Predicting type 2 diabetes using genetic and environmental risk factors in a multi-ethnic Malaysian cohort

Body composition and risk factors for cardiovascular disease in global multi-ethnic populations

BACKGROUND

No large-scale studies have compared associations between body composition and cardiovascular risk factors across multi-ethnic populations.

METHODS

Population-based surveys included 30,721 Malay, 10,865 Indian and 25,296 Chinese adults from The Malaysian Cohort, and 413,737 White adults from UK Biobank. Sex-specific linear regression models estimated associations of anthropometry and body composition (body mass index [BMI], waist circumference [WC], fat mass, appendicular lean mass) with systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), triglycerides and HbA1c.

RESULTS

Compared to Malay and Indian participants, Chinese adults had lower BMI and fat mass while White participants were taller with more appendicular lean mass. For BMI and fat mass, positive associations with SBP and HbA1c were strongest among the Chinese and Malay and weaker in White participants. Associations with triglycerides were considerably weaker in those of Indian ethnicity (eg 0.09 [0.02] mmol/L per 5 kg/m2 BMI in men, vs 0.38 [0.02] in Chinese). For appendicular lean mass, there were weak associations among men; but stronger positive associations with SBP, triglycerides, and HbA1c, and inverse associations with LDL-C, among Malay and Indian women. Associations between WC and risk factors were generally strongest in Chinese and weakest in Indian ethnicities, although this pattern was reversed for HbA1c.

CONCLUSION

There were distinct patterns of adiposity and body composition and cardiovascular risk factors across ethnic groups. We need to better understand the mechanisms relating body composition with cardiovascular risk to attenuate the increasing global burden of obesity-related disease.

Stability of glycated haemoglobin (HbA1c) measurements from whole blood samples kept at -196°C for seven to eight years in The Malaysian Cohort study

OBJECTIVE

Glycated haemoglobin (HbA1c) is a standard indication for screening type 2 diabetes that also has been widely used in large-scale epidemiological studies. However, its long-term quality (in terms of reproducibility) stored in liquid nitrogen is still unknown. This study is aimed to evaluate the stability and reproducibility of HbA1c measurements from frozen whole blood samples kept at -196°C for more than 7 years.

METHODS

A total of 401 whole blood samples with a fresh HbA1c measurement were randomly selected from The Malaysian Cohort's (TMC) biobank. The HbA1c measurements of fresh and frozen (stored for 7-8 years) samples were assayed using different high-performance liquid chromatography (HPLC) systems. The HbA1c values of the fresh samples were then calculated and corrected according to the later system. The reproducibility of HbA1c measurements between calculated-fresh and frozen samples was assessed using a Passing-Bablok linear regression model. The Bland-Altman plot was then used to evaluate the concordance of HbA1c values.

RESULTS

The different HPLC systems highly correlated (r = 0.99) and agreed (ICC = 0.96) with each other. Furthermore, the HbA1c measurements for frozen samples strongly correlate with the corrected HbA1c values of the fresh samples (r = 0.875) with a mean difference of -0.02 (SD: -0.38 to 0.38). Although the mean difference is small, discrepancies were observed within the diabetic and non-diabetic samples.

CONCLUSION

These data demonstrate that the HbA1c measurements between fresh and frozen samples are highly correlated and reproducible.

Implementation of Nutrigenetics and Nutrigenomics Research and Training Activities for Developing Precision Nutrition Strategies in Malaysia

Nutritional epidemiological studies show a triple burden of malnutrition with disparate prevalence across the coexisting ethnicities in Malaysia. To tackle malnutrition and related conditions in Malaysia, research in the new and evolving field of nutrigenetics and nutrigenomics is essential. As part of the Gene-Nutrient Interactions (GeNuIne) Collaboration, the Nutrigenetics and Nutrigenomics Research and Training Unit (N2RTU) aims to solve the malnutrition paradox. This review discusses and presents a conceptual framework that shows the pathway to implementing and strengthening precision nutrition strategies in Malaysia. The framework is divided into: (1) Research and (2) Training and Resource Development. The first arm collects data from genetics, genomics, transcriptomics, metabolomics, gut microbiome, and phenotypic and lifestyle factors to conduct nutrigenetic, nutrigenomic, and nutri-epigenetic studies. The second arm is focused on training and resource development to improve the capacity of the stakeholders (academia, healthcare professionals, policymakers, and the food industry) to utilise the findings generated by research in their respective fields. Finally, the N2RTU framework foresees its applications in artificial intelligence and the implementation of precision nutrition through the action of stakeholders.

Analysis of OCT1, OCT2 and OCT3 gene polymorphisms among Type 2 diabetes mellitus subjects in Indian ethnicity, Malaysia

Background

Type 2 Diabetes mellitus (T2DM) is a chronic metabolic disorder. It is a major non-communicable disease affecting 463 million people globally in 2019 and is expected to be double to about 700 million by 2045. The majority are Asians with Indian ethnicity in Malaysia reported as the highest prevalence of T2DM. Cardiovascular disease, renal failure, blindness and neuropathy, as well as premature death are the known morbidity and mortality resulted from T2DM. T2DM is characterized by the dysfunctional insulin physiology that causes reduction of glucose transport into the cells which lead to hyperglycaemia. Hence, one of the important treatments is an oral antidiabetic drug that lowers the serum glucose level in patients with T2DM. This drug will be transported across cell membranes by organic cation transporters (OCT). Therefore, it is important to identify the OCT candidate gene polymorphisms related to T2DM especially among the Indian ethnicity in Malaysia.

Methods

Blood samples were collected from 132 T2DM patients and 133 controls. Genotyping of OCT1 (rs628031), OCT2 (rs145450955), OCT3 (rs3088442 and rs2292334) was performed using (PCR-RFLP).

Results

No association was observed for genotypic and allelic distributions in all the gene polymorphisms of OCT genes (P > 0.05). However, a logistic regression analysis stratified by gender in a dominant model showed a significant difference for OCT3 among males with T2DM (P = 0.006). Significant association was also observed for OCT3 when stratified to subjects aged > 45 years old (P = 0.009).

Conclusion

Based on these findings, the association of OCT3 (rs2292334) could be considered as a possible genetic risk factor for the development of T2DM among Indian males alone.

A Genetic Risk Score Improves the Prediction of Type 2 Diabetes Mellitus in Mexican Youths but Has Lower Predictive Utility Compared With Non-Genetic Factors

Background

Type 2 diabetes (T2D) is a multifactorial disease caused by a complex interplay between environmental risk factors and genetic predisposition. To date, a total of 10 single nucleotide polymorphism (SNPs) have been associated with pediatric-onset T2D in Mexicans, with a small individual effect size. A genetic risk score (GRS) that combines these SNPs could serve as a predictor of the risk for pediatric-onset T2D.

Objective

To assess the clinical utility of a GRS that combines 10 SNPs to improve risk prediction of pediatric-onset T2D in Mexicans.

Methods

This case-control study included 97 individuals with pediatric-onset T2D and 84 controls below 18 years old without T2D. Information regarding family history of T2D, demographics, perinatal risk factors, anthropometric measurements, biochemical variables, lifestyle, and fitness scores were then obtained. Moreover, 10 single nucleotide polymorphisms (SNPs) previously associated with pediatric-onset T2D in Mexicans were genotyped. The GRS was calculated by summing the 10 risk alleles. Pediatric-onset T2D risk variance was assessed using multivariable logistic regression models and the area under the receiver operating characteristic curve (AUC).

Results

The body mass index Z-score (Z-BMI) [odds ratio (OR) = 1.7; p = 0.009] and maternal history of T2D (OR = 7.1; p < 0.001) were found to be independently associated with pediatric-onset T2D. No association with other clinical risk factors was observed. The GRS also showed a significant association with pediatric-onset T2D (OR = 1.3 per risk allele; p = 0.006). The GRS, clinical risk factors, and GRS plus clinical risk factors had an AUC of 0.66 (95% CI 0.56-0.75), 0.72 (95% CI 0.62-0.81), and 0.78 (95% CI 0.70-0.87), respectively (p < 0.01).

Conclusion

The GRS based on 10 SNPs was associated with pediatric-onset T2D in Mexicans and improved its prediction with modest significance. However, clinical factors, such the Z-BMI and family history of T2D, continue to have the highest predictive utility in this population.

Association of the JAZF1 Variant in Adults With a Parental History of Type 2 Diabetes Mellitus In Pakistan

Background

Type 2 diabetes mellitus (T2DM) is a chronic multifactorial condition and quickly growing disease in Pakistan. Many genes together with Zinc finger protein 1 (JAZF1) have already been described earlier in the literature but the role of JAZF1 in this subset of the population is yet to define. This study was aimed at identifying JAZF1 polymorphism and the risk of developing T2DM in persons with a parental history of T2DM in the Pakistani population.

Methods

DNA samples from 75 non-diabetic Pakistani participants with a family history of T2DM and 75 controls were evaluated by using a polymerase chain reaction (PCR) and the restriction fragment length polymorphism method.

Results

The alleles AA and AG and the GG genotype of JAZF1 (rs864745) varied considerably in frequency distribution between cases and control (p<0.05). The GG was independently and significantly associated with cases who had a family history of T2DM [odds ratio (OR) 2.6 (95% confidence interval (Cl) 1.3-5.1); p=0.005] while the AA allele was significantly associated with controls without a family history of T2DM [odds ratio (OR) 0.39 (95% confidence interval (Cl) 0.2-0.7); p=0.0059] and the allele AG has no significance and was equally distributed among control and cases with p-value=1.000.

Conclusion

Genotype GG of the JAZF1 variant was found significantly associated with the risk of developing type 2 diabetes mellitus in the Pakistani subset of the population.

Prediction of Type 2 Diabetes Risk and Its Effect Evaluation Based on the XGBoost Model

In view of the harm of diabetes to the population, we have introduced an ensemble learning algorithm—EXtreme Gradient Boosting (XGBoost) to predict the risk of type 2 diabetes and compared it with Support Vector Machines (SVM), the Random Forest (RF) and K-Nearest Neighbor (K-NN) algorithm in order to improve the prediction effect of existing models. The combination of convenient sampling and snowball sampling in Xicheng District, Beijing was used to conduct a questionnaire survey on the personal data, eating habits, exercise status and family medical history of 380 middle-aged and elderly people. Then, we trained the models and obtained the disease risk index for each sample with 10-fold cross-validation. Experiments were made to compare the commonly used machine learning algorithms mentioned above and we found that XGBoost had the best prediction effect, with an average accuracy of 0.8909 and the area under the receiver’s working characteristic curve (AUC) was 0.9182. Therefore, due to the superiority of its architecture, XGBoost has more outstanding prediction accuracy and generalization ability than existing algorithms in predicting the risk of type 2 diabetes, which is conducive to the intelligent prevention and control of diabetes in the future.

Association between MIR499A rs3746444 polymorphism and breast cancer susceptibility: a meta-analysis

Numerous studies have investigated the association of MIR499A rs3746444 polymorphism with breast cancer susceptibility, but the results have been inconsistent. In this work, we performed a meta-analysis to obtain a more reliable estimate of the association between the polymorphism and susceptibility to breast cancer. A comprehensive literature search was conducted on PubMed, Scopus, Web of Science (WoS), China National Knowledge Infrastructure (CNKI), VIP and Wanfang databases up to January 2020. A total of 14 studies involving 6,797 cases and 8,534 controls were included for analysis under five genetic models: homozygous (GG vs. AA), heterozygous (AG vs. AA), dominant (AG + GG vs. AA), recessive (GG vs. AA + AG) and allele (G vs. A). A statistically significant association was observed between the polymorphism and an increased breast cancer susceptibility under all genetic models (homozygous, OR = 1.33, 95% CI = 1.03-1.71, P = 0.03; heterozygous, OR = 1.08, 95% CI = 1.00-1.16, P = 0.04; dominant, OR = 1.15, 95% CI = 1.02-1.30; P = 0.03; recessive, OR = 1.35, 95% CI = 1.06-1.72, P = 0.01; allele, OR = 1.12, 95% CI = 1.00-1.26, P = 0.04). Subgroup analysis based on ethnicity suggested that significant association was present only among Asians, but not Caucasians. In conclusion, MIR499A rs3746444 polymorphism was significantly associated with breast cancer susceptibility among Asians, suggesting its potential use as a genetic risk marker in this population.

Ethnicity-Specific Association Between Ghrelin Leu72Met Polymorphism and Type 2 Diabetes Mellitus Susceptibility: An Updated Meta-Analysis

Background: The Leu72Met polymorphism of ghrelin gene has been associated with genetic predisposition to type 2 diabetes mellitus (T2DM), while conclusions remain conflicting. Hence, we performed this updated meta-analysis to clarify the association between Leu72Met polymorphism and T2DM susceptibility. Methods: Six electronic databases were consulted for articles published before 1 January, 2018. Pooled odds ratios (OR) and 95% confidence intervals (CI) were calculated under five genetic models to assess this association. We used I ²-test and Q statistics to measure heterogeneity across the included studies. Subgroup analyses and publication bias were also performed. Results: Thirteen case-control studies involving 4720 T2DM patients and 4206 controls were included in this meta-analysis. The overall results using fixed-effects models showed that Leu72Met polymorphism was significantly associated with an increased risk of T2DM under homozygous model (OR = 1.307, 95%CI 1.001-1.705, p = 0.049). Further subgroup analyses stratified by ethnicity revealed that the risk for T2DM was only increased in Asians (homozygous model: OR = 1.335, 95%CI 1.014-1.758, p = 0.040), while decreased in Caucasians (dominant model: OR = 0.788, 95%CI 0.635-0.978, p = 0.030; heterozygous model: OR = 0.779, 95%CI 0.626-0.969, p = 0.025; allelic model: OR = 0.811, 95%CI 0.661-0.995, p = 0.045). Funnel plots were basically symmetrical, and all p-values of Egger's test under five genetic models were >0.050, which indicated no evidence of publication bias. Conclusions: Our results demonstrate that the Leu72Met polymorphism of ghrelin gene may be protective against T2DM in Caucasians, while predisposing to T2DM in Asians.