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1.  Identification of heart rate–associated loci and their effects on cardiac conduction and rhythm disorders 
den Hoed, Marcel | Eijgelsheim, Mark | Esko, Tõnu | Brundel, Bianca J J M | Peal, David S | Evans, David M | Nolte, Ilja M | Segrè, Ayellet V | Holm, Hilma | Handsaker, Robert E | Westra, Harm-Jan | Johnson, Toby | Isaacs, Aaron | Yang, Jian | Lundby, Alicia | Zhao, Jing Hua | Kim, Young Jin | Go, Min Jin | Almgren, Peter | Bochud, Murielle | Boucher, Gabrielle | Cornelis, Marilyn C | Gudbjartsson, Daniel | Hadley, David | Van Der Harst, Pim | Hayward, Caroline | Heijer, Martin Den | Igl, Wilmar | Jackson, Anne U | Kutalik, Zoltán | Luan, Jian’an | Kemp, John P | Kristiansson, Kati | Ladenvall, Claes | Lorentzon, Mattias | Montasser, May E | Njajou, Omer T | O’Reilly, Paul F | Padmanabhan, Sandosh | Pourcain, Beate St. | Rankinen, Tuomo | Salo, Perttu | Tanaka, Toshiko | Timpson, Nicholas J | Vitart, Veronique | Waite, Lindsay | Wheeler, William | Zhang, Weihua | Draisma, Harmen H M | Feitosa, Mary F | Kerr, Kathleen F | Lind, Penelope A | Mihailov, Evelin | Onland-Moret, N Charlotte | Song, Ci | Weedon, Michael N | Xie, Weijia | Yengo, Loic | Absher, Devin | Albert, Christine M | Alonso, Alvaro | Arking, Dan E | de Bakker, Paul I W | Balkau, Beverley | Barlassina, Cristina | Benaglio, Paola | Bis, Joshua C | Bouatia-Naji, Nabila | Brage, Søren | Chanock, Stephen J | Chines, Peter S | Chung, Mina | Darbar, Dawood | Dina, Christian | Dörr, Marcus | Elliott, Paul | Felix, Stephan B | Fischer, Krista | Fuchsberger, Christian | de Geus, Eco J C | Goyette, Philippe | Gudnason, Vilmundur | Harris, Tamara B | Hartikainen, Anna-liisa | Havulinna, Aki S | Heckbert, Susan R | Hicks, Andrew A | Hofman, Albert | Holewijn, Suzanne | Hoogstra-Berends, Femke | Hottenga, Jouke-Jan | Jensen, Majken K | Johansson, Åsa | Junttila, Juhani | Kääb, Stefan | Kanon, Bart | Ketkar, Shamika | Khaw, Kay-Tee | Knowles, Joshua W | Kooner, Angrad S | Kors, Jan A | Kumari, Meena | Milani, Lili | Laiho, Päivi | Lakatta, Edward G | Langenberg, Claudia | Leusink, Maarten | Liu, Yongmei | Luben, Robert N | Lunetta, Kathryn L | Lynch, Stacey N | Markus, Marcello R P | Marques-Vidal, Pedro | Leach, Irene Mateo | McArdle, Wendy L | McCarroll, Steven A | Medland, Sarah E | Miller, Kathryn A | Montgomery, Grant W | Morrison, Alanna C | Müller-Nurasyid, Martina | Navarro, Pau | Nelis, Mari | O’Connell, Jeffrey R | O’Donnell, Christopher J | Ong, Ken K | Newman, Anne B | Peters, Annette | Polasek, Ozren | Pouta, Anneli | Pramstaller, Peter P | Psaty, Bruce M | Rao, Dabeeru C | Ring, Susan M | Rossin, Elizabeth J | Rudan, Diana | Sanna, Serena | Scott, Robert A | Sehmi, Jaban S | Sharp, Stephen | Shin, Jordan T | Singleton, Andrew B | Smith, Albert V | Soranzo, Nicole | Spector, Tim D | Stewart, Chip | Stringham, Heather M | Tarasov, Kirill V | Uitterlinden, André G | Vandenput, Liesbeth | Hwang, Shih-Jen | Whitfield, John B | Wijmenga, Cisca | Wild, Sarah H | Willemsen, Gonneke | Wilson, James F | Witteman, Jacqueline C M | Wong, Andrew | Wong, Quenna | Jamshidi, Yalda | Zitting, Paavo | Boer, Jolanda M A | Boomsma, Dorret I | Borecki, Ingrid B | Van Duijn, Cornelia M | Ekelund, Ulf | Forouhi, Nita G | Froguel, Philippe | Hingorani, Aroon | Ingelsson, Erik | Kivimaki, Mika | Kronmal, Richard A | Kuh, Diana | Lind, Lars | Martin, Nicholas G | Oostra, Ben A | Pedersen, Nancy L | Quertermous, Thomas | Rotter, Jerome I | van der Schouw, Yvonne T | Verschuren, W M Monique | Walker, Mark | Albanes, Demetrius | Arnar, David O | Assimes, Themistocles L | Bandinelli, Stefania | Boehnke, Michael | de Boer, Rudolf A | Bouchard, Claude | Caulfield, W L Mark | Chambers, John C | Curhan, Gary | Cusi, Daniele | Eriksson, Johan | Ferrucci, Luigi | van Gilst, Wiek H | Glorioso, Nicola | de Graaf, Jacqueline | Groop, Leif | Gyllensten, Ulf | Hsueh, Wen-Chi | Hu, Frank B | Huikuri, Heikki V | Hunter, David J | Iribarren, Carlos | Isomaa, Bo | Jarvelin, Marjo-Riitta | Jula, Antti | Kähönen, Mika | Kiemeney, Lambertus A | van der Klauw, Melanie M | Kooner, Jaspal S | Kraft, Peter | Iacoviello, Licia | Lehtimäki, Terho | Lokki, Marja-Liisa L | Mitchell, Braxton D | Navis, Gerjan | Nieminen, Markku S | Ohlsson, Claes | Poulter, Neil R | Qi, Lu | Raitakari, Olli T | Rimm, Eric B | Rioux, John D | Rizzi, Federica | Rudan, Igor | Salomaa, Veikko | Sever, Peter S | Shields, Denis C | Shuldiner, Alan R | Sinisalo, Juha | Stanton, Alice V | Stolk, Ronald P | Strachan, David P | Tardif, Jean-Claude | Thorsteinsdottir, Unnur | Tuomilehto, Jaako | van Veldhuisen, Dirk J | Virtamo, Jarmo | Viikari, Jorma | Vollenweider, Peter | Waeber, Gérard | Widen, Elisabeth | Cho, Yoon Shin | Olsen, Jesper V | Visscher, Peter M | Willer, Cristen | Franke, Lude | Erdmann, Jeanette | Thompson, John R | Pfeufer, Arne | Sotoodehnia, Nona | Newton-Cheh, Christopher | Ellinor, Patrick T | Stricker, Bruno H Ch | Metspalu, Andres | Perola, Markus | Beckmann, Jacques S | Smith, George Davey | Stefansson, Kari | Wareham, Nicholas J | Munroe, Patricia B | Sibon, Ody C M | Milan, David J | Snieder, Harold | Samani, Nilesh J | Loos, Ruth J F
Nature genetics  2013;45(6):621-631.
Elevated resting heart rate is associated with greater risk of cardiovascular disease and mortality. In a 2-stage meta-analysis of genome-wide association studies in up to 181,171 individuals, we identified 14 new loci associated with heart rate and confirmed associations with all 7 previously established loci. Experimental downregulation of gene expression in Drosophila melanogaster and Danio rerio identified 20 genes at 11 loci that are relevant for heart rate regulation and highlight a role for genes involved in signal transmission, embryonic cardiac development and the pathophysiology of dilated cardiomyopathy, congenital heart failure and/or sudden cardiac death. In addition, genetic susceptibility to increased heart rate is associated with altered cardiac conduction and reduced risk of sick sinus syndrome, and both heart rate–increasing and heart rate–decreasing variants associate with risk of atrial fibrillation. Our findings provide fresh insights into the mechanisms regulating heart rate and identify new therapeutic targets.
doi:10.1038/ng.2610
PMCID: PMC3696959  PMID: 23583979
2.  Genome-wide meta-analysis of observational studies shows common genetic variants associated with macronutrient intake1234 
Background: Macronutrient intake varies substantially between individuals, and there is evidence that this variation is partly accounted for by genetic variants.
Objective: The objective of the study was to identify common genetic variants that are associated with macronutrient intake.
Design: We performed 2-stage genome-wide association (GWA) meta-analysis of macronutrient intake in populations of European descent. Macronutrients were assessed by using food-frequency questionnaires and analyzed as percentages of total energy consumption from total fat, protein, and carbohydrate. From the discovery GWA (n = 38,360), 35 independent loci associated with macronutrient intake at P < 5 × 10−6 were identified and taken forward to replication in 3 additional cohorts (n = 33,533) from the DietGen Consortium. For one locus, fat mass obesity-associated protein (FTO), cohorts with Illumina MetaboChip genotype data (n = 7724) provided additional replication data.
Results: A variant in the chromosome 19 locus (rs838145) was associated with higher carbohydrate (β ± SE: 0.25 ± 0.04%; P = 1.68 × 10−8) and lower fat (β ± SE: −0.21 ± 0.04%; P = 1.57 × 10−9) consumption. A candidate gene in this region, fibroblast growth factor 21 (FGF21), encodes a fibroblast growth factor involved in glucose and lipid metabolism. The variants in this locus were associated with circulating FGF21 protein concentrations (P < 0.05) but not mRNA concentrations in blood or brain. The body mass index (BMI)–increasing allele of the FTO variant (rs1421085) was associated with higher protein intake (β ± SE: 0.10 ± 0.02%; P = 9.96 × 10−10), independent of BMI (after adjustment for BMI, β ± SE: 0.08 ± 0.02%; P = 3.15 × 10−7).
Conclusion: Our results indicate that variants in genes involved in nutrient metabolism and obesity are associated with macronutrient consumption in humans. Trials related to this study were registered at clinicaltrials.gov as NCT00005131 (Atherosclerosis Risk in Communities), NCT00005133 (Cardiovascular Health Study), NCT00005136 (Family Heart Study), NCT00005121 (Framingham Heart Study), NCT00083369 (Genetic and Environmental Determinants of Triglycerides), NCT01331512 (InCHIANTI Study), and NCT00005487 (Multi-Ethnic Study of Atherosclerosis).
doi:10.3945/ajcn.112.052183
PMCID: PMC3652928  PMID: 23636237
3.  Gene-Lifestyle Interaction and Type 2 Diabetes: The EPIC InterAct Case-Cohort Study 
PLoS Medicine  2014;11(5):e1001647.
In this study, Wareham and colleagues quantified the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention. The authors found that the relative effect of a type 2 diabetes genetic risk score is greater in younger and leaner participants, and the high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.
Please see later in the article for the Editors' Summary
Background
Understanding of the genetic basis of type 2 diabetes (T2D) has progressed rapidly, but the interactions between common genetic variants and lifestyle risk factors have not been systematically investigated in studies with adequate statistical power. Therefore, we aimed to quantify the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention.
Methods and Findings
The InterAct study includes 12,403 incident T2D cases and a representative sub-cohort of 16,154 individuals from a cohort of 340,234 European participants with 3.99 million person-years of follow-up. We studied the combined effects of an additive genetic T2D risk score and modifiable and non-modifiable risk factors using Prentice-weighted Cox regression and random effects meta-analysis methods. The effect of the genetic score was significantly greater in younger individuals (p for interaction  = 1.20×10−4). Relative genetic risk (per standard deviation [4.4 risk alleles]) was also larger in participants who were leaner, both in terms of body mass index (p for interaction  = 1.50×10−3) and waist circumference (p for interaction  = 7.49×10−9). Examination of absolute risks by strata showed the importance of obesity for T2D risk. The 10-y cumulative incidence of T2D rose from 0.25% to 0.89% across extreme quartiles of the genetic score in normal weight individuals, compared to 4.22% to 7.99% in obese individuals. We detected no significant interactions between the genetic score and sex, diabetes family history, physical activity, or dietary habits assessed by a Mediterranean diet score.
Conclusions
The relative effect of a T2D genetic risk score is greater in younger and leaner participants. However, this sub-group is at low absolute risk and would not be a logical target for preventive interventions. The high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
Worldwide, more than 380 million people currently have diabetes, and the condition is becoming increasingly common. Diabetes is characterized by high levels of glucose (sugar) in the blood. Blood sugar levels are usually controlled by insulin, a hormone released by the pancreas after meals (digestion of food produces glucose). In people with type 2 diabetes (the commonest type of diabetes), blood sugar control fails because the fat and muscle cells that normally respond to insulin by removing excess sugar from the blood become less responsive to insulin. Type 2 diabetes can often initially be controlled with diet and exercise (lifestyle changes) and with antidiabetic drugs such as metformin and sulfonylureas, but patients may eventually need insulin injections to control their blood sugar levels. Long-term complications of diabetes, which include an increased risk of heart disease and stroke, reduce the life expectancy of people with diabetes by about ten years compared to people without diabetes.
Why Was This Study Done?
Type 2 diabetes is thought to originate from the interplay between genetic and lifestyle factors. But although rapid progress is being made in understanding the genetic basis of type 2 diabetes, it is not known whether the consequences of adverse lifestyles (for example, being overweight and/or physically inactive) differ according to an individual's underlying genetic risk of diabetes. It is important to investigate this question to inform strategies for prevention. If, for example, obese individuals with a high level of genetic risk have a higher risk of developing diabetes than obese individuals with a low level of genetic risk, then preventative strategies that target lifestyle interventions to obese individuals with a high genetic risk would be more effective than strategies that target all obese individuals. In this case-cohort study, researchers from the InterAct consortium quantify the combined effects of genetic and lifestyle factors on the risk of type 2 diabetes. A case-cohort study measures exposure to potential risk factors in a group (cohort) of people and compares the occurrence of these risk factors in people who later develop the disease with those who remain disease free.
What Did the Researchers Do and Find?
The InterAct study involves 12,403 middle-aged individuals who developed type 2 diabetes after enrollment (incident cases) into the European Prospective Investigation into Cancer and Nutrition (EPIC) and a sub-cohort of 16,154 EPIC participants. The researchers calculated a genetic type 2 diabetes risk score for most of these individuals by determining which of 49 gene variants associated with type 2 diabetes each person carried, and collected baseline information about exposure to lifestyle risk factors for type 2 diabetes. They then used various statistical approaches to examine the combined effects of the genetic risk score and lifestyle factors on diabetes development. The effect of the genetic score was greater in younger individuals than in older individuals and greater in leaner participants than in participants with larger amounts of body fat. The absolute risk of type 2 diabetes, expressed as the ten-year cumulative incidence of type 2 diabetes (the percentage of participants who developed diabetes over a ten-year period) increased with increasing genetic score in normal weight individuals from 0.25% in people with the lowest genetic risk scores to 0.89% in those with the highest scores; in obese people, the ten-year cumulative incidence rose from 4.22% to 7.99% with increasing genetic risk score.
What Do These Findings Mean?
These findings show that in this middle-aged cohort, the relative association with type 2 diabetes of a genetic risk score comprised of a large number of gene variants is greatest in individuals who are younger and leaner at baseline. This finding may in part reflect the methods used to originally identify gene variants associated with type 2 diabetes, and future investigations that include other genetic variants, other lifestyle factors, and individuals living in other settings should be undertaken to confirm this finding. Importantly, however, this study shows that young, lean individuals with a high genetic risk score have a low absolute risk of developing type 2 diabetes. Thus, this sub-group of individuals is not a logical target for preventative interventions. Rather, suggest the researchers, the high absolute risk of type 2 diabetes associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001647.
The US National Diabetes Information Clearinghouse provides information about diabetes for patients, health-care professionals and the general public, including detailed information on diabetes prevention (in English and Spanish)
The UK National Health Service Choices website provides information for patients and carers about type 2 diabetes and about living with diabetes; it also provides people's stories about diabetes
The charity Diabetes UK provides detailed information for patients and carers in several languages, including information on healthy lifestyles for people with diabetes
The UK-based non-profit organization Healthtalkonline has interviews with people about their experiences of diabetes
The Genetic Landscape of Diabetes is published by the US National Center for Biotechnology Information
More information on the InterAct study is available
MedlinePlus provides links to further resources and advice about diabetes and diabetes prevention (in English and Spanish)
doi:10.1371/journal.pmed.1001647
PMCID: PMC4028183  PMID: 24845081
4.  Age at Menopause, Reproductive Life Span, and Type 2 Diabetes Risk 
Diabetes Care  2013;36(4):1012-1019.
OBJECTIVE
Age at menopause is an important determinant of future health outcomes, but little is known about its relationship with type 2 diabetes. We examined the associations of menopausal age and reproductive life span (menopausal age minus menarcheal age) with diabetes risk.
RESEARCH DESIGN AND METHODS
Data were obtained from the InterAct study, a prospective case-cohort study nested within the European Prospective Investigation into Cancer and Nutrition. A total of 3,691 postmenopausal type 2 diabetic case subjects and 4,408 subcohort members were included in the analysis, with a median follow-up of 11 years. Prentice weighted Cox proportional hazards models were adjusted for age, known risk factors for diabetes, and reproductive factors, and effect modification by BMI, waist circumference, and smoking was studied.
RESULTS
Mean (SD) age of the subcohort was 59.2 (5.8) years. After multivariable adjustment, hazard ratios (HRs) of type 2 diabetes were 1.32 (95% CI 1.04–1.69), 1.09 (0.90–1.31), 0.97 (0.86–1.10), and 0.85 (0.70–1.03) for women with menopause at ages <40, 40–44, 45–49, and ≥55 years, respectively, relative to those with menopause at age 50–54 years. The HR per SD younger age at menopause was 1.08 (1.02–1.14). Similarly, a shorter reproductive life span was associated with a higher diabetes risk (HR per SD lower reproductive life span 1.06 [1.01–1.12]). No effect modification by BMI, waist circumference, or smoking was observed (P interaction all > 0.05).
CONCLUSIONS
Early menopause is associated with a greater risk of type 2 diabetes.
doi:10.2337/dc12-1020
PMCID: PMC3609516  PMID: 23230098
5.  A new tool for converting food frequency questionnaire data into nutrient and food group values: FETA research methods and availability 
BMJ Open  2014;4(3):e004503.
Objectives
To describe the research methods for the development of a new open source, cross-platform tool which processes data from the European Prospective Investigation into Cancer and Nutrition Norfolk Food Frequency Questionnaire (EPIC-Norfolk FFQ). A further aim was to compare nutrient and food group values derived from the current tool (FETA, FFQ EPIC Tool for Analysis) with the previously validated but less accessible tool, CAFÉ (Compositional Analyses from Frequency Estimates). The effect of text matching on intake data was also investigated.
Design
Cross-sectional analysis of a prospective cohort study—EPIC-Norfolk.
Setting
East England population (city of Norwich and its surrounding small towns and rural areas).
Participants
Complete FFQ data from 11 250 men and 13 602 women (mean age 59 years; range 40–79 years).
Outcome measures
Nutrient and food group intakes derived from FETA and CAFÉ analyses of EPIC-Norfolk FFQ data.
Results
Nutrient outputs from FETA and CAFÉ were similar; mean (SD) energy intake from FETA was 9222 kJ (2633) in men, 8113 kJ (2296) in women, compared with CAFÉ intakes of 9175 kJ (2630) in men, 8091 kJ (2298) in women. The majority of differences resulted in one or less quintile change (98.7%). Only mean daily fruit and vegetable food group intakes were higher in women than in men (278 vs 212 and 284 vs 255 g, respectively). Quintile changes were evident for all nutrients, with the exception of alcohol, when text matching was not executed; however, only the cereals food group was affected.
Conclusions
FETA produces similar nutrient and food group values to the previously validated CAFÉ but has the advantages of being open source, cross-platform and complete with a data-entry form directly compatible with the software. The tool will facilitate research using the EPIC-Norfolk FFQ, and can be customised for different study populations.
doi:10.1136/bmjopen-2013-004503
PMCID: PMC3975761  PMID: 24674997
6.  Dietary dairy product intake and incident type 2 diabetes: a prospective study using dietary data from a 7-day food diary 
Diabetologia  2014;57:909-917.
Aim/hypothesis
The aim of this study was to investigate the association between total and types of dairy product intake and risk of developing incident type 2 diabetes, using a food diary.
Methods
A nested case-cohort within the EPIC-Norfolk Study was examined, including a random subcohort (n = 4,000) and cases of incident diabetes (n = 892, including 143 cases in the subcohort) followed-up for 11 years. Diet was assessed using a prospective 7-day food diary. Total dairy intake (g/day) was estimated and categorised into high-fat (≥3.9%) and low-fat (<3.9% fat) dairy, and by subtype into yoghurt, cheese and milk. Combined fermented dairy product intake (yoghurt, cheese, sour cream) was estimated and categorised into high- and low-fat. Prentice-weighted Cox regression HRs were calculated.
Results
Total dairy, high-fat dairy, milk, cheese and high-fat fermented dairy product intakes were not associated with the development of incident diabetes. Low-fat dairy intake was inversely associated with diabetes in age- and sex-adjusted analyses (tertile [T] 3 vs T1, HR 0.81 [95% CI 0.66, 0.98]), but further adjustment for anthropometric, dietary and diabetes risk factors attenuated this association. In addition, an inverse association was found between diabetes and low-fat fermented dairy product intake (T3 vs T1, HR 0.76 [95% CI 0.60, 0.99]; ptrend = 0.049) and specifically with yoghurt intake (HR 0.72 [95% CI 0.55, 0.95]; ptrend = 0.017) in multivariable adjusted analyses.
Conclusions/interpretation
Greater low-fat fermented dairy product intake, largely driven by yoghurt intake, was associated with a decreased risk of type 2 diabetes development in prospective analyses. These findings suggest that the consumption of specific dairy types may be beneficial for the prevention of diabetes, highlighting the importance of food group subtypes for public health messages.
Electronic supplementary material
The online version of this article (doi:10.1007/s00125-014-3176-1) contains peer-reviewed but unedited supplementary material, which is available to authorised users.
doi:10.1007/s00125-014-3176-1
PMCID: PMC3980034  PMID: 24510203
Cheese; Dairy; Fermented dairy; Milk; Type 2 diabetes; Yoghurt
7.  Insulin Resistance and Truncal Obesity as Important Determinants of the Greater Incidence of Diabetes in Indian Asians and African Caribbeans Compared With Europeans 
Diabetes Care  2013;36(2):383-393.
OBJECTIVE
To determine the extent of, and reasons for, ethnic differences in type 2 diabetes incidence in the U.K.
RESEARCH DESIGN AND METHODS
Population-based triethnic cohort. Participants were without diabetes, aged 40–69 at baseline (1989–1991), and followed-up for 20 years. Baseline measurements included fasting and postglucose bloods, anthropometry, and lifestyle questionnaire. Incident diabetes was identified from medical records and participant recall. Ethnic differences in diabetes incidence were examined using competing risks regression.
RESULTS
Incident diabetes was identified in 196 of 1,354 (14%) Europeans, 282 of 839 (34%) Indian Asians, and 100 of 335 (30%) African Caribbeans. All Indian Asians and African Caribbeans were first-generation migrants. Compared with Europeans, age-adjusted subhazard ratios (SHRs [95% CI]) for men and women, respectively, were 2.88 (95%, 2.36–3.53; P < 0.001) and 1.91 (1.18–3.10; P = 0.008) in Indian Asians, and 2.23 (1.64–3.03; P < 0.001) and 2.51 (1.63–3.87; P < 0.001) in African Caribbeans. Differences in baseline insulin resistance and truncal obesity largely attenuated the ethnic minority excess in women (adjusted SHRs: Indian Asians 0.77 [0.49–1.42]; P = 0.3; African Caribbeans 1.48 [0.89–2.45]; P = 0.13), but not in men (adjusted SHRs: Indian Asians 1.98 [1.52–2.58]; P < 0.001 and African Caribbeans, 2.05 [1.46–2.89; P < 0.001]).
CONCLUSIONS
Insulin resistance and truncal obesity account for the twofold excess incidence of diabetes in Indian Asian and African Caribbean women, but not men. Explanations for the excess diabetes risk in ethnic minority men remains unclear. Further study requires more precise measures of conventional risk factors and identification of novel risk factors.
doi:10.2337/dc12-0544
PMCID: PMC3554271  PMID: 22966089
8.  Social relationships and healthful dietary behaviour: Evidence from over-50s in the EPIC cohort, UK☆ 
Social Science & Medicine (1982)  2014;100(100):167-175.
Social relationships are an important aspect of a person's social environment that can protect against a wide range of chronic conditions and facilitate recovery from disease. Social relationships have also been linked to dietary behaviour which may be an important pathway through which social circumstances exert their influence on health. Yet, questions remain about which structural aspects of social relationships most affect healthful dietary behaviours and whether different structural components interact to produce a combined effect. Using data from adults (≥50 years) in the European Prospective Investigation of Cancer-Norfolk study (1996–2002), we examined marital status, living arrangement and social isolation in relation to scores for variety of fruit and vegetable intake as a marker of diet quality associated with adverse health outcomes. Data were analysed with multivariable linear regression models for gender-specific and interaction associations. We found that being single or widowed was associated with a lower variety score, particularly vegetable variety, and associations were enhanced when combined with male gender, living alone or infrequent friend contact. Lower variety scores for lone-living were also observed, especially for men. Infrequent friend contact interacted with living arrangement to amplify negative associations of lone-living with variety, with statistically significant differences in contact frequency for vegetable variety. Lower levels of friend contact were associated with reduced variety of fruits and vegetables in a graded trend for both genders; the trend was more pronounced among men. Family contact appeared to have limited association with vegetable variety in men; among women, weekly contact was significantly and positively associated with vegetable variety compared to daily family contact. Results highlight the importance of considering living arrangement and/or frequency of social contact when assessing whether widowed, single or lone-living older adults are at risk of lower fruit and vegetable variety.
Highlights
•Social relationships affect health and can also influence dietary behaviour.•We examined joint effects of diverse structural components of relationships on diet.•We used data from over-50s in an established epidemiological cohort.•Men fared worse than women in reduced variety from poorer structural social ties.•Social isolation altered dietary effects of marital status and living arrangement.
doi:10.1016/j.socscimed.2013.08.018
PMCID: PMC3969105  PMID: 24035440
Social relationships; Social ties; Gender; Interactions; Diet variety; Health behaviour; Chronic disease; UK
9.  Dietary Intakes of Individual Flavanols and Flavonols Are Inversely Associated with Incident Type 2 Diabetes in European Populations123 
The Journal of Nutrition  2013;144(3):335-343.
Dietary flavanols and flavonols, flavonoid subclasses, have been recently associated with a lower risk of type 2 diabetes (T2D) in Europe. Even within the same subclass, flavonoids may differ considerably in bioavailability and bioactivity. We aimed to examine the association between individual flavanol and flavonol intakes and risk of developing T2D across European countries. The European Prospective Investigation into Cancer and Nutrition (EPIC)–InterAct case-cohort study was conducted in 8 European countries across 26 study centers with 340,234 participants contributing 3.99 million person-years of follow-up, among whom 12,403 incident T2D cases were ascertained and a center-stratified subcohort of 16,154 individuals was defined. We estimated flavonoid intake at baseline from validated dietary questionnaires using a database developed from Phenol-Explorer and USDA databases. We used country-specific Prentice-weighted Cox regression models and random-effects meta-analysis methods to estimate HRs. Among the flavanol subclass, we observed significant inverse trends between intakes of all individual flavan-3-ol monomers and risk of T2D in multivariable models (all P-trend < 0.05). We also observed significant trends for the intakes of proanthocyanidin dimers (HR for the highest vs. the lowest quintile: 0.81; 95% CI: 0.71, 0.92; P-trend = 0.003) and trimers (HR: 0.91; 95% CI: 0.80, 1.04; P-trend = 0.07) but not for proanthocyanidins with a greater polymerization degree. Among the flavonol subclass, myricetin (HR: 0.77; 95% CI: 0.64, 0.93; P-trend = 0.001) was associated with a lower incidence of T2D. This large and heterogeneous European study showed inverse associations between all individual flavan-3-ol monomers, proanthocyanidins with a low polymerization degree, and the flavonol myricetin and incident T2D. These results suggest that individual flavonoids have different roles in the etiology of T2D.
doi:10.3945/jn.113.184945
PMCID: PMC3927546  PMID: 24368432
10.  The effects of vitamin D2 or D3 supplementation on glycaemic control and related metabolic parameters in people at risk of type 2 diabetes: protocol of a randomised double-blind placebo-controlled trial 
BMC Public Health  2013;13:999.
Background
The global prevalence of type 2 diabetes is increasing. Effective strategies to address this public health challenge are currently lacking. A number of epidemiological studies have reported associations between low concentrations of 25-hydroxy vitamin D and the incidence of diabetes, but a causal link has not been established. We investigate the effect of vitamin D supplementation on the metabolic status of individuals at increased risk of developing type 2 diabetes.
Methods/design
In a randomised double-blind placebo-controlled trial individuals identified as having a high risk of type 2 diabetes (non-diabetic hyperglycaemia or positive diabetes risk score) are randomised into one of three groups and given 4 doses of either placebo, or 100,000 IU Vitamin D2 (ergocalciferol) or 100,000 IU Vitamin D3 (cholecalciferol) at monthly intervals. The primary outcome measure is the change in glycated haemoglobin level between baseline and 4 months. Secondary outcome measures include blood pressure, lipid levels, apolipoproteins, highly sensitive C-reactive protein, parathyroid hormone (PTH) and safety of supplementation. and C-reactive protein. The trial is being conducted at two sites (London and Cambridge, U.K.) and a total of 342 participants are being recruited.
Discussion
Trial data examining whether supplementation of vitamin D improves glycaemic status and other metabolic parameters in people at risk of developing type 2 diabetes are sparse. This trial will evaluate the causal role of vitamin D in hyperglycaemia and risk of type 2 diabetes. Specific features of this trial include recruitment of participants from different ethnic groups, investigation of the relative effectiveness and safety of vitamin D2 and D3 and an evidence based approach to determination of the dose of supplementation.
Trial registration
EudraCT2009-011264-11; ISRCTN86515510
doi:10.1186/1471-2458-13-999
PMCID: PMC3819003  PMID: 24152375
Vitamin D2; Vitamin D3; Placebo; Type 2 diabetes; Randomised; Trial; Intervention
11.  Estimation of CT-Derived Abdominal Visceral and Subcutaneous Adipose Tissue Depots from Anthropometry in Europeans, South Asians and African Caribbeans 
PLoS ONE  2013;8(9):e75085.
Background
South Asians and African Caribbeans experience more cardiometabolic disease than Europeans. Risk factors include visceral (VAT) and subcutaneous abdominal (SAT) adipose tissue, which vary with ethnicity and are difficult to quantify using anthropometry.
Objective
We developed and cross-validated ethnicity and gender-specific equations using anthropometrics to predict VAT and SAT.
Design
669 Europeans, 514 South Asians and 227 African Caribbeans (70±7 years) underwent anthropometric measurement and abdominal CT scanning. South Asian and African Caribbean participants were first-generation migrants living in London. Prediction equations were derived for CT-measured VAT and SAT using stepwise regression, then cross-validated by comparing actual and predicted means.
Results
South Asians had more and African Caribbeans less VAT than Europeans. For basic VAT prediction equations (age and waist circumference), model fit was better in men (R2 range 0.59-0.71) than women (range 0.35-0.59). Expanded equations (+ weight, height, hip and thigh circumference) improved fit for South Asian and African Caribbean women (R2 0.35 to 0.55, and 0.43 to 0.56 respectively). For basic SAT equations, R2 was 0.69-0.77, and for expanded equations it was 0.72-0.86. Cross-validation showed differences between actual and estimated VAT of <7%, and SAT of <8% in all groups, apart from VAT in South Asian women which disagreed by 16%.
Conclusion
We provide ethnicity- and gender-specific VAT and SAT prediction equations, derived from a large tri-ethnic sample. Model fit was reasonable for SAT and VAT in men, while basic VAT models should be used cautiously in South Asian and African Caribbean women. These equations will aid studies of mechanisms of cardiometabolic disease in later life, where imaging data are not available.
doi:10.1371/journal.pone.0075085
PMCID: PMC3775834  PMID: 24069381
12.  Genetic loci influencing kidney function and chronic kidney disease in man 
Chambers, John C | Zhang, Weihua | Lord, Graham M | van der Harst, Pim | Lawlor, Debbie A | Sehmi, Joban S | Gale, Daniel P | Wass, Mark N | Ahmadi, Kourosh R | Bakker, Stephan JL | Beckmann, Jacqui | Bilo, Henk JG | Bochud, Murielle | Brown, Morris J | Caulfield, Mark J | Connell, John M C | Cook, Terence | Cotlarciuc, Ioana | Smith, George Davey | de Silva, Ranil | Deng, Guohong | Devuyst, Olivier | Dikkeschei, Lambert D. | Dimkovic, Nada | Dockrell, Mark | Dominiczak, Anna | Ebrahim, Shah | Eggermann, Thomas | Farrall, Martin | Ferrucci, Luigi | Floege, Jurgen | Forouhi, Nita G | Gansevoort, Ron T | Han, Xijin | Hedblad, Bo | van der Heide, Jaap J Homan | Hepkema, Bouke G | Hernandez-Fuentes, Maria | Hypponen, Elina | Johnson, Toby | de Jong, Paul E | Kleefstra, Nanne | Lagou, Vasiliki | Lapsley, Marta | Li, Yun | Loos, Ruth J F | Luan, Jian'an | Luttropp, Karin | Maréchal, Céline | Melander, Olle | Munroe, Patricia B | Nordfors, Louise | Parsa, Afshin | Penninx, Brenda W. | Perucha, Esperanza | Pouta, Anneli | Prokopenko, Inga | Roderick, Paul J | Ruokonen, Aimo | Samani, Nilesh | Sanna, Serena | Schalling, Martin | Schlessinger, David | Schlieper, Georg | Seelen, Marc AJ | Shuldiner, Alan R | Sjögren, Marketa | Smit, Johannes H. | Snieder, Harold | Soranzo, Nicole | Spector, Timothy D | Stenvinkel, Peter | Sternberg, Michael JE | Swaminathan, Ramasamyiyer | Tanaka, Toshiko | Ubink-Veltmaat, Lielith J. | Uda, Manuela | Vollenweider, Peter | Wallace, Chris | Waterworth, Dawn | Zerres, Klaus | Waeber, Gerard | Wareham, Nicholas J | Maxwell, Patrick H | McCarthy, Mark I | Jarvelin, Marjo-Riitta | Mooser, Vincent | Abecasis, Goncalo R | Lightstone, Liz | Scott, James | Navis, Gerjan | Elliott, Paul | Kooner., Jaspal S
Nature genetics  2010;42(5):373-375.
Chronic kidney disease (CKD), the result of permanent loss of kidney function, is a major global problem. We identify common genetic variants at chr2p12-p13, chr6q26, chr17q23 and chr19q13 associated with serum creatinine, a marker of kidney function (P=10−10 to 10−15). SNPs rs10206899 (near NAT8, chr2p12-p13) and rs4805834 (near SLC7A9, chr19q13) were also associated with CKD. Our findings provide new insight into metabolic, solute and drug-transport pathways underlying susceptibility to CKD.
doi:10.1038/ng.566
PMCID: PMC3748585  PMID: 20383145
13.  A Prospective Study of the Association Between Quantity and Variety of Fruit and Vegetable Intake and Incident Type 2 Diabetes 
Diabetes Care  2012;35(6):1293-1300.
OBJECTIVE
The association between quantity of fruit and vegetable (F&V) intake and risk of type 2 diabetes (T2D) is not clear, and the relationship with variety of intake is unknown. The current study examined the association of both quantity and variety of F&V intake and risk of T2D.
RESEARCH DESIGN AND METHODS
We examined the 11-year incidence of T2D in relation to quantity and variety of fruit, vegetables, and combined F&V intake in a case-cohort study of 3,704 participants (n = 653 diabetes cases) nested within the European Prospective Investigation into Cancer and Nutrition-Norfolk study, who completed 7-day prospective food diaries. Variety of intake was derived from the total number of different items consumed in a 1-week period. Multivariable, Prentice-weighted Cox regression was used to estimate hazard ratios (HRs) and 95% CIs.
RESULTS
A greater quantity of combined F&V intake was associated with 21% lower hazard of T2D (HR 0.79 [95% CI 0.62–1.00]) comparing extreme tertiles, in adjusted analyses including variety. Separately, quantity of vegetable intake (0.76 [0.60–0.97]), but not fruit, was inversely associated with T2D in adjusted analysis. Greater variety in fruit (0.70 [0.53–0.91]), vegetable (0.77 [0.61–0.98]), and combined F&V (0.61 [0.48–0.78]) intake was associated with a lower hazard of T2D, independent of known confounders and quantity of intake comparing extreme tertiles.
CONCLUSIONS
These findings suggest that a diet characterized by a greater quantity of vegetables and a greater variety of both F&V intake is associated with a reduced risk of T2D.
doi:10.2337/dc11-2388
PMCID: PMC3357245  PMID: 22474042
14.  The prospective association between total and type of fish intake and type 2 diabetes in 8 European countries: EPIC-InterAct Study123 
Background: Epidemiologic evidence of an association between fish intake and type 2 diabetes (T2D) is inconsistent and unresolved.
Objective: The objective was to examine the association between total and type of fish intake and T2D in 8 European countries.
Design: This was a case-cohort study, nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study, with 3.99 million person-years of follow-up, 12,403 incident diabetes cases, and a random subcohort of 16,835 individuals from 8 European countries. Habitual fish intake (lean fish, fatty fish, total fish, shellfish, and combined fish and shellfish) was assessed by country-specific dietary questionnaires. HRs were estimated in each country by using Prentice-weighted Cox regression models and pooled by using a random-effects meta-analysis.
Results: No overall association was found between combined fish and shellfish intake and incident T2D per quartile (adjusted HR: 1.00; 95% CI: 0.94, 1.06; P-trend = 0.99). Total fish, lean fish, and shellfish intakes separately were also not associated with T2D, but fatty fish intake was weakly inversely associated with T2D: adjusted HR per quartile 0.97 (0.94, 1.00), with an HR of 0.84 (0.70, 1.01), 0.85 (0.76, 0.95), and 0.87 (0.78, 0.97) for a comparison of the second, third, and fourth quartiles with the lowest quartile of intake, respectively (P-trend = 0.06).
Conclusions: These findings suggest that lean fish, total fish, and shellfish intakes are not associated with incident diabetes but that fatty fish intake may be weakly inversely associated. Replication of these findings in other populations and investigation of the mechanisms underlying these associations are warranted. Meanwhile, current public health recommendations on fish intake should remain unchanged.
doi:10.3945/ajcn.111.029314
PMCID: PMC3623039  PMID: 22572642
15.  Fruit and vegetable intake and type 2 diabetes: EPIC-InterAct prospective study and meta-analysis 
European journal of clinical nutrition  2012;66(10):1082-1092.
Background/Objective
Fruit and vegetable intake (FVI) may reduce the risk of type 2 diabetes (T2D), but the epidemiological evidence is inconclusive. The aim of this study is to examine the prospective association of FVI with T2D and conduct an updated meta-analysis.
Subjects/Methods
In the EPIC-InterAct (European Prospective Investigation into Cancer-InterAct) prospective case-cohort study nested within eight European countries, a representative sample of 16 154 participants and 12 403 incident cases of T2D were identified from 340 234 individuals with 3.99 million person-years of follow-up. For the meta-analysis we identified prospective studies on FVI and T2D risk by systematic searches of MEDLINE and EMBASE until April 2011.
Results
In EPIC-InterAct, estimated FVI by dietary questionnaires varied more than two-fold between countries. In adjusted analyses the hazard ratio (95% confidence interval) comparing the highest with lowest quartile of reported intake was 0.90 (0.80-1.01) for FVI; 0.89 (0.76-1.04) for fruit, and 0.94 (0.84-1.05) for vegetables. Among FV sub-types, only root vegetables were inversely associated with diabetes 0.87 (0.77-0.99). In meta-analysis using pooled data from five studies including EPIC-InterAct, comparing the highest with lowest category for FVI was associated with a lower relative risk of diabetes (0.93 (0.87-1.00)). Fruit or vegetables separately were not associated with diabetes. Among FV sub-types, only green leafy vegetable intake (RR: 0.84 (0.74-0.94)) was inversely associated with diabetes.
Conclusions
Sub-types of vegetables, such as root vegetables or green leafy vegetables may be beneficial for the prevention of diabetes, while total FVI may exert a weaker overall effect.
doi:10.1038/ejcn.2012.85
PMCID: PMC3652306  PMID: 22854878
Fruit; vegetables; type 2 diabetes mellitus; epidemiology; meta-analysis; review
16.  No Interactions Between Previously Associated 2-Hour Glucose Gene Variants and Physical Activity or BMI on 2-Hour Glucose Levels 
Scott, Robert A. | Chu, Audrey Y. | Grarup, Niels | Manning, Alisa K. | Hivert, Marie-France | Shungin, Dmitry | Tönjes, Anke | Yesupriya, Ajay | Barnes, Daniel | Bouatia-Naji, Nabila | Glazer, Nicole L. | Jackson, Anne U. | Kutalik, Zoltán | Lagou, Vasiliki | Marek, Diana | Rasmussen-Torvik, Laura J. | Stringham, Heather M. | Tanaka, Toshiko | Aadahl, Mette | Arking, Dan E. | Bergmann, Sven | Boerwinkle, Eric | Bonnycastle, Lori L. | Bornstein, Stefan R. | Brunner, Eric | Bumpstead, Suzannah J. | Brage, Soren | Carlson, Olga D. | Chen, Han | Chen, Yii-Der Ida | Chines, Peter S. | Collins, Francis S. | Couper, David J. | Dennison, Elaine M. | Dowling, Nicole F. | Egan, Josephine S. | Ekelund, Ulf | Erdos, Michael R. | Forouhi, Nita G. | Fox, Caroline S. | Goodarzi, Mark O. | Grässler, Jürgen | Gustafsson, Stefan | Hallmans, Göran | Hansen, Torben | Hingorani, Aroon | Holloway, John W. | Hu, Frank B. | Isomaa, Bo | Jameson, Karen A. | Johansson, Ingegerd | Jonsson, Anna | Jørgensen, Torben | Kivimaki, Mika | Kovacs, Peter | Kumari, Meena | Kuusisto, Johanna | Laakso, Markku | Lecoeur, Cécile | Lévy-Marchal, Claire | Li, Guo | Loos, Ruth J.F. | Lyssenko, Valeri | Marmot, Michael | Marques-Vidal, Pedro | Morken, Mario A. | Müller, Gabriele | North, Kari E. | Pankow, James S. | Payne, Felicity | Prokopenko, Inga | Psaty, Bruce M. | Renström, Frida | Rice, Ken | Rotter, Jerome I. | Rybin, Denis | Sandholt, Camilla H. | Sayer, Avan A. | Shrader, Peter | Schwarz, Peter E.H. | Siscovick, David S. | Stančáková, Alena | Stumvoll, Michael | Teslovich, Tanya M. | Waeber, Gérard | Williams, Gordon H. | Witte, Daniel R. | Wood, Andrew R. | Xie, Weijia | Boehnke, Michael | Cooper, Cyrus | Ferrucci, Luigi | Froguel, Philippe | Groop, Leif | Kao, W.H. Linda | Vollenweider, Peter | Walker, Mark | Watanabe, Richard M. | Pedersen, Oluf | Meigs, James B. | Ingelsson, Erik | Barroso, Inês | Florez, Jose C. | Franks, Paul W. | Dupuis, Josée | Wareham, Nicholas J. | Langenberg, Claudia
Diabetes  2012;61(5):1291-1296.
Gene–lifestyle interactions have been suggested to contribute to the development of type 2 diabetes. Glucose levels 2 h after a standard 75-g glucose challenge are used to diagnose diabetes and are associated with both genetic and lifestyle factors. However, whether these factors interact to determine 2-h glucose levels is unknown. We meta-analyzed single nucleotide polymorphism (SNP) × BMI and SNP × physical activity (PA) interaction regression models for five SNPs previously associated with 2-h glucose levels from up to 22 studies comprising 54,884 individuals without diabetes. PA levels were dichotomized, with individuals below the first quintile classified as inactive (20%) and the remainder as active (80%). BMI was considered a continuous trait. Inactive individuals had higher 2-h glucose levels than active individuals (β = 0.22 mmol/L [95% CI 0.13–0.31], P = 1.63 × 10−6). All SNPs were associated with 2-h glucose (β = 0.06–0.12 mmol/allele, P ≤ 1.53 × 10−7), but no significant interactions were found with PA (P > 0.18) or BMI (P ≥ 0.04). In this large study of gene–lifestyle interaction, we observed no interactions between genetic and lifestyle factors, both of which were associated with 2-h glucose. It is perhaps unlikely that top loci from genome-wide association studies will exhibit strong subgroup-specific effects, and may not, therefore, make the best candidates for the study of interactions.
doi:10.2337/db11-0973
PMCID: PMC3331745  PMID: 22415877
17.  The Relationship Between Metabolic Risk Factors and Incident Cardiovascular Disease in Europeans, South Asians, and African Caribbeans 
Objectives
This study sought to determine whether ethnic differences in diabetes, dyslipidemia, and ectopic fat deposition account for ethnic differences in incident cardiovascular disease.
Background
Coronary heart disease risks are elevated in South Asians and are lower in African Caribbeans compared with Europeans. These ethnic differences map to lipid patterns and ectopic fat deposition.
Methods
Cardiovascular risk factors were assessed in 2,049 Europeans, 1,517 South Asians, and 630 African Caribbeans from 1988 through 1991 (mean age: 52.4 ± 6.9 years). Fatal and nonfatal events were captured over a median 20.5-year follow-up. Subhazard ratios (SHR) were calculated using competing risks regression.
Results
Baseline diabetes prevalence was more than 3 times greater in South Asians and African Caribbeans than in Europeans. South Asians were more and African Caribbeans were less centrally obese and dyslipidemic than Europeans. Compared with Europeans, coronary heart disease incidence was greater in South Asians and less in African Caribbeans. The age- and sex-adjusted South Asian versus European SHR was 1.70 (95% confidence interval [CI]: 1.52 to 1.91, p < 0.001) and remained significant (1.45, 95% CI: 1.28 to 1.64, p < 0.001) when adjusted for waist-to-hip ratio. The African Caribbean versus European age- and sex-adjusted SHR of 0.64 (95% CI: 0.52 to 0.79, p < 0.001) remained significant when adjusted for high-density lipoprotein and low-density lipoprotein cholesterol (0.74, 95% CI: 0.60 to 0.92, p = 0.008). Compared with Europeans, South Asians and African Caribbeans experienced more strokes (age- and sex-adjusted SHR: 1.45 [95% CI: 1.17 to 1.80, p = 0.001] and 1.50 [95% CI: 1.13 to 2.00, p = 0.005], respectively), and this differential was more marked in those with diabetes (age-adjusted SHR: 1.97 [95% CI: 1.16 to 3.35, p = 0.038 for interaction] and 2.21 [95% CI: 1.14 to 4.30, p = 0.019 for interaction]).
Conclusions
Ethnic differences in measured metabolic risk factors did not explain differences in coronary heart disease incidence. The apparently greater association between diabetes and stroke risk in South Asians and African Caribbeans compared with Europeans merits further study.
doi:10.1016/j.jacc.2012.12.046
PMCID: PMC3677086  PMID: 23500273
coronary heart disease; ethnicity; incidence; stroke; CHD, coronary heart disease; CVD, cardiovascular disease; HDL, high-density lipoprotein; ICD, International Classification of Diseases; IR, insulin resistance; LDL, low-density lipoprotein; SHR, subhazard ratio
18.  Development and validation of a robust automated analysis of plasma phospholipid fatty acids for metabolic phenotyping of large epidemiological studies 
Genome Medicine  2013;5(4):39.
A fully automated, high-throughput method was developed to profile the fatty acids of phospholipids from human plasma samples for application to a large epidemiological sample set (n > 25,000). We report here on the data obtained for the quality-control materials used with the first 860 batches, and the validation process used. The method consists of two robotic systems combined with gas chromatography, performing lipid extraction, phospholipid isolation, hydrolysis and derivatization to fatty-acid methyl esters, and on-line analysis. This is the first report showing that fatty-acid profiling is an achievable strategy for metabolic phenotyping in very large epidemiological and genetic studies.
doi:10.1186/gm443
PMCID: PMC3706814  PMID: 23618465
19.  Fish Consumption, Dietary Long-Chain n-3 Fatty Acids, and Risk of Type 2 Diabetes 
Diabetes Care  2012;35(4):918-929.
OBJECTIVE
The evidence on the association between fish consumption, dietary long-chain n-3 fatty acids, and risk of type 2 diabetes is inconsistent. We therefore performed a systematic review and meta-analysis of the available prospective evidence.
RESEARCH DESIGN AND METHODS
Studies were identified by searching the PubMed and EMBASE databases through 15 December 2011 and by reviewing the reference lists of retrieved articles. Prospective studies were included if they reported relative risk (RR) estimates with 95% CIs for the association between fish consumption and/or dietary long-chain n-3 fatty acids and incidence of type 2 diabetes. A dose-response random-effects model was used to combine study-specific RRs. Potential sources of heterogeneity were explored by prespecified stratifications.
RESULTS
Sixteen studies involving 527,441 participants and 24,082 diabetes cases were included. Considerable statistical heterogeneity in the overall summary estimates was partly explained by geographical differences. For each serving per week increment in fish consumption, the RRs (95% CIs) of type 2 diabetes were 1.05 (1.02–1.09), 1.03 (0.96–1.11), and 0.98 (0.97–1.00) combining U.S., European, and Asian/Australian studies, respectively. For each 0.30 g per day increment in long-chain n-3 fatty acids, the corresponding summary estimates were 1.17 (1.09–1.26), 0.98 (0.70–1.37), and 0.90 (0.82–0.98).
CONCLUSIONS
Results from this meta-analysis indicate differences between geographical regions in observed associations of fish consumption and dietary intake of long-chain n-3 fatty acids with risk of type 2 diabetes. In consideration of the heterogeneous results, the relationship warrants further investigation. Meanwhile, current public health recommendations on fish consumption should be upheld unchanged.
doi:10.2337/dc11-1631
PMCID: PMC3308304  PMID: 22442397
20.  A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance 
Manning, Alisa K. | Hivert, Marie-France | Scott, Robert A. | Grimsby, Jonna L. | Bouatia-Naji, Nabila | Chen, Han | Rybin, Denis | Liu, Ching-Ti | Bielak, Lawrence F. | Prokopenko, Inga | Amin, Najaf | Barnes, Daniel | Cadby, Gemma | Hottenga, Jouke-Jan | Ingelsson, Erik | Jackson, Anne U. | Johnson, Toby | Kanoni, Stavroula | Ladenvall, Claes | Lagou, Vasiliki | Lahti, Jari | Lecoeur, Cecile | Liu, Yongmei | Martinez-Larrad, Maria Teresa | Montasser, May E. | Navarro, Pau | Perry, John R. B. | Rasmussen-Torvik, Laura J. | Salo, Perttu | Sattar, Naveed | Shungin, Dmitry | Strawbridge, Rona J. | Tanaka, Toshiko | van Duijn, Cornelia M. | An, Ping | de Andrade, Mariza | Andrews, Jeanette S. | Aspelund, Thor | Atalay, Mustafa | Aulchenko, Yurii | Balkau, Beverley | Bandinelli, Stefania | Beckmann, Jacques S. | Beilby, John P. | Bellis, Claire | Bergman, Richard N. | Blangero, John | Boban, Mladen | Boehnke, Michael | Boerwinkle, Eric | Bonnycastle, Lori L. | Boomsma, Dorret I. | Borecki, Ingrid B. | Böttcher, Yvonne | Bouchard, Claude | Brunner, Eric | Budimir, Danijela | Campbell, Harry | Carlson, Olga | Chines, Peter S. | Clarke, Robert | Collins, Francis S. | Corbatón-Anchuelo, Arturo | Couper, David | de Faire, Ulf | Dedoussis, George V | Deloukas, Panos | Dimitriou, Maria | Egan, Josephine M | Eiriksdottir, Gudny | Erdos, Michael R. | Eriksson, Johan G. | Eury, Elodie | Ferrucci, Luigi | Ford, Ian | Forouhi, Nita G. | Fox, Caroline S | Franzosi, Maria Grazia | Franks, Paul W | Frayling, Timothy M | Froguel, Philippe | Galan, Pilar | de Geus, Eco | Gigante, Bruna | Glazer, Nicole L. | Goel, Anuj | Groop, Leif | Gudnason, Vilmundur | Hallmans, Göran | Hamsten, Anders | Hansson, Ola | Harris, Tamara B. | Hayward, Caroline | Heath, Simon | Hercberg, Serge | Hicks, Andrew A. | Hingorani, Aroon | Hofman, Albert | Hui, Jennie | Hung, Joseph | Jarvelin, Marjo Riitta | Jhun, Min A. | Johnson, Paul C.D. | Jukema, J Wouter | Jula, Antti | Kao, W.H. | Kaprio, Jaakko | Kardia, Sharon L. R. | Keinanen-Kiukaanniemi, Sirkka | Kivimaki, Mika | Kolcic, Ivana | Kovacs, Peter | Kumari, Meena | Kuusisto, Johanna | Kyvik, Kirsten Ohm | Laakso, Markku | Lakka, Timo | Lannfelt, Lars | Lathrop, G Mark | Launer, Lenore J. | Leander, Karin | Li, Guo | Lind, Lars | Lindstrom, Jaana | Lobbens, Stéphane | Loos, Ruth J. F. | Luan, Jian’an | Lyssenko, Valeriya | Mägi, Reedik | Magnusson, Patrik K. E. | Marmot, Michael | Meneton, Pierre | Mohlke, Karen L. | Mooser, Vincent | Morken, Mario A. | Miljkovic, Iva | Narisu, Narisu | O’Connell, Jeff | Ong, Ken K. | Oostra, Ben A. | Palmer, Lyle J. | Palotie, Aarno | Pankow, James S. | Peden, John F. | Pedersen, Nancy L. | Pehlic, Marina | Peltonen, Leena | Penninx, Brenda | Pericic, Marijana | Perola, Markus | Perusse, Louis | Peyser, Patricia A | Polasek, Ozren | Pramstaller, Peter P. | Province, Michael A. | Räikkönen, Katri | Rauramaa, Rainer | Rehnberg, Emil | Rice, Ken | Rotter, Jerome I. | Rudan, Igor | Ruokonen, Aimo | Saaristo, Timo | Sabater-Lleal, Maria | Salomaa, Veikko | Savage, David B. | Saxena, Richa | Schwarz, Peter | Seedorf, Udo | Sennblad, Bengt | Serrano-Rios, Manuel | Shuldiner, Alan R. | Sijbrands, Eric J.G. | Siscovick, David S. | Smit, Johannes H. | Small, Kerrin S. | Smith, Nicholas L. | Smith, Albert Vernon | Stančáková, Alena | Stirrups, Kathleen | Stumvoll, Michael | Sun, Yan V. | Swift, Amy J. | Tönjes, Anke | Tuomilehto, Jaakko | Trompet, Stella | Uitterlinden, Andre G. | Uusitupa, Matti | Vikström, Max | Vitart, Veronique | Vohl, Marie-Claude | Voight, Benjamin F. | Vollenweider, Peter | Waeber, Gerard | Waterworth, Dawn M | Watkins, Hugh | Wheeler, Eleanor | Widen, Elisabeth | Wild, Sarah H. | Willems, Sara M. | Willemsen, Gonneke | Wilson, James F. | Witteman, Jacqueline C.M. | Wright, Alan F. | Yaghootkar, Hanieh | Zelenika, Diana | Zemunik, Tatijana | Zgaga, Lina | Wareham, Nicholas J. | McCarthy, Mark I. | Barroso, Ines | Watanabe, Richard M. | Florez, Jose C. | Dupuis, Josée | Meigs, James B. | Langenberg, Claudia
Nature genetics  2012;44(6):659-669.
Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and beta-cell dysfunction, but contributed little to our understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways may be uncovered by accounting for differences in body mass index (BMI) and potential interaction between BMI and genetic variants. We applied a novel joint meta-analytical approach to test associations with fasting insulin (FI) and glucose (FG) on a genome-wide scale. We present six previously unknown FI loci at P<5×10−8 in combined discovery and follow-up analyses of 52 studies comprising up to 96,496non-diabetic individuals. Risk variants were associated with higher triglyceride and lower HDL cholesterol levels, suggestive of a role for these FI loci in insulin resistance pathways. The localization of these additional loci will aid further characterization of the role of insulin resistance in T2D pathophysiology.
doi:10.1038/ng.2274
PMCID: PMC3613127  PMID: 22581228
21.  Genetic variation at the SLC23A1 locus is associated with circulating levels of L-ascorbic acid (Vitamin C). Evidence from 5 independent studies with over 15000 participants 
Background
L-ascorbic acid is an essential part of the human diet and has been associated with a wide-range of chronic complex diseases including cardiovascular outcomes. To date, there are no confirmed genetic correlates of circulating levels of L-ascorbic acid.
Objectives
We aimed to confirm the existence of association between common variation at the SLC23A1 gene locus and circulating levels of L-ascorbic acid.
Design
We employed a two-stage design which used a discovery cohort (the British Women’s Heart and Health Study) and a series of follow-up cohorts and meta-analysis (totalling 15087 participants) to assess the relationship between variation at SLC23A1 and circulating levels of L-ascorbic acid.
Results
In the discovery cohort, variation at rs33972313 was associated with a reduction in circulating levels of L-ascorbic acid (−4.15μmol/L (95%CI −0.49, −7.81), p=0.03 reduction per minor allele). Pooled analysis of the relationship between rs33972313 and circulating L-ascorbic acid across all studies confirmed this, showing that each additional rare allele was associated with a reduction in circulating levels of L-ascorbic acid of −5.98μmol/L (95%CI −8.23, −3.73), p=2.0×10−7 per minor allele.
Conclusion
Work here has identified a genetic variant (rs33972313) in the SLC23A1 vitamin C active transporter locus that is reliably associated with circulating levels of L-ascorbic acid in the general population. This finding has implications more generally for the epidemiological investigation of relationships between circulating L-ascorbic acid and health outcomes.
doi:10.3945/ajcn.2010.29438
PMCID: PMC3605792  PMID: 20519558
Vitamin C; genotype; L-ascorbic acid
22.  Self-rated health and type 2 diabetes risk in the European Prospective Investigation into Cancer and Nutrition-InterAct study: a case-cohort study 
BMJ Open  2013;3(3):e002436.
Objectives
To investigate the association between self-rated health and risk of type 2 diabetes and whether the strength of this association is consistent across five European centres.
Design
Population-based prospective case-cohort study.
Setting
Enrolment took place between 1992 and 2000 in five European centres (Bilthoven, Cambridge, Heidelberg, Potsdam and Umeå).
Participants
Self-rated health was assessed by a baseline questionnaire in 3399 incident type 2 diabetic case participants and a centre-stratified subcohort of 4619 individuals from the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct study which was drawn from a total cohort of 340 234 participants in the EPIC.
Primary outcome measure
Prentice-weighted Cox regression was used to estimate centre-specific HRs and 95% CIs for incident type 2 diabetes controlling for age, sex, centre, education, body mass index (BMI), smoking, alcohol consumption, energy intake, physical activity and hypertension. The centre-specific HRs were pooled across centres by random effects meta-analysis.
Results
Low self-rated health was associated with a higher hazard of type 2 diabetes after adjusting for age and sex (pooled HR 1.67, 95% CI 1.48 to 1.88). After additional adjustment for health-related variables including BMI, the association was attenuated but remained statistically significant (pooled HR 1.29, 95% CI 1.09 to 1.53). I2 index for heterogeneity across centres was 13.3% (p=0.33).
Conclusions
Low self-rated health was associated with a higher risk of type 2 diabetes. The association could be only partly explained by other health-related variables, of which obesity was the strongest. We found no indication of heterogeneity in the association between self-rated health and type 2 diabetes mellitus across the European centres.
doi:10.1136/bmjopen-2012-002436
PMCID: PMC3612773  PMID: 23471609
Diabetes & Endocrinology; Preventive Medicine
23.  Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways 
Scott, Robert A | Lagou, Vasiliki | Welch, Ryan P | Wheeler, Eleanor | Montasser, May E | Luan, Jian’an | Mägi, Reedik | Strawbridge, Rona J | Rehnberg, Emil | Gustafsson, Stefan | Kanoni, Stavroula | Rasmussen-Torvik, Laura J | Yengo, Loïc | Lecoeur, Cecile | Shungin, Dmitry | Sanna, Serena | Sidore, Carlo | Johnson, Paul C D | Jukema, J Wouter | Johnson, Toby | Mahajan, Anubha | Verweij, Niek | Thorleifsson, Gudmar | Hottenga, Jouke-Jan | Shah, Sonia | Smith, Albert V | Sennblad, Bengt | Gieger, Christian | Salo, Perttu | Perola, Markus | Timpson, Nicholas J | Evans, David M | Pourcain, Beate St | Wu, Ying | Andrews, Jeanette S | Hui, Jennie | Bielak, Lawrence F | Zhao, Wei | Horikoshi, Momoko | Navarro, Pau | Isaacs, Aaron | O’Connell, Jeffrey R | Stirrups, Kathleen | Vitart, Veronique | Hayward, Caroline | Esko, Tönu | Mihailov, Evelin | Fraser, Ross M | Fall, Tove | Voight, Benjamin F | Raychaudhuri, Soumya | Chen, Han | Lindgren, Cecilia M | Morris, Andrew P | Rayner, Nigel W | Robertson, Neil | Rybin, Denis | Liu, Ching-Ti | Beckmann, Jacques S | Willems, Sara M | Chines, Peter S | Jackson, Anne U | Kang, Hyun Min | Stringham, Heather M | Song, Kijoung | Tanaka, Toshiko | Peden, John F | Goel, Anuj | Hicks, Andrew A | An, Ping | Müller-Nurasyid, Martina | Franco-Cereceda, Anders | Folkersen, Lasse | Marullo, Letizia | Jansen, Hanneke | Oldehinkel, Albertine J | Bruinenberg, Marcel | Pankow, James S | North, Kari E | Forouhi, Nita G | Loos, Ruth J F | Edkins, Sarah | Varga, Tibor V | Hallmans, Göran | Oksa, Heikki | Antonella, Mulas | Nagaraja, Ramaiah | Trompet, Stella | Ford, Ian | Bakker, Stephan J L | Kong, Augustine | Kumari, Meena | Gigante, Bruna | Herder, Christian | Munroe, Patricia B | Caulfield, Mark | Antti, Jula | Mangino, Massimo | Small, Kerrin | Miljkovic, Iva | Liu, Yongmei | Atalay, Mustafa | Kiess, Wieland | James, Alan L | Rivadeneira, Fernando | Uitterlinden, Andre G | Palmer, Colin N A | Doney, Alex S F | Willemsen, Gonneke | Smit, Johannes H | Campbell, Susan | Polasek, Ozren | Bonnycastle, Lori L | Hercberg, Serge | Dimitriou, Maria | Bolton, Jennifer L | Fowkes, Gerard R | Kovacs, Peter | Lindström, Jaana | Zemunik, Tatijana | Bandinelli, Stefania | Wild, Sarah H | Basart, Hanneke V | Rathmann, Wolfgang | Grallert, Harald | Maerz, Winfried | Kleber, Marcus E | Boehm, Bernhard O | Peters, Annette | Pramstaller, Peter P | Province, Michael A | Borecki, Ingrid B | Hastie, Nicholas D | Rudan, Igor | Campbell, Harry | Watkins, Hugh | Farrall, Martin | Stumvoll, Michael | Ferrucci, Luigi | Waterworth, Dawn M | Bergman, Richard N | Collins, Francis S | Tuomilehto, Jaakko | Watanabe, Richard M | de Geus, Eco J C | Penninx, Brenda W | Hofman, Albert | Oostra, Ben A | Psaty, Bruce M | Vollenweider, Peter | Wilson, James F | Wright, Alan F | Hovingh, G Kees | Metspalu, Andres | Uusitupa, Matti | Magnusson, Patrik K E | Kyvik, Kirsten O | Kaprio, Jaakko | Price, Jackie F | Dedoussis, George V | Deloukas, Panos | Meneton, Pierre | Lind, Lars | Boehnke, Michael | Shuldiner, Alan R | van Duijn, Cornelia M | Morris, Andrew D | Toenjes, Anke | Peyser, Patricia A | Beilby, John P | Körner, Antje | Kuusisto, Johanna | Laakso, Markku | Bornstein, Stefan R | Schwarz, Peter E H | Lakka, Timo A | Rauramaa, Rainer | Adair, Linda S | Smith, George Davey | Spector, Tim D | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Gudnason, Vilmundur | Kivimaki, Mika | Hingorani, Aroon | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Boomsma, Dorret I | Stefansson, Kari | van der Harst, Pim | Dupuis, Josée | Pedersen, Nancy L | Sattar, Naveed | Harris, Tamara B | Cucca, Francesco | Ripatti, Samuli | Salomaa, Veikko | Mohlke, Karen L | Balkau, Beverley | Froguel, Philippe | Pouta, Anneli | Jarvelin, Marjo-Riitta | Wareham, Nicholas J | Bouatia-Naji, Nabila | McCarthy, Mark I | Franks, Paul W | Meigs, James B | Teslovich, Tanya M | Florez, Jose C | Langenberg, Claudia | Ingelsson, Erik | Prokopenko, Inga | Barroso, Inês
Nature genetics  2012;44(9):991-1005.
Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have raised the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional follow-up of these newly discovered loci will further improve our understanding of glycemic control.
doi:10.1038/ng.2385
PMCID: PMC3433394  PMID: 22885924
24.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes 
Morris, Andrew P | Voight, Benjamin F | Teslovich, Tanya M | Ferreira, Teresa | Segrè, Ayellet V | Steinthorsdottir, Valgerdur | Strawbridge, Rona J | Khan, Hassan | Grallert, Harald | Mahajan, Anubha | Prokopenko, Inga | Kang, Hyun Min | Dina, Christian | Esko, Tonu | Fraser, Ross M | Kanoni, Stavroula | Kumar, Ashish | Lagou, Vasiliki | Langenberg, Claudia | Luan, Jian'an | Lindgren, Cecilia M | Müller-Nurasyid, Martina | Pechlivanis, Sonali | Rayner, N William | Scott, Laura J | Wiltshire, Steven | Yengo, Loic | Kinnunen, Leena | Rossin, Elizabeth J | Raychaudhuri, Soumya | Johnson, Andrew D | Dimas, Antigone S | Loos, Ruth J F | Vedantam, Sailaja | Chen, Han | Florez, Jose C | Fox, Caroline | Liu, Ching-Ti | Rybin, Denis | Couper, David J | Kao, Wen Hong L | Li, Man | Cornelis, Marilyn C | Kraft, Peter | Sun, Qi | van Dam, Rob M | Stringham, Heather M | Chines, Peter S | Fischer, Krista | Fontanillas, Pierre | Holmen, Oddgeir L | Hunt, Sarah E | Jackson, Anne U | Kong, Augustine | Lawrence, Robert | Meyer, Julia | Perry, John RB | Platou, Carl GP | Potter, Simon | Rehnberg, Emil | Robertson, Neil | Sivapalaratnam, Suthesh | Stančáková, Alena | Stirrups, Kathleen | Thorleifsson, Gudmar | Tikkanen, Emmi | Wood, Andrew R | Almgren, Peter | Atalay, Mustafa | Benediktsson, Rafn | Bonnycastle, Lori L | Burtt, Noël | Carey, Jason | Charpentier, Guillaume | Crenshaw, Andrew T | Doney, Alex S F | Dorkhan, Mozhgan | Edkins, Sarah | Emilsson, Valur | Eury, Elodie | Forsen, Tom | Gertow, Karl | Gigante, Bruna | Grant, George B | Groves, Christopher J | Guiducci, Candace | Herder, Christian | Hreidarsson, Astradur B | Hui, Jennie | James, Alan | Jonsson, Anna | Rathmann, Wolfgang | Klopp, Norman | Kravic, Jasmina | Krjutškov, Kaarel | Langford, Cordelia | Leander, Karin | Lindholm, Eero | Lobbens, Stéphane | Männistö, Satu | Mirza, Ghazala | Mühleisen, Thomas W | Musk, Bill | Parkin, Melissa | Rallidis, Loukianos | Saramies, Jouko | Sennblad, Bengt | Shah, Sonia | Sigurðsson, Gunnar | Silveira, Angela | Steinbach, Gerald | Thorand, Barbara | Trakalo, Joseph | Veglia, Fabrizio | Wennauer, Roman | Winckler, Wendy | Zabaneh, Delilah | Campbell, Harry | van Duijn, Cornelia | Uitterlinden89-, Andre G | Hofman, Albert | Sijbrands, Eric | Abecasis, Goncalo R | Owen, Katharine R | Zeggini, Eleftheria | Trip, Mieke D | Forouhi, Nita G | Syvänen, Ann-Christine | Eriksson, Johan G | Peltonen, Leena | Nöthen, Markus M | Balkau, Beverley | Palmer, Colin N A | Lyssenko, Valeriya | Tuomi, Tiinamaija | Isomaa, Bo | Hunter, David J | Qi, Lu | Shuldiner, Alan R | Roden, Michael | Barroso, Ines | Wilsgaard, Tom | Beilby, John | Hovingh, Kees | Price, Jackie F | Wilson, James F | Rauramaa, Rainer | Lakka, Timo A | Lind, Lars | Dedoussis, George | Njølstad, Inger | Pedersen, Nancy L | Khaw, Kay-Tee | Wareham, Nicholas J | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Korpi-Hyövälti, Eeva | Saltevo, Juha | Laakso, Markku | Kuusisto, Johanna | Metspalu, Andres | Collins, Francis S | Mohlke, Karen L | Bergman, Richard N | Tuomilehto, Jaakko | Boehm, Bernhard O | Gieger, Christian | Hveem, Kristian | Cauchi, Stephane | Froguel, Philippe | Baldassarre, Damiano | Tremoli, Elena | Humphries, Steve E | Saleheen, Danish | Danesh, John | Ingelsson, Erik | Ripatti, Samuli | Salomaa, Veikko | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Peters, Annette | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Morris, Andrew D | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | Boerwinkle, Eric | Melander, Olle | Kathiresan, Sekar | Nilsson, Peter M | Deloukas, Panos | Thorsteinsdottir, Unnur | Groop, Leif C | Stefansson, Kari | Hu, Frank | Pankow, James S | Dupuis, Josée | Meigs, James B | Altshuler, David | Boehnke, Michael | McCarthy, Mark I
Nature genetics  2012;44(9):981-990.
To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.
doi:10.1038/ng.2383
PMCID: PMC3442244  PMID: 22885922
25.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes 
Morris, Andrew P | Voight, Benjamin F | Teslovich, Tanya M | Ferreira, Teresa | Segré, Ayellet V | Steinthorsdottir, Valgerdur | Strawbridge, Rona J | Khan, Hassan | Grallert, Harald | Mahajan, Anubha | Prokopenko, Inga | Kang, Hyun Min | Dina, Christian | Esko, Tonu | Fraser, Ross M | Kanoni, Stavroula | Kumar, Ashish | Lagou, Vasiliki | Langenberg, Claudia | Luan, Jian’an | Lindgren, Cecilia M | Müller-Nurasyid, Martina | Pechlivanis, Sonali | Rayner, N William | Scott, Laura J | Wiltshire, Steven | Yengo, Loic | Kinnunen, Leena | Rossin, Elizabeth J | Raychaudhuri, Soumya | Johnson, Andrew D | Dimas, Antigone S | Loos, Ruth J F | Vedantam, Sailaja | Chen, Han | Florez, Jose C | Fox, Caroline | Liu, Ching-Ti | Rybin, Denis | Couper, David J | Kao, Wen Hong L | Li, Man | Cornelis, Marilyn C | Kraft, Peter | Sun, Qi | van Dam, Rob M | Stringham, Heather M | Chines, Peter S | Fischer, Krista | Fontanillas, Pierre | Holmen, Oddgeir L | Hunt, Sarah E | Jackson, Anne U | Kong, Augustine | Lawrence, Robert | Meyer, Julia | Perry, John R B | Platou, Carl G P | Potter, Simon | Rehnberg, Emil | Robertson, Neil | Sivapalaratnam, Suthesh | Stančáková, Alena | Stirrups, Kathleen | Thorleifsson, Gudmar | Tikkanen, Emmi | Wood, Andrew R | Almgren, Peter | Atalay, Mustafa | Benediktsson, Rafn | Bonnycastle, Lori L | Burtt, Noël | Carey, Jason | Charpentier, Guillaume | Crenshaw, Andrew T | Doney, Alex S F | Dorkhan, Mozhgan | Edkins, Sarah | Emilsson, Valur | Eury, Elodie | Forsen, Tom | Gertow, Karl | Gigante, Bruna | Grant, George B | Groves, Christopher J | Guiducci, Candace | Herder, Christian | Hreidarsson, Astradur B | Hui, Jennie | James, Alan | Jonsson, Anna | Rathmann, Wolfgang | Klopp, Norman | Kravic, Jasmina | Krjutškov, Kaarel | Langford, Cordelia | Leander, Karin | Lindholm, Eero | Lobbens, Stéphane | Männistö, Satu | Mirza, Ghazala | Mühleisen, Thomas W | Musk, Bill | Parkin, Melissa | Rallidis, Loukianos | Saramies, Jouko | Sennblad, Bengt | Shah, Sonia | Sigurðsson, Gunnar | Silveira, Angela | Steinbach, Gerald | Thorand, Barbara | Trakalo, Joseph | Veglia, Fabrizio | Wennauer, Roman | Winckler, Wendy | Zabaneh, Delilah | Campbell, Harry | van Duijn, Cornelia | Uitterlinden, Andre G | Hofman, Albert | Sijbrands, Eric | Abecasis, Goncalo R | Owen, Katharine R | Zeggini, Eleftheria | Trip, Mieke D | Forouhi, Nita G | Syvänen, Ann-Christine | Eriksson, Johan G | Peltonen, Leena | Nöthen, Markus M | Balkau, Beverley | Palmer, Colin N A | Lyssenko, Valeriya | Tuomi, Tiinamaija | Isomaa, Bo | Hunter, David J | Qi, Lu | Shuldiner, Alan R | Roden, Michael | Barroso, Ines | Wilsgaard, Tom | Beilby, John | Hovingh, Kees | Price, Jackie F | Wilson, James F | Rauramaa, Rainer | Lakka, Timo A | Lind, Lars | Dedoussis, George | Njølstad, Inger | Pedersen, Nancy L | Khaw, Kay-Tee | Wareham, Nicholas J | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Korpi-Hyövälti, Eeva | Saltevo, Juha | Laakso, Markku | Kuusisto, Johanna | Metspalu, Andres | Collins, Francis S | Mohlke, Karen L | Bergman, Richard N | Tuomilehto, Jaakko | Boehm, Bernhard O | Gieger, Christian | Hveem, Kristian | Cauchi, Stephane | Froguel, Philippe | Baldassarre, Damiano | Tremoli, Elena | Humphries, Steve E | Saleheen, Danish | Danesh, John | Ingelsson, Erik | Ripatti, Samuli | Salomaa, Veikko | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Peters, Annette | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Morris, Andrew D | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | Boerwinkle, Eric | Melander, Olle | Kathiresan, Sekar | Nilsson, Peter M | Deloukas, Panos | Thorsteinsdottir, Unnur | Groop, Leif C | Stefansson, Kari | Hu, Frank | Pankow, James S | Dupuis, Josée | Meigs, James B | Altshuler, David | Boehnke, Michael | McCarthy, Mark I
Nature genetics  2012;44(9):981-990.
To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.
doi:10.1038/ng.2383
PMCID: PMC3442244  PMID: 22885922

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