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Yoo L, Mendoza D, Richard AJ, Stephens JM. KAT8 beyond Acetylation: A Survey of Its Epigenetic Regulation, Genetic Variability, and Implications for Human Health. Genes (Basel) 2024; 15:639. [PMID: 38790268 PMCID: PMC11121512 DOI: 10.3390/genes15050639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Revised: 05/07/2024] [Accepted: 05/09/2024] [Indexed: 05/26/2024] Open
Abstract
Lysine acetyltransferase 8, also known as KAT8, is an enzyme involved in epigenetic regulation, primarily recognized for its ability to modulate histone acetylation. This review presents an overview of KAT8, emphasizing its biological functions, which impact many cellular processes and range from chromatin remodeling to genetic and epigenetic regulation. In many model systems, KAT8's acetylation of histone H4 lysine 16 (H4K16) is critical for chromatin structure modification, which influences gene expression, cell proliferation, differentiation, and apoptosis. Furthermore, this review summarizes the observed genetic variability within the KAT8 gene, underscoring the implications of various single nucleotide polymorphisms (SNPs) that affect its functional efficacy and are linked to diverse phenotypic outcomes, ranging from metabolic traits to neurological disorders. Advanced insights into the structural biology of KAT8 reveal its interaction with multiprotein assemblies, such as the male-specific lethal (MSL) and non-specific lethal (NSL) complexes, which regulate a wide range of transcriptional activities and developmental functions. Additionally, this review focuses on KAT8's roles in cellular homeostasis, stem cell identity, DNA damage repair, and immune response, highlighting its potential as a therapeutic target. The implications of KAT8 in health and disease, as evidenced by recent studies, affirm its importance in cellular physiology and human pathology.
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Affiliation(s)
- Lindsey Yoo
- Adipocyte Biology Laboratory, Pennington Biomedical, Baton Rouge, LA 70808, USA; (L.Y.); (D.M.); (A.J.R.)
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - David Mendoza
- Adipocyte Biology Laboratory, Pennington Biomedical, Baton Rouge, LA 70808, USA; (L.Y.); (D.M.); (A.J.R.)
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Allison J. Richard
- Adipocyte Biology Laboratory, Pennington Biomedical, Baton Rouge, LA 70808, USA; (L.Y.); (D.M.); (A.J.R.)
| | - Jacqueline M. Stephens
- Adipocyte Biology Laboratory, Pennington Biomedical, Baton Rouge, LA 70808, USA; (L.Y.); (D.M.); (A.J.R.)
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
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Viljakainen H, Sorlí JV, Dahlström E, Agrawal N, Portolés O, Corella D. Interaction between genetic susceptibility to obesity and food intake on BMI in Finnish school-aged children. Sci Rep 2023; 13:15265. [PMID: 37709841 PMCID: PMC10502078 DOI: 10.1038/s41598-023-42430-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 09/10/2023] [Indexed: 09/16/2023] Open
Abstract
Diet modulates the genetic risk of obesity, but the modulation has been rarely studied using genetic risk scores (GRSs) in children. Our objectives were to identify single nucleotide polymorphisms (SNPs) that drive the interaction of specific foods with obesity and combine these into GRSs. Genetic and food frequency data from Finnish Health in Teens study was utilized. In total, 1142 11-year-old subjects were genotyped on the Metabochip array. BMI-GRS with 30 well-known SNPs was computed and the interaction of individual SNPs with food items and their summary dietary scores were examined in relation to age- and sex-specific BMI z-score (BMIz). The whole BMI-GRS interacted with several foods on BMIz. We identified 7-11 SNPs responsible for each interaction and these were combined into food-specific GRS. The most predominant interaction was witnessed for pizza (p < 0.001): the effect on BMIz was b - 0.130 (95% CI - 0.23; - 0.031) in those with low-risk, and 0.153 (95% CI 0.072; 0.234) in high-risk. Corresponding, but weaker interactions were verified for sweets and chocolate, sugary juice drink, and hamburger and hotdog. In total 5 SNPs close to genes NEGR1, SEC16B, TMEM18, GNPDA2, and FTO were shared between these interactions. Our results suggested that children genetically prone to obesity showed a stronger association of unhealthy foods with BMIz than those with lower genetic susceptibility. Shared SNPs of the interactions suggest common differences in metabolic gene-diet interactions, which warrants further investigation.
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Affiliation(s)
- Heli Viljakainen
- Folkhälsan Research Center, Topeliuksenkatu 20, 00250, Helsinki, Finland.
- Faculty of Medicine, University of Helsinki, Helsinki, Finland.
| | - Jose V Sorlí
- Department of Preventive Medicine and Public Health, University of Valencia, Valencia, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición, Madrid, Spain
| | - Emma Dahlström
- Folkhälsan Research Center, Topeliuksenkatu 20, 00250, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, 00290, Helsinki, Finland
- Department of Nephrology, University of Helsinki and Helsinki University Hospital, 00290, Helsinki, Finland
| | - Nitin Agrawal
- Folkhälsan Research Center, Topeliuksenkatu 20, 00250, Helsinki, Finland
- Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Olga Portolés
- Department of Preventive Medicine and Public Health, University of Valencia, Valencia, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición, Madrid, Spain
| | - Dolores Corella
- Department of Preventive Medicine and Public Health, University of Valencia, Valencia, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición, Madrid, Spain
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Functionally Significant Variants in Genes Associated with Abdominal Obesity: A Review. J Pers Med 2023; 13:jpm13030460. [PMID: 36983642 PMCID: PMC10056771 DOI: 10.3390/jpm13030460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 02/23/2023] [Accepted: 02/26/2023] [Indexed: 03/05/2023] Open
Abstract
The high prevalence of obesity and of its associated diseases is a major problem worldwide. Genetic predisposition and the influence of environmental factors contribute to the development of obesity. Changes in the structure and functional activity of genes encoding adipocytokines are involved in the predisposition to weight gain and obesity. In this review, variants in genes associated with adipocyte function are examined, as are variants in genes associated with metabolic aberrations and the accompanying disorders in visceral obesity.
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Genetic risk score for common obesity and anthropometry in Spanish schoolchildren. ENDOCRINOL DIAB NUTR 2023; 70:107-114. [PMID: 36868927 DOI: 10.1016/j.endien.2022.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 09/22/2022] [Indexed: 03/05/2023]
Abstract
IntroductionCommon or non-syndromic obesity is a complex polygenic trait conditioned by biallelic or single-base polymorphisms called SNPs (Single-Nucleotide Polymorphisms) that present an additive effect and act synergistically. Most genotype-obese phenotype association studies include body mass index (BMI) or waist-to-height ratio (WtHR), and very few introduce a broad anthropometric profile. ObjectiveTo verify whether a genetic risk score (GRS) developed from 10 SNPs is associated with the obesity phenotype assessed from anthropometric measures indicative of excess weight, adiposity and fat distribution. Material and methodsA series of 438 Spanish schoolchildren (6-16 years old) were evaluated anthropometrically (weight, height, waist circumference, skinfold thickness, BMI, WtHR, body fat percentage [%BF]). Ten SNPs were genotyped from saliva samples, generating a GRS for obesity, establishing genotype-phenotype association. ResultsSchoolchildren categorised as obese by BMI, ICT and %BF had higher GRS than their non-obese peers. The prevalence of overweight and adiposity was higher in subjects with a GRS above the median. Similarly, between 11 and 16 years of age, all anthropometric variables presented higher averages. ConclusionsGRS estimated from the 10 SNPs can be a diagnostic tool for the potential risk of obesity in Spanish schoolchildren and could be useful from the preventive perspective.
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Puntuación de riesgo genético para la obesidad común y antropometría en escolares españoles. ENDOCRINOL DIAB NUTR 2022. [DOI: 10.1016/j.endinu.2022.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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How does age determine the development of human immune-mediated arthritis? Nat Rev Rheumatol 2022; 18:501-512. [PMID: 35948692 PMCID: PMC9363867 DOI: 10.1038/s41584-022-00814-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/01/2022] [Indexed: 11/08/2022]
Abstract
Does age substantially affect the emergence of human immune-mediated arthritis? Children do not usually develop immune-mediated articular inflammation during their first year of life. In patients with juvenile idiopathic arthritis, this apparent ‘immune privilege’ disintegrates, and chronic inflammation is associated with variable autoantibody signatures and patterns of disease that resemble adult arthritis phenotypes. Numerous mechanisms might be involved in this shift, including genetic and epigenetic predisposing factors, maturation of the immune system with a progressive modulation of putative tolerogenic controls, parallel development of microbial dysbiosis, accumulation of a pro-inflammatory burden driven by environmental exposures (the exposome) and comorbidity-related drivers. By exploring these mechanisms, we expand the discussion of three (not mutually exclusive) hypotheses on how these factors can contribute to the differences and similarities between the loss of immune tolerance in children and the development of established immune-mediated arthritis in adults. These three hypotheses relate to a critical window in genetics and epigenetics, immune maturation, and the accumulation of burden. The varied manifestation of the underlying mechanisms among individuals is only beginning to be clarified, but the establishment of a framework can facilitate the development of an integrated understanding of the pathogenesis of arthritis across all ages. In this Review, the authors discuss age-related arthropathy and the similarities and differences between childhood loss of immune tolerance and adult development of immune-mediated arthritis, and develop three hypotheses describing age-related mechanisms that contribute to the onset of arthritis. The arthritis-free ‘immune privilege’ of early childhood is overridden by multiple mechanisms, progressively and age-dependently, generating recognizable patterns of chronic inflammatory arthritis. The emergence of arthritis involves interconnected mechanisms related to immune priming, to a situational susceptibility and to the accumulation of an inflammatory burden. The accumulation of epigenetic drift may contribute to differences across ages. The exposome is expected to contribute to arthritis emergence in adults as well as in children.
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Naess M, Sund ER, Vie GÅ, Bjørngaard JH, Åsvold BO, Holmen TL, Kvaløy K. Intergenerational polygenic obesity risk throughout adolescence in a cross-sectional study design: The HUNT study, Norway. Obesity (Silver Spring) 2021; 29:1916-1924. [PMID: 34651441 DOI: 10.1002/oby.23284] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 07/08/2021] [Accepted: 08/06/2021] [Indexed: 12/15/2022]
Abstract
OBJECTIVE This study examined the relationship between parental obesity polygenic risk and children's BMI throughout adolescence. Additionally, from a smaller subsample, the objective was to assess whether parental polygenic risk score (PRS) may act as a proxy for offspring PRS in studies lacking offspring genetic data. METHODS A total of 8,561 parent-offspring (age 13-19 years) trios from the Trøndelag Health Study (the HUNT Study) were included, of which, 1,286 adolescents had available genetic data. Weighted parental PRSs from 900 single-nucleotide polymorphisms robustly associated with adult BMI were constructed and applied in linear mixed-effects models. RESULTS A positive association between parental PRS and offspring sex- and age-adjusted BMI (iso-BMI) throughout adolescence was identified. The estimated marginal effects per standard deviation increase in parental PRS were 0.26 (95% CI: 0.18-0.33), 0.36 (95% CI: 0.29-0.43), and 0.62 kg/m2 (95% CI: 0.51-0.72) for maternal, paternal, and combined parental PRS, respectively. In subsample analyses, the magnitude of association of the parental PRS versus offspring PRS with iso-BMI in adolescents was similar. CONCLUSIONS Parental PRS was consistently associated with offspring iso-BMI throughout adolescence. Results from subsample analyses support the use of parental PRS of obesity as a proxy for adolescent PRS in the absence of offspring genetic data.
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Affiliation(s)
- Marit Naess
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Nord-Trøndelag Hospital Trust, Levanger Hospital, Levanger, Norway
| | - Erik R Sund
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Nord-Trøndelag Hospital Trust, Levanger Hospital, Levanger, Norway
- Faculty of Nursing and Health Sciences, Nord University, Levanger, Norway
| | - Gunnhild Å Vie
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Johan H Bjørngaard
- Faculty of Nursing and Health Sciences, Nord University, Levanger, Norway
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Bjørn Olav Åsvold
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Endocrinology, St. Olav's University Hospital, Trondheim University Hospital, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Turid Lingaas Holmen
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Kirsti Kvaløy
- HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Nord-Trøndelag Hospital Trust, Levanger Hospital, Levanger, Norway
- Centre for Sami Health Research, Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
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Development of a Genetic Risk Score to predict the risk of overweight and obesity in European adolescents from the HELENA study. Sci Rep 2021; 11:3067. [PMID: 33542408 PMCID: PMC7862459 DOI: 10.1038/s41598-021-82712-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 01/22/2021] [Indexed: 11/08/2022] Open
Abstract
Obesity is the result of interactions between genes and environmental factors. Since monogenic etiology is only known in some obesity-related genes, a genetic risk score (GRS) could be useful to determine the genetic predisposition to obesity. Therefore, the aim of our study was to build a GRS able to predict genetic predisposition to overweight and obesity in European adolescents. A total of 1069 adolescents (51.3% female), aged 11-19 years participating in the Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) cross-sectional study were genotyped. The sample was divided in non-overweight (non-OW) and overweight/obesity (OW/OB). From 611 single nucleotide polymorphisms (SNP) available, a first screening of 104 SNPs univariately associated with obesity (p < 0.20) was established selecting 21 significant SNPs (p < 0.05) in the multivariate model. Unweighted GRS (uGRS) was calculated by summing the number of risk alleles and weighted GRS (wGRS) by multiplying the risk alleles to each estimated coefficient. The area under curve (AUC) was calculated in uGRS (0.723) and wGRS (0.734) using tenfold internal cross-validation. Both uGRS and wGRS were significantly associated with body mass index (BMI) (p < .001). Both GRSs could potentially be considered as useful genetic tools to evaluate individual's predisposition to overweight/obesity in European adolescents.
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Exclusive breastfeeding can attenuate body-mass-index increase among genetically susceptible children: A longitudinal study from the ALSPAC cohort. PLoS Genet 2020; 16:e1008790. [PMID: 32525877 PMCID: PMC7289340 DOI: 10.1371/journal.pgen.1008790] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 04/22/2020] [Indexed: 02/02/2023] Open
Abstract
Recent discoveries from large-scale genome-wide association studies (GWASs) explain a larger proportion of the genetic variability to BMI and obesity. The genetic risk associated with BMI and obesity can be assessed by an obesity-specific genetic risk score (GRS) constructed from genome-wide significant genetic variants. The aim of our study is to examine whether the duration and exclusivity of breastfeeding can attenuate BMI increase during childhood and adolescence due to genetic risks. A total sample of 5,266 children (2,690 boys and 2,576 girls) from the Avon Longitudinal Study of Parents and Children (ALSPAC) was used for the analysis. We evaluated the role of breastfeeding (exclusivity and duration) in modulating BMI increase attributed to the GRS from birth to 18 years of age. The GRS was composed of 69 variants associated with adult BMI and 25 non-overlapping SNPs associated with pediatric BMI. In the high genetic susceptible group (upper GRS quartile), exclusive breastfeeding (EBF) to 5 months reduces BMI by 1.14 kg/m2 (95% CI, 0.37 to 1.91, p = 0.0037) in 18-year-old boys, which compensates a 3.9-decile GRS increase. In 18-year-old girls, EBF to 5 months decreases BMI by 1.53 kg/m2 (95% CI, 0.76 to 2.29, p<0.0001), which compensates a 7.0-decile GRS increase. EBF acts early in life by delaying the age at adiposity peak and at adiposity rebound. EBF to 3 months or non-exclusive breastfeeding was associated with a significantly diminished impact on reducing BMI growth during childhood. EBF influences early life growth and development and thus may play a critical role in preventing overweight and obesity among children at high-risk due to genetic factors. Previous studies have shown that EBF is associated with lower BMI during childhood and adolescence. Moreover, a GRS based on 97 genetic variants has been derived from large GWASs and is predictive of BMI in adults and children. However, it remains unclear whether EBF can attenuate the increase in BMI attributed to the GRS in children. Our study was able to characterize the effect of the GRS in children from birth to 18 years of age. Our main results showed that EBF to 5 months has substantial effect in decreasing BMI among children at higher genetic risks. EBF to 3 months or non-exclusive breastfeeding had a significantly diminished effect on reducing BMI growth during childhood. Our study suggests that interventions aimed at reducing the risks of overweight and obesity across the lifespan should start in very early childhood to be impactful, which makes EBF a key candidate intervention. While EBF is beneficial to all children, targeting those carrying multiple BMI/obesity alleles should be a priority to reduce obesity and associated non-communicable diseases.
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Slob EAW, Burgess S. A comparison of robust Mendelian randomization methods using summary data. Genet Epidemiol 2020; 44:313-329. [PMID: 32249995 PMCID: PMC7317850 DOI: 10.1002/gepi.22295] [Citation(s) in RCA: 372] [Impact Index Per Article: 74.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 08/22/2019] [Accepted: 03/11/2020] [Indexed: 01/20/2023]
Abstract
The number of Mendelian randomization (MR) analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. Since it is unlikely that all genetic variants will be valid instrumental variables, several robust methods have been proposed. We compare nine robust methods for MR based on summary data that can be implemented using standard statistical software. Methods were compared in three ways: by reviewing their theoretical properties, in an extensive simulation study, and in an empirical example. In the simulation study, the best method, judged by mean squared error was the contamination mixture method. This method had well-controlled Type 1 error rates with up to 50% invalid instruments across a range of scenarios. Other methods performed well according to different metrics. Outlier-robust methods had the narrowest confidence intervals in the empirical example. With isolated exceptions, all methods performed badly when over 50% of the variants were invalid instruments. Our recommendation for investigators is to perform a variety of robust methods that operate in different ways and rely on different assumptions for valid inferences to assess the reliability of MR analyses.
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Affiliation(s)
- Eric A. W. Slob
- Erasmus School of EconomicsErasmus University RotterdamRotterdamThe Netherlands
- Erasmus University Rotterdam Institute for Behavior and BiologyRotterdamThe Netherlands
| | - Stephen Burgess
- Department of Public Health and Primary CareUniversity of CambridgeCambridgeUK
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
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Hüls A, Czamara D. Methodological challenges in constructing DNA methylation risk scores. Epigenetics 2020; 15:1-11. [PMID: 31318318 PMCID: PMC6961658 DOI: 10.1080/15592294.2019.1644879] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 06/28/2019] [Accepted: 07/09/2019] [Indexed: 12/23/2022] Open
Abstract
Polygenic approaches often access more variance of complex traits than is possible by single variant approaches. For genotype data, genetic risk scores (GRS) are widely used for risk prediction as well as in association and interaction studies. Recently, interest has been growing in transferring GRS approaches to DNA methylation data (methylation risk scores, MRS), which can be used 1) as biomarkers for environmental exposures, 2) in association analyses in which single CpG sites do not achieve significance, 3) as dimension reduction approach in interaction and mediation analyses, and 4) to predict individual risks of disease or treatment success. Most GRS approaches can directly be transferred to methylation data. However, since methylation data is more sensitive to confounding, e.g. by age and tissue, it is more complex to find appropriate external weights. In this review, we will outline the adaption of current GRS approaches to methylation data and highlight occurring challenges.
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Affiliation(s)
- Anke Hüls
- Department of Human Genetics, Emory University, Atlanta, GA, USA
- Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital Research Institute, and Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Darina Czamara
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
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Sebert S, Lowry E, Aumüller N, Bermúdez MG, Bjerregaard LG, de Rooij SR, De Silva M, El Marroun H, Hummel N, Juola T, Mason G, Much D, Oliveros E, Poupakis S, Rautio N, Schwarzfischer P, Tzala E, Uhl O, van de Beek C, Vehmeijer F, Verdejo-Román J, Wasenius N, Webster C, Ala-Mursula L, Herzig KH, Keinänen-Kiukaanniemi S, Miettunen J, Baker JL, Campoy C, Conti G, Eriksson JG, Hummel S, Jaddoe V, Koletzko B, Lewin A, Rodriguez-Palermo M, Roseboom T, Rueda R, Evans J, Felix JF, Prokopenko I, Sørensen TIA, Järvelin MR. Cohort Profile: The DynaHEALTH consortium - a European consortium for a life-course bio-psychosocial model of healthy ageing of glucose homeostasis. Int J Epidemiol 2019; 48:1051-1051k. [PMID: 31321419 PMCID: PMC6693805 DOI: 10.1093/ije/dyz056] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/02/2019] [Indexed: 01/12/2023] Open
Affiliation(s)
- Sylvain Sebert
- Centre for Life Course Health Research, Faculty of Medicine, University of Oulu, Finland
- Biocenter Oulu, University of Oulu, Finland
- Department of Genomics of Complex Diseases, School of Public Health, Imperial College London, UK
| | - Estelle Lowry
- Centre for Life Course Health Research, Faculty of Medicine, University of Oulu, Finland
- Biocenter Oulu, University of Oulu, Finland
| | - Nicole Aumüller
- Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children’s Hospital, Ludwig-Maximilians Universität München, Munich, Germany
| | | | - Lise G Bjerregaard
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, The Capital Region, Copenhagen, Denmark
| | - Susanne R de Rooij
- Department of Clinical Epidemiology, Biostatistics & Bio informatics, Amsterdam University Medical Centre)
| | - Maneka De Silva
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, UK
| | - Hanan El Marroun
- Generation R Study Group, Department of Pediatrics, Department of Child & Adolescent Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Nadine Hummel
- Institute of Diabetes Research, Helmholtz Zentrum München, and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, Neuherberg, Germany
| | - Teija Juola
- Centre for Life Course Health Research, Faculty of Medicine, University of Oulu, Finland
| | | | - Daniela Much
- Institute of Diabetes Research, Helmholtz Zentrum München, and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, Neuherberg, Germany
| | | | | | - Nina Rautio
- Centre for Life Course Health Research, Faculty of Medicine, University of Oulu, Finland
- Biocenter Oulu, University of Oulu, Finland
| | - Phillipp Schwarzfischer
- Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children’s Hospital, Ludwig-Maximilians Universität München, Munich, Germany
| | - Evangelia Tzala
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, UK
| | - Olaf Uhl
- Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children’s Hospital, Ludwig-Maximilians Universität München, Munich, Germany
| | - Cornelieke van de Beek
- Department of Obstetrics & Gynaecology, Amsterdam University Medical Centers, The Netherlands
| | - Florianne Vehmeijer
- Generation R Study Group, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Juan Verdejo-Román
- Mind, Brain and Behavior Research Centre (CIMCYC), University of Granada, Spain
- Department of Experimental Psychology, Psychological Processes and Speech Therapy, Universidad Complutense de Madrid
| | - Niko Wasenius
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland
- Folkhälsan Research Center, Helsinki, Finland
| | | | - Leena Ala-Mursula
- Centre for Life Course Health Research, Faculty of Medicine, University of Oulu, Finland
| | - Karl-Heinz Herzig
- Research Unit of Biomedicine, Department of Physiology & Biocenter of Oulu, University of Oulu, Oulu, Finland
- Department of Gastroenterology and Metabolism, Poznan University of Medical Sciences, Poznan, Poland
| | | | - Jouko Miettunen
- Centre for Life Course Health Research, Faculty of Medicine, University of Oulu, Finland
- Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Finland
| | - Jennifer L Baker
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, The Capital Region, Copenhagen, Denmark
- NovoNordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Cristina Campoy
- Department of Paediatrics, School of Medicine, University of Granada, Spain
| | | | - Johan G Eriksson
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, Finland
- Folkhälsan Research Center, Helsinki, Finland
- National Institute for Health and Welfare, Finland
| | - Sandra Hummel
- Institute of Diabetes Research, Helmholtz Zentrum München, and Forschergruppe Diabetes, Klinikum rechts der Isar, Technische Universität München, Neuherberg, Germany
| | - Vincent Jaddoe
- Generation R Study Group, Department of Pediatrics, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Harvard Medical School, USA
| | - Berthold Koletzko
- Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children’s Hospital, Ludwig-Maximilians Universität München, Munich, Germany
| | - Alex Lewin
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, UK
| | | | | | | | - Jayne Evans
- Folkhälsan Research Center, Helsinki, Finland
| | - Janine F Felix
- Generation R Study Group, Department of Pediatrics, Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Inga Prokopenko
- Department of Genomics of Complex Diseases, School of Public Health, Imperial College London, UK
| | - Thorkild I A Sørensen
- Department of Gastroenterology and Metabolism, Poznan University of Medical Sciences, Poznan, Poland
- Department of Public Health, Section of Epidemiology, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Marjo-Riitta Järvelin
- Centre for Life Course Health Research, Faculty of Medicine, University of Oulu, Finland
- Biocenter Oulu, University of Oulu, Finland
- Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, UK
- Department of Life Sciences, College of Health and Life Sciences, Brunel University London, UK
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13
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Tekola-Ayele F, Lee A, Workalemahu T, Sánchez-Pozos K. Shared genetic underpinnings of childhood obesity and adult cardiometabolic diseases. Hum Genomics 2019; 13:17. [PMID: 30947744 PMCID: PMC6449964 DOI: 10.1186/s40246-019-0202-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 03/20/2019] [Indexed: 12/12/2022] Open
Abstract
Background Obesity during childhood can lead to increased risk of adverse cardiometabolic diseases such as type 2 diabetes and coronary artery disease during adult life. Evidence for strong genetic correlations between child and adult body mass index (BMI) suggest the possibility of shared genetic effects. We performed a test for pleiotropy (shared genetics) and functional enrichment of single nucleotide polymorphisms (SNPs) associated with childhood BMI and 15 adult cardiometabolic traits using a unified statistical approach that integrates pleiotropy and functional annotation data. Results Pleiotropic genetic effects were significantly abundant in 13 out of 15 childhood BMI-adult cardiometabolic trait tests (P < 3.3 × 10−3). SNPs associated with both childhood BMI and adult traits were more likely to be functionally deleterious than SNPs associated with neither trait. Genetic variants associated with increased childhood obesity tend to increase risk of cardiometabolic diseases in adulthood. We replicated 39 genetic loci that are known to be associated with childhood BMI and adult traits (coronary artery disease, HDL cholesterol, myocardial infarction, triglycerides, total cholesterol, type 2 diabetes, BMI, waist circumference, and waist-to-hip ratio) in previous genome-wide association studies. We also found a novel association of rs12446632 near GPRC5B, which is highly expressed in adipose tissue and the central nervous system, with adult HDL cholesterol. Conclusions This study found significant pleiotropic genetic effects and enrichment of functional annotations in genetic variants that were jointly associated with childhood obesity and adult cardiometabolic diseases. The findings provide new avenues to disentangle the genetic basis of life course associations between childhood obesity and adult cardiometabolic diseases. Electronic supplementary material The online version of this article (10.1186/s40246-019-0202-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Fasil Tekola-Ayele
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 6710B Rockledge Drive, Room 3204, Bethesda, MD, 20892-7004, USA.
| | - Anthony Lee
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 6710B Rockledge Drive, Room 3204, Bethesda, MD, 20892-7004, USA
| | - Tsegaselassie Workalemahu
- Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 6710B Rockledge Drive, Room 3204, Bethesda, MD, 20892-7004, USA
| | - Katy Sánchez-Pozos
- Laboratorio de Endocrinologia Molecular, Hospital Juárez de México, Mexico City, Mexico
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14
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Budu-Aggrey A, Brumpton B, Tyrrell J, Watkins S, Modalsli EH, Celis-Morales C, Ferguson LD, Vie GÅ, Palmer T, Fritsche LG, Løset M, Nielsen JB, Zhou W, Tsoi LC, Wood AR, Jones SE, Beaumont R, Saunes M, Romundstad PR, Siebert S, McInnes IB, Elder JT, Davey Smith G, Frayling TM, Åsvold BO, Brown SJ, Sattar N, Paternoster L. Evidence of a causal relationship between body mass index and psoriasis: A mendelian randomization study. PLoS Med 2019; 16:e1002739. [PMID: 30703100 PMCID: PMC6354959 DOI: 10.1371/journal.pmed.1002739] [Citation(s) in RCA: 157] [Impact Index Per Article: 26.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 12/27/2018] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Psoriasis is a common inflammatory skin disease that has been reported to be associated with obesity. We aimed to investigate a possible causal relationship between body mass index (BMI) and psoriasis. METHODS AND FINDINGS Following a review of published epidemiological evidence of the association between obesity and psoriasis, mendelian randomization (MR) was used to test for a causal relationship with BMI. We used a genetic instrument comprising 97 single-nucleotide polymorphisms (SNPs) associated with BMI as a proxy for BMI (expected to be much less confounded than measured BMI). One-sample MR was conducted using individual-level data (396,495 individuals) from the UK Biobank and the Nord-Trøndelag Health Study (HUNT), Norway. Two-sample MR was performed with summary-level data (356,926 individuals) from published BMI and psoriasis genome-wide association studies (GWASs). The one-sample and two-sample MR estimates were meta-analysed using a fixed-effect model. To test for a potential reverse causal effect, MR analysis with genetic instruments comprising variants from recent genome-wide analyses for psoriasis were used to test whether genetic risk for this skin disease has a causal effect on BMI. Published observational data showed an association of higher BMI with psoriasis. A mean difference in BMI of 1.26 kg/m2 (95% CI 1.02-1.51) between psoriasis cases and controls was observed in adults, while a 1.55 kg/m2 mean difference (95% CI 1.13-1.98) was observed in children. The observational association was confirmed in UK Biobank and HUNT data sets. Overall, a 1 kg/m2 increase in BMI was associated with 4% higher odds of psoriasis (meta-analysis odds ratio [OR] = 1.04; 95% CI 1.03-1.04; P = 1.73 × 10(-60)). MR analyses provided evidence that higher BMI causally increases the odds of psoriasis (by 9% per 1 unit increase in BMI; OR = 1.09 (1.06-1.12) per 1 kg/m2; P = 4.67 × 10(-9)). In contrast, MR estimates gave little support to a possible causal effect of psoriasis genetic risk on BMI (0.004 kg/m2 change in BMI per doubling odds of psoriasis (-0.003 to 0.011). Limitations of our study include possible misreporting of psoriasis by patients, as well as potential misdiagnosis by clinicians. In addition, there is also limited ethnic variation in the cohorts studied. CONCLUSIONS Our study, using genetic variants as instrumental variables for BMI, provides evidence that higher BMI leads to a higher risk of psoriasis. This supports the prioritization of therapies and lifestyle interventions aimed at controlling weight for the prevention or treatment of this common skin disease. Mechanistic studies are required to improve understanding of this relationship.
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Affiliation(s)
- Ashley Budu-Aggrey
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Ben Brumpton
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Thoracic Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Jess Tyrrell
- Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, United Kingdom
- European Centre for Environment and Human Health, University of Exeter Medical School, The Knowledge Spa, Truro, United Kingdom
| | - Sarah Watkins
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Ellen H. Modalsli
- Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Dermatology, St. Olav’s Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Carlos Celis-Morales
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Lyn D. Ferguson
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Gunnhild Åberge Vie
- Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tom Palmer
- Department of Mathematics and Statistics, Lancaster University, Lancaster, United Kingdom
| | - Lars G. Fritsche
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Mari Løset
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Dermatology, St. Olav’s Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Jonas Bille Nielsen
- Department of Internal Medicine, Division of Cardiology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Wei Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Lam C. Tsoi
- Department of Dermatology, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Andrew R. Wood
- Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, United Kingdom
| | - Samuel E. Jones
- Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, United Kingdom
| | - Robin Beaumont
- Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, United Kingdom
| | - Marit Saunes
- Department of Dermatology, St. Olav’s Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Pål Richard Romundstad
- Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Stefan Siebert
- Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom
| | - Iain B. McInnes
- Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom
| | - James T. Elder
- Department of Dermatology, University of Michigan, Ann Arbor, Michigan, United States of America
- Ann Arbor Veterans Affairs Hospital, Ann Arbor, Michigan, United States of America
| | - George Davey Smith
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Timothy M. Frayling
- Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, United Kingdom
| | - Bjørn Olav Åsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Endocrinology, St. Olav’s Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Sara J. Brown
- Skin Research Group, School of Medicine, University of Dundee, Dundee, United Kingdom
- Department of Dermatology, Ninewells Hospital and Medical School, Dundee, United Kingdom
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Lavinia Paternoster
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, United Kingdom
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15
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Hollensted M, Fogh M, Schnurr TM, Kloppenborg JT, Have CT, Ruest Haarmark Nielsen T, Rask J, Asp Vonsild Lund M, Frithioff-Bøjsøe C, Østergaard Johansen M, Vincent Rosenbaum Appel E, Mahendran Y, Grarup N, Kadarmideen HN, Pedersen O, Holm JC, Hansen T. Genetic Susceptibility for Childhood BMI has no Impact on Weight Loss Following Lifestyle Intervention in Danish Children. Obesity (Silver Spring) 2018; 26:1915-1922. [PMID: 30460774 DOI: 10.1002/oby.22308] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 08/10/2018] [Indexed: 01/27/2023]
Abstract
OBJECTIVE This study aimed to investigate the effect of a genetic risk score (GRS) comprising 15 single-nucleotide polymorphisms, previously shown to associate with childhood BMI, on the baseline cardiometabolic traits and the response to a lifestyle intervention in Danish children and adolescents. METHODS Children and adolescents with overweight or obesity (n = 920) and a population-based control sample (n = 698) were recruited. Anthropometric and biochemical measures were obtained at baseline and in a subgroup of children and adolescents with overweight or obesity again after 6 to 24 months of lifestyle intervention (n = 754). The effects of the GRS were examined by multiple linear regressions using additive genetic models. RESULTS At baseline, the GRS associated with BMI standard deviation score (SDS) both in children and adolescents with overweight or obesity (β = 0.033 [SE = 0.01]; P = 0.001) and in the population-based sample (β = 0.065 [SE = 0.02]; P = 0.001). No associations were observed for cardiometabolic traits. The GRS did not influence changes in BMI SDS or cardiometabolic traits following lifestyle intervention. CONCLUSIONS A GRS for childhood BMI was associated with BMI SDS but not with other cardiometabolic traits in Danish children and adolescents. The GRS did not influence treatment response following lifestyle intervention.
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Affiliation(s)
- Mette Hollensted
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
- The Danish Diabetes Academy, Odense, Denmark
| | - Mette Fogh
- The Children's Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbaek, Holbaek, Denmark
- Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark
| | - Theresia M Schnurr
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
- The Danish Diabetes Academy, Odense, Denmark
| | - Julie T Kloppenborg
- The Children's Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbaek, Holbaek, Denmark
| | - Christian T Have
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Tenna Ruest Haarmark Nielsen
- The Children's Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbaek, Holbaek, Denmark
| | - Johanne Rask
- The Children's Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbaek, Holbaek, Denmark
| | - Morten Asp Vonsild Lund
- The Children's Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbaek, Holbaek, Denmark
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Christine Frithioff-Bøjsøe
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
- The Children's Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbaek, Holbaek, Denmark
| | - Mia Østergaard Johansen
- The Children's Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbaek, Holbaek, Denmark
| | | | - Yuvaraj Mahendran
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Haja N Kadarmideen
- Department of Bio and Health Informatics, Section of Systems Genomics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Jens-Christian Holm
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
- The Children's Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbaek, Holbaek, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
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16
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Munthali RJ, Sahibdeen V, Kagura J, Hendry LM, Norris SA, Ong KK, Day FR, Lombard Z. Genetic risk score for adult body mass index associations with childhood and adolescent weight gain in an African population. GENES AND NUTRITION 2018; 13:24. [PMID: 30123368 PMCID: PMC6090951 DOI: 10.1186/s12263-018-0613-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Accepted: 07/13/2018] [Indexed: 11/10/2022]
Abstract
Background Ninety-seven independent single nucleotide polymorphisms (SNPs) are robustly associated with adult body mass index (BMI kg/m2) in Caucasian populations. The relevance of such variants in African populations at different stages of the life course (such as childhood) is unclear. We tested whether a genetic risk score composed of the aforementioned SNPs was associated with BMI from infancy to early adulthood. We further tested whether this genetic effect was mediated by conditional weight gain at different growth periods. We used data from the Birth to Twenty Plus Cohort (Bt20+), for 971 urban South African black children from birth to 18 years. DNA was collected at 13 years old and was genotyped using the Metabochip (Illumina) array. The weighted genetic risk score (wGRS) for BMI was constructed based on 71 of the 97 previously reported SNPs. Results The cross-sectional association between the wGRS and BMI strengthened with age from 5 to 18 years. The significant associations were observed from 11 to 18 years, and peak effect sizes were observed at 13 and 14 years of age. Results from the linear mixed effects models showed significant interactions between the wGRS and age on longitudinal BMI but no such interactions were observed in sex and the wGRS. A higher wGRS was associated with an increased relative risk of belonging to the early onset obese longitudinal BMI trajectory (relative risk = 1.88; 95%CI 1.28 to 2.76) compared to belonging to a normal longitudinal BMI trajectory. Adolescent conditional relative weight gain had a suggestive mediation effect of 56% on the association between wGRS and obesity risk at 18 years. Conclusions The results suggest that genetic susceptibility to higher adult BMI can be tracked from childhood in this African population. This supports the notion that prevention of adult obesity should begin early in life. The genetic risk score combined with other non-genetic risk factors, such as BMI trajectory membership in our case, has the potential to be used to screen for early identification of individuals at increased risk of obesity and other related NCD risk factors in order to reduce the adverse health risk outcomes later. Electronic supplementary material The online version of this article (10.1186/s12263-018-0613-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Richard J Munthali
- 1Faculty of Science, School of Molecular and Cell Biology, University of the Witwatersrand, Johannesburg, South Africa.,2Sydney Brenner Institute for Molecular Bioscience (SBIMB), University of the Witwatersrand, The Mount, 9 Jubilee Road, Parktown, Johannesburg, Gauteng 2193 South Africa.,3MRC/Wits Developmental Pathways for Health Research Unit (DPHRU), University of the Witwatersrand, Johannesburg, South Africa
| | - Venesa Sahibdeen
- 2Sydney Brenner Institute for Molecular Bioscience (SBIMB), University of the Witwatersrand, The Mount, 9 Jubilee Road, Parktown, Johannesburg, Gauteng 2193 South Africa.,4Faculty of Health Sciences, Division of Human Genetics, School of Pathology, University of the Witwatersrand and National Health Laboratory Service, Johannesburg, South Africa
| | - Juliana Kagura
- 3MRC/Wits Developmental Pathways for Health Research Unit (DPHRU), University of the Witwatersrand, Johannesburg, South Africa
| | - Liesl M Hendry
- 1Faculty of Science, School of Molecular and Cell Biology, University of the Witwatersrand, Johannesburg, South Africa.,2Sydney Brenner Institute for Molecular Bioscience (SBIMB), University of the Witwatersrand, The Mount, 9 Jubilee Road, Parktown, Johannesburg, Gauteng 2193 South Africa
| | - Shane A Norris
- 3MRC/Wits Developmental Pathways for Health Research Unit (DPHRU), University of the Witwatersrand, Johannesburg, South Africa
| | - Ken K Ong
- 3MRC/Wits Developmental Pathways for Health Research Unit (DPHRU), University of the Witwatersrand, Johannesburg, South Africa.,5MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Felix R Day
- 5MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Zané Lombard
- 1Faculty of Science, School of Molecular and Cell Biology, University of the Witwatersrand, Johannesburg, South Africa.,2Sydney Brenner Institute for Molecular Bioscience (SBIMB), University of the Witwatersrand, The Mount, 9 Jubilee Road, Parktown, Johannesburg, Gauteng 2193 South Africa.,4Faculty of Health Sciences, Division of Human Genetics, School of Pathology, University of the Witwatersrand and National Health Laboratory Service, Johannesburg, South Africa
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17
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Associations of adult genetic risk scores for adiposity with childhood abdominal, liver and pericardial fat assessed by magnetic resonance imaging. Int J Obes (Lond) 2017; 42:897-904. [PMID: 29437161 PMCID: PMC5985956 DOI: 10.1038/ijo.2017.302] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Revised: 10/05/2017] [Accepted: 11/19/2017] [Indexed: 02/07/2023]
Abstract
Background Genome-wide association studies (GWAS) identified single nucleotide polymorphisms (SNPs) involved in adult fat distribution. Whether these SNPs also affect abdominal and organ-specific fat accumulation in children is unknown. Methods In a population-based prospective cohort study among 1 995 children (median age: 9.8 years, 95% range 9.4;10.8), We tested the associations of six genetic risk scores based on previously identified SNPs for childhood BMI, adult BMI, liver fat, WHR, pericardial fat mass, visceral- and subcutaneous adipose tissue ratio (VAT/SAT ratio), and four individual SAT and VAT associated SNPs, for association with SAT (N=1 746), VAT (N=1 742), VAT/SAT ratio (N=1 738), liver fat fraction (N=1 950), and pericardial fat mass (N=1 803) measured by Magnetic Resonance Imaging. Results Per additional risk allele in the childhood BMI genetic risk score, SAT increased 0.020 standard deviation scores (SDS), (95% confidence interval (CI) 0.009;0.031, p-value:3.28*10-4) and VAT increased 0.021 SDS, 95% CI:0.009;0.032, p-value:4.68*10-4). The adult BMI risk score was positively associated with SAT (0.022 SDS increase, CI:0.015;0.029, p-value:1.33*10-9), VAT (0.017 SDS increase, CI:0.010;0.025, p-value:7.00*10-6), and negatively with VAT/SAT ratio (-0.012 SDS decrease, CI:-0.019;-0.006, p-value:2.88*10-4). The liver fat risk score was associated with liver fat fraction (0.121 SDS, CI:0.086;0.157, p-value:2.65*10-11). Rs7185735 (SAT), was associated with SAT (0.151 SDS, CI:0.087;0.214, p-value:3.00*10-6) and VAT/SAT ratio (-0.126 SDS, CI:-0.186;-0.065, p-value:4.70*10-5). After stratification by sex the associations of the adult BMI risk score with SAT and VAT and of the liver fat risk score with liver fat fraction remained in both sexes. Associations of the childhood BMI risk score with SAT, and the adult BMI risk score with VAT/SAT ratio were present among boys only, whereas the association of the pericardial fat risk score with pericardial fat was present among girls only. Conclusion Genetic variants associated with BMI, body fat distribution, liver and pericardial fat already affect body fat distribution in childhood.
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18
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Monnereau C, Jansen PW, Tiemeier H, Jaddoe VWV, Felix JF. Influence of genetic variants associated with body mass index on eating behavior in childhood. Obesity (Silver Spring) 2017; 25:765-772. [PMID: 28245097 PMCID: PMC5496668 DOI: 10.1002/oby.21778] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Revised: 12/23/2016] [Accepted: 12/26/2016] [Indexed: 12/31/2022]
Abstract
OBJECTIVE Childhood eating behaviors are associated with body mass index (BMI). Recent genome-wide association studies have identified many single-nucleotide polymorphisms (SNPs) associated with adult and childhood BMI. This study hypothesized that these SNPs also influence eating behavior. METHODS In a population-based prospective cohort study among 3,031 children (mean age [standard deviation]: 4.0 [0.1] years), two weighted genetic risk scores, based on 15 childhood and 97 adult BMI SNPs, and ten individual appetite- and/or satiety-related SNPs were tested for association with food fussiness, food responsiveness, enjoyment of food, satiety responsiveness, and slowness in eating. RESULTS The 15 SNP-based childhood BMI genetic risk score was not associated with the eating behavior subscales. The 97 SNP-based adult BMI genetic risk score was nominally associated with satiety responsiveness (β: -0.007 standard deviation, 95% confidence interval [CI] -0.013, 0.000). Of the 10 individual SNPs, rs11030104 in BDNF and rs10733682 in LMX1B were nominally associated with satiety responsiveness (β: -0.057 standard deviation, 95% CI -0.112, -0.002). CONCLUSIONS These findings do not strongly support the hypothesis that BMI-associated SNPs also influence eating behavior at this age. A potential role for BMI SNPs in satiety responsiveness during childhood was observed; however, no associations with the other eating behavior subscales were found.
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Affiliation(s)
- Claire Monnereau
- The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Pauline W Jansen
- Institute of Psychology, Erasmus University, Rotterdam, the Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Henning Tiemeier
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Psychiatry, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Janine F Felix
- The Generation R Study Group, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
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