You are viewing the site in preview mode

Skip to main content
Download PDF
Download ePub

Atherogenic lipid parameters in people with normal glucose tolerance: implications from elevated 1-hour post-load plasma glucose

Cardiovascular Diabetology volume 24, Article number: 207 (2025) Cite this article

Abstract

Background

Existing evidence suggests that elevated 1-hour post-load plasma glucose (1-h PG ≥ 8.6 mmol/L) during an oral glucose tolerance test (OGTT) is associated with atherogenic lipid parameters which are linked to an increased risk of cardiovascular disease (CVD). However, it remains unclear whether normal glucose tolerance (NGT) individuals with elevated 1-h PG (NGT-1hPG-high) should still be considered low-risk. Therefore, this study aims to demonstrate comprehensive lipid characteristics in individuals with different glycemic status stratified by 1-h PG, with a particular focus on those with NGT-1hPG-high.

Methods

This cross-sectional study included individuals aged 25–55 years with high-risk of diabetes from the Daqing Diabetes Prevention Study II (Daqing DPS-II). Individuals were categorized into different glycemic status based on the World Health Organization’s 1999 criteria and the International Diabetes Federation’s 2024 position statement on 1-h PG. Traditional (TC, TG, HDL-C, LDL-C) and non-traditional lipid parameters [ApoA-1, ApoB, sdLDL-C, Lp(a), non-HDL-C, remnant cholesterol (RC), ApoB/ApoA-1, LDL-C/ApoB] were measured. Dyslipidemia was defined according to the 2023 Chinese Guidelines for Lipid Management. The China-PAR equation was used to estimate 10-year CVD risk. Spearman’s correlation coefficients were calculated to evaluate the correlation between lipid parameters and 10-year CVD risk. Logistic and multiple linear regression models were performed to assess the association between 1-h PG and dyslipidemia as well as lipid parameters adjusting for covariates.

Results

Among 2 469 individuals, 22.7% had NGT with normal 1-h PG (NGT-1hPG-normal), 19.9% had NGT-1hPG-high, 2.6% had prediabetes with normal 1-h PG (PDM-1hPG-normal), 34.2% had prediabetes with elevated 1-h PG (PDM-1hPG-high), and 20.6% had newly diagnosed diabetes. The prevalence of dyslipidemia did not significantly differ between NGT-1hPG-high and PDM-1hPG-high (OR = 1.13, 95%CI: 0.88–1.44, P > 0.05). Higher 1-h PG levels were consistently associated with an atherogenic lipid profile, characterized by increased TC, TG, LDL-C, ApoB, sdLDL-C, non-HDL-C, RC and ApoB/ApoA-1, along with decreased ApoA-1, HDL-C and LDL-C/ApoB (all P < 0.05). Among lipid parameters, TG, sdLDL-C, RC, ApoB/ApoA-1, LDL-C/ApoB and HDL-C showed the strongest correlation with 10-year CVD risk, with Spearman’s correlation coefficients of 0.41, 0.38, 0.35, 0.31, − 0.37 and − 0.36, respectively. In the NGT-1hPG-high, TG, sdLDL-C, and ApoB/ApoA-1 levels were significantly higher, while HDL-C and LDL-C/ApoB levels were significantly lower compared to counterparts with NGT-1hPG-normal (all P < 0.05). Moreover, except for TG and RC (both P < 0.01), the majority of lipid parameter levels in NGT-1hPG-high did not significantly differ from those in PDM (all P > 0.05).

Conclusions

NGT-1hPG-high exhibited a similar atherogenic lipid profile to that observed in PDM. 1-h PG could serve as a potential indicator for the early identification of at-risk individuals who may otherwise go undetected among NGT population.

Graphical Abstract

Introduction

The prevalence of diabetes mellitus (DM) and prediabetes (PDM) has been rising rapidly worldwide, placing an increasing burden on global healthcare systems. The International Diabetes Federation (IDF) estimates that by 2045, the number of DM and PDM in adults aged 20–79 years will reach 783.7 million (11.2%) and 1.2 billion (17.5%), respectively [1]. Individuals with DM and PDM, or even early-stage of hyperglycemia often experience dyslipidemia, a well-established risk factor for atherosclerosis that significantly increases the risk of heart attacks, strokes, and other cardiovascular diseases (CVD) [2, 3].

To counteract the rising prevalence of DM, early identification and precise stratification of high-risk population of DM are crucial, as they allow timely interventions to manage glucose and lipid levels, and delay DM progression as well as reduce the risk of associated cardiovascular complications [4]. Recent studies have highlighted the potential of 1-hour post-load plasma glucose (1-h PG) during an oral glucose tolerance test (OGTT) as a predictor of DM progression [5]. The IDF’s 2024 position statement underscored that current glucose tests may be inadequate. Even among individuals with normal glucose tolerance (NGT), those with 1-h PG levels exceeding 8.6 mmol/L face a significantly higher risk of developing DM, DM related CVD and mortality [4].

Despite the growing recognition of 1-h PG as a valuable biomarker, existing studies on cardiometabolic characteristics have primarily focused on individuals with DM and PDM rather than those with NGT but elevated 1-h PG (NGT-1hPG-high). Existing study has shown that PDM is associated with lower high-density lipoprotein-cholesterol (HDL-C), higher total cholesterol (TC), low-density lipoprotein-cholesterol (LDL-C), apolipoprotein B (ApoB) and triglycerides (TG) levels compared to NGT individuals [6]. These lipid abnormalities are even more pronounced in people with DM [7, 8]. Although a few studies have explored the association between 1-h PG and traditional lipid parameters (TG, TC, HDL-C and LDL-C) [9], further study is needed into non-traditional lipid parameters. Apolipoprotein, lipoprotein(a) [Lp(a)], small dense low-density lipoprotein cholesterol (sdLDL-C), non-HDL-C, remnant cholesterol (RC), ApoB/apolipoprotein A-1 (ApoA-1) and LDL-C/ApoB are critical in understanding atherogenesis and have gained increasing attention in recent research due to their associations with CVD [10,11,12]. For instance, sdLDL-C is a subtype of LDL-C whose particles are smaller and denser than typical LDL-C, enabling them to penetrate the arterial wall more easily and sustain oxidation, which promotes atherosclerotic plaque formation [13]. The LDL-C/ApoB ratio also reflects small LDL particle size and can predict CVD and all-cause mortality in the general population [14]. The ApoB/ApoA-1 ratio serves as a potential marker of plasma atherogenicity, assessing the balance between atherogenic and protective lipid factors [15]. However, evidence regarding these non-traditional lipid parameters in different glycemic status stratified by 1-h PG remains limited.

Therefore, this study aims to provide a comprehensive analysis of lipid characteristics in individuals with different glycemic status defined by 1-h PG. Particular attention is given to NGT-1hPG-high individuals to determine whether they should still be considered low-risk.

Materials and methods

Study design and participants

Data was obtained from the screening stage of “Daqing Diabetes Prevention Study II (Daqing DPS-II)”, which included high-risk population screening stage and lifestyle intervention stage. The study was conducted in Daqing, Heilongjiang province in the northeast of China. We used 2023 health checkup data and an e-questionnaire for high-risk assessment to recruit employees aged 25–55 years from 8 oilfield factories. Those who met any one of the following conditions were considered high-risk: venous fasting plasma glucose (FPG) 5.6–6.9 mmol/L in 2023 health checkup data; capillary FPG 5.6–6.9 mmol/L; random capillary blood glucose 7.8–11.0 mmol/L; overweight or obese [body mass index (BMI) ≥ 24 kg/m2]; family history of DM. For future lifestyle interventions, subjects were excluded if they met any one of the following exclusion criteria: self-reported presence of DM or regular use of hypoglycemic agents; persistent uncontrolled severe hypertension; pregnancy, lactation, or planned pregnancy within the next 36 months; severe diseases related to glucose metabolism disorder; active cancer, recent cancer treatment (within the past 6 months) or other conditions deemed inappropriate for participation by the researchers (Figure S1).

Eligible individuals were invited to undergo an OGTT, complete anthropometric measurements, and participate in a face-to-face structured questionnaire in the screening stage, which took place between August, 2023 and January, 2024. The study enrolled 2 492 individuals and data points with missing values were excluded. Ultimately, 2 469 (99.1%) subjects were included in the final analysis. Written informed consent was obtained from each individual. This study was approved by the Institutional Ethics Committee of the Chinese Academy of Medical Sciences & Peking Union Medical College (CAMS&PUMC-IEC-2022-061).

Glucose and lipid parameters measurement and 10-year CVD risk calculation

After fasting for at least 8 h, fasting blood samples were collected for examining FPG and other biochemical parameters. A 75 g OGTT was then performed, with additional blood samples taken at 60 and 120 min to assess post-load glucose and insulin tests. Plasma glucose was measured using the hexokinase method (Roche cobas® c701 analyzers, Germany), while insulin was measured using immunoradiometric assay kit (DiaSource® INS-IRMA, Belgium). Lipid parameters including TC, TG, HDL-C, LDL-C, sdLDL-C, Lp(a), ApoA-1 and ApoB were measured using enzymatic methods (Roche cobas® c701 analyzers, Germany). The following lipid indices were calculated: non-HDL-C = TC– HDL-C; RC = TC– HDL-C– LDL-C; ApoB/ApoA-1; LDL-C/ApoB. The 10-year CVD risk was calculated using the Prediction for Atherosclerotic cardiovascular disease Risk in China (China-PAR) equation as recommended by the Guideline on the assessment and management of cardiovascular risk in China [16, 17]. The gender-specific China-PAR equation was developed and validated using data from 4 large, contemporary, population-based Chinese cohorts. It has demonstrated excellent performance in predicting atherosclerotic CVD risk with good internal consistency and external validation [16]. The equation accounts for sociodemographic, anthropometric, biochemical, lifestyle characteristics and family history, and is adjusted for baseline survival. Insulin resistance was evaluated using homeostasis model assessment of insulin resistance (HOMA-IR), calculated as follows: HOMA-IR = fasting insulin (µU/mL) × FPG (mmol/L)/22.5.

Definition of glycemic status

Based on the World Health Organization (WHO)’s 1999 criteria for the diagnosis of glycemic status and the IDF’s 2024 position statement on 1-h PG for the diagnosis of intermediate hyperglycemia and DM [4, 18], the study population was classified as follows:

  1. (1)

    NGT: FPG < 6.1 mmol/L and 2-h PG < 7.8 mmol/L

    • NGT-1hPG-normal: 1-h PG < 8.6 mmol/L

    • NGT-1hPG-high: 1-h PG ≥ 8.6 mmol/L

  2. (2)

    PDM: FPG 6.1–6.9 mmol/L and/or 2-h PG 7.8–11.0 mmol/L

    • PDM-1hPG-normal: 1-h PG < 8.6 mmol/L

    • PDM-1hPG-high: 1-h PG ≥ 8.6 mmol/L

  3. (3)

    DM: FPG ≥ 7.0 mmol/L and/or 2-h PG ≥ 11.1 mmol/L

Definition of dyslipidemia

Based on the 2023 Chinese guidelines for lipid management [19], individuals were defined as having dyslipidemia if they met either of the following criteria:

  1. (1)

    TC ≥ 6.2 mmol/L or TG ≥ 2.3 mmol/L or LDL-C ≥ 4.1 mmol/L or HDL-C < 1.0 mmol/L.

  2. (2)

    With a history of diagnosed dyslipidemia or the use of lipid-lowering drugs.

Covariates

To adjust for potential confounding factors, we collected a range of covariates. Body height, weight, waist circumference (WC) and blood pressure were measured by trained survey personnel. BMI was calculated as body weight (kg) divided by the square of body height (m2). Blood pressure was measured three times for each individual after 5 minutes’ rest (Omron HEM-7122, Japan), and with the average of the second and third measurements used for analysis.

A questionnaire was used to obtain information on sociodemographic characteristics, disease history and lifestyle information, including diet, physical activity, smoking status and frequency of alcohol consumption. Dietary quality was assessed using the Diet Quality Questionnaire (DQQ), and the Global Diet Recommendation (GDR) score which integrated various healthy and unhealthy food categories was calculated to evaluate individual’s adherence to WHO global dietary recommendations [20,21,22]. Physical activity levels were assessed using the International Physical Activity Questionnaire– Short Form (IPAQ-SF) [23]. Smoking status and alcohol consumption frequency were investigated using a self-compiled questionnaire adapted from the China Chronic Disease and Risk Factor Surveillance survey. Individuals who were currently smoking were considered as current smokers. Those who had consumed alcohol within the past 12 months were considered as current drinkers. Hypertension was defined as an average systolic blood pressure ≥ 140 mmHg, or an average diastolic blood pressure ≥ 90 mmHg, or a self-reported history of hypertension diagnosis in the questionnaire [24]. Hyperuricemia was defined as serum uric acid ≥ 420 µmol/L or a self-reported history of hyperuricemia diagnosis in the questionnaire [25].

Statistical analysis

Data analysis was conducted using R statistical software (version 4.2.2). Categorical variables were described as n (%) and using with the Pearson’s chi-square (χ2) test or Fisher’s exact test. Continuous variables were presented as mean (SD) or median (P25, P75) and tested using one-way ANOVA and Kruskal-Wallis rank sum test, respectively.

Individuals were categorized by glycemic status, or divided into 4 groups based on the number of elevated glucose indicators (FPG, 1-h PG, and 2-h PG). Then, logistic regression was used to estimate the association between glycemic status and dyslipidemia, as well as between the number of elevated glucose indicators and dyslipidemia. The Receiver Operating Characteristics (ROC) curve was employed to evaluate the performance of glucose indicators in identifying the dyslipidemia, with the area under curve (AUC) determined and tested using the DeLong’s test. Subgroup analyses of the association between 1-h PG and dyslipidemia were also performed based on gender, age, hypertension, hyperuricemia, and 10-year CVD risk stratification (Supplementary material).

To identify the most atherogenic lipid parameters, we calculated Spearman’s correlation coefficients between lipid parameters and 10-year CVD risk. Restricted cubic splines were used to examine nonlinear trends between 1-h PG (independent variable) and each lipid parameter (dependent variable) before conducting linear regression analysis. Multiple linear regression or log-log regression (if the distribution was significantly skewed) was applied to assess the correlation between 1-h PG (independent variable) and each of the lipid parameters (dependent variables). Bonferroni post hoc correction was applied for lipid parameter comparisons among different glycemic status. The multivariate analyses mentioned above incorporated several potential confounding factors for model adjustment. Model 1 was a crude model without covariate adjustment. Model 2 was adjusted for age, gender, education and occupation. Model 3 was further adjusted for smoking status, alcohol consumption, GDR score and physical activity level based on Model 2. Model 4 included additional adjustments for BMI, hypertension and use of lipid-lowering drugs (LLDs) based on Model 3. All P values were two-sided with significance level of 0.05.

Results

A total of 2 469 subjects were included in the data analysis, of whom 60.1% were male, with an average age of 45.76 ± 6.20 years. Among the subjects, 560 (22.7%) had NGT-1hPG-normal, 492 (19.9%) had NGT-1hPG-high, 64 (2.6%) had PDM-1hPG-normal, 844 (34.2%) had PDM-1hPG-high, 509 (20.6%) had newly diagnosed DM.

Metabolic characteristics

Table 1 described the sociodemographic, anthropometric, biochemical and lifestyle characteristics of subjects by glycemic status. BMI and WC were higher in DM compared to counterparts with other glycemic status (both P < 0.001). TC, TG, ApoB, sdLDL-C, non-HDL-C, ApoB/ApoA-1, fasting insulin and the HOMA-IR index followed a similar trend, with the highest values observed in those with DM, followed by those with PDM-1hPG-high, PDM-1hPG-normal, NGT-1hPG-high and NGT-1hPG-normal (all P < 0.001). 1-h insulin levels were significantly higher in NGT-1hPG-high compared to others. Education and the 4 main lifestyles (diet, physical activity, smoking status and alcohol consumption) distributions differed significantly among groups and were therefore considered as covariates.

Table 1 Sociodemographic, anthropometric, biochemical and lifestyle characteristics according to glycemic status
Full size table

Individuals were also divided into 4 groups based on the number of elevated glucose indicators (FPG, 1-h PG, and 2-h PG): 0, 1, 2, and 3 elevated indicators (Table S1). As the number of elevated glucose indicators increased, the prevalence of dyslipidemia rose accordingly (P < 0.001). TC, TG, ApoB, sdLDL-C, non-HDL-C, RC, and ApoB/ApoA-1 ratio showed significant upward trends (all P < 0.001). In contrast, HDL-C, LDL-C/ApoB ratio and Lp(a) levels decreased (all P < 0.01, Table S1).

Dyslipidemia and glycemic status

There was no significant difference in the prevalence of dyslipidemia between NGT-1hPG-high and PDM-1hPG-high (OR = 1.13, 95%CI: 0.88–1.44; Table 2); however, NGT-1hPG-normal exhibited a statistically significant lower risk of dyslipidemia compared to NGT-1hPG-high (OR = 0.63, 95%CI: 0.48–0.81; Table 2).

Table 2 Odd ratios (ORs) and 95% confidence intervals (CIs) of dyslipidemia with different glycemic status
Full size table

Dyslipidemia and elevated 1-h PG

Among individuals with only 1 elevated glucose indicator, elevated 1-h PG was associated with a higher risk of dyslipidemia (OR = 1.60, 95%CI: 1.23–2.08), whereas no significant association was found for elevated FPG levels or 2-h PG levels (both P > 0.05; Table S2). Subgroup analysis across gender, age, hypertension and hyperuricemia demonstrated that elevated 1-h PG levels were consistently associated with an increased risk of dyslipidemia (all P < 0.01). A significant association between 1-h PG and dyslipidemia was observed in the low 10-year CVD risk subgroup (P < 0.001, Table S3).

Atherogenic lipid parameters and elevated 1-h PG

TG, sdLDL-C, RC, ApoB/ApoA-1, LDL-C/ApoB and HDL-C showed the strongest correlation with 10-year CVD risk, with Spearman’s correlation coefficients of 0.41, 0.38, 0.35, 0.31, − 0.37 and − 0.36, respectively (Fig. 1). Higher 1-h PG levels were consistently and linearly associated with an atherogenic lipid profile, characterized by increased TC, TG, LDL-C, ApoB, sdLDL-C, non-HDL-C, RC and ApoB/ApoA-1, and decreased HDL-C, Lp(a), LDL-C/ApoB after adjusting for potential confounding factors (all P < 0.01, Table S4, Figure S2).

Fig. 1
figure 1

Correlation between lipid parameters and 10-year CVD risk. This heatmap displays the Spearman’s correlation coefficients between lipid parameters and 10-year CVD risk. The color gradient represents the strength and direction of correlations, where red indicates positive correlations and green indicates negative correlations

Full size image

Atherogenic lipid parameters and glycemic status

NGT-1hPG-high showed significantly higher TG, sdLDL-C, and ApoB/ApoA-1 levels and lower HDL-C and LDL-C/ApoB levels compared to counterparts with NGT-1hPG-normal (all P < 0.05). There was no significant difference between NGT-1hPG-high and PDM in the majority of lipid parameters (all P > 0.05, Fig. 2), except for TG and RC (both P < 0.01, Fig. 2).

Fig. 2
figure 2

Comparisons of lipid level among different glycemic status. Box plots showed the median and interquartile range (IQR). Group differences were assessed using a general linear model adjusted for covariates (age, gender, education, occupation, smoking status, alcohol consumption, GDR score, physical activity level, BMI, LLDs use and hypertension) with post-hoc Bonferroni correction. ns = no significant difference; *P < 0.05; **P < 0.01; ***P < 0.001

Full size image

Discussion

Our study comprehensively investigated the correlation between 1-h PG and the atherogenic lipid parameters. The main findings were as follows: (1) Elevated 1-h PG was associated with an increased risk of dyslipidemia and the atherogenic lipid parameters; (2) NGT-1hPG-high individuals exhibited significantly more atherogenic lipid parameters relevant to 10-year CVD risk, including higher TG, sdLDL-C, ApoB/ApoA-1 and lower HDL-C, LDL-C/ApoB compared to counterparts with NGT-1hPG-normal. Additionally, their lipid profile resembled to that observed in PDM. Therefore, 1-h PG may serve as a valuable indicator for identifying NGT individuals with elevated CVD risk.

Our findings align with previous studies. Andreozzi et al. demonstrated that NGT individuals with elevated 1-h PG had significantly higher TG, TG/HDL-C, ApoB, and ApoB/ApoA-1 and lower HDL-C compared to NGT-1hPG-normal individuals, resembling the lipid profiles of individuals with impaired glucose tolerance (IGT) [9]. Similarly, Shimodaira et al. reported a negative correlation between 1-h PG levels and HDL-C levels, as well as a positive correlation between 1-h PG levels and TG/HDL-C ratio in NGT individuals [26]. Oduncu et al. highlighted significant differences in HDL-C and TG levels between individuals with 1-h PG > 8.6 mmol/L and those with 1-h PG ≤ 8.6 mmol/L in PDM [27]. It is well-established that these lipid parameters play a crucial role in atherosclerosis and CVD. Elevated TG levels are associated with a higher risk of CVD, and should be routinely evaluated in all patients, even those considered to be at low-risk [28, 29]. A U-shaped relationship has been observed between HDL-C levels and CVD risk, with both low (< 40 mg/dL in men, < 50 mg/dL in women) and very high (≥ 80 mg/dL in men) HDL-C values being linked to increased risks of CVD and overall mortality [30]. The ApoB/ApoA-1 ratio reflects the balance between atherogenic and anti-atherogenic lipoproteins, with a high ApoB/ApoA-1 ratio being strongly correlated with stroke [31] and major adverse cardiovascular events (MACEs), and may also signal an increased risk of cardiovascular events decades before their onset [32]. These findings suggest that elevated 1-h PG in individuals with NGT may be associated with a higher risk of atherosclerosis and CVD.

We also observed elevated levels of sdLDL-C and a decreased LDL-C/ApoB ratio in NGT-1hPG-high individuals. Growing attention has been directed toward sdLDL-C, a specific form of LDL-C comprising smaller and denser particles [33]. The enhanced atherogenicity of sdLDL-C may be explained by its lower affinity for the LDL-C receptor, greater ability to penetrate arterial walls, increased retention due to proteoglycan binding [34], and heightened susceptibility to oxidation [35]. As these sdLDL-C particles contain less cholesterol, their presence may not be reflected by LDL-C levels and can often be overlooked [36]. Furthermore, the LDL-C/ApoB ratio, a more accessible and cost-effective tool for estimating LDL particle size, has been shown to independently predict both CVD and all-cause mortality [14]. Additionally, our study corroborated the previously reported phenomenon of the “Lp(a) paradox”. To date, there is no evidence to suggest that lowering Lp(a) has a negative impact on cardiovascular outcomes [37]. Therefore, non-traditional parameters involved in CVD pathogenesis should not be disregarded and further investigation is warranted.

The natural progression from normal glucose regulation to DM can be described in three distinct stages [4, 38]. Initially, β-cells compensate for reduced insulin sensitivity by increasing insulin production. In the second phase, although β-cell function is still preserved, β-cells mass begins to diminish, leading to reduced insulin responsiveness. Eventually in the third phase, β-cells can no longer produce sufficient insulin, resulting in the breakdown of glucose homeostasis and the onset of DM. Insulin resistance is a potential mechanism linking hyperglycemia to atherogenic lipid parameters. Impaired insulin sensitivity may hinder glucose uptake for glycogen synthesis in peripheral tissues, such as skeletal muscle, resulting in a redirection of excess glucose to the liver [39]. This process promotes hepatic lipogenesis, ultimately contributing to elevated circulating lipid levels and atherogenic lipid parameters. In NGT-1hPG-high individuals, insulin resistance may result in a delayed insulin peak and prolonged postprandial hyperglycemia [40], which could potentially explain the association between 1-h PG and atherogenic lipid parameters. Our findings support this observation, as 1-h insulin levels were the highest in NGT-1hPG-high compared to those with other glycemic status, indicating an abnormal insulin peak.

Data from the HOMA-IR index suggest that NGT-1hPG-high represents an intermediate state of insulin resistance, positioned between NGT-1hPG-normal and PDM. Under insulin-resistance conditions, very low-density lipoprotein (VLDL) levels increase, facilitating the exchange of cholesterol esters and TG between VLDL and low-density lipoprotein (LDL) [41]. This process produces TG-rich LDL particles that are depleted of cholesterol esters. These particles serve as an optimal substrate for hepatic lipase, which hydrolyzes their TG content, leading to the formation of small dense LDL particles [42]. Additionally, the increased presence of TG-rich VLDL and LDL fosters the redistribution of TG to high-density lipoprotein (HDL) via lecithin-cholesterol acyltransferase and cholesterol ester transfer protein, accelerating HDL catabolism [9]. As a result, NGT-1hPG-high individuals exhibit a lipid pattern similar to PDM, including increased TG and sdLDL-C, as well as decreased LDL-C/ApoB and HDL-C, suggesting that NGT-1hPG-high should no longer to be considered low-risk.

Testing of 1-h PG in high-risk populations is essential for precise stratification. Because the progression from normal glucose regulation to DM can span over a decade, during which the traditional glucose indicators, like FPG and 2-h PG, may still remain within normal ranges [4]. The San Antonio Heart Study (SAHS) revealed that individuals with NGT and 1-h PG ≥ 8.6 mmol/L had a significantly higher 8-year risk (15.3%) of developing to type 2 diabetes (T2D), comparable to those with IFG (10.8%) or IGT (17.8%) but with 1-h PG < 8.6 mmol/L [43]. The Botnia Study reported similar findings [44]. Moreover, extensive research highlights that individuals with NGT but elevated 1-h PG exhibit an adverse cardio-metabolic risk profile, including elevated plasma biomarkers of systemic inflammation increasing their risk of cardiovascular target-organ damage [4]. Among patients with coronary heart disease, those with NGT but elevated 1-h PG demonstrated more severe coronary artery lesions and faced a higher risk of adverse cardiovascular events and hospital re-admission [45]. Therefore, despite normal FPG and 2-h PG levels, maintained due to residual β-cell function, elevated 1-h PG could still serve as an early indicator for future DM and CVD risk, as it signals underlying metabolic dysfunctions not yet detected by traditional glucose tolerance tests [4, 46, 47]. Our subgroup analysis, showing a significant association between 1-h PG and dyslipidemia in the low 10-year CVD risk group, further underscores its potential as an early predictor of cardiometabolic abnormalities. From a clinical perspective, 1-h PG should be considered in OGTT, as it may provide additional prognostic value beyond traditional FPG and 2-h PG measurements. Incorporating 1-h PG testing in diabetes screening could allow for more precise stratification of high-risk population and the identification of at-risk people, who might otherwise go undetected. Additionally, it could refine current risk assessments and provides a new perspective on the prevention of DM and CVD.

This study offers several advantages. First, it includes a large sample size of participants with varying glycemic status categorized by 1-h PG, providing a novel and comprehensive understanding of lipid parameters and cardiometabolic characteristics across different glycemic conditions. The identification of NGT-1hPG-high as an undetected at-risk population highlights the potential for early intervention and improved cardiovascular risk management. Additionally, standardized screening protocols and rigorous quality control measures ensure the reliability and robustness of the results.

However, our study also has several limitations. First, the cross-sectional design limits our ability to assess changes in risk factors or outcomes over time, necessitating further prospective longitudinal studies for confirmation. Second, although we exclusively included individuals with self-reported no prior history of DM or the use of hypoglycemic agents, potential selection bias may have occurred because the individuals with NGT in our study were selected from a high-risk population rather than the general population. This could have led to an overestimation of the associations with cardiovascular risk factors and limited the generalizability to the broader population. Third, although our results have adjusted for LLDs use, the potential influence of other medications on the outcomes cannot be entirely excluded. Fourth, although the 10-year CVD risk was calculated using the China-PAR equations, which was validated and widely used in the Chinese population, it remains an estimation, rather than data directly derived from long-term follow-up. Finally, despite applying multivariate adjustments and conducting subgroup analyses, residual confounding remains a possibility. Although our findings provide important insights, caution should be exercised when extrapolating these results to other populations or settings.

Conclusion

NGT-1hPG-high exhibited an atherogenic lipid profile similar to that observed in PDM. 1-h PG could serve as a potential indicator for the early identification of undetected at-risk individuals among NGT population.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

NGT:

Normal glucose tolerance

PDM:

Prediabetes

DM:

Diabetes mellitus

NGT-1hPG-normal:

NGT with normal 1-h PG

NGT-1hPG-high:

NGT with elevated 1-h PG

PDM-1hPG-normal:

PDM with normal 1-h PG

PDM-1hPG-high:

PDM with elevated 1-h PG

BMI:

Body Mass Index

WC:

Waist circumference

FPG:

Fasting plasma glucose

1-h PG:

1-hour post-load plasma glucose

2-h PG:

2-hour post-load plasma glucose

LLDs:

Lipid-lowering drugs

ApoA-1:

Apolipoprotein A-1

ApoB:

Apolipoprotein B

Lp(a):

Lipoprotein (a)

sdLDL-C:

Small dense low-density lipoprotein-cholesterol

TC:

Total cholesterol

TG:

Triglycerides

HDL-C:

High-density lipoprotein-cholesterol

LDL-C:

Low-density lipoprotein-cholesterol

HDL:

High-density lipoprotein

LDL:

Low-density lipoprotein

Non-HDL-C:

Non-high-density lipoprotein-cholesterol

RC:

Remnant cholesterol

VLDL:

Very low-density lipoprotein

China-PAR:

Prediction for atherosclerotic cardiovascular disease risk in China

HOMA-IR:

Homeostatic model assessment for insulin resistance

GDR:

Global diet recommendation

PA:

Physical activity

OGTT:

Oral glucose tolerance test

CVD:

Cardiovascular disease

Daqing DPS-II:

Daqing Diabetes Prevention Study II

WHO:

World Health Organization

IDF:

International Diabetes Federation

OR:

Odds ratio

CI:

Confidence Interval

SD:

Standard deviation

ANOVA:

Analysis of variance

ROC:

Receiver operating characteristic

AUC:

Area under curve

MACEs:

Major adverse cardiovascular events

References

  1. International Diabetes Federation. Global diabetes data report 2000–2045. https://diabetesatlas.org/data/en/world/

  2. Haffner SM, Stern MP, Hazuda HP, et al. Cardiovascular risk factors in confirmed prediabetic individuals. Does the clock for coronary heart disease start ticking before the onset of clinical diabetes? JAMA. 1990;263(21):2893–8.

    Article  CAS  PubMed  Google Scholar 

  3. Schlesinger S, Neuenschwander M, Barbaresko J, et al. Prediabetes and risk of mortality, diabetes-related complications and comorbidities: umbrella review of meta-analyses of prospective studies. Diabetologia. 2022;65(2):275–85.

    Article  CAS  PubMed  Google Scholar 

  4. Bergman M, Tuomilehto J. International diabetes federation position statement on the 1-hour post-load plasma glucose for the diagnosis of intermediate hyperglycaemia and type 2 diabetes. Diabetes Res Clin Pract. 2024;210:111636.

    Article  CAS  PubMed  Google Scholar 

  5. Rong L, Luo N, Gong Y, et al. One-hour plasma glucose concentration can identify elderly Chinese male subjects at high risk for future type 2 diabetes mellitus: A 20-year retrospective and prospective study. Diabetes Res Clin Pract. 2021;173:108683.

    Article  CAS  PubMed  Google Scholar 

  6. Lorenzo C, Hartnett S, Hanley AJ, et al. Impaired fasting glucose and impaired glucose tolerance have distinct lipoprotein and Apolipoprotein changes: the insulin resistance atherosclerosis study. J Clin Endocrinol Metab. 2013;98(4):1622–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Schofield JD, Liu Y, Rao-Balakrishna P, et al. Diabetes Dyslipidemia Diabetes Ther. 2016;7(2):203–19.

    Article  CAS  PubMed  Google Scholar 

  8. Kaze AD, Santhanam P, Musani SK, et al. Metabolic dyslipidemia and cardiovascular outcomes in type 2 diabetes mellitus: findings from the look AHEAD study. J Am Heart Assoc. 2021;10(7):e016947.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Andreozzi F, Mannino GC, Perticone M, et al. Elevated 1-h post-load plasma glucose levels in subjects with normal glucose tolerance are associated with a pro-atherogenic lipid profile. Atherosclerosis. 2017;256:15–20.

    Article  CAS  PubMed  Google Scholar 

  10. Yang T, Liu Y, Li L, et al. Correlation between the triglyceride-to-high-density lipoprotein cholesterol ratio and other unconventional lipid parameters with the risk of prediabetes and type 2 diabetes in patients with coronary heart disease: a RCSCD-TCM study in China. Cardiovasc Diabetol. 2022;21(1):93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Li M, Zhang W, Zhang M, et al. Nonlinear relationship between untraditional lipid parameters and the risk of prediabetes: a large retrospective study based on Chinese adults. Cardiovasc Diabetol. 2024;23(1):12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Rikhi R, Schaich CL, Hafzalla GW, et al. Small dense low-density lipoprotein cholesterol and coronary artery calcification in the Multi-Ethnic study of atherosclerosis. Eur J Prev Cardiol. 2024;31(8):1048–54.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Ikezaki H, Lim E, Cupples LA, et al. Small dense Low-Density lipoprotein cholesterol is the most atherogenic lipoprotein parameter in the prospective Framingham offspring study. J Am Heart Assoc. 2021;10(5):e019140.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Xiao L, Zhang K, Wang F, et al. The LDL-C/ApoB ratio predicts cardiovascular and all-cause mortality in the general population. Lipids Health Dis. 2023;22(1):104.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Kaneva AM, Potolitsyna NN, Bojko ER, et al. The Apolipoprotein B/apolipoprotein A-I ratio as a potential marker of plasma atherogenicity. Dis Markers. 2015;2015:591454.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Yang X, Li J, Hu D, et al. Predicting the 10-Year risks of atherosclerotic cardiovascular disease in Chinese population: the China-PAR project (Prediction for ASCVD risk in China). Circulation. 2016;134(19):1430–40.

    Article  PubMed  Google Scholar 

  17. Joint Task Force for Guideline on the Assessment and Management of Cardiovascular Risk in China. Guideline on the assessment and management of cardiovascular risk in China. 2019;53(1):13–35.

  18. Chinese Diabetes Society. Guideline for the prevention and treatment of type 2 diabetes mellitus in China (2020 edition) (Part 1). Chin J Practical Intern Med. 2020;41(8):668–95.

    Google Scholar 

  19. Joint Committee on the Chinese Guidelines for Lipid Management. Chinese guidelines for lipid management (2023). Chin J Cardiol. 2023;53(3):221–5.

    Google Scholar 

  20. Gao D, Wang H, Wang Y et al. Association between diet quality and stroke among Chinese adults: results from China health and nutrition survey 2011. Nutrients. 2023;15(14):3229.

    Article  PubMed  PubMed Central  Google Scholar 

  21. The Global Alliance for Improved Nutrition (GAIN) and The Columbia Climate School and Cornell University College of Agriculture and Life Sciences. The Food Systems Dashboard; 2024. https://www.foodsystemsdashboard.org/indicators/outcomes/dietary-intake/gdr-score/table

  22. Wang H, Herforth AW, Xi B et al. Validation of the diet quality questionnaire in Chinese children and adolescents and relationship with pediatric overweight and obesity. Nutrients. 2022;14(17):3551.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Craig CL, Marshall AL, Sjöström M, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–95.

    Article  PubMed  Google Scholar 

  24. Bureau of Disease Prevention and Control; National Health Commission of People’s Republic of China. National guideline for hypertension management in China (2019). Chin J Cardiol. 2020;48(1):10–46.

    Google Scholar 

  25. Critical Metabolism Branch of China National Health Association; Multi-disciplinary Expert Group on Diagnosis and Treatment of Hyperuricemia and Related Diseases. Chinese multi-disciplinary consensus on the diagnosis and treatment of hyperuricemia and its related diseases. Chin J Practical Intern Med. 2017;56(3):235–48.

    Google Scholar 

  26. Shimodaira M, Niwa T, Nakajima K, et al. Correlation between serum lipids and 1-hour postload plasma glucose levels in normoglycemic individuals. J Clin Lipidol. 2014;8(2):217–22.

    Article  PubMed  Google Scholar 

  27. Oduncu EA, Tural E, Dayan A. Evaluation of the relationship between oral glucose tolerance test, blood sugar levels and cholesterol levels in prediabetic individuals. Pak J Med Sci. 2024;40(5):907–12.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Wu Z, Gao J, Chen S, et al. Triglyceride levels and risk of cardiovascular disease and all-cause mortality in Chinese adults younger than 40 years old: a prospective cohort study. Cardiovasc Diagn Ther. 2024;14(2):240–50.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Arca M, Veronesi C, D’Erasmo L, et al. Association of hypertriglyceridemia with All-Cause mortality and atherosclerotic cardiovascular events in a Low-Risk Italian population: the TG-REAL retrospective cohort analysis. J Am Heart Assoc. 2020;9(19):e015801.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Razavi AC, Mehta A, Jain V, et al. High-Density lipoprotein cholesterol in atherosclerotic cardiovascular disease risk assessment: exploring and explaining the U-Shaped curve. Curr Cardiol Rep. 2023;25(12):1725–33.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Liu D, Zhang Y, Wang C, et al. Association of the ApoB/ApoA-I ratio with stroke risk: findings from the China health and nutrition survey (CHNS). Nutr Metab Cardiovasc Dis. 2022;32(1):203–9.

    Article  CAS  PubMed  Google Scholar 

  32. Walldius G, de Faire U, Alfredsson L, et al. Long-term risk of a major cardiovascular event by ApoB, apoA-1, and the apoB/apoA-1 ratio-Experience from the Swedish AMORIS cohort: A cohort study. PLoS Med. 2021;18(12):e1003853.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Rizvi AA, Stoian AP, Janez A et al. Lipoproteins and cardiovascular disease: an update on the clinical significance of atherogenic small, dense LDL and new therapeutical options. Biomedicines. 2021;9(11):1579.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Anber V, Griffin BA, McConnell M, et al. Influence of plasma lipid and LDL-subfraction profile on the interaction between low density lipoprotein with human arterial wall proteoglycans. Atherosclerosis. 1996;124(2):261–71.

    Article  CAS  PubMed  Google Scholar 

  35. Sattar N, Petrie JR, Jaap AJ. The atherogenic lipoprotein phenotype and vascular endothelial dysfunction. Atherosclerosis. 1998;138(2):229–35.

    Article  CAS  PubMed  Google Scholar 

  36. Hoogeveen RC, Gaubatz JW, Sun W, et al. Small dense low-density lipoprotein-cholesterol concentrations predict risk for coronary heart disease: the atherosclerosis risk in communities (ARIC) study. Arterioscler Thromb Vasc Biol. 2014;34(5):1069–77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kostner KM, Kostner GM. Lp(a) and the risk for cardiovascular disease: focus on the Lp(a) paradox in diabetes mellitus. Int J Mol Sci. 2022;23(7):3584.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Hulman A, Simmons RK, Brunner EJ, et al. Trajectories of glycaemia, insulin sensitivity and insulin secretion in South Asian and white individuals before diagnosis of type 2 diabetes: a longitudinal analysis from the Whitehall II cohort study. Diabetologia. 2017;60(7):1252–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Petersen KF, Dufour S, Savage DB, et al. The role of skeletal muscle insulin resistance in the pathogenesis of the metabolic syndrome. Proc Natl Acad Sci U S A. 2007;104(31):12587–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Ferrannini E, Gastaldelli A, Miyazaki Y, et al. beta-Cell function in subjects spanning the range from normal glucose tolerance to overt diabetes: a new analysis. J Clin Endocrinol Metab. 2005;90(1):493–500.

    Article  CAS  PubMed  Google Scholar 

  41. Malmström R, Packard CJ, Caslake M, et al. Effects of insulin and acipimox on VLDL1 and VLDL2 Apolipoprotein B production in normal subjects. Diabetes. 1998;47(5):779–87.

    Article  PubMed  Google Scholar 

  42. Bjornstad P, Eckel RH. Pathogenesis of lipid disorders in insulin resistance: a brief review. Curr Diab Rep. 2018;18(12):127.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Abdul-Ghani MA, Abdul-Ghani T, Ali N, et al. One-hour plasma glucose concentration and the metabolic syndrome identify subjects at high risk for future type 2 diabetes. Diabetes Care. 2008;31(8):1650–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Abdul-Ghani MA, Lyssenko V, Tuomi T, et al. Fasting versus postload plasma glucose concentration and the risk for future type 2 diabetes: results from the Botnia study. Diabetes Care. 2009;32(2):281–6.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Cao L, Wang P, Luan H, et al. Elevated 1-h postload plasma glucose levels identify coronary heart disease patients with greater severity of coronary artery lesions and higher risk of 1-year re-admission. Diab Vasc Dis Res. 2020;17(1):1479164119896978.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Mann BK, Bhandohal JS, Hong J. An overall glance of evidence supportive of One-Hour and Two-Hour postload plasma glucose levels as predictors of Long-Term cardiovascular events. Int J Endocrinol. 2019;2019:6048954.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Pareek M, Bhatt DL, Nielsen ML, et al. Enhanced predictive capability of a 1-Hour oral glucose tolerance test: A prospective Population-Based cohort study. Diabetes Care. 2018;41(1):171–7.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank all of our study participants for their contributions to this study.

Funding

This work was supported by School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College [grant number WH10022022010, the Disciplines Construction Project: Population Medicine] and Key Research and Development Program of Department of Science and Technology of Heilongjiang Province [grant number 2024ZX12C38]. The funding bodies had no role in the design of the study, data collection, analysis, interpretation of data, or in writing the manuscript.

Author information

Authors and Affiliations

  1. School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China

    Chunyu Yang, Xin Chai, Yachen Wang, Kaipeng Liang, Juan Zhang & Ruitai Shao

  2. Daqing Oilfield General Hospital (Daqing First Hospital), Daqing, 163000, China

    Di Li, Dongli Zhu, Jinping Wang & Zhiwei Yang

  3. Center of Endocrinology, National Center of Cardiology & Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China

    Qiuhong Gong

Authors
  1. Chunyu Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xin Chai
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yachen Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Di Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Dongli Zhu
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Kaipeng Liang
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Jinping Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Zhiwei Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Qiuhong Gong
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Juan Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Ruitai Shao
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

CY.Y: Conceptualization, Methodology, Data curation, Formal analysis, Investigation, Validation, Visualization, Writing—original draft, Writing—review & editing. X.C: Data curation, Formal analysis, Investigation, Validation, Visualization, Writing—original draft, Writing—review & editing. YC.W: Data curation, Investigation, Validation, Visualization, Writing—review & editing. D.L, DL.Z, KP.L: Data curation, Investigation, Validation. JP.W: Project administration, Supervision, Validation. ZW.Y: Project administration, Supervision, Funding acquisition. QH.G: Project administration, Validation. J.Z: Conceptualization, Methodology, Project administration, Supervision, Validation, Visualization, Writing—review & editing. RT.S: Conceptualization, Funding acquisition, Supervision, Validation, Writing—review & editing.

Corresponding author

Correspondence to Juan Zhang.

Ethics declarations

Ethics approval and consent to participate

This study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Ethics Committee of the Chinese Academy of Medical Sciences & Peking Union Medical College under approval number [CAMS-PUMC-IEC-2022-061]. Written informed consent was obtained from all participants prior to their inclusion in the study.

Consent for publication

All participants provided written informed consent for the publication of anonymized data.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, C., Chai, X., Wang, Y. et al. Atherogenic lipid parameters in people with normal glucose tolerance: implications from elevated 1-hour post-load plasma glucose. Cardiovasc Diabetol 24, 207 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12933-025-02722-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12933-025-02722-8

Keywords

Cardiovascular Diabetology

ISSN: 1475-2840