The occurance of genetic variations in the MYH9 gene and their association with CKD in a mixed South African population

Thesis (MScMedSc)--Stellenbosch University, 2012.ENGLISH ABSTRACT: The purpose of this study was to investigate the association of the selected MYH9 single nucleotide polymorphisms (SNPs) with chronic kidney disease (CKD) and its related co-morbidities in the South African mixed ancestry population residing in Bellville South, Cape Town. In 2008, two landmark studies identified SNPs in the MYH9 gene which explained most of the increased risk for non-diabetic CKD in African Americans. These polymorphisms were later found to be weakly associated with diabetic nephropathy. Three SNPs that exhibited independent evidence for association with CKD were selected (rs5756152, rs4821480 and rs12107). These were genotyped using a Taqman genotyping assay on a BioRad MiniOpticon and confirmed by sequencing in 724 subjects from Bellville South, Cape Town, South Africa. Prevalent CKD was defined based on the estimated glomerular filtration rate calculated using the modification of diet in renal disease (MDRD) formula. Chronic kidney disease was present in 214 subjects (29.6%), 96.3% were stage 3 and only 8 subjects were stage 4. In additive allelic models, adjusted for age and gender, rs5756152 demonstrated an association with kidney function whereby each G allele of rs5756152 increased eGFR by 3.67 ml/min/1.73, reduced serum creatinine by 4.5% and increased fasting plasma glucose by 0.51 mmol/L. When an interaction model was used, the effect of rs5756152 on serum creatinine, eGFR and blood glucose levels was retained, and enhanced, but only in diabetic subjects. In addition, rs4821480 T allele increased eGFR while rs12107 A allele decreased glucose levels in diabetic subjects. In contrast to reports that MYH9 SNPs are strongly associated with non-diabetic end stage renal disease, our study demonstrated that rs5756152 and rs4821480 are associated with early kidney function derangements in type 2 diabetes whilst rs12107 is associated with glucose metabolism. Our findings, along with previous reports, suggest that the MYH9 gene may have a broader genetic risk effect on different types of kidney diseases than previously thought.AFRIKAANSE OPSOMMING: Hierdie studie het ondersoek ingestel na die verband tussen drie gekose MYH9-enkelnukleotied-polimorfismes (SNP’s) en chroniese niersiekte (hierna ‘niersiekte’), wat verwante ko-morbiditeite insluit, onder ’n Suid-Afrikaanse populasie van gemengde afkoms in Bellville-Suid, Kaapstad. Twee rigpuntstudies het in 2008 op SNP’s in die MYH9-geen afgekom wat verklaar het waarom Afro-Amerikaners ’n hoër risiko vir niediabetiese niersiekte toon. Later is bevind dat hierdie polimorfismes ook ’n swak verband met diabetiese nefropatie het. Drie SNP’s wat elk onafhanklik bewys gelewer het van ’n verband met niersiekte is vervolgens gekies (rs5756152, rs4821480 en rs12107). Die SNP’s is daarná met behulp van die Taqman-toets op ’n BioRad MiniOpticon aan genotipering onderwerp, en is toe deur middel van reeksbepaling by 724 proefpersone van Bellville-Suid, Kaapstad, Suid-Afrika, bevestig. Die voorkoms van niersiekte is bepaal op grond van die geraamde glomerulêre filtrasietempo (eGFR), wat aan die hand van die ‘niersiekte-dieetveranderings’- (MDRD-)formule bereken is. Daar is bevind dat 214 proefpersone (29,6%) aan chroniese niersiekte ly – 96,3% was in fase 3 en slegs agt proefpersone in fase 4. In toegevoegde alleliese modelle wat vir ouderdom en geslag aangepas is, het rs5756152 ’n verband met nierfunksie getoon: Elke G-allel van rs5756152 het eGFR met 3,67 ml/min/1,73 verhoog, serumkreatinien met 4,5% verlaag en vastende plasmaglukose met 0,51 mmol/L verhoog. Toe ’n interaksiemodel gebruik is, is die effek van rs5756152 op serumkreatinien, eGFR en bloedglukosevlakke behou en versterk, hoewel slegs by diabetiese proefpersone. Daarbenewens het die T-allel van rs4821480 eGFR verhoog, terwyl die A-allel van rs12107 ook glukosevlakke by diabetiese proefpersone verlaag het. In teenstelling met bewerings dat MYH9-SNP’s ’n sterk verband met niediabetiese eindstadiumniersiekte toon, het hierdie studie bewys dat rs5756152 en rs4821480 met vroeë nierfunksieversteurings by tipe 2-diabetes verband hou, terwyl rs12107 weer met glukosemetabolisme verbind word. Tesame met vorige studies, doen hierdie navorsingsbevindinge dus aan die hand dat die MYH9-geen dalk ’n groter genetiese risiko-effek op verskillende tipes niersiekte het as wat voorheen vermoed is.Cape Peninsula University of Technology Research FundUniversity of Stellenbosch Merit Bursar

Discrete Event Simulation for Decision Modeling in Health Care: Lessons from Abdominal Aortic Aneurysm Screening

Markov models are often used to evaluate the cost-effectiveness of new healthcare interventions but they are sometimes not flexible enough to allow accurate modeling or investigation of alternative scenarios and policies. A Markov model previously demonstrated that a one-off invitation to screening for abdominal aortic aneurysm (AAA) for men aged 65 y in the UK and subsequent follow-up of identified AAAs was likely to be highly cost-effective at thresholds commonly adopted in the UK (£20,000 to £30,000 per quality adjusted life-year). However, new evidence has emerged and the decision problem has evolved to include exploration of the circumstances under which AAA screening may be cost-effective, which the Markov model is not easily able to address. A new model to handle this more complex decision problem was needed, and the case of AAA screening thus provides an illustration of the relative merits of Markov models and discrete event simulation (DES) models. An individual-level DES model was built using the R programming language to reflect possible events and pathways of individuals invited to screening v. those not invited. The model was validated against key events and cost-effectiveness, as observed in a large, randomized trial. Different screening protocol scenarios were investigated to demonstrate the flexibility of the DES. The case of AAA screening highlights the benefits of DES, particularly in the context of screening studies

Independent external validation and comparison of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa

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

Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review

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

Effects of different missing data imputation techniques on the performance of undiagnosed diabetes risk prediction models in a mixed-ancestry population of South Africa

BACKGROUND: Imputation techniques used to handle missing data are based on the principle of replacement. It is widely advocated that multiple imputation is superior to other imputation methods, however studies have suggested that simple methods for filling missing data can be just as accurate as complex methods. The objective of this study was to implement a number of simple and more complex imputation methods, and assess the effect of these techniques on the performance of undiagnosed diabetes risk prediction models during external validation. METHODS: Data from the Cape Town Bellville-South cohort served as the basis for this study. Imputation methods and models were identified via recent systematic reviews. Models’ discrimination was assessed and compared using C-statistic and non-parametric methods, before and after recalibration through simple intercept adjustment. 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. Family history had the highest proportion of missing data (25%). Imputation of the outcome, undiagnosed diabetes, was highest in stochastic regression imputation (163 individuals). Overall, deletion resulted in the lowest model performances while simple imputation yielded the highest C-statistic for the Cambridge Diabetes Risk model, Kuwaiti Risk model, Omani Diabetes Risk model and Rotterdam Predictive model. Multiple imputation only yielded the highest C-statistic for the Rotterdam Predictive model, which were matched by simpler imputation methods. CONCLUSIONS: Deletion was confirmed as a poor technique for handling missing data. However, despite the emphasized disadvantages of simpler imputation methods, this study showed that implementing these methods results in similar predictive utility for undiagnosed diabetes when compared to multiple imputation

APOL1 genetic variants, chronic kidney diseases and hypertension in mixed ancestry South Africans

BackgroundThe frequencies of apolipoprotein L1 (APOL1) variants and their associations with chronic kidney disease (CKD) vary substantially in populations from Africa. Moreover, available studies have used very small sample sizes to provide reliable estimates of the frequencies of these variants in the general population. We determined the frequency of the two APOL1 risk alleles (G1 and G2) and investigated their association with renal traits in a relatively large sample of mixed-ancestry South Africans. APOL1 risk variants (G1: rs60910145 and rs73885319; G2: rs71785313) were genotyped in 859 African mixed ancestry individuals using allele-specific TaqMan technology. Glomerular filtration rate (eGFR) was estimated using the Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations.ResultsThe frequencies of rs73885319, rs60910145 and rs71785313 risk alleles were respectively, 3.6%, 3.4%, and 5.8%, resulting in a 1.01% frequency of the APOL1 two-risk allele (G1:G1 or G1:G2 or G2:G2). The presence of the two-risk allele increased serum creatinine with a corresponding reduction in eGFR (either MDRD or CKD-EPI based). In dominant and log-additive genetic models, significant associations were found between rs71785313 and systolic blood pressure (both p ≤ 0.025), with a significant statistical interaction by diabetes status, p = 0.022, reflecting a negative non-significant effect in nondiabetics and a positive effect in diabetics.ConclusionsAlthough the APOL1 variants are not common in the mixed ancestry population of South Africa, the study does provide an indication that APOL1 variants may play a role in conferring an increased risk for renal and cardiovascular risk in this population

Effect of interpregnancy weight change on perinatal outcomes: systematic review and meta-analysis

Abstract: Background: Although obesity is a well-known risk factor for adverse pregnancy outcomes, evidence is sparse about the effects of interpregnancy weight change on the risk of adverse perinatal complications in a subsequent pregnancy. The current study aims to assess the effect of interpregnancy weight change on the risk of developing gestational diabetes, pre-eclampsia, pregnancy induced hypertension, preterm birth, or delivering a large- or small-for-gestational age neonate. Methods: Pubmed, Ovid Embase, ClinicalTrial.gov and the Cochrane library were systematically searched up until July 24th, 2019. Interpregnancy weight change was defined as the difference between pre-pregnancy weight of an index pregnancy and a consecutive pregnancy. Inclusion criteria included full text original articles reporting quantitative data about interpregnancy weight change in multiparous women with any time interval between consecutive births and the risk of any perinatal complication of interest. Studies reporting adjusted odds ratios and a reference group of − 1 to + 1 BMI unit change between pregnancies were harmonised by meta-analysis. Results: Twenty-three cohort studies identified a total of 671,906 women with two or more consecutive pregnancies. Seven of these studies were included in the meta-analysis (280,672 women). Interpregnancy weight gain was consistently associated with a higher risk of gestational diabetes, pre-eclampsia, pregnancy induced hypertension and large-for-gestational age births. In contrast, interpregnancy weight loss was associated with a lower risk of delivering a large-for-gestational age neonate. The effect magnitude (relative risk) of interpregnancy weight gain on pregnancy induced hypertension or delivering a large-for-gestational age neonate was greater among women with a normal BMI in the index pregnancy compared to women with a starting BMI ≥25 kg/m2. Conclusion: These findings confirm that interpregnancy weight change impacts the risk of developing perinatal complications in a subsequent pregnancy. This provides evidence in support of guidelines encouraging women to achieve post-partum weight loss, as their risk of perinatal complications might be minimised if they return to their pre-pregnancy weight before conceiving again. Prospectively registered with PROSPERO (CRD42017067326)

Discrete Event Simulation for Decision Modelling in Health Care: Lessons from Abdominal Aortic Aneurysm Screening

Markov models are often used to evaluate the cost-effectiveness of new healthcare interventions, but they are sometimes not flexible enough to allow accurate modelling or investigation of alternative scenarios and policies. A Markov model previously demonstrated that a one-off invitation to screening for abdominal aortic aneurysm (AAA) for men aged 65 in the UK and subsequent follow-up of identified AAAs was likely to be highly cost-effective at thresholds commonly adopted in the UK (£20,000-£30,000 per quality adjusted life-year). However, new evidence has emerged and the decision problem has evolved to include the exploration of the circumstances under which AAA screening may be cost-effective, which the Markov model is not easily able to address. A new model to handle this more complex decision problem was needed and the case of AAA screening thus provides an illustration of the relative merits of Markov models and discrete event simulation (DES) models. An individual level DES model was built using the R programming language to reflect possible events and pathways of individuals invited to screening versus those not invited. The model was validated against key events and cost-effectiveness as observed in a large randomized trial. Different screening protocol scenarios were investigated to demonstrate the flexibility of the DES. The case of AAA screening highlights the benefits of DES, particularly in the context of screening studies

Discrete Event Simulation for Decision Modeling in Health Care: Lessons from Abdominal Aortic Aneurysm Screening.

Markov models are often used to evaluate the cost-effectiveness of new healthcare interventions but they are sometimes not flexible enough to allow accurate modeling or investigation of alternative scenarios and policies. A Markov model previously demonstrated that a one-off invitation to screening for abdominal aortic aneurysm (AAA) for men aged 65 y in the UK and subsequent follow-up of identified AAAs was likely to be highly cost-effective at thresholds commonly adopted in the UK (£20,000 to £30,000 per quality adjusted life-year). However, new evidence has emerged and the decision problem has evolved to include exploration of the circumstances under which AAA screening may be cost-effective, which the Markov model is not easily able to address. A new model to handle this more complex decision problem was needed, and the case of AAA screening thus provides an illustration of the relative merits of Markov models and discrete event simulation (DES) models. An individual-level DES model was built using the R programming language to reflect possible events and pathways of individuals invited to screening v. those not invited. The model was validated against key events and cost-effectiveness, as observed in a large, randomized trial. Different screening protocol scenarios were investigated to demonstrate the flexibility of the DES. The case of AAA screening highlights the benefits of DES, particularly in the context of screening studies

Analysis of clinical benefit, harms, and cost-effectiveness of screening women for abdominal aortic aneurysm.

BACKGROUND: A third of deaths in the UK from ruptured abdominal aortic aneurysm (AAA) are in women. In men, national screening programmes reduce deaths from AAA and are cost-effective. The benefits, harms, and cost-effectiveness in offering a similar programme to women have not been formally assessed, and this was the aim of this study. METHODS: We developed a decision model to assess predefined outcomes of death caused by AAA, life years, quality-adjusted life years, costs, and the incremental cost-effectiveness ratio for a population of women invited to AAA screening versus a population who were not invited to screening. A discrete event simulation model was set up for AAA screening, surveillance, and intervention. Relevant women-specific parameters were obtained from sources including systematic literature reviews, national registry or administrative databases, major AAA surgery trials, and UK National Health Service reference costs. FINDINGS: AAA screening for women, as currently offered to UK men (at age 65 years, with an AAA diagnosis at an aortic diameter of ≥3·0 cm, and elective repair considered at ≥5·5cm) gave, over 30 years, an estimated incremental cost-effectiveness ratio of £30 000 (95% CI 12 000-87 000) per quality-adjusted life year gained, with 3900 invitations to screening required to prevent one AAA-related death and an overdiagnosis rate of 33%. A modified option for women (screening at age 70 years, diagnosis at 2·5 cm and repair at 5·0 cm) was estimated to have an incremental cost-effectiveness ratio of £23 000 (9500-71 000) per quality-adjusted life year and 1800 invitations to screening required to prevent one AAA-death, but an overdiagnosis rate of 55%. There was considerable uncertainty in the cost-effectiveness ratio, largely driven by uncertainty about AAA prevalence, the distribution of aortic sizes for women at different ages, and the effect of screening on quality of life. INTERPRETATION: By UK standards, an AAA screening programme for women, designed to be similar to that used to screen men, is unlikely to be cost-effective. Further research on the aortic diameter distribution in women and potential quality of life decrements associated with screening are needed to assess the full benefits and harms of modified options. FUNDING: UK National Institute for Health Research Health Technology Assessment programme