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- Kaptoge, S., et al.
(author)
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World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions
- 2019
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In: Lancet Global Health. - : Elsevier BV. - 2214-109X. ; 7:10
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Journal article (peer-reviewed)abstract
- 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. Copyright (C) 2019 The Author(s). Published by Elsevier Ltd.
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- Tai, F, et al.
(author)
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Abdominal Wall Miscellaneous
- 2015
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In: Hernia : the journal of hernias and abdominal wall surgery. - 1248-9204. ; 19 Suppl 1, s. S5-S12
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Journal article (peer-reviewed)
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- Jin, Guangfu, et al.
(author)
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Validation of prostate cancer risk-related loci identified from genome-wide association studies using family-based association analysis : evidence from the International Consortium for Prostate Cancer Genetics (ICPCG)
- 2012
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In: Human Genetics. - : Springer Science and Business Media LLC. - 0340-6717 .- 1432-1203. ; 131:7, s. 1095-1103
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Journal article (peer-reviewed)abstract
- Multiple prostate cancer (PCa) risk-related loci have been discovered by genome-wide association studies (GWAS) based on case-control designs. However, GWAS findings may be confounded by population stratification if cases and controls are inadvertently drawn from different genetic backgrounds. In addition, since these loci were identified in cases with predominantly sporadic disease, little is known about their relationships with hereditary prostate cancer (HPC). The association between seventeen reported PCa susceptibility loci was evaluated with a family-based association test using 1,979 hereditary PCa families of European descent collected by members of the International Consortium for Prostate Cancer Genetics, with a total of 5,730 affected men. The risk alleles for 8 of the 17 loci were significantly over-transmitted from parents to affected offspring, including SNPs residing in 8q24 (regions 1, 2 and 3), 10q11, 11q13, 17q12 (region 1), 17q24 and Xp11. In subgroup analyses, three loci, at 8q24 (regions 1 and 2) plus 17q12, were significantly over-transmitted in hereditary PCa families with five or more affected members, while loci at 3p12, 8q24 (region 2), 11q13, 17q12 (region 1), 17q24 and Xp11 were significantly over-transmitted in HPC families with an average age of diagnosis at 65 years or less. Our results indicate that at least a subset of PCa risk-related loci identified by case-control GWAS are also associated with disease risk in HPC families.
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- Kaput, J, et al.
(author)
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The case for strategic international alliances to harness nutritional genomics for public and personal health
- 2005
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In: The British journal of nutrition. - : Cambridge University Press (CUP). - 0007-1145 .- 1475-2662. ; 94:5, s. 623-632
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Journal article (peer-reviewed)abstract
- Nutrigenomics is the study of how constituents of the diet interact with genes, and their products, to alter phenotype and, conversely, how genes and their products metabolise these constituents into nutrients, antinutrients, and bioactive compounds. Results from molecular and genetic epidemiological studies indicate that dietary unbalance can alter gene–nutrient interactions in ways that increase the risk of developing chronic disease. The interplay of human genetic variation and environmental factors will make identifying causative genes and nutrients a formidable, but not intractable, challenge. We provide specific recommendations for how to best meet this challenge and discuss the need for new methodologies and the use of comprehensive analyses of nutrient–genotype interactions involving large and diverse populations. The objective of the present paper is to stimulate discourse and collaboration among nutrigenomic researchers and stakeholders, a process that will lead to an increase in global health and wellness by reducing health disparities in developed and developing countries.
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- Lee, Man K. S., et al.
(author)
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Glycolysis Is Required for LPS-Induced Activation and Adhesion of Human CD14(+)CD16(-) Monocytes
- 2019
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In: Frontiers in Immunology. - : FRONTIERS MEDIA SA. - 1664-3224. ; 10
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Journal article (peer-reviewed)abstract
- Monocytes in humans consist of 3 subsets; CD14(+)CD16(-) (classical), CD14(+)CD16(+) (intermediate) and CD14(dim)CD16+ (non-classical), which exhibit distinct and heterogeneous responses to activation. During acute inflammation CD14(+)CD16(-) monocytes are significantly elevated and migrate to the sites of injury via the adhesion cascade. The field of immunometabolism has begun to elucidate the importance of the engagement of specific metabolic pathways in immune cell function. Yet, little is known about monocyte metabolism and the role of metabolism in mediating monocyte activation and adherence to vessels. Accordingly, we aimed to determine whether manipulating the metabolism of CD14(+)CD16(-) monocytes alters their ability to become activated and adhere. We discovered that LPS stimulation increased the rate of glycolysis in human CD14(+)CD16(-) monocytes. Inhibition of glycolysis with 2-deoxy-D-glucose blunted LPS-induced activation and adhesion of monocytes. Mechanistically, we found that increased glycolysis was regulated by mTOR-induced glucose transporter (GLUT)- 1. Furthermore, enhanced glycolysis increased accumulation of reactive oxygen species (ROS) and activation of p38 MAPK, which lead to activation and adhesion of monocytes. These findings reveal that glycolytic metabolism is critical for the activation of CD14(+)CD16(-) monocytes and contributes to our understanding of the interplay between metabolic substrate preference and immune cell function.
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- Teerlink, Craig C., et al.
(author)
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Association analysis of 9,560 prostate cancer cases from the International Consortium of Prostate Cancer Genetics confirms the role of reported prostate cancer associated SNPs for familial disease
- 2014
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In: Human Genetics. - : Springer. - 0340-6717 .- 1432-1203. ; 133:3, s. 347-356
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Journal article (peer-reviewed)abstract
- Previous GWAS studies have reported significant associations between various common SNPs and prostate cancer risk using cases unselected for family history. How these variants influence risk in familial prostate cancer is not well studied. Here, we analyzed 25 previously reported SNPs across 14 loci from prior prostate cancer GWAS. The International Consortium for Prostate Cancer Genetics (ICPCG) previously validated some of these using a family-based association method (FBAT). However, this approach suffered reduced power due to the conditional statistics implemented in FBAT. Here, we use a case-control design with an empirical analysis strategy to analyze the ICPCG resource for association between these 25 SNPs and familial prostate cancer risk. Fourteen sites contributed 12,506 samples (9,560 prostate cancer cases, 3,368 with aggressive disease, and 2,946 controls from 2,283 pedigrees). We performed association analysis with Genie software which accounts for relationships. We analyzed all familial prostate cancer cases and the subset of aggressive cases. For the familial prostate cancer phenotype, 20 of the 25 SNPs were at least nominally associated with prostate cancer and 16 remained significant after multiple testing correction (p a parts per thousand currency sign 1E (-3)) occurring on chromosomal bands 6q25, 7p15, 8q24, 10q11, 11q13, 17q12, 17q24, and Xp11. For aggressive disease, 16 of the SNPs had at least nominal evidence and 8 were statistically significant including 2p15. The results indicate that the majority of common, low-risk alleles identified in GWAS studies for all prostate cancer also contribute risk for familial prostate cancer, and that some may contribute risk to aggressive disease.
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