Children with lower SES suffering childhood leukemia do not seem to equally enjoy the impressive recent survival gains. Special health policy strategies and increased awareness of health providers might minimize the effects of socioeconomic disparities.

Autoimmune diseases (ADs) are rapidly increasing worldwide and accumulating data support a key role of disrupted metabolism in ADs. This study aimed to identify an improved combination of Total Fatty Acids (TFAs) biomarkers as a predictive factor for the presence of autoimmune diseases. A retrospective nested case-control study was conducted in 403 individuals. In the case group, 240 patients diagnosed with rheumatoid arthritis, thyroid disease, multiple sclerosis, vitiligo, psoriasis, inflammatory bowel disease, and other AD were included and compared to 163 healthy individuals. Targeted metabolomic analysis of serum TFAs was performed using GC-MS, and 28 variables were used as input for the predictive models. The primary analysis identified 12 variables that were statistically significantly different between the two groups, and metabolite-metabolite correlation analysis revealed 653 significant correlation coefficients with 90% level of significance (p < 0.05). Three predictive models were developed, namely (a) a logistic regression based on Principal Component Analysis (PCA), (b) a straightforward logistic regression model and (c) an Artificial Neural Network (ANN) model. PCA and straightforward logistic regression analysis, indicated reasonably well adequacy (74.7 and 78.9%, respectively). For the ANN, a model using two hidden layers and 11 variables was developed, resulting in 76.2% total predictive accuracy. The models identified important biomarkers: lauric acid (C12:0), myristic acid (C14:0), stearic acid (C18:0), lignoceric acid (C24:0), palmitic acid (C16:0) and heptadecanoic acid (C17:0) among saturated fatty acids, Cis-10-pentadecanoic acid (C15:1), Cis-11-eicosenoic acid (C20:1n9), and erucic acid (C22:1n9) among monounsaturated fatty acids and the Gamma-linolenic acid (C18:3n6) polyunsaturated fatty acid. The metabolic pathways of the candidate biomarkers are discussed in relation to ADs. The findings indicate that the metabolic profile of serum TFAs is associated with the presence of ADs and can be an adjunct tool for the early diagnosis of ADs.

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The role of reproductive factors, such as parental age, in the pathogenesis of childhood leukemias is being intensively examined; the results of individual studies are controversial. This meta-analysis aims to quantitatively synthesize the published data on the association between parental age and risk of two major distinct childhood leukemia types in the offspring. Eligible studies were identified and pooled relative risk (RR) estimates were calculated using random-effects models, separately for childhood acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). Subgroup analyses were performed by study design, geographical region, adjustment factors; sensitivity analyses and meta-regression analyses were also undertaken. 77 studies (69 case-control and eight cohort) were deemed eligible. Older maternal and paternal age were associated with increased risk for childhood ALL (pooled RR = 1.05, 95 % CI 1.01-1.10; pooled RR = 1.04, 95 % CI 1.00-1.08, per 5 year increments, respectively). The association between maternal age and risk of childhood AML showed a U-shaped pattern, with symmetrically associated increased risk in the oldest (pooled RR = 1.23, 95 % CI 1.06-1.43) and the youngest (pooled RR = 1.23, 95 % CI 1.07-1.40) extremes. Lastly, only younger fathers were at increased risk of having a child with AML (pooled RR = 1.28, 95 % CI 1.04-1.59). In conclusion, maternal and paternal age represents a meaningful risk factor for childhood leukemia, albeit of different effect size by leukemia subtype. Genetic and socio-economic factors may underlie the observed associations. Well-adjusted studies, scheduled by large consortia, are anticipated to satisfactorily address methodological issues, whereas the potential underlying genetic mechanisms should be elucidated by basic research studies.

Fatty acids (FAs) play critical roles in health and disease. The detection of FA imbalances through metabolomics can provide an overview of an individual’s health status, particularly as regards chronic inflammatory disorders. In this study, we aimed to establish sensitive reference value ranges for targeted plasma FAs in a well-defined population of healthy adults. Plasma samples were collected from 159 participants admitted as outpatients. A total of 24 FAs were analyzed using gas chromatography-mass spectrometry, and physiological values and 95% reference intervals were calculated using an approximate method of analysis. The differences among the age groups for the relative levels of stearic acid (P=0.005), the omega-6/omega-3 ratio (P=0.027), the arachidonic acid/eicosapentaenoic acid ratio (P<0.001) and the linoleic acid-produced dihomo-gamma-linolenic acid (P=0.046) were statistically significant. The majority of relative FA levels were higher in males than in females. The levels of myristic acid (P=0.0170) and docosahexaenoic acid (P=0.033) were signifi-cantly different between the sexes. The reference values for the FAs examined in this study represent a baseline for further studies examining the reproducibility of this methodology and sensitivities for nutrient deficiency detection and investigating the biochemical background of pathological conditions. The application of these values to clinical practice will allow for the discrimination between health and disease and contribute to early prevention and treatment.