Mixed-ancestry South Africans Research Articles

Machine learning and discriminant function analysis in the formulation of generic models for sex prediction using patella measurements

Sex prediction from bone measurements that display sexual dimorphism is one of the most important aspects of forensic anthropology. Some bones like the skull and pelvis display distinct morphological traits that are based on shape. These morphological traits which are sexually dimorphic across different population groups have been shown to provide an acceptably high degree of accuracy in the prediction of sex. A sample of 100 patella of Mixed Ancestry South Africans (MASA) was collected from the Dart collection. Six parameters: maximum height (maxh), maximum breadth (maxw), maximum thickness (maxt), the height of articular facet (haf), lateral articular facet breadth (lafb), and medial articular facet breath (mafb) were used in this study. Stepwise and direct discriminant function analyses were performed for measurements that exhibited significant differences between male and female mean measurements, and the “leave-one-out” approach was used for validation. Moreover, we have used eight classical machine learning techniques along with feature ranking techniques to identify the best feature combinations for sex prediction. A stacking machine learning technique was trained and validated to classify the sex of the subject. Here, we have used the top performing three ML classifiers as base learners and the predictions of these models were used as inputs to different machine learning classifiers as meta learners to make the final decision. The measurements of the patella of South Africans are sexually dimorphic and this observation is consistent with previous studies on the patella of different countries. The range of average accuracies obtained for pooled multivariate discriminant function equations is 81.9–84.2%, while the stacking ML technique provides 90.8% accuracy which compares well with those presented for previous studies in other parts of the world. In conclusion, the models proposed in this study from measurements of the patella of different population groups in South Africa are useful resent with reasonably high average accuracies.

Combining HbA1c and glycated albumin improves detection of dysglycaemia in mixed-ancestry South Africans

SummaryBackgroundCombining HbA1c with glycated albumin (GA) may improve detection of dysglycaemia. As BMI correlates positively with HbA1c and negatively with GA, HbA1c may be more effective in obese and GA in nonobese individuals.MethodsTo relate these findings to Africans, we assessed in 1274 South Africans living in CapeTown (male 26%; age 48±16y; BMI 28.7 kg/m2 (range 15.6–73.8); obesity 39.9% and no prior diabetes history) the: (1) correlation of BMI with HbA1c and GA, (2) ability of HbA1c and GA separately and jointly, to detect OGTT-diagnosed dysglycaemia (diabetes plus prediabetes). Data collection took place between 2014 and 2016 in the City of Cape Town. Dysglycaemia was diagnosed by glucose criteria for the OGTT. Youden index was used to optimize diagnostic thresholds for HbA1c and GA.FindingsNormal glucose tolerance, prediabetes and diabetes occurred in 76%, 17% and 7%, respectively. BMI positively correlated with HbA1c [r = 0·34 [95%CI: 0·29,0·39)] and negatively with GA [-0·08 (0·13,0·03)]. For HbA1c the optimal threshold by Youden-index for dysglycaemia diagnosis was: 6·0% (95%CI: 5·8,6·2) and for GA: 13·44% (12·72,14·71). In the nonobese, obese and total cohort, HbA1c-alone detected: 51% (42–60), 72% (65,78), 63% (57,68), respectively; GA-alone detected 55% (52% (46,63), 52% (44, 59) and 53% (47,53), respectively; whereas: HbA1c+GA detected: 69% (60,76), 82% (75,87) and 76% (71, 81). Therefore, for the total cohort detection of dysglycaemia HbA1c-alone vs HbA1c+GA detected 63% (57,68) vs 76% (71,81).InterpretationThe opposite correlations of HbA1c and GA with BMI have now been demonstrated in an African-based population. Improving detection of dysglycaemia by combining HbA1c and GA has important implications for diabetes risk screening.FundingAES is supported by the intramural programs of the National Institute of Diabetes and Digestive and Kidney Diseases and the National Institute of Minority Health and Health Disparities of the National Institutes of Health (NIH, Bethesda, Maryland, USA). DBS is supported by the intramural program of the Clinical Center of NIH. The South African Medical Research Council (SAMRC) funded the VMH study with funds from the National Treasury under its Economic Competitiveness and Support Package (MRC-RFA-UFSP-01–2013/VMH Study).

1004-P: The Influence of Body Mass Index in Mixed-Ancestry South Africans on the Ability of A1C and Glycated Albumin to Detect Dysglycemia

As A1C and glycated albumin (GA) correlate in opposite directions with body mass index (BMI), combining these two biomarkers has led to improved detection of dysglycemia. As this has not been studied in Africa, we assessed in 1274 mixed ancestry South Africans in Cape Town with no prior history of diabetes (male 26%; obese 40%; mean age 48±16y), the correlation of A1C and GA with BMI and the sensitivity of A1C and GA separately and jointly in detecting OGTT diagnosed dysglycemia. A1C and GA levels were obtained. OGTT performed. Dysglycemia was defined as either newly detected diabetes (FPG≥7.0 mmol/L and/or 2hPG≥11.1 mmol/L) or prediabetes (6.1≥FPG<7.0 mmol/L and/or 7.8<2hPG<11.1 mmol/L). Dysglycemia occurred in 24% of participants (prediabetes: 17%, diabetes:7%). By robust analysis, the correlation between BMI and A1C was positive [r=0.34 [95%CI: 0.29-0.39)] and negative for BMI and GA [-0.08 (-0.13- -0.03)]. The C-statistic for A1C diagnosis of dysglycemia in the total cohort was 0.77 (95%CI: 0.73-0.80) and for GA was 0.67 (0.64-0.71). In the nonobese the C-statistic for A1C was 0.71 (0.65-0.76) and for GA was 0.68 (0.62-0.73). In the obese the C-statistic for A1C was 0.79 (0.74-0.83) and for GA was 0.69 (0.64-0.74). Optimal thresholds by Youden index for dysglycemia diagnosis by A1C was 6.0% (95%CI: 5.8-6.2) and for GA was 13.44% (12.72-14.71). At these thresholds, sensitivities for A1C-alone, GA-alone, and A1C+GA in the total cohort were 63% (95%CI: 57-68), 53% (47-59) and 76% (71-81); in the nonobese were 51% (42-60), 55% (46-63) and 69% (60-76); and in the obese 72% (65-78), 52% (44-59) and 82% (75-87), resp. In short, this is the first demonstration in Africa of the positive correlation between A1C and BMI and the negative correlation between GA and BMI. The inverse correlations of A1C and GA with BMI may explain why there is improved detection of dysglycemia in the nonobese when the two biomarkers were combined and has important implications for screening. Disclosure A. Kengne: None. T. E. Matsha: None. D. B. Sacks: Research Support; Self; Sebia, Trinity Biotech plc. A. E. Zemlin: None. R. T. Erasmus: None. A. E. Sumner: None.

Accuracies of discriminant function equations for sex estimation using long bones of upper extremities.

One of the scopes of practice of forensic anthropologists is the estimation of sex from skeletal remains. As a result, population-specific discriminant function equations have been developed from measurements of various bones of the human skeletons. Steyn, Patriquin (Forensic Sci Int 191 (1-3):113, 2009) noted that the lack of skeletal collections and data from most parts of the world has made this process impractical. Previous attempts to develop global discriminant function equations from measurements of the pelvis showed that population-specific equations are not necessary as equations derived from other populations yielded high sex estimation scores when applied to a different population. However, information on the suitability and applicability of generalised equations in sex estimation using long bones is still scarce. It is, therefore, the aim of this study to assess the accuracies of population-specific discriminant function equations derived from measurements of long bones of the upper limb of South African population groups. Data analysed in the current study were obtained from Mokoena, Billings, Bidmos, Mazengenya (Forensic Sci Int 278:404, 2017) and Mokoena, Billings, Gibbon, Bidmos, Mazengenya (Science & Justice 6(59):660-666, 2019) in which a total sample of 988 bones (humeri, radii, and ulnae) of South Africans of African descent (SAAD), South Africans of European descent (SAED) and Mixed Ancestry South Africans (MASA) were measured. Stepwise and direct discriminant function analyses were performed on the pooled data. Each function was used to estimate the sex of cases in each population group separately and average accuracies calculated. Thereafter, population-specific discriminant function equations were formulated for each population group and then applied to other population groups. The average accuracies of functions for pooled data ranged between 80.7 and 86.5%. The cross-validation average accuracies remained unchanged for most functions, confirming the validity of derived functions. A drop in average accuracies (0.8-5.3%) was observed when the functions were tested on a sample of SAAD while increased average accuracy was observed for the SAED and MASA (0.5-6.9%). When population-specific functions for a particular population group were applied to other groups, a wide range of a drop in average accuracies was observed (1.3 to 22.4%). This thereby confirms that population-specific equations should not be applied to other population groups. However, discriminant function equations from the pooled data of South Africans are accurate in the estimation of sex and efforts should be made towards the development and validation of such equations from as many bones of the human skeleton.

Changes in Obesity Phenotype Distribution in Mixed-ancestry South Africans in Cape Town Between 2008/09 and 2014/16.

Background: The concept of obesity phenotypes encompasses a different approach to evaluating the relationship between obesity and cardiometabolic diseases. Considering the minimal research on obesity phenotypes in Africa, we investigated these changes from 2008/09 to 2014/16 in the mixed ancestry population in Cape Town, South Africa.Methods: In all, 928 (2008/09) and 1969 (2014/16) ≥20 year old participants were included in two community-based cross-sectional studies. For obesity phenotype classification, a combination of body mass index (BMI) categories and prevalent cardiometabolic disease risk factors were used, with the presence of ≥2 cardiometabolic abnormalities defining abnormal metabolic status. Interaction tests were used to investigate changes in their distribution across the years of study.Results: Distribution of BMI categories differed significantly between the 2 years; normal weight, overweight and obese: 27.4, 27.4, and 45.3% in 2008/09 vs. 34.2, 23.6, and 42.2% in 2014/16 (p = 0.001). There was no differential effect in the distribution of obesity phenotypes pattern across the two time-points (interaction p = 0.126). Across BMI categories, levels of cardiometabolic risk factors linearly deteriorated in both metabolically healthy and abnormal participants (all p ≤ 0.018 for linear trends). Findings were not sensitive to the number of metabolic abnormalities included in the definition of obesity phenotypes.Conclusions: Our study showed negligible differences in obesity phenotypes over time, but a high burden of metabolic abnormalities among normal weight participants, and a significant proportion of metabolically health obese individuals. Further investigation is needed to improve risk stratification and cost-effective identification of individuals at high risk for cardiometabolic diseases.

THE RELATIONSHIP BETWEEN BODY COMPOSITION AND BLOOD PRESSURE DIFFERS IN THOSE WITH AND WITHOUT CHRONIC KIDNEY DISEASE

Objective: Thought it is generally accepted that measures of adiposity are related to cardiovascular disease risk, this relationship may not be consistent in disease states, particularly chronic kidney disease (CKD). This study aimed to investigate the relationship between measures of adiposity and blood pressure indices in a community-based sample of adults with and without prevalent CKD. Design and method: Mixed-ancestry South Africans residing in Cape Town (n = 1621) were examined between February and November 2015. Glomerular filtration rate (GFR) was estimated using the Modification of Diet in Renal Disease (MDRD) equation, without ethnicity correction. CKD was defined as an estimated GFR (eGFR) <60 ml/min/1.73m2. Body composition was assessed using anthropometry [body mass index (BMI) and waist circumference (WC)] and dual-energy x-ray absorptiometry (DXA) (in a sub-sample, n = 152). Blood pressure was recorded using a semi-automated digital monitor. Pulse pressure (PP) and mean arterial pressure (MAP) were calculated from mean systolic and diastolic blood pressure (SBP and DBP). Results: In bivariate correlation analysis, in those without CKD, an increase in anthropometric and DXA-derived measures of body composition correlated with an increase in SBP, DBP and MAP (all, p < 0.02); with BMI, WC and visceral adipose tissue also having a positive association with PP (all, p < 0.004). Contrary, in those with CKD (6%, n = 96), measures of body composition and fat distribution were not associated with blood pressure indices, accept for PP where the anthropometric-derived measures were inversely associated with PP (rho: -0.21 and -0.19 respectively, both p < 0.004). In multivariable analysis, only BMI and WC were positively associated with SBP [β (95 %CI): 0.27(0.13–0.41), p < 0.0001; 0.13(0.06–0.19), p < 0.0001], DBP [β (95% CI): 0.27(0.18–0.36), p < 0.0001; 0.13(0.09–0.17), p < 0.0001] and MAP [β (95% CI): 0.25(0.16–0.35), p < 0.0001; 0.12(0.08–0.17), p < 0.0001], independent of age, gender and CKD status. However, this association differed by CKD status, such that BMI and WC were only associated with SBP in those without CKD and inversely associated with PP in those with CKD (interaction, p < 0.098). Conclusions: In those without CKD there are parallel increases in brachial blood pressure as BMI, WC and fat depots increases, but these relationships were not observed in the presence of CKD.

Development of discriminant functions to estimate sex in upper limb bones for mixed ancestry South Africans

South Africa has one of the highest murder rates in the world, which is associated with an increasing number of unidentified individuals. Forensic anthropologists can assist in these cases to reduce the number of potential victims the remains may belong to. Sex estimation potentially decreases the number of possible victims by half. The mixed ancestry population in South Africa is the second largest group of people; however, there remains a paucity of data and population-specific methods for sex estimation in this group. The aim of this study was to assess the potential for metrices obtained around the nutrient foramen and the maximum length of upper limb long bones to estimate sex in mixed ancestry South Africans using discriminant function analysis. A total of 328 humeri, radii and ulnae from individuals of mixed ancestry were analysed. Sex was correctly classified with an average classification accuracy of 84.3% in the humeri, 88.3% for radii and 83.5% for the ulnae. Total length was the single best predictor of sex; the combination of total length with dimensions related to the nutrient foramen produced high classification accuracies in the current study. Overall, sexual dimorphism was observed in mixed ancestry South Africans upper limb long bones. The findings of this study further emphasise the need for population-specific standards of sexing in an attempt to improve current methods of forensic identification of descendants.

Leucocyte Telomere Length and Glucose Tolerance Status in Mixed-Ancestry South Africans.

Telomeres are DNA-tandem repeats situated at the ends of chromosomes and are responsible for genome stabilization. They are eroded by increased cell division, age and oxidative stress with shortened leucocyte telomeres (LTL) being associated with inflammatory disorders, including Type II diabetes. We assessed LTL in 205 participants across glucose tolerance groups at baseline and after three years in the mixed ancestry population of South Africa which have been shown to have high rates of obesity and T2DM. Baseline and follow-up data included glucose tolerance status, anthropometric measurements, lipids, insulin, γ-glutamyl transferase (GGT), cotinine, and HbA1c. Telomere length was measured using the absolute telomere q-PCR method performed on a Bio-Rad MiniOpticon Detector. No significant difference was detected in LTL across glucose tolerance groups at both time points, including in subjects who showed a deterioration of their glucose tolerance status. There was, however, a significant negative correlation between LTL and age which was more pronounced in diabetes (r = −0.18, p = 0.04) and with GGT (r = −0.16, p = 0.027). This longitudinal study has demonstrated that LTL shortening is not evident within three years, nor is it associated with glycaemia. Further studies in a larger sample and over a longer time period is required to confirm these results.

5.10-P3Time trend in absolute BP levels, prevalent hypertension and determinants in Mixed Ancestry South Africans

5.10-P3Time trend in absolute BP levels, prevalent hypertension and determinants in Mixed Ancestry South Africans

Association of Kidney Function and Waist Circumference with Uric Acid Levels in South Africans

Recent evidence that hyperuricemia is associated with incident chronic kidney disease (CKD) provides a potential therapeutic target for CKD that has not been explored in Africans. With hyperuricemia and gout increasing globally, we sought to determine their prevalence in South Africans with varying kidney function levels. This was a cross-sectional study of ambulatory adult patients presenting at a General Internal Medicine Outpatients Clinic between September 2012 and March 2014. Demographic, clinical, and laboratory data collected were analyzed using STATA11. Odds ratios (ORs) and 95% confidence intervals were determined using multivariable logistic regression with bootstrapping. There were 225/261 (86.2%) black/Africans, 31/261 (11.9%) Indian South Africans, 3/261 (1.1%) Caucasians, and 2/261 (<1%) mixed ancestry South Africans. Mean age was 51.3 ± 14.5 years. Median (interquartile range) estimated glomerular filtration rate (eGFR) was 71 (38) mL/min/1.73 m2 and 39.8% (104/261) of patients had CKD. Hyperuricemia prevalence was 43.7% (114/261) and increased from 16.7% in patients with eGFR ≥90 mL/min/1.73 m2 to 74.2% with eGFR <30 mL/min/1.73 m2 (P < 0.001). Gout prevalence was 5.4% (14/261), with equal distribution across eGFR categories (0.814). Factors independently associated with hyperuricemia were eGFR <90 [ORs 3.24 (1.15-9.14), 7.28 (2.26-23.49), and 7.88 (1.95-31.82) for eGFR 60-89.9, 30-60, and <30, respectively], albuminuria [2.32 (1.11-4.85)], and waist circumference [1.04 (1.01-1.06) per 1 cm increase]. In univariate and multivariable analysis, gout was positively associated with male gender and cardiovascular disease, while it was negatively associated with African ancestry, but none of these factors remained significant after bootstrapping; ORs 6.65 (0.64-69.24), 4.14 (0.61-28.07), and 0.18 (0.01-2.21), respectively. Hyperuricemia prevalence was high, with CKD and waist circumference being the strongest predictors. Gout was uncommon in black Africans. With population data lacking, screening high-risk individuals may provide insight into the burden of hyperuricemia and gout in South Africa.

Alternative indices of glucose homeostasis as biochemical diagnostic tests for abnormal glucose tolerance in an African setting

AimsAccurate diabetes diagnosis is important in Africa, where rates are increasing, and the disease largely undiagnosed. The cumbersome oral glucose tolerance test (OGTT) remains the reference standard, while alternative diagnostic methods are not yet established in Africans. We assessed the ability of fasting plasma glucose (FPG), HbA1c and fructosamine, to diagnose OGTT-based abnormal glucose tolerance in mixed-ancestry South Africans. MethodsMixed-ancestry adults, residing in Cape Town were examined between February and November 2015. OGTT values were used to classify glucose tolerance status as: screen-detected diabetes, prediabetes, dysglycaemia (combination of diabetes and prediabetes) and normal glucose tolerance. ResultsOf the 793 participants included, 65 (8.2%) had screen-detected diabetes, 157 (19.8%) prediabetes and 571 (72.0%) normal glucose tolerance. Correlations of FPG and 2-h glucose with HbA1c (r=0.51 and 0.52) were higher than those with fructosamine (0.34 and 0.30), both p<0.0001. The highest c-statistic for the prediction of abnormal glucose tolerance was recorded with 2-h glucose [c-statistic=0.997 (screen-detected diabetes), 0.979 (prediabetes) and 0.984 (dysglycaemia)] and the lowest with fructosamine (0.865, 0.596 and 0.677). At recommended or data-specific optimal cut-offs, no combination of FPG, HbA1c and fructosamine did better than 2-h glucose, while FPG was better than HbA1c and fructosamine on a range of performance measures. ConclusionsAbnormal glucose tolerance in this population is overwhelmingly expressed through 2-h glucose’s abnormalities; and no combination of FPG, HbA1c and fructosamine was effective at accurately discriminating OGTT-defined abnormal glucose tolerance. Tested non-glucose based strategies are unreliable alternatives to OGTT for dysglycaemia diagnosis in this population.

Platelet, monocyte and neutrophil activation and glucose tolerance in South African Mixed Ancestry individuals

Platelet activation has been described in patients with chronic inflammation, however in type 2 diabetes mellitus it remains controversial. We compared levels of platelet leucocyte aggregates, monocyte and granulocyte activation across glucose tolerance statuses in mixed ancestry South Africans. Individuals (206) were recruited from Bellville-South, Cape Town, and included 66% with normal glucose tolerance, 18.7% pre-diabetes, 8.7% screen-detected diabetes and 6.3% known diabetes. Monocyte and neutrophil activation were measured by calculating the percentage of cells expressing CD142 and CD69 while platelet monocyte aggregates were defined as CD14++ CD42b+ events and platelet neutrophil aggregates as CD16++ CD42b+ events. The percentage of monocytes and neutrophils expressing CD69 and CD142 was significantly higher in known diabetes and prediabetes, but, lowest in screen-detected diabetes (both p ≤ 0.016). The pattern was similar for platelet monocyte and neutrophil aggregates (both p ≤ 0.003). In robust linear regressions adjusted for age and gender, known diabetes was significantly and positively associated with the percentage of monocytes expressing CD69 [beta 11.06 (p = 0.016)] and CD42b (PMAs) [19.51 (0.003)] as well as the percentage of neutrophils expressing CD69 [14.19 (<0.0001)] and CD42b [17.7 (0.001)]. We conclude that monitoring platelet activation in diagnosed diabetic patients may have a role in the management and risk stratification.

High Molecular Weight Adiponectin Levels are Neither Influenced by Adiponectin Polymorphisms Nor Associated with Insulin Resistance in Mixed-ancestry Hyperglycemic Subjects from South Africa.

SummaryBackgroundHigh molecular weight (HMW) adiponectin has antiatherogenic, antiinflammatory and antidiabetic properties and these effects have been linked to its effect on high density lipoprotein cholesterol (HDL-c). Single nucleotide polymorphisms (SNPs) in the adiponectin gene influence adiponectin levels. We examined the relationship between HMW-adiponectin levels and cardiometabolic traits in normo- and hyperglycemic mixed ancestry South Africans and correlated these levels to two common polymorphisms.MethodsHMW-adiponectin was determined in 101 subjects from the Cape Town Bellville South community-based study on a mixed ancestry population. Comparisons were made between individuals with normo- and hyperglycemia. Two common SNPs, ADIPOQ SNPs rs17300539 and rs266729, known to affect adiponectin levels were also tested for. Levels of HMW-adiponectin were then correlated with cardiometabolic traits in all groups.ResultsLevels of HMW-adiponectin were not significantly different in the normo- and hyperglycemic groups (median 11.6 vs. 10.5 μg/mL, p=0.3060) and in men and women (8.44 vs. 11.34 μg/mL, p=0.67). ADIPOQ SNPs rs17300539 and rs266729 did not influence levels of HMW-adiponectin. Robust correlation analyses revealed a significant positive correlation between HMW-adiponectin and HDL-c (r=0.45; 95%CI: 0.27–0.59), similarly in normo- and hyperglycemic participants (p > 0.99). This association was substantially attenuated in robust linear regressions adjusted for age, gender and adiposity.ConclusionsAdiponectin levels in this population were not determined by the commonest SNPs of the adiponectin gene, were unaffected by glycemic status; but were significantly correlated with HDL-c levels. Previous studies have attributed some of the beneficial effects of adiponectin to its effect on HDL-c.

Correlation of E-selectin concentrations with carotid intima-media thickness and cardio-metabolic profile of mixed ancestry South Africans: a cross-sectional study.

Background E-selectin, an adhesion molecule, is a specific marker of endothelial dysfunction. High concentrations have been reported in type 2 diabetes and disorders with high risk of cardiovascular disease (CVD). Measurement of carotid intima-media thickness (CIMT) is a surrogate marker of early atherosclerosis. We examined the relationship between E-selectin concentrations, CIMT and cardio-metabolic traits in normo- and hyperglycaemic mixed ancestry South Africans. Methods E-selectin concentrations were determined in 308 subjects from the Cape Town Bellville South Community-based study on a mixed ancestry population. Their correlation with CIMT and cardio-metabolic profile used robust correlations and linear regression models. Results E-selectin concentrations were significantly higher in the hyperglycaemic (median 139.8 µg/L) compared to the normoglycaemic group (median 118.8 µg/L), ( p = 0.0007). Significant differences between the two groups were found for markers of glycaemia and adiposity, but not for 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 in robust linear regression models. In similar regressions models, age and gender were the main predictors of CIMT, which was not associated with E-selectin. Conclusions E-selectin concentrations in this study were associated with hyperglycaemia, possibly reflecting early endothelial damage. However, E-selectin was not useful to assess CIMT, a marker of subclinical atherosclerosis, which appeared to be determined by ageing and male gender.

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

BackgroundChronic 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.MethodsDiscrimination and calibration were assessed overall and by major subgroups. CKD was defined as ‘estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m2’ or ‘any nephropathy’. eGFR was based on the 4-variable Modification of Diet in Renal Disease (MDRD) formula.ResultsIn all 902 participants (mean age 55 years) included, 259 (28.7 %) had prevalent undiagnosed CKD. C-statistics were 0.76 (95 % CI: 0.73–0.79) for ‘eGFR <60 ml/min/1.73 m2’ 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 <60 ml/min/1.73 m2’ 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.ConclusionAsian 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12882-015-0093-6) contains supplementary material, which is available to authorized users.

Derivation and validation of an HbA1c optimal cutoff for diagnosing prediabetes in a South African mixed ancestry population

IntroductionPrediabetes compromises impaired fasting glucose and impaired glucose tolerance and is a high risk for future diabetes mellitus and cardiovascular disease. Traditional diagnostic methods involve a fasting sample or oral glucose tolerance test, which is cumbersome, time-consuming and inconvenient. An HbA1c-based approach has been incorporated into new guidelines, but cut-offs may vary and have not been defined for all population groups. We derived and validated HbA1c cut-offs to diagnose prediabetes in mixed ancestry South Africans. MethodsParticipants were 667 (derivation sample), 234 (validation sample 1) and 674 (validation sample 2) diabetes-free individuals. They underwent standard 2-hour OGTT with HbA1c test. Receiver–operator characteristic curves were used to determine optimal HbA1c cut-off to predict prediabetes. ResultsA total of 27.7% participants in the derivation sample had prediabetes versus 17.5% (validation sample 1) and 15.4% (validation sample 2). The optimal cut-off was 5.75% in all three cohorts with sensitivity and specificity of 64.8% and 60.4% in combined derivation and validation sample 1, and 59.6% and 69.8% in validation sample 2. ConclusionThe discriminatory capacity of HbA1c for predicting prediabetes in this population is modest at the derived cut-off. The use of HbA1c alone in this setting may result in an inaccurate diagnosis.

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.

Deterioration, improvement of kidney function over time and determinants in the Cape Town Bellville South cohort.

The aim of the present study was to assess the trajectories of glomerular filtration rate (GFR) and determinants of change during a 3-year period in free-living mixed-ancestry South Africans. In all 320 (78.1% women) adults, aged 56.2 years, from Cape Town were examined in 2008 and 2011. Estimated glomerular filtration rate (eGFR) was based on the Modification of Diet in Renal Disease model; and staging of eGFR used the National Kidney Foundation's classification. Mean eGFR (mL/min per 1.73 m2) was 68.6 at baseline and eGFR stages were: > 90 (9.4%), 60-90 (58.7%), 30-60 (28.1%) and < 30 (0.9%). eGFR increased by 8 mL/min during follow-up, reflecting variable trajectories by baseline eGFR stages, sex, hypertension and glucose tolerance (all P-interaction ≤ 0.012). Movements across eGFR stages during follow-up favoured improvement in 113 participants (35.3%), and worsened in 23 (7.2%). In adjusted multinomial logistic regressions, men had a 72% (43-86%) lower chance of improvement, while each mmHg higher systolic blood pressure conferred a 7% (3-11%) risk of deterioration. Equivalent for each 1% HbA1c was 30% (8-56%). Participants with glucose intolerance had 102% (3-297%) higher chances of improvement than diabetics. Variable trajectories of eGFR with time were observed in this cohort, reflecting the effects of modifiable risk factors such as hypertension and dysglycaemia.

OP11 Relationship of paraoxonase (PON) 1 and oxidative profile with sub-clinical cardiovascular disease in mixed-ancestry South Africans

OP11 Relationship of paraoxonase (PON) 1 and oxidative profile with sub-clinical cardiovascular disease in mixed-ancestry South Africans

OP1 The validation and comparison of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa

Introduction: The use of multivariable risk prediction models have been advocated as practical and potentially affordable approaches for improving the detection of undiagnosed diabetes or screening for future risk of diabetes. However, no one single model can perform well in all settings and available models must be tested before implementation in new populations. The objective is to assess and compare the performance of six prevalent diabetes risk models updated with intercept adjustment in mixed-ancestry South Africans Methods: The data from the Cape Town Bellville-South cohort served as the basis for this study. Models were identified via recent systematic reviews. Models’ discrimination was assessed and compared using C-statistic and non-parametric methods. Calibration was assessed via the Hosmer and Lemeshow statistics, before and after recalibration through simple intercept adjustment. Results: 737 participants (27% male), aged 52.2 years, were included, among who 130 (17.6%) had prevalent undiagnosed diabetes. The highest C-statistic was recorded with the San Antonio Heart Study (SAHS) model [C-statistic 0.925 (95% confidence: 0.895–0.954)] and the lowest with the Framingham model [0.557 (0.528–0.626)]; with always significant statistical differences when SAHS was compared with the other models (best fit-MESA Framingham, best-fit MESA San Antonio, Cambridge and KORA risk predictive models). The intercept adjustment improved calibration across all models, most significantly in the SAHS. Conclusion: The wide range of performances of different models in our sample highlights the challenges of selecting an appropriate model for diabetes risk prediction in different settings.