Figures
Abstract
Aim
Given a lack of data on diabetes care performance in Malaysia, we conducted a cross-sectional study to understand the clinical characteristics, control of cardiometabolic risk factors, and patterns of use of guideline-directed medical therapy (GDMT) among patients with type 2 diabetes (T2D), who were managed at publicly-funded hospitals between December 2021 and June 2022.
Methods
Patients aged ≥18 years with T2D from eight publicly-funded hospitals in the Greater Kuala Lumpur region, who had ≥2 outpatient visits within the preceding year and irrespective of treatment regimen, were eligible. The primary outcome was ≥2 treatment target attainment (defined as either HbA1c <7.0%, blood pressure [BP] <130/80 mmHg, or low-density lipoprotein cholesterol [LDL-C] <1.8 mmol/L). The secondary outcomes were the individual treatment target, a combination of all three treatment targets, and patterns of GDMT use. To assess for potential heterogeneity of study findings, all outcomes were stratified according to prespecified baseline characteristics namely 1) history of atherosclerotic cardiovascular disease (ASCVD; yes/no) and 2) clinic type (Diabetes specialist versus General medicine).
Results
Among 5094 patients (mean±SD age 59.0±13.2 years; T2D duration 14.8±9.2 years; HbA1c 8.2±1.9% (66±21 mmol/mol); BMI 29.6±6.2 kg/m2; 45.6% men), 99% were at high/very high cardiorenal risk. Attainment of ≥2 treatment targets was at 18%, being higher in General medicine than in Diabetes specialist clinics (20.8% versus 17.5%; p = 0.039). The overall statin coverage was 90%. More patients with prior ASCVD attained LDL-C <1.4 mmol/L than those without (13.5% versus 8.4%; p<0.001). Use of sodium-glucose cotransporter-2 (SGLT2) inhibitors (13.2% versus 43.2%), glucagon-like peptide-1 receptor agonists (GLP1-RAs) (1.0% versus 6.2%), and insulin (27.7% versus 58.1%) were lower in General medicine than in Diabetes specialist clinics.
Citation: Lim L-L, Hussein Z, Noor NM, Raof ASA, Mustafa N, Bidin MBL, et al. (2024) Real-world evaluation of care for type 2 diabetes in Malaysia: A cross-sectional analysis of the treatment adherence to guideline evaluation in type 2 diabetes (TARGET-T2D) study. PLoS ONE 19(1): e0296298. https://doi.org/10.1371/journal.pone.0296298
Editor: Hean Teik Ong, HT Ong Heart Clinic, MALAYSIA
Received: July 25, 2023; Accepted: December 8, 2023; Published: January 2, 2024
Copyright: © 2024 Lim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data cannot be shared publicly due to local regulation imposed by the Medical Review and Ethics Committee, Ministry of Health, Malaysia. Researchers who are interested and meet the criteria for research access to our data may apply to the Steering Committee of the TARGET-T2D study. For data availability, researchers can email nmrr@nmrr.gov.my for any inquiry on data access.
Funding: The TARGET-T2D study is funded by Boehringer Ingelheim (website URL: boehringer-ingelheim.com) via an unrestricted educational grant to the Malaysian Endocrine and Metabolic Society. The funder contributed scientific expertise to the study design. Co-authors from the funder had reviewed and edited the manuscript for intellectual content; however, the funder had no role in data collection, data analysis, and final review and approval of the manuscript for submission. The corresponding authors have full access to all data in the study and have the final responsibility for the decision to submit for publication.
Competing interests: LLL report receiving grants through her affiliated institutions and/or honoraria for consultancy and speaker bureau from Abbott, AstraZeneca, Boehringer Ingelheim, Merck Sharp & Dohme, Novo Nordisk, Roche, Sanofi, Servier, and Zuellig Pharma. SPC report receiving the research grant through her affiliated Society (Malaysian Endocrine and Metabolic Society) and honoraria for consultancy and speaker bureau from Abbott, AstraZeneca, Boehringer Ingelheim, Novo Nordisk, Roche, Sanofi, Servier, and Zuellig Pharma. KR and PS are employee of Boehringer Ingelheim. Other co-authors declared no potential conflict of interest. There are no patents, products in development or marketed products associated with this research to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
1. Introduction
Type 2 diabetes (T2D) is one of the major public health concerns in Malaysia. Based on the Malaysian National Health and Morbidity Survey in 2019, the prevalence of diabetes in adults aged ≥18 years has increased from 11.2% in 2011 to 18.3% [1]. This brings the total number of adults living with diabetes to 3.9 million [1]. Key cardiometabolic risk factors namely T2D, hypertension, and dyslipidaemia often occur together. To date, 3.4 million patients have two or more of these risk factors [1]. This highlights the importance of multicomponent interventions in managing patients with T2D to mitigate the long-term risk of complications and improve quality of life [2].
The increasing burden of T2D and its complications imposes substantial healthcare costs (both direct and indirect), especially when the healthcare system is heavily subsidized by the Malaysian government [3,4]. Although atherosclerotic cardiovascular disease (ASCVD) and heart failure are leading causes of disability and premature death, the 2019 GBD-NHLBI-JACC Global Burden of Cardiovascular Diseases Study reported the lack of improvement in mitigating these risks on a global level in the past three decades [5]. High systolic BP, high low-density lipoprotein cholesterol (LDL-C), high body mass index (BMI), high fasting plasma glucose, and kidney dysfunction remain the top five modifiable risk factors for ASCVD and heart failure between 1990 and 2019 [5].
Large-scale randomized clinical trials reported that sustained reduction of cardiometabolic risk factors for 2–5 years improved clinical outcomes in patients with T2D [2]. Several meta-analyses had also reported that reduction of HbA1c by 0.9% (10 mmol/mol) [6,7], systolic BP by 10 mmHg [8], and LDL-C by 1 mmol/L [9] independently reduced the risk of ASCVD and/or all-cause death by 10–20%. Since the landmark EMPA-REG OUTCOME trial in 2015 [10], several randomized clinical trials with sodium-glucose cotransporter-2 (SGLT2) inhibitors confirmed the marked reduction in risks for ASCVD, heart failure, and kidney dysfunction in patients with T2D and even improved cardiorenal outcomes in patients without T2D [11–17]. Glucagon-like peptide-1 receptor agonists (GLP1-RAs) have also shown risk reductions in ASCVD and kidney dysfunction in patients with T2D [18–22]. These findings have led to a changing landscape of the management of T2D [23,24].
Given its high burden and the paradigm shift in T2D management, evidence-based consensus guidance and new quality indicators have been developed for increasing the efficiency of care delivery. In Malaysia, the National Diabetes Registry was established in 2009 to evaluate the variations in care delivery and to uncover opportunities for quality improvement at publicly-funded primary care clinics [25]. However, similar mechanisms in hospital-based settings have thus far been limited.
2. Methods
2.1. Study design and population
The TARGET-T2D study is the first, large-scale quality improvement initiative for understanding the care patterns of patients with T2D in hospital-based settings in Malaysia. This was a cross-sectional study to describe the clinical characteristics, control of cardiometabolic risk factors, and patterns of medication use at eight publicly-funded specialist hospitals in the Greater Kuala Lumpur region, the capital of Malaysia. Three hospitals were academic institutions under the Ministry of Higher Education (MOHE) whilst the remaining hospitals were managed by the Ministry of Health (MOH) (Fig 1).
We included patients aged ≥18 years with T2D treated with oral glucose-lowering drugs and/or injectable therapy, who had at least two outpatient visits at either Diabetes specialist or General medicine clinics within the preceding year of data collection. We excluded patients with 1) type 1 diabetes, defined as a presentation with either diabetic ketoacidosis, unprovoked ketosis, or continuous insulin requirement within 12 months of diagnosis; 2) gestational diabetes mellitus, and 3) secondary diabetes mellitus.
We used the convenience sampling method and conducted data collection at all study sites from 13 December 2021 to 30 June 2022 for a total duration of six months. Before each clinic day, the study team prepared the outpatient appointment lists to facilitate data collection. The study team was trained to extract relevant data (including sociodemographic, comorbidities, medications, anthropometric, and laboratory measurements) from health records (either electronic or manual, depending on the site facility). We standardized data collection using established definitions, uniform data entry, and periodic data quality assurance by the Steering Committee of the study. We developed a TARGET-T2D web portal in collaboration with the Department of Software Engineering, University of Malaya. All data were pseudonymized and stored in the web portal in a manner compliant with local regulations. Any data not meeting predefined clinical plausibility thresholds were flagged for manual review with each study site.
The present study was approved by the Medical Research & Ethics Committee, Ministry of Health (NMRR ID-21-02100-BPE [IIR]) and three MOHE institutional ethics review boards. Given that there was no data collection beyond that of routine care, a waiver of written informed consent was granted.
2.2. Study outcomes
The primary outcome was the proportion of patients with T2D attaining ≥2 treatment targets, defined as 1) HbA1c <7.0% (53 mmol/mol); 2) BP <130/80 mmHg; and 3) LDL-C <1.8 mmol/L [23,26–28]. Secondary outcomes were the individual target, a combination of all three targets, and medication patterns. To assess for potential heterogeneity of results, all study outcomes were stratified according to prespecified baseline characteristics namely prior ASCVD and clinic type (Diabetes specialist versus General medicine).
Based on the recommendations of the Malaysian Clinical Practice Guideline of Type 2 Diabetes [23], we recorded glycaemic parameters namely fasting plasma glucose and HbA1c that were available up to six months prior to data collection. For non-glycaemic parameters namely lipid profile (including total cholesterol, LDL-C, triglyceride, and high-density lipoprotein cholesterol [HDL-C]), kidney function, liver function, and albuminuria, we recorded these measurements up to 12 months prior to data collection. To standardize the definition of albuminuria, we converted the values of urinary protein:creatinine ratio to urinary albumin:creatinine ratio (ACR) based on the equation developed by the CKD Prognosis Consortium [29]. We used the most recent laboratory measurements to define study outcomes.
2.3. Statistical analysis
Data were presented as mean±standard deviation (SD) or median (interquartile range [IQR]) for continuous variables with either normal or skewed distribution, respectively. We assessed for normality of data using the histograms, QQ plots, Shapiro Wilk, or Kolmogorov Smirnov test. We logarithmically transformed continuous variables with skewed distributions for analysis. Categorical variables were presented as numbers and percentages. Patients with T2D were categorized into either moderate, high, or very high cardiovascular risk based on the 2019 European Society of Cardiology (ESC) risk criteria [26].
For the two-group comparison of continuous variables, we used an independent t-test for data with normal distributions whilst Wilcoxon rank-sum test was for data with skewed distributions. We used the Chi-square test for between-group comparisons of categorical variables. We performed subgroup analyses, stratified by history of ASCVD and clinic type (Diabetes specialist versus General medicine).
We performed pairwise deletion for variables with missing values. All analyses were performed using R 4.2.1 [30]. A 2-tailed p-value <0.05 denoted statistical significance.
3. Results
Fig 1 describes the study flow. Among 5102 patients with T2D, we included 5094 patients aged ≥18 years in the present analysis, of whom 45.6% were men. Table 1 shows the baseline characteristics of the overall cohort, stratified by ASCVD status. The cohort was predominantly of Malay ethnicity (58.2%), followed by Indian (23.6%) and Chinese (18.2%). Fewer than 10% of them were current smokers.
In the entire cohort, the mean±SD HbA1c was 8.2±1.9% (66±21 mmol/mol) and one-third attained HbA1c<7% (53 mmol/mol) at baseline. Compared with those without prior ASCVD, a lower proportion of patients with prior ASCVD attained HbA1c<7% (53 mmol/mol) (27.3% versus 30.6%; p = 0.029) (Table 1 and Fig 2). The proportion of patients who attained BP<130/80 mmHg was 22.8% with no significant difference by ASCVD status (Table 1 and Fig 2). Compared with those without prior ASCVD, a higher proportion of patients with prior ASCVD attained either LDL-C<1.4 mmol/L (13.5% versus 8.4%) or LDL-C<1.8 mmol/L (32.6% versus 23.7%) (p<0.001 for both; Table 1 and Fig 2). A total of 18% of the entire cohort attained ≥2 treatment targets, which was higher in the General medicine clinics compared with the Diabetes specialist clinics (20.8% versus 17.5%; p = 0.039) (Table 2 and Fig 2).
#Treatment targets were based on HbA1c <7%, BP <130/80 mmHg, and LDL-C <1.8 mmol/L. “Audit” means treatment targets were based on the audit criteria of the 2020 Malaysian Clinical Practice Guideline on Type 2 Diabetes, defined as HbA1c <8.5%, blood pressure (BP) <130/80 mmHg, and low-density lipoprotein cholesterol (LDL-C) <1.8 mmol/L.
There were high rates of general and central obesity (Table 1). The mean±SD BMI of the entire cohort was 29.6±6.2 kg/m2, wherein only 22.9% patients had a BMI of <25 kg/m2. The mean±SD waist circumference was 99.7±13.8 cm in males and 95.1±12.7 cm in females. One-third of the entire cohort had an eGFR <60 mL/min/1.73m2, whilst about 4% had prior hospitalization for heart failure. Compared with those without prior ASCVD, patients with prior ASCVD reported higher proportions of eGFR <60 mL/min/1.73m2 (40.0% versus 25.5%) and heart failure (9.0% versus 1.7%) (p<0.001 for both; Table 1). Of note, 99% of the entire cohort were at high-/very high cardiorenal risk according to the ESC 2019 classification, irrespective of clinic type (Tables 1 and 2).
Compared with those without ASCVD, use of SGLT2 inhibitors (42.2% versus 35.7%; p<0.001) was significantly higher among patients with prior ASCVD, but not for GLP1-RAs (3.7% versus 5.9%) (Table 1 and Fig 3). Similar results were observed for RAS inhibitors (72.2% versus 63.1%), beta-blockers (60.4% versus 20.7%), statin therapy (95.1% versus 87.0%), and antiplatelet therapy (86.9% versus 12.1%) (p<0.001 for all; Table 1 and Fig 3). Of note, compared with the Diabetes specialist clinics, use of SGLT2 inhibitors (13.2% versus 43.2%), GLP1-RAs (1.0% versus 6.2%), DPP4 inhibitors (15.4% versus 47.9%), and insulin (27.7% versus 58.1%) were lower in the General medicine clinics (Table 2 and Fig 3). Regarding lipid-lowering medications, use of fenofibrate (5.5% versus 12.8%) and ezetimibe (2.2% versus 5.9%) were also lower at the General medicine clinics with no significant between-group differences in lipid profile. Use of BP-lowering medications was not significantly different between the Diabetes specialist and General medicine clinics.
GLP1-RAs, glucagon-like peptide-1 receptor agonists; RAS inhibitors, renin-angiotensin system inhibitors; SGLT2 inhibitors, sodium-glucose cotransporter-2 inhibitors.
Compared with those managed in the Diabetes specialist clinics, patients in the General medicine clinics were older (60.5±12.0 versus 58.7±13.4 years) with a shorter duration of T2D (10.6±7.4 versus 15.7±9.3 years) (Table 2). Both clinic types had similar cardiometabolic risk factors and comorbidity profiles, except for a lower HbA1c level (7.9±1.9% (63±21 mmol/mol) versus 8.2±1.9% (66±21 mmol/mol)) and a higher proportion of ASCVD (37.6% versus 28.0%) and albuminuria (66.3% versus 57.6%) in the General medicine clinics. Compared with those managed in the Diabetes specialist clinics, a higher proportion of patients in the General medicine clinics attained HbA1c<7% (53 mmol/mol) (39.1% versus 27.9%) and ≥2 treatment targets (20.8% versus 17.5%).
4. Discussion
In this real-world cohort of >5000 patients with T2D from eight urban, publicly-funded, hospital-based clinics, we highlighted several findings in relation to the cardiometabolic risk profiles and quality of care. More than 90% of the cohort were either high- or very high-risk for cardiorenal diseases, showing no significant difference between the Diabetes specialist and General medicine clinics. The present cohort also had high rates of general and central obesity, especially in women with T2D. Of note, one in five patients attained ≥2 treatment targets (HbA1c<7% (53 mmol/mol), BP<130/80 mmHg, and LDL-C<1.8 mmol/L), showing a higher proportion among those either with prior ASCVD or managed in the General medicine clinics. Although statin coverage was >90%, there was suboptimal attainment of LDL-C targets even among patients with prior ASCVD, wherein fewer than 15% of them attained LDL-C<1.4 mmol/L. Despite having a high proportion of at-risk patients, use of newer guideline-directed medical therapy (GDMT) such as SGLT2 inhibitors and GLP1-RAs was suboptimal, especially among those managed in the General medicine clinics.
Compared with previous hospital-based studies that were conducted in 2008 and 2013, the control of glycaemia among patients with T2D in the present study has modestly improved [31,32]. About one-third of the present cohort attained HbA1c <7% (53 mmol/mol), which was consistent with T2D populations in other middle-income countries such as China and India [33]. However, this was lower than what had been reported in high-income countries, which ranged between 45% and 80% [33]. The selection of HbA1c <7% (53 mmol/mol) as a treatment target in the present study could be debated. This target was based on the 2022 American Diabetes Association Standard of Medical Care [27] and our local guideline [23]. In real-world practice, the HbA1c target can be individualized between 7% (53 mmol/mol) and 8.5% (69 mmol/mol) according to the patient’s age, comorbidities, and hypoglycaemia risk [27,34]. On the other hand, therapeutic inertia could contribute to suboptimal control of glycaemia. In a retrospective analysis of the Malaysian primary care-based National Diabetes Registry, among non-insulin-treated patients with T2D and HbA1c ≥8% (64 mmol/mol), 54% had delayed treatment intensification with a median time of 13 months [35].
Obesity, hypertension, and dyslipidaemia can interact with hyperglycaemia in the development and progression of cardiorenal diseases, cancer, and other microvascular complications [2]. In the present cohort, three-quarters had general obesity with a mean BMI of 30 kg/m2 and >90% of women had central obesity. These findings were consistent with a multinational CAPTURE study which involved a predominantly White population [36]. According to the 2019 population-based Malaysian National Health and Morbidity Survey, 50% of adults had a BMI of ≥25 kg/m2, being more common in women than in men [1]. Indeed, the mean BMI among patients with T2D has increased from 28 kg/m2 in DiabCare 2008 [31] to 29 kg/m2 in DiabCare 2013 [32], and 30 kg/m2 in the present cohort.
Achieving hypertension control has been challenging wherein only 23% of the present cohort reported having BP <130/80 mmHg. Although the presence of multiple comorbidities among patients at hospital-based clinics was common and could be associated with suboptimal control, the proportion of patients at primary care clinics attaining BP ≤135/75 mmHg, who tended to have fewer comorbidities, was similar at 26% [37]. One possibility was white-coat hypertension as clinic BP, but not home BP, was recorded. Other potential reasons include the lack of disease awareness, infrequent home BP monitoring, and suboptimal treatment adherence due to polypharmacy, medication side effects, and self-care behaviour [2,38]. In addition, salt intake is an important determinant of BP. Alarmingly, 79% of adults in Malaysia reported consuming >5 grams (1 teaspoon) daily, which was beyond the recommended intake by the World Health Organization [39].
We report that one in four high- and very high-risk patients had adequate control of LDL-C level (<1.8 mmol/L), which is consistent with other Asian countries/regions. In a large retrospective cohort of >100,000 high-risk patients with diabetes in Korea, the mean LDL-C level was 2.9 mmol/L and 12% attained LDL-C <1.8 mmol/L [40]. In a multinational SUrvey of Risk Factors (SURF) study, the proportion of high-risk patients attaining LDL-C <1.8 mmol/L was 15% in China and Taiwan, compared with 33% and 35% in European and Middle-Eastern countries [41]. Future analysis by the type and dose of statin therapy (high- versus moderate intensity), as well as the different combinations of lipid-lowering therapy, may provide insights on how to close these gaps in care. The lack of patient-provider communication on the safety of statin therapy such as muscle symptoms, may also affect patient treatment adherence [42]. In a meta-analysis of 19 randomized clinical trials, statin therapy was associated with an absolute excess rate of 6–16 events per 1000 person-years of muscle symptoms during year 1 [43]. There was no significant excess risk during subsequent years [43]. Indeed, 90% of all reports of muscle symptoms among statin-treated patients were not due to statin therapy [43]. Apart from the aforementioned patient-level factors, the lack of a reliable cardiovascular risk stratification tool and therapeutic inertia could be associated with suboptimal risk-based LDL-C management [44].
Our findings indicate that one-third of the present high-risk cohort were treated with SGLT2 inhibitors, with a much lower proportion of 13% in General medicine clinics. The latter is consistent with other reports with similar time periods from the US [45] and the CAPTURE study [36]. Use of GLP1-RAs was only 4% among patients with ASCVD, reflecting the lack of availability of this class of GDMT. The uptake of these GDMT may change with the recent updates to consensus guidance that now recommend an SGLT2 inhibitor or GLP1-RA as first or second-line treatment in patients with either high-risk T2D or cardiorenal diseases [23,24,34,46]. The influence of these consensus updates on real-world practice is of interest and hence, the present study provides a benchmark for quality improvement. On the other hand, access and availability of SGLT2 inhibitors and GLP1-RAs depend on the purchasing decisions, medication quota, and prescribing rights in individual publicly-funded hospitals. For instance, at the time of the TARGET-T2D study, SGLT2 inhibitors were not approved for use at General medicine clinics and hence, patients who were indicated for treatment would need to be referred to Endocrinologists.
The major strength of the present study is the shared protocol for standardized data collection and quality assurance at eight publicly-funded tertiary care hospitals. In addition, use of a structured care record form, established data definitions, and an electronic data capture system with quality control may be key measures for periodic performance tracking and identification of gaps in care [2]. Importantly, our hospital-based data will complement the findings from the National Diabetes Registry which involves publicly-funded primary care clinics. Taken together, we are hopeful that our data will offer an impetus for the improvement of care standards over time among patients with T2D in Malaysia.
We acknowledge several study limitations. First, due to the pragmatic nature of the present study, there was potential selection bias due to the enthusiasm of participating study sites. Patients who were younger, with shorter disease duration, and less complex diseases might be under-represented, biasing our findings to those with more severe disease. Second, given that all study sites were urban publicly-funded hospitals in the Greater Kuala Lumpur region, there is limited generalizability to patients with T2D living in rural areas, patients managed in private healthcare facilities, and on a nationwide level. Last, given that not all study sites had electronic health records systems and manual data extraction was necessary, we limited the number of data collected in the real-world practice by excluding questionnaires on hypoglycaemia risk and patient-reported outcomes.
In conclusion, compared with previous audits (although with different hospital-based patient groups), we have reported modest improvement in cardiometabolic risk factors and treatment target attainment in the public hospital setting. Given finite resources, our data highlight potential gaps in care which can facilitate effective resource allocation. To address the epidemic of T2D in Malaysia, the present TARGET-T2D study is well-positioned to enable future data collection on a nationwide level, monitoring of trends, and longitudinal evaluation of health outcomes.
Acknowledgments
We thank the Director General of Health, Malaysia for this permission to publish this article. We thank all patients with diabetes, the study team members, staff in all study sites, UM Innovations Sdn. Bhd, and organisations involved in setting up, maintaining, and overseeing data collection for the TARGET-T2D study.
References
- 1. Institute for Public Health (IPH), National Institutes of Health, Ministry of Health Malaysia. 2020. National Health and Morbidity Survey (NHMS) 2019: Vol. I: NCDs–Non-Communicable Diseases: Risk Factors and other Health Problems.
- 2. Chan JCN, Lim LL, Wareham NJ, Shaw JE, Orchard TJ, Zhang P, et al. The Lancet Commission on diabetes: using data to transform diabetes care and patient lives. Lancet. 2021;396(10267):2019–82. Epub 2020/11/16. pmid:33189186.
- 3.
Direct Health-care Cost of Noncommunicable Diseases in Malaysia (2022). Putrajaya, Malaysia: Ministry of Health Malaysia.
- 4.
The Impact of Noncommunicable Diseases and Their Risk Factors on Malaysia’s Gross Domestic Product (2020). Putrajaya, Malaysia: Ministry of Health Malaysia.
- 5. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020;76(25):2982–3021. Epub 2020/12/15. pmid:33309175; PubMed Central PMCID: PMC7755038.
- 6. Ray KK, Seshasai SR, Wijesuriya S, Sivakumaran R, Nethercott S, Preiss D, et al. Effect of intensive control of glucose on cardiovascular outcomes and death in patients with diabetes mellitus: a meta-analysis of randomised controlled trials. Lancet. 2009;373(9677):1765–72. Epub 2009/05/26. pmid:19465231.
- 7. Turnbull FM, Abraira C, Anderson RJ, Byington RP, Chalmers JP, Duckworth WC, et al. Intensive glucose control and macrovascular outcomes in type 2 diabetes. Diabetologia. 2009;52(11):2288–98. Epub 2009/08/06. pmid:19655124.
- 8. Emdin CA, Rahimi K, Neal B, Callender T, Perkovic V, Patel A. Blood pressure lowering in type 2 diabetes: a systematic review and meta-analysis. JAMA. 2015;313(6):603–15. pmid:25668264.
- 9. Cholesterol Treatment Trialists Collaborators, Kearney PM, Blackwell L, Collins R, Keech A, Simes J, et al. Efficacy of cholesterol-lowering therapy in 18,686 people with diabetes in 14 randomised trials of statins: a meta-analysis. Lancet. 2008;371(9607):117–25. pmid:18191683.
- 10. Zinman B, Wanner C, Lachin JM, Fitchett D, Bluhmki E, Hantel S, et al. Empagliflozin, Cardiovascular Outcomes, and Mortality in Type 2 Diabetes. N Engl J Med. 2015;373(22):2117–28. Epub 20150917. pmid:26378978.
- 11. Neal B, Perkovic V, Mahaffey KW, de Zeeuw D, Fulcher G, Erondu N, et al. Canagliflozin and Cardiovascular and Renal Events in Type 2 Diabetes. N Engl J Med. 2017;377(7):644–57. Epub 20170612. pmid:28605608.
- 12. Wiviott SD, Raz I, Bonaca MP, Mosenzon O, Kato ET, Cahn A, et al. Dapagliflozin and Cardiovascular Outcomes in Type 2 Diabetes. N Engl J Med. 2019;380(4):347–57. Epub 20181110. pmid:30415602.
- 13. Packer M, Anker SD, Butler J, Filippatos G, Pocock SJ, Carson P, et al. Cardiovascular and Renal Outcomes with Empagliflozin in Heart Failure. N Engl J Med. 2020;383(15):1413–24. Epub 2020/09/01. pmid:32865377.
- 14. Perkovic V, Jardine MJ, Neal B, Bompoint S, Heerspink HJL, Charytan DM, et al. Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy. N Engl J Med. 2019;380(24):2295–306. Epub 2019/04/17. pmid:30990260.
- 15. McMurray JJV, Solomon SD, Inzucchi SE, Køber L, Kosiborod MN, Martinez FA, et al. Dapagliflozin in Patients with Heart Failure and Reduced Ejection Fraction. N Engl J Med. 2019;381(21):1995–2008. Epub 2019/09/19. pmid:31535829.
- 16. Heerspink HJL, Stefánsson BV, Correa-Rotter R, Chertow GM, Greene T, Hou FF, et al. Dapagliflozin in Patients with Chronic Kidney Disease. N Engl J Med. 2020;383(15):1436–46. Epub 2020/09/25. pmid:32970396.
- 17. Herrington WG, Staplin N, Wanner C, Green JB, Hauske SJ, Emberson JR, et al. Empagliflozin in Patients with Chronic Kidney Disease. N Engl J Med. 2022. Epub 20221104. pmid:36331190.
- 18. Marso SP, Bain SC, Consoli A, Eliaschewitz FG, Jódar E, Leiter LA, et al. Semaglutide and Cardiovascular Outcomes in Patients with Type 2 Diabetes. N Engl J Med. 2016;375(19):1834–44. Epub 20160915. pmid:27633186.
- 19. Marso SP, Daniels GH, Brown-Frandsen K, Kristensen P, Mann JF, Nauck MA, et al. Liraglutide and Cardiovascular Outcomes in Type 2 Diabetes. N Engl J Med. 2016;375(4):311–22. Epub 20160613. pmid:27295427; PubMed Central PMCID: PMC4985288.
- 20. Gerstein HC, Colhoun HM, Dagenais GR, Diaz R, Lakshmanan M, Pais P, et al. Dulaglutide and renal outcomes in type 2 diabetes: an exploratory analysis of the REWIND randomised, placebo-controlled trial. Lancet. 2019;394(10193):131–8. Epub 2019/06/14. pmid:31189509.
- 21. Gerstein HC, Colhoun HM, Dagenais GR, Diaz R, Lakshmanan M, Pais P, et al. Dulaglutide and cardiovascular outcomes in type 2 diabetes (REWIND): a double-blind, randomised placebo-controlled trial. Lancet. 2019;394(10193):121–30. Epub 2019/06/14. pmid:31189511.
- 22. Hernandez AF, Green JB, Janmohamed S, D’Agostino RB, Sr., Granger CB, Jones NP, et al. Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial. Lancet. 2018;392(10157):1519–29. Epub 2018/10/07. pmid:30291013.
- 23.
Malaysian Clinical Practice Guidelines on Management of Type 2 Diabetes, 6th Edition, December 2020. Available from http://www.acadmed.org.my/index.cfm?&menuid=67.
- 24. Davies MJ, Aroda VR, Collins BS, Gabbay RA, Green J, Maruthur NM, et al. Management of Hyperglycemia in Type 2 Diabetes, 2022. A Consensus Report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care. 2022. Epub 20220923. pmid:36148880.
- 25.
Feisul MI, Azmi S. (Eds). National Diabetes Registry Report, Volume 1, 2009–2012. Kuala Lumpur; Ministry of Health Malaysia; 2013 Jul.
- 26. Cosentino F, Grant PJ, Aboyans V, Bailey CJ, Ceriello A, Delgado V, et al. 2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD. Eur Heart J. 2020;41(2):255–323. pmid:31497854.
- 27. Draznin B, Aroda VR, Bakris G, Benson G, Brown FM, Freeman R, et al. 6. Glycemic Targets: Standards of Medical Care in Diabetes-2022. Diabetes Care. 2022;45(Supplement_1):S83–s96. pmid:34964868.
- 28. Draznin B, Aroda VR, Bakris G, Benson G, Brown FM, Freeman R, et al. 10. Cardiovascular Disease and Risk Management: Standards of Medical Care in Diabetes-2022. Diabetes Care. 2022;45(Supplement_1):S144–s74. pmid:34964815.
- 29. Sumida K, Nadkarni GN, Grams ME, Sang Y, Ballew SH, Coresh J, et al. Conversion of Urine Protein-Creatinine Ratio or Urine Dipstick Protein to Urine Albumin-Creatinine Ratio for Use in Chronic Kidney Disease Screening and Prognosis: An Individual Participant-Based Meta-analysis. Ann Intern Med. 2020;173(6):426–35. Epub 20200714. pmid:32658569; PubMed Central PMCID: PMC7780415.
- 30.
R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available from https://www.R-project.org/.
- 31. Mafauzy M, Hussein Z, Chan SP. The status of diabetes control in Malaysia: results of DiabCare 2008. Med J Malaysia. 2011;66(3):175–81. Epub 2011/11/25. pmid:22111435.
- 32. Mafauzy M, Zanariah H, Nazeri A, Chan SP. DiabCare 2013: A cross-sectional study of hospital based diabetes care delivery and prevention of diabetes related complications in Malaysia. Med J Malaysia. 2016;71(4):177–85. pmid:27770116.
- 33. Chan JCN, Lim LL, Luk AOY, Ozaki R, Kong APS, Ma RCW, et al. From Hong Kong Diabetes Register to JADE Program to RAMP-DM for Data-Driven Actions. Diabetes Care. 2019;42(11):2022–31. Epub 2019/09/19. pmid:31530658.
- 34. de Boer IH, Khunti K, Sadusky T, Tuttle KR, Neumiller JJ, Rhee CM, et al. Diabetes Management in Chronic Kidney Disease: A Consensus Report by the American Diabetes Association (ADA) and Kidney Disease: Improving Global Outcomes (KDIGO). Diabetes Care. 2022. Epub 20221003. pmid:36189689.
- 35. Wan KS, Moy FM, Mohd Yusof K, Mustapha FI, Mohd Ali Z, Hairi NN. Clinical inertia in type 2 diabetes management in a middle-income country: A retrospective cohort study. PLoS One. 2020;15(10):e0240531. Epub 20201009. pmid:33035261; PubMed Central PMCID: PMC7546487.
- 36. Mosenzon O, Alguwaihes A, Leon JLA, Bayram F, Darmon P, Davis TME, et al. CAPTURE: a multinational, cross-sectional study of cardiovascular disease prevalence in adults with type 2 diabetes across 13 countries. Cardiovasc Diabetol. 2021;20(1):154. Epub 20210727. pmid:34315481; PubMed Central PMCID: PMC8317423.
- 37. Ministry of Health Malaysia. 2021. National Diabetes Registry Report 2020.
- 38. Chia YC, Kario K. Asian management of hypertension: Current status, home blood pressure, and specific concerns in Malaysia. J Clin Hypertens (Greenwich). 2020;22(3):497–500. Epub 20191106. pmid:31693281; PubMed Central PMCID: PMC8029851.
- 39. Institute for Public Health (IPH) 2019. Population-Based Salt Intake Survey To Support The National Salt Reduction Programme For Malaysia (Malaysian Community Salt Survey–MyCoSS). Available from https://iku.moh.gov.my/images/IKU/Document/SALT-FULL_Report.pdf. Accessed 29 Jan 2022.
- 40. Yang YS, Yang BR, Kim MS, Hwang Y, Choi SH. Low-density lipoprotein cholesterol goal attainment rates in high-risk patients with cardiovascular diseases and diabetes mellitus in Korea: a retrospective cohort study. Lipids Health Dis. 2020;19(1):5. Epub 20200111. pmid:31926562; PubMed Central PMCID: PMC6954559.
- 41. Zhao M, Cooney MT, Klipstein-Grobusch K, Vaartjes I, De Bacquer D, De Sutter J, et al. Simplifying the audit of risk factor recording and control: A report from an international study in 11 countries. Eur J Prev Cardiol. 2016;23(11):1202–10. Epub 20160426. pmid:27118362.
- 42. Lim LL, Lau ESH, Kong APS, Davies MJ, Levitt NS, Eliasson B, et al. Aspects of Multicomponent Integrated Care Promote Sustained Improvement in Surrogate Clinical Outcomes: A Systematic Review and Meta-analysis. Diabetes Care. 2018;41(6):1312–20. Epub 2018/05/23. pmid:29784698.
- 43. Effect of statin therapy on muscle symptoms: an individual participant data meta-analysis of large-scale, randomised, double-blind trials. Lancet. 2022;400(10355):832–45. Epub 20220829. pmid:36049498; PubMed Central PMCID: PMC7613583.
- 44. Morieri ML, Lamacchia O, Manzato E, Giaccari A, Avogaro A. Physicians’ misperceived cardiovascular risk and therapeutic inertia as determinants of low LDL-cholesterol targets achievement in diabetes. Cardiovasc Diabetol. 2022;21(1):57. Epub 20220426. pmid:35473579; PubMed Central PMCID: PMC9044595.
- 45. Nanna MG, Kolkailah AA, Page C, Peterson ED, Navar AM. Use of Sodium-Glucose Cotransporter 2 Inhibitors and Glucagonlike Peptide-1 Receptor Agonists in Patients With Diabetes and Cardiovascular Disease in Community Practice. JAMA Cardiol. 2022. Epub 20221102. pmid:36322056.
- 46. Heidenreich PA, Bozkurt B, Aguilar D, Allen LA, Byun JJ, Colvin MM, et al. 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2022:101161cir0000000000001063. Epub 20220401. pmid:35363499.