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Nutritional quality and health risks of wheat grains from organic and conventional cropping systems
Journal Pre-proofs Nutritional quality and health risks of wheat grains from organic and conventional cropping systems Yang Zhang, Suzhen Cao, Zhiyong Zhang, Xiaodan Meng, Chien Hsiaoping, Changbin Yin, Hao Jiang, Shu Wang PII: DOI: Reference:
S0308-8146(19)31708-X https://doi.org/10.1016/j.foodchem.2019.125584 FOCH 125584
To appear in:
Food Chemistry
Received Date: Revised Date: Accepted Date:
9 April 2019 26 August 2019 23 September 2019
Please cite this article as: Zhang, Y., Cao, S., Zhang, Z., Meng, X., Hsiaoping, C., Yin, C., Jiang, H., Wang, S., Nutritional quality and health risks of wheat grains from organic and conventional cropping systems, Food Chemistry (2019), doi: https://doi.org/10.1016/j.foodchem.2019.125584
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Nutritional quality and health risks of wheat grains from organic and conventional cropping systems Yang Zhanga,b, Suzhen Caoc, Zhiyong Zhangd,e, Xiaodan Mengd,e, Chien Hsiaopingf, Changbin Yina,b*, Hao Jianga, Shu Wanga a. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China b. Key laboratory of Nonpoint Source Pollution Control, Ministry of Agriculture, Beijing 100081, China c. School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China d. College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China e. The Collaborative Innovation Center of Henan Food Crops, College of Agronomy, Henan Agriculture University, Zhengzhou 450002, China f.
Japan International Research Center for Agricultural Sciences, 1-1 Ohwashi, Tsukuba, Ibaraki, 305-8686, Japan
Abstract The influence of cropping systems on nutrition and food safety is controversial. This study aimed to evaluate the effects of an organic cropping system (OCS) on wheat nutrition and food safety at the molecular level by using a comprehensive research method. Nutrient deviation in samples from an OCS and a conventional cropping system (CCS) were detected, and 58 biomarkers were selected through multivariate statistical analysis and were further qualitatively 1
and quantitatively analyzed. The health risk of heavy metal(loid)s (HMs) for different populations was assessed based on the estimated average daily dose and recommended ingestion reference dose, which indicated that populations ingesting grains from OCSs had higher non-carcinogenic and carcinogenic risks. Additionally, HMs posed greater non-carcinogenic risks to children under five years old and greater carcinogenic risks to adults. This study highlights the need to consider the potential risk from HMs and nutritive ingredient differences in organic food. Keywords: Metabolomics, nutrition, heavy metals, health risk assessment, wheat List of abbreviations: OCS: organic cropping system; CCS: conventional cropping system; GC-Q/TOF: gas chromatography–quadrupole time of flight–mass spectrometry; UPLC-Q/TOF: ultra performance liquid chromatography–quadrupole time of flight–mass spectrometry; ICP-MS: inductively coupled plasma–mass spectrometry; HMs: heavy metal(loid)s; ADD: average daily dose; HQ: hazard quotient; HI: hazard index; OPLS-DA: orthogonal projection to latent structures–discriminant analysis; PCA: principal component analysis; RfD: ingestion reference dose. 1.
Introduction Organic farming has become increasingly attractive worldwide with the increase in
concerns about environmental degradation, food safety, and human health (Lairon, 2010). Organic farming is defined as a way to grow crops without synthetic fertilizers, herbicides, or pesticides, instead applying organic fertilizer and utilizing crop rotation to maintain soil 2
productivity (Andersen et al., 2015). Global-scale data published by the Research Institute of Organic Agriculture and the International Federation of Organic Agriculture Movements in 2017 showed that organic agriculture was practiced in 179 countries, with 9.06 million hectares of agricultural land managed organically by 2,400,000 producers, and that the area of organic production quadrupled during 1999–2015. Whether organic cropping systems (OCSs) can improve food nutritional quality compared with conventional cropping systems (CCSs) has long been a matter of interest and debate (Popa, Mitelut, Popa, Stan, & Popa, 2018). Previous studies have shown that organic foods may have some advantages, such as an overall trend of higher levels of nutrients, including phenolic compounds, vitamins, and some micronutrients (Mie et al., 2017); however, other studies have revealed no consistent difference in macronutrient content between conventionally and organically grown foods (Thorupkristensen, Dresbøll, & Kristensen, 2012) and have documented a higher average protein content in food from CCSs than in food from OCSs (Vrček et al., 2014). Various analytical methods have been used to differentiate the chemical components of organic and conventional products. Among the analytical approaches used for this purpose, metabolomics has emerged as an important method; this approach is mainly related to metabolites such as small-molecule compounds and can be used to indicate changes in organisms as well as their tissues and cells. Metabolite profiling has recently been carried out for many plant species, such as tomatoes (Vallverdú-Queralt, Medina-Remón, Casals-Ribes, Amat, & Lamuela-Raventós, 2011), potatoes (Shepherd et al., 2014), white cabbage (Mie et 3
al., 2014), peppers (Novotnã et al., 2012), strawberries (D'Urso, D'Aquino, Pizza, & Montoro, 2015), and onions and carrots (Cubero-Leon, De Rudder, & Maquet, 2018) to compare nutritionally desirable metabolites in products grown in OCSs and CCSs. These studies demonstrated that metabolomics could be applied to reliably, precisely, and effectively identify crops grown with different cultivation practices and to determine which organic products have higher concentrations of a wide range of plant secondary metabolites (Barański et al., 2014). Contradictory findings have been reported for wheat (Triticum aestivum), however, which is the main cereal crop consumed by humans in many areas worldwide and is a staple food in many regions of China (Zhang et al., 2017). Some studies have documented higher levels of phytochemical compounds, such as ferulic, p-coumaric, syringic, vanillic, and caffeic acids, in organic wheat than in conventionally grown wheat (Okarter, Liu, Sorrells, & Liu, 2010). In contrast, Zorb et al. (2006) found no remarkable difference in the contents of 44 secondary metabolites that were measured in wheat grains in an OCS and a CCS. The nutritional level of wheat is of extreme importance because it is a significant human dietary source of nutrients, including protein, carbohydrates, minerals, vitamins, fiber, and phyto-compounds (Dziki, Różyło, Gawlik-Dziki, & Świeca, 2014); therefore, untargeted metabolite profiling is needed to confirm the impacts of OCSs and CCSs on the overall chemical composition of wheat grains. Compared with CCSs, OCSs may have severe heavy metal(loid) (HM) pollution due to the use of organic fertilizer, which is made up mainly of livestock and poultry waste and thus contains large amounts of trace elements. These elements accumulate in crops through the 4
livestock–fodder and poultry waste–soil–crop pathways (Petersen et al., 2007). High contents of Cd, Cu, and Zn can accumulate under OCSs due to excessive application of commercial organic fertilizer (Chen, Huang, Hu, Weindorf, Liu, & Yang, 2014). Zaccone et al. (2010) found higher contents of Ni, Zn, and Pb in food samples obtained from OCSs due to long-term fertilizer application. Through a wide inventory of HM content in organic manures, Lopes, Herva, Franco-Uría, & Roca, 2011) determined that the high biotransfer potential of Zn led to significant concentrations in food, exceeding recommended doses; they proposed that measures to regulate the Zn content in organic fertilizers should match specific management goals. The appearance of high levels of HMs in crops may pose potential harm to human health due to high exposure doses from food ingestion and toxic effects of HMs to human beings (Qureshi, Hussain, Ismail, & Khan, 2016); quantitative assessment of the risks caused by HMs in food is thus essential for environmental management and food safety control. Human health risk assessment estimates the probability and nature of adverse health effects to populations that may be exposed to pollutants (Aschberger et al., 2010) and is necessary to determine the risks associated with wheat grains from OCSs and CCSs. The application of no synthetic fertilizers, herbicides, or pesticides in OCSs could lead to nutrient deficiencies and pose potential health risks to the population due to the harmful HMs present in organic fertilizers, so this issue should be heavily studied. The goals of this study were therefore to 1) confirm the effects of an OCS and a CCS on wheat grain nutrition, 2) reveal the dynamic characteristics of metabolites in OCS and CCS wheat, 3) summarize the 5
impacts of cropping systems on HMs in wheat grains, 4) determine the HM exposure, and 5) identify health risks for the local population resulting from the oral ingestion of wheat grains. We compared the nutritional quality and food safety of wheat grains from an OCS and a CCS on a molecular level. Our results could further our realization of the metabolism of wheat grains produced under OCSs and CCSs and will help the government and policymakers to implement measures to reduce or control HMs in wheat grains. 2. Materials and Methods 2.1 Experimental site and study design This study was part of a long-term experiment conducted at the Scientific and Educational Park of Henan Agricultural University (113°35'51"E, 34°52'13"N), Henan Province, China, where the effects of an OCS and a CCS were compared using a 6-year (2011–2017) durum wheat–green manure crop rotation. One set of fields was managed using traditional agricultural practices, which has been detailed previously (Zhang, Yin, Cao, Cheng, Wu, & Guo, 2018). Another set of fields was managed according to the Chinese requirements for certified organic production (GB/T 19630.1-2011). The common organic fertilizer in Henan Province was applied as basal fertilizer at 30 kg·ha–1·yr–1. Details on the fertilizers used in both cropping systems can be seen in Supplementary Tables 1 and 2. Grain yields from the OCS and CCS were monitored annually (Supplementary Table 3). For each cropping system, 48 wheat samples (1 g each) were collected for elemental analysis by GC-Q/TOF, UPLC-Q/TOF, and ICP-MS in June 2017.
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2.2 Sample preparation Samples were prepared for GC-Q/TOF analysis by modifying a previously described method (Palmer, Dias, Boughton, Roessner, Graham, & Stangoulis, 2014). For UPLC-Q/TOF analysis, the procedure of sample treatments was conducted using a well-designed method (Zhang et al., 2017); the ICP-MS analysis referred to details described in Cao et al. (2014). For quality assurance and quality control, the reference samples of wheat as well as reagent blank samples were included during sample pretreatment and detection. 2.3 Sample testing GC-Q/TOF analysis was conducted using a modification of a previously described method (Palmer et al., 2014). GC was performed using a 30-m VF-5MS column with 0.2-μm-thick film and a 10-m Integra guard column (Agilent J & W GC Column; Agilent Scientific Technology Ltd., California, USA). The injection temperature was regulated at 280 °C, the MS transfer line was set at 300 °C, the ion source was set at 280 °C, and the quadrupole was set at 180 °C. Helium was applied as the carrier gas at a flow rate of 1.0 mL ·min–1. For the analysis of polar metabolites, we used the following program: injection at 70 °C and holding for 1 min, and then establishment of a 7 °C·min–1 oven temperature that was increased to 325 °C and held at this final heat temperature for 6 min. UPLC-Q/TOF analysis was conducted using an established protocol (Zhang et al., 2017). The compounds of all wheat samples were detected with 2 μL methanol filtrate and a UPLC-Q/TOF; the detailed pretreatment and detection conditions were described in a previously published study (Zhang et al., 2017). For the concentration measurement of HMs, 7
i.e., the content of Pb, Cd, Cr, As, Ni, Zn, Cu, and Mn in samples, ICP-MS analysis was conducted using the established protocol (Zhang et al., 2018). 2.4 Exposure and risk assessment In addition to considering the nutrients in food, the detrimental health effects of HMs from food ingestion should also be considered. Therefore, on the basis of concentrations of HMs in the grains of wheat, the exposure dose and consequent health risks of HMs for the local population due to grain ingestion were assessed according to the Chinese Exposure Factors Handbook (Duan, 2012; USEPA, 2011a). 2.4.1 Exposure assessment On the basis of the concentration of HMs and exposure factors, the exposure dose is presented as the average daily dose (ADD; mg·kg–1·day–1). The ADD of each HM through grain ingestion, according to the Exposure Factors Handbook, could be estimated via the following model (USEPA, 2011a):
ADDingest=
C IngR EF ED , BW AT
(1)
where C is the HM content in mg·kg–1, IngR is the food ingestion rate in mg·d–1, EF is the exposure frequency in d·year–1, ED is the exposure duration in years, BW is the body weight in kg, and AT is the average time in days. AT was set as equal to EF × ED for non-carcinogenic assessment and to 25200 days for carcinogenic assessment (Duan, 2012; USEPA, 2011a). The exposure factors such as the IngR of wheat grains, BW of different populations, and ED were obtained from the field questionnaire survey. 2.4.2 Risk characterization 8
A hazard quotient (HQ) was used to indicate lifetime non-cancer risk. In this study, the HQ was evaluated by dividing the ADD from ingestion exposure pathways by a specific ingestion reference dose (RfD). The HQ was defined as the following equation (USEPA, 2011a):
HQ=
ADD , RfD
(2)
where RfD reflects the estimated maximum acceptable risk to humans through daily ingestion exposure (mg·kg–1·day–1). Detrimental health effects are unlikely if HQ ≤ 1, whereas HQ > 1 is likely to raise concerns about adverse non-carcinogenic effects (Al-Saleh, Nester, DeVol, Shinwari, & Al-Shahria, 1999). To assess the cumulative potential non-carcinogenic health risks of multiple chemicals, a hazard index (HI) was used to identify the risks by summing the HQs from each chemical. HI was defined as follows (USEPA, 2011a):
HI= 1 HQ , i
(3)
If HI ≤ 1, the adverse cumulative non-carcinogenic health risks are unlikely to happen, whereas there may be potential chronic risks if HI > 1, and pollutants should then be segregated for analysis. For carcinogenic health risk assessment, the incremental lifetime cancer risk (ILCR) was applied to indicate the incremental probability of suffering from potential carcinogenic effects over a lifetime. The ILCR is calculated using the following equation (USEPA, 2011a): ILCR = ADD × SF,
(4)
where SF denotes the cancer slope factor in (mg·kg-1·d-1)-1; SF values of each of the 9
carcinogens are listed in Table 1. When there are multiple carcinogenic pollutants, the individual carcinogenic risks and exposure routes can then be summed (assuming additive effects) and applied for comparison with the acceptable risk. The potential carcinogenic values in the range of 1.0 × 10–6 to 1.0 × 10–4 were regarded as acceptable risks. In line with the classification criteria defined by the International Agency for Research on Cancer (IRAC, 2011), Zn, Cd, Mn, Pb, and Cu were set as non-carcinogenic pollutants, while Cr and As were regarded as potential carcinogens that can pose carcinogenic effects to the human body by ingestion exposure pathways. SF values of carcinogens and RfD values of non-carcinogens related to this study are listed in Table 1 (USEPA, 2011a). 2.5 Statistical analyses The preprocessing of metabolomics data, such as retention time correction, peak integration, alignment, and peak detection, was carried out using the Markerlynx XS software (Waters Corporation). Orthogonal projection to latent structures–discriminant analysis (OPLS-DA) and principal component analysis (PCA) were then applied for multivariable analysis based on the SIMCA-P software (v. 12.0; Umetric, Umea, Sweden). We also performed Kruskal–Wallis tests, univariate statistical analysis, and multi-regression analysis employing the software of SPSS 20.0 and Origin 8.0 and regarded the statistical results to be significant with a p value of no more than 0.01. Since the HM contents showed a normal distribution, Pearson’s correlation was applied to evaluate linear correlations among different metals.
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3. Results 3.1 Profiling of wheat grain metabolites Plant nutritional quality is related closely to the types and contents of metabolites. In order to give a summarization of the metabolite variation between OCS and CCS samples, GC-Q/TOF-MS and UPLC-Q/TOF-MS data were examined using PCA. This analysis was conducted based on specified MS information, including ion peak intensity, retention time, ion peak area, and MS (m/z) ions. The PCA results indicated that the cropping system could influence the metabolite variation significantly among samples (Figure 1). Seven principal components (PCs) were kept in the ultimate PCA models, with two PCs representing 61.4% (PC1, 39.3%; PC2, 22.1%) of the GC-Q/TOF-MS dataset variance (R2 X = 0.776, Q2 = 0.491; Figure 1a) and 47% (PC1, 29.8%; PC2, 17.2%) of the UPLC-Q/TOF-MS dataset variance (R2 X = 0.569, Q2 = 0.432; Figure 1b). Using GC-Q/TOF and UPLC-Q/TOF methods, we detected a total of 432 and 2756 primary and secondary metabolites, respectively, in the wheat grains. To explore the biomarkers related to the difference between the two cropping systems, an S-plot of the OPLS-DA model was applied, which is illustrated in Supplementary Figure 1. The sample classification could be mainly identified by the compounds that were much closer to the upper right and lower left corners. This result met the requirement that the value of variable importance in projection (VIP) be higher than 1; higher VIPs have greater variable importance, according to the model. On the basis of the distribution that was illustrated by the S-plot of the OPLS-DA model, 53 primary metabolites and 19 secondary metabolites were screened. The 11
unpaired parametric Welch t test was employed to identify the remarkable differences (p ≤ 0.001, ≥ 2-fold change) among screened metabolites. Finally, 46 primary metabolites and 12 secondary metabolites were selected as potential biomarkers for the classification of wheat grain samples under the organic and non-organic cropping system treatments. 3.2 Qualitative and quantitative biomarker analysis In this study, GC-Q/TOF was used mainly to detect primary metabolites in wheat grains. Based on the acquired mass spectra, 31 primary metabolites were identified as biomarkers using mass spectral databases, including that of the National Institute of Standards and Technology and the Golm Metabolome Database. These biomarkers belong to diverse nutrient categories and comprise seven free amino acids, six sugars, three sugar alcohols, six organic acids, three fatty acids, and six other metabolites (Supplementary Table 4). UPLC-Q/TOF was used mainly to detect secondary metabolites in wheat grains. To identify biomarkers among the secondary metabolites, we used the Markerlynx software (Waters Corporation, Milford, MA, USA) to determine the elemental compositions of unknown compounds as well as the refined formula weight, degrees of unsaturation, and isotope abundance patterns. Based on MS information and quasi-molecular ion peaks, we measured fragments of the ion peaks of chemicals in the second-order MS. In total, six secondary metabolites belonging to flavonoids and their derivatives were identified through searches of the Kyoto Encyclopedia of Genes and Genomes, MassBank, METLIN, Madison Metabolomics Consortium Database, PubChem, and other network databases and related reports (Supplementary Table 5). 12
Kruskal–Wallis and multiple comparison tests were used to compare biomarker content among samples. Secondary metabolite biomarker content was significantly higher in OCS samples than in CCS samples (Figure 2). As primary metabolite biomarkers were too numerous to perform multiple comparisons, we used Bayesian network analysis to compare the content between the OCS and CCS samples; the results were translated into Pearson correlation coefficients, which are presented as a heat map (Supplementary Figure 2). Dynamic characteristics of biomarkers could reflect the effect of cropping systems on wheat grain metabolism, and further identification of biomarkers could indicate the affected metabolic pathways, which could contribute to exploring the relationship between nutrition metabolism and planting patterns. 3.3 Contents of heavy metals in wheat grains The concentrations of all investigated HMs, including Ni, Pb, Zn, Cd, Cr, Cu, Mn, and As, in wheat grains are shown in Table 2. The order of HM content in wheat grains from the OCS and CCS was Zn > Cu > Mn > Cr > Ni > As > Pb > Cd. Cu, Zn, As, Cd, and Pb concentrations were observably higher in OCS than in CCS samples; no significant difference in the content of any other examined HM was observed between sample groups. The mean contents of each target HM did not exceed the relative thresholds of the national standards for food contaminants (GB 2762-2012). However, wheat grains from the OCS contained a greater excess of HMs, especially Cr and As (at excess rates of 17.1% and 10.5%, respectively). HM contents of wheat grains from the CCS generally fell below the standard thresholds (GB 2762-2012), except for some samples that exceeded standard levels 13
of Cd, Pb, and As. 3.4 Daily exposure dose of heavy metal(loid)s According to the field investigation, we found that wheat is the main staple food for the population in Henan Province. Thus, the assessment of exposure dose and health risks from HMs in wheat is important in this region. Combining the concentrations of HMs in wheat and the exposure behavior patterns of the local population to wheat, the ADD of each HM via wheat ingestion was assessed for the local children (i.e., 0–5 years and 6–17 years) and adults. Since the mean ADD was identical to the median ADD value, the mean ADD value of each population group is displayed in Figure 3 and is applied to assess the health risks. Within the three population groups, the ADD values for Mn, Ni, Zn, As, Cd, and Pb under OCS and CCS treatments were 5.05–5.50, 30.26–35.08, 5.77–8.89, 67.44–150.52, 14.81–27.81, and 4.21–7.49 times lower, respectively, than the relevant average daily intake values based on the Food and Agriculture Organization/World Health Organization regulations (0.06, 0.12, 1, 0.15, 0.007, and 0.0035 mg·kg–1·day–1, respectively; Figure 3). Therefore, the ADDs obtained in the present study are acceptable and likely pose little harm to the local population. Furthermore, the ADD values for HMs in OCS and CCS samples were in the order of Zn > Cu > Mn > Cr > Ni > As > Pb > Cd. The total ADD values for Cr, Mn, Ni, Cu, and Zn were statistically higher for the OCS treatment than for the CCS treatment, and those for As, Cd, and Pb differed indistinctively between the two cropping systems. However, ADD values from OCS samples reached maximum levels of 0.26, 0.18, and 0.21 mg·kg–1·day–1 for 0–5- and 6–17-year-olds and adults, respectively. The mean cumulative ADD values for 14
0–5-year-old children were 1.44 and 1.24 times higher than for the 6–17-year-old children and adults, respectively. 3.5 Health risks of wheat grains 3.5.1
Non-cancer risks On the basis of the estimated ADD value and recommended RfD through ingestion
exposure pathways for each HM, the non-cancer risk of each HM, expressed as HQ, was assessed for the three population groups. The HQ values are shown by age group in Figure 4. HIs were 1.3 times higher for OCS than for CCS wheat in all three population groups. As and Cr exposure levels were the greatest contributors to the HI for the three groups, followed by exposure to Zn, Pb, Cu, Cd, Ni, and Mn, accounting for 64.89% and 13.22% of the cumulative HI for wheat grown under OCS and CCS, respectively. Ni and Mn contributed the least to the HIs for wheat grown under OCS and CCS (1.75% and 0.70%, respectively). Notably, cumulative HIs for OCS and CCS wheat exceeded 1 in all three population groups because the HQs for As and Cr were high (5.37 and 1.38, respectively). Due to the potential non-cancerous health risks, further research on each separate HM is required; if HI > 1 in some cases (USEPA, 2011a), then local populations could experience non-cancerous health effects via wheat ingestion pathways due to HM exposure. Thus, our findings suggest that each single HM, including Pb, Zn, Cd, Ni, Mn, and Cu, in wheat may pose few health effects to the local population, although As and Cr ingestion poses certain health risks. The HI values of three population groups decreased as follows: 0–5-year-old children > adults > 6–17-year-old children, which was similar to the trend of cumulative ADD values. 15
Separately, the HQs for HMs increased in the order of Ni < Cu < Mn < Zn < Pb < Cd < As < Cr, following exposure to grains cultivated under the OCS, whereas they increased in the order of Ni < Cu < Mn < Zn < Pb < Cd < Cr < As for populations due to grain exposure from grains cultivated under the CCS. When considering the distributions of the HM content and ADD values for each metal in the OCS and CCS samples, these findings further validated the fact that metals can accumulate under various conditions and the important roles of human behavior patterns and toxicity characteristics in the determination of health risks via wheat ingestion exposure pathways for local residents. 3.5.2
Cancer risks The risks attributable to exposure to carcinogenic HMs are shown by age group in Table 1.
Since the potential carcinogenic risk was within the range defined as acceptable or inconsequential risk (USEPA, 2011b), there might be acceptable carcinogenic health effects to the local population; thus, the ILCR for the three population groups due to wheat grain ingestion was much lower than the acceptable maximum level, i.e., 1.0 × 10–4. Similar to the distribution of HQs under the two cropping systems, either for each HM or for each population group, ILCRs were lower for CCS wheat. Nevertheless, the cancer risk was greater for adults than for children, and it was the lowest for children aged 0–5 years, which differs from the trend of HQs. 3.5.3
Uncertainty analysis There might be some uncertainties in the process of assessing the health risk from
exposure to HMs in wheat grains; the assessment of health risks could be more accurate if the 16
bioavailable or bioaccessible concentrations were considered. Furthermore, various uncertainties might be included in the extrapolation of toxic effects from animal experiments to humans (from high to low doses). Consequently, the non-carcinogenic and carcinogenic health risks according to the total contents of HMs in this study might be slightly overestimated. Nonetheless, although absolute perfect risk assessment was not conducted, this study could be regarded as a perspective study on health risks with the consideration of different crop outputs. 4. Discussion Cultivation factors greatly influence the chemical composition of plants (Palmer et al., 2013); these factors can include the fertilizer level, location, soil type, and year of harvest (Jørgensen, Knudsen, & Lauridsen, 2012). CCSs rely heavily on the application of a range of modern management practices and external inputs, especially fertilizer with high nitrogen concentrations (related closely to amino acid metabolism), to achieve high yields. Under sufficient nitrogen conditions, large amounts of glutamic acid are produced by the glutamine synthetase/glutamate synthase metabolism pathway (GS/GOGAT), and more amino acids connected to this pathway (e.g., lysine, threonine, lactamic acid, and asparagine) are derived (Ferrario-Méry et al., 2000; Glevarec et al., 2004). Our results are consistent with these characteristics. In contrast, OCSs have been developed in response to bans on pesticide and chemical fertilizer use, such that OCS crops are exposed to pest attacks and nutrient-deficient conditions. Therefore, crops grown under organic practices must synthesize more metabolites 17
to defend against increased stress. In this study, the contents of sugars and their derivatives, including glucose, maltose, trehalose, threitol, xylitol, and sorbitol, were obviously higher in organic wheat grains than in CCS-grown wheat grains; the elevation of these compound contents may be a protective mechanism under undesirable growth conditions. Similar to hormones, sugars can play a role as primary messengers to regulate signals controlling the expression of various genes to promote abiotic/biotic stress tolerance (Gupta & Kaur, 2005). In addition to sugars and their derivatives, as a class of low-weight polyphenolic secondary metabolic compounds, flavonoids have outstanding effects on resistance to adversity and stress (Zhang et al., 2017). In our study, the biomarkers identified among secondary metabolites were tricin, kaempferol3-o-glucoside, kaempferol rhamnoside, luteolin, rutin, and quercetin 3-methyl ether, all of which are flavonoids. Higher flavonoid levels have been detected previously in organic wheat grains and could provide defense against stresses such as low nutrition and serious pest infestation in OCSs (Luthria, Singh, Wilson, Vorsa, Banuelos, & Vinyard, 2010; Mazzoncini, Antichi, Silvestri, Ciantelli, & Sgherri, 2015). Recently, much attention has been focused on the functions of flavonoids in response to environmental stresses. In a six-year fixed experiment, Ren et al. (2017) compared the secondary metabolites in onions grown by OCSs and CCSs and found higher secondary metabolite contents, including those of flavonoids and anthocyanins, in organic onions; they concluded that the use of different soil management practices in organic agriculture was responsible for these differences. Similar results have been reported for rice, carrots, and grapes (Xiao, Ma, Zhang, & Qian, 2018). Another study suggested that high microbial 18
activity levels in organically managed soils are another main reason for high secondary metabolite concentrations in crops (Reilly et al., 2014). ADD values were biggest for children aged 0–5 years, due mainly to the different pollution levels of various HMs and differentiation among wheat intake rates. Although the food intake rate was less for children aged 0–5 years, their body weights were also significantly lower; thus, ADD values based on unit body weight were larger under similar exposure conditions. Different exposure behavior patterns may result in different health effects in various populations in a similar exposure environment, especially in terms of health risks caused by HMs. Overall, the non-cancer risk findings for the three population groups suggest that a single HM, including Pb, Ni, Zn, Cd, Mn, and Cu in wheat grains, may pose few harmful health effects to the local population due to wheat ingestion in the study area, regardless of the presence of other toxic HMs. Non-carcinogenic health risks due to As and Cr in wheat grains remain of concern, especially for children aged 0–5 years. As reported previously for Hunan Province (Cao et al., 2014), we found that potential cancer risks in the ingestion pathway were due mainly to Cr. The ILCR for Cr from wheat grain intake was nearly two to three orders of magnitude higher than that for Ni or As ingestion exposure. The cancer risks for adults due to Ni and As exposure were about three and five times larger than those for children aged 0–5 and 6–17 years, respectively, demonstrating that the contributions of HMs to cancer risk differ among age groups.
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5. Conclusion In this study, remarkable variability was found for the nutrient compounds of wheat grains cultivated under OCS and CCS. A total of 46 primary and 12 secondary metabolites were selected as biomarkers based on multivariate statistical analysis, quantitative and qualitative analyses of which indicated that grains cultivated under OCS may be at a disadvantage in terms of nutrition. Additionally, the analysis of concentrations of HMs in wheat grains, including Ni, Pb, Zn, Cd, Cr, Cu, Mn, and As, showed that wheat grains from the OCS contained higher levels of HMs. Furthermore, ADD values of each HM via wheat ingestion were calculated to assess the health risk of HMs for the local population. Health risk assessment indicated that people who ingested wheat grains from the OCS had higher non-cancer and cancer risks. As and Cr exposure were the greatest contributors to the HI, accounting for 64.89% and 13.22%, respectively, and Cr exposure accounted for more than 90% of the total potential cancer risks. HMs posed a higher non-cancer risk to children aged 0–5 years and a higher cancer risk to adults. This study strengthened the hypothesis that both nutrition and potential detrimental health effects should be considered and addressed before beginning organic farming.
Funding
This research was supported by China Agriculture Research System- Green Manure (CARS-22), Major Program of National Social Science Foundation of China (Grant No. 20
18ZDA048), the National Key Research and Development Plan of China (grant Nos. 2016YFC0206203), and the U.S. National Institutes of Health (K02HD70324).
References
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27
Tables
Table1. Summary of cancer risks for the local population (Children in 0-5 yrs, 6-17 yrs and adult) via grain ingestion, which were cultivated under OCS and CCS Cr
Ni
As
0-5 yrs
6-17 yrs
Adult
0-5 yrs
6-17 yrs
Adult
0-5 yrs
6-17 yrs
Adult
OCS
4.43E-05
6.41 E-05
7.15 E-05
8.28 E-07
1.20 E-06
4.02 E-06
7.74 E-07
1.12E-06
3.75 E-06
CCS
3.65 E-05
5.28 E-05
6.77 E-05
7.15 E-07
1.03 E-06
3.47 E-06
3.47 E-07
5.02 E-07
1.68 E-06
Conditions
28
Table2. HMs concentrations（mg·kg-1）in wheat grains determined by ICP-MS HMs concentrations wheat grains from OCS HMs
Rang
Average
HMs concentrations wheat grains from CCS
Exceeding ratio
Rang
Average
Exceeding ratio
（%） Cr
0.70-1.10
0.89±0.14
17.1
0.51-1.03
0.73±0.16
4.2
Mn
1.99-2.97
2.31±0.38
0
1.76-2.66
2.12±0.25
0
Ni
0.59-1.01
0.77±0.14
4.2
0.46-0.93
0.67±0.16
0
Cu
3.02-5.95
4.34±0.90
0
2.01-3.69
2.91±0.58
0
Zn
23.35-46.78
33.76±6.85
0
16.77-30.12
21.90±4.26
0
As
0.33-0.66
0.43±0.11
10.5
0.11-0.30
0.19±0.06
0
Cd
0.06-0.15
0.09±0.03
8.3
0.03-0.07
0.05±0.01
6.3
Pb
0.06-0.22
0.16±0.05
8.8
0.06-0.14
0.09±0.03
4.2
29
Figures
(a)
(b) Figure 1a: Scores plots of PCA based on GC-QTOF/MS data; Figure 1b: Scores plots of PCA based on UPLC-QTOF/MS data. () wheat grain samples from CCS; (◆) wheat grain samples from OCS.
30
Figure 2: Box plots of the identified biomarkers of secondary metabolites in wheat grain samples.
31
Figure 3: The contribution of CCS and OCS to the average daily expose dose of each metal(loid)s through food ingestion.
32
Figure 4: The HQ values for each metal(loid)s under CCS and OCS treatments through ingestion exposure pathways for the local children and adult.
33
The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
34
The impacts of OCS and CCS on wheat nutrition and food safety were evaluated.
Biomarkers of OCS, including primary and secondary metabolites, were qualitative and quantitative analyzed.
The local population who ingested grains from OCS experienced a higher non-cancer and cancer risks.
As and Cr exposure were the greatest contributor to Hazard Index (HI).
HMs posed relatively higher non-cancer to children aged 0–5 years and higher cancer risks to adults.
35
S0308-8146(19)31708-X https://doi.org/10.1016/j.foodchem.2019.125584 FOCH 125584
To appear in:
Food Chemistry
Received Date: Revised Date: Accepted Date:
9 April 2019 26 August 2019 23 September 2019
Please cite this article as: Zhang, Y., Cao, S., Zhang, Z., Meng, X., Hsiaoping, C., Yin, C., Jiang, H., Wang, S., Nutritional quality and health risks of wheat grains from organic and conventional cropping systems, Food Chemistry (2019), doi: https://doi.org/10.1016/j.foodchem.2019.125584
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Nutritional quality and health risks of wheat grains from organic and conventional cropping systems Yang Zhanga,b, Suzhen Caoc, Zhiyong Zhangd,e, Xiaodan Mengd,e, Chien Hsiaopingf, Changbin Yina,b*, Hao Jianga, Shu Wanga a. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China b. Key laboratory of Nonpoint Source Pollution Control, Ministry of Agriculture, Beijing 100081, China c. School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China d. College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China e. The Collaborative Innovation Center of Henan Food Crops, College of Agronomy, Henan Agriculture University, Zhengzhou 450002, China f.
Japan International Research Center for Agricultural Sciences, 1-1 Ohwashi, Tsukuba, Ibaraki, 305-8686, Japan
Abstract The influence of cropping systems on nutrition and food safety is controversial. This study aimed to evaluate the effects of an organic cropping system (OCS) on wheat nutrition and food safety at the molecular level by using a comprehensive research method. Nutrient deviation in samples from an OCS and a conventional cropping system (CCS) were detected, and 58 biomarkers were selected through multivariate statistical analysis and were further qualitatively 1
and quantitatively analyzed. The health risk of heavy metal(loid)s (HMs) for different populations was assessed based on the estimated average daily dose and recommended ingestion reference dose, which indicated that populations ingesting grains from OCSs had higher non-carcinogenic and carcinogenic risks. Additionally, HMs posed greater non-carcinogenic risks to children under five years old and greater carcinogenic risks to adults. This study highlights the need to consider the potential risk from HMs and nutritive ingredient differences in organic food. Keywords: Metabolomics, nutrition, heavy metals, health risk assessment, wheat List of abbreviations: OCS: organic cropping system; CCS: conventional cropping system; GC-Q/TOF: gas chromatography–quadrupole time of flight–mass spectrometry; UPLC-Q/TOF: ultra performance liquid chromatography–quadrupole time of flight–mass spectrometry; ICP-MS: inductively coupled plasma–mass spectrometry; HMs: heavy metal(loid)s; ADD: average daily dose; HQ: hazard quotient; HI: hazard index; OPLS-DA: orthogonal projection to latent structures–discriminant analysis; PCA: principal component analysis; RfD: ingestion reference dose. 1.
Introduction Organic farming has become increasingly attractive worldwide with the increase in
concerns about environmental degradation, food safety, and human health (Lairon, 2010). Organic farming is defined as a way to grow crops without synthetic fertilizers, herbicides, or pesticides, instead applying organic fertilizer and utilizing crop rotation to maintain soil 2
productivity (Andersen et al., 2015). Global-scale data published by the Research Institute of Organic Agriculture and the International Federation of Organic Agriculture Movements in 2017 showed that organic agriculture was practiced in 179 countries, with 9.06 million hectares of agricultural land managed organically by 2,400,000 producers, and that the area of organic production quadrupled during 1999–2015. Whether organic cropping systems (OCSs) can improve food nutritional quality compared with conventional cropping systems (CCSs) has long been a matter of interest and debate (Popa, Mitelut, Popa, Stan, & Popa, 2018). Previous studies have shown that organic foods may have some advantages, such as an overall trend of higher levels of nutrients, including phenolic compounds, vitamins, and some micronutrients (Mie et al., 2017); however, other studies have revealed no consistent difference in macronutrient content between conventionally and organically grown foods (Thorupkristensen, Dresbøll, & Kristensen, 2012) and have documented a higher average protein content in food from CCSs than in food from OCSs (Vrček et al., 2014). Various analytical methods have been used to differentiate the chemical components of organic and conventional products. Among the analytical approaches used for this purpose, metabolomics has emerged as an important method; this approach is mainly related to metabolites such as small-molecule compounds and can be used to indicate changes in organisms as well as their tissues and cells. Metabolite profiling has recently been carried out for many plant species, such as tomatoes (Vallverdú-Queralt, Medina-Remón, Casals-Ribes, Amat, & Lamuela-Raventós, 2011), potatoes (Shepherd et al., 2014), white cabbage (Mie et 3
al., 2014), peppers (Novotnã et al., 2012), strawberries (D'Urso, D'Aquino, Pizza, & Montoro, 2015), and onions and carrots (Cubero-Leon, De Rudder, & Maquet, 2018) to compare nutritionally desirable metabolites in products grown in OCSs and CCSs. These studies demonstrated that metabolomics could be applied to reliably, precisely, and effectively identify crops grown with different cultivation practices and to determine which organic products have higher concentrations of a wide range of plant secondary metabolites (Barański et al., 2014). Contradictory findings have been reported for wheat (Triticum aestivum), however, which is the main cereal crop consumed by humans in many areas worldwide and is a staple food in many regions of China (Zhang et al., 2017). Some studies have documented higher levels of phytochemical compounds, such as ferulic, p-coumaric, syringic, vanillic, and caffeic acids, in organic wheat than in conventionally grown wheat (Okarter, Liu, Sorrells, & Liu, 2010). In contrast, Zorb et al. (2006) found no remarkable difference in the contents of 44 secondary metabolites that were measured in wheat grains in an OCS and a CCS. The nutritional level of wheat is of extreme importance because it is a significant human dietary source of nutrients, including protein, carbohydrates, minerals, vitamins, fiber, and phyto-compounds (Dziki, Różyło, Gawlik-Dziki, & Świeca, 2014); therefore, untargeted metabolite profiling is needed to confirm the impacts of OCSs and CCSs on the overall chemical composition of wheat grains. Compared with CCSs, OCSs may have severe heavy metal(loid) (HM) pollution due to the use of organic fertilizer, which is made up mainly of livestock and poultry waste and thus contains large amounts of trace elements. These elements accumulate in crops through the 4
livestock–fodder and poultry waste–soil–crop pathways (Petersen et al., 2007). High contents of Cd, Cu, and Zn can accumulate under OCSs due to excessive application of commercial organic fertilizer (Chen, Huang, Hu, Weindorf, Liu, & Yang, 2014). Zaccone et al. (2010) found higher contents of Ni, Zn, and Pb in food samples obtained from OCSs due to long-term fertilizer application. Through a wide inventory of HM content in organic manures, Lopes, Herva, Franco-Uría, & Roca, 2011) determined that the high biotransfer potential of Zn led to significant concentrations in food, exceeding recommended doses; they proposed that measures to regulate the Zn content in organic fertilizers should match specific management goals. The appearance of high levels of HMs in crops may pose potential harm to human health due to high exposure doses from food ingestion and toxic effects of HMs to human beings (Qureshi, Hussain, Ismail, & Khan, 2016); quantitative assessment of the risks caused by HMs in food is thus essential for environmental management and food safety control. Human health risk assessment estimates the probability and nature of adverse health effects to populations that may be exposed to pollutants (Aschberger et al., 2010) and is necessary to determine the risks associated with wheat grains from OCSs and CCSs. The application of no synthetic fertilizers, herbicides, or pesticides in OCSs could lead to nutrient deficiencies and pose potential health risks to the population due to the harmful HMs present in organic fertilizers, so this issue should be heavily studied. The goals of this study were therefore to 1) confirm the effects of an OCS and a CCS on wheat grain nutrition, 2) reveal the dynamic characteristics of metabolites in OCS and CCS wheat, 3) summarize the 5
impacts of cropping systems on HMs in wheat grains, 4) determine the HM exposure, and 5) identify health risks for the local population resulting from the oral ingestion of wheat grains. We compared the nutritional quality and food safety of wheat grains from an OCS and a CCS on a molecular level. Our results could further our realization of the metabolism of wheat grains produced under OCSs and CCSs and will help the government and policymakers to implement measures to reduce or control HMs in wheat grains. 2. Materials and Methods 2.1 Experimental site and study design This study was part of a long-term experiment conducted at the Scientific and Educational Park of Henan Agricultural University (113°35'51"E, 34°52'13"N), Henan Province, China, where the effects of an OCS and a CCS were compared using a 6-year (2011–2017) durum wheat–green manure crop rotation. One set of fields was managed using traditional agricultural practices, which has been detailed previously (Zhang, Yin, Cao, Cheng, Wu, & Guo, 2018). Another set of fields was managed according to the Chinese requirements for certified organic production (GB/T 19630.1-2011). The common organic fertilizer in Henan Province was applied as basal fertilizer at 30 kg·ha–1·yr–1. Details on the fertilizers used in both cropping systems can be seen in Supplementary Tables 1 and 2. Grain yields from the OCS and CCS were monitored annually (Supplementary Table 3). For each cropping system, 48 wheat samples (1 g each) were collected for elemental analysis by GC-Q/TOF, UPLC-Q/TOF, and ICP-MS in June 2017.
6
2.2 Sample preparation Samples were prepared for GC-Q/TOF analysis by modifying a previously described method (Palmer, Dias, Boughton, Roessner, Graham, & Stangoulis, 2014). For UPLC-Q/TOF analysis, the procedure of sample treatments was conducted using a well-designed method (Zhang et al., 2017); the ICP-MS analysis referred to details described in Cao et al. (2014). For quality assurance and quality control, the reference samples of wheat as well as reagent blank samples were included during sample pretreatment and detection. 2.3 Sample testing GC-Q/TOF analysis was conducted using a modification of a previously described method (Palmer et al., 2014). GC was performed using a 30-m VF-5MS column with 0.2-μm-thick film and a 10-m Integra guard column (Agilent J & W GC Column; Agilent Scientific Technology Ltd., California, USA). The injection temperature was regulated at 280 °C, the MS transfer line was set at 300 °C, the ion source was set at 280 °C, and the quadrupole was set at 180 °C. Helium was applied as the carrier gas at a flow rate of 1.0 mL ·min–1. For the analysis of polar metabolites, we used the following program: injection at 70 °C and holding for 1 min, and then establishment of a 7 °C·min–1 oven temperature that was increased to 325 °C and held at this final heat temperature for 6 min. UPLC-Q/TOF analysis was conducted using an established protocol (Zhang et al., 2017). The compounds of all wheat samples were detected with 2 μL methanol filtrate and a UPLC-Q/TOF; the detailed pretreatment and detection conditions were described in a previously published study (Zhang et al., 2017). For the concentration measurement of HMs, 7
i.e., the content of Pb, Cd, Cr, As, Ni, Zn, Cu, and Mn in samples, ICP-MS analysis was conducted using the established protocol (Zhang et al., 2018). 2.4 Exposure and risk assessment In addition to considering the nutrients in food, the detrimental health effects of HMs from food ingestion should also be considered. Therefore, on the basis of concentrations of HMs in the grains of wheat, the exposure dose and consequent health risks of HMs for the local population due to grain ingestion were assessed according to the Chinese Exposure Factors Handbook (Duan, 2012; USEPA, 2011a). 2.4.1 Exposure assessment On the basis of the concentration of HMs and exposure factors, the exposure dose is presented as the average daily dose (ADD; mg·kg–1·day–1). The ADD of each HM through grain ingestion, according to the Exposure Factors Handbook, could be estimated via the following model (USEPA, 2011a):
ADDingest=
C IngR EF ED , BW AT
(1)
where C is the HM content in mg·kg–1, IngR is the food ingestion rate in mg·d–1, EF is the exposure frequency in d·year–1, ED is the exposure duration in years, BW is the body weight in kg, and AT is the average time in days. AT was set as equal to EF × ED for non-carcinogenic assessment and to 25200 days for carcinogenic assessment (Duan, 2012; USEPA, 2011a). The exposure factors such as the IngR of wheat grains, BW of different populations, and ED were obtained from the field questionnaire survey. 2.4.2 Risk characterization 8
A hazard quotient (HQ) was used to indicate lifetime non-cancer risk. In this study, the HQ was evaluated by dividing the ADD from ingestion exposure pathways by a specific ingestion reference dose (RfD). The HQ was defined as the following equation (USEPA, 2011a):
HQ=
ADD , RfD
(2)
where RfD reflects the estimated maximum acceptable risk to humans through daily ingestion exposure (mg·kg–1·day–1). Detrimental health effects are unlikely if HQ ≤ 1, whereas HQ > 1 is likely to raise concerns about adverse non-carcinogenic effects (Al-Saleh, Nester, DeVol, Shinwari, & Al-Shahria, 1999). To assess the cumulative potential non-carcinogenic health risks of multiple chemicals, a hazard index (HI) was used to identify the risks by summing the HQs from each chemical. HI was defined as follows (USEPA, 2011a):
HI= 1 HQ , i
(3)
If HI ≤ 1, the adverse cumulative non-carcinogenic health risks are unlikely to happen, whereas there may be potential chronic risks if HI > 1, and pollutants should then be segregated for analysis. For carcinogenic health risk assessment, the incremental lifetime cancer risk (ILCR) was applied to indicate the incremental probability of suffering from potential carcinogenic effects over a lifetime. The ILCR is calculated using the following equation (USEPA, 2011a): ILCR = ADD × SF,
(4)
where SF denotes the cancer slope factor in (mg·kg-1·d-1)-1; SF values of each of the 9
carcinogens are listed in Table 1. When there are multiple carcinogenic pollutants, the individual carcinogenic risks and exposure routes can then be summed (assuming additive effects) and applied for comparison with the acceptable risk. The potential carcinogenic values in the range of 1.0 × 10–6 to 1.0 × 10–4 were regarded as acceptable risks. In line with the classification criteria defined by the International Agency for Research on Cancer (IRAC, 2011), Zn, Cd, Mn, Pb, and Cu were set as non-carcinogenic pollutants, while Cr and As were regarded as potential carcinogens that can pose carcinogenic effects to the human body by ingestion exposure pathways. SF values of carcinogens and RfD values of non-carcinogens related to this study are listed in Table 1 (USEPA, 2011a). 2.5 Statistical analyses The preprocessing of metabolomics data, such as retention time correction, peak integration, alignment, and peak detection, was carried out using the Markerlynx XS software (Waters Corporation). Orthogonal projection to latent structures–discriminant analysis (OPLS-DA) and principal component analysis (PCA) were then applied for multivariable analysis based on the SIMCA-P software (v. 12.0; Umetric, Umea, Sweden). We also performed Kruskal–Wallis tests, univariate statistical analysis, and multi-regression analysis employing the software of SPSS 20.0 and Origin 8.0 and regarded the statistical results to be significant with a p value of no more than 0.01. Since the HM contents showed a normal distribution, Pearson’s correlation was applied to evaluate linear correlations among different metals.
10
3. Results 3.1 Profiling of wheat grain metabolites Plant nutritional quality is related closely to the types and contents of metabolites. In order to give a summarization of the metabolite variation between OCS and CCS samples, GC-Q/TOF-MS and UPLC-Q/TOF-MS data were examined using PCA. This analysis was conducted based on specified MS information, including ion peak intensity, retention time, ion peak area, and MS (m/z) ions. The PCA results indicated that the cropping system could influence the metabolite variation significantly among samples (Figure 1). Seven principal components (PCs) were kept in the ultimate PCA models, with two PCs representing 61.4% (PC1, 39.3%; PC2, 22.1%) of the GC-Q/TOF-MS dataset variance (R2 X = 0.776, Q2 = 0.491; Figure 1a) and 47% (PC1, 29.8%; PC2, 17.2%) of the UPLC-Q/TOF-MS dataset variance (R2 X = 0.569, Q2 = 0.432; Figure 1b). Using GC-Q/TOF and UPLC-Q/TOF methods, we detected a total of 432 and 2756 primary and secondary metabolites, respectively, in the wheat grains. To explore the biomarkers related to the difference between the two cropping systems, an S-plot of the OPLS-DA model was applied, which is illustrated in Supplementary Figure 1. The sample classification could be mainly identified by the compounds that were much closer to the upper right and lower left corners. This result met the requirement that the value of variable importance in projection (VIP) be higher than 1; higher VIPs have greater variable importance, according to the model. On the basis of the distribution that was illustrated by the S-plot of the OPLS-DA model, 53 primary metabolites and 19 secondary metabolites were screened. The 11
unpaired parametric Welch t test was employed to identify the remarkable differences (p ≤ 0.001, ≥ 2-fold change) among screened metabolites. Finally, 46 primary metabolites and 12 secondary metabolites were selected as potential biomarkers for the classification of wheat grain samples under the organic and non-organic cropping system treatments. 3.2 Qualitative and quantitative biomarker analysis In this study, GC-Q/TOF was used mainly to detect primary metabolites in wheat grains. Based on the acquired mass spectra, 31 primary metabolites were identified as biomarkers using mass spectral databases, including that of the National Institute of Standards and Technology and the Golm Metabolome Database. These biomarkers belong to diverse nutrient categories and comprise seven free amino acids, six sugars, three sugar alcohols, six organic acids, three fatty acids, and six other metabolites (Supplementary Table 4). UPLC-Q/TOF was used mainly to detect secondary metabolites in wheat grains. To identify biomarkers among the secondary metabolites, we used the Markerlynx software (Waters Corporation, Milford, MA, USA) to determine the elemental compositions of unknown compounds as well as the refined formula weight, degrees of unsaturation, and isotope abundance patterns. Based on MS information and quasi-molecular ion peaks, we measured fragments of the ion peaks of chemicals in the second-order MS. In total, six secondary metabolites belonging to flavonoids and their derivatives were identified through searches of the Kyoto Encyclopedia of Genes and Genomes, MassBank, METLIN, Madison Metabolomics Consortium Database, PubChem, and other network databases and related reports (Supplementary Table 5). 12
Kruskal–Wallis and multiple comparison tests were used to compare biomarker content among samples. Secondary metabolite biomarker content was significantly higher in OCS samples than in CCS samples (Figure 2). As primary metabolite biomarkers were too numerous to perform multiple comparisons, we used Bayesian network analysis to compare the content between the OCS and CCS samples; the results were translated into Pearson correlation coefficients, which are presented as a heat map (Supplementary Figure 2). Dynamic characteristics of biomarkers could reflect the effect of cropping systems on wheat grain metabolism, and further identification of biomarkers could indicate the affected metabolic pathways, which could contribute to exploring the relationship between nutrition metabolism and planting patterns. 3.3 Contents of heavy metals in wheat grains The concentrations of all investigated HMs, including Ni, Pb, Zn, Cd, Cr, Cu, Mn, and As, in wheat grains are shown in Table 2. The order of HM content in wheat grains from the OCS and CCS was Zn > Cu > Mn > Cr > Ni > As > Pb > Cd. Cu, Zn, As, Cd, and Pb concentrations were observably higher in OCS than in CCS samples; no significant difference in the content of any other examined HM was observed between sample groups. The mean contents of each target HM did not exceed the relative thresholds of the national standards for food contaminants (GB 2762-2012). However, wheat grains from the OCS contained a greater excess of HMs, especially Cr and As (at excess rates of 17.1% and 10.5%, respectively). HM contents of wheat grains from the CCS generally fell below the standard thresholds (GB 2762-2012), except for some samples that exceeded standard levels 13
of Cd, Pb, and As. 3.4 Daily exposure dose of heavy metal(loid)s According to the field investigation, we found that wheat is the main staple food for the population in Henan Province. Thus, the assessment of exposure dose and health risks from HMs in wheat is important in this region. Combining the concentrations of HMs in wheat and the exposure behavior patterns of the local population to wheat, the ADD of each HM via wheat ingestion was assessed for the local children (i.e., 0–5 years and 6–17 years) and adults. Since the mean ADD was identical to the median ADD value, the mean ADD value of each population group is displayed in Figure 3 and is applied to assess the health risks. Within the three population groups, the ADD values for Mn, Ni, Zn, As, Cd, and Pb under OCS and CCS treatments were 5.05–5.50, 30.26–35.08, 5.77–8.89, 67.44–150.52, 14.81–27.81, and 4.21–7.49 times lower, respectively, than the relevant average daily intake values based on the Food and Agriculture Organization/World Health Organization regulations (0.06, 0.12, 1, 0.15, 0.007, and 0.0035 mg·kg–1·day–1, respectively; Figure 3). Therefore, the ADDs obtained in the present study are acceptable and likely pose little harm to the local population. Furthermore, the ADD values for HMs in OCS and CCS samples were in the order of Zn > Cu > Mn > Cr > Ni > As > Pb > Cd. The total ADD values for Cr, Mn, Ni, Cu, and Zn were statistically higher for the OCS treatment than for the CCS treatment, and those for As, Cd, and Pb differed indistinctively between the two cropping systems. However, ADD values from OCS samples reached maximum levels of 0.26, 0.18, and 0.21 mg·kg–1·day–1 for 0–5- and 6–17-year-olds and adults, respectively. The mean cumulative ADD values for 14
0–5-year-old children were 1.44 and 1.24 times higher than for the 6–17-year-old children and adults, respectively. 3.5 Health risks of wheat grains 3.5.1
Non-cancer risks On the basis of the estimated ADD value and recommended RfD through ingestion
exposure pathways for each HM, the non-cancer risk of each HM, expressed as HQ, was assessed for the three population groups. The HQ values are shown by age group in Figure 4. HIs were 1.3 times higher for OCS than for CCS wheat in all three population groups. As and Cr exposure levels were the greatest contributors to the HI for the three groups, followed by exposure to Zn, Pb, Cu, Cd, Ni, and Mn, accounting for 64.89% and 13.22% of the cumulative HI for wheat grown under OCS and CCS, respectively. Ni and Mn contributed the least to the HIs for wheat grown under OCS and CCS (1.75% and 0.70%, respectively). Notably, cumulative HIs for OCS and CCS wheat exceeded 1 in all three population groups because the HQs for As and Cr were high (5.37 and 1.38, respectively). Due to the potential non-cancerous health risks, further research on each separate HM is required; if HI > 1 in some cases (USEPA, 2011a), then local populations could experience non-cancerous health effects via wheat ingestion pathways due to HM exposure. Thus, our findings suggest that each single HM, including Pb, Zn, Cd, Ni, Mn, and Cu, in wheat may pose few health effects to the local population, although As and Cr ingestion poses certain health risks. The HI values of three population groups decreased as follows: 0–5-year-old children > adults > 6–17-year-old children, which was similar to the trend of cumulative ADD values. 15
Separately, the HQs for HMs increased in the order of Ni < Cu < Mn < Zn < Pb < Cd < As < Cr, following exposure to grains cultivated under the OCS, whereas they increased in the order of Ni < Cu < Mn < Zn < Pb < Cd < Cr < As for populations due to grain exposure from grains cultivated under the CCS. When considering the distributions of the HM content and ADD values for each metal in the OCS and CCS samples, these findings further validated the fact that metals can accumulate under various conditions and the important roles of human behavior patterns and toxicity characteristics in the determination of health risks via wheat ingestion exposure pathways for local residents. 3.5.2
Cancer risks The risks attributable to exposure to carcinogenic HMs are shown by age group in Table 1.
Since the potential carcinogenic risk was within the range defined as acceptable or inconsequential risk (USEPA, 2011b), there might be acceptable carcinogenic health effects to the local population; thus, the ILCR for the three population groups due to wheat grain ingestion was much lower than the acceptable maximum level, i.e., 1.0 × 10–4. Similar to the distribution of HQs under the two cropping systems, either for each HM or for each population group, ILCRs were lower for CCS wheat. Nevertheless, the cancer risk was greater for adults than for children, and it was the lowest for children aged 0–5 years, which differs from the trend of HQs. 3.5.3
Uncertainty analysis There might be some uncertainties in the process of assessing the health risk from
exposure to HMs in wheat grains; the assessment of health risks could be more accurate if the 16
bioavailable or bioaccessible concentrations were considered. Furthermore, various uncertainties might be included in the extrapolation of toxic effects from animal experiments to humans (from high to low doses). Consequently, the non-carcinogenic and carcinogenic health risks according to the total contents of HMs in this study might be slightly overestimated. Nonetheless, although absolute perfect risk assessment was not conducted, this study could be regarded as a perspective study on health risks with the consideration of different crop outputs. 4. Discussion Cultivation factors greatly influence the chemical composition of plants (Palmer et al., 2013); these factors can include the fertilizer level, location, soil type, and year of harvest (Jørgensen, Knudsen, & Lauridsen, 2012). CCSs rely heavily on the application of a range of modern management practices and external inputs, especially fertilizer with high nitrogen concentrations (related closely to amino acid metabolism), to achieve high yields. Under sufficient nitrogen conditions, large amounts of glutamic acid are produced by the glutamine synthetase/glutamate synthase metabolism pathway (GS/GOGAT), and more amino acids connected to this pathway (e.g., lysine, threonine, lactamic acid, and asparagine) are derived (Ferrario-Méry et al., 2000; Glevarec et al., 2004). Our results are consistent with these characteristics. In contrast, OCSs have been developed in response to bans on pesticide and chemical fertilizer use, such that OCS crops are exposed to pest attacks and nutrient-deficient conditions. Therefore, crops grown under organic practices must synthesize more metabolites 17
to defend against increased stress. In this study, the contents of sugars and their derivatives, including glucose, maltose, trehalose, threitol, xylitol, and sorbitol, were obviously higher in organic wheat grains than in CCS-grown wheat grains; the elevation of these compound contents may be a protective mechanism under undesirable growth conditions. Similar to hormones, sugars can play a role as primary messengers to regulate signals controlling the expression of various genes to promote abiotic/biotic stress tolerance (Gupta & Kaur, 2005). In addition to sugars and their derivatives, as a class of low-weight polyphenolic secondary metabolic compounds, flavonoids have outstanding effects on resistance to adversity and stress (Zhang et al., 2017). In our study, the biomarkers identified among secondary metabolites were tricin, kaempferol3-o-glucoside, kaempferol rhamnoside, luteolin, rutin, and quercetin 3-methyl ether, all of which are flavonoids. Higher flavonoid levels have been detected previously in organic wheat grains and could provide defense against stresses such as low nutrition and serious pest infestation in OCSs (Luthria, Singh, Wilson, Vorsa, Banuelos, & Vinyard, 2010; Mazzoncini, Antichi, Silvestri, Ciantelli, & Sgherri, 2015). Recently, much attention has been focused on the functions of flavonoids in response to environmental stresses. In a six-year fixed experiment, Ren et al. (2017) compared the secondary metabolites in onions grown by OCSs and CCSs and found higher secondary metabolite contents, including those of flavonoids and anthocyanins, in organic onions; they concluded that the use of different soil management practices in organic agriculture was responsible for these differences. Similar results have been reported for rice, carrots, and grapes (Xiao, Ma, Zhang, & Qian, 2018). Another study suggested that high microbial 18
activity levels in organically managed soils are another main reason for high secondary metabolite concentrations in crops (Reilly et al., 2014). ADD values were biggest for children aged 0–5 years, due mainly to the different pollution levels of various HMs and differentiation among wheat intake rates. Although the food intake rate was less for children aged 0–5 years, their body weights were also significantly lower; thus, ADD values based on unit body weight were larger under similar exposure conditions. Different exposure behavior patterns may result in different health effects in various populations in a similar exposure environment, especially in terms of health risks caused by HMs. Overall, the non-cancer risk findings for the three population groups suggest that a single HM, including Pb, Ni, Zn, Cd, Mn, and Cu in wheat grains, may pose few harmful health effects to the local population due to wheat ingestion in the study area, regardless of the presence of other toxic HMs. Non-carcinogenic health risks due to As and Cr in wheat grains remain of concern, especially for children aged 0–5 years. As reported previously for Hunan Province (Cao et al., 2014), we found that potential cancer risks in the ingestion pathway were due mainly to Cr. The ILCR for Cr from wheat grain intake was nearly two to three orders of magnitude higher than that for Ni or As ingestion exposure. The cancer risks for adults due to Ni and As exposure were about three and five times larger than those for children aged 0–5 and 6–17 years, respectively, demonstrating that the contributions of HMs to cancer risk differ among age groups.
19
5. Conclusion In this study, remarkable variability was found for the nutrient compounds of wheat grains cultivated under OCS and CCS. A total of 46 primary and 12 secondary metabolites were selected as biomarkers based on multivariate statistical analysis, quantitative and qualitative analyses of which indicated that grains cultivated under OCS may be at a disadvantage in terms of nutrition. Additionally, the analysis of concentrations of HMs in wheat grains, including Ni, Pb, Zn, Cd, Cr, Cu, Mn, and As, showed that wheat grains from the OCS contained higher levels of HMs. Furthermore, ADD values of each HM via wheat ingestion were calculated to assess the health risk of HMs for the local population. Health risk assessment indicated that people who ingested wheat grains from the OCS had higher non-cancer and cancer risks. As and Cr exposure were the greatest contributors to the HI, accounting for 64.89% and 13.22%, respectively, and Cr exposure accounted for more than 90% of the total potential cancer risks. HMs posed a higher non-cancer risk to children aged 0–5 years and a higher cancer risk to adults. This study strengthened the hypothesis that both nutrition and potential detrimental health effects should be considered and addressed before beginning organic farming.
Funding
This research was supported by China Agriculture Research System- Green Manure (CARS-22), Major Program of National Social Science Foundation of China (Grant No. 20
18ZDA048), the National Key Research and Development Plan of China (grant Nos. 2016YFC0206203), and the U.S. National Institutes of Health (K02HD70324).
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27
Tables
Table1. Summary of cancer risks for the local population (Children in 0-5 yrs, 6-17 yrs and adult) via grain ingestion, which were cultivated under OCS and CCS Cr
Ni
As
0-5 yrs
6-17 yrs
Adult
0-5 yrs
6-17 yrs
Adult
0-5 yrs
6-17 yrs
Adult
OCS
4.43E-05
6.41 E-05
7.15 E-05
8.28 E-07
1.20 E-06
4.02 E-06
7.74 E-07
1.12E-06
3.75 E-06
CCS
3.65 E-05
5.28 E-05
6.77 E-05
7.15 E-07
1.03 E-06
3.47 E-06
3.47 E-07
5.02 E-07
1.68 E-06
Conditions
28
Table2. HMs concentrations（mg·kg-1）in wheat grains determined by ICP-MS HMs concentrations wheat grains from OCS HMs
Rang
Average
HMs concentrations wheat grains from CCS
Exceeding ratio
Rang
Average
Exceeding ratio
（%） Cr
0.70-1.10
0.89±0.14
17.1
0.51-1.03
0.73±0.16
4.2
Mn
1.99-2.97
2.31±0.38
0
1.76-2.66
2.12±0.25
0
Ni
0.59-1.01
0.77±0.14
4.2
0.46-0.93
0.67±0.16
0
Cu
3.02-5.95
4.34±0.90
0
2.01-3.69
2.91±0.58
0
Zn
23.35-46.78
33.76±6.85
0
16.77-30.12
21.90±4.26
0
As
0.33-0.66
0.43±0.11
10.5
0.11-0.30
0.19±0.06
0
Cd
0.06-0.15
0.09±0.03
8.3
0.03-0.07
0.05±0.01
6.3
Pb
0.06-0.22
0.16±0.05
8.8
0.06-0.14
0.09±0.03
4.2
29
Figures
(a)
(b) Figure 1a: Scores plots of PCA based on GC-QTOF/MS data; Figure 1b: Scores plots of PCA based on UPLC-QTOF/MS data. () wheat grain samples from CCS; (◆) wheat grain samples from OCS.
30
Figure 2: Box plots of the identified biomarkers of secondary metabolites in wheat grain samples.
31
Figure 3: The contribution of CCS and OCS to the average daily expose dose of each metal(loid)s through food ingestion.
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Figure 4: The HQ values for each metal(loid)s under CCS and OCS treatments through ingestion exposure pathways for the local children and adult.
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The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
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The impacts of OCS and CCS on wheat nutrition and food safety were evaluated.
Biomarkers of OCS, including primary and secondary metabolites, were qualitative and quantitative analyzed.
The local population who ingested grains from OCS experienced a higher non-cancer and cancer risks.
As and Cr exposure were the greatest contributor to Hazard Index (HI).
HMs posed relatively higher non-cancer to children aged 0–5 years and higher cancer risks to adults.
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