A picture of the population dynamics buy Cisplatin (the changing genotypic landscape within the microbial population in the presence of antibiotics) will provide valuable insights into the aforementioned questions and contribute to the elucidation of the fundamental principles underlying how microbial pathogens evolve resistance to antimicrobial agents. Among human fungal pathogens, Candida spp. is recognized as a major challenge in public health, causing potentially life-threatening invasive infections in immunocompromised patients. Candida

spp. is the fourth most common cause of blood stream infections with a mortality rate approaching 50% in US hospitals (Zaoutis et al., 2005; Pfaller & Diekema, 2007). The species distribution among clinical Candida isolates varies depending MS-275 cost on the geographic regions, with Candida albicans (C. albicans) being

the most commonly isolated species in Candidaemia according to a 10.5-year global survey (Pfaller et al., 2010), from the lowest frequency (48.9%) in North America to the highest one (67.9%) in European; however, there is an upward trend in the frequency of isolation of non-albicans species (NAC), likely due to reduced susceptibility to antifungal agents in some NAC (Lai et al., 2008; Pfaller & Diekema, 2010; Pfaller, 2012). In the management of fungal infections, there have been significant recent advances in antifungal therapy, including the introduction of a new generation of antifungal agents, the use of combination therapy, and improved standardization of susceptibility testing; however, drug resistance still poses a challenge in the management and treatment of fungal infections (Kanafani & Perfect, 2008; Chapeland-Leclerc et al., 2010; Pfaller, 2012). In the United States, the treatment associated with Candidemia cost more than US $1 billion annually (Beck-Sague & Jarvis, 1993; Miller et al., 2001). The high mortality rate, the rapid Megestrol Acetate development of drug resistance, and the high cost associated with therapeutic treatment make Candida spp. a medically important group of fungal pathogens. Antimicrobial resistance has become increasingly

important in antifungal therapy. Resistance to nearly all major antifungal agents has been reported in clinical isolates of Candida spp. (Marr et al., 1998; Sanglard & Odds, 2002; Katiyar et al., 2006), which poses a major public health concern as the arsenal of antifungal agents is limited. Single nucleotide polymorphism, loss-of-heterozygosity (LOH) and gross chromosomal rearrangements have been found to be important processes in the development of drug resistance (Selmecki et al., 2006, 2008, 2009). Research within the past couple of decades has identified numerous drug resistance mechanisms. Mutations in drug targets, such as ERG11 in fluconazole resistance and FKS1 in echinocandin resistance (Loffler et al., 1997; Lamb et al., 2000; White et al., 2002; Park et al.

In general, the principal events that shape a bacterial chromosome are gene duplication, horizontal gene transfer, gene loss and chromosomal rearrangements (Andersson & Hughes, 2009). Of these, gene duplication seems to contribute only modestly, horizontal gene transfer seem to be quite important, and gene deletion and genetic drift, which are countered by positive selection, probably vary with ecological niche and the type of chromosome rearrangements. Of these three contributions, it is likely that gene deletion and genetic drift are the most related to evolutionary time because such events are largely dependent on repeated sequences and mobile elements (Ventura et al., 2007). However, up

to the present, no reliable method of tracing the evolutionary development of chromosomes in terms of these various BTK inhibitor events has been successful. Nonetheless, there is evidence to suggest that the Actinomycetales might have enough coherence across their chromosomes to allow some insights into this problem. Chromosome diversity and similarity within the Actinomycetales are made more interesting because of the topological diversity of their chromosomes; specifically, some families seem to have a preference for linear chromosomes, whereas the majority prefer circular chromosomes (Lin et al., 1993; Reeves et al., 1998; Redenbach et al., 2000; Bentley et al., 2002;

Goshi et al., 2002; Ikeda et al., 2003; Bentley & Parkhill, 2004; McLeod et al., 2006; Ohnishi et al., 2008). In fact, the frequency of linear chromosomes GSK269962 chemical structure within the Actinomycetales is high compared with all other orders in the kingdom Bacteria. What evolutionary factors lead to a linear vs. a circular chromosome remain open to debate (Chen, 1996; Chen et al., 2002; Qin & Cohen, 2002), but it is important to realize that linearity vs. circularity does not seem to affect chromosome structure dramatically.

Here, we will examine the chromosome diversity and similarity of the Actinomycetales, as displayed by the complete chromosome sequences available, and suggest that changes vary PLEK2 across the chromosome (Ventura et al., 2007; Hsaio & Kirby, 2008; Kirby et al., 2008). As the number of chromosome sequences available for the Actinomycetales increases and the genera from which they come broadens, it becomes important to try and understand how chromosome evolution in this order has occurred and is occurring. This is not least because over 80% of the world’s antibiotics originally were identified as being produced by a member of the Actinomycetales (Hopwood, 2006). The majority of prokaryote chromosomes are believed to be circular. However, it can also be stated that biochemical proof of the circularity of many of these chromosomes is lacking and that they are circular by default. This remains true for the Actinobacteria and the Actinomycetales.

In general, the principal events that shape a bacterial chromosome are gene duplication, horizontal gene transfer, gene loss and chromosomal rearrangements (Andersson & Hughes, 2009). Of these, gene duplication seems to contribute only modestly, horizontal gene transfer seem to be quite important, and gene deletion and genetic drift, which are countered by positive selection, probably vary with ecological niche and the type of chromosome rearrangements. Of these three contributions, it is likely that gene deletion and genetic drift are the most related to evolutionary time because such events are largely dependent on repeated sequences and mobile elements (Ventura et al., 2007). However, up

to the present, no reliable method of tracing the evolutionary development of chromosomes in terms of these various find protocol events has been successful. Nonetheless, there is evidence to suggest that the Actinomycetales might have enough coherence across their chromosomes to allow some insights into this problem. Chromosome diversity and similarity within the Actinomycetales are made more interesting because of the topological diversity of their chromosomes; specifically, some families seem to have a preference for linear chromosomes, whereas the majority prefer circular chromosomes (Lin et al., 1993; Reeves et al., 1998; Redenbach et al., 2000; Bentley et al., 2002;

Goshi et al., 2002; Ikeda et al., 2003; Bentley & Parkhill, 2004; McLeod et al., 2006; Ohnishi et al., 2008). In fact, the frequency of linear chromosomes selleck products within the Actinomycetales is high compared with all other orders in the kingdom Bacteria. What evolutionary factors lead to a linear vs. a circular chromosome remain open to debate (Chen, 1996; Chen et al., 2002; Qin & Cohen, 2002), but it is important to realize that linearity vs. circularity does not seem to affect chromosome structure dramatically.

Here, we will examine the chromosome diversity and similarity of the Actinomycetales, as displayed by the complete chromosome sequences available, and suggest that changes vary Casein kinase 1 across the chromosome (Ventura et al., 2007; Hsaio & Kirby, 2008; Kirby et al., 2008). As the number of chromosome sequences available for the Actinomycetales increases and the genera from which they come broadens, it becomes important to try and understand how chromosome evolution in this order has occurred and is occurring. This is not least because over 80% of the world’s antibiotics originally were identified as being produced by a member of the Actinomycetales (Hopwood, 2006). The majority of prokaryote chromosomes are believed to be circular. However, it can also be stated that biochemical proof of the circularity of many of these chromosomes is lacking and that they are circular by default. This remains true for the Actinobacteria and the Actinomycetales.

MT Ivan: Exchange of E132, E147 or H168 in MT Ivan led to a complete loss of activity and zinc was not detected in the enzyme (see the asterisks in Fig. 2a). Hence, we click here believe that these amino acids are the zinc-binding partners. The exchange of all other amino acids tested did not result in a loss of zinc. Potential adjacent binding partners of E132, E147 or H168 were E133, H146 and H166. The activity of the enzymes mutated in these positions

was significantly reduced with vanillate as a substrate. MT Iver: Exchange of the amino acids D83, C111 or C151, respectively, led to a complete loss of the activity (see the asterisks in Fig. 2b); in all these mutants, the zinc content was <0.05 mol mol−1 enzyme, whereas the zinc content of the native enzyme was 1 mol mol−1. This result indicates that the

three amino acids involved in zinc binding of MT Iver are one aspartate and two cysteine residues. C151 is flanked by two potential find protocol zinc-binding amino acids: D150 and H152. To exclude that one of these amino acids rather than C151 is involved in zinc binding, D150 and H152 were also exchanged in separate experiments and the activity and the zinc content were determined. In these recombinant enzymes, the zinc content was between 0.96 and 1.03 mol mol−1 enzyme, indicating that none of these amino acids is involved in zinc binding. The activity of the latter mutants with veratrol as a substrate, however, was significantly reduced to <5% of the activity of the native enzyme. When C151 was exchanged for aspartate as a potential zinc-binding partner, the zinc content was reduced to about 0.07 mol mol−1 enzyme and no activity was detected. In separate experiments, H152 or D150 was exchanged for cysteine and simultaneously C151 for alanine to elucidate the impact of the position of the zinc-binding cysteine. In these mutants, neither zinc binding nor activity was detected.

These results reveal that not only the amino acid position but also the kind of amino acid is important. The exchange of single acidic amino acids close to the zinc-binding motif for alanine resulted in a significant loss of activity to ≤60% (Fig. 2b). The mutants obtained show partially Flucloronide restricted substrate spectra (data not shown). All these mutants studied still contained approximately 1 mol zinc mol−1 protein. In the MT I, zinc is believed to have a catalytic rather than a structural function. This assumption is based on (1) the kind of amino acid as a binding partner for zinc, which should be cysteine for a structural function (Auld, 2001), (2) the comparison with methanogenic corrinoid-dependent methyltransferases (Hagemeier et al., 2006) and (3) the location of the assumed zinc-binding amino acids in MT I (Fig. 3).

Omptins impact bacterial virulence by degrading or processing a number of host proteins or peptides (Haiko et al., 2009). Escherichia coli K12 OmpT was reported to efficiently degrade the AMP protamine (Stumpe et al., 1998). Other studies have shown that S. Typhimurium PgtE and Yersinia pestis Pla cleave α-helical AMPs such as C18G and human LL-37 (Guina et al., 2000; Galvan et al., 2008). CroP, the omptin of the murine enteric pathogen C. rodentium,

Cetuximab price was shown to degrade α-helical AMPs, including mCRAMP (Le Sage et al., 2009) (Fig. 1a). CroP-mediated degradation of AMPs occurred before they reached the periplasmic space and triggered a PhoPQ-mediated adaptive response. OmpT of enterohemorrhagic E. coli (EHEC) was shown to inactivate human LL-37 by cleaving it twice at dibasic sites (Thomassin et al., 2012). selleck chemicals Structures external to the bacterial cell envelope such as capsule polysaccharides (CPS), curli fimbriae,

exopolysaccharides involved in biofilm formation, and the O-polysaccharide of lipopolysaccharide play a role in AMP resistance. They are proposed to act as a decoy by binding AMPs and reducing the amount of AMPs reaching the bacterial membrane (Fig. 1b). Campos et al. (2004) reported that a K. pneumoniae CPS mutant is more sensitive to AMPs than the wild-type strain with a concomitant increase in AMP-mediated OM disruption, indicating that CPS acts as a shield against AMPs. Consistent with the cationic nature of AMPs, another study reported that only anionic CPSs decreased the bactericidal activity of AMPs (Llobet et al., 2008). A similar protective role for CPS was observed in Neisseria meningitidis. An unencapsulated serogroup B strain of N. meningitidis was more susceptible to the bacterially derived AMP polymyxin B, α- and β-defensins as well as the cathelicidins LL-37 and mCRAMP (Spinosa et al., 2007). Interestingly, sublethal concentrations of AMPs upregulated the transcription of the capsule genes in N. meningitidis, suggesting that increased capsule synthesis is a bacterial adaptation downstream of AMP sensing (Spinosa et al., 2007; Jones et al.,

2009). Bacterial exopolysaccharides are the major constituent of the extracellular biofilm matrix (Sutherland, 2001). Exopolysaccharides are most often Montelukast Sodium anionic polymers that are proposed to play a role in the resistance of bacterial biofilms to innate host defenses. For example, the β-d-manuronate and α-l-guluronate polymer alginate produced by P. aeruginosa was shown to promote the formation of interacting complexes with LL-37 (Herasimenka et al., 2005). Pseudomonas aeruginosa alginate and exopolysaccharides from other lung pathogens were reported to inhibit the bactericidal activity of LL-37, indicating that sequestration of LL-37 by exopolysaccharides lowers the concentration of AMP at its target site (Foschiatti et al., 2009).

It has been extensively debated that inflammation can exert a noxious effect on the vasculature and heart via two pathways: chronic, low-grade inflammation and an acute systemic inflammatory response. The former has been implicated in atherosclerotic processes [31], while the latter accounts for adverse cardiovascular events following severe inflammatory stimulation. Both pathways compromise cardiovascular integrity; they may trigger the progression and destabilization of inflamed vulnerable arterial plaques and subsequently lead to adverse

cardiovascular events Veliparib mouse [32]. In the presence of HIV infection, elevated levels of inflammatory and coagulation markers (IL-6 and D-dimers, respectively) are strongly associated with vascular dysfunction and increased all-cause mortality [33,34]. Thus, further insights can be obtained by including the aforementioned biomarkers in the design of studies assessing the cardiovascular risk of therapeutic interventions in patients with HIV infection. Following administration of a vaccine, the white blood cell count rises. This is a result of mobilization from the marginated pool and egress from the bone marrow [35]. Administration of the novel influenza A/H1N1 vaccine resulted in increased levels of circulating white blood cells in our group of HIV-infected patients.

The interaction of white blood cells with the selleck screening library endothelium is facilitated by adhesion molecules [36]. Thereby, selectins and cell adhesion molecules play an active role in leucocyte rolling on the endothelial lining and subsequent transendothelial migration. U0126 The soluble isoforms of adhesion molecules, such as ICAM-1, vascular cell adhesion molecule-1 (VCAM-1) and selectins, result from proteolytic cleavage and ‘shedding’ from the cell surface; numerous studies have linked increased plasma levels of their soluble forms

to higher inflammatory status and an increase in the number of subsequent adverse events [37,38]. Nevertheless, the kinetics of adhesion molecules in the first few hours following an inflammatory insult are largely unknown; moreover, diurnal variation should be accounted for [39]. In our study, a paradoxical drop in the sICAM-1 level was noted following vaccination, but not in the control group. Relapsing responses of sICAM-1 levels, i.e. an initial fall with a subsequent increase, have previously been reported in systemic inflammatory states [40]. This may reflect a compensatory mechanism following the acute stimulus of vaccination and merits further research. In ‘healthy’ individuals, IL-6 is the major regulator of the acute-phase response. Combined with an increase in IL-1 levels, it results in CRP up-regulation. Apart from being an inflammatory mediator, IL-6 also participates in immune responses. It acts directly on B cells and induces immunoglobulin M, G and A production by promoting the differentiation of B cells into immunoglobulin-secreting cells.

2004, Schernewski & Neumann 2005, Neumann & Schernewski 2005, 2008); however, validation of the model did not include validation of the pCO2 data. Here, a simple carbon

cycle has been included in the model to deal specifically with the pCO2 at the sea surface. This was accomplished by the addition to the model of the variable CT  , the total CO2 inorganic NVP-BEZ235 in vivo carbon ( eq. (33)). The equations for CT   are similar to those for other nutrients (phosphate, nitrate etc.). The exchange process at the air-sea border, i.e. the CO2 flux, is calculated according to equation(1) CTflux=k×k0×(pCO2−pCO2atm),where k   is the gas-transfer velocity, k  0 the CO2 solubility constant, pCO2 the surface-water CO2 partial pressure, and pCO2atm the atmospheric CO2 partial pressure. The pCO2atm was described as a function of the Julian day using the seasonality of the CO2 molar fraction in dry air ( Schneider 2011) and taking into account water vapour saturation at the sea surface. pCO2atm ranges from 365 to 392 μatm during the year. The two CO2 system parameters applied to calculate pCO2 were total CO2CT Talazoparib cost and total alkalinity AT. The CO2 solubility constant k0 was calculated according to the method of Weiss (1974). To calculate pCO2 at the sea surface, the value-iteration method based on the equations of DOE (1994) was

used. These calculations entailed the use of thermodynamic equilibrium constants, after Dickson & Millero (1987). The gas-transfer velocity k was calculated according to the method of Liss & Merlivat (1986). CT was determined from the model ( eq. (33)) and AT was assumed to be constant. For the latter, Methocarbamol the mean AT (1580 μmol kg−1, as determined by Schneider et al. (2003)) for the eastern Gotland

Sea was used. The assumption of constant alkalinity is justified because calcifying organisms are virtually absent in the central Baltic ( Tyrrell et al. 2008) and thus no significant internal changes in AT occur except the negligible AT increase by nitrate assimilation. Nevertheless, AT variations are observed in the central Baltic (see ICES dataset http://www.ices.dk/ocean), but these are due to the lateral mixing of water masses which have different background AT ( Hjalmarsson et al. 2008). However, the seasonal changes in pCO2 are almost independent of the background AT level. Furthermore, it is not possible to take into account changes in the alkalinity due to the lateral fluxes simply by adjusting it to observations, as at the same time one should adjust CT and other biochemical parameters, and that would render all the results of a one-dimensional model meaningless. Sensitivity tests of the model with different AT constant values were performed. The results of these tests showed that a spin-up period of 3 years was enough to adapt the model to various AT resulting in similar pCO2 values during the 4th year. Observations have shown that the elemental composition of cyanobacteria can change dramatically during the growing season.

To further explore the changes within the cortex, the segmented cortical compartment was electronically partitioned into an outer and an inner cortex, where the outer cortex covered two-thirds and the inner cortex covered one-third of the total cortical thickness. For each compartment, vBMD and volume were measured and BMC calculated from the product

of vBMD and volume. To evaluate the consistency between QCT and DXA, changes at the total hip using scans from Hologic, Selleck GSI-IX Inc. (Bedford, MA, USA; n = 57) or GE Healthcare Lunar (Waukesha, WI, USA; n = 5) DXA machines available from the subjects in the QCT study also were compared at baseline and months 12, 24, and 36. Endpoints buy PF-02341066 for this substudy included changes in total hip integral, trabecular, subcortical, and cortical vBMD and BMC from baseline and compared with placebo at months 12, 24, and 36. In addition, the outer and

inner cortex regions were assessed. Subjects had to have a baseline scan and ≥ 1 post-baseline scan analyzed by MIAF to be included in the analysis. Hip QCT scans at each annual visit for each subject were included in the analyses. There was no imputation of missing data. The percentage and absolute changes from baseline for vBMD and BMC were determined. Data analyses assessed changes over time relative to baseline for each treatment group and also compared with placebo. The percentage and absolute changes from baseline were analyzed using an analysis of covariance (ANCOVA) model including treatment and adjusting for baseline value and age strata SDHB (stratification factor). Least-squares means and 95% confidence intervals (CIs) for each treatment and for the treatment difference (denosumab — placebo) at each time point were generated. All analyses were exploratory and post hoc. P-values and CIs were not adjusted for multiplicity. This substudy included 62 postmenopausal women with osteoporosis (placebo

N = 26; denosumab N = 36). Subject demographics were balanced between treatment groups (Table 1). Most women were White/Caucasian (53.8% placebo; 61.1% denosumab), with a mean age of 74.2 years in the placebo group and 72.8 years in the denosumab group. Mean total hip integral vBMD was 216 mg/cm3 and 224 mg/cm3 for the placebo and denosumab groups, respectively, and mean total hip aBMD was 0.70 g/cm2 for the placebo group and 0.74 g/cm2 for the denosumab group. Mean total hip integral BMC as measured by QCT was 15 603 mg and 16 843 mg for the placebo and denosumab groups, respectively. At baseline, the proportion that each compartment contributed to BMC was 69% for the cortical compartment, 18% for the trabecular compartment, and 13% for the subcortical compartment.

Of these 45 patients with EGFR mutation-positive

tumors, 27 (60%) had received gefitinib and 18 (40%) carboplatin/paclitaxel. Of the 116 cytology samples, 31 (19%) were evaluable check details for EGFR mutation of which nine (29%) were EGFR mutation-positive. Of these nine patients with EGFR mutation-positive tumors, six (67%) had received gefitinib and three (33%) carboplatin/paclitaxel. A total of 20 histology samples (20%) and 85 cytology samples (73%) were not evaluable for EGFR mutation status (insufficient DNA for mutation analysis or no material available for DNA extraction and subsequent analysis). Fig. 3 summarizes the number of evaluable and EGFR mutation-positive samples observed, according to tumor cell content. A total of 52 cytology samples (45%) had <100 tumor cells; eleven of these samples provided an evaluable EGFR mutation result, of which two (18%) were EGFR mutation-positive. A total of 64 cytology samples (55%) had >100 tumor cells; twenty of these samples provided an evaluable EGFR mutation result, of which seven (35%) were EGFR mutation-positive. Data from the previously unanalyzed histology samples showed that 73 samples (74%) had <100 tumor cells, with 59 samples providing an evaluable EGFR mutation result; thirty (51%) were EGFR mutation-positive. A total of 26 histology samples (26%) had >100 tumor cells. These samples had previously been excluded from the main IPASS study on the

basis that they did not meet the pre-specified thresholds regarding tumor content and sample quality/quantity (described in

Section 2). Twenty samples provided an evaluable EGFR mutation result; 15 (75%) were EGFR mutation-positive. In total, therefore, selleck compound EGFR mutation-positive tumors were detected in 54 patients which had previously been described as EGFR mutation-unknown. Of the EGFR mutation-positive cytology samples, 5 (55.6%) were positive for exon 19 deletions and 4 (44.4%) were positive for exon 21 L858R. Of the EGFR mutation-positive histology CYTH4 samples, 22 (48.9%) were positive for exon 19 deletions, 18 (40%) for exon 21 L858R, and two (4.4%) for exon 18 G719S/A/C. A total of three samples were identified as having double mutations: two (4.4%) for exon 19 deletions and exon 21 L858R, and one sample (2.2%) for exon 18 G719S/A/C and exon 21 L861Q. Data from the previously analyzed samples demonstrated the differential efficacy in terms of ORRs for patients with gefitinib, with 1% of patients (n = 1/100) having an objective response in the EGFR mutation-negative subgroup, 43% (n = 167/386) in the mutation-unknown subgroup, and 71% (n = 94/132) in the mutation-positive subgroup [4] and [5]. Note that in the previous analysis, the EGFR mutation-unknown subgroup consisted of 386 patients, including 61 patients described as not previously analyzed and who are described here. Fig. 4 summarizes the ORR in the previously unanalyzed cytology and histology samples by EGFR mutation status for patients with gefitinib.

1

Frailty itself has a series of negative consequences, including a future risk of disability,2 institutionalization,3 fracture,4 hospitalization,5 and mortality.4 and 6 Identification of modifiable risk factors for frailty7 selleck kinase inhibitor is clearly important in the prevention of the syndrome. One such modifiable predictor of frailty may be diabetes8 and its risk factors. Diabetes risk factors that have recently been shown to be related to an elevated risk of frailty include adiposity,9 low high-density lipoprotein (HDL)-cholesterol level,10 high blood pressure,11 and cigarette smoking.12 However, this evidence base is modest; studies are typically small in scale and cross-sectional in design, and the influence, if any, of other diabetes risk factors (history of high blood glucose, physical activity, consumption of fruit and vegetables, fasting glucose, and triglycerides) on future frailty is unknown. Additionally, in

the clinical setting, predictive risk algorithms that are in frequent use for the purposes of predicting diabetes and that comprise these risk factors offer value in estimating the likelihood of future disease and therefore provide clinical guidance in prevention and treatment. In the present analyses, we examined the longitudinal association between a comprehensive range of individual diabetes risk factors, validated diabetes risk algorithms (Framingham Offspring,13 Cambridge,14 and Finnish15), and future frailty. If a strong association Edoxaban between the diabetes risk scores and frailty is confirmed, these Belnacasan mw scores would present

a convenient way to identify individuals at an increased risk of frailty later in life and in need of early preventive measures. Described in detail elsewhere,16 data were drawn from the Whitehall II study, an ongoing longitudinal study of 10,308 (67% men) London-based British civil servants aged 35 to 55 years at study induction.17 The first screening (phase 1) took place from 1985 to 1988, involving a clinical examination and self-administered questionnaire. Subsequent phases of data collection have alternated between postal questionnaire alone (phases 2 [1988–1990], 4 [1995–1996], 6 [2001], 8 [2006], and 10 [2011]), and postal questionnaire accompanied by a clinical examination approximately every 5 to 6 years (phases 3 [1991–1993], 5 [1997–1999], 7 [2002–2004], and 9 [2007–2009]). We used diabetes risk factors measured at phase 5, the “baseline” for the purposes of our analyses. Frailty was assessed approximately 10 years later, at phase 9, when its components were measured for the first time. Diabetes status was assessed at phases 5, 7, and 9. Prevalent diabetes cases at phase 5 were excluded from the population. Ethical approval for the Whitehall II study was obtained from the University College London Medical School Committee on the ethics of human research (London, UK).