Inverse relationship between dietary fiber intake and environmental exposure to acrylamide

doi:10.21203/rs.3.rs-2099707/v1

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Inverse relationship between dietary fiber intake and environmental exposure to acrylamide

https://doi.org/10.21203/rs.3.rs-2099707/v1

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Dietary fiber intake was thought to decrease some environmental pollutants exposure by increasing gastrointestinal excretion. While diet is considered the major source of exposure to acrylamide (AA), and the impact of dietary fiber intake on acrylamide (AA) exposure is still unknown. We analyzed the associations between dietary fiber intake and AA hemoglobin biomarkers [hemoglobin adducts of acrylamide (HbAA) and glycinamide (HbGA) and sum of HbAA and HbGA (HbAA + HbGA)] among 3448 US adults who participated in the National Health and Nutrition Examination Survey (NHANES) 2013–2016. Multivariable linear regression and cubic spline models were conducted to estimate the associations between energy-adjusted fiber intake and AA hemoglobin biomarkers. Energy-adjusted fiber intake had a strong inverse and J shaped association with AA hemoglobin biomarkers. In the fully adjusted linear regression model, compared with participants in the lowest dietary fiber quantile, the adjusted percent change with 95% confidence intervals (CIs) in HbAA for the highest dietary fiber quantile was − 19.7% (-26.7%, -13.1%); for HbGA, it was − 12.2% (-18.9%, -4.9%), and for HbAA + HbGA, it was − 17.3% (-23.7%, -10.4%). Associations between higher dietary fiber intake and lower levels of environmental exposure to acrylamide hemoglobin biomarkers suggest the need to increase dietary fiber intake.

dietary fiber

acrylamide

inverse

relationship

NHANES

exposure.

Acrylamide (AA) exposure has been identified as a probable carcinogen to humans and has also been associated with a wide variety of health outcomes in humans, including cardiovascular disease(Zhang et al. 2018), diabetes (Yin et al. 2021), lung disease (Liu et al. 2021a) and obesity (Huang et al. 2018). AA exposure can occur from tobacco smoke and a wide variety of commonly consumed dry-heated food or beverages, including fried potatoes, biscuits, potato chips, and coffee(Tareke et al. 2002). However, in the general population, diet is considered the major source of exposure to AA (Pedersen et al. 2022).

Dietary fiber intake is increasingly recognized as critical for the prevention of diseases, including cardiovascular disease (Pereira et al. 2004; Zhang et al. 2022), depression(Liu et al. 2021b), hyperuricemia (Sun et al. 2019), hypertension (Sun et al. 2018) and stroke (Larsson and Wolk 2014), and the study of fiber intake has recently extended to the field of environmental toxicology (Dzierlenga et al. 2021; Shi et al. 2022). Dietary fiber intake was thought to decrease serum environmental pollutants such as perfluoroalkyl acids (Dzierlenga et al. 2021) and isopentanaldehyde (Shi et al. 2022) by increasing gastrointestinal excretion. Since diet is considered the major source of exposure to AA, and dietary fiber increases the volume of feces and decreased the transit time in the intestine, dietary fiber intake may reduce the blood concentration of AA hemoglobin biomarkers. Therefore, our study focused on the correlations between dietary fiber intake and the blood concentrations of AA hemoglobin biomarkers.

2.1 Study population

Publicly available data regarding participants who were recruited for the NHANES were used. NHANES is an ongoing cross-sectional study conducted by the National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC). Detailed survey operation manuals, survey design, methods, available data and consent documents are available on the NHANES website (https://www.cdc.gov/nchs/nhanes/). The data were from two cycles of the NHANES (2013–2014, 2015–2016) and included a total of 20,146 participants. Participants with missing data on acrylamide and glycinamide were excluded (n = 15183). A total of 466 individuals lacked data on dietary fiber intake. We selected 3448 participants aged 18 years and older who were not pregnant (Fig. 1).

2.2 Environmental AA measurements

Blood levels of hemoglobin adducts of acrylamide (HbAA) and glycinamide (HbGA) were measured in fresh or frozen erythrocytes or ethylenediaminetetraacetic acid (EDTA)-anticoagulated whole blood specimens. Details regarding the experimental methods for HbAA and HbGA are available on the NHANES website. For concentrations of HbAA and HbGA below the limit of detection (LOD), we recorded the values as the LOD divided by the square root of 2.

2.3. Dietary information

Dietary fiber intake was obtained from two 24-h dietary recall interviews. The two interviews were conducted in person in the mobile examination center (MEC) and by telephone at an interval of 3 to 10 days. Total dietary fiber was recoded as the fiber remaining after a food’s digestion by α-amylase, protease, and amyloglucosidase. Dietary fiber intake was calculated based on an average of the two interviews according to the United States Department of Agriculture (USDA) Food and Nutrient Databases for Dietary Studies (Oza-Frank et al. 2009).

2.4. Covariates

The following potential confounding factors were included in our analyses: age, sex, race, educational level, self-reported history of hypertension and diabetes, smoking (Smoking at least 100 cigarettes in life or not), drinking (Having at least 12 alcohol drinks per year or not), physical activity (vigorous activity, moderate activity, and never), body mass index (BMI), family poverty-income ratio (PIR) and total daily energy intake. Total daily energy intake was obtained from two 24 h dietary recall interviews by trained interviewers.

2.5. Statistical analysis

Measurement data were described by the mean (standard deviation, SD) or median (interquartile range, IQR) according to the normality of the data, and analysis of variance or Kruskal–Wallis H rank-sum test was used for comparison between multiple groups. Categorical variables were described by the numbers and percentage [n (%)], and the chi-square test was used to perform the comparison between groups. Energy-adjusted fiber intake was calculated as the residual of the regression of dietary fiber intake on total caloric intake (Willett et al. 1997). Since the distribution of environmental AA exposure was skewed, we log2-transformed the concentrations of HbAA and HbGA to normalize the distributions for regression analysis.

Generalized linear regression models were performed to assess the association between energy-adjusted fiber intake and the blood concentrations of HbAA, HbGA and HbAA + HbGA. All regression analyses were adjusted by age, sex, education level, race, smoker, alcohol user, diabetes, hypertension, body mass index, poverty-income ratio and physical activity. Cubic splines with 3 knots located at the 10th, 50th, and 90th percentiles of the distribution were conducted to explore the relationship between dietary fiber intake and the blood concentrations of HbAA, HbGA and HbAA + HbGA. The percentage differences (% diffs) and 95% CIs in blood concentrations of HbAA, HbGA and HbAA + HbGA across quantiles of energy-adjusted fiber intake after adjusting for confounding factors were calculated. Quartiles of energy-adjusted fiber intake were regarded as a continuous variable in the multivariable linear regression model to test for significant linear trends. All statistical analyses were carried out in R (version 4.0.3). A two-sided p value < 0.05 was considered statistically significant.

3.1 Study population characteristics

The baseline characteristics of the study population by quantiles of energy-adjusted fiber intake are shown in Table 1. Participants with low fiber intake were more likely to be younger or male, have lower incidences of smoking, alcohol use, diabetes and hypertension, and have a lower PIR.

Table 1

Characteristics of the study population
Variable	Overall (N = 3488)	Energy adjusted dietary fiber (g/day)
Variable	Overall (N = 3488)	≤ 11.44 (n = 862)	11.45–15.11 (n = 862)	15.12–20.02 (n = 862)	> 20.03 (n = 862)	p value
Age, years	47.2 (17.8)	42.5 (16.3)	47.1 (18.9)	48.9 (17.9)	50.1 (16.9)	< 0.001
Male, %	1734 (50.3%)	502 (58.2%)	410 (47.6%)	381 (44.2%)	441 (51.2%)	< 0.001
Education level, %						< 0.001
< 9th grade	302 (8.8%)	43 (5.0%)	51 (5.9%)	80 (9.3%)	128 (14.8%)
9–11th grade	467 (13.5%)	171 (19.8%)	115 (13.3%)	99 (11.5%)	82 (9.5%)
High school	837 (24.3%)	246 (28.5%)	233 (27.0%)	211 (24.5%)	147 (17.1%)
College	1042 (30.2%)	281 (32.6%)	295 (34.2%)	260 (30.2%)	206 (23.9%)
Graduate	800 (23.2%)	121 (14.0%)	168 (19.5%)	212 (24.6%)	299 (34.7%)
Race/ethnicity, %						< 0.001
Mexican American	505 (14.6%)	67 (7.8%)	92 (10.7%)	140 (16.2%)	206 (23.9%)
Other Hispanic	361 (10.5%)	57 (6.6%)	87 (10.1%)	90 (10.4%)	127 (14.7%)
Non-Hispanic White	1444 (41.9%)	402 (46.6%)	403 (46.8%)	358 (41.5%)	281 (32.6%)
Non-Hispanic Black	701 (20.3%)	261 (30.3%)	180 (20.9%)	158 (18.3%)	102 (11.8%)
Other race	437 (12.7%)	75 (8.7%)	100 (11.6%)	116 (13.5%)	146 (16.9%)
Hypertension, %	1200 (34.8%)	259 (30.0%)	334 (38.7%)	303 (35.2%)	304 (35.3%)	0.002
Diabetes	437 (12.7%)	75 (8.7%)	119 (13.8%)	123 (14.3%)	120 (13.9%)	0.001
Body mass index, kg/m2	29.1 (7.16)	29.2 (7.89)	29.5 (7.17)	29.2 (7.00)	28.6 (6.49)	0.073
Smoker, %	1755 (50.9%)	594 (68.9%)	470 (54.5%)	368 (42.7%)	323 (37.5%)	< 0.001
Alcohol user, %	2510 (72.8%)	698 (81.0%)	634 (73.5%)	605 (70.2%)	573 (66.5%)	< 0.001
Energy intake, kcal/day	2070 (881)	2260 (1020)	1890 (807)	1950 (814)	2190 (810)	< 0.001
Physical activity
Never	1984 (57.5%)	439 (50.9%)	497 (57.7%)	515 (59.7%)	533 (61.8%)
Moderate	707 (20.5%)	165 (19.1%)	179 (20.8%)	192 (22.3%)	171 (19.8%)
Vigorous	757 (22.0%)	258 (29.9%)	186 (21.6%)	155 (18.0%)	158 (18.3%)
Poverty-income ratio	2.19 (1.55)	1.79 (1.40)	2.10 (1.49)	2.31 (1.56)	2.55 (1.66)	< 0.001
HbAA, pmol/g Hb	47.80 (34.80–88.10)	76.05 (40.95-139.25)	50.05 (34.38–100.00)	42.55 (33.20–65.60)	43.05 (34.10-57.13)	< 0.001
HbGA, pmol/g Hb	41.10 (29.00-63.88)	51.80 (32.20-88.68)	41.80 (29.30-72.18)	38.10 (28.18–54.93)	37.90 (27.70–50.30)	< 0.001
HbAA + HbGA, pmol/g Hb	90.00 (64.90-152.48)	128.25 (73.35-226.58)	92.10 (64.28-174.38)	80.65 (62.30-121.88)	81.10 (62.38-107.95)	< 0.001
Data are presented as mean (standard deviation, SD), medians (interquartile range, IQR) or n (%).

3.2 Multivariable linear regression model to assess the association between energy-adjusted fiber intake and blood concentrations of HbAA, HbGA and HbAA + HbGA

The results of the multivariable linear regression are shown in Table 2. After adjusting for all covariates, including age, sex, education level, race, smoking, alcohol use, diabetes, hypertension, body mass index, poverty-income ratio and physical activity, energy-adjusted fiber intake was negatively associated with the concentrations of HbAA, HbGA and HbAA + HbGA. Compared with the lowest quantile, the β values with 95% confidence intervals (CIs) across the quantiles of energy-adjusted fiber intake were − 0.13 (-0.21 to -0.05), -0.24 (-0.31 to -0.16), and − 0.22 (-0.31 to -0.14) for HbAA, HbGA and HbAA + HbGA, respectively. Energy-adjusted fiber intake was approximately a J shaped relationship with the concentrations of HbAA, HbGA and HbAA + HbGA, and tests for nonlinearity were significant (Fig. 2)

Table 2

Association between energy adjusted dietary fiber and serum concentration of acrylamide hemoglobin biomarkers.
	HbAA	HbGA	HbAA + HbGA
Adjusted dietary fiber intake	β (95% CI)	β (95% CI)	β (95% CI)
Q1	Ref.	Ref.	Ref.
Q2	-0.13(-0.21, -0.05)^***	-0.04(-0.12, 0.03)	-0.10(-0.17, -0.02)^**
Q3	-0.24(-0.31, -0.16)^***	-0.13(-0.21, -0.06)^***	-0.20(-0.27, -0.12)^***
Q4	-0.22(-0.31, -0.14)^***	-0.13(-0.21, -0.05)^**	-0.19(-0.27, -0.11)^***
p-trend	< 0.001	< 0.001	< 0.001
Analyses were adjusted for age, sex, education level, race, smoking, drinking, diabetes, hypertension, body mass index, poverty-income ratio and physical activity. Serum concentration of acrylamide hemoglobin biomarkers were log2-transformed to fit the regression model. Q, quantile. ^p < 0.01, ^*p < 0.001 .

Examination of the percent difference in AA hemoglobin biomarkers by quartile of energy-adjusted fiber intake is presented in Fig. 3. In the fully adjusted linear regression model, every interquartile range (IQR) increase in energy-adjusted fiber intake was associated with − 12.1% (-18.0%, -5.8%) lower levels of HbAA, -5.2% (-11.2%, 1.5%) lower levels of HbGA and − 9.9% (-15.7% to -3.6%) lower levels of HbAA + HbGA. Compared with participants in the lowest dietary fiber quantile, the adjusted percent change with 95% confidence intervals (CIs) in HbAA for the highest dietary fiber quantile was − 19.7% (-26.7%, -13.1%); for HbGA, it was − 12.2% (-18.9%, -4.9%), and for HbAA + HbGA, it was − 17.3% (-23.7%, -10.4%).

3.3 Subgroup analysis by obesity

In subgroup analysis by obesity, the results among obesity and non-obesity were inconsistent with those among the total population (detailed regression result data were presented in Table 3). BMI-stratified models suggest that the association between energy-adjusted fiber intake and the blood concentrations of HbAA, HbGA and HbAA + HbGA was more significant among non-obese adults (all p value for interaction < 0.05).

Table 3

Association between energy adjusted dietary fiber and serum concentration of acrylamide hemoglobin biomarkers stratified by BMI.
	HbAA	HbGA	HbAA + HbGA
	β (95% CI)	β (95% CI)	β (95% CI)
Obesity (BMI > = 30kg/m²)
Q1	Ref.	Ref.	Ref.
Q2	-0.09(-0.20, 0.02)	-0.03(-0.14, 0.08)	-0.07(-0.17, 0.04)
Q3	-0.15(-0.27, -0.04)^*	-0.11(-0.22, 0.01)	-0.14(-0.25, -0.02)^*
Q4	-0.14(-0.27, -0.02)^*	-004(-0.17, 0.08)	-0.10(-0.22, 0.02)
Non-obesity (BMI < 30kg/m²)
Q1	Ref.	Ref.	Ref.
Q2	-0.14(-0.24, -0.03)^**	-0.05(-0.16, 0.05)	-0.10(-0.21, -0.01)^*
Q3	-0.27(-0.38, -0.17)^***	-0.15(-0.25, -0.04) ^**	-0.23(-0.33, -0.12)^***
Q4	-0.25(-0.36, -0.14)^***	-0.17(-0.28, -0.06) ^**	-0.22(-0.33, -0.12)^***
P for interaction	< 0.001	0.014	< 0.001
Analyses were adjusted for age, sex, education level, race, smoking, drinking, diabetes, hypertension, poverty-income ratio and physical activity. Serum concentration of acrylamide hemoglobin biomarkers were log2-transformed to fit the regression model. Q, quantile. ^p < 0.05, ^p < 0.01, ^**p < 0.001 .

To our knowledge, our study is the first to explore the associations between energy-adjusted fiber intake and AA hemoglobin biomarkers among the general population. Our results indicated that dietary fiber intake displayed an inverse and nonlinear relationship with the blood concentration levels of AA hemoglobin biomarkers.

We have observed J-shaped relationship between dietary fiber intake and AA hemoglobin biomarkers. The regression splines suggested a possible threshold effect for dietary fiber intake of approximately 20g/day on AA hemoglobin biomarkers. On the left side of threshold effect point, dietary fiber intake showed a linear and inverse relationship with the concentrations of AA hemoglobin biomarkers and the effect became flat on the right side of threshold effect point. In addition, our results showed a significant interaction between dietary fiber intake and obesity status in relation to the concentrations of AA hemoglobin biomarkers. The associations between dietary fiber intake and AA hemoglobin biomarkers were less significant among obese adults compared with non-obese adults.

There have been no previous studies investigating dietary fiber intake that modifies blood concentration levels of AA hemoglobin biomarkers in general populations. Our results are consistent with previous data reporting the inverse association of fiber intake with environmental pollutants such as perfluoroalkyl acids (Dzierlenga et al. 2021) and isopentanaldehyde (Shi et al. 2022). In the study conducted by Dzierlenga et al., dietary fiber was associated with lower serum concentrations of perfluoroalkyl substances due to increased gastrointestinal excretion. Additionally, in the study conducted by Shi et al., dietary fiber intake was inversely associated with blood levels of isopentanaldehyde and propionaldehyde. Our results add information of interest to the available research on this topic.

The underlying mechanism of dietary fiber intake-induced decreases in blood concentration levels of AA hemoglobin biomarkers remains unknown. Possible mechanisms are as follows. First, diet was considered the major source of exposure to AA, though insoluble dietary fiber helped to reduce the absorption of AA because it increased the volume of feces and decreased the transit time in the intestine. Second, epidemiological study showed that acrylamide exposure was positively associated with systemic inflammatory mediator plasma C-reactive protein. Animal experiments demonstrated that dietary fiber mediated oxidation by activating the Acly/Nrf2/NF-κB signaling pathway, which exerted anti-inflammatory effects by inhibiting the differentiation of macrophages into pro-inflammatory M1 macrophages (Shao et al. 2022). Thus, it is plausible to consider that the pathogenesis of dietary fiber may be related to decreased concentrations of AA hemoglobin biomarkers’s through its anti-inflammatory effects. Third, the liver is in the first line to help our body defense against harmful substances outside including environmental pollutants. A recent study clarified the inverse associations of dietary fiber intake with nonalcoholic fatty liver disease (NAFLD) (Zhao et al. 2020). NAFLD patients are likely to have decreased dietary fiber intake and impaired liver function, while the liver plays a crucial role in AA metabolism. Finally, intestinal microorganisms are fermented by dietary fiber to produce short-chain fatty acids, which can improve liver metabolism (Lundin et al. 2004), thus increasing AA metabolism.

There are some limitations in our study. First, we cannot infer causal relationships directly and which factor came first because of the cross-sectional nature of this study. Second, dietary fiber intakes were obtained from two 24 h dietary recall interviews at an interval of 3 to 10 days, while AA hemoglobin biomarkers can reflect the exposure level within 120 days (Bergmark et al. 1993). Dietary fiber intake evaluated over 2 days may not be long enough to estimate the cumulative effect of fiber intake on AA exposure. Third, the dietary data of fiber intake were obtained from two 24 h dietary recall interviews, and ineluctable recall bias may exist. Finally, although we try to control a series of confounding factors, unmeasured confounding factors may also play a role.

4.Conclusion

Dietary fiber intake was inversely and nonlinearly associated with environmental exposure to AA. The possible obesity-specific effect of dietary fiber intake on AA hemoglobin biomarkers was suggested in our study. Our results also suggested that high-fiber diets are recommended for populations exposed to elevated AA levels. However, further studies should be employed to elucidate these relationships.

Author information

Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University

Shengen Liao

Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University

Huanhuan Gong

Corresponding author:

Shengen Liao

Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Guangzhou Road 300, Nanjing, 210029, China. Tel: +86 15851851983; E-mail: [email protected]

Contributions

Shengen Liao designed the research. Huanhuan Gong performed the data analysis, wrote and edited the original draft of the paper. All authors contributed to the interpretation of the results, and all authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

The data we used and analyzed in our study are available from https://www.cdc.gov/nchs/nhanes.

Conflict of interest

The authors declare no conflicts of interest.

Funding

Not applicable. No funding was received to assist with the preparation of this manuscript.

Acknowledgements

The authors would like to thank all participants enrolled in the National Center for Health Statistics and the director of the NHANES.

Bergmark E, Calleman CJ, He F, Costa LG (1993) Determination of hemoglobin adducts in humans occupationally exposed to acrylamide. Toxicol Appl Pharmacol 120:45–54. doi:10.1006/taap.1993.1085
Dzierlenga MW, Keast DR, Longnecker MP (2021) The concentration of several perfluoroalkyl acids in serum appears to be reduced by dietary fiber. Environ Int 146:106292. doi:10.1016/j.envint.2020.106292
Huang M, Zhuang P, Jiao J, Wang J, Zhang Y (2018) Association of acrylamide hemoglobin biomarkers with obesity, abdominal obesity and overweight in general US population: NHANES 2003–2006. Sci Total Environ 631–632:589–596. doi:10.1016/j.scitotenv.2018.02.338
Larsson SC, Wolk A (2014) Dietary fiber intake is inversely associated with stroke incidence in healthy Swedish adults. J Nutr 144:1952–1955. doi:10.3945/jn.114.200634
Liu S, Ben X, Liang H, Fei Q, Guo X, Weng X, Wu Y, Wen L, Wang R, Chen J, Jing C (2021a) Association of acrylamide hemoglobin biomarkers with chronic obstructive pulmonary disease in the general population in the US: NHANES 2013–2016. Food Funct 12:12765–12773. doi:10.1039/d1fo02612g
Liu Y, Ju Y, Cui L, Liu T, Hou Y, Wu Q, Ojo O, Du X, Wang X (2021b) Association between Dietary Fiber Intake and Incidence of Depression and Anxiety in Patients with Essential Hypertension. Nutrients 13. doi:10.3390/nu13114159
Lundin EA, Zhang JX, Lairon D, Tidehag P, Aman P, Adlercreutz H, Hallmans G (2004) Effects of meal frequency and high-fibre rye-bread diet on glucose and lipid metabolism and ileal excretion of energy and sterols in ileostomy subjects. Eur J Clin Nutr 58:1410–1419. doi:10.1038/sj.ejcn.1601985
Pedersen M, Vryonidis E, Joensen A, Törnqvist M (2022) Hemoglobin adducts of acrylamide in human blood - What has been done and what is next? Food Chem Toxicol 161:112799. doi:10.1016/j.fct.2021.112799
Pereira MA, O'Reilly E, Augustsson K, Fraser GE, Goldbourt U, Heitmann BL, Hallmans G, Knekt P, Liu S, Pietinen P, Spiegelman D, Stevens J, Virtamo J, Willett WC, Ascherio A (2004) Dietary fiber and risk of coronary heart disease: a pooled analysis of cohort studies. Arch Intern Med 164:370–376. doi:10.1001/archinte.164.4.370
Shao J, Ge T, Wei Y, Zhou Y, Shi M, Liu H, Chen Z, Xia Y (2022) Co-interventions with Clostridium butyricum and soluble dietary fiber targeting the gut microbiota improve MAFLD via the Acly/Nrf2/NF-κB signaling pathway. Food Funct 13:5807–5819. doi:10.1039/d1fo04224f
Shi S, Zhu Q, Liao S, Zhu X, Tang X, Zhou Y (2022) The association between dietary fiber intake and the concentrations of aldehydes in serum. Environ Sci Pollut Res Int 29:25790–25798. doi:10.1007/s11356-021-17638-5
Sun B, Shi X, Wang T, Zhang D (2018) Exploration of the Association between Dietary Fiber Intake and Hypertension among U.S. Adults Using 2017 American College of Cardiology/American Heart Association Blood Pressure Guidelines: NHANES 2007â»2014. Nutrients 10. doi:10.3390/nu10081091
Sun Y, Sun J, Zhang P, Zhong F, Cai J, Ma A (2019) Association of dietary fiber intake with hyperuricemia in U.S. adults. Food Funct 10:4932–4940. doi:10.1039/c8fo01917g
Tareke E, Rydberg P, Karlsson P, Eriksson S, Törnqvist M (2002) Analysis of acrylamide, a carcinogen formed in heated foodstuffs. J Agric Food Chem 50:4998–5006. doi:10.1021/jf020302f
Yin G, Liao S, Gong D, Qiu H (2021) Association of acrylamide and glycidamide haemoglobin adduct levels with diabetes mellitus in the general population. Environ Pollut 277:116816. doi:10.1016/j.envpol.2021.116816
Zhang S, Tian J, Lei M, Zhong C, Zhang Y (2022) Association between dietary fiber intake and atherosclerotic cardiovascular disease risk in adults: a cross-sectional study of 14,947 population based on the National Health and Nutrition Examination Surveys. BMC Public Health 22:1076. doi:10.1186/s12889-022-13419-y
Zhang Y, Huang M, Zhuang P, Jiao J, Chen X, Wang J, Wu Y (2018) Exposure to acrylamide and the risk of cardiovascular diseases in the National Health and Nutrition Examination Survey 2003–2006. Environ Int 117:154–163. doi:10.1016/j.envint.2018.04.047
Zhao H, Yang A, Mao L, Quan Y, Cui J, Sun Y (2020) Association Between Dietary Fiber Intake and Non-alcoholic Fatty Liver Disease in Adults. Front Nutr 7:593735. doi:10.3389/fnut.2020.593735

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Inverse relationship between dietary fiber intake and environmental exposure to acrylamide

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Version 1

Abstract

Figures

1. introduction

2. Method

2.1 Study population

2.2 Environmental AA measurements

2.3. Dietary information

2.4. Covariates

2.5. Statistical analysis

3. Results

3.1 Study population characteristics

3.3 Subgroup analysis by obesity

4. Discussion

Declarations

References

Status:

Version 1