Deviant Peer Affiliation and Antisocial Behavior: Interaction with Monoamine Oxidase A (MAOA) Genotype

Although genetic and environmental factors are separately implicated in the development of antisocial behavior (ASB), interactive models have emerged relatively recently, particularly those incorporating molecular genetic data. Using a large sample of male Caucasian adolescents and young adults from the National Longitudinal Study of Adolescent Health (Add Health), the association of deviant peer affiliation, the 30-base pair variable number tandem repeat polymorphism in promoter region of the monoamine oxidase-A (MAOA) gene, and their interaction, with antisocial behavior (ASB) was investigated. Weighted analyses accounting for over-sampling and clustering within schools as well as controlling for age and wave suggested that deviant peer affiliation and MAOA genotype were each significantly associated with levels of overt ASB across a 6-year period. Only deviant peer affiliation was significantly related to covert ASB, however. Additionally, there was evidence suggestive of a gene-environment interaction (G × E) where the influence of deviant peer affiliation on overt ASB was significantly stronger among individuals with the high-activity MAOA genotype than the low-activity genotype. MAOA was not significantly associated with deviant peer affiliation, thus strengthening the inference of G × E rather than gene-environment correlation (rGE). Different forms of gene-environment interplay and implications for future research on ASB are discussed

Family physician and endocrinologist coordination as the basis for diabetes care in clinical practice

<p>Abstract</p> <p>Background</p> <p>To estimate the proportion of diabetic patients (DPts) with peripheral vascular disease treated at a primary health care site after an endocrinologist-based intervention, who meet ATP III and Steno targets of metabolic control, as well as to compare the outcome with the results of the patients treated by endocrinologists.</p> <p>Methods</p> <p>A controlled, prospective over 30-months period study was conducted in area 7 of Madrid. One hundred twenty six eligible diabetic patients diagnosed as having peripheral vascular disease between January 2003 and June 2004 were included in the study. After a treatment period of three months by the Diabetes team at St Carlos Hospital, 63 patients were randomly assigned to continue their follow up by diabetes team (Group A) and other 63 to be treated by the family physicians (FP) at primary care level with continuous diabetes team coordination (Group B). 57 DPts from Group A and 59 from Group B, completed the 30 months follow-up period. At baseline both groups were similar in age, weight, time from diagnosis and metabolic control. The main outcomes of this study were the proportion of patients meeting ATP III and Steno goals for HbA1c (%), Cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, blood pressure, albumine-to-creatinine excretion ratio (ACR), body mass index (BMI), waist circumference (WC), anti-aggregation treatment and smoking status.</p> <p>Results</p> <p>At the end of the follow up, no differences were found between the groups. More than 37% of diabetic patients assigned to be treated by FP achieved a HbA1c < 6.5%, more than 50% a ACR < 30 mg/g, and more than 80% reached low risk values for cholesterol, LDL cholesterol, triglycerides, diastolic blood pressure and were anti-aggregated, and 12% remained smokers. In contrast, less than 45% achieved a systolic blood pressure < 130 mm Hg, less than 12% had a BMI < 25 Kg.m-2 (versus 23% in group A; p < 0.05) and 49%/30% (men/women) had a waist circumference of low risk.</p> <p>Conclusion</p> <p>Improvements in metabolic control among diabetic patients with peripheral vascular disease treated at a primary health care setting is possible, reaching similar results to the patients treated at a specialized level. Despite such an improvement, body weight control remains more than poor in both levels, mainly at primary care level. General practitioner and endocrinologist coordination care may be important to enhance diabetes management in primary care settings.</p> <p>Trial registration</p> <p>Clinical Trial number ISRCTN75037597</p

Deep Sequencing Whole Transcriptome Exploration of the σE Regulon in Neisseria meningitidis

Bacteria live in an ever-changing environment and must alter protein expression promptly to adapt to these changes and survive. Specific response genes that are regulated by a subset of alternative σ70-like transcription factors have evolved in order to respond to this changing environment. Recently, we have described the existence of a σE regulon including the anti-σ-factor MseR in the obligate human bacterial pathogen Neisseria meningitidis. To unravel the complete σE regulon in N. meningitidis, we sequenced total RNA transcriptional content of wild type meningococci and compared it with that of mseR mutant cells (ΔmseR) in which σE is highly expressed. Eleven coding genes and one non-coding gene were found to be differentially expressed between H44/76 wildtype and H44/76ΔmseR cells. Five of the 6 genes of the σE operon, msrA/msrB, and the gene encoding a pepSY-associated TM helix family protein showed enhanced transcription, whilst aniA encoding a nitrite reductase and nspA encoding the vaccine candidate Neisserial surface protein A showed decreased transcription. Analysis of differential expression in IGRs showed enhanced transcription of a non-coding RNA molecule, identifying a σE dependent small non-coding RNA. Together this constitutes the first complete exploration of an alternative σ-factor regulon in N. meningitidis. The results direct to a relatively small regulon indicative for a strictly defined response consistent with a relatively stable niche, the human throat, where N. meningitidis resides

Comparative analyses imply that the enigmatic sigma factor 54 is a central controller of the bacterial exterior

Contains fulltext : 95738.pdf (publisher's version ) (Open Access)BACKGROUND: Sigma-54 is a central regulator in many pathogenic bacteria and has been linked to a multitude of cellular processes like nitrogen assimilation and important functional traits such as motility, virulence, and biofilm formation. Until now it has remained obscure whether these phenomena and the control by Sigma-54 share an underlying theme. RESULTS: We have uncovered the commonality by performing a range of comparative genome analyses. A) The presence of Sigma-54 and its associated activators was determined for all sequenced prokaryotes. We observed a phylum-dependent distribution that is suggestive of an evolutionary relationship between Sigma-54 and lipopolysaccharide and flagellar biosynthesis. B) All Sigma-54 activators were identified and annotated. The relation with phosphotransfer-mediated signaling (TCS and PTS) and the transport and assimilation of carboxylates and nitrogen containing metabolites was substantiated. C) The function annotations, that were represented within the genomic context of all genes encoding Sigma-54, its activators and its promoters, were analyzed for intra-phylum representation and inter-phylum conservation. Promoters were localized using a straightforward scoring strategy that was formulated to identify similar motifs. We found clear highly-represented and conserved genetic associations with genes that concern the transport and biosynthesis of the metabolic intermediates of exopolysaccharides, flagella, lipids, lipopolysaccharides, lipoproteins and peptidoglycan. CONCLUSION: Our analyses directly implicate Sigma-54 as a central player in the control over the processes that involve the physical interaction of an organism with its environment like in the colonization of a host (virulence) or the formation of biofilm

Pharmaceutical cost and multimorbidity with type 2 diabetes mellitus using electronic health record data

© 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.[EN] Background: The objective of the study is to estimate the frequency of multimorbidity in type 2 diabetes patients classified by health statuses in a European region and to determine the impact on pharmaceutical expenditure. Methods: Cross-sectional study of the inhabitants of a southeastern European region with a population of 5,150,054, using data extracted from Electronic Health Records for 2012. 491,854 diabetic individuals were identified and selected through clinical codes, Clinical Risk Groups and diabetes treatment and/or blood glucose reagent strips. Patients with type 1 diabetes and gestational diabetes were excluded. All measurements were obtained at individual level. The prevalence of common chronic diseases and co-occurrence of diseases was established using factorial analysis. Results: The estimated prevalence of diabetes was 9.6 %, with nearly 70 % of diabetic patients suffering from more than two comorbidities. The most frequent of these was hypertension, which for the groups of patients in Clinical Risk Groups (CRG) 6 and 7 was 84.3 % and 97.1 % respectively. Regarding age, elderly patients have more probability of suffering complications than younger people. Moreover, women suffer complications more frequently than men, except for retinopathy, which is more common in males. The highest use of insulins, oral antidiabetics (OAD) and combinations was found in diabetic patients who also suffered cardiovascular disease and neoplasms. The average cost for insulin was 153€ and that of OADs 306€. Regarding total pharmaceutical cost, the greatest consumers were patients with comorbidities of respiratory illness and neoplasms, with respective average costs of 2,034.2€ and 1,886.9€. Conclusions: Diabetes is characterized by the co-occurrence of other diseases, which has implications for disease management and leads to a considerable increase in consumption of medicines for this pathology and, as such, pharmaceutical expenditure.This study was financed by a grant from the Fondo de Investigaciones de la Seguridad Social Instituto de Salud Carlos III, the Spanish Ministry of Health (FIS PI12/0037).Sancho Mestre, C.; Vivas Consuelo, DJJ.; Alvis, L.; Romero, M.; Usó Talamantes, R.; Caballer Tarazona, V. (2016). Pharmaceutical cost and multimorbidity with type 2 diabetes mellitus using electronic health record data. BMC Health Services Research. 16(394):1-8. https://doi.org/10.1186/s12913-016-1649-2S1816394Whiting DR, Guariguata L, Weil C, Shaw J. IDF Diabetes Atlas: Global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res Clin Pract. 2011;94:311–21. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22079683Soriguer F, Goday A, Bosch-Comas A, Bordiu E, Calle-Pascual A, Carmena R, et al. Prevalence of diabetes mellitus and impaired glucose regulation in Spain: the [email protected] Study. Diabetologia. 2012;55:88–93. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21987347WHO | Rio Political Declaration on Social Determinants of Health. WHO. World Health Organization; 2011. Available from: http://www.who.int/sdhconference/declaration/Rio_political_declaration.pdf?ua=1Fortin M, Soubhi H, Hudon C, Bayliss EA, van den Akker M. Multimorbidity’s many challenges. BMJ. 2007;334:1016–7. BMJ Group [cited 2016 Aug 4]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17510108Valderas JM, Starfield B, Sibbald B, Salisbury C, Roland M. Defining comorbidity: implications for understanding health and health services. Ann Fam Med. 2009;7:357–63. [cited 2016 Aug 4]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19597174Sundararajan V, Henderson T, Perry C, Muggivan A, Quan H, Ghali WA. New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality. J Clin Epidemiol. 2004;57:1288–94. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15617955Glynn LG, Valderas JM, Healy P, Burke E, Newell J, Gillespie P, et al. The prevalence of multimorbidity in primary care and its effect on health care utilization and cost. Fam Pract. 2011;28:516–23. [Internet]. 2011 [cited 2016 Aug 4]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21436204Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012;380:37–43. [cited 2014 Nov 5] Available from: http://www.ncbi.nlm.nih.gov/pubmed/22579043Holden L, Scuffham PA, Hilton MF, Muspratt A, Ng SK, Whiteford HA. Patterns of multimorbidity in working Australians. Popul Heal Metr. 2011;9:15. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21635787Starfield B. Threads and yarns: weaving the tapestry of comorbidity. Ann Fam Med. 2006;4:101–3. [cited 2016 Aug 4]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16569711Abdul-Rahim HF, Holmboe-Ottesen G, Stene LCM, Husseini A, Giacaman R, Jervell J, et al. Obesity in a rural and an urban Palestinian West Bank population. Int J Obes. 2003;27:140–6. Available from: http://dx.doi.org/10.1038/sj.ijo.0802160Boutayeb A, Boutayeb S, Boutayeb W. Multi-morbidity of non communicable diseases and equity in WHO Eastern Mediterranean countries. Int J Equity Heal. 2013;12:60. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23961989Teljeur C, Smith SM, Paul G, Kelly A, O’Dowd T. Multimorbidity in a cohort of patients with type 2 diabetes. Eur J Gen Pract. 2013;19:17–22. Available from: http://informahealthcare.com/doi/abs/10.3109/13814788.2012.714768 , http://www.ncbi.nlm.nih.gov/pubmed/23432037Hughes JS, Averill RF, Eisenhandler J, Goldfield NI, Muldoon J, Neff JM, et al. Clinical Risk Groups (CRGs): a classification system for risk-adjusted capitation-based payment and health care management. Med Care. 2004;42:81–90. [cited 2016 Feb 29]. Available from: http://www.ncbi.nlm.nih.gov/pubmed/14713742Vivas-Consuelo D, Alvis-Estrada L, Uso-Talamantes R, Caballer-Tarazona V, Buigues-Pastor L, Sancho-Mestre C. Multimorbidity Pharmaceutical Cost of Diabetes Mellitus. Value in Health. 2014;17:A341–2. Elsevier [cited 2016 Apr 21]. Available from: http://www.sciencedirect.com/science/article/pii/S1098301514026102Inoriza JM, Pérez M, Cols M, Sánchez I, Carreras M. Análisis de la población diabética de una comarca : perfil de morbilidad, utilización de recursos, complicaciones y control metabólico. Aten Primaria. 2016;45. Available from: http://www.sciencedirect.com/science/article/pii/S0212656713001340Vivas-Consuelo D, Usó-Talamantes R, Trillo-Mata JL, Caballer-Tarazona M, Barrachina-Martínez I, Buigues-Pastor L. Predictability of pharmaceutical spending in primary health services using Clinical Risk Groups. Health Policy. 2014;116:188–95. Available from: http://www.sciencedirect.com/science/article/pii/S0168851014000256Kho AN, Hayes MG, Rasmussen-Torvik L, Pacheco JA, Thompson WK, Armstrong LL, et al. Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study. J Am Med Inform Assoc. 2016;19:212–8. [cited 2016 Feb 18]. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3277617&tool=pmcentrez&rendertype=abstractPrados-Torres A, Poblador-Plou B, Calderón-Larrañaga A, Gimeno-Feliu LA, González-Rubio F, Poncel-Falcó A, et al. Multimorbidity patterns in primary care: interactions among chronic diseases using factor analysis. PLoS One. 2012;7:e32190. Public Library of Science [cited 2016 Apr 21]. Available from: http://dx.doi.org/10.1371/journal.pone.0032190Islam MM, Valderas JM, Yen L, Dawda P, Jowsey T, McRae IS. Multimorbidity and comorbidity of chronic diseases among the senior Australians: prevalence and patterns. PLoS One. 2014;9:e83783. Public Library of Science [cited 2016 Mar 25]. Available from: http://dx.doi.org/10.1371/journal.pone.0083783Fortin M, Bravo G, Hudon C, Lapointe L, Dubois MF, Almirall J. Psychological distress and multimorbidity in primary care. Ann Fam Med. 2006;4:417–22. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17003141Nuttall M, van der Meulen J, Emberton M. Charlson scores based on ICD-10 administrative data were valid in assessing comorbidity in patients undergoing urological cancer surgery. J Clin Epidemiol. 2006;59:265–73. Available from: http://www.ncbi.nlm.nih.gov/pubmed/16488357Klompas M, Eggleston E, McVetta J, Lazarus R, Li L, Platt R. Automated detection and classification of type 1 versus type 2 diabetes using electronic health record data. Diabetes Care. 2013;36:914–21. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23193215Alonso-Moran E, Orueta JF, Fraile Esteban JI, Arteagoitia Axpe JM, Luz Marques Gonzalez M, Toro Polanco N, et al. The prevalence of diabetes-related complications and multimorbidity in the population with type 2 diabetes mellitus in the Basque Country. BMC Public Health. 2014;14. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4197247/Pantalone KM, Hobbs TM, Wells BJ, Kong SX, Kattan MW, Bouchard J, et al. Clinical characteristics, complications, comorbidities and treatment patterns among patients with type 2 diabetes mellitus in a large integrated health system. BMJ open diabetes Res Care. 2015;3:e000093. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4513350&tool=pmcentrez&rendertype=abstractAlonso-Moran E, Satylganova A, Orueta JF, Nuno-Solinis R. Prevalence of depression in adults with type 2 diabetes in the Basque Country: relationship with glycaemic control and health care costs. BMC Public Health. 2014;14. Available from: http://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-14-769Kilzieh N, Rastam S, Maziak W, Ward KD. Comorbidity of depression with chronic diseases: a population-based study in Aleppo, Syria. Int J Psychiatry Med. 2008;38:169–84. Available from: http://www.ncbi.nlm.nih.gov/pubmed/18724568Almawi W, Tamim H, Al-Sayed N, Arekat MR, Al-Khateeb GM, Baqer A, et al. Association of comorbid depression, anxiety, and stress disorders with Type 2 diabetes in Bahrain, a country with a very high prevalence of Type 2 diabetes. J Endocrinol Invest. 2008;31:1020–4. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19169060Giralt Muiña P, Gutiérrez Ávila G, Ballester Herrera MJ, Botella Romero F, Angulo Donado JJ. Prevalencia de diabetes y diabetes oculta en adultos de Castilla-La Mancha. TITLEREVISTA. 2011;137:484–90. Available from: http://zl.elsevier.es/es/revista/medicina-clinica-2/prevalencia-diabetes-diabetes-oculta-adultos-castilla-la-mancha-90028329-originales-2011Mata-Cases M, Roura-Olmeda P, Berengué-Iglesias M, Birulés-Pons M, Mundet-Tuduri X, Franch-Nadal J, et al. Fifteen years of continuous improvement of quality care of type 2 diabetes mellitus in primary care in Catalonia, Spain. Int J Clin Pract. 2012;66:289–98. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3584513&tool=pmcentrez&rendertype=abstractEgede LE, Gebregziabher M, Zhao Y, Dismuke CE, Walker RJ, Hunt KJ, et al. Differential Impact of Mental Health. 2015;21:535–44. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26295353Huber CA, Diem P, Schwenkglenks M, Rapold R, Reich O. Estimating the prevalence of comorbid conditions and their effect on health care costs in patients with diabetes mellitus in Switzerland. Diabetes Metab Syndr Obes. 2014;7:455–65. Dove Press [cited 2016 Aug 4]. Available from: https://www.dovepress.com/estimating-the-prevalence-of-comorbid-conditions-and-their-effect-on-h-peer-reviewed-article-DMS

High frequency of HIF-1 alpha overexpression in BRCA1 related breast cancer

Hypoxia is a hallmark of cancer. Hypoxia inducible factor-1 alpha (HIF-1 alpha) is the key regulator of the hypoxia response. HIF-1 alpha is overexpressed during sporadic breast carcinogenesis and correlated with poor prognosis. Little is known on the role of HIF-1 alpha in hereditary breast carcinogenesis. A recent study suggests a role for BRCA1 in HIF-1 alpha regulation. We therefore examined the expression of HIF-1 alpha in BRCA1 related breast cancers. By immunohistochemistry we studied expression of HIF-1 alpha and some of its downstream targets in 30 hereditary invasive breast cancers in comparison with 200 sporadic controls. HIF-1 alpha overexpression was significantly more frequent in BRCA1 related breast cancers (27/30, 90%) than in sporadic controls (88/200, 44%) (P <0.0001). 19/30 (63%) of BRCA1 tumors showed perinecrotic (hypoxia induced) and 8/30 (27%) a diffuse HIF-1 alpha overexpression pattern, the latter more likely related to genetic alterations in oncogenes and tumor suppressor genes. In contrast, sporadic breast cancer HIF-1 expressing tumors showed an inverse ratio of perinecrotic/diffuse expression (31 vs. 69%, P = 0.0002). Glut-1 and CAIX, downstream HIF1 targets, were expressed in 27/30 (90%) and 15/21 (71%) of hereditary cases versus 54/183 (29%) and 24/183 (13%) in sporadic cases. p300 levels, necessary for HIF-1 downstream activation, were significantly higher in hereditary cases (20/21, 95%) compared to sporadic cases (91/183, 50%, P = 0.0001). In conclusion, in BRCA1 germline mutation related breast cancer, functional HIF-1 alpha overexpression is seen at a much higher frequency than in sporadic breast cancer, mostly hypoxia induced. This points to an important role of hypoxia and its key regulator HIF-1 alpha in hereditary breast carcinogenesis