Effects of diabetes mellitus on amyotrophic lateral sclerosis: a systematic review

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

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

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

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

Validation of two prediction models of undiagnosed chronic kidney disease in mixed-ancestry South Africans

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

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

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

Metabolic Syndrome in People Living with Human Immunodeficiency Virus: An Assessment of the Prevalence and the Agreement between Diagnostic Criteria

Objectives. We determined metabolic syndrome (MetS) prevalence and assessed the agreement between different diagnostic criteria in HIV-infected South Africans. Method. A random sample included 748 HIV-infected adult patients (79% women) across 17 HIV healthcare facilities in the Western Cape Province. MetS was defined using the Joint Interim Statement (JIS 2009), International Diabetes Federation (IDF 2005), and Adult Treatment Panel III (ATPIII 2005) criteria. Results. Median values were 38 years (age), 5 years (diagnosed HIV duration), and 392 cells/mm3 (CD4 count), and 93% of the participants were on antiretroviral therapy (ART). MetS prevalence was 28.2% (95%CI: 25–31.4), 26.5% (23.3–29.6), and 24.1% (21–27.1) by the JIS, IDF, and ATPIII 2005 criteria, respectively. Prevalence was always higher in women than in men (all ), in participants with longer duration of diagnosed HIV (all ), and in ART users not receiving 1st-line regimens (all ). The agreement among the three criteria was very good overall and in most subgroups (all ). Conclusions. The three most popular diagnostic criteria yielded similarly high MetS prevalence in this relatively young population receiving care for HIV infection. Very good levels of agreement between criteria are unaffected by some HIV-specific features highlighting the likely comparable diagnostic utility of those criteria in routine HIV care settings

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

Genome-wide DNA methylation in mixed ancestry individuals with diabetes and prediabetes from South Africa

Aims. To conduct a genome-wide DNA methylation in individuals with type 2 diabetes, individuals with prediabetes, and control mixed ancestry individuals from South Africa. Methods. We used peripheral blood to perform genome-wide DNA methylation analysis in 3 individuals with screen detected diabetes, 3 individuals with prediabetes, and 3 individuals with normoglycaemia from the Bellville South Community, Cape Town, South Africa, who were age-, gender-, body mass index-, and duration of residency-matched. Methylated DNA immunoprecipitation (MeDIP) was performed by Arraystar Inc. (Rockville, MD, USA). Results. Hypermethylated DMRs were 1160 (81.97%) and 124 (43.20%), respectively, in individuals with diabetes and prediabetes when both were compared to subjects with normoglycaemia. Our data shows that genes related to the immune system, signal transduction, glucose transport, and pancreas development have altered DNA methylation in subjects with prediabetes and diabetes. Pathway analysis based on the functional analysis mapping of genes to KEGG pathways suggested that the linoleic acid metabolism and arachidonic acid metabolism pathways are hypomethylated in prediabetes and diabetes. Conclusions. Our study suggests that epigenetic changes are likely to be an early process that occurs before the onset of overt diabetes. Detailed analysis of DMRs that shows gradual methylation differences from control versus prediabetes to prediabetes versus diabetes in a larger sample size is required to confirm these findings