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      Association between prediabetes and risk of all cause mortality and cardiovascular disease: updated meta-analysis

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          Abstract

          Objective

          To evaluate the associations between prediabetes and the risk of all cause mortality and incident cardiovascular disease in the general population and in patients with a history of atherosclerotic cardiovascular disease.

          Design

          Updated meta-analysis.

          Data sources

          Electronic databases (PubMed, Embase, and Google Scholar) up to 25 April 2020.

          Review methods

          Prospective cohort studies or post hoc analysis of clinical trials were included for analysis if they reported adjusted relative risks, odds ratios, or hazard ratios of all cause mortality or cardiovascular disease for prediabetes compared with normoglycaemia. Data were extracted independently by two investigators. Random effects models were used to calculate the relative risks and 95% confidence intervals. The primary outcomes were all cause mortality and composite cardiovascular disease. The secondary outcomes were the risk of coronary heart disease and stroke.

          Results

          A total of 129 studies were included, involving 10 069 955 individuals for analysis. In the general population, prediabetes was associated with an increased risk of all cause mortality (relative risk 1.13, 95% confidence interval 1.10 to 1.17), composite cardiovascular disease (1.15, 1.11 to 1.18), coronary heart disease (1.16, 1.11 to 1.21), and stroke (1.14, 1.08 to 1.20) in a median follow-up time of 9.8 years. Compared with normoglycaemia, the absolute risk difference in prediabetes for all cause mortality, composite cardiovascular disease, coronary heart disease, and stroke was 7.36 (95% confidence interval 9.59 to 12.51), 8.75 (6.41 to 10.49), 6.59 (4.53 to 8.65), and 3.68 (2.10 to 5.26) per 10 000 person years, respectively. Impaired glucose tolerance carried a higher risk of all cause mortality, coronary heart disease, and stroke than impaired fasting glucose. In patients with atherosclerotic cardiovascular disease, prediabetes was associated with an increased risk of all cause mortality (relative risk 1.36, 95% confidence interval 1.21 to 1.54), composite cardiovascular disease (1.37, 1.23 to 1.53), and coronary heart disease (1.15, 1.02 to 1.29) in a median follow-up time of 3.2 years, but no difference was seen for the risk of stroke (1.05, 0.81 to 1.36). Compared with normoglycaemia, in patients with atherosclerotic cardiovascular disease, the absolute risk difference in prediabetes for all cause mortality, composite cardiovascular disease, coronary heart disease, and stroke was 66.19 (95% confidence interval 38.60 to 99.25), 189.77 (117.97 to 271.84), 40.62 (5.42 to 78.53), and 8.54 (32.43 to 61.45) per 10 000 person years, respectively. No significant heterogeneity was found for the risk of all outcomes seen for the different definitions of prediabetes in patients with atherosclerotic cardiovascular disease (all P>0.10).

          Conclusions

          Results indicated that prediabetes was associated with an increased risk of all cause mortality and cardiovascular disease in the general population and in patients with atherosclerotic cardiovascular disease. Screening and appropriate management of prediabetes might contribute to primary and secondary prevention of cardiovascular disease.

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          Most cited references30

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          Race/ethnic difference in diabetes and diabetic complications.

          Health disparities in diabetes and its complications and comorbidities exist globally. A recent Endocrine Society Scientific Statement described the Health Disparities in several endocrine disorders, including type 2 diabetes. In this review, we summarize that statement and provide novel updates on race/ethnic differences in children and adults with type 1 diabetes, children with type 2 diabetes, and in Latino subpopulations. We also review race/ethnic differences in the epidemiology of diabetes, prediabetes, and diabetes complications and mortality in the United States and globally. Finally, we discuss biological, behavioral, social, environmental, and health system contributors to diabetes disparities to identify areas for future preventive interventions.
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            Morbidity and mortality after lifestyle intervention for people with impaired glucose tolerance: 30-year results of the Da Qing Diabetes Prevention Outcome Study

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              Current methods of the US Preventive Services Task Force: a review of the process.

              The U.S. Preventive Services Task Force (USPSTF/Task Force) represents one of several efforts to take a more evidence-based approach to the development of clinical practice guidelines. As methods have matured for assembling and reviewing evidence and for translating evidence into guidelines, so too have the methods of the USPSTF. This paper summarizes the current methods of the third USPSTF, supported by the Agency for Healthcare Research and Quality (AHRQ) and two of the AHRQ Evidence-based Practice Centers (EPCs). The Task Force limits the topics it reviews to those conditions that cause a large burden of suffering to society and that also have available a potentially effective preventive service. It focuses its reviews on the questions and evidence most critical to making a recommendation. It uses analytic frameworks to specify the linkages and key questions connecting the preventive service with health outcomes. These linkages, together with explicit inclusion criteria, guide the literature searches for admissible evidence. Once assembled, admissible evidence is reviewed at three strata: (1) the individual study, (2) the body of evidence concerning a single linkage in the analytic framework, and (3) the body of evidence concerning the entire preventive service. For each stratum, the Task Force uses explicit criteria as general guidelines to assign one of three grades of evidence: good, fair, or poor. Good or fair quality evidence for the entire preventive service must include studies of sufficient design and quality to provide an unbroken chain of evidence-supported linkages, generalizable to the general primary care population, that connect the preventive service with health outcomes. Poor evidence contains a formidable break in the evidence chain such that the connection between the preventive service and health outcomes is uncertain. For services supported by overall good or fair evidence, the Task Force uses outcomes tables to help categorize the magnitude of benefits, harms, and net benefit from implementation of the preventive service into one of four categories: substantial, moderate, small, or zero/negative. The Task Force uses its assessment of the evidence and magnitude of net benefit to make a recommendation, coded as a letter: from A (strongly recommended) to D (recommend against). It gives an I recommendation in situations in which the evidence is insufficient to determine net benefit. The third Task Force and the EPCs will continue to examine a variety of methodologic issues and document work group progress in future communications.
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                Author and article information

                Contributors
                Role: associate professor
                Role: associate professor
                Role: resident doctor
                Role: associate professor
                Role: attending physician
                Role: attending physician
                Role: professor
                Role: professor
                Role: professor
                Journal
                BMJ
                BMJ
                BMJ-UK
                bmj
                The BMJ
                BMJ Publishing Group Ltd.
                0959-8138
                1756-1833
                2020
                15 July 2020
                : 370
                : m2297
                Affiliations
                [1 ]Department of Scientific Research and Education, Shunde Hospital, Southern Medical University, Foshan, China
                [2 ]Key Laboratory of Neuroscience, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China
                [3 ]Department of Cardiology, Shunde Hospital, Southern Medical University, Jiazi Road, Lunjiao Town, Shunde District, Foshan 528300, China
                [4 ]George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
                [5 ]VIP Clinic Centre, Shunde Hospital, Southern Medical University, Foshan, China
                [6 ]Department of Geriatrics, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China
                Author notes
                Correspondence to: Y Huang hyuli821@ 123456smu.edu.cn
                Author information
                http://orcid.org/0000-0001-5423-5487
                Article
                caix056552
                10.1136/bmj.m2297
                7362233
                32669282
                b0c72b52-de76-4c78-b26a-028cc905deee
                © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 20 May 2020
                Categories
                Research

                Medicine
                Medicine

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