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Dashti HS, Scheer FAJL, Saxena R, Garaulet M. Impact of polygenic score for BMI on weight loss effectiveness and genome-wide association analysis. Int J Obes (Lond) 2024; 48:694-701. [PMID: 38267484 DOI: 10.1038/s41366-024-01470-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 01/05/2024] [Accepted: 01/12/2024] [Indexed: 01/26/2024]
Abstract
BACKGROUND While environmental factors play an important role in weight loss effectiveness, genetics may also influence its success. We examined whether a genome-wide polygenic score for BMI was associated with weight loss effectiveness and aimed to identify common genetic variants associated with weight loss. METHODS Participants in the ONTIME study (n = 1210) followed a uniform, multimodal behavioral weight-loss intervention. We first tested associations between a genome-wide polygenic score for higher BMI and weight loss effectiveness (total weight loss, rate of weight loss, and attrition). We then conducted a genome-wide association study (GWAS) for weight loss in the ONTIME study and performed the largest weight loss meta-analysis with earlier studies (n = 3056). Lastly, we ran exploratory GWAS in the ONTIME study for other weight loss outcomes and related factors. RESULTS We found that each standard deviation increment in the polygenic score was associated with a decrease in the rate of weight loss (Beta (95% CI) = -0.04 kg per week (-0.06, -0.01); P = 3.7 × 10-03) and with higher attrition after adjusting by treatment duration. No associations reached genome-wide significance in meta-analysis with previous GWAS studies for weight loss. However, associations in the ONTIME study showed effects consistent with published studies for rs545936 (MIR486/NKX6.3/ANK1), a previously noted weight loss locus. In the meta-analysis, each copy of the minor A allele was associated with 0.12 (0.03) kg/m2 higher BMI at week five of treatment (P = 3.9 × 10-06). In the ONTIME study, we also identified two genome-wide significant (P < 5×10-08) loci for the rate of weight loss near genes implicated in lipolysis, body weight, and metabolic regulation: rs146905606 near NFIP1/SPRY4/FGF1; and rs151313458 near LSAMP. CONCLUSION Our findings are expected to help in developing personalized weight loss approaches based on genetics. CLINICAL TRIAL REGISTRATION Obesity, Nutrigenetics, Timing, and Mediterranean (ONTIME; clinicaltrials.gov: NCT02829619) study.
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Affiliation(s)
- Hassan S Dashti
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Broad Institute, Cambridge, MA, USA.
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.
| | - Frank A J L Scheer
- Broad Institute, Cambridge, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Richa Saxena
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Marta Garaulet
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women's Hospital, Boston, MA, USA.
- Department of Physiology, Regional Campus of International Excellence, University of Murcia, 30100, Murcia, Spain.
- Biomedical Research Institute of Murcia, IMIB-Arrixaca-UMU, University Clinical Hospital, 30120, Murcia, Spain.
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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] [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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3
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Pierantozzi E, Raucci L, Buonocore S, Rubino EM, Ding Q, Laurino A, Fiore F, Soldaini M, Chen J, Rossi D, Vangheluwe P, Chen H, Sorrentino V. Skeletal muscle overexpression of sAnk1.5 in transgenic mice does not predispose to type 2 diabetes. Sci Rep 2023; 13:8195. [PMID: 37210436 PMCID: PMC10199891 DOI: 10.1038/s41598-023-35393-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 05/17/2023] [Indexed: 05/22/2023] Open
Abstract
Genome-wide association studies (GWAS) and cis-expression quantitative trait locus (cis-eQTL) analyses indicated an association of the rs508419 single nucleotide polymorphism (SNP) with type 2 diabetes (T2D). rs508419 is localized in the muscle-specific internal promoter (P2) of the ANK1 gene, which drives the expression of the sAnk1.5 isoform. Functional studies showed that the rs508419 C/C variant results in increased transcriptional activity of the P2 promoter, leading to higher levels of sAnk1.5 mRNA and protein in skeletal muscle biopsies of individuals carrying the C/C genotype. To investigate whether sAnk1.5 overexpression in skeletal muscle might predispose to T2D development, we generated transgenic mice (TgsAnk1.5/+) in which the sAnk1.5 coding sequence was selectively overexpressed in skeletal muscle tissue. TgsAnk1.5/+ mice expressed up to 50% as much sAnk1.5 protein as wild-type (WT) muscles, mirroring the difference reported between individuals with the C/C or T/T genotype at rs508419. However, fasting glucose levels, glucose tolerance, insulin levels and insulin response in TgsAnk1.5/+ mice did not differ from those of age-matched WT mice monitored over a 12-month period. Even when fed a high-fat diet, TgsAnk1.5/+ mice only presented increased caloric intake, but glucose disposal, insulin tolerance and weight gain were comparable to those of WT mice fed a similar diet. Altogether, these data indicate that sAnk1.5 overexpression in skeletal muscle does not predispose mice to T2D susceptibility.
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Affiliation(s)
- E Pierantozzi
- Department of Molecular and Developmental Medicine, University of Siena, 53100, Siena, Italy
| | - L Raucci
- Department of Molecular and Developmental Medicine, University of Siena, 53100, Siena, Italy
| | - S Buonocore
- Department of Molecular and Developmental Medicine, University of Siena, 53100, Siena, Italy
| | - E M Rubino
- Department of Molecular and Developmental Medicine, University of Siena, 53100, Siena, Italy
| | - Q Ding
- Department of Cardio-Thoracic Surgery, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, 210008, Jiangsu, China
| | - A Laurino
- Department of Molecular and Developmental Medicine, University of Siena, 53100, Siena, Italy
| | - F Fiore
- Department of Molecular and Developmental Medicine, University of Siena, 53100, Siena, Italy
| | - M Soldaini
- Department of Molecular and Developmental Medicine, University of Siena, 53100, Siena, Italy
| | - J Chen
- Laboratory of Cellular Transport Systems, Department of Cellular and Molecular Medicine, Katholieke Universiteit Leuven (KU Leuven), 3000, Leuven, Belgium
| | - D Rossi
- Department of Molecular and Developmental Medicine, University of Siena, 53100, Siena, Italy
- Interdepartmental Program of Molecular Diagnosis and Pathogenetic Mechanisms of Rare Genetic Diseases, Azienda Ospedaliera Universitaria Senese, 53100, Siena, Italy
| | - P Vangheluwe
- Laboratory of Cellular Transport Systems, Department of Cellular and Molecular Medicine, Katholieke Universiteit Leuven (KU Leuven), 3000, Leuven, Belgium
| | - H Chen
- Department of Cardio-Thoracic Surgery, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, 210008, Jiangsu, China
- Programme in Cardiovascular and Metabolic Disorders, Duke-NUS Medical School, 8 College Road, Singapore, 169857, Singapore
| | - V Sorrentino
- Department of Molecular and Developmental Medicine, University of Siena, 53100, Siena, Italy.
- Interdepartmental Program of Molecular Diagnosis and Pathogenetic Mechanisms of Rare Genetic Diseases, Azienda Ospedaliera Universitaria Senese, 53100, Siena, Italy.
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4
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Lai ML, Li AQ, Senior AM, Neely GG, Simpson SJ, Wang QP. Nutritional geometry framework of sleep. Life Sci 2023; 316:121381. [PMID: 36640899 DOI: 10.1016/j.lfs.2023.121381] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 12/30/2022] [Accepted: 01/07/2023] [Indexed: 01/13/2023]
Abstract
AIMS Sleep is a fundamental physiological function and is essential for all animals. Sleep is affected by diet compositions including protein (P) and carbohydrates (C), but there has not been a systematic investigation on the effect of dietary macronutrient balance on sleep. MAIN METHODS We used the nutritional geometry framework (NGF) to explore the interactive effects on sleep of protein (P) and carbohydrates (C) in the model organism Drosophila. Both female and male flies were fed various diets containing seven ratios of protein-to-carbohydrates at different energetic levels for 5 days and sleep was monitored by the Drosophila Activity Monitor (DAM) system. KEY FINDINGS Our results showed that the combination of low protein and high carbohydrates (LPHC) prolonged sleep time and sleep quality, with fewer sleep episodes and longer sleep duration. We further found that the effects of macronutrients on sleep mirrored levels of hemolymph glucose and whole-body glycogen. Moreover, transcriptomic analyses revealed that a high-protein, low-carbohydrate (HPLC) diet significantly elevated the gene expression of metabolic pathways when compared to the LPHC diet, with the glycine, serine, and threonine metabolism pathway being most strongly elevated. Further studies confirmed that the contents of glycine, serine, and threonine affected sleep. SIGNIFICANCE Our results demonstrate that sleep is affected by the dietary balance of protein and carbohydrates possibly mediated by the change in glucose, glycogen, glycine, serine, and threonine.
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Affiliation(s)
- Mei-Ling Lai
- Laboratory of Metabolism and Aging, School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen 518107, China
| | - An-Qi Li
- Laboratory of Metabolism and Aging, School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen 518107, China
| | - Alistair M Senior
- Charles Perkins Centre and School of Life & Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - G Gregory Neely
- The Dr. John and Anne Chong Laboratory for Functional Genomics, Charles Perkins Centre and School of Life & Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Stephen J Simpson
- Charles Perkins Centre and School of Life & Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Qiao-Ping Wang
- Laboratory of Metabolism and Aging, School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen 518107, China.
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5
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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: 1181] [Impact Index Per Article: 1181.0] [Reference Citation Analysis] [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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6
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dSec16 Acting in Insulin-like Peptide Producing Cells Controls Energy Homeostasis in Drosophila. LIFE (BASEL, SWITZERLAND) 2022; 13:life13010081. [PMID: 36676030 PMCID: PMC9862641 DOI: 10.3390/life13010081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 11/29/2022] [Accepted: 12/08/2022] [Indexed: 12/29/2022]
Abstract
Many studies show that genetics play a major contribution to the onset of obesity. Human genome-wide association studies (GWASs) have identified hundreds of genes that are associated with obesity. However, the majority of them have not been functionally validated. SEC16B has been identified in multiple obesity GWASs but its physiological role in energy homeostasis remains unknown. Here, we use Drosophila to determine the physiological functions of dSec16 in energy metabolism. Our results showed that global RNAi of dSec16 increased food intake and triglyceride (TAG) levels. Furthermore, this TAG increase was observed in flies with a specific RNAi of dSec16 in insulin-like peptide producing cells (IPCs) with an alteration of endocrine peptides. Together, our study demonstrates that dSec16 acting in IPCs controls energy balance and advances the molecular understanding of obesity.
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7
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Li X, Qi L. Epigenetics in Precision Nutrition. J Pers Med 2022; 12:jpm12040533. [PMID: 35455649 PMCID: PMC9027461 DOI: 10.3390/jpm12040533] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 03/14/2022] [Accepted: 03/24/2022] [Indexed: 02/01/2023] Open
Abstract
Precision nutrition is an emerging area of nutrition research, with primary focus on the individual variability in response to dietary and lifestyle factors, which are mainly determined by an individual’s intrinsic variations, such as those in genome, epigenome, and gut microbiome. The current research on precision nutrition is heavily focused on genome and gut microbiome, while epigenome (DNA methylation, non-coding RNAs, and histone modification) is largely neglected. The epigenome acts as the interface between the human genome and environmental stressors, including diets and lifestyle. Increasing evidence has suggested that epigenetic modifications, particularly DNA methylation, may determine the individual variability in metabolic health and response to dietary and lifestyle factors and, therefore, hold great promise in discovering novel markers for precision nutrition and potential targets for precision interventions. This review summarized recent studies on DNA methylation with obesity, diabetes, and cardiovascular disease, with more emphasis put in the relations of DNA methylation with nutrition and diet/lifestyle interventions. We also briefly reviewed other epigenetic events, such as non-coding RNAs, in relation to human health and nutrition, and discussed the potential role of epigenetics in the precision nutrition research.
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Affiliation(s)
- Xiang Li
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA;
| | - Lu Qi
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70112, USA;
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Correspondence: ; Tel.: +1-504-988-7259
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Antoine D, Guéant-Rodriguez RM, Chèvre JC, Hergalant S, Sharma T, Li Z, Rouyer P, Chery C, Halvick S, Bui C, Oussalah A, Ziegler O, Quilliot D, Brunaud L, Guéant JL, Meyre D. Low-frequency Coding Variants Associated With Body Mass Index Affect the Success of Bariatric Surgery. J Clin Endocrinol Metab 2022; 107:e1074-e1084. [PMID: 34718599 DOI: 10.1210/clinem/dgab774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Indexed: 11/19/2022]
Abstract
CONTEXT A recent study identified 14 low-frequency coding variants associated with body mass index (BMI) in 718 734 individuals predominantly of European ancestry. OBJECTIVE We investigated the association of 2 genetic scores (GS) with i) the risk of severe/morbid obesity, ii) BMI variation before weight-loss intervention, iii) BMI change in response to an 18-month lifestyle/behavioral intervention program, and iv) BMI change up to 24 months after bariatric surgery. METHODS The 14 low-frequency coding variants were genotyped or sequenced in 342 French adults with severe/morbid obesity and 574 French adult controls from the general population. We built risk and protective GS based on 6 BMI-increasing and 5 BMI-decreasing low-frequency coding variants that were polymorphic in our study. RESULTS While the risk GS was not associated with severe/morbid obesity status, BMI-decreasing low-frequency coding variants were significantly less frequent in patients with severe/morbid obesity than in French adults from the general population. Neither the risk nor the protective GS was associated with BMI before intervention in patients with severe/morbid obesity, nor did they affect BMI change in response to a lifestyle/behavioral modification program. The protective GS was associated with a greater BMI decrease following bariatric surgery. The risk and protective GS were associated with a higher and lower risk of BMI regain after bariatric surgery. CONCLUSION Our data indicate that in populations of European descent, low-frequency coding variants associated with BMI in the general population also affect the outcomes of bariatric surgery in patients with severe/morbid obesity.
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Affiliation(s)
- Darlène Antoine
- Inserm UMR_S1256 Nutrition-Genetics-Environmental Risk Exposure, University of Lorraine, 54500 Nancy, France
- FHU ARRIMAGE, department of Biochemistry-Molecular Biology-Nutrition, University Hospital Centre of Nancy, 54500 Nancy, France
| | - Rosa-Maria Guéant-Rodriguez
- Inserm UMR_S1256 Nutrition-Genetics-Environmental Risk Exposure, University of Lorraine, 54500 Nancy, France
- FHU ARRIMAGE, department of Biochemistry-Molecular Biology-Nutrition, University Hospital Centre of Nancy, 54500 Nancy, France
| | - Jean-Claude Chèvre
- Inserm UMR_S1256 Nutrition-Genetics-Environmental Risk Exposure, University of Lorraine, 54500 Nancy, France
- FHU ARRIMAGE, department of Biochemistry-Molecular Biology-Nutrition, University Hospital Centre of Nancy, 54500 Nancy, France
| | - Sébastien Hergalant
- Inserm UMR_S1256 Nutrition-Genetics-Environmental Risk Exposure, University of Lorraine, 54500 Nancy, France
- FHU ARRIMAGE, department of Biochemistry-Molecular Biology-Nutrition, University Hospital Centre of Nancy, 54500 Nancy, France
| | - Tanmay Sharma
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario L8S 4L8, Canada
| | - Zhen Li
- Inserm UMR_S1256 Nutrition-Genetics-Environmental Risk Exposure, University of Lorraine, 54500 Nancy, France
- FHU ARRIMAGE, department of Biochemistry-Molecular Biology-Nutrition, University Hospital Centre of Nancy, 54500 Nancy, France
- Specialized Obesity Center and Endocrinology, Diabetology, department of Nutrition, Brabois Hospital, CHRU of Nancy, 54500 Vandoeuvre-Les-Nancy, France
| | - Pierre Rouyer
- Inserm UMR_S1256 Nutrition-Genetics-Environmental Risk Exposure, University of Lorraine, 54500 Nancy, France
- FHU ARRIMAGE, department of Biochemistry-Molecular Biology-Nutrition, University Hospital Centre of Nancy, 54500 Nancy, France
| | - Céline Chery
- Inserm UMR_S1256 Nutrition-Genetics-Environmental Risk Exposure, University of Lorraine, 54500 Nancy, France
- FHU ARRIMAGE, department of Biochemistry-Molecular Biology-Nutrition, University Hospital Centre of Nancy, 54500 Nancy, France
| | - Sarah Halvick
- Inserm UMR_S1256 Nutrition-Genetics-Environmental Risk Exposure, University of Lorraine, 54500 Nancy, France
- FHU ARRIMAGE, department of Biochemistry-Molecular Biology-Nutrition, University Hospital Centre of Nancy, 54500 Nancy, France
| | - Catherine Bui
- Inserm UMR_S1256 Nutrition-Genetics-Environmental Risk Exposure, University of Lorraine, 54500 Nancy, France
- FHU ARRIMAGE, department of Biochemistry-Molecular Biology-Nutrition, University Hospital Centre of Nancy, 54500 Nancy, France
| | - Abderrahim Oussalah
- Inserm UMR_S1256 Nutrition-Genetics-Environmental Risk Exposure, University of Lorraine, 54500 Nancy, France
- FHU ARRIMAGE, department of Biochemistry-Molecular Biology-Nutrition, University Hospital Centre of Nancy, 54500 Nancy, France
| | - Olivier Ziegler
- Inserm UMR_S1256 Nutrition-Genetics-Environmental Risk Exposure, University of Lorraine, 54500 Nancy, France
- FHU ARRIMAGE, department of Biochemistry-Molecular Biology-Nutrition, University Hospital Centre of Nancy, 54500 Nancy, France
- Specialized Obesity Center and Endocrinology, Diabetology, department of Nutrition, Brabois Hospital, CHRU of Nancy, 54500 Vandoeuvre-Les-Nancy, France
- Department of Surgery, Endocrine and metabolic surgery, Multidisciplinary unit for obesity surgery (CVMC), University Hospital Centre of Nancy, Brabois Hospital, 54500 Nancy, France
| | - Didier Quilliot
- Inserm UMR_S1256 Nutrition-Genetics-Environmental Risk Exposure, University of Lorraine, 54500 Nancy, France
- FHU ARRIMAGE, department of Biochemistry-Molecular Biology-Nutrition, University Hospital Centre of Nancy, 54500 Nancy, France
- Specialized Obesity Center and Endocrinology, Diabetology, department of Nutrition, Brabois Hospital, CHRU of Nancy, 54500 Vandoeuvre-Les-Nancy, France
- Department of Surgery, Endocrine and metabolic surgery, Multidisciplinary unit for obesity surgery (CVMC), University Hospital Centre of Nancy, Brabois Hospital, 54500 Nancy, France
| | - Laurent Brunaud
- Inserm UMR_S1256 Nutrition-Genetics-Environmental Risk Exposure, University of Lorraine, 54500 Nancy, France
- FHU ARRIMAGE, department of Biochemistry-Molecular Biology-Nutrition, University Hospital Centre of Nancy, 54500 Nancy, France
- Department of Surgery, Endocrine and metabolic surgery, Multidisciplinary unit for obesity surgery (CVMC), University Hospital Centre of Nancy, Brabois Hospital, 54500 Nancy, France
| | - Jean-Louis Guéant
- Inserm UMR_S1256 Nutrition-Genetics-Environmental Risk Exposure, University of Lorraine, 54500 Nancy, France
- FHU ARRIMAGE, department of Biochemistry-Molecular Biology-Nutrition, University Hospital Centre of Nancy, 54500 Nancy, France
| | - David Meyre
- Inserm UMR_S1256 Nutrition-Genetics-Environmental Risk Exposure, University of Lorraine, 54500 Nancy, France
- FHU ARRIMAGE, department of Biochemistry-Molecular Biology-Nutrition, University Hospital Centre of Nancy, 54500 Nancy, France
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario L8S 4L8, Canada
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Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, Boehme AK, Buxton AE, Carson AP, Commodore-Mensah Y, Elkind MSV, Evenson KR, Eze-Nliam C, Ferguson JF, Generoso G, Ho JE, Kalani R, Khan SS, Kissela BM, Knutson KL, Levine DA, Lewis TT, Liu J, Loop MS, Ma J, Mussolino ME, Navaneethan SD, Perak AM, Poudel R, Rezk-Hanna M, Roth GA, Schroeder EB, Shah SH, Thacker EL, VanWagner LB, Virani SS, Voecks JH, Wang NY, Yaffe K, Martin SS. Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association. Circulation 2022; 145:e153-e639. [PMID: 35078371 DOI: 10.1161/cir.0000000000001052] [Citation(s) in RCA: 2397] [Impact Index Per Article: 1198.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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 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. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy. 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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10
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Moraes KCM, Montagne J. Drosophila melanogaster: A Powerful Tiny Animal Model for the Study of Metabolic Hepatic Diseases. Front Physiol 2021; 12:728407. [PMID: 34603083 PMCID: PMC8481879 DOI: 10.3389/fphys.2021.728407] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 08/27/2021] [Indexed: 12/25/2022] Open
Abstract
Animal experimentation is limited by unethical procedures, time-consuming protocols, and high cost. Thus, the development of innovative approaches for disease treatment based on alternative models in a fast, safe, and economic manner is an important, yet challenging goal. In this paradigm, the fruit-fly Drosophila melanogaster has become a powerful model for biomedical research, considering its short life cycle and low-cost maintenance. In addition, biological processes are conserved and homologs of ∼75% of human disease-related genes are found in the fruit-fly. Therefore, this model has been used in innovative approaches to evaluate and validate the functional activities of candidate molecules identified via in vitro large-scale analyses, as putative agents to treat or reverse pathological conditions. In this context, Drosophila offers a powerful alternative to investigate the molecular aspects of liver diseases, since no effective therapies are available for those pathologies. Non-alcoholic fatty liver disease is the most common form of chronic hepatic dysfunctions, which may progress to the development of chronic hepatitis and ultimately to cirrhosis, thereby increasing the risk for hepatocellular carcinoma (HCC). This deleterious situation reinforces the use of the Drosophila model to accelerate functional research aimed at deciphering the mechanisms that sustain the disease. In this short review, we illustrate the relevance of using the fruit-fly to address aspects of liver pathologies to contribute to the biomedical area.
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Affiliation(s)
- Karen C M Moraes
- Laboratório de Sinalização Celular e Expressão Gênica, Departamento de Biologia Geral e Aplicada, Instituto de Biociências, UNESP, Rio Claro, Brazil
| | - Jacques Montagne
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Saclay, Gif-sur-Yvette, France
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11
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Gasmi A, Mujawdiya PK, Noor S, Piscopo S, Menzel A. Lifestyle Genetics-Based Reports in the Treatment of Obesity. ARCHIVES OF RAZI INSTITUTE 2021; 76:707-719. [PMID: 35096307 PMCID: PMC8790989 DOI: 10.22092/ari.2021.356057.1768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 10/12/2021] [Indexed: 10/07/2022]
Abstract
Obesity becomes a chronic disease due to the increasing number of mortality and morbidity cases around the world. In most regions, chronic illnesses, such as obesity, are important sources of morbidity and mortality. Due to a lack of effective strategies for prevention and management, the adverse effects of obesity and related diseases on health continue to be a serious problem. Relevant information was searched from Google Scholar, Scopus, and PubMed using such different terms as "Obesity", "Obesity Management", "Obesity AND Physical activity", "Obesity AND Genetics", "Obesity AND Diet", and "Obesity AND Nutrigenomics". Obesity is characterized by a complex interaction of hereditary and lifestyle factors, which includes food. Diet is an environmental element that plays an important and considerable role in the management of health and reduces the risk of obesity and its comorbidities. Changes in lifestyle patterns not only help burn extra calories but also prevent the development of obesity via its modulating effect on genetic factors. Different people respond differently to an obesogenic environment. The notion of nutrigenetics emerged as a result of various genetic variations that may explain this heterogeneity. Nutritional genomics, also known as nutrigenetics, is the study that investigates and analyses gene variations linked to varied responses to certain foods; moreover, it links this variation to diseases, such as obesity. As a result, tailored nutrition advice based on a person's genetic profile may improve the outcomes of a specific dietary strategy and offer a novel dietary strategy to improve life quality and preventing obesity. This study concluded that physical activity and dietary interventions play an effective role in the management of obesity. Moreover, understanding of the function of the most prominent obesity-related genes, as well as the interaction between nutrition and gene expression, will help researchers design personalized treatment strategies for humans.
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Affiliation(s)
- A Gasmi
- Société Francophone de Nutrithérapie et de Nutrigénétique Appliquée, Villeurbanne, France
| | - P K Mujawdiya
- Inochi Care Private Limited, New Delhi-110017, India
| | - S Noor
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University Multan, Pakistan
| | - S Piscopo
- Société Francophone de Nutrithérapie et de Nutrigénétique Appliquée, Villeurbanne, France
- Research and Development Department, Nutri-Logics SA, Weiswampach, Luxembourg
| | - A Menzel
- Laboratoires Réunis, Junglinster, Luxembourg
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12
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Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Cheng S, Delling FN, Elkind MSV, Evenson KR, Ferguson JF, Gupta DK, Khan SS, Kissela BM, Knutson KL, Lee CD, Lewis TT, Liu J, Loop MS, Lutsey PL, Ma J, Mackey J, Martin SS, Matchar DB, Mussolino ME, Navaneethan SD, Perak AM, Roth GA, Samad Z, Satou GM, Schroeder EB, Shah SH, Shay CM, Stokes A, VanWagner LB, Wang NY, Tsao CW. Heart Disease and Stroke Statistics-2021 Update: A Report From the American Heart Association. Circulation 2021; 143:e254-e743. [PMID: 33501848 DOI: 10.1161/cir.0000000000000950] [Citation(s) in RCA: 3040] [Impact Index Per Article: 1013.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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 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. The 2021 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors related to cardiovascular disease. RESULTS Each of the 27 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, policy makers, 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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13
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Dent R, McPherson R, Harper ME. Factors affecting weight loss variability in obesity. Metabolism 2020; 113:154388. [PMID: 33035570 DOI: 10.1016/j.metabol.2020.154388] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 09/19/2020] [Accepted: 09/23/2020] [Indexed: 12/25/2022]
Abstract
Current obesity treatment strategies include diet, exercise, bariatric surgery, and a limited but growing repertoire of medications. Individual weight loss in response to each of these strategies is highly variable. Here we review research into factors potentially contributing to inter-individual variability in response to treatments for obesity, with a focus on studies in humans. Well-recognized factors associated with weight loss capacity include diet adherence, physical activity, sex, age, and specific medications. However, following control for each of these, differences in weight loss appear to persist in response to behavioral, pharmacological and surgical interventions. Adaptation to energy deficit involves complex feedback mechanisms, and inter-individual differences likely to arise from a host of poorly defined genetic factors, as well as differential responses in neurohormonal mechanisms (including gastrointestinal peptides), metabolic efficiency and capacity of tissues, non-exercise activity thermogenesis, thermogenic response to food, and in gut microbiome. A better understanding of the factors involved in inter-individual variability in response to therapies will guide more personalized approaches to the treatment of obesity.
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Affiliation(s)
- Robert Dent
- Department of Medicine, Division of Endocrinology and The Ottawa Hospital, University of Ottawa, 210 Melrose Ave, Ottawa, ON K1Y 4K7, Canada
| | - Ruth McPherson
- Atherogenomics Laboratory, Division of Cardiology, University of Ottawa Heart Institute, 40 Ruskin St., Ottawa, ON K1Y 4W7, Canada
| | - Mary-Ellen Harper
- Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, Ottawa Institute of Systems Biology, University of Ottawa, 451 Smyth Rd., Ottawa, ON K1H 8M5, Canada.
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14
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Gkouskou K, Vlastos I, Karkalousos P, Chaniotis D, Sanoudou D, Eliopoulos AG. The "Virtual Digital Twins" Concept in Precision Nutrition. Adv Nutr 2020; 11:1405-1413. [PMID: 32770212 PMCID: PMC7666894 DOI: 10.1093/advances/nmaa089] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 06/15/2020] [Accepted: 07/06/2020] [Indexed: 12/13/2022] Open
Abstract
Nutritional and lifestyle changes remain at the core of healthy aging and disease prevention. Accumulating evidence underscores the impact of genetic, metabolic, and host gut microbial factors on individual responses to nutrients, paving the way for the stratification of nutritional guidelines. However, technological advances that incorporate biological, nutritional, lifestyle, and health data at an unprecedented scale and depth conceptualize a future where preventative dietary interventions will exceed stratification and will be highly individualized. We herein discuss how genetic information combined with longitudinal metabolomic, immune, behavioral, and gut microbial parameters, and bioclinical variables could define a digital replica of oneself, a "virtual digital twin," which could serve to guide nutrition in a personalized manner. Such a model may revolutionize the management of obesity and its comorbidities, and provide a pillar for healthy aging.
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Affiliation(s)
| | - Ioannis Vlastos
- Department of Biology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Petros Karkalousos
- Department of Biomedical Sciences, University of West Attica, Athens, Greece
| | - Dimitrios Chaniotis
- Department of Biomedical Sciences, University of West Attica, Athens, Greece
| | - Despina Sanoudou
- Clinical Genomics and Pharmacogenomics Unit, 4th Department of Internal Medicine, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece,Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece,Center of Basic Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
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15
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Valsesia A, Chakrabarti A, Hager J, Langin D, Saris WHM, Astrup A, Blaak EE, Viguerie N, Masoodi M. Integrative phenotyping of glycemic responders upon clinical weight loss using multi-omics. Sci Rep 2020; 10:9236. [PMID: 32514005 PMCID: PMC7280519 DOI: 10.1038/s41598-020-65936-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 05/11/2020] [Indexed: 12/18/2022] Open
Abstract
Weight loss aims to improve glycemic control in obese but strong variability is observed. Using a multi-omics approach, we investigated differences between 174 responders and 201 non-responders, that had lost >8% body weight following a low-caloric diet (LCD, 800 kcal/d for 8 weeks). The two groups were comparable at baseline for body composition, glycemic control, adipose tissue transcriptomics and plasma ketone bodies. But they differed significantly in their response to LCD, including improvements in visceral fat, overall insulin resistance (IR) and tissue-specific IR. Transcriptomics analyses found down-regulation in key lipogenic genes (e.g. SCD, ELOVL5) in responders relative to non-responders; metabolomics showed increase in ketone bodies; while proteomics revealed differences in lipoproteins. Findings were consistent between genders; with women displaying smaller improvements owing to a better baseline metabolic condition. Integrative analyses identified a plasma omics model that was able to predict non-responders with strong performance (on a testing dataset, the Receiving Operating Curve Area Under the Curve (ROC AUC) was 75% with 95% Confidence Intervals (CI) [67%, 83%]). This model was based on baseline parameters without the need for intrusive measurements and outperformed clinical models (p = 0.00075, with a +14% difference on the ROC AUCs). Our approach document differences between responders and non-responders, with strong contributions from liver and adipose tissues. Differences may be due to de novo lipogenesis, keto-metabolism and lipoprotein metabolism. These findings are useful for clinical practice to better characterize non-responders both prior and during weight loss.
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Affiliation(s)
| | | | - Jörg Hager
- Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | - Dominique Langin
- INSERM, UMR 1048, Institute of Metabolic and Cardiovascular Diseases, Toulouse, France.,University of Toulouse, Paul Sabatier University, Toulouse, France.,Toulouse University Hospitals, Laboratory of Clinical Biochemistry, Toulouse, France
| | - Wim H M Saris
- Department of Human Biology, NUTRIM, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+(MUMC+), Maastricht, The Netherlands
| | - Arne Astrup
- University of Copenhagen, Department of Nutrition, Exercise and Sports, Faculty of Science, Copenhagen, Denmark
| | - Ellen E Blaak
- Department of Human Biology, NUTRIM, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+(MUMC+), Maastricht, The Netherlands
| | - Nathalie Viguerie
- INSERM, UMR 1048, Institute of Metabolic and Cardiovascular Diseases, Toulouse, France
| | - Mojgan Masoodi
- Nestlé Institute of Health Sciences, Lausanne, Switzerland. .,Institute of Clinical Chemistry, Inselspital, Bern University Hospital, Bern, Switzerland.
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16
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A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma. PLoS Comput Biol 2020; 16:e1007882. [PMID: 32492067 PMCID: PMC7295243 DOI: 10.1371/journal.pcbi.1007882] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 06/15/2020] [Accepted: 04/16/2020] [Indexed: 11/19/2022] Open
Abstract
Molecular quantitative trait locus (QTL) analyses are increasingly popular to explore the genetic architecture of complex traits, but existing studies do not leverage shared regulatory patterns and suffer from a large multiplicity burden, which hampers the detection of weak signals such as trans associations. Here, we present a fully multivariate proteomic QTL (pQTL) analysis performed with our recently proposed Bayesian method LOCUS on data from two clinical cohorts, with plasma protein levels quantified by mass-spectrometry and aptamer-based assays. Our two-stage study identifies 136 pQTL associations in the first cohort, of which >80% replicate in the second independent cohort and have significant enrichment with functional genomic elements and disease risk loci. Moreover, 78% of the pQTLs whose protein abundance was quantified by both proteomic techniques are confirmed across assays. Our thorough comparisons with standard univariate QTL mapping on (1) these data and (2) synthetic data emulating the real data show how LOCUS borrows strength across correlated protein levels and markers on a genome-wide scale to effectively increase statistical power. Notably, 15% of the pQTLs uncovered by LOCUS would be missed by the univariate approach, including several trans and pleiotropic hits with successful independent validation. Finally, the analysis of extensive clinical data from the two cohorts indicates that the genetically-driven proteins identified by LOCUS are enriched in associations with low-grade inflammation, insulin resistance and dyslipidemia and might therefore act as endophenotypes for metabolic diseases. While considerations on the clinical role of the pQTLs are beyond the scope of our work, these findings generate useful hypotheses to be explored in future research; all results are accessible online from our searchable database. Thanks to its efficient variational Bayes implementation, LOCUS can analyze jointly thousands of traits and millions of markers. Its applicability goes beyond pQTL studies, opening new perspectives for large-scale genome-wide association and QTL analyses. Diet, Obesity and Genes (DiOGenes) trial registration number: NCT00390637. Exploring the functional mechanisms between the genotype and disease endpoints in view of identifying innovative therapeutic targets has prompted molecular quantitative trait locus studies, which assess how genetic variants (single nucleotide polymorphisms, SNPs) affect intermediate gene (eQTL), protein (pQTL) or metabolite (mQTL) levels. However, conventional univariate screening approaches do not account for local dependencies and association structures shared by multiple molecular levels and markers. Conversely, the current joint modelling approaches are restricted to small datasets by computational constraints. We illustrate and exploit the advantages of our recently introduced Bayesian framework LOCUS in a fully multivariate pQTL study, with ≈300K tag SNPs (capturing information from 4M markers) and 100 − 1, 000 plasma protein levels measured by two distinct technologies. LOCUS identifies novel pQTLs that replicate in an independent cohort, confirms signals documented in studies 2 − 18 times larger, and detects more pQTLs than a conventional two-stage univariate analysis of our datasets. Moreover, some of these pQTLs might be of biomedical relevance and would therefore deserve dedicated investigation. Our extensive numerical experiments on these data and on simulated data demonstrate that the increased statistical power of LOCUS over standard approaches is largely attributable to its ability to exploit shared information across outcomes while efficiently accounting for the genetic correlation structures at a genome-wide level.
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17
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Atashi H, Salavati M, De Koster J, Crowe MA, Opsomer G, Hostens M. Genome-wide association for metabolic clusters in early-lactation Holstein dairy cows. J Dairy Sci 2020; 103:6392-6406. [PMID: 32331880 DOI: 10.3168/jds.2019-17369] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 01/22/2020] [Indexed: 11/19/2022]
Abstract
The aim of this study was to detect the genomic region or regions associated with metabolic clusters in early-lactation Holstein cows. This study was carried out in 2 experiments. In experiment I, which was carried out on 105 multiparous Holstein cows, animals were classified through k-means clustering on log-transformed and standardized concentrations of blood glucose, insulin-like growth factor I, free fatty acids, and β-hydroxybutyrate at 14 and 35 d in milk (DIM), into metabolic clusters, either balanced (BAL) or other (OTR). Forty percent of the animals were categorized in the BAL group, and the remainder were categorized as OTR. The cows were genotyped for a total of 777,962 SNP. A genome-wide association study was performed, using a case-control approach through the GEMMA software, accounting for population structure. We found 8 SNP (BTA11, BTA23, and BTAX) associated with the predicted metabolic clusters. In experiment II, carried out on 4,267 second-parity Holstein cows, milk samples collected starting from the first week until 50 DIM were used to determine Fourier-transform mid-infrared (FT-MIR) spectra and subsequently to classify the animals into the same metabolic clusters (BAL vs. OTR). Twenty-eight percent of the animals were categorized in the BAL group, and the remainder were classified in the OTR category. Although daily milk yield was lower in BAL cows, we found no difference in daily fat- and protein-corrected milk yield in cows from the BAL metabolic cluster compared with those in the OTR metabolic cluster. In the next step, a single-step genomic BLUP was used to identify the genomic region(s) associated with the predicted metabolic clusters. The results revealed that prediction of metabolic clusters is a highly polygenic trait regulated by many small-sized effects. The region of 36,258 to 36,295 kb on BTA27 was the highly associated region for the predicted metabolic clusters, with the closest genes to this region (ANK1 and miR-486) being related to hematopoiesis, erythropoiesis, and mammary gland development. The heritability for metabolic clustering was 0.17 (SD 0.03), indicating that the use of FT-MIR spectra in milk to predict metabolic clusters in early-lactation across a large number of cows has satisfactory potential to be included in genetic selection programs for modern dairy cows.
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Affiliation(s)
- H Atashi
- Department of Reproduction, Obstetrics and Herd Health, Ghent University, Merelbeke 9820, Belgium; Department of Animal Science, Shiraz University, Shiraz 71441-65186, Iran
| | - M Salavati
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian EH25 9RG, UK
| | - J De Koster
- Department of Reproduction, Obstetrics and Herd Health, Ghent University, Merelbeke 9820, Belgium
| | - M A Crowe
- University College Dublin, 4 Dublin, Ireland
| | - G Opsomer
- Department of Reproduction, Obstetrics and Herd Health, Ghent University, Merelbeke 9820, Belgium
| | | | - M Hostens
- Department of Reproduction, Obstetrics and Herd Health, Ghent University, Merelbeke 9820, Belgium.
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18
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Virani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Delling FN, Djousse L, Elkind MSV, Ferguson JF, Fornage M, Khan SS, Kissela BM, Knutson KL, Kwan TW, Lackland DT, Lewis TT, Lichtman JH, Longenecker CT, Loop MS, Lutsey PL, Martin SS, Matsushita K, Moran AE, Mussolino ME, Perak AM, Rosamond WD, Roth GA, Sampson UKA, Satou GM, Schroeder EB, Shah SH, Shay CM, Spartano NL, Stokes A, Tirschwell DL, VanWagner LB, Tsao CW. Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association. Circulation 2020; 141:e139-e596. [PMID: 31992061 DOI: 10.1161/cir.0000000000000757] [Citation(s) in RCA: 4779] [Impact Index Per Article: 1194.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND The American Heart Association, in conjunction with the National Institutes of Health, annually reports on 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 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. The 2020 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population, metrics to assess and monitor healthy diets, an enhanced focus on social determinants of health, a focus on the global burden of cardiovascular disease, and further evidence-based approaches to changing behaviors, implementation strategies, and implications of the American Heart Association's 2020 Impact Goals. RESULTS Each of the 26 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, policy makers, media professionals, clinicians, healthcare administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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