Longitudinal Associations Between Income Changes and Incident Cardiovascular Disease

Wang, Stephen Y; Tan, Andy S.L.; Claggett, Brian; Chandra, Alvin; Khatana, Sameed Ahmed M.; Lutsey, Pamela L.; Kucharska‐Newton, Anna; Koton, Silvia; Solomon, Scott D.; Kawachi, Ichiro

doi:10.1001/jamacardio.2019.3788

Cited by 40 publications

(28 citation statements)

References 38 publications

(71 reference statements)

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“…Consistent with previous research, our findings reveal that Gender, Age, BMI, and smoking were important predictors of CVD risk 12 , 21 . Earlier studies have established an association between parental income 72 , sedentary duration 73 , skipping breakfast 74 , self-image 75 , and depressive symptoms 76 and a higher likelihood of CVD risk, and our study results further substantiated this as these factors are found to be critical predictors of CVD risk. In addition, some predictors emerged as important for specific algorithms but not for others.…”

Section: Discussionsupporting

confidence: 87%

Explainable machine learning framework for predicting long-term cardiovascular disease risk among adolescents

Salah

Srinivas

2022

Sci Rep

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Although cardiovascular disease (CVD) is the leading cause of death worldwide, over 80% of it is preventable through early intervention and lifestyle changes. Most cases of CVD are detected in adulthood, but the risk factors leading to CVD begin at a younger age. This research is the first to develop an explainable machine learning (ML)-based framework for long-term CVD risk prediction (low vs. high) among adolescents. This study uses longitudinal data from a nationally representative sample of individuals who participated in the Add Health study. A total of 14,083 participants who completed relevant survey questionnaires and health tests from adolescence to young adulthood were chosen. Four ML classifiers [decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and deep neural networks (DNN)] and 36 adolescent predictors are used to predict adulthood CVD risk. While all ML models demonstrated good prediction capability, XGBoost achieved the best performance (AUC-ROC: 84.5% and AUC-PR: 96.9% on testing data). Besides, critical predictors of long-term CVD risk and its impact on risk prediction are obtained using an explainable technique for interpreting ML predictions. The results suggest that ML can be employed to detect adulthood CVD very early in life, and such an approach may facilitate primordial prevention and personalized intervention.

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Section: Discussionsupporting

confidence: 87%

Explainable machine learning framework for predicting long-term cardiovascular disease risk among adolescents

Salah

Srinivas

2022

Sci Rep

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“… 13 The causal relationship between lower socioeconomic status and poorer prognosis has also been confirmed from a longitudinal study exploring income changes with incident cardiovascular events including HF. 14 A recent global between-country analysis showed that income inequality, rather than income level alone, may impact on HF outcomes to a similar degree as do major comorbidities. 15 The relationship between lower socioeconomic status and worse clinical outcome could be bidirectional due to higher economic burden imposed by HF per se or HF-related comorbidities.…”

Section: Discussionmentioning

confidence: 99%

Income level and outcomes in patients with heart failure with universal health coverage

Hung

Chao

et al. 2020

Heart

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ObjectiveWe aimed to investigate the influence of income level on guideline-directed medical therapy (GDMT) prescription rates and prognosis of patients with heart failure (HF) following implementation of a nationwide health insurance programme.MethodsA total of 633 098 hospitalised patients with HF from 1996 to 2013 were identified from Taiwan National Health Insurance Research Database. Participants were classified into low-income, median-income and high-income groups. GDMT utilisation, in-hospital mortality and postdischarge HF readmission, and mortality rates were compared.ResultsThe low-income group had a higher comorbidity burden and was less likely to receive GDMT than the other two groups. The in-hospital mortality rate in the low-income group (5.07%) was higher than in the median-income (2.47%) and high-income (2.51%) groups. Compared with the high-income group, the low-income group had a significantly higher risk of postdischarge HF readmission (adjusted HR (aHR): 1.29, 95% CI 1.27 to 1.31), all-cause mortality (aHR: 1.98, 95% CI 1.95 to 2.02) and composite HF readmission/all-cause mortality (aHR: 1.54, 95% CI 1.52 to 1.56). These results were generally consistent among the population after propensity matching (low vs high: HR=2.08 for mortality and 1.36 for HF readmission; median vs high: HR=1.23 for mortality and 1.12 for HF readmission; all p<0.001) and after inverse probability of treatment weighting (low-income vs high-income group: HR: 2.19 for mortality and 1.16 for HF readmission; median-income vs high-income group: HR: 1.53 for mortality and 1.09 for HF readmission; all p<0.001). Lower utilisation of GDMT and poorer prognosis in lower-income hospitalised patients with HF appeared to mitigate over time.ConclusionsLow-income patients with HF had nearly a twofold increase in the risk of in-hospital mortality and postdischarge events compared with the high-income group, partly due to lower GDMT utilisation. The differences between postdischarge HF outcomes among various income groups appeared to mitigate over time following the implementation of nationwide universal health coverage.

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“…Atherosclerosis (AS) is a chronic multifactorial disease with high incidence and mortality rates worldwide [ 1 ]. Currently, it is well-known that the pathophysiological mechanisms and aetiology of AS include the dysregulation of lipid metabolism, activation of inflammatory factors and functional activation of various cells in the blood vessels, such as lymphocytes and macrophages.…”

Section: Introductionmentioning

confidence: 99%

Reduced SULT2B1b expression alleviates ox-LDL-induced inflammation by upregulating miR-148-3P via inhibiting the IKKβ/NF-κB pathway in macrophages

Yin

Guo

et al. 2021

Aging

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Atherosclerosis is a lipid-driven chronic inflammatory disease in which lipid-laden macrophage foam cells lead to inflamed lesions in arteries. Previous studies have proven that sulfotransferase 2B1b (SULT2B1b) has several roles in the regulation of lipid metabolism and the inflammatory response. However, little is known about the functions of SULT2B1b in ox-LDL-induced inflammation in macrophages. In this study, after treatment with either ox-LDL alone or combined with transfection of siRNAs targeting SULT2B1b, IL-6, TNF-α, NF-κB, IKKβ and IκB mRNA and protein expression were determined in Raw264.7 cells by real-time PCR and Western blot, respectively. The proliferative capacity was determined by EdU staining and Cell Counting Kit-8. Our data demonstrated that SULT2B1b knockdown could reduce phosphorylated NF-κB levels and downregulate IKKβ protein levels. Additionally, IκB levels were increased and the proliferation of ox-LDL stimulated cells was inhibited after SULT2B1b silencing. Downregulation of SULT2B1b expression was found to upregulate miR-148a-3p expression by microarray assay, while IKKβ was a miR-148a-3p target gene. Our study suggests that SULT2B1b knockdown could promote miR148a-3p expression and inhibit activation of the IKKβ/NF-κB signalling pathway, which suppressed the inflammatory response in macrophages. Therefore, targeting the SULT2B1b gene might be potentially beneficial for atherosclerosis prevention by decreasing the inflammatory response.

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Longitudinal Associations Between Income Changes and Incident Cardiovascular Disease

Cited by 40 publications

References 38 publications

Explainable machine learning framework for predicting long-term cardiovascular disease risk among adolescents

Explainable machine learning framework for predicting long-term cardiovascular disease risk among adolescents

Income level and outcomes in patients with heart failure with universal health coverage

Reduced SULT2B1b expression alleviates ox-LDL-induced inflammation by upregulating miR-148-3P via inhibiting the IKKβ/NF-κB pathway in macrophages

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