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      The effects of bariatric surgery on clinical profile, DNA methylation, and ageing in severely obese patients

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          Abstract

          Background

          Severe obesity is a growing, worldwide burden and conventional therapies including radical change of diet and/or increased physical activity have limited results. Bariatric surgery has been proposed as an alternative therapy showing promising results. It leads to substantial weight loss and improvement of comorbidities such as type 2 diabetes. Increased adiposity is associated with changes in epigenetic profile, including DNA methylation. We investigated the effect of bariatric surgery on clinical profile, DNA methylation, and biological age estimated using Horvath’s epigenetic clock.

          Results

          To determine the impact of bariatric surgery and subsequent weight loss on clinical traits, a cohort of 40 severely obese individuals (BMI = 30–73 kg/m 2) was examined at the time of surgery and at three follow-up visits, i.e., 3, 6, and 12 months after surgery. The majority of the individuals were women (65%) and the mean age at surgery was 45.1 ± 8.1 years. We observed a significant decrease over time in BMI, fasting glucose, HbA1c, HOMA-IR, insulin, total cholesterol, triglycerides, LDL and free fatty acids levels, and a significant small increase in HDL levels (all p values < 0.05). Epigenome-wide association analysis revealed 4857 differentially methylated CpG sites 12 months after surgery (at Bonferroni-corrected p value < 1.09 × 10 −7). Including BMI change in the model decreased the number of significantly differentially methylated CpG sites by 51%. Gene set enrichment analysis identified overrepresentation of multiple processes including regulation of transcription, RNA metabolic, and biosynthetic processes in the cell. Bariatric surgery in severely obese patients resulted in a decrease in both biological age and epigenetic age acceleration (EAA) (mean = − 0.92, p value = 0.039).

          Conclusions

          Our study shows that bariatric surgery leads to substantial BMI decrease and improvement of clinical outcomes observed 12 months after surgery. These changes explained part of the association between bariatric surgery and DNA methylation. We also observed a small, but significant improvement of biological age. These epigenetic changes may be modifiable by environmental lifestyle factors and could be used as potential biomarkers for obesity and in the future for obesity related comorbidities.

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          Most cited references32

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          An epigenetic biomarker of aging for lifespan and healthspan

          Identifying reliable biomarkers of aging is a major goal in geroscience. While the first generation of epigenetic biomarkers of aging were developed using chronological age as a surrogate for biological age, we hypothesized that incorporation of composite clinical measures of phenotypic age that capture differences in lifespan and healthspan may identify novel CpGs and facilitate the development of a more powerful epigenetic biomarker of aging. Using an innovative two-step process, we develop a new epigenetic biomarker of aging, DNAm PhenoAge, that strongly outperforms previous measures in regards to predictions for a variety of aging outcomes, including all-cause mortality, cancers, healthspan, physical functioning, and Alzheimer's disease. While this biomarker was developed using data from whole blood, it correlates strongly with age in every tissue and cell tested. Based on an in-depth transcriptional analysis in sorted cells, we find that increased epigenetic, relative to chronological age, is associated with increased activation of pro-inflammatory and interferon pathways, and decreased activation of transcriptional/translational machinery, DNA damage response, and mitochondrial signatures. Overall, this single epigenetic biomarker of aging is able to capture risks for an array of diverse outcomes across multiple tissues and cells, and provide insight into important pathways in aging.
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            The medical risks of obesity.

            Obesity is at epidemic proportions in the United States and in other developed and developing countries. The prevalence of obesity is increasing not only in adults, but especially among children and adolescents. In the United States in 2003 to 2004, 17.1% of children and adolescents were overweight, and 32.2% of adults were obese. Obesity is a significant risk factor for and contributor to increased morbidity and mortality, most importantly from cardiovascular disease (CVD) and diabetes, but also from cancer and chronic diseases, including osteoarthritis, liver and kidney disease, sleep apnea, and depression. The prevalence of obesity has increased steadily over the past 5 decades, and obesity may have a significant impact on quality-adjusted life years. Obesity is also strongly associated with an increased risk of all-cause mortality as well as cardiovascular and cancer mortality. Despite the substantial effects of obesity, weight loss can result in a significant reduction in risk for the majority of these comorbid conditions. Those comorbidities most closely linked to obesity must be identified to increase awareness of potential adverse outcomes. This will allow health care professionals to identify and implement appropriate interventions to reduce patient risk and mortality. A systematic search strategy was used to identify published literature between 1995 and 2008 that reported data from prospective longitudinal studies of obesity and comorbid medical conditions. This article will review evidence for significant associations of obesity with comorbidities to provide information useful for optimal patient management.
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              Obesity accelerates epigenetic aging of human liver.

              Because of the dearth of biomarkers of aging, it has been difficult to test the hypothesis that obesity increases tissue age. Here we use a novel epigenetic biomarker of aging (referred to as an "epigenetic clock") to study the relationship between high body mass index (BMI) and the DNA methylation ages of human blood, liver, muscle, and adipose tissue. A significant correlation between BMI and epigenetic age acceleration could only be observed for liver (r = 0.42, P = 6.8 × 10(-4) in dataset 1 and r = 0.42, P = 1.2 × 10(-4) in dataset 2). On average, epigenetic age increased by 3.3 y for each 10 BMI units. The detected age acceleration in liver is not associated with the Nonalcoholic Fatty Liver Disease Activity Score or any of its component traits after adjustment for BMI. The 279 genes that are underexpressed in older liver samples are highly enriched (1.2 × 10(-9)) with nuclear mitochondrial genes that play a role in oxidative phosphorylation and electron transport. The epigenetic age acceleration, which is not reversible in the short term after rapid weight loss induced by bariatric surgery, may play a role in liver-related comorbidities of obesity, such as insulin resistance and liver cancer.
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                Author and article information

                Contributors
                j.v.van.vliet@umcg.nl
                Journal
                Clin Epigenetics
                Clin Epigenetics
                Clinical Epigenetics
                BioMed Central (London )
                1868-7075
                1868-7083
                20 January 2020
                20 January 2020
                2020
                : 12
                : 14
                Affiliations
                [1 ]ISNI 0000 0000 9558 4598, GRID grid.4494.d, Department of Epidemiology, University of Groningen, , University Medical Center Groningen, ; Groningen, The Netherlands
                [2 ]ISNI 0000 0001 2208 0118, GRID grid.31147.30, Centre for Health Protection, , National Institute for Public Health and the Environment (RIVM), ; Bilthoven, The Netherlands
                [3 ]ISNI 0000 0001 2208 0118, GRID grid.31147.30, Centre for Nutrition, Prevention and Health services, , National Institute for Public Health and the Environment (RIVM), ; Bilthoven, The Netherlands
                [4 ]ISNI 0000 0000 9558 4598, GRID grid.4494.d, Department of Pediatrics, section of Molecular Metabolism and Nutrition, University of Groningen, , University Medical Center Groningen, ; Groningen, The Netherlands
                [5 ]ISNI 0000 0004 1937 0722, GRID grid.11899.38, Laboratory of Nutrigenomics Studies, Department of Internal Medicine, Ribeirão Preto Medical School, , University of Sao Paulo, ; Sao Paulo, Brazil
                [6 ]ISNI 0000 0004 1937 0722, GRID grid.11899.38, Laboratory of Nutrigenomics Studies, Department of Health Sciences, Ribeirão Preto Medical School, , University of Sao Paulo, ; Sao Paulo, Brazil
                [7 ]ISNI 0000000109410645, GRID grid.11794.3a, Epigenomics in Endocrinology and Nutrition, Health Research Institute of Santiago (IDIS), , University Clinical Hospital of Santiago (CHUS/SERGAS) and Santiago de Compostela University (USC), ; Santiago de Compostela, Spain
                [8 ]ISNI 0000 0000 9314 1427, GRID grid.413448.e, CIBER Fisiopatologia de la Obesidad y Nutricion (CIBERobn), ; Madrid, Spain
                [9 ]ISNI 0000 0001 0481 6099, GRID grid.5012.6, School for Mental Health and Neuroscience, , Maastricht University, ; Maastricht, The Netherlands
                [10 ]Department of Surgery, Zuyderland Medical Center Heerlen, Dutch Obesity Clinic South, Heerlen, The Netherlands
                [11 ]Department of Surgery, Maastricht University Medical Center, NUTRIM School for Nutrition and Translational Research in Metabolism, Maastricht, The Netherlands
                [12 ]ISNI 0000 0000 9558 4598, GRID grid.4494.d, Department of Endocrinology, University of Groningen, , University Medical Center Groningen, ; Groningen, The Netherlands
                [13 ]ISNI 0000 0000 9558 4598, GRID grid.4494.d, Genomics Coordination Center, Department of Genetics, University of Groningen, , University Medical Center Groningen, ; Hanzeplein 1, 9713 GZ Groningen, The Netherlands
                Author information
                http://orcid.org/0000-0002-2163-1560
                Article
                790
                10.1186/s13148-019-0790-2
                6972025
                31959221
                b5f50454-e7ad-42d5-824a-830b5cea8e1e
                © The Author(s). 2020

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 1 October 2019
                : 27 November 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003092, Diabetes Fonds;
                Award ID: 2013.81.1673
                Funded by: National Consortium for Healthy Ageing
                Award ID: 050-060-810
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                Genetics
                obesity,bariatric surgery,morbid obesity,dna methylation,epigenetics,epigenetic clock,biological age,ewas

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