Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: A comparative risk assessment

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Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: a comparative risk assessment

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Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: a comparative risk assessment

BackgroundHigh blood pressure, blood glucose, serum cholesterol, and BMI are risk factors for cardiovascular diseases and some of these factors also increase the risk of chronic kidney disease and diabetes. We estimated mortality from cardiovascular diseases, chronic kidney disease, and diabetes that was attributable to these four cardiometabolic risk factors for all countries and regions from 1980 to 2010.MethodsWe used data for exposure to risk factors by country, age group, and sex from pooled analyses of population-based health surveys. We obtained relative risks for the effects of risk factors on cause-specific mortality from meta-analyses of large prospective studies. We calculated the population attributable fractions for each risk factor alone, and for the combination of all risk factors, accounting for multicausality and for mediation of the effects of BMI by the other three risks. We calculated attributable deaths by multiplying the cause-specific population attributable fractions by the number of disease-specific deaths. We obtained cause-specific mortality from the Global Burden of Diseases, Injuries, and Risk Factors 2010 Study. We propagated the uncertainties of all the inputs to the final estimates.FindingsIn 2010, high blood pressure was the leading risk factor for deaths due to cardiovascular diseases, chronic kidney disease, and diabetes in every region, causing more than 40% of worldwide deaths from these diseases; high BMI and glucose were each responsible for about 15% of deaths, and high cholesterol for more than 10%. After accounting for multicausality, 63% (10·8 million deaths, 95% CI 10·1-11·5) of deaths from these diseases in 2010 were attributable to the combined effect of these four metabolic risk factors, compared with 67% (7·1 million deaths, 6·6-7·6) in 1980. The mortality burden of high BMI and glucose nearly doubled from 1980 to 2010. At the country level, age-standardised death rates from these diseases attributable to the combined effects of these four risk factors surpassed 925 deaths per 100 000 for men in Belarus, Kazakhstan, and Mongolia, but were less than 130 deaths per 100 000 for women and less than 200 for men in some high-income countries including Australia, Canada, France, Japan, the Netherlands, Singapore, South Korea, and Spain.InterpretationThe salient features of the cardiometabolic disease and risk factor epidemic at the beginning of the 21st century are high blood pressure and an increasing effect of obesity and diabetes. The mortality burden of cardiometabolic risk factors has shifted from high-income to low-income and middle-income countries. Lowering cardiometabolic risks through dietary, behavioural, and pharmacological interventions should be a part of the global response to non-communicable diseases.FundingUK Medical Research Council, US National Institutes of Health

Cardiovascular disease, chronic kidney disease, and diabetes mortality burden of cardiometabolic risk factors from 1980 to 2010: a comparative risk assessment

High blood pressure, blood glucose, serum cholesterol, and BMI are risk factors for cardiovascular diseases and some of these factors also increase the risk of chronic kidney disease and diabetes. We estimated mortality from cardiovascular diseases, chronic kidney disease, and diabetes that was attributable to these four cardiometabolic risk factors for all countries and regions from 1980 to 2010. 0 info:eu-repo/semantics/publishe

Dairy consumption and cardiometabolic diseases: systematic review and updated meta-analyses of prospective cohort studies

Purpose of Review Dairy products contain both beneficial and harmful nutrients in relation to cardiometabolic diseases. Here, we provide the latest scientific evidence regarding the relationship between dairy products and cardiometabolic diseases by reviewing the literature and updating meta-analyses of observational studies. Recent Findings We updated our previous meta-analyses of cohort studies on type 2 diabetes, coronary heart disease (CHD), and stroke with nine studies and confirmed previous results. Total dairy and low-fat dairy (per 200 g/d) were inversely associated with a 3–4% lower risk of diabetes. Yogurt was non-linearly inversely associatedwith diabetes (RR = 0.86, 95%CI: 0.83–0.90 at 80 g/ d). Total dairy and milk were not associated with CHD (RR~1.0). An increment of 200 g of daily milk intake was associated with an 8% lower risk of stroke. Summary The latest scientific evidence confirmed neutral or beneficial associations between dairy products and risk of cardiometabolic diseases

Intraindividual double burden of overweight or obesity and micronutrient deficiencies or anemia among women of reproductive age in 17 population-based surveys

Background: Rising prevalence of overweight/obesity (OWOB) alongside persistent micronutrient deficiencies suggests many women face concomitant OWOB and undernutrition. Objectives: We aimed to 1) describe the prevalence of the double burden of malnutrition (DBM) among nonpregnant women of reproductive age, defined as intraindividual OWOB and either ≥1 micronutrient deficiency [micronutrient deficiency index (MDI) \u3e 0; DBM-MDI] or anemia (DBM-anemia); 2) test whether the components of the DBM were independent; and 3) identify factors associated with DBM-MDI and DBM-anemia. Methods: With data from 17 national surveys spanning low- and middle-income countries (LMICs) and high-income countries from the Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia project (n = 419 to n = 9029), we tested independence of over- and undernutrition using the Rao–Scott chi-square test and examined predictors of the DBM and its components using logistic regression for each survey. Results: Median DBM-MDI was 21.9% (range: 1.6%–39.2%); median DBM-anemia was 8.6% (range: 1.0%–18.6%). OWOB and micronutrient deficiencies or anemia were independent in most surveys. Where associations existed, OWOB was negatively associated with micronutrient deficiencies and anemia in LMICs. In 1 high-income country, OWOB women were more likely to experience micronutrient deficiencies and anemia. Age was consistently positively associated with OWOB and the DBM, whereas the associations with other sociodemographic characteristics varied. Higher socioeconomic status tended to be positively associated with OWOB and the DBM in LMICs, whereas in higher-income countries the association was reversed. Conclusions: The independence of OWOB and micronutrient deficiencies or anemia within individuals suggests that these forms of over- and undernutrition may have unique etiologies. Decision-makers should still consider the prevalence, consequences, and etiology of the individual components of the DBM as programs move towards double-duty interventions aimed at addressing OWOB and undernutrition simultaneously

Association between depression, anxiety and weight change in young adults

Background To investigate whether there are bi-directional associations between anxiety and mood disorders and body mass index (BMI) in a cohort of young adults. Methods We analysed data from the 2004–2006 (baseline) and 2009–2011 (follow-up) waves of the Childhood Determinants of Adult Health study. Lifetime DSM-IV anxiety and mood disorders were retrospectively diagnosed with the Composite International Diagnostic Interview. Potential mediators were individually added to the base models to assess their potential role as a mediator of the associations. Results In males, presence of mood disorder history at baseline was positively associated with BMI gain (β = 0.77, 95% CI: 0.14–1.40), but baseline BMI was not associated with subsequent risk of mood disorder. Further adjustment for covariates, including dietary pattern, physical activity, and smoking reduced the coefficient (β) to 0.70 (95% CI: 0.01–1.39), suggesting that the increase in BMI was partly mediated by these factors. In females, presence of mood disorder history at baseline was not associated with subsequent weight gain, however, BMI at baseline was associated with higher risk of episode of mood disorder (RR per kg/m2: 1.04, 95% CI: 1.01–1.08), which was strengthened (RR per kg/m2 = 1.07, 95% CI: 1.00–1.15) after additional adjustment in the full model. There was no significant association between anxiety and change in BMI and vice-versa. Conclusion The results do not suggest bidirectional associations between anxiety and mood disorders, and change in BMI. Interventions promoting healthy lifestyle could contribute to reducing increase in BMI associated with mood disorder in males, and excess risk of mood disorder associated with BMI in females