School and Community Screening Shows Malawi, Africa, to Have a High Prevalence of Latent Rheumatic Heart Disease

Rheumatic heart disease (RHD) is the largest cardiac cause of morbidity and mortality in the world's youth. Early detection of RHD through echocardiographic screening in asymptomatic children may identify an early stage of disease, when secondary prophylaxis has the greatest chance of stopping disease progression. Latent RHD signifies echocardiographic evidence of RHD with no known history of acute rheumatic fever and no clinical symptoms.

OBJECTIVE: Determine the prevalence of latent RHD among children ages 5-16 in Lilongwe, Malawi.

DESIGN: This is a cross-sectional study in which children ages 5 through 16 were screened for RHD using echocardiography.

SETTING: Screening was conducted in 3 schools and surrounding communities in the Lilongwe district of Malawi between February and April 2014.

OUTCOME MEASURES: Children were diagnosed as having no, borderline, or definite RHD as defined by World Heart Federation criteria. The primary reader completed offline reads of all studies. A second reader reviewed all of the studies diagnosed as RHD, plus a selection of normal studies. A third reader served as tiebreaker for discordant diagnoses. The distribution of results was compared between gender, location, and age categories using Fisher's exact test.

RESULTS: The prevalence of latent RHD was 3.4% (95% CI = 2.45, 4.31), with 0.7% definite RHD and 2.7% borderline RHD. There was no significant differences in prevalence between gender (P = .44), site (P = .6), urban vs. peri-urban (P = .75), or age (P = .79). Of those with definite RHD, all were diagnosed because of pathologic mitral regurgitation (MR) and 2 morphologic features of the mitral valve. Of those with borderline RHD, most met the criteria by having pathological MR (92.3%).

CONCLUSION: Malawi has a high rate of latent RHD, which is consistent with other results from sub-Saharan Africa. This study strongly supports the need for a RHD prevention and control program in Malawi.

Common polygenic variation enhances risk prediction for Alzheimer's disease.

The identification of subjects at high risk for Alzheimer’s disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer’s disease and the accuracy of Alzheimer’s disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer’s Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer’s disease (P = 4.9 × 10−26). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10−19). The best prediction accuracy AUC = 78.2% (95% confidence interval 77–80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer’s disease has a significant polygenic component, which has predictive utility for Alzheimer’s disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes.

Alleles that increase risk for type 2 diabetes mellitus are not associated with increased risk for Alzheimer's disease

Although epidemiological studies suggest that type 2 diabetes mellitus (T2DM) increases the risk of late-onset Alzheimer's disease (LOAD), the biological basis of this relationship is not well understood. The aim of this study was to examine the genetic comorbidity between the 2 disorders and to investigate whether genetic liability to T2DM, estimated by a genotype risk scores based on T2DM associated loci, is associated with increased risk of LOAD. This study was performed in 2 stages. In stage 1, we combined genotypes for the top 15 T2DM-associated polymorphisms drawn from approximately 3000 individuals (1349 cases and 1351 control subjects) with extracted and/or imputed data from 6 genome-wide studies (>10,000 individuals; 4507 cases, 2183 controls, 4989 population controls) to form a genotype risk score and examined if this was associated with increased LOAD risk in a combined meta-analysis. In stage 2, we investigated the association of LOAD with an expanded T2DM score made of 45 well-established variants drawn from the 6 genome-wide studies. Results were combined in a meta-analysis. Both stage 1 and stage 2 T2DM risk scores were not associated with LOAD risk (odds ratio = 0.988; 95% confidence interval, 0.972-1.004; p = 0.144 and odds ratio = 0.993; 95% confidence interval, 0.983-1.003; p = 0.149 per allele, respectively). Contrary to expectation, genotype risk scores based on established T2DM candidates were not associated with increased risk of LOAD. The observed epidemiological associations between T2DM and LOAD could therefore be a consequence of secondary disease processes, pleiotropic mechanisms, and/or common environmental risk factors. Future work should focus on well-characterized longitudinal cohorts with extensive phenotypic and genetic data relevant to both LOAD and T2DM.