@article {931, title = {Regression dilution methods for meta-analysis: assessing long-term variability in plasma fibrinogen among 27,247 adults in 15 prospective studies.}, journal = {Int J Epidemiol}, volume = {35}, year = {2006}, month = {2006 Dec}, pages = {1570-8}, abstract = {

BACKGROUND: Within-person variability in measured values of a risk factor can bias its association with disease. The extent of this regression dilution bias for plasma fibrinogen was investigated using repeat measurement data collected at varying time intervals on 27 247 adults in 15 prospective studies.

METHODS: Regression dilution ratios (RDRs) were estimated from a linear regression of repeat measurements on baseline values in each study and for each time interval, and pooled allowing for within- and between-study heterogeneity. RDRs were estimated both without and with adjustment for confounders, and factors were investigated that might influence the RDRs.

RESULTS: The unadjusted overall RDR was 0.51 (95\% CI: 0.47, 0.55), which decreased to 0.46 (95\% CI: 0.42, 0.49) after adjustment for age, sex and measured values of other established vascular risk factors. The RDR did not vary materially by assay method, age, sex or smoking status, but decreased at higher levels of baseline fibrinogen.

CONCLUSION: It is appropriate to use an RDR of 0.5 to correct approximately for regression dilution bias in plasma fibrinogen values; however, this correction factor may produce somewhat conservative hazard ratios in adjusted analyses, at higher fibrinogen concentrations and in follow-up beyond a decade. More generally, the methods described in this report have widespread applicability to quantifying regression dilution bias in repeatability data from multiple prospective studies.

}, keywords = {Adult, Age Factors, Bias, Cardiovascular Diseases, Female, Fibrinogen, Humans, Male, Meta-Analysis as Topic, Prospective Studies, Regression Analysis, Risk Factors, Sex Factors, Smoking, Time Factors}, issn = {0300-5771}, doi = {10.1093/ije/dyl233}, author = {Wood, Angela M and White, Ian and Thompson, Simon G and Lewington, Sarah and Danesh, John} } @article {1116, title = {Lipoprotein(a) concentration and the risk of coronary heart disease, stroke, and nonvascular mortality.}, journal = {JAMA}, volume = {302}, year = {2009}, month = {2009 Jul 22}, pages = {412-23}, abstract = {

CONTEXT: Circulating concentration of lipoprotein(a) (Lp[a]), a large glycoprotein attached to a low-density lipoprotein-like particle, may be associated with risk of coronary heart disease (CHD) and stroke.

OBJECTIVE: To assess the relationship of Lp(a) concentration with risk of major vascular and nonvascular outcomes.

STUDY SELECTION: Long-term prospective studies that recorded Lp(a) concentration and subsequent major vascular morbidity and/or cause-specific mortality published between January 1970 and March 2009 were identified through electronic searches of MEDLINE and other databases, manual searches of reference lists, and discussion with collaborators.

DATA EXTRACTION: Individual records were provided for each of 126,634 participants in 36 prospective studies. During 1.3 million person-years of follow-up, 22,076 first-ever fatal or nonfatal vascular disease outcomes or nonvascular deaths were recorded, including 9336 CHD outcomes, 1903 ischemic strokes, 338 hemorrhagic strokes, 751 unclassified strokes, 1091 other vascular deaths, 8114 nonvascular deaths, and 242 deaths of unknown cause. Within-study regression analyses were adjusted for within-person variation and combined using meta-analysis. Analyses excluded participants with known preexisting CHD or stroke at baseline.

DATA SYNTHESIS: Lipoprotein(a) concentration was weakly correlated with several conventional vascular risk factors and it was highly consistent within individuals over several years. Associations of Lp(a) with CHD risk were broadly continuous in shape. In the 24 cohort studies, the rates of CHD in the top and bottom thirds of baseline Lp(a) distributions, respectively, were 5.6 (95\% confidence interval [CI], 5.4-5.9) per 1000 person-years and 4.4 (95\% CI, 4.2-4.6) per 1000 person-years. The risk ratio for CHD, adjusted for age and sex only, was 1.16 (95\% CI, 1.11-1.22) per 3.5-fold higher usual Lp(a) concentration (ie, per 1 SD), and it was 1.13 (95\% CI, 1.09-1.18) following further adjustment for lipids and other conventional risk factors. The corresponding adjusted risk ratios were 1.10 (95\% CI, 1.02-1.18) for ischemic stroke, 1.01 (95\% CI, 0.98-1.05) for the aggregate of nonvascular mortality, 1.00 (95\% CI, 0.97-1.04) for cancer deaths, and 1.00 (95\% CI, 0.95-1.06) for nonvascular deaths other than cancer.

CONCLUSION: Under a wide range of circumstances, there are continuous, independent, and modest associations of Lp(a) concentration with risk of CHD and stroke that appear exclusive to vascular outcomes.

}, keywords = {Cause of Death, Coronary Disease, Humans, Lipoprotein(a), Risk Factors, Stroke}, issn = {1538-3598}, doi = {10.1001/jama.2009.1063}, author = {Erqou, Sebhat and Kaptoge, Stephen and Perry, Philip L and Di Angelantonio, Emanuele and Thompson, Alexander and White, Ian R and Marcovina, Santica M and Collins, Rory and Thompson, Simon G and Danesh, John} } @article {1171, title = {Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables.}, journal = {Stat Med}, volume = {29}, year = {2010}, month = {2010 May 30}, pages = {1298-311}, abstract = {

Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context of multiple genetic markers measured in multiple studies, based on the analysis of individual participant data. First, for a single genetic marker in one study, we show that the usual ratio of coefficients approach can be reformulated as a regression with heterogeneous error in the explanatory variable. This can be implemented using a Bayesian approach, which is next extended to include multiple genetic markers. We then propose a hierarchical model for undertaking a meta-analysis of multiple studies, in which it is not necessary that the same genetic markers are measured in each study. This provides an overall estimate of the causal relationship between the phenotype and the outcome, and an assessment of its heterogeneity across studies. As an example, we estimate the causal relationship of blood concentrations of C-reactive protein on fibrinogen levels using data from 11 studies. These methods provide a flexible framework for efficient estimation of causal relationships derived from multiple studies. Issues discussed include weak instrument bias, analysis of binary outcome data such as disease risk, missing genetic data, and the use of haplotypes.

}, keywords = {Bayes Theorem, Biostatistics, C-Reactive Protein, Fibrinogen, Genetic Markers, Humans, Meta-Analysis as Topic, Models, Statistical, Phenotype, Polymorphism, Single Nucleotide}, issn = {1097-0258}, doi = {10.1002/sim.3843}, author = {Burgess, Stephen and Thompson, Simon G and Burgess, S and Thompson, S G and Andrews, G and Samani, N J and Hall, A and Whincup, P and Morris, R and Lawlor, D A and Davey Smith, G and Timpson, N and Ebrahim, S and Ben-Shlomo, Y and Davey Smith, G and Timpson, N and Brown, M and Ricketts, S and Sandhu, M and Reiner, A and Psaty, B and Lange, L and Cushman, M and Hung, J and Thompson, P and Beilby, J and Warrington, N and Palmer, L J and Nordestgaard, B G and Tybjaerg-Hansen, A and Zacho, J and Wu, C and Lowe, G and Tzoulaki, I and Kumari, M and Sandhu, M and Yamamoto, J F and Chiodini, B and Franzosi, M and Hankey, G J and Jamrozik, K and Palmer, L and Rimm, E and Pai, J and Psaty, B and Heckbert, S and Bis, J and Anand, S and Engert, J and Collins, R and Clarke, R and Melander, O and Berglund, G and Ladenvall, P and Johansson, L and Jansson, J-H and Hallmans, G and Hingorani, A and Humphries, S and Rimm, E and Manson, J and Pai, J and Watkins, H and Clarke, R and Hopewell, J and Saleheen, D and Frossard, R and Danesh, J and Sattar, N and Robertson, M and Shepherd, J and Schaefer, E and Hofman, A and Witteman, J C M and Kardys, I and Ben-Shlomo, Y and Davey Smith, G and Timpson, N and de Faire, U and Bennet, A and Sattar, N and Ford, I and Packard, C and Kumari, M and Manson, J and Lawlor, Debbie A and Davey Smith, George and Anand, S and Collins, R and Casas, J P and Danesh, J and Davey Smith, G and Franzosi, M and Hingorani, A and Lawlor, D A and Manson, J and Nordestgaard, B G and Samani, N J and Sandhu, M and Smeeth, L and Wensley, F and Anand, S and Bowden, J and Burgess, S and Casas, J P and Di Angelantonio, E and Engert, J and Gao, P and Shah, T and Smeeth, L and Thompson, S G and Verzilli, C and Walker, M and Whittaker, J and Hingorani, A and Danesh, J} } @article {1151, title = {C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis.}, journal = {Lancet}, volume = {375}, year = {2010}, month = {2010 Jan 09}, pages = {132-40}, abstract = {

BACKGROUND: Associations of C-reactive protein (CRP) concentration with risk of major diseases can best be assessed by long-term prospective follow-up of large numbers of people. We assessed the associations of CRP concentration with risk of vascular and non-vascular outcomes under different circumstances.

METHODS: We meta-analysed individual records of 160 309 people without a history of vascular disease (ie, 1.31 million person-years at risk, 27 769 fatal or non-fatal disease outcomes) from 54 long-term prospective studies. Within-study regression analyses were adjusted for within-person variation in risk factor levels.

RESULTS: Log(e) CRP concentration was linearly associated with several conventional risk factors and inflammatory markers, and nearly log-linearly with the risk of ischaemic vascular disease and non-vascular mortality. Risk ratios (RRs) for coronary heart disease per 1-SD higher log(e) CRP concentration (three-fold higher) were 1.63 (95\% CI 1.51-1.76) when initially adjusted for age and sex only, and 1.37 (1.27-1.48) when adjusted further for conventional risk factors; 1.44 (1.32-1.57) and 1.27 (1.15-1.40) for ischaemic stroke; 1.71 (1.53-1.91) and 1.55 (1.37-1.76) for vascular mortality; and 1.55 (1.41-1.69) and 1.54 (1.40-1.68) for non-vascular mortality. RRs were largely unchanged after exclusion of smokers or initial follow-up. After further adjustment for fibrinogen, the corresponding RRs were 1.23 (1.07-1.42) for coronary heart disease; 1.32 (1.18-1.49) for ischaemic stroke; 1.34 (1.18-1.52) for vascular mortality; and 1.34 (1.20-1.50) for non-vascular mortality.

INTERPRETATION: CRP concentration has continuous associations with the risk of coronary heart disease, ischaemic stroke, vascular mortality, and death from several cancers and lung disease that are each of broadly similar size. The relevance of CRP to such a range of disorders is unclear. Associations with ischaemic vascular disease depend considerably on conventional risk factors and other markers of inflammation.

FUNDING: British Heart Foundation, UK Medical Research Council, BUPA Foundation, and GlaxoSmithKline.

}, keywords = {Alcohol Drinking, Biomarkers, Blood Pressure, Body Mass Index, C-Reactive Protein, Cholesterol, Coronary Disease, Databases, Factual, Diabetes Mellitus, Female, Fibrinogen, Humans, Interleukin-6, Leukocyte Count, Lung Diseases, Male, Middle Aged, Motor Activity, Neoplasms, Regression Analysis, Risk Assessment, Risk Factors, Serum Albumin, Sex Factors, Smoking, Stroke, Triglycerides}, issn = {1474-547X}, doi = {10.1016/S0140-6736(09)61717-7}, author = {Kaptoge, Stephen and Di Angelantonio, Emanuele and Lowe, Gordon and Pepys, Mark B and Thompson, Simon G and Collins, Rory and Danesh, John} } @article {583, title = {Lipoprotein-associated phospholipase A(2) and risk of coronary disease, stroke, and mortality: collaborative analysis of 32 prospective studies.}, journal = {Lancet}, volume = {375}, year = {2010}, month = {2010 May 01}, pages = {1536-44}, abstract = {

BACKGROUND: Lipoprotein-associated phospholipase A(2) (Lp-PLA(2)), an inflammatory enzyme expressed in atherosclerotic plaques, is a therapeutic target being assessed in trials of vascular disease prevention. We investigated associations of circulating Lp-PLA(2) mass and activity with risk of coronary heart disease, stroke, and mortality under different circumstances.

METHODS: With use of individual records from 79 036 participants in 32 prospective studies (yielding 17 722 incident fatal or non-fatal outcomes during 474 976 person-years at risk), we did a meta-analysis of within-study regressions to calculate risk ratios (RRs) per 1 SD higher value of Lp-PLA(2) or other risk factor. The primary outcome was coronary heart disease.

FINDINGS: Lp-PLA(2) activity and mass were associated with each other (r=0.51, 95\% CI 0.47-0.56) and proatherogenic lipids. We noted roughly log-linear associations of Lp-PLA(2) activity and mass with risk of coronary heart disease and vascular death. RRs, adjusted for conventional risk factors, were: 1.10 (95\% CI 1.05-1.16) with Lp-PLA(2) activity and 1.11 (1.07-1.16) with Lp-PLA(2) mass for coronary heart disease; 1.08 (0.97-1.20) and 1.14 (1.02-1.27) for ischaemic stroke; 1.16 (1.09-1.24) and 1.13 (1.05-1.22) for vascular mortality; and 1.10 (1.04-1.17) and 1.10 (1.03-1.18) for non-vascular mortality, respectively. RRs with Lp-PLA(2) did not differ significantly in people with and without initial stable vascular disease, apart from for vascular death with Lp-PLA(2) mass. Adjusted RRs for coronary heart disease were 1.10 (1.02-1.18) with non-HDL cholesterol and 1.10 (1.00-1.21) with systolic blood pressure.

INTERPRETATION: Lp-PLA(2) activity and mass each show continuous associations with risk of coronary heart disease, similar in magnitude to that with non-HDL cholesterol or systolic blood pressure in this population. Associations of Lp-PLA(2) mass and activity are not exclusive to vascular outcomes, and the vascular associations depend at least partly on lipids.

FUNDING: UK Medical Research Council, GlaxoSmithKline, and British Heart Foundation.

}, keywords = {1-Alkyl-2-acetylglycerophosphocholine Esterase, Blood Pressure, Coronary Disease, Female, Humans, Linear Models, Lipids, Male, Middle Aged, Prospective Studies, Risk Assessment, Risk Factors, Stroke, Systole}, issn = {1474-547X}, doi = {10.1016/S0140-6736(10)60319-4}, author = {Thompson, Alexander and Gao, Pei and Orfei, Lia and Watson, Sarah and Di Angelantonio, Emanuele and Kaptoge, Stephen and Ballantyne, Christie and Cannon, Christopher P and Criqui, Michael and Cushman, Mary and Hofman, Albert and Packard, Chris and Thompson, Simon G and Collins, Rory and Danesh, John} } @article {1272, title = {Diabetes mellitus, fasting glucose, and risk of cause-specific death.}, journal = {N Engl J Med}, volume = {364}, year = {2011}, month = {2011 Mar 03}, pages = {829-841}, abstract = {

BACKGROUND: The extent to which diabetes mellitus or hyperglycemia is related to risk of death from cancer or other nonvascular conditions is uncertain.

METHODS: We calculated hazard ratios for cause-specific death, according to baseline diabetes status or fasting glucose level, from individual-participant data on 123,205 deaths among 820,900 people in 97 prospective studies.

RESULTS: After adjustment for age, sex, smoking status, and body-mass index, hazard ratios among persons with diabetes as compared with persons without diabetes were as follows: 1.80 (95\% confidence interval [CI], 1.71 to 1.90) for death from any cause, 1.25 (95\% CI, 1.19 to 1.31) for death from cancer, 2.32 (95\% CI, 2.11 to 2.56) for death from vascular causes, and 1.73 (95\% CI, 1.62 to 1.85) for death from other causes. Diabetes (vs. no diabetes) was moderately associated with death from cancers of the liver, pancreas, ovary, colorectum, lung, bladder, and breast. Aside from cancer and vascular disease, diabetes (vs. no diabetes) was also associated with death from renal disease, liver disease, pneumonia and other infectious diseases, mental disorders, nonhepatic digestive diseases, external causes, intentional self-harm, nervous-system disorders, and chronic obstructive pulmonary disease. Hazard ratios were appreciably reduced after further adjustment for glycemia measures, but not after adjustment for systolic blood pressure, lipid levels, inflammation or renal markers. Fasting glucose levels exceeding 100 mg per deciliter (5.6 mmol per liter), but not levels of 70 to 100 mg per deciliter (3.9 to 5.6 mmol per liter), were associated with death. A 50-year-old with diabetes died, on average, 6 years earlier than a counterpart without diabetes, with about 40\% of the difference in survival attributable to excess nonvascular deaths.

CONCLUSIONS: In addition to vascular disease, diabetes is associated with substantial premature death from several cancers, infectious diseases, external causes, intentional self-harm, and degenerative disorders, independent of several major risk factors. (Funded by the British Heart Foundation and others.).

}, keywords = {Blood Glucose, Cause of Death, Diabetes Mellitus, Female, Humans, Hyperglycemia, Life Expectancy, Male, Middle Aged, Risk, Survival Analysis}, issn = {1533-4406}, doi = {10.1056/NEJMoa1008862}, author = {Rao Kondapally Seshasai, Sreenivasa and Kaptoge, Stephen and Thompson, Alexander and Di Angelantonio, Emanuele and Gao, Pei and Sarwar, Nadeem and Whincup, Peter H and Mukamal, Kenneth J and Gillum, Richard F and Holme, Ingar and Nj{\o}lstad, Inger and Fletcher, Astrid and Nilsson, Peter and Lewington, Sarah and Collins, Rory and Gudnason, Vilmundur and Thompson, Simon G and Sattar, Naveed and Selvin, Elizabeth and Hu, Frank B and Danesh, John} } @article {1563, title = {Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies.}, journal = {Lancet}, volume = {377}, year = {2011}, month = {2011 Mar 26}, pages = {1085-95}, abstract = {

BACKGROUND: Guidelines differ about the value of assessment of adiposity measures for cardiovascular disease risk prediction when information is available for other risk factors. We studied the separate and combined associations of body-mass index (BMI), waist circumference, and waist-to-hip ratio with risk of first-onset cardiovascular disease.

METHODS: We used individual records from 58 cohorts to calculate hazard ratios (HRs) per 1 SD higher baseline values (4.56 kg/m(2) higher BMI, 12.6 cm higher waist circumference, and 0.083 higher waist-to-hip ratio) and measures of risk discrimination and reclassification. Serial adiposity assessments were used to calculate regression dilution ratios.

RESULTS: Individual records were available for 221,934 people in 17 countries (14,297 incident cardiovascular disease outcomes; 1.87 million person-years at risk). Serial adiposity assessments were made in up to 63,821 people (mean interval 5.7 years [SD 3.9]). In people with BMI of 20 kg/m(2) or higher, HRs for cardiovascular disease were 1.23 (95\% CI 1.17-1.29) with BMI, 1.27 (1.20-1.33) with waist circumference, and 1.25 (1.19-1.31) with waist-to-hip ratio, after adjustment for age, sex, and smoking status. After further adjustment for baseline systolic blood pressure, history of diabetes, and total and HDL cholesterol, corresponding HRs were 1.07 (1.03-1.11) with BMI, 1.10 (1.05-1.14) with waist circumference, and 1.12 (1.08-1.15) with waist-to-hip ratio. Addition of information on BMI, waist circumference, or waist-to-hip ratio to a cardiovascular disease risk prediction model containing conventional risk factors did not importantly improve risk discrimination (C-index changes of -0.0001, -0.0001, and 0.0008, respectively), nor classification of participants to categories of predicted 10-year risk (net reclassification improvement -0.19\%, -0.05\%, and -0.05\%, respectively). Findings were similar when adiposity measures were considered in combination. Reproducibility was greater for BMI (regression dilution ratio 0.95, 95\% CI 0.93-0.97) than for waist circumference (0.86, 0.83-0.89) or waist-to-hip ratio (0.63, 0.57-0.70).

INTERPRETATION: BMI, waist circumference, and waist-to-hip ratio, whether assessed singly or in combination, do not importantly improve cardiovascular disease risk prediction in people in developed countries when additional information is available for systolic blood pressure, history of diabetes, and lipids.

FUNDING: British Heart Foundation and UK Medical Research Council.

}, keywords = {Age Factors, Blood Pressure, Body Mass Index, Cardiovascular Diseases, Cholesterol, Cholesterol, HDL, Diabetes Mellitus, Female, Humans, Male, Middle Aged, Obesity, Abdominal, Proportional Hazards Models, Prospective Studies, Risk Assessment, Sex Factors, Smoking, Systole, Waist Circumference, Waist-Hip Ratio}, issn = {1474-547X}, doi = {10.1016/S0140-6736(11)60105-0}, author = {Wormser, David and Kaptoge, Stephen and Di Angelantonio, Emanuele and Wood, Angela M and Pennells, Lisa and Thompson, Alex and Sarwar, Nadeem and Kizer, Jorge R and Lawlor, Debbie A and Nordestgaard, B{\o}rge G and Ridker, Paul and Salomaa, Veikko and Stevens, June and Woodward, Mark and Sattar, Naveed and Collins, Rory and Thompson, Simon G and Whitlock, Gary and Danesh, John} } @article {1382, title = {Carotid intima-media thickness progression to predict cardiovascular events in the general population (the PROG-IMT collaborative project): a meta-analysis of individual participant data.}, journal = {Lancet}, volume = {379}, year = {2012}, month = {2012 Jun 02}, pages = {2053-62}, abstract = {

BACKGROUND: Carotid intima-media thickness (cIMT) is related to the risk of cardiovascular events in the general population. An association between changes in cIMT and cardiovascular risk is frequently assumed but has rarely been reported. Our aim was to test this association.

METHODS: We identified general population studies that assessed cIMT at least twice and followed up participants for myocardial infarction, stroke, or death. The study teams collaborated in an individual participant data meta-analysis. Excluding individuals with previous myocardial infarction or stroke, we assessed the association between cIMT progression and the risk of cardiovascular events (myocardial infarction, stroke, vascular death, or a combination of these) for each study with Cox regression. The log hazard ratios (HRs) per SD difference were pooled by random effects meta-analysis.

FINDINGS: Of 21 eligible studies, 16 with 36,984 participants were included. During a mean follow-up of 7{\textperiodcentered}0 years, 1519 myocardial infarctions, 1339 strokes, and 2028 combined endpoints (myocardial infarction, stroke, vascular death) occurred. Yearly cIMT progression was derived from two ultrasound visits 2-7 years (median 4 years) apart. For mean common carotid artery intima-media thickness progression, the overall HR of the combined endpoint was 0{\textperiodcentered}97 (95\% CI 0{\textperiodcentered}94-1{\textperiodcentered}00) when adjusted for age, sex, and mean common carotid artery intima-media thickness, and 0{\textperiodcentered}98 (0{\textperiodcentered}95-1{\textperiodcentered}01) when also adjusted for vascular risk factors. Although we detected no associations with cIMT progression in sensitivity analyses, the mean cIMT of the two ultrasound scans was positively and robustly associated with cardiovascular risk (HR for the combined endpoint 1{\textperiodcentered}16, 95\% CI 1{\textperiodcentered}10-1{\textperiodcentered}22, adjusted for age, sex, mean common carotid artery intima-media thickness progression, and vascular risk factors). In three studies including 3439 participants who had four ultrasound scans, cIMT progression did not correlate between occassions (reproducibility correlations between r=-0{\textperiodcentered}06 and r=-0{\textperiodcentered}02).

INTERPRETATION: The association between cIMT progression assessed from two ultrasound scans and cardiovascular risk in the general population remains unproven. No conclusion can be derived for the use of cIMT progression as a surrogate in clinical trials.

FUNDING: Deutsche Forschungsgemeinschaft.

}, keywords = {Cardiovascular Diseases, Carotid Intima-Media Thickness, Disease Progression, Follow-Up Studies, Humans, Myocardial Infarction, Prognosis, Risk Assessment, Stroke}, issn = {1474-547X}, doi = {10.1016/S0140-6736(12)60441-3}, author = {Lorenz, Matthias W and Polak, Joseph F and Kavousi, Maryam and Mathiesen, Ellisiv B and V{\"o}lzke, Henry and Tuomainen, Tomi-Pekka and Sander, Dirk and Plichart, Matthieu and Catapano, Alberico L and Robertson, Christine M and Kiechl, Stefan and Rundek, Tatjana and Desvarieux, Mo{\"\i}se and Lind, Lars and Schmid, Caroline and DasMahapatra, Pronabesh and Gao, Lu and Ziegelbauer, Kathrin and Bots, Michiel L and Thompson, Simon G} } @article {1399, title = {Lipid-related markers and cardiovascular disease prediction.}, journal = {JAMA}, volume = {307}, year = {2012}, month = {2012 Jun 20}, pages = {2499-506}, abstract = {

CONTEXT: The value of assessing various emerging lipid-related markers for prediction of first cardiovascular events is debated.

OBJECTIVE: To determine whether adding information on apolipoprotein B and apolipoprotein A-I, lipoprotein(a), or lipoprotein-associated phospholipase A2 to total cholesterol and high-density lipoprotein cholesterol (HDL-C) improves cardiovascular disease (CVD) risk prediction.

DESIGN, SETTING, AND PARTICIPANTS: Individual records were available for 165,544 participants without baseline CVD in 37 prospective cohorts (calendar years of recruitment: 1968-2007) with up to 15,126 incident fatal or nonfatal CVD outcomes (10,132 CHD and 4994 stroke outcomes) during a median follow-up of 10.4 years (interquartile range, 7.6-14 years).

MAIN OUTCOME MEASURES: Discrimination of CVD outcomes and reclassification of participants across predicted 10-year risk categories of low (<10\%), intermediate (10\%-<20\%), and high (>=20\%) risk.

RESULTS: The addition of information on various lipid-related markers to total cholesterol, HDL-C, and other conventional risk factors yielded improvement in the model{\textquoteright}s discrimination: C-index change, 0.0006 (95\% CI, 0.0002-0.0009) for the combination of apolipoprotein B and A-I; 0.0016 (95\% CI, 0.0009-0.0023) for lipoprotein(a); and 0.0018 (95\% CI, 0.0010-0.0026) for lipoprotein-associated phospholipase A2 mass. Net reclassification improvements were less than 1\% with the addition of each of these markers to risk scores containing conventional risk factors. We estimated that for 100,000 adults aged 40 years or older, 15,436 would be initially classified at intermediate risk using conventional risk factors alone. Additional testing with a combination of apolipoprotein B and A-I would reclassify 1.1\%; lipoprotein(a), 4.1\%; and lipoprotein-associated phospholipase A2 mass, 2.7\% of people to a 20\% or higher predicted CVD risk category and, therefore, in need of statin treatment under Adult Treatment Panel III guidelines.

CONCLUSION: In a study of individuals without known CVD, the addition of information on the combination of apolipoprotein B and A-I, lipoprotein(a), or lipoprotein-associated phospholipase A2 mass to risk scores containing total cholesterol and HDL-C led to slight improvement in CVD prediction.

}, keywords = {Aged, Biomarkers, Cardiovascular Diseases, Cholesterol, HDL, Cohort Studies, Female, Humans, Lipoproteins, Male, Middle Aged, Risk Assessment}, issn = {1538-3598}, doi = {10.1001/jama.2012.6571}, author = {Di Angelantonio, Emanuele and Gao, Pei and Pennells, Lisa and Kaptoge, Stephen and Caslake, Muriel and Thompson, Alexander and Butterworth, Adam S and Sarwar, Nadeem and Wormser, David and Saleheen, Danish and Ballantyne, Christie M and Psaty, Bruce M and Sundstr{\"o}m, Johan and Ridker, Paul M and Nagel, Dorothea and Gillum, Richard F and Ford, Ian and Ducimetiere, Pierre and Kiechl, Stefan and Koenig, Wolfgang and Dullaart, Robin P F and Assmann, Gerd and D{\textquoteright}Agostino, Ralph B and Dagenais, Gilles R and Cooper, Jackie A and Kromhout, Daan and Onat, Altan and Tipping, Robert W and G{\'o}mez-de-la-C{\'a}mara, Agust{\'\i}n and Rosengren, Annika and Sutherland, Susan E and Gallacher, John and Fowkes, F Gerry R and Casiglia, Edoardo and Hofman, Albert and Salomaa, Veikko and Barrett-Connor, Elizabeth and Clarke, Robert and Brunner, Eric and Jukema, J Wouter and Simons, Leon A and Sandhu, Manjinder and Wareham, Nicholas J and Khaw, Kay-Tee and Kauhanen, Jussi and Salonen, Jukka T and Howard, William J and Nordestgaard, B{\o}rge G and Wood, Angela M and Thompson, Simon G and Boekholdt, S Matthijs and Sattar, Naveed and Packard, Chris and Gudnason, Vilmundur and Danesh, John} } @article {6559, title = {Glycated hemoglobin measurement and prediction of cardiovascular disease.}, journal = {JAMA}, volume = {311}, year = {2014}, month = {2014 Mar 26}, pages = {1225-33}, abstract = {

IMPORTANCE: The value of measuring levels of glycated hemoglobin (HbA1c) for the prediction of first cardiovascular events is uncertain.

OBJECTIVE: To determine whether adding information on HbA1c values to conventional cardiovascular risk factors is associated with improvement in prediction of cardiovascular disease (CVD) risk.

DESIGN, SETTING, AND PARTICIPANTS: Analysis of individual-participant data available from 73 prospective studies involving 294,998 participants without a known history of diabetes mellitus or CVD at the baseline assessment.

MAIN OUTCOMES AND MEASURES: Measures of risk discrimination for CVD outcomes (eg, C-index) and reclassification (eg, net reclassification improvement) of participants across predicted 10-year risk categories of low (<5\%), intermediate (5\% to <7.5\%), and high (>= 7.5\%) risk.

RESULTS: During a median follow-up of 9.9 (interquartile range, 7.6-13.2) years, 20,840 incident fatal and nonfatal CVD outcomes (13,237 coronary heart disease and 7603 stroke outcomes) were recorded. In analyses adjusted for several conventional cardiovascular risk factors, there was an approximately J-shaped association between HbA1c values and CVD risk. The association between HbA1c values and CVD risk changed only slightly after adjustment for total cholesterol and triglyceride concentrations or estimated glomerular filtration rate, but this association attenuated somewhat after adjustment for concentrations of high-density lipoprotein cholesterol and C-reactive protein. The C-index for a CVD risk prediction model containing conventional cardiovascular risk factors alone was 0.7434 (95\% CI, 0.7350 to 0.7517). The addition of information on HbA1c was associated with a C-index change of 0.0018 (0.0003 to 0.0033) and a net reclassification improvement of 0.42 (-0.63 to 1.48) for the categories of predicted 10-year CVD risk. The improvement provided by HbA1c assessment in prediction of CVD risk was equal to or better than estimated improvements for measurement of fasting, random, or postload plasma glucose levels.

CONCLUSIONS AND RELEVANCE: In a study of individuals without known CVD or diabetes, additional assessment of HbA1c values in the context of CVD risk assessment provided little incremental benefit for prediction of CVD risk.

}, keywords = {Aged, C-Reactive Protein, Cholesterol, HDL, Coronary Disease, Diabetes Mellitus, Female, Glycated Hemoglobin A, Humans, Male, Middle Aged, Predictive Value of Tests, Prospective Studies, Risk Assessment, Stroke}, issn = {1538-3598}, doi = {10.1001/jama.2014.1873}, author = {Di Angelantonio, Emanuele and Gao, Pei and Khan, Hassan and Butterworth, Adam S and Wormser, David and Kaptoge, Stephen and Kondapally Seshasai, Sreenivasa Rao and Thompson, Alex and Sarwar, Nadeem and Willeit, Peter and Ridker, Paul M and Barr, Elizabeth L M and Khaw, Kay-Tee and Psaty, Bruce M and Brenner, Hermann and Balkau, Beverley and Dekker, Jacqueline M and Lawlor, Debbie A and Daimon, Makoto and Willeit, Johann and Nj{\o}lstad, Inger and Nissinen, Aulikki and Brunner, Eric J and Kuller, Lewis H and Price, Jackie F and Sundstr{\"o}m, Johan and Knuiman, Matthew W and Feskens, Edith J M and Verschuren, W M M and Wald, Nicholas and Bakker, Stephan J L and Whincup, Peter H and Ford, Ian and Goldbourt, Uri and G{\'o}mez-de-la-C{\'a}mara, Agust{\'\i}n and Gallacher, John and Simons, Leon A and Rosengren, Annika and Sutherland, Susan E and Bj{\"o}rkelund, Cecilia and Blazer, Dan G and Wassertheil-Smoller, Sylvia and Onat, Altan and Mar{\'\i}n Iba{\~n}ez, Alejandro and Casiglia, Edoardo and Jukema, J Wouter and Simpson, Lara M and Giampaoli, Simona and Nordestgaard, B{\o}rge G and Selmer, Randi and Wennberg, Patrik and Kauhanen, Jussi and Salonen, Jukka T and Dankner, Rachel and Barrett-Connor, Elizabeth and Kavousi, Maryam and Gudnason, Vilmundur and Evans, Denis and Wallace, Robert B and Cushman, Mary and D{\textquoteright}Agostino, Ralph B and Umans, Jason G and Kiyohara, Yutaka and Nakagawa, Hidaeki and Sato, Shinichi and Gillum, Richard F and Folsom, Aaron R and van der Schouw, Yvonne T and Moons, Karel G and Griffin, Simon J and Sattar, Naveed and Wareham, Nicholas J and Selvin, Elizabeth and Thompson, Simon G and Danesh, John} } @article {6811, title = {Association of Cardiometabolic Multimorbidity With Mortality.}, journal = {JAMA}, volume = {314}, year = {2015}, month = {2015 Jul 7}, pages = {52-60}, abstract = {

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 MEASURES: All-cause mortality and estimated reductions in life expectancy.

RESULTS: 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.

CONCLUSIONS AND RELEVANCE: 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.

}, keywords = {Adult, Aged, Comorbidity, Diabetes Mellitus, Female, Humans, Life Expectancy, Male, Middle Aged, Mortality, Myocardial Infarction, Risk Factors, Stroke}, issn = {1538-3598}, doi = {10.1001/jama.2015.7008}, author = {Di Angelantonio, Emanuele and Kaptoge, Stephen and Wormser, David and Willeit, Peter and Butterworth, Adam S and Bansal, Narinder and O{\textquoteright}Keeffe, Linda M and Gao, Pei and Wood, Angela M and Burgess, Stephen and Freitag, Daniel F and Pennells, Lisa and Peters, Sanne A and Hart, Carole L and H{\r a}heim, Lise Lund and Gillum, Richard F and Nordestgaard, B{\o}rge G and Psaty, Bruce M and Yeap, Bu B and Knuiman, Matthew W and Nietert, Paul J and Kauhanen, Jussi and Salonen, Jukka T and Kuller, Lewis H and Simons, Leon A and van der Schouw, Yvonne T and Barrett-Connor, Elizabeth and Selmer, Randi and Crespo, Carlos J and Rodriguez, Beatriz and Verschuren, W M Monique and Salomaa, Veikko and Sv{\"a}rdsudd, Kurt and van der Harst, Pim and Bj{\"o}rkelund, Cecilia and Wilhelmsen, Lars and Wallace, Robert B and Brenner, Hermann and Amouyel, Philippe and Barr, Elizabeth L M and Iso, Hiroyasu and Onat, Altan and Trevisan, Maurizio and D{\textquoteright}Agostino, Ralph B and Cooper, Cyrus and Kavousi, Maryam and Welin, Lennart and Roussel, Ronan and Hu, Frank B and Sato, Shinichi and Davidson, Karina W and Howard, Barbara V and Leening, Maarten J G and Leening, Maarten and Rosengren, Annika and D{\"o}rr, Marcus and Deeg, Dorly J H and Kiechl, Stefan and Stehouwer, Coen D A and Nissinen, Aulikki and Giampaoli, Simona and Donfrancesco, Chiara and Kromhout, Daan and Price, Jackie F and Peters, Annette and Meade, Tom W and Casiglia, Edoardo and Lawlor, Debbie A and Gallacher, John and Nagel, Dorothea and Franco, Oscar H and Assmann, Gerd and Dagenais, Gilles R and Jukema, J Wouter and Sundstr{\"o}m, Johan and Woodward, Mark and Brunner, Eric J and Khaw, Kay-Tee and Wareham, Nicholas J and Whitsel, Eric A and Nj{\o}lstad, Inger and Hedblad, Bo and Wassertheil-Smoller, Sylvia and Engstr{\"o}m, Gunnar and Rosamond, Wayne D and Selvin, Elizabeth and Sattar, Naveed and Thompson, Simon G and Danesh, John} } @article {6612, title = {Inflammatory markers and extent and progression of early atherosclerosis: Meta-analysis of individual-participant-data from 20 prospective studies of the PROG-IMT collaboration.}, journal = {Eur J Prev Cardiol}, volume = {23}, year = {2016}, month = {2016 Jan}, pages = {194-205}, abstract = {

BACKGROUND: Large-scale epidemiological evidence on the role of inflammation in early atherosclerosis, assessed by carotid ultrasound, is lacking. We aimed to quantify cross-sectional and longitudinal associations of inflammatory markers with common-carotid-artery intima-media thickness (CCA-IMT) in the general population.

METHODS: Information on high-sensitivity C-reactive protein, fibrinogen, leucocyte count and CCA-IMT was available in 20 prospective cohort studies of the PROG-IMT collaboration involving 49,097 participants free of pre-existing cardiovascular disease. Estimates of associations were calculated within each study and then combined using random-effects meta-analyses.

RESULTS: Mean baseline CCA-IMT amounted to 0.74 mm (SD = 0.18) and mean CCA-IMT progression over a mean of 3.9 years to 0.011 mm/year (SD = 0.039). Cross-sectional analyses showed positive linear associations between inflammatory markers and baseline CCA-IMT. After adjustment for traditional cardiovascular risk factors, mean differences in baseline CCA-IMT per one-SD higher inflammatory marker were: 0.0082 mm for high-sensitivity C-reactive protein (p < 0.001); 0.0072 mm for fibrinogen (p < 0.001); and 0.0025 mm for leucocyte count (p = 0.033). {\textquoteright}Inflammatory load{\textquoteright}, defined as the number of elevated inflammatory markers (i.e. in upper two quintiles), showed a positive linear association with baseline CCA-IMT (p < 0.001). Longitudinal associations of baseline inflammatory markers and changes therein with CCA-IMT progression were null or at most weak. Participants with the highest {\textquoteright}inflammatory load{\textquoteright} had a greater CCA-IMT progression (p = 0.015).

CONCLUSION: Inflammation was independently associated with CCA-IMT cross-sectionally. The lack of clear associations with CCA-IMT progression may be explained by imprecision in its assessment within a limited time period. Our findings for {\textquoteright}inflammatory load{\textquoteright} suggest important combined effects of the three inflammatory markers on early atherosclerosis.

}, issn = {2047-4881}, doi = {10.1177/2047487314560664}, author = {Willeit, Peter and Thompson, Simon G and Agewall, Stefan and Bergstr{\"o}m, G{\"o}ran and Bickel, Horst and Catapano, Alberico L and Chien, Kuo-Liong and de Groot, Eric and Empana, Jean-Philippe and Etgen, Thorleif and Franco, Oscar H and Iglseder, Bernhard and Johnsen, Stein H and Kavousi, Maryam and Lind, Lars and Liu, Jing and Mathiesen, Ellisiv B and Norata, Giuseppe D and Olsen, Michael H and Papagianni, Aikaterini and Poppert, Holger and Price, Jackie F and Sacco, Ralph L and Yanez, David N and Zhao, Dong and Schminke, Ulf and B{\"u}lb{\"u}l, Alpaslan and Polak, Joseph F and Sitzer, Matthias and Hofman, Albert and Grigore, Liliana and D{\"o}rr, Marcus and Su, Ta-Chen and Ducimetiere, Pierre and Xie, Wuxiang and Ronkainen, Kimmo and Kiechl, Stefan and Rundek, Tatjana and Robertson, Christine and Fagerberg, Bj{\"o}rn and Bokemark, Lena and Steinmetz, Helmuth and Ikram, M Arfan and V{\"o}lzke, Henry and Lin, Hung-Ju and Plichart, Matthieu and Tuomainen, Tomi-Pekka and Desvarieux, Mo{\"\i}se and McLachlan, Stela and Schmidt, Caroline and Kauhanen, Jussi and Willeit, Johann and Lorenz, Matthias W and Sander, Dirk} } @article {7451, title = {REPEATED MEASUREMENTS OF BLOOD PRESSURE AND CHOLESTEROL IMPROVES CARDIOVASCULAR DISEASE RISK PREDICTION: AN INDIVIDUAL-PARTICIPANT-DATA META-ANALYSIS.}, journal = {Am J Epidemiol}, year = {2017}, month = {2017 May 26}, abstract = {

The added value of incorporating information from repeated measurements of blood pressure and cholesterol for cardiovascular disease (CVD) risk prediction has not been rigorously assessed. We used data from the Emerging Risk Factors Collaboration on 191,445 adults (38 cohorts from across 17 countries with data from 1962-2014) with~>~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 means of repeated measurements and summary measures from longitudinal modelling of the repeated measurements were compared to models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analysed across studies. Compared to the single time point model, the cumulative means and the longitudinal models increased the C-index by 0.0040 (95\% CI: 0.0023, 0.0057) and 0.0023 (0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared to the single time point model, overall net reclassification improvements were 0.0369 (0.0303, 0.0436) for the cumulative means model and 0.0177 (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.

}, issn = {1476-6256}, doi = {10.1093/aje/kwx149}, author = {Paige, Ellie and Barrett, Jessica and Pennells, Lisa and Sweeting, Michael and Willeit, Peter and Di Angelantonio, Emanuele and Gudnason, Vilmundur and Nordestgaard, B{\o}rge G and Psaty, Bruce M and Goldbourt, Uri and Best, Lyle G and Assmann, Gerd and Salonen, Jukka T and Nietert, Paul J and Verschuren, Wm Monique and Brunner, Eric J and Kronmal, Richard A and Salomaa, Veikko and Bakker, Stephan Jl and Dagenais, Gilles R and Sato, Shinichi and Jansson, Jan-H{\r a}kan and Willeit, Johann and Onat, Altan and de la C{\'a}mara, Agustin G{\'o}mez and Roussel, Ronan and V{\"o}lzke, Henry and Dankner, Rachel and Tipping, Robert W and Meade, Tom W and Donfrancesco, Chiara and Kuller, Lewis H and Peters, Annette and Gallacher, John and Kromhout, Daan and Iso, Hiroyasu and Knuiman, Matthew and Casiglia, Edoardo and Kavousi, Maryam and Palmieri, Luigi and Sundstr{\"o}m, Johan and Davis, Barry R and Nj{\o}lstad, Inger and Couper, David and Danesh, John and Thompson, Simon G and Wood, Angela} } @article {7923, title = {Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies.}, journal = {Eur Heart J}, year = {2018}, month = {2018 Nov 22}, abstract = {

Aims: There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after {\textquoteright}recalibration{\textquoteright}, a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied.

Methods and results: Using individual-participant data on 360 737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at {\textquoteright}high{\textquoteright} 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE over-predicted CVD risk on average by 10\%, 52\%, and 41\%, respectively, whereas RRS under-predicted by 10\%. Original versions of algorithms classified 29-39\% of individuals aged >=40 years as high risk. By contrast, recalibration reduced this proportion to 22-24\% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44-51 such individuals using original algorithms, in contrast to 37-39 individuals with recalibrated algorithms.

Conclusion: Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need.

}, issn = {1522-9645}, doi = {10.1093/eurheartj/ehy653}, author = {Pennells, Lisa and Kaptoge, Stephen and Wood, Angela and Sweeting, Mike and Zhao, Xiaohui and White, Ian and Burgess, Stephen and Willeit, Peter and Bolton, Thomas and Moons, Karel G M and van der Schouw, Yvonne T and Selmer, Randi and Khaw, Kay-Tee and Gudnason, Vilmundur and Assmann, Gerd and Amouyel, Philippe and Salomaa, Veikko and Kivimaki, Mika and Nordestgaard, B{\o}rge G and Blaha, Michael J and Kuller, Lewis H and Brenner, Hermann and Gillum, Richard F and Meisinger, Christa and Ford, Ian and Knuiman, Matthew W and Rosengren, Annika and Lawlor, Debbie A and V{\"o}lzke, Henry and Cooper, Cyrus and Mar{\'\i}n Iba{\~n}ez, Alejandro and Casiglia, Edoardo and Kauhanen, Jussi and Cooper, Jackie A and Rodriguez, Beatriz and Sundstr{\"o}m, Johan and Barrett-Connor, Elizabeth and Dankner, Rachel and Nietert, Paul J and Davidson, Karina W and Wallace, Robert B and Blazer, Dan G and Bj{\"o}rkelund, Cecilia and Donfrancesco, Chiara and Krumholz, Harlan M and Nissinen, Aulikki and Davis, Barry R and Coady, Sean and Whincup, Peter H and J{\o}rgensen, Torben and Ducimetiere, Pierre and Trevisan, Maurizio and Engstr{\"o}m, Gunnar and Crespo, Carlos J and Meade, Tom W and Visser, Marjolein and Kromhout, Daan and Kiechl, Stefan and Daimon, Makoto and Price, Jackie F and G{\'o}mez de la C{\'a}mara, Agustin and Wouter Jukema, J and Lamarche, Beno{\^\i}t and Onat, Altan and Simons, Leon A and Kavousi, Maryam and Ben-Shlomo, Yoav and Gallacher, John and Dekker, Jacqueline M and Arima, Hisatomi and Shara, Nawar and Tipping, Robert W and Roussel, Ronan and Brunner, Eric J and Koenig, Wolfgang and Sakurai, Masaru and Pavlovic, Jelena and Gansevoort, Ron T and Nagel, Dorothea and Goldbourt, Uri and Barr, Elizabeth L M and Palmieri, Luigi and Nj{\o}lstad, Inger and Sato, Shinichi and Monique Verschuren, W M and Varghese, Cherian V and Graham, Ian and Onuma, Oyere and Greenland, Philip and Woodward, Mark and Ezzati, Majid and Psaty, Bruce M and Sattar, Naveed and Jackson, Rod and Ridker, Paul M and Cook, Nancy R and D{\textquoteright}Agostino, Ralph B and Thompson, Simon G and Danesh, John and Di Angelantonio, Emanuele} } @article {7846, title = {Predictive value for cardiovascular events of common carotid intima media thickness and its rate of change in individuals at high cardiovascular risk - Results from the PROG-IMT collaboration.}, journal = {PLoS One}, volume = {13}, year = {2018}, month = {2018}, pages = {e0191172}, abstract = {

AIMS: Carotid intima media thickness (CIMT) predicts cardiovascular (CVD) events, but the predictive value of CIMT change is debated. We assessed the relation between CIMT change and events in individuals at high cardiovascular risk.

METHODS AND RESULTS: From 31 cohorts with two CIMT scans (total n = 89070) on average 3.6 years apart and clinical follow-up, subcohorts were drawn: (A) individuals with at least 3 cardiovascular risk factors without previous CVD events, (B) individuals with carotid plaques without previous CVD events, and (C) individuals with previous CVD events. Cox regression models were fit to estimate the hazard ratio (HR) of the combined endpoint (myocardial infarction, stroke or vascular death) per standard deviation (SD) of CIMT change, adjusted for CVD risk factors. These HRs were pooled across studies. In groups A, B and C we observed 3483, 2845 and 1165 endpoint events, respectively. Average common CIMT was 0.79mm (SD 0.16mm), and annual common CIMT change was 0.01mm (SD 0.07mm), both in group A. The pooled HR per SD of annual common CIMT change (0.02 to 0.43mm) was 0.99 (95\% confidence interval: 0.95-1.02) in group A, 0.98 (0.93-1.04) in group B, and 0.95 (0.89-1.04) in group C. The HR per SD of common CIMT (average of the first and the second CIMT scan, 0.09 to 0.75mm) was 1.15 (1.07-1.23) in group A, 1.13 (1.05-1.22) in group B, and 1.12 (1.05-1.20) in group C.

CONCLUSIONS: We confirm that common CIMT is associated with future CVD events in individuals at high risk. CIMT change does not relate to future event risk in high-risk individuals.

}, keywords = {Aged, Cardiovascular Diseases, Carotid Intima-Media Thickness, Female, Humans, Intersectoral Collaboration, Male, Middle Aged, Prognosis, Risk Factors}, issn = {1932-6203}, doi = {10.1371/journal.pone.0191172}, author = {Lorenz, Matthias W and Gao, Lu and Ziegelbauer, Kathrin and Norata, Giuseppe Danilo and Empana, Jean Philippe and Schmidtmann, Irene and Lin, Hung-Ju and McLachlan, Stela and Bokemark, Lena and Ronkainen, Kimmo and Amato, Mauro and Schminke, Ulf and Srinivasan, Sathanur R and Lind, Lars and Okazaki, Shuhei and Stehouwer, Coen D A and Willeit, Peter and Polak, Joseph F and Steinmetz, Helmuth and Sander, Dirk and Poppert, Holger and Desvarieux, Mo{\"\i}se and Ikram, M Arfan and Johnsen, Stein Harald and Staub, Daniel and Sirtori, Cesare R and Iglseder, Bernhard and Beloqui, Oscar and Engstr{\"o}m, Gunnar and Friera, Alfonso and Rozza, Francesco and Xie, Wuxiang and Parraga, Grace and Grigore, Liliana and Plichart, Matthieu and Blankenberg, Stefan and Su, Ta-Chen and Schmidt, Caroline and Tuomainen, Tomi-Pekka and Veglia, Fabrizio and V{\"o}lzke, Henry and Nijpels, Giel and Willeit, Johann and Sacco, Ralph L and Franco, Oscar H and Uthoff, Heiko and Hedblad, Bo and Suarez, Carmen and Izzo, Raffaele and Zhao, Dong and Wannarong, Thapat and Catapano, Alberico and Ducimetiere, Pierre and Espinola-Klein, Christine and Chien, Kuo-Liong and Price, Jackie F and Bergstr{\"o}m, G{\"o}ran and Kauhanen, Jussi and Tremoli, Elena and D{\"o}rr, Marcus and Berenson, Gerald and Kitagawa, Kazuo and Dekker, Jacqueline M and Kiechl, Stefan and Sitzer, Matthias and Bickel, Horst and Rundek, Tatjana and Hofman, Albert and Mathiesen, Ellisiv B and Castelnuovo, Samuela and Landecho, Manuel F and Rosvall, Maria and Gabriel, Rafael and de Luca, Nicola and Liu, Jing and Baldassarre, Damiano and Kavousi, Maryam and de Groot, Eric and Bots, Michiel L and Yanez, David N and Thompson, Simon G} } @article {7664, title = {Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies.}, journal = {Lancet}, volume = {391}, year = {2018}, month = {2018 04 14}, pages = {1513-1523}, abstract = {

BACKGROUND: Low-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.

METHODS: We 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{\textperiodcentered}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{\textperiodcentered}6 years [5th-95th percentile 1{\textperiodcentered}04-13{\textperiodcentered}5]) from 71 011 participants from 37 studies.

FINDINGS: In the 599 912 current drinkers included in the analysis, we recorded 40 310 deaths and 39 018 incident cardiovascular disease events during 5{\textperiodcentered}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{\textperiodcentered}14, 95\% CI, 1{\textperiodcentered}10-1{\textperiodcentered}17), coronary disease excluding myocardial infarction (1{\textperiodcentered}06, 1{\textperiodcentered}00-1{\textperiodcentered}11), heart failure (1{\textperiodcentered}09, 1{\textperiodcentered}03-1{\textperiodcentered}15), fatal hypertensive disease (1{\textperiodcentered}24, 1{\textperiodcentered}15-1{\textperiodcentered}33); and fatal aortic aneurysm (1{\textperiodcentered}15, 1{\textperiodcentered}03-1{\textperiodcentered}28). By contrast, increased alcohol consumption was log-linearly associated with a lower risk of myocardial infarction (HR 0{\textperiodcentered}94, 0{\textperiodcentered}91-0{\textperiodcentered}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.

INTERPRETATION: In 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.

FUNDING: UK Medical Research Council, British Heart Foundation, National Institute for Health Research, European Union Framework 7, and European Research Council.

}, issn = {1474-547X}, doi = {10.1016/S0140-6736(18)30134-X}, author = {Wood, Angela M and Kaptoge, Stephen and Butterworth, Adam S and Willeit, Peter and Warnakula, Samantha and Bolton, Thomas and Paige, Ellie and Paul, Dirk S and Sweeting, Michael and Burgess, Stephen and Bell, Steven and Astle, William and Stevens, David and Koulman, Albert and Selmer, Randi M and Verschuren, W M Monique and Sato, Shinichi and Nj{\o}lstad, Inger and Woodward, Mark and Salomaa, Veikko and Nordestgaard, B{\o}rge G and Yeap, Bu B and Fletcher, Astrid and Melander, Olle and Kuller, Lewis H and Balkau, Beverley and Marmot, Michael and Koenig, Wolfgang and Casiglia, Edoardo and Cooper, Cyrus and Arndt, Volker and Franco, Oscar H and Wennberg, Patrik and Gallacher, John and de la C{\'a}mara, Agustin G{\'o}mez and V{\"o}lzke, Henry and Dahm, Christina C and Dale, Caroline E and Bergmann, Manuela M and Crespo, Carlos J and van der Schouw, Yvonne T and Kaaks, Rudolf and Simons, Leon A and Lagiou, Pagona and Schoufour, Josje D and Boer, Jolanda M A and Key, Timothy J and Rodriguez, Beatriz and Moreno-Iribas, Conchi and Davidson, Karina W and Taylor, James O and Sacerdote, Carlotta and Wallace, Robert B and Quiros, J Ramon and Tumino, Rosario and Blazer, Dan G and Linneberg, Allan and Daimon, Makoto and Panico, Salvatore and Howard, Barbara and Skeie, Guri and Strandberg, Timo and Weiderpass, Elisabete and Nietert, Paul J and Psaty, Bruce M and Kromhout, Daan and Salamanca-Fernandez, Elena and Kiechl, Stefan and Krumholz, Harlan M and Grioni, Sara and Palli, Domenico and Huerta, Jos{\'e} M and Price, Jackie and Sundstr{\"o}m, Johan and Arriola, Larraitz and Arima, Hisatomi and Travis, Ruth C and Panagiotakos, Demosthenes B and Karakatsani, Anna and Trichopoulou, Antonia and K{\"u}hn, Tilman and Grobbee, Diederick E and Barrett-Connor, Elizabeth and van Schoor, Natasja and Boeing, Heiner and Overvad, Kim and Kauhanen, Jussi and Wareham, Nick and Langenberg, Claudia and Forouhi, Nita and Wennberg, Maria and Despr{\'e}s, Jean-Pierre and Cushman, Mary and Cooper, Jackie A and Rodriguez, Carlos J and Sakurai, Masaru and Shaw, Jonathan E and Knuiman, Matthew and Voortman, Trudy and Meisinger, Christa and Tj{\o}nneland, Anne and Brenner, Hermann and Palmieri, Luigi and Dallongeville, Jean and Brunner, Eric J and Assmann, Gerd and Trevisan, Maurizio and Gillum, Richard F and Ford, Ian and Sattar, Naveed and Lazo, Mariana and Thompson, Simon G and Ferrari, Pietro and Leon, David A and Smith, George Davey and Peto, Richard and Jackson, Rod and Banks, Emily and Di Angelantonio, Emanuele and Danesh, John} }