During active disease, patients with systemic-onset juvenile chronic arthritis (S-JCA) demonstrate a rise and fall in serum interleukin-6 (IL-6) that parallels the classic quotidian fever. To investigate the possibility that this cytokine profile results from a difference in the control of IL-6 expression, we examined the 5 Ј flanking region of the IL-6 gene for polymorphisms. A G/C polymorphism was detected at position Ϫ 174. In a group of 383 healthy men and women from a general practice in North London, the frequency of the C allele was 0.403 (95% confidence interval 0.37-0.44). In comparison, 92 patients with S-JCA had a different overall genotype frequency, especially those with onset of disease at Ͻ 5 yr of age. This was mainly due to the statistically significant lower frequency of the CC genotype in this subgroup. When comparing constructs of the 5 Ј flanking region ( Ϫ 550-ϩ 61 bp) in a luciferase reporter vector transiently transfected into HeLa cells, the Ϫ 174C construct showed 0.624 Ϯ 0.15-fold lower expression than the Ϫ 174G construct. After stimulation with LPS or IL-1, expression from the Ϫ 174C construct did not significantly change after 24 h, whereas expression from the Ϫ 174G construct increased by 2.35 Ϯ 0.10-and 3.60 Ϯ 0.26-fold, respectively, compared with the unstimulated level. Plasma levels of IL-6 were also measured in 102 of the healthy subjects, and the C allele was found to be associated with significantly lower levels of plasma IL-6. These results suggest that there is a genetically determined difference in the degree of the IL-6 response to stressful stimuli between individuals. The reduced frequency of the potentially protective CC genotype in young S-JCA patients may contribute to its pathogenesis. Similarly the individual's IL-6 genotype may be highly relevant in other conditions where IL-6 has been implicated, such as atherosclerosis. ( J. Clin. Invest. 1998. 102:1369-1376.)

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, we conducted genome-wide association meta-analyses of waist and hip circumference-related traits in up to 224,459 individuals. We identified 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (WHRadjBMI) and an additional 19 loci newly associated with related waist and hip circumference measures (P<5×10−8). Twenty of the 49 WHRadjBMI loci showed significant sexual dimorphism, 19 of which displayed a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation, and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms.

To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS) including 26,488 cases and 83,964 controls of European, East Asian, South Asian, and Mexican and Mexican American ancestry. We observed significant excess in directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals of association from the trans-ethnic meta-analysis in an additional 21,491 cases and 55,647 controls of European ancestry, we identified seven novel T2D susceptibility loci. Furthermore, we observed considerable improvements in fine-mapping resolution of common variant association signals at several T2D susceptibility loci. These observations highlight the benefits of trans-ethnic GWAS for the discovery and characterisation of complex trait loci, and emphasize an exciting opportunity to extend insight into the genetic architecture and pathogenesis of human diseases across populations of diverse ancestry.

Other experts who contributed to parts of the guidelines: Edmond Walma, Tony Fitzgerald, Marie Therese Cooney, Alexandra Dudina European Society of Cardiology (ESC) Committee for Practice Guidelines (CPG): Alec Vahanian (Chairperson), John Camm, Raffaele De Caterina, Veronica Dean, Kenneth Dickstein, Christian Funck-Brentano, Gerasimos Filippatos, Irene Hellemans, Steen Dalby Kristensen, Keith McGregor, Udo Sechtem, Sigmund Silber, Michal Tendera, Petr Widimsky, Jose Luis Zamorano Document reviewers: Irene Hellemans (CPG Review Co-ordinator), Attila Altiner, Enzo Bonora, Paul N. Durrington, Robert Fagard, Simona Giampaoli, Harry Hemingway, Jan Hakansson, Sverre Erik Kjeldsen, Mogens Lytken Larsen, Giuseppe Mancia, Athanasios J. Manolis, Kristina Orth-Gomer, Terje Pedersen, Mike Rayner, Lars Ryden, Mario Sammut, Neil Schneiderman, Anton F. Stalenhoef, Lale Tokgözoglu, Olov Wiklund, Antonis Zampelas

Background To help adapt cardiovascular disease risk prediction approaches to low-income and middle-income countries, WHO has convened an effort to develop, evaluate, and illustrate revised risk models. Here, we report the derivation, validation, and illustration of the revised WHO cardiovascular disease risk prediction charts that have been adapted to the circumstances of 21 global regions. Methods In this model revision initiative, we derived 10-year risk prediction models for fatal and non-fatal cardiovascular disease (ie, myocardial infarction and stroke) using individual participant data from the Emerging Risk Factors Collaboration. Models included information on age, smoking status, systolic blood pressure, history of diabetes, and total cholesterol. For derivation, we included participants aged 40-80 years without a known baseline history of cardiovascular disease, who were followed up until the first myocardial infarction, fatal coronary heart disease, or stroke event. We recalibrated models using age-specific and sex-specific incidences and risk factor values available from 21 global regions. For external validation, we analysed individual participant data from studies distinct from those used in model derivation. We illustrated models by analysing data on a further 123 743 individuals from surveys in 79 countries collected with the WHO STEPwise Approach to Surveillance. Findings Our risk model derivation involved 376 177 individuals from 85 cohorts, and 19 333 incident cardiovascular events recorded during 10 years of follow-up. The derived risk prediction models discriminated well in external validation cohorts (19 cohorts, 1 096 061 individuals, 25 950 cardiovascular disease events), with Harrell's C indices ranging from 0•685 (95% CI 0•629-0•741) to 0•833 (0•783-0•882). For a given risk factor profile, we found substantial variation across global regions in the estimated 10-year predicted risk. For example, estimated cardiovascular disease risk for a 60-year-old male smoker without diabetes and with systolic blood pressure of 140 mm Hg and total cholesterol of 5 mmol/L ranged from 11% in Andean Latin America to 30% in central Asia. When applied to data from 79 countries (mostly low-income and middle-income countries), the proportion of individuals aged 40-64 years estimated to be at greater than 20% risk ranged from less than 1% in Uganda to more than 16% in Egypt. Interpretation We have derived, calibrated, and validated new WHO risk prediction models to estimate cardiovascular disease risk in 21 Global Burden of Disease regions. The widespread use of these models could enhance the accuracy, practicability, and sustainability of efforts to reduce the burden of cardiovascular disease worldwide.