Background

We previously have shown that chronic kidney disease (CKD) is associated with cardiovascular and all-cause mortality in community-dwelling people 75 years and older. The present study addresses the hypothesis that CKD is associated with a higher rate of hospital admission at an older age.

Study Design

Cohort study.

Setting & Participants

15,336 participants from 53 UK general practices underwent comprehensive health assessment between 1994 and 1999.

Predictor

Data for estimated glomerular filtration rate (eGFR, derived from creatinine levels using the CKD Epidemiology Collaboration [CKD-EPI] study equation) and dipstick proteinuria were available for 12,371 participants.

Outcomes

Hospital admissions collected from hospital discharge letters for 2 years after assessment.

Measurements

Age, sex, cardiovascular risk factors, possible biochemical and health consequences of kidney disease (hemoglobin, phosphate, and albumin levels; physical and mental health problems).

Results

2,310 (17%) participants had 1 hospital admission, and 981 (7%) had 2 or more. After adjusting for age, sex, and cardiovascular risk factors, HRs were 1.66 (95% CI, 1.21-2.27), 1.17 (95% CI, 0.95-1.43), 1.08 (95% CI, 0.90-1.30), and 1.11 (95% CI, 0.91-1.35) for eGFRs <30, 30-44, 45-59, and ≥75 mL/min/1.73 m2, respectively, compared with eGFRs of 60-74 mL/min/1.73 m2 for hospitalizations during <6 months of follow-up. HRs were weaker for follow-up of 6-18 months. Dipstick-positive proteinuria was associated with an increased HR throughout follow-up (HR, 1.29 [95% CI, 1.11-1.49], adjusting for cardiovascular risk factors). Dipstick-positive proteinuria and eGFR <30 mL/min/1.73 m2 were independently associated with 2 or more hospital admissions during the 2-year follow-up. Adjustment for other health factors and laboratory measurements attenuated the effect of eGFR, but not the effect of proteinuria.

Limitations

Follow-up limited to 2 years, selection bias due to nonparticipation in study, missing data for potential covariates, and single noncalibrated measurements from multiple laboratories.

Conclusions

The study indicates that community-dwelling older people who have dipstick-positive proteinuria and/or eGFR <30 mL/min/1.73 m2 are at increased risk of hospitalization.

Objective

We investigated whether previously reported single nucleotide polymorphisms (SNPs) of EPHA2 in European studies are associated with cataract in India.

Methods

We carried out a population-based genetic association study. We enumerated randomly sampled villages in two areas of north and south India to identify people aged 40 and over. Participants attended a clinical examination including lens photography and provided a blood sample for genotyping. Lens images were graded by the Lens Opacification Classification System (LOCS III). Cataract was defined as a LOCS III grade of nuclear ≥4, cortical ≥3, posterior sub-capsular (PSC) ≥2, or dense opacities or aphakia/pseudophakia in either eye. We genotyped SNPs rs3754334, rs7543472 and rs11260867 on genomic DNA extracted from peripheral blood leukocytes using TaqMan assays in an ABI 7900 real-time PCR. We used logistic regression with robust standard errors to examine the association between cataract and the EPHA2 SNPs, adjusting for age, sex and location.

Results

7418 participants had data on at least one of the SNPs investigated. Genotype frequencies of controls were in Hardy-Weinberg Equilibrium (p>0.05). There was no association of rs3754334 with cataract or type of cataract. Minor allele homozygous genotypes of rs7543472 and rs11260867 compared to the major homozygote genotype were associated with cortical cataract, Odds ratio (OR) = 1.8, 95% Confidence Interval (CI) (1.1, 3.1) p = 0.03 and 2.9 (1.2, 7.1) p = 0.01 respectively, and with PSC cataract, OR = 1.5 (1.1, 2.2) p = 0.02 and 1.8 (0.9, 3.6) p = 0.07 respectively. There was no consistent association of SNPs with nuclear cataract or a combined variable of any type of cataract including operated cataract.

Conclusions

Our results in the Indian population agree with previous studies of the association of EPHA2 variants with cortical cataracts. We report new findings for the association with PSC which is particularly prevalent in Indians.

Based on published data, this ecological correlation study showed evidence to support the hypothesis that variation in the risk allele frequency of the Y402H polymorphism across ethnicities explains variation in prevalence of late AMD when data on people of African ancestry are excluded.

Purpose.

To investigate whether variation in the distribution of the risk allele frequency of the Y402H single-nucleotide polymorphism (SNP) across various ethnicities and geographic regions reflects differences in the prevalence of late age-related macular degeneration (AMD) in those ethnicities.

Methods.

Published data were obtained via a systematic search. Study samples were grouped into clusters by ethnicity and geographic location and the Spearman correlation coefficient of the prevalence of late AMD and risk allele frequencies was calculated across clusters.

Results.

Across all ethnicities, AMD prevalence was seen to increase with age. Populations of European descent had both higher risk allele frequencies and prevalence of late AMD than did Japanese, Chinese, and Hispanic descendants. Results for African descendants were anomalous: although allele frequency was similar to that in European populations, the age-specific prevalence of late AMD was considerably lower. The correlation coefficient for the association between allele frequency and AMD prevalence was 0.40 (95% confidence interval [CI] = −0.36 to 0.84, P = 0.28) in all populations combined and 0.71 (95% CI = 0.02–0.94, P = 0.04) when people of African descent were excluded.

Conclusions.

Evidence was found at the population level to support a positive association between the Y204H risk allele and the prevalence of AMD after exclusion of studies undertaken on persons of African ancestry. Data in African, Middle Eastern, and South American populations are needed to provide a better understanding of the association of late AMD genetic risk across ethnicities.

Background

Rapid and cost-effective methods for HIV-1 diagnosis and viral load monitoring would greatly enhance the clinical management of HIV-1 infected adults and children in limited-resource settings. Recent recommendations to treat perinatally infected infants within the first year of life are feasible only if early diagnosis is routinely available. Dried blood spots (DBS) on filter paper are an easy and convenient way to collect and transport blood samples. A rapid and cost effective method to diagnose and quantify HIV-1 from DBS is urgently needed to facilitate early diagnosis of HIV-1 infection and monitoring of antiretroviral therapy.

Methods and Findings

We have developed a real-time LightCycler (rtLC) PCR assay to detect and quantify HIV-1 from DBS. HIV-1 RNA extracted from DBS was amplified in a one-step, single-tube system using primers specific for long-terminal repeat sequences that are conserved across all HIV-1 clades. SYBR Green dye was used to quantify PCR amplicons and HIV-1 RNA copy numbers were determined from a standard curve generated using serially diluted known copies of HIV-1 RNA. This assay detected samples across clades, has a dynamic range of 5 log10, and %CV <8% up to 4 log10 dilution. Plasma HIV-1 RNA copy numbers obtained using this method correlated well with the Roche Ultrasensitive (r = 0.91) and branched DNA (r = 0.89) assays. The lower limit of detection (95%) was estimated to be 136 copies. The rtLC DBS assay was 2.5 fold rapid as well as 40-fold cheaper when compared to commercial assays. Adaptation of the assay into other real-time systems demonstrated similar performance.

Conclusions

The accuracy, reliability, genotype inclusivity and affordability, along with the small volumes of blood required for the assay suggest that the rtLC DBS assay will be useful for early diagnosis and monitoring of pediatric HIV-1 infection in resource-limited settings.

Background

Population-based investigations aimed at uncovering genotype-trait associations often involve high-dimensional genetic polymorphism data as well as information on multiple environmental and clinical parameters. Machine learning (ML) algorithms offer a straightforward analytic approach for selecting subsets of these inputs that are most predictive of a pre-defined trait. The performance of these algorithms, however, in the presence of covariates is not well characterized.

Methods and Results

In this manuscript, we investigate two approaches: Random Forests (RFs) and Multivariate Adaptive Regression Splines (MARS). Through multiple simulation studies, the performance under several underlying models is evaluated. An application to a cohort of HIV-1 infected individuals receiving anti-retroviral therapies is also provided.

Conclusion

Consistent with more traditional regression modeling theory, our findings highlight the importance of considering the nature of underlying gene-covariate-trait relationships before applying ML algorithms, particularly when there is potential confounding or effect mediation.