The Impact of Obstructive Sleep Apnea on Metabolic and Inflammatory Markers in Consecutive Patients with Metabolic Syndrome

Background: Obstructive Sleep Apnea (OSA) is tightly linked to some components of Metabolic Syndrome (MetS). However, most of the evidence evaluated individual components of the MetS or patients with a diagnosis of OSA that were referred for sleep studies due to sleep complaints. Therefore, it is not clear whether OSA exacerbates the metabolic abnormalities in a representative sample of patients with MetS. Methodology/Principal Findings: We studied 152 consecutive patients (age 48 +/- 9 years, body mass index 32.3 +/- 3.4 Kg/m(2)) newly diagnosed with MetS (Adult Treatment Panel III). All participants underwent standard polysomnography irrespective of sleep complaints, and laboratory measurements (glucose, lipid profile, uric acid and C-reactive protein). The prevalence of OSA (apnea-hypopnea index >= 15 events per hour of sleep) was 60.5%. Patients with OSA exhibited significantly higher levels of blood pressure, glucose, triglycerides, cholesterol, LDL, cholesterol/HDL ratio, triglycerides/HDL ratio, uric acid and C-reactive protein than patients without OSA. OSA was independently associated with 2 MetS criteria: triglycerides: OR: 3.26 (1.47-7.21) and glucose: OR: 2.31 (1.12-4.80). OSA was also independently associated with increased cholesterol/HDL ratio: OR: 2.38 (1.08-5.24), uric acid: OR: 4.19 (1.70-10.35) and C-reactive protein: OR: 6.10 (2.64-14.11). Indices of sleep apnea severity, apnea-hypopnea index and minimum oxygen saturation, were independently associated with increased levels of triglycerides, glucose as well as cholesterol/HDL ratio, uric acid and C-reactive protein. Excessive daytime sleepiness had no effect on the metabolic and inflammatory parameters. Conclusions/Significance: Unrecognized OSA is common in consecutive patients with MetS. OSA may contribute to metabolic dysregulation and systemic inflammation in patients with MetS, regardless of symptoms of daytime sleepiness.Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)[200032/2009-7]Fundacao Zerbini, BrazilNational Sleep Foundation/American Lung Association Pickwick[SF-78568 N]National Institutes of Health (NIH)[HL07534]National Institutes of Health (NIH)[R01 HL80105]National Institutes of Health (NIH)[5P50HL084945]American Heart Association[0765293U](BSF) United States Israel Binational Science Foundation[2005265

Increasing leadership capacity for HIV/AIDS programmes by strengthening public health epidemiology and management training in Zimbabwe

<p>Abstract</p> <p>Background</p> <p>Increased funding for global human immunodeficiency virus prevention and control in developing countries has created both a challenge and an opportunity for achieving long-term global health goals. This paper describes a programme in Zimbabwe aimed at responding more effectively to the HIV/AIDS epidemic by reinforcing a critical competence-based training institution and producing public health leaders.</p> <p>Methods</p> <p>The programme used new HIV/AIDS programme-specific funds to build on the assets of a local education institution to strengthen and expand the general public health leadership capacity in Zimbabwe, simultaneously ensuring that they were trained in HIV interventions.</p> <p>Results</p> <p>The programme increased both numbers of graduates and retention of faculty. The expanded HIV/AIDS curriculum was associated with a substantial increase in trainee projects related to HIV. The increased number of public health professionals has led to a number of practically trained persons working in public health leadership positions in the ministry, including in HIV/AIDS programmes.</p> <p>Conclusion</p> <p>Investment of a modest proportion of new HIV/AIDS resources in targeted public health leadership training programmes can assist in building capacity to lead and manage national HIV and other public health programmes.</p

Consequences of Lineage-Specific Gene Loss on Functional Evolution of Surviving Paralogs: ALDH1A and Retinoic Acid Signaling in Vertebrate Genomes

Genome duplications increase genetic diversity and may facilitate the evolution of gene subfunctions. Little attention, however, has focused on the evolutionary impact of lineage-specific gene loss. Here, we show that identifying lineage-specific gene loss after genome duplication is important for understanding the evolution of gene subfunctions in surviving paralogs and for improving functional connectivity among human and model organism genomes. We examine the general principles of gene loss following duplication, coupled with expression analysis of the retinaldehyde dehydrogenase Aldh1a gene family during retinoic acid signaling in eye development as a case study. Humans have three ALDH1A genes, but teleosts have just one or two. We used comparative genomics and conserved syntenies to identify loss of ohnologs (paralogs derived from genome duplication) and to clarify uncertain phylogenies. Analysis showed that Aldh1a1 and Aldh1a2 form a clade that is sister to Aldh1a3-related genes. Genome comparisons showed secondarily loss of aldh1a1 in teleosts, revealing that Aldh1a1 is not a tetrapod innovation and that aldh1a3 was recently lost in medaka, making it the first known vertebrate with a single aldh1a gene. Interestingly, results revealed asymmetric distribution of surviving ohnologs between co-orthologous teleost chromosome segments, suggesting that local genome architecture can influence ohnolog survival. We propose a model that reconstructs the chromosomal history of the Aldh1a family in the ancestral vertebrate genome, coupled with the evolution of gene functions in surviving Aldh1a ohnologs after R1, R2, and R3 genome duplications. Results provide evidence for early subfunctionalization and late subfunction-partitioning and suggest a mechanistic model based on altered regulation leading to heterochronic gene expression to explain the acquisition or modification of subfunctions by surviving ohnologs that preserve unaltered ancestral developmental programs in the face of gene loss

Proximal correlates of metabolic phenotypes during ‘at-risk' and ‘case' stages of the metabolic disease continuum

Extent: 11p.OBJECTIVE: To examine the social and behavioural correlates of metabolic phenotypes during ‘at-risk’ and ‘case’ stages of the metabolic disease continuum. DESIGN: Cross-sectional study of a random population sample. PARTICIPANTS: A total of 718 community-dwelling adults (57% female), aged 18--92 years from a regional South Australian city. MEASUREMENTS: Total body fat and lean mass and abdominal fat mass were assessed by dual energy x-ray absorptiometry. Fasting venous blood was collected in the morning for assessment of glycated haemoglobin, plasma glucose, serum triglycerides, cholesterol lipoproteins and insulin. Seated blood pressure (BP) was measured. Physical activity and smoking, alcohol and diet (96-item food frequency), sleep duration and frequency of sleep disordered breathing (SDB) symptoms, and family history of cardiometabolic disease, education, lifetime occupation and household income were assessed by questionnaire. Current medications were determined by clinical inventory. RESULTS: 36.5% were pharmacologically managed for a metabolic risk factor or had known diabetes (‘cases’), otherwise were classified as the ‘at-risk’ population. In both ‘at-risk’ and ‘cases’, four major metabolic phenotypes were identified using principal components analysis that explained over 77% of the metabolic variance between people: fat mass/insulinemia (FMI); BP; lipidaemia/lean mass (LLM) and glycaemia (GLY). The BP phenotype was uncorrelated with other phenotypes in ‘cases’, whereas all phenotypes were inter-correlated in the ‘at-risk’. Over and above other socioeconomic and behavioural factors, medications were the dominant correlates of all phenotypes in ‘cases’ and SDB symptom frequency was most strongly associated with FMI, LLM and GLY phenotypes in the ‘at-risk’. CONCLUSION: Previous research has shown FMI, LLM and GLY phenotypes to be most strongly predictive of diabetes development. Reducing SDB symptom frequency and optimising the duration of sleep may be important concomitant interventions to standard diabetes risk reduction interventions. Prospective studies are required to examine this hypothesis.MT Haren, G Misan, JF Grant, JD Buckley, PRC Howe, AW Taylor, J Newbury and RA McDermot

Proximal correlates of metabolic phenotypes during ‘at-risk' and ‘case' stages of the metabolic disease continuum

Extent: 11p.OBJECTIVE: To examine the social and behavioural correlates of metabolic phenotypes during ‘at-risk’ and ‘case’ stages of the metabolic disease continuum. DESIGN: Cross-sectional study of a random population sample. PARTICIPANTS: A total of 718 community-dwelling adults (57% female), aged 18--92 years from a regional South Australian city. MEASUREMENTS: Total body fat and lean mass and abdominal fat mass were assessed by dual energy x-ray absorptiometry. Fasting venous blood was collected in the morning for assessment of glycated haemoglobin, plasma glucose, serum triglycerides, cholesterol lipoproteins and insulin. Seated blood pressure (BP) was measured. Physical activity and smoking, alcohol and diet (96-item food frequency), sleep duration and frequency of sleep disordered breathing (SDB) symptoms, and family history of cardiometabolic disease, education, lifetime occupation and household income were assessed by questionnaire. Current medications were determined by clinical inventory. RESULTS: 36.5% were pharmacologically managed for a metabolic risk factor or had known diabetes (‘cases’), otherwise were classified as the ‘at-risk’ population. In both ‘at-risk’ and ‘cases’, four major metabolic phenotypes were identified using principal components analysis that explained over 77% of the metabolic variance between people: fat mass/insulinemia (FMI); BP; lipidaemia/lean mass (LLM) and glycaemia (GLY). The BP phenotype was uncorrelated with other phenotypes in ‘cases’, whereas all phenotypes were inter-correlated in the ‘at-risk’. Over and above other socioeconomic and behavioural factors, medications were the dominant correlates of all phenotypes in ‘cases’ and SDB symptom frequency was most strongly associated with FMI, LLM and GLY phenotypes in the ‘at-risk’. CONCLUSION: Previous research has shown FMI, LLM and GLY phenotypes to be most strongly predictive of diabetes development. Reducing SDB symptom frequency and optimising the duration of sleep may be important concomitant interventions to standard diabetes risk reduction interventions. Prospective studies are required to examine this hypothesis.MT Haren, G Misan, JF Grant, JD Buckley, PRC Howe, AW Taylor, J Newbury and RA McDermot