Differences in the haematological profile of healthy 70 year old men and women: normal ranges with confirmatory factor analysis

<p>Abstract</p> <p>Background</p> <p>Reference ranges are available for different blood cell counts. These ranges treat each cell type independently and do not consider possible correlations between cell types.</p> <p>Methods</p> <p>Participants were identified from the Community Health Index as survivors of the 1947 Scottish Mental Survey, all born in 1936, who were resident in Lothian (potential n = 3,810) and invited to participate in the study. Those who consented were invited to attend a Clinical Research Facility where, amongst other assessments, blood was taken for full blood count. First we described cell count data and bivariate correlations. Next we performed principal components analysis to identify common factors. Finally we performed confirmatory factor analysis to evaluate suitable models explaining relationships between cell counts in men and women.</p> <p>Results</p> <p>We examined blood cell counts in 1027 community-resident people with mean age 69.5 (range 67.6-71.3) years. We determined normal ranges for each cell type using Q-Q plots which showed that these ranges were significantly different between men and women for all cell types except basophils. We identified three principal components explaining around 60% of total variance of cell counts. Varimax rotation indicated that these could be considered as erythropoietic, leukopoietic and thrombopoietic factors. We showed that these factors were distinct for men and women by confirmatory factor analysis: in men neutrophil count was part of a 'thrombopoietic' trait whereas for women it was part of a 'leukopoietic' trait.</p> <p>Conclusions</p> <p>First, normal ranges for haematological indices should be sex-specific; at present this only pertains to those associated with erythrocytes. Second, differences between individuals across a range of blood cell counts can be explained to a considerable extent by three major components, but these components are not the same in men and women.</p

Combined effects of cigarette smoking, gene polymorphisms and methylations of tumor suppressor genes on non small cell lung cancer: a hospital-based case-control study in China

<p>Abstract</p> <p>Background</p> <p>Cigarette smoking is the most established risk factor, and genetic variants and/or gene promoter methylations are also considered to play an essential role in development of lung cancer, but the pathogenesis of lung cancer is still unclear.</p> <p>Methods</p> <p>We collected the data of 150 cases and 150 age-matched and sex-matched controls on a Hospital-Based Case-Control Study in China. Face to face interviews were conducted using a standardized questionnaire. Gene polymorphism and methylation status were measured by RFLP-PCR and MSP, respectively. Logistic regressive model was used to estimate the odds ratios (OR) for different levels of exposure.</p> <p>Results</p> <p>After adjusted age and other potential confounding factors, smoking was still main risk factor and significantly increased 3.70-fold greater risk of NSCLC as compared with nonsmokers, and the ORs across increasing levels of pack years were 1, 3.54, 3.65 and 7.76, which the general dose-response trend was confirmed. Our striking findings were that the risk increased 5.16, 8.28 and 4.10-fold, respectively, for NSCLC with promoter hypermethylation of the <it>p16</it>, <it>DAPK </it>or <it>RARβ </it>gene in smokers with <it>CYP1A1 </it>variants, and the higher risk significantly increased in smokers with null <it>GSTM1 </it>and the OR was 17.84 for NSCLC with <it>p16 </it>promoter hypermethylation, 17.41 for <it>DAPK</it>, and 8.18 for <it>RARβ </it>in smokers with null <it>GSTM1 </it>compared with controls (all p < 0.01).</p> <p>Conclusion</p> <p>Our study suggests the strong combined effects of cigarette smoke, <it>CYP1A1 </it>and <it>GSTM1 </it>Polymorphisms, hypermethylations of <it>p16</it>, <it>DAPK </it>and <it>RARβ </it>promoters in NSCLC, implying complex pathogenesis of NSCLC should be given top priority in future research.</p

The distribution of standard deviations applied to high throughput screening

High throughput screening (HTS) assesses compound libraries for “activity” using target assays. A subset of HTS data contains a large number of sample measurements replicated a small number of times providing an opportunity to introduce the distribution of standard deviations (DSD). Applying the DSD to some HTS data sets revealed signs of bias in some of the data and discovered a sub-population of compounds exhibiting high variability which may be difficult to screen. In the data examined, 21% of 1189 such compounds were pan-assay interference compounds. This proportion reached 57% for the most closely related compounds within the sub-population. Using the DSD, large HTS data sets can be modelled in many cases as two distributions: a large group of nearly normally distributed “inactive” compounds and a residual distribution of “active” compounds. The latter were not normally distributed, overlapped inactive distributions – on both sides –, and were larger than typically assumed. As such, a large number of compounds are being misclassified as “inactive” or are invisible to current methods which could become the next generation of drugs. Although applied here to HTS, it is applicable to data sets with a large number of samples measured a small number of times