Dataset Information

Assessing the risk of an emerging zoonosis of worldwide concern: anisakiasis.

ABSTRACT: Anisakiasis is an emerging zoonosis caused by the fish parasitic nematode Anisakis. Spain appears to have the highest reported incidence in Europe and marinated anchovies are recognised as the main food vehicle. Using data on fishery landings, fish infection rates and consumption habits of the Spanish population from questionnaires, we developed a quantitative risk assessment (QRA) model for the anchovy value chain. Spaniards were estimated to consume on average 0.66 Anisakis per untreated (non-frozen) raw or marinated anchovy meal. A dose-response relationship was generated and the probability of anisakiasis was calculated to be 9.56?×?10^-5 per meal, and the number of annual anisakiasis cases requiring medical attention was predicted between 7,700 and 8,320. Monte Carlo simulations estimated post-mortem migration of Anisakis from viscera to flesh increases the disease burden by >1000% whilst an education campaign to freeze anchovy before consumption may reduce cases by 80%. However, most of the questionnaire respondents who ate untreated meals knew how to prevent Anisakis infection. The QRA suggests that previously reported figures of 500 anisakiasis per year in Europe is a considerable underestimate. The QRA tool can be used by policy makers and informs industry, health professionals and consumers about this underdiagnosed zoonosis.

SUBMITTER: Bao M

PROVIDER: S-EPMC5347442 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Assessing the risk of an emerging zoonosis of worldwide concern: anisakiasis.

Bao Miguel M Pierce Graham J GJ Pascual Santiago S González-Muñoz Miguel M Mattiucci Simonetta S Mladineo Ivona I Cipriani Paolo P Bušelić Ivana I Strachan Norval J C NJ

Scientific reports 20170313

Anisakiasis is an emerging zoonosis caused by the fish parasitic nematode Anisakis. Spain appears to have the highest reported incidence in Europe and marinated anchovies are recognised as the main food vehicle. Using data on fishery landings, fish infection rates and consumption habits of the Spanish population from questionnaires, we developed a quantitative risk assessment (QRA) model for the anchovy value chain. Spaniards were estimated to consume on average 0.66 Anisakis per untreated (non- ...[more]

PMID: 28287609

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Project description:Today, many contaminants of emerging concern can be measured in waters across the United States, including the tributaries of the Great Lakes. However, just because the chemicals can be measured does not mean that they necessarily result in harm to fish and other aquatic species. Complicating risk assessment in these waters is the fact that aquatic species are encountering the chemicals as mixtures, which may have additive or synergistic risks that cannot be calculated using single chemical hazard and concentration-response information. We developed an in vitro effects-based screening approach to help us predict potential liver toxicity and cancer in aquatic organisms using water from specific Great Lakes tributaries: St. Louis River (MN), Bad River (WI), Fox River (WI), Manitowoc River (WI), Milwaukee River (WI), Indiana Harbor Canal (IN), St. Joseph River (MI), Grand River (MI), Clinton River (MI), River Rouge (MI), Maumee River (OH), Vermilion River (OH), Cuyahoga River (OH), Genesee River (NY), and Oswego River (NY). We exposed HepG2 cells for 48hrs to medium spiked with either field collected water (final concentration of environmental samples in the exposure medium were 75% of the field-collected water samples) or purified water. Using a deep neural network we clustered our collection sites from each tributary based on water chemistry. We also performed high throughput transcriptomics on the RNA obtained from the HepG2 cells. We used the transcriptomics data with our Bayesian Inferene for Sustance and Chemical Toxicity (BISCT) Bayesian Network for Steatosis to predict the probability of the field samples yielding a gene expression pattern consistent with predicting steatosis as an outcome. Surprisingly, we found that the probability of steatosis did not correspond to the surface water chemistry clustering. Our analysis suggests that chemical signatures are not informative in predicting biological effects. Furthermore, recent reports published after we obtained our samples, suggest that chemical levels in the sediment may be more relevant for predicting potential biological effects in the fish species developing tumors in the Great Lakes basin.

Dataset Information

Assessing the risk of an emerging zoonosis of worldwide concern: anisakiasis.

Publications

Assessing the risk of an emerging zoonosis of worldwide concern: anisakiasis.

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