults may be at risk of developing diabetes if proper measures are not taken\u0026nbsp;\u003csup\u003e20\u003c/sup\u003e. Studies have indicated that the standardized incidence rate of prediabetes in the overall population of China is 62.6 cases per 1000 person-years (73.8 cases per 1000 person-years among males and 51.2 cases per 1000 person-years among females)\u0026nbsp;\u003csup\u003e21\u003c/sup\u003e. Additionally, another cohort study in China, which included 4093 Chinese adults with a median follow-up time of 3.25 years, found that 26.2% of participants developed prediabetes\u0026nbsp;\u003csup\u003e22\u003c/sup\u003e. IFG is a state of prediabetes, and the primary outcome variable studied here was IFG. Our study reveals that over a 5-year period, 22.9% of participants developed impaired fasting glucose (IFG), with an incidence rate of 73.19 per 1000 person-years. The differences in prediabetes incidence rates among various studies could be attributed to variations in participants\u0026rsquo; age, follow-up duration, and ethnicity. Notably, less than 11% of individuals are aware of their condition\u0026nbsp;\u003csup\u003e23\u003c/sup\u003e. Therefore, identifying factors leading to IFG is crucial for preventing diabetes and its complications.\u003c/p\u003e\n\u003cp\u003eA cohort study in China, involving 201,298 individuals, found that a high TyG index was independently associated with the risk of developing diabetes (HR) = 3.34; 95% CI = 3.11\u0026ndash;3.60)\u0026nbsp;\u003csup\u003e24\u003c/sup\u003e . Another cohort study involving 4543 Chinese adults utilized logistic regression analysis adjusted for several confounders, showing that for each standard deviation increase in the TyG index, the risk of prediabetes increased by 1.38 times (95% CI = 1.28\u0026ndash;1.48)\u0026nbsp;\u003csup\u003e22\u003c/sup\u003e. Hence, we hypothesize that an increase in the TyG index might be related to an increased risk of IFG in the elderly. Unfortunately, reports on their relationship are scarce. Linhao Zhang et al. found a non-linear relationship between the TyG index and impaired fasting blood glucose by analyzing data from 25,159 patients\u0026nbsp;\u003csup\u003e25\u003c/sup\u003e. Xiaoxia Li et al. demonstrated that, based on baseline data, logistic analysis showed that after multivariate adjustment, the TyG index was significantly positively correlated with IFG. However, the association was not significant after further adjustment (HR, 1.06; 95% CI, 0.58\u0026ndash;1.96; p for trend = 0.784)\u0026nbsp;\u003csup\u003e26\u003c/sup\u003e. This study may differ from ours because the sample size was not as large as ours and the influence of age was not analyzed separately. However, our study found a positive, linear correlation between the TyG index and the risk of IFG in the elderly. Our research adds to the existing literature, supporting the hypothesis that an elevated TyG index is associated with an increased IFG risk. Compared to other studies, in our research, the TyG index was utilized in both categorical and continuous forms to examine the correlation between TyG index and the risk of IFG. Our aim was to minimize loss of information and accurately measure the association between the two variables. Furthermore, the confounding factors adjusted in our study differ from previous ones. We adjusted for a wider variety of variables, such as age, gender, systolic blood pressure, diastolic blood pressure, body mass index, alanine aminotransferase, aspartate aminotransferase, blood urea nitrogen, serum creatinine, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, diabetes family history, alcohol consumption, and tobacco use. Sensitivity analysis confirmed that this relationship persists even after excluding participants with age\u0026ge;80 years, BMI\u0026ge;28kg/m2, and SBP\u0026ge;140mmHg. Additionally, subgroup analyses and interaction tests on age, DBP, BMI, SBP, gender, and family history of diabetes showed no interactions, confirming the stability of the relationship between the TyG index and IFG risk. This finding provides a reference for clinical interventions to reduce the probability of IFG in the elderly by targeting the TyG index.\u003c/p\u003e\n\u003cp\u003eThe study has several strengths. Firstly, we have established a linear relationship between the TyG index and the risk of IFG among the elderly for the first time. In addition, the research involved a substantial group of 17,746 senior citizens and accounted for variables like BMI, age, TC, SBP, BUN, ALT, AST, DBP, Scr, LDL-C, HDL-C, gender, diabetic family history, alcohol consumption, and tobacco use to reduce possible distortions. To ensure the reliability and robustness of the results, sensitivity analysis was performed. Moreover, subgroup analysis and tests for interactions were conducted. The results indicate that the TyG index has different effects on IFG risk across various subgroups, further validating the experiment\u0026rsquo;s stability.\u003c/p\u003e\n\u003cp\u003eDespite these strengths, our study has several limitations. First, the average follow-up duration of the study participants was only 5.0 years, which is relatively short. Second, participants were only followed up once, with information available only at two time points (baseline and follow-up). Third, there were no available serum insulin level data, thus the predictive value of the TyG index could not be compared. Fourth, values for impaired glucose tolerance at 2 hours or HbA1c were not collected, hence further analysis of the relationship between the TyG index and changes in glucose tolerance and HbA1c was not possible. Fifth, the study focused only on individuals aged over 60 years, and may not represent all populations; further research should expand to include various age groups.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study revealed a linear relationship between the TyG index and the IFG in Chinese individuals aged over 60. Understanding this linear relationship can help clinicians identify high-risk elder individuals and implement focused interventions to reduce the risk of developing diabetes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study utilized data from a previous retrospective cohort study conducted by Chinese researchers (Chen et al.)\u0026nbsp;\u003csup\u003e27\u003c/sup\u003e. The target independent variable was the TyG at baseline. The outcome variable was the development from normoglycemia to IFG at follow-up.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData source\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccess to the original dataset was granted at no cost through the DATADRYAD platform (www.datadryad.org), courtesy of Ying Chen et al\u0026nbsp;\u003csup\u003e27\u003c/sup\u003e. In accordance with Dryad\u0026rsquo;s usage policy, the data is available for academic and research purposes, allowing users to share, adapt, alter, and build upon the material, provided it is not for commercial use and proper attribution is given to the original authors and source. The dataset was sourced from a publicly accessible study published in\u0026nbsp;2018 titled \u0026ldquo;Association of body mass index and age with diabetes onset in Chinese adults: a population-based cohort study,\u0026rdquo; which can be found at http://dx.doi.org/10.1136/bmjopen-2018-021768. For those interested, the dataset can be retrieved from the following link: https://doi.org/10.5061/dryad.ft8750v.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe prior study received ethical approval from the Rich Healthcare Group Review Board. Given that the current study involves a secondary analysis of existing data, there was no need for obtaining informed consent or additional ethical approval. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003eResearch Population\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe primary research included 685,277 individuals aged 20 years and above, all of whom had undergone a minimum of two health assessments. The study focused on participants who, during follow-up, had FPG levels ranging from 6.1 to 6.9 mmol/l without any prior diagnosis of diabetes. The initial selection excluded participants based on several factors: (1) lack of detailed information regarding weight, height, or gender; (2) BMI values outside the normal range (\u0026lt;15kg/m2 or \u0026gt;55kg/m2); (3) intervals between visits shorter than 2 years; (4) missing FPG readings; (5) individuals diagnosed with diabetes at the start or with uncertain diabetes status at the time of follow-up. Following these criteria, the study retained 211,833 participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurther analysis led to the exclusion of an additional 194,087 participants for reasons including: 1) absence of follow-up FPG readings, 2) baseline FPG levels \u0026ge;5.6 mmol/l, 3) FPG levels \u0026gt;6.9 mmol/l during follow-up, 4) unclear diabetes diagnosis at follow-up, and 5) lack of triglyceride (TG) values or being less than 60 years of age. Elder people are defined as those aged over 60 years old or older \u003csup\u003e25\u003c/sup\u003e. Ultimately, the study included 17,746 participants. The process of selecting participants for this study is depicted in Figure 4.\u003c/p\u003e\n\u003cp\u003eFigure. 4 Flowchart illustrating the selection process of study participants\u003c/p\u003e\n\u003cp\u003eData collection\u003c/p\u003e\n\u003cp\u003eIn this study, data collection included demographic information such as age, diastolic blood pressure (DBP), systolic blood pressure (SBP), height, and weight, from which body mass index (BMI) was calculated. To ensure consistency in data collection, staff received specialized training focusing on demographic data and key measurements, including blood pressure. Tests were uniformly conducted in a standardized laboratory environment for triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), total cholesterol (TC), blood urea nitrogen (BUN), serum creatinine (Scr), alanine aminotransferase (ALT), low-density lipoprotein cholesterol (LDL-c), and FPG. Additionally, the study collected information on the patients\u0026rsquo; smoking and drinking histories, defining current drinking as 1, former drinking as 2, never drinking as 3, and unknown drinking status as 4. Similarly, current smoking was coded as 1, former smoking as 2, never smoking as 3, and unknown smoking status as 4.\u003c/p\u003e\n\u003cp\u003eOutcome and definitions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt follow-up, our focus was on identifying individuals who had an impaired fasting glucose (IFG) condition. This was determined by having fasting plasma glucose (FPG) levels fall within the range of 6.1-6.9 mmol/l, with no reported cases of new-onset diabetes.\u0026nbsp;\u003csup\u003e28\u003c/sup\u003e. We defined \u0026ldquo;elderly patients\u0026rdquo; as aged over 60\u0026nbsp;\u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMissing data processing\u003c/p\u003e\n\u003cp\u003eIn this study, the number of participants with missing data was as follows: 7 (0.00%) each for DBP and SBP, 1 person (0.00%) for TC, 869 (0.69%) for ALT, 72640 (58%) for AST, 60634 (48.39%) for LDL-c, 6192 (4.9%) for Scr, 60579 (48.34%) for HDL-c, and 11452 (9.1%) for BUN. To mitigate the uncertainty caused by missing data, this study utilized multiple imputation techniques. The imputation model included ALT, LDL-c, AST, Scr, HDL-c, and BUN, with 5 iterations using linear regression. Data analysis assumed that missingness was random (MAR)\u003csup\u003e30, 31\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eThe main variable examined in this research is the TyG index, which is defined by the equation: TyG index = ln [FPG (mg/dL) \u0026times; TG (mg/dL)/2]\u003csup\u003e32\u003c/sup\u003e. We divided it into four quartiles and considered it as a continuous variable. We presented the mean and standard deviation for continuous variables that followed a normal distribution, while the median was reported for data that did not follow a normal distribution. For categorical variables, we presented the frequency and proportion of the data in our study. To analyze the differences between different TyG index groups, we utilized the Kruskal-Wallis H test for data that was not normally distributed, one-way analysis of variance for normally distributed data, and the chi-square test for categorical variables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe developed numerous models to evaluate the correlation between the TyG index and the risk of IFG: a baseline model without any adjustments, a simplified model adjusting for gender and age only (Model I), and a comprehensive model adjusting for multiple covariates (Model II: recording a variety of demographic and health-related variables, such as age, gender, BMI, blood pressure, liver enzymes, cholesterol levels, serum creatinine, family history of diabetes, alcohol consumption, and smoking status, in our study. From each model, we noted the effect size (hazard ratio HR) along with its 95% confidence interval (CI).\u003c/p\u003e\n\u003cp\u003eAfter considering potential confounding variables through clinical expertise, reviewing literature, and analyzing data univariately, we incorporated a multifaceted Cox proportional hazards model. This model introduced cubic spline functions and implemented smooth curve fitting to investigate potential nonlinear links between the TyG index and IFG risk. Furthermore, applying a segmented Cox proportional hazards model aided in elucidating this nonlinear association.\u003c/p\u003e\n\u003cp\u003eTo validate our findings, we conducted a series of sensitivity analyses. By incorporating continuous variables into a generalized additive model (GAM) in curve form, we further confirmed the robustness of the results. Additionally, we conducted analyses using stratified Cox proportional hazards models in different subgroups (such as age, gender, blood pressure, smoking, and drinking status). Finally, we used likelihood ratio tests to examine whether there were interactions in the model, both in models including interaction terms and those without. All analyses were performed using Empower Stats (X\u0026amp;Y Solutions, Inc., Boston, MA, http://www.empowerstats.com), with a statistical significance level set at a two-sided P value less than 0.05.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset was sourced from a publicly accessible study published in 2018 titled \u0026ldquo;Association of body mass index and age with diabetes onset in Chinese adults: a population-based cohort study,\u0026rdquo; which can be found at http://dx.doi.org/10.1136/bmjopen-2018-021768. For those interested, the dataset can be retrieved from the following link: https://doi.org/10.5061/dryad.ft8750v.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by The Rich Healthcare Group Review Board. The ethics committee waived the need for written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJie Liu, Feng Yi, and Kai Duan contributed to the study concept and design, researched, and interpreted the data, and drafted the manuscript. Haibo Liu analyzed the data and reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\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\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by Rich Healthcare Group Review Board. The ethics committee waived the requirement of written informed consent for participation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStandards of Medical Care in Diabetes\u0026ndash;2010. Diabetes Care. 33 Suppl 1, S11-S61 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJorge-Galarza, E. et al. Adipose Tissue Dysfunction Increases Fatty Liver Association with Pre Diabetes and Newly Diagnosed Type 2 Diabetes Mellitus. Diabetol. Metab. Syndr. 8, 73 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEk, A. E., R\u0026ouml;ssner, S. M., Hagman, E. \u0026amp; Marcus, C. High Prevalence of Prediabetes in a Swedish Cohort of Severely Obese Children. Pediatr. Diabetes. 16, 117\u0026ndash;128 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, M. K. et al. Cumulative Exposure to Impaired Fasting Glucose and Future Risk of Type 2 Diabetes Mellitus. Diabetes. Res. Clin. Pract. 175, 108799 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim, J. H. \u0026amp; Lim, J. S. Trends of Diabetes and Prediabetes Prevalence Among Korean Adolescents From 2007 to 2018. J. Korean Med. Sci. 36, e112 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZuo, Y. et al. Association of Impaired Fasting Glucose with Cardiovascular Disease in the Absence of Risk Factor. J. Clin. Endocrinol. Metab. 107, e1710-e1718 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLind, V. et al. Impaired Fasting Glucose: A Risk Factor for Atrial Fibrillation and Heart Failure. Cardiovasc. Diabetol. 20, 227 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, S. H., Han, K., Kwon, H. S. \u0026amp; Kim, M. K. Frequency of Exposure to Impaired Fasting Glucose and Risk of Mortality and Cardiovascular Outcomes. Endocrinol. Metab. 36, 1007\u0026ndash;1015 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnger, G., Benozzi, S. F., Perruzza, F. \u0026amp; Pennacchiotti, G. L. Triglycerides and Glucose Index: A Useful Indicator of Insulin Resistance. Endocrinol Nutr. 61, 533\u0026ndash;540 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFritz, J. et al. The Triglyceride-Glucose Index as a Measure of Insulin Resistance and Risk of Obesity-Related Cancers. Int. J. Epidemiol. 49, 193\u0026ndash;204 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDarshan, A. V. et al. Comparison of Triglyceride Glucose Index and Hba1C as a Marker of Prediabetes - A Preliminary Study. Diabetes Metab. Syndr.-Clin. Res. Rev. 16, 102605 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, L. \u0026amp; Zeng, L. Non-Linear Association of Triglyceride-Glucose Index with Prevalence of Prediabetes and Diabetes: A Cross-Sectional Study. Front. Endocrinol. 14, 1295641 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong, T. et al. Triglyceride-Glucose Index Predicts the Risk of Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis. Gynecol. Endocrinol. 38, 10\u0026ndash;15 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTao, L. C., Xu, J. N., Wang, T. 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Endocrinol. 11, 522883 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"TyG index, Impaired fasting glucose, retrospective cohort study, Chinese elder adults","lastPublishedDoi":"10.21203/rs.3.rs-4413051/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4413051/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe relationship between the triglyceride-glucose (TyG) index and impaired fasting glucose (IFG) in elderly individuals remains uncertain. Our study aimed to explore the association between the TyG index and the risk of future IFG in this population. This retrospective cohort study included 17,746 elderly individuals over 60. In this population, Cox regression models proportional to hazards, along with smooth curve fitting and cubic spline functions, were employed to examine the association between the baseline TyG index and the risk of IFG. Subgroup analyses and sensitivity were also performed to ensure the robustness of the study findings. After adjusting for covariates, a positive relationship between the TyG index and the risk of IFG was found (HR\u0026thinsp;=\u0026thinsp;1.43, 95% CI: 1.27\u0026ndash;1.60, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The likelihood of IFG rose steadily as the TyG index quartiles (from Q1 to Q4) increased, with Q4 demonstrating a 62% elevated risk compared to Q1 (adjusted HR\u0026thinsp;=\u0026thinsp;1.62, 95% CI: 1.37\u0026ndash;1.90). Additionally, we found the association between TyG index and risk of IFG was a linear. Sensitivity and subgroup analyses confirmed the stability of the results. Our study observed a linear association between the TyG index and the development of IFG in elderly Chinese individuals. Recognizing this relationship can help clinicians identify high-risk individuals and implement targeted interventions to reduce their risk of progressing to diabetes.\u003c/p\u003e","manuscriptTitle":"Triglyceride-glucose index is associated with the risk of impaired fasting glucose: A 5-year retrospective cohort study in Chinese elderly people","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-06 15:50:40","doi":"10.21203/rs.3.rs-4413051/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-13T01:42:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-11T18:14:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264755537915932468193782616544370155014","date":"2024-06-11T14:21:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-28T02:31:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147354673925411141234419699715235601415","date":"2024-05-27T07:24:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-25T10:00:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-25T09:52:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-21T06:24:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-21T06:21:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-05-13T11:54:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"699375dd-8506-41db-9095-e34b1e6c8a54","owner":[],"postedDate":"June 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":32601250,"name":"Health sciences/Endocrinology"},{"id":32601251,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-07-08T05:15:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-06 15:50:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4413051","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4413051","identity":"rs-4413051","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}