Inverse Association Between HDL (High-Density Lipoprotein) Cholesterol and Stroke Risk Among Patients With Type 2 Diabetes Mellitus

Shen, Yun; Shi, Lizheng; Nauman, Elizabeth; Katzmarzyk, Peter T.; Price‐Haywood, Eboni G.; Bazzano, Alessandra N.; Nigam, Somesh; Hu, Gang

doi:10.1161/strokeaha.118.023682

Cited by 28 publications

(23 citation statements)

References 29 publications

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“…A study including multivariate analysis conducted in Spain found 41.2% T2DM patients with atherothrombotic stroke and 35.1% with lacunar infarction ( 18 ). Shen et al ( 19 ) performed a retrospective cohort study composed of 27,113 blacks and 40,431 whites with T2DM and found that 8,496 (12.57%) participants developed stroke during a mean follow-up period of 3 years. A Chinese study was conducted on 9,374 T2DM patients in total to establish a risk score system; among all the participants, 11.48% developed ischemic stroke with a mean follow-up of 8 years ( 12 ).…”

Section: Discussionmentioning

confidence: 99%

“…A meta-analysis on data of 61 studies indicated a strong association between HDL-C cholesterol and high risk of CVD and death ( 48 ). Through a retrospective cohort study and a mean follow-up of 3 years, including 67,544 T2DM patients, a significant adverse connection was found between HDL-C cholesterol among T2DM patients and the risk of total, ischemic, and hemorrhagic stroke ( 19 ). High-density lipoprotein cholesterol was an influencing factor involved in a Chinese retrospective cohort study aiming at establishing a predictive model of ischemic stroke among T2DM patients ( 12 ).…”

Section: Discussionmentioning

confidence: 99%

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Establishment of Clinical Prediction Model Based on the Study of Risk Factors of Stroke in Patients With Type 2 Diabetes Mellitus

et al. 2020

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Purpose: Stroke has sparked global concern as it seriously threatens people's life, bringing about dramatic health burdens on patients, especially for type 2 diabetes mellitus (T2DM) patients. Therefore, a risk scoring model is urgently valuable for T2DM patients to predict the risk of stroke incidence and for positive health intervention. Methods: We randomly divided 4,335 T2DM patients into two groups, training set ( n = 3,252) and validation set ( n = 1,083), at the ratio of 3:1. Characteristic variables were then selected based on the data of training set through least absolute shrinkage and selection operator regression. Three models were established to verify predictive ability. Foundation model was composed of basic information and physical indicators. Biochemical model consisted of biochemical indexes. Integrated model combined the above two models. Data of three models were then put into logistic regression analysis to form nomogram prediction models. Tools including C index, calibration plot, and curve analysis were implemented to test discrimination, calibration, and clinical use. To select the best predicting model, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were put into effect. Results: Eleven risk factors were determined, including age, duration of T2DM, estimated glomerular filtration rate, systolic blood pressure, diastolic blood pressure, low-density lipoprotein, high-density lipoprotein, triglyceride, body mass index, uric acid, and glycosylated hemoglobin A 1c , all with significant P -values through logistic regression analysis. In the training set, areas under the curve of three models were 0.810, 0.819, and 0.884, whereas in the validation set, they were 0.836, 0.832, and 0.909. Through calibration plot, the S:P values in the training set were 0.836, 0.754, and 0.621 and were 0.918, 0.682, and 0.666 separately in the validation set. In terms of the decision curve analysis, the risk thresholds were, respectively, 8–73%, 8–98%, and 8%~ in the training set and 8–70%, 8–90%, and 8–95% in the validation set. With the aid of NRI and IDI, integrated model is proved to be the best model in training set and validation set. Besides, internal validation was conducted on all the subjects in this study, and the C index was 0.890 (0.873–0.907). Conclusion: This study established a model predicting risk of stroke for T2DM patients through a community-based survey.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Establishment of Clinical Prediction Model Based on the Study of Risk Factors of Stroke in Patients With Type 2 Diabetes Mellitus

et al. 2020

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“…Data from patients with type 2 diabetes in the Louisiana Experiment Assessing Diabetes outcomes (LEAD) cohort study were obtained through the Research Action for Health Network (REACHnet) 9,10 . The dataset included electronic health record data for the study cohort from 1 January 2013 to 30 April 2018.…”

Section: Methodsmentioning

confidence: 99%

Association between visit‐to‐visit HbA1c variability and the risk of cardiovascular disease in patients withtype 2diabetes

Shen

Zhou

Shi

et al. 2020

Diabetes Obesity Metabolism

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Aim To investigate the association between visit‐to‐visit HbA1c variability and the risk of cardiovascular disease in patients with type 2 diabetes. Materials and methods We performed a retrospective cohort study of 29 260 patients with at least four HbA1c measurements obtained within 2 years of their first diagnosis of type 2 diabetes. Different HbA1c variability markers were calculated, including the standard deviation (SD), coefficient of variation (CV) and adjusted SD. Cox proportional hazards regression models were used to estimate the association of these HbA1c variability markers with incident cardiovascular disease. Results During a mean follow‐up of 4.18 years, a total of 3746 incident cardiovascular disease cases were diagnosed. Multivariate‐adjusted hazard ratios for cardiovascular disease across the first, second, third and fourth quartiles of HbA1c SD values were 1.00, 1.30 (95% confidence interval [CI] 1.18‐1.42), 1.40 (95% CI 1.26‐1.55) and 1.59 (95% CI 1.41‐1.77) (P for trend <.001), respectively. When we utilized HbA1c CV and adjusted HbA1c SD values as exposures, similar positive associations were observed. HbA1c variability was also associated with the risk of first and recurrent severe hypoglycaemic events. A mediating effect of severe hypoglycaemia was observed between HbA1c variability and incident cardiovascular disease. Conclusions Large visit‐to‐visit HbA1c variability is associated with an increased risk of cardiovascular disease in patients with type 2 diabetes. Severe hypoglycaemia may mediate the association between HbA1c variability and incident cardiovascular disease.

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“…Data on patients with type 2 diabetes in the Louisiana Experiment Assessing Diabetes outcomes (LEAD) cohort study were obtained through the Research Action for Health Network (REACHnet). 6,7 REACHnet is a partnership of health systems, academic centers, and public health organizations that constitute an innovative data network for conducting efficient, multi-site research. Data from three REACHnet partner health systems were used in the current study including Ochsner Health System, Tulane Medical Center, and Access Health Louisiana.…”

Section: Study Participantsmentioning

confidence: 99%

Association between Hemoglobin A1c and Stroke Risk in Patients with Type 2 Diabetes

et al. 2020

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Background and Purpose The association between hemoglobin A1c (HbA1c) and stroke risk along with its subtypes is rarely reported. We aimed to investigate the association between HbA1c and the risk of incident stroke in patients with type 2 diabetes based on real world data from three healthcare systems.Methods We performed a retrospective cohort study of 27,113 African Americans and 40,431 whites with type 2 diabetes. Demographic, anthropometric, laboratory, and medication information were abstracted from the National Patient-Centered Clinical Research Network common data model. Incident stroke events including both ischemic and hemorrhagic stroke were defined.Results During a mean follow-up period of 3.79±1.68 years, 7,735 patients developed stroke (6,862 ischemic and 873 hemorrhagic). Multivariable-adjusted hazard ratios across levels of HbA1c at baseline (<6.0%, 6.0% to 6.9% [reference group], 7.0% to 7.9%, 8.0% to 8.9%, 9.0% to 9.9%, and ≥10%) were 1.07, 1.00, 1.13, 1.23, 1.27, and 1.37 (Ptrend <0.001) for total stroke, 1.02, 1.00, 1.13, 1.20, 1.24, and 1.35 (Ptrend <0.001) for ischemic stroke, and 1.40, 1.00, 1.14, 1.47, 1.47, and 1.51 (Ptrend=0.002) for hemorrhagic stroke. When we used an updated mean value of HbA1c, the U-shaped association of HbA1c with stroke risk did not change. This U-shaped association was consistent among patients of different subgroups. The U-shaped association was more pronounced among patients taking antidiabetic, lipid-lowering, and antihypertensive medications compared with those without these medications.Conclusions These data suggest that diabetes management may have to be individualized according to the guideline recommendations rather than intensively attempting to lower HbA1c.

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Inverse Association Between HDL (High-Density Lipoprotein) Cholesterol and Stroke Risk Among Patients With Type 2 Diabetes Mellitus

Cited by 28 publications

References 29 publications

Establishment of Clinical Prediction Model Based on the Study of Risk Factors of Stroke in Patients With Type 2 Diabetes Mellitus

Establishment of Clinical Prediction Model Based on the Study of Risk Factors of Stroke in Patients With Type 2 Diabetes Mellitus

Association between visit‐to‐visit HbA1c variability and the risk of cardiovascular disease in patients withtype 2diabetes

Association between Hemoglobin A1c and Stroke Risk in Patients with Type 2 Diabetes

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