Quantifying Importance of Major Risk Factors for Coronary Heart Disease

Pencina, Michael J.; Návar, Ann Marie; Wojdyla, Daniel; Sanchez, Robert J.; Khan, Irfan; Elassal, Joseph; D’Agostino, Ralph B.; Peterson, Eric D.; Sniderman, Allan D.

doi:10.1161/circulationaha.117.031855

Cited by 133 publications

(96 citation statements)

References 24 publications

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“…Also, the impact of aging on cardiovascular risk is more severe in the presence of diabetes, with a transition from a low to a moderate 10-year CHD risk category at only 35 years of age in men and 45 years in women [39]. Thus, age represents a major determinant of the prognostic information included in all estimation risk models [36], whereas modifiable factors, such as body weight, play only a marginal role [22,40]. These considerations may explain our findings of raised CVD risk estimates after 5 years of treatment with Liraglutide, supporting a recent call for novel age-and sex-specific risk prediction models for the assessment of CVD [41].…”

Section: Discussionmentioning

confidence: 99%

Long-Term Effectiveness of Liraglutide for Weight Management and Glycemic Control in Type 2 Diabetes

Mirabelli

Chiefari

Caroleo

et al. 2019

IJERPH

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Background: Liraglutide is the first glucagon-like peptide-1 receptor agonist (GLP-1 RA) based on the human GLP-1 sequence, with potential weight loss benefits, approved for the treatment of type 2 diabetes (T2D) mellitus. Herein, we aimed to assess the 5-year effectiveness of Liraglutide in the management of weight and glycometabolic control in a Southern Italian cohort of overweight/obese T2D patients, who were naïve to GLP-1 RAs. Patients and Methods: Forty overweight or obese patients treated with Liraglutide at doses up to 1.8 mg/day, in combination with one or more oral antidiabetic agents, were retrospectively assessed at baseline, during, and after 60 months of continuous therapy. Results: After 5 years of Liraglutide treatment, body weight decreased from 92.1 ± 20.5 kg to 87.3 ± 20.0 Kg (p < 0.001), with a mean reduction of 5.0 ± 7.0 Kg and a body mass index (BMI) decrement of −2.0 ± 3.1 Kg/m2. On Spearman’s univariate analysis, change in body weight was correlated with female gender and baseline BMI. Hemoglobin A1c (HbA1c) decreased from 7.9 ± 0.9% at baseline to 7.0 ± 0.7% at the end of the study period (p < 0.001), followed by a significant reduction in fasting plasma glucose. No significant differences emerged in other biochemical parameters, despite a trend toward improvement in lipid profile. Notwithstanding encouraging effects on several markers of cardiovascular disease (CVD), increments in the 5- and 10-year risk for the first atherosclerotic cardiovascular event were documented, as four incident cases of myocardial infarction. Conclusions: Prolonging treatment with Liraglutide can lead to durable benefits in relation to weight and glycemic control, with a greater impact on women. These results extend and corroborate previous observations, suggesting that gender per se may modulate the response to Liraglutide. Despite favorable effects on some established CVD risks factors, the long-term role of Liraglutide in primary prevention of CVD in patients with T2D remains controversial.

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Section: Discussionmentioning

confidence: 99%

Long-Term Effectiveness of Liraglutide for Weight Management and Glycemic Control in Type 2 Diabetes

Mirabelli

Chiefari

Caroleo

et al. 2019

IJERPH

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“…Although the identi cation of CVD risk factors seems to be relatively comprehensive, the precise and indirect mechanisms of the associated factors underlying CVD remains unclear (37). It is a critical gap since healthy behaviours lead to both reduced risk and more effective treatments (17,38). This is the rst study in the eld of CVD, to the best of our knowledge, that extensively examined the direct and indirect effects of comprehensive cardiovascular-related factors using BSEM in a population-based cohort platform.…”

Section: Discussionmentioning

confidence: 99%

“…Although the indirect effects of family history of CVD, age, lipid pro les, anthropometric indices, healthy lifestyle, quality of life, high-risk behaviours, having hypertension and high blood sugar on CVDs were not covered in the literature, many studies discussed the direct effect of such factors in which these results are in line with (9,11,12,15,(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50). Causal networks evaluate causal relationships among variables beyond partial correlations and thus play a fundamental step in risk prediction.…”

Section: Discussionmentioning

confidence: 99%

Pathways Leading to Prevention of Fatal and Non-Fatal Cardiovascular Disease: An Interaction Model on 15 years Population-Based Cohort study

Shakibaei

Hassannejad

Mohammadifard

et al. 2020

Preprint

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Background: A comprehensive study on the interaction of cardiovascular disease (CVD) risk factors is critical to prevent cardiovascular events. The main focus of this study is thus to understand direct and indirect relationships between different CVD risk factors. Methods: A longitudinal data on adults aged ≥35 years, who were free of CVD at baseline, were used in this study. The endpoints were CVD events, whereas their measurements were demographic, lifestyle components, socio-economics, anthropometric measures, laboratory findings, quality of life status, and psychological factors. A Bayesian structural equation modelling was used to determine the relationships among 21 relevant factors associated with total CVD, stroke, acute coronary syndrome (ACS), and fatal CVDs. Results: In this study, a total of 3161 individuals with complete information were involved in the study. A total of 407 CVD events, with an average age of 54.77(10.66) years, occurred during follow-up. The causal associations between six latent variables were identified in the causal network for fatal and non-fatal CVDs. Lipid profile, with the coefficient of 0.26 (0.01), influenced the occurrence of CVD events as the most critical factor, while it was indirectly mediated through risky behaviours and comorbidities. Lipid profile at baseline was influenced by a wide range of other protective factors, such as quality of life and healthy lifestyle components. Conclusions: Analysing a causal network of risk factors revealed the flow of information in direct and indirect paths. It also determined predictors and demonstrated the utility of integrating multi-factor data in a complex framework to identify novel preventable pathways to reduce the risk of CVDs.

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“…Landmark analyses starting in 1987 identified certain key CVD risk factors and remarkably quickly these were standardised as age, gender, smoking, blood pressure, diabetes and cholesterol (later divided into total and high‐density lipoprotein (HDL) cholesterol) . A multitude of additional CVD risk factors have since been described but all of these added little to the basic predictive model which is mostly driven by age, gender and ethnicity . Risk factor counting and set intervention levels were the basis of defining high‐risk patients for intervention.…”

Section: Suggested Data Transparency and Quality Assessment Criteria mentioning

confidence: 99%

“…4,6,7 A multitude of additional CVD risk factors have since been described but all of these added little to the basic predictive model which is mostly driven by age, gender and ethnicity. 8 Risk factor counting and set intervention levels were the basis of defining high-risk patients for intervention. These still persist in modern guidelines, for example, stage 2 hypertension or total cholesterol > 7.5mmol/L and more usefully the concept of two CVD risk factors predicting lifetime risk from age 55.…”

mentioning

confidence: 99%

Computational models and neural nets: Fantastic models—Where to find them and how to identify them

Wierzbicki

Reynolds

2019

Int J Clin Pract

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Quantifying Importance of Major Risk Factors for Coronary Heart Disease

Abstract: Supplemental Digital Content is available in the text.

Cited by 133 publications

References 24 publications

Long-Term Effectiveness of Liraglutide for Weight Management and Glycemic Control in Type 2 Diabetes

Long-Term Effectiveness of Liraglutide for Weight Management and Glycemic Control in Type 2 Diabetes

Pathways Leading to Prevention of Fatal and Non-Fatal Cardiovascular Disease: An Interaction Model on 15 years Population-Based Cohort study

Computational models and neural nets: Fantastic models—Where to find them and how to identify them

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