Acrylamide Exposure and Cardiovascular Risk: A Systematic Review
Abstract
:1. Introduction
2. Materials and Methods
2.1. Search Strategy
2.2. Study Selection
2.3. Outcomes
2.4. Data Extraction
2.5. Risk of Bias
2.6. Data Synthesis
3. Results
3.1. Search Results
3.2. Characteristics of the Included Studies
3.3. Risk of Bias
3.4. Association Between Acrylamide Exposure and CVD Mortality and CVD Risk
3.5. Association Between Acrylamide Exposure and Diabetes and Glucose Metabolism
3.6. Association Between Acrylamide Exposure and Dyslipidemia and Lipid Metabolism
3.7. Association Between Acrylamide Exposure and Obesity and Body Composition
3.8. Association Between Acrylamide Exposure and Hypertension and Blood Pressure
3.9. Association Between Acrylamide Exposure and Metabolic Syndrome
3.10. Association Between Acrylamide Exposure and Cardiovascular Risk Factors in Non-Smokers
3.11. Association Between Acrylamide Exposure and Cardiovascular Risk Factors in Smokers
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Exon, J.H. A Review of the Toxicology of Acrylamide. J. Toxicol. Environ. Health Part B 2006, 9, 397–412. [Google Scholar] [CrossRef] [PubMed]
- Substance Information—ECHA. Available online: https://echa.europa.eu/es/substance-information/-/substanceinfo/100.001.067 (accessed on 31 July 2024).
- Sarion, C.; Codină, G.G.; Dabija, A. Acrylamide in Bakery Products: A Review on Health Risks, Legal Regulations and Strategies to Reduce Its Formation. Int. J. Environ. Res. Public Health 2021, 18, 4332. [Google Scholar] [CrossRef] [PubMed]
- Petersen, B.J.; Tran, N. Exposure to Acrylamide. In Chemistry and Safety of Acrylamide in Food; Springer: Boston, MA, USA, 2005; pp. 63–76. [Google Scholar] [CrossRef]
- Peris-Camarasa, B.; Pardo, O.; Fernández, S.F.; Dualde, P.; Coscollà, C. Assessment of Acrylamide Exposure in Spain by Human Biomonitoring: Risk and Predictors of Exposure. Environ. Pollut. 2023, 331, 121896. [Google Scholar] [CrossRef] [PubMed]
- Schettgen, T.; Rossbach, B.; Kütting, B.; Letzel, S.; Drexler, H.; Angerer, J. Determination of Haemoglobin Adducts of Acrylamide and Glycidamide in Smoking and Non-Smoking Persons of the General Population. Int. J. Hyg. Environ. Health 2004, 207, 531–539. [Google Scholar] [CrossRef] [PubMed]
- Besaratinia, A.; Pfeifer, G.P. Genotoxicity of Acrylamide and Glycidamide. J. Natl. Cancer Inst. 2004, 96, 1023–1029. [Google Scholar] [CrossRef]
- Pedersen, M.; Vryonidis, E.; Joensen, A.; Törnqvist, M. Hemoglobin Adducts of Acrylamide in Human Blood—What Has Been Done and What Is Next? Food Chem. Toxicol. 2022, 161, 112799. [Google Scholar] [CrossRef]
- Bjellaas, T.; Stølen, L.H.; Haugen, M.; Paulsen, J.E.; Alexander, J.; Lundanes, E.; Becher, G. Urinary Acrylamide Metabolites as Biomarkers for Short-Term Dietary Exposure to Acrylamide. Food Chem. Toxicol. 2007, 45, 1020–1026. [Google Scholar] [CrossRef]
- Obón-Santacana, M.; Lujan-Barroso, L.; Freisling, H.; Cadeau, C.; Fagherazzi, G.; Boutron-Ruault, M.C.; Kaaks, R.; Fortner, R.T.; Boeing, H.; Ramón Quirós, J.; et al. Dietary and Lifestyle Determinants of Acrylamide and Glycidamide Hemoglobin Adducts in Non-Smoking Postmenopausal Women from the EPIC Cohort. Eur. J. Nutr. 2017, 56, 1157. [Google Scholar] [CrossRef]
- Shipp, A.; Lawrence, G.; Gentry, R.; McDonald, T.; Bartow, H.; Bounds, J.; Macdonald, N.; Clewell, H.; Allen, B.; Van Landingham, C. Acrylamide: Review of Toxicity Data and Dose-Response Analyses for Cancer and Noncancer Effects. Crit. Rev. Toxicol. 2006, 36, 481–608. [Google Scholar] [CrossRef]
- Filippini, T.; Halldorsson, T.I.; Capitão, C.; Martins, R.; Giannakou, K.; Hogervorst, J.; Vinceti, M.; Åkesson, A.; Leander, K.; Katsonouri, A.; et al. Dietary Acrylamide Exposure and Risk of Site-Specific Cancer: A Systematic Review and Dose-Response Meta-Analysis of Epidemiological Studies. Front. Nutr. 2022, 9, 875607. [Google Scholar] [CrossRef]
- Adani, G.; Filippini, T.; Wise, L.A.; Halldorsson, T.I.; Blaha, L.; Vinceti, M. Dietary Intake of Acrylamide and Risk of Breast, Endometrial, and Ovarian Cancers: A Systematic Review and Dose-Response Meta-Analysis. Cancer Epidemiol. Biomark. Prev. 2020, 29, 1095–1106. [Google Scholar] [CrossRef] [PubMed]
- Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef] [PubMed]
- PROSPERO. Available online: https://www.crd.york.ac.uk/prospero/ (accessed on 31 July 2024).
- JBI Critical Appraisal Tools|JBI. Available online: https://jbi.global/critical-appraisal-tools (accessed on 31 July 2024).
- Marsh, G.M.; Youk, A.O.; Buchanich, J.M.; Kant, I.J.; Swaen, G. Mortality Patterns among Workers Exposed to Acrylamide: Updated Follow Up. J. Occup. Environ. Med. 2007, 49, 82–95. [Google Scholar] [CrossRef] [PubMed]
- Swaen, G.M.H.; Haidar, S.; Burns, C.J.; Bodner, K.; Parsons, T.; Collins, J.J.; Baase, C. Mortality Study Update of Acrylamide Workers. Occup. Environ. Med. 2007, 64, 396. [Google Scholar] [CrossRef] [PubMed]
- Huang, M.; Jiao, J.; Wang, J.; Chen, X.; Zhang, Y. Associations of Hemoglobin Biomarker Levels of Acrylamide and All-Cause and Cardiovascular Disease Mortality among U.S. Adults: National Health and Nutrition Examination Survey 2003–2006. Environ. Pollut. 2018, 238, 852–858. [Google Scholar] [CrossRef]
- Wu, H.; Sun, X.; Jiang, H.; Hu, C.; Xu, J.; Sun, C.; Wei, W.; Han, T.; Jiang, W. The Association Between Exposure to Acrylamide and Mortalities of Cardiovascular Disease and All-Cause Among People with Hyperglycemia. Front. Cardiovasc. Med. 2022, 9, 930135. [Google Scholar] [CrossRef]
- Marques, C.; Frenoy, P.; Elbaz, A.; Laouali, N.; Shah, S.; Severi, G.; Mancini, F.R. Association between Dietary Intake of Acrylamide and Increased Risk of Mortality in Women: Evidence from the E3N Prospective Cohort. Sci. Total Environ. 2024, 906, 167514. [Google Scholar] [CrossRef]
- Feng, X.; Qiu, F.; Zheng, L.; Zhang, Y.; Wang, Y.; Wang, M.; Xia, H.; Tang, B.; Yan, C.; Liang, R. Exposure to Volatile Organic Compounds and Mortality in US Adults: A Population-Based Prospective Cohort Study. Sci. Total Environ. 2024, 928, 172512. [Google Scholar] [CrossRef]
- Nalini, M.; Poustchi, H.; Bhandari, D.; Chang, C.M.; Blount, B.C.; Wang, L.; Feng, J.; Gross, A.; Khoshnia, M.; Pourshams, A.; et al. Volatile Organic Compounds and Mortality from Ischemic Heart Disease: A Case-Cohort Study. Am. J. Prev. Cardiol. 2024, 19, 100700. [Google Scholar] [CrossRef]
- Zhang, Y.; Huang, M.; Zhuang, P.; Jiao, J.; Chen, X.; Wang, J.; Wu, Y. Exposure to Acrylamide and the Risk of Cardiovascular Diseases in the National Health and Nutrition Examination Survey 2003–2006. Environ. Int. 2018, 117, 154–163. [Google Scholar] [CrossRef]
- Wang, B.; Wang, X.; Yu, L.; Liu, W.; Song, J.; Fan, L.; Zhou, M.; Yang, M.; Ma, J.; Cheng, M.; et al. Acrylamide Exposure Increases Cardiovascular Risk of General Adult Population Probably by Inducing Oxidative Stress, Inflammation, and TGF-Β1: A Prospective Cohort Study. Environ. Int. 2022, 164, 107261. [Google Scholar] [CrossRef] [PubMed]
- Wang, X.; Chen, Z.; Cheng, D.; Cao, Y.; Xie, X.; Zhou, J.; Wu, Y.; Li, X.; Yu, J.; Yang, B. Association between Urinary Metabolites of Volatile Organic Compounds and Cardiovascular Disease in the General Population from NHANES 2011–2018. Ecotoxicol. Environ. Saf. 2023, 264, 115412. [Google Scholar] [CrossRef] [PubMed]
- Ma, M.; Zhu, X.; Li, F.; Guan, G.; Hui, R.; Zhu, L.; Pang, H.; Zhang, Y. Associations of Urinary Volatile Organic Compounds with Cardiovascular Disease among the General Adult Population. Int. J. Environ. Health Res. 2024, 34, 3876–3890. [Google Scholar] [CrossRef] [PubMed]
- Han, S.; Xie, M.; Cheng, S.; Han, Y.; Li, P.; Guo, J. Associations between Specific Volatile Organic Chemical Exposures and Cardiovascular Disease Risks: Insights from NHANES. Front. Public Health 2024, 12, 1378444. [Google Scholar] [CrossRef]
- Lin, C.Y.; Lin, Y.C.; Kuo, H.K.; Hwang, J.J.; Lin, J.L.; Chen, P.C.; Lin, L.Y. Association among Acrylamide, Blood Insulin, and Insulin Resistance in Adults. Diabetes Care 2009, 32, 2206–2211. [Google Scholar] [CrossRef]
- Lin, C.Y.; Lee, H.L.; Chen, Y.C.; Lien, G.W.; Lin, L.Y.; Wen, L.L.; Liao, C.C.; Chien, K.L.; Sung, F.C.; Chen, P.C.; et al. Positive Association between Urinary Levels of 8-Hydroxydeoxyguanosine and the Acrylamide Metabolite N-Acetyl-S-(Propionamide)-Cysteine in Adolescents and Young Adults. J. Hazard. Mater. 2013, 261, 372–377. [Google Scholar] [CrossRef]
- Wang, B.; Qiu, W.; Yang, S.; Cao, L.; Zhu, C.; Ma, J.; Li, W.; Zhang, Z.; Xu, T.; Wang, X.; et al. Acrylamide Exposure and Oxidative DNA Damage, Lipid Peroxidation, and Fasting Plasma Glucose Alteration: Association and Mediation Analyses in Chinese Urban Adults. Diabetes Care 2020, 43, 1479–1486. [Google Scholar] [CrossRef]
- Yin, G.; Liao, S.; Gong, D.; Qiu, H. Association of Acrylamide and Glycidamide Haemoglobin Adduct Levels with Diabetes Mellitus in the General Population. Environ. Pollut. 2021, 277, 116816. [Google Scholar] [CrossRef]
- Hosseini-Esfahani, F.; Beheshti, N.; Nematollahi, A.; Koochakpoor, G.; Verij-Kazemi, S.; Mirmiran, P.; Azizi, F. The Association between Dietary Acrylamide Intake and the Risk of Type 2 Diabetes Incidence in the Tehran Lipid and Glucose Study. Sci. Rep. 2023, 13, 8235. [Google Scholar] [CrossRef]
- Cheang, I.; Liao, S.; Zhu, X.; Lu, X.; Zhu, Q.; Yao, W.; Zhou, Y.; Zhang, H.; Li, X. Association of Acrylamide Hemoglobin Biomarkers with Serum Lipid Levels in General US Population: NHANES 2013–2016. Ecotoxicol. Environ. Saf. 2021, 214, 112111. [Google Scholar] [CrossRef]
- Chen, W.Y.; Fu, Y.P.; Tu, H.; Zhong, W.; Zhou, L. The Association between Exposure to Volatile Organic Compounds and Serum Lipids in the US Adult Population. Lipids Health Dis. 2023, 22, 129. [Google Scholar] [CrossRef] [PubMed]
- Chu, P.L.; Lin, L.Y.; Chen, P.C.; Su, T.C.; Lin, C.Y. Negative Association between Acrylamide Exposure and Body Composition in Adults: NHANES, 2003–2004. Nutr. Diabetes 2017, 7, e246. [Google Scholar] [CrossRef] [PubMed]
- Huang, M.; Zhuang, P.; Jiao, J.; Wang, J.; Zhang, Y. Association of Acrylamide Hemoglobin Biomarkers with Obesity, Abdominal Obesity and Overweight in General US Population: NHANES 2003–2006. Sci. Total Environ. 2018, 631–632, 589–596. [Google Scholar] [CrossRef] [PubMed]
- Yin, T.; Xu, F.; Shi, S.; Liao, S.; Tang, X.; Zhang, H.; Zhou, Y.; Li, X. Vitamin D Mediates the Association between Acrylamide Hemoglobin Biomarkers and Obesity. Environ. Sci. Pollut. Res. Int. 2022, 29, 17162–17172. [Google Scholar] [CrossRef]
- Lei, T.; Qian, H.; Yang, J.; Hu, Y. The Association Analysis between Exposure to Volatile Organic Chemicals and Obesity in the General USA Population: A Cross-Sectional Study from NHANES Program. Chemosphere 2023, 315, 137738. [Google Scholar] [CrossRef]
- Liang, J.; Xu, C.; Liu, Q.; Weng, Z.; Zhang, X.; Xu, J.; Gu, A. Total Cholesterol: A Potential Mediator of the Association between Exposure to Acrylamide and Hypertension Risk in Adolescent Females. Environ. Sci. Pollut. Res. 2022, 29, 38425–38434. [Google Scholar] [CrossRef]
- McGraw, K.E.; Konkle, S.L.; Riggs, D.W.; Rai, S.N.; DeJarnett, N.; Xie, Z.; Keith, R.J.; Oshunbade, A.; Hall, M.E.; Shimbo, D.; et al. Exposure to Volatile Organic Compounds Is Associated with Hypertension in Black Adults: The Jackson Heart Study. Environ. Res. 2023, 223, 115384. [Google Scholar] [CrossRef]
- Hung, C.C.; Cheng, Y.W.; Chen, W.L.; Fang, W.H. Negative Association between Acrylamide Exposure and Metabolic Syndrome Markers in Adult Population. Int. J. Environ. Res. Public Health 2021, 18, 11949. [Google Scholar] [CrossRef]
- Wan, X.; Zhu, F.; Zhuang, P.; Liu, X.; Zhang, L.; Jia, W.; Jiao, J.; Xu, C.; Zhang, Y. Associations of Hemoglobin Adducts of Acrylamide and Glycidamide with Prevalent Metabolic Syndrome in a Nationwide Population-Based Study. J. Agric. Food Chem. 2022, 70, 8755–8766. [Google Scholar] [CrossRef]
- Tan, L.; Liu, Y.; Liu, J.; Liu, Z.; Shi, R. Associations of Individual and Mixture Exposure to Volatile Organic Compounds with Metabolic Syndrome and Its Components among US Adults. Chemosphere 2024, 347, 140683. [Google Scholar] [CrossRef]
- Tanaka, E.; Terada, M.; Misawa, S. Cytochrome P450 2E1: Its Clinical and Toxicological Role. J. Clin. Pharm. Ther. 2000, 25, 165–175. [Google Scholar] [CrossRef] [PubMed]
- Vryonidis, E.; Törnqvist, M.; Lignell, S.; Rosén, J.; Aasa, J. Estimation of Intake and Quantification of Hemoglobin Adducts of Acrylamide in Adolescents in Sweden. Front. Nutr. 2024, 11, 1371612. [Google Scholar] [CrossRef] [PubMed]
- Fernández, S.F.; Poteser, M.; Govarts, E.; Pardo, O.; Coscollà, C.; Schettgen, T.; Vogel, N.; Weber, T.; Murawski, A.; Kolossa-Gehring, M.; et al. Determinants of Exposure to Acrylamide in European Children and Adults Based on Urinary Biomarkers: Results from the “European Human Biomonitoring Initiative” HBM4EU Participating Studies. Sci. Rep. 2023, 13, 21291. [Google Scholar] [CrossRef] [PubMed]
- Duke, T.J.; Ruestow, P.S.; Marsh, G.M. The Influence of Demographic, Physical, Behavioral, and Dietary Factors on Hemoglobin Adduct Levels of Acrylamide and Glycidamide in the General U.S. Population. Crit. Rev. Food Sci. Nutr. 2018, 58, 700–710. [Google Scholar] [CrossRef]
- Wang, L.; Martínez Steele, E.; Du, M.; Pomeranz, J.L.; O’Connor, L.E.; Herrick, K.A.; Luo, H.; Zhang, X.; Mozaffarian, D.; Zhang, F.F. Trends in Consumption of Ultraprocessed Foods Among US Youths Aged 2–19 Years, 1999–2018. JAMA 2021, 326, 519–530. [Google Scholar] [CrossRef]
- Lauria, F.; Dello Russo, M.; Formisano, A.; De Henauw, S.; Hebestreit, A.; Hunsberger, M.; Krogh, V.; Intemann, T.; Lissner, L.; Molnar, D.; et al. Ultra-Processed Foods Consumption and Diet Quality of European Children, Adolescents and Adults: Results from the I.Family Study. Nutr. Metab. Cardiovasc. Dis. 2021, 31, 3031–3043. [Google Scholar] [CrossRef]
- Lee, H.W.; Pyo, S. Acrylamide Induces Adipocyte Differentiation and Obesity in Mice. Chem. Biol. Interact. 2019, 298, 24–34. [Google Scholar] [CrossRef]
- Egusquiza, R.J.; Blumberg, B. Environmental Obesogens and Their Impact on Susceptibility to Obesity: New Mechanisms and Chemicals. Endocrinology 2020, 161, bqaa024. [Google Scholar] [CrossRef]
- Marion-Letellier, R.; Savoye, G.; Ghosh, S. Fatty Acids, Eicosanoids and PPAR Gamma. Eur. J. Pharmacol. 2016, 785, 44–49. [Google Scholar] [CrossRef]
- Wang, Q.; Imam, M.U.; Yida, Z.; Wang, F. Peroxisome Proliferator-Activated Receptor Gamma (PPARγ) as a Target for Concurrent Management of Diabetes and Obesity-Related Cancer. Curr. Pharm. Des. 2017, 23, 3677–3688. [Google Scholar] [CrossRef]
- Yue, Z.; Chen, Y.; Song, Y.; Zhang, J.; Yang, X.; Wang, J.; Li, L.; Sun, Z. Effect of Acrylamide on Glucose Homeostasis in Female Rats and Its Mechanisms. Food Chem. Toxicol. 2020, 135, 110894. [Google Scholar] [CrossRef] [PubMed]
- Michalak, J.; Gujska, E.; Czarnowska-Kujawska, M.; Nowak, F. Effect of Different Home-Cooking Methods on Acrylamide Formation in Pre-Prepared Croquettes. J. Food Compos. Anal. 2017, 56, 134–139. [Google Scholar] [CrossRef]
- Navruz-Varlı, S.; Mortaş, H. Acrylamide Formation in Air-Fried versus Deep and Oven-Fried Potatoes. Front. Nutr. 2023, 10, 1297069. [Google Scholar] [CrossRef] [PubMed]
- Commission Regulation (EU) 2017/2158 Establishing Mitigation Measures and Benchmark Levels for the Reduction of the Presence of Acrylamide in Food.|FAOLEX. Available online: https://www.fao.org/faolex/results/details/en/c/LEX-FAOC171340/ (accessed on 31 July 2024).
- Guidance for Industry: Acrylamide in Foods|FDA. Available online: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/guidance-industry-acrylamide-foods (accessed on 31 July 2024).
- Aesan—Agencia Española de Seguridad Alimentaria y Nutrición. Available online: https://www.aesan.gob.es/AECOSAN/web/seguridad_alimentaria/campanyas/acrilamida.htm (accessed on 31 July 2024).
- Acrylamide Toolbox—FoodDrinkEurope: FoodDrinkEurope. Available online: https://www.fooddrinkeurope.eu/resource/acrylamide-toolbox/ (accessed on 31 July 2024).
Author (Year) | Country (Cohort) | Study Design | Sample Size | Age in Years | Men (%) | Acrylamide Exposure (Mean or Median) | Outcomes | Covariates in the Fully Adjusted Model |
---|---|---|---|---|---|---|---|---|
CVD mortality | ||||||||
Marsh GM et al. (2007) [17] | US and NL | Prospective (US follow-up: 1925–2002; the NL follow-up: 1965–2004) | US: 8508; NL: 344 | NA | 100 | Cumulative acrylamide exposure, mg/m3-year (US mean: 0.098; NL mean: 0.114) | -CVD mortality: US: ICD 8th (390–548); NL: ICD 9th (390–548) | Age, duration of employment, race, and time since first employment |
Swaen GMH et al. (2007) [18] | US | Retrospective cohort (follow-up: 1955–2001) | 696 | NA | 94.1 | Cumulative acrylamide exposure, mg/m3 (mean: 4.6) | -Stroke mortality -Heart disease mortality | Age, duration of follow-up, sex, and time interval |
Huang M et al. (2018) [19] | US (NHANES 2003–2006) | Prospective (6.7-year follow-up) | 5504 | ≥25 | 47.9 | Blood (pmol/g Hb): -HbAA (median: 61.25) -HbGA (median: 52.50) | -CVD mortality: ICD 10th (I00-I78) | Age, alcohol consumption, BMI, education, energy intake, family history of CVD, PIR, HTN, log-cotinine, log-HbAA, log-HbGA, physical activity, race, and sex |
Wu H et al. (2022) [20] | US (NHANES 2003–2006, 2013–2014) | Prospective (follow-up until 2015) | 3601 | >18 | NA | Blood (pmol/g Hb): Diabetics -HbAA (median: 49.7) -HbGA (median: 47.2) | -CVD mortality: ICD 10th (I00–I09, I11, I13, I20–I51, I60–I69) | Age, alcohol consumption, blood pressure, BMI, education, energy intake, income, occupation, physical activity, race, sex, smoking, and taking medication for DM, HTN, or cholesterol |
Marques C et al. (2023) [21] | France (E3N French cohort) | Prospective (follow-up: 1993–2014) | 72,585 | Mean: 52.9 | 0 | Dietary acrylamide intake (µg/day) (mean: 32.6) | -CVD mortality: ICD 9th (390–459) and ICD 10th (I00–I99) | Age, alcohol consumption, birth cohort, BMI, education, energy intake, lipids consumption, menopausal status, physical activity, and smoking |
Feng X et al. (2024) [22] | US (NHANES 2005–2006, 2011–2018) | Prospective (6.2-year follow-up) | 8799 | ≥20 | 49.2 | Urine (μg/g Cr): AAMA (median: 54.6) | -CVD mortality: ICD 10th (I00–I09, I11, I13, I20–I51, and I60–I69 | Age, alcohol consumption, BMI, education, NHANES cycles, PIR, physical activity, race, sex, and smoking |
Nalini M et al. (2024) [23] | Iran (Golestan Cohort Study) | Prospective (8.4-year follow-up) | 1176 | 40–75 | 62.0 | Urine (µg/g creatinine): AAMA (median: 152.2) GAMA (median: 18.8) | -IHD mortality: ICD 10th (I20–I25) | Age, BMI, cotinine, education, enrolment year, marital status, nass use, physical activity, race, and wealth score |
CVD risk | ||||||||
Zhang Y et al. (2018) [24] | US (NHANES 2003–2006) | Cross-sectional | 8290 | ≥20 | 48.2 | Blood (pmol/g Hb): -HbAA (mean: 60.70) -HbGA (mean: 51.58) | -CVD (congestive heart failure, coronary heart disease, angina pectoris, heart attack, stroke) | Age, alcohol consumption, BMI, caffeine intake, C-reactive protein, energy intake, family history of CVD, homocysteine, HTN, log-TC, log-cotinine, log-HbAA, log-HBbGA, race, sex, and trans-fat intake |
Wang B et al. (2022) [25] | China (Wuhan-Zhuhai cohort) | Cross-sectional | 3024 | Mean: 53.82 | 29.4 | Urine (µg/mmol Creatinine): -AAMA (median: 3.29) -GAMA (median: 0.50) | -10-year CVD risk | Alcohol consumption, BMI, education, income, LDL, passive smoking, physical activity, region, and sex |
Wang X et al. (2023) [26] | US (NHANES 2011–2018) | Cross-sectional | 6814 | ≥20 | 49.4 | Urine (ng/mL): AAMA (mean: 1.70) | -CVD (congestive heart failure, coronary heart disease, angina, heart attack) | Age, cotinine, education, DBP, DM, HDL, HTN, LDL, PIR, race, second-hand smoke, sex, and volatile toxicant questionnaire scores |
Ma M et al. (2023) [27] | US (NHANES 2011–2016) | Cross-sectional | 5213 | Median: 47 | 48.8 | Urine AAMA | -CVD | Age, alcohol consumption, BMI, DM, education, energy intake, eGFR, HDL, HTN, poverty, race, sedentary time, sex, smoking, and TC |
Han S et al. (2024) [28] | US (NHANES 2005–2006, 2011–2018) | Cross-sectional | 9119 | Mean: 66.1 (CVD) | 58.6 (CVD) | Urine (ng/mL): AAMA (median: 51.5) | -CVD (congestive heart failure, coronary heart disease, angina, heart attack, or stroke) | Age, alcohol consumption, BMI, DM, education, HTN, PIR, physical activity, race, sex, smoking, TC, urinary albumin, and creatinine |
Diabetes and glucose metabolism | ||||||||
Lin CY et al. (2009) [29] | US (NHANES 2003–2004) | Cross-sectional | 1356 | ≥20 | 48.7 | Blood (pmol/g Hb): -HbAA (median: 61.6) -HbGA (median: 57.4) | -Glucose (mmol/L) -HbA1c -Insulin (pmol/L) -HOMA-IR -TC -HDL -TG -WC (cm) | Age, alcohol consumption, BMI, education, HbA1c/insulin/glucose/HOMA-IR, income, race, sex, and smoking |
Lin CY et al. (2013) [30] | Taiwan | Cross-sectional | 800 | Mean 21.3 | 37.3 | Urine (µg/g Creatinine): -AAMA (mean: 52.8) | -Glucose (mg/dL) -Insulin (pM) -HOMA-IR -TC (mg/dL) -HDL (mg/dL) -LDL (mg/dL) -BMI (kg/m2) | Age, alcohol consumption, BMI, sex, and smoking |
Wang B et al. (2020) [31] | China (Wuhan-Zhuhai cohort) | Cross-sectional | 3270 | Mean: 53.0 | 29.9 | Urine (µg/mmol Creatinine) -AAMA (median: 3.28) -GAMA (median: 0.50) | -Glucose (mmol/L) | Age, alcohol consumption, BMI, education, family history of DM, income, mean arterial pressure, physical activity, sex, smoking, TC, and TG |
Yin G et al. (2021) [32] | US (NHANES 2005–2006, 2013–2016) | Cross-sectional | 3577 | Mean: 51.4 | 52.4 | Blood (pmol/g Hb): -HbAA (median: 50.4) -HbGA (median: 43.0) | -DM: self-reported diabetes or HbA1c ≥ 6.5% or fasting plasma glucose ≥ 126 mg/dL or 2 h plasma glucose after OGTT ≥ 200 mg/dL | Age, alcohol consumption, BMI, education, HTN, race, sex, and smoking |
Hosseini-Esfahani F et al. (2023) [33] | Iran (Tehran lipid and glucose study) | Prospective (6.6-year follow-up) | 6022 | Mean: 41.5 (men) | 45.0 | Dietary acrylamide intake (µg/day) (mean: 57.0) | -DM: fasting plasma glucose ≥ 126 mg/dL or 2 h plasma glucose ≥ 200 mg/dL or taking medication for diabetes | Age, BMI, education, energy intake family history of diabetes, HDL, physical activity, sex, smoking, and TG |
Dyslipidemia and lipid metabolism | ||||||||
Cheang I et al. (2020) [34] | US (NHANES 2013–2016) | Cross-sectional | 2899 | Mean: 45.1 | 50.1 | Blood (pmol/g Hb): -HbAA (median: 49.2) -HbGA (median: 42.2) | -TC (mg/dL) -HDL (mg/dL) -TG (mg/dL) -LDL (mg/dL) | Age, alcohol consumption, BMI, DM, education, energy intake, PIR, HTN, physical activity, race, sex, and smoking |
Chen WY et al. (2023) [35] | US (NHANES 2011–2015) | Cross-sectional | 1410 | Mean: 48.2 | 51.8 | Urine (ng/mL): AAMA | -TC (mg/dL) -HDL (mg/dL) -TG (mg/dL) -LDL (mg/dL) | Age, BMI, creatinine, education, energy intake, marital status, PIR, physical activity, race, sex, and smoking |
Obesity and body composition | ||||||||
Chu PL et al. (2017) [36] | US (NHANES 2003–2004) | Cross-sectional | 3623 | ≥20 | 48.4 | Blood (pmol/g Hb): -HbAA (median: 60.5) -HbGA (median: 55.6) | -BMI (kg/m2) -WC (cm) | Age, caffeine intake, education, energy intake, income, metabolic equivalent intensity level for activity, protein intake, race, saturated fatty acids intake, sex, smoking, and sugar intake |
Huang M et al. (2018) [37] | US (NHANES 2003–2006) | Cross-sectional | 8364 | ≥20 | 48.2 | Blood (pmol/g Hb): -HbAA (median: 53.0) -HbGA (median: 50.9) | -GO: BMI ≥ 30 kg/m2 -AO: WC >102 cm (men) and >88 cm (women) | Age, alcohol consumption, education, energy intake, PIR, HTN, marital status, physical activity, race, sex, and smoking |
Yin T et al. (2022) [38] | US (NHANES 2003–2006, 2013–2014) | Cross-sectional | 10,377 | Mean: 46.8 | 51.2 | Blood (pmol/g Hb): -HbAA (median: 54.2) -HbGA (median: 49.5) | -GO: BMI ≥ 30 kg/m2 -AO: WC >102 cm (men) and >88 cm (women) | Age, alcohol consumption, DM, education, energy intake, HTN, race, sex, and smoking |
Lei T et al. (2023) [39] | US (NHANES 2005–2006, 2011–2020) | Cross-sectional | 17,524 | Mean: 45.09 (obesity) | 44.9 (obesity) | Urine (ng/mL): -AAMA (median: 28.7) | -GO: BMI ≥ 30 kg/m2 -AO: WC > 102 cm (men) and >88 cm (women) | Age, alcohol consumption, DM, education, energy intake, HTN, marital status, physical activity, race, sex, smoking, urinary albumin, and creatinine |
Hypertension and blood pressure | ||||||||
Liang J et al. (2022) [40] | US (NHANES 2003–2006, 2013–2016) | Cross-sectional | 3981 | Mean: 16.0 | 51.3 | Blood (pmol/g Hb): -HbAA (mean: 65.0) -HbGA (mean: 57.5) | - HTN: blood pressure ≥ 130/80 mmHg or taking antihypertensive treatment -TC (mg/dL) -TG (mg/dL) -LDL (mg/dL) -SBP (mmHg) -DBP (mmHg) | BMI, cotinine, dietary intake of calcium, sodium, and potassium, PIR, gender, and race |
McGraw KE et al. (2023) [41] | US (Jackson Heart Study cohort) | Cross-sectional | 1194 | Mean: 51.4 | 49.8 | Urine (ng/mg creatinine): AAMA (median: 53.7) | -HTA: blood pressure ≥ 140/90 mmHg or taking antihypertensive treatment -SBP -DBP | Age, ambient PM2.5 levels, BMI, DM, education, eGFR, HDL, physical activity, taking medication for HTN, and TG |
Metabolic syndrome | ||||||||
Hung CC et al. (2021) [42] | US (NHANES 2003–2006) | Cross-sectional | 4813 | ≥18 | 50.5 (non-MetS); 49.7 (MetS) | Blood (pmol/g Hb): -HbAA (mean: 49.8) -HbGA (mean: 50.0) | -MetS: Clinical definition by the NCEP-ATP III (3 or more criteria) * -High glucose -Low HDL -High TG -AO -HTN | Age, angina/angina pectoris, aspartate aminotransferase, creatinine, emphysema, race, sex, and smoking |
Wan X et al. (2022) [43] | US (NHANES 2003–2006, 2013–2016) | Cross-sectional | 4790 | ≥20 | 49.6 | Blood (pmol/g Hb): -HbAA (mean: 47.7) -HbGA (mean: 60.4) | -MetS: Clinical definition by the NCEP-ATP III (3 or more criteria) * -Glucose -HbA1c -TC -HDL -TG -LDL -AO -HTN -SBP -DBP | Age, alcohol consumption, BMI, caffeine intake, education, energy, intake, log-HbAA or log-HbGA, physical activity, PIR, race, sex, and smoking |
Tan L et al. (2024) [44] | US (NHANES 2005–2006, 2011–2020) | Cross-sectional | 8223 | ≥16 | 50.5 | Urine (ng/mL): AAMA (median: 54) | -MetS: Clinical definition by the NCEP-ATP III (3 or more criteria) * -High glucose -Low HDL -High TG -AO -HTN | Age, alcohol consumption, education, marital status, PIR, race, sex, smoking, urinary albumin, and creatinine |
Study | Country (Sample) | Acrylamide Metabolite | Unit of Comparison | Outcome | Results | |||||
---|---|---|---|---|---|---|---|---|---|---|
CVD Mortality | Stroke Mortality | Heart Disease Mortality | IHD Mortality | CVD | 10-y CVD Risk | |||||
Marsh GM et al. (2007) [17] | US and NL | Cumulative acrylamide exposure | Observed mortality vs. expected mortality for the US or NL | ↓ | CVD mortality (US): SMR = 0.88 (0.84–0.92), p-value: < 0.01 | |||||
NSA | CVD morality (NL): SMR = 0.73 (0.51–1.02) | |||||||||
Swaen GMH et al. (2007) [18] | US | Cumulative acrylamide exposure | Observed mortality vs. expected mortality for the US | NSA | NSA | Stroke mortality: SMR = 65.3 (21.2–152.30) Heart disease mortality: SMR = 91.8 (68.9–119.7) | ||||
Wu H et al. (2022) [20] | US (NHANES 2003–2006, 2013–2014) | HbAA | Q4vsQ1 | ↑ | CVD mortality: HR = 1.84 (1.00–3.37), p-trend: 0.002 | |||||
HbGA | Q4vsQ1 | NSA | CVD mortality: HR = 0.60 (0.33–1.07), p-trend: 0.448 | |||||||
HbAA/HbGA | Q4vsQ1 | ↑ | CVD mortality: HR = 1.61 (1.09–2.39), p-trend: 0.062 | |||||||
Marques C et al. (2023) [21] | France (E3N French cohort) | Dietary acrylamide intake | Q4vsQ1 | ↑ | CVD mortality: HR = 1.33 (1.09–1.63), p-trend: 0.003 | |||||
Feng X et al. (2024) [22] | US (NHANES 2005–2006, 2011–2018) | AAMA | 1-unit in log2 AAMA | NSA | CVD mortality: HR = 1.09 (0.95–1.25) | |||||
Nalini M et al. (2024) [23] | Iran (Golestan Cohort Study) | AAMA | T3vsT1 | ↑ | IHD mortality: HR = 1.68 (1.05, 2.69), p-trend: 0.025 | |||||
GAMA | T3vsT1 | NSA | IHD mortality: HR = 1.15 (0.75, 1.78), p-trend: 0.522 | |||||||
CVD risk | ||||||||||
Wang B et al. (2022) [25] | China (Wuhan-Zhuhai cohort) | AAMA | Q4vsQ1 | ↑ | 10 y CVD risk: OR = 1.47 (1.16–1.88), p-trend: 0.004 | |||||
GAMA | Q4vsQ1 | ↑ | 10 y CVD risk: OR = 1.67 (1.32–2.11), p-trend: <0.001 | |||||||
ΣUAAM | Q4vsQ1 | ↑ | 10 y CVD risk: OR = 1.51 (1.19–1.91), p-trend: 0.001 | |||||||
GAMA/AAMA | Q4vsQ1 | ↑ | 10 y CVD risk: OR = 1.42 (1.10–1.83), p-trend: 0.002 | |||||||
Wang X et al. (2023) [26] | US (NHANES 2011–2018) | AAMA | 1-unit increase in AAMA | NSA | CVD: OR = 1.1 (0.85–1.43), p-value: 0.46 | |||||
Han S et al. (2024) [28] | US (NHANES 2005–2006, 2011–2018) | AAMA | Q4vsQ1 | ↑ | CVD: OR = 1.54 (1.01–2.35), p-trend: 0.021 | |||||
Ma M et al. (2023) [27] | US (NHANES 2011–2016) | AAMA | Q4vsQ1 | ↑ | CVD: OR = 1.95 (1.09–3.51), p-trend: 0.020 |
Study | Sample | Acrylamide Metabolite | Unit of Comparison | Outcome | Results | |||||
---|---|---|---|---|---|---|---|---|---|---|
Glucose | HbA1c | Insulin | HOMA-IR | DM | High Glucose | |||||
Lin CY et al. (2009) [29] | US (NHANES 2003–2004) | HbAA | 1-unit increase in natural log HbAA | NSA | NSA | ↓ | ↓ | Glucose: β = −0.09 (−0.25, −0.07), p-value: 0.262 Log-HbA1c: β = 0.01 (−0.10, 0.03), p-value: 0.253 Log-insulin: β = −0.20 (−0.30, −0.10), p-value: 0.001 Log-HOMA-IR: β = −0.23 (−0.33, −0.13), p-value: <0.001 | ||
Lin CY et al. (2013) [30] | Taiwan | AAMA | 1-unit increase in natural log AAMA | NSA | NSA | NSA | Glucose: β = −0.192 (−1.60, 1.22), p-value: 0.789 Log-insulin: β = 0.023 (−0.04, 0.09), p-value: 0.473 Log-HOMA-IR: β = 0.023 (−0.04, 0.09), p-value: 0.492 | |||
Wang B et al. (2020) [31] | China (Wuhan-Zhuhai cohort) | AAMA | Q4vsQ1 | ↑ | Glucose: β = 0.20 (0.05, 0.35), p-trend: 0.008 | |||||
GAMA | Q4vsQ1 | NSA | Glucose: β = 0.05 (−0.10, 0.19), p-trend: 0.581 | |||||||
ΣUAAM | Q4vsQ1 | ↑ | Glucose: β = 0.17 (0.02, 0.32), p-trend: 0.014 | |||||||
Yin G et al. (2021) [32] | US (NHANES 2005–2006, 2013–2016) | HbAA | Q4vsQ1 | ↓ | DM: OR = 0.71 (0.55–0.93), p-trend: 0.013 | |||||
HbGA | Q4vsQ1 | NSA | DM: OR = 0.92 (0.71–1.18), p-trend: 0.859 | |||||||
HbAA + HbGA | Q4vsQ1 | NSA | DM: OR = 0.80 (0.62–1.03), p-trend: 0.194 | |||||||
HbGA/HbAA | Q4vsQ1 | ↑ | DM: OR = 1.95 (1.51–2.51), p-trend: <0.001 | |||||||
Hosseini-Esfahani F et al. (2023) [33] | Iran (Tehran lipid and glucose study) | Acrylamide intake | Q4vsQ1 | NSA | DM: HR = 1.06 (0.98–1.16), p-trend:0.13 | |||||
Hung CC et al. (2021) [42] | US (NHANES 2003–2006) | HbAA | 1-unit increase in natural log HbAA | NSA | High glucose: Graphically reported | |||||
HbGA | 1-unit increase in natural log HbGA | ↓ | High glucose: Graphically reported | |||||||
Wan X et al. (2022) [43] | US (NHANES 2003–2006, 2013–2016) | HbAA | 1-unit increase in natural log HbAA | ↓ | ↓ | Glucose: β = −5.13 (−7.89, −2.37), p-value: < 0.001 HbA1c: β = −0.18 (−0.26, −0.10), p-value: < 0.001 | ||||
HbGA | 1-unit increase in natural log HbGA | ↑ | ↑ | Glucose: β = 2.51 (0.16, 4.86), p-value: 0.036 HbA1c: β = 0.19 (0.12, 0.26), p-value: <0.001 | ||||||
HbAA + HbGA | 1-unit increase in natural log HbAA + HbGA | ↓ | NSA | Glucose: β = −2.46 (−4.46, −0.46), p-value: 0.016 HbA1c: β = 0.02 (−0.04, 0.08), p-value: 0.447 | ||||||
HbGA/HbAA | 1-unit increase in natural log HbGA/HbAA | ↑ | ↑ | Glucose: β = 3.12 (0.82, 5.42), p-value: 0.008 HbA1c: β = 0.19 (0.12, 0.26), p-value: <0.001 | ||||||
Tan L et al. (2024) [44] | US (NHANES 2005–2006, 2011–2020) | AAMA | Q4vsQ1 | NSA | High glucose: OR = 1.13 (0.93, 1.37), p-trend: 0.628 |
Study | Country (Sample) | Acrylamide Metabolite | Unit of Comparison | Outcome | Results | |||||
---|---|---|---|---|---|---|---|---|---|---|
TC | HDL | TG | LDL | Low HDL | High TG | |||||
Lin CY et al. (2009) [29] | US (NHANES 2003–2004) | HbAA | 1-unit increase in natural log HbAA | NSA | NSA | NSA | TC: β = 0.02 (−0.14, 0.18), p-value: 0.839 HDL: β = −0.05 (−0.11, 0.01), p-value: 0.072 Log-TG: β = −0.01 (−0.07, 0.05), p-value: 0.742 | |||
Lin CY et al. (2013) [30] | Taiwan | AAMA | 1-unit increase in natural log AAMA | NSA | NSA | NSA | TC: β = −1.85 (−4.31, 0.61), p-value: 0.141 HDL: β = −0.10 (−0.76, 0.57), p-value: 0.770 LDL: β = 0.28 (−1.95, 2.51), p-value: 0.804 | |||
Cheang I et al. (2020) [34] | US (NHANES 2013–2016) | HbAA | Q4vsQ1 | NSA | NSA | ↑ | NSA | TC: β = 1.74 (−3.22, 6.70), p-trend: 0.606 HDL: β = −0.32 (−2.17, 1.53), p-trend: 0.718 TG: β = 23.63 (6.01, 41.24), p-trend: 0.047 LDL: β = 1.12 (−5.59, 7.82), p-trend: 0.909 | ||
HbGA | Q4vsQ1 | ↑ | ↓ | ↑ | NSA | TC: β= 6.78 (2.17, 11.40), p-trend: 0.002 HDL: β= −5.12 (−6.84, −3.40), p-trend: < 0.001 TG: β= 25.32 (8.98, 41.65), p-trend: 0.004 LDL: β= 5.89 (−0.32, 12.10), p-trend: 0.026 | ||||
HbAA + HbGA | Q4vsQ1 | NSA | ↓ | ↑ | NSA | TC: β= 3.81 (−1.02, 8.63), p-trend: 0.106 HDL: β= −2.09 (−3.89, −0.29), p-trend: 0.049 TG: β= 25.65 (8.63, 42.68), p-trend: 0.017 LDL: β= 1.87 (−4.59, 8.34), p-trend: 0.443 | ||||
HbGA/HbAA | Q4vsQ1 | ↑ | ↓ | ↑ | ↑ | TC: β= 12.79 (8.18, 17.40), p-trend: <0.001 HDL: β= −7.24 (−8.94, −5.53), p-trend: <0.001 TG: β= 27.33 (10.72, 43.94), p-trend: 0.002 LDL: β= 11.10 (4.82,17.39), p-trend: 0.001 | ||||
Liang J et al. (2022) [40] | US (NHANES 2003–2006, 2013–2016) | HbGA | 1-unit increase in Natural log HbGA | ↑ | NSA | NSA | NSA | TC: β= 2.83 (0.49, 5.18), p-value: 0.018 HDL: β= 0.65 (−0.33, 1.63), p-value: 0.192 TG: β= 5.23 (−0.65, 11.11), p-value: 0.081 LDL: β= 1.52 (−1.59, 4.63), p-value: 0.337 | ||
Hung CC et al. (2021) [42] | US (NHANES 2003–2006) | HbAA | 1-unit increase in natural log HbAA | ↓ | ↓ | Low HDL and High TG: Graphically reported | ||||
HbGA | 1-unit increase in natural log HbGA | NSA | NSA | Low HDL and High TG: Graphically reported | ||||||
Wan X et al. (2022) [43] | US (NHANES 2003–2006, 2013–2016) | HbAA | 1-unit increase in natural log HbAA | ↓ | ↑ | ↓ | ↓ | TC: β= −4.62 (−8.28, −0.96), p-value: 0.013 HDL: β= 3.89 (2.67, 5.10), p-value: <0.001 TG: β= −18.85 (−29.69, −8.00), p-value: 0.001 LDL: β= −5.41 (−8.49, −2.33), p-value: 0.001 | ||
HbGA | 1-unit increase in natural log HbGA | ↑ | ↓ | ↑ | ↑ | TC: β= 6.60 (3.49, 9.72), p-value: <0.001 HDL: β= −4.20 (−5.23, −3.17), p-value: <0.001 TG: β= 16.86 (7.64, 26.08), p-value: <0.001 LDL: β= 7.47 (4.85, 10.08), p-value: <0.001 | ||||
HbAA + HbGA | 1-unit increase in natural log HbAA + HbGA | ↑ | NSA | NSA | ↑ | TC: β= 3.27 (0.61, 5.92), p-value: 0.016 HDL: β= −0.59 (−1.48, 0.29), p-value: 0.191 TG: β= 1.56 (−6.31, 9.42), p-value: 0.698 LDL: β= 2.98 (0.74, 5.21), p-value: 0.009 | ||||
HbGA/HbAA | 1-unit increase in natural log HbGA/HbAA | ↑ | ↓ | ↑ | ↑ | TC: β= 6.14 (3.09, 9.19), p-value: <0.001 HDL: β= −4.13 (−5.14, −3.11), p-value: <0.001 TG: 17.32 (8.28, 26.37), p-value: <0.001 LDL: β= 6.99 (4.42, 9.55), p-value: <0.001 | ||||
Chen WY et al. (2023) [35] | US (NHANES 2011–2015) | AAMA | 1-unit increase in natural log AAMA | NSA | ↑ | NSA | NSA | TC: β= −0.63 (−4.46, 3.2), p-value: 0.75 ln-HDL: 1.28 (0.21, 2.36), p-value: 0.03 ln-TG: β= −0.09 (−6.82, 6.64), p-value: 0.98 LDL: −1.87 (−5.33,1.58), p-value: 0.3 | ||
Tan L et al. (2024) [44] | US (NHANES 2005–2006, 2011–2020) | AAMA | Q4vsQ1 | NSA | NSA | Low HDL: OR= 0.89 (0.73, 1.07), p-trend: 0.137 High TG: OR= 0.90 (0.75, 1.08), p-trend: 0.097 |
Study | Country (Sample) | Acrylamide Metabolite | Unit of Comparison | Outcome | Results | |||
---|---|---|---|---|---|---|---|---|
BMI | WC | GO | AO | |||||
Lin CY et al. (2009) [29] | US (NHANES 2003–2004) | HbAA | 1-unit increase in natural log HbAA | NSA | WC: β = 0.44 (−1.74, 2.62), p-value: 0.697 | |||
Lin CY et al. (2013) [30] | Taiwan | AAMA | 1-unit increase in natural log AAMA | NSA | BMI: β = −0.094 (−0.39, 0.20), p-value: 0.535 | |||
Chu PL et al. (2017) [36] | US (NHANES 2003–2004) | HbAA | 1-unit increase in natural log HbAA | ↓ | ↓ | BMI: β = −1.46 (−2.07, −0.85), p-value: <0.001 WC: β = −3.72 (−5.33, −2.11), p-value: <0.001 | ||
HbGA | 1-unit increase in natural log HbGA | NSA | BMI: β = 0.38 (−0.19, 0.95), p-value: 0.217 WC: β = 0.58 (−0.81, 1.97), p-value: 0.429 | |||||
Huang M et al. (2018) [37] | US (NHANES 2003–2006) | HbAA | Q4vsQ1 | ↓ | ↓ | GO: OR = 0.67 (0.55–0.81), p-trend: <0.0001 AO: OR = 0.66 (0.57–0.75), p-trend: <0.0001 | ||
HbGA | Q4vsQ1 | ↑ | ↑ | GO: OR = 1.40 (1.17–1.68), p-trend: 0.0004 AO: OR = 1.43 (1.19–1.72), p-trend: 0.0008 | ||||
HbAA + HbGA | Q4vsQ1 | NSA | NSA | GO: OR = 0.90 (0.73–1.13), p-trend: 0.4877 AO: OR = 0.88 (0.74–1.05), p-trend: 0.4151 | ||||
HbGA/HbAA | Q4vsQ1 | ↑ | ↑ | GO: OR = 2.86 (2.43–3.38), p-trend: <0.0001 AO: OR = 2.91 (2.40–3.54), p-trend: <0.0001 | ||||
Yin T et al. (2022) [38] | US (NHANES 2003–2006, 2013–2014) | HbAA | 1-unit in log2 HbAA | ↓ | ↓ | GO: OR = 0.80 (0.76–0.85), p-value: <0.001 AO: OR= 0.79 (0.75–0.83), p-value: <0.001 | ||
HbGA | 1-unit in log2 HbGA | NSA | NSA | GO: OR = 1.03 (0.98–1.08) AO: OR = 1.03 (0.98–1.08) | ||||
HbAA + HbGA | 1-unit in log2 HbAA + HbGA | ↓ | ↓ | GO: OR = 0.88 (0.83–0.93), p-value: <0.001 AO: OR = 0.86 (0.82–0.91), p-value: <0.001 | ||||
Lei T et al. (2023) [39] | US (NHANES 2005–2006, 2011–2020) | AAMA | Q4vsQ1 | ↓ | ↓ | GO: OR = 0.79 (0.67–0.92), p-value: <0.01 AO: OR = 0.79 (0.65–0.90), p-value: <0.001 | ||
Hung CC et al. (2021) [42] | US (NHANES 2003–2006) | HbAA | 1-unit increase in natural log HbAA | NSA | AO: Graphically reported | |||
HbGA | 1-unit increase in natural log HbGA | NSA | AO: Graphically reported | |||||
Wan X et al. (2022) [43] | US (NHANES 2003–2006, 2013–2016) | HbAA | Q4vsQ1 | ↓ | AO: OR = 0.36 (0.23–0.58), p-trend: <0.001 | |||
HbGA | Q4vsQ1 | ↑ | AO: OR = 1.78 (1.03–3.08), p-trend: 0.192 | |||||
HbAA + HbGA | Q4vsQ1 | NSA | AO: OR = 0.70 (0.47–1.03), p-trend: 0.051 | |||||
HbGA/HbAA | Q4vsQ1 | ↑ | AO: OR = 1.95 (1.28–2.99), p-trend: 0.010 | |||||
Tan L et al. (2024) [44] | US (NHANES 2005–2006, 2011–2020) | AAMA | Q4vsQ1 | NSA | AO: OR = 1.01 (0.84, 1.21), p-trend: 0.289 |
Study | Country (Sample) | Acrylamide Metabolite | Unit of Comparison | Outcome | Results | ||
---|---|---|---|---|---|---|---|
SBP | DBP | HTN | |||||
Liang J et al. (2022) [40] | US (NHANES 2003–2006, 2013–2016) | HbAA | Q4vsQ1 | NSA | HTN: OR = 1.01 (0.72–1.42), p-trend: 0.794 | ||
HbGA | Q4vsQ1 | NSA | HTN: OR = 1.01 (0.71–1.42), p-trend: 0.795 | ||||
HbAA | 1-unit increase in natural log HbAA | NSA | NSA | ln-SBP: β = 0.27 (−0.37, 0.90), p-value: 0.413 ln-DBP: β = −0.39 (−1.18, 0.39), p-value: 0.325 | |||
HbGA | 1-unit increase in natural log HbGA | ↑ | NSA | ln-SBP: β = 0.49 (0, 0.97), p-value: 0.048 ln-DBP: β = −0.59 (−1.19, 0), p-value: 0.051 | |||
Hung CC et al. (2021) [42] | US (NHANES 2003–2006) | HbAA | 1-unit increase in natural log HbAA | NSA | HTN: Graphically reported | ||
HbGA | 1-unit increase in natural log HbGA | NSA | HTN: Graphically reported | ||||
Wan X et al. (2022) [43] | US (NHANES 2003–2006, 2013–2016) | HbAA | Q4vsQ1 | NSA | HTN: OR = 0.70 (0.47–1.03), p-trend: 0.046 | ||
HbGA | Q4vsQ1 | NSA | HTN: OR = 0.76 (0.54–1.08), p-trend: 0.129 | ||||
HbAA + HbGA | Q4vsQ1 | NSA | HTN: OR = 0.83 (0.62–1.11), p-trend: 0.847 | ||||
HbGA/HbAA | Q4vsQ1 | NSA | HTN: OR = 0.99 (0.74–1.33), p-trend: 0.905 | ||||
HbAA | 1-unit increase in natural log HbAA | NSA | NSA | SBP: β = 0.58 (−0.84, 1.99), p-value: 0.425 DBP: β= 0.49 (−0.62, 1.60), p-value: 0.385 | |||
HbGA | 1-unit increase in natural log HbGA | NSA | NSA | SBP: β = 0.67 (−0.53, 1.88), p-value: 0.272 DBP: β = −0.81 (−1.75,0.14), p-value: 0.094 | |||
HbAA + HbGA | 1-unit increase in natural log HbAA + HbGA | ↑ | NSA | SBP: β = 1.29 (0.26, 2.31), p-value: 0.014 DBP: β = −0.46 (−1.26, 0.35), p-value: 0.267 | |||
HbGA/HbAA | 1-unit increase in natural log HbGA/HbAA | NSA | NSA | SBP: β = 0.38 (−0.80, 1.56), p-value: 0.525 DBP: β = −0.73 (−1.30, 0.55), p-value: 0.120 | |||
McGraw KE et al. (2023) [41] | US (Jackson Heart Study cohort) | AAMA | Per IQR of AAMA | NSA | NSA | NSA | HTN: RR = 1.02 (0.97–1.08), p-value: 0.38 SBP β = 0.24 (−1.20, 1.68), p-value: 0.75 SBP: DBP: β = −0.07 (−0.81, 0.67), p-value: 0.85 |
Tan L et al. (2024) [44] | US (NHANES 2005–2006, 2011–2020) | AAMA | Q4vsQ1 | ↓ | HTN: OR = 0.60 (0.47–0.76), p-trend: 0.003 |
Study | Country (Sample) | Acrylamide Metabolite | Unit of Comparison | MetS | Results |
---|---|---|---|---|---|
Hung CC et al. (2021) [42] | US (NHANES 2003–2006) | HbAA | Q4vsQ1 | ↓ | MetS: OR = 0.60 (0.40–0.88), p-value: 0.009 |
HbGA | Q4vsQ1 | NSA | MetS: OR = 1.02 (0.72–1.45), p-value: 0.911 | ||
Wan X et al. (2022) [43] | US (NHANES 2003–2006, 2013–2016) | HbAA | Q4vsQ1 | ↓ | MetS: OR = 0.60 (0.40–0.89), p-trend: 0.001 |
HbGA | Q4vsQ1 | NSA | MetS: OR = 1.26 (0.84–1.89), p-trend: 0.232 | ||
HbAA + HbGA | Q4vsQ1 | NSA | MetS: OR = 0.93 (0.71–1.21), p-trend: 0.371 | ||
HbGA/HbAA | Q4vsQ1 | ↑ | MetS: OR = 1.61 (1.18–2.20), p-trend: 0.001 | ||
Tan L et al. (2024) [44] | US (NHANES 2005–2006, 2011–2020) | AAMA | Q4vsQ1 | ↓ | MetS: OR = 0.78 (0.64–0.95), p-trend: < 0.001 |
Outcome | Main Conclusion | Study |
---|---|---|
CVD mortality | HbAA, dietary acrylamide intake, and AAMA are associated with a higher risk of CVD mortality. | Wu H et al. (2022) [20] Marques C et al. (2023) [21] Nalini M et al. (2024) [23] |
CVD risk | AAMA is associated with higher 10 y CVD risk and CVD prevalence. | Wang B et al. (2022) [25] Han S et al. (2024) [28] |
GAMA is associated with higher 10 y CVD risk. | Wang B et al. (2022) [25] | |
Diabetes and glucose metabolism | AAMA is positively associated with glucose levels. HbAA is associated with lower DM prevalence, glucose, and HbA1c levels. | Wang B et al. (2020) [31] Yin G et al. (2021) [32] Wan X et al. (2022) [43] |
HbGA is positively associated with glucose and HbA1c levels. | Wan X et al. (2022) [43] | |
Dyslipidemia and lipid metabolism | HbAA is negatively associated with TC, TG, and LDL levels. HbAA and AAMA are positively associated with HDL levels. | Wan X et al. (2022) [43] Chen WY et al. (2023) [35] |
HbGA is positively associated with TC, TG, and LDL levels and negatively associated with HDL levels in US adolescents. | Liang J et al. (2022) [40] Wan X et al. (2022) [43] | |
Obesity and body composition | AAMA is associated with a lower prevalence of GO and AO. AAMA and HbAA are associated with a lower prevalence of AO. | Lei T et al. (2023) [39] Wan X et al. (2022) [43] |
HbGA is associated with a higher prevalence of AO. | Wan X et al. (2022) [43] | |
Hypertension and blood pressure | AAMA is associated with a lower prevalence of HTN. | Tan L et al. (2024) [44] |
HbGA is associated with higher SBP levels in US adolescents. | Liang J et al. (2022) [40] | |
Metabolic syndrome | HbAA and AAMA are associated with a lower prevalence of MetS. | Wan X et al. (2022) [43] Tan L et al. (2024) [44] |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Mérida, D.M.; Rey-García, J.; Moreno-Franco, B.; Guallar-Castillón, P. Acrylamide Exposure and Cardiovascular Risk: A Systematic Review. Nutrients 2024, 16, 4279. https://doi.org/10.3390/nu16244279
Mérida DM, Rey-García J, Moreno-Franco B, Guallar-Castillón P. Acrylamide Exposure and Cardiovascular Risk: A Systematic Review. Nutrients. 2024; 16(24):4279. https://doi.org/10.3390/nu16244279
Chicago/Turabian StyleMérida, Diana María, Jimena Rey-García, Belén Moreno-Franco, and Pilar Guallar-Castillón. 2024. "Acrylamide Exposure and Cardiovascular Risk: A Systematic Review" Nutrients 16, no. 24: 4279. https://doi.org/10.3390/nu16244279
APA StyleMérida, D. M., Rey-García, J., Moreno-Franco, B., & Guallar-Castillón, P. (2024). Acrylamide Exposure and Cardiovascular Risk: A Systematic Review. Nutrients, 16(24), 4279. https://doi.org/10.3390/nu16244279