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Association between the atherogenic index of plasma and long-term risk of type 2 diabetes: a 12-year cohort study based on the Japanese population

Cardiovascular Diabetology volume 24, Article number: 50 (2025) Cite this article

Abstract

Background

Atherosclerotic dyslipidemia is associated with an increased risk of type 2 diabetes (T2D). Although previous studies have demonstrated an association between the atherogenic index of plasma (AIP) and insulin resistance, there remains a scarcity of large cohort studies investigating the association between AIP and the long-term risk of T2D in the general population. This study aims to investigate the potential association between AIP and the long-term risk of T2D in individuals with normal fasting plasma glucose levels.

Methods

This retrospective cohort study included 15,453 participants. The AIP was calculated using the formula log [triglyceride (mmol/L)/high-density lipoprotein cholesterol (mmol/L)]. Cox proportional hazard regression models were employed to assess the association between AIP and T2D risk. The nonlinear association was examined using a restricted cubic spline (RCS) model.

Results

During an average follow-up period of 6.05 years, 373 participants developed T2D. After adjusting for confounding factors, elevated AIP was independently associated with an increased risk of developing T2D (HR 1.763, 95%CI 1.210–2.568, P = 0.003). The RCS analysis revealed a J-shaped association between AIP and T2D risk, with a sharp increase in risk when AIP levels exceeded − 0.268. Moreover, time-dependent receiver operating characteristic analysis consistently demonstrated a moderate predictability of AIP for new-onset T2D within 1 to 12 years.

Conclusion

The AIP exhibits a J-shaped association with the risk of developing T2D. Therefore, maintaining AIP levels below a certain threshold (-0.268) might help prevent the onset of T2D.

Graphical abstract

Introduction

Type 2 diabetes (T2D) is a prevalent chronic metabolic disease that poses a growing global health concern. According to the International Diabetes Federation, the global number of adults with diabetes reached 537 million in 2021, with projections indicating an increase to 783 million by 2045 [1]. T2D significantly amplifies the risk of cardiovascular disease, renal insufficiency, retinopathy, and dementia [2,3,4,5]. Consequently, early identification and intervention for individuals at high risk of developing T2D are crucial from a public health perspective.

T2D is commonly associated with dyslipidemia, characterized by hypertriglyceridemia, decreased high-density lipoprotein cholesterol (HDL-C), and the presence of small, dense low-density lipoprotein cholesterol (sdLDL-C) particles [6]. These sdLDL-C particles, defined as low-density lipoprotein cholesterol (LDL-C) with a density of > 1.034 g/mL and an average diameter of < 25.5 nm, possess unique physical and chemical properties that enhance their atherogenic potential compared to other LDL-C subclasses [7]. Hypertriglyceridemia impairs glucose utilization by reducing the number and activity of insulin receptors on adipocytes. This reduction competes with glucose for cellular entry, leading to compensatory hyperinsulinemia [8]. Additionally, HDL-C, a stable protein complex with multiple critical functions, including acute phase response, protease inhibition, complement activation, and lipid metabolism, also plays a significant role in glucose regulation [9]. HDL-C influences insulin secretion from pancreatic β cells and modulates glucose uptake in skeletal muscle [10,11,12]. Reduced HDL-C levels are associated with decreased insulin sensitivity and secretion, which in turn impairs β cell function and results in insulin resistance (IR) [13]. Moreover, IR contributes to increased triglyceride (TG) and plasma-free fatty acid levels, while concurrently reducing HDL-C [14]. This interplay of factors supports the hypothesis of a “vicious cycle”, where dyslipidemia, IR, and hyperinsulinemia mutually reinforce each other, ultimately promoting T2D development [15].

The atherogenic index of plasma (AIP), defined as the logarithmic ratio of TG to HDL-C, was first proposed by Dobiásová [16]. The AIP is inversely proportional to the LDL-C particle size, making it an effective substitute for sdLDL-C particle size. Given its comprehensive reflection of dyslipidemia and its ability to capture lipoprotein-driven atherosclerosis, AIP has proven to be a valuable tool for assessing the risk of cardiovascular disease [17], stroke [18], diabetic kidney disease [19], metabolic syndrome [20, 21], depression [22, 23], and mortality [24]. Additionally, AIP has been shown to indicate the severity of IR and exhibits a nonlinear positive association with prediabetes risk [25, 26]. Accumulating evidence further underscores a robust association between AIP and the risk of T2D. A meta-analysis involving 4010 individuals reported that high AIP value significantly increased T2D risk and AIP outperformed other lipid parameters in predicting T2D risk [27]. However, it is noteworthy that most research has been confined to cross-sectional studies [28,29,30]. To date, only a limited number of cohort studies have focused on high-risk populations, specifically individuals with prediabetes and middle-aged to elderly adults [31,32,33]. For the general population, particularly those with normal baseline fasting plasma glucose (FPG) levels, cohort studies investigating the association between AIP and the long-term risk of T2D are remarkably scarce. Consequently, we conducted a comprehensive analysis of data obtained from a large-scale, long-term cohort study, aiming to elucidate the role of AIP in predicting the long-term risk of T2D among individuals with normal FPG levels and to establish corresponding risk thresholds.

Method

Study population

Data for this study were obtained from the nonalcoholic fatty liver disease (NAFLD) in the Gifu Area, Longitudinal Analysis (NAGALA) database, available on the Dryad Data Platform (https://datadryad.org/). The Gifu area, situated in central Japan, has a population density and economic scale similar to the national average, rendering it a suitable model for population-based research in Japan. The NAGALA study is a population-based cohort study conducted at Murakami Memorial Hospital in Gifu Prefecture, Japan, from 1994 to 2016. It was designed to detect chronic diseases such as T2D and identify risk factors for public health promotion. Participants underwent one to two physical examinations annually. In the initial study, Okamura T et al. [34] extracted medical data from 20,944 participants (12498 males and 8446 females). The exclusion criteria were as follows: (1) missing data (n = 863); (2) known liver disease (except NAFLD) (n = 416); (3) excessive alcohol consumption at baseline (over 420 g/week for males and 280 g/week for females) (n = 739); (4) medication usage at baseline (n = 2321); (5) a baseline diabetes diagnosis or FPG of > 6.1 mmol/L (n = 1131); and (6) unexplained withdrawal from the study (n = 10). Consequently, a cohort of 15,464 participants was established for the initial study. This study is a secondary analysis of the NAGALA study, specifically investigating the association between AIP and T2D risk. Participants with an FPG of 6.1 mmol/L (n = 0) and those lacking TG or HDL-C data (n = 11) were further excluded, resulting in a final sample of 15,453 participants with normal FPG levels [35] (Fig. 1). According to the service terms of the Dryad database, this dataset is available for analysis to explore new research hypotheses.

Fig. 1
figure 1

Flowchart of the study population

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Data collection

Physical examination personnel conducted data collection and measurements. A questionnaire survey was administered to gather basic demographic information, including sex, age, smoking status (categorized as never, past, and current smokers), alcohol consumption levels, and exercise habits (defined as engaging in any form of physical activity at least once a week). Standard methods were utilized to measure participants’ height, weight, systolic and diastolic blood pressure (SBP/DBP), and waist circumference (WC). Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (kg/m²). NAFLD diagnosis was confirmed via abdominal ultrasound examinations performed by gastroenterologists. Forearm venous blood samples were collected from participants who had fasted for at least 8 h. Biochemical parameters, including aspartate aminotransferase (AST), alanine aminotransferase (ALT), γ-glutamate transferase (GGT), TG, total cholesterol (TC), HDL-C, FPG, and glycated hemoglobin A1c (HbA1c), were measured using an automated biochemical analyzer. AIP was calculated using the formula log [TG (mmol/L) / HDL-C (mmol/L)].

Outcome

The primary outcome of this study was the incidence rate of T2D. T2D was defined based on the diagnostic criteria established by the American Diabetes Association, which encompass an FPG level of ≥ 7.0 mmol/L, an HbA1c level of ≥ 6.5% measured during follow-up, or self-reported T2D [36].

Statistical analyses

Statistical analyses were performed using IBM SPSS Statistics software for Mac (Version 26.0, SPSS Inc. Chicago IL, USA), EmpowerStats (http://www.empowerstats.com, X&Y Solutions, Inc., Boston, MA), and R version 4.2.0. Normally distributed data were presented as means ± standard deviations, and skewed data were presented as medians with interquartile ranges. Continuous variables were compared between groups using t-tests, one-way analysis of variance, or rank-sum tests. Categorical variables were reported as frequencies (%), and intergroup comparisons were performed using chi-square tests. Additionally, due to the substantial difference in the number of participants with and without T2D, standardized differences (SDs) between the two groups were added. SD was calculated as the differences in means or proportions divided by the pooled estimate of the standard deviation [37]. SD > 0.1 was generally considered clinically significant.

Initially, Pearson’s or Spearman’s rank correlation coefficient analyses were conducted to assess the correlation between AIP and other clinical risk factors. Kaplan-Meier curves were generated to visually assess time-related events, and comparisons were made using the log-rank test. The variance inflation factor (VIF) was calculated to evaluate multicollinearity among variables. Covariables with a VIF of > 5 were considered collinear and excluded from the multivariable Cox proportional hazards regression models.

Following the Strengthening the Reporting of Observational Studies in Epidemiology guidelines, four sequential multivariable Cox proportional hazards regression models were developed to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for evaluating the association between AIP and T2D risk. Model 1 adjusted for age and sex. Model 2 built upon Model 1 by additionally adjusting for drinking status, smoking status, and exercise habits. Model 3 added BMI, NAFLD, and SBP to the variables adjusted for in Model 2. Model 4 adjusted for all noncollinear covariables, including age, sex, BMI, SBP, drinking status, smoking status, exercise habits, NAFLD, ALT, AST, GGT, TC, FPG, and HbA1c. The proportional hazards assumption underwent testing through the Schoenfeld residuals test for continuous variables and visual inspection for categorical variables (Supplementary Fig. 1). The E value was employed to investigate the likelihood of unmeasured confounding factors influencing the association between AIP and T2D risk.

The nonlinear association between AIP and T2D risk was investigated using Cox proportional hazards regression models and restricted cubic spline (RCS) analysis with four knots (at the 5th, 35th, 65th, and 95th percentiles). A two-piecewise Cox regression model and recursive algorithm were applied to identify the inflection point on the regression curve. Subgroup analyses were conducted to examine the interaction effects of confounding categorical variables, including age, sex, BMI, WC, SBP, DBP, smoking status, drinking status, exercise habits, and NAFLD.

Furthermore, time-dependent receiver operating characteristic (ROC) curve analysis was used to evaluate the predictability of AIP for T2D at various time points ranging from 1 to 12 years. The area under the ROC curve (AUC), optimal threshold, sensitivity, and specificity were calculated to assess how time affects the ability of AIP to predict T2D risk. Statistical significance was defined as a two-tailed P value of < 0.05.

Results

Baseline characteristics and clinical outcomes

This study included 15,453 participants, with a mean age of 43.71 ± 8.90 years, of whom 54.48% were males. Over an average follow-up period of 6.05 years, 373 individuals (2.41%) developed T2D. Table 1 displays the baseline characteristics of the study population, categorized by T2D diagnosis. Compared to the non-T2D group, the T2D group had significantly higher levels of age, BMI, WC, SBP, DBP, ALT, AST, GGT, TG, TC, FPG, HbA1c, and AIP (P < 0.001, SD > 0.1). Additionally, participants in the T2D group were more likely to be males, current smokers, drinkers (> 140 g/week), and irregular exercisers, and had a higher prevalence of NAFLD compared to those without T2D (P < 0.05, SD > 0.1). Conversely, the HDL-C levels were lower in the T2D group (P < 0.001, SD > 0.1). Participants were further divided into four groups based on AIP quartiles. Supplementary Table 1 demonstrates statistically significant differences in the aforementioned baseline variables across these groups. Importantly, the incidence of T2D progressively increased across higher AIP quartiles (0.57%, 1.22%, 2.12%, and 5.75%, respectively; P < 0.001). These findings were corroborated by the Kaplan-Meier curve for cumulative incidence (Fig. 2).

Fig. 2
figure 2

Kaplan-Meier curve of the cumulative incidence of T2D by AIP level categories

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Table 1 Baseline characteristics of all participants
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T2D: Type 2 diabetes; BMI: Body mass index; WC: Waist circumference; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; NAFLD: Nonalcoholic fatty liver disease; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; GGT: γ-glutamate transferase; TG: Triglyceride; TC: Total cholesterol; HDL-C: High-density lipoprotein cholesterol; FPG: Fasting plasma glucose; HbA1c: Glycated hemoglobin A1c; AIP: Atherogenic index of plasma.

Correlation between AIP and clinical indicators

The results of Pearson’s and Spearman’s rank analyses, as shown in Supplementary Table 2, demonstrated that the AIP was positively correlated with several established risk factors for T2D. These factors included age, BMI, WC, SBP, DBP, ALT, AST, GGT, TG, TC, FPG, and HbA1c. Conversely, AIP was found to be negatively correlated with HDL-C.

Cox regression analysis of the association between AIP and T2D risk

Supplementary Fig. 2 presents the results of the univariable Cox regression analysis, identifying various risk factors associated with T2D. These risk factors included AIP, age, BMI, WC, SBP, DBP, smoking, drinking, NAFLD, ALT, AST, GGT, TG, TC, FPG, and HbA1c. In contrast, HDL-C and exercise habits were identified as protective factors against T2D. Collinearity analysis (Supplementary Fig. 3) revealed that DBP exhibited high collinearity, with a VIF of 5.71. Moreover, a high correlation was observed between BMI and WC, with VIF values nearing 5. As a result, DBP and WC were excluded from the multivariable analysis. The multivariable analysis was employed to assess the independent effects of AIP on T2D risk (Table 2). In Model 1, which was adjusted for age and sex, AIP demonstrated a robust association with an increased T2D risk (HR 8.565, 95%CI 6.207–11.818, P < 0.001). This association remained robust in Model 2 (HR 7.984, 95%CI 5.751–11.083, P < 0.001), which included additional adjustments for smoking status, drinking status, and exercise habits. In Model 3, after additionally adjusting for BMI, SBP, and NAFLD, the association between AIP and T2D risk persisted (HR 2.566, 95%CI 1.764–3.732, P < 0.001). Finally, in Model 4, which accounted for all noncollinear variables (including age, sex, BMI, SBP, drinking status, smoking status, exercise habits, NAFLD, ALT, AST, GGT, TC, FPG, and HbA1c), AIP remained a significant independent risk factor for T2D (HR 1.763, 95%CI 1.210–2.568, P = 0.003). The E value was calculated to quantify the potential impact of unmeasured confounding factors. The E value of 2.92 indicated that the influence of unmeasured confounding factors on the association between AIP and T2D risk was negligible.

Table 2 Multivariable Cox proportional hazards regression analysis of the association between AIP and T2D risk
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Nonlinear association between AIP and T2D risk

A two-piecewise Cox regression model and RCS analysis were employed to investigate the nonlinearity between AIP and T2D risk. The adjusted smooth curve, depicted in Fig. 3, revealed a J-shaped association between AIP and T2D risk. The inflection point occurred at an AIP value of -0.268, as presented in Supplementary Table 3. Below this threshold, (AIP < -0.268), AIP exhibited a negative, albeit not statistically significant, association with T2D risk (HR 0.589, 95%CI 0.196–1.772, P = 0.347). Conversely, when AIP was ≥ -0.268, a significant positive association with T2D risk was observed (HR 2.250, 95%CI 1.447–3.498, P < 0.001).

Subgroup analyses

Further estimation of the association between AIP and T2D risk was performed in various subgroups of the population, according to age, sex, BMI, WC, SBP, DBP, drinking status, smoking status, exercise habits, and NAFLD. As shown in Fig. 4, no significant interactions were detected between these stratified variables and AIP, indicating that these variables did not significantly alter the association between AIP and T2D risk.

Fig. 3
figure 3

A nonlinear association was shown between the AIP and T2D risk. Adjusted for confounding factors including age, sex, BMI, SBP, drinking status, smoking status, exercise habits, NAFLD, ALT, AST, GGT, TC, FPG, and HbA1c

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Performance of AIP in predicting T2D

Time-dependent ROC analysis was performed to evaluate the predictability of the AIP for T2D across a follow-up period ranging from 1 to 12 years. As shown in Supplementary Tables 4 and Fig. 5, the optimal thresholds for identifying T2D varied between 0.321 and 0.399, with AUC values remaining relatively consistent between 0.701 and 0.746. The highest AUC was observed at the 7-year follow-up, with a specificity of 62.55% and a sensitivity of 75.29%. These findings emphasized the robust predictability of AIP in assessing future T2D risk. Furthermore, AIP demonstrated higher AUC values in certain subgroups, including females (0.780 vs. 0.694, P = 0.004), individuals with a BMI of < 25 kg/m2 (0.715 vs. 0.649, P = 0.022), those with an SBP of < 140 mmHg (0.746 vs. 0.644, P = 0.021), and those without NAFLD (0.684 vs. 0.607, P = 0.011) (Fig. 6).

Fig. 4
figure 4

Forest plot for the subgroup analyses of the association between AIP and T2D risk. Models adjusted for the same covariables as in model 4, except for the stratification variables

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Fig. 5
figure 5

The recognition ability of AIP for the incidence of T2D at different time points ranging from 1 to 12 years

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Fig. 6
figure 6

ROC curve analysis of the AIP for identifying T2D across different subgroups. A: Age; B: Sex; C: BMI; D: WC; E: SBP; F: DBP; G: Drinking status; H: Smoking status; I: Exercise habits; J: NAFLD

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Discussion

In this large-scale retrospective study, we investigated the significance of AIP as a predictor of T2D risk within a regional population. Our findings demonstrated a nonlinear positive association between AIP and T2D risk, confirming AIP as an independent risk factor. Stratified variables did not significantly alter the association between AIP and T2D risk. Notably, this study is the first to establish stable AUC values and optimal thresholds for AIP in predicting T2D risk over 1–12 years, positioning AIP as a promising biomarker for long-term T2D risk assessment.

T2D remains a significant global health challenge, with approximately 6.7 million adults worldwide succumbing to diabetes or its complications in 2021 alone [1]. This underscores the urgent need for effective prognostic and diagnostic markers to improve preventive care in high-risk populations. Although a few cohort studies have assessed the association between AIP and T2D risk, particularly in individuals with prediabetes and middle-aged to elderly adults [31,32,33], there is currently limited evidence supporting the predictability of AIP for long-term T2D risk in individuals with normal FPG levels. Therefore, we conducted a comprehensive analysis of the NAGALA study data. Our findings underscored that elevated AIP could work as an independent risk factor for new-onset T2D. Specifically, after adjusting for traditional risk markers such as FPG and HbA1c, we found that each unit rise in AIP was associated with a 76.3% increased probability of developing T2D. A nine-year cohort study conducted in Taiwan has demonstrated a strong association between AIP and T2D risk among individuals aged 40 to 64 [38]. Compared with this research, our study excluded participants with impaired FPG and extended the follow-up period. We delved deeper into the nonlinear association between AIP and T2D risk, offering fresh perspectives on the AUC values and optimal thresholds of AIP in predicting T2D risk within 1–12 years. These discoveries hold substantial significance for long-term T2D risk assessment and monitoring. Furthermore, our study revealed a J-shaped association between AIP and T2D risk, with a notable surge in T2D risk observed when AIP levels surpass − 0.268. This finding is consistent with previously reported nonlinear associations in prediabetic and obese populations [29, 32]. This nonlinear association might result from multiple factors, including nonlinear conversion calculation methods, the complexity of lipid interactions, and individual differences. However, the inflection points in those studies were − 0.07 and 0.17, respectively, differing from our results. This discrepancy may stem from our study’s focus on individuals with normal FPG, leading to generally lower AIP levels. Additionally, Qian et al. [31] investigated the influence of longitudinal AIP changes on T2D progression. Their findings indicated that individuals with persistent high AIP levels or those experiencing transitions from high to low or low to high AIP levels had approximately 1.5 times the risk of developing T2D compared to those maintaining low AIP levels. Taken together, these findings emphasize the potential of maintaining AIP levels below a specific threshold as a preventive strategy for T2D.

AIP, which integrates TG and HDL-C, serves as a comprehensive indicator for assessing T2D risk. Elevated AIP typically indicates an increase in TG levels and/or a decrease in HDL-C levels. Previous research has demonstrated that fibrates, which effectively lower TG levels, can reduce lipotoxicity, improve peripheral tissue IR, and protect pancreatic β-cell function, thereby mitigating the onset of T2D [39, 40]. Additionally, increasing HDL-C levels has been proposed as a therapeutic approach to reduce T2D risk [41]. A double-blind, placebo-controlled crossover study further demonstrated that intravenous infusion of recombinant HDL-C can effectively lower blood glucose levels in T2D patients by augmenting plasma insulin levels and activating AMP-activated protein kinase in skeletal muscle [12]. These findings underscore the role of therapeutic strategies aimed at reducing TG levels and/or increasing HDL-C levels to decrease AIP in the prevention and treatment of T2D.

BMI and WC are widely recognized as effective measures of overweight and obesity at the population level and they are considered primary risk factors for T2D. Interestingly, the ROC analyses in our study revealed that AIP demonstrated higher AUC values in individuals with a BMI of < 25 kg/m2 than in individuals with a BMI of ≥ 25 kg/m2. This pattern aligned with findings from other studies. For instance, Yi et al. [42] reported that AIP had predictive value for adverse cardiovascular outcomes solely in individuals with acute coronary syndrome and a BMI of < 24 kg/m2. Another study found a significant association between AIP and in-stent restenosis exclusively in patients with a BMI of < 25 kg/m2 [43]. Moreover, the association between AIP and hyperinsulinemia was stronger in non-obese populations compared to obese populations [44]. These observations suggest that there may be other obesity related mechanisms that transcend the role of AIP in the development of T2D in obese individuals. Specifically, when compared to nonobese counterparts, obese individuals demonstrate perturbations in adipocyte function, which subsequently promote the infiltration of proinflammatory macrophages, culminating in chronic low-grade inflammation within adipose tissue [45, 46]. Furthermore, obesity disrupts the endocrine function of adipose tissue, leading to an augmented release of adipokines, notably tumor necrosis factor-α and interleukin-6 [47]. These perturbations may disrupt insulin signaling pathways via diverse mechanisms or further stimulate and amplify the activation of other inflammatory cascades [48]. Notably, obesity-associated metabolites, particularly elevated glucose levels and nonesterified fatty acids, exert inhibitory effects on insulin receptor activation and their downstream signaling axis, thereby eliciting endoplasmic reticulum stress and oxidative stress responses in insulin-sensitive tissues and ultimately expediting the progression of T2D [49]. Inflammation and dyslipidemia are interrelated biological processes that occur early in IR and persist over time [50, 51]. For example, a study on middle-aged and elderly Turkish individuals found a positive association between AIP and C-reactive protein (CRP) levels [52]. Lan et al. [53] demonstrated, through unique temporal analysis, a significant interaction between AIP and high-sensitivity CRP associated with developing T2D, indicating that inflammation has a more substantial impact on future AIP changes than the reverse. A recent study has shown that inflammatory cells such as neutrophils, white blood cells, and monocytes mediate 4.66%, 4.16%, and 1.93% of the association between AIP and T2D risk in overweight and obese populations [29].

Previous studies have highlighted a sex-specific association between AIP and T2D risk [54, 55]. Our study found that elevated AIP levels were associated with an increased risk of T2D predominantly in males, with no significant association observed in females. This suggests that other mediators, such as estrogen, might play a more prominent role in females, mitigating the impact of AIP on T2D risk [56]. Furthermore, our study revealed a novel finding: the association between AIP and T2D risk only existed in individuals without exercise habits. Although the underlying mechanisms for this observation remain unclear, our results indicate that enhancing lipid management based on AIP values in individuals without exercise habits could significantly reduce their residual risk of developing T2D. Overall, our findings provide valuable insights into the clinical utility of AIP as a risk marker for T2D and have implications for public health strategies aimed at T2D prevention.

This study offers several notable advantages. First, it is a large-scale cohort study with rigorous control measures and adherence to a strict protocol, ensuring data validity and reliability. The 12-year follow-up period provides robust evidence for the findings. Second, the data analysis was carefully adjusted for potential confounding factors and included subgroup analyses based on multiple categorical variables, enhancing the reliability of the research findings. Moreover, this study is the first to demonstrate that AIP is suitable for predicting long-term T2D risk in individuals with normal FPG levels through the application of time-dependent ROC analysis.

However, several limitations must be acknowledged in this study. First, the definition of T2D used in this study did not include oral glucose tolerance testing, which might have underestimated T2D incidence. Second, the absence of insulin data precluded the evaluation of IR. To mitigate this limitation, NAFLD, a well-known risk factor for T2D, was adjusted. Third, due to the lack of repeated AIP measurements, the impact of longitudinal dynamic changes in AIP on T2D risk could not be assessed. Fourth, the absence of data records on inflammatory markers such as CRP, interleukin-6, and procalcitonin in the current database precluded a systematic elucidation of the association between inflammation and AIP. Fifth, the lack of data on lipid-lowering therapy prevents stratified analysis to assess its impact on the association between AIP and T2D risk. Finally, given the significant variations in demographics, socioeconomic factors, and genetic profiles across different regions, caution is warranted when generalizing our findings to other regions and populations, as the study focused on the general population in the Gifu region of Japan.

Conclusion

Our study provides compelling evidence of a strong association between AIP and long-term risk of T2D in individuals with normal FPG levels. These findings highlight the clinical significance of AIP in identifying individuals at risk for T2D and guiding preventive strategies. However, additional research is required to fully elucidate the underlying mechanisms linking AIP to T2D risk.

Availability of data and materials

Data for this study are available online on the Dryad Data Platform (https://datadryad.org/stash/dataset/doi:https://doiorg.publicaciones.saludcastillayleon.es/10.5061/dryad.8q0p192 ).

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Acknowledgements

We sincerely express our gratitude to Bullet Edits Limited for the linguistic editing of the manuscript.

Funding

This study was supported by Jiangsu Provincial Medical Key Discipline (Laboratory) (ZDXK202207).

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Authors and Affiliations

  1. Department of Cardiology, Zhongda Hospital, Southeast University, No. 87 Dingjiaqiao, Nanjing, 210009, Jiangsu, China

    Qianxing Zhou & Mingkang Li

  2. Reproductive Medical Center, Hainan Women and Children’s Medical Center, Haikou, 570206, Hainan, China

    Yamei Wu

  3. School of Medicine, Southeast University, Nanjing, Jiangsu, China

    Mingkang Li

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Contributions

Qianxing Zhou, Yamei Wu, and Mingkang Li contributed to the conception and design of the study, advised on all statistical aspects, and interpreted the data. Qianxing Zhou and Yamei Wu analyzed and interpreted the data, and drafted the manuscript. Mingkang Li assisted with data analysis, interpreted the data, and revised the manuscript. All authors critically reviewed the manuscript and approved the final draft for submission, with final responsibility for publication.

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Correspondence to Mingkang Li.

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The NAGALA study was approved by the Ethics Committee of Murakami Memorial Hospital, and informed consent was obtained from all participants. Ethical review and approval were waived for this study because no additional institutional review board approval was required for the secondary analysis.

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Zhou, Q., Wu, Y. & Li, M. Association between the atherogenic index of plasma and long-term risk of type 2 diabetes: a 12-year cohort study based on the Japanese population. Cardiovasc Diabetol 24, 50 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12933-025-02605-y

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Keywords

Cardiovascular Diabetology

ISSN: 1475-2840