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A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance.

ABSTRACT: Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and ?-cell dysfunction but have contributed little to the understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways might be uncovered by accounting for differences in body mass index (BMI) and potential interactions between BMI and genetic variants. We applied a joint meta-analysis approach to test associations with fasting insulin and glucose on a genome-wide scale. We present six previously unknown loci associated with fasting insulin at P < 5 × 10(-8) in combined discovery and follow-up analyses of 52 studies comprising up to 96,496 non-diabetic individuals. Risk variants were associated with higher triglyceride and lower high-density lipoprotein (HDL) cholesterol levels, suggesting a role for these loci in insulin resistance pathways. The discovery of these loci will aid further characterization of the role of insulin resistance in T2D pathophysiology.

SUBMITTER: Manning AK

PROVIDER: S-EPMC3613127 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature

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A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance.

Manning Alisa K AK Hivert Marie-France MF Scott Robert A RA Grimsby Jonna L JL Bouatia-Naji Nabila N Chen Han H Rybin Denis D Liu Ching-Ti CT Bielak Lawrence F LF Prokopenko Inga I Amin Najaf N Barnes Daniel D Cadby Gemma G Hottenga Jouke-Jan JJ Ingelsson Erik E Jackson Anne U AU Johnson Toby T Kanoni Stavroula S Ladenvall Claes C Lagou Vasiliki V Lahti Jari J Lecoeur Cecile C Liu Yongmei Y Martinez-Larrad Maria Teresa MT Montasser May E ME Navarro Pau P Perry John R B JR Rasmussen-Torvik Laura J LJ Salo Perttu P Sattar Naveed N Shungin Dmitry D Strawbridge Rona J RJ Tanaka Toshiko T van Duijn Cornelia M CM An Ping P de Andrade Mariza M Andrews Jeanette S JS Aspelund Thor T Atalay Mustafa M Aulchenko Yurii Y Balkau Beverley B Bandinelli Stefania S Beckmann Jacques S JS Beilby John P JP Bellis Claire C Bergman Richard N RN Blangero John J Boban Mladen M Boehnke Michael M Boerwinkle Eric E Bonnycastle Lori L LL Boomsma Dorret I DI Borecki Ingrid B IB Böttcher Yvonne Y Bouchard Claude C Brunner Eric E Budimir Danijela D Campbell Harry H Carlson Olga O Chines Peter S PS Clarke Robert R Collins Francis S FS Corbatón-Anchuelo Arturo A Couper David D de Faire Ulf U Dedoussis George V GV Deloukas Panos P Dimitriou Maria M Egan Josephine M JM Eiriksdottir Gudny G Erdos Michael R MR Eriksson Johan G JG Eury Elodie E Ferrucci Luigi L Ford Ian I Forouhi Nita G NG Fox Caroline S CS Franzosi Maria Grazia MG Franks Paul W PW Frayling Timothy M TM Froguel Philippe P Galan Pilar P de Geus Eco E Gigante Bruna B Glazer Nicole L NL Goel Anuj A Groop Leif L Gudnason Vilmundur V Hallmans Göran G Hamsten Anders A Hansson Ola O Harris Tamara B TB Hayward Caroline C Heath Simon S Hercberg Serge S Hicks Andrew A AA Hingorani Aroon A Hofman Albert A Hui Jennie J Hung Joseph J Jarvelin Marjo-Riitta MR Jhun Min A MA Johnson Paul C D PC Jukema J Wouter JW Jula Antti A Kao W H WH Kaprio Jaakko J Kardia Sharon L R SL Keinanen-Kiukaanniemi Sirkka S Kivimaki Mika M Kolcic Ivana I Kovacs Peter P Kumari Meena M Kuusisto Johanna J Kyvik Kirsten Ohm KO Laakso Markku M Lakka Timo T Lannfelt Lars L Lathrop G Mark GM Launer Lenore J LJ Leander Karin K Li Guo G Lind Lars L Lindstrom Jaana J Lobbens Stéphane S Loos Ruth J F RJ Luan Jian'an J Lyssenko Valeriya V Mägi Reedik R Magnusson Patrik K E PK Marmot Michael M Meneton Pierre P Mohlke Karen L KL Mooser Vincent V Morken Mario A MA Miljkovic Iva I Narisu Narisu N O'Connell Jeff J Ong Ken K KK Oostra Ben A BA Palmer Lyle J LJ Palotie Aarno A Pankow James S JS Peden John F JF Pedersen Nancy L NL Pehlic Marina M Peltonen Leena L Penninx Brenda B Pericic Marijana M Perola Markus M Perusse Louis L Peyser Patricia A PA Polasek Ozren O Pramstaller Peter P PP Province Michael A MA Räikkönen Katri K Rauramaa Rainer R Rehnberg Emil E Rice Ken K Rotter Jerome I JI Rudan Igor I Ruokonen Aimo A Saaristo Timo T Sabater-Lleal Maria M Salomaa Veikko V Savage David B DB Saxena Richa R Schwarz Peter P Seedorf Udo U Sennblad Bengt B Serrano-Rios Manuel M Shuldiner Alan R AR Sijbrands Eric J G EJ Siscovick David S DS Smit Johannes H JH Small Kerrin S KS Smith Nicholas L NL Smith Albert Vernon AV Stančáková Alena A Stirrups Kathleen K Stumvoll Michael M Sun Yan V YV Swift Amy J AJ Tönjes Anke A Tuomilehto Jaakko J Trompet Stella S Uitterlinden Andre G AG Uusitupa Matti M Vikström Max M Vitart Veronique V Vohl Marie-Claude MC Voight Benjamin F BF Vollenweider Peter P Waeber Gerard G Waterworth Dawn M DM Watkins Hugh H Wheeler Eleanor E Widen Elisabeth E Wild Sarah H SH Willems Sara M SM Willemsen Gonneke G Wilson James F JF Witteman Jacqueline C M JC Wright Alan F AF Yaghootkar Hanieh H Zelenika Diana D Zemunik Tatijana T Zgaga Lina L Wareham Nicholas J NJ McCarthy Mark I MI Barroso Ines I Watanabe Richard M RM Florez Jose C JC Dupuis Josée J Meigs James B JB Langenberg Claudia C

Nature genetics 20120513 6

Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and β-cell dysfunction but have contributed little to the understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways might be uncovered by accounting for differences in body mass index (BMI) and potential interactions between BMI and genetic variants. We applied a joint meta-analysis approach to test associations w ...[more]

PMID: 22581228

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Project description:ImportanceObesity is associated with a number of noncommunicable chronic diseases and is purported to cause premature death.ObjectiveTo summarize evidence on the temporality of the association between higher body mass index (BMI) and 2 potential mediators: chronic inflammation and hyperinsulinemia.Data sourcesMEDLINE (1946 to August 20, 2019) and Embase (from 1974 to August 19, 2019) were searched, although only studies published in 2018 were included because of a high volume of results. The data analysis was conducted between January 2020 and October 2020.Study selection and measuresLongitudinal studies and randomized clinical trials that measured fasting insulin level and/or an inflammation marker and BMI with at least 3 commensurate time points were selected.Data extraction and synthesisSlopes of these markers were calculated between time points and standardized. Standardized slopes were meta-regressed in later periods (period 2) with standardized slopes in earlier periods (period 1). Evidence-based items potentially indicating risk of bias were assessed.ResultsOf 1865 records, 60 eligible studies with 112 cohorts of 5603 participants were identified. Most standardized slopes were negative, meaning that participants in most studies experienced decreases in BMI, fasting insulin level, and C-reactive protein level. The association between period 1 fasting insulin level and period 2 BMI was positive and significant (β = 0.26; 95% CI, 0.13-0.38; I2 = 79%): for every unit of SD change in period 1 insulin level, there was an ensuing associated change in 0.26 units of SD in period 2 BMI. The association of period 1 fasting insulin level with period 2 BMI remained significant when period 1 C-reactive protein level was added to the model (β = 0.57; 95% CI, 0.27-0.86). In this bivariable model, period 1 C-reactive protein level was not significantly associated with period 2 BMI (β = -0.07; 95% CI, -0.42 to 0.29; I2 = 81%).Conclusions and relevanceIn this meta-analysis, the finding of temporal sequencing (in which changes in fasting insulin level precede changes in weight) is not consistent with the assertion that obesity causes noncommunicable chronic diseases and premature death by increasing levels of fasting insulin.

Project description:BackgroundOverweight and obesity is a serious problem worldwide related to cardiovascular and other diseases. Personality traits are associated with the abnormal body mass indices (BMIs) indicative of overweight and obesity. However, the links between personality traits and BMI have been little studied in Korea.MethodsWe evaluated the association between personality traits and BMI in men and women using the rural Ansung and urban Ansan cohort from the Korean Genome Epidemiology Study, and the Kangbuk Samsung Hospital Cohort Study datasets. A shorter version of the original Revised Neuroticism-Extroversion-Openness Personality Inventory (NEO-PI-R) was used to measure the five-factor model of personality (neuroticism, extraversion, openness to experience, agreeableness, and conscientiousness).ResultsData from a total of 1,495 men (mean age 60.0 ± 9.8 years; mean BMI 24.3 ± 3.0 kg/m2) and 2,547 women (mean age 47.0 ± 15.5 years; mean BMI 22.8 ± 3.4 kg/m2) were included in the analysis. Compared with the normal weight groups, overweight and obese men scored higher on openness to experience and lower on conscientiousness. Overweight and obese women scored lower on neuroticism and openness to experience and higher on agreeableness. Extraversion was positively associated with BMI in men (?=0.032, P<0.05). BMI and waist circumference were significantly increased in individuals who were less dutiful. In women, neuroticism was inversely associated with BMI (?=-0.026, P<0.05). Openness to experience was negatively, and agreeableness was positively, associated with BMI (openness to experience: ?=-0.072, agreeableness ?=0.068) and waist circumference (openness to experience: ?=-0.202, agreeableness: ?=0.227) (P<0.05).ConclusionPersonality traits were associated with underweight, overweight, and obesity in men and women. Increased understanding of the underlying factors contributing to this association will aid in the prevention and treatment of abnormal BMI.

Project description:ObjectiveThe worldwide prevalence of obesity mandates a widely accessible tool to categorize adiposity that can best predict associated health risks. The body adiposity index (BAI) was designed as a single equation to predict body adiposity in pooled analysis of both genders. We compared body adiposity index (BAI), body mass index (BMI), and other anthropometric measures, including percent body fat (PBF), in their correlations with cardiometabolic risk factors. We also compared BAI with BMI to determine which index is a better predictor of PBF.MethodsThe cohort consisted of 698 Mexican Americans. We calculated correlations of BAI, BMI, and other anthropometric measurements (PBF measured by dual energy X-ray absorptiometry, waist and hip circumference, height, weight) with glucose homeostasis indices (including insulin sensitivity and insulin clearance from euglycemic clamp), lipid parameters, cardiovascular traits (including carotid intima-media thickness), and biomarkers (C-reactive protein, plasminogen activator inhibitor-1 and adiponectin). Correlations between each anthropometric measure and cardiometabolic trait were compared in both sex-pooled and sex-stratified groups.ResultsBMI was associated with all but two measured traits (carotid intima-media thickness and fasting glucose in men), while BAI lacked association with several variables. BAI did not outperform BMI in its associations with any cardiometabolic trait. BAI was correlated more strongly than BMI with PBF in sex-pooled analyses (r?=?0.78 versus r?=?0.51), but not in sex-stratified analyses (men, r?=?0.63 versus r?=?0.79; women, r?=?0.69 versus r?=?0.77). Additionally, PBF showed fewer correlations with cardiometabolic risk factors than BMI. Weight was more strongly correlated than hip with many of the cardiometabolic risk factors examined.ConclusionsBAI is inferior to the widely used BMI as a correlate of the cardiometabolic risk factors studied. Additionally, BMI's relationship with total adiposity may not be the sole determinate of its association with cardiometabolic risk.

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A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance.

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A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance.

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