Higher BMI is linked to an increased risk of heart attacks in European adults: a Mendelian randomisation study

Adams, Benjamin; Jacocks, Lauren; Guo, Hui

doi:10.1186/s12872-020-01542-w

Cited by 10 publications

(10 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The result of Framingham Heart Study showed the relative risk of coronary heart disease in obese women increased by 64% and was statistically significant differences compared with a 46% increase in men. The relationship between obesity and cardiovascular disease is becoming more and more clear [18]. The regression analysis in this study shows that the relative risk in obese (overweight) women with ACS was more than 400% of those in normal young women without ACS(P < 0.01).…”

Section: Discussionmentioning

confidence: 57%

“…(17) It was recorded as hypothyroidism that patient suffered from hypothyroidism and was taking thyroid hormone therapy or plasma thyroxine level was lower than the normal level, and thyrotrophin was elevated at the same time. (18) It was recorded as an autoimmune disease that patient suffered from autoimmune disease in the past week and was taking related drugs treatment. (19) If the patients had been diagnosed with depression or anxiety previously and were taking antidepressant medication, these patients were recorded as having depression or anxiety.…”

Section: The Judging Criteria For Risk Factorsmentioning

confidence: 99%

See 1 more Smart Citation

The characteristics of risk factors in Chinese young women with acute coronary syndrome

Liu

Zhou

et al. 2020

BMC Cardiovasc Disord

View full text Add to dashboard Cite

Background In recent years, the prevalence rate of acute coronary syndrome (ACS) in Chinese young women has been increasing significantly, becoming one of the main causes of death in young females. A matter of constant concern is what is the characteristics and differences in risk factors between young women with ACS and without ACS. This study aimed to investigate the characteristics and difference of risk factors in Chinese young women with ACS and to provide references for ACS prevention and treatment. Methods A 1:1 case-control study was conducted to evaluate risk factors of 415 young females with ACS (ACS group) who underwent PCI treatment and 415 young females without ACS (control group) who were hospitalized and confirmed by coronary angiography to exclude coronary heart disease from January 2010 to August 2016. The average age of the cases in groups was respectively (40.77 ± 4.02) and (40.57 ± 4.01) years-old (P > 0.05). Results The risk factors in ACS group were overweight (64.10%), hypertension (49.88%), hyperlipidemia (40.72%), diabetes (23.37%), depression or anxiety (16.63%), gynecological diseases (16.39%), Hyperuricemia (14.94%), family history of early-onset CHD (14.94%), hyperhomocysteinemia (11.33%), hypothyroidism (9.64%), hypercholesterolemia (8.43%) and high C-reactive protein (7.47%), and were significant difference (P < 0.01) compared with that of the control group. The average number of risk factors per case in ACS group was significantly more than that of control groups (P < 0.01). Regression analysis showed that hyperlipidemia, hyperhomocysteinemia, overweight (obesity), high CRP, hypertension, hypothyroidism, gynecological diseases, depression or anxiety, cardiac insufficiency, hypercholesterolemia, diabetes, oral contraceptives, family history of early-onset CHD, and autoimmune diseases were independent risk factors (P < 0.01). The bivariate correlation analysis between CRP level and age was r = − 0.158 (P < 0.01). The result showed the younger ACS patient is the higher serum CRP. Conclusion The independent risk factors of ACS in young women are hyperlipidemia, hyperhomocysteinemia, overweight, high CRP, hypertension, hypothyroidism, gynecological diseases, depression or anxiety, cardiac insufficiency, hypercholesterolemia, diabetes, oral contraceptives, family history of early-onset CHD, and autoimmune diseases. The co-existence of multiple risk factors is the main cause suffering from ACS in young women.

show abstract

Section: Discussionmentioning

confidence: 57%

Section: The Judging Criteria For Risk Factorsmentioning

confidence: 99%

The characteristics of risk factors in Chinese young women with acute coronary syndrome

Liu

Zhou

et al. 2020

BMC Cardiovasc Disord

View full text Add to dashboard Cite

show abstract

“…Higher BMI tends to indicate overweight or obesity, which is associated with increased morbidity and mortality from various diseases (44,45). Similarly, higher BMI is associated with increased risk of acquiring infections with worse outcomes (46).…”

Section: Discussionmentioning

confidence: 99%

Body mass index associated with severity and mortality of patients with coronavirus disease 2019: A systematic review and meta-analysis

Fang

Zhou

et al. 2020

Preprint

View full text Add to dashboard Cite

Background As an important indicator to measure obesity or underweight, body mass index (BMI) can be used to assess the potential risk for various diseases. The present study systematically examined the relationship between BMI and severity and mortality of patients with coronavirus disease 2019 (COVID-19). Methods We systematically searched PubMed, Embase, Cochrane, and China National Knowledge Infrastructure (CNKI) for studies published as of September 3, 2020 and extracted the relevant data of research endpoints in each study. Results This study included 16 studies with 6087 patients. This study observed a significant increase in BMI on admission in patients with severe COVID-19 compared with those with non-severe COVID-19 (Mean difference [MD] = 1.95, 95% confidence interval [CI], 1.52 − 2.37, I2 = 33%, P < 0.00001). A significant increase in BMI on admission was observed in patients who died from COVID-19 compared with (MD = 3.01, 95% CI: 1.83 to 4.19, I2 = 0%, P < 0.00001). In the intensive care unit (ICU) or geriatric ward, the study observed a significant decrease in BMI in the non-survivor group compared with the survivor group (MD = -1.61, 95% CI: -3.07 to -0.16, I2 = 72%, P = 0.03). Conclusions Higher BMI on admission is associated with severity and mortality of patients with COVID-19, but lower BMI is associated with mortality of patients with COVID-19 in the ICU or geriatric ward. Thus, we strongly recommend that clinicians should closely monitor the BMI of patients with COVID-19, especially those from the ICU or geriatric ward.

show abstract

“…Now we specify priors in the Bayesian model ( 2 )-( 10 ). Previous GWAS studies show that individual SNPs explain a tiny proportion of exposure variance [ 14 – 16 ], corresponding to small magnitudes of the α parameters in our model. In accord with this finding and previous MR simulation studies [ 17 ], we set IV strength parameters α s to be independent and identically distributed with mean zero and a small variance: α 1 ∼ N L ( 0 ,0.3 2 I ), α 2 ∼ N K ( 0 ,0.3 2 I ), α 31 ∼ N M ( 0 ,0.3 2 I ), and α 32 ∼ N M ( 0 ,0.3 2 I ).…”

Section: Methodsmentioning

confidence: 97%

Bayesian mendelian randomization with study heterogeneity and data partitioning for large studies

Linyi

Guo

Berzuini

2022

BMC Med Res Methodol

Self Cite

View full text Add to dashboard Cite

Background Mendelian randomization (MR) is a useful approach to causal inference from observational studies when randomised controlled trials are not feasible. However, study heterogeneity of two association studies required in MR is often overlooked. When dealing with large studies, recently developed Bayesian MR can be computationally challenging, and sometimes even prohibitive. Methods We addressed study heterogeneity by proposing a random effect Bayesian MR model with multiple exposures and outcomes. For large studies, we adopted a subset posterior aggregation method to overcome the problem of computational expensiveness of Markov chain Monte Carlo. In particular, we divided data into subsets and combined estimated causal effects obtained from the subsets. The performance of our method was evaluated by a number of simulations, in which exposure data was partly missing. Results Random effect Bayesian MR outperformed conventional inverse-variance weighted estimation, whether the true causal effects were zero or non-zero. Data partitioning of large studies had little impact on variations of the estimated causal effects, whereas it notably affected unbiasedness of the estimates with weak instruments and high missing rate of data. For the cases being simulated in our study, the results have indicated that the “divide (data) and combine (estimated subset causal effects)” can help improve computational efficiency, for an acceptable cost in terms of bias in the causal effect estimates, as long as the size of the subsets is reasonably large. Conclusions We further elaborated our Bayesian MR method to explicitly account for study heterogeneity. We also adopted a subset posterior aggregation method to ease computational burden, which is important especially when dealing with large studies. Despite the simplicity of the model we have used in the simulations, we hope the present work would effectively point to MR studies that allow modelling flexibility, especially in relation to the integration of heterogeneous studies and computational practicality.

show abstract

Higher BMI is linked to an increased risk of heart attacks in European adults: a Mendelian randomisation study

Cited by 10 publications

References 26 publications

The characteristics of risk factors in Chinese young women with acute coronary syndrome

The characteristics of risk factors in Chinese young women with acute coronary syndrome

Body mass index associated with severity and mortality of patients with coronavirus disease 2019: A systematic review and meta-analysis

Bayesian mendelian randomization with study heterogeneity and data partitioning for large studies

Contact Info

Product

Resources

About