Association of Low-Density Lipoprotein Cholesterol with Risk of Coronary Heart Disease and Stroke among Middle-Aged Japanese Workers: An Analysis using Inverse Probability Weighting

Al-Shoaibi, Abubakr Ahmed Abdullah; Li, Yuanying; Song, Zean; Chiang, Chifa; Hirakawa, Yoshihisa; Saif‐Ur‐Rahman, K. M.; Shimoda, Masako; Nakano, Yoshihisa; Matsunaga, Masaaki; Aoyama, Atsuko; Koji, Takehiko; Ota, Atsuhiko; Yatsuya, Hiroshi

doi:10.5551/jat.63519

Cited by 4 publications

(3 citation statements)

References 45 publications

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“…The greatest predictors for coronary artery disease included age, total cholesterol, total platelets, and family history of a heart attack. The visualizations completed for the top four covariates were concordant with current literature around the relationship between these covariates and coronary artery disease: there is strong epidemiological and physiological evidence for the link between increased age and cholesterol as major risk factors for coronary artery disease [26][27][28]. The non-linear relationship between cholesterol and coronary artery disease matches survival-modeling and restricted PLOS ONE cubic spline analysis from other studies [28][29][30][31][32][33][34][35][36].…”

Section: Plos Onesupporting

confidence: 72%

Use of machine learning to identify risk factors for coronary artery disease

Huang

2023

PLoS ONE

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Coronary artery disease (CAD) is the leading cause of death in both developed and developing nations. The objective of this study was to identify risk factors for coronary artery disease through machine-learning and assess this methodology. A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. Univariate logistic models, with CAD as the outcome, were used to identify covariates that were associated with CAD. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the Cover statistic to identify risk factors for CAD. Shapely Additive Explanations (SHAP) explanations were utilized to visualize the relationship between these potential risk factors and CAD. Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51%) were female, 2,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of other race. A total of 338 (4.5%) of patients had coronary artery disease. These were fitted into the XGBoost model and an AUROC = 0.89, Sensitivity = 0.85, Specificity = 0.87 were observed (Fig 1). The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were age (Cover = 21.1%), Platelet count (Cover = 5.1%), family history of heart disease (Cover = 4.8%), and Total Cholesterol (Cover = 4.1%). Machine learning models can effectively predict coronary artery disease using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.

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Section: Plos Onesupporting

confidence: 72%

Use of machine learning to identify risk factors for coronary artery disease

Huang

2023

PLoS ONE

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“…In this issue of Journal of Atherosclerosis and Thrombosis, Al-Shoaibi et al 1) reported the associations between low-density lipoprotein cholesterol (LDL-C) and the risk of coronary heart disease (CHD) and stroke subtypes among middleaged Japanese workers. They revealed that LDL-C levels were significantly and positively associated with CHD and inversely associated with intracerebral hemorrhage.…”

Section: See Article Vol 29: 000-000mentioning

confidence: 99%

Current Status of Low-Density Lipoprotein Cholesterol and Cardiovascular Diseases in Japan

Sun

Yamagishi

Iso

2023

JAT

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“…However, over recent decades, blood cholesterol levels 4) and thus the incidence of coronary heart disease 3) have been rising. A number of large prospective cohort studies published over the last decade have reported that LDL-C levels are a major risk factor for morbidity and mortality from coronary artery disease (CAD) in the medium to long terms in Japan [5][6][7][8] . These trends emphasize the need for effective therapeutic measures to control hypercholesterolemia.…”

Section: Introductionmentioning

confidence: 99%

Achievement Rates for Low-Density Lipoprotein Cholesterol Goals in Patients at High Risk of Atherosclerotic Cardiovascular Disease in a Real-World Setting in Japan

Mitani

Suzuki

Ako

et al. 2023

JAT

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The study aimed to investigate low-density lipoprotein cholesterol (LDL-C) goal achievement rates in patients receiving LDL-C-lowering therapy using recent real-world data, following the 2017 revision of the Japan Atherosclerosis Society Guidelines for Prevention of Atherosclerotic Cardiovascular Diseases (JAS GL2017).Methods: Patients with documented LDL-C test results were extracted from the Medical Data Vision claims database between July 2018 and June 2021 and divided into three groups according to JAS GL2017: primary prevention high risk (Group I, LDL-C goal ＜120 mg/dL), secondary prevention (Group II, LDL-C goal ＜100 mg/dL), and secondary prevention high risk (Group III, LDL-C goal ＜70 mg/dL). Results:The mean LDL-C value was 108.7 mg/dL (n=125,235), 94.4 mg/dL (n=57,910), and 90.6 mg/dL (n=33,850) in Groups I, II, and III, respectively. Intensive statin monotherapy (pitavastatin, rosuvastatin, or atorvastatin) was the most frequently prescribed lipid-lowering treatment (21.6%, 30.8%, and 42.7% in Groups I, II, and III, respectively), followed by ezetimibe (2.5%, 7.1%, and 8.5% in Groups I, II, and III, respectively). LDL-C goals were achieved by 65.5%, 60.6%, and 25.4% of patients overall in Groups I, II, and III, respectively. Achievement rates were 83.9%, 75.3%, and 29.5% in patients prescribed intensive statin monotherapy and 82.3%, 86.4%, and 46.4% in those prescribed statin and ezetimibe combinations in Groups I, II, and III, respectively. In Group III, the proportion of patients with familial hypercholesterolemia prescribed statin and ezetimibe combinations achieving LDL-C goals was low (32.5%). Conclusions:The proportion of patients achieving LDL-C goals for secondary prevention in the high-risk group remains low even with statin and ezetimibe combination therapy.

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Association of Low-Density Lipoprotein Cholesterol with Risk of Coronary Heart Disease and Stroke among Middle-Aged Japanese Workers: An Analysis using Inverse Probability Weighting

Cited by 4 publications

References 45 publications

Use of machine learning to identify risk factors for coronary artery disease

Use of machine learning to identify risk factors for coronary artery disease

Current Status of Low-Density Lipoprotein Cholesterol and Cardiovascular Diseases in Japan

Achievement Rates for Low-Density Lipoprotein Cholesterol Goals in Patients at High Risk of Atherosclerotic Cardiovascular Disease in a Real-World Setting in Japan

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