Emerging Risk Factors Collaboration Research Articles

Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries: 23 million person-years of observation

The prevalence of type 2 diabetes is increasing rapidly, particularly among younger age groups. Estimates suggest that people with diabetes die, on average, 6 years earlier than people without diabetes. We aimed to provide reliable estimates of the associations between age at diagnosis of diabetes and all-cause mortality, cause-specific mortality, and reductions in life expectancy. For this observational study, we conducted a combined analysis of individual-participant data from 19 high-income countries using two large-scale data sources: the Emerging Risk Factors Collaboration (96 cohorts, median baseline years 1961-2007, median latest follow-up years 1980-2013) and the UK Biobank (median baseline year 2006, median latest follow-up year 2020). We calculated age-adjusted and sex-adjusted hazard ratios (HRs) for all-cause mortality according to age at diagnosis of diabetes using data from 1 515 718 participants, in whom deaths were recorded during 23·1 million person-years of follow-up. We estimated cumulative survival by applying age-specific HRs to age-specific death rates from 2015 for the USA and the EU. For participants with diabetes, we observed a linear dose-response association between earlier age at diagnosis and higher risk of all-cause mortality compared with participants without diabetes. HRs were 2·69 (95% CI 2·43-2·97) when diagnosed at 30-39 years, 2·26 (2·08-2·45) at 40-49 years, 1·84 (1·72-1·97) at 50-59 years, 1·57 (1·47-1·67) at 60-69 years, and 1·39 (1·29-1·51) at 70 years and older. HRs per decade of earlier diagnosis were similar for men and women. Using death rates from the USA, a 50-year-old individual with diabetes died on average 14 years earlier when diagnosed aged 30 years, 10 years earlier when diagnosed aged 40 years, or 6 years earlier when diagnosed aged 50 years than an individual without diabetes. Using EU death rates, the corresponding estimates were 13, 9, or 5 years earlier. Every decade of earlier diagnosis of diabetes was associated with about 3-4 years of lower life expectancy, highlighting the need to develop and implement interventions that prevent or delay the onset of diabetes and to intensify the treatment of risk factors among young adults diagnosed with diabetes. British Heart Foundation, Medical Research Council, National Institute for Health and Care Research, and Health Data Research UK.

Research round-up

With data from the UK Biobank (n=401 219; median follow-up 8·1 years) and 21 cohorts included in the Emerging Risk Factors Collaboration (ERFC; n=162 036; median follow-up 9·5 years), Eric Harshfield and colleagues investigated the association between depressive symptoms and cardiovascular disease (CVD). Results from the ERFC showed that for each 1-SD higher depression score, there was an increased risk of stroke (hazard ratio [HR] 1·05 [95% CI 1·01–1·10]), coronary heart disease (1·07 [1·03–1·11]), or a composite of the two outcomes (1·06 [1·04–1·08]).

Association Between Depressive Symptoms and Incident Cardiovascular Diseases

It is uncertain whether depressive symptoms are independently associated with subsequent risk of cardiovascular diseases (CVDs). To characterize the association between depressive symptoms and CVD incidence across the spectrum of lower mood. A pooled analysis of individual-participant data from the Emerging Risk Factors Collaboration (ERFC; 162 036 participants; 21 cohorts; baseline surveys, 1960-2008; latest follow-up, March 2020) and the UK Biobank (401 219 participants; baseline surveys, 2006-2010; latest follow-up, March 2020). Eligible participants had information about self-reported depressive symptoms and no CVD history at baseline. Depressive symptoms were recorded using validated instruments. ERFC scores were harmonized across studies to a scale representative of the Center for Epidemiological Studies Depression (CES-D) scale (range, 0-60; ≥16 indicates possible depressive disorder). The UK Biobank recorded the 2-item Patient Health Questionnaire 2 (PHQ-2; range, 0-6; ≥3 indicates possible depressive disorder). Primary outcomes were incident fatal or nonfatal coronary heart disease (CHD), stroke, and CVD (composite of the 2). Hazard ratios (HRs) per 1-SD higher log CES-D or PHQ-2 adjusted for age, sex, smoking, and diabetes were reported. Among 162 036 participants from the ERFC (73%, women; mean age at baseline, 63 years [SD, 9 years]), 5078 CHD and 3932 stroke events were recorded (median follow-up, 9.5 years). Associations with CHD, stroke, and CVD were log linear. The HR per 1-SD higher depression score for CHD was 1.07 (95% CI, 1.03-1.11); stroke, 1.05 (95% CI, 1.01-1.10); and CVD, 1.06 (95% CI, 1.04-1.08). The corresponding incidence rates per 10 000 person-years of follow-up in the highest vs the lowest quintile of CES-D score (geometric mean CES-D score, 19 vs 1) were 36.3 vs 29.0 for CHD events, 28.0 vs 24.7 for stroke events, and 62.8 vs 53.5 for CVD events. Among 401 219 participants from the UK Biobank (55% were women, mean age at baseline, 56 years [SD, 8 years]), 4607 CHD and 3253 stroke events were recorded (median follow-up, 8.1 years). The HR per 1-SD higher depression score for CHD was 1.11 (95% CI, 1.08-1.14); stroke, 1.10 (95% CI, 1.06-1.14); and CVD, 1.10 (95% CI, 1.08-1.13). The corresponding incidence rates per 10 000 person-years of follow-up among individuals with PHQ-2 scores of 4 or higher vs 0 were 20.9 vs 14.2 for CHD events, 15.3 vs 10.2 for stroke events, and 36.2 vs 24.5 for CVD events. The magnitude and statistical significance of the HRs were not materially changed after adjustment for additional risk factors. In a pooled analysis of 563 255 participants in 22 cohorts, baseline depressive symptoms were associated with CVD incidence, including at symptom levels lower than the threshold indicative of a depressive disorder. However, the magnitude of associations was modest.

Quantifying the contribution of established risk factors to cardiovascular mortality differences between Russia and Norway

Surprisingly few attempts have been made to quantify the simultaneous contribution of well-established risk factors to CVD mortality differences between countries. We aimed to develop and critically appraise an approach to doing so, applying it to the substantial CVD mortality gap between Russia and Norway using survey data in three cities and mortality risks from the Emerging Risk Factor Collaboration. We estimated the absolute and relative differences in CVD mortality at ages 40–69 years between countries attributable to the risk factors, under the counterfactual that the age- and sex-specific risk factor profile in Russia was as in Norway, and vice-versa. Under the counterfactual that Russia had the Norwegian risk factor profile, the absolute age-standardized CVD mortality gap would decline by 33.3% (95% CI 25.1–40.1) among men and 22.1% (10.4–31.3) among women. In relative terms, the mortality rate ratio (Russia/Norway) would decline from 9–10 to 7–8. Under the counterfactual that Norway had the Russian risk factor profile, the mortality gap reduced less. Well-established CVD risk factors account for a third of the male and around a quarter of the female CVD mortality gap between Russia and Norway. However, these estimates are based on widely held epidemiological assumptions that deserve further scrutiny.

World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions

SummaryBackgroundTo help adapt cardiovascular disease risk prediction approaches to low-income and middle-income countries, WHO has convened an effort to develop, evaluate, and illustrate revised risk models. Here, we report the derivation, validation, and illustration of the revised WHO cardiovascular disease risk prediction charts that have been adapted to the circumstances of 21 global regions.MethodsIn this model revision initiative, we derived 10-year risk prediction models for fatal and non-fatal cardiovascular disease (ie, myocardial infarction and stroke) using individual participant data from the Emerging Risk Factors Collaboration. Models included information on age, smoking status, systolic blood pressure, history of diabetes, and total cholesterol. For derivation, we included participants aged 40–80 years without a known baseline history of cardiovascular disease, who were followed up until the first myocardial infarction, fatal coronary heart disease, or stroke event. We recalibrated models using age-specific and sex-specific incidences and risk factor values available from 21 global regions. For external validation, we analysed individual participant data from studies distinct from those used in model derivation. We illustrated models by analysing data on a further 123 743 individuals from surveys in 79 countries collected with the WHO STEPwise Approach to Surveillance.FindingsOur risk model derivation involved 376 177 individuals from 85 cohorts, and 19 333 incident cardiovascular events recorded during 10 years of follow-up. The derived risk prediction models discriminated well in external validation cohorts (19 cohorts, 1 096 061 individuals, 25 950 cardiovascular disease events), with Harrell's C indices ranging from 0·685 (95% CI 0·629–0·741) to 0·833 (0·783–0·882). For a given risk factor profile, we found substantial variation across global regions in the estimated 10-year predicted risk. For example, estimated cardiovascular disease risk for a 60-year-old male smoker without diabetes and with systolic blood pressure of 140 mm Hg and total cholesterol of 5 mmol/L ranged from 11% in Andean Latin America to 30% in central Asia. When applied to data from 79 countries (mostly low-income and middle-income countries), the proportion of individuals aged 40–64 years estimated to be at greater than 20% risk ranged from less than 1% in Uganda to more than 16% in Egypt.InterpretationWe have derived, calibrated, and validated new WHO risk prediction models to estimate cardiovascular disease risk in 21 Global Burden of Disease regions. The widespread use of these models could enhance the accuracy, practicability, and sustainability of efforts to reduce the burden of cardiovascular disease worldwide.FundingWorld Health Organization, British Heart Foundation (BHF), BHF Cambridge Centre for Research Excellence, UK Medical Research Council, and National Institute for Health Research.

Cardiovascular Risk Factors Associated With Venous Thromboembolism

It is uncertain to what extent established cardiovascular risk factors are associated with venous thromboembolism (VTE). To estimate the associations of major cardiovascular risk factors with VTE, ie, deep vein thrombosis and pulmonary embolism. This study included individual participant data mostly from essentially population-based cohort studies from the Emerging Risk Factors Collaboration (ERFC; 731 728 participants; 75 cohorts; years of baseline surveys, February 1960 to June 2008; latest date of follow-up, December 2015) and the UK Biobank (421 537 participants; years of baseline surveys, March 2006 to September 2010; latest date of follow-up, February 2016). Participants without cardiovascular disease at baseline were included. Data were analyzed from June 2017 to September 2018. A panel of several established cardiovascular risk factors. Hazard ratios (HRs) per 1-SD higher usual risk factor levels (or presence/absence). Incident fatal outcomes in ERFC (VTE, 1041; coronary heart disease [CHD], 25 131) and incident fatal/nonfatal outcomes in UK Biobank (VTE, 2321; CHD, 3385). Hazard ratios were adjusted for age, sex, smoking status, diabetes, and body mass index (BMI). Of the 731 728 participants from the ERFC, 403 396 (55.1%) were female, and the mean (SD) age at the time of the survey was 51.9 (9.0) years; of the 421 537 participants from the UK Biobank, 233 699 (55.4%) were female, and the mean (SD) age at the time of the survey was 56.4 (8.1) years. Risk factors for VTE included older age (ERFC: HR per decade, 2.67; 95% CI, 2.45-2.91; UK Biobank: HR, 1.81; 95% CI, 1.71-1.92), current smoking (ERFC: HR, 1.38; 95% CI, 1.20-1.58; UK Biobank: HR, 1.23; 95% CI, 1.08-1.40), and BMI (ERFC: HR per 1-SD higher BMI, 1.43; 95% CI, 1.35-1.50; UK Biobank: HR, 1.37; 95% CI, 1.32-1.41). For these factors, there were similar HRs for pulmonary embolism and deep vein thrombosis in UK Biobank (except adiposity was more strongly associated with pulmonary embolism) and similar HRs for unprovoked vs provoked VTE. Apart from adiposity, these risk factors were less strongly associated with VTE than CHD. There were inconsistent associations of VTEs with diabetes and blood pressure across ERFC and UK Biobank, and there was limited ability to study lipid and inflammation markers. Older age, smoking, and adiposity were consistently associated with higher VTE risk.

Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

SummaryBackgroundLow-risk limits recommended for alcohol consumption vary substantially across different national guidelines. To define thresholds associated with lowest risk for all-cause mortality and cardiovascular disease, we studied individual-participant data from 599 912 current drinkers without previous cardiovascular disease.MethodsWe did a combined analysis of individual-participant data from three large-scale data sources in 19 high-income countries (the Emerging Risk Factors Collaboration, EPIC-CVD, and the UK Biobank). We characterised dose–response associations and calculated hazard ratios (HRs) per 100 g per week of alcohol (12·5 units per week) across 83 prospective studies, adjusting at least for study or centre, age, sex, smoking, and diabetes. To be eligible for the analysis, participants had to have information recorded about their alcohol consumption amount and status (ie, non-drinker vs current drinker), plus age, sex, history of diabetes and smoking status, at least 1 year of follow-up after baseline, and no baseline history of cardiovascular disease. The main analyses focused on current drinkers, whose baseline alcohol consumption was categorised into eight predefined groups according to the amount in grams consumed per week. We assessed alcohol consumption in relation to all-cause mortality, total cardiovascular disease, and several cardiovascular disease subtypes. We corrected HRs for estimated long-term variability in alcohol consumption using 152 640 serial alcohol assessments obtained some years apart (median interval 5·6 years [5th–95th percentile 1·04–13·5]) from 71 011 participants from 37 studies.FindingsIn the 599 912 current drinkers included in the analysis, we recorded 40 310 deaths and 39 018 incident cardiovascular disease events during 5·4 million person-years of follow-up. For all-cause mortality, we recorded a positive and curvilinear association with the level of alcohol consumption, with the minimum mortality risk around or below 100 g per week. Alcohol consumption was roughly linearly associated with a higher risk of stroke (HR per 100 g per week higher consumption 1·14, 95% CI, 1·10–1·17), coronary disease excluding myocardial infarction (1·06, 1·00–1·11), heart failure (1·09, 1·03–1·15), fatal hypertensive disease (1·24, 1·15–1·33); and fatal aortic aneurysm (1·15, 1·03–1·28). By contrast, increased alcohol consumption was log-linearly associated with a lower risk of myocardial infarction (HR 0·94, 0·91–0·97). In comparison to those who reported drinking >0–≤100 g per week, those who reported drinking >100–≤200 g per week, >200–≤350 g per week, or >350 g per week had lower life expectancy at age 40 years of approximately 6 months, 1–2 years, or 4–5 years, respectively.InterpretationIn current drinkers of alcohol in high-income countries, the threshold for lowest risk of all-cause mortality was about 100 g/week. For cardiovascular disease subtypes other than myocardial infarction, there were no clear risk thresholds below which lower alcohol consumption stopped being associated with lower disease risk. These data support limits for alcohol consumption that are lower than those recommended in most current guidelines.FundingUK Medical Research Council, British Heart Foundation, National Institute for Health Research, European Union Framework 7, and European Research Council.

Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis.

The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962–2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.

Research digest

Where high-income countries lead, others need help to follow. Studies done in high-income countries have shown that while adults with diabetes are at increased risks of death from various causes, such risks are now far lower than ever before. For example, analyses by the Emerging Risk Factors Collaboration showed that death from any cause, death from cardiovascular disease, and death from cancer were 80%, 130%, and 25% higher, respectively, in people with diabetes compared with those without diabetes in recent decades. And this gap in outcomes continues to shrink with the widespread and earlier use of clinically effective therapies to lower glucose, blood pressure, and cholesterol. In fact, diabetes-associated mortality risks in high-income countries are so improved that better survival, rather than increased incidence, is now the key driver for rising diabetes prevalence.

Identifying Causes for Excess Mortality in Patients With Diabetes: Closer but Not There Yet

Diabetes is a chronic disease associated with increased morbidity and mortality (1), mainly from cardiovascular disease (2–6). Treatment of diabetes includes normalizing hyperglycemia to attain glycemic targets and treatment of cardiovascular risk factors such as hypertension and dyslipidemia. This multifactorial intervention strategy has been shown to decrease cardiovascular and all-cause mortality among patients with type 2 diabetes (7). Nevertheless, mortality in diabetes remains elevated (2,5). A number of epidemiological studies have quantified the risk of death among patients with diabetes and assessed the causes of death (2–6), with highly varying results (Table 1). The South Tees Diabetes Mortality Study (2) found an over threefold increase in all-cause mortality, mainly attributed to increased cardiovascular deaths, but found no increased risk of cancer mortality. The Australian Diabetes, Obesity and Lifestyle Study (AusDiab) (3) of over 10,000 individuals reported a little over twofold increase in the risk of all-cause mortality, with the majority due to cardiovascular causes. The Emerging Risk Factors Collaboration (ERFC) study (6), involving over 800,000 individuals, reported a little under twofold increase in the risk of all-cause mortality associated with diabetes. It also found that diabetes was associated with an increased risk of death from cancer (hazard ratio [HR] 1.25 [95% CI 1.19–1.31]), from vascular disease (HR 2.32 [95% CI 2.11–2.56]), and from nonvascular and noncancer etiologies (HR 1.73 [95% CI 1.62–1.85]). More recently, a study using the Swedish National Diabetes Register (5) on over 400,000 patients with type 2 diabetes matched to more than 2 million subjects …

Association of Cardiometabolic Multimorbidity With Mortality.

The prevalence of cardiometabolic multimorbidity is increasing. To estimate reductions in life expectancy associated with cardiometabolic multimorbidity. 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. A history of 2 or more of the following: diabetes mellitus, stroke, myocardial infarction (MI). All-cause mortality and estimated reductions in life expectancy. In 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. Mortality 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.

Adult height, coronary heart disease and stroke: a multi-locus Mendelian randomization meta-analysis.

Background: We investigated causal effect of completed growth, measured by adult height, on coronary heart disease (CHD), stroke and cardiovascular traits, using instrumental variable (IV) Mendelian randomization meta-analysis. Methods: We developed an allele score based on 69 single nucleotide polymorphisms (SNPs) associated with adult height, identified by the IBCCardioChip, and used it for IV analysis against cardiovascular risk factors and events in 21 studies and 60 028 participants. IV analysis on CHD was supplemented by summary data from 180 height-SNPs from the GIANT consortium and their corresponding CHD estimates derived from CARDIoGRAMplusC4D. Results: IV estimates from IBCCardioChip and GIANT-CARDIoGRAMplusC4D showed that a 6.5-cm increase in height reduced the odds of CHD by 10% [odds ratios 0.90; 95% confidence intervals (CIs): 0.78 to 1.03 and 0.85 to 0.95, respectively],which agrees with the estimate from the Emerging Risk Factors Collaboration (hazard ratio 0.93; 95% CI: 0.91 to 0.94). IV analysis revealed no association with stroke (odds ratio 0.97; 95% CI: 0.79 to 1.19). IV analysis showed that a 6.5-cm increase in height resulted in lower levels of body mass index (P < 0.001), triglycerides (P < 0.001), non high-density (non-HDL) cholesterol (P < 0.001), C-reactive protein (P = 0.042), and systolic blood pressure (P = 0.064) and higher levels of forced expiratory volume in 1 s and forced vital capacity (P < 0.001 for both). Conclusions: Taller individuals have a lower risk of CHD with potential explanations being that taller people have a better lung function and lower levels of body mass index, cholesterol and blood pressure.

Risk Factors for Cardiovascular Disease: Evidence from Studies

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in the world [1,2]. Risk factors such as hypertension, metabolic syndrome, cigarette smoking, diabetes mellitus, elevated glucose and cholesterol levels, and obesity or being overweight are the top causes of death globally [3,4]. The Emerging Risk Factors Collaboration incorporating 160,309 study participants demonstrated the independent predictive value of C-reactive protein (CRP) for coronary heart disease (CHD) and stroke, the clinical utility of CRP and fibrinogen in CVD risk prediction, and the limited role of adding the novel lipid markers apolipoprotein A-I, lipoprotein (a), apolipoprotein B, and lipoprotein-associated phospholipase A2 to traditional lipid measures [5-7].

Covariate-adjusted measures of discrimination for survival data.

MotivationDiscrimination statistics describe the ability of a survival model to assign higher risks to individuals who experience earlier events: examples are Harrell’s C-index and Royston and Sauerbrei’s D, which we call the D-index. Prognostic covariates whose distributions are controlled by the study design (e.g. age and sex) influence discrimination and can make it difficult to compare model discrimination between studies. Although covariate adjustment is a standard procedure for quantifying disease-risk factor associations, there are no covariate adjustment methods for discrimination statistics in censored survival data.ObjectiveTo develop extensions of the C-index and D-index that describe the prognostic ability of a model adjusted for one or more covariate(s).MethodWe define a covariate-adjusted C-index and D-index for censored survival data, propose several estimators, and investigate their performance in simulation studies and in data from a large individual participant data meta-analysis, the Emerging Risk Factors Collaboration.ResultsThe proposed methods perform well in simulations. In the Emerging Risk Factors Collaboration data, the age-adjusted C-index and D-index were substantially smaller than unadjusted values. The study-specific standard deviation of baseline age was strongly associated with the unadjusted C-index and D-index but not significantly associated with the age-adjusted indices.ConclusionsThe proposed estimators improve meta-analysis comparisons, are easy to implement and give a more meaningful clinical interpretation.

Genetic determination of lipoprotein(a) and its association with cardiovascular disease: convenient does not always mean better

Genetic determination of lipoprotein(a) and its association with cardiovascular disease: convenient does not always mean better

Assessing Risk Prediction Models Using Individual Participant Data From Multiple Studies

Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous.

Targeting inflammatory pathways for the treatment of cardiovascular disease

This editorial refers to ‘Inflammatory cytokines and risk of coronary heart disease: new prospective study and updated meta-analysis’[†][1], by S. Kaptoge et al ., on page 578 In the 15 years that have passed since C-reactive protein was linked to future cardiovascular events in otherwise healthy individuals,1 no alternative biomarker has proved to be a superior surrogate for vascular inflammation in the prediction of global cardiovascular risk. This observation is re-affirmed in population-based prospective cohort data from the Danish Research Center of Prevention and Health published by Kaptoge and colleagues in this issue of the European Heart Journal .2 In brief, in age- and gender-adjusted analyses, as well as in full multivariate models, basal levels of the ‘upstream’ pro-inflammatory cytokines interleukin-6 (IL-6), interleukin-18, and tumour necrosis factor-α (TNF-α) were all associated with future vascular events in an approximately log-linear manner, as were levels of matrix metalloproteinase-9. However, and also consistent with prior work in this arena, the magnitudes of these effects were smaller than that associated with the ‘downstream’ inflammatory biomarker, C-reactive protein. Furthermore, among those study participants in whom direct comparison could be performed, only two biomarkers—downstream C-reactive protein (hazard ratio 1.69 per 1 SD higher baseline level) and upstream TNF-α (hazard ratio 1.32 per 1 SD higher baseline level)—remained statistically significant predictors of future vascular risk when simultaneously adjusting for the full panel of cytokines and chemokines. The core question raised in the present analysis, however, does not relate to which inflammatory biomarker is best suited for clinical practice. That issue was addressed in a prior comprehensive meta-analysis from the Emerging Risk Factors Collaboration, in which the incremental utility of C-reactive protein for predicting vascular risk was found to be fully comparable in magnitude to that of total and high-density lipoprotein cholesterol.3 Rather, what the present … [1]: #fn-2

Diabetes and Noncommunicable Disease

In this issue of JAMA, Xu et al1 provide 2010 estimates of the prevalence of diabetes and prediabetes in China. This is the first large survey to use the latest American Diabetes Association criteria, which include glycated hemoglobin A1c (HbA1c), fasting plasma glucose, and 2-hour glucose during a 75-g oral-glucose tolerance test. The study was a substantial undertaking, striving for representation across 31 provinces and performing all 3 glucose measures in 98 658 Chinese adults. The authors report a prevalence of 11.6% for diabetes (4.5% by fasting plasma glucose ≥125 mg/dL and 4.6% by HbA1c ≥6.5%) and 50.1% for prediabetes (35.4% by HbA1c ≥5.7%-6.4% and 27.2% by fasting plasma glucose ≥100-125 mg/dL, ). Among those with diabetes, only 30% had been previously diagnosed and only 26% were treated. Among those treated, only 40% had HbA1c concentrations lower than 7%.1 Despite the widely publicized report of 9.7% for diabetes and 15.5% for prediabetes byYang et al2 in the 2007China survey, the findings reported by Xu et al1 indicate that the epidemic of diabetes andprediabetes inChinahas shownno sign of abating. In examining the testing patterns bywhich the diabetes and prediabetes diagnoses weremade, the 2-hour glucosemeasure contributed the least to these diagnoses, uniquely establishing the diagnosis in only 15%of thosewith newly diagnoseddiabetes and 4% of those with prediabetes. This finding has 2 important ramifications. First, the increased prevalence of obesity may have affected the testing characteristics of the population with diabetes or prediabetes. In contrast to this report, themajority of adults with diabetes or prediabetes in the mid 1990-2000s had high post– glucose loading plasma glucose levels.3 In the 2010 survey, 1 in3Chineseadultshadeither centralorgeneralobesity.Among theobeseparticipants, 1 in 2hadprediabetes and1 in5haddiabetes. Furthermore, there were associations between diabetes andprediabeteswithother cardiometabolic risk factors includingdyslipidemia andhighbloodpressure.1 Second, these data support a high diagnostic yield from the combination of themore easily obtained fasting plasma glucose andHbA1c in the Chinese population. Inadditionto thevalueof fastingplasmaglucoseandHbA1c in diabetes screening, these diagnostic tests also have prognostic value in predicting the onset of chronic disease. In the Emerging Risk Factors Collaboration involving 97 prospectivestudiesof820 900peoplewithanaccrualof 123 205deaths, peoplewithdiabeteshada 1.3to 3-fold increased riskofdeath due to multiple causes including cardiovascular disease, renal failure,mental illnesses, all-site cancer, hepatobiliary disease, and sepsis. Overall, diabetes was associated with reduced life expectancy of 6 years, especially in persons with young onset of disease. Importantly, there were linear relationships between fastingplasmaglucose andhazard ratios of all vascular, cancer, nonvascular, andnoncancer events starting from a threshold plasma glucose value of 90 to 108 mg/ dL. These hazards were attenuated with adjustment of fasting plasma glucose only, supporting the causal nature of glucotoxicity inmultipleorgandysfunction.4Similarly, thenear linear relationships between HbA1c and risk of diabetes, cardiovasculardisease5 andcancer6havebeen reported inpeople withorwithoutdiabetes. Inadequate control of fastingplasma glucose andHbA1c inChinaherald a looming andmassive epidemicof chronicdiseases, if promptpreventiveactionsarenot taken. Given themagitude of the problem,what are the priorities in preventive actions? According to the study, 50% of Chinese men were current smokers. Although Xu et al found negative associations of diabetes and prediabeteswith smoking, which might be due to reverse causality, the dose-response associationbetweensmokinganddiabeteshasbeenreported inametaanalysis of prospective studies.7 Although it remains to be proven whether tobacco control would reduce the diabetesprediabetes epidemic, smoking cessation shouldhelpmitigate the amplifying effects of tobacco on the adverse effects of glucotoxicityonorgandamage.Asiahas thehighestpopulationof tobaccousers.Although the levels of development, systemsof government, and population size differ greatly among countries, there have been successful reports of tobacco control through strong political will and restrictive policies in some areas, such as in New Zealand and Hong Kong.8 In the study by Xu et al,1 5% to 8% of those in the 18to 40-year age group had diabetes and 40% to 50% had prediabetes. Apart from phenotypic and genotypic heterogeneity, which pose therapeutic challenges, these young adults are at high risk of prematuremortality and cardiovascular-renal disease during the prime of life. Yet, in part, due to the nonurgent nature of the condition and competing priorities, these young to middle-aged persons often have high rates of nonadherence, default, and suboptimal risk factor control.9 For such populations, a life course and proactive approach starting with protecting maternal and childhood health, promotingahealthy lifestyle, andavoidingobesity fromchildhoodand by launchingoutreachawareness anddetectionprograms targeting theworkforce and early intervention of high-risk individuals with prediabetes, notably those with gestational diabetes and family history of diabetes, are measures that are in linewith theWorldHealthOrganization (WHO)Global Planof Action for Control and Prevention of Noncommunicable Diseases.10 Diabetes is a societal and a health care challenge due to complex interplays amonggenetic, perinatal, lifestyle, andenvironmental factors, to name but a few. Rapidmodernization has resulted in an obesogenic environment characterized by food abundance, physical inactivity, and psychosocial stress. Related article 948 Opinion Editorial

Insulin as an early treatment for type 2 diabetes: ORIGIN or end of an old question?

The Emerging Risk Factors Collaboration (1) has recently confirmed that type 2 diabetes remains associated with substantial premature mortality. Compared with nondiabetic individuals, those with diabetes have a hazard ratio (HR) of 1.80 (95% CI 1.71–1.90) for death from any cause, 1.25 (1.19–1.31) for death from cancer, 2.32 (2.11–2.56) for death from vascular causes, and 1.73 (1.62–1.85) for death from other causes. As is apparent, the greater risk is owing to cardiovascular (CV) disease, which accounts for >60% of the years of life lost because of diabetes. Although this increase in CV risk is largely attributable to the coexistence of multiple metabolic and hemodynamic disorders, elevation of plasma glucose levels remains strongly associated with increased CV morbidity and mortality (2–4). In support of a potential direct effect of plasma glucose elevation is the association between fasting plasma glucose levels >100 mg/dL (5.6 mmol/L) and vascular death (1). Since this a near-linear association (1,5) it has been postulated that reduction of plasma glucose levels should exert a positive impact on CV morbidity and mortality. In the UK Prospective Diabetes Study (UKPDS), improvement of glycemic control was associated with a 16% reduction in the risk of myocardial infarction without achieving statistical significance ( P < 0.052) (6). Glycemic control in that seminal trial was, however, relatively unsuccessful. Though intensively treated patients achieved an average A1C of 7%, a progressive worsening in glycemic control occurred after the initial improvement. A more aggressive and successful approach was adopted in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) (7) and Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation (ADVANCE) (8) trials and Veterans Affairs Diabetes Trial (VADT) (9). In all these three large studies, glycemic control was achieved (A1C 6.5–7.0%) and maintained over a substantial period of …

Combining multiple imputation and meta‐analysis with individual participant data

Multiple imputation is a strategy for the analysis of incomplete data such that the impact of the missingness on the power and bias of estimates is mitigated. When data from multiple studies are collated, we can propose both within-study and multilevel imputation models to impute missing data on covariates. It is not clear how to choose between imputation models or how to combine imputation and inverse-variance weighted meta-analysis methods. This is especially important as often different studies measure data on different variables, meaning that we may need to impute data on a variable which is systematically missing in a particular study. In this paper, we consider a simulation analysis of sporadically missing data in a single covariate with a linear analysis model and discuss how the results would be applicable to the case of systematically missing data. We find in this context that ensuring the congeniality of the imputation and analysis models is important to give correct standard errors and confidence intervals. For example, if the analysis model allows between-study heterogeneity of a parameter, then we should incorporate this heterogeneity into the imputation model to maintain the congeniality of the two models. In an inverse-variance weighted meta-analysis, we should impute missing data and apply Rubin's rules at the study level prior to meta-analysis, rather than meta-analyzing each of the multiple imputations and then combining the meta-analysis estimates using Rubin's rules. We illustrate the results using data from the Emerging Risk Factors Collaboration.