Metabolic health and cardiometabolic risk clusters: implications for prediction, prevention, and treatment
Tài liệu tham khảo
2016, Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19·2 million participants, Lancet, 387, 1377, 10.1016/S0140-6736(16)30054-X
Bhaskaran, 2018, Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3·6 million adults in the UK, Lancet Diabetes Endocrinol, 6, 944, 10.1016/S2213-8587(18)30288-2
Foreman, 2018, Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016–40 for 195 countries and territories, Lancet, 392, 2052, 10.1016/S0140-6736(18)31694-5
Malik, 2020, Nearly a decade on—trends, risk factors and policy implications in global obesity, Nat Rev Endocrinol, 16, 615, 10.1038/s41574-020-00411-y
Fan, 2013, Combined effect of obesity and cardio-metabolic abnormality on the risk of cardiovascular disease: a meta-analysis of prospective cohort studies, Int J Cardiol, 168, 4761, 10.1016/j.ijcard.2013.07.230
Eckel, 2016, Metabolically healthy obesity and cardiovascular events: a systematic review and meta-analysis, Eur J Prev Cardiol, 23, 956, 10.1177/2047487315623884
Putra, 2022, Metabolically unhealthy phenotype in normal weight population and risk of mortality and major adverse cardiac events: a meta-analysis of 41 prospective cohort studies, Diabetes Metab Syndr, 16, 10.1016/j.dsx.2022.102635
Zheng, 2016, The long-term prognosis of cardiovascular disease and all-cause mortality for metabolically healthy obesity: a systematic review and meta-analysis, J Epidemiol Community Health, 70, 1024, 10.1136/jech-2015-206948
Yeh, 2019, The relationship between metabolically healthy obesity and the risk of cardiovascular disease: a systematic review and meta-analysis, J Clin Med, 8, 10.3390/jcm8081228
Opio, 2020, Metabolically healthy overweight/obesity are associated with increased risk of cardiovascular disease in adults, even in the absence of metabolic risk factors: a systematic review and meta-analysis of prospective cohort studies, Obes Rev, 21, 10.1111/obr.13127
Stefan, 2017, Causes, characteristics, and consequences of metabolically unhealthy normal weight in humans, Cell Metab, 26, 292, 10.1016/j.cmet.2017.07.008
Stefan, 2018, Metabolically healthy obesity: the low-hanging fruit in obesity treatment?, Lancet Diabetes Endocrinol, 6, 249, 10.1016/S2213-8587(17)30292-9
Schulze, 2019, Metabolic health in normal-weight and obese individuals, Diabetologia, 62, 558, 10.1007/s00125-018-4787-8
Smith, 2019, Metabolically healthy obesity: facts and fantasies, J Clin Invest, 129, 3978, 10.1172/JCI129186
Stefan, 2020, Causes, consequences, and treatment of metabolically unhealthy fat distribution, Lancet Diabetes Endocrinol, 8, 616, 10.1016/S2213-8587(20)30110-8
Blüher, 2020, Metabolically healthy obesity, Endocr Rev, 41, 10.1210/endrev/bnaa004
Yusuf, 2005, Obesity and the risk of myocardial infarction in 27 000 participants from 52 countries: a case-control study, Lancet, 366, 1640, 10.1016/S0140-6736(05)67663-5
Canoy, 2007, Body fat distribution and risk of coronary heart disease in men and women in the European prospective investigation into cancer and nutrition in Norfolk cohort: a population-based prospective study, Circulation, 116, 2933, 10.1161/CIRCULATIONAHA.106.673756
Wormser, 2011, Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies, Lancet, 377, 1085, 10.1016/S0140-6736(11)60105-0
Basu, 2017, Development and validation of Risk Equations for Complications Of type 2 Diabetes (RECODe) using individual participant data from randomised trials, Lancet Diabetes Endocrinol, 5, 788, 10.1016/S2213-8587(17)30221-8
Herder, 2022, A novel diabetes typology: towards precision diabetology from pathogenesis to treatment, Diabetologia, 65, 1770, 10.1007/s00125-021-05625-x
Deutsch, 2022, Phenotypic and genetic classification of diabetes, Diabetologia, 65, 1758, 10.1007/s00125-022-05769-4
Buijsse, 2011, Risk assessment tools for identifying individuals at risk of developing type 2 diabetes, Epidemiol Rev, 33, 46, 10.1093/epirev/mxq019
Abbasi, 2012, Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study, BMJ, 345, 10.1136/bmj.e5900
Lucaroni, 2019, Can risk be predicted? An umbrella systematic review of current risk prediction models for cardiovascular diseases, diabetes and hypertension, BMJ Open, 9, 10.1136/bmjopen-2019-030234
Hippisley-Cox, 2017, Development and validation of QDiabetes-2018 risk prediction algorithm to estimate future risk of type 2 diabetes: cohort study, BMJ, 359
Chen, 2010, AUSDRISK: an Australian type 2 diabetes risk assessment tool based on demographic, lifestyle and simple anthropometric measures, Med J Aust, 192, 197, 10.5694/j.1326-5377.2010.tb03478.x
Schiborn, 2022, German diabetes risk score for the determination of the individual type 2 diabetes risk, Dtsch Arztebl Int, 119, 651
Kengne, 2014, Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models, Lancet Diabetes Endocrinol, 2, 19, 10.1016/S2213-8587(13)70103-7
Goff, 2014, 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines, Circulation, 129, S49
Hageman, 2021, SCORE2 working group and ESC cardiovascular risk collaboration. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe, Eur Heart J, 42, 2439, 10.1093/eurheartj/ehab309
Wagner, 2021, Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes, Nat Med, 27, 49, 10.1038/s41591-020-1116-9
Ahlqvist, 2018, Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables, Lancet Diabetes Endocrinol, 6, 361, 10.1016/S2213-8587(18)30051-2
Udler, 2018, Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: a soft clustering analysis, PLoS Med, 15, 10.1371/journal.pmed.1002654
Dennis, 2019, Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data, Lancet Diabetes Endocrinol, 7, 442, 10.1016/S2213-8587(19)30087-7
Wesolowska-Andersen, 2022, Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: an IMI DIRECT study, Cell Rep Med, 3
Nair, 2022, Heterogeneity in phenotype, disease progression and drug response in type 2 diabetes, Nat Med, 28, 982, 10.1038/s41591-022-01790-7
Visseren, 2021, 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice, Eur Heart J, 42, 3227, 10.1093/eurheartj/ehab484
Arnett, 2019, 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on clinical practice guidelines, Circulation, 140, e596
Schiborn, 2022, Precision prognostics for the development of complications in diabetes, Diabetologia, 65, 1867, 10.1007/s00125-022-05731-4
Mühlenbruch, 2013, The value of genetic information for diabetes risk prediction—differences according to sex, age, family history and obesity, PLoS One, 8, 10.1371/journal.pone.0064307
Eckel, 2015, Characterization of metabolically unhealthy normal-weight individuals: risk factors and their associations with type 2 diabetes, Metabolism, 64, 862, 10.1016/j.metabol.2015.03.009
Chowdhury, 2019, Prognostic tools for cardiovascular disease in patients with type 2 diabetes: a systematic review and meta-analysis of C-statistics, J Diabetes Complications, 33, 98, 10.1016/j.jdiacomp.2018.10.010
Chowdhury, 2019, Predicting the risk of stroke among patients with type 2 diabetes: a systematic review and meta-analysis of C-statistics, BMJ Open, 9, 10.1136/bmjopen-2018-025579
Buchan, 2021, Predictive models for cardiovascular and kidney outcomes in patients with type 2 diabetes: systematic review and meta-analyses, Heart, 107, 1962, 10.1136/heartjnl-2021-319243
Stevens, 2001, The UKPDS risk engine: a model for the risk of coronary heart disease in Type II diabetes (UKPDS 56), Clin Sci (Lond), 101, 671, 10.1042/cs1010671
Davis, 2010, An Australian cardiovascular risk equation for type 2 diabetes: the fremantle diabetes study, Intern Med J, 40, 286, 10.1111/j.1445-5994.2009.01958.x
Rao Kondapally Seshasai, 2011, Diabetes mellitus, fasting glucose, and risk of cause-specific death, N Engl J Med, 364, 829, 10.1056/NEJMoa1008862
Gerstein, 2007, Annual incidence and relative risk of diabetes in people with various categories of dysglycemia: a systematic overview and meta-analysis of prospective studies, Diabetes Res Clin Pract, 78, 305, 10.1016/j.diabres.2007.05.004
Schlesinger, 2022, Prediabetes and risk of mortality, diabetes-related complications and comorbidities: umbrella review of meta-analyses of prospective studies, Diabetologia, 65, 275, 10.1007/s00125-021-05592-3
Tirosh, 2005, Normal fasting plasma glucose levels and type 2 diabetes in young men, N Engl J Med, 353, 1454, 10.1056/NEJMoa050080
Schulze, 2010, Fasting plasma glucose and type 2 diabetes risk: a non-linear relationship, Diabet Med, 27, 473, 10.1111/j.1464-5491.2009.02919.x
Lee, 2019, Comparing different definitions of prediabetes with subsequent risk of diabetes: an individual participant data meta-analysis involving 76 513 individuals and 8208 cases of incident diabetes, BMJ Open Diabetes Res Care, 7, 10.1136/bmjdrc-2019-000794
Zimmet, 2005, The metabolic syndrome: a global public health problem and a new definition, J Atheroscler Thromb, 12, 295, 10.5551/jat.12.295
Eckel, 2005, The metabolic syndrome, Lancet, 365, 1415, 10.1016/S0140-6736(05)66378-7
Grundy, 2005, Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement, Circulation, 112, 2735, 10.1161/CIRCULATIONAHA.105.169404
Alberti, 2009, Circulation, 120, 1640, 10.1161/CIRCULATIONAHA.109.192644
Stefan, 2013, Metabolically healthy obesity: epidemiology, mechanisms, and clinical implications, Lancet Diabetes Endocrinol, 1, 152, 10.1016/S2213-8587(13)70062-7
Neeland, 2018, Cardiovascular and metabolic heterogeneity of obesity: clinical challenges and implications for management, Circulation, 137, 1391, 10.1161/CIRCULATIONAHA.117.029617
Zembic, 2021, An empirically derived definition of metabolically healthy obesity based on risk of cardiovascular and total mortality, JAMA Netw Open, 4, 10.1001/jamanetworkopen.2021.8505
Lassale, 2018, Separate and combined associations of obesity and metabolic health with coronary heart disease: a pan-European case-cohort analysis, Eur Heart J, 39, 397, 10.1093/eurheartj/ehx448
Selvin, 2010, Glycated hemoglobin, diabetes, and cardiovascular risk in nondiabetic adults, N Engl J Med, 362, 800, 10.1056/NEJMoa0908359
Vasan, 2001, Impact of high-normal blood pressure on the risk of cardiovascular disease, N Engl J Med, 345, 1291, 10.1056/NEJMoa003417
Eckel, 2018, Transition from metabolic healthy to unhealthy phenotypes and association with cardiovascular disease risk across BMI categories in 90 257 women (the Nurses' Health Study): 30 year follow-up from a prospective cohort study, Lancet Diabetes Endocrinol, 6, 714, 10.1016/S2213-8587(18)30137-2
Abiri, 2022, Transition from metabolically healthy to unhealthy overweight/obesity and risk of cardiovascular disease incidence: a systematic review and meta-analysis, Nutr Metab Cardiovasc Dis, 32, 2041, 10.1016/j.numecd.2022.06.010
Kim, 2023, High-throughput genetic clustering of type 2 diabetes loci reveals heterogeneous mechanistic pathways of metabolic disease, Diabetologia, 66, 495, 10.1007/s00125-022-05848-6
Qiu, 2017, Reversed graph embedding resolves complex single-cell trajectories, Nat Methods, 14, 979, 10.1038/nmeth.4402
Zaharia, 2019, Risk of diabetes-associated diseases in subgroups of patients with recent-onset diabetes: a 5-year follow-up study, Lancet Diabetes Endocrinol, 7, 684, 10.1016/S2213-8587(19)30187-1
Estruch, 2018, Primary prevention of cardiovascular disease with a Mediterranean diet supplemented with extra-virgin olive oil or nuts, N Engl J Med, 378, e34, 10.1056/NEJMoa1800389
Guo, 2017, Cardiometabolic disease staging predicts effectiveness of weight loss therapy to prevent type 2 diabetes: pooled results from phase III clinical trials assessing phentermine/topiramate extended release, Diabetes Care, 40, 856, 10.2337/dc17-0088
Pigeyre, 2023, Identifying blood biomarkers for type 2 diabetes subtyping: a report from the ORIGIN trial, Diabetologia, 10.1007/s00125-023-05887-7
Dennis, 2020, Precision medicine in type 2 diabetes: using individualized prediction models to optimize selection of treatment, Diabetes, 69, 2075, 10.2337/dbi20-0002
Bancks, 2021, Type 2 diabetes subgroups, risk for complications, and differential effects due to an intensive lifestyle intervention, Diabetes Care, 44, 1203, 10.2337/dc20-2372
Raverdy, 2022, Data-driven subgroups of type 2 diabetes, metabolic response, and renal risk profile after bariatric surgery: a retrospective cohort study, Lancet Diabetes Endocrinol, 10, 167, 10.1016/S2213-8587(22)00005-5
Sjöström, 2012, Bariatric surgery and long-term cardiovascular events, JAMA, 307, 56, 10.1001/jama.2011.1914
Sjöström, 2013, Review of the key results from the Swedish Obese Subjects (SOS) trial—a prospective controlled intervention study of bariatric surgery, J Intern Med, 273, 219, 10.1111/joim.12012
Sattar, 2021, Cardiovascular, mortality, and kidney outcomes with GLP-1 receptor agonists in patients with type 2 diabetes: a systematic review and meta-analysis of randomised trials, Lancet Diabetes Endocrinol, 9, 653, 10.1016/S2213-8587(21)00203-5
Hodkinson, 2022, Comparative effectiveness of statins on non-high density lipoprotein cholesterol in people with diabetes and at risk of cardiovascular disease: systematic review and network meta-analysis, BMJ, 376
Shields, 2023, Patient stratification for determining optimal second-line and third-line therapy for type 2 diabetes: the TriMaster study, Nat Med, 29, 376, 10.1038/s41591-022-02120-7