Quantitative electroencephalography reveals different physiological profiles between benign and remitting-relapsing multiple sclerosis patients

BACKGROUND A possible method of finding physiological markers of multiple sclerosis (MS) is the application of EEG quantification (QEEG) of brain activity when the subject is stressed by the demands of a cognitive task. In particular, modulations of the spectral content that take place in the EEG of patients with multiple sclerosis remitting-relapsing (RRMS) and benign multiple sclerosis (BMS) during a visuo-spatial task need to be observed. METHODS The sample consisted of 19 patients with RRMS, 10 with BMS, and 21 control subjects. All patients were free of medication and had not relapsed within the last month. The power spectral density (PSD) of different EEG bands was calculated by Fast-Fourier-Transformation (FFT), those analysed being delta, theta, alpha, beta and gamma. Z-transformation was performed to observe individual profiles in each experimental group for spectral modulations. Lastly, correlation analyses was performed between QEEG values and other variables from participants in the study (age, EDSS, years of evolution and cognitive performance). RESULTS Nearly half (42%) the RRMS patients showed a statistically significant increase of two or more standard deviations (SD) compared to the control mean value for the beta-2 and gamma bands (F = 2.074, p = 0.004). These alterations were localized to the anterior regions of the right hemisphere, and bilaterally to the posterior areas of the scalp. None of the BMS patients or control subjects had values outside the range of +/- 2 SD. There were no significant correlations between these values and the other variables analysed (age, EDSS, years of evolution or behavioural performance). CONCLUSION During the attentional processing, changes in the high EEG spectrum (beta-2 and gamma) in MS patients exhibit physiological alterations that are not normally detected by spontaneous EEG analysis. The different spectral pattern between pathological and controls groups could represent specific changes for the RRMS patients, indicative of compensatory mechanisms or cortical excitatory states representative of some phases during the RRMS course that are not present in the BMS group.

The involvement of thaumatin-like proteins in plant food cross-reactivity: a multicenter study using a specific protein microarray

Cross-reactivity of plant foods is an important phenomenon in allergy, with geographical variations with respect to the number and prevalence of the allergens involved in this process, whose complexity requires detailed studies. We have addressed the role of thaumatin-like proteins (TLPs) in cross-reactivity between fruit and pollen allergies. A representative panel of 16 purified TLPs was printed onto an allergen microarray. The proteins selected belonged to the sources most frequently associated with peach allergy in representative regions of Spain. Sera from two groups of well characterized patients, one with allergy to Rosaceae fruit (FAG) and another against pollens but tolerant to food-plant allergens (PAG), were obtained from seven geographical areas with different environmental pollen profiles. Cross-reactivity between members of this family was demonstrated by inhibition assays. Only 6 out of 16 purified TLPs showed noticeable allergenic activity in the studied populations. Pru p 2.0201, the peach TLP (41%), chestnut TLP (24%) and plane pollen TLP (22%) proved to be allergens of probable relevance to fruit allergy, being mainly associated with pollen sensitization, and strongly linked to specific geographical areas such as Barcelona, Bilbao, the Canary Islands and Madrid. The patients exhibited >50% positive response to Pru p 2.0201 and to chestnut TLP in these specific areas. Therefore, their recognition patterns were associated with the geographical area, suggesting a role for pollen in the sensitization of these allergens. Finally, the co-sensitizations of patients considering pairs of TLP allergens were analyzed by using the co-sensitization graph associated with an allergen microarray immunoassay. Our data indicate that TLPs are significant allergens in plant food allergy and should be considered when diagnosing and treating pollen-food allergy.

Nutrient patterns and their food sources in an International Study Setting: report from the EPIC study.

BACKGROUND Compared to food patterns, nutrient patterns have been rarely used particularly at international level. We studied, in the context of a multi-center study with heterogeneous data, the methodological challenges regarding pattern analyses. METHODOLOGY/PRINCIPAL FINDINGS We identified nutrient patterns from food frequency questionnaires (FFQ) in the European Prospective Investigation into Cancer and Nutrition (EPIC) Study and used 24-hour dietary recall (24-HDR) data to validate and describe the nutrient patterns and their related food sources. Associations between lifestyle factors and the nutrient patterns were also examined. Principal component analysis (PCA) was applied on 23 nutrients derived from country-specific FFQ combining data from all EPIC centers (N = 477,312). Harmonized 24-HDRs available for a representative sample of the EPIC populations (N = 34,436) provided accurate mean group estimates of nutrients and foods by quintiles of pattern scores, presented graphically. An overall PCA combining all data captured a good proportion of the variance explained in each EPIC center. Four nutrient patterns were identified explaining 67% of the total variance: Principle component (PC) 1 was characterized by a high contribution of nutrients from plant food sources and a low contribution of nutrients from animal food sources; PC2 by a high contribution of micro-nutrients and proteins; PC3 was characterized by polyunsaturated fatty acids and vitamin D; PC4 was characterized by calcium, proteins, riboflavin, and phosphorus. The nutrients with high loadings on a particular pattern as derived from country-specific FFQ also showed high deviations in their mean EPIC intakes by quintiles of pattern scores when estimated from 24-HDR. Center and energy intake explained most of the variability in pattern scores. CONCLUSION/SIGNIFICANCE The use of 24-HDR enabled internal validation and facilitated the interpretation of the nutrient patterns derived from FFQs in term of food sources. These outcomes open research opportunities and perspectives of using nutrient patterns in future studies particularly at international level.

Gene-lifestyle interaction and type 2 diabetes: the EPIC interact case-cohort study.

BACKGROUND Understanding of the genetic basis of type 2 diabetes (T2D) has progressed rapidly, but the interactions between common genetic variants and lifestyle risk factors have not been systematically investigated in studies with adequate statistical power. Therefore, we aimed to quantify the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention. METHODS AND FINDINGS The InterAct study includes 12,403 incident T2D cases and a representative sub-cohort of 16,154 individuals from a cohort of 340,234 European participants with 3.99 million person-years of follow-up. We studied the combined effects of an additive genetic T2D risk score and modifiable and non-modifiable risk factors using Prentice-weighted Cox regression and random effects meta-analysis methods. The effect of the genetic score was significantly greater in younger individuals (p for interaction  = 1.20×10-4). Relative genetic risk (per standard deviation [4.4 risk alleles]) was also larger in participants who were leaner, both in terms of body mass index (p for interaction  = 1.50×10-3) and waist circumference (p for interaction  = 7.49×10-9). Examination of absolute risks by strata showed the importance of obesity for T2D risk. The 10-y cumulative incidence of T2D rose from 0.25% to 0.89% across extreme quartiles of the genetic score in normal weight individuals, compared to 4.22% to 7.99% in obese individuals. We detected no significant interactions between the genetic score and sex, diabetes family history, physical activity, or dietary habits assessed by a Mediterranean diet score. CONCLUSIONS The relative effect of a T2D genetic risk score is greater in younger and leaner participants. However, this sub-group is at low absolute risk and would not be a logical target for preventive interventions. The high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.