Doctoral Degrees (Chemical Pathology)

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Browsing Doctoral Degrees (Chemical Pathology) by browse.metadata.advisor "Matsha, Tandi E."

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    HbA1c as a screening tool for diabetes mellitus and its use with traditional and novel biochemical parameters to predict cardiovascular risk in a local urban community
    (Stellenbosch : Stellenbosh University, 2016-12) Zemlin, Annalise E.; Erasmus, Rajiv T.; Matsha, Tandi E.; Kengne, Andre P.; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Pathology: Chemical Pathology.
    ENGLISH SUMMARY: Introduction The global obesity pandemic has reached Africa and the diabetes mellitus (DM) prevalence is increasing in parallel. A high prevalence of DM and risk for cardiovascular disease (CVD) has been described in the South African mixed ancestry population. Recent guidelines advocate using HbA1c as a diagnostic tool for DM and prediabetes, which is more convenient. However, various studies have challenged these cut-offs. There is a paucity of studies validating these cut-offs in Africa. As DM is considered a CVD risk equivalent, emerging markers of CVD and adiposity also need evaluation. The adipokine adiponectin has anti-diabetic, anti-atherogenic and anti-inflammatory properties and levels decrease in obesity. E-selectin, a marker of endothelial cell dysfunction, is associated with subclinical atherosclerosis and hyperglycaemia. Carotid intima-media thickness (CIMT) is a noninvasive measure of subclinical atherosclerosis. The aim of this investigation was to verify recommended HbA1c cut-offs to diagnose DM and prediabetes and to examine the usefulness of emerging markers of subclinical CVD in our population. Methods This investigation consists of four substudies and was performed on participants of the Bellville South Africa Study. In the first, we challenged the recommended HbA1c cut-off of 6.5% to diagnose DM in 946 participants using oral glucose tolerance test (OGTT), fasting blood glucose (FBG), and receiver operator characteristic (ROC) curves. In the second, we derived an optimal HbA1c cut-off to detect prediabetes in 667 participants and validated this in two populations, using OGTT and ROC curves. In the third, we determined high molecular weight (hmw)-adiponectin levels in 101 participants, compared these in participants with and without hyperglycaemia and investigated their relationship with two polymorphisms (rs17300539 and rs266729) reported to affect adiponectin values. In the fourth, we determined E-selectin levels in 307 participants, compared these in participants with and without hyperglycaemia and assessed their effect on CIMT. Results The recommended HbA1c cut-off was not sensitive enough to detect DM. Using FBG, 117 (14%) participants were diagnosed with DM and 50% had an HbA1c of  6.5%; using OGTT 147 (18%) had DM and 46% had an HbA1c of  6.5%. Comparing HbA1c to FBG and OGTT, a cut-off of 6.1% gave a better sensitivity and specificity (area under curve (AUC) 0.85 and 0.82 respectively). Also, the recommended HbA1c cut-off to detect prediabetes was not appropriate and we determined that 5.75% was best. However, the low sensitivity and specificity (64.8% and 60.4% respectively for the derivation and first validation sample and 59.6% and 69.8% for the second validation sample), confirmed that HbA1c alone would miss a significant number of prediabetics. Hmw-adiponectin levels were not affected by glycaemia (median 11.6 g/mL in normoglycaemia vs. 10.5 g/mL in hyperglycaemia; p=0.3060) nor by two common polymorphisms. Using robust correlations, a significant correlation was found between hmw-adiponectin and high density lipoprotein cholesterol (HDL-c) (r=0.45; 95%CI: 0.27-0.59), which was similar in both normo-and hyperglycaemia (p>0.99). This association was attenuated in robust linear regressions adjusted for gender and adiposity. Eselectin levels were significantly higher in hyperglycaemia (median 139.8 g/L vs. 118.8 g/L in normoglycaemia; p=0.0007) but not associated with CIMT. Significant correlations were found between E-selectin and age, markers of glycaemia and inflammation, central obesity and lipid variables. Associations remained significant only with age, hyperglycaemia and C-reactive protein (CRP) in multivariable robust linear regression models. In similar regressions models, age and gender were the main predictors of CIMT, which was not associated with E-selectin. Conclusion The international HbA1c cut-offs recommended to detect DM and prediabetes were not appropriate in our population. Though a cut-off of 6.5% to diagnose DM is a good diagnostic tool with high specificity, the low sensitivity limits its screening use. Similarly, recommended HbA1c values to detect prediabetes may underestimate the true numbers. This emphasizes the importance of local evidence-based values being established. Additionally, hmw-adiponectin was not affected by glycaemia or polymorphisms, but correlated significantly with HDL-c which may explain its beneficial cardiovascular effect. Though Eselectin was influenced by glycaemia, possibly reflecting early endothelial damage, it did not correlate with CIMT, which was determined by age and male gender.
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    Methodological issues around the validation of models for predicting diabetes risk in developing countries
    (Stellenbosch : Stellenbosh University, 2016-12) Masconi, Katya Laura; Kengne, Andre Pascal; Erasmus, Rajiv T.; Matsha, Tandi E.; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Pathology: Chemical Pathology.
    ENGLISH SUMMARY: Background: Multivariable diabetes risk prediction models have the potential to contribute to screening strategies, combining several risk factors to predict undiagnosed diabetes or future risk of developing diabetes. The focus of this study is the prediction of undiagnosed diabetes and diabetes risk prediction in a developing country where no population-specific diabetes risk prediction model currently exists. Existent models have been developed in unrelated populations with different disease prevalence, predictor weightings and methods used for risk factor determination and diabetes diagnosis. For accurate diabetes risk prediction in the mixed ancestry population of Bellville South, Cape Town, methodological issues regarding the validation and performance of these models needs to be addressed. Methodology: Cross-sectional data from the Cape Town Bellville South cohort was used for this study. Missing data in risk prediction research was investigated through a systematic review and a number of imputation methods were explored to deal with missing data in this dataset. Models were identified via recent systematic reviews and validated in the mixed-ancestry population. Discrimination was assessed and compared using the C-statistic and calibration was assessed via calibration plots. Model recalibration in diabetes risk prediction was investigated through a systematic review. In an effort to improve model performance in the new setting, model recalibration and updating strategies were used and performance was compared before and after implementation. Results: The study sample consisted of 1256 individuals, of whom 173 were excluded due to previously diagnosed diabetes. Of the final 1083 individuals, 329 (30.4%) had missing data. Deletion resulted in the lowest model performance and simple imputation, the simplest method, resulted in the highest model performance and was employed for further analysis. A systematic review highlighted the gross underreporting and mishandling of missing data in diabetes risk prediction research. Original model performance during validation was poor-to-average, with both over- and underestimation present: Cambridge [C-statistic: 0.67 (0.62-0.72); E/O: 1.81 (1.09-2.52)], Kuwaiti [C-statistic: 0.68 (0.63-0.73); E/O: 0.72 (0.43-1.12)], Omani [C-statistic: 0.66 (0.61-0.70); E/O: 1.28 (0.63-1.93)], Rotterdam [C-statistic: 0.64 (0.59-0.69); E/O: 0.54 (0.50-1.04)] and Simplified Finnish [C-statistic: 0.67 (0.62-0.71); E/O: 0.26 (0.13-0.39)] diabetes risk prediction models. Recalibration, as shown through a systematic review, was undertaken only in models predicting incident diabetes, and was reported in 22.9% of validation studies, with 77.8% achieving an increase in model performance. Updating results applied to this validation dataset showed an increase in both discrimination and calibration in varying levels across all five models. Overall, the re-estimation of the Cambridge diabetes risk model yielded the best model performance [C-statistic: 0.71 (0.67 – 0.75); E/O: 1.00 (0.86 – 1.17)]. Discussion and conclusion: The frequency of missing data, underreporting and mishandling of missing data, complexity of updating methods and overall model performance of validated models in new settings highlight the challenges in diabetes risk prediction research. This is the first validation study of prevalent diabetes risk prediction models in Sub-Saharan Africa and highlighted important methodological issues. While both simpler imputation and updating methods resulted in similar predictive utility when compared to more complex techniques, model performance was not increased sufficiently to suggest recommendation.
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    Molecular investigation of genetic factors associated with insulin resistance and obesity in a South African population
    (Stellenbosch : Stellenbosh University, 2015-12) Vergotine, Zelda; Erasmus, Rajiv T.; Matsha, Tandi E.; Pillay, Tahir S.; Kotze, Maritha J.; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Pathology: Chemical Pathology.
    ENGLISH ABSTRACT: Background: The aetiopathogenesis of type 2 diabetes and the associated insulin resistance have been shown to have a strong genetic basis. Several genetic variants of the peroxisome proliferatoractivated receptor gamma (PPARG) and the insulin receptor substrate (IRS) 1 genes have been associated with the metabolic states of obesity, insulin resistance and type 2 diabetes in Caucasian populations. Furthermore, insulin resistance is strongly associated with diabetes and subsequent cardiovascular disease. These are increasingly common in low- to middle -income countries, including South Africa. Limited information is currently available regarding genetic associations with insulin resistance in African populations. Objectives: (1) To identify subjects with insulin resistance and determine the frequencies of the single nucleotide polymorphisms in the PPARG and IRS1 genes and examine the associated risk of insulin resistance and type 2 diabetes mellitus in a mixed-ancestry South African population. (2) To investigate the relationship between indices of insulin resistance and carotid intima media thickness, a marker of subclinical cardiovascular disease/atherosclerosis. Methods: A total of 856 (235 males) mixed-ancestry adults drawn from an urban community of Bellville South, Cape Town were genotyped for PPARG Pro12Ala (rs1801282, G>C), Pro115Gln (rs1800571, G>T), Val290Met (rs72551362, G>A), Pheu388Leu (rs72551363, T>A), Arg397Cys (rs72551364, C>T), His449His (rs3856806, C>T) and IRS1 Gly972Arg (rs 1801278, G>A). The oral glucose tolerance test was performed and cardiometabolic risk factors measured. Insulin resistance was estimated by the homeostasis model assessment of insulin resistance, the homeostasis model assessment of functional beta-cells, the quantitative insulin-sensitivity check index, the fasting insulin resistance index and the glucose/insulin ratio. Carotid intima media thickness was measured in longitudinal section at the far wall of the distal common carotid arteries, 2 cm from the bifurcation, at three consecutive end-points, 5-10 mm apart. Results: The genotype frequencies of PPARG Pro12Ala, IRS1 Gly972Arg and PPARG His449His were 10,4%, 7,7% and 23,8% respectively. No mutations were found for PPARG Pro115Gln, Val290Met, Pheu388Leu and Arg379Cys. In a model containing both PPARG Pro12Ala and IRS1 Gly972Arg alleles and their interaction term, the presence of the PPARG Pro12 resulted in a 64% risk of prevalent type 2 diabetes mellitus and was associated with higher 2 hour post-OGTT insulin levels in subjects with normoglycaemia. The PPARG Pro12 was associated with insulin resistance and interacted with IRS1 Gly972Arg, increasing the risk of type 2 diabetes mellitus. The PPARG His449His allele T frequency was about 14% and in an additive genetic model significantly reduced the risk of diabetes by 44%. After adjustment for age, gender, body mass index and diabetes status, the fasting plasma glucose (β=0,087;p=0,042) and glucose/insulin ratio (β=0,026; p=0,026) were associated with carotid intima media thickness. However, the effect on the overall model performance was marginal, R2<29,7%. Conclusion: The PPARG Pro12 was associated with insulin resistance and showed a gene-gene interaction with the unfavorable polymorphism IRS1 Gly972Arg, leading to an increased risk of type 2 diabetes mellitus. In contrast, the PPARG His449His T allele showed a protective effect against the risk of developing diabetes. Furthermore, indices of insulin resistance such as homeostatis model assessment of insulin resistance, quantitative insulin-sensitivity check index, fasting insulin resistance index and the glucose/insulin ratio were weakly associated with carotid intima media thickness in the risk stratification of cardiovascular disease in this population.