Unravelling the association of glycosylated haemoglobin A1c, blood pressure, and LDL-cholesterol (ABC) with all-cause mortality in Type 2 diabetes patients: insights from a middle-income country

Abstract

Introduction

This study evaluated the risk of all-cause mortality among Type 2 Diabetes (T2D) patients in Malaysia, correlating it with glycosylated haemoglobin A1c (HbA1c), blood pressure (BP), and LDL-Cholesterol (LDL-C) – the ABC parameters. This would fill the evidence gap from middle-income countries like Malaysia.

Methods

This retrospective cohort study analysed data from National Diabetes Registry and death records for 90,933 T2D patients in southern Malaysia (2011–2021). ABC parameters were categorized into quantiles, and adjusted hazard ratios (aHR) were estimated using Cox regression with the lowest-risk quantile as reference.

Results

All-cause mortality showed a 'J-shaped' association across ABC parameters. For HbA1c, aHRs (95% CI) were 1.11 (1.03–1.19) and 1.51 (1.40–1.63) in the first and last deciles (reference: fourth decile). For BP and LDL-C (reference: third quantile), aHRs were 1.11 (1.05–1.17) and 1.19 (1.13–1.24) for systolic BP, and 1.08 (1.03–1.14) and 1.16 (1.11–1.22) for LDL-C at the lowest and highest quintiles. For diastolic BP, aHRs were 1.09 (1.02–1.16) and 1.11 (1.04–1.19) at the lowest and highest quartiles.

Conclusion

Maintaining optimal ABC parameters is crucial to reduce mortality in T2D patients. These findings fill critical gap in the literature, particularly for the Malaysian population.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-025-01620-w.

Introduction

Diabetes is a significant global health challenge. In Malaysia, the prevalence of the disease consistently exceeds the global average, with the latest diabetes prevalence reported to be at 15.6% among the adult population [1, 2]. Type 2 Diabetes (T2D) accounts for about 90% of all diabetes cases [3]. Clinical trials focused on lowering individuals’ blood glucose, blood pressure (BP), and blood lipids have shown substantial reductions in the risk of complications among T2D patients [4–6]. As a result, clinical guidelines for diabetes management emphasize the “ABC” targets, which are the glycosylated haemoglobin A1c (HbA1c), BP, and low-density lipoprotein cholesterol (LDL-C) as main parameters for diabetes control [7, 8].

The interplay between diabetes and blood pressure is a critical aspect of diabetes management. Hypertension frequently coexists with T2D due to shared risk factors such as obesity and insulin resistance, as well as diabetes-specific mechanisms, including endothelial dysfunction and vascular damage from chronic hyperglycemia [9, 10]. Furthermore, individuals with diabetes frequently exhibit dyslipidemia, including elevated levels of low-density lipoprotein cholesterol (LDL-C). High LDL-C levels further aggravate vascular complications by promoting atherosclerosis, which restricts blood flow to vital organs, increasing the likelihood of ischemic heart disease, stroke, and other circulatory disorders [11]. The combined impact of hyperglycemia, BP dysregulation, and dyslipidemia accelerates vascular complications, underscoring the necessity of a comprehensive approach to diabetes management that includes BP and lipid control alongside glycemic regulation [7, 9, 10].

The Malaysian Clinical Practice Guidelines (CPG) for T2D management set specific ABC targets tailored to different patient groups [8]. These targets were adopted based on guidelines from high-income countries (HICs), including those from the American Diabetes Association (ADA), the European Association for the Study of Diabetes (EASD), the National Institute for Health and Care Excellence (NICE), and other international bodies [8]. However, concerns have been raised regarding the applicability of these international guidelines to populations in low- and middle-income countries (LMICs) such as Malaysia [12], where population characteristics, healthcare infrastructure, and lifestyle factors may differ significantly from those in Malaysia [13]. Besides, variations in genetic, cultural, and environmental factors could result in differing clinical outcomes, making it necessary to obtain local evidence to support the adoption of international targets in the Malaysian context. However, limited research in LMICs, including Malaysia, makes it unclear whether these international targets are appropriate, highlighting the need for local evidence [14, 15].

This study was conducted to examine the association between ABC targets and all-cause mortality among T2D patients, with a focus on the Malaysian population. T2D was chosen as the focus of this study because it accounts for the vast majority of diabetes cases [3]. The objective was to evaluate the risk of all-cause mortality at different levels of HbA1c, BP, and LDL-C, contributing valuable insights into global diabetes management strategies, particularly in middle-income settings. All-cause mortality was selected as the primary outcome as it provides a comprehensive measure of the overall impact of various health factors, including both cardiovascular and non-cardiovascular complications, on patient survival [16]. By identifying the nature of this association in the Malaysian context, the study provides valuable insights into whether the ABC targets recommended by international guidelines are optimal for the local populations. This study is important not only for improving diabetes management in Malaysia but also for contributing to the global discourse on adapting clinical guidelines to suit the unique characteristics of LMICs populations.

Methods

Study design

This study was conducted as a retrospective open cohort study using secondary data originally collected for routine clinical audit purposes from the clinical audit dataset of the Malaysian National Diabetes Registry (NDR), linked with the Malaysian Death Register for T2D patients in southern Malaysia from 2011 to 2021. The clinical audit dataset has been routinely collected annually using a structured protocol established since the registry’s inception in 2009 [17]. This protocol governs patient sampling, data collection, and quality control measures to ensure consistency and comparability across audit cycles. It serves as a surveillance tool to monitor the quality of diabetes care in public health clinics, inform policy decisions, and support research efforts [17, 18].

The clinical audit dataset in the Malaysian NDR collects various information to assess the quality of care and clinical outcomes in T2D patients [17]. Among the data collected are patient demographics, diabetes duration, and status updates such as active follow-up, loss to follow-up, or death. The dataset also includes information on diabetes-related complications and co-morbidities. Additionally, clinical investigation results such as glycemic control, BP, renal profiles, and lipid profiles are documented. The dataset also captures information on medications used for diabetes, hypertension, dyslipidemia, and cardiovascular risk management.

Malaysia's death register has been well-established for decades, with mandatory registration of all deaths with the National Registration Department, as required by law [19]. This system ensures comprehensive coverage, with only minimal gaps in very remote areas.

Data linkage between the clinical audit and the death register was performed using patients’ identity card numbers. Patients entered the cohort at their first audit within the study period and were considered to exit upon death or were censored if they were alive at the end of the study period.

Study population

The study population included Malaysian patients with T2D who received diabetes care at public health clinics in southern Malaysia. The southern region was selected for the study because it has the highest diabetes prevalence in the country [18]. Additionally, the demographic characteristics of T2D patients in this region (age, gender, and ethnicity) closely match national averages, making it an ideal representative sample [18]. The inclusion criteria were Malaysian with T2D aged 18 years and above. Patients with incomplete identity card records were excluded as the data linkage with the death register could not be conducted.

Study variables

The primary outcome of the study was all-cause mortality, defined as death from any cause during the follow-up period. The date of death was obtained from the Malaysian Death Register. The primary independent variables of interest were the ABC parameters, including HbA1c, systolic and diastolic BP, and LDL-C. Additional covariates included in the analysis were demographic and clinical factors such as age, sex, ethnicity, duration of diabetes, smoking history, comorbidities (e.g., hypertension, dyslipidaemia, and the presence of diabetes complications such as nephropathy, retinopathy, ischemic heart disease, and cerebrovascular disease), body mass index (BMI), waist circumference, fasting blood sugar, total cholesterol, triglycerides, serum creatinine, and urine albumin levels. Medications such as oral hypoglycaemic agents, insulin, antiplatelet agents, and antihypertensives were also included as covariates.

Data management

All data management and analyses were performed using StataMP version 17, and Jupyter Notebook version 6.4.12. Data extraction, linkage, and cleaning were performed prior to analysis. Data cleaning involved removing duplicates, correcting any data inconsistencies, and handling missing values. The dataset contained missing data with a complex missingness pattern, exhibiting a combination of missing completely at random and missing at random mechanisms.

Of the 48 variables in the dataset, 25 contained missing values, ranging from 0.02% to 43.3% (Supplementary Fig. 1). Only 7.8% of the total patients had complete data (no missing values), while the patients with the most missing values had 35.8% of their information missing. Missing data imputation was conducted using MissForest, a machine learning-based imputation method that has been shown to provide reliable imputed values for up to 50% of missing data across various missing mechanisms [20, 21]. MissForest is a non-parametric machine learning-based algorithm that utilizes Random Forest models to predict missing values. It iteratively imputes missing values for each variable based on the observed values of other variables, effectively handling both continuous and categorical data [20, 21]. This approach is particularly useful for datasets with complex missingness patterns, as it captures nonlinear relationships and interactions between variables, improving the accuracy of imputed values.

Statistical analysis

The results for the descriptive analysis of numerical variables were reported as median, first quartile (Q1), and third quartile (Q3). Comparisons between groups (those who experienced mortality versus survived) were conducted using the Mann–Whitney U test due to the non-normal data distribution. For categorical variables, counts and percentages were reported, with group comparisons performed using the chi-square test. A p-value < 0.05 was considered statistically significant. The descriptive analysis of the ABC parameters and key sociodemographic variables was presented in this article, while analysis of other variables was provided as supplementary material.

The Cox proportional hazard regression was used to examine the risk of all-cause mortality among T2D patients at different levels of diabetes control via an adjusted model. In this model, HbA1c, systolic BP (SBP), diastolic BP (DBP), and LDL-C were divided into quantiles to allow the examination of non-linear relationships between these variables and all-cause mortality. HbA1c was divided into deciles, SBP and LDL-C into quintiles, and DBP into quartiles. This division was determined experimentally to ensure the most interpretable results. The division into specific quantiles was influenced by the distribution of each variable in the dataset. For example, BP values exhibited significant clustering around central values, making division into smaller quantiles, such as deciles, less meaningful and harder to interpret.

The analysis began with univariable Cox regression for each independent variable to assess its association with all-cause mortality. Variables with a p-value < 0.25 in the univariable analysis were included in the initial multivariable Cox regression model. Subsequently, a stepwise backward elimination process was applied by iteratively removing variables with the highest p-values until all remaining variables had a p-value < 0.05 in the final model. We selected this approach to ensure that only statistically significant predictors remained in the final model while minimizing overfitting and multicollinearity. Stepwise backward elimination is particularly useful in large datasets with multiple potential predictors, as it helps refine the model by retaining only variables that contribute meaningfully to the outcome of interest. Additionally, this method aligns with the goal of creating an interpretable and parsimonious model, which is essential for clinical applicability [22].

After obtaining the final model, the log-minus-log survival plots for HbA1c, SBP, DBP, and LDL-C were assessed to check for the proportional hazard assumption to ensure model validity. The findings were reported as adjusted hazard ratios (aHR) with their respective 95% confidence intervals (CI). The quantile with the lowest risk was the reference for aHR estimation.

Ethics statement

This study was approved by the Malaysian Medical Research and Ethics Committee with registration number NMRR ID- 22–00928-MMB (IIR) and received permission from the respective State Health Departments.

Results

Characteristics of patients

This study includes 90,933 T2D patients from southern Malaysia. Overall, the whole cohort was followed up for a median of 8 years. At baseline, the median age of the cohort was 59 years old, with diabetes duration of 4 years. For the ABC parameter, the median HbA1c was 7.30%, SBP was 135 mmHg, DBP was 80 mmHg, and the LDL-C was 2.71 mmol/L. The dataset predominantly comprised female participants (60.89%). The majority were of Malay ethnicity (66.03%), followed by Chinese (19.89%), Indian (13.59%), and a small proportion of other ethnicities (0.49%). Table 1 summarises the main characteristics of patients included in the study, with detailed information provided in the supplementary table.

Table 1.

Characteristics of the patients in the study

		Dead	Survived	Overall
		n = 16180	n = 74753	n = 90933
		median (Q1, Q3)/n (%)	median (Q1, Q3)/n (%)	median (Q1, Q3)/n (%)
Age (years)		65.0 (58.0, 73.0)	58.0 (51.0, 65.0)	59.0 (52.0, 66.0)
T2D duration (years)		6.0 (3.0, 10.0)	4.0 (2.0, 7.0)	4.0 (2.0, 8.0)
HbA1c (%)		7.6 (6.5, 9.5)	7.2 (6.4, 8.8)	7.3 (6.4, 8.9)
Systolic BP (mmHg)		138.0 (126.0, 150.0)	134.0 (124.0, 146.0)	135.0 (124.0, 147.0)
Diastolic BP (mmHg)		80.0 (71.0, 85.0)	79.0 (70.0, 84.0)	80.0 (71.0, 84.0)
LDL-C (mmol/L)		2.73 (2.10, 3.50)	2.70 (2.10, 3.40)	2.71 (2.10, 3.40)
Gender
	Male	7705 (21.7%)	27862 (78.3%)	35567 (100%)
	Female	8475 (15.3%)	46891 (84.7%)	55366 (100%)
Ethnic
	Malay	10629 (17.7%)	49417 (82.3%)	60046 (100%)
	Chinese	3443 (19.0%)	14640 (81.0%)	18083 (100%)
	Indian	2042 (16.5%	10313 (83.5%)	12355 (100%)
	Others	66 (14.7%)	383 (85.3%)	449 (100%)

Comparison between groups (dead vs survived) was conducted using the Mann–Whitney U test for numerical variables and the chi-square test for categorical variables. All results were statistically significant p-value < 0.01

Incidence of all-cause mortality

A total of 16,180 (17.79%) of the total cohort died by the end of the study. The incidence rate of all-cause mortality observed was 23.6 per 1,000 patient-years.

Characteristics of patients who experience mortality

Patients with diabetes who experienced mortality in this study had a higher median age (65 years vs 58 years), longer diabetes duration (six years vs four years), higher HbA1c (7.60% vs 7.20%), higher SBP (138 mmHg vs 134 mmHg), higher DBP (80 mmHg vs 79 mmHg), and higher LDL-C levels (2.73 mmol/L vs 2.70 mmol/L) compared to those who survived. All comparisons between these groups were statistically significant, with p-values < 0.01.

Male patients had a higher mortality rate in this study, with 21.7% experiencing mortality as compared to 15.9% of female patients. Additionally, Chinese patients had the highest mortality rate (19.0%), followed by Malay (17.7%), Indian (16.5%) and other ethnicities (14.7%). These differences were statistically significant, with p-values < 0.01.

Risk of all-cause mortality at different levels of ABC parameters

All-cause mortality exhibited a J-shaped association across ABC parameters. For HbA1c, mortality risk was elevated at both extremes, with adjusted hazard ratios (aHRs) of 1.11 (95% CI: 1.03–1.19) in the first decile and 1.51 (95% CI: 1.40–1.63) in the tenth decile, using the fourth decile as the reference. Similarly, for BP and LDL-C, with the third quantile as the reference, increased mortality risk was observed at both low and high levels. For SBP, the lowest and highest quintiles had aHRs of 1.11 (95% CI: 1.05–1.17) and 1.19 (95% CI: 1.13–1.24), respectively. For DBP, the lowest and highest quartiles were associated with aHRs of 1.09 (95% CI: 1.02–1.16) and 1.11 (95% CI: 1.04–1.19). A similar J-shaped trend was seen for LDL-C, where the first and second quintiles had aHRs of 1.08 (95% CI: 1.03–1.14), while the highest quintile showed an aHR of 1.16 (95% CI: 1.11–1.22). These findings suggest that mortality risk is minimized within the mid-range of ABC parameters, with increased risk at both lower and higher levels. These findings are summarised in Table 2 and Fig. 1.

Table 2.

Risk of all-cause mortality among T2D patients at different levels of ABC parameters

		aHR (95% CI)a	p-value	The lower and upper borders of the quantilesb
Deciles of HbA1c
	1	1.11 (1.03–1.19)	< 0.01	4.0–5.9
	2	1.03 (0.96–1.11)	0.41	5.9–6.3
	3	1.01 (0.94–1.09)	0.76	6.3–6.5
	4	Reference		6.5–6.9
	5	1.00 (0.93–1.08)	0.98	6.9–7.3
	6	1.03 (0.96–1.11)	0.39	7.3–7.8
	7	1.06 (0.99–1.14)	0.1	7.8–8.5
	8	1.12 (1.04–1.20)	< 0.01	8.5–9.4
	9	1.24 (1.15–1.34)	< 0.01	9.4–10.8
	10	1.51 (1.40–1.63)	< 0.01	10.8–20.0
Quintiles of Systolic BP
	1	1.11 (1.05–1.17)	< 0.01	71–120
	2	1.02 (0.97–1.07)	0.39	121–130
	3	Reference		131–140
	4	1.06 (1.01–1.12)	0.02	141–150
	5	1.19 (1.13–1.24)	< 0.01	151–250
Quartiles of Diastolic BP
	1	1.09 (1.02–1.16)	0.01	30–71
	2	1.02 (0.96–1.09)	0.43	72–80
	3	Reference		81–84
	4	1.11 (1.04–1.19)	< 0.01	85–144
Quintiles of LDL-C
	1	1.08 (1.03–1.14)	< 0.01	1.0–2.0
	2	1.08 (1.03–1.13)	< 0.01	2.0–2.5
	3	Reference		2.5–3.0
	4	1.07 (1.02–1.13)	< 0.01	3.0–3.6
	5	1.16 (1.11–1.22)	< 0.01	3.6–15

This model was also adjusted with gender, ethnicity, T2D duration, age at diagnosis, body mass index, random blood sugar, fasting blood sugar, triglyceride, creatinine, urine protein, fundus, foot examination, retinopathy status, ischaemic heart disease status, cerebrovascular disease status, diabetic foot ulcer status, amputation status, dyslipidaemia, metformin, insulin, antiplatelet/anticoagulation, diuretics, a-blocker, and smoking history

^aAdjusted Hazard Ratio (95% confidence interval)

^bThe lower and upper border of the quantiles were measured in % for HbA1c, mmHg for BP, and mmol/L for LDL-C

Fig. 1.

Risk of all-cause mortality at different levels of ABC parameters

Discussion

The role of ABC parameters in diabetes complications is well established, with specific targets set in the Malaysian CPG [8]. This study used Cox proportional hazards regression to examine their association with all-cause mortality, revealing J-shaped relationships. While evidence from LMICs is limited, these findings align with studies from developed countries [14, 23, 24].

J-shaped association observations and potential mechanisms

The J-shaped association between HbA1c and mortality suggests that while high HbA1c levels increase mortality risk [25], very low levels may also be harmful, likely due to hypoglycemia-related complications from overly tight glycemic control [26, 27]. This supports the Malaysian CPG recommendation to maintain HbA1c within an optimal range [8]. A similar J-shaped pattern was observed for BP, where both low and high levels were linked to increased mortality risk. Very low BP may cause inadequate organ perfusion, while high BP contributes to cardiovascular strain [28]. The CPG-recommended BP targets (SBP: 130–139 mmHg, DBP: 70–79 mmHg) align with these findings [8].

The J-shaped association for LDL-C appears counterintuitive, as lower LDL-C is generally recommended to reduce cardiovascular risk [8, 29–31]. However, similar findings, known as the"lipid paradox,"have been reported in other studies [32–34]. Confounding by indication may play a role, as patients with very low LDL-C often have severe comorbidities, requiring intensive therapy and inherently carrying higher mortality risk [8]. Extremely low LDL-C may also signal underlying health issues, such as chronic illness or immune dysfunction, further contributing to increased mortality [35–37].

Notably, the lowest mortality risk was observed in the third quintile of LDL-C (2.5–3.0 mmol/L), slightly above the CPG target of < 2.6 mmol/L, which is frequently initiated in patients with LDL-C levels within this range. This could reflect the benefits of statin therapy, which not only lowers LDL-C but also provides anti-inflammatory and cardioprotective effects [38]. Additionally, patients within this range may have lower baseline mortality risk compared to those requiring stricter LDL-C control due to severe cardiovascular conditions. The interplay of therapeutic benefits and baseline risk likely contributes to the observed J-shaped association.

A key factor that may contribute to the increased mortality observed at the higher levels of HbA1c, blood pressure, and LDL-C is inflammation. It is well-established that poorly controlled diabetes, hypertension, and dyslipidemia are associated with increased inflammation. Specifically, elevated HbA1c promotes the formation of advanced glycation end products (AGEs), which trigger inflammatory pathways and contribute to diabetic complications [39]. Elevated blood pressure is associated with increased vascular inflammation, which can damage blood vessels and contribute to organ damage [40]. High levels of LDL cholesterol and triglycerides can induce inflammation in the arterial wall, promoting atherosclerosis —a key process in cardiovascular disease [41, 42]. More broadly, chronic inflammation is implicated in the development and progression of various chronic diseases, ultimately increasing the risk of premature death [41]. Our finding that these conditions are associated with increased all-cause mortality aligns with the understanding that inflammation plays a key role in their pathogenesis. The cumulative inflammatory burden from these conditions may synergistically increase the risk of death.

Clinical and public health significance

The Malaysian T2D CPG was developed using evidence from international guidelines, including those from the ADA, EASD, NICE, and Canadian guidelines [8]. While these are reputable sources, their applicability to Malaysia must be considered, as they are based on high-income populations with different socioeconomic contexts. The variable accuracy of the Framingham risk score outside the U.S. [43], highlights the need to validate clinical guidelines within local settings. This study provides crucial evidence supporting the relevance of Malaysia’s CPG.

A key finding is the J-shaped association between ABC parameters and all-cause mortality, emphasizing that both excessively high and low levels can be harmful. For HbA1c and BP, early and moderate control is more beneficial than overtreatment, which may lead to chronic hypoglycemia or hypoperfusion. This aligns with the Malaysian CPG, which recommends maintaining HbA1c and BP within optimal ranges [8]. For LDL-C, while a lower target remains valid, potential confounding by indication should be considered.

This study underscores the importance of robust data systems in diabetes management. The Malaysian NDR [44], is a vital tool for tracking T2D outcomes but could be enhanced by expanding data collection to other important variables such as physical activity, diet, and mental health. Improving data quality through stringent collection methods and standardized reporting would further enhance its reliability. Additionally, integrating the NDR with hospital and mortality registries would enable seamless tracking of diabetes progression, facilitating more effective resource allocation and targeted interventions. Strengthening the NDR would ultimately improve patient care and inform healthcare policies, enhancing diabetes management in Malaysia.

Strengths and limitations of the study

A key strength of this study is the utilisation of a large dataset for analysis. It included all eligible patients from the Malaysian NDR dataset, a substantial secondary data source for diabetes research in the Malaysian context. The dataset's size enables the detection of subtle but important associations, such as the J-shaped relationship between certain variables and outcomes, while providing smaller margins of error and more precise confidence intervals. Additionally, the vast sample sizes allow for sufficient statistical power in conducting detailed analyses, such as the division of the samples into quantiles for the detection of the non-linear relationship. Furthermore, the extensive nature of the dataset ensures that it encompasses data from a diverse range of subpopulations with various characteristics. This diversity enhances the generalizability of the study's findings, making them more applicable to a broader population.

However, registry-based studies are limited by the available variables, restricting the inclusion of important factors such as health literacy, family history, and physical inactivity, which are not recorded in the NDR. Additionally, other key confounders, including socioeconomic status, medication adherence, detailed medication records, and dietary factors, may influence mortality risk but were not available in our dataset. The absence of these variables may introduce residual confounding; however, this was mitigated by adjusting for key clinical factors, which are strong proxies for overall health status. Future studies integrating supplementary datasets or external data sources could further address this limitation and enhance risk prediction models.

Data quality and completeness issues, including inconsistencies and missing values, stem from variations in healthcare settings, recording habits, and data entry systems. These were mitigated through thorough preprocessing, leveraging an in-depth understanding of data issues. Additionally, the NDR does not include T2D patients from tertiary centers, but given that over 80% of T2D patients receive treatment in primary care [45], the data still represent a substantial proportion of T2D patients in the country.

Mortality data in this study was obtained from the Malaysian Death Register, sourced from the Malaysian National Registration Department. However, around 50% of the records were non-medically certified deaths, which prevents specific-cause mortality analysis from being conducted [46]. Nonetheless, all-cause mortality remains a comprehensive measure that reflects overall patient outcomes.

Due to the nature of the collected data, this study can only utilize baseline data to assess the association between ABC parameters and all-cause mortality. This approach may not fully capture the true dynamics of diabetes control, as changes in the ABC parameter over time could profoundly impact outcomes, leading to potential underestimation or overestimation of the long-term effects of diabetes control on mortality risks. Nevertheless, the use of baseline measurements aligns with established methodologies in diabetes research. Numerous landmark studies have relied on such data to draw significant associations with long-term health outcomes [14]. While these studies do not account for time-varying covariates, they still provide foundational insights into disease progression and risk, thereby supporting the relevance and usefulness of our study's findings. By offering a snapshot of risk at cohort entry, our study contributes to understanding initial risk stratification, emphasizing the utility of baseline assessments in predicting future complications.

Future works

While this study provides valuable insights into diabetes management in Malaysia, validating these findings in other middle-income countries would help assess regional differences in diabetes care and outcomes. Comparative studies across similar healthcare settings could enhance the generalizability of our results and inform the refinement of diabetes management strategies globally.

A periodic reassessment of ABC targets in Malaysia is recommended to ensure they reflect emerging evidence and the specific clinical characteristics of the local population. Further research should investigate time-varying changes in ABC levels and their effects on long-term outcomes to support more adaptive and personalized control strategies. Additionally, expanding local studies on risk factors not currently captured in registries, such as physical activity and family history, would provide a more comprehensive understanding and guide more effective diabetes management. Integrating supplementary datasets, such as pharmacy records for medication adherence or socioeconomic data, could further enhance future analyses and improve risk prediction models for diabetes management.

Conclusion

This study provides important insights into the association of HbA1c, BP, and LDL-C levels with all-cause mortality among T2D patients in Malaysia, highlighting a J-shaped relationship across these parameters. Both extremely low and high levels of each parameter were associated with increased mortality risk. For HbA1c and BP, this reinforces the importance of maintaining moderate control to minimize risk. While revealing some complexities, the finding regarding LDL-C levels may be influenced by factors such as confounding by indication and dataset limitations. Despite these nuances, the broader evidence supports the continued validity of the current Malaysian CPG recommendations. These findings support the tailored and individualised targets for ABC parameters in the Malaysian population, emphasizing optimized management to reduce mortality risk effectively. Overall, the study reinforces that the current Malaysian CPG guidelines remain valid and applicable, providing evidence that international recommendations are relevant to local contexts, particularly in LMICs like Malaysia.

Supplementary Information

Below is the link to the electronic supplementary material.

Authors’ contributions

M.Z.A. conceptualized the research, performed data analysis, and drafted the manuscript. N.N.H. and W.Y.C. supervised the overall project, provided essential input throughout the study, and contributed to the final review of the manuscript. K.L. provided guidance on model development and data analysis. K.S.W. contributed to the study design, provided critical revisions, and guided the interpretation of results. All authors read and approved the final version of the manuscript for submission.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Data availability

The data utilized in this study are owned by the Ministry of Health Malaysia and are not publicly available. Researchers interested in accessing the data may submit a formal request to the Disease Control Division, Ministry of Health Malaysia, for consideration.

Declarations

Conflict of interest

The authors declare no competing interests.