BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
BACKGROUND: 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. METHODS: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. RESULTS: 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. CONCLUSIONS: 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.
Authors: G P Bagnara; L Bonsi; P Strippoli; F Bonifazi; R Tonelli; S D'Addato; R Paganelli; E Scala; U Fagiolo; D Monti; A Cossarizza; M Bonafé; C Franceschi Journal: J Gerontol A Biol Sci Med Sci Date: 2000-02 Impact factor: 6.053
Authors: Linda A Kirschenbaum; Daryl Adler; Mark E Astiz; Rajat S Barua; Dhanonjoy Saha; Eric C Rackow Journal: Shock Date: 2002-06 Impact factor: 3.454
Authors: Riccardo E Marioni; Sonia Shah; Allan F McRae; Brian H Chen; Elena Colicino; Sarah E Harris; Jude Gibson; Anjali K Henders; Paul Redmond; Simon R Cox; Alison Pattie; Janie Corley; Lee Murphy; Nicholas G Martin; Grant W Montgomery; Andrew P Feinberg; M Daniele Fallin; Michael L Multhaup; Andrew E Jaffe; Roby Joehanes; Joel Schwartz; Allan C Just; Kathryn L Lunetta; Joanne M Murabito; John M Starr; Steve Horvath; Andrea A Baccarelli; Daniel Levy; Peter M Visscher; Naomi R Wray; Ian J Deary Journal: Genome Biol Date: 2015-01-30 Impact factor: 13.583
Authors: Riccardo E Marioni; Sonia Shah; Allan F McRae; Stuart J Ritchie; Graciela Muniz-Terrera; Sarah E Harris; Jude Gibson; Paul Redmond; Simon R Cox; Alison Pattie; Janie Corley; Adele Taylor; Lee Murphy; John M Starr; Steve Horvath; Peter M Visscher; Naomi R Wray; Ian J Deary Journal: Int J Epidemiol Date: 2015-01-22 Impact factor: 7.196
Authors: Catharine R Gale; Riccardo E Marioni; Sarah E Harris; John M Starr; Ian J Deary Journal: Clin Epigenetics Date: 2018-08-03 Impact factor: 6.551