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Risk of Exposure to Multiple Mycotoxins from Maize-Based Complementary Foods in Tanzania Analice Kamala,†,§ Martin Kimanya,*,# Carl Lachat,† Liesbeth Jacxsens,† Geert Haesaert,‡ Patrick Kolsteren,† Johana Ortiz,†,⊥ Bendantuguka Tiisekwa,⊗ and Bruno De Meulenaer† †

nutriFOODchem unit, Department of Food Safety and Food Quality, partner in Food2Know, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgium § Directorate of Food Safety, Tanzania Food and Drugs Authority, P.O. Box 77150, Dar es Salaam, Tanzania # School of Life Sciences and Bio-Engineering, The Nelson Mandela African Institution of Science and Technology (NM-AIST), P.O. Box 447, Arusha, Tanzania ‡ Department of Applied Biosciences, Faculty of Bioscience Engineering, Ghent University, Valentin Vaerwyckweg 1, BE-9000 Ghent, Belgium ⊥ Department of Biosciences, Faculty of Chemical Sciences, Cuenca University, Av. 12 de Abril s/n Cdla, Universitaria, 010201 Cuenca, Ecuador ⊗ College of Agriculture, Sokoine University of Agriculture, P.O. Box 3005, Morogoro, Tanzania ABSTRACT: This study estimated exposure to multiple mycotoxins in 249 infants aged between 6 and 12 months in three agroecological zones of Tanzania. Maize-based complementary food intakes were estimated using two 24 h dietary recalls. Using @ Risk software, probabilistic exposure assessment was conducted by modeling maize intake data (kg/kg body weight/day) with previously determined multiple mycotoxin (except for ochratoxin A (OTA) and zearalenone (ZEA), present in only a few samples) contamination data (μg/kg) in maize. Maize intakes ranged from 0.13 to 185 g/child/day (average = 59 ± 36 g/child/ day). The estimated mean exposures were higher for aflatoxins (6-fold), fumonisins (3-fold), and deoxynivalenol (2-fold) than health-based guidance values of 0.017 ng/kg body weight/day, 2 μg/kg body weight/day, and 1 μg/kg body weight/day, respectively. The population at risk of exposures above the limits of health concern ranged from 12% for HT-2 toxin through 35% for deoxynivalenol to 100% for aflatoxins. The exposure varied among the agro-ecological zones. Strategies targeting multiple mycotoxins in maize are urgently needed to minimize exposures in Tanzania. KEYWORDS: multiple mycotoxins, maize intake, exposure assessment, probabilistic analysis, Monte Carlo simulation

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INTRODUCTION

there were no data available on the carcinogenicity to humans and there is only limited evidence of carcinogenicity in experimental animals.8 In addition, AFs and FBs have been associated with reduced growth in children.10,11 Emerging evidence from certain studies suggests that mycotoxins may have synergistic, additive, or antagonistic toxicological effects in humans or animals.12−14 It is assumed that mycotoxins with the same mode of action and/ or with the same cellular target would have a synergistic or additive effect when present together.12 Studies have indicated that FB1 synergistically promotes liver tumors initiated by AFB1.15 Similarly, a synergistic effect on the reduction of body weight gain in male Wistar rats receiving oral AFB1 and FB1 has been reported.16 Furthermore, synergistic and additive interaction were observed at the intestinal level when animals were fed diets co-contaminated with DON and FBs.17 Recently, it has been suggested that the child growth impairment

Just like in other sub-Saharan Africa countries, in many parts of Tanzania, maize is the main staple food and used as a main ingredient in complementary foods.1 The maize crop can be infested by different toxigenic fungi and is susceptible to contamination by multiple mycotoxins.2 Mycotoxins are secondary metabolites produced by species of toxigenic fungi and found in several kinds of food, especially cereals and cereal products.3−5 Hundreds of mycotoxins have been discovered; however, on the basis of their frequency of occurrence in food and feed worldwide and/or level of toxicity, aflatoxins (AFs), ochratoxin A (OTA), fumonisins (FBs), deoxynivalenol (DON), T-2 toxin (T-2), HT-2 toxin (HT-2), and zearalenone (ZEA) are of particular concern.6 Consumption of a food contaminated by mycotoxins may induce acute or chronic disease episodes, with carcinogenic, mutagenic, teratogenic, estrogenic, hemorrhagic, nephrotoxic, hepatotoxic, neurotoxic, and/or immunosuppressive effects.7 The International Agency for Research on Cancer (IARC) classified AFB1 in group 1: “carcinogenic to humans”.8 OTA as well as FBs (FB1 and FB2) are classified by IARC in group 2B: “possibly carcinogenic to humans”.9 Other mycotoxins, DON, T-2, and ZEA, are classified by the same agency in group 3: “not classifiable as to their carcinogenicity to humans” because © XXXX American Chemical Society

Special Issue: Public Health Perspectives of Mycotoxins in Food Received: Revised: Accepted: Published: A

July 31, 2016 November 5, 2016 November 9, 2016 November 9, 2016 DOI: 10.1021/acs.jafc.6b03429 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

Article

Journal of Agricultural and Food Chemistry Table 1. Occurrence and Levels of Multiple Mycotoxins in Maize by Location locationa

frequency, n > LODb

AFs

Kilosa Hanang’ Rungwe overall

19 9 5 33

124 3.3 4.2 70

FBs


19 12 17 48

2808 193 5267 3025

DON


11 13 14 38

532 515 433 490

HT-2


1 2 12 15

N/Ae 18 (0.2) 19 (3) 20 (3)

OTA


1 1 NDf 2

ZEA


1 ND 2 3

mycotoxin

mean (SDc) (μg/kg) (208) (1.1) (3.5) (231) (3881) (108) (13364) (8389) (714) (598) (567) (607)

median (μg/kg) 10.2 3.2 6.3 12.

range (μg/kg) 1.0−1081 1.2−5 0.44−8.3

1379 222 1267 1125

44−16203 18−622 31−56400

111 198 148 149

68−2196 79−1925 84−1931

N/A 18.5 18.3 18.4

23d 18−19 15−25

N/A N/A N/A 45 (40)

N/A N/A N/A 45

73d 16d N/A

N/A N/A 1057 (575) 729 (699)

N/A N/A 345 55

73d N/A 651−1464

a Each location (n = 20; overall n = 60). bLOD, limit of detection (μg/kg). LOD for AFB1 = 0.8, AFB2 = 0.6, AFG1= 0.4, AFG2 = 0.6, FB1 = 4, FB2 = 86, DON = 38, HT-2 = 0.6, OTA = 6, and ZEA = 30. n = number of samples. cSD, standard deviation. dOnly one sample contained levels above LOD. eN/A, not applicable. fND, not detected.

findings will add to both profiles and backgrounds of multiple mycotoxin exposures through maize-based complementary food in Tanzania and will enable prioritizing actions to reduce risks.

mechanism of AFs together with FBs and DON is through targeting the intestinal tract and inducing environmental enteropathy (EE).18 Environmental enteropathy, which is highly prevalent in the developing world, is a subclinical condition characterized by villous atrophy, crypt hyperplasia, increased small intestinal permeability, and inflammatory cell infiltrate. This condition leads to chronic systemic immune activation and malabsorption of nutrients, which in turn may lead to growth retardation. Infants and children are considered to be more susceptible to different toxins than adults due to their immature detoxification capacity, rapid growth, and high energy intake relative to their body size. Generally, children have more future years of life than adults, and they have more time to develop chronic diseases triggered by early exposures.19 Although available reports in Tanzania indicate contamination of maize with various mycotoxins,20−22 exposure assessment has been done for few groups of mycotoxins, namely, AF, DON, and FB23−25 and for limited zones of the country. The objective of this study was to quantitatively assess the exposure to a wide range of mycotoxins from maize-based complementary food by estimating maize consumption among the infants and combining the consumption data with the multiple mycotoxin contamination data, previously collected.21 The study further examines differences in mycotoxin exposure patterns from three agro-ecological zones of Tanzania and estimates the risk of exposures exceeding health-based guidance values such as the tolerable daily intake of particular toxins. The

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MATERIALS AND METHODS

Study Area. The study was conducted in three different agroecological zones of Tanzania; these are Northern Highland, Eastern Lowland, and Southwestern Highland. One district in each zone, Hanang’ representing the Northern Highland zone, Kilosa the Eastern Lowland zone, and Rungwe the Southwestern Highland zone, was chosen for the study. The zones and respective districts were chosen on the basis of the outcome of a previous survey of multiple mycotoxin contamination in maize, conducted in 2012, which showed that maize from the zones had high rates of contamination at important levels.21 Sampling plans, development of analytical methods, and analysis of the multiple mycotoxins were previously described.21 Briefly, the multiple mycotoxins were analyzed using an ultrahigh-performance liquid chromatography−time-of-flight mass spectrometry (UHPLCTOFMS) method with a QuEChERS-based procedure for sample extraction. Occurrence and contamination levels of all mycotoxins in maize per agro-ecological zone are presented in Table 1. Particularly high frequency of FBs (80%), AFs (55%), DON (63%), and HT-2, a deacetylated form of T-2 (25%), were observed. T-2 toxin was below the limit of detection (LOD). T-2 has been reported to occur more frequently and at higher levels than HT-226,27 The observed occurrence of HT-2 alone in this study is in accordance with the results found in Norway.28 The differences could be due to variations in the Fusarium species producing the toxins. It has also been shown B

DOI: 10.1021/acs.jafc.6b03429 J. Agric. Food Chem. XXXX, XXX, XXX−XXX

Article

Journal of Agricultural and Food Chemistry

Table 2. Best-Fit Distributions, Minimum, Mean, and Maximum Determined for the Lower Bound Scenario of Mycotoxin Concentrationsa and for Maize Intakeb location

a

description

function

minimum

mean

maximum

Kilosa

maize intake AFs DON FBs

RiskTriang (−0.00040875; 0.0063392; 0.016323) RiskLognorm (79858.3; 620295.9; RiskShift (−78.072) RiskLognorm (746.49; 5005.9; RiskShift (40.74) RiskLognorm (3255.9; 8241.3; RiskShift (−54.469)

−0.001 76 41 −52

0.007 37922 784 3198

0.016 48106600 41601 458671

Rungwe

maize intake AFs DON FBs HT-2

RiskWeibull (1.6102; 0.0053894; RiskShift (−0.00026443) RiskInvgauss (3981.2; 1210.9; RiskShift (155.13) RiskInvgauss (361.57; 126.81; RiskShift (24.627) RiskLognorm (7080.2; 41801.3; RiskShift (140.38) RiskLoglogistic (14.5085; 3.5689; 2.8017)

−0.000 216 30 140 15

0.006 4137 386 7290 19

0.024 190272 20657 7852175 224

Hanang’

maize intake AFs DON FBs

RiskWeibull (2.0841; 0.011106; RiskShift (−0.0012976) RiskInvgauss (450.78; 76.926; RiskShift (64.703) RiskLogistic (3243.29; 590.52) RiskLognorm (246.85; 840.32; RiskShift (16.364)

−0.002 67 −3243 16.415

0.009 516 3243 263

0.034 31139 9706 63602

all locations combined

maize intake AFs DON FBs HT-2

RiskWeibull (1.7809; 0.0094515; RiskShift (−0.00056967) RiskLoglogistic (380.84; 5231; 1.2857) RiskLognorm (469.4; 1336.2; RiskShift (37.992) RiskGamma (0.41139; 7464.6; RiskShift (18.679) RiskLoglogistic (14.3432; 3.9927; 3.1238)

−0.001 384 38 18 14

0.008 27187 506 3089 19

0.032 100909900 59453 60027 92

Aflatoxin concentration as ng/kg, other mycotoxins as μg/kg. bMaize intake expressed as kg/kg bw/day.

that the degree of deacetylation depends on various factors including the grain species.29 Contamination ranges of the mycotoxins were 18− 56,400, 0.44−1081, 68−2196, and 15−25 μg/kg for FBs, AFs, DON, and HT-2, respectively. Occurrence of the studied mycotoxins differed significantly (p < 0.05) among the three agro-ecological zones. Participants (Sample Size, Selection Criteria, and Recruitment). The study targeted 300 infants aged from 6 to 12 months. The sample size was determined according to the method of Naing, Winn, and Rusli30 using the formula n =

Z2P(1 − P) d2

and Eastern Lowland and August 2014 in the Southwestern Highland zones. Using replicas, a 24 h dietary recall to estimate amounts of maize-based food consumed by the infants was conducted.31 Two visits at an interval of 1−2 weeks were made to the home of each of the infants. Information about type of foods consumed by the child was collected. The type of food was classified into eight food groups (Food Group Index-8) according to the method of Dewey et al.,32 which included (1) grains, roots, and tubers; (2) legumes and nuts; (3) dairy products; (4) flesh foods; (5) eggs; (6) vitamin A rich fruits and vegetables; (7) other fruits and vegetables; and (8) fats and oils. In this survey, only the maize-based meal consumed by a child was quantified, and the portion size was estimated by recording the equivalent amount of maize-based meal eaten. In addition to 24 h recalls, a food frequency questionnaire was administered to assess the frequency of consumption of maize-based food per week. A questionnaire that was previously used by other researchers in communities of Tanzania that have food consumption habits similar to the communities in the areas of our study was used.24 Adjustment of the dry matter to the moisture content of maize flour was done as described previously by Kimanya23 Intake data were entered in the Lucille food intake software of Ghent University.33 Food composition was analyzed using the Tanzania food composition tables.34 To obtain a better estimate of the habitual maize-based food intake of the child, the average maize-based meal consumption of each child was adjusted by multiplying it with his/her weekly frequency of maize-based food consumption divided by seven. The daily maize intake was expressed as g/day and later transformed to kg/kg bw/day using the body weight (bw) obtained at the time of the complementary food survey to enable exposure assessment. The body weight was measured according to World Health Organization (WHO) standardized procedures for taking anthropometric measurement using calibrated instruments.35 Children were weighed in light clothing using a portable spring scale (Salter model 235 6M), and weights were recorded to the nearest 0.1 kg. In addition to exposure assessment, the body weight obtained was used to determine a weight-for-age-Z-score (WAZ). WAZ is an aggregate index for malnutrition, considering both stunting and wasting. Each child’s date of birth was obtained from his or her growth card. Age in months was calculated from difference between date when anthropometric measurements were taken and birth date. The WAZ was calculated using WHO Anthro software.36

, where n = sample size, Z

= Z statistic for a level of confidence (=1.96 for 95% confidence level), P = expected proportion of samples containing mycotoxins (=0.5 based on previous study,21 which found that between 10 and 73% of maize samples were contaminated with multiple mycotoxin, and d = precision (=0.05). This resulted in a sample size of 384 participants. However, due to resource constraints the sample size was reduced to 300. The sampling unit was a village of which a total of 30 villages (10 for each district) were included in the study. Villages and households visited were randomly selected from the census register of the three districts. The selection criteria for the households were to be a permanent resident and maize grower, having capacity of storing maize for a period of not fewer than 6 months after harvest, and children aged between 0 and 6 months at the time of recruitment. Six months before the survey, infants were recruited from the register of births in child and reproductive health clinics. Infants were identified using their registration number and date of birth. In Tanzania, all infants born in clinics are registered soon after birth. In the case of home deliveries, registration is done on the day the child is taken to the clinic for immunization. On registration, each child is allocated a registration number, and the child’s particulars including date and place of birth are recorded. Ethical Considerations. Ethical approval was obtained from the National Institute of Medical Research in Tanzania (NIMR/HQ/R. 8a/Vol. IX/1660). Local government nutritionists, resident nurses, and sociologists were involved in this study. With the help of village executive officers, mothers of the eligible infants were requested to gather at a nearby health facility or school, where informed written consent was obtained from the mother of each participating infant. Food Intake Assessment. A food intake survey was conducted 6 months after maize harvest: December 2013 in the Northern Highland C


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Journal of Agricultural and Food Chemistry Table 3. Characteristics and Food Intakes by Location location characteristics no. at food survey females (%) no. of children introduced to food age (in months) at introduction to feeding (%) 0−2 3−5 6 mean age, months (SDa) mean weight, kg (SD) WAZ, mean (SD) maize intake mean, g/day (SD) range (g/day) other food groups (%) legumes vitamin A rich fruits and vegetablesb,c dairyd fleshe other fruits and vegetables

Kilosa

Rungwe

Hanang’

locations combined

93 40 91

81 47 74

87 48 84

261 45 249

10 41 49 8.9 (2) 8 (1.2) −0.66 (1.08)

12 48 40 8.4 (2) 8.8 (1.6) 0.27 (1.4)

11 42 47 8.7 (2) 8.4 (1.3) −0.11 (1.04)

10 48 42 8.7 (2) 8.4 (1.4) −0.17 (1.2)

57 (27) 0.39−124

40 (28) 0.13−114

72 (43) 0.16−185

59 (36)

28 59 12 24 6

22 27 35 20 10

15 28 34 41 6

27 42 25 27 8

a

SD, standard deviation. bIncludes pumpkin, carrots, yellow/orange sweet potatoes, ripe mango or papaya, passion fruit, any dark green leafy vegetables (spinach/amaranth/cassava), and other locally grown yellow/orange fleshed fruits or vegetables. cDifference is between Kilosa and Rungwe (p < 0.001), Kilosa and Hanang’ (p < 0.001). dDifference is between Rungwe and Kilosa (p < 0.001), Hanang’ and Kilosa (p = 0.003). e Difference is between Hanang’ and Rungwe (P = 0.019), Hanang’ and Kilosa (P = 0.040). Exposure Assessment. The exposure assessment was performed, probabilistically, by combining the mycotoxin contamination data (obtained in 2012) with maize consumption data (obtained in 2013/ 14). Calculations were carried out using the software of @Risk for Microsoft Excel version 6.1.2 (Palisade Corp., USA). Probabilistic models take into account every possible value that each variable can take and weigh each possible scenario by the probability of its occurrence.37 This approach ensures that any variability or uncertainty in variables is reflected in the model output,38 allowing a more accurate mycotoxin intake estimation. To carry out exposure assessment of the mycotoxins, nondetects, that is, concentrations below the LOD, were treated in three different scenarios, lower, medium, and upper bound, to provide an impression of the variation between an optimistic and the worst-case scenario. Accordingly, nondetects were considered as zero, 1/2LOD, and LOD for lower, medium, and upper bound, respectively. AFs and FBs intake was estimated as total aflatoxins (AFB1+AFG1+AFB2+AFG2) and total fumonisins (FB1+FB2), respectively. Exposure assessment was performed per each zone and for all zones combined. The distribution fitting was possible when there were at least five data values, among which three data values had to be positive39 Consequently, the mycotoxins T-2, which was not detected, OTA, and ZEA, present only in very few samples, were excluded from the exposure assessment. Best-fit distributions were constructed to the lower, medium, and upper bound scenarios of all mycotoxin concentrations in the three agro-ecological zones and to the respective consumption data. The type of distribution selected as best fitted (Table 2) was applied. Best fit was based on Chi-square statistics. Also, the probability/probability plots (P/P) and the quantile/quantile plots (Q/Q), resulting from the cumulative distributions, were a parameter if the cumulative distributions corresponded to the theoretical cumulative distributions. First-order Monte Carlo simulations were performed considering 10,000 iterations. The estimated exposures of the mycotoxins (mean, standard deviation, and percentiles) were determined separately per zone and for all zones combined. The total intake (all three zones combined) was calculated by considering all mycotoxin concentration data of maize and the consumption data according to the estimates from the 2 day 24 h recalls and food frequency questionnaire.

Risk Characterization. Because AF is a genotoxic carcinogen, JECFA cannot set a tolerable daily intake (TDI) for it. Therefore, risk characterization for this toxin was based on margins of exposure (MOE).40,41 The MOE for a child was calculated by dividing the benchmark dose lower limit (BMDL10) of 170 ng/kg bw/day by the exposure estimated for him/her. According to EFSA,41 a MOE of 10,000, which is equivalent to an exposure of 0.017 ng/kg bw/day (obtained by dividing the BMDL of 170 ng/kg bw/day by 10,000), is considered a cutoff point of low public health concern. Therefore, estimated exposure above 0.017 ng/kg bw/day was considered of concern from a public health point of view. Exposure estimates for FBs, DON, and HT-2 were compared with the TDIs of 2 μg/kg bw/day (as set for FB1 and FB2, alone or in combination), 1 μg/kg bw/day, and 0.1 μg/kg bw/day (as set for the sum of HT-2 and T-2 toxins), respectively, as set by JECFA42 and EFSA.43 The results were reported as mean, standard deviation, percentile, and percentage of the population at risk of exceeding the corresponding TDI. Exposures above the TDIs were considered of public health concern. Statistics Analysis. The Stata software version 12.0 program was used to determine descriptive statistics of the children characteristics and 95% confidence intervals of the proportion of the population exceeding the tolerable daily intake. Differences in means of maize intake, age, and body weight of the children between agro-ecological zones were compared using analysis of variance. The Chi-square test was used to compare proportions of the population consuming types of food other than maize. StatTool was used to compute 95% confidence intervals of the estimated exposures. The level of confidence required for significance was set at p < 0.05.

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RESULTS AND DISCUSSION Study Subject Characteristics. Characteristics of the children participating in the food intake survey are presented in Table 3. A total of 260 (87% of the 300 recruited) infants were available for food intake. The missed (40) children were not available either because of not being at home during the day of visit or not being willing to participate in the study. Of the 260 infants, 249 were already introduced to complementary food. D


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Table 4. Probabilistic Exposures of Multiple Mycotoxins (Mean, Standard Deviation, and Percentiles (95% CI)) by Locationa location mycotoxin

parameter

Kilosa

Hanang’

AFs

mean SD P50 P75 P95 P99 population exceeding TDIb (%) MOE at mean exposure %TDIc

708 (700−716) 3471 66 (64−68) 272 (265−279) 1661 (1591−1735) 12409 (11285−13739) 100 0.24 4 × 106

14 (13.4−14.3) 19 0 38 (37−39) 63 (62−64) 87 (84−90) 34 (24−44) 12 8 × 104

9.4 (9.2−9.7) 32 0 6.6 (6.5−6.8) 43 (42−45) 135 (129−141) 50 (34−61) 19 5 × 104

Rungwe

all locations combined 133 (129−138) 5984 0 35 (34−37) 234 (229−240) 946 (936−956) 49 (43−55) 1.3 8 × 105

DON

mean SD P50 P75 P95 P99 population exceeding TDI (%) %TDIc

2.9 (2.6−3.5) 21.82 0.02 1.05 (0.9−1.6) 10.2 (9−11) 44 (40−53) 25 (16−33) 2.9 × 102

2.2 (2.1−2.3) 7.9 0.04 (0.03−0.05) 1.53 (1.50−1.56) 9.4 (9.0−9.8) 33 (31−35) 35 (24−45) 2.2 × 102

1.9 (1.7−2.1) 0.73 0.02 (0.01−0.04) 0.72 (0.70−0.73) 4.2 (4.0−4.3) 12 (11−13) 20 (10−29) 1.9 × 102

1.9 (1.8−2.0) 7.8 0.015 (0.01−0.018) 1.3 (1.1−1.5) 0.8 (0.75−0.85) 27 (25−29) 29 (23−34) 1.9 × 102

FBs


23.5 (23−24) 65 7.3 (7.2−7.4) 21 (20.9−21.7) 91 (90−96) 250 (241−264) 78 (70−87) 1.2 × 103

1.2 (1.10−1.24) 6.5 0.0 0.64 (0.63−0.66) 4.8 (4.6−4.9) 17 (16−18) 12 (5−18) 60

16.9 (15−19) 211 0.47 (0.44−0.49) 4.9 (4.6−5.3) 59 (53−65) 262 (233−320) 35 (25−45) 8 × 102

12 (11.2−13) 34 0.03 (0.02−0.04) 6.9 (6.4−7.5) 72 (65−79) 174 (167−179) 35 (29−41) 6 × 102

HT-2


N/Ad

0.07 (0.060−0.08) 0.10 (0.07−0.14) 0.01 (0.005−0.015) 0.13 (0.11−0.15) 0.28 (0.20−0.32) 0.38 (0.29−0.42a 18 (9−26) 70

0.04 (0.03−0.05) 0.01 (0.005−0.016) 0.00 0.08 (0.06−0.10) 0.17 (0.12−0.22) 0.25 (0.19−0.31) 12 (5−19) 40

Lower bound corresponds to mycotoxin < LOD treated as zero (0). bThe tolerable daily intake (TDI) for AF (0.017 ng/kg bw/day), DON (1 μg/ kg bw/day), FB (2 μg/kg bw/day), and HT-2 (0.1 μg/kg bw/day). c%TDI calculated as (estimated mean exposure/TDI) × 100; mean, SD, and percentile expressed as ng/kg bw/day for aflatoxins, μg/kg bw/day for other mycotoxins. dN/A, not applicable. a

249 children who had been introduced to complementary food. All of the 249 children were partially complemented (i.e., given complementary food and being breastfed). Results indicate that maize intake differed significantly among the three locations. Hanang’ showed significantly higher mean maize intake than Kilosa (p = 0.031) and Rungwe (p < 0.001). The mean maize intake in Kilosa was also significantly higher than in Rungwe (p = 0.001). In addition to maize-based complementary food, other types of food groups consumed by children include vitamin A rich fruits and vegetables, legumes, dairy, flesh, and other fruits and vegetables (Table 3). Consumption of vitamin A rich fruits and vegetables, dairy, and flesh showed significant differences (p < 0.05) across the zones. Previous studies in Tanzania46 and in other African countries47 reported similar findings on variation of maize intake by geographical location. The differences could be due to the availability of other foods and eating habits of the particular community. Probabilistic Exposures. Table 4 shows the results of probabilistic calculation of the mycotoxin exposures (mean,

Of the 249 children, 91, 84, and 74 were from Kilosa, Hanang’, and Rungwe, respectively. The mean age did not differ significantly among the agro-ecological zones (p > 0.05), whereas the mean WAZ was significantly lower in Kilosa than in Hanang’ (p = 0.016) and Rungwe (p < 0.001). No significant difference in WAZ was observed between Hanang’ and Rungwe. About 10% of infants between 0 and 2 months of age had started receiving complementary food. These findings on infant feeding practices compare well with published Demographic and Health Survey data for Tanzania44 and other countries in Africa.45 Introducing complementary food to infants before 6 months predisposes them to the risk of exposure to pathogens and chemical toxicants and risk of effects from the exposures because the gut is not yet fully developed and is more permeable.19 Maize Intake and Other Foods Introduced to Infants. Table 3 further shows children’s maize intake by location. The study found that maize-based food is commonly prepared and consumed as thin or stiff porridge. The intake was estimated for E


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exposures to DON and FB were highest in this zone. Similar high exposures, particularly to aflatoxins, are expected in communities of other regions in Tanzania such as Shinyanga and Tabora, where aflatoxin contamination in maize has been reported to be high.56,57 Because most of the prevailing environmental conditions and mycotoxin contamination are not easily controllable, attention is needed to maize handling practices, which predispose maize to mycotoxin contamination and can be controlled by human effort. Several pre- and postharvest methods such as crop rotation, timely harvests, proper drying, and storage and processing techniques (e.g., sorting, cleaning, and milling)58,59 can be used to tackle this agro-food problem. High exposure can result from either high consumption of moderately contaminated foods or high contamination levels of foods consumed in moderate amounts. High exposure to aflatoxins and fumonisins from maize-based complementary food in Kilosa was due to the high contamination level at the same time as a high intake of maize. Despite the comparable low levels of aflatoxins in Rungwe and Hanang’, fumonisins in Hanang’, and HT-2 in Rungwe, the high intake of maize has resulted in the high potential risk of the respective mycotoxin exposure in these zones. The findings support the previous views by other researchers60 that an effective way of lowering the risk of exceeding tolerable limits for mycotoxins in Tanzania is by limiting both the consumption of maize and the level of the mycotoxins in the maize. Therefore, advocacy of diet diversification based on locally available foods that are less prone to mycotoxin contamination together with the aforementioned pre- and postharvest strategies targeting multiple mycotoxins is urgently needed to minimize mycotoxin exposures from maize in Tanzania. Comparison of exposure estimated in different studies is hard due to differences in the sampling strategies, applied analytical methods, number of samples, and calculation methodology and should be used only approximately and with caution. Previous studies on mycotoxin intakes in other parts of Tanzania also reported that children are exposed to high levels of mycotoxins, particularly aflatoxins, deoxynivalenol, and fumomonisins.23,25 The population at risk was estimated to be 32% for aflatoxins, 29% for DON, and 46% for fumonisins. The AFs MOE from the same study ranged from 0.22 to 169. Consequently, an imported health threat was here also identified. High exposure obtained from this study has been also reported in other African countries.51,61,62 On the other hand, published studies in other countries such as France, 63 Japan,64 Nordic countries,65 and South Korea66 reported lower exposure of the mycotoxins than reported by this study. The finding is in agreement with other researchers24,61 who reported that the risk of mycotoxin exposures in Africa is higher than in other parts of the world because of the high maize/cereal-based food consumption and high contamination of the crop with mycotoxins in this region. For instance, the estimated average daily maize intake of 59 g per infant in this study is >7 times higher than the average intake in adults in Europe as reported by WHO GEMS/Food Regional Diets.67 Mycotoxin exposure data generated by this study used contamination data for stored maize intended for human consumption. The exposure of mycotoxin at the point of consumption may differ depending on the processing factors applied prior to consumption. Processes such as dehulling of maize prior to milling, removing spoiled kernels, and flotation have been shown to result in a significantly decreased

standard deviation, percentiles) by location and by all locations aggregated for the lower bound scenario (optimistic case scenario). However, there was no significant difference (p > 0.05) of DON, FBs, and HT-2 exposures between the lower, medium, and upper bounds. This can be explained by the low LOD of the analytical method used and the low percentage of samples below LOD. The results reveal that in the studied zones, children consuming maize-based complementary food are exposed to carcinogenic mycotoxins, aflatoxins, and possible human carcinogen, fumonisins, together with another fusarium toxin, DON, at levels that exceed the reference toxicological values. Mean exposure for HT-2 was below TDI; however, the P75, P95, and P99 exceeded the TDI. Exposure to HT-2 in Tanzania is reported for the first time in this study, reflecting a previous study,21 which reported for the first time the occurrence of HT-2 in maize grown and consumed in Tanzania. With regard to the MOE values for intake of AFs, the application of the calculated mean exposure determined in this study and taking into consideration a BMDL10 of 170 ng/ kg bw/day,41 the recalculated MOE values (Table 4) are considerably lower than 10,000 and therefore considered to be of concern from a public health point of view, which clearly highlights the need for urgent action. Overall, the estimated mean exposure in relation to the reference toxicological value was highest for AF, followed by FB, DON, and then HT-2. Table 4 further shows the percentage of population exceeding the tolerable daily intake. The population at risk of exposures above the limits of health concern ranged from 12% for HT-2 through 35% for deoxynivalenol to 100% for aflatoxins. The reported exposure to multiple mycotoxins by this study is high and could raise serious public health concerns. This is of particular concern because their combined effect can be additive or synergistic.48 Currently, risk assessments are mainly performed for a single mycotoxin basis. On the basis of these findings and other reports on the prevalence of multiple mycotoxins in agricultural commodities,49−51 there is a need to consider the impact of multiple exposures and combined risks for exposed populations by developing a methodology to address the cumulative exposure to multiple mycotoxins, as recommended by EFSA.52 The aggregated zone data hide significant variation between the agro-ecological zones. For example, the mean exposures to AFs, DON, and FBs were significantly higher in Kilosa than in Hanang’ and Rungwe. Similarly, the mean exposure to FBs in Rungwe was also significantly higher than in Hanang’. Except for DON, the proportion of population at risk of unacceptable exposures among the three agro-ecological zones followed the same trend. The observation of the difference of mycotoxin exposure by geographical location is further supported by other studies in Tanzania46 and other parts of Africa.51,53,54 The main reasons for these differences are considered to be variation in food intake and contamination patterns. The differences in climatic conditions across the different agro-ecological zones and pre- and postharvest practices, which could lead to different levels of fungal contamination and subsequent mycotoxin production between the zones, are the main factors for the variation in mycotoxin contaminations.55 The present findings identify populations at risk and prioritize areas of action when risk management plans are made. Particular attention should be paid to the high-risk Eastern Lowland zone (Kilosa). In this zone all children were exposed to AFs above the reference toxicological value of 0.017 ng/kg bw/day. Furthermore, F


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Journal of Agricultural and Food Chemistry mycotoxin contamination.22,68,69 Thus, determination of mycotoxin levels in ready-to-use maize flour would give a more realistic figure for mycotoxin presence in the maize and the corresponding exposures. In any case, the calculated values in this study should be treated as approximate values for the mycotoxin exposure of the children through maize-based complementary food. To improve exposure estimates, determination of mycotoxin levels in ready-to-use maize is needed. However, regardless of its limitation, the results of the present study provide a strong corroboration to reduce mycotoxin exposure. Studies conducted in Tanzania showed that processing steps applied to obtain ready-to-use maize flour for complementary food are sorting and dehulling.24,57 However, a significant proportion of households do not sort maize to remove the spoiled or damaged kernels or cobs before storage and consumption.24,57 In addition, sorting and dehulling can only partly reduce the exposure.24 The effectiveness of the above strategy will hence depend on the initial mycotoxin contamination of maize.70 Generally, use of biomarkers is the most reliable method for determination of exposure to chemicals. However, food-based exposure assessments are also reliable because some studies have indicated a significant correlation, at the individual level, between AF-alb (aflatoxins biomarker of exposure) and aflatoxin intake through maize-based food.71 Similarly, DON intakes estimated by multiplying DON contamination in maize and maize consumption were significantly correlated with urinary DON levels estimated through maize-based food.72 Given the good correlation between maize-based exposures and biomarker-based exposures and the fact that maize is the main ingredient for complementary food in Tanzania,24 it is reasonable to rely on the outcome of this exposure assessment in developing strategies to mitigate the problem of mycotoxin contamination in maize. Uncertainties Related to Exposure Assessment. Exposure assessment is associated with uncertainties that need to be considered to understand correctly the strength and limitations of its results.73 In this study, estimation of portion sizes, which can be interpreted subjectively by consumers and under- or over-reporting of consumption data, could contribute to an under- or overestimation of food consumption and affect the exposure assessment. Another uncertainty could arise from the consideration of concentration of mycotoxins below LOD. In this study, the exposures for AFs in Rungwe as were reported for lower bound scenario (i.e., concentrations below LOD were considered as zero) could underestimate the reported exposures. Another uncertainty can arise from the extrapolation of zone data to Tanzania as a whole. Such extrapolation can lead to exposures in particular areas becoming better or considerably worse. In conclusion, the present study corroborates and to a great extent expands the previous mycotoxin exposure data and reports for the first time the exposure to HT-2 through maizebased food in Tanzania. The study further highlights the fact that children receiving maize-based complementary foods in Tanzania are at a high risk of exposure to multiple mycotoxins at levels that exceed the reference toxicological values. Strategies targeting multiple mycotoxins are urgently needed to minimize mycotoxin exposures from maize in Tanzania. The risk reported by this study and the toxicological significance confirm that aflatoxins and fumonisins are of public health concern and should be considered a priority for risk management actions. In addition, policy makers and agricultural

and health stakeholders should be sensitized to the health risks associated with exposure to mycotoxins and advised to include the mycotoxin component in food safety, nutrition, and food security programs.

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AUTHOR INFORMATION

Corresponding Author

*(M.K.) Phone: +255 754 317 687. E-mail: martin.kimanya@ nm-aist.ac.tz. ORCID

Martin Kimanya: 0000-0003-3576-5516 Funding

This work was supported by Flemish Interuniversity Council− Institutional University cooperation (VLIR-UOS) (Grant ZEIN2011PR388). Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We acknowledge Ghent University (Belgium), the Management of Tanzania Food and Drugs Authority, and District Councils’ authorities of Hanang’, Kilosa, and Rungwe for valuable support during implementation of the study.

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REFERENCES

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