The Emerging Risk Factors Collaboration IMPORTANCE The prevalence of cardiometabolic multimorbidity is increasing. OBJECTIVE To estimate reductions in life expectancy associated with cardiometabolic multimorbidity. DESIGN, SETTING, AND PARTICIPANTS Age-and sex-adjusted mortality rates and hazard ratios (HRs) were calculated using individual participant data from the Emerging Risk Factors Collaboration (689 300 participants; 91 cohorts; years of baseline surveys: 1960-2007; latest mortality follow-up: April 2013; 128 843 deaths). The HRs from the Emerging Risk Factors Collaboration were compared with those from the UK Biobank (499 808 participants; years of baseline surveys: 2006-2010; latest mortality follow-up: November 2013; 7995 deaths).Cumulative survival was estimated by applying calculated age-specific HRs for mortality to contemporary US age-specific death rates.EXPOSURES A history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI). MAIN OUTCOMES AND MEASURESAll-cause mortality and estimated reductions in life expectancy. RESULTSIn participants in the Emerging Risk Factors Collaboration without a history of diabetes, stroke, or MI at baseline (reference group), the all-cause mortality rate adjusted to the age of 60 years was 6.8 per 1000 person-years. Mortality rates per 1000 person-years were 15.6 in participants with a history of diabetes, 16.1 in those with stroke, 16.8 in those with MI, 32.0 in those with both diabetes and MI, 32.5 in those with both diabetes and stroke, 32.8 in those with both stroke and MI, and 59.5 in those with diabetes, stroke, and MI. Compared with the reference group, the HRs for all-cause mortality were 1.9 (95% CI, 1.8-2.0) in participants with a history of diabetes, 2.1 (95% CI, 2.0-2.2) in those with stroke, 2.0 (95% CI, 1.9-2.2) in those with MI, 3.7 (95% CI, 3.3-4.1) in those with both diabetes and MI, 3.8 (95% CI, 3.5-4.2) in those with both diabetes and stroke, 3.5 (95% CI, 3.1-4.0) in those with both stroke and MI, and 6.9 (95% CI, 5.7-8.3) in those with diabetes, stroke, and MI. The HRs from the Emerging Risk Factors Collaboration were similar to those from the more recently recruited UK Biobank. The HRs were little changed after further adjustment for markers of established intermediate pathways (eg, levels of lipids and blood pressure) and lifestyle factors (eg, smoking, diet). At the age of 60 years, a history of any 2 of these conditions was associated with 12 years of reduced life expectancy and a history of all 3 of these conditions was associated with 15 years of reduced life expectancy. CONCLUSIONS AND RELEVANCEMortality associated with a history of diabetes, stroke, or MI was similar for each condition. Because any combination of these conditions was associated with multiplicative mortality risk, life expectancy was substantially lower in people with multimorbidity.

BACKGROUND There is debate about the value of assessing levels of C-reactive protein (CRP) and other biomarkers of inflammation for the prediction of first cardiovascular events. METHODS We analyzed data from 52 prospective studies that included 246,669 participants without a history of cardiovascular disease to investigate the value of adding CRP or fibrinogen levels to conventional risk factors for the prediction of cardiovascular risk. We calculated measures of discrimination and reclassification during follow-up and modeled the clinical implications of initiation of statin therapy after the assessment of CRP or fibrinogen. RESULTS The addition of information on high-density lipoprotein cholesterol to a prognostic model for cardiovascular disease that included age, sex, smoking status, blood pressure, history of diabetes, and total cholesterol level increased the C-index, a measure of risk discrimination, by 0.0050. The further addition to this model of information on CRP or fibrinogen increased the C-index by 0.0039 and 0.0027, respectively (P<0.001), and yielded a net reclassification improvement of 1.52% and 0.83%, respectively, for the predicted 10-year risk categories of “low” (<10%), “intermediate” (10% to <20%), and “high” (≥20%) (P<0.02 for both comparisons). We estimated that among 100,000 adults 40 years of age or older, 15,025 persons would initially be classified as being at intermediate risk for a cardiovascular event if conventional risk factors alone were used to calculate risk. Assuming that statin therapy would be initiated in accordance with Adult Treatment Panel III guidelines (i.e., for persons with a predicted risk of ≥20% and for those with certain other risk factors, such as diabetes, irrespective of their 10-year predicted risk), additional targeted assessment of CRP or fibrinogen levels in the 13,199 remaining participants at intermediate risk could help prevent approximately 30 additional cardiovascular events over the course of 10 years. CONCLUSIONS In a study of people without known cardiovascular disease, we estimated that under current treatment guidelines, assessment of the CRP or fibrinogen level in people at intermediate risk for a cardiovascular event could help prevent one additional event over a period of 10 years for every 400 to 500 people screened. (Funded by the British Heart Foundation and others.)

We thank Drs Ding, Mekary, and Katz for their interest in our recent findings showing a positive association between television viewing time and mortality. 1 Drs Ding and Mekary helpfully bring to our attention the relevance of their isotemporal substitution model to our findings. Consistent with their perspective, we acknowledge that the basic partition model applied in our analyses has limitations with respect to not being able to account for total activity time. As a potential explanation of the relationship between television viewing time and mortality, their "displacement" hypothesis is intriguing. However, as noted in our article, we and others, including Mekary et al, 2 have previously shown television viewing time to be only weakly correlated with moderate to vigorous leisure-time exercise. Studies using objective measures of physical activity (mainly accelerometers) show clearly that only a small proportion of total waking hours is spent engaging in moderate to vigorous exercise (Ͻ5%), with the balance being distributed between sedentary time and light-intensity physical activity. 3 With this in mind, we speculated in our article that television viewing may displace opportunities to engage in lightintensity activities, which have been shown to be beneficially associated with cardiometabolic biomarkers, including 2-hour postchallenge blood glucose. However, capturing time spent in lightintensity physical activity with the use of self-reported measures has been notoriously problematic for physical activity epidemiology research and is also a problem for sedentary behavior studies. This is largely due to recall and measurement error. We certainly see scientific virtue in addressing this "displacement across the full 24-hour day" hypothesis as a potential explanation for the relationships that we have reported. The isotemporal substitution model could be considered in future studies to better understand such relationships. A significant limitation in doing so will be that self-reported measures of physical activity, including our own, have less than perfect reliability and validity, which inevitably will compromise what can be derived from these sophisticated models. The increasing adoption of objectively measured physical activity and sedentary time approaches may offer new insights into the relevance of the displacement hypothesis and help to minimize the residual bias that presently exists with self-reported measures.Dr Katz correctly points out (as we also have done in our article) that a baseline-only assessment of television viewing and leisuretime exercise and the inability to account for all other unmeasured or unknown confounding factors are limitations. However, these are inherent limitations that our findings have in common with a plethora of prospective epidemiological cohort studies, all showing significant relationships of exercise and physical activity with premature mortality. We are encouraged that since the publication of our findings, positive associations of television viewing with prema...

Objective To assess what proportions of studies reported increasing, stable, or declining trends in the incidence of diagnosed diabetes. Design Systematic review of studies reporting trends of diabetes incidence in adults from 1980 to 2017 according to PRISMA guidelines. Data sources Medline, Embase, CINAHL, and reference lists of relevant publications. Eligibility criteria Studies of open population based cohorts, diabetes registries, and administrative and health insurance databases on secular trends in the incidence of total diabetes or type 2 diabetes in adults were included. Poisson regression was used to model data by age group and year. Results Among the 22 833 screened abstracts, 47 studies were included, providing data on 121 separate sex specific or ethnicity specific populations; 42 (89%) of the included studies reported on diagnosed diabetes. In 1960-89, 36% (8/22) of the populations studied had increasing trends in incidence of diabetes, 55% (12/22) had stable trends, and 9% (2/22) had decreasing trends. In 1990-2005, diabetes incidence increased in 66% (33/50) of populations, was stable in 32% (16/50), and decreased in 2% (1/50). In 2006-14, increasing trends were reported in only 33% (11/33) of populations, whereas 30% (10/33) and 36% (12/33) had stable or declining incidence, respectively. Conclusions The incidence of clinically diagnosed diabetes has continued to rise in only a minority of populations studied since 2006, with over a third of populations having a fall in incidence in this time period. Preventive strategies could have contributed to the fall in diabetes incidence in recent years. Data are limited in low and middle income countries, where trends in diabetes incidence could be different. Systematic review registration Prospero CRD42018092287.

Adult height has directionally opposing relationships with risk of death from several different major causes of chronic diseases.