How subchronic and chronic health effects can be neglected for GMOs, pesticides or chemicals

Abstract

Chronic health effects are increasing in the world such as cancers, hormonal, reproductive, nervous, or immune diseases, even in young people. During regulatory toxicological subchronic tests to prevent these on mammalian health, prior commercialization of chemicals, including pesticides and drugs, or GMOs, some statistically significant findings may be revealed. This discussion is about the need to investigate the relevant criteria to consider those as biologically significant. The sex differences and the non linear dose or time related effects should be considered in contrast to the claims of a Monsanto-supported expert panel about a GMO, the MON 863 Bt maize, but also for pesticides or drugs, in particular to reveal hormone-dependent diseases and first signs of toxicities.

Keywords: GMO; MON 863; Pesticides; side effects; toxicological tests..

Figure 1

Comparison of regulatory toxicity tests generally performed in vivo on mammals, for instance with rats, the most used model, before commercialization of various products. These are GMOs used for food or feed, pesticides, drugs, or the best tested chemicals. The choice of how to apply standards is made by scientific commissions of regulatory instances. This figure does not include reproductive, developmental or trans-generational tests that are not requested for commercialized GMOs for food or feed. Nutritional tests are not represented either because they do not require blood analyses, which are very informative on health secondary effects. Some mammalian nutritional tests are performed with pigs or cows, for instance for GMOs, and may last longer with fewer animals. Subchronic toxicity tests are in the last case performed, if any, only with rats for most GMOs. Then it is only with 10 animals fully assessed on 20 for each of two doses, and per sex. There are 3 mammalian species used for other products. This is to measure short-term effects. The so-called chronic tests (lasting more than 3 months) give more chances to reveal metabolic, nervous, immune, hormonal or cancer diseases. They are widely performed for pesticides and drugs and for some chemicals over a certain production, but not for actual commercialized GMOs released in the environment (1995-2009). This is a matter of debate, since 99.9% of those are genetically modified to contain new pesticide residues that they tolerate (ex. Roundup Ready soya) or that they produce (ex. insecticides Bt in maize, that are newly modified proteic toxins). (d: day; m: month; y: year).

Figure 2

A. Principal Component Analysis for liver parameters in all rats of the MON 863 experiment. It was performed according to Hotelling in order to study the scattering of the different factors. The scheme obtained for parameters at week 14 explains 42.42% of the total data variability (inertia) expressed on 2 axes (32.01% for factor 1; 10.41% for factor 2), scale d=2. This demonstrates the clear separation of parameters values according to sex. B. Principal Component Analysis for kidney parameters in all rats of the MON 863 experiment. The scheme obtained for parameters at week 14 explains 47.73% of the total data variability (inertia) expressed on 2 axes (26.95% for factor 1; 20.78% for factor 2), scale d=5. This demonstrates the clear separation of parameters values according to sex.

Figure 2

A. Principal Component Analysis for liver parameters in all rats of the MON 863 experiment. It was performed according to Hotelling in order to study the scattering of the different factors. The scheme obtained for parameters at week 14 explains 42.42% of the total data variability (inertia) expressed on 2 axes (32.01% for factor 1; 10.41% for factor 2), scale d=2. This demonstrates the clear separation of parameters values according to sex. B. Principal Component Analysis for kidney parameters in all rats of the MON 863 experiment. The scheme obtained for parameters at week 14 explains 47.73% of the total data variability (inertia) expressed on 2 axes (26.95% for factor 1; 20.78% for factor 2), scale d=5. This demonstrates the clear separation of parameters values according to sex.