http://informahealthcare.com/bmk ISSN: 1354-750X (print), 1366-5804 (electronic) Biomarkers, 2014; 19(5): 430–435 ! 2014 Informa UK Ltd. DOI: 10.3109/1354750X.2014.924998

RESEARCH ARTICLE

Quantitative correlation of aflatoxin biomarker with dietary intake of aflatoxin in Tanzanian children Michael N. Routledge1, Martin E. Kimanya2,3, Candida P. Shirima2, Christopher P. Wild4, and Yun Yun Gong1,5# School of Medicine, University of Leeds, Leeds, UK, 2Tanzania Food and Drugs Authority (TFDA), Dar es Salaam, Tanzania, 3The Nelson Mandela Institute of Science and Technology (NM-AIST), School of Life Sciences and Bioengineering, Arusha, Tanzania, 4International Agency for Research on Cancer (IARC), Lyon, France, and 5Institute for Global Food Security, School of Biological Sciences, Queen’s University, Belfast, UK

Biomarkers Downloaded from informahealthcare.com by University of Otago on 07/09/15 For personal use only.

1

Abstract

Keywords

The association between aflatoxin intake from maize-based weaning food and aflatoxin albumin adducts (AF-alb) was investigated in 148 Tanzanian children aged between 12 and 22 months, at 2 visits 6 months apart. At the first visit (storage season) there was a significant correlation at the individual level between AF-alb (geometric mean 43.2 pg/mg albumin) and aflatoxin intake (geometric mean 81.7 ng/kg b.w./d) through maize-based weaning food (r ¼ 0.51, p50.01). Overall, this correlation was r ¼ 0.43 (p50.01). The AF-alb level in weaning-age children in Tanzania closely reflects aflatoxin intake from maize in weaning food. Exposure levels suggest children may be at risk from aflatoxin associated health effects.

Aflatoxin albumin adduct, aflatoxins, biomarker, dietary intake, early life exposure

Introduction The development of validated biomarkers has been key to human health research into the effects of aflatoxin over the last 40 years (Kensler et al., 2011). Aflatoxin is metabolized into a highly active epoxide which reacts to form a covalent bond with DNA, to yield the aflatoxin-N7-guanine adduct, with covalently bound aflatoxin albumin (AF-alb) adduct also being found in the peripheral blood. The DNA adduct is involved in the fundamental mechanism of aflatoxin induced human liver cancer (IARC, 2002), and the repair product of this adduct can be detected in human urine. Both urinary DNA adduct and AF-alb adduct in blood have been used as a biomarker of exposure to aflatoxin (Groopman et al., 1993; Routledge & Gong, 2011; Wild et al., 1992). The AF-alb adduct has a longer half-life than the urinary DNA adduct, and therefore better represents chronic exposure (Wild et al., 1986). The high throughput ELISA detection method for AF-alb adduct has previously been validated against an isotope dilution MS/MS method (McCoy et al., 2008) and has enabled large amounts of exposure data to be collected over the past two decades (Wild et al., 1990; Wild & Gong, 2010), establishing this biomarker as a well-validated tool for

#Yun Yun Gong is responsible for statistical analysis. E-mail: [email protected] Address for correspondence: Dr. Yun Yun Gong, Institute for Global Food Security, School of Biological Sciences, Queen’s University, Belfast BT9 5AG, UK. Tel: +44(0)2890974388. Fax: +44(0)2890976513. E-mail: [email protected]

History Received 8 April 2014 Revised 13 May 2014 Accepted 13 May 2014 Published online 5 June 2014

studying aflatoxin associated human health effects. The studies using the AF-alb adduct have not only greatly improved understanding of human carcinogenicity of aflatoxin (Wild & Gong, 2010), but also promoted research into other health problems including child growth impairment (Gong et al., 2002, 2003, 2004), immune modification (Turner et al., 2003, 2007) and childhood hepatomegaly (Gong et al., 2012). Early in the 1990s, Wild et al. (1992) demonstrated the quantitative association between dietary aflatoxin exposure and AF-alb adduct in Gambian adults. Although it was previously assumed that the AF-alb adduct was also a valid biomarker for aflatoxin exposure in children, the association between dietary intake and AF-alb adduct level has not been measured in children. In the present study, we have undertaken this assessment to further clarify the validity of the AF-alb adduct biomarker in children. Understanding the dietary intake of aflatoxin by children during the weaning period is particularly difficult because during this period of life the diet is usually composed of several components; breastfeeding, weaning food and family food. The proportions of these components shift dynamically during this period, and vary from family to family, as well as across geographical locations. This introduces high variability and consequent difficulty in accurately assessing dietary exposure to contaminants when using food-based methods. Therefore, using the AF-alb adduct is critical for understanding the contribution of aflatoxin to health outcomes in such children. Maize is the staple food in many parts of Tanzania. Tanzanian children are given primarily maize-based weaning food from about 4 months of age. Their diet gradually shifts to

DOI: 10.3109/1354750X.2014.924998

a virtually wholly maize-based family food. Our recent report from this same study shows Tanzanian children are exposed to high levels of both aflatoxin and fumonisin, another type of mycotoxin, through contaminated maize in their diet (Shirima et al., 2013). The uniformity of the diet provides an ideal setting to study the association between aflatoxin intake and the blood biomarker. In this study, we investigated the correlation between ingestion of aflatoxin, from maizebased weaning food, and blood AF-alb adduct levels in children from different regions of Tanzania.

Materials and methods

Biomarkers Downloaded from informahealthcare.com by University of Otago on 07/09/15 For personal use only.

Participants A total of 166 apparently healthy children, one per family from the villages Nyabula, Kikelelwa and Kigwa of the Iringa, Kilimanjaro and Tabora districts, respectively, were surveyed during the maize harvest and were then followed up twice at 6-month intervals. The samples were collected between April 2009 and January 2010. Blood samples were collected from 148 children (56, 55 and 37 from Nyabula, Kikelelwa and Kigwa, respectively) and representative maize flour samples (used for cooking weaning and family foods) were obtained from their families at the second visit, when maize had been stored for 6 months and at the third visit, which fell during the maize harvest. The children were aged between 12 and 22 months old at the second visit, based on the birth registration at the village dispensary. In the rest of the manuscript these two visits are referred to as ‘‘storage’’ and ‘‘harvest’’ visit, respectively. Two consecutive days dietary intake, on both weaning food and family food, was assessed for each child using a 24 h recall questionnaire method. A food frequency questionnaire (FFQ) assessing the dietary intake in the previous week was also used in order to cross-check the accuracy of the 24 h recall data. Both methods were validated for the study population in a previous study (Kimanya et al., 2008). The mean of the 2 d intake from the 24 h recall alone was used as the consumption data for each food item. Informed consent was obtained from mothers or care givers of the children and ethical approval from the National Institute of Medical Research in Tanzania and the University of Leeds. Trained health workers administered the questionnaire to the mother of each child recording the information on child age, sex, feeding, as well as family socio-economic status (SES). SES was calculated on a weighted-basis score system based on family house type in terms of floor, wall and roof. Blood AF-alb adduct analysis Two millilitres of venous blood was collected from each child by a qualified nurse and the plasma was separated by centrifugation at a local district/regional hospital. The plasma was stored in an ice box before being transferred to a 20  C freezer within 8 h. It was shipped on dry ice to the University of Leeds where AF-alb adduct analysis was performed using an ELISA method, as previously described (Chapot & Wild, 1991). Briefly, following albumin extraction, enzyme digestion of albumin and purification, a competitive ELISA assay

Dietary intake of aflatoxin in Tanzanian children

431

was employed to measure AF-alb adduct using a rabbit polyclonal antibody. Three positive and one negative control samples were run with each batch of samples. Coefficients of variation must be less than 25% between repeats on two different days. The detection limit for the assay was 3 pg/mg albumin. Any samples with AF-alb adduct below this limit were assigned a value of 1.5 for data analysis purposes. Maize aflatoxin analysis A portion of maize flour used for both weaning and family food preparation was collected from each household during the two surveys. A representative sampling approach was followed to ensure an even sample was taken from each corner of the maize storage container. Maize samples were analysed for aflatoxin B1 (AFB1), B2 (AFB2), G1 (AFG1), and G2 (AFG2) in the Tanzania Food and Drug Authority (TFDA) laboratory according to a standard HPLC method (Stroka et al., 2000). The limit of detection (LOD) was 0.53, 0.15, 0.24, 0.01 ng/g maize for AFB1, AFB2, AFG1 and AFG2, respectively. Any value below the LOD was assigned as 1/2 LOD for data analysis purposes. Aflatoxin total (AFT) was calculated by summing the AFB1, AFB2 and AFG1 and AFG2. Aflatoxin intake The intake of AFB1 (or AFT) of each child was calculated by multiplying the maize AFB1 (or AFT) as measured in the flour sample from the household and the maize consumption of the child from all sources (including weaning foods) obtained from the 24 h dietary recall data. Statistical analysis To adjust for body weight difference, maize consumption, maize AFB1 and AFT intake of the children are all expressed as mass/kg body weight/d (kg b.w./d). Maize AFB1, AFT and AF-alb adduct data were natural log transformed to normal distribution for statistical analysis. Geometric mean (GM) and 95% confidence intervals (CI) were used to summarize these variables. Linear correlation and regression analyses were performed to establish the association between aflatoxin dietary intake and the biomarker. Multivariate regression analysis was used to investigate the association after adjustment for other confounding factors (village and age). A p value  0.05 was considered statistically significant. All analysis was performed using STATA software version 10 (STATAÕ , College Station, TX).

Results General information The general information at the storage visit is shown in Table 1, and has also been described previously (Shirima et al., 2013). The mean age of the children was 17 months (range 12–22 months). The mean age at the start of weaning was 4 months (range 0–6). At 18 months and 24 months, 50% and 90%, respectively, of the children were no longer being breastfed. More of the mothers of the children (24%) from Kigwa had received no formal school education as

432

M. N. Routledge et al.

Biomarkers, 2014; 19(5): 430–435

Table 1. Child and family information at the storage visit. Variables

Nyabula

Kikelelwa

Kigwa

Total

No of children % of boys Age in month, mean ± SD (range) Weaning age in months, mean ± SD % of children fully weaned Socio-economic status (Low:Medium:High) % of mother receiving no education % of families using dehulled maize

56 51 17 ± 2 (13–20) 4.5 ± 1.6 23 66:32:2 4 96a

55 54 16 ± 2* (12–20) 3.4 ± 1.3** 18 0:75:25** 2 7b

37 38 18 ± 1 (15–22) 3.7 ± 1.8* 27 51:30:19 24** 49c

148 49 17 ± 2 (12–22) 3.9 ± 1.6 22 – – –

a,b,c Figures with different letters differ significantly from each other. The figure is statistically different to the unmarked figure(s) in that row, *p50.05; **p50.01.

Biomarkers Downloaded from informahealthcare.com by University of Otago on 07/09/15 For personal use only.

Table 2. Aflatoxin contamination of maize at the storage visit (upper) and the harvest visit (lower), geometric mean (95% CI) unless specified. Variables

Nyabula

Kikelelwa

Kigwa

Total

% of maize with AFB1 detected AFB1 (ng/g maize) AFT (ng/g maize) % of maize with AFB1 detected AFB1 (ng/g maize) AFT (ng/g maize)

10 0.3 0.6 8 0.3 0.6

8 0.3 0.6 27 0.5 0.8

97** 5.3 (3.4–8.5)** 10.5 (6.7–16.4)** 45** 0.5 (0.4–0.7) 1.1 (0.8–1.6)*

31 0.67 1.23 23 0.41 0.75

(0.3–0.4) (0.5–0.7) (0.3–0.3) (0.5–0.6)

(0.3–0.4) (0.5–0.7) (0.3–0.7) (0.6–1.2)

(0.52–0.87) (0.94–1.59) (0.35–0.49) (0.64–0.89)

**Significantly different to the other villages, p50.01; *significantly different to Nyabula, p50.05.

Table 3. Groundnut consumption, maize consumption, AFB1/AFT intake and AF-alb at the storage visit (upper) and the harvest visit (lower), geometric mean (95% CI). Variables

Nyabula

Kikelelwa

Kigwa

Total

% of children consuming groundnut on at least 1 day per week Maize intake (g/kg b.w./d) AFB1 intake (ng/kg b.w./d) AFT intake (ng/kg b.w./d) AF-alb (pg/mg albumin) % of children consuming groundnuts on at least 1 day per week Maize intake (g/kg b.w./d) AFB1 intake (ng/kg b.w./d) AFT intake (ng/kg b.w./d) AF-alb (pg/mg albumin)

13%

15%

32%*

19%

11.3 (10.1–12.7)** 3.8 (2.8–3.6) 6.9 (5.5–8.6) 19.9 (13.5–29.2) 24%

7.2 (6.3–8.1) 2.4 (1.8–3.1) 4.1 (3.2–5.2) 3.6 (2.8–4.7) 25%

7.7 (6.3–9.5) 41.9 (24.7–71.0)** 81.7 (48.8–136.7)** 43.2 (28.7–65.0)** 78%**

8.7 (7.9–9.4) 5.8 (4.5–7.6) 10.5 (8.1–13.8) 12.9 (9.9–16.7) 38%

12.9 (11.7–14.2) 3.9 (3.3–4.5) 7.1 (6.2–8.2) 20.8 (16.6–26.1)

8.1 (6.6–9.9)** 4.1 (2.7–6.2) 6.7 (4.7–9.7) 16.1 (12.6–20.7)

12.0 (10.4–13.8) 6.2 (4.4–8.7) 13.2 (9.1–19.0)** 48.8 (34.5–69.1)**

12.0 (11.1–12.9) 4.4 (3.7–5.3) 8.0 (6.8–9.6) 23.4 (19.9–27.7)

The mean level is significantly different to levels in the other villages, **p50.01; *p50.05.

compared to mothers in the other villages (2–4%). The SES scores were higher in Kikelelwa than in the other two villages (p50.01). Maize aflatoxin contamination Table 2 shows that 97% of maize samples were contaminated with aflatoxin in Kigwa, in contrast to 10% and 8% in Nyabula and Kikelelwa, respectively, at the storage visit. The AFT and AFB1 levels in maize were significantly higher in Kigwa when compared to other villages (AFT, GM 10.5 versus 0.6 ng/g; AFB1, GM 5.3 versus 0.3 ng/g, both p50.01). Although both the detectable percentage and the contamination level were lower in Kigwa at the harvest visit as compared to the storage season visit, they were still slightly higher than other villages, see Table 2. The overall ratio of AFB1:AFB2:AFG1:AFG2, based on the arithmetic mean, was 70:7:20:3 (results not shown).

It is a common practice in some areas of Tanzania that maize is dehulled before cooking, a process that could markedly reduce mycotoxins in maize. Almost all families (96%) consumed dehulled maize in Nyabula as compared to 7% and 49% in Kikelelwa and Kigwa, respectively, at the storage visit (p50.01), see Table 1. This percentage was 98% in Nyabula, 8% in Kikelelwa and 83% in Kigwa at the harvest visit (p50.01). Maize, groundnuts, and other food consumption Table 3 shows that at the storage visit, maize intake was significantly higher in Nyabula children than in children from the other two villages (11.3 versus 7.2 & 7.7 g/kg b.w./d, p50.01), but at the harvest visit the consumption levels were similar between Nyabula and Kigwa (12.9 and 12.0 g/kg b.w./d), with both higher than that in Kikelelwa (8.1 g/kg b.w./d).

Dietary intake of aflatoxin in Tanzanian children

Biomarkers Downloaded from informahealthcare.com by University of Otago on 07/09/15 For personal use only.

Aflatoxin intake In this study, the aflatoxin intake was assessed from maize food only, because maize is the primary ingredient of the weaning food and family food being consumed by the children. In Kigwa, the AFT intake GM of 81.7 ng/kg b.w./d was markedly higher (p50.01) than in Nyabula (6.9 ng/kg b.w./d) and Kikelelwa (4.1 ng/kg b.w./d) at the storage visit. The AFB1 intake followed the same pattern as AFT intake in these villages with the former representing about 50% of the total AFT. The same trend was observed at the harvest visit, see Table 3. AF-alb in blood At the storage visit, 123 of the 148 children had detectable AF-alb adduct in their blood. Children in Kigwa had the highest AF-alb adduct level with a GM of 43.2 pg/mg (Table 3). AF-alb adducts were lower in Nyabula at 19.9 pg/mg and lowest in Kikelelwa at 3.6 pg/mg (p50.01). The harvest visit showed a similar trend but an overall higher AF-alb adduct level than at the storage visit (23.4 versus 12.9 pg/mg), see Table 3. All but one child had detectable biomarker at the harvest season. Overall, high maize AFB1 intake was associated with high AF-alb adduct in blood at the storage visit (r ¼ 0.51, p50.01). The correlation coefficient between AFT intake and blood AF-alb followed a similar trend (not shown). At the harvest visit, a similar trend was observed but with a weaker association than the storage visit (r ¼ 0.32, p50.01). When data from the two visits were combined together the correlation coefficient was r ¼ 0.43, p50.01, see Figure 1. The association between AFT and AF-alb followed the same trend. When the correlation analysis was restricted to children who did not consume groundnuts, in order to avoid interference as an additional source of aflatoxin exposure, a similar trend to the above analysis result was found (data not shown). Multivariate linear regression analysis was conducted with maize AFB1 or AFT intake, age, visit, and village included in

6 4

ln(AF-alb)(pg/mg)

2 0

Groundnut consumption was recorded simply as the frequency of consumption over the previous week. More children ate groundnuts at least once a week in Kigwa than in other villages at the storage visit (32% versus 13–15%, p50.05), and the harvest visit (78% versus 24–25%, p50.01). The frequency of groundnut consumption was significantly higher in the harvest visit than in the storage visit (38% versus 19%, p50.05). The exact composition of complementary foods in different families was not recorded and there will be variation in the inclusion of other cereals such as rice and millet with the main component of maize in flour used to make porridge. Children in Kikelelwa ate substantial amounts of bananas usually in the form of banana porridge, but no children from the other villages consumed bananas. Potatoes, rice and wheat consumption were also more frequent in Kikelelwa than in other villages. Cassava was not a common food in any of the three villages under study (data not shown).

433

8

DOI: 10.3109/1354750X.2014.924998

0

2

4

6

8

ln(AFB1intake)(ng/kg bw/day)

Figure 1. Scatter plot with linear curve fit (red line) and 95% CI (grey-shaded area) showing the correlation relationship between AFB1 intake through maize food and blood AF-alb using pooled data at both visits, both in natural log transformed data. Correlation coef. ¼ 0.43, p50.01.

the model to determine the key contributors for AF-alb adduct. It showed that AFB1 intake ( ¼ 0.36, p50.01), and age ( ¼ 0.13, p50.01) were the key determinants of AF-alb, in addition to residence in village Kikelewa being a protective factor ( ¼ 0.91, p50.01).

Discussion Aflatoxin biomarker analysis It is widely acknowledged that estimating the intake of aflatoxin by measuring aflatoxin levels in food samples and recording food intake through use of 24 h recall questionnaires and FFQ will be somewhat inaccurate due to the heterogeneous distribution of aflatoxin in the food and error in recall. The AF-alb adduct biomarker is accepted as giving a more accurate individual exposure estimate (IARC, 2002) and when used to study associations between exposure and health outcomes is also more informative because it takes account of inter-individual variations in absorption and metabolism (Shephard, 2008). In addition, the long half-life of albumin in blood (19 d) means that measurements of AF-alb integrate exposure over the previous weeks rather than days (Sleep et al., 2013). Comparison of aflatoxin biomarker with dietary aflatoxin intake In the present study, we have compared the dietary intake estimate of aflatoxin, measured during carefully monitored conditions, with the AF-alb biomarker levels and found a strong correlation at the individual level. The strength of correlation is similar to the finding reported by Wild et al. (1992), in 20 Gambian adults (r ¼ 0.55, p50.05). In that study, aflatoxin intake over a 7-d period was measured using a duplicate diet method and compared to AF-alb adduct level on day 8, with a mean level of AF-alb adduct of 44 pg/mg albumin. In the current study, the mean AF-alb adduct level was 17 pg/mg, and the AFT diet intake was estimated at 90 ng/d. However, such quantitative comparisons should be interpreted with caution as the intake in Tanzania was

434

M. N. Routledge et al.

assessed based on maize only which is likely to lead to an underestimate of total aflatoxin intake and the likely underestimated intakes in The Gambia were also discussed by Wild et al. (1992). Nevertheless, given the complexity of diet in young children, the strong correlation between aflatoxin intake and AF-alb adduct found here further validates the use of the AF-alb biomarker in young children for comparing aflatoxin exposure to health endpoints.

Biomarkers Downloaded from informahealthcare.com by University of Otago on 07/09/15 For personal use only.

Effect of diet on aflatoxin contamination and biomarker levels Aflatoxins frequently contaminate maize and groundnuts in many areas of sub-Saharan Africa, where hot and humid conditions favor Aspergillus fungal growth. The toxin can be produced in crops growing in the field or during harvest, and production can accelerate during the storage period, depending on conditions. In this study, the maize contamination was higher at the storage visit compared to the harvest visit. It was, however, somewhat unexpected that the AF-alb levels were generally higher at harvest visit than at the storage visit. The reason for this could be that maize consumption increased significantly over the 6 months growing period of a child since it is both the contamination levels and the consumption of food that influence the aflatoxin exposure. Additionally, the above observation may reflect the more frequent consumption of other commonly aflatoxin-contaminated foods at the time of harvest, notably groundnuts, which would contribute to the AF-alb levels. In turn, the higher consumption of groundnuts may reflect the older age of the children at the later (harvest) visit accompanied by a more diverse diet. Groundnut consumption in these villages was lower than in West Africa, where they contribute significantly to aflatoxin exposure. In the statistical model in this study, groundnut consumption was not a significant contributor to aflatoxin biomarker levels, although the crude estimates of consumption should be recognised. However, it is notable that the frequency of consumption was consistently higher in Kigwa than in the other two villages, indicating a possible contribution role to the higher AF-alb in this village. Influence of village on aflatoxin contamination and biomarker levels The village of residence had a strong influence on aflatoxin exposure and this may be a combination of maize consumption, maize processing, other sources of dietary aflatoxins and general socio-economic status. Kigwa had the highest AFT intake at both visits, at around 12-fold and 20-fold higher than that of Nyabula and Kikelelwa, respectively, at the storage visit. This is largely due to the high level of contamination, even though maize consumption was not the highest in Kigwa. This was one of the two villages with lower SES and had the lowest educational level among the mothers interviewed. In the other two villages (Nyabula and Kikelelwa), levels of maize contamination with aflatoxins were similar, however the AF-alb adduct in children from Nyabula was markedly higher than in children from Kikelelwa (19.9 versus 3.6 pg/mg). The higher maize intake in Nyabula may be one reason for this difference. It has previously been observed that when maize

Biomarkers, 2014; 19(5): 430–435

production is high, the best quality maize is often sold to commercial millers (Kimanya et al., 2008), so it is also possible that aflatoxin intake is affected by consumption of lower quality maize. In addition to the storage practice, the processing of maize may also contribute to aflatoxin contamination level. De-hulling, a process to remove the pericarp of the maize kernel, has been shown to reduce aflatoxin levels by up to 90% (Mutungi et al., 2008; Siwela et al., 2005). Villagers in Nyabula typically de-hulled their maize more frequently than those in Kigwa, where only half of the maize samples were de-hulled at the storage visit. This is likely to have contributed to the higher contamination of aflatoxins in Kigwa maize. However, despite only 7% of the families in Kikelelwa using de-hulled maize, it had an equally low AFT contamination as Nyabula. Kikelelwa is located further North in a mountainous region, where the climatic conditions are less favorable to Aspergillus growth or aflatoxin production. Also, the available food types, and hence diets, in this village are more diverse; villagers did consume less maize when compared to the other two villages, although differences were not major.

Conclusions The findings from this study show that dietary intake of aflatoxins at the weaning stage is significantly correlated with the blood AF-alb adduct biomarker. Levels of exposure increase with aflatoxin (both AFT and AFB1) intake. The aflatoxin contamination at storage and the variety of diet and food processing make village location a key contributor to the exposure. Nevertheless, the biomarker reflected the overall aflatoxin intake, confirming that the AF-alb adduct is a valid approach in child exposure assessment, and a useful tool when studying health consequences of early life exposure to aflatoxin.

Acknowledgements The authors acknowledge the Tanzania Food and Drugs Authority for facilitating this study. We thank the administration authorities, study participants and field workers from all the areas where the study was conducted. We acknowledge the valuable laboratory technical assistance provided by Jovita Castelino and Chou Srey.

Declaration of interest This project was supported by the Leverhulme-Royal Society Africa Award. Dr YY Gong and Dr CP Wild acknowledge funding support from the National Institute of Environmental Health Sciences, USA (grant N : ES06052).

References Chapot B, Wild CP. (1991). ELISA for quantification of aflatoxinalbumin adduct and their application to human exposure assessment. In: Warhol M, van Velzen D, Bullock GR, eds. Techniques in diagnostic pathology. San Diego (CA): Academic Press, 135–55. Gong YY, Cardwell K, Hounsa A, et al. (2002). Dietary aflatoxin exposure and impaired growth in young children from Benin and Togo: cross sectional study. Br Med J 325:20–2. Gong YY, Egal S, Hounsa A, et al. (2003). Determinants of aflatoxin exposure in young children from Benin and Togo, West Africa: the critical role of weaning. Int J Epidemiol 32:556–62.

Biomarkers Downloaded from informahealthcare.com by University of Otago on 07/09/15 For personal use only.

DOI: 10.3109/1354750X.2014.924998

Gong YY, Hounsa A, Egal S, et al. (2004). Postweaning exposure to aflatoxin results in impaired child growth: a longitudinal study in Benin, West Africa. Environ Health Perspect 112: 1334–8. Gong YY, Wilson S, Mwatha JK, et al. (2012). Aflatoxin exposure may contribute to chronic hepatomegaly in Kenyan school children. Environ Health Perspect 120:893–6. Groopman JD, Wild CP, Hasler H, et al. (1993). Molecular epidemiology of aflatoxin exposures - validation of aflatoxin-N7-guanine levels in urine as a biomarker in experimental rat models and humans. Environ Health Perspect 99:107–13. IARC Working Group on the Evaluation of Carcinogenic Risks to Humans. Some traditional herbal medicines, some mycotoxins, naphthalene and styrene. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans 82. Lyon: International Agency for Research on Cancer; 2002. Kensler TW, Roebuck BD, Wogan GN, Groopman JD. (2011). Aflatoxin: a 50-year odyssey of mechanistic and translational toxicology. Toxicol Sci 99:S28–48. Kimanya ME, De Meulenaer B, Tiisekwa B, et al. (2008). Co-occurrence of fumonisins with aflatoxins in home stored maize for human consumption in rural villages of Tanzania. Food Chem Contaminant A 25:1353–64. McCoy LF, Scholl PF, Sutcliffe AE, et al. (2008). Human aflatoxin albumin adducts quantitatively compared by ELISA, HPLC with fluorescence detection, and HPLC with isotope dilution mass spectrometry. Cancer Epidemiol Biomark Prev 17: 1653–7. Mutungi C, Lamuka P, Arimi S, et al. (2008). The fate of aflatoxins during processing of maize into muthokoi – a traditional Kenyan food. Food Control 19:714–21. Routledge MN, Gong YY. (2011). In: De Saeger S, ed. Determining mycotoxins and mycotoxigenic fungi in food and feed. Cambridge: Woodhead Publishing in Food Science Technology and Nutrition 203, Woodhead Publishing Ltd, 225–44.

Dietary intake of aflatoxin in Tanzanian children

435

Shephard GS. (2008). Risk assessment of aflatoxins in food in Africa. Food Addit Contamin 25:1246–56. Shirima CP, Kimanya ME, Kiabo JL, et al. (2013). Dietary exposure to aflatoxin and fumonisin among Tanzanian children as determined using biomarkers of exposure. Mol Nutr Food Res 57:1874–81. Siwela AH, Siwela M, Matindi G, et al. (2005). Decontamination of aflatoxin-contaminated maize by dehulling. J Sci Food Agri 85: 2535–8. Sleep D, Cameron J, Evans LR. (2013). Albumin as a versatile platform for drug half-life extension. Biochim Biophys Acta 1830:5526–34. Stroka J, Anklam E, Joissen U, Gilbert J. (2000). Immunoaffirnity column cleanup with liquid chromatography using post column bromination for determination of aflatoxins in peanut butter, Pistachio 191Paste, Fig Paste, and Paprica powder: collaborative study. J AOAC Int 83:320–40. Turner PC, Collinson AC, Cheung YC, et al. (2007). Aflatoxin exposure in utero causes growth faltering in Gambian infants. Int J Epidemiol 36:1119–25. Turner PC, Moore SE, Hall AJ, et al. (2003). Modification of immune function through exposure to dietary aflatoxin in Gambian children. Environ Health Perspect 111:217–20. Wild CP, Garner RC, Montesano R, Tursi F. (1986). Aflatoxin B1 binding to plasma albumin and liver DNA upon chronic administration to rats. Carcinogenesis 7:853–8. Wild CP, Gong YY. (2010). Mycotoxins and human disease: a largely ignored global health issue. Carcinogenesis 31:71–82. Wild CP, Hudson GJ, Sabbioni R, et al. (1992). Dietary intake of aflatoxins and the level of albumin bound aflatoxin in peripheral blood in the Gambia, West Africa. Canc Epidemiol Biomark Prev 1:229–34. Wild CP, Jiang YZ, Allen SJ, et al. (1990). Aflatoxin albumin adduct in human sera from different regions of the world. Carcinogenesis 11: 2271–4.

Quantitative correlation of aflatoxin biomarker with dietary intake of aflatoxin in Tanzanian children.

The association between aflatoxin intake from maize-based weaning food and aflatoxin albumin adducts (AF-alb) was investigated in 148 Tanzanian childr...
173KB Sizes 1 Downloads 3 Views