Browsing by Author "Matsha, Tandi"

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    Open Access
    Effects of diabetes mellitus on amyotrophic lateral sclerosis: a systematic review
    (BioMed Central Ltd, 2014) Lekoubou, Alain; Matsha, Tandi; Sobngwi, Eugene; Kengne, Andre
    BACKGROUND:Amyotrophic lateral sclerosis (ALS) is an incurable motor neuron degenerative disease which onset and course may be affected by concurrent diabetes mellitus (DM). We performed a systematic review to assess the effect of DM/dysglycemic states on ALS. METHODS: We searched PubMed MEDLINE, from inception to March 2013 for original articles published in English and in French languages on DM (and related states) and ALS. We made no restriction per study designs. RESULTS: Seven studies/1410 citations (5 case-control and 2 cross-sectional) were included in the final selection. The number of participants with ALS ranged from 18 to 2371. The outcome of interest was ALS and DM/dysglycemic states respectively in three and two case control-studies. DM/impaired glucose tolerance status did not affect disease progression, survival, disease severity and disease duration in ALS participants but ALS participants with DM were found to be older in one study. DM/IGT prevalence was similar in both ALS and non ALS participants. This review was limited by the absence of prospective cohort studies and the heterogeneity in ALS and DM diagnosis criteria. CONCLUSIONS: This systematic review suggests that evidences for the association of ALS and DM are rather limited and derived from cross-sectional studies. Prospective studies supplemented by ALS registries and animal studies are needed to better understand the relationship between both conditions.
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    Independent external validation and comparison of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa
    (BioMed Central Ltd, 2015) Masconi, Katya L; Matsha, Tandi; Erasmus, Rajiv; Kengne, Andre
    BACKGROUND: Guidelines increasingly encourage the use of multivariable risk models to predict the presence of prevalent undiagnosed type 2 diabetes mellitus worldwide. However, no single model can perform well in all settings and available models must be tested before implementation in new populations. We assessed and compared the performance of five prevalent diabetes risk models in mixed-ancestry South Africans. METHODS: Data from the Cape Town Bellville-South cohort were used for this study. Models were identified via recent systematic reviews. Discrimination was assessed and compared using C-statistic and non-parametric methods. Calibration was assessed via calibration plots, before and after recalibration through intercept adjustment. RESULTS: Seven hundred thirty-seven participants (27% male), mean age, 52.2years, were included, among whom 130 (17.6%) had prevalent undiagnosed diabetes. The highest c-statistic for the five prediction models was recorded with the Kuwaiti model [C-statistic 0.68: 95% confidence: 0.63-0.73] and the lowest with the Rotterdam model [0. 64 (0.59-0.69)]; with no significant statistical differences when the models were compared with each other (Cambridge, Omani and the simplified Finnish models). Calibration ranged from acceptable to good, however over- and underestimation was prevalent. The Rotterdam and the Finnish models showed significant improvement following intercept adjustment. CONCLUSIONS: The wide range of performances of different models in our sample highlights the challenges of selecting an appropriate model for prevalent diabetes risk prediction in different settings.
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    Proliferator-activated receptor gamma Pro12Ala interacts with the insulin receptor substrate 1 Gly972Arg and increase the risk of insulin resistance and diabetes in the mixed ancestry population from South Africa
    (BioMed Central Ltd, 2014) Vergotine, Zelda; Yako, Yandiswa; Kengne, Andre; Erasmus, Rajiv; Matsha, Tandi
    BACKGROUND: The peroxisome proliferator-activated receptor gamma (PPARG), Pro12Ala and the insulin receptor substrate (IRS1), Gly972Arg confer opposite effects on insulin resistance and type 2 diabetes mellitus (T2DM). We investigated the independent and joint effects of PPARG Pro12Ala and IRS1 Gly972Arg on markers of insulin resistance and T2DM in an African population with elevated risk of T2DM. In all 787 (176 men) mixed-ancestry adults from the Bellville-South community in Cape Town were genotyped for PPARG Pro12Ala and IRS1 Gly972Arg by two independent laboratories. Glucose tolerance status and insulin resistance/sensitivity were assessed. RESULTS: Genotype frequencies were 10.4% (PPARG Pro12Ala) and 7.7% (IRS1 Gly972Arg). Alone, none of the polymorphisms predicted prevalent T2DM, but in regression models containing both alleles and their interaction term, PPARG Pro12 conferred a 64% higher risk of T2DM. Furthermore PPARG Pro12 was positively associated in adjusted linear regressions with increased 2-hour post-load insulin in non-diabetic but not in diabetic participants. CONCLUSION: The PPARG Pro12 is associated with insulin resistance and this polymorphism interacts with IRS1 Gly972Arg, to increase the risk of T2DM in the mixed-ancestry population of South Africa. Our findings require replication in a larger study before any generalisation and possible application for risk stratification.
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    Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review
    (BioMed Central Ltd, 2015) Masconi, Katya L; Matsha, Tandi; Echouffo-Tcheugui, Justin; Erasmus, Rajiv; Kengne, Andre
    Missing values are common in health research and omitting participants with missing data often leads to loss of statistical power, biased estimates and, consequently, inaccurate inferences. We critically reviewed the challenges posed by missing data in medical research and approaches to address them. To achieve this more efficiently, these issues were analyzed and illustrated through a systematic review on the reporting of missing data and imputation methods (prediction of missing values through relationships within and between variables) undertaken in risk prediction studies of undiagnosed diabetes. Prevalent diabetes risk models were selected based on a recent comprehensive systematic review, supplemented by an updated search of English-language studies published between 1997 and 2014. Reporting of missing data has been limited in studies of prevalent diabetes prediction. Of the 48 articles identified, 62.5% (n=30) did not report any information on missing data or handling techniques. In 21 (43.8%) studies, researchers opted out of imputation, completing case-wise deletion of participants missing any predictor values. Although imputation methods are encouraged to handle missing data and ensure the accuracy of inferences, this has seldom been the case in studies of diabetes risk prediction. Hence, we elaborated on the various types and patterns of missing data, the limitations of case-wise deletion and state-of the-art methods of imputations and their challenges. This review highlights the inexperience or disregard of investigators of the effect of missing data in risk prediction research. Formal guidelines may enhance the reporting and appropriate handling of missing data in scientific journals.
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    Validation of two prediction models of undiagnosed chronic kidney disease in mixed-ancestry South Africans
    (BioMed Central Ltd, 2015) Mogueo, Amelie; Echouffo-Tcheugui, Justin; Matsha, Tandi; Erasmus, Rajiv; Kengne, Andre
    BACKGROUND: Chronic kidney disease (CKD) is a global challenge. Risk models to predict prevalent undiagnosed CKD have been published. However, none was developed or validated in an African population. We validated the Korean and Thai CKD prediction model in mixed-ancestry South Africans. METHODS: Discrimination and calibration were assessed overall and by major subgroups. CKD was defined as 'estimated glomerular filtration rate (eGFR) <60ml/min/1.73m 2 ' or 'any nephropathy'. eGFR was based on the 4-variable Modification of Diet in Renal Disease (MDRD) formula. RESULTS: In all 902 participants (mean age 55years) included, 259 (28.7%) had prevalent undiagnosed CKD. C-statistics were 0.76 (95 % CI: 0.73-0.79) for 'eGFR <60ml/min/1.73m 2 ' and 0.81 (0.78-0.84) for 'any nephropathy' for the Korean model; corresponding values for the Thai model were 0.80 (0.77-0.83) and 0.77 (0.74-0.81). Discrimination was better in men, older and normal weight individuals. The model underestimated CKD risk by 10% to 13% for the Thai and 9% to 93% for the Korean model. Intercept adjustment significantly improved the calibration with an expected/observed risk of 'eGFR <60ml/min/1.73m 2 ' and 'any nephropathy' respectively of 0.98 (0.87-1.10) and 0.97 (0.86-1.09) for the Thai model; but resulted in an underestimation by 24% with the Korean model. Results were broadly similar for CKD derived from the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula. CONCLUSION: Asian prevalent CKD risk models had acceptable performances in mixed-ancestry South Africans. This highlights the potential importance of using existing models for risk CKD screening in developing countries.