Higher body mass index and lower intake of dairy products predict poor glycaemic control among Type 2 Diabetes patients in Malaysia

Epidemiology of Poor Glycaemic Control among Patients with Type 2 Diabetes Mellitus in Terengganu State of Malaysia

Background: Type 2 diabetes mellitus (T2DM) is among the most common non-communicable diseases worldwide and Malaysia. Poor glycaemic control among T2DM patients lead to serious life-long complications. Therefore, it is imperative to study the prevalence of poor glycaemic control and its risk factors to facilitate public health physicians and clinicians in developing the best strategies to optimize glycaemic control among T2DM patients. Materials and Methods: A comparative cross-sectional study between groups of good glycaemic control and poor glycaemic control was conducted among type 2 diabetes mellitus (T2DM) patients who fulfilled study criteria in Terengganu state of Malaysia. Eligible samples in the National Diabetes Registry registered from 1st January 2021 until 31st December 2021 were included into the study. Descriptive statistics, simple and multiple logistic regressions were employed for data analysis. Result: A total of 17,165 samples were recruited in the descriptive part of the study. The prevalence of patients with poor glycaemic control in Terengganu state was 65.3% (95% CI: 0.62, 0.67). In the inferential part of the study, a total 3,700 samples were randomly selected. Multivariable analysis using multiple logistic regression revealed age, duration of diabetes, body mass index, cigarette smoking, presence of retinopathy and presence of hypertension were the significant factors associated with poor glycaemic control among T2DM patients in Terengganu state with an adjusted odds ratio (AOR) of 0.95 (95%CI:0.94, 0.96); p<0.001), AOR 1.15 (95%CI:1.13, 1.17; p<0.001), AOR 1.03 (95%CI:1.01, 1.04; p<0.001), AOR 1.45 (95%CI:1.01, 2.10; p=0.047), AOR 1.32 (95%CI:1.01, 1.73; p=0.043) and AOR 1.39 (95%CI:1.16, 1.67; p<0.001) respectively. Conclusion: Strategies focusing on the identified risk factors may improve diabetes mellitus management and avert life-long diabetic complications.

Diet Quality of Malaysians across Lifespan: A Scoping Review of Evidence in a Multi-Ethnic Population

Malaysia is a rapidly developing economy experiencing a nutrition transition. It suffers from a double burden of over- and undernutrition, making it essential to understand diet quality in the population. In this scoping review, we have collated the existing literature on Malaysian diet quality, including factors that influence it, and the association between diet quality and health outcomes across the lifespan of Malaysians. Overall, diet quality was poor in all age groups studied. The Healthy Eating Index (HEI) and its iterations were predominantly used in urban and clinical settings to evaluate diet-chronic disease relationships. These indices were significantly associated with cardio-metabolic and disease risks in adults. The Diet Diversity Score (DDS) and Food Variety Score (FVS) were used to gauge diet quality in maternal and child nutrition studies and were associated with appropriate growth and caloric intake. Deficiencies were found in fruit, vegetable, legumes, and dairy intake. Meat, salt, and sugar intake were found to be excessive in many studies. The findings can inform policies to improve diet quality in this population. The review also identified knowledge gaps that require further investigation.

Diet quality and its association with glycemic parameters in different diabetes progression stages. A cross-sectional questionnaire study at a primary care clinic

BACKGROUND

Diabetes mellitus is a leading cause of preventable deaths and becomes a major public health concern in Malaysia. Multiple studies have reported the association between diet quality and glycemic parameters among known diabetic subjects. Its influence in individuals with borderline diabetes (i.e. pre-diabetes) or unknown diabetes is still unclear.

OBJECTIVE

The objective of this study was to assess the association between diet quality evaluated by healthy eating index (HEI) with the glucose outcome in individuals with distinct diabetes progression stages, as well as to identify causal factors in relation to their diabetes status.

METHOD

A cross-sectional study was conducted at clinical care setting in Universiti Sains Malaysia (USM) between October 2018-March 2019. Normoglycemic controls (n = 47), at-risk of pre-diabetes (n = 58), pre-diabetes (n = 24) as well as individuals with undiagnosed diabetes (n = 18) were queried about their habitual diet by using Food Frequency Questionnaire (FFQ). Correlation analyses were performed to examine the relationship between HEI score and 1) Fasting plasma glucose (FPG) 2) postprandial blood glucose (2-HPP) and glycosylated hemoglobin (HbA1c). Multinomial regression was performed to identify predictors associated with diabetes status of study participants.

RESULT

Overall, diet quality of study participants was unsatisfactory with the mean score of 58.05 ± 9.07 that need improvement. Total HEI score was negatively correlated with the 2-HPP levels in pre-diabetic patients (r = - 0.45, p = 0.05). No significant association was revealed between glycemic parameters and total HEI score among other groups. Age, body mass index (BMI), triglycerides and female gender were positively correlated with the risk of pre-diabetes, at-risk of pre-diabetes and undiagnosed diabetes (p < 0.01).

CONCLUSION

Diet quality was not strongly associated with glycemic parameters among study participants. Age, BMI, triglycerides and female gender are crucial factors, that needed to be prioritized by primary care providers when managing pre-diabetes/diabetes to achieve possible reversion. Further in-depth investigations with large sample are warranted to confirm study findings.

Who benefits most in disease management programs: Improving target efficiency

Disease management programs aim to reduce cost by improving the quality of care for chronic diseases. Evidence of their effectiveness is mixed. Reducing health care spending sufficiently to cover program costs has proved particularly challenging. This study uses a difference in differences design to examine the impact of a diabetes disease management program for high risk patients on preventive tests, health outcomes, and cost of care. Heterogeneity is examined along the dimensions of severity (measured using the proxy of poor glycemic control) and preventive testing received in the baseline year. Although disease management programs tend to focus on the sickest, the impact of this program concentrates in the group of people who had not received recommended tests in the preintervention period. If confirmed, such findings are practically important to improve cost-effectiveness in disease management programs by targeting relevant subgroups defined both based on severity and on (missing) test information.

Associations between local descriptive norms for overweight/obesity and insufficient fruit intake, individual-level diet, and 10-year change in body mass index and glycosylated haemoglobin in an Australian cohort

Background

Descriptive norms (what other people do) relate to individual-level dietary behaviour and health outcome including overweight and obesity. Descriptive norms vary across residential areas but the impact of spatial variation in norms on individual-level diet and health is poorly understood. This study assessed spatial associations between local descriptive norms for overweight/obesity and insufficient fruit intake (spatially-specific local prevalence), and individual-level dietary intakes (fruit, vegetable and sugary drinks) and 10-year change in body mass index (BMI) and glycosylated haemoglobin (HbA_1c).

Methods

HbA_1c and BMI were clinically measured three times over 10 years for a population-based adult cohort (n = 4056) in Adelaide, South Australia. Local descriptive norms for both overweight/obesity and insufficient fruit intake specific to each cohort participant were calculated as the prevalence of these factors, constructed from geocoded population surveillance data aggregated for 1600 m road-network buffers centred on cohort participants’ residential addresses. Latent growth models estimated the effect of local descriptive norms on dietary behaviours and change in HbA_1c and BMI, accounting for spatial clustering and covariates (individual-level age, sex, smoking status, employment and education, and area-level median household income).

Results

Local descriptive overweight/obesity norms were associated with individual-level fruit intake (inversely) and sugary drink consumption (positively), and worsening HbA_1c and BMI. Spatially-specific local norms for insufficient fruit intake were associated with individual-level fruit intake (inversely) and sugary drink consumption (positively) and worsening HbA_1c but not change in BMI. Individual-level fruit and vegetable intakes were not associated with change in HbA_1c or BMI. Sugary drink consumption was also not associated with change in HbA_1c but rather with increasing BMI.

Conclusion

Adverse local descriptive norms for overweight/obesity and insufficient fruit intake are associated with unhealthful dietary intakes and worsening HbA_1c and BMI. As such, spatial variation in lifestyle-related norms is an important consideration relevant to the design of population health interventions. Adverse local norms influence health behaviours and outcomes and stand to inhibit the effectiveness of traditional intervention efforts not spatially tailored to local population characteristics. Spatially targeted social de-normalisation strategies for regions with high levels of unhealthful norms may hold promise in concert with individual, environmental and policy intervention approaches.

Poor glycaemic control and its metabolic and demographic risk factors in a Malaysian community-based study

OBJECTIVES

The lack of population-based evidence on the risk factors for poor glycaemic control in diabetics, particularly in resource-poor settings, is a challenge for the prevention of long-term complications. This study aimed to identify the metabolic and demographic risk factors for poor glycaemic control among diabetics in a rural community in Malaysia.

METHODS

A total of 1844 (780 males and 1064 females) known diabetics aged ≥ 35 years were identified from the South East Asia Community Observatory (SEACO) health and demographic surveillance site database.

RESULTS

41.3% of the sample had poor glycaemic control. Poor glycaemic control was associated with age and ethnicity, with older participants (65+) better controlled than younger adults (45-54), and Malaysian Indians most poorly controlled, followed by Malay and then Chinese participants. Metabolic risk factors were also highly associated with poor glycaemic control.

CONCLUSIONS

There is a critical need for evidence for a better understanding of the mechanisms of the associations between risk factors and glycaemic control.

Linear regression and the normality assumption

OBJECTIVES

Researchers often perform arbitrary outcome transformations to fulfill the normality assumption of a linear regression model. This commentary explains and illustrates that in large data settings, such transformations are often unnecessary, and worse may bias model estimates.

STUDY DESIGN AND SETTING

Linear regression assumptions are illustrated using simulated data and an empirical example on the relation between time since type 2 diabetes diagnosis and glycated hemoglobin levels. Simulation results were evaluated on coverage; i.e., the number of times the 95% confidence interval included the true slope coefficient.

RESULTS

Although outcome transformations bias point estimates, violations of the normality assumption in linear regression analyses do not. The normality assumption is necessary to unbiasedly estimate standard errors, and hence confidence intervals and P-values. However, in large sample sizes (e.g., where the number of observations per variable is >10) violations of this normality assumption often do not noticeably impact results. Contrary to this, assumptions on, the parametric model, absence of extreme observations, homoscedasticity, and independency of the errors, remain influential even in large sample size settings.

CONCLUSION

Given that modern healthcare research typically includes thousands of subjects focusing on the normality assumption is often unnecessary, does not guarantee valid results, and worse may bias estimates due to the practice of outcome transformations.