C-reactive protein modifies lipoprotein(a)-related risk for coronary heart disease: the BiomarCaRE project.

BACKGROUND AND AIMS: Recent investigations have suggested an interdependence of lipoprotein(a) [Lp(a)]-related risk for cardiovascular disease with background inflammatory burden. The aim the present analysis was to investigate whether high-sensitive C-reactive protein (hsCRP) modulates the association between Lp(a) and coronary heart disease (CHD) in the general population. METHODS: Data from 71 678 participants from 8 European prospective population-based cohort studies were used (65 661 without/6017 with established CHD at baseline; median follow-up 9.8/13.8 years, respectively). Fine and Gray competing risk-adjusted models were calculated according to accompanying hsCRP concentration (<2 and ≥2 mg/L). RESULTS: Among CHD-free individuals, increased Lp(a) levels were associated with incident CHD irrespective of hsCRP concentration: fully adjusted sub-distribution hazard ratios [sHRs (95% confidence interval)] for the highest vs. lowest fifth of Lp(a) distribution were 1.45 (1.23-1.72) and 1.48 (1.23-1.78) for a hsCRP group of <2 and ≥2 mg/L, respectively, with no interaction found between these two biomarkers on CHD risk (Pinteraction = 0.82). In those with established CHD, similar associations were seen only among individuals with hsCRP ≥ 2 mg/L [1.34 (1.03-1.76)], whereas among participants with a hsCRP concentration <2 mg/L, there was no clear association between Lp(a) and future CHD events [1.29 (0.98-1.71)] (highest vs. lowest fifth, fully adjusted models; Pinteraction = 0.024). CONCLUSIONS: While among CHD-free individuals Lp(a) was significantly associated with incident CHD regardless of hsCRP, in participants with CHD at baseline, Lp(a) was related to recurrent CHD events only in those with residual inflammatory risk. These findings might guide adequate selection of high-risk patients for forthcoming Lp(a)-targeting compounds.

National health examination surveys; a source of critical data.

The aim of this paper is to contribute technical arguments to the debate about the importance of health examination surveys and their continued use during the post-pandemic health financing crisis, and in the context of a technological innovation boom that offers new ways of collecting and analysing individual health data (e.g. artificial intelligence). Technical considerations demonstrate that health examination surveys make an irreplaceable contribution to the local availability of primary health data that can be used in a range of further studies (e.g. normative, burden-of-disease, care cascade, cost and policy impact studies) essential for informing several phases of the health planning cycle (e.g. surveillance, prioritization, resource mobilization and policy development). Examples of the use of health examination survey data in the World Health Organization (WHO) European Region (i.e. Finland, Italy, Malta and the United Kingdom of Great Britain and Northern Ireland) and the WHO Region of the Americas (i.e. Chile, Mexico, Peru and the United States of America) are presented, and reasons why health provider-led data cannot replace health examination survey data are discussed (e.g. underestimation of morbidity and susceptibility to bias). In addition, the importance of having nationally representative random samples of the general population is highlighted and we argue that health examination surveys make a critical contribution to external quality control for a country's health system by increasing the transparency and accountability of health spending. Finally, we consider future technological advances that can improve survey fieldwork and suggest ways of ensuring health examination surveys are sustainable in low-resource settings.

Cet article a pour objet d'apporter des arguments techniques au débat sur l'importance des enquêtes de santé par examen et sur leur utilisation continue pendant la crise post-pandémique du financement de la santé et dans le contexte d'un essor de l'innovation technologique qui offre de nouvelles façons de collecter et d'analyser les données individuelles sur la santé (comme l'intelligence artificielle). Les considérations techniques démontrent que les enquêtes de santé par examen apportent une contribution irremplaçable à la disponibilité locale de données de santé primaires qui peuvent servir dans une série d'études complémentaires (telles que des études normatives, sur la charge de morbidité, la cascade des soins, les coûts et l'impact des politiques). Ces études sont essentielles pour renseigner plusieurs phases du cycle de planification sanitaire (par exemple: surveillance, priorisation, mobilisation de ressources et élaboration de politiques). Cet article présente des exemples d'utilisation des données d'enquêtes de santé par examen dans la Région OMS de l'Europe (Finlande, Italie, Malte et Royaume-Uni de Grande-Bretagne et d'Irlande du Nord) et dans la Région OMS des Amériques (Chili, États-Unis d'Amérique, Mexique et Pérou) et aborde les raisons pour lesquelles les données fournies par les prestataires de soins de santé ne peuvent pas remplacer les données d'enquêtes de santé par examen (par exemple la sous-estimation de la morbidité et la vulnérabilité aux biais). En outre, il soulignet l'importance de disposer d'échantillons aléatoires représentatifs de la population générale au niveau national, et nous soutenons que les enquêtes de santé par examen apportent une contribution essentielle au contrôle externe de la qualité du système de santé d'un pays en renforçant la transparence des dépenses de santé et l'obligation de rendre des comptes à leur sujet. Enfin, nous envisageons les futures avancées technologiques susceptibles d'améliorer le travail d'enquête sur le terrain et suggérons des moyens d'assurer la viabilité des enquêtes de santé par examen dans les environnements à faibles ressources.

El objetivo de este artículo es aportar argumentos técnicos al debate sobre la importancia de las encuestas de salud y su uso continuado durante la crisis de financiación sanitaria pospandémica y en el contexto de un auge de la innovación tecnológica que ofrece nuevas formas de recopilar y analizar datos sanitarios individuales (por ejemplo, la inteligencia artificial). Las consideraciones técnicas demuestran que las encuestas de salud contribuyen de manera insustituible a la disponibilidad local de datos sanitarios primarios que pueden utilizarse en toda una serie de estudios posteriores (por ejemplo, estudios normativos, de carga de morbilidad, de cascada asistencial, de costes y de impacto de las políticas) esenciales para fundamentar varias fases del ciclo de planificación sanitaria (por ejemplo, vigilancia, establecimiento de prioridades, movilización de recursos y elaboración de políticas). Se presentan ejemplos del uso de los datos de las encuestas de salud en la Región Europea de la Organización Mundial de la Salud (Finlandia, Italia, Malta y el Reino Unido de Gran Bretaña e Irlanda del Norte) y en la Región de las Américas de la OMS (Chile, Estados Unidos de América, México y Perú) y se analizan las razones por las que los datos obtenidos por los proveedores sanitarios no pueden sustituir a los de las encuestas de salud (por ejemplo, la subestimación de la morbilidad y la posibilidad de sesgo). Además, se destaca la importancia de contar con muestras aleatorias representativas de la población general a escala nacional y se argumenta que las encuestas de salud contribuyen de forma decisiva al control de calidad externo del sistema sanitario de un país, al aumentar la transparencia y la rendición de cuentas del gasto sanitario. Por último, se examinan los futuros avances tecnológicos que pueden mejorar el trabajo de campo de las encuestas y se sugieren métodos para garantizar que las encuestas de salud sean sostenibles en entornos con pocos recursos.

Prognostic Value of Cardiovascular Biomarkers in the Population.

Importance: Identification of individuals at high risk for atherosclerotic cardiovascular disease within the population is important to inform primary prevention strategies. Objective: To evaluate the prognostic value of routinely available cardiovascular biomarkers when added to established risk factors. Design, Setting, and Participants: Individual-level analysis including data on cardiovascular biomarkers from 28 general population-based cohorts from 12 countries and 4 continents with assessments by participant age. The median follow-up was 11.8 years. Exposure: Measurement of high-sensitivity cardiac troponin I, high-sensitivity cardiac troponin T, N-terminal pro-B-type natriuretic peptide, B-type natriuretic peptide, or high-sensitivity C-reactive protein. Main Outcomes and Measures: The primary outcome was incident atherosclerotic cardiovascular disease, which included all fatal and nonfatal events. The secondary outcomes were all-cause mortality, heart failure, ischemic stroke, and myocardial infarction. Subdistribution hazard ratios (HRs) for the association of biomarkers and outcomes were calculated after adjustment for established risk factors. The additional predictive value of the biomarkers was assessed using the C statistic and reclassification analyses. Results: The analyses included 164 054 individuals (median age, 53.1 years [IQR, 42.7-62.9 years] and 52.4% were women). There were 17 211 incident atherosclerotic cardiovascular disease events. All biomarkers were significantly associated with incident atherosclerotic cardiovascular disease (subdistribution HR per 1-SD change, 1.13 [95% CI, 1.11-1.16] for high-sensitivity cardiac troponin I; 1.18 [95% CI, 1.12-1.23] for high-sensitivity cardiac troponin T; 1.21 [95% CI, 1.18-1.24] for N-terminal pro-B-type natriuretic peptide; 1.14 [95% CI, 1.08-1.22] for B-type natriuretic peptide; and 1.14 [95% CI, 1.12-1.16] for high-sensitivity C-reactive protein) and all secondary outcomes. The addition of each single biomarker to a model that included established risk factors improved the C statistic. For 10-year incident atherosclerotic cardiovascular disease in younger people (aged <65 years), the combination of high-sensitivity cardiac troponin I, N-terminal pro-B-type natriuretic peptide, and high-sensitivity C-reactive protein resulted in a C statistic improvement from 0.812 (95% CI, 0.8021-0.8208) to 0.8194 (95% CI, 0.8089-0.8277). The combination of these biomarkers also improved reclassification compared with the conventional model. Improvements in risk prediction were most pronounced for the secondary outcomes of heart failure and all-cause mortality. The incremental value of biomarkers was greater in people aged 65 years or older vs younger people. Conclusions and Relevance: Cardiovascular biomarkers were strongly associated with fatal and nonfatal cardiovascular events and mortality. The addition of biomarkers to established risk factors led to only a small improvement in risk prediction metrics for atherosclerotic cardiovascular disease, but was more favorable for heart failure and mortality.

Nutrition, Physical Activity and Smoking Habit in the Italian General Adult Population: CUORE Project Health Examination Survey 2018-2019.

BACKGROUND: Tobacco consumption, incorrect nutrition and insufficient physical activity/sedentariness represent modifiable NCDs risk factors in Western countries. To evaluate recent lifestyle indicators in Italy, data from the national Health Examination Survey (HES), implemented in 2018-2019 within the CUORE Project, were assessed. METHODS: Age-sex standardized results from random samples of Italian general population (35-74 years) were reported by sex, age-class, educational level and geographical area. From 2106 participants, 2090 were considered for smoking habit, 2016 for physical activity and 1578 for nutrition. Standardized questionnaires were used for smoking habit and physical activity, and the EPIC questionnaire for nutrition. RESULTS: Total cigarette current smokers were 23% in men and 19% in women; sedentariness during leisure time was 34% in men and 45% in women and at work 45% and 47% in men and women, respectively. Prevalence of balanced eating behaviours for vegetables was 28% in men and 39% in women; and for fruits 50% and 52%, respectively; prevalence of correct lifestyle (not smoker, regular physical activity and following at least five correct eating behaviours) was 7% and 12% for men and women, respectively. CONCLUSIONS: In 2018-2019, levels of unhealthy lifestyles were found to be still epidemic and basically stable compared to 10 years earlier (slight smoking habit decrease, slight sedentariness increase and slight nutrition improvements); intersectoral strategies and monitoring need to be continued.

Cost-effectiveness of applying high-sensitivity troponin I to a score for cardiovascular risk prediction in asymptomatic population.

INTRODUCTION: Risk stratification scores such as the European Systematic COronary Risk Evaluation (SCORE) are used to guide individuals on cardiovascular disease (CVD) prevention. Adding high-sensitivity troponin I (hsTnI) to such risk scores has the potential to improve accuracy of CVD prediction. We investigated how applying hsTnI in addition to SCORE may impact management, outcome, and cost-effectiveness. METHODS: Characteristics of 72,190 apparently healthy individuals from the Biomarker for Cardiovascular Risk Assessment in Europe (BiomarCaRE) project were included into a discrete-event simulation comparing two strategies for assessing CVD risk. The standard strategy reflecting current practice employed SCORE (SCORE); the alternative strategy involved adding hsTnI information for further stratifying SCORE risk categories (S-SCORE). Individuals were followed over ten years from baseline examination to CVD event, death or end of follow-up. The model tracked the occurrence of events and calculated direct costs of screening, prevention, and treatment from a European health system perspective. Cost-effectiveness was expressed as incremental cost-effectiveness ratio (ICER) in € per quality-adjusted life year (QALYs) gained during 10 years of follow-up. Outputs were validated against observed rates, and results were tested in deterministic and probabilistic sensitivity analyses. RESULTS: S-SCORE yielded a change in management for 10.0% of individuals, and a reduction in CVD events (4.85% vs. 5.38%, p<0.001) and mortality (6.80% vs. 7.04%, p<0.001). S-SCORE led to 23 (95%CI: 20-26) additional event-free years and 7 (95%CI: 5-9) additional QALYs per 1,000 subjects screened, and resulted in a relative risk reduction for CVD of 9.9% (95%CI: 7.3-13.5%) with a number needed to screen to prevent one event of 183 (95%CI: 172 to 203). S-SCORE increased costs per subject by 187€ (95%CI: 177 € to 196 €), leading to an ICER of 27,440€/QALY gained. Sensitivity analysis was performed with eligibility for treatment being the most sensitive. CONCLUSION: Adding a person's hsTnI value to SCORE can impact clinical decision making and eventually improves QALYs and is cost-effective compared to CVD prevention strategies using SCORE alone. Stratifying SCORE risk classes for hsTnI would likely offer cost-effective alternatives, particularly when targeting higher risk groups.

Impact of Lipoprotein(a) Level on Low-Density Lipoprotein Cholesterol- or Apolipoprotein B-Related Risk of Coronary Heart Disease.

BACKGROUND: Conventional low-density lipoprotein cholesterol (LDL-C) quantification includes cholesterol attributable to lipoprotein(a) (Lp(a)-C) due to their overlapping densities. OBJECTIVES: The purposes of this study were to compare the association between LDL-C and LDL-C corrected for Lp(a)-C (LDLLp(a)corr) with incident coronary heart disease (CHD) in the general population and to investigate whether concomitant Lp(a) values influence the association of LDL-C or apolipoprotein B (apoB) with coronary events. METHODS: Among 68,748 CHD-free subjects at baseline LDLLp(a)corr was calculated as "LDL-C-Lp(a)-C," where Lp(a)-C was 30% or 17.3% of total Lp(a) mass. Fine and Gray competing risk-adjusted models were applied for the association between the outcome incident CHD and: 1) LDL-C and LDLLp(a)corr in the total sample; and 2) LDL-C and apoB after stratification by Lp(a) mass (≥/<90th percentile). RESULTS: Similar risk estimates for incident CHD were found for LDL-C and LDL-CLp(a)corr30 or LDL-CLp(a)corr17.3 (subdistribution HR with 95% CI) were 2.73 (95% CI: 2.34-3.20) vs 2.51 (95% CI: 2.15-2.93) vs 2.64 (95% CI: 2.26-3.10), respectively (top vs bottom fifth; fully adjusted models). Categorization by Lp(a) mass resulted in higher subdistribution HRs for uncorrected LDL-C and incident CHD at Lp(a) ≥90th percentile (4.38 [95% CI: 2.08-9.22]) vs 2.60 [95% CI: 2.21-3.07]) at Lp(a) <90th percentile (top vs bottom fifth; Pinteraction0.39). In contrast, apoB risk estimates were lower in subjects with higher Lp(a) mass (2.43 [95% CI: 1.34-4.40]) than in Lp(a) <90th percentile (3.34 [95% CI: 2.78-4.01]) (Pinteraction0.49). CONCLUSIONS: Correction of LDL-C for its Lp(a)-C content provided no meaningful information on CHD-risk estimation at the population level. Simple categorization of Lp(a) mass (≥/<90th percentile) influenced the association between LDL-C or apoB with future CHD mostly at higher Lp(a) levels.