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11.
  • Hageman, Steven H. J., et al. (author)
  • Prediction of individual lifetime cardiovascular risk and potential treatment benefit: development and recalibration of the LIFE-CVD2 model to four European risk regions
  • 2024
  • In: EUROPEAN JOURNAL OF PREVENTIVE CARDIOLOGY. - 2047-4873 .- 2047-4881.
  • Journal article (peer-reviewed)abstract
    • Aims The 2021 European Society of Cardiology prevention guidelines recommend the use of (lifetime) risk prediction models to aid decisions regarding initiation of prevention. We aimed to update and systematically recalibrate the LIFEtime-perspective CardioVascular Disease (LIFE-CVD) model to four European risk regions for the estimation of lifetime CVD risk for apparently healthy individuals.Methods and results The updated LIFE-CVD (i.e. LIFE-CVD2) models were derived using individual participant data from 44 cohorts in 13 countries (687 135 individuals without established CVD, 30 939 CVD events in median 10.7 years of follow-up). LIFE-CVD2 uses sex-specific functions to estimate the lifetime risk of fatal and non-fatal CVD events with adjustment for the competing risk of non-CVD death and is systematically recalibrated to four distinct European risk regions. The updated models showed good discrimination in external validation among 1 657 707 individuals (61 311 CVD events) from eight additional European cohorts in seven countries, with a pooled C-index of 0.795 (95% confidence interval 0.767-0.822). Predicted and observed CVD event risks were well calibrated in population-wide electronic health records data in the UK (Clinical Practice Research Datalink) and the Netherlands (Extramural LUMC Academic Network). When using LIFE-CVD2 to estimate potential gain in CVD-free life expectancy from preventive therapy, projections varied by risk region reflecting important regional differences in absolute lifetime risk. For example, a 50-year-old smoking woman with a systolic blood pressure (SBP) of 140 mmHg was estimated to gain 0.9 years in the low-risk region vs. 1.6 years in the very high-risk region from lifelong 10 mmHg SBP reduction. The benefit of smoking cessation for this individual ranged from 3.6 years in the low-risk region to 4.8 years in the very high-risk region.Conclusion By taking into account geographical differences in CVD incidence using contemporary representative data sources, the recalibrated LIFE-CVD2 model provides a more accurate tool for the prediction of lifetime risk and CVD-free life expectancy for individuals without previous CVD, facilitating shared decision-making for cardiovascular prevention as recommended by 2021 European guidelines. The study introduces LIFE-CVD2, a new tool that helps predict the risk of heart disease over a person's lifetime, and highlights how where you live in Europe can affect this risk. Using health information from over 687 000 people, LIFE-CVD2 looks at things like blood pressure and whether someone smokes to figure out their chance of having heart problems later in life. Health information from another 1.6 million people in seven different European countries was used to show that it did a good job of predicting who might develop heart disease.Knowing your heart disease risk over your whole life helps doctors give you the best advice to keep your heart healthy. Let us say there is a 50-year-old woman who smokes and has a bit high blood pressure. Right now, she might not look like she is in danger. But with the LIFE-CVD2 tool, doctors can show her how making changes today, like lowering her blood pressure or stopping smoking, could mean many more years without heart problems. These healthy changes can make a big difference over many years.
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12.
  • Justice, Anne E., et al. (author)
  • Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution
  • 2019
  • In: Nature Genetics. - : Nature Publishing Group. - 1061-4036 .- 1546-1718. ; 51:3, s. 452-469
  • Journal article (peer-reviewed)abstract
    • Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF >= 5%) and nine low-frequency or rare (MAF < 5%) coding novel variants. Pathway/gene set enrichment analyses identified lipid particle, adiponectin, abnormal white adipose tissue physiology and bone development and morphology as important contributors to fat distribution, while cross-trait associations highlight cardiometabolic traits. In functional follow-up analyses, specifically in Drosophila RNAi-knockdowns, we observed a significant increase in the total body triglyceride levels for two genes (DNAH10 and PLXND1). We implicate novel genes in fat distribution, stressing the importance of interrogating low-frequency and protein-coding variants.
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13.
  • Kaptoge, Stephen, et al. (author)
  • C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality : an individual participant meta-analysis
  • 2010
  • In: The Lancet. - 0140-6736 .- 1474-547X. ; 375:9709, s. 132-140
  • Journal article (peer-reviewed)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.
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14.
  • Kaptoge, Stephen, et al. (author)
  • World Health Organization cardiovascular disease risk charts : revised models to estimate risk in 21 global regions
  • 2019
  • In: The Lancet Global Health. - 2214-109X. ; 7:10, s. E1332-E1345
  • Journal article (peer-reviewed)abstract
    • Background: To 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.Methods: In 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.Findings: Our 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.Interpretation: We 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.Funding: World Health Organization, British Heart Foundation (BHF), BHF Cambridge Centre for Research Excellence, UK Medical Research Council, and National Institute for Health Research.
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15.
  • Key, Timothy J., et al. (author)
  • Consumption of Meat, Fish, Dairy Products, Eggs and Risk of Ischemic Heart Disease : A Prospective Study of 7198 Incident Cases Among 409,885 Participants in the Pan-European EPIC Cohort
  • 2019
  • In: Circulation. - : Wolters Kluwer. - 0009-7322 .- 1524-4539. ; 139:25, s. 2835-2845
  • Journal article (peer-reviewed)abstract
    • BACKGROUND: There is uncertainty about the relevance of animal foods to the etiology of ischemic heart disease (IHD). We examined meat, fish, dairy products and eggs and risk for IHD in the pan-European EPIC cohort.METHODS: A prospective study of 409,885 men and women in nine European countries. Diet was assessed using validated questionnaires, calibrated using 24-hour recalls. Lipids and blood pressure were measured in a subsample. During 12.6 years mean follow up, 7198 participants had a myocardial infarction or died from IHD. The relationships of animal foods with risk were examined using Cox regression with adjustment for other animal foods and relevant covariates.RESULTS: The hazard ratio (HR) for IHD was 1.19 (95% CI 1.06-1.33) for a 100 g/d increment in intake of red and processed meat, and this remained significant after excluding the first 4 years of follow-up (HR 1.25 [1.09-1.42]). Risk was inversely associated with intakes of yogurt (HR 0.93 [0.89-0.98] per 100 g/d increment), cheese (HR 0.92 [0.86-0.98] per 30 g/d increment) and eggs (HR 0.93 [0.88-0.99] per 20 g/d increment); the associations with yogurt and eggs were attenuated and non-significant after excluding the first 4 years of follow-up. Risk was not significantly associated with intakes of poultry, fish or milk. In analyses modelling dietary substitutions, replacement of 100 kcal/d from red and processed meat with 100 kcal/d from fatty fish, yogurt, cheese or eggs was associated with approximately 20% lower risk of IHD. Consumption of red and processed meat was positively associated with serum non-HDL cholesterol concentration and systolic blood pressure, and consumption of cheese was inversely associated with serum non-HDL cholesterol.CONCLUSIONS: Risk for IHD was positively associated with consumption of red and processed meat, and inversely associated with consumption of yogurt, cheese and eggs, although the associations with yogurt and eggs may be influenced by reverse causation bias. It is not clear whether the associations with red and processed meat and cheese reflect causality, but they were consistent with the associations of these foods with plasma non-HDL cholesterol, and for red and processed meat with systolic blood pressure, which could mediate such effects.
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16.
  • Lassale, Camille, et al. (author)
  • Separate and combined associations of obesity and metabolic health with coronary heart disease : a pan-European case-cohort analysis
  • 2018
  • In: European Heart Journal. - : Oxford University Press. - 0195-668X .- 1522-9645. ; 39:5, s. 397-406
  • Journal article (peer-reviewed)abstract
    • Aims: The hypothesis of 'metabolically healthy obesity' implies that, in the absence of metabolic dysfunction, individuals with excess adiposity are not at greater cardiovascular risk. We tested this hypothesis in a large pan-European prospective study.Methods and results: We conducted a case-cohort analysis in the 520 000-person European Prospective Investigation into Cancer and Nutrition study ('EPIC-CVD'). During a median follow-up of 12.2 years, we recorded 7637 incident coronary heart disease (CHD) cases. Using cut-offs recommended by guidelines, we defined obesity and overweight using body mass index (BMI), and metabolic dysfunction ('unhealthy') as ≥ 3 of elevated blood pressure, hypertriglyceridaemia, low HDL-cholesterol, hyperglycaemia, and elevated waist circumference. We calculated hazard ratios (HRs) and 95% confidence intervals (95% CI) within each country using Prentice-weighted Cox proportional hazard regressions, accounting for age, sex, centre, education, smoking, diet, and physical activity. Compared with metabolically healthy normal weight people (reference), HRs were 2.15 (95% CI: 1.79; 2.57) for unhealthy normal weight, 2.33 (1.97; 2.76) for unhealthy overweight, and 2.54 (2.21; 2.92) for unhealthy obese people. Compared with the reference group, HRs were 1.26 (1.14; 1.40) and 1.28 (1.03; 1.58) for metabolically healthy overweight and obese people, respectively. These results were robust to various sensitivity analyses.Conclusion: Irrespective of BMI, metabolically unhealthy individuals had higher CHD risk than their healthy counterparts. Conversely, irrespective of metabolic health, overweight and obese people had higher CHD risk than lean people. These findings challenge the concept of 'metabolically healthy obesity', encouraging population-wide strategies to tackle obesity.
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17.
  • Lee, Crystal, et al. (author)
  • Association of anthropometry and weight change with risk of dementia and its major subtypes: a meta-analysis consisting 2.8 million adults with 57,294 cases of dementia
  • 2020
  • In: Obesity Reviews. - : Wiley. - 1467-7881 .- 1467-789X. ; 21:4
  • Journal article (peer-reviewed)abstract
    • Uncertainty exists regarding the relation of body size and weight change with dementia risk. As populations continue to age and the global obesity epidemic shows no sign of waning, reliable quantification of such associations is important. We examined the relationship of body mass index, waist circumference, and annual percent weight change with risk of dementia and its subtypes by pooling data from 19 prospective cohort studies and four clinical trials using meta-analysis. Compared with body mass index-defined lower-normal weight (18.5–22.4 kg/m2), the risk of all-cause dementia was higher among underweight individuals but lower among those with upper-normal (22.5–24.9 kg/m2) levels. Obesity was associated with higher risk in vascular dementia. Similarly, relative to the lowest fifth of waist circumference, those in the highest fifth had non-significant higher vascular dementia risk. Weight loss was associated with higher all-cause dementia risk relative to weight maintenance. Weight gain was weakly associated with higher vascular dementia risk. The relationship between body size, weight change and dementia is complex and exhibits nonlinear associations depending on dementia subtype under scrutiny. Weight loss was associated with an elevated risk most likely due to reverse causality and/or pathophysiological changes in the brain, although the latter remains speculative.
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18.
  • Lee, Wei-Hsuan, et al. (author)
  • Genetically predicted plasma cortisol and common chronic diseases : A Mendelian randomization study.
  • 2023
  • In: Clinical Endocrinology. - 0300-0664 .- 1365-2265.
  • Journal article (peer-reviewed)abstract
    • OBJECTIVE: Cushing's syndrome is characterized by hypercortisolaemia and is frequently accompanied by comorbidities such as type 2 diabetes, hypertension, osteoporosis, depression and schizophrenia. It is unclear whether moderate but lifelong hypercortisolaemia is causally associated with these diseases in the general population. We aimed to address this research gap using a Mendelian randomization approach.METHODS: We used three cortisol-associated genetic variants in the SERPINA6/SERPINA1 region as genetic instruments in a two-sample, inverse-variance-weighted Mendelian randomization analysis. We obtained summary-level statistics for cortisol and disease outcomes from publicly available genetic consortia, and meta-analysed them as appropriate. We conducted a multivariable Mendelian randomization analysis to assess potential mediating effects.RESULTS: A 1 standard deviation higher genetically predicted plasma cortisol was associated with greater odds of hypertension (odds ratio: 1.12; 95% confidence interval [CI]: 1.05-1.18) as well as higher systolic blood pressure (mean difference [MD]: 0.03 SD change; 95% CI: 0.01-0.05) and diastolic blood pressure (MD: 0.03 SD change; 95% CI: 0.01-0.04). There was no evidence of association with type 2 diabetes, osteoporosis, depression and schizophrenia. The association with hypertension was attenuated upon adjustment for waist circumference, suggesting potential mediation through central obesity.CONCLUSION: There is strong evidence for a causal association between plasma cortisol and greater risk for hypertension, potentially mediated by obesity.
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19.
  • Marouli, Eirini, et al. (author)
  • Rare and low-frequency coding variants alter human adult height
  • 2017
  • In: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 542:7640, s. 186-190
  • Journal article (peer-reviewed)abstract
    • Height is a highly heritable, classic polygenic trait with approximately 700 common associated variants identified through genome-wide association studies so far. Here, we report 83 height-associated coding variants with lower minor-allele frequencies (in the range of 0.1-4.8%) and effects of up to 2 centimetres per allele (such as those in IHH, STC2, AR and CRISPLD2), greater than ten times the average effect of common variants. In functional follow-up studies, rare height increasing alleles of STC2 (giving an increase of 1-2 centimetres per allele) compromised proteolytic inhibition of PAPP-A and increased cleavage of IGFBP-4 in vitro, resulting in higher bioavailability of insulin-like growth factors. These 83 height-associated variants overlap genes that are mutated in monogenic growth disorders and highlight new biological candidates (such as ADAMTS3, IL11RA and NOX4) and pathways (such as proteoglycan and glycosaminoglycan synthesis) involved in growth. Our results demonstrate that sufficiently large sample sizes can uncover rare and low-frequency variants of moderate-to-large effect associated with polygenic human phenotypes, and that these variants implicate relevant genes and pathways.
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20.
  • Paige, Ellie, et al. (author)
  • Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction : An Individual-Participant-Data Meta-Analysis
  • 2017
  • In: American Journal of Epidemiology. - : Oxford University Press. - 0002-9262 .- 1476-6256. ; 186:8, s. 899-907
  • Research review (peer-reviewed)abstract
    • 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.
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