Abstract
Background
Advanced glycation end products (AGEs) have been identified as potential dietary risk factors for cardiometabolic disorders. Despite studies in Western populations, little is known about the impact of AGEs in elderly Asian with different dietary patterns and aging dynamics. This study aimed to examine the association between dietary intake of three representative AGEs—Nɛ-(carboxymethyl)lysine (CML), Nɛ-(1-carboxyethyl)lysine (CEL), and Nδ-(5-hydro-5-methyl-4-imidazolone-2-yl)-ornithine (MGH1)—and the risk of cardiometabolic disorders among older Korean adults.
Methods
A total of 6,731 individuals aged 65 years and older participated in the Korea National Health and Nutrition Examination Survey (KNHANES) from which cross-sectional data collected between 2007 and 2021 were analyzed. Dietary AGEs intake was estimated using a food-based AGEs composition database linked to 24-hour dietary recall data. Participants were categorized into quartiles according to energy-adjusted AGEs intake. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) to evaluate the associations between dietary AGEs intake and cardiometabolic disorders including hypertension, type 2 diabetes mellitus, hypercholesterolemia, and hypertriglyceridemia.
Results
Higher dietary intake of CML and MGH1 was significantly associated with increased odds of type 2 diabetes (OR, 95% CI, CML = 1.33, 1.12–1.58, P for trend < 0.001; MGH1 = 1.25, 1.05–1.49, P for trend = 0.007) and hypercholesterolemia (OR, 95% CI, CML = 1.61, 1.34–1.92, P for trend < 0.001; MGH1 = 1.24, 1.03–1.48, P for trend = 0.032). CEL intake was independently associated with hypercholesterolemia (OR, 95% CI = 1.54, 1.29–1.84, P for trend < 0.001). In contrast, no significant associations were observed between AGEs intake and hypertension or hypertriglyceridemia. Although absolute differences in continuous biomarkers such as fasting glucose and total cholesterol across AGEs quartiles were small, consistent dose–response trends were observed, suggesting cumulative metabolic alterations associated with higher dietary AGEs intake.
Conclusions
High intake of dietary AGEs, particularly CML and MGH1, may contribute to increased risk of type 2 diabetes and hypercholesterolemia in older Korean adults. Given the cross-sectional design of this study, causal inferences cannot be drawn; however, these findings suggest that considering AGEs may be relevant for dietary strategies and cardiometabolic health in aging societies.
Keywords: Advanced glycation end products, Cardiometabolic disorders, Hypercholesterolemia, Type 2 diabetes mellitus, Older adults
Introduction
Cardiometabolic disorders including hypertension, type 2 diabetes mellitus, hypercholesterolemia, and hypertriglyceridemia are highly prevalent among the elderly and represent a substantial global public health challenge [1, 2]. These conditions frequently coexist, thereby compounding morbidity and mortality risks in aging populations and placing a significant burden on healthcare systems worldwide [3, 4]. Their prevalence and clinical management vary across regions, influenced by a complex interplay of factors such as dietary habits, lifestyle behaviors, socioeconomic conditions, and access to healthcare services [1, 5]. As the prevalence of cardiometabolic disorders continues to rise among South Korea’s aging population, these conditions have become critical public health concerns [6].
Concurrently, shifts in dietary patterns driven by modernization and westernization have led to increased intake of ultra-processed and heat-treated foods, raising critical concerns about diet-related cardiometabolic risk in older adults [7, 8]. The influence of diet on cardiometabolic disease is multifactorial, involving direct nutritional effects, interactions with the gut microbiota, and modulation of metabolic pathways [9, 10]. Previous epidemiological studies have consistently demonstrated that dietary patterns rich in whole grains, fruits, vegetables, and short-chain fatty acids such as the Mediterranean and Dietary Approaches to Stop Hypertension (DASH) diets are associated with a reduced risk of cardiometabolic disorders [11, 12]. In contrast, diets high in processed foods and saturated fats have been linked to an increased risk [8].
Advanced glycation end products (AGEs) are increasingly recognized as dietary components that may contribute to the development of cardiometabolic disorders [13]. AGEs represent a heterogeneous group of compounds formed endogenously through nonenzymatic glycation and oxidation reactions, and are also abundantly present in foods prepared under high-temperature cooking conditions such as baking, roasting, and frying [13, 14]. Accumulating evidence suggests that excessive dietary AGEs exposure is associated with oxidative stress, chronic low-grade inflammation, endothelial dysfunction, and insulin resistance, which are pathophysiological processes central to type 2 diabetes and atherosclerotic cardiovascular disease [15, 16]. Among the diverse AGEs species, Nε-(carboxymethyl)lysine (CML), Nε-(1-carboxyethyl)lysine (CEL), and Nδ-(5-hydro-5-methyl-4-imidazolon-2-yl)-ornithine (MGH1) are the most extensively characterized dietary AGEs in human studies [15, 16]. These compounds were selected because they are quantitatively dominant in commonly consumed foods, reflect distinct AGEs formation pathways, and have been shown to differentially activate receptor for AGEs (RAGE) mediated inflammatory and metabolic signaling, including the nuclear factor-kappa B (NF-κB) related pathways relevant to lipid metabolism and insulin sensitivity. Together, they provide a representative yet mechanistically diverse profile for evaluating the cardiometabolic relevance of dietary AGEs exposure.
A recent epidemiological evidence, largely derived from Western populations, has demonstrated associations between high dietary AGEs intake and an increased risk of metabolic disorders [14]. However, the generalizability of these findings to Asian populations remains uncertain. In Korea, traditional dietary patterns characterized by fermented and vegetable-based foods have progressively shifted since the early 2000s toward greater consumption of processed, convenience, and high-temperature–prepared foods, including grilled and barbecued meats, which are known to be major dietary sources of AGEs [17]. These dietary transitions suggest a substantial increase in population-level exposure to dietary AGEs, yet quantitative assessments of AGEs intake within Korean diets remain limited. At present, a comprehensive AGEs food database tailored to Korean dietary patterns has not been established. The development of such a database and the systematic quantification of dietary AGEs exposure would enable more accurate population-level nutritional risk assessment in Korea [18]. Furthermore, evaluating the cumulative effects of chronic dietary AGEs intake and its associations with metabolic health biomarkers in older adults may provide important insights into cardiometabolic disease pathogenesis and prevention [19]. To date, no nationally representative studies have examined the relationship between dietary AGEs intake and cardiometabolic disease risk among older Korean adults.
The present study aimed to evaluate the association between dietary intake of three representative AGEs (CML, CEL, and MGH1) and the risk of cardiometabolic disorders, including hypertension, type 2 diabetes mellitus, hypercholesterolemia, and hypertriglyceridemia among older Korean adults. We hypothesized that higher dietary intake of these AGEs would be positively associated with an increased prevalence of cardiometabolic disorders, with potential variations in the strength of association across specific AGEs and metabolic outcomes. Using cross-sectional data from the 2007–2021 Korea National Health and Nutrition Examination Survey (KNHANES), we sought to determine whether dietary AGEs intake is independently associated with cardiometabolic risk after adjustment for potential confounders. Findings from this study may help inform evidence-based dietary guidelines and public health strategies for aging population.
Materials and methods
Study population
This cross-sectional study aimed to investigate the potential associations between lifestyle factors and the prevalence of chronic diseases utilizing data of a nationally representative survey from KNHANES conducted by the Korea Centers for Disease Control and Prevention (KDCA). The methodological details of the rationale, design, and baseline characteristics of KNHANES study have been described elsewhere [20]. KNHANES applies a stratified, multistage probability sampling design to represent the Korean civilian population. This study focused on adults aged ≥ 65 years to reflect the population most vulnerable to AGEs-related cardiometabolic risk. From the 120,181 participants enrolled in the 2007–2021 KNHANES, individuals younger than 65 years (n = 96,617) were excluded. Among the remaining participants, those with incomplete structured questionnaire and physical examination data (n = 14,433), as well as individuals reporting implausible daily energy intake (< 500 kcal or > 5,000 kcal; n = 2,400), were also excluded. Consequently, a final analytic sample of 6,731 adults aged 65 years or older was identified for the assessment of dietary AGEs and their associations with the risk of cardiometabolic disorders (Fig. 1). All data collection for KNHANES was conducted under a standardized protocol and comprised three components including a health interview, a physical examination, and a nutritional survey [20]. All procedures adhered to ethical standards and were approved by the Institutional Review Board (IRB) of the KDCA (IRB No. 2018-01-03–5 C-A). Additional approval for the present study was granted by the IRB of Changwon National University (IRB No. 7001066-202503-HR-012).
Fig. 1.
Flow chart of study subjects
Assessment of dietary AGEs and covariates
Demographic characteristics, lifestyle and dietary intake information were obtained using structured questionnaires administered by a trained interviewer. Habitual dietary intake was assessed via a 24-hour dietary recall interview. Dietary intake data were collected through face-to-face 24-hour dietary recall interviews administered by trained dietitians following the standardized KNHANES multiple-pass protocol. To estimate individual intake of dietary AGEs, including CML, CEL, and MGH1, each reported food item was matched to a validated food composition database quantifying AGEs content using liquid chromatography–tandem mass spectrometry (LC-MS/MS) methods [21, 22]. In total, 5,105 distinct food items were recorded through the 24-hour recall data in KNHANES. Corresponding AGEs values were identified for 3,217 foods for CML, 3,172 for CEL, and 3,514 for MGH1. Dietary AGEs intake was estimated using only food items with available CML, CEL, or MGH1 values in the AGEs food composition database. Food items without matched AGEs values were excluded from intake calculations, and no imputation or zero-value assignment was applied to missing data. These matched entries were used to calculate daily AGEs intake and evaluate their associations with cardiometabolic risk factors. Covariates were considered a priori based on prior epidemiological evidence demonstrating their associations with both dietary patterns and cardiometabolic risk, including age [23], sex [24], smoking status [25], alcohol consumption [26], physical activity (moderate and vigorous intensity) [27], body mass index (BMI) [28], and socioeconomic factors [29].
Anthropometric and biochemical parameters
All anthropometric and clinical measurements were performed by trained personnel following standardized protocols of KNHANES, with rigorous quality assurance and quality control procedures implemented by the Korea Disease Control and Prevention Agency (KDCA). Anthropometric and biochemical indicators related to cardiometabolic risk were assessed, including BMI (kg/m²), waist circumference (WC, cm), systolic blood pressure (SBP, mmHg), diastolic blood pressure (DBP, mmHg), fasting blood glucose (FBG, mg/dL), total cholesterol (Total-C, mg/dL), triglycerides (TG, mg/dL), high-density lipoprotein cholesterol (HDL-C, mg/dL), and glycated hemoglobin (HbA1c). Body weight and height were measured by trained personnel using a calibrated digital scale and audiometer, with participants wearing light indoor clothing and no shoes. BMI was calculated as weight in kilograms divided by height in meters squared (kg/m²). WC was measured to the nearest 0.1 cm using a flexible, non-stretchable measuring tape placed at the midpoint between the lower margin of the last rib and the iliac crest, with participants standing upright and breathing normally. Blood pressure was recorded using a standard mercury sphygmomanometer and appropriately sized cuff on the right upper arm after the participant had been seated at rest for at least five minutes. Three consecutive measurements were obtained at 30-second intervals, and the average of the second and third readings was used for analysis. Both SBP and DBP were reported in mmHg. All biochemical variables (FBG, Total-C, TG, HDL- C, and HbA1c) were measured in KNHANES central laboratory using standardized automated assays, under the national QA/QC program that includes internal and external quality control, use of certified reference materials, and inter-laboratory comparison. Assay-specific limits of detection and coefficients of variation are reported in KNHANES laboratory quality reports but are not available in the public microdata. FBG concentrations were determined using the ultraviolet hexokinase method on a Cobas 8000 modular analyzer (Roche Diagnostics, Mannheim, Germany). Serum concentrations of Total-C, TG, and HDL-C were measured via enzymatic colorimetric assays, following the manufacturer’s protocols (Labospect 008AS, Hitachi, Japan).
Diagnosis of cardiometabolic disorders
Cardiometabolic disorders assessed in this study included hypertension, type 2 diabetes mellitus, hypercholesterolemia, and hypertriglyceridemia, based on biochemical markers obtained from fasting blood samples and self-reported medication use. Hypertension was defined as a SBP of ≥ 140 mmHg, DBP of ≥ 90 mmHg, or current use of antihypertensive medications. Type 2 diabetes mellitus was identified by a FBG concentration ≥ 126 mg/dL, HbA1c ≥ 6.5%, or use of glucose-lowering medications. Hypercholesterolemia was defined as fasting serum Total-C ≥ 240 mg/dL or active use of lipid-lowering agents. Hypertriglyceridemia was defined as a fasting triglyceride level ≥ 200 mg/dL. Diagnostic cut-offs were based on international clinical guidelines (e.g., WHO 2020; Korean Diabetes Association 2022; Korean Society of Lipid & Atherosclerosis 2023), and medication use was verified through structured interviews and prescription checks.
Statistical analysis
Daily dietary intake of AGEs including CML, CEL, and MGH1 was quantified in milligrams per day (mg/day) and adjusted for total energy intake using the residual method, in order to minimize potential confounding by overall caloric intake [30]. Participants were categorized into quartiles based on their energy-adjusted intake levels for each AGEs compound to examine associations with risk of cardiometabolic disorders. Sociodemographic, lifestyle, anthropometric, and biochemical characteristics were summarized as means and standard deviations (SD) for continuous variables and as frequencies and percentages for categorical variables across quartiles. Linear regression was used to evaluate trends in BMI across quartiles, while the Jonckheere–Terpstra test was applied to assess trends in other continuous variables. For categorical variables, the Cochran–Mantel–Haenszel test was employed, where appropriate. Associations between AGEs intake and cardiometabolic risk were assessed using multivariable logistic regression, estimating odds ratios (ORs) and 95% confidence intervals (CIs). Multivariable models were adjusted for potential confounders, including continuous variables (age, BMI) and categorical covariates (sex, smoking status, alcohol consumption, and levels of moderate-to-vigorous physical activity). For trend analyses, the median value of each quartile of AGEs intake was treated as a continuous variable. All statistical analyses were conducted using SAS software version 9.4 (SAS Institute Inc., Cary, NC, USA) with statistical significance defined as a two-sided p-value < 0.05. No formal adjustment for multiple comparisons was applied; therefore, results should be interpreted with consideration of the correlated nature of the outcomes and potential for type I error.
Results
Dietary CML intake and its association with risk of type 2 diabetes and hypercholesterolemia
As shown in Table 1, participants in the highest quartile of dietary CML intake were older and more likely to be female (p for trend < 0.001). Participants with higher CML intake were less likely to be smoking (p for trend < 0.001) and alcohol consumption (p for trend < 0.001), as well as reduced engagement in moderate-intensity physical activity (p for trend = 0.008). Total energy intake declined across quartiles (p for trend < 0.001), whereas BMI (p for trend = 0.019), SBP (p for trend = 0.009), FBG (p for trend < 0.001), Total-C (p for trend < 0.001), and HDL-C (p for trend = 0.013) increased significantly. TG and WC showed no consistent trends. Table 2 presents that higher dietary CML intake was significantly associated with increased cardiometabolic risk including type 2 diabetes and hypercholesterolemia. Higher CML intake was significantly increased adjusted ORs for type 2 diabetes (OR, 95% CI = 1.33, 1.12–1.58, p for trend < 0.001) and hypercholesterolemia (OR, 95% CI = 1.61, 1.34–1.92, p for trend < 0.001). Although a significant crude model with hypertension was observed, the association was attenuated after multivariable adjustment. No significant relationship was found between CML intake and hypertriglyceridemia. After multivariable adjustment, the association between CML intake and hypertension was markedly attenuated, whereas associations with type 2 diabetes and hypercholesterolemia remained statistically significant.
Table 1.
Sociodemographic, lifestyle, and metabolic parameters across quartile of dietary CML
| Q1 | Q2 | Q3 | Q4 | P for trend | |
|---|---|---|---|---|---|
| N | 1683 | 1682 | 1684 | 1682 | |
| Dietary CML intake (mg/day) | < 0.53 | 0.53–0.90 | 0.90–1.47 | > 1.47 | |
| Age (years) | 71.5 ± 4.5 | 71.9 ± 4.6 | 71.9 ± 4.6 | 72.3 ± 4.7 | < 0.001 |
| Sex (n, %) | |||||
| Men | 845 (50.2) | 770 (45.8) | 692 (41.1) | 572 (34.0) | < 0.001 |
| Women | 838 (49.8) | 912 (54.2) | 992 (58.9) | 1110 (66.0) | |
| Smoking (n, %) | |||||
| No | 890 (52.9) | 975 (58.0) | 1006 (59.7) | 1084 (64.5) | < 0.001 |
| Yes | 793 (47.1) | 707 (42.0) | 678 (40.3) | 598 (35.6) | |
| Alcohol consumption (n, %) | |||||
| No | 416 (24.7) | 493 (29.3) | 521 (30.9) | 558 (33.2) | < 0.001 |
| Yes | 1267 (75.3) | 1189 (70.7) | 1163 (69.1) | 1124 (66.8) | |
| Moderate-intensity of physical activity (n, %) | |||||
| No | 1492 (88.7) | 1512 (89.9) | 1514 (89.9) | 1540 (91.6) | 0.008 |
| Yes | 191 (11.4) | 170 (10.1) | 170 (10.1) | 142 (8.4) | |
| Vigorous-intensity of physical activity (n, %) | |||||
| No | 1525 (90.6) | 1529 (90.9) | 1520 (90.3) | 1546 (91.9) | 0.30 |
| Yes | 158 (9.4) | 153 (9.1) | 164 (9.7) | 136 (8.1) | |
| Total energy intake (kcal/day) | 1889.64 ± 632.49 | 1745.58 ± 554.93 | 1572.83 ± 520.81 | 1321.10 ± 475.50 | < 0.001 |
| BMI (kg/m2) | 23.70 ± 3.13 | 23.82 ± 3.25 | 23.80 ± 3.19 | 23.98 ± 3.24 | 0.019 |
| WC (cm) | 84.24 ± 9.36 | 83.97 ± 9.30 | 83.62 ± 9.47 | 83.87 ± 9.29 | 0.14 |
| SBP (mmHg) | 129.73 ± 18.36 | 129.37 ± 16.56 | 130.53 ± 18.15 | 130.93 ± 17.71 | 0.009 |
| DBP (mmHg) | 75.51 ± 10.19 | 75.00 ± 10.03 | 75.38 ± 10.18 | 75.42 ± 10.12 | 0.84 |
| FBG (mg/dL) | 101.38 ± 22.46 | 103.12 ± 25.06 | 104.31 ± 24.51 | 104.39 ± 25.12 | < 0.001 |
| Total-C (mg/dL) | 189.11 ± 36.60 | 189.92 ± 36.00 | 192.48 ± 38.16 | 194.61 ± 39.26 | < 0.001 |
| TG (mg/dL) | 141.70 ± 88.46 | 139.66 ± 81.55 | 138.82 ± 91.55 | 139.67 ± 85.79 | 0.41 |
| HDL-C (mg/dL) | 46.23 ± 10.58 | 46.50 ± 11.46 | 46.57 ± 11.29 | 47.17 ± 11.17 | 0.013 |
Values for categorical variables are shown as number and percentage (n, %), and continuous variables as mean ± SD. P values for trend were calculated with the use of linear regression (BMI), the Jonckheere-Terpstra test (other continuous variables), or the Cochran-Mantel-Haenszel test (categorical variables), where appropriate. The P for trend for total energy intake across quartiles was < 0.001. Statistically significant results (P < 0.05) are presented in bold
CML Nɛ-(carboxymethyl)lysine, DBP diastolic blood pressure, FBG fasting blood glucose, HDL-C high-density lipoprotein cholesterol, SBP systolic blood pressure, TG triglyceride, Total-C total cholesterol, WC waist circumference
Table 2.
Association between dietary CML and cardiometabolic disease risk
| Dietary CML Intake (mg/day) | P for trend | ||||
|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | ||
| Hypertension | |||||
| Prevalence (%) | 954 (56.7) | 1062 (63.1) | 1032 (61.3) | 1060 (63.0) | 0.001 |
| Crude OR (95% CI) | 1.0 (ref) | 1.31 (1.14–1.50) | 1.21 (1.05–1.38) | 1.30 (1.13–1.50) | 0.004 |
| Multivariable OR (95% CI) | 1.0 (ref) | 1.27 (1.10–1.46) | 1.16 (1.00-1.33) | 1.18 (1.02–1.36) | 0.19 |
| Type 2 diabetes mellitus | |||||
| Prevalence (%) | 315 (18.7) | 322 (19.1) | 362 (21.5) | 392 (23.3) | < 0.001 |
| Crude OR (95% CI) | 1.0 (ref) | 1.03 (0.87–1.22) | 1.19 (1.00-1.41) | 1.32 (1.12–1.56) | < 0.001 |
| Multivariable OR (95% CI) | 1.0 (ref) | 1.03 (0.87–1.23) | 1.21 (1.02–1.43) | 1.33 (1.12–1.58) | < 0.001 |
| Hypercholesterolemia | |||||
| Prevalence (%) | 255 (15.2) | 306 (18.2) | 372 (22.1) | 398 (23.7) | < 0.001 |
| Crude OR (95% CI) | 1.0 (ref) | 1.25 (1.04–1.49) | 1.59 (1.33–1.89) | 1.74 (1.46–2.07) | < 0.001 |
| Multivariable OR (95% CI) | 1.0 (ref) | 1.23 (1.02–1.48) | 1.54 (1.29–1.84) | 1.61 (1.34–1.92) | < 0.001 |
| Hypertriglyceridemia | |||||
| Prevalence (%) | 288 (17.1) | 284 (16.9) | 257 (15.3) | 278 (16.5) | 0.40 |
| Crude OR (95% CI) | 1.0 (ref) | 0.98 (0.82–1.18) | 0.87 (0.73–1.05) | 0.96 (0.80–1.15) | 0.60 |
| Multivariable OR (95% CI) | 1.0 (ref) | 0.98 (0.82–1.18) | 0.86 (0.71–1.04) | 0.93 (0.77–1.11) | 0.36 |
The multivariable models were adjusted for age, sex, smoking status, alcohol consumption, and physical activity (both vigorous and moderate intensity). Smoking status and alcohol consumption differed significantly across AGEs intake quartiles. P values for trends were calculated using logistic regression by treating the order of the quantile group as a continuous variable. Statistically significant results (P < 0.05) are presented in bold
CML Nɛ-(carboxymethyl)lysine
Dietary CEL intake and its association with hypercholesterolemia
As shown in Table 3, higher dietary CEL intake was associated with older age (p for trend < 0.001), higher proportion of female participants (p for trend < 0.001), and higher rates of smoking (p for trend = 0.019) and alcohol use (p for trend = 0.017). Moderate-intensity of physical activity declined as CEL intake increased (p for trend = 0.006). Total energy intake declined significantly across quartiles (p for trend < 0.001), while FBG (p for trend < 0.001), Total-C (p for trend = 0.005), TG (p for trend = 0.005), and HDL-C (p for trend < 0.001) levels increased. A slight but significant increase in BMI was also observed (p for trend = 0.019), whereas SBP and DBP showed no meaningful variation. Physical activity declined as CEL intake increased. In Table 4, higher CEL intake was significantly associated with increased odds of hypercholesterolemia. Participants with high intake of CEL had a 54% greater likelihood of hypercholesterolemia compared to those with low intake (OR, 95% CI: 1.54, 1.29–1.84, p for trend < 0.001). For CEL, no significant associations with hypertension were observed in either crude or adjusted models. Similarly, no significant associations were detected for type 2 diabetes or hypertriglyceridemia.
Table 3.
Sociodemographic, lifestyle, and metabolic parameters across quartile of dietary CEL
| Q1 | Q2 | Q3 | Q4 | P for trend | |
|---|---|---|---|---|---|
| N | 1683 | 1682 | 1683 | 1683 | |
| Dietary CEL intake (mg/day) | < 0.24 | 0.24–0.49 | 0.49–0.95 | > 0.95 | |
| Age (years) | 71.7 ± 4.6 | 71.8 ± 4.6 | 71.9 ± 4.6 | 72.2 ± 4.7 | < 0.001 |
| Sex (n, %) | |||||
| Men | 762 (45.3) | 777 (46.2) | 723 (43.0) | 617 (36.7) | < 0.001 |
| Women | 921 (54.7) | 905 (53.8) | 960 (57.0) | 1066 (63.3) | |
| Smoking (n, %) | |||||
| No | 961 (57.1) | 956 (56.8) | 984 (58.5) | 1054 (62.6) | 0.019 |
| Yes | 722 (42.9) | 726 (43.2) | 699 (41.5) | 629 (37.4) | |
| Alcohol consumption (n, %) | |||||
| No | 460 (27.3) | 501 (29.8) | 500 (29.7) | 527 (31.3) | 0.017 |
| Yes | 1223 (72.7) | 1181 (70.2) | 1183 (70.3) | 1156 (68.7) | |
| Moderate-intensity of physical activity (n, %) | |||||
| No | 1505 (89.4) | 1488 (88.5) | 1520 (90.3) | 1545 (91.8) | 0.006 |
| Yes | 178 (10.6) | 194 (11.5) | 163 (9.7) | 138 (8.2) | |
| Vigorous-intensity of physical activity (n, %) | |||||
| No | 1539 (91.4) | 1511 (89.8) | 1529 (90.9) | 1541 (91.6) | 0.66 |
| Yes | 144 (8.6) | 171 (10.2) | 154 (9.2) | 142 (8.4) | |
| Total energy intake (kcal/day) | 1790.72 ± 597.04 | 1720.85 ± 602.97 | 1616.49 ± 558.44 | 1401.22 ± 516.11 | < 0.001 |
| BMI (kg/m2) | 23.71 ± 3.20 | 23.85 ± 3.28 | 23.76 ± 3.09 | 23.99 ± 3.24 | 0.019 |
| WC (cm) | 84.05 ± 9.35 | 84.07 ± 9.53 | 83.62 ± 9.26 | 83.96 ± 9.28 | 0.44 |
| SBP (mmHg) | 129.97 ± 18.30 | 129.40 ± 17.09 | 130.40 ± 17.58 | 130.79 ± 17.86 | 0.08 |
| DBP (mmHg) | 75.54 ± 10.20 | 75.34 ± 10.19 | 75.21 ± 10.14 | 75.22 ± 10.00 | 0.22 |
| FBG (mg/dL) | 101.84 ± 23.68 | 103.82 ± 27.02 | 104.01 ± 24.32 | 103.53 ± 22.01 | < 0.001 |
| Total-C (mg/dL) | 190.07 ± 37.19 | 189.95 ± 36.76 | 192.59 ± 37.09 | 193.51 ± 39.15 | 0.005 |
| TG (mg/dL) | 143.52 ± 90.45 | 141.21 ± 80.19 | 138.07 ± 90.33 | 137.05 ± 86.17 | 0.005 |
| HDL-C (mg/dL) | 46.20 ± 10.56 | 46.11 ± 11.31 | 46.88 ± 11.52 | 47.28 ± 11.08 | < 0.001 |
Values for categorical variables are shown as number and percentage (n, %), and continuous variables as mean ± SD. P values for trend were calculated with the use of linear regression (BMI), the Jonckheere-Terpstra test (other continuous variables), or the Cochran-Mantel-Haenszel test (categorical variables), where appropriate. The P for trend for total energy intake across quartiles was < 0.001. Statistically significant results (P < 0.05) are presented in bold
CEL Nɛ-(1-carboxyethyl)lysine, DBP diastolic blood pressure, FBG fasting blood glucose, HDL-C high-density lipoprotein cholesterol, SBP systolic blood pressure, TG triglyceride, Total-C total cholesterol, WC waist circumference
Table 4.
Association between dietary CEL and cardiometabolic disease risk
| Dietary CEL Intake (mg/day) | P for trend | ||||
|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | ||
| Hypertension | |||||
| Prevalence (%) | 981 (58.3) | 1030 (61.2) | 1058 (62.9) | 1039 (61.7) | 0.024 |
| Crude OR (95% CI) | 1.0 (ref) | 1.13 (0.99–1.30) | 1.21 (1.06–1.39) | 1.16 (1.01–1.33) | 0.12 |
| Multivariable OR (95% CI) | 1.0 (ref) | 1.11 (0.96–1.28) | 1.18 (1.02–1.36) | 1.06 (0.92–1.22) | 0.77 |
| Type 2 diabetes mellitus | |||||
| Prevalence (%) | 332 (19.7) | 345 (20.5) | 344 (20.4) | 370 (22.0) | 0.13 |
| Crude OR (95% CI) | 1.0 (ref) | 1.05 (0.89–1.24) | 1.05 (0.88–1.24) | 1.15 (0.97–1.35) | 0.11 |
| Multivariable OR (95% CI) | 1.0 (ref) | 1.04 (0.88–1.24) | 1.05 (0.89–1.25) | 1.14 (0.96–1.35) | 0.13 |
| Hypercholesterolemia | |||||
| Prevalence (%) | 269 (16.0) | 329 (19.6) | 341 (20.3) | 392 (23.3) | < 0.001 |
| Crude OR (95% CI) | 1.0 (ref) | 1.28 (1.07–1.53) | 1.34 (1.12–1.59) | 1.60 (1.34–1.90) | < 0.001 |
| Multivariable OR (95% CI) | 1.0 (ref) | 1.30 (1.08–1.56) | 1.35 (1.13–1.62) | 1.54 (1.29–1.84) | < 0.001 |
| Hypertriglyceridemia | |||||
| Prevalence (%) | 299 (17.8) | 288 (17.1) | 251 (14.9) | 269 (16.0) | 0.06 |
| Crude OR (95% CI) | 1.0 (ref) | 0.96 (0.80–1.14) | 0.81 (0.68–0.98) | 0.88 (0.74–1.06) | 0.18 |
| Multivariable OR (95% CI) | 1.0 (ref) | 0.95 (0.79–1.13) | 0.80 (0.67–0.97) | 0.85 (0.71–1.03) | 0.10 |
The multivariable models were adjusted for age, sex, smoking status, alcohol consumption, and physical activity (both vigorous and moderate intensity). Smoking status and alcohol consumption differed significantly across AGEs intake quartiles. P values for trends were calculated using logistic regression by treating the order of the quantile group as a continuous variable. Statistically significant results (P < 0.05) are presented in bold
CEL Nɛ-(1-carboxyethyl)lysine
Dietary MGH1intake and its association with risk of type 2 diabetes and hypercholesterolemia
As detailed in Table 5, increased intake of MGH1 was associated with older age (p for trend < 0.001), a higher proportion of women (p for trend < 0.001), and lower levels of smoking (p for trend < 0.001) and alcohol consumption (p for trend < 0.001). Moderate-intensity (p for trend = 0.019) and vigorous-intensity physical activity (p for trend = 0.003) was inversely associated with MGH1 intake. Total energy intake declined significantly across quartiles (p for trend < 0.001), while SBP (p for trend = 0.007), FBG (p for trend = 0.011), and Total-C (p for trend < 0.001) increased. No clear trends were observed for BMI, HDL-C, or DBP. Physical activity was inversely associated with MGH1 intake. As shown in Table 6, dietary MGH1 intake was positively associated with type 2 diabetes and hypercholesterolemia. Participants in the highest quartile had higher odds of type 2 diabetes (OR, 95% CI: 1.25, 1.05–1.49, p for trend = 0.007) and hypercholesterolemia (OR, 95% CI: 1.24, 1.03–1.48, p for trend = 0.032). Although a significant crude association with hypertension was observed for MGH1, this association was attenuated and no longer statistically evident after multivariable adjustment.
Table 5.
Sociodemographic, lifestyle, and metabolic parameters across quartile of dietary MGH1
| Q1 | Q2 | Q3 | Q4 | P for trend | |
|---|---|---|---|---|---|
| N | 1682 | 1684 | 1682 | 1683 | |
| Dietary MGH1 intake (mg/day) | < 8.45 | 8.45–12.07 | 12.07–17.02 | > 17.02 | |
| Age (years) | 71.1 ± 4.5 | 71.5 ± 4.4 | 72.2 ± 4.7 | 72.8 ± 4.7 | < 0.001 |
| Sex (n, %) | |||||
| Men | 957 (56.9) | 852 (50.6) | 615 (36.6) | 455 (27.0) | < 0.001 |
| Women | 725 (43.1) | 832 (49.4) | 1067 (63.4) | 1228 (73.0) | |
| Smoking (n, %) | |||||
| No | 814 (48.4) | 906 (53.8) | 1087 (64.6) | 1148 (68.2) | < 0.001 |
| Yes | 868 (51.6) | 778 (46.2) | 595 (35.4) | 535 (31.8) | |
| Alcohol consumption (n, %) | |||||
| No | 368 (21.9) | 449 (26.7) | 547 (32.5) | 624 (37.1) | < 0.001 |
| Yes | 1314 (78.1) | 1235 (73.3) | 1135 (67.5) | 1059 (62.9) | |
| Moderate-intensity of physical activity (n, %) | |||||
| No | 1492 (88.7) | 1514 (89.9) | 1518 (90.3) | 1534 (91.2) | 0.019 |
| Yes | 190 (11.3) | 170 (10.1) | 164 (9.8) | 149 (8.9) | |
| Vigorous-intensity of physical activity (n, %) | |||||
| No | 1509 (89.7) | 1518 (90.1) | 1539 (91.5) | 1554 (92.3) | 0.003 |
| Yes | 173 (10.3) | 166 (9.9) | 143 (8.5) | 129 (7.7) | |
| Total energy intake (kcal/day) | 2115.74 ± 674.58 | 1828.59 ± 350.58 | 1453.92 ± 301.18 | 1131.05 ± 400.92 | < 0.001 |
| BMI (kg/m2) | 23.81 ± 3.10 | 23.80 ± 3.13 | 23.82 ± 3.28 | 23.88 ± 3.30 | 0.68 |
| WC (cm) | 84.84 ± 9.13 | 84.03 ± 9.18 | 83.50 ± 9.45 | 83.33 ± 9.60 | < 0.001 |
| SBP (mmHg) | 129.61 ± 18.21 | 129.69 ± 17.07 | 130.31 ± 17.39 | 130.94 ± 18.15 | 0.007 |
| DBP (mmHg) | 75.51 ± 10.11 | 75.36 ± 10.11 | 75.31 ± 10.24 | 75.13 ± 10.07 | 0.15 |
| FBG (mg/dL) | 102.68 ± 25.18 | 103.18 ± 24.45 | 103.35 ± 23.22 | 103.99 ± 24.45 | 0.011 |
| Total-C (mg/dL) | 189.65 ± 36.21 | 188.94 ± 36.39 | 191.74 ± 38.58 | 195.79 ± 38.73 | < 0.001 |
| TG (mg/dL) | 139.12 ± 86.99 | 138.21 ± 79.84 | 139.36 ± 86.86 | 143.15 ± 93.40 | 0.18 |
| HDL-C (mg/dL) | 46.97 ± 10.91 | 46.40 ± 11.19 | 46.44 ± 11.33 | 46.66 ± 11.10 | 0.61 |
Values for categorical variables are shown as number and percentage (n, %), and continuous variables as mean ± SD. P values for trend were calculated with the use of linear regression (BMI), the Jonckheere-Terpstra test (other continuous variables), or the Cochran-Mantel-Haenszel test (categorical variables), where appropriate. The P for trend for total energy intake across quartiles was < 0.001. Statistically significant results (P < 0.05) are presented in bold
DBP diastolic blood pressure, FBG fasting blood glucose, HDL-C high-density lipoprotein cholesterol, MGH1 Nδ-(5-hydro-5-methyl-4-imidazolon-2-yl)-ornithine, SBP systolic blood pressure, TG triglyceride, Total-C total cholesterol, WC waist circumference
Table 6.
Association between dietary MGH1 and cardiometabolic disease risk
| Dietary MGH1 Intake (mg/day) | P for trend | ||||
|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | ||
| Hypertension | |||||
| Prevalence (%) | 979 (58.2) | 1010 (60.0) | 1054 (62.7) | 1065 (63.3) | < 0.001 |
| Crude OR (95% CI) | 1.0 (ref) | 1.08 (0.94–1.24) | 1.21 (1.05–1.38) | 1.24 (1.08–1.42) | 0.002 |
| Multivariable OR (95% CI) | 1.0 (ref) | 1.04 (0.90–1.20) | 1.11 (0.96–1.28) | 1.07 (0.92–1.24) | 0.36 |
| Type 2 diabetes mellitus | |||||
| Prevalence (%) | 322 (19.1) | 333 (19.8) | 364 (21.6) | 372 (22.1) | 0.015 |
| Crude OR (95% CI) | 1.0 (ref) | 1.04 (0.88–1.24) | 1.17 (0.99–1.38) | 1.20 (1.01–1.42) | 0.020 |
| Multivariable OR (95% CI) | 1.0 (ref) | 1.06 (0.89–1.26) | 1.22 (1.02–1.44) | 1.25 (1.05–1.49) | 0.007 |
| Hypercholesterolemia | |||||
| Prevalence (%) | 284 (16.9) | 326 (19.4) | 347 (20.6) | 374 (22.2) | < 0.001 |
| Crude OR (95% CI) | 1.0 (ref) | 1.18 (0.99–1.41) | 1.28 (1.08–1.52) | 1.41 (1.19–1.67) | 0.001 |
| Multivariable OR (95% CI) | 1.0 (ref) | 1.15 (0.96–1.38) | 1.17 (0.98–1.41) | 1.24 (1.03–1.48) | 0.032 |
| Hypertriglyceridemia | |||||
| Prevalence (%) | 264 (15.7) | 284 (16.9) | 275 (16.4) | 284 (16.9) | 0.45 |
| Crude OR (95% CI) | 1.0 (ref) | 1.09 (0.91–1.31) | 1.05 (0.87–1.26) | 1.09 (0.91–1.31) | 0.46 |
| Multivariable OR (95% CI) | 1.0 (ref) | 1.09 (0.91–1.31) | 1.04 (0.86–1.26) | 1.07 (0.88–1.29) | 0.66 |
The multivariable models were adjusted for age, sex, smoking status, alcohol consumption, and physical activity (both vigorous and moderate intensity). Smoking status and alcohol consumption differed significantly across AGEs intake quartiles. P values for trends were calculated using logistic regression by treating the order of the quantile group as a continuous variable. Statistically significant results (P < 0.05) are presented in bold
MGH1 Nδ-(5-hydro-5-methyl-4-imidazolon-2-yl)-ornithine
Discussion
This study examined differences in sociodemographic, lifestyle, and metabolic characteristics among older Korean adults according to dietary intake of three representative AGEs including CML, CEL, and MGH1. Higher dietary AGEs intake was significantly associated with increased risk of cardiometabolic disorders, particularly in type 2 diabetes mellitus and hypercholesterolemia. Among the assessed AGEs, CML and MGH1 exhibited the strongest associations with both type 2 diabetes mellitus and hypercholesterolemia accompanied by elevated FBG and Total-C levels. In contrast, CEL was specifically associated with increased odds of hypercholesterolemia. No significant associations were observed between dietary AGEs intake and hypertension or hypertriglyceridemia in multivariable adjusted models. The outcome-specific attenuation observed after adjustment suggests that shared confounders may differentially influence blood pressure, glycemic control, and lipid metabolism. In addition, although statistically significant dose–response trends were observed for FBG and Total-C across AGEs quartiles, the absolute differences between groups were relatively small. Although the absolute differences in continuous metabolic biomarkers across dietary AGEs intake quartiles were relatively small, these findings should be interpreted in the context of population-level risk. Even modest shifts in fasting glucose or total cholesterol may contribute to clinically meaningful increases in the odds of cardiometabolic diseases when thresholds for disease classification are exceeded. Accordingly, the moderate-to-substantial odds ratios observed for type 2 diabetes and hypercholesterolemia indicate that dietary AGEs may have important implications for cardiometabolic risk, despite subtle changes in mean biomarker levels. These subtle variations likely reflect cumulative metabolic responses to chronic AGEs exposure rather than acute pathological changes. Nonetheless, the consistency of these trends supports the hypothesis that long-term dietary AGEs intake may contribute to gradual metabolic dysregulation over time.
Exogenous AGEs enter the body primarily through the consumption of foods processed or cooked at high temperatures such as grilling, frying, or roasting [31, 32]. Unlike endogenously formed AGEs that arise as metabolic byproducts, dietary AGEs constitute a modifiable external source contributing to the overall oxidative and inflammatory load. Protein- and fat-rich foods are particularly susceptible to AGEs formation through the Maillard reaction, a nonenzymatic glycation process that occurs during dry-heat cooking [32, 33]. In contrast, moist-heat methods such as steaming and boiling substantially reduce AGEs formation, suggesting that dietary practices and cooking techniques can directly influence metabolic risk through AGEs exposure.
Epidemiological studies in Western populations have shown that dietary patterns high in processed and heat-treated foods, major sources of AGEs, are associated with greater incidence of metabolic syndrome and cardiovascular disease [34, 35]. Conversely, adherence to low-AGEs or minimally processed diets improves insulin sensitivity and reduces inflammatory markers, reinforcing the biological plausibility of dietary AGEs restriction as a preventive strategy [35]. Furthermore, serum AGEs accumulation was positively associated with both all-cause and cardiovascular mortality [36]. Our findings extend these observations to older adults in Korea, a population undergoing dietary westernization but maintaining distinct culinary traditions. This suggests that the detrimental effects of dietary AGEs may be universal, though their magnitude may vary depending on local dietary composition and food preparation methods. At the same time, inconsistent findings reported in other Asian cohorts, such as the Chinese study showing reduced cardiovascular and diabetes risk across both traditional and modern diets, point to possible modifying factors [37]. These may include differences in the AGEs content of regional cuisines, variations in genetic predisposition affecting AGEs metabolism, or the influence of gut microbiota composition on AGEs absorption and systemic effects. Such cross-cultural variability underscores the importance of context-specific dietary recommendations and supports the need for future research exploring gene–diet and microbiome–diet interactions in the context of AGEs exposure. Furthermore, the metabolic fate and bioavailability of dietary AGEs remain incompletely understood. Emerging evidence indicates that AGEs may also act indirectly by altering gut microbial diversity, intestinal permeability, and short-chain fatty acid production, all of which have downstream implications for systemic inflammation and cardiometabolic regulation [38]. Incorporating these mechanistic dimensions into prospective and interventional studies will be critical to fully elucidate the causal pathways linking dietary AGEs to cardiometabolic diseases. Together, these insights emphasize that reducing dietary AGEs exposure, through both food selection and cooking practices, could represent an effective, culturally adaptable strategy for mitigating age-related metabolic risk.
The association between specific AGEs and distinct cardiometabolic diseases appears to be compound-specific reflecting the differential biochemical properties and mechanisms of action of individual AGEs. Among the AGEs species examined, CML and MGH1 are primarily generated through nonenzymatic glycation and oxidation of proteins, particularly under hyperglycemic and oxidative conditions [39]. These reactions irreversibly modify key structural and enzymatic proteins involved in vascular and metabolic regulation. CML formation through lysine glycation promotes endothelial stiffening and microvascular injury, thereby contributing to diabetic complications such as retinopathy and nephropathy [40–42]. MGH1 is produced from the reaction of methylglyoxal, a reactive byproduct of glucose metabolism with arginine residues and accumulates rapidly during oxidative and metabolic stress [43–46]. Its elevated serum concentrations have been linked to reduced estimated glomerular filtration rate (eGFR) and diabetic nephropathy, suggesting direct renal toxicity and impaired detoxification capacity. These mechanistic observations correspond with our findings that higher intake of CML and MGH1 was associated with increased risks of type 2 diabetes and hypercholesterolemia, indicating that glucose-derived AGEs may act synergistically to impair insulin sensitivity and lipid regulation through oxidative and inflammatory pathways. While prior studies have linked circulating MGH1 to renal dysfunction in diabetic populations, our findings relate to dietary MGH1 intake. Thus, the observed associations should be interpreted as indirect and hypothesis-generating, highlighting the need for future studies integrating dietary assessment with direct measurement of circulating AGEs biomarkers. In contrast, CEL exerts its primary influence on lipid metabolism [47]. CEL accumulation enhances the atherogenicity of low-density lipoprotein (LDL) by promoting glycation, reducing receptor-mediated clearance, and facilitating macrophage uptake, key steps in foam cell formation and atherogenesis [47, 48]. Furthermore, CEL disrupts cholesterol homeostasis by suppressing ATP-binding cassette transporters A1 and G1 (ABCA1, ABCG1) responsible for reverse cholesterol transport, while simultaneously upregulating scavenger receptors such as CD36 and AGEs with their RAGE that drive lipid accumulation and vascular inflammation [49, 50]. These mechanisms may help explain the strong association between CEL intake and hypercholesterolemia observed in this study. Previous studies have demonstrated elevated levels of AGEs including CEL in patients with coronary heart disease suggesting a critical role for CEL in the progression of atherosclerosis in both diabetic and non-diabetic populations [51]. Collectively, these compound-specific patterns emphasize that AGEs act through diverse yet convergent mechanisms involving oxidative stress, inflammation, and lipid dysregulation [13, 48]. In the present study, CML and MGH1 exhibited strong associations with type 2 diabetes mellitus and hypercholesterolemia, whereas CEL demonstrated a more specific association with hypercholesterolemia. This heterogeneity underscores the multifactorial nature of AGEs-related cardiometabolic disorders and supports the therapeutic rationale for targeting specific AGEs–RAGE pathways rather than treating AGEs as a uniform group.
AGEs exert their deleterious effects through interconnected molecular pathways involving oxidative stress and inflammation [32]. Their accumulation stimulates reactive oxygen species (ROS) generation and activates RAGE, which in turn triggers NF-κB-mediated pro-inflammatory cascade [52]. This signaling network induces endothelial dysfunction and insulin resistance, contributing to glucose intolerance, atherogenesis, and dyslipidemia [53, 54]. The resulting increase in pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) further disrupts lipid metabolism and cholesterol homeostasis [55–57].
A major consequence of AGEs–RAGE interaction is the induction of oxidative stress, which drives lipid oxidation and metabolic imbalance [32, 58]. The resulting ROS promote oxidation of LDL, making it poorly recognized by its receptors and more readily internalized by macrophages to form foam cells, a key step in atherogenesis [59]. Concurrently, oxidative stress compromises the function of HDL and impairs reverse cholesterol transport [60]. Sustained RAGE activation also alters the expression of genes regulated by peroxisome proliferator-activated receptors (PPARs), enhancing very-low-density lipoprotein (VLDL) synthesis, reducing fatty acid oxidation, and promoting triglyceride accumulation [54, 61]. These alterations collectively produce an atherogenic lipid profile characterized by elevated LDL, decreased HDL, and increased triglycerides [54]. Furthermore, RAGE-mediated signaling contributes to insulin resistance, stimulating the release of free fatty acids that are converted into cholesterol-rich lipoproteins, thereby amplifying dyslipidemia and cardiovascular risk [53, 54].
In this study, stratification of participants by quartiles of dietary AGEs intake revealed a consistent dose–response relationship, with individuals in the highest quartile exhibiting significantly greater odds of metabolic syndrome and its components. Notably, total energy intake was inversely associated with dietary AGEs consumption. Higher intake of CML, CEL, and MGH1 was accompanied by lower overall energy intake, whereas lower AGEs intake was associated with higher energy consumption. Despite reduced caloric intake, individuals with higher AGEs consumption demonstrated adverse metabolic profiles, including elevated triglyceride levels, reduced HDL cholesterol, and impaired fasting glucose. Although higher dietary AGEs intake was accompanied by lower reported total energy intake, this inverse relationship should be interpreted with caution. Participants in higher AGEs intake quartiles were older on average, and age-related reductions in appetite and overall energy intake may partly explain this pattern. In addition, differential underreporting of energy intake in older adults cannot be excluded given the reliance on a single 24-hour dietary recall. Furthermore, although AGEs intake was adjusted for total energy using the residual method, the pronounced inverse energy gradient suggests that dietary AGEs may reflect qualitative aspects of dietary patterns, including food choices and cooking practices, rather than caloric quantity alone. Therefore, the observed associations may represent the metabolic impact of AGEs-rich dietary patterns rather than a purely energy-independent effect of AGEs. These observations reinforce the role of dietary AGEs as modifiable risk factors for metabolic health and underscore their relevance in the prevention and management of cardiometabolic disorders.
The public health implications of these findings are substantial. With the proportion of adults aged 65 years and older in Korea projected to exceed 40% of the total population by 2050, the identification of modifiable dietary risk factors for cardiometabolic disease is of increasing importance [62]. The Korean diet reflects a blend of traditional and westernized eating patterns. While the traditional diet characterized by fermented vegetables and steamed dishes is generally low in AGEs content, the growing consumption of grilled meats, processed snacks, and convenience foods has likely contributed to increased dietary AGEs exposure in recent decades. These findings support the consideration of public health strategies aimed at reducing AGEs intake, such as nutrition education, improved food labeling, and the promotion of low-AGEs cooking practices among older adults.
Beyond public health perspectives, these findings also have direct clinical relevance. Dietary AGEs intake is a modifiable factor that can be addressed through practical changes in food selection and cooking methods. For older adults at risk of type 2 diabetes or dyslipidemia, reducing the consumption of AGEs-rich foods such as fried, grilled, or processed products and adopting cooking methods like steaming or boiling may improve glycemic control and lipid metabolism. These results suggest that clinicians and dietitians could incorporate AGEs-related dietary education into individualized nutrition counseling for elderly patients with metabolic disorders. Integrating such dietary advice into clinical practice could help mitigate cardiometabolic risk and enhance healthy aging.
A strength of this study lies in the improved accuracy of dietary AGEs estimation including CML, CEL, and MGH1 through the use of a food composition database reflecting items commonly consumed in Korea. This approach may have enhanced the ability to detect associations between dietary AGEs intake and cardiometabolic risk in the elderly population. Nonetheless, several limitations should be considered. First, the cross-sectional study design precludes causal inference. Although significant associations were identified between dietary AGEs intake and cardiometabolic risk, temporal relationships cannot be established. To mitigate this limitation, we applied rigorous multivariable adjustments including major confounders such as age, sex, BMI, smoking, alcohol consumption, and physical activity to strengthen the internal validity of associations. Nevertheless, prospective cohort or longitudinal studies are warranted to confirm the temporal direction of these relationships. Second, dietary AGEs intake was assessed using a 24-hour dietary recall, which may not fully capture habitual intake because of day-to-day variability and cooking-method-dependent variation in AGEs content. This method is also susceptible to self-reporting bias including recall error. However, all dietary data were collected by trained dietitians following the standardized protocol of the KDCA, ensuring high data quality and consistency across participants. Despite these precautions, the potential for day-to-day variability in individual food intake cannot be completely ruled out. In addition, dietary intake in KNHANES is based on a single 24-hour recall per participant, and repeated recalls or seasonal adjustments were not available in the public-use dataset, which may further limit the assessment of long-term habitual dietary AGEs exposure. Additionally, although total energy intake was adjusted in all models, detailed macronutrient composition including protein, fat, and carbohydrate intake was not incorporated as covariates. Dietary AGEs are highly correlated with protein- and fat-rich foods, and including these variables could result in over-adjustment and attenuation of the true association between dietary AGEs and metabolic outcomes. However, the absence of macronutrient adjustment, particularly for dietary protein, represents a methodological limitation and should be considered when interpreting the findings. Future studies incorporating comprehensive dietary analyses of macronutrient distributions are warranted to further delineate the independent effects of dietary AGEs from those of general nutrient intake. Third, direct measurement of biomarkers such as serum AGEs was not feasible limiting the ability to validate dietary AGEs exposure biochemically. The study relied on estimated dietary AGEs intake rather than direct measurement of circulating AGEs biomarkers. Nonetheless, we emphasized that future research integrating dietary assessments with biochemical validation will be essential to verify the physiological relevance of dietary AGEs exposure. Lastly, although the AGEs food database was comprehensive, it may not fully capture the diversity of Korean cuisine, particularly traditional home-prepared dishes. Further development of a comprehensive Korean-specific AGEs database would enhance future accuracy. In addition, dietary AGEs intake was estimated using only food items with available AGEs values, which may have resulted in underestimation of absolute dietary AGEs intake. However, this conservative approach enhances internal validity by avoiding systematic bias arising from speculative assignment of AGEs values. Importantly, relative comparisons across intake quantiles are expected to remain robust under this framework. In addition, multiple cardiometabolic outcomes were evaluated without formal adjustment for multiple comparisons. Because these outcomes are biologically interrelated and reflect overlapping pathophysiological processes, applying highly conservative corrections such as Bonferroni adjustment could increase the risk of type II error and obscure potentially meaningful associations. Accordingly, the absence of multiple-testing correction may increase the risk of type I error, and the findings should therefore be interpreted with caution, with greater emphasis placed on consistency and biological plausibility across outcomes rather than on isolated nominal p-values.
Future research should employ prospective cohort designs to investigate the longitudinal associations between dietary AGEs intake and cardiometabolic risk in older populations. The inclusion of biochemical AGEs biomarkers may offer deeper insights into the biological relevance and systemic impact of dietary AGEs. Furthermore, replication studies in other populations are needed to support the generalizability of these findings.
In conclusion, the findings indicate that high dietary intake of AGEs is significantly associated with an increased risk of type 2 diabetes mellitus and hypercholesterolemia among older adults. This age-specific focus underscores the importance of incorporating AGEs exposure into dietary guidelines and chronic disease prevention strategies, particularly in the context of rapidly aging population. Reducing AGEs intake through healthier food choices and cooking methods may contribute to the mitigation of cardiometabolic disease risk in later life. Moreover, these findings provide a foundation for developing age-targeted dietary interventions aimed at minimizing AGEs exposure and preventing chronic disease in older populations.
Acknowledgements
The authors thank the efforts of those who contributed to this study.
Abbreviations
- ABCA1
ATP-binding cassette transporter A1
- ABCG1
ATP-binding cassette transporter G1
- AGEs
Advanced glycation end products
- CEL
Nɛ-(1-carboxyethyl)lysine
- CD36
Cluster of differentiation 36
- CML
Nɛ-(carboxymethyl)lysine
- 95% CI
95% confidence interval
- eGFR
Estimated glomerular filtration rate
- FBG
Fasting blood glucose
- HDL-C
High-density lipoprotein cholesterol
- IL-6
Interleukin-6
- LDL
Low-density lipoprotein
- MGH1
Nδ-(5-hydro-5-methyl-4-imidazolon-2-yl)-ornithine
- NF-κB
Nuclear factor-kappa B
- OR
Odds ratio
- PPAR
Peroxisome proliferator-activated receptor
- RAGE
Receptor for advanced glycation end products
- ROS
Reactive oxygen species
- TG
Triglycerides
- TNF-α
Tumor necrosis factor-alpha
- Total–C
Total cholesterol
- VLDL
Very-low-density lipoprotein
Authors’ contributions
Conceptualization: AS and JK. Methodology: AS and JK. Investigation: AS and JK. Writing – Original Draft: AS and JK. Writing – Review & Editing: AS and JK. Supervision: JK.
Funding
This research was supported by the Foundational and Protective Field of Studies Support Project of Changwon National University in 2024, the Glocal University Project supported through the RISE (Regional Innovation System & Education) program funded by the Ministry of Education (MOE), and the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT [MSIT]) (Grant No. RS-2025-16071386).
Data availability
The datasets analysed during the current study are available from the Korea National Health and Nutrition Examination Survey (KNHANES) website, operated by the Korea Disease Control and Prevention Agency, at https://knhanes.kdca.go.kr/knhanes/main.do. The data used in this study were obtained from KNHANES 2007–2021. Access to KNHANES data is subject to the data access procedures and data use policy of the Korea Disease Control and Prevention Agency.
Declarations
Ethics approval and consent to participate
All procedures adhered to ethical standards and were approved by the Institutional Review Board (IRB) of the Korea Disease Control and Prevention Agency (IRB No. 2018-01-03–5 C-A). Additional approval for the present study was granted by the IRB of Changwon National University (IRB No. 7001066-202503-HR-012).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets analysed during the current study are available from the Korea National Health and Nutrition Examination Survey (KNHANES) website, operated by the Korea Disease Control and Prevention Agency, at https://knhanes.kdca.go.kr/knhanes/main.do. The data used in this study were obtained from KNHANES 2007–2021. Access to KNHANES data is subject to the data access procedures and data use policy of the Korea Disease Control and Prevention Agency.