Effect of Multifactorial Treatment Targets and Relative Importance of Hemoglobin A1c, Blood Pressure, and Low‐Density Lipoprotein‐Cholesterol on Cardiovascular Diseases in Chinese Primary Care Patients With Type 2 Diabetes Mellitus: A Population‐Based Retrospective Cohort Study

Abstract

BackgroundThe relative effect of hemoglobin A1c, blood pressure, and low‐density lipoprotein‐cholesterol (LDL‐C) (“ABC” factors) on the prevention of cardiovascular diseases (CVD) among patients with type 2 diabetes mellitus is poorly understood. This study aimed to evaluate the association of key clinical parameters on CVD risk using a multifactorial optimal control approach in Chinese primary care patients with type 2 diabetes mellitus.Methods and ResultsA population‐based retrospective cohort study was conducted on 144 271 Chinese type 2 diabetes mellitus primary care patients, aged 18 to 79 and without prior clinical diagnosis of CVD in 2008–2011. Cox regressions were conducted to examine the association between the combinations of ABC targets (hemoglobin A1c <7%, blood pressure <130/90 mm Hg, and LDL‐C <2.6 mmol/L) and risks of CVD (overall), coronary heart disease, stroke, and heart failure. Achieving more ABC targets incrementally reduced the incidence of total CVD and individual disease including coronary heart disease, stroke, and heart failure, irrespective of other patient characteristics. Compared with suboptimal control in all ABC levels, achieving any 1, 2, and all 3 ABC targets reduced the relative risk of CVD by 13% to 42%, 31% to 52%, and 55%, respectively. Among those achieving only 1 ABC target, LDL‐C reduction was associated with the greatest CVD risk reduction (42%), followed by blood pressure reduction (18%), and hemoglobin A1c reduction (13%).ConclusionsTo achieve the greatest risk reduction for the incidence of CVD, the ultimate goal of treatment should be to achieve target control of hemoglobin A1c, blood pressure, and LDL‐C. If it is not possible to achieve all 3 targets, efforts should be prioritized on treating the LDL‐C to minimize CVD risk.

The challenge of multiple cardiovascular risk factor control outside Western Europe: Findings from the International ChoLesterol management Practice Study

“…This poor control of cardiovascular risk was similar across all participating countries, and was consistent with low goal attainment rates reported previously for studies conducted predominantly in North America and Western Europe. [5][6][7][8][9][10][11][12][13] As ICLPS was performed in countries and geographical regions often overlooked by other international studies, these findings indicate that poor control of multiple risk factors is a worldwide problem.…”

“…[5][6][7][8] Studies in non-Western populations are limited, but generally report multifactor control rates of up to $10%. [10][11][12] Variations between countries have not been studied extensively; however, poor control of risk factors has been implicated in the higher rates of cardiovascular events and mortality observed in lowand middle-income countries (LMICs) compared with high-income countries (HICs), despite the lower riskfactor burden in LMICs. 14 For each patient, multiple factors interact to determine the likelihood of good risk factor control.…”

Review of Ongoing Activities and Challenges to Improve the Care of Patients With Type 2 Diabetes Across Africa and the Implications for the Future

“…K. Prospective Diabetes Study Group, 1995;UK Prospective Diabetes Study Group, 1998;MRC/BHF Heart Protection, 2002;Wan et al, 2017). Currently, only a minority of patients with diabetes in Africa achieve optimal therapeutic targets (Sobngwi et al, 2012;Pinchevsky et al, 2015;Manne-Goehler et al, 2019), which is a real concern.…”

Ten-year risk prediction models of complications and mortality of Chinese patients with diabetes mellitus in primary care in Hong Kong: a study protocol

“… 41 Thus, multiple imputation has become one of the most widely used methods for handling missing data in medical research. 42 43 This approach was also used in the development of the CVD prediction model in the QRisk study. 9 10 …”