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      The metabolic syndrome – What is it and how should it be managed?

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          Abstract

          A cluster of metabolic factors have been merged into an entity named the metabolic syndrome. Although the characteristics of this syndrome have varied over time the presently used definition was established in 2009. The presence of three abnormal findings out of five components qualifies a person for the metabolic syndrome: elevated waist circumference, elevated triglycerides, reduced high-density lipoprotein cholesterol, elevated blood pressure and elevated fasting plasma glucose. Cut points have been defined for all components apart from waist circumference, for which national or regional values are used. The metabolic syndrome predicts cardiovascular disease and type 2 diabetes. This associated risk does not exceed its components whereof elevated blood pressure is the most frequent. A successful management should, however, address all factors involved. The management is always based on healthy lifestyle choices but has not infrequently to be supported by pharmacological treatment, especially blood pressure lowering drugs. The metabolic syndrome is a useful example of the importance of multiple targets for preventive interventions. To be successful management has to be individualized not the least when it comes to pharmacological therapy. Frail elderly people should not be over-treated. Knowledge transfer of how risk factors act should be accompanied by continuous trust building and motivation. In complex situations with a mix of biological risk factors, adverse social conditions and unhealthy lifestyle, everything cannot be changed at once. It is better to aim for small steps that are lasting than large, unsustainable steps with relapses to unhealthy behaviours. A person with the metabolic syndrome will always be afflicted by its components, which is the reason that management has to be sustained over a very long time. This review summarizes the knowledge on the metabolic syndrome and its management according to present state of the art.

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          General cardiovascular risk profile for use in primary care: the Framingham Heart Study.

          Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD and of its constituents. We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between 30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions ("general CVD" algorithms) were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted CVD risk (multivariable-adjusted P<0.0001). The general CVD algorithm demonstrated good discrimination (C statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional risk factors and the other based on non-laboratory-based predictors. A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.
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            Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity.

            A cluster of risk factors for cardiovascular disease and type 2 diabetes mellitus, which occur together more often than by chance alone, have become known as the metabolic syndrome. The risk factors include raised blood pressure, dyslipidemia (raised triglycerides and lowered high-density lipoprotein cholesterol), raised fasting glucose, and central obesity. Various diagnostic criteria have been proposed by different organizations over the past decade. Most recently, these have come from the International Diabetes Federation and the American Heart Association/National Heart, Lung, and Blood Institute. The main difference concerns the measure for central obesity, with this being an obligatory component in the International Diabetes Federation definition, lower than in the American Heart Association/National Heart, Lung, and Blood Institute criteria, and ethnic specific. The present article represents the outcome of a meeting between several major organizations in an attempt to unify criteria. It was agreed that there should not be an obligatory component, but that waist measurement would continue to be a useful preliminary screening tool. Three abnormal findings out of 5 would qualify a person for the metabolic syndrome. A single set of cut points would be used for all components except waist circumference, for which further work is required. In the interim, national or regional cut points for waist circumference can be used.
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              Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.

              Type 2 diabetes affects approximately 8 percent of adults in the United States. Some risk factors--elevated plasma glucose concentrations in the fasting state and after an oral glucose load, overweight, and a sedentary lifestyle--are potentially reversible. We hypothesized that modifying these factors with a lifestyle-intervention program or the administration of metformin would prevent or delay the development of diabetes. We randomly assigned 3234 nondiabetic persons with elevated fasting and post-load plasma glucose concentrations to placebo, metformin (850 mg twice daily), or a lifestyle-modification program with the goals of at least a 7 percent weight loss and at least 150 minutes of physical activity per week. The mean age of the participants was 51 years, and the mean body-mass index (the weight in kilograms divided by the square of the height in meters) was 34.0; 68 percent were women, and 45 percent were members of minority groups. The average follow-up was 2.8 years. The incidence of diabetes was 11.0, 7.8, and 4.8 cases per 100 person-years in the placebo, metformin, and lifestyle groups, respectively. The lifestyle intervention reduced the incidence by 58 percent (95 percent confidence interval, 48 to 66 percent) and metformin by 31 percent (95 percent confidence interval, 17 to 43 percent), as compared with placebo; the lifestyle intervention was significantly more effective than metformin. To prevent one case of diabetes during a period of three years, 6.9 persons would have to participate in the lifestyle-intervention program, and 13.9 would have to receive metformin. Lifestyle changes and treatment with metformin both reduced the incidence of diabetes in persons at high risk. The lifestyle intervention was more effective than metformin.
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                Author and article information

                Journal
                European Journal of Preventive Cardiology
                Eur J Prev Cardiolog
                SAGE Publications
                2047-4873
                2047-4881
                December 2019
                November 26 2019
                December 2019
                : 26
                : 2_suppl
                : 33-46
                Affiliations
                [1 ]Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
                [2 ]Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
                [3 ]Department of Public Health, University of Helsinki, Finland
                [4 ]Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
                [5 ]Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
                Article
                10.1177/2047487319886404
                31766917
                f4c12d1c-ef76-48be-9ec5-e48514b4b23b
                © 2019

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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