Published papers
Featured Publications
Association of Body Mass Index With Cardiometabolic Disease in the UK Biobank
Type: article, Author: Donald M. Lyall and Carlos Celis-Morales and Joey Ward, Date: 2017-07-05
Comorbidities in the diseasome are more apparent than real: What Bayesian filtering reveals about the comorbidities of depression.
Type: article, Author: P Marx and P Antal B Bolgar and G Bagdy and B Deakin and G Juhasz , Date: 2017-06-23
Cardiovascular and type 2 diabetes morbidity and all-cause mortality among diverse chronic inflammatory disorders.
Type: article, Author: A Dregan and P Chowienczyk and M Molokhia, Date: 2017-06-10
Last updated on July 7th, 2016
2017 |
UE Ntuk CA Celis-Morales, DF Mackay Sattar JP Pell JM N Association between grip strength and diabetes prevalence in black, South-Asian, and white European ethnic groups: a cross-sectional analysis of 418 656 participants in the UK Biobank study. Journal Article In: Diabetic Medicine, 2017. Abstract | BibTeX | Tags: 7155, diabetes, grip strength @article{Ntuk2017, title = {Association between grip strength and diabetes prevalence in black, South-Asian, and white European ethnic groups: a cross-sectional analysis of 418 656 participants in the UK Biobank study.}, author = {UE Ntuk, CA Celis-Morales, DF Mackay, N Sattar, JP Pell, JM }, year = {2017}, date = {2017-02-01}, journal = {Diabetic Medicine}, abstract = {AIMS: To quantify the extent to which ethnic differences in muscular strength might account for the substantially higher prevalence of diabetes in black and South-Asian compared with white European adults. METHODS: This cross-sectional study used baseline data from the UK Biobank study on 418 656 white European, black and South-Asian participants, aged 40-69 years, who had complete data on diabetes status and hand-grip strength. Associations between hand-grip strength and diabetes were assessed using logistic regression and were adjusted for potential confounding factors. RESULTS: Lower grip strength was associated with higher prevalence of diabetes, independent of confounding factors, across all ethnicities in both men and women. Diabetes prevalence was approximately three- to fourfold higher in South-Asian and two- to threefold higher in black participants compared with white European participants across all levels of grip strength, but grip strength in South-Asian men and women was ~ 5-6 kg lower than in the other ethnic groups. Thus, the attributable risk for diabetes associated with low grip strength was substantially higher in South-Asian participants (3.9 and 4.2 cases per 100 men and women, respectively) than in white participants (2.0 and 0.6 cases per 100 men and women, respectively). Attributable risk associated with low grip strength was also high in black men (4.3 cases) but not in black women (0.4 cases). CONCLUSIONS: Low strength is associated with a disproportionately large number of diabetes cases in South-Asian men and women and in black men. Trials are needed to determine whether interventions to improve strength in these groups could help reduce ethnic inequalities in diabetes prevalence.}, keywords = {7155, diabetes, grip strength}, pubstate = {published}, tppubtype = {article} } AIMS: To quantify the extent to which ethnic differences in muscular strength might account for the substantially higher prevalence of diabetes in black and South-Asian compared with white European adults. METHODS: This cross-sectional study used baseline data from the UK Biobank study on 418 656 white European, black and South-Asian participants, aged 40-69 years, who had complete data on diabetes status and hand-grip strength. Associations between hand-grip strength and diabetes were assessed using logistic regression and were adjusted for potential confounding factors. RESULTS: Lower grip strength was associated with higher prevalence of diabetes, independent of confounding factors, across all ethnicities in both men and women. Diabetes prevalence was approximately three- to fourfold higher in South-Asian and two- to threefold higher in black participants compared with white European participants across all levels of grip strength, but grip strength in South-Asian men and women was ~ 5-6 kg lower than in the other ethnic groups. Thus, the attributable risk for diabetes associated with low grip strength was substantially higher in South-Asian participants (3.9 and 4.2 cases per 100 men and women, respectively) than in white participants (2.0 and 0.6 cases per 100 men and women, respectively). Attributable risk associated with low grip strength was also high in black men (4.3 cases) but not in black women (0.4 cases). CONCLUSIONS: Low strength is associated with a disproportionately large number of diabetes cases in South-Asian men and women and in black men. Trials are needed to determine whether interventions to improve strength in these groups could help reduce ethnic inequalities in diabetes prevalence. |
2016 |
Lyall D. Celis, Anderson Gill Mackay McIntosh Smith Deary Sattar Pell C J J M D A D J I N J Associations between single and multiple cardiometabolic diseases and cognitive abilities in 474 129 UK Biobank participants Journal Article In: European Heart Journal, 2016. Abstract | Links | BibTeX | Tags: cognitive ability, coronary artery disease, diabetes, hypertension @article{LyallDM2016b, title = {Associations between single and multiple cardiometabolic diseases and cognitive abilities in 474 129 UK Biobank participants}, author = {Lyall, D. Celis, C. Anderson, J. Gill, J. M. Mackay, D. McIntosh, A. Smith, D. J. Deary, I. Sattar, N. Pell, J.}, url = {http://eurheartj.oxfordjournals.org/content/early/2016/11/13/eurheartj.ehw528}, year = {2016}, date = {2016-11-16}, journal = {European Heart Journal}, abstract = {Aims Cardiometabolic diseases (hypertension, coronary artery disease [CAD] and diabetes are known to associate with poorer cognitive ability but there are limited data on whether having more than one of these conditions is associated with additive effects. We aimed to quantify the magnitude of their associations with non-demented cognitive abilities and determine the extent to which these associations were additive. Methods and results We examined cognitive test scores in domains of reasoning, information processing speed and memory, included as part of the baseline UK Biobank cohort assessment (N = 474 129 with relevant data), adjusting for a range of potentially confounding variables. The presence of hypertension, CAD and diabetes generally associated with poorer cognitive scores on all tests, compared with a control group that reported none of these diseases. There was evidence of an additive deleterious dose effect of an increasing number of cardiometabolic diseases, for reasoning scores (unstandardized additive dose beta per disease = −0.052 score points out of 13, 95% CI [confidence intervals] −0.063 to − 0.041, P < 0.001), log reaction time scores (exponentiated beta = 1.005, i.e. 0.5% slower, 95% CI 1.004–1.005, P < 0.001) and log memory errors (exponentiated beta = 1.005 i.e. 0.5% more errors; 95% CI 1.003–1.008). Conclusion Cardiometabolic diseases are associated with worse cognitive abilities, and the potential effect of an increasing number of cardiometabolic conditions appears additive. These results reinforce the notion that preventing or delaying cardiovascular disease or diabetes may delay cognitive decline and possible dementia.}, keywords = {cognitive ability, coronary artery disease, diabetes, hypertension}, pubstate = {published}, tppubtype = {article} } Aims Cardiometabolic diseases (hypertension, coronary artery disease [CAD] and diabetes are known to associate with poorer cognitive ability but there are limited data on whether having more than one of these conditions is associated with additive effects. We aimed to quantify the magnitude of their associations with non-demented cognitive abilities and determine the extent to which these associations were additive. Methods and results We examined cognitive test scores in domains of reasoning, information processing speed and memory, included as part of the baseline UK Biobank cohort assessment (N = 474 129 with relevant data), adjusting for a range of potentially confounding variables. The presence of hypertension, CAD and diabetes generally associated with poorer cognitive scores on all tests, compared with a control group that reported none of these diseases. There was evidence of an additive deleterious dose effect of an increasing number of cardiometabolic diseases, for reasoning scores (unstandardized additive dose beta per disease = −0.052 score points out of 13, 95% CI [confidence intervals] −0.063 to − 0.041, P < 0.001), log reaction time scores (exponentiated beta = 1.005, i.e. 0.5% slower, 95% CI 1.004–1.005, P < 0.001) and log memory errors (exponentiated beta = 1.005 i.e. 0.5% more errors; 95% CI 1.003–1.008). Conclusion Cardiometabolic diseases are associated with worse cognitive abilities, and the potential effect of an increasing number of cardiometabolic conditions appears additive. These results reinforce the notion that preventing or delaying cardiovascular disease or diabetes may delay cognitive decline and possible dementia. |
van der der Lotta L.A. Sharp, Burgess Perry Stewart Willems Luan Ardanaz Arriola Balkau Boeing Deloukas Forouhi Franks Grioni Kaaks Key Navarro Nilsson Overvad Palli Panico Quiros Riboli Rolandsson Sacerdote Salamanca-Fernandez Slimani Spijkerman Tjonneland Tumino Van Schouw McCarthy Barroso O'Rahilly Savage Sattar Langenberg Scott Wareham S J S J R I D S M J E L B H P N G P S R T J C P M K D S J R E O C E N A M W A R A D L Y T M I S D B N C R A N J Association between low-density lipoprotein choloesterol-lowering genetic variants and risk of type 2 diabetes Journal Article In: JAMA, 2016. Abstract | Links | BibTeX | Tags: cholesterol, diabetes, LDL, type 2 @article{LottLA2016, title = {Association between low-density lipoprotein choloesterol-lowering genetic variants and risk of type 2 diabetes}, author = {Lotta, L.A. Sharp, S.J. Burgess, S. Perry, J.R. Stewart, I.D. Willems, S.M. Luan, J. Ardanaz, E. Arriola, L. Balkau, B. Boeing, H. Deloukas, P. Forouhi, N.G. Franks, P. Grioni, S. Kaaks, R. Key, T. J. Navarro, C. Nilsson, P.M. Overvad, K. Palli, D. Panico, S. Quiros, J.R. Riboli, E. Rolandsson, O. Sacerdote, C. Salamanca-Fernandez, E. Slimani, N. Spijkerman, A.M.W. Tjonneland, A. Tumino, R. van der A, D.L. Van der Schouw, Y.T. McCarthy, M. Barroso, I. O'Rahilly, S. Savage, D.B. Sattar, N. Langenberg, C. Scott, R.A. Wareham, N.J.}, url = {https://www.ncbi.nlm.nih.gov/pubmed/27701660}, year = {2016}, date = {2016-10-04}, journal = {JAMA}, abstract = {IMPORTANCE: Low-density lipoprotein cholesterol (LDL-C)-lowering alleles in or near NPC1L1 or HMGCR, encoding the respective molecular targets of ezetimibe and statins, have previously been used as proxies to study the efficacy of these lipid-lowering drugs. Alleles near HMGCR are associated with a higher risk of type 2 diabetes, similar to the increased incidence of new-onset diabetes associated with statin treatment in randomized clinical trials. It is unknown whether alleles near NPC1L1 are associated with the risk of type 2 diabetes. OBJECTIVE: To investigate whether LDL-C-lowering alleles in or near NPC1L1 and other genes encoding current or prospective molecular targets of lipid-lowering therapy (ie, HMGCR, PCSK9, ABCG5/G8, LDLR) are associated with the risk of type 2 diabetes. DESIGN, SETTING, AND PARTICIPANTS: The associations with type 2 diabetes and coronary artery disease of LDL-C-lowering genetic variants were investigated in meta-analyses of genetic association studies. Meta-analyses included 50 775 individuals with type 2 diabetes and 270 269 controls and 60 801 individuals with coronary artery disease and 123 504 controls. Data collection took place in Europe and the United States between 1991 and 2016. EXPOSURES: Low-density lipoprotein cholesterol-lowering alleles in or near NPC1L1, HMGCR, PCSK9, ABCG5/G8, and LDLR. MAIN OUTCOMES AND MEASURES: Odds ratios (ORs) for type 2 diabetes and coronary artery disease. RESULTS: Low-density lipoprotein cholesterol-lowering genetic variants at NPC1L1 were inversely associated with coronary artery disease (OR for a genetically predicted 1-mmol/L [38.7-mg/dL] reduction in LDL-C of 0.61 [95% CI, 0.42-0.88]; P = .008) and directly associated with type 2 diabetes (OR for a genetically predicted 1-mmol/L reduction in LDL-C of 2.42 [95% CI, 1.70-3.43]; P < .001). For PCSK9 genetic variants, the OR for type 2 diabetes per 1-mmol/L genetically predicted reduction in LDL-C was 1.19 (95% CI, 1.02-1.38; P = .03). For a given reduction in LDL-C, genetic variants were associated with a similar reduction in coronary artery disease risk (I2 = 0% for heterogeneity in genetic associations; P = .93). However, associations with type 2 diabetes were heterogeneous (I2 = 77.2%; P = .002), indicating gene-specific associations with metabolic risk of LDL-C-lowering alleles. CONCLUSIONS AND RELEVANCE: In this meta-analysis, exposure to LDL-C-lowering genetic variants in or near NPC1L1 and other genes was associated with a higher risk of type 2 diabetes. These data provide insights into potential adverse effects of LDL-C-lowering therapy.}, keywords = {cholesterol, diabetes, LDL, type 2}, pubstate = {published}, tppubtype = {article} } IMPORTANCE: Low-density lipoprotein cholesterol (LDL-C)-lowering alleles in or near NPC1L1 or HMGCR, encoding the respective molecular targets of ezetimibe and statins, have previously been used as proxies to study the efficacy of these lipid-lowering drugs. Alleles near HMGCR are associated with a higher risk of type 2 diabetes, similar to the increased incidence of new-onset diabetes associated with statin treatment in randomized clinical trials. It is unknown whether alleles near NPC1L1 are associated with the risk of type 2 diabetes. OBJECTIVE: To investigate whether LDL-C-lowering alleles in or near NPC1L1 and other genes encoding current or prospective molecular targets of lipid-lowering therapy (ie, HMGCR, PCSK9, ABCG5/G8, LDLR) are associated with the risk of type 2 diabetes. DESIGN, SETTING, AND PARTICIPANTS: The associations with type 2 diabetes and coronary artery disease of LDL-C-lowering genetic variants were investigated in meta-analyses of genetic association studies. Meta-analyses included 50 775 individuals with type 2 diabetes and 270 269 controls and 60 801 individuals with coronary artery disease and 123 504 controls. Data collection took place in Europe and the United States between 1991 and 2016. EXPOSURES: Low-density lipoprotein cholesterol-lowering alleles in or near NPC1L1, HMGCR, PCSK9, ABCG5/G8, and LDLR. MAIN OUTCOMES AND MEASURES: Odds ratios (ORs) for type 2 diabetes and coronary artery disease. RESULTS: Low-density lipoprotein cholesterol-lowering genetic variants at NPC1L1 were inversely associated with coronary artery disease (OR for a genetically predicted 1-mmol/L [38.7-mg/dL] reduction in LDL-C of 0.61 [95% CI, 0.42-0.88]; P = .008) and directly associated with type 2 diabetes (OR for a genetically predicted 1-mmol/L reduction in LDL-C of 2.42 [95% CI, 1.70-3.43]; P < .001). For PCSK9 genetic variants, the OR for type 2 diabetes per 1-mmol/L genetically predicted reduction in LDL-C was 1.19 (95% CI, 1.02-1.38; P = .03). For a given reduction in LDL-C, genetic variants were associated with a similar reduction in coronary artery disease risk (I2 = 0% for heterogeneity in genetic associations; P = .93). However, associations with type 2 diabetes were heterogeneous (I2 = 77.2%; P = .002), indicating gene-specific associations with metabolic risk of LDL-C-lowering alleles. CONCLUSIONS AND RELEVANCE: In this meta-analysis, exposure to LDL-C-lowering genetic variants in or near NPC1L1 and other genes was associated with a higher risk of type 2 diabetes. These data provide insights into potential adverse effects of LDL-C-lowering therapy. |
de Eastwood S. V. Mathur, Atkinson Brophy Sudlow Flaig Lusignan Allen Chaturvedi R M S C R S N N Algorithms for the Capture and Adjudication of Prevalent and Incident Diabetes in UK Biobank Journal Article In: PLoS One, 2016. Abstract | Links | BibTeX | Tags: diabetes, incident, Prevalent @article{EastwoodSV2016, title = {Algorithms for the Capture and Adjudication of Prevalent and Incident Diabetes in UK Biobank}, author = {Eastwood, S. V. Mathur, R. Atkinson, M. Brophy, S. Sudlow, C. Flaig, R. de Lusignan, S. Allen, N. Chaturvedi, N.}, url = {https://www.ncbi.nlm.nih.gov/pubmed/27631769}, year = {2016}, date = {2016-09-16}, journal = {PLoS One}, abstract = {OBJECTIVES: UK Biobank is a UK-wide cohort of 502,655 people aged 40-69, recruited from National Health Service registrants between 2006-10, with healthcare data linkage. Type 2 diabetes is a key exposure and outcome. We developed algorithms to define prevalent and incident diabetes for UK Biobank. The algorithms will be implemented by UK Biobank and their results made available to researchers on request. METHODS: We used UK Biobank self-reported medical history and medication to assign prevalent diabetes and type, and tested this against linked primary and secondary care data in Welsh UK Biobank participants. Additionally, we derived and tested algorithms for incident diabetes using linked primary and secondary care data in the English Clinical Practice Research Datalink, and ran these on secondary care data in UK Biobank. RESULTS AND SIGNIFICANCE: For prevalent diabetes, 0.001% and 0.002% of people classified as "diabetes unlikely" in UK Biobank had evidence of diabetes in their primary or secondary care record respectively. Of those classified as "probable" type 2 diabetes, 75% and 96% had specific type 2 diabetes codes in their primary and secondary care records. For incidence, 95% of people with the type 2 diabetes-specific C10F Read code in primary care had corroborative evidence of diabetes from medications, blood testing or diabetes specific process of care codes. Only 41% of people identified with type 2 diabetes in primary care had secondary care evidence of type 2 diabetes. In contrast, of incident cases using ICD-10 type 2 diabetes specific codes in secondary care, 77% had corroborative evidence of diabetes in primary care. We suggest our definition of prevalent diabetes from UK Biobank baseline data has external validity, and recommend that specific primary care Read codes should be used for incident diabetes to ensure precision. Secondary care data should be used for incident diabetes with caution, as around half of all cases are missed, and a quarter have no corroborative evidence of diabetes in primary care.}, keywords = {diabetes, incident, Prevalent}, pubstate = {published}, tppubtype = {article} } OBJECTIVES: UK Biobank is a UK-wide cohort of 502,655 people aged 40-69, recruited from National Health Service registrants between 2006-10, with healthcare data linkage. Type 2 diabetes is a key exposure and outcome. We developed algorithms to define prevalent and incident diabetes for UK Biobank. The algorithms will be implemented by UK Biobank and their results made available to researchers on request. METHODS: We used UK Biobank self-reported medical history and medication to assign prevalent diabetes and type, and tested this against linked primary and secondary care data in Welsh UK Biobank participants. Additionally, we derived and tested algorithms for incident diabetes using linked primary and secondary care data in the English Clinical Practice Research Datalink, and ran these on secondary care data in UK Biobank. RESULTS AND SIGNIFICANCE: For prevalent diabetes, 0.001% and 0.002% of people classified as "diabetes unlikely" in UK Biobank had evidence of diabetes in their primary or secondary care record respectively. Of those classified as "probable" type 2 diabetes, 75% and 96% had specific type 2 diabetes codes in their primary and secondary care records. For incidence, 95% of people with the type 2 diabetes-specific C10F Read code in primary care had corroborative evidence of diabetes from medications, blood testing or diabetes specific process of care codes. Only 41% of people identified with type 2 diabetes in primary care had secondary care evidence of type 2 diabetes. In contrast, of incident cases using ICD-10 type 2 diabetes specific codes in secondary care, 77% had corroborative evidence of diabetes in primary care. We suggest our definition of prevalent diabetes from UK Biobank baseline data has external validity, and recommend that specific primary care Read codes should be used for incident diabetes to ensure precision. Secondary care data should be used for incident diabetes with caution, as around half of all cases are missed, and a quarter have no corroborative evidence of diabetes in primary care. |
Jessica Tyrrell Hanieh Yaghootkar, Robin Beaumont Samuel Jones Ryan Ames Marcus Tuke Katherine Ruth Zoltán Kutalik Rachel Freathy Anna Murray Andrew Wood Michael Weedon Timothy Frayling E M A S M R N M Gene-obesogenic environment interactions in the UK Biobank study Presentation 14.09.2016. Abstract | Links | BibTeX | Tags: 9072, diabetes @misc{Tyrrell2016b, title = {Gene-obesogenic environment interactions in the UK Biobank study}, author = {Jessica Tyrrell, Hanieh Yaghootkar, Robin Beaumont, Samuel E Jones, Ryan M Ames, Marcus A Tuke, Katherine S Ruth, Zoltán Kutalik, Rachel M Freathy, Anna Murray, Andrew R Wood, Michael N Weedon, Timothy M Frayling}, url = {http://www.easd.org/index.php?option=com_content&view=article&id=69&Itemid=509}, year = {2016}, date = {2016-09-14}, abstract = {Background and Aims Susceptibility to obesity and type 2 diabetes in today’s environment has a strong genetic component. However, little is known about how genetic variation interacts with the modern environment to predispose some individuals to obesity and type 2 diabetes whilst others remain slim. Previous gene-obesogenic environment studies have been limited by the need to perform meta-analyses of many heterogeneous studies. We aimed to use 120,000 individuals from the UK Biobank to test the hypothesis that high risk obesogenic environments accentuate genetic susceptibility to obesity and therefore increase type 2 diabetes risk. Materials and Methods We used 120,000 individuals from the UK Biobank study to test the hypothesis that high risk obesogenic environments and behaviours accentuate genetic susceptibility to obesity. We used BMI as the outcome and genetics and self-reported estimates of the obesogenic environment as exposures. We used a 69-variant genetic risk score (GRS) for obesity as the genetic exposure and 9 self-reported measures, including TV watching, westernised diet and physical activity and a composite of these factors, as obesogenic environment/behaviour exposures. We tested the association of the genetic risk score with BMI in high and low environment groups and tested for interactions. Results The self-reported measures of the obesogenic environment and behaviour were all associated with BMI in the expected directions (all P<0.001). We found evidence of gene-environment interactions with self-reported TV-watching (Pinteraction=7x10-5), and self-reported physical activity (Pinteraction=5x10-6). For example, within individuals reporting watching ≥4 hours TV per day, carrying 10 additional BMI-raising alleles was associated with approximately 4.0kg extra weight in someone 1.73m tall. In contrast, within individuals reporting watching <4 hours TV per day, carrying 10 additional BMI-raising alleles was associated with approximately 3.1kg extra weight. Evidence of interaction using a composite measure of the obesogenic environment (Pinteraction=2x10-4) and permutations of the data based on randomly selecting groups of individuals of different BMIs, suggested that these differences were not specific to one aspect of the environment. The main limitations of our findings are that the environmental measures are complex mixes of environment and behaviour and are based on self-report. Conclusions Our findings suggest that the there is no particular aspect of the environment or behaviour that if altered would have a preferential benefit over others. It is premature to suggest public health measures should be targeted specifically at fried food reduction, fizzy drink consumption and diet in those genetically predisposed to obesity. Instead, public health measures aiming to alter all aspects of the obesogenic environment in small ways may have more impact in lowering the prevalence of obesity and type 2 diabetes than targeting a single or few aspects. }, keywords = {9072, diabetes}, pubstate = {published}, tppubtype = {presentation} } Background and Aims Susceptibility to obesity and type 2 diabetes in today’s environment has a strong genetic component. However, little is known about how genetic variation interacts with the modern environment to predispose some individuals to obesity and type 2 diabetes whilst others remain slim. Previous gene-obesogenic environment studies have been limited by the need to perform meta-analyses of many heterogeneous studies. We aimed to use 120,000 individuals from the UK Biobank to test the hypothesis that high risk obesogenic environments accentuate genetic susceptibility to obesity and therefore increase type 2 diabetes risk. Materials and Methods We used 120,000 individuals from the UK Biobank study to test the hypothesis that high risk obesogenic environments and behaviours accentuate genetic susceptibility to obesity. We used BMI as the outcome and genetics and self-reported estimates of the obesogenic environment as exposures. We used a 69-variant genetic risk score (GRS) for obesity as the genetic exposure and 9 self-reported measures, including TV watching, westernised diet and physical activity and a composite of these factors, as obesogenic environment/behaviour exposures. We tested the association of the genetic risk score with BMI in high and low environment groups and tested for interactions. Results The self-reported measures of the obesogenic environment and behaviour were all associated with BMI in the expected directions (all P<0.001). We found evidence of gene-environment interactions with self-reported TV-watching (Pinteraction=7x10-5), and self-reported physical activity (Pinteraction=5x10-6). For example, within individuals reporting watching ≥4 hours TV per day, carrying 10 additional BMI-raising alleles was associated with approximately 4.0kg extra weight in someone 1.73m tall. In contrast, within individuals reporting watching <4 hours TV per day, carrying 10 additional BMI-raising alleles was associated with approximately 3.1kg extra weight. Evidence of interaction using a composite measure of the obesogenic environment (Pinteraction=2x10-4) and permutations of the data based on randomly selecting groups of individuals of different BMIs, suggested that these differences were not specific to one aspect of the environment. The main limitations of our findings are that the environmental measures are complex mixes of environment and behaviour and are based on self-report. Conclusions Our findings suggest that the there is no particular aspect of the environment or behaviour that if altered would have a preferential benefit over others. It is premature to suggest public health measures should be targeted specifically at fried food reduction, fizzy drink consumption and diet in those genetically predisposed to obesity. Instead, public health measures aiming to alter all aspects of the obesogenic environment in small ways may have more impact in lowering the prevalence of obesity and type 2 diabetes than targeting a single or few aspects. |
Samuel E. Jones Jessica Tyrrell, Andrew Wood Robin Beaumont Katherine Ruth Marcus Tuke Hanieh Yaghootkar Youna Hu Maris Teder-Laving Caroline Hayward Till Roenneberg James Wilson Fabiola Del Greco Andrew Hicks Chol Shin Chang-Ho Yun Seung Ku Lee Andres Metspalu Enda Byrne Philip Gehrman Henning Tiemeier Karla Allebrandt Rachel Freathy Anna Murray David Hinds Timothy Frayling Michael Weedon R N S A F A M R V M A M N Genome-Wide Association Analyses in 128,266 Individuals Identifies New Morningness and Sleep Duration Loci Journal Article In: PLOS Genetics, 2016. Abstract | Links | BibTeX | Tags: 9055, circadian rhythm, diabetes @article{Jones2016b, title = {Genome-Wide Association Analyses in 128,266 Individuals Identifies New Morningness and Sleep Duration Loci}, author = {Samuel E. Jones, Jessica Tyrrell, Andrew R. Wood, Robin N. Beaumont, Katherine S. Ruth, Marcus A. Tuke, Hanieh Yaghootkar, Youna Hu, Maris Teder-Laving, Caroline Hayward, Till Roenneberg, James F. Wilson, Fabiola Del Greco, Andrew A. Hicks, Chol Shin, Chang-Ho Yun, Seung Ku Lee, Andres Metspalu, Enda M. Byrne, Philip R. Gehrman, Henning Tiemeier, Karla V. Allebrandt, Rachel M. Freathy, Anna Murray, David A. Hinds, Timothy M. Frayling , Michael N. Weedon}, url = {http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1006125}, year = {2016}, date = {2016-08-05}, journal = {PLOS Genetics}, abstract = {Disrupted circadian rhythms and reduced sleep duration are associated with several human diseases, particularly obesity and type 2 diabetes, but until recently, little was known about the genetic factors influencing these heritable traits. We performed genome-wide association studies of self-reported chronotype (morning/evening person) and self-reported sleep duration in 128,266 white British individuals from the UK Biobank study. Sixteen variants were associated with chronotype (P<5x10-8), including variants near the known circadian rhythm genes RGS16 (1.21 odds of morningness, 95% CI [1.15, 1.27], P = 3x10-12) and PER2 (1.09 odds of morningness, 95% CI [1.06, 1.12], P = 4x10-10). The PER2 signal has previously been associated with iris function. We sought replication using self-reported data from 89,283 23andMe participants; thirteen of the chronotype signals remained associated at P<5x10-8 on meta-analysis and eleven of these reached P<0.05 in the same direction in the 23andMe study. We also replicated 9 additional variants identified when the 23andMe study was used as a discovery GWAS of chronotype (all P<0.05 and meta-analysis P<5x10-8). For sleep duration, we replicated one known signal in PAX8 (2.6 minutes per allele, 95% CI [1.9, 3.2], P = 5.7x10-16) and identified and replicated two novel associations at VRK2 (2.0 minutes per allele, 95% CI [1.3, 2.7], P = 1.2x10-9; and 1.6 minutes per allele, 95% CI [1.1, 2.2], P = 7.6x10-9). Although we found genetic correlation between chronotype and BMI (rG = 0.056, P = 0.05); undersleeping and BMI (rG = 0.147, P = 1x10-5) and oversleeping and BMI (rG = 0.097, P = 0.04), Mendelian Randomisation analyses, with limited power, provided no consistent evidence of causal associations between BMI or type 2 diabetes and chronotype or sleep duration. Our study brings the total number of loci associated with chronotype to 22 and with sleep duration to three, and provides new insights into the biology of sleep and circadian rhythms in humans.}, keywords = {9055, circadian rhythm, diabetes}, pubstate = {published}, tppubtype = {article} } Disrupted circadian rhythms and reduced sleep duration are associated with several human diseases, particularly obesity and type 2 diabetes, but until recently, little was known about the genetic factors influencing these heritable traits. We performed genome-wide association studies of self-reported chronotype (morning/evening person) and self-reported sleep duration in 128,266 white British individuals from the UK Biobank study. Sixteen variants were associated with chronotype (P<5x10-8), including variants near the known circadian rhythm genes RGS16 (1.21 odds of morningness, 95% CI [1.15, 1.27], P = 3x10-12) and PER2 (1.09 odds of morningness, 95% CI [1.06, 1.12], P = 4x10-10). The PER2 signal has previously been associated with iris function. We sought replication using self-reported data from 89,283 23andMe participants; thirteen of the chronotype signals remained associated at P<5x10-8 on meta-analysis and eleven of these reached P<0.05 in the same direction in the 23andMe study. We also replicated 9 additional variants identified when the 23andMe study was used as a discovery GWAS of chronotype (all P<0.05 and meta-analysis P<5x10-8). For sleep duration, we replicated one known signal in PAX8 (2.6 minutes per allele, 95% CI [1.9, 3.2], P = 5.7x10-16) and identified and replicated two novel associations at VRK2 (2.0 minutes per allele, 95% CI [1.3, 2.7], P = 1.2x10-9; and 1.6 minutes per allele, 95% CI [1.1, 2.2], P = 7.6x10-9). Although we found genetic correlation between chronotype and BMI (rG = 0.056, P = 0.05); undersleeping and BMI (rG = 0.147, P = 1x10-5) and oversleeping and BMI (rG = 0.097, P = 0.04), Mendelian Randomisation analyses, with limited power, provided no consistent evidence of causal associations between BMI or type 2 diabetes and chronotype or sleep duration. Our study brings the total number of loci associated with chronotype to 22 and with sleep duration to three, and provides new insights into the biology of sleep and circadian rhythms in humans. |
Samuel E. Jones Jessica Tyrrell, Andrew Wood Robin Beaumont Katherine Ruth Marcus Tuke Hanieh Yaghootkar Maris Teder-Laving Caroline Hayward Till Roenneberg James Wilson Fabiola Del Greco Andrew Hicks Andres Metspalu Enda Byrne Philip Gehrman Henning Tiemeier Karla Allebrandt Rachel Freathy Anna Murray Timothy Frayling Michael Weedon R N S A F A M R V M M N Genetic Studies of Sleep and Morningness and their Relationship with Obesity and Type 2 Diabetes. Presentation 13.06.2016. Abstract | Links | BibTeX | Tags: 9055, 9072, chronotype, diabetes @misc{Jones2016, title = {Genetic Studies of Sleep and Morningness and their Relationship with Obesity and Type 2 Diabetes. }, author = {Samuel E. Jones, Jessica Tyrrell, Andrew R. Wood, Robin N. Beaumont, Katherine S. Ruth, Marcus A. Tuke, Hanieh Yaghootkar, Maris Teder-Laving, Caroline Hayward, Till Roenneberg, James F. Wilson, Fabiola Del Greco, Andrew A. Hicks, Andres Metspalu, Enda M. Byrne, Philip R. Gehrman, Henning Tiemeier, Karla V. Allebrandt, Rachel M. Freathy, Anna Murray, Timothy M. Frayling, Michael N. Weedon}, url = {http://www.abstractsonline.com/pp8/#!/4008/session/192}, year = {2016}, date = {2016-06-13}, abstract = {Presented at the American Diabetes Association 13 June 2016. Chronotype (morning or evening person) and sleep duration are associated with obesity and type 2 diabetes but the causal directions of these associations are uncertain. Using 107,634 individuals from the UK Biobank study and self-reported "morningness", we generated a chronotype score and observed strong associations between this score and BMI (effect size = -0.08 kg/m2 per unit score increase in "morningness", P=1.7x10-12) and type 2 diabetes (OR = 0.94 per unit score increase in "morningness", P= 4.1x10-6) and between sleep duration and BMI (effect size = -0.09 kg/m2 per extra hour slept, P=4.6x10-12) and type 2 diabetes (OR = 1.03 per extra hour slept, P=3.1x10-2). We performed a genome wide association study to identify genetic variants associated with chronotype and sleep duration. We identified 9 loci (P<5x10-8) influencing chronotype. The top signal was in RGS16 which has a known role in circadian rhythm regulation. Other signals include a variant downstream of TRAF3IP1 which has previously been associated with iris development and function, and another within HTR6, a serotonin receptor gene. We also replicated a previously reported association with sleep duration (0.04 hours per allele, P=7x10-14) near PAX8 and identified two putative variants in VRK2 (0.033 and 0.027 hours per allele P=1.2x10-9, P=7.6x10-9). To investigate the link between obesity and type 2 diabetes with chronotype and sleep duration, we performed Mendelian randomization. We constructed weighted genetic risk scores for BMI and type 2 diabetes, using previously published results. We found evidence that higher BMI has a causal effect on Chronotype (P=8x10-3) with a 1 SD higher BMI (4.6kgm2) being causally associated with a 0.06 SD move towards morningness. We found no evidence for the effect of either BMI (P=0.44) or type 2 diabetes risk (P=0.35) on sleep duration. In conclusion we identified novel variants associated with sleep phenotypes and showed that higher BMI has a likely causal effect on chronotype.}, keywords = {9055, 9072, chronotype, diabetes}, pubstate = {published}, tppubtype = {presentation} } Presented at the American Diabetes Association 13 June 2016. Chronotype (morning or evening person) and sleep duration are associated with obesity and type 2 diabetes but the causal directions of these associations are uncertain. Using 107,634 individuals from the UK Biobank study and self-reported "morningness", we generated a chronotype score and observed strong associations between this score and BMI (effect size = -0.08 kg/m2 per unit score increase in "morningness", P=1.7x10-12) and type 2 diabetes (OR = 0.94 per unit score increase in "morningness", P= 4.1x10-6) and between sleep duration and BMI (effect size = -0.09 kg/m2 per extra hour slept, P=4.6x10-12) and type 2 diabetes (OR = 1.03 per extra hour slept, P=3.1x10-2). We performed a genome wide association study to identify genetic variants associated with chronotype and sleep duration. We identified 9 loci (P<5x10-8) influencing chronotype. The top signal was in RGS16 which has a known role in circadian rhythm regulation. Other signals include a variant downstream of TRAF3IP1 which has previously been associated with iris development and function, and another within HTR6, a serotonin receptor gene. We also replicated a previously reported association with sleep duration (0.04 hours per allele, P=7x10-14) near PAX8 and identified two putative variants in VRK2 (0.033 and 0.027 hours per allele P=1.2x10-9, P=7.6x10-9). To investigate the link between obesity and type 2 diabetes with chronotype and sleep duration, we performed Mendelian randomization. We constructed weighted genetic risk scores for BMI and type 2 diabetes, using previously published results. We found evidence that higher BMI has a causal effect on Chronotype (P=8x10-3) with a 1 SD higher BMI (4.6kgm2) being causally associated with a 0.06 SD move towards morningness. We found no evidence for the effect of either BMI (P=0.44) or type 2 diabetes risk (P=0.35) on sleep duration. In conclusion we identified novel variants associated with sleep phenotypes and showed that higher BMI has a likely causal effect on chronotype. |
Hanieh Yaghootkar Luca A. Lotta, Jessica Tyrrell Roelof Smit Sam Jones Louise Donnelly Robin Beaumont Archie Campbell Marcus Tuke Caroline Hayward Katherine Ruth Sandosh Padmanabhan Wouter Jukema Colin Palmer Andrew Hattersley Rachel Freathy Claudia Langenberg Nicholas Wareham Andrew Wood Anna Murray Michael Weedon Naveed Sattar Ewan Pearson Robert Scott A J E A S J C M J R N A; Frayling, Timothy M Genetic evidence for a link between favorable adiposity and lower risk of type 2 diabetes, hypertension and heart disease. Journal Article In: Diabetes, 2016. Abstract | Links | BibTeX | Tags: 9055, diabetes, genetics @article{Yaghootkar2016, title = {Genetic evidence for a link between favorable adiposity and lower risk of type 2 diabetes, hypertension and heart disease.}, author = {Hanieh Yaghootkar, Luca A. Lotta, Jessica Tyrrell, Roelof A. J. Smit, Sam E. Jones, Louise Donnelly, Robin Beaumont, Archie Campbell, Marcus A. Tuke, Caroline Hayward, Katherine S. Ruth, Sandosh Padmanabhan, J. Wouter Jukema, Colin C. Palmer, Andrew Hattersley, Rachel M. Freathy, Claudia Langenberg, Nicholas J. Wareham, Andrew R. Wood, Anna Murray, Michael N. Weedon, Naveed Sattar, Ewan Pearson, Robert A. Scott and Timothy M. Frayling }, url = {http://diabetes.diabetesjournals.org/content/early/2016/04/25/db15-1671.abstract}, year = {2016}, date = {2016-04-27}, journal = {Diabetes}, abstract = {Recent genetic studies have identified some alleles associated with higher BMI but lower risk of type 2 diabetes, hypertension and heart disease. These “favorable adiposity” alleles are collectively associated with lower insulin levels and higher subcutaneous-to-visceral adipose tissue ratio and may protect from disease through higher adipose storage capacity. We aimed to use data from 164,609 individuals from the UK Biobank and five other studies to replicate associations between a genetic score of 11 favorable adiposity variants and adiposity and risk of disease, test for interactions between BMI and favorable adiposity genetics and test effects separately in men and women. In the UK Biobank the 50% of individuals carrying the most favorable adiposity alleles had higher BMIs (0.120 Kg/m2 [0.066,0.174]; p=1E-5) and higher body fat percentage (0.301 % [0.230,0.372]; p=1E-16) compared to the 50% of individuals carrying the fewest alleles. For a given BMI, the 50% of individuals carrying the most favourable adiposity alleles were at: 0.837 OR [0.784,0.894] lower risk of type 2 diabetes (p=1E-7), -0.859 mmHg [-1.099,-0.618] lower systolic (p=3E-12) and -0.394 mmHg [-0.534,-0.254] lower diastolic blood pressure (p=4E-8), 0.935 OR [0.911,0.958] lower risk of hypertension (p=1E-7) and 0.921 OR [0.872,0.973] lower risk of heart disease (p=3E-3). In women, these associations could be explained by the observation that the alleles associated with higher BMI but lower risk of disease were also associated with a favourable body fat distribution, with a lower waist-hip ratio (-0.004 [-0.005,-0.003] 50% vs 50%; p=3E-14) but in men, the favourable adiposity alleles were associated with higher waist circumference (0.454 cm [0.267,0.641] 50% vs 50%; p=2E-6) and higher waist-hip ratio (0.0013 [0.0003,0.0024] 50% vs 50%; p=0.01). Results were strengthened when meta-analysing with five additional studies. There was no evidence of interaction between a genetic score consisting of known BMI variants and the favorable adiposity genetic score. In conclusion, different molecular mechanisms that lead to higher body fat percentage (with greater subcutaneous storage capacity) can have different impacts on cardiometabolic disease risk. While higher BMI is associated with higher risk of diseases, better fat storage capacity could reduce the risk. }, keywords = {9055, diabetes, genetics}, pubstate = {published}, tppubtype = {article} } Recent genetic studies have identified some alleles associated with higher BMI but lower risk of type 2 diabetes, hypertension and heart disease. These “favorable adiposity” alleles are collectively associated with lower insulin levels and higher subcutaneous-to-visceral adipose tissue ratio and may protect from disease through higher adipose storage capacity. We aimed to use data from 164,609 individuals from the UK Biobank and five other studies to replicate associations between a genetic score of 11 favorable adiposity variants and adiposity and risk of disease, test for interactions between BMI and favorable adiposity genetics and test effects separately in men and women. In the UK Biobank the 50% of individuals carrying the most favorable adiposity alleles had higher BMIs (0.120 Kg/m2 [0.066,0.174]; p=1E-5) and higher body fat percentage (0.301 % [0.230,0.372]; p=1E-16) compared to the 50% of individuals carrying the fewest alleles. For a given BMI, the 50% of individuals carrying the most favourable adiposity alleles were at: 0.837 OR [0.784,0.894] lower risk of type 2 diabetes (p=1E-7), -0.859 mmHg [-1.099,-0.618] lower systolic (p=3E-12) and -0.394 mmHg [-0.534,-0.254] lower diastolic blood pressure (p=4E-8), 0.935 OR [0.911,0.958] lower risk of hypertension (p=1E-7) and 0.921 OR [0.872,0.973] lower risk of heart disease (p=3E-3). In women, these associations could be explained by the observation that the alleles associated with higher BMI but lower risk of disease were also associated with a favourable body fat distribution, with a lower waist-hip ratio (-0.004 [-0.005,-0.003] 50% vs 50%; p=3E-14) but in men, the favourable adiposity alleles were associated with higher waist circumference (0.454 cm [0.267,0.641] 50% vs 50%; p=2E-6) and higher waist-hip ratio (0.0013 [0.0003,0.0024] 50% vs 50%; p=0.01). Results were strengthened when meta-analysing with five additional studies. There was no evidence of interaction between a genetic score consisting of known BMI variants and the favorable adiposity genetic score. In conclusion, different molecular mechanisms that lead to higher body fat percentage (with greater subcutaneous storage capacity) can have different impacts on cardiometabolic disease risk. While higher BMI is associated with higher risk of diseases, better fat storage capacity could reduce the risk. |
Sophie Cassidy Josephine Y Chau, Michael Catt Adrian Bauman Michael Trenell In: BMJ Open, 2016. Abstract | Links | BibTeX | Tags: 12184, cardiovascular, diabetes, sleep @article{Cassidy2016, title = {Cross-sectional study of diet, physical activity, television viewing and sleep duration in 233 110 adults from the UK Biobank; the behavioural phenotype of cardiovascular disease and type 2 diabetes }, author = { Sophie Cassidy, Josephine Y Chau, Michael Catt, Adrian Bauman, Michael Trenell}, url = {http://bmjopen.bmj.com/content/6/3/e010038.short?rss=1}, year = {2016}, date = {2016-03-16}, journal = {BMJ Open}, abstract = { Objectives Simultaneously define diet, physical activity, television (TV) viewing, and sleep duration across cardiometabolic disease groups, and investigate clustering of non-diet lifestyle behaviours. Design Cross-sectional observational study. Setting 22 UK Biobank assessment centres across the UK. Participants 502 664 adults aged 37–63 years old, 54% women. 4 groups were defined based on disease status; ‘No disease’ (n=103 993), ‘cardiovascular disease’ (CVD n=113 469), ‘Type 2 diabetes without CVD’ (n=4074) and ‘Type 2 diabetes + CVD’ (n=11 574). Main outcomes Diet, physical activity, TV viewing and sleep duration. Results People with ‘CVD’ report low levels of physical activity (<918 MET min/week, OR (95% CI) 1.23 (1.20 to 1.25)), high levels of TV viewing (>3 h/day; 1.42 (1.39 to 1.45)), and poor sleep duration (<7, >8 h/night; 1.37 (1.34 to 1.39)) relative to people without disease. People with ‘Type 2 diabetes + CVD’ were more likely to report low physical activity (1.71 (1.64 to 1.78)), high levels of TV viewing (1.92 (1.85 to 1.99)) and poor sleep duration (1.52 (1.46 to1.58)) relative to people without disease. Non-diet behaviours were clustered, with people with ‘CVD’ or ‘Type 2 diabetes + CVD’ more likely to report simultaneous low physical activity, high TV viewing and poor sleep duration than those without disease (2.15 (2.03 to 2.28) and 3.29 (3.02 to 3.58), respectively). By contrast, 3 in 4 adults with ‘Type 2 diabetes’, and 2 in 4 adults with ‘CVD’ have changed their diet in the past 5 years, compared with only 1 in 4 in the ‘No disease’ group. Models were adjusted for gender, age, body mass index, Townsend Deprivation Index, ethnicity, alcohol intake, smoking and meeting fruit/vegetable guidelines. Conclusions Low physical activity, high TV and poor sleep duration are prominent unaddressed high-risk characteristics of both CVD and type 2 diabetes, and are likely to be clustered together. }, keywords = {12184, cardiovascular, diabetes, sleep}, pubstate = {published}, tppubtype = {article} } Objectives Simultaneously define diet, physical activity, television (TV) viewing, and sleep duration across cardiometabolic disease groups, and investigate clustering of non-diet lifestyle behaviours. Design Cross-sectional observational study. Setting 22 UK Biobank assessment centres across the UK. Participants 502 664 adults aged 37–63 years old, 54% women. 4 groups were defined based on disease status; ‘No disease’ (n=103 993), ‘cardiovascular disease’ (CVD n=113 469), ‘Type 2 diabetes without CVD’ (n=4074) and ‘Type 2 diabetes + CVD’ (n=11 574). Main outcomes Diet, physical activity, TV viewing and sleep duration. Results People with ‘CVD’ report low levels of physical activity (<918 MET min/week, OR (95% CI) 1.23 (1.20 to 1.25)), high levels of TV viewing (>3 h/day; 1.42 (1.39 to 1.45)), and poor sleep duration (<7, >8 h/night; 1.37 (1.34 to 1.39)) relative to people without disease. People with ‘Type 2 diabetes + CVD’ were more likely to report low physical activity (1.71 (1.64 to 1.78)), high levels of TV viewing (1.92 (1.85 to 1.99)) and poor sleep duration (1.52 (1.46 to1.58)) relative to people without disease. Non-diet behaviours were clustered, with people with ‘CVD’ or ‘Type 2 diabetes + CVD’ more likely to report simultaneous low physical activity, high TV viewing and poor sleep duration than those without disease (2.15 (2.03 to 2.28) and 3.29 (3.02 to 3.58), respectively). By contrast, 3 in 4 adults with ‘Type 2 diabetes’, and 2 in 4 adults with ‘CVD’ have changed their diet in the past 5 years, compared with only 1 in 4 in the ‘No disease’ group. Models were adjusted for gender, age, body mass index, Townsend Deprivation Index, ethnicity, alcohol intake, smoking and meeting fruit/vegetable guidelines. Conclusions Low physical activity, high TV and poor sleep duration are prominent unaddressed high-risk characteristics of both CVD and type 2 diabetes, and are likely to be clustered together. |
S Cassidy JY Chau JY, Catt Bauman MI Trenell M M A A M In: BMJ Open, 2016. Abstract | Links | BibTeX | Tags: 12184, cardiovascular health, diabetes @article{Cassidy2016b, title = {Cross-sectional study of diet, physical activity, television viewing and sleep duration in 233 110 adults from the UK Biobank; the behavioural phenotype of cardiovascular disease and type 2 diabetes.}, author = {S Cassidy, JY Chau JY, M Catt M, A Bauman A, MI Trenell M}, url = {http://bmjopen.bmj.com/content/6/3/e010038.long}, year = {2016}, date = {2016-03-15}, journal = {BMJ Open}, abstract = {Simultaneously define diet, physical activity, television (TV) viewing, and sleep duration across cardiometabolic disease groups, and investigate clustering of non-diet lifestyle behaviours. DESIGN: Cross-sectional observational study. SETTING: 22 UK Biobank assessment centres across the UK. PARTICIPANTS: 502 664 adults aged 37-63 years old, 54% women. 4 groups were defined based on disease status; 'No disease' (n=103 993), 'cardiovascular disease' (CVD n=113 469), 'Type 2 diabetes without CVD' (n=4074) and 'Type 2 diabetes + CVD' (n=11 574). MAIN OUTCOMES: Diet, physical activity, TV viewing and sleep duration. RESULTS: People with 'CVD' report low levels of physical activity (<918 MET min/week, OR (95% CI) 1.23 (1.20 to 1.25)), high levels of TV viewing (>3 h/day; 1.42 (1.39 to 1.45)), and poor sleep duration (<7, >8 h/night; 1.37 (1.34 to 1.39)) relative to people without disease. People with 'Type 2 diabetes + CVD' were more likely to report low physical activity (1.71 (1.64 to 1.78)), high levels of TV viewing (1.92 (1.85 to 1.99)) and poor sleep duration (1.52 (1.46 to1.58)) relative to people without disease. Non-diet behaviours were clustered, with people with 'CVD' or 'Type 2 diabetes + CVD' more likely to report simultaneous low physical activity, high TV viewing and poor sleep duration than those without disease (2.15 (2.03 to 2.28) and 3.29 (3.02 to 3.58), respectively). By contrast, 3 in 4 adults with 'Type 2 diabetes', and 2 in 4 adults with 'CVD' have changed their diet in the past 5 years, compared with only 1 in 4 in the 'No disease' group. Models were adjusted for gender, age, body mass index, Townsend Deprivation Index, ethnicity, alcohol intake, smoking and meeting fruit/vegetable guidelines. CONCLUSIONS: Low physical activity, high TV and poor sleep duration are prominent unaddressed high-risk characteristics of both CVD and type 2 diabetes, and are likely to be clustered together.}, keywords = {12184, cardiovascular health, diabetes}, pubstate = {published}, tppubtype = {article} } Simultaneously define diet, physical activity, television (TV) viewing, and sleep duration across cardiometabolic disease groups, and investigate clustering of non-diet lifestyle behaviours. DESIGN: Cross-sectional observational study. SETTING: 22 UK Biobank assessment centres across the UK. PARTICIPANTS: 502 664 adults aged 37-63 years old, 54% women. 4 groups were defined based on disease status; 'No disease' (n=103 993), 'cardiovascular disease' (CVD n=113 469), 'Type 2 diabetes without CVD' (n=4074) and 'Type 2 diabetes + CVD' (n=11 574). MAIN OUTCOMES: Diet, physical activity, TV viewing and sleep duration. RESULTS: People with 'CVD' report low levels of physical activity (<918 MET min/week, OR (95% CI) 1.23 (1.20 to 1.25)), high levels of TV viewing (>3 h/day; 1.42 (1.39 to 1.45)), and poor sleep duration (<7, >8 h/night; 1.37 (1.34 to 1.39)) relative to people without disease. People with 'Type 2 diabetes + CVD' were more likely to report low physical activity (1.71 (1.64 to 1.78)), high levels of TV viewing (1.92 (1.85 to 1.99)) and poor sleep duration (1.52 (1.46 to1.58)) relative to people without disease. Non-diet behaviours were clustered, with people with 'CVD' or 'Type 2 diabetes + CVD' more likely to report simultaneous low physical activity, high TV viewing and poor sleep duration than those without disease (2.15 (2.03 to 2.28) and 3.29 (3.02 to 3.58), respectively). By contrast, 3 in 4 adults with 'Type 2 diabetes', and 2 in 4 adults with 'CVD' have changed their diet in the past 5 years, compared with only 1 in 4 in the 'No disease' group. Models were adjusted for gender, age, body mass index, Townsend Deprivation Index, ethnicity, alcohol intake, smoking and meeting fruit/vegetable guidelines. CONCLUSIONS: Low physical activity, high TV and poor sleep duration are prominent unaddressed high-risk characteristics of both CVD and type 2 diabetes, and are likely to be clustered together. |
consortium Andrew R. Wood Jessica Tyrrell, Robin Beaumont Samuel Jones Marcus Tuke Katherine Ruth The GIANT Hanieh Yaghootkar Rachel Freathy E A S M Variants in the FTO and CDKAL1 loci have recessive effects on risk of obesity and type 2 diabetes, respectively Journal Article In: Diabetologia, 2016. Abstract | Links | BibTeX | Tags: diabetes, genetics @article{Wood2016, title = {Variants in the FTO and CDKAL1 loci have recessive effects on risk of obesity and type 2 diabetes, respectively}, author = {Andrew R. Wood, Jessica Tyrrell, Robin Beaumont, Samuel E. Jones, Marcus A. Tuke, Katherine S. Ruth, The GIANT consortium, Hanieh Yaghootkar, Rachel M. Freathy }, url = {http://link.springer.com/article/10.1007/s00125-016-3908-5}, year = {2016}, date = {2016-03-10}, journal = {Diabetologia}, abstract = {Genome-wide association (GWA) studies have identified hundreds of common genetic variants associated with obesity and type 2 diabetes. These studies have usually focused on additive association tests. Identifying deviations from additivity may provide new biological insights and explain some of the missing heritability for these diseases. Methods We performed a GWA study using a dominance deviation model for BMI, obesity (29,925 cases) and type 2 diabetes (4,040 cases) in 120,286 individuals of British ancestry from the UK Biobank study. We also investigated whether single nucleotide polymorphisms previously shown to be associated with these traits showed any enrichment for departures from additivity. Results Known obesity-associated variants in FTO showed strong evidence of deviation from additivity (p DOMDEV = 3 × 10−5) through a recessive effect of the allele associated with higher BMI. The average BMI of individuals carrying zero, one or two BMI-raising alleles was 27.27 (95% CI 27.22, 27.31) kg/m2, 27.54 (95% CI 27.50, 27.58) kg/m2 and 28.07 (95% CI 28.00, 28.14) kg/m2, respectively. A similar effect was observed in 105,643 individuals from the GIANT Consortium (p DOMDEV = 0.003; meta-analysis p DOMDEV = 1 × 10−7). For type 2 diabetes, we detected a recessive effect (p DOMDEV = 5 × 10−4) at CDKAL1. Relative to homozygous non-risk allele carriers, homozygous risk allele carriers had an OR of 1.48 (95% CI 1.32, 1.65), while the heterozygous group had an OR of 1.06 (95% CI 0.99, 1.14), a result consistent with that of a previous study. We did not identify any novel associations at genome-wide significance. Conclusions/interpretation Although we found no evidence of widespread non-additive genetic effects contributing to obesity and type 2 diabetes risk, we did find robust examples of recessive effects at the FTO and CDKAL1 loci}, keywords = {diabetes, genetics}, pubstate = {published}, tppubtype = {article} } Genome-wide association (GWA) studies have identified hundreds of common genetic variants associated with obesity and type 2 diabetes. These studies have usually focused on additive association tests. Identifying deviations from additivity may provide new biological insights and explain some of the missing heritability for these diseases. Methods We performed a GWA study using a dominance deviation model for BMI, obesity (29,925 cases) and type 2 diabetes (4,040 cases) in 120,286 individuals of British ancestry from the UK Biobank study. We also investigated whether single nucleotide polymorphisms previously shown to be associated with these traits showed any enrichment for departures from additivity. Results Known obesity-associated variants in FTO showed strong evidence of deviation from additivity (p DOMDEV = 3 × 10−5) through a recessive effect of the allele associated with higher BMI. The average BMI of individuals carrying zero, one or two BMI-raising alleles was 27.27 (95% CI 27.22, 27.31) kg/m2, 27.54 (95% CI 27.50, 27.58) kg/m2 and 28.07 (95% CI 28.00, 28.14) kg/m2, respectively. A similar effect was observed in 105,643 individuals from the GIANT Consortium (p DOMDEV = 0.003; meta-analysis p DOMDEV = 1 × 10−7). For type 2 diabetes, we detected a recessive effect (p DOMDEV = 5 × 10−4) at CDKAL1. Relative to homozygous non-risk allele carriers, homozygous risk allele carriers had an OR of 1.48 (95% CI 1.32, 1.65), while the heterozygous group had an OR of 1.06 (95% CI 0.99, 1.14), a result consistent with that of a previous study. We did not identify any novel associations at genome-wide significance. Conclusions/interpretation Although we found no evidence of widespread non-additive genetic effects contributing to obesity and type 2 diabetes risk, we did find robust examples of recessive effects at the FTO and CDKAL1 loci |
Sanne A E Peters Rachel R Huxley, Mark Woodward Sex differences in body anthropometry and composition in individuals with and without diabetes in the UK Biobank Journal Article In: BMJ Open, 2016. Abstract | Links | BibTeX | Tags: 2495, diabetes @article{Peters2016, title = {Sex differences in body anthropometry and composition in individuals with and without diabetes in the UK Biobank}, author = {Sanne A E Peters, Rachel R Huxley, Mark Woodward}, url = {http://bmjopen.bmj.com/content/6/1/e010007.full}, year = {2016}, date = {2016-01-06}, journal = {BMJ Open}, abstract = { Objective Type I and II diabetes are associated with a greater relative risk of cardiovascular diseases (CVD) in women than in men. Sex differences in adiposity storage may explain these findings. Methods A cross-sectional study of 480 813 participants from the UK Biobank without history of CVD was conducted to assess whether the difference in body size in people with and without diabetes was greater in women than in men. Age-adjusted linear regression analyses were used to obtain the mean difference in women minus men in the difference in body size measures, separately for type I and II diabetes. Results Body size was higher in individuals with diabetes than in individuals without diabetes, particularly in type II diabetes. Differences in body size between individuals with and without type II diabetes were more extreme in women than in men; compared to those without type II diabetes, body mass index and waist circumference were 1.94 (95% CI 1.82 to 2.07) and 4.84 (4.53 to 5.16) higher in women than in men, respectively. In type I diabetes, body size differed to a similar extent between those with and without diabetes in women as in men. This pattern was observed across all prespecified subgroups. Conclusions Differences in body size associated with diabetes were significantly greater in women than in men in type II diabetes but not in type I diabetes. Prospective studies can determine whether sex differences in body size associated with diabetes underpin some of the excess risk for CVD in women with type II diabetes. }, keywords = {2495, diabetes}, pubstate = {published}, tppubtype = {article} } Objective Type I and II diabetes are associated with a greater relative risk of cardiovascular diseases (CVD) in women than in men. Sex differences in adiposity storage may explain these findings. Methods A cross-sectional study of 480 813 participants from the UK Biobank without history of CVD was conducted to assess whether the difference in body size in people with and without diabetes was greater in women than in men. Age-adjusted linear regression analyses were used to obtain the mean difference in women minus men in the difference in body size measures, separately for type I and II diabetes. Results Body size was higher in individuals with diabetes than in individuals without diabetes, particularly in type II diabetes. Differences in body size between individuals with and without type II diabetes were more extreme in women than in men; compared to those without type II diabetes, body mass index and waist circumference were 1.94 (95% CI 1.82 to 2.07) and 4.84 (4.53 to 5.16) higher in women than in men, respectively. In type I diabetes, body size differed to a similar extent between those with and without diabetes in women as in men. This pattern was observed across all prespecified subgroups. Conclusions Differences in body size associated with diabetes were significantly greater in women than in men in type II diabetes but not in type I diabetes. Prospective studies can determine whether sex differences in body size associated with diabetes underpin some of the excess risk for CVD in women with type II diabetes. |
2015 |
Samuel E. Jones Jessica Tyrrell, Andrew Wood Robin Beaumont Marcus Tuke Hanieh Yaghootkar Rachel Freathy Anna Murray Timothy Frayling Michael Weedon M N Using novel genetic variants associated with chronotype to assess the causal role of disrupted circadian rhythms in obesity and type 2 diabetes: a UK Biobank study Presentation 04.09.2015. Abstract | BibTeX | Tags: 9055, chronotype, diabetes @misc{Jones2015, title = {Using novel genetic variants associated with chronotype to assess the causal role of disrupted circadian rhythms in obesity and type 2 diabetes: a UK Biobank study}, author = {Samuel E. Jones, Jessica Tyrrell, Andrew Wood, Robin Beaumont, Marcus Tuke, Hanieh Yaghootkar, Rachel Freathy, Anna Murray, Timothy M. Frayling, Michael N. Weedon}, year = {2015}, date = {2015-09-04}, abstract = {There are strong epidemiological associations between chronotype (morning or evening person) and sleep duration and metabolic disease, particularly obesity and type 2 diabetes. The UK Biobank study provides an excellent opportunity to test the causal nature of these associations. We performed genome-wide association studies in 107,634 White British individuals to identify genetic variants associated with chronotype and sleep duration. We then used the associated variants as instrumental variables in Mendelian Randomization analyses to test the causality of the association between chronotype and sleep duration on BMI and type 2 diabetes. Using self-reported “morningness”, we generated a continuous chronotype score and observed strong observational associations between this score and BMI (effect size = 0.10 kg/m2 per unit score increase, P=1.6x10-12) and type 2 diabetes (OR = 1.18 per unit score increase, P=8.6x10-24) and between hours slept and BMI (effect size = -0.08 kg/m2 per extra hour slept, P=5.6x10-8) and type 2 diabetes (OR = 1.08 per extra hour slept, P=1.4x10-10). We identified 9 genome-wide significant loci (P<5x10-8) influencing chronotype. The top signal was in RGS16 which has a known role in circadian rhythm regulation. Other signals include a variant downstream of TRAF3IP1 which has previously been associated with iris development and function, and another within HTR6, a serotonin receptor gene. We also replicated a previously reported association with sleep duration (0.04 hours per allele, P=7x10-14) near PAX8. We constructed a chronotype weighted genetic risk score (GRS) using the GWAS significant variants and used it as an instrument to test causal relationships with BMI, obesity and type 2 diabetes (N=4,040 cases). We did not find any evidence that chronotype causally influences BMI (P=0.50), obesity (P=0.77), or type 2 diabetes (P=0.83). The SNP associated with sleep duration was not associated with BMI (P=0.86), type 2 diabetes (P=0.05) or obesity (P=0.77). We have identified novel genetic associations for chronotype. These loci cluster near genes known to be important in determining circadian rhythms. We found no evidence that these variants associate with obesity or type 2 diabetes, suggesting that variation in circadian rhythm is not an important causal factor for metabolic disease. }, keywords = {9055, chronotype, diabetes}, pubstate = {published}, tppubtype = {presentation} } There are strong epidemiological associations between chronotype (morning or evening person) and sleep duration and metabolic disease, particularly obesity and type 2 diabetes. The UK Biobank study provides an excellent opportunity to test the causal nature of these associations. We performed genome-wide association studies in 107,634 White British individuals to identify genetic variants associated with chronotype and sleep duration. We then used the associated variants as instrumental variables in Mendelian Randomization analyses to test the causality of the association between chronotype and sleep duration on BMI and type 2 diabetes. Using self-reported “morningness”, we generated a continuous chronotype score and observed strong observational associations between this score and BMI (effect size = 0.10 kg/m2 per unit score increase, P=1.6x10-12) and type 2 diabetes (OR = 1.18 per unit score increase, P=8.6x10-24) and between hours slept and BMI (effect size = -0.08 kg/m2 per extra hour slept, P=5.6x10-8) and type 2 diabetes (OR = 1.08 per extra hour slept, P=1.4x10-10). We identified 9 genome-wide significant loci (P<5x10-8) influencing chronotype. The top signal was in RGS16 which has a known role in circadian rhythm regulation. Other signals include a variant downstream of TRAF3IP1 which has previously been associated with iris development and function, and another within HTR6, a serotonin receptor gene. We also replicated a previously reported association with sleep duration (0.04 hours per allele, P=7x10-14) near PAX8. We constructed a chronotype weighted genetic risk score (GRS) using the GWAS significant variants and used it as an instrument to test causal relationships with BMI, obesity and type 2 diabetes (N=4,040 cases). We did not find any evidence that chronotype causally influences BMI (P=0.50), obesity (P=0.77), or type 2 diabetes (P=0.83). The SNP associated with sleep duration was not associated with BMI (P=0.86), type 2 diabetes (P=0.05) or obesity (P=0.77). We have identified novel genetic associations for chronotype. These loci cluster near genes known to be important in determining circadian rhythms. We found no evidence that these variants associate with obesity or type 2 diabetes, suggesting that variation in circadian rhythm is not an important causal factor for metabolic disease. |
Hanieh Yaghootkar Robin Beaumont, Jessica Tyrrell Samuel Jones Andrew Wood Marcus Tuke Katherine Ruth Rachel Freathy Anna Murray Michael Weedon Timothy Frayling GENETIC VARIANTS ASSOCIATED WITH LOWER BMI AND LOWER BODY FAT PERCENTAGE INCREASE THE RISK OF TYPE 2 DIABETES, HYPERTENSION AND CORONARY ARTERY DISEASE IN THE UK BIOBANK STUDY. Presentation 04.09.2015. Abstract | BibTeX | Tags: 9055, 9072, coronary artery disease, diabetes, hypertension @misc{Yaghootkar2015, title = {GENETIC VARIANTS ASSOCIATED WITH LOWER BMI AND LOWER BODY FAT PERCENTAGE INCREASE THE RISK OF TYPE 2 DIABETES, HYPERTENSION AND CORONARY ARTERY DISEASE IN THE UK BIOBANK STUDY.}, author = {Hanieh Yaghootkar, Robin Beaumont, Jessica Tyrrell, Samuel Jones, Andrew Wood, Marcus Tuke, Katherine Ruth, Rachel Freathy, Anna Murray, Michael Weedon, Timothy Frayling}, year = {2015}, date = {2015-09-04}, abstract = {The UK Biobank study was designed to understand the role of genes, environment and their interaction in disease. Previous smaller studies have shown that many obese individuals are metabolically healthy whilst many normal weight individuals can have an elevated risk of metabolic diseases such as type 2 diabetes, coronary artery disease and hypertension. Further studies are needed to investigate the potential role of a shared genetic etiology between these diseases that is independent from obesity. We aimed to use the initial release of genetic data from 120,000 ancestrally British UK Biobank individuals to test the hypothesis that some individuals are genetically predisposed to metabolic disease independently of higher BMI. We selected 11 common genetic variants previously associated with insulin resistance and metabolic diseases from previously published GWAS data. We tested these variants individually and as a genetic risk score in the 120,000 UK Biobank individuals. Alleles in or near the LYPLAL1, GRB14, IRS1, PPARG, FAM13A, PDGFC, PEPD and ANKRD55 genes, previously associated with higher insulin resistance, were associated with reduced body fat percentage in UK Biobank (N=118,012, p-values ranging from 0.05 to 9E-11). An insulin resistance genetic risk score consisting of all 11 variants was associated with lower BMI (-0.05kg/m2 per weighted allele [-0.06,-0.03], p=2E-7), lower body fat percentage (-0.13% [-0.15,-0.1], p=2E-27) but only nominally with fat free mass (0.03Kg [0.01,0.05], p=0.01). Despite this association with lower adiposity the insulin resistance genetic risk score was associated with higher systolic (0.26mmHg [0.18,0.33], p=2E-10) and diastolic blood pressure(0.10 mmHg [0.05,0.14], p=6E-5), and greater risk of hypertension(OR 1.02 [1.01,1.03], p=3E-6, 65,976 cases), type 2 diabetes (OR 1.06 [1.04,1.08], p=2E-8, 4,040 cases) and coronary artery disease(OR 1.03 [1.02,1.05], p=1E-4, 5,807 cases). The UK Biobank data provide evidence for shared genetic factors that predispose to type 2 diabetes, hypertension and coronary artery disease independently of obesity and suggest that reduced adipose tissue capacity is a likely contributory mechanism.}, keywords = {9055, 9072, coronary artery disease, diabetes, hypertension}, pubstate = {published}, tppubtype = {presentation} } The UK Biobank study was designed to understand the role of genes, environment and their interaction in disease. Previous smaller studies have shown that many obese individuals are metabolically healthy whilst many normal weight individuals can have an elevated risk of metabolic diseases such as type 2 diabetes, coronary artery disease and hypertension. Further studies are needed to investigate the potential role of a shared genetic etiology between these diseases that is independent from obesity. We aimed to use the initial release of genetic data from 120,000 ancestrally British UK Biobank individuals to test the hypothesis that some individuals are genetically predisposed to metabolic disease independently of higher BMI. We selected 11 common genetic variants previously associated with insulin resistance and metabolic diseases from previously published GWAS data. We tested these variants individually and as a genetic risk score in the 120,000 UK Biobank individuals. Alleles in or near the LYPLAL1, GRB14, IRS1, PPARG, FAM13A, PDGFC, PEPD and ANKRD55 genes, previously associated with higher insulin resistance, were associated with reduced body fat percentage in UK Biobank (N=118,012, p-values ranging from 0.05 to 9E-11). An insulin resistance genetic risk score consisting of all 11 variants was associated with lower BMI (-0.05kg/m2 per weighted allele [-0.06,-0.03], p=2E-7), lower body fat percentage (-0.13% [-0.15,-0.1], p=2E-27) but only nominally with fat free mass (0.03Kg [0.01,0.05], p=0.01). Despite this association with lower adiposity the insulin resistance genetic risk score was associated with higher systolic (0.26mmHg [0.18,0.33], p=2E-10) and diastolic blood pressure(0.10 mmHg [0.05,0.14], p=6E-5), and greater risk of hypertension(OR 1.02 [1.01,1.03], p=3E-6, 65,976 cases), type 2 diabetes (OR 1.06 [1.04,1.08], p=2E-8, 4,040 cases) and coronary artery disease(OR 1.03 [1.02,1.05], p=1E-4, 5,807 cases). The UK Biobank data provide evidence for shared genetic factors that predispose to type 2 diabetes, hypertension and coronary artery disease independently of obesity and suggest that reduced adipose tissue capacity is a likely contributory mechanism. |
Samuel E. Jones Jessica Tyrrell, Andrew Woo Robin Beaumont Marcus Tuke Hanieh Yaghootkar Rachel Freathy Anna Murray Timothy Frayling Michael Weedon M N Using novel genetic variants associated with chronotype to assess the causal role of disrupted circadian rhythms in obesity and type 2 diabetes: a UK Biobank study Presentation 04.09.2015. Abstract | BibTeX | Tags: 9055, chronotype, diabetes @misc{Jones2015, title = {Using novel genetic variants associated with chronotype to assess the causal role of disrupted circadian rhythms in obesity and type 2 diabetes: a UK Biobank study}, author = {Samuel E. Jones, Jessica Tyrrell, Andrew Woo, Robin Beaumont, Marcus Tuke, Hanieh Yaghootkar, Rachel Freathy, Anna Murray, Timothy M. Frayling, Michael N. Weedon}, year = {2015}, date = {2015-09-04}, abstract = {There are strong epidemiological associations between chronotype (morning or evening person) and sleep duration and metabolic disease, particularly obesity and type 2 diabetes. The UK Biobank study provides an excellent opportunity to test the causal nature of these associations. We performed genome-wide association studies in 107,634 White British individuals to identify genetic variants associated with chronotype and sleep duration. We then used the associated variants as instrumental variables in Mendelian Randomization analyses to test the causality of the association between chronotype and sleep duration on BMI and type 2 diabetes. Using self-reported “morningness”, we generated a continuous chronotype score and observed strong observational associations between this score and BMI (effect size = 0.10 kg/m2 per unit score increase, P=1.6x10-12) and type 2 diabetes (OR = 1.18 per unit score increase, P=8.6x10-24) and between hours slept and BMI (effect size = -0.08 kg/m2 per extra hour slept, P=5.6x10-8) and type 2 diabetes (OR = 1.08 per extra hour slept, P=1.4x10-10). We identified 9 genome-wide significant loci (P<5x10-8) influencing chronotype. The top signal was in RGS16 which has a known role in circadian rhythm regulation. Other signals include a variant downstream of TRAF3IP1 which has previously been associated with iris development and function, and another within HTR6, a serotonin receptor gene. We also replicated a previously reported association with sleep duration (0.04 hours per allele, P=7x10-14) near PAX8. We constructed a chronotype weighted genetic risk score (GRS) using the GWAS significant variants and used it as an instrument to test causal relationships with BMI, obesity and type 2 diabetes (N=4,040 cases). We did not find any evidence that chronotype causally influences BMI (P=0.50), obesity (P=0.77), or type 2 diabetes (P=0.83). The SNP associated with sleep duration was not associated with BMI (P=0.86), type 2 diabetes (P=0.05) or obesity (P=0.77). We have identified novel genetic associations for chronotype. These loci cluster near genes known to be important in determining circadian rhythms. We found no evidence that these variants associate with obesity or type 2 diabetes, suggesting that variation in circadian rhythm is not an important causal factor for metabolic disease.}, keywords = {9055, chronotype, diabetes}, pubstate = {published}, tppubtype = {presentation} } There are strong epidemiological associations between chronotype (morning or evening person) and sleep duration and metabolic disease, particularly obesity and type 2 diabetes. The UK Biobank study provides an excellent opportunity to test the causal nature of these associations. We performed genome-wide association studies in 107,634 White British individuals to identify genetic variants associated with chronotype and sleep duration. We then used the associated variants as instrumental variables in Mendelian Randomization analyses to test the causality of the association between chronotype and sleep duration on BMI and type 2 diabetes. Using self-reported “morningness”, we generated a continuous chronotype score and observed strong observational associations between this score and BMI (effect size = 0.10 kg/m2 per unit score increase, P=1.6x10-12) and type 2 diabetes (OR = 1.18 per unit score increase, P=8.6x10-24) and between hours slept and BMI (effect size = -0.08 kg/m2 per extra hour slept, P=5.6x10-8) and type 2 diabetes (OR = 1.08 per extra hour slept, P=1.4x10-10). We identified 9 genome-wide significant loci (P<5x10-8) influencing chronotype. The top signal was in RGS16 which has a known role in circadian rhythm regulation. Other signals include a variant downstream of TRAF3IP1 which has previously been associated with iris development and function, and another within HTR6, a serotonin receptor gene. We also replicated a previously reported association with sleep duration (0.04 hours per allele, P=7x10-14) near PAX8. We constructed a chronotype weighted genetic risk score (GRS) using the GWAS significant variants and used it as an instrument to test causal relationships with BMI, obesity and type 2 diabetes (N=4,040 cases). We did not find any evidence that chronotype causally influences BMI (P=0.50), obesity (P=0.77), or type 2 diabetes (P=0.83). The SNP associated with sleep duration was not associated with BMI (P=0.86), type 2 diabetes (P=0.05) or obesity (P=0.77). We have identified novel genetic associations for chronotype. These loci cluster near genes known to be important in determining circadian rhythms. We found no evidence that these variants associate with obesity or type 2 diabetes, suggesting that variation in circadian rhythm is not an important causal factor for metabolic disease. |
2014 |
Ntuk, UE; Gill, JM; Mackay, DF; Sattar, N; Pell, JP Ethnic-specific obesity cutoffs for diabetes risk: cross-sectional study of 490,288 UK biobank participants. Journal Article In: Diabetes Care, 37 (9), pp. 2500-7, 2014. Abstract | Links | BibTeX | Tags: diabetes, obesity @article{Ntuk2014, title = {Ethnic-specific obesity cutoffs for diabetes risk: cross-sectional study of 490,288 UK biobank participants.}, author = {UE Ntuk and JM Gill and DF Mackay and N Sattar and JP Pell}, url = {http://www.ncbi.nlm.nih.gov/pubmed/24974975}, year = {2014}, date = {2014-09-01}, journal = {Diabetes Care}, volume = {37}, number = {9}, pages = {2500-7}, abstract = {OBJECTIVE: To compare the relationship between adiposity and prevalent diabetes across ethnic groups in the UK Biobank cohort and to derive ethnic-specific obesity cutoffs that equate to those developed in white populations in terms of diabetes prevalence. RESEARCH DESIGN AND METHODS: UK Biobank recruited 502,682 U.K. residents aged 40-69 years. We used baseline data on the 490,288 participants from the four largest ethnic subgroups: 471,174 (96.1%) white, 9,631 (2.0%) South Asian, 7,949 (1.6%) black, and 1,534 (0.3%) Chinese. Regression models were developed for the association between anthropometric measures (BMI, waist circumference, percentage body fat, and waist-to-hip ratio) and prevalent diabetes, stratified by sex and adjusted for age, physical activity, socioeconomic status, and heart disease. RESULTS: Nonwhite participants were two- to fourfold more likely to have diabetes. For the equivalent prevalence of diabetes at 30 kg/m(2) in white participants, BMI equated to the following: South Asians, 22.0 kg/m(2); black, 26.0 kg/m(2); Chinese women, 24.0 kg/m(2); and Chinese men, 26.0 kg/m(2). Among women, a waist circumference of 88 cm in the white subgroup equated to the following: South Asians, 70 cm; black, 79 cm; and Chinese, 74 cm. Among men, a waist circumference of 102 cm equated to 79, 88, and 88 cm for South Asian, black, and Chinese participants, respectively. CONCLUSIONS: Obesity should be defined at lower thresholds in nonwhite populations to ensure that interventions are targeted equitably based on equivalent diabetes prevalence. Furthermore, within the Asian population, a substantially lower obesity threshold should be applied to South Asian compared with Chinese groups.}, keywords = {diabetes, obesity}, pubstate = {published}, tppubtype = {article} } OBJECTIVE: To compare the relationship between adiposity and prevalent diabetes across ethnic groups in the UK Biobank cohort and to derive ethnic-specific obesity cutoffs that equate to those developed in white populations in terms of diabetes prevalence. RESEARCH DESIGN AND METHODS: UK Biobank recruited 502,682 U.K. residents aged 40-69 years. We used baseline data on the 490,288 participants from the four largest ethnic subgroups: 471,174 (96.1%) white, 9,631 (2.0%) South Asian, 7,949 (1.6%) black, and 1,534 (0.3%) Chinese. Regression models were developed for the association between anthropometric measures (BMI, waist circumference, percentage body fat, and waist-to-hip ratio) and prevalent diabetes, stratified by sex and adjusted for age, physical activity, socioeconomic status, and heart disease. RESULTS: Nonwhite participants were two- to fourfold more likely to have diabetes. For the equivalent prevalence of diabetes at 30 kg/m(2) in white participants, BMI equated to the following: South Asians, 22.0 kg/m(2); black, 26.0 kg/m(2); Chinese women, 24.0 kg/m(2); and Chinese men, 26.0 kg/m(2). Among women, a waist circumference of 88 cm in the white subgroup equated to the following: South Asians, 70 cm; black, 79 cm; and Chinese, 74 cm. Among men, a waist circumference of 102 cm equated to 79, 88, and 88 cm for South Asian, black, and Chinese participants, respectively. CONCLUSIONS: Obesity should be defined at lower thresholds in nonwhite populations to ensure that interventions are targeted equitably based on equivalent diabetes prevalence. Furthermore, within the Asian population, a substantially lower obesity threshold should be applied to South Asian compared with Chinese groups. |
2013 |
Tyrrell, Jessica S; Yaghootkar, Hanieh; Freathy, Rachel M; Hattersley, Andrew T; Frayling, Timothy M Parental diabetes and birthweight in 236 030 individuals in the UK Biobank Study Journal Article In: International Journal of Epidemiology, 42 (6), pp. 1714-1723, 2013. Abstract | Links | BibTeX | Tags: diabetes @article{Tyrrell2013, title = {Parental diabetes and birthweight in 236 030 individuals in the UK Biobank Study}, author = {Jessica S Tyrrell and Hanieh Yaghootkar and Rachel M Freathy and Andrew T Hattersley and Timothy M Frayling}, url = {http://www.ukbiobank.ac.uk/2013/12/dads-influence-on-birth-weight-linked-to-diabetes-genes/}, year = {2013}, date = {2013-12-11}, journal = {International Journal of Epidemiology}, volume = {42}, number = {6}, pages = {1714-1723}, abstract = {Background The UK Biobank study provides a unique opportunity to study the causes and consequences of disease. We aimed to use the UK Biobank data to study the well-established, but poorly understood, association between low birthweight and type 2 diabetes. Methods We used logistic regression to calculate the odds ratio for participants’ risk of type 2 diabetes given a one standard deviation increase in birthweight. To test for an association between parental diabetes and birthweight, we performed linear regression of self-reported parental diabetes status against birthweight. We performed path and mediation analyses to test the hypothesis that birthweight partly mediates the association between parental diabetes and participant type 2 diabetes status. Results Of the UK Biobank participants, 277 261 reported their birthweight. Of 257 715 individuals of White ethnicity and singleton pregnancies, 6576 had type 2 diabetes, 19 478 reported maternal diabetes (but not paternal), 20 057 reported paternal diabetes (but not maternal) and 2754 participants reported both parents as having diabetes. Lower birthweight was associated with type 2 diabetes in the UK Biobank participants. A one kilogram increase in birthweight was associated with a lower risk of type 2 diabetes (odds ratio: 0.74; 95% CI: 0.71, 0.76; P = 2 × 10−57). Paternal diabetes was associated with lower birthweight (45 g lower; 95% CI: 36, 54; P = 2 × 10−23) relative to individuals with no parental diabetes. Maternal diabetes was associated with higher birthweight (59 g increase; 95% CI: 50, 68; P = 3 × 10−37). Participants’ lower birthweight was a mediator of the association between reported paternal diabetes and participants’ type 2 diabetes status, explaining 1.1% of the association, and participants’ higher birthweight was a mediator of the association between reported maternal diabetes and participants’ type 2 diabetes status, explaining 1.2% of the association. Conclusions Data from the UK Biobank provides the strongest evidence by far that paternal diabetes is associated with lower birthweight, whereas maternal diabetes is associated with increased birthweight. Our findings with paternal diabetes are consistent with a role for the same genetic factors influencing foetal growth and type 2 diabetes.}, keywords = {diabetes}, pubstate = {published}, tppubtype = {article} } Background The UK Biobank study provides a unique opportunity to study the causes and consequences of disease. We aimed to use the UK Biobank data to study the well-established, but poorly understood, association between low birthweight and type 2 diabetes. Methods We used logistic regression to calculate the odds ratio for participants’ risk of type 2 diabetes given a one standard deviation increase in birthweight. To test for an association between parental diabetes and birthweight, we performed linear regression of self-reported parental diabetes status against birthweight. We performed path and mediation analyses to test the hypothesis that birthweight partly mediates the association between parental diabetes and participant type 2 diabetes status. Results Of the UK Biobank participants, 277 261 reported their birthweight. Of 257 715 individuals of White ethnicity and singleton pregnancies, 6576 had type 2 diabetes, 19 478 reported maternal diabetes (but not paternal), 20 057 reported paternal diabetes (but not maternal) and 2754 participants reported both parents as having diabetes. Lower birthweight was associated with type 2 diabetes in the UK Biobank participants. A one kilogram increase in birthweight was associated with a lower risk of type 2 diabetes (odds ratio: 0.74; 95% CI: 0.71, 0.76; P = 2 × 10−57). Paternal diabetes was associated with lower birthweight (45 g lower; 95% CI: 36, 54; P = 2 × 10−23) relative to individuals with no parental diabetes. Maternal diabetes was associated with higher birthweight (59 g increase; 95% CI: 50, 68; P = 3 × 10−37). Participants’ lower birthweight was a mediator of the association between reported paternal diabetes and participants’ type 2 diabetes status, explaining 1.1% of the association, and participants’ higher birthweight was a mediator of the association between reported maternal diabetes and participants’ type 2 diabetes status, explaining 1.2% of the association. Conclusions Data from the UK Biobank provides the strongest evidence by far that paternal diabetes is associated with lower birthweight, whereas maternal diabetes is associated with increased birthweight. Our findings with paternal diabetes are consistent with a role for the same genetic factors influencing foetal growth and type 2 diabetes. |
