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- Bixby, H., et al.
(author)
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Rising rural body-mass index is the main driver of the global obesity epidemic in adults
- 2019
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In: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 569:7755, s. 260-4
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Journal article (peer-reviewed)abstract
- Body-mass index (BMI) has increased steadily in most countries in parallel with a rise in the proportion of the population who live in cities(.)(1,2) This has led to a widely reported view that urbanization is one of the most important drivers of the global rise in obesity(3-6). Here we use 2,009 population-based studies, with measurements of height and weight in more than 112 million adults, to report national, regional and global trends in mean BMI segregated by place of residence (a rural or urban area) from 1985 to 2017. We show that, contrary to the dominant paradigm, more than 55% of the global rise in mean BMI from 1985 to 2017-and more than 80% in some low- and middle-income regions-was due to increases in BMI in rural areas. This large contribution stems from the fact that, with the exception of women in sub-Saharan Africa, BMI is increasing at the same rate or faster in rural areas than in cities in low- and middle-income regions. These trends have in turn resulted in a closing-and in some countries reversal-of the gap in BMI between urban and rural areas in low- and middle-income countries, especially for women. In high-income and industrialized countries, we noted a persistently higher rural BMI, especially for women. There is an urgent need for an integrated approach to rural nutrition that enhances financial and physical access to healthy foods, to avoid replacing the rural undernutrition disadvantage in poor countries with a more general malnutrition disadvantage that entails excessive consumption of low-quality calories.
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| 3. |
- Taddei, C, et al.
(author)
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Repositioning of the global epicentre of non-optimal cholesterol
- 2020
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In: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 582:7810, s. 73-
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Journal article (peer-reviewed)abstract
- High blood cholesterol is typically considered a feature of wealthy western countries1,2. However, dietary and behavioural determinants of blood cholesterol are changing rapidly throughout the world3 and countries are using lipid-lowering medications at varying rates. These changes can have distinct effects on the levels of high-density lipoprotein (HDL) cholesterol and non-HDL cholesterol, which have different effects on human health4,5. However, the trends of HDL and non-HDL cholesterol levels over time have not been previously reported in a global analysis. Here we pooled 1,127 population-based studies that measured blood lipids in 102.6 million individuals aged 18 years and older to estimate trends from 1980 to 2018 in mean total, non-HDL and HDL cholesterol levels for 200 countries. Globally, there was little change in total or non-HDL cholesterol from 1980 to 2018. This was a net effect of increases in low- and middle-income countries, especially in east and southeast Asia, and decreases in high-income western countries, especially those in northwestern Europe, and in central and eastern Europe. As a result, countries with the highest level of non-HDL cholesterol—which is a marker of cardiovascular risk—changed from those in western Europe such as Belgium, Finland, Greenland, Iceland, Norway, Sweden, Switzerland and Malta in 1980 to those in Asia and the Pacific, such as Tokelau, Malaysia, The Philippines and Thailand. In 2017, high non-HDL cholesterol was responsible for an estimated 3.9 million (95% credible interval 3.7 million–4.2 million) worldwide deaths, half of which occurred in east, southeast and south Asia. The global repositioning of lipid-related risk, with non-optimal cholesterol shifting from a distinct feature of high-income countries in northwestern Europe, north America and Australasia to one that affects countries in east and southeast Asia and Oceania should motivate the use of population-based policies and personal interventions to improve nutrition and enhance access to treatment throughout the world.
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| 8. |
- Cozen, W., et al.
(author)
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A meta-analysis of Hodgkin lymphoma reveals 19p13.3 TCF3 as a novel susceptibility locus
- 2014
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In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 5, s. 3856-
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Journal article (peer-reviewed)abstract
- Recent genome-wide association studies (GWAS) of Hodgkin lymphoma (HL) have identified associations with genetic variation at both HLA and non-HLA loci; however, much of heritable HL susceptibility remains unexplained. Here we perform a meta-analysis of three HL GWAS totaling 1,816 cases and 7,877 controls followed by replication in an independent set of 1,281 cases and 3,218 controls to find novel risk loci. We identify a novel variant at 19p13.3 associated with HL (rs1860661; odds ratio (OR) = 0.81, 95% confidence interval (95% CI) = 0.76-0.86, P-combined 3.5 x 10(-10)), located in intron 2 of TCF3 (also known as E2A), a regulator of B-and T-cell lineage commitment known to be involved in HL pathogenesis. This meta-analysis also notes associations between previously published loci at 2p16, 5q31, 6p31, 8q24 and 10p14 and HL subtypes. We conclude that our data suggest a link between the 19p13.3 locus, including TCF3, and HL risk.
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| 9. |
- Johansson, Mattias, et al.
(author)
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The influence of obesity-related factors in the etiology of renal cell carcinoma—A mendelian randomization study
- 2019
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In: PLoS Medicine. - : Public Library of Science (PLoS). - 1549-1277 .- 1549-1676. ; 16:1
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Journal article (peer-reviewed)abstract
- Background: Several obesity-related factors have been associated with renal cell carcinoma (RCC), but it is unclear which individual factors directly influence risk. We addressed this question using genetic markers as proxies for putative risk factors and evaluated their relation to RCC risk in a mendelian randomization (MR) framework. This methodology limits bias due to confounding and is not affected by reverse causation.Methods and findings: Genetic markers associated with obesity measures, blood pressure, lipids, type 2 diabetes, insulin, and glucose were initially identified as instrumental variables, and their association with RCC risk was subsequently evaluated in a genome-wide association study (GWAS) of 10,784 RCC patients and 20,406 control participants in a 2-sample MR framework. The effect on RCC risk was estimated by calculating odds ratios (ORSD) for a standard deviation (SD) increment in each risk factor. The MR analysis indicated that higher body mass index increases the risk of RCC (ORSD: 1.56, 95% confidence interval [CI] 1.44–1.70), with comparable results for waist-to-hip ratio (ORSD: 1.63, 95% CI 1.40–1.90) and body fat percentage (ORSD: 1.66, 95% CI 1.44–1.90). This analysis further indicated that higher fasting insulin (ORSD: 1.82, 95% CI 1.30–2.55) and diastolic blood pressure (DBP; ORSD: 1.28, 95% CI 1.11–1.47), but not systolic blood pressure (ORSD: 0.98, 95% CI 0.84–1.14), increase the risk for RCC. No association with RCC risk was seen for lipids, overall type 2 diabetes, or fasting glucose.Conclusions: This study provides novel evidence for an etiological role of insulin in RCC, as well as confirmatory evidence that obesity and DBP influence RCC risk.
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| 10. |
- Scelo, Ghislaine, et al.
(author)
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Genome-wide association study identifies multiple risk loci for renal cell carcinoma.
- 2017
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In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 8
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Journal article (peer-reviewed)abstract
- Previous genome-wide association studies (GWAS) have identified six risk loci for renal cell carcinoma (RCC). We conducted a meta-analysis of two new scans of 5,198 cases and 7,331 controls together with four existing scans, totalling 10,784 cases and 20,406 controls of European ancestry. Twenty-four loci were tested in an additional 3,182 cases and 6,301 controls. We confirm the six known RCC risk loci and identify seven new loci at 1p32.3 (rs4381241, P=3.1 × 10-10), 3p22.1 (rs67311347, P=2.5 × 10-8), 3q26.2 (rs10936602, P=8.8 × 10-9), 8p21.3 (rs2241261, P=5.8 × 10-9), 10q24.33-q25.1 (rs11813268, P=3.9 × 10-8), 11q22.3 (rs74911261, P=2.1 × 10-10) and 14q24.2 (rs4903064, P=2.2 × 10-24). Expression quantitative trait analyses suggest plausible candidate genes at these regions that may contribute to RCC susceptibility.
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