GRAPHICAL ABSTRACT

Highlights

• High 1-h plasma glucose (1-h PG) is a strong predictor of T2D risk.

• Elevated 1-h PG is associated with lower disposition index.

• Higher genetic risk for T2D is associated with faster increase in 1-h PG over time.

• Healthy lifestyle mitigates 1-h PG increase across all genetic risk groups.

INTRODUCTION

The escalating global prevalence of type 2 diabetes mellitus (T2DM) highlights the need for more effective strategies to identify high-risk individuals early and implement timely interventions [1]. Current prediabetes (intermediate hyperglycemia) diagnostic criteria based on fasting plasma glucose (FPG) and 2-hour plasma glucose (2-h PG) from an oral glucose tolerance test (OGTT) often fail to detect a significant number of high-risk individuals [2,3]. To address this limitation, the International Diabetes Federation (IDF) has proposed using 1-hour plasma glucose (1-h PG) to better identify individuals at increased risk of T2DM and for diagnosing T2DM [4].

The IDF recommendation is supported by substantial evidence demonstrating that the 1-h PG is an earlier and more sensitive marker of T2DM than other metabolic markers [5-11]. Studies have also shown that a high 1-h PG is significantly correlated with key pathophysiological defects in T2DM, including decreased insulin sensitivity and impaired β-cell function [6,12,13]. Yet, most of this evidence is based on single time-point measurements of 1-h PG, which may not fully capture individual differences in 1-h PG trajectories. Therefore, investigating these differences is necessary to enable more personalized risk assessment and intervention strategies for T2DM. Furthermore, T2DM progression is influenced by genetic predisposition and lifestyle factors [14,15], determining whether these factors affect the rate of change in the 1-h PG is crucial for predicting individual differences in the 1-h PG trajectory [16,17].

In this study, we examined the 1-h PG in predicting T2DM risk and the factors influencing the 1-h PG trajectory, hypothesizing that the rate of change of the 1-h PG is influenced by both genetic and lifestyle factors. We therefore analyzed data from a large, prospective, community-based cohort in South Korea with biennial 1-h PG measurements collected over a 14- year follow-up period. We examined the impact of genetic risk on 1-h PG trajectories using a T2DM polygenic risk score (PRS), which includes a substantial number of variants associated with β-cell function. Additionally, we examined whether lifestyle can mitigate the effects of genetic risk on 1-h PG trajectories using comprehensive survey data.

METHODS

Ethics approval and informed consent

The study protocol was approved by the ethics committee of the Korean Center for Disease Control and the Institutional Review Board of Seoul National University Hospital (1801-095-916). All participants provided written informed consent.

Study population

The Ansan-Ansung Cohort Study is a prospective, community-based cohort study in South Korea, previously described in detail [18]. It is part of the Korean Genome and Epidemiology Study (KoGES), which investigates genetic and environmental factors contributing to common complex diseases, including diabetes. The cohort enrolled individuals aged 40 to 69 years residing in urban Ansan or rural Ansung during 2001 and 2002 as baseline, with follow-up examinations conducted biennially. For this study, we analyzed data collected from 2001 to 2016. Validation of the association between the 1-h PG and risk of T2DM was conducted using the Seoul National University Hospital Gestational Diabetes Mellitus (SNUH GDM) prospective cohort that has been described previously [19,20].

Study procedures

The study procedures have been previously described in detail [18]. Anthropometric parameters and blood pressure were measured using standard methods and blood samples were collected after a 12-hour fast. Low-density lipoprotein cholesterol was calculated using the Friedewald equation [21]. At enrollment, participants underwent a 2-hour 75-g OGTT, with biennial follow-up testing. Plasma samples were collected at 0, 60, and 120 minutes to measure plasma glucose and insulin levels. Participants with prior diabetes diagnosis or those using diabetes medication were excluded from the OGTT. Diabetes was defined using FPG, 2-h PG, and glycosylated hemoglobin (HbA1c) according to American Diabetes Association criteria [2]. The 1-h PG cutoff values were based on the IDF Position Statement, with 155 mg/dL as a threshold for increased risk of T2DM (prediabetes or intermediate hyperglycemia) and 209 mg/dL as the diagnostic threshold of T2DM [4]. Among the 8,840 participants in the Ansan-Ansung cohort, 7,523 participants did not have T2DM at baseline. Of these, 7,464 participants with baseline 1-h PG, age, sex, and body mass index (BMI) data were included (Supplementary Fig. 1). For the area under the receiver operating characteristic curve analysis, 5,712 participants with baseline normoglycemia based on FPG and 2-h PG, and with baseline 1-h and 2-h PG data were included (Supplementary Fig. 2). For the 1-h PG trajectory analyses, 6,588 participants who had at least two 1-h PG measurements during follow-up were included (Supplementary Fig. 3). In the SNUH GDM cohort, 423 participants with at least 1 year of follow-up were assessed for T2DM diagnosis based on their 1-h PG measured at 6 weeks postpartum.

Insulin sensitivity and β-cell function

Insulin sensitivity was assessed using the composite (Matsuda) insulin sensitivity index (ISI) and the homeostasis model assessment of insulin resistance (Supplementary Methods) [22,23]. Pancreatic β-cell function or insulin secretory response was assessed using the 60-minute insulinogenic index (IGI₆₀), and the homeostasis model assessment of β-cell function (Supplementary Methods) [22,24]. To assess β-cell function adjusted for insulin sensitivity, the OGTT-derived disposition index (DI) was calculated as (IGI₆₀)×(composite ISI).

Genotype data and genome-wide association analysis for 1-h PG level

Participants were genotyped using the Affymetrix Genome-Wide Human single nucleotide polymorphism (SNP) Array 5.0 and imputation was performed using the TransOmics for Precision Medicine (TOPMed) Imputation Server with the TOPMed r2 reference panel [25]. We performed a genomewide association study (GWAS) for 1-h PG level in 7,514 participants who were not diagnosed of T2DM and data on 1-PG, age, sex and BMI (Supplementary Fig. 4). Details are elaborated in the Supplementary Methods. The overall genomic inflation factor was 1.05, and a total of two lead SNPs were identified (Supplementary Figs. 5 and 6).

SNP-based heritability estimation for 1-h PG

We performed a univariate Genome-wide Complex Trait Analysis (GCTA)-genomic-relatedness-based restricted maximum-likelihood (GREML) analysis using common SNPs to estimate the narrow-sense heritability, which is defined as the proportion of phenotypic variance in 1-h PG explained by additive genetic variation (Supplementary Table 1) [26,27].

Polygenic risk score calculation

The association between the 1-h PG trajectory and genetic risk was assessed using T2DM PRS, as few genome-wide significant SNPs have been identified for the 1-h PG, and most large-scale GWAS to date have focused on T2DM. T2DM PRS was calculated in unrelated participants using polygenic risk score estimated using Bayesian regression with continuous shrinkage priors (PRS-CSx), a Bayesian regression method that improves cross-population polygenic prediction by integrating GWAS summary statistics from multiple ancestry groups, leading to more accurate effect size estimation [28]. We used ancestry-specific summary statistics from the European and East Asian cohorts of the Type 2 Diabetes Global Genomics Initiative (T2DGGI) consortium to estimate effect sizes for 1,015,552 HapMap 3 variants (Supplementary Table 2) [29,30]. Individual-level PRS was calculated using Plink software and the scores were standardized (mean=0, standard deviation [SD]=1) [31].

Assessment of lifestyle factors

Lifestyle was assessed using five behavioral components from Life’s Essential 8: (1) avoidance of nicotine, (2) healthy weight, (3) healthy diet, (4) participation in physical activity, and (5) healthy sleep [32]. Self-reported data at enrollment were used. Missing data rates for each lifestyle factor were below 5%, and missing values were imputed using the median for continuous or mode for categorical variables. No current smoking and BMI <23 kg/m² were classified as healthy lifestyle [33]. Diet was evaluated using a semi-quantitative food frequency questionnaire and scored using the healthy plant-based diet index (hPDI) [34]. The hPDI increases with higher intake of healthy plant foods (e.g., whole grains, fruits, vegetables, nuts, legumes, tea/coffee) and decreases with higher intake of less healthy plant foods (e.g., refined grains, potatoes, sugar-sweetened beverages, sweets, desserts, salty foods) and animal foods (e.g., animal fat, dairy eggs, fish, meat). A healthy diet was defined as hPDI >48 based on previous results [35]. Participation in physical activity was defined as ≥150 min/week of moderate or ≥75 min/week of vigorous activity. Healthy sleep was defined as ≥7 and <9 hours of sleep per day [36]. Participants were categorized into three lifestyle groups based on the number of healthy lifestyle components they met: favorable lifestyle (≥4), intermediate lifestyle (3), and unfavorable lifestyle (≤2) (Supplementary Table 3).

Statistical analysis

Data are presented as counts (%) for categorical variables and mean±SD for continuous variables. Variables with non-Gaussian distributions were log-transformed. Categorical variables were compared using the χ² test, and means were compared using analysis of variance (ANOVA) or analysis of covariance (ANCOVA). Trend analysis was performed using linear regression for continuous variables. Participants were categorized by 1-h PG levels and genetic risk for T2DM. For 1-h PG, participants were grouped into <155 mg/dL (normal), 155–208 mg/dL (prediabetes or intermediate hyperglycemia), and ≥209 mg/dL (diagnostic of T2DM). Genetic risk was determined using a genome-wide T2DM PRS, with participants grouped into low (1st quintile), intermediate (2nd–4th quintiles), and high (5th quintile) genetic risk groups. The potential of the 1-h PG as marker of T2DM was assessed using Cox proportional hazards regression and logistic regression analysis.

Longitudinal analysis of the 1-h PG was conducted using a linear mixed-effects model to estimate its trajectory over 14 years [37]. The model included time, genetic risk group and their interaction as fixed effects, with individuals as a random effect. Biennial OGTT data were used until diabetes diagnosis, as interventions including medications could alter the results. Data points with negative IGI₆₀ values (11.5%) were excluded from analyses, as negative values likely reflect non-physiologic insulin secretory responses or measurement variability and cannot be meaningfully interpreted, particularly given the log-transformation applied to the indices. Age, sex, BMI, and the first 10 principal components of ancestry were included as covariates in all models. All statistical analyses were conducted in R software v4.4.2 (R Foundation for Statistical Computing, Vienna, Austria).

Data and resource availability

The Ansan-Ansung cohort study data are available through the Korean National Institute of Health (https://biobank.nih.go.kr). Datasets generated and/or analyzed in the current study can be obtained from the corresponding author upon reasonable request.

RESULTS

Baseline characteristics according to the 1-h PG

Baseline characteristics of the 7,464 participants in the Ansan-Ansung Cohort Study are presented in Table 1. Participants with higher baseline 1-h PG were more likely to have a family history of diabetes, and exhibited higher FPG, 2-h PG and HbA1c levels. Participants with higher baseline 1-h PG were also older at baseline, and more likely to be male. They exhibited higher systolic and diastolic blood pressures, total cholesterol levels, and greater waist circumference. BMI was highest in the 1-h PG 155–208 mg/dL group, followed by the 1-h PG ≥209 mg/dL group, and lowest in the 1-h PG <155 mg/dL group.

High 1-h PG is associated with increased risk of T2DM

We first sought to validate that the 1-h PG is associated with progression to T2DM. Over a median follow-up period of 14 years, a total of 1,530 participants (20.5%) developed T2DM. The incidence of T2DM increased with higher baseline 1-h PG levels (Fig. 1). Specifically, T2DM developed in 524 participants (11.0%) in the 1-h PG <155 mg/dL group, 780 participants (34.1%) in the 155–208 mg/dL group, and 226 participants (53.4%) in the ≥209 mg/dL group. In a Cox proportional hazards model, individuals with 1-h PG levels of 155–208 mg/dL had a 3.34-fold (95% confidence interval [CI], 2.99 to 3.74; P<0.001) higher risk of developing T2DM compared to the reference group of 1-h PG <155 mg/dL (Supplementary Table 4). Additionally, each 10 mg/dL increase in 1-h PG was associated with a 1.26-fold higher odds of developing T2DM (95% CI, 1.24 to 1.28; P<0.001). The increased risk was also observed in the SNUH GDM cohort, in which the postpartum 1-h PG measured at 6 weeks was associated with future T2DM risk over 5 years of follow-up (Supplementary Fig. 7). T2DM developed in 28 participants (17.1%) in the 1-h PG <155 mg/dL group, 58 participants (32.4%) in the 155–208 mg/dL group, and 47 participants (58.8%) in the ≥209 mg/dL group. Compared with the reference group (1-h PG <155 mg/dL), women with 1-h PG levels of 155–208 mg/dL had a 1.94-fold (95% CI, 1.23 to 3.06; P<0.001) higher risk of developing T2DM, while those with 1-h PG levels ≥209 mg/dL had a 4.61-fold (95% CI, 2.86 to 7.42; P<0.001) higher risk.

High 1-h PG is associated with impaired β-cell function

We next investigated whether high 1-h PG levels were associated with the pathophysiology of T2DM by investigating their relationship with β-cell function and insulin sensitivity. Higher 1-h PG levels were significantly associated with lower insulin sensitivity (composite ISI), reduced β-cell secretory response (IGI₆₀), and impaired β-cell function relative to insulin sensitivity (DI) (Table 1, Supplementary Fig. 8). Baseline composite ISI decreased with each 10 mg/dL increase in 1-h PG (β_ISI=–0.041, P<2×10^–16). Similarly, baseline IGI₆₀ decreased with each 10 mg/dL increase in 1-h PG (βIGI₆₀=–0.122, P<2×10^–16). The most pronounced reduction was in baseline DI, which reflects β-cell function adjusted for insulin sensitivity. Baseline DI decreased significantly with each 10 mg/dL increase in 1-h PG (β_DI=–0.160, P<2×10^–16). Compared with the <155 mg/dL group, DI was 57.3% and 72.7% lower in the 155–208 and ≥209 mg/dL groups, respectively (P_trend<2×10^–16).

1-h PG shows higher sensitivity than 2-h PG in predicting T2DM

Among individuals with baseline normoglycemia, the 1-h PG demonstrated greater sensitivity than 2-h PG in predicting future T2DM. At the optimal cutoff based on Youden index, the sensitivity of the 1-h PG was 71% compared to 53% for 2-h PG (Supplementary Table 5).

1-h PG level is influenced by genetic risk factors

To elucidate the determinants of the 1-h PG level, we investigated the influence of genetic risk factors. We first performed a single variant association analysis on the 1-h PG and identified two lead variants that reached genome-wide significance (P<5.0×10^–8): an intronic CDKAL1 variant (rs34499031) on chromosome 6, and an exonic ALDH2 variant (rs671, NP_000681.2: p.Glu504Lys) on chromosome 12 (Supplementary Table 6). rs34499031 was also significantly associated with baseline IGI₆₀ and DI but not ISI (Supplementary Table 7). The SNP-based heritability of 1-h PG estimated using common variants was 13.7% (standard error=0.049, P=0.002) (Supplementary Table 1).

As our study lacked sufficient power to uncover all genetic variants associated with 1-h PG levels, we investigated whether known T2DM genetic risk factors aggregated as a PRS were associated with 1-h PG levels (Supplementary Fig. 9). We observed a significantly higher baseline 1-h PG per 1-SD increase in T2DM PRS (β_{1-h PG}=7.583, P_SD<2×10^–16). Compared with the low genetic risk group, the intermediate and high genetic risk groups exhibited 7.6% and 15.3% higher baseline 1-h PG levels, respectively (P_trend<2×10^–16).

Genetic risk and longitudinal trajectory of 1-h PG level

To assess the impact of genetic risk on the longitudinal trajectory of 1-h PG level, we analyzed 1-h PG levels over a median follow-up of 14 years (mean 5.90 measurements per person) (Supplementary Table 8). Individuals with a higher T2DM PRS exhibited a more rapid increase in 1-h PG over time (Fig. 2, Table 2, Supplementary Table 9). Throughout the follow-up period, 1-h PG levels increased in all genetic risk groups, with the high T2DM genetic risk group experiencing a 1.62-fold faster increase compared to the low genetic risk group (2.21 mg/dL/year vs. 1.36 mg/dL/year, P=3.58×10^–14), while the intermediate genetic risk group exhibited a 1.35-fold faster increase compared to the low genetic risk group (1.85 mg/dL/year vs. 1.36 mg/dL/year, P=7.23×10^–8). After 14 years, the low genetic risk group exhibited a 13.4% increase in 1-h PG from baseline, whereas the high genetic risk group showed a more pronounced 18.6% increase. Additionally, each 1-SD increase in T2DM PRS was associated with an additional 2.90 mg/dL increase in 1-h PG over 10 years (P_SD<2×10^–16).

1-h PG level can be reduced through a healthy lifestyle

Finally, we investigated whether a healthy lifestyle could mitigate the effect of genetic risk factors on the 1-h PG over time. Overall, a favorable lifestyle was associated with a 26% slower increase in 1-h PG compared to an unfavorable lifestyle (1.55 mg/dL/year vs. 2.10 mg/dL/year, P=2.28×10^–10) (Supplementary Table 10). When stratified by genetic risk, adherence to a healthy lifestyle was consistently associated with a slower increase in the 1-h PG (Fig. 3, Supplementary Table 11). In the high genetic risk group, a favorable lifestyle resulted in a 37% slower increase compared to an unfavorable lifestyle (1.72 mg/dL/year vs. 2.71 mg/dL/year, P=3.13×10^–6), corresponding to a 9.87 mg/dL smaller increase in 1-h PG after 10 years for those with a favorable lifestyle. Similar findings were observed in the intermediate and low genetic risk groups.

A significant reduction in the rate of increase in the 1-h PG was observed with each additional healthy lifestyle component across all genetic risk groups (Supplementary Table 11). The high genetic risk group benefited the most, showing the largest reduction per one additional component of healthy lifestyle. Specifically, each additional component of healthy lifestyle was associated with a 3.24 mg/dL (P=2.74×10^–5), 2.39 mg/dL (P=1.79×10^–8), and 1.43 mg/dL (P=0.033) smaller increase over 10 years in the high, intermediate, and low genetic risk groups, respectively.

DISCUSSION

In this study, we investigated the significance of the 1-h PG in predicting T2DM risk by assessing its association with progression to T2DM and key pathophysiological factors of T2DM. Importantly, we also investigated the determinants of 1-h PG levels and their longitudinal impact, and found that while genetic risk influences the 1-h PG, this effect can be mitigated with a healthy lifestyle.

Our study supports three notable conclusions. First, we confirm that the 1-h PG is a strong predictor of T2DM risk. We identified a clear gradient in T2DM risk based on 1-h PG thresholds of 155 and 209 mg/dL, with individuals having 1-h PG levels between 155 and 208 mg/dL exhibiting a 3.34-fold higher risk of developing T2DM. This finding aligns with previous studies using the same cohort [5,9,10]. Notably, one study in participants with normal glucose tolerance (NGT) at baseline found an optimal cutoff of 144 mg/dL for predicting T2DM, and showed that individuals above this threshold had a 2.84-fold higher risk of developing T2DM [10]. Two recent studies showed that a high 1-h PG, even in the context of NGT, was associated with 2.7- to 3.9-fold higher risk of T2DM [5,9]. These consistent findings support the clinical utility of 1-h PG in stratifying T2DM risk. Furthermore, we observed that higher 1-h PG levels were associated with lower baseline β-cell function. This finding aligns with a recent study from the same cohort, which provides additional insight into the longitudinal impact of the 1-h PG. Individuals with NGT but 1-h PG ≥155 mg/dL had lower model-derived β-cell function at baseline and remained lower over 10 years [5]. Taken together, these findings support the conclusion that the 1-h PG is closely linked to β-cell dysfunction both cross-sectionally and longitudinally, underscoring its role as a strong marker of T2DM risk.

Second, 1-h PG levels are influenced by both genetic risk and lifestyle factors. We found that the SNP-based heritability of the 1-h PG, estimated using common variants was 13.7%. We further examined the contribution of genetic risk to 1-h PG levels by testing whether T2DM PRS was associated with 1-h PG levels. Our findings indicate that genetic risk plays a significant role in shaping 1-h PG levels, likely through its influence on β-cell function. This is supported by evidence that a substantial portion of T2DM-related variants are associated with β-cell dysfunction [38]. We observed that higher 1-h PG levels were consistently associated with impaired β-cell function, reinforcing the genetic connection. Additionally, our findings indicate that a healthy lifestyle can mitigate the effect of genetic risk on the 1-h PG. Our previous study showed that improving one lifestyle component prevented a decline of β-cell function by 4.4% after 10 years in the high genetic risk group [17]. Consistent with this, the present study showed that improving one lifestyle component reduced the rate of increase in 1-h PG by 3.25 mg/dL after 10 years in the high genetic risk group. Together, these results support our conclusion that lifestyle interventions can slow the genetically driven increase in the 1-h PG possibly through mechanisms related to β-cell function.

Third, the 1-h PG trajectory varies across individuals and can be predicted using the T2DM PRS. A previous study demonstrated that the trajectory of the 1-h PG differs between progressors and non-progressors of T2DM [39]. More than one year before the diagnosis of T2DM, progressors had a mean 1-h PG level that was 60 mg/dL (41%) higher than non-progressors (P<0.001), followed by a steep increase in progressors immediately before diagnosis [39]. Our study expands this concept by showing that differences in the 1-h PG trajectory can be predicted using the T2DM PRS. Specifically, for each 1-SD increase in T2DM PRS, the 1-h PG increased by an additional 2.90 mg/dL over a 10-year period. Given the wide variation in 1-h PG trajectories, it is important to consider the rate of increase in 1-h PG, especially in individuals with high genetic risk, as their levels are expected to rise more rapidly. Monitoring the 1-h PG and estimating genetic risk of T2DM using a PRS could enable more timely interventions, ultimately improving outcomes, because a healthy lifestyle can mitigate the genetic risk of T2DM. Additionally, as genetic variants linked to longitudinal changes in the FPG have already been investigated, identifying more genetic variants associated with longitudinal changes in the 1-h PG in the future could further enhance the prediction of the 1-h PG trajectory [40,41].

The main strengths of this study are the comprehensive approach to understanding the 1-h PG and its role in predicting T2DM risk. First, we used a large, prospective cohort with biennial follow-up visits spanning 14 years, enabling a robust longitudinal analysis of 1-h PG trajectories. Second, we investigated the pathophysiology of the 1-h PG by examining its associations with IGI₆₀, ISI, and DI. Third, we applied the most up-to-date PRS method to enhance the accuracy of T2DM genetic risk estimation in East Asian populations. Notably, this is the first study to comprehensively evaluate the impact of overall genetic influence captured through a PRS on 1-h PG levels. Lastly, we conducted a detailed evaluation of lifestyle factors, allowing for a comprehensive assessment of the interaction between genetics and lifestyle.

However, this study has some limitations. First, because our cohort consisted entirely of East Asian individuals, differences in genetic architecture, lifestyle patterns, and optimal 1-h PG thresholds across ethnic groups may limit the generalizability of our findings. Additionally, PRS transferability across ancestries may be incomplete due to differences in allele frequencies and linkage disequilibrium structure [42]. Furthermore, the independent validation of the association between 1-h PG and T2DM risk was conducted in the SNUH GDM cohort, which represents a high-risk, sex-specific population, and the findings should be interpreted within this specific clinical context. Future studies using multi-ancestry PRS approaches and diverse populations are warranted. Second, although different 1-h PG thresholds have been reported across Asian populations, we adopted the IDF-recommended cutoffs (155 and 209 mg/dL for prediabetes or intermediate hyperglycemia and T2DM, respectively) to ensure alignment with international diagnostic criteria and to facilitate cross-study comparability. Although receiver operating characteristic analysis in our cohort identified a lower optimal cutoff (136.5 mg/dL) for predicting incident T2DM, this value reflects dataset-specific statistical optimization rather than a clinically established threshold. Prior studies in Korean and other East Asian cohorts have shown that 1-h PG values around 155 mg/dL could predict incident T2DM and are associated with impaired β-cell function and insulin sensitivity [5,43-45]. These discrepancies may be due to differences in participant characteristics or study duration rather than biological divergence, as numerous studies in Asian populations support this threshold for its strong association with impaired β-cell function and its predictive value for T2DM and related complications [5,6,9,43,46]. Third, our study lacked sufficient power to fully uncover the genetic variants associated with 1-h PG levels, and we used a T2DM PRS rather than a 1-h PG-specific PRS due to limited genome-wide data on the 1-h PG. While the T2DM PRS captures polygenic influences on β-cell dysfunction [17], future GWAS of the 1-h PG would help refine polygenic prediction and improve trait-specific risk estimation. Lastly, lifestyle behaviors were assessed at baseline using self-reported data, which may be subject to recall bias and may not capture lifestyle changes during the 14-year follow-up. Future studies incorporating repeated lifestyle assessments are needed to better delineate longitudinal effects.

In conclusion, we validated that the 1-h PG is a strong predictor of T2DM risk by demonstrating its association with both the probability of progression to T2DM and key pathophysiological factors of T2DM. Additionally, we showed that the 1-h PG trajectory can be predicted based on genetic risk assessed by a genome-wide PRS, while a healthy lifestyle mitigates this genetic risk. These findings highlight the clinical utility of the 1-h PG in predicting T2DM risk and suggest the potential use of the 1-h PG trajectory in guiding personalized risk assessment and intervention strategies.

SUPPLEMENTARY MATERIALS

NOTES

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

AUTHOR CONTRIBUTIONS

Conception or design: S.C., H.L., J.H., M.B., S.H.K.

Acquisition, analysis, or interpretation of data: S.C., H.L., K.S.P., N.H.C., S.H.K.

Drafting the work or revising: all authors.

Final approval of the manuscript: all authors.

FUNDING

This study was supported by grants from the National Research Foundation of Korea funded by the Ministry of Science and ICT (RS-2023-00262002), the Ministry of Food and Drug Safety (23212MFDS202), and the Korea National Institute of Health (2024-ER1104-00, -01, -02; 2025-ER1103-00, -01) awarded to Soo Heon Kwak.

Hyunsuk Lee is supported by the Phase III (Postdoctoral fellowship) grant of the SPST (SNU-SNUH Physician Scientist Training) Program, Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2020-NR049600), Korea Basic Science Institute (National Research Facilities and Equipment Center) grant funded by the Ministry of Education (RS2021-NF000547) and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare (RS-2026-25516569).

Joon Ha is supported by National Science Foundation (DMS2401921).

ACKNOWLEDGMENTS

Parts of this study will be presented in a poster presentation at the American Diabetes Association 85th Scientific Sessions, Chicago, IL, 20 to 23 June 2025.

The authors thank the participants and staff of the Korean Genome and Epidemiology Study for their dedication and contribution to the research. The biospecimens and data used in this study were provided by the National Biobank of Korea.

Fig. 1.

Kaplan-Meier plot illustrating the risk of type 2 diabetes mellitus (T2DM) based on 1-hour plasma glucose (1-h PG) levels. The Kaplan-Meier plot depicts the risk of T2DM stratified by baseline 1-h PG levels using cutoffs of 155 and 209 mg/dL. Adjusted hazard ratios (HRs) for T2DM across 1-h PG groups are presented, with 95% confidence intervals (CI) provided in parentheses. HRs represent the relative risk of T2DM compared to the reference group (1-h PG <155 mg/dL) based on the Cox regression model. The model was adjusted for baseline age, sex, body mass index, and the first 10 principal components of ancestry.

Fig. 2.

Trajectory of 1-hour plasma glucose (1-h PG) stratified by genetic risk for type 2 diabetes mellitus (T2DM). The trajectory of 1-h PG by genetic risk groups for T2DM is shown. The data are adjusted for baseline age, sex, body mass index, and the first 10 principal components of ancestry. Mean 1-h PG levels are plotted from baseline to year 14 at 2-year intervals. Baseline and year 14 values are shown with the percentage change from baseline provided in parentheses. Error bars represent the standard error of the mean. The rate of change was compared across genetic risk groups, with statistically significant differences indicated ^aP<0.05.

Fig. 3.

Rate of change in 1-hour plasma glucose (1-h PG) by genetic risk for type 2 diabetes mellitus (T2DM) and lifestyle factors. The rate of change in 1-h PG by genetic risk for T2DM and lifestyle factors is shown. Lifestyle factors include no current smoking, not overweight, regular physical activity, healthy diet and adequate sleep duration. The data are adjusted for baseline age, sex, body mass index, and the first 10 principal components of ancestry. P values for the increase in the rate of change in 1-h PG per one additional healthy lifestyle component is provided in Supplementary Table 11. Error bars represent the standard error of the mean. ^aP<0.05 in post hoc analysis.

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Cho S, Lee H, Ha J, Park Y, Moon JH, Jang HC, Park KS, Cho NH, Bergman M, Kwak SH. Genetic and Lifestyle Factors Influence High 1-Hour Plasma Glucose, a Predictor of Type 2 Diabetes Mellitus. Diabetes Metab J. 2026 Apr 22. doi: 10.4093/dmj.2025.0362. Epub ahead of print.

Received: Apr 25, 2025; Accepted: Feb 03, 2026

DOI: https://doi.org/10.4093/dmj.2025.0362.

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