Advanced glycation end product accumulation was associated with renal function impairment in males in large health examination population

Figures Abstract The accumulation of advanced glycation end products (AGEs) is a risk factor for renal dysfunction. However, no investigation has been conducted on the association between AGEs and renal function in health screening participants. Therefore, this study aimed to examine the association between AGEs and kidney function in health screening participants who underwent health screening examination. Overall, 1,651 health screening examinees without a history of renal dysfunction diagnosis were recruited and AGE accumulation was measured by skin autofluorescence (SAF). The association between estimated glomerular filtration rate (eGFR) and AGEs was subsequently investigated in all examinees. The mean age was 56.6 ± 9.5 years; 812 (49.1%) were males, and the mean eGFR was 73.3 ± 13.4 mL/min. Multiple regression analysis identified that AGEs were significantly negatively associated with eGFR. This finding was also observed in examinees with normal eGFR (≧ 90 mL/min/1.73 m2, G1 stage according to the Japanese Society of Nephrology: N = 207). In conclusion, skin AGEs were significantly negatively associated with eGFR. To clarify whether AGEs contribute to renal dysfunction progression, additional research is required. Citation: Asaoka R, Muto S, Obana A (2026) Advanced glycation end product accumulation was associated with renal function impairment in males in large health examination population. PLoS One 21(6): e0351398. https://doi.org/10.1371/journal.pone.0351398 Editor: Donovan Anthony McGrowder, The University of the West Indies, JAMAICA Received: October 1, 2025; Accepted: May 27, 2026; Published: June 8, 2026 Copyright: © 2026 Asaoka et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The data that support the findings of this study are not publicly available due to privacy restrictions and ethical constraints. Public sharing of the data would compromise patient confidentiality and breach the protocol approved by the Research Ethics Board of Seirei Hamamatsu General Hospital. The data are available from the corresponding author and the Seirei Research Center (hmchiken@sis.seirei.or.jp) upon reasonable request. Funding: Grants 19H01114, 18KK0253, and 20K09784 from the Ministry of Education, Culture, Sports, Science, and Technology of Japan, and the Research Funding for Longevity Sciences (22-25) from National Center for Geriatrics and Gerontology (NCGG). Competing interests: No authors have competing interests. Introduction Advanced glycation end products (AGEs) are the irreversible products of nonenzymatic glycation and protein and lipid oxidation [1,2]. Under normal conditions, AGEs gradually accumulate in tissues and plasma with aging [3]. However, during oxidative and/or glycemic stress, AGEs can be rapidly produced and have been associated with the progression of conditions, such as diabetes [4], atherosclerosis [5], cardiovascular diseases [6], diabetic retinopathy [7–9], age-related macular degeneration [10], and inflammatory disorders. Recently, their association with renal dysfunction has been recognized [11–15]. Smith et al. reported that increased AGE levels lead to tissue injury by activating proinflammatory and prooxidative pathways in renal dysfunction [13]. Moreover, decreased kidney function causes lower levels of AGE excretion, thereby leading to AGE accumulation [13]. Therefore, AGE accumulation induces decreased renal function, and renal dysfunction induces AGE accumulation. To assess the concentrations of various substances within AGEs, blood tests have been commonly employed. Blood tests are costly and, owing to their invasiveness, performing them on a large scale such as in the health examination population is challenging. Nowadays, the amount of AGEs accumulated in skin can be noninvasively evaluated by measuring skin autofluorescence (SAF) [16]. Using this method, it has been reported that increased skin AGEs measured by SAF is associated with cardiovascular events and all-cause mortality in Chronic Kidney Disease (CKD) stage 3 [14,17]. Furthermore, skin AGEs measured by SAF has been reported to be an independent predictor of cardiovascular mortality in patients undergoing dialysis [11]. However, there is no report regarding the association between skin AGEs measured by SAF and renal function in health screening participants. In this study, we aimed to examine the association between AGEs measured by SAF and kidney function in health screening participants without a history of renal dysfunction who underwent a health screening examination. Materials and methods Ethics approval Participants and clinical data The participants’ smoking habits and alcohol consumption were also assessed. Alcohol score was counted as 1: none, 2: < 180 ml/day, 3: ≧ 180 ml/day and < 360 ml/day, 4: ≧ 360 ml/day and < 540, 5: ≧ 540 ml/day. Measurement of AGEs Measurement of skin AGEs was performed using an AGE sensor (Air Water Biodesign Inc., Hyogo, Japan). The AGE sensor measures skin autofluorescence derived from AGEs and enables noninvasive assessment of skin AGE accumulation. The validity of this measurement has been supported by comparisons with skin biopsy results [16]. The measurement values are expressed in arbitrary units. The interrater reliability and two-timepoint agreement of the AGE sensor have been reported in detail, demonstrating high reliability [18]. A light source emitting light at a 365-nm wavelength excites fluorescent moieties in compounds in the skin to produce fluorescence at 440 nm. The output represents the ratio between autofluorescence in the 440-nm range and excitation light in the 365-nm range and is reported in arbitrary units (AU). Before measurement, calibration was performed according to the manufacturer’s instructions. Skin AGE measurements were performed on the palmar side of the middle finger tip. Statistical analysis All results were expressed as means ± SDs. Comparisons were made using the nonpaired t-test and chi-square test for numerical and categorical variables, respectively. The association between eGFR and the values of skin AGEs, age, sex, BMI, SBP, DBP, Alb, LDL-Cho, HDL-Cho, TG, HbA1c, WBC, RBC, Hb, Ht, Plt, and CRP levels, presence of smoking habit and alcohol score, usage of antihypertensive medicine and lipid/cholesterol-lowering agents, as well as the interaction term between skin AGE and sex was analyzed using multivariate linear regression. Then, model selection was performed through a two-stage model selection. First, most important 10 parameters were selected from all 22 clinical parameters using the least absolute shrinkage and selection operator regression [19,20]. Then the final optimum model was selected using the second-order bias-corrected Akaike information criterion (AICc) index; the optimal model for the occurrence of hypotony complications was identified from all 210 patterns using the 10 candidate variables. This two-stage method was necessary to reduce the total number of variables in the exhaustive AICc model selection. The AIC is a well-known statistical measurement used in model selection, whereas the AICc is a corrected version of the AIC, which provides an accurate estimation even when the sample size is small [21]. These analyses were iterated in examinees in the G1 (normal: eGFR ≧ 90 mL/min/1.73 m2) stage, according to the Japanese Society of Nephrology guidelines (https://jsn.or.jp/en/guideline/guideline.php)[22]. Results The subject data of participants including skin AGE score (measurement values obtained using AGE sensor), age, BMI, SBP, DBP, Alb, eGFR, LDL-Cho, HDL-Cho, TG, HbA1c, WBC, RBC, Hb, Ht, Plt, and CRP levels, the presence of smoking habit and alcohol score, as well as the usage of antihypertensive medicine and lipid/cholesterol-lowering agents are shown in Table 1. 340 among the 1,651 participants were patients with diabetes (Table 1). The two-stage model selection with LASSO regression and AICc model selection resulted in the variables of skin AGEs, age, BMI, HbA1c, Ht, Plt, UA, CRP, smoking habits, and usage of antihypertensive medicine. Conducting the multiple regression analysis revealed a significant correlation between eGFR and AGE score, age, BMI, UA, HbA1c, Plt, and, as well as the presence of smoking habit (Table 2). In the subanalysis of the screening examinees in the G1 stage (N = 207), AGE score, BMI and Hb were significantly associated with eGFR (Table 3). Discussion In the present study, the variable significantly associated with eGFR was identified in 1,651 health screening participants without a history of renal dysfunction who underwent health screening examination. AGEs showed a significantly negative correlation with eGFR in males but not in females. Several studies have suggested an association between AGEs and kidney dysfunction. For instance, in kidney injury, endothelial cells in the kidney stimulated by AGEs release proinflammatory mediators and adhesion molecules [23–25], which lead to tissue damage and dysfunction in the kidney. Consistent with this finding, Schutte et al. reported that elevated AGE levels were associated with lower baseline eGFR in patients with peripheral artery disease [26]. Moreover, Osawa et al. reported that AGEs is an independent predictor for diabetic nephropathy in Japanese patients with type 2 diabetes mellitus [27]. A previous study has suggested that AGEs is an independent risk factor for cardiovascular events and all-cause mortality in patients with CKD stage 3 [17]. Moreover, the current results suggest that elevated AGEs was associated with eGFR deterioration even in health screening participants (Table 1), and even in those in G1 stage (Table 2). There have been contradicting results regarding the sex difference in AGEs [28–31]. A complex relationship exists between gender and kidney diseases, with males and females having different biological susceptibilities to the disease [32]. Previous studies have shown that although more females have renal dysfunction than males, there is a greater likelihood of males to develop kidney failure faster than females. For this reason, the male gender is a risk factor for developing kidney failure with greater severity [33,34]. The reason for these gender differences is not clearly understood; however, the current results suggested that the effect of skin AGEs on eGFR did not differ between males and females (Tables 2 and 3). HbA1c is a product of glycation similar to AGEs, and elevated HbA1c levels were significantly associated with increased eGFR in all examinees in the current study. This may be due to the glomerular hyperfiltration in the early stage of diabetes [35]. Higher BMI was associated with increased eGFR in all examinees and also in those in G1 stage. This may be due to the obesity-related glomerulopathy [36]. Higher UA and smoking habits are established risk factor for renal dysfunction. Both of these were confirmed to have significant association with eGFR in all examinees, in the current study. Age was significantly associated with eGFR in all examinees. A careful consideration is needed when interpreting the effect of age on eGFR, because age is a component of the eGFR formula used to estimate creatinine production and muscle mass, however inclusion as an independent covariate in the regression model is essential to adjust for biological aging, vascular integrity, and the cumulative impact of lifestyle factors—factors that influence clinical outcomes independently of renal function. Our study had some limitations. First, this was a cross-sectional study; therefore, it was neither controlled nor randomized. Second, no detailed dietary questionnaire was used in this study. Finally, the relatively older age of the population could limit the generalizability of our results to younger populations. In addition, proteinuria, albuminuria and dietary intake of AGEs were not measured in all participants. Moreover, the very low prevalence of smoking, especially in female. A further study is needed to investigate the effect of smoking using another dataset with higher prevalence. Finally, in people> 50 years old, eGFR decline is a natural consequence of age-related glomerulosclerosis. This is certainly associated with AGEs accumulation. However, our study had several strengths, including the large number of examinees and comprehensive assessments of their clinical characteristics and laboratory data. In conclusion, the current study suggested that AGEs were significantly negatively associated with eGFR in health screening participants. Similar result was also observed in those with normal eGFR. To clarify whether AGEs contribute to the longitudinal progression of renal dysfunction, additional research is required. Acknowledgments We thank Mr. Yamanaka (Shimadzu corporation Future Strategy Department, Technology Research Laboratory) for his assistance with the use of the AGE sensor and the interpretation of the measurement results. References - 1. Hartog JWL, Voors AA, Bakker SJL, Smit AJ, van Veldhuisen DJ. Advanced glycation end-products (AGEs) and heart failure: pathophysiology and clinical implications. Eur J Heart Fail. 2007;9(12):1146–55. pmid:18023248 - 2. Miyata T, Sugiyama S, Saito A, Kurokawa K. Reactive carbonyl compounds related uremic toxicity (“carbonyl stress”). Kidney Int Suppl. 2001;78:S25-31. pmid:11168978 - 3. Schleicher ED, Wagner E, Nerlich AG. Increased accumulation of the glycoxidation product N(epsilon)-(carboxymethyl)lysine in human tissues in diabetes and aging. J Clin Invest. 1997;99(3):457–68. pmid:9022079 - 4. Stitt AW. Advanced glycation: an important pathological event in diabetic and age related ocular disease. Br J Ophthalmol. 2001;85(6):746–53. pmid:11371498 - 5. Bucciarelli LG, Kaneko M, Ananthakrishnan R, Harja E, Lee LK, Hwang YC, et al. Receptor for advanced-glycation end products: key modulator of myocardial ischemic injury. Circulation. 2006;113(9):1226–34. pmid:16505177 - 6. Makulska I, Szczepańska M, Drożdż D, Polak-Jonkisz D, Zwolińska D. Skin autofluorescence as a marker of cardiovascular risk in children with chronic kidney disease. Pediatr Nephrol. 2013;28(1):121–8. pmid:22976887 - 7. Yasuda M, Shimura M, Kunikata H, Kanazawa H, Yasuda K, Tanaka Y, et al. Relationship of skin autofluorescence to severity of retinopathy in type 2 diabetes. Curr Eye Res. 2015;40(3):338–45. pmid:24871684 - 8. Stitt AW, Curtis TM. Advanced glycation and retinal pathology during diabetes. Pharmacol Rep. 2005;57 Suppl:156–68. pmid:16415497 - 9. Hirano T, Iesato Y, Toriyama Y, Imai A, Chiba D, Murata T. Correlation between diabetic retinopathy severity and elevated skin autofluorescence as a marker of advanced glycation end-product accumulation in type 2 diabetic patients. J Diabetes Complications. 2014;28(5):729–34. pmid:24795072 - 10. Semba RD, Cotch MF, Gudnason V, Eiríksdottir G, Harris TB, Sun K, et al. Serum Carboxymethyllysine, an Advanced Glycation End Product, and Age-Related Macular Degeneration. JAMA Ophthalmol. 2014;132(4):464. - 11. Meerwaldt R, Hartog JWL, Graaff R, Huisman RJ, Links TP, den Hollander NC, et al. Skin autofluorescence, a measure of cumulative metabolic stress and advanced glycation end products, predicts mortality in hemodialysis patients. J Am Soc Nephrol. 2005;16(12):3687–93. pmid:16280473 - 12. Semba RD, Fink JC, Sun K, Windham BG, Ferrucci L. Serum carboxymethyl-lysine, a dominant advanced glycation end product, is associated with chronic kidney disease: the Baltimore longitudinal study of aging. J Ren Nutr. 2010;20(2):74–81. pmid:19853477 - 13. Smit AJ, Gerrits EG. Skin autofluorescence as a measure of advanced glycation endproduct deposition: a novel risk marker in chronic kidney disease. Curr Opin Nephrol Hypertens. 2010;19(6):527–33. pmid:20844429 - 14. McIntyre NJ, Fluck RJ, McIntyre CW, Taal MW. Skin autofluorescence and the association with renal and cardiovascular risk factors in chronic kidney disease stage 3. Clin J Am Soc Nephrol. 2011;6(10):2356–63. pmid:21885790 - 15. Tanaka K, Nakayama M, Kanno M, Kimura H, Watanabe K, Tani Y, et al. Skin autofluorescence is associated with the progression of chronic kidney disease: a prospective observational study. PLoS One. 2013;8(12):e83799. pmid:24349550 - 16. Meerwaldt R, Graaff R, Oomen PHN, Links TP, Jager JJ, Alderson NL, et al. Simple non-invasive assessment of advanced glycation endproduct accumulation. Diabetologia. 2004;47(7):1324–30. pmid:15243705 - 17. Shardlow A, McIntyre NJ, Kolhe NV, Nellums LB, Fluck RJ, McIntyre CW, et al. The association of skin autofluorescence with cardiovascular events and all-cause mortality in persons with chronic kidney disease stage 3: A prospective cohort study. PLoS Med. 2020;17(7):e1003163. pmid:32658890 - 18. Adl Amini D, Moser M, Chiapparelli E, Oezel L, Zhu J, Okano I, et al. A Prospective Analysis of Skin and Fingertip Advanced Glycation End-Product Devices in Healthy Volunteers. J Clin Med. 2022;11(16):4709. pmid:36012948 - 19. Tibshirani R. Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology. 1996;58(1):267–88. - 20. Friedman J, Hastie T, Tibshirani R. Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw. 2010;33(1):1–22. pmid:20808728 - 21. Burnham KP, Anderson DR. Multimodel Inference. Sociological Methods & Research. 2016;33(2):261–304. - 22. Japanese Society of Nephrology. Essential points from evidence-based clinical practice guideline for chronic kidney disease 2023. Clin Exp Nephrol. 2024;28(6):473–95. pmid:38713253 - 23. Hartog JWL, Smit AJ, van Son WJ, Navis G, Gans ROB, Wolffenbuttel BHR, et al. Advanced glycation end products in kidney transplant patients: a putative role in the development of chronic renal transplant dysfunction. Am J Kidney Dis. 2004;43(6):966–75. pmid:15168376 - 24. Basta G, Lazzerini G, Massaro M, Simoncini T, Tanganelli P, Fu C, et al. Advanced glycation end products activate endothelium through signal-transduction receptor RAGE: a mechanism for amplification of inflammatory responses. Circulation. 2002;105(7):816–22. pmid:11854121 - 25. Rader DJ. Inflammatory markers of coronary risk. N Engl J Med. 2000;343(16):1179–82. pmid:11036126 - 26. Schutte E, de Vos LC, Lutgers HL, Lambers Heerspink HJ, Wolffenbuttel BHR, Vart P, et al. Association of Skin Autofluorescence Levels With Kidney Function Decline in Patients With Peripheral Artery Disease. Arterioscler Thromb Vasc Biol. 2016;36(8):1709–14. pmid:27365407 - 27. Osawa S, Katakami N, Sato I, Ninomiya H, Omori K, Yamamoto Y, et al. Skin autofluorescence is associated with vascular complications in patients with type 2 diabetes. J Diabetes Complications. 2018;32(9):839–44. pmid:30099985 - 28. Yue X, Hu H, Koetsier M, Graaff R, Han C. Reference values for the Chinese population of skin autofluorescence as a marker of advanced glycation end products accumulated in tissue. Diabet Med. 2011;28(7):818–23. pmid:21204956 - 29. Simon Klenovics K, Kollárová R, Hodosy J, Celec P, Sebeková K. Reference values of skin autofluorescence as an estimation of tissue accumulation of advanced glycation end products in a general Slovak population. Diabet Med. 2014;31(5):581–5. pmid:24111899 - 30. Koetsier M, Lutgers HL, de Jonge C, Links TP, Smit AJ, Graaff R. Reference values of skin autofluorescence. Diabetes Technol Ther. 2010;12(5):399–403. pmid:20388050 - 31. Wolffenbuttel BHR, Boersma HE, van Waateringe R, Paterson AD, van der Klauw MM. Reference values and biological factors influencing skin autofluorescence. Front Endocrinol (Lausanne). 2025;16:1700892. pmid:41282282 - 32. Murphy D, McCulloch CE, Lin F, Banerjee T, Bragg-Gresham JL, Eberhardt MS, et al. Trends in Prevalence of Chronic Kidney Disease in the United States. Ann Intern Med. 2016;165(7):473–81. pmid:27479614 - 33. Ricardo AC, Yang W, Sha D, Appel LJ, Chen J, Krousel-Wood M, et al. Sex-Related Disparities in CKD Progression. J Am Soc Nephrol. 2019;30(1):137–46. pmid:30510134 - 34. Harris RC, Zhang M-Z. The role of gender disparities in kidney injury. Ann Transl Med. 2020;8(7):514. pmid:32395558 - 35. Tonneijck L, Muskiet MHA, Smits MM, van Bommel EJ, Heerspink HJL, van Raalte DH, et al. Glomerular Hyperfiltration in Diabetes: Mechanisms, Clinical Significance, and Treatment. J Am Soc Nephrol. 2017;28(4):1023–39. pmid:28143897 - 36. Chagnac A, Weinstein T, Herman M, Hirsh J, Gafter U, Ori Y. The effects of weight loss on renal function in patients with severe obesity. J Am Soc Nephrol. 2003;14(6):1480–6. pmid:12761248