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Gupta OT, Gupta RK. The Expanding Problem of Regional Adiposity: Revisiting a 1985 Diabetes Classic by Ohlson et al. Diabetes 2024; 73:649-652. [PMID: 38640415 DOI: 10.2337/dbi24-0021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 02/20/2024] [Indexed: 04/21/2024]
Abstract
Body fat distribution is a predictor of metabolic health in obesity. In this Classics in Diabetes article, we revisit a 1985 Diabetes article by Swedish investigators Ohlson et al. This work was one of the first prospective population-based studies that established a relationship between abdominal adiposity and the risk for developing diabetes. Here, we discuss evolving concepts regarding the link between regional adiposity and diabetes and other chronic disorders. Moreover, we highlight fundamental questions that remain unresolved.
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Affiliation(s)
- Olga T Gupta
- Division of Endocrinology and Diabetes, Department of Pediatrics, Duke University, Durham, NC
| | - Rana K Gupta
- Division of Endocrinology, Department of Medicine, Duke Molecular Physiology Institute, Duke University, Durham, NC
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2
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Ghouse J, Sveinbjörnsson G, Vujkovic M, Seidelin AS, Gellert-Kristensen H, Ahlberg G, Tragante V, Rand SA, Brancale J, Vilarinho S, Lundegaard PR, Sørensen E, Erikstrup C, Bruun MT, Jensen BA, Brunak S, Banasik K, Ullum H, Verweij N, Lotta L, Baras A, Mirshahi T, Carey DJ, Kaplan DE, Lynch J, Morgan T, Schwantes-An TH, Dochtermann DR, Pyarajan S, Tsao PS, Laisk T, Mägi R, Kozlitina J, Tybjærg-Hansen A, Jones D, Knowlton KU, Nadauld L, Ferkingstad E, Björnsson ES, Ulfarsson MO, Sturluson Á, Sulem P, Pedersen OB, Ostrowski SR, Gudbjartsson DF, Stefansson K, Olesen MS, Chang KM, Holm H, Bundgaard H, Stender S. Integrative common and rare variant analyses provide insights into the genetic architecture of liver cirrhosis. Nat Genet 2024:10.1038/s41588-024-01720-y. [PMID: 38632349 DOI: 10.1038/s41588-024-01720-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 03/18/2024] [Indexed: 04/19/2024]
Abstract
We report a multi-ancestry genome-wide association study on liver cirrhosis and its associated endophenotypes, alanine aminotransferase (ALT) and γ-glutamyl transferase. Using data from 12 cohorts, including 18,265 cases with cirrhosis, 1,782,047 controls, up to 1 million individuals with liver function tests and a validation cohort of 21,689 cases and 617,729 controls, we identify and validate 14 risk associations for cirrhosis. Many variants are located near genes involved in hepatic lipid metabolism. One of these, PNPLA3 p.Ile148Met, interacts with alcohol intake, obesity and diabetes on the risk of cirrhosis and hepatocellular carcinoma (HCC). We develop a polygenic risk score that associates with the progression from cirrhosis to HCC. By focusing on prioritized genes from common variant analyses, we find that rare coding variants in GPAM associate with lower ALT, supporting GPAM as a potential target for therapeutic inhibition. In conclusion, this study provides insights into the genetic underpinnings of cirrhosis.
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Affiliation(s)
- Jonas Ghouse
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | | | - Marijana Vujkovic
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anne-Sofie Seidelin
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Helene Gellert-Kristensen
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Gustav Ahlberg
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Søren A Rand
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Joseph Brancale
- Section of Digestive Diseases, Department of Internal Medicine, and Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Silvia Vilarinho
- Section of Digestive Diseases, Department of Internal Medicine, and Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Pia Rengtved Lundegaard
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Erik Sørensen
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
| | - Mie Topholm Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | | | - Søren Brunak
- Translational Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Karina Banasik
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Copenhagen, Denmark
| | | | - Niek Verweij
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Luca Lotta
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Aris Baras
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Tooraj Mirshahi
- Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA, USA
| | - David J Carey
- Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA, USA
| | - David E Kaplan
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Julie Lynch
- VA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Timothy Morgan
- Gastroenterology Section, Veterans Affairs Long Beach Healthcare System, Long Beach, CA, USA
- Department of Medicine, University of California, Irvine, CA, USA
| | - Tae-Hwi Schwantes-An
- Gastroenterology Section, Veterans Affairs Long Beach Healthcare System, Long Beach, CA, USA
- Department of Medical and Molecular Genetics, Indiana University, Indianapolis, IN, USA
| | - Daniel R Dochtermann
- Center for Data and Computational Sciences, VA Boston Healthcare System, Boston, MA, USA
| | - Saiju Pyarajan
- Center for Data and Computational Sciences, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Philip S Tsao
- Palo Alto Epidemiology Research and Information Center for Genomics, VA Palo Alto, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Triin Laisk
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Julia Kozlitina
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Anne Tybjærg-Hansen
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - David Jones
- Precision Genomics, Intermountain Healthcare, Saint George, UT, USA
| | - Kirk U Knowlton
- Intermountain Medical Center, Intermountain Heart Institute, Salt Lake City, UT, USA
- University of Utah, School of Medicine, Salt Lake City, UT, USA
| | - Lincoln Nadauld
- Precision Genomics, Intermountain Healthcare, Saint George, UT, USA
- Stanford University, School of Medicine, Stanford, CA, USA
| | | | - Einar S Björnsson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Internal Medicine and Emergency Services, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | - Magnus O Ulfarsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland
| | | | | | - Ole B Pedersen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Morten Salling Olesen
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kyong-Mi Chang
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hilma Holm
- deCODE Genetics/Amgen, Reykjavik, Iceland
| | - Henning Bundgaard
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Stefan Stender
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
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Opsahl JO, Fragoso-Bargas N, Lee Y, Carlsen EØ, Lekanova N, Qvigstad E, Sletner L, Jenum AK, Lee-Ødegård S, Prasad RB, Birkeland KI, Moen GH, Sommer C. Epigenome-wide association study of DNA methylation in maternal blood leukocytes with BMI in pregnancy and gestational weight gain. Int J Obes (Lond) 2024; 48:584-593. [PMID: 38219005 PMCID: PMC10978488 DOI: 10.1038/s41366-024-01458-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 12/17/2023] [Accepted: 01/02/2024] [Indexed: 01/15/2024]
Abstract
OBJECTIVES We aimed to discover CpG sites with differential DNA methylation in peripheral blood leukocytes associated with body mass index (BMI) in pregnancy and gestational weight gain (GWG) in women of European and South Asian ancestry. Furthermore, we aimed to investigate how the identified sites were associated with methylation quantitative trait loci, gene ontology, and cardiometabolic parameters. METHODS In the Epigenetics in pregnancy (EPIPREG) sample we quantified maternal DNA methylation in peripheral blood leukocytes in gestational week 28 with Illumina's MethylationEPIC BeadChip. In women with European (n = 303) and South Asian (n = 164) ancestry, we performed an epigenome-wide association study of BMI in gestational week 28 and GWG between gestational weeks 15 and 28 using a meta-analysis approach. Replication was performed in the Norwegian Mother, Father, and Child Cohort Study, the Study of Assisted Reproductive Technologies (MoBa-START) (n = 877, mainly European/Norwegian). RESULTS We identified one CpG site significantly associated with GWG (p 5.8 × 10-8) and five CpG sites associated with BMI at gestational week 28 (p from 4.0 × 10-8 to 2.1 × 10-10). Of these, we were able to replicate three in MoBa-START; cg02786370, cg19758958 and cg10472537. Two sites are located in genes previously associated with blood pressure and BMI. DNA methylation at the three replicated CpG sites were associated with levels of blood pressure, lipids and glucose in EPIPREG (p from 1.2 × 10-8 to 0.04). CONCLUSIONS We identified five CpG sites associated with BMI at gestational week 28, and one with GWG. Three of the sites were replicated in an independent cohort. Several genetic variants were associated with DNA methylation at cg02786379 and cg16733643 suggesting a genetic component influencing differential methylation. The identified CpG sites were associated with cardiometabolic traits. CLINICALTRIALS GOV REGISTRATION NO Not applicable.
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Affiliation(s)
- J O Opsahl
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - N Fragoso-Bargas
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
| | - Y Lee
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - E Ø Carlsen
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - N Lekanova
- Department of Biosciences, The Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | - E Qvigstad
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
| | - L Sletner
- Department of Pediatric and Adolescents Medicine, Akershus University Hospital, Lørenskog, Norway
| | - A K Jenum
- General Practice Research Unit, Department of General Practice, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - S Lee-Ødegård
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Nutrition, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - R B Prasad
- Lund University Diabetes Centre, Malmö, Sweden
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - K I Birkeland
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
| | - G-H Moen
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, 4102, Australia
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK
- Institute of Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
| | - C Sommer
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway.
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Novakov V, Novakova O, Churnosova M, Aristova I, Ponomarenko M, Reshetnikova Y, Churnosov V, Sorokina I, Ponomarenko I, Efremova O, Orlova V, Batlutskaya I, Polonikov A, Reshetnikov E, Churnosov M. Polymorphism rs143384 GDF5 reduces the risk of knee osteoarthritis development in obese individuals and increases the disease risk in non-obese population. Arthroplasty 2024; 6:12. [PMID: 38424630 PMCID: PMC10905832 DOI: 10.1186/s42836-023-00229-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 12/26/2023] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND We investigated the effect of obesity on the association of genome-wide associative studies (GWAS)-significant genes with the risk of knee osteoarthritis (KOA). METHODS All study participants (n = 1,100) were divided into 2 groups in terms of body mass index (BMI): BMI ≥ 30 (255 KOA patients and 167 controls) and BMI < 30 (245 KOA and 433 controls). The eight GWAS-significant KOA single nucleotide polymorphisms (SNP) of six candidate genes, such as LYPLAL1 (rs2820436, rs2820443), SBNO1 (rs1060105, rs56116847), WWP2 (rs34195470), NFAT5 (rs6499244), TGFA (rs3771501), GDF5 (rs143384), were genotyped. Logistic regression analysis (gPLINK online program) was used for SNPs associations study with the risk of developing KOA into 2 groups (BMI ≥ 30 and BMI < 30) separately. The functional effects of KOA risk loci were evaluated using in silico bioinformatic analysis. RESULTS Multidirectional relationships of the rs143384 GDF5 with KOA in BMI-different groups were found: This SNP was KOA protective locus among individuals with BMI ≥ 30 (OR 0.41 [95%CI 0.20-0.94] recessive model) and was disorder risk locus among individuals with BMI < 30 (OR 1.32 [95%CI 1.05-1.65] allele model, OR 1.44 [95%CI 1.10-1.86] additive model, OR 1.67 [95%CI 1.10-2.52] dominant model). Polymorphism rs143384 GDF5 manifested its regulatory effects in relation to nine genes (GDF5, CPNE1, EDEM2, ERGIC3, GDF5OS, PROCR, RBM39, RPL36P4, UQCC1) in adipose tissue, which were involved in the regulation of pathways of apoptosis of striated muscle cells. CONCLUSIONS In summary, the effect of obesity on the association of the rs143384 GDF5 with KOA was shown: the "protective" value of this polymorphism in the BMI ≥ 30 group and the "risk" meaning in BMI < 30 cohort.
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Affiliation(s)
- Vitaly Novakov
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Olga Novakova
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Maria Churnosova
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Inna Aristova
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Marina Ponomarenko
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Yuliya Reshetnikova
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Vladimir Churnosov
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Inna Sorokina
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Irina Ponomarenko
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Olga Efremova
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Valentina Orlova
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Irina Batlutskaya
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Alexey Polonikov
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
- Department of Biology, Medical Genetics and Ecology and Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, Kursk, 305041, Russia
| | - Evgeny Reshetnikov
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia
| | - Mikhail Churnosov
- Department of Medical Biological Disciplines, Belgorod State National Research University, Belgorod, 308015, Russia.
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Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Barone Gibbs B, Beaton AZ, Boehme AK, Commodore-Mensah Y, Currie ME, Elkind MSV, Evenson KR, Generoso G, Heard DG, Hiremath S, Johansen MC, Kalani R, Kazi DS, Ko D, Liu J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Perman SM, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Tsao CW, Urbut SM, Van Spall HGC, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Palaniappan LP. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation 2024; 149:e347-e913. [PMID: 38264914 DOI: 10.1161/cir.0000000000001209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
BACKGROUND The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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Loh WJ, Yaligar J, Hooper AJ, Sadananthan SA, Kway Y, Lim SC, Watts GF, Velan SS, Leow MKS, Khoo J. Clinical and imaging features of women with polygenic partial lipodystrophy: a case series. Nutr Diabetes 2024; 14:3. [PMID: 38321009 PMCID: PMC10847407 DOI: 10.1038/s41387-024-00260-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 01/13/2024] [Accepted: 01/19/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Familial partial lipodystrophy (FPLD) is an inherited disorder of white adipose tissue that causes premature cardiometabolic disease. There is no clear diagnostic criteria for FPLD, and this may explain the under-detection of this condition. AIM This pilot study aimed to describe the clinical features of women with FPLD and to explore the value of adipose tissue measurements that could be useful in diagnosis. METHODS In 8 women with FPLD and 4 controls, skinfold measurements, DXA and whole-body MRI were undertaken. RESULTS Whole genome sequencing was negative for monogenic metabolic causes, but polygenic scores for partial lipodystrophy were elevated in keeping with FPLD type 1. The mean age of diagnosis of DM was 31 years in the FPLD group. Compared with controls, the FPLD group had increased HOMA-IR (10.3 vs 2.9, p = 0.028) and lower mean thigh skinfold thickness (19.5 mm vs 48.2 mm, p = 0.008). The FPLD group had lower percentage of leg fat and an increased ratio of trunk to leg fat percentage on DXA. By MRI, the FPLD group had decreased subcutaneous adipose tissue (SAT) volume in the femoral and calf regions (p < 0.01); abdominal SAT, visceral adipose tissue, and femoral and calf muscle volumes were not different from controls. CONCLUSION Women with FPLD1 in Singapore have significant loss of adipose but not muscle tissue in lower limbs and have early onset of diabetes. Reduced thigh skinfold, and increased ratio of trunk to leg fat percentage on DXA are potentially clinically useful markers to identify FPLD1.
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Affiliation(s)
- Wann Jia Loh
- Department of Endocrinology, Changi General Hospital, Singapore, Singapore.
- Duke-NUS Medical School, Singapore, Singapore.
| | - Jadegoud Yaligar
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
| | - Amanda J Hooper
- Department of Biochemistry, Pathwest and Fiona Stanley Hospital Network, Perth, Australia
- School of Medicine, University of Western Australia, Perth, Australia
| | - Suresh Anand Sadananthan
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
| | - Yeshe Kway
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
- Departments of Medicine and Physiology, NUS Yong Loo School of Medicine, NUS, Singapore, Singapore
| | - Su Chi Lim
- Diabetes Centre, Admiralty Medical Centre, Singapore, Singapore
| | - Gerald F Watts
- School of Medicine, University of Western Australia, Perth, Australia
- Department of Cardiology and Internal Medicine, Royal Perth Hospital, Perth, Australia
| | - Sambasivam Sendhil Velan
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
- Departments of Medicine and Physiology, NUS Yong Loo School of Medicine, NUS, Singapore, Singapore
| | - Melvin Khee Shing Leow
- Duke-NUS Medical School, Singapore, Singapore
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
- Department of Endocrinology, Tan Tock Seng Hospital, Singapore, Singapore
- LKC School of Medicine, NTU, Singapore, Singapore
| | - Joan Khoo
- Department of Endocrinology, Changi General Hospital, Singapore, Singapore
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7
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Manco L, Albuquerque D, Rodrigues D, Machado-Rodrigues AM, Padez C. Protective Association of APOC1/rs4420638 with Risk of Obesity: A case-control Study in Portuguese Children. Biochem Genet 2024; 62:254-263. [PMID: 37328602 PMCID: PMC10902077 DOI: 10.1007/s10528-023-10427-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/07/2023] [Indexed: 06/18/2023]
Abstract
The association of the rs4420638 polymorphism, near the APOC1 gene, was examined with the risk of obesity among Portuguese children. A sample of 446 Portuguese individuals (231 boys and 215 girls) of European descent, aged 3.2 to 13.7 years old (mean age: 7.98 years), were selected to conduct a case-control study. Body mass index (BMI), BMI Z-scores, and waist circumference were calculated. Genotyping was performed by real time PCR using a pre-designed TaqMan probe. Logistic regression and the nonparametric Mann-Whitney test were used to test the associations. The association results revealed a significant protective effect from the minor G-allele of SNP rs4420638 against obesity, with an odds ratio (OR) of 0.619 (95% CI 0.421-0.913; p = 0.0155) in the additive model, and OR of 0.587 (95% CI 0.383-0.9; p = 0.0145) in the dominant model. Moreover, comparing genotype groups (AA vs. AG + GG), significantly lower values (p < 0.05) for the anthropometric traits weight, height, BMI, BMI Z-score and waist circumference, were observed in the carriers of allele G. The present study provides further evidence for the APOE/APOC1 candidate-region association with the risk of obesity. This was the first study to describe the protective association of the rs4420638 minor G-allele against obesity in childhood exclusively.
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Affiliation(s)
- Licínio Manco
- Research Centre for Anthropology and Health (CIAS), University of Coimbra, Coimbra, 3000, Portugal.
- Department of Life Sciences, University of Coimbra, Coimbra, Portugal.
| | - David Albuquerque
- Research Centre for Anthropology and Health (CIAS), University of Coimbra, Coimbra, 3000, Portugal
| | - Daniela Rodrigues
- Research Centre for Anthropology and Health (CIAS), University of Coimbra, Coimbra, 3000, Portugal
| | - Aristides M Machado-Rodrigues
- Research Centre for Anthropology and Health (CIAS), University of Coimbra, Coimbra, 3000, Portugal
- Faculty of Sport Sciences and Physical Education, University of Coimbra, Coimbra, Portugal
| | - Cristina Padez
- Research Centre for Anthropology and Health (CIAS), University of Coimbra, Coimbra, 3000, Portugal
- Department of Life Sciences, University of Coimbra, Coimbra, Portugal
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8
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Gholami M. Genetic variants and haplotype structures of miRNA host genes in cancer and obesity. J Biomol Struct Dyn 2024:1-7. [PMID: 38174558 DOI: 10.1080/07391102.2023.2300056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/13/2023] [Indexed: 01/05/2024]
Abstract
Cancer and obesity are two important public health problems. This study aimed to investigate the role of genetic variants and haplotypes of miRNA host genes in cancer and obesity. Data from the catalog of genome-wide association studies (GWAS) were used to find significant variants (index). Then, 1000-genome phase 3 data were used to find haplotypic variants (proxy) associated with these diseases. The candidate variants and haplotypes were identified from proxy and index variants. Finally, SNP function analysis was performed. All GWAS-significant cancer-associated miRNA host gene variants, including MIR4713HG, MIR663AHG, MIR99AHG and MIR4435-2HG, were also significantly associated with obesity. The rs703764 variant was common between cutaneous melanoma and obesity traits in the European population (P ≤ 5E-8). The rs2414098 variant was associated with endometrial cancer (P ≤ 5E-13), and the rs7173595 variant was associated with waist-hip ratio (P ≤ 5E-13) and new CGGCATCA haplotypic located at MIR4713HG was identified in the European population. In addition, the ATCTTGTT haplotype for rs17041868 in MIR4435-2HG was identified to be associated with obesity traits (waist-hip ratio and BMI) in the European population (P ≤ 5E-8). This study found that rs703764 is a common genetic marker between cancer and obesity. The CGGCATCA haplotype is common between endometrial cancer and waist-hip ratio. Also, ATCTTGTT haplotype is associated with obesity traits. These results indicate that the variants and haplotypes of miRNAs host genes play an important role between cancer and obesity in the European population. It is suggested to investigate the effect of these structures in other populations.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Morteza Gholami
- Metabolic Disorders Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
- Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
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9
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Zhu Y, Wang Q, Dai H, Hou T, Wang T, Zhao Z, Li M, Miao W, Yang J, Lu J, Xu Y, Chen Y, Ning G, Zheng J, Bi Y, Xu M, Wang W. Sex-specific causality of MRI-derived body compositions on glycaemic traits: Mendelian randomization and observational study. Diabetes Obes Metab 2024; 26:373-384. [PMID: 37920887 DOI: 10.1111/dom.15326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/13/2023] [Accepted: 09/25/2023] [Indexed: 11/04/2023]
Abstract
AIM To investigate the sex-specific causality of body compositions in type 2 diabetes and related glycaemic traits using Mendelian randomization (MR). MATERIALS AND METHODS We leveraged sex-specific summary-level statistics from genome-wide association studies for three adipose deposits adjusted for body mass index and height, including abdominal subcutaneous adipose tissue, visceral adipose tissue (VATadj) and gluteofemoral adipose tissue (GFATadj), measured by MRI (20 038 women; 19 038 men), and fat mass-adjusted appendicular lean mass (ALMadj) (244 730 women; 205 513 men) in the UK Biobank. Sex-specific statistics of type 2 diabetes were from the Diabetes Genetics Replication and Meta-analysis Consortium and those for fasting glucose and insulin were from the Meta-analyses of Glucose and Insulin-related Traits Consortium. Univariable and multivariable MR (MVMR) were performed. We also performed MR analyses of anthropometric traits and genetic association analyses using individual-level data of body composition as validation. RESULTS Univariable MR analysis showed that, in women, higher GFATadj and ALMadj exerted a causally protective effect on type 2 diabetes (GFATadj: odds ratio [OR] 0.59, 95% confidence interval [CI; 0.50, 0.69]; ALMadj: OR 0.84, 95% CI [0.77, 0.91]) and VATadj to be riskier in glycaemic traits. MVMR showed that GFATadj retained a robust effect on type 2 diabetes (OR 0.57, 95% CI [0.42, 0.77]; P = 2.6 × 10-4 ) in women, while it was nominally significant in men (OR 0.58, 95% CI [0.35, 0.96]; P = 3.3 × 10-2 ), after adjustment for ASATadj and VATadj. MR analyses of anthropometric measures and genetic association analyses of glycaemic traits confirmed the results. CONCLUSIONS Body composition has a sex-specific effect on type 2 diabetes, and higher GFATadj has an independent protective effect on type 2 diabetes in both sexes.
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Affiliation(s)
- Yijie Zhu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qi Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huajie Dai
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianzhichao Hou
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tiange Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhiyun Zhao
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Mian Li
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Miao
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jian Yang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jieli Lu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yu Xu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuhong Chen
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Guang Ning
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, UK
| | - Yufang Bi
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Min Xu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Weiqing Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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10
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Elmaleh-Sachs A, Schwartz JL, Bramante CT, Nicklas JM, Gudzune KA, Jay M. Obesity Management in Adults: A Review. JAMA 2023; 330:2000-2015. [PMID: 38015216 DOI: 10.1001/jama.2023.19897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Importance Obesity affects approximately 42% of US adults and is associated with increased rates of type 2 diabetes, hypertension, cardiovascular disease, sleep disorders, osteoarthritis, and premature death. Observations A body mass index (BMI) of 25 or greater is commonly used to define overweight, and a BMI of 30 or greater to define obesity, with lower thresholds for Asian populations (BMI ≥25-27.5), although use of BMI alone is not recommended to determine individual risk. Individuals with obesity have higher rates of incident cardiovascular disease. In men with a BMI of 30 to 39, cardiovascular event rates are 20.21 per 1000 person-years compared with 13.72 per 1000 person-years in men with a normal BMI. In women with a BMI of 30 to 39.9, cardiovascular event rates are 9.97 per 1000 person-years compared with 6.37 per 1000 person-years in women with a normal BMI. Among people with obesity, 5% to 10% weight loss improves systolic blood pressure by about 3 mm Hg for those with hypertension, and may decrease hemoglobin A1c by 0.6% to 1% for those with type 2 diabetes. Evidence-based obesity treatment includes interventions addressing 5 major categories: behavioral interventions, nutrition, physical activity, pharmacotherapy, and metabolic/bariatric procedures. Comprehensive obesity care plans combine appropriate interventions for individual patients. Multicomponent behavioral interventions, ideally consisting of at least 14 sessions in 6 months to promote lifestyle changes, including components such as weight self-monitoring, dietary and physical activity counseling, and problem solving, often produce 5% to 10% weight loss, although weight regain occurs in 25% or more of participants at 2-year follow-up. Effective nutritional approaches focus on reducing total caloric intake and dietary strategies based on patient preferences. Physical activity without calorie reduction typically causes less weight loss (2-3 kg) but is important for weight-loss maintenance. Commonly prescribed medications such as antidepressants (eg, mirtazapine, amitriptyline) and antihyperglycemics such as glyburide or insulin cause weight gain, and clinicians should review and consider alternatives. Antiobesity medications are recommended for nonpregnant patients with obesity or overweight and weight-related comorbidities in conjunction with lifestyle modifications. Six medications are currently approved by the US Food and Drug Administration for long-term use: glucagon-like peptide receptor 1 (GLP-1) agonists (semaglutide and liraglutide only), tirzepatide (a glucose-dependent insulinotropic polypeptide/GLP-1 agonist), phentermine-topiramate, naltrexone-bupropion, and orlistat. Of these, tirzepatide has the greatest effect, with mean weight loss of 21% at 72 weeks. Endoscopic procedures (ie, intragastric balloon and endoscopic sleeve gastroplasty) can attain 10% to 13% weight loss at 6 months. Weight loss from metabolic and bariatric surgeries (ie, laparoscopic sleeve gastrectomy and Roux-en-Y gastric bypass) ranges from 25% to 30% at 12 months. Maintaining long-term weight loss is difficult, and clinical guidelines support the use of long-term antiobesity medications when weight maintenance is inadequate with lifestyle interventions alone. Conclusion and Relevance Obesity affects approximately 42% of adults in the US. Behavioral interventions can attain approximately 5% to 10% weight loss, GLP-1 agonists and glucose-dependent insulinotropic polypeptide/GLP-1 receptor agonists can attain approximately 8% to 21% weight loss, and bariatric surgery can attain approximately 25% to 30% weight loss. Comprehensive, evidence-based obesity treatment combines behavioral interventions, nutrition, physical activity, pharmacotherapy, and metabolic/bariatric procedures as appropriate for individual patients.
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Affiliation(s)
- Arielle Elmaleh-Sachs
- Department of Medicine, Division of General Internal Medicine and Clinical Innovation, New York University Grossman School of Medicine, New York, New York
- Department of Population Health, New York University Grossman School of Medicine, New York, New York
- Family Health Centers at NYU Langone, New York, New York
| | - Jessica L Schwartz
- Division of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Carolyn T Bramante
- Division of General Internal Medicine, University of Minnesota Medical School, Minneapolis
| | - Jacinda M Nicklas
- Division of General Internal Medicine, University of Colorado School of Medicine, Aurora
| | - Kimberly A Gudzune
- Division of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Melanie Jay
- Department of Medicine, Division of General Internal Medicine and Clinical Innovation, New York University Grossman School of Medicine, New York, New York
- Department of Population Health, New York University Grossman School of Medicine, New York, New York
- New York Harbor Veteran Affairs, New York, New York
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11
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Zhang Y, Qi Q. Can healthy lifestyle offset the genetic predisposition to obesity to prevent coronary heart disease? Am J Clin Nutr 2023; 118:841-842. [PMID: 37923496 DOI: 10.1016/j.ajcnut.2023.08.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 08/30/2023] [Indexed: 11/07/2023] Open
Affiliation(s)
- Yanbo Zhang
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States.
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12
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Wang M, Au Yeung SL, Luo S, Jang H, Ho HS, Sharp SJ, Wijndaele K, Brage S, Wareham NJ, Kim Y. Adherence to a healthy lifestyle, genetic susceptibility to abdominal obesity, cardiometabolic risk markers, and risk of coronary heart disease. Am J Clin Nutr 2023; 118:911-920. [PMID: 37923500 DOI: 10.1016/j.ajcnut.2023.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 06/20/2023] [Accepted: 08/01/2023] [Indexed: 11/07/2023] Open
Abstract
BACKGROUND Little is known about whether the association between genetic susceptibility to high waist-to-hip ratio (WHR), a measure of abdominal obesity, and incident coronary heart disease (CHD) is modified by adherence to a healthy lifestyle. OBJECTIVES To explore the interplay of genetic susceptibility to high WHR and adherence to a healthy lifestyle on incident CHD. METHODS This study included 282,316 white British individuals from the UK Biobank study. Genetic risk for high WHR was estimated in the form of weighted polygenic risk scores (PRSs), calculated based on 156 single-nucleotide polymorphisms. Lifestyle scores were calculated based on 5 healthy lifestyle factors: regular physical activity, no current smoking, a healthy diet, <3 times/wk of alcohol consumption and 7-9 h/d of sleep. Incident CHD (n = 11,635) was accrued over a median 13.8 y of follow-up, and 12 individual cardiovascular disease risk markers assessed at baseline. RESULTS Adhering to a favorable lifestyle (4-5 healthy factors) was associated with a 25% (hazard ratio: 0.75, 95% confidence interval: 0.70, 0.81) lower hazard of CHD compared with an unfavorable lifestyle (0-1 factor), independent of PRS for high WHR. Estimated 12-y absolute risk of CHD was lower for a favorable lifestyle at high genetic risk (1.73%) and medium genetic risk (1.67%) than for an unfavorable lifestyle at low genetic risk (2.08%). Adhering to a favorable lifestyle was associated with healthier levels of cardiovascular disease risk markers (except random glucose and high-density lipoprotein), independent of PRS for high WHR. CONCLUSIONS Individuals who have high or medium genetic risk of abdominal obesity but adhere to a healthy lifestyle may have a lower risk of developing CHD, compared with those who have low genetic risk and an unhealthy lifestyle. Future clinical trials of lifestyle modification could be implemented for individuals at high genetic risk of abdominal obesity for the primary prevention of CHD events.
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Affiliation(s)
- Mengyao Wang
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong SAR, China
| | - Shiu Lun Au Yeung
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong SAR, China
| | - Shan Luo
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong SAR, China
| | - Haeyoon Jang
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong SAR, China
| | - Hin Sheung Ho
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong SAR, China
| | - Stephen J Sharp
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, Cambridgeshire, United Kingdom
| | - Katrien Wijndaele
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, Cambridgeshire, United Kingdom
| | - Soren Brage
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, Cambridgeshire, United Kingdom
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, Cambridgeshire, United Kingdom
| | - Youngwon Kim
- School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Hong Kong SAR, China; MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, Cambridgeshire, United Kingdom.
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Pray R, Riskin S. The History and Faults of the Body Mass Index and Where to Look Next: A Literature Review. Cureus 2023; 15:e48230. [PMID: 38050494 PMCID: PMC10693914 DOI: 10.7759/cureus.48230] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 11/03/2023] [Indexed: 12/06/2023] Open
Abstract
Body mass index (BMI) is an anthropometric index that is commonly used in the medical setting and is a factor in assessing various disease risks but its origins are unknown by many. More importantly, BMI does not properly assess body fat percentage and muscle mass or distinguish abdominal fat from gluteofemoral fat, which is important to note because abdominal fat is associated with insulin resistance, metabolic disease, and cardiovascular complications. Using a less accurate index to assess the relationship between weight and disease risk is conceptually invalid because the use of BMI ultimately trickles into patient treatment, preventive medicine, and overall health outcomes. Several different anthropometric indices that more accurately assess abdominal adiposity through the incorporation of waist circumference exist and have been extensively studied, such as waist-to-hip ratio, waist-to-height ratio, and a body shape index. It is important that we consider replacing BMI's usage in the healthcare setting with a different anthropometric index: one that considers height, sex, and race differences, accounts for abdominal adiposity, and more accurately predicts the relationship between obesity, mortality, and diseases such as cardiovascular disease, hypertension, insulin resistance, and diabetes.
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Affiliation(s)
- Rachel Pray
- Medicine, Nova Southeastern University Dr. Kiran C. Patel College of Osteopathic Medicine, Clearwater, USA
| | - Suzanne Riskin
- Internal Medicine, Nova Southeastern University Dr. Kiran C. Patel College of Osteopathic Medicine, Clearwater, USA
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14
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Szczerbinski L, Florez JC. Precision medicine of obesity as an integral part of type 2 diabetes management - past, present, and future. Lancet Diabetes Endocrinol 2023; 11:861-878. [PMID: 37804854 DOI: 10.1016/s2213-8587(23)00232-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/29/2023] [Accepted: 08/01/2023] [Indexed: 10/09/2023]
Abstract
Obesity is a complex and heterogeneous condition that leads to various metabolic complications, including type 2 diabetes. Unfortunately, for some, treatment options to date for obesity are insufficient, with many people not reaching sustained weight loss or having improvements in metabolic health. In this Review, we discuss advances in the genetics of obesity from the past decade-with emphasis on developments from the past 5 years-with a focus on metabolic consequences, and their potential implications for precision management of the disease. We also provide an overview of the potential role of genetics in guiding weight loss strategies. Finally, we propose a vision for the future of precision obesity management that includes developing an obesity-centred multidisease management algorithm that targets both obesity and its comorbidities. However, further collaborative efforts and research are necessary to fully realise its potential and improve metabolic health outcomes.
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Affiliation(s)
- Lukasz Szczerbinski
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland; Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
| | - Jose C Florez
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
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15
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Huang B, DePaolo J, Judy RL, Shakt G, Witschey WR, Levin MG, Gershuni VM. Relationships between body fat distribution and metabolic syndrome traits and outcomes: A mendelian randomization study. PLoS One 2023; 18:e0293017. [PMID: 37883456 PMCID: PMC10602264 DOI: 10.1371/journal.pone.0293017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 10/04/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Obesity is a complex, multifactorial disease associated with substantial morbidity and mortality worldwide. Although it is frequently assessed using BMI, many epidemiological studies have shown links between body fat distribution and obesity-related outcomes. This study examined the relationships between body fat distribution and metabolic syndrome traits using Mendelian Randomization (MR). METHODS/FINDINGS Genetic variants associated with visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT), and gluteofemoral adipose tissue (GFAT), as well as their relative ratios, were identified from a genome wide association study (GWAS) performed with the United Kingdom BioBank. GWAS summary statistics for traits and outcomes related to metabolic syndrome were obtained from the IEU Open GWAS Project. Two-sample MR and BMI-controlled multivariable MR (MVMR) were performed to examine relationships between each body fat measure and ratio with the outcomes. Increases in absolute GFAT were associated with a protective cardiometabolic profile, including lower low density lipoprotein cholesterol (β: -0.19, [95% CI: -0.28, -0.10], p < 0.001), higher high density lipoprotein cholesterol (β: 0.23, [95% CI: 0.03, 0.43], p = 0.025), lower triglycerides (β: -0.28, [95% CI: -0.45, -0.10], p = 0.0021), and decreased systolic (β: -1.65, [95% CI: -2.69, -0.61], p = 0.0019) and diastolic blood pressures (β: -0.95, [95% CI: -1.65, -0.25], p = 0.0075). These relationships were largely maintained in BMI-controlled MVMR analyses. Decreases in relative GFAT were linked with a worse cardiometabolic profile, with higher levels of detrimental lipids and increases in systolic and diastolic blood pressures. CONCLUSION A MR analysis of ASAT, GFAT, and VAT depots and their relative ratios with metabolic syndrome related traits and outcomes revealed that increased absolute and relative GFAT were associated with a favorable cardiometabolic profile independently of BMI. These associations highlight the importance of body fat distribution in obesity and more precise means to categorize obesity beyond BMI.
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Affiliation(s)
- Brian Huang
- Department of Medicine, Massachusetts General Hospital, Boston, MA, United States of America
| | - John DePaolo
- Department of Surgery, Hospital of the University of Pennsylvania, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Renae L. Judy
- Department of Surgery, Hospital of the University of Pennsylvania, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Gabrielle Shakt
- Department of Surgery, Hospital of the University of Pennsylvania, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Walter R. Witschey
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Michael G. Levin
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, United States of America
| | - Victoria M. Gershuni
- Department of Surgery, Hospital of the University of Pennsylvania, University of Pennsylvania, Philadelphia, PA, United States of America
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
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Roshandel D, Lu T, Paterson AD, Dash S. Beyond apples and pears: sex-specific genetics of body fat percentage. Front Endocrinol (Lausanne) 2023; 14:1274791. [PMID: 37867527 PMCID: PMC10585153 DOI: 10.3389/fendo.2023.1274791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 09/18/2023] [Indexed: 10/24/2023] Open
Abstract
Introduction Biological sex influences both overall adiposity and fat distribution. Further, testosterone and sex hormone binding globulin (SHBG) influence adiposity and metabolic function, with differential effects of testosterone in men and women. Here, we aimed to perform sex-stratified genome-wide association studies (GWAS) of body fat percentage (BFPAdj) (adjusting for testosterone and sex hormone binding globulin (SHBG)) to increase statistical power. Methods GWAS were performed in white British individuals from the UK Biobank (157,937 males and 154,337 females). To avoid collider bias, loci associated with SHBG or testosterone were excluded. We investigated association of BFPAdj loci with high density cholesterol (HDL), triglyceride (TG), type 2 diabetes (T2D), coronary artery disease (CAD), and MRI-derived abdominal subcutaneous adipose tissue (ASAT), visceral adipose tissue (VAT) and gluteofemoral adipose tissue (GFAT) using publicly available data from large GWAS. We also performed 2-sample Mendelian Randomization (MR) using identified BFPAdj variants as instruments to investigate causal effect of BFPAdj on HDL, TG, T2D and CAD in males and females separately. Results We identified 195 and 174 loci explaining 3.35% and 2.60% of the variation in BFPAdj in males and females, respectively at genome-wide significance (GWS, p<5x10-8). Although the direction of effect at these loci was generally concordant in males and females, only 38 loci were common to both sexes at GWS. Seven loci in males and ten loci in females have not been associated with any adiposity/cardiometabolic traits previously. BFPAdj loci generally did not associate with cardiometabolic traits; several had paradoxically beneficial cardiometabolic effects with favourable fat distribution. MR analyses did not find convincing supportive evidence that increased BFPAdj has deleterious cardiometabolic effects in either sex with highly significant heterogeneity. Conclusions There was limited genetic overlap between BFPAdj in males and females at GWS. BFPAdj loci generally did not have adverse cardiometabolic effects which may reflect the effects of favourable fat distribution and cardiometabolic risk modulation by testosterone and SHBG.
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Affiliation(s)
- Delnaz Roshandel
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, Canada
| | - Tianyuan Lu
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Andrew D. Paterson
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, ON, Canada
- Divisions of Epidemiology and Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Satya Dash
- Department of Medicine, University Health Network, and University of Toronto, Toronto, ON, Canada
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17
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Blaak EE, Goossens GH. Metabolic phenotyping in people living with obesity: Implications for dietary prevention. Rev Endocr Metab Disord 2023; 24:825-838. [PMID: 37581871 PMCID: PMC10492670 DOI: 10.1007/s11154-023-09830-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/01/2023] [Indexed: 08/16/2023]
Abstract
Given the increasing number of people living with obesity and related chronic metabolic disease, precision nutrition approaches are required to increase the effectiveness of prevention strategies. This review addresses these approaches in different metabolic phenotypes (metabotypes) in obesity. Although obesity is typically associated with an increased cardiometabolic disease risk, some people with obesity are relatively protected against the detrimental effects of excess adiposity on cardiometabolic health, also referred to as 'metabolically healthy obesity' (MHO). Underlying mechanisms, the extent to which MHO is a transient state as well as lifestyle strategies to counteract the transition from MHO to metabolically unhealthy obesity (MUO) are discussed. Based on the limited resources that are available for dietary lifestyle interventions, it may be reasonable to prioritize interventions for people with MUO, since targeting high-risk patients for specific nutritional, lifestyle or weight-loss strategies may enhance the cost-effectiveness of these interventions. Additionally, the concept of tissue insulin resistant (IR) metabotypes is discussed, representing distinct etiologies towards type 2 diabetes (T2D) as well as cardiovascular disease (CVD). Recent evidence indicates that these tissue IR metabotypes, already present in individuals with obesity with a normal glucose homeostasis, respond differentially to diet. Modulation of dietary macronutrient composition according to these metabotypes may considerably improve cardiometabolic health benefits. Thus, nutritional or lifestyle intervention may improve cardiometabolic health, even with only minor or no weight loss, which stresses the importance of focusing on a healthy lifestyle and not on weight loss only. Targeting different metabotypes towards T2D and cardiometabolic diseases may lead to more effective lifestyle prevention and treatment strategies. Age and sex-related differences in tissue metabotypes and related microbial composition and functionality (fermentation), as important drivers and/or mediators of dietary intervention response, have to be taken into account. For the implementation of these approaches, more prospective trials are required to provide the knowledge base for precision nutrition in the prevention of chronic metabolic diseases.
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Affiliation(s)
- Ellen E Blaak
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, P.O. Box 616, 6200 MD, Maastricht, The Netherlands.
| | - Gijs H Goossens
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
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18
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Felix JF, Grant SF. Keeping It in the Family: Consanguinity Reveals P4HTM as a Novel Syndromic Obesity Gene. Diabetes 2023; 72:1184-1186. [PMID: 37603723 PMCID: PMC10450820 DOI: 10.2337/dbi23-0010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 06/07/2023] [Indexed: 08/23/2023]
Affiliation(s)
- Janine F. Felix
- The Generation R Study Group, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Struan F.A. Grant
- Divisions of Human Genetics and Endocrinology & Diabetes, Children’s Hospital of Philadelphia, Philadelphia, PA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
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19
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Adam RC, Pryce DS, Lee JS, Zhao Y, Mintah IJ, Min S, Halasz G, Mastaitis J, Atwal GS, Aykul S, Idone V, Economides AN, Lotta LA, Murphy AJ, Yancopoulos GD, Sleeman MW, Gusarova V. Activin E-ACVR1C cross talk controls energy storage via suppression of adipose lipolysis in mice. Proc Natl Acad Sci U S A 2023; 120:e2309967120. [PMID: 37523551 PMCID: PMC10410708 DOI: 10.1073/pnas.2309967120] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 06/13/2023] [Indexed: 08/02/2023] Open
Abstract
Body fat distribution is a heritable risk factor for cardiovascular and metabolic disease. In humans, rare Inhibin beta E (INHBE, activin E) loss-of-function variants are associated with a lower waist-to-hip ratio and protection from type 2 diabetes. Hepatic fatty acid sensing promotes INHBE expression during fasting and in obese individuals, yet it is unclear how the hepatokine activin E governs body shape and energy metabolism. Here, we uncover activin E as a regulator of adipose energy storage. By suppressing β-agonist-induced lipolysis, activin E promotes fat accumulation and adipocyte hypertrophy and contributes to adipose dysfunction in mice. Mechanistically, we demonstrate that activin E elicits its effect on adipose tissue through ACVR1C, activating SMAD2/3 signaling and suppressing PPARG target genes. Conversely, loss of activin E or ACVR1C in mice increases fat utilization, lowers adiposity, and drives PPARG-regulated gene signatures indicative of healthy adipose function. Our studies identify activin E-ACVR1C as a metabolic rheostat promoting liver-adipose cross talk to restrain excessive fat breakdown and preserve fat mass during prolonged fasting, a mechanism that is maladaptive in obese individuals.
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Affiliation(s)
| | | | | | - Yuanqi Zhao
- Regeneron Pharmaceuticals, Tarrytown, NY10591
| | | | - Soo Min
- Regeneron Pharmaceuticals, Tarrytown, NY10591
| | | | | | | | - Senem Aykul
- Regeneron Pharmaceuticals, Tarrytown, NY10591
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20
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Arruda AL, Hartley A, Katsoula G, Smith GD, Morris AP, Zeggini E. Genetic underpinning of the comorbidity between type 2 diabetes and osteoarthritis. Am J Hum Genet 2023; 110:1304-1318. [PMID: 37433298 PMCID: PMC10432145 DOI: 10.1016/j.ajhg.2023.06.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/13/2023] Open
Abstract
Multimorbidity is a rising public health challenge with important implications for health management and policy. The most common multimorbidity pattern is the combination of cardiometabolic and osteoarticular diseases. Here, we study the genetic underpinning of the comorbidity between type 2 diabetes and osteoarthritis. We find genome-wide genetic correlation between the two diseases and robust evidence for association-signal colocalization at 18 genomic regions. We integrate multi-omics and functional information to resolve the colocalizing signals and identify high-confidence effector genes, including FTO and IRX3, which provide proof-of-concept insights into the epidemiologic link between obesity and both diseases. We find enrichment for lipid metabolism and skeletal formation pathways for signals underpinning the knee and hip osteoarthritis comorbidities with type 2 diabetes, respectively. Causal inference analysis identifies complex effects of tissue-specific gene expression on comorbidity outcomes. Our findings provide insights into the biological basis for the type 2 diabetes-osteoarthritis disease co-occurrence.
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Affiliation(s)
- Ana Luiza Arruda
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Munich School of Data Science, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Technical University of Munich (TUM), School of Medicine, Graduate School of Experimental Medicine, 81675 Munich, Germany
| | - April Hartley
- MRC Integrative Epidemiology Unit, University of Bristol, BS8 2BN Bristol, UK
| | - Georgia Katsoula
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Technical University of Munich (TUM), School of Medicine, Graduate School of Experimental Medicine, 81675 Munich, Germany
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, BS8 2BN Bristol, UK
| | - Andrew P Morris
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany; Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, M13 9PT Manchester, UK
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany; TUM School of Medicine, Technical University Munich and Klinikum Rechts der Isar, 81675 Munich, Germany.
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21
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Zuber V, Lewin A, Levin MG, Haglund A, Ben-Aicha S, Emanueli C, Damrauer S, Burgess S, Gill D, Bottolo L. Multi-response Mendelian randomization: Identification of shared and distinct exposures for multimorbidity and multiple related disease outcomes. Am J Hum Genet 2023; 110:1177-1199. [PMID: 37419091 PMCID: PMC10357504 DOI: 10.1016/j.ajhg.2023.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 06/11/2023] [Accepted: 06/11/2023] [Indexed: 07/09/2023] Open
Abstract
The existing framework of Mendelian randomization (MR) infers the causal effect of one or multiple exposures on one single outcome. It is not designed to jointly model multiple outcomes, as would be necessary to detect causes of more than one outcome and would be relevant to model multimorbidity or other related disease outcomes. Here, we introduce multi-response Mendelian randomization (MR2), an MR method specifically designed for multiple outcomes to identify exposures that cause more than one outcome or, conversely, exposures that exert their effect on distinct responses. MR2 uses a sparse Bayesian Gaussian copula regression framework to detect causal effects while estimating the residual correlation between summary-level outcomes, i.e., the correlation that cannot be explained by the exposures, and vice versa. We show both theoretically and in a comprehensive simulation study how unmeasured shared pleiotropy induces residual correlation between outcomes irrespective of sample overlap. We also reveal how non-genetic factors that affect more than one outcome contribute to their correlation. We demonstrate that by accounting for residual correlation, MR2 has higher power to detect shared exposures causing more than one outcome. It also provides more accurate causal effect estimates than existing methods that ignore the dependence between related responses. Finally, we illustrate how MR2 detects shared and distinct causal exposures for five cardiovascular diseases in two applications considering cardiometabolic and lipidomic exposures and uncovers residual correlation between summary-level outcomes reflecting known relationships between cardiovascular diseases.
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Affiliation(s)
- Verena Zuber
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK; UK Dementia Research Institute, Imperial College London, London, UK.
| | - Alex Lewin
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Michael G Levin
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Corporal Michael J. Crescenz VA Medical Center, Philadelphia, USA
| | - Alexander Haglund
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - Soumaya Ben-Aicha
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Costanza Emanueli
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Scott Damrauer
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Corporal Michael J. Crescenz VA Medical Center, Philadelphia, USA
| | - Stephen Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Cardiovascular Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; Chief Scientific Advisor Office, Research and Early Development, Novo Nordisk, Copenhagen, Denmark
| | - Leonardo Bottolo
- Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Alan Turing Institute, London, UK; MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
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22
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Baptista LS, Silva KR, Jobeili L, Guillot L, Sigaudo-Roussel D. Unraveling White Adipose Tissue Heterogeneity and Obesity by Adipose Stem/Stromal Cell Biology and 3D Culture Models. Cells 2023; 12:1583. [PMID: 37371053 DOI: 10.3390/cells12121583] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/30/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023] Open
Abstract
The immune and endocrine dysfunctions of white adipose tissue are a hallmark of metabolic disorders such as obesity and type 2 diabetes. In humans, white adipose tissue comprises distinct depots broadly distributed under the skin (hypodermis) and as internal depots (visceral). Depot-specific ASCs could account for visceral and subcutaneous adipose tissue properties, by regulating adipogenesis and immunomodulation. More importantly, visceral and subcutaneous depots account for distinct contributions to obesity and its metabolic comorbidities. Recently, distinct ASCs subpopulations were also described in subcutaneous adipose tissue. Interestingly, the superficial layer closer to the dermis shows hyperplastic and angiogenic capacities, whereas the deep layer is considered as having inflammatory properties similar to visceral. The aim of this focus review is to bring the light of recent discoveries into white adipose tissue heterogeneity together with the biology of distinct ASCs subpopulations and to explore adipose tissue 3D models revealing their advantages, disadvantages, and contributions to elucidate the role of ASCs in obesity development. Recent advances in adipose tissue organoids opened an avenue of possibilities to recreate the main cellular and molecular events of obesity leading to a deep understanding of this inflammatory disease besides contributing to drug discovery. Furthermore, 3D organ-on-a-chip will add reproducibility to these adipose tissue models contributing to their translation to the pharmaceutical industry.
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Affiliation(s)
- Leandra S Baptista
- Numpex-bio, Campus UFRJ Duque de Caxias Prof Geraldo Cidade, Universidade Federal do Rio de Janeiro, Rio de Janeiro 25240005, Brazil
| | - Karina R Silva
- Laboratory of Stem Cell Research, Histology and Embryology Department, Biology Institute, State University of Rio de Janeiro, Rio de Janeiro 20550900, Brazil
- Teaching and Research Division, National Institute of Traumatology and Orthopedics, Rio de Janeiro 20940070, Brazil
| | - Lara Jobeili
- Laboratory of Tissue Biology and Therapeutic Engineering, University of Lyon, Claude Bernard University Lyon 1, CNRS, LBTI UMR 5305, 69367 Lyon, France
| | - Lucile Guillot
- Laboratory of Tissue Biology and Therapeutic Engineering, University of Lyon, Claude Bernard University Lyon 1, CNRS, LBTI UMR 5305, 69367 Lyon, France
- Urgo Research Innovation and Development, 21300 Chenôve, France
| | - Dominique Sigaudo-Roussel
- Laboratory of Tissue Biology and Therapeutic Engineering, University of Lyon, Claude Bernard University Lyon 1, CNRS, LBTI UMR 5305, 69367 Lyon, France
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23
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Wang N, Yu B, Jun G, Qi Q, Durazo-Arvizu RA, Lindstrom S, Morrison AC, Kaplan RC, Boerwinkle E, Chen H. StocSum: stochastic summary statistics for whole genome sequencing studies. bioRxiv 2023:2023.04.06.535886. [PMID: 37066281 PMCID: PMC10104122 DOI: 10.1101/2023.04.06.535886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Genomic summary statistics, usually defined as single-variant test results from genome-wide association studies, have been widely used to advance the genetics field in a wide range of applications. Applications that involve multiple genetic variants also require their correlations or linkage disequilibrium (LD) information, often obtained from an external reference panel. In practice, it is usually difficult to find suitable external reference panels that represent the LD structure for underrepresented and admixed populations, or rare genetic variants from whole genome sequencing (WGS) studies, limiting the scope of applications for genomic summary statistics. Here we introduce StocSum, a novel reference-panel-free statistical framework for generating, managing, and analyzing stochastic summary statistics using random vectors. We develop various downstream applications using StocSum including single-variant tests, conditional association tests, gene-environment interaction tests, variant set tests, as well as meta-analysis and LD score regression tools. We demonstrate the accuracy and computational efficiency of StocSum using two cohorts from the Trans-Omics for Precision Medicine Program. StocSum will facilitate sharing and utilization of genomic summary statistics from WGS studies, especially for underrepresented and admixed populations.
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Affiliation(s)
- Nannan Wang
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Bing Yu
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Goo Jun
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Qibin Qi
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Ramon A. Durazo-Arvizu
- The Saban Research Institute, Children’s Hospital Los Angeles, Los Angeles, California
- Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sara Lindstrom
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, School of Public Health, University of Washington, 3980 15th Ave NE, Seattle, WA, USA
| | - Alanna C. Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Robert C. Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
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24
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Seo MW, Lee JM, Jung HC. Prevalence of combined metabolic health and weight status by various diagnosis criteria and association with cardiometabolic disease in Korean adults. Obes Res Clin Pract 2023; 17:137-143. [PMID: 37024380 DOI: 10.1016/j.orcp.2023.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 02/20/2023] [Accepted: 03/16/2023] [Indexed: 04/08/2023]
Abstract
The purpose of this study was to compare the cardiometabolic disease prevalence and risk factors between individuals categorized as metabolically unhealthy and healthy (MU vs. MH), with normal-weight and obesity (Nw vs. Ob), according to different established criteria for combined metabolic health and weight status; and to assess the optimal metabolic health diagnostic classifications to predict cardiometabolic disease risk factors. Data were obtained from the 2019 and 2020 Korean National Health and Nutrition Examination Surveys. We applied the nine accepted metabolic health diagnostic classification criteria. Statistical analysis was applied to frequency, multiple logistic regression, and ROC curve analysis. The prevalence of MHNw ranged from 24.6% to 53.9%, MUNw from 3.7% to 37.9%, MHOb from 3.4% to 25.9%, and MUOb from 16.3% to 39.1%. For hypertension, the MUNw had an increased risk ranging from 1.90 to 3.24 times compared with MHNw; MHOb ranged from 1.84 to 3.76 times; MUOb ranged from 4.18 to 6.97 times (all p < .05). For dyslipidemia, the MUNw had an increased risk ranging from 1.33 to 2.25 times compared with MHNw; MHOb ranged from 1.47 to 2.33 times; MUOb ranged from 2.31 to 2.67 times (all p < .05). For diabetes, the MUNw had an increased risk ranging from 2.27 to 11.93 times compared with MHNW; MHOb ranged from 1.36 to 1.95 times; MUOb ranged from 3.60 to 18.45 times (all p < .05). Our study findings revealed that AHA/NHLBI-02 and NCEP-02 can be the best diagnostic classifications criteria for cardiometabolic diseases risk factors.
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Affiliation(s)
- Myong-Won Seo
- Department of Exercise Science, David B. Falk College of Sports & Human Dynamics, Syracuse University, USA
| | - Jung-Min Lee
- Sports Science Research Center, Kyung Hee University, Yongin-si, South Korea; Department of Physical Education, College of Physical Education, Kyung Hee University, Yongin-si, South Korea
| | - Hyun Chul Jung
- Sports Science Research Center, Kyung Hee University, Yongin-si, South Korea; Department of Sports Coaching, College of Physical Education, Kyung Hee University, Yongin-si, South Korea.
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25
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Gnatiuc Friedrichs L, Trichia E, Aguilar-Ramirez D, Preiss D. Metabolic profiling of MRI-measured liver fat in the UK Biobank. Obesity (Silver Spring) 2023; 31:1121-1132. [PMID: 36872307 DOI: 10.1002/oby.23687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 11/21/2022] [Accepted: 11/28/2022] [Indexed: 03/07/2023]
Abstract
OBJECTIVE Liver fat associates with obesity-related metabolic disturbances and may precede incident diseases. Metabolomic profiles of liver fat in the UK Biobank were investigated. METHODS Regression models assessed the associations between 180 metabolites and proton density liver fat fraction (PDFF) measured 5 years later through magnetic resonance imaging, as the difference (in SD units) of each log metabolite measure with 1-SD higher PDFF among those without chronic disease and not taking statins, and by diabetes and cardiovascular diseases. RESULTS After accounting for confounders, multiple metabolites were associated positively with liver fat (p < 0.0001 for 152 traits), particularly extremely large and very large lipoprotein particle concentrations, very low-density lipoprotein triglycerides, small high-density lipoprotein particles, glycoprotein acetyls, monounsaturated and saturated fatty acids, and amino acids. Extremely large and large high-density lipoprotein concentrations had strong inverse associations with liver fat. Associations were broadly comparable among those with versus without vascular metabolic conditions, although negative, rather than positive, associations were observed between intermediate-density and large low-density lipoprotein particles among those with BMI ≥25 kg/m2 , diabetes, or cardiovascular diseases. Metabolite principal components showed a 15% significant improvement in risk prediction for PDFF relative to BMI, which was twice as great (but nonsignificant) compared with conventional high-density lipoprotein cholesterol and triglycerides. CONCLUSIONS Hazardous metabolomic profiles are associated with ectopic hepatic fat and are relevant to risk of vascular-metabolic disease.
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Affiliation(s)
- Louisa Gnatiuc Friedrichs
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Eirini Trichia
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Diego Aguilar-Ramirez
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - David Preiss
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
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26
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Ruiz-Cortés M, Múzquiz-Barberá P, Herrero R, Vara MD, Escrivá-Martínez T, Carcelén R, Rodilla E, Baños RM, Lisón JF. How the Presence of a Doctor Known to Patients Impacts a Web-Based Intervention to Promote Physical Activity and Healthy Eating Behaviour in Individuals with an Overweight/Obesity–Hypertension Phenotype: A Randomised Clinical Trial. Nutrients 2023; 15:nu15071624. [PMID: 37049465 PMCID: PMC10097159 DOI: 10.3390/nu15071624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 03/22/2023] [Accepted: 03/24/2023] [Indexed: 03/30/2023] Open
Abstract
(1) Background: The ‘Living Better’ web-based programme has shown short- and long-term benefits for body composition and psychological variables in obese patients with hypertension by promoting a healthier lifestyle. To further explore the potential of this programme, in this work we aimed to explore the possible effect of the patient’s ‘own doctor’ appearing in the video content of the Living Better intervention. (2) Methods: A total of 132 patients were randomly assigned either to the experimental (EG, n = 70) or control (CG, n = 62) group (with a doctor the patient knew as ‘their own’ or an ‘unknown doctor’, respectively). The body mass index (BMI), motivation towards physical activity (PA), PA levels, motivation to change one’s eating habits, adherence to the Mediterranean diet, and eating behaviour were all assessed and compared at baseline and post-intervention (12 weeks). (3) Results: The results of this study confirmed the positive effects of the Living Better programme on BMI and external eating style, with significant improvements in these variables in both groups. In addition, in the EG there was higher intrinsic motivation to change eating behaviour (mean difference of 0.9, 95% CI [0.1, 1.6], p = 0.032) and lower amotivation (mean difference of −0.6, 95% CI [−1.2, −0.1], p = 0.027) compared to the CG. (4) Conclusions: This study suggests that the presence of the patients’ own doctor in the audiovisual content of the Living Better intervention did not have significant additional benefits in terms of BMI or external eating style. However, their presence did improve intrinsic motivation and amotivation related to eating habits.
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Affiliation(s)
- Marta Ruiz-Cortés
- Department of Biomedical Sciences, Faculty of Health Sciences, University CEU-Cardenal Herrera, CEU Universities, 46115 Valencia, Spain
| | - Pedro Múzquiz-Barberá
- Department of Nursing and Physiotherapy, Faculty of Health Sciences, University CEU-Cardenal Herrera, CEU Universities, 46115 Valencia, Spain
| | - Rocío Herrero
- Department of Psychology and Sociology, Universidad de Zaragoza, 50009 Teruel, Spain
- Centre of Physiopathology of Obesity and Nutrition (CIBERobn), CB06/03/0052, Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - María Dolores Vara
- Centre of Physiopathology of Obesity and Nutrition (CIBERobn), CB06/03/0052, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Polibienestar Research Institute, Universitat de València, 46022 Valencia, Spain
| | - Tamara Escrivá-Martínez
- Centre of Physiopathology of Obesity and Nutrition (CIBERobn), CB06/03/0052, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Polibienestar Research Institute, Universitat de València, 46022 Valencia, Spain
| | - Raquel Carcelén
- Department of Medicine and Surgery, Faculty of Health Sciences, University CEU-Cardenal Herrera, CEU Universities, 46115 Valencia, Spain
| | - Enrique Rodilla
- Department of Medicine and Surgery, Faculty of Health Sciences, University CEU-Cardenal Herrera, CEU Universities, 46115 Valencia, Spain
- Hypertension and Vascular Risk Unit, Hospital Universitario de Sagunto, 46115 Valencia, Spain
| | - Rosa María Baños
- Centre of Physiopathology of Obesity and Nutrition (CIBERobn), CB06/03/0052, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Polibienestar Research Institute, Universitat de València, 46022 Valencia, Spain
| | - Juan Francisco Lisón
- Department of Biomedical Sciences, Faculty of Health Sciences, University CEU-Cardenal Herrera, CEU Universities, 46115 Valencia, Spain
- Centre of Physiopathology of Obesity and Nutrition (CIBERobn), CB06/03/0052, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Correspondence: ; Tel.: +34-961369000 (ext. 64540)
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Huang M, Coral D, Ardalani H, Spegel P, Saadat A, Claussnitzer M, Mulder H, Franks PW, Kalamajski S. Identification of a weight loss-associated causal eQTL in MTIF3 and the effects of MTIF3 deficiency on human adipocyte function. eLife 2023; 12:84168. [PMID: 36876906 PMCID: PMC10023155 DOI: 10.7554/elife.84168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 03/05/2023] [Indexed: 03/07/2023] Open
Abstract
Genetic variation at the MTIF3 (Mitochondrial Translational Initiation Factor 3) locus has been robustly associated with obesity in humans, but the functional basis behind this association is not known. Here, we applied luciferase reporter assay to map potential functional variants in the haplotype block tagged by rs1885988 and used CRISPR-Cas9 to edit the potential functional variants to confirm the regulatory effects on MTIF3 expression. We further conducted functional studies on MTIF3-deficient differentiated human white adipocyte cell line (hWAs-iCas9), generated through inducible expression of CRISPR-Cas9 combined with delivery of synthetic MTIF3-targeting guide RNA. We demonstrate that rs67785913-centered DNA fragment (in LD with rs1885988, r2 > 0.8) enhances transcription in a luciferase reporter assay, and CRISPR-Cas9-edited rs67785913 CTCT cells show significantly higher MTIF3 expression than rs67785913 CT cells. Perturbed MTIF3 expression led to reduced mitochondrial respiration and endogenous fatty acid oxidation, as well as altered expression of mitochondrial DNA-encoded genes and proteins, and disturbed mitochondrial OXPHOS complex assembly. Furthermore, after glucose restriction, the MTIF3 knockout cells retained more triglycerides than control cells. This study demonstrates an adipocyte function-specific role of MTIF3, which originates in the maintenance of mitochondrial function, providing potential explanations for why MTIF3 genetic variation at rs67785913 is associated with body corpulence and response to weight loss interventions.
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Affiliation(s)
- Mi Huang
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Clinical Research Centre, Lund UniversityMalmöSweden
| | - Daniel Coral
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Clinical Research Centre, Lund UniversityMalmöSweden
| | - Hamidreza Ardalani
- Department of Chemistry, Centre for Analysis and Synthesis, Lund UniversityLundSweden
| | - Peter Spegel
- Department of Chemistry, Centre for Analysis and Synthesis, Lund UniversityLundSweden
| | - Alham Saadat
- Metabolism Program, Broad Institute of MIT and HarvardCambridgeUnited States
| | - Melina Claussnitzer
- Metabolism Program, Broad Institute of MIT and HarvardCambridgeUnited States
| | - Hindrik Mulder
- Unit of Molecular Metabolism, Department of Clinical Sciences, Clinical Research Centre, Lund UniversityMalmöSweden
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Clinical Research Centre, Lund UniversityMalmöSweden
- Department of Nutrition, Harvard T.H. Chan School of Public HealthBostonUnited States
| | - Sebastian Kalamajski
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Clinical Research Centre, Lund UniversityMalmöSweden
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Xing Z, Xiao B, Hu X, Chai X. Relationship Between Regional Adiposity Distribution and Incident Heart Failure in General Populations without Cardiovascular Disease. Am J Med 2023; 136:277-283.e2. [PMID: 36495933 DOI: 10.1016/j.amjmed.2022.11.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 11/15/2022] [Accepted: 11/16/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Obesity is associated with a high risk of heart failure. However, the contribution of regional fat distribution evaluated using bioimpedance analysis toward heart failure risk in the general population without cardiovascular disease has rarely been studied. METHODS This study included 483,316 participants without heart failure and cardiovascular disease from the UK Biobank study. The regional fat mass was determined by bioimpedance analysis and calculated by dividing the square of height in meters (kg/m2). This study evaluated the association of regional fat mass (arm fat index [AFI], trunk fat index [TFI], and leg fat index [LFI]) with the risk of incident heart failure and whether regional fat mass adds a further prognostic value for heart failure besides body mass index (BMI) in a large prospective cohort study. RESULTS During the median 12.1 years, 3134 incident heart failure cases occurred. After adjustment for BMI and other confounding factors, each 1-standard deviation increase in LFI was associated with a 21% lower heart failure risk even after adjusting for BMI and other confounding factors (hazard ratio [HR] 0.79; 95% confidence interval [CI], 0.73-0.85). However, we did not observe heart failure-associated risks with AFI and TFI (HR 1.04; 95% CI, 0.99-1.09; HR 0.97, 95% CI, 0.91-1.04, respectively). Subgroup analysis demonstrated that the protective role of LFI was more prominent in the elderly and female participants (P < .01). CONCLUSION Regional fat measurement other than BMI can improve heart failure risk stratification; leg fat plays a protective role, yet arm and trunk fat do not, in the general population without cardiovascular disease.
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Affiliation(s)
- Zhenhua Xing
- Department of Emergency Medicine, Second Xiangya Hospital, Central South University, Changsha, China; Emergency Medicine and Difficult Diseases Institute
| | - Bing Xiao
- Department of Emergency Medicine, Second Xiangya Hospital, Central South University, Changsha, China; Emergency Medicine and Difficult Diseases Institute
| | - Xinqun Hu
- Department of Cardiovascular Medicine, Central South University, Changsha, China
| | - Xiangping Chai
- Department of Emergency Medicine, Second Xiangya Hospital, Central South University, Changsha, China; Emergency Medicine and Difficult Diseases Institute.
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Koprulu M, Carrasco-Zanini J, Wheeler E, Lockhart S, Kerrison ND, Wareham NJ, Pietzner M, Langenberg C. Proteogenomic links to human metabolic diseases. Nat Metab 2023; 5:516-528. [PMID: 36823471 PMCID: PMC7614946 DOI: 10.1038/s42255-023-00753-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 02/01/2023] [Indexed: 02/25/2023]
Abstract
Studying the plasma proteome as the intermediate layer between the genome and the phenome has the potential to identify new disease processes. Here, we conducted a cis-focused proteogenomic analysis of 2,923 plasma proteins measured in 1,180 individuals using antibody-based assays. We (1) identify 256 unreported protein quantitative trait loci (pQTL); (2) demonstrate shared genetic regulation of 224 cis-pQTLs with 575 specific health outcomes, revealing examples for notable metabolic diseases (such as gastrin-releasing peptide as a potential therapeutic target for type 2 diabetes); (3) improve causal gene assignment at 40% (n = 192) of overlapping risk loci; and (4) observe convergence of phenotypic consequences of cis-pQTLs and rare loss-of-function gene burden for 12 proteins, such as TIMD4 for lipoprotein metabolism. Our findings demonstrate the value of integrating complementary proteomic technologies with genomics even at moderate scale to identify new mediators of metabolic diseases with the potential for therapeutic interventions.
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Affiliation(s)
- Mine Koprulu
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Julia Carrasco-Zanini
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Eleanor Wheeler
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Sam Lockhart
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
- MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Nicola D Kerrison
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
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30
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Tsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Beaton AZ, Boehme AK, Buxton AE, Commodore-Mensah Y, Elkind MSV, Evenson KR, Eze-Nliam C, Fugar S, Generoso G, Heard DG, Hiremath S, Ho JE, Kalani R, Kazi DS, Ko D, Levine DA, Liu J, Ma J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Virani SS, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Martin SS. Heart Disease and Stroke Statistics-2023 Update: A Report From the American Heart Association. Circulation 2023; 147:e93-e621. [PMID: 36695182 DOI: 10.1161/cir.0000000000001123] [Citation(s) in RCA: 828] [Impact Index Per Article: 828.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The American Heart Association, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2023 Statistical Update is the product of a full year's worth of effort in 2022 by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. The American Heart Association strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional COVID-19 (coronavirus disease 2019) publications, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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Coral DE, Fernandez-Tajes J, Tsereteli N, Pomares-Millan H, Fitipaldi H, Mutie PM, Atabaki-Pasdar N, Kalamajski S, Poveda A, Miller-Fleming TW, Zhong X, Giordano GN, Pearson ER, Cox NJ, Franks PW. A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes. Nat Metab 2023; 5:237-247. [PMID: 36703017 PMCID: PMC9970876 DOI: 10.1038/s42255-022-00731-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 12/20/2022] [Indexed: 01/27/2023]
Abstract
Obesity and type 2 diabetes are causally related, yet there is considerable heterogeneity in the consequences of both conditions and the mechanisms of action are poorly defined. Here we show a genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects. We annotate and then compare association signals for these profiles across clinical and molecular phenotypic layers. Key differences are identified in a wide range of traits, including cardiovascular mortality, fat distribution, liver metabolism, blood pressure, specific lipid fractions and blood levels of proteins involved in extracellular matrix remodelling. We find marginal differences in abundance of Bacteroidetes and Firmicutes bacteria in the gut. Instrumental analyses reveal prominent causal roles for waist-to-hip ratio, blood pressure and cholesterol content of high-density lipoprotein particles in the development of diabetes in obesity. We prioritize 17 genes from the discordant signature that convey protection against type 2 diabetes in obesity, which may represent logical targets for precision medicine approaches.
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Affiliation(s)
- Daniel E Coral
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden.
| | - Juan Fernandez-Tajes
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Neli Tsereteli
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Hugo Pomares-Millan
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Hugo Fitipaldi
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Pascal M Mutie
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Naeimeh Atabaki-Pasdar
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Sebastian Kalamajski
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Alaitz Poveda
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Tyne W Miller-Fleming
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xue Zhong
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Giuseppe N Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Ewan R Pearson
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden
- Population Health and Genomics, University of Dundee, Dundee, UK
| | - Nancy J Cox
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Skåne University Hospital, Malmö, Sweden.
- Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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Kentistou KA, Luan J, Wittemans LBL, Hambly C, Klaric L, Kutalik Z, Speakman JR, Wareham NJ, Kendall TJ, Langenberg C, Wilson JF, Joshi PK, Morton NM. Large scale phenotype imputation and in vivo functional validation implicate ADAMTS14 as an adiposity gene. Nat Commun 2023; 14:307. [PMID: 36658113 PMCID: PMC9852585 DOI: 10.1038/s41467-022-35563-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 12/09/2022] [Indexed: 01/20/2023] Open
Abstract
Obesity remains an unmet global health burden. Detrimental anatomical distribution of body fat is a major driver of obesity-mediated mortality risk and is demonstrably heritable. However, our understanding of the full genetic contribution to human adiposity is incomplete, as few studies measure adiposity directly. To address this, we impute whole-body imaging adiposity phenotypes in UK Biobank from the 4,366 directly measured participants onto the rest of the cohort, greatly increasing our discovery power. Using these imputed phenotypes in 392,535 participants yielded hundreds of genome-wide significant associations, six of which replicate in independent cohorts. The leading causal gene candidate, ADAMTS14, is further investigated in a mouse knockout model. Concordant with the human association data, the Adamts14-/- mice exhibit reduced adiposity and weight-gain under obesogenic conditions, alongside an improved metabolic rate and health. Thus, we show that phenotypic imputation at scale offers deeper biological insights into the genetics of human adiposity that could lead to therapeutic targets.
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Affiliation(s)
- Katherine A Kentistou
- Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, EH16 4TJ, UK
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, UK
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Laura B L Wittemans
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Catherine Hambly
- Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, AB24 2TZ, UK
| | - Lucija Klaric
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Zoltán Kutalik
- Centre for Primary Care and Public Health, University of Lausanne, Lausanne, 1010, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
| | - John R Speakman
- Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, AB24 2TZ, UK
- Centre for Energy Metabolism and Reproduction, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen Key Laboratory of Metabolic Health, CAS Centre of Excellence in Animal Evolution and Genetics, Kunming, China
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Timothy J Kendall
- Centre for Inflammation Research, University of Edinburgh, Edinburgh, EH16 4TJ, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, CB2 0QQ, UK
- Computational Medicine, Berlin Institute of Health (BIH) Charité University Medicine, Berlin, Germany
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, UK
| | - Nicholas M Morton
- Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, EH16 4TJ, UK.
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Honda M, Tsuboi A, Minato-Inokawa S, Takeuchi M, Kurata M, Wu B, Kazumi T, Fukuo K. Reduced gluteofemoral (subcutaneous) fat mass in young Japanese women with family history of type 2 diabetes: an exploratory analysis. Sci Rep 2022; 12:12579. [PMID: 35869280 PMCID: PMC9307820 DOI: 10.1038/s41598-022-16890-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 07/18/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractLimited expandability of subcutaneous adipose tissue may be characteristics of first-degree relatives of type 2 diabetes. We tested the hypothesis that family history of type 2 diabetes (FHD) may be associated with reduced peripheral fat mass. Body composition and metabolic variables were compared between 18 and 111 Japanese female collegiate athletes, and between 55 and 148 nonathletes with positive (FHD +) and negative FHD (FHD-), respectively. We had multivariate logistic regression analyses for FHD + as dependent variable in a total population.BMI averaged < 21 kg/m2 and did not differ between FHD + and FHD- nonathletes. Despite comparable BMI, body fat percentage and serum leptin were lower in FHD + nonathletes. This was due to lower arm and gluteofemoral fat percentage (both p = 0.02) whereas the difference in trunk fat percentage was not significant (p = 0.08). These differences were not found between two groups of athletes. FHD + women had lower HDL cholesterol despite lower BMI in a total population. Fasting insulin, serum adiponectin and high-sensitivity C-reactive protein did not differ between FHD + and FHD- athletes or nonathletes. Multivariate logistic regression analyses revealed independent associations of FHD + with BMI (odds ratio, 0.869; 95% confidential interval, 0.768–0.984; p = 0.02) and HDL cholesterol (odds ratio, 0.977; 95% confidential interval, 0.957–0.997, p = 0.02). In conclusion, FHD may be associated with reduced subcutaneous fat mass in young Japanese women, suggesting impaired adipose tissue expandability.
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Gradidge PJL, Jaff NG, Norris SA, Toman M, Crowther NJ. The negative association of lower body fat mass with cardiometabolic disease risk factors is partially mediated by adiponectin. Endocr Connect 2022; 11:e220156. [PMID: 36169024 PMCID: PMC9641776 DOI: 10.1530/ec-22-0156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 09/28/2022] [Indexed: 11/08/2022]
Abstract
Gluteofemoral fat correlates negatively with a number of cardiometabolic disease risk factors, but the mechanisms involved in these relationships are unknown. The aim of this study was to test the hypothesis that gluteofemoral fat attenuates the risk of cardiometabolic disease by increasing blood adiponectin levels. This was a cross-sectional study in which arm, leg, gluteofemoral, abdominal s.c. and visceral fat levels were measured by dual-energy X-ray absorptiometry in 648 African females. Fasting serum adiponectin, lipid, insulin and plasma glucose levels and blood pressure were measured. Relationships between variables were analysed using multivariable linear regression and structural equation modelling. Adiponectin correlated positively (β = 0.45, P < 0.0001) with gluteofemoral fat in a multivariable regression model that included age, height, and arm, s.c. and visceral fat levels. In further regression models, there was a negative correlation of gluteofemoral fat with fasting glucose (β = -0.28; P < 0.0001) and triglyceride levels (β = -0.29; P < 0.0001) and insulin resistance (HOMA; β = -0.26; P < 0.0001). Structural equation modelling demonstrated that adiponectin mediated 20.7% (P < 0.01) of the association of gluteofemoral fat with insulin resistance and 16.1% (P < 0.01) of the association with triglyceride levels but only 6.67% (P = 0.31) of the association with glucose levels. These results demonstrate that gluteofemoral and leg fat are positively associated with adiponectin levels and that the negative association of lower body fat with insulin resistance and triglyceride levels may partially be mediated by this adipokine. Further studies are required to determine other factors that mediate the effect of lower body fat on cardiometabolic disease risk factors.
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Affiliation(s)
- Philippe Jean-Luc Gradidge
- Centre for Exercise Science and Sports Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Nicole G Jaff
- Department of Chemical Pathology, National Health Laboratory Service, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Shane A Norris
- SAMRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Global Health Research Institute, School of Human Development and Health, University of Southampton, Southampton, UK
| | - Marketa Toman
- Department of Chemical Pathology, National Health Laboratory Service, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Nigel J Crowther
- Department of Chemical Pathology, National Health Laboratory Service, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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Ke J, Gao W, Wang B, Cao W, Lv J, Yu C, Huang T, Sun D, Liao C, Pang Y, Pang Z, Cong L, Wang H, Wu X, Liu Y, Li L. Exploring the Genetic Association between Obesity and Serum Lipid Levels Using Bivariate Methods. Twin Res Hum Genet 2022; 25:234-44. [PMID: 36606461 DOI: 10.1017/thg.2022.39] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
It is crucial to understand the genetic mechanisms and biological pathways underlying the relationship between obesity and serum lipid levels. Structural equation models (SEMs) were constructed to calculate heritability for body mass index (BMI), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and the genetic connections between BMI and the four classes of lipids using 1197 pairs of twins from the Chinese National Twin Registry (CNTR). Bivariate genomewide association studies (GWAS) were performed to identify genetic variants associated with BMI and lipids using the records of 457 individuals, and the results were further validated in 289 individuals. The genetic background affecting BMI may differ by gender, and the heritability of males and females was 71% (95% CI [.66, .75]) and 39% (95% CI [.15, .71]) respectively. BMI was positively correlated with TC, TG and LDL-C in phenotypic and genetic correlation, while negatively correlated with HDL-C. There were gender differences in the correlation between BMI and lipids. Bivariate GWAS analysis and validation stage found 7 genes (LOC105378740, LINC02506, CSMD1, MELK, FAM81A, ERAL1 and MIR144) that were possibly related to BMI and lipid levels. The significant biological pathways were the regulation of cholesterol reverse transport and the regulation of high-density lipoprotein particle clearance (p < .001). BMI and blood lipid levels were affected by genetic factors, and they were genetically correlated. There might be gender differences in their genetic correlation. Bivariate GWAS analysis found MIR144 gene and its related biological pathways may influence obesity and lipid levels.
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Abstract
Type 2 diabetes accounts for nearly 90% of the approximately 537 million cases of diabetes worldwide. The number affected is increasing rapidly with alarming trends in children and young adults (up to age 40 years). Early detection and proactive management are crucial for prevention and mitigation of microvascular and macrovascular complications and mortality burden. Access to novel therapies improves person-centred outcomes beyond glycaemic control. Precision medicine, including multiomics and pharmacogenomics, hold promise to enhance understanding of disease heterogeneity, leading to targeted therapies. Technology might improve outcomes, but its potential is yet to be realised. Despite advances, substantial barriers to changing the course of the epidemic remain. This Seminar offers a clinically focused review of the recent developments in type 2 diabetes care including controversies and future directions.
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Affiliation(s)
- Ehtasham Ahmad
- Diabetes Research Centre, University of Leicester and the Leicester NIHR Biomedical Research Centre, Leicester General Hospital, Leicester, UK
| | - Soo Lim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea
| | - Roberta Lamptey
- Family Medicine Department, Korle Bu Teaching Hospital, Accra Ghana and Community Health Department, University of Ghana Medical School, Accra, Ghana
| | - David R Webb
- Diabetes Research Centre, University of Leicester and the Leicester NIHR Biomedical Research Centre, Leicester General Hospital, Leicester, UK
| | - Melanie J Davies
- Diabetes Research Centre, University of Leicester and the Leicester NIHR Biomedical Research Centre, Leicester General Hospital, Leicester, UK.
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Carrasco-Zanini J, Pietzner M, Lindbohm JV, Wheeler E, Oerton E, Kerrison N, Simpson M, Westacott M, Drolet D, Kivimaki M, Ostroff R, Williams SA, Wareham NJ, Langenberg C. Proteomic signatures for identification of impaired glucose tolerance. Nat Med 2022; 28:2293-2300. [PMID: 36357677 PMCID: PMC7614638 DOI: 10.1038/s41591-022-02055-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 09/27/2022] [Indexed: 11/12/2022]
Abstract
The implementation of recommendations for type 2 diabetes (T2D) screening and diagnosis focuses on the measurement of glycated hemoglobin (HbA1c) and fasting glucose. This approach leaves a large number of individuals with isolated impaired glucose tolerance (iIGT), who are only detectable through oral glucose tolerance tests (OGTTs), at risk of diabetes and its severe complications. We applied machine learning to the proteomic profiles of a single fasted sample from 11,546 participants of the Fenland study to test discrimination of iIGT defined using the gold-standard OGTTs. We observed significantly improved discriminative performance by adding only three proteins (RTN4R, CBPM and GHR) to the best clinical model (AUROC = 0.80 (95% confidence interval: 0.79-0.86), P = 0.004), which we validated in an external cohort. Increased plasma levels of these candidate proteins were associated with an increased risk for future T2D in an independent cohort and were also increased in individuals genetically susceptible to impaired glucose homeostasis and T2D. Assessment of a limited number of proteins can identify individuals likely to be missed by current diagnostic strategies and at high risk of T2D and its complications.
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Affiliation(s)
- Julia Carrasco-Zanini
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Maik Pietzner
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Joni V Lindbohm
- Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland
- Department of Epidemiology and Public Health, University College London, London, UK
- The Klarman Cell Observatory, Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Eleanor Wheeler
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Erin Oerton
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Nicola Kerrison
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | | | | | | | - Mika Kivimaki
- Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland
- Department of Epidemiology and Public Health, University College London, London, UK
| | | | | | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
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Boulet N, Briot A, Galitzky J, Bouloumié A. The Sexual Dimorphism of Human Adipose Depots. Biomedicines 2022; 10:2615. [PMID: 36289874 PMCID: PMC9599294 DOI: 10.3390/biomedicines10102615] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 10/11/2022] [Accepted: 10/13/2022] [Indexed: 08/21/2023] Open
Abstract
The amount and the distribution of body fat exhibit trajectories that are sex- and human species-specific and both are determinants for health. The enhanced accumulation of fat in the truncal part of the body as a risk factor for cardiovascular and metabolic diseases is well supported by epidemiological studies. In addition, a possible independent protective role of the gluteofemoral fat compartment and of the brown adipose tissue is emerging. The present narrative review summarizes the current knowledge on sexual dimorphism in fat depot amount and repartition and consequences on cardiometabolic and reproductive health. The drivers of the sex differences and fat depot repartition, considered to be the results of complex interactions between sex determination pathways determined by the sex chromosome composition, genetic variability, sex hormones and the environment, are discussed. Finally, the inter- and intra-depot heterogeneity in adipocytes and progenitors, emphasized recently by unbiased large-scale approaches, is highlighted.
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Affiliation(s)
| | | | | | - Anne Bouloumié
- Inserm, Unité Mixte de Recherche (UMR) 1297, Team 1, Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Université de Toulouse, F-31432 Toulouse, France
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Bell JA, Richardson TG, Wang Q, Sanderson E, Palmer T, Walker V, O'Keeffe LM, Timpson NJ, Cichonska A, Julkunen H, Würtz P, Holmes MV, Davey Smith G. Effects of general and central adiposity on circulating lipoprotein, lipid, and metabolite levels in UK Biobank: A multivariable Mendelian randomization study. Lancet Reg Health Eur 2022; 21:100457. [PMID: 35832062 PMCID: PMC9272390 DOI: 10.1016/j.lanepe.2022.100457] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Background The direct effects of general adiposity (body mass index (BMI)) and central adiposity (waist-to-hip-ratio (WHR)) on circulating lipoproteins, lipids, and metabolites are unknown. Methods We used new metabolic data from UK Biobank (N=109,532, a five-fold higher N over previous studies). EDTA-plasma was used to quantify 249 traits with nuclear-magnetic-resonance spectroscopy including subclass-specific lipoprotein concentrations and lipid content, plus pre-glycemic and inflammatory metabolites. We used univariable and multivariable two-stage least-squares regression models with genetic risk scores for BMI and WHR as instruments to estimate total (unadjusted) and direct (mutually-adjusted) effects of BMI and WHR on metabolic traits; plus effects on statin use and interaction by sex, statin use, and age (proxy for medication use). Findings Higher BMI decreased apolipoprotein B and low-density lipoprotein cholesterol (LDL-C) before and after WHR-adjustment, whilst BMI increased triglycerides only before WHR-adjustment. These effects of WHR were larger and BMI-independent. Direct effects differed markedly by sex, e.g., triglycerides increased only with BMI among men, and only with WHR among women. Adiposity measures increased statin use and showed metabolic effects which differed by statin use and age. Among the youngest (38-53y, statins-5%), BMI and WHR (per-SD) increased LDL-C (total effects: 0.04-SD, 95%CI=-0.01,0.08 and 0.10-SD, 95%CI=0.02,0.17 respectively), but only WHR directly. Among the oldest (63-73y, statins-29%), BMI and WHR directly lowered LDL-C (-0.19-SD, 95%CI=-0.27,-0.11 and -0.05-SD, 95%CI=-0.16,0.06 respectively). Interpretation Excess adiposity likely raises atherogenic lipid and metabolite levels exclusively via adiposity stored centrally, particularly among women. Apparent effects of adiposity on lowering LDL-C are likely explained by an effect of adiposity on statin use. Funding UK Medical Research Council; British Heart Foundation; Novo Nordisk; National Institute for Health Research; Wellcome Trust; Cancer Research UK.
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Affiliation(s)
- Joshua A. Bell
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom G. Richardson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Novo Nordisk Research Centre Oxford, Old Road Campus, Oxford, UK
| | - Qin Wang
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Eleanor Sanderson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom Palmer
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Venexia Walker
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Linda M. O'Keeffe
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- School of Public Health, Western Gateway Building, University College Cork, Ireland
| | - Nicholas J. Timpson
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | | | | | - Michael V. Holmes
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit at the University of Oxford, Oxford, UK
- National Institute for Health Research, Oxford Biomedical Research Centre, Oxford University Hospital, Oxford, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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Brettle H, Tran V, Drummond GR, Franks AE, Petrovski S, Vinh A, Jelinic M. Sex hormones, intestinal inflammation, and the gut microbiome: Major influencers of the sexual dimorphisms in obesity. Front Immunol 2022; 13:971048. [PMID: 36248832 PMCID: PMC9554749 DOI: 10.3389/fimmu.2022.971048] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 08/16/2022] [Indexed: 11/13/2022] Open
Abstract
Obesity is defined as the excessive accumulation of body fat and is associated with an increased risk of developing major health problems such as cardiovascular disease, diabetes and stroke. There are clear sexual dimorphisms in the epidemiology, pathophysiology and sequelae of obesity and its accompanying metabolic disorders, with females often better protected compared to males. This protection has predominantly been attributed to the female sex hormone estrogen and differences in fat distribution. More recently, the sexual dimorphisms of obesity have also been attributed to the differences in the composition and function of the gut microbiota, and the intestinal immune system. This review will comprehensively summarize the pre-clinical and clinical evidence for these sexual dimorphisms and discuss the interplay between sex hormones, intestinal inflammation and the gut microbiome in obesity. Major gaps and limitations of this rapidly growing area of research will also be highlighted in this review.
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Affiliation(s)
- Holly Brettle
- Centre for Cardiovascular Biology and Disease Research, Department of Microbiology, Anatomy Physiology and Pharmacology, School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, VIC, Australia
| | - Vivian Tran
- Centre for Cardiovascular Biology and Disease Research, Department of Microbiology, Anatomy Physiology and Pharmacology, School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, VIC, Australia
| | - Grant R. Drummond
- Centre for Cardiovascular Biology and Disease Research, Department of Microbiology, Anatomy Physiology and Pharmacology, School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, VIC, Australia
| | - Ashley E. Franks
- Department of Microbiology, Anatomy Physiology and Pharmacology, School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, VIC, Australia
| | - Steve Petrovski
- Centre for Cardiovascular Biology and Disease Research, Department of Microbiology, Anatomy Physiology and Pharmacology, School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, VIC, Australia
| | - Antony Vinh
- Centre for Cardiovascular Biology and Disease Research, Department of Microbiology, Anatomy Physiology and Pharmacology, School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, VIC, Australia
| | - Maria Jelinic
- Centre for Cardiovascular Biology and Disease Research, Department of Microbiology, Anatomy Physiology and Pharmacology, School of Agriculture, Biomedicine and Environment, La Trobe University, Bundoora, VIC, Australia
- *Correspondence: Maria Jelinic,
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Lim P, Bleich D. Revisiting cardiovascular risk reduction in type 2 diabetes and dyslipidemia. International Journal of Cardiology Cardiovascular Risk and Prevention 2022; 14:200141. [PMID: 36060284 PMCID: PMC9434405 DOI: 10.1016/j.ijcrp.2022.200141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/10/2022] [Accepted: 06/16/2022] [Indexed: 11/25/2022]
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Nguyen A, Khafagy R, Meerasa A, Roshandel D, Paterson AD, Dash S. Insulin Response to Oral Glucose and Cardiometabolic Disease: A Mendelian Randomization Study to Assess Potential Causality. Diabetes 2022; 71:1880-1890. [PMID: 35748295 DOI: 10.2337/db22-0138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022]
Abstract
Mendelian randomization (MR) suggests that postprandial hyperinsulinemia (unadjusted for plasma glucose) increases BMI, but its impact on cardiometabolic disease, a leading cause for mortality and morbidity in people with obesity, is not established. Fat distribution i.e., increased centripetal and/or reduced femoro-gluteal adiposity, is causally associated with and better predicts cardiometabolic disease than BMI. We therefore undertook bidirectional MR to assess the effect of corrected insulin response (CIR) (insulin 30 min after a glucose challenge adjusted for plasma glucose) on BMI, waist-to-hip ratio (WHR), leg fat, type 2 diabetes (T2D), triglyceride (TG), HDL, liver fat, hypertension (HTN), and coronary artery disease (CAD) in people of European descent. Inverse variance-weighted MR suggests a potential causal association between increased CIR and increased BMI (b = 0.048 ± 0.02, P = 0.03), increased leg fat (b = 0.029 ± 0.012, P = 0.01), reduced T2D (b = -0.73 ± 0.15, P = 6 × 10-7, odds ratio [OR] 0.48 [95% CI 0.36-0.64]), reduced TG (b = -0.07 ± 0.02, P = 0.003), and increased HDL (b = 0.04 ± 0.01, P = 0.006) with some evidence of horizontal pleiotropy. CIR had neutral effects on WHR (b = 0.009 ± 0.02, P = 0.69), liver fat (b = -0.08 ± 0.04, P = 0.06), HTN (b = -0.001 ± 0.004, P = 0.7, OR 1.00 [95% CI 0.99-1.01]), and CAD (b = -0.002 ± 0.002, P = 0.48, OR 0.99 [95% CI 0.81-1.21]). T2D decreased CIR (b -0.22 ± 0.04, P = 1.3 × 10-7), with no evidence that BMI, TG, HDL, liver fat, HTN, and CAD modulate CIR. In conclusion, we did not find evidence that increased CIR increases cardiometabolic disease. It might increase BMI with favorable fat distribution, reduce T2D, and improve lipids.
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Affiliation(s)
- Anthony Nguyen
- Department of Medicine, University Health Network, and University of Toronto, Toronto, Canada
| | - Rana Khafagy
- Department of Medicine, University Health Network, and University of Toronto, Toronto, Canada
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada
- Divisions of Epidemiology and Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Ameena Meerasa
- Department of Medicine, University Health Network, and University of Toronto, Toronto, Canada
| | - Delnaz Roshandel
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada
| | - Andrew D Paterson
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Canada
- Divisions of Epidemiology and Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Satya Dash
- Department of Medicine, University Health Network, and University of Toronto, Toronto, Canada
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Cao H, Zhao H, Shen L. Depression increased risk of coronary heart disease: A meta-analysis of prospective cohort studies. Front Cardiovasc Med 2022; 9:913888. [PMID: 36110417 PMCID: PMC9468274 DOI: 10.3389/fcvm.2022.913888] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 08/08/2022] [Indexed: 11/18/2022] Open
Abstract
Background Depression, as an independent risk factor, can lead to a substantially increased risk of coronary heart disease (CHD). The overall body of evidence involving depression and CHD is not consistent. Therefore, we performed an update meta-analysis to evaluate the association between depression and the risk of patients with CHD. Methods Studies were identified through a comprehensive literature search of the PubMed, Embase, and the Cochrane Library database from its inception to 28 September 2021 for titles/abstracts with restricted to English language articles. The literature was screened according to the inclusion and exclusion criteria. Along with data extraction, we evaluated the quality of eligible studies using the Newcastle-Ottawa Scale (NOS). The primary outcome was fatal or non-fatal CHD. We calculated relative risk (RR) with 95% confidence intervals (CIs) using a random-effects models. The protocol was registered in the PROSPERO registration (registration number CRD42021271259). Results From 9,151 records, we included 26 prospective cohort studies published from 1998 to 2018, consisting of 402,597 patients. Either in depression-exposured group or non-depression-exposured group, the mean age of all participants ranged from 18 to 99 years. Moreover, the NOS scores of these studies are eventually indicated that the quality of these eligible studies was reliable. In general, the pooled results showed that patients with depression had a higher risk of CHD compared to patients without depression (RR = 1.21, 95% CI: 1.14–1.29). Additionally, the funnel plot appeared to be asymmetry, indicating there existing publication bias for the pooled results between depression and CHD. A sensitivity analysis was used to assess the stability of the relationship between depression and CHD that indicating the results robust (RR = 1.15, 95% CI: 1.09–1.21). Conclusion Depression may increase risk of CHD. Future studies on the share pathogenic mechanisms of both depression and CHD may develop novel therapies.
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Affiliation(s)
- Hongfu Cao
- Gulou Hospital of Traditional Chinese Medicine of Beijing, Beijing, China
| | - Hui Zhao
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Li Shen
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
- *Correspondence: Li Shen,
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Akbari P, Sosina OA, Bovijn J, Landheer K, Nielsen JB, Kim M, Aykul S, De T, Haas ME, Hindy G, Lin N, Dinsmore IR, Luo JZ, Hectors S, Geraghty B, Germino M, Panagis L, Parasoglou P, Walls JR, Halasz G, Atwal GS, Jones M, LeBlanc MG, Still CD, Carey DJ, Giontella A, Orho-Melander M, Berumen J, Kuri-Morales P, Alegre-Díaz J, Torres JM, Emberson JR, Collins R, Rader DJ, Zambrowicz B, Murphy AJ, Balasubramanian S, Overton JD, Reid JG, Shuldiner AR, Cantor M, Abecasis GR, Ferreira MAR, Sleeman MW, Gusarova V, Altarejos J, Harris C, Economides AN, Idone V, Karalis K, Della Gatta G, Mirshahi T, Yancopoulos GD, Melander O, Marchini J, Tapia-Conyer R, Locke AE, Baras A, Verweij N, Lotta LA. Multiancestry exome sequencing reveals INHBE mutations associated with favorable fat distribution and protection from diabetes. Nat Commun 2022; 13:4844. [PMID: 35999217 PMCID: PMC9399235 DOI: 10.1038/s41467-022-32398-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 07/28/2022] [Indexed: 12/13/2022] Open
Abstract
Body fat distribution is a major, heritable risk factor for cardiometabolic disease, independent of overall adiposity. Using exome-sequencing in 618,375 individuals (including 160,058 non-Europeans) from the UK, Sweden and Mexico, we identify 16 genes associated with fat distribution at exome-wide significance. We show 6-fold larger effect for fat-distribution associated rare coding variants compared with fine-mapped common alleles, enrichment for genes expressed in adipose tissue and causal genes for partial lipodystrophies, and evidence of sex-dimorphism. We describe an association with favorable fat distribution (p = 1.8 × 10-09), favorable metabolic profile and protection from type 2 diabetes (~28% lower odds; p = 0.004) for heterozygous protein-truncating mutations in INHBE, which encodes a circulating growth factor of the activin family, highly and specifically expressed in hepatocytes. Our results suggest that inhibin βE is a liver-expressed negative regulator of adipose storage whose blockade may be beneficial in fat distribution-associated metabolic disease.
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Affiliation(s)
- Parsa Akbari
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Olukayode A. Sosina
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Jonas Bovijn
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Karl Landheer
- grid.418961.30000 0004 0472 2713Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Jonas B. Nielsen
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Minhee Kim
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Senem Aykul
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Tanima De
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Mary E. Haas
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - George Hindy
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Nan Lin
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Ian R. Dinsmore
- grid.280776.c0000 0004 0394 1447Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA USA
| | - Jonathan Z. Luo
- grid.280776.c0000 0004 0394 1447Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA USA
| | - Stefanie Hectors
- grid.418961.30000 0004 0472 2713Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Benjamin Geraghty
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Mary Germino
- grid.418961.30000 0004 0472 2713Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Lampros Panagis
- grid.418961.30000 0004 0472 2713Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Prodromos Parasoglou
- grid.418961.30000 0004 0472 2713Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Johnathon R. Walls
- grid.418961.30000 0004 0472 2713Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Gabor Halasz
- grid.418961.30000 0004 0472 2713Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Gurinder S. Atwal
- grid.418961.30000 0004 0472 2713Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | | | | | - Marcus Jones
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Michelle G. LeBlanc
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Christopher D. Still
- grid.280776.c0000 0004 0394 1447Geisinger Obesity Institute, Geisinger Health System, Danville, PA USA
| | - David J. Carey
- grid.280776.c0000 0004 0394 1447Geisinger Obesity Institute, Geisinger Health System, Danville, PA USA
| | - Alice Giontella
- grid.4514.40000 0001 0930 2361Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden ,grid.5611.30000 0004 1763 1124Department of Medicine, University of Verona, Verona, Italy
| | - Marju Orho-Melander
- grid.4514.40000 0001 0930 2361Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | - Jaime Berumen
- grid.9486.30000 0001 2159 0001Unidad de Medicina Experimental de la Facultad de Medicina de la Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Pablo Kuri-Morales
- grid.9486.30000 0001 2159 0001Unidad de Medicina Experimental de la Facultad de Medicina de la Universidad Nacional Autónoma de México, Mexico City, Mexico ,grid.419886.a0000 0001 2203 4701Instituto Tecnológico y de Estudios Superiores de Monterrey, Monterrey, Mexico
| | - Jesus Alegre-Díaz
- grid.9486.30000 0001 2159 0001Unidad de Medicina Experimental de la Facultad de Medicina de la Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - Jason M. Torres
- grid.4991.50000 0004 1936 8948MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK ,grid.4991.50000 0004 1936 8948Clinical Trial Service Unit & Epidemiological Studies Unit Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jonathan R. Emberson
- grid.4991.50000 0004 1936 8948MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK ,grid.4991.50000 0004 1936 8948Clinical Trial Service Unit & Epidemiological Studies Unit Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Rory Collins
- grid.4991.50000 0004 1936 8948Clinical Trial Service Unit & Epidemiological Studies Unit Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Daniel J. Rader
- grid.25879.310000 0004 1936 8972Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Brian Zambrowicz
- grid.418961.30000 0004 0472 2713Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Andrew J. Murphy
- grid.418961.30000 0004 0472 2713Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Suganthi Balasubramanian
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - John D. Overton
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Jeffrey G. Reid
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Alan R. Shuldiner
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Michael Cantor
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Goncalo R. Abecasis
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Manuel A. R. Ferreira
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Mark W. Sleeman
- grid.418961.30000 0004 0472 2713Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Viktoria Gusarova
- grid.418961.30000 0004 0472 2713Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Judith Altarejos
- grid.418961.30000 0004 0472 2713Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Charles Harris
- grid.418961.30000 0004 0472 2713Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Aris N. Economides
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA ,grid.418961.30000 0004 0472 2713Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Vincent Idone
- grid.418961.30000 0004 0472 2713Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Katia Karalis
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Giusy Della Gatta
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Tooraj Mirshahi
- grid.280776.c0000 0004 0394 1447Geisinger Obesity Institute, Geisinger Health System, Danville, PA USA
| | | | - Olle Melander
- grid.4514.40000 0001 0930 2361Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden ,grid.411843.b0000 0004 0623 9987Department of Emergency and Internal Medicine, Skåne University Hospital, Malmö, Sweden
| | - Jonathan Marchini
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Roberto Tapia-Conyer
- grid.419886.a0000 0001 2203 4701Instituto Tecnológico y de Estudios Superiores de Monterrey, Monterrey, Mexico
| | - Adam E. Locke
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Aris Baras
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA.
| | - Niek Verweij
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
| | - Luca A. Lotta
- grid.418961.30000 0004 0472 2713Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY USA
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45
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Li H, Konja D, Wang L, Wang Y. Sex Differences in Adiposity and Cardiovascular Diseases. Int J Mol Sci 2022; 23:ijms23169338. [PMID: 36012601 PMCID: PMC9409326 DOI: 10.3390/ijms23169338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/11/2022] [Accepted: 08/17/2022] [Indexed: 11/16/2022] Open
Abstract
Body fat distribution is a well-established predictor of adverse medical outcomes, independent of overall adiposity. Studying body fat distribution sheds insights into the causes of obesity and provides valuable information about the development of various comorbidities. Compared to total adiposity, body fat distribution is more closely associated with risks of cardiovascular diseases. The present review specifically focuses on the sexual dimorphism in body fat distribution, the biological clues, as well as the genetic traits that are distinct from overall obesity. Understanding the sex determinations on body fat distribution and adiposity will aid in the improvement of the prevention and treatment of cardiovascular diseases (CVD).
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46
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Alalwani J, Eljazzar S, Basil M, Tayyem R. The impact of health status, diet and lifestyle on non-alcoholic fatty liver disease: Narrative review. Clin Obes 2022; 12:e12525. [PMID: 35412016 DOI: 10.1111/cob.12525] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 03/30/2022] [Accepted: 03/31/2022] [Indexed: 12/13/2022]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is defined as the abnormal accumulation of triglycerides in the liver. NAFLD has a global prevalence of almost 30%, while incidence is rising with increasing levels of obesity, type 2 diabetes mellitus (T2DM) and metabolic syndrome. Nutrition plays a significant role in both the prevention and treatment of NAFLD. Therefore, the aim of this literature review is to explore the associations between dietary, lifestyle and other risk factors and the risk for developing NAFLD. Dietary patterns, lifestyle behaviours, comorbidities, or a combination of any may contribute to either the progression or prevention of NAFLD. Having diabetes, hypertension, or having obesity might increase the progression of NAFLD if not well treated and controlled. Diet influences the progression of NAFLD; following a western diet or simply a high-fat diet may contribute to the worsening of NAFLD and further progression to non-alcoholic steatohepatitis (NASH) and cirrhosis in later stages. On the other hand, the Mediterranean diet is the gold standard for both the treatment and prevention of NAFLD. Social behaviours, such as smoking, caffeine consumption and physical activity also play a role in the pathophysiology of NAFLD. Nutrition contributes significantly to the prevention or treatment of NAFLD, since this disease can be managed by diet and physical activity. However, further studies are still needed for a better understanding of the mechanisms of action. Randomized control trials are also needed to confirm findings in observational studies.
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Affiliation(s)
- Joud Alalwani
- Human Nutrition Department, College of Health Sciences, Qatar University, Doha, Qatar
| | - Sereen Eljazzar
- Human Nutrition Department, College of Health Sciences, Qatar University, Doha, Qatar
| | - Maya Basil
- Human Nutrition Department, College of Health Sciences, Qatar University, Doha, Qatar
| | - Reema Tayyem
- Human Nutrition Department, College of Health Sciences, Qatar University, Doha, Qatar
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47
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Kuo FC, Neville MJ, Sabaratnam R, Wesolowska-Andersen A, Phillips D, Wittemans LBL, van Dam AD, Loh NY, Todorčević M, Denton N, Kentistou KA, Joshi PK, Christodoulides C, Langenberg C, Collas P, Karpe F, Pinnick KE. HOTAIR interacts with PRC2 complex regulating the regional preadipocyte transcriptome and human fat distribution. Cell Rep 2022; 40:111136. [PMID: 35905723 PMCID: PMC10073411 DOI: 10.1016/j.celrep.2022.111136] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 05/06/2022] [Accepted: 07/01/2022] [Indexed: 12/12/2022] Open
Abstract
Mechanisms governing regional human adipose tissue (AT) development remain undefined. Here, we show that the long non-coding RNA HOTAIR (HOX transcript antisense RNA) is exclusively expressed in gluteofemoral AT, where it is essential for adipocyte development. We find that HOTAIR interacts with polycomb repressive complex 2 (PRC2) and we identify core HOTAIR-PRC2 target genes involved in adipocyte lineage determination. Repression of target genes coincides with PRC2 promoter occupancy and H3K27 trimethylation. HOTAIR is also involved in modifying the gluteal adipocyte transcriptome through alternative splicing. Gluteal-specific expression of HOTAIR is maintained by defined regions of open chromatin across the HOTAIR promoter. HOTAIR expression levels can be modified by hormonal (estrogen, glucocorticoids) and genetic variation (rs1443512 is a HOTAIR eQTL associated with reduced gynoid fat mass). These data identify HOTAIR as a dynamic regulator of the gluteal adipocyte transcriptome and epigenome with functional importance for human regional AT development.
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Affiliation(s)
- Feng-Chih Kuo
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK; Division of Endocrinology and Metabolism, Department of Internal Medicine, Tri-Service General Hospital, National Defence Medical Centre, Taipei, Taiwan
| | - Matt J Neville
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK; NIHR Oxford Biomedical Research Centre, OUH Foundation Trust, Oxford, UK
| | - Rugivan Sabaratnam
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK; Institute of Clinical Research, University of Southern Denmark, 5000 Odense C, Denmark; Steno Diabetes Center Odense, Odense University Hospital, 5000 Odense C, Denmark
| | | | - Daniel Phillips
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK
| | - Laura B L Wittemans
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK; The Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Andrea D van Dam
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK
| | - Nellie Y Loh
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK
| | - Marijana Todorčević
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK
| | - Nathan Denton
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK
| | - Katherine A Kentistou
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, UK; Centre for Cardiovascular Sciences, Queen's Medical Research Institute, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, UK
| | - Constantinos Christodoulides
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Philippe Collas
- Department of Molecular Medicine, Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway; Department of Immunology, Oslo University Hospital, Oslo, Norway
| | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK; NIHR Oxford Biomedical Research Centre, OUH Foundation Trust, Oxford, UK.
| | - Katherine E Pinnick
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Churchill Hospital, Headington OX3 7LE, UK.
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48
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Morillas Blasco P, Gómez Moreno S, Febles Palenzuela T, Pallarés Carratalá V. Approach to Patients with Obesity and Other Cardiovascular Risk Factors in Primary Care Using the Delphi Methodology. J Clin Med 2022; 11:4130. [PMID: 35887894 DOI: 10.3390/jcm11144130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 07/07/2022] [Accepted: 07/13/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Implementing preventive strategies for patients with obesity would improve the future burden of cardiovascular diseases. The objective was to present the opinions of experts on the approach to treating patients with obesity and other cardiovascular risk factors from a primary care perspective in Spain; Methods: Using the Delphi technique, a 42-question questionnaire was developed based on results from the scientific literature, and sent to 42 experts in primary care. Two rounds of participation were held; Results: There is a close relationship between obesity and cardiovascular risk factors among primary care physicians. It is necessary to use a checklist in primary care that includes metabolic parameters such as body mass index, waist circumference, and levels of C-reactive protein and ferritin. It is also useful to combine pharmacological treatment, such as liraglutide, with a change in lifestyle to achieve therapeutic goals in this population; Conclusions: There is a high level of awareness among experts in Spain regarding obesity and other cardiovascular risk factors, and the need to address this pathology comprehensively. The need to incorporate specific tools in primary care consultations that allow for better assessment and follow-up of these patients, such as cuffs adapted to arm size or imaging techniques to assess body fat, is evident. Teleconsultation is imposed as a helpful tool for follow-up. Experts recommend that patients with obesity and associated comorbidities modify their lifestyle, incorporate a Mediterranean diet, and administer liraglutide.
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49
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Agrawal S, Wang M, Klarqvist MDR, Smith K, Shin J, Dashti H, Diamant N, Choi SH, Jurgens SJ, Ellinor PT, Philippakis A, Claussnitzer M, Ng K, Udler MS, Batra P, Khera AV. Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots. Nat Commun 2022; 13:3771. [PMID: 35773277 PMCID: PMC9247093 DOI: 10.1038/s41467-022-30931-2] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 05/25/2022] [Indexed: 12/11/2022] Open
Abstract
For any given level of overall adiposity, individuals vary considerably in fat distribution. The inherited basis of fat distribution in the general population is not fully understood. Here, we study up to 38,965 UK Biobank participants with MRI-derived visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes. Because these fat depot volumes are highly correlated with BMI, we additionally study six local adiposity traits: VAT adjusted for BMI and height (VATadj), ASATadj, GFATadj, VAT/ASAT, VAT/GFAT, and ASAT/GFAT. We identify 250 independent common variants (39 newly-identified) associated with at least one trait, with many associations more pronounced in female participants. Rare variant association studies extend prior evidence for PDE3B as an important modulator of fat distribution. Local adiposity traits (1) highlight depot-specific genetic architecture and (2) enable construction of depot-specific polygenic scores that have divergent associations with type 2 diabetes and coronary artery disease. These results - using MRI-derived, BMI-independent measures of local adiposity - confirm fat distribution as a highly heritable trait with important implications for cardiometabolic health outcomes.
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Affiliation(s)
- Saaket Agrawal
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Minxian Wang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | | | - Kirk Smith
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Joseph Shin
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Hesam Dashti
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nathaniel Diamant
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Seung Hoan Choi
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Sean J Jurgens
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Patrick T Ellinor
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Anthony Philippakis
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Melina Claussnitzer
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kenney Ng
- Center for Computational Health, IBM Research, Cambridge, MA, USA
| | - Miriam S Udler
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amit V Khera
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- Verve Therapeutics, Cambridge, MA, USA.
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50
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Shan B, Barker CS, Shao M, Zhang Q, Gupta RK, Wu Y. Multilayered omics reveal sex- and depot-dependent adipose progenitor cell heterogeneity. Cell Metab 2022; 34:783-799.e7. [PMID: 35447091 PMCID: PMC9986218 DOI: 10.1016/j.cmet.2022.03.012] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 01/17/2022] [Accepted: 03/28/2022] [Indexed: 01/25/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) has revealed that adult white adipose tissue (WAT) harbors functionally diverse subpopulations of mesenchymal stromal cells that differentially impact tissue plasticity. To date, the molecular basis of this cellular heterogeneity has not been fully defined. Here, we describe a multilayered omics approach to dissect adipose progenitor cell heterogeneity in three dimensions: progenitor subpopulation, sex, and anatomical localization. We applied state-of-the-art mass spectrometry methods to quantify 4,870 proteins in eight different stromal cell populations from perigonadal and inguinal WAT of male and female mice and acquired transcript expression levels of 15,477 genes using RNA-seq. Our data unveil molecular signatures defining sex differences in preadipocyte differentiation and identify regulatory pathways that functionally distinguish adipose progenitor subpopulations. This multilayered omics analysis, freely accessible at http://preadprofiler.net/, provides unprecedented insights into adipose stromal cell heterogeneity and highlights the benefit of complementary proteomics to support findings from scRNA-seq studies.
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Affiliation(s)
- Bo Shan
- Touchstone Diabetes Center, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Clive S Barker
- YCI Laboratory for Next-Generation Proteomics, RIKEN Center of Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
| | - Mengle Shao
- Touchstone Diabetes Center, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Qianbin Zhang
- Touchstone Diabetes Center, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Rana K Gupta
- Touchstone Diabetes Center, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Yibo Wu
- YCI Laboratory for Next-Generation Proteomics, RIKEN Center of Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
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