Testing the thresholds of toxicological concern values using a new database for food-related substances.

The Threshold of Toxicological Concern (TTC) concept integrates data on exposure, chemical structure, toxicity and metabolism to identify a safe exposure threshold value for chemicals with insufficient toxicity data for risk assessment. The TTC values were originally derived from a non-cancer dataset of 613 compounds with a potentially small domain of applicability. There is interest to test whether the TTC values are applicable to a broader range of substances, particularly relevant to food safety using EFSA's new OpenFoodTox database. After exclusion of genotoxic compounds, organophosphates or carbamates or those belonging to the TTC exclusion categories, the remaining 329 substances in the EFSA OpenFoodTox database were categorized under the Cramer decision tree, into low (Class I), moderate (II), or high (III) toxicity profile. For Cramer Classes I and III the threshold values were 1000 µg/person per day (90% confidence interval: 187-2190) and 87 µg/person per day (90% confidence interval: 60-153), respectively, compared to the corresponding original threshold values of 1800 and 90 µg/person per day. This confirms the applicability of the TTC values to substances relevant to food safety. Cramer Class II was excluded from our analysis because of containing too few compounds. Comparison with the Globally Harmonized System of classification confirmed that the Cramer classification scheme in the TTC approach is conservative for substances relevant to food safety.

Effect on public health of a possible increase of the maximum level for 'aflatoxin total' from 4 to 10 µg/kg in peanuts and processed products thereof, intended for direct human consumption or use as an ingredient in foodstuffs.

EFSA was asked to deliver a scientific opinion regarding the effect on public health of a possible increase of the maximum level (ML) for 'aflatoxin total' (AFT; sum of aflatoxin B1, aflatoxin B2, aflatoxin G1 and aflatoxin G2) from 4 to 10 µg/kg in peanuts and processed products thereof. Aflatoxins are genotoxic and cause hepatocellular carcinomas in humans. The Panel on Contaminants in the Food Chain (CONTAM Panel) evaluated 8,085 samples of peanuts and 472 samples of peanut butter, with > 60% left-censored. The mean concentration of AFT in peanuts was 2.65/3.56 µg/kg (lower bound (LB)/upper bound (UB)) with a maximum of 1,429 µg/kg. The mean concentration in peanut butter was 1.47/1.92 µg/kg (LB/UB) with a maximum of 407 µg/kg. Peanut oil was not included since all data were left-censored and the ML does not apply for oil. Exposure was calculated for a 'Current ML' and 'Increased ML' scenario, and mean chronic exposure estimates for consumers only, amounted to 0.04-2.74 ng/kg body weight (bw) per day and 0.07-4.28 ng/kg bw per day, respectively. The highest exposures were calculated for adolescents and other children. The CONTAM Panel used the cancer potencies estimated by the Joint FAO/WHO Expert Committee on Food Additives for the risk characterisation. Under the scenario of the current ML, the cancer risk was estimated to range between 0.001 and 0.213 aflatoxin-induced cancers per 100,000 person years. Under the scenario of the increased ML, it ranged between 0.001 and 0.333 aflatoxin-induced cancers per 100,000 person years. Comparing these data calculated under the current ML scenario with the yearly excess cancer risk of 0.014 shows a higher risk for consumers of peanuts and peanut butter in some surveys. The calculated cancer risks indicate that an increase of the ML would further increase the risk by a factor of 1.6-1.8.

Re-evaluation of potassium nitrite (E 249) and sodium nitrite (E 250) as food additives.

The Panel on Food Additives and Nutrient Sources added to Food (ANS) provided a scientific opinion re-evaluating the safety of potassium nitrite (E 249) and sodium nitrite (E 250) when used as food additives. The ADIs established by the SCF (1997) and by JECFA (2002) for nitrite were 0-0.06 and 0-0.07 mg/kg bw per day, respectively. The available information did not indicate in vivo genotoxic potential for sodium and potassium nitrite. Overall, an ADI for nitrite per se could be derived from the available repeated dose toxicity studies in animals, also considering the negative carcinogenicity results. The Panel concluded that an increased methaemoglobin level, observed in human and animals, was a relevant effect for the derivation of the ADI. The Panel, using a BMD approach, derived an ADI of 0.07 mg nitrite ion/kg bw per day. The exposure to nitrite resulting from its use as food additive did not exceed this ADI for the general population, except for a slight exceedance in children at the highest percentile. The Panel assessed the endogenous formation of nitrosamines from nitrites based on the theoretical calculation of the NDMA produced upon ingestion of nitrites at the ADI and estimated a MoE > 10,000. The Panel estimated the MoE to exogenous nitrosamines in meat products to be < 10,000 in all age groups at high level exposure. Based on the results of a systematic review, it was not possible to clearly discern nitrosamines produced from the nitrite added at the authorised levels, from those found in the food matrix without addition of external nitrite. In epidemiological studies there was some evidence to link (i) dietary nitrite and gastric cancers and (ii) the combination of nitrite plus nitrate from processed meat and colorectal cancers. There was evidence to link preformed NDMA and colorectal cancers.

Clasificación de dengue hemorrágico utilizando árboles de decisión en la fase temprana de la enfermedad / Classification of dengue hemorrhagic fever using decision trees in the early phase of the disease

Introducción: el dengue es una enfermedad viral con comportamiento epidémico, a su inicio no es posible saber qué pacientes evolucionarán desfavorablemente, sin embargo, pueden presentar signos de alarma que anuncian deterioro clínico. Objetivo: aplicar la técnica de árboles de decisión a la búsqueda de signos de alarma de gravedad en la fase temprana de la enfermedad. Métodos: la muestra de estudio la constituyeron 230 pacientes ingresados con dengue en el Instituto de Medicina Tropical "Pedro Kourí" en 2001. Las variables consideradas para la clasificación fueron los signos, síntomas y exámenes de laboratorio al tercer día de evolución de la enfermedad. Se aplicó el algoritmo de árboles de clasificación y regresión utilizando el índice de Gini. Se consideraron diferentes matrices de pérdida para mejorar la sensibilidad. Resultados: el algoritmo ARC, correspondiente a la mejor pérdida, tuvo una sensibilidad de 98,68 por ciento y error global de 0,36. Sin considerar pérdida, el árbol resultante obtuvo una sensibilidad de 74 por ciento con un error de 0,25. En ambos casos las variables de mayor importancia fueron plaqueta y hemoglobina. Conclusiones: se proponen reglas de decisión con alta sensibilidad y valor predictivo negativo de utilidad en la práctica clínica. Las variables de laboratorio resultan tener mayor importancia que las clínicas para discriminar las formas clínicas de dengue.

Introduction: dengue is a viral disease with endemic behavior. At the beginning of the illness it is not possible to know which patients will have an unfavorable evolution and develop a severe form of dengue. However, some warning symptoms and signs may be present. Objective: to apply decision tree techniques to the exploration of signs of severity in the early phase of the illness. Methods: the study sample was made up of 230 patients admitted with dengue to "Pedro Kourí" Institute of Tropical Medicine in 2001. The variables considered for the classification were the signs, symptoms and laboratory exams on the third day of evolution of the illness. The algorithm of classification and regression trees using the Gini's index was applied. Different loss matrices to improve the sensitivity were considered. Results: the algorithm CART, corresponding to the best loss, had a sensitivity of 98.68 percent and global error of 0.36. Without considering loss, it obtained its sensitivity reached 74 percent with an error of 0.25. In both cases, the most important variables were platelets and hemoglobin. Conclusions: the study submitted rules of decision with high sensitivity and negative predictive value of utility in the clinical practice. The laboratory variables resulted more important from the informational viewpoint than the clinical ones to discriminate clinical forms of dengue.