Author
Yoav Ben-Shlomo
Other affiliations: University of Vermont, Imperial College London, University of Oulu ...read more
Bio: Yoav Ben-Shlomo is an academic researcher from University of Bristol. The author has contributed to research in topics: Population & Medicine. The author has an hindex of 110, co-authored 628 publications receiving 59538 citations. Previous affiliations of Yoav Ben-Shlomo include University of Vermont & Imperial College London.
Topics: Population, Medicine, Odds ratio, Cohort study, Dementia
Papers published on a yearly basis
Papers
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University of Bristol1, Wellcome Trust Centre for Human Genetics2, University of Oxford3, Churchill Hospital4, National Health Service5, University of London6, Imperial College London7, University of Oulu8, National Institutes of Health9, Medical Research Council10, Wellcome Trust Sanger Institute11, Newcastle University12, Queen Mary University of London13, Royal London Hospital14, University of Dundee15, Ninewells Hospital16
TL;DR: A genome-wide search for type 2 diabetes–susceptibility genes identified a common variant in the FTO (fat mass and obesity associated) gene that predisposes to diabetes through an effect on body mass index (BMI).
Abstract: Obesity is a serious international health problem that increases the risk of several common diseases. The genetic factors predisposing to obesity are poorly understood. A genome-wide search for type 2 diabetes-susceptibility genes identified a common variant in the FTO (fat mass and obesity associated) gene that predisposes to diabetes through an effect on body mass index (BMI). An additive association of the variant with BMI was replicated in 13 cohorts with 38,759 participants. The 16% of adults who are homozygous for the risk allele weighed about 3 kilograms more and had 1.67-fold increased odds of obesity when compared with those not inheriting a risk allele. This association was observed from age 7 years upward and reflects a specific increase in fat mass.
4,184 citations
Elizabeth K. Speliotes1, Elizabeth K. Speliotes2, Cristen J. Willer3, Sonja I. Berndt +410 more•Institutions (86)
TL;DR: Genetic loci associated with body mass index map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor, which may provide new insights into human body weight regulation.
Abstract: Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and similar to 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 x 10(-8)), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.
2,632 citations
2,337 citations
01 Apr 2004
TL;DR: The Fetal growth and development: the role of nutrition and other factors and Should the authors intervene to improve fetal growth?
Abstract: PART I - BACKGROUND 1. Introduction 2. The life course and adult chronic disease: an historical perspective with particular reference to coronary heart disease PART II - LIFE COURSE INFLUENCES ON ADULT CHRONIC DISEASE 3. Pre-adult influences on cardiovascular disease 4. Life course approaches to socioeconomic differentials in health 5. Ischaemic heart disease and cerebrovascular disease mortality trends with special reference to England and Wales: are there cohort effects? 6. Geography and migration with special reference to cardiovascular disease 7. A life course approach to diabetes 8. A life course approach to obesity 9. A life course approach to blood pressure 10. A life course approach to respiratory and allergic diseases 11. A life course approach to cancer epidemiology 12. Time trends in cancer incidence and mortality 13. A life course approach to biological ageing 14. A life course approach to neuropsychiatric outcomes PART III - BIOLOGICAL AND SOCIAL PROCESSES 15. Fetal growth and development: the role of nutrition and other factors 16. Socioeconomic pathways between childhood and adult health PART IV - IMPLICATIONS FOR POLICY AND FUTURE RESEARCH 17. Should we intervene to improve fetal and infant growth? 18. Should we intervene to improve childhood circumstances? 19. Conclusions
2,289 citations
TL;DR: It is demonstrated that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes.
Abstract: Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes.
2,022 citations
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Journal Article•
28,685 citations
28 Jul 2005
TL;DR: PfPMP1）与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。
18,940 citations
Journal Article•
TL;DR: It is suggested that the natural selection against large insertion/deletion is so weak that a large amount of variation is maintained in a population.
11,521 citations
Journal Article•
TL;DR: For the next few weeks the course is going to be exploring a field that’s actually older than classical population genetics, although the approach it’ll be taking to it involves the use of population genetic machinery.
Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to
9,847 citations
TL;DR: In this paper, the authors estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent effects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010.
9,324 citations