LOAD risk loci may also contribute to variation in age of onset (AAO) of LOAD, as do the allelic variants in APOE, however roles in AAO for the other newly identified risk loci (CLU, BIN1, and others) have not been explored. We examined variants at ten confirmed LOAD risk loci (APOE, CLU, PICALM, CR1, BIN1, CD2AP, EPHA1, the MS4A region, ABCA7, and CD33) to determine if they contribute to variation in AAO among 9,160 LOAD cases in the Alzheimer's disease Genetics Consortium (ADGC). We tested association with AAO for each locus using linear modeling with covariate adjustment for population substructure and performed a random-effects meta-analysis across datasets. We also examined genetic burden using genotype scores weighted by risk effect sizes to examine the aggregate contribution of these loci to variation in AAO. Analyses confirmed association of APOE regional variation with AAO (rs6857, P =3.30×10 -96), with statistically significant associations with AAO (P<0.005) demonstrated at several other LOAD risk loci, including rs6701713 in CR1 (P =0.00717), rs7561528 in BIN1 (P =0.00478), rs561655 in PICALM (P =0.00223). Associations remained largely unchanged after additional adjustment for dosage of APOE ε4 alleles. Burden analyses showed APOE contributes to 3.1% of variation in AAO (R 2 =0.078) whereas the other nine genes contribute to 1.1% of variation (R 2 =0.058) over baseline (R 2 =0.047). Secondary analyses of genome-wide association with AAO among non-risk loci identified several regions with multiple SNPs demonstrating suggestive associations (P<10 -6), including one nearing genome-wide statistical significance: MYO16 (47 SNPs; most significant: rs9521011, P =7.62×10 -8), CDH20 (4 SNPs; most significant: rs12956834, P =6.17×10 -6), and SGCZ (10 SNPs; most significant: rs7016159, P =7.70×10 -6). We confirmed the association of APOE variants with AAO among LOAD cases, and observe associations with AAO in CR1, BIN1, and PICALM. In contrast to earlier hypothetical modeling, we show that the combined effects of other loci do not exceed the effect of APOE on AAO, and if additional genetic contributions to AAO exist, they are likely very small individually or are hidden in gene-gene interactions.
While studies of late-onset Alzheimer Disease (LOAD) have found multiple loci (APOE, CLU, PICALM, CR1, and BIN1) contributing to risk, genetic contributors to onset age of LOAD have not been widely studied, with variation at only a few loci (e.g., APOE, GSTO1) known to contribute. We examined the association of genomic variation with age-at-onset (AAO) in 9,160 LOAD cases from the Alzheimer's Disease Genetics Consortium (ADGC). We examined association with AAO for 2,324,889 genotyped and imputed SNPs among 9,160 LOAD cases from 14 datasets of the ADGC. AAO was modeled linearly using generalized estimating equations (GEE) with adjustment for population substructure and cohort effects. Extended models adjusted for sex and dosage of the APOE e4 allele (0, 1, or 2 copies) as covariates. We also examined the AAO effect by comparing differences in genotype frequency between the youngest (lowest quartile, N = 2,450) and oldest (highest quartile, N = 2,757) cases using GEE with a logistic model. Primary AAO analyses using all 9,160 cases confirmed association of APOE region variation with AAO (APOC1, P = 4.72×10−100). After adjustment for sex and dosage of the APOE e4 allele, with which associations in APOE faded as expected, the AD candidate gene VDR demonstrated association with AAO near genome-wide significance (P = 6.18×10−7), as did several other regions: the 8q24.11 region including RAD21, P = 2.38×10−6; 6q16.1, P = 6.17×10−6; CIB4, P = 7.70×10−6; ABCC4, P = 9.25×10−6. Dichotomized analyses of youngest/oldest case quartiles demonstrated differences in AAO association among loci near APOE (e.g., APOC1, P = 3.17×10−69), with more modest associations (P < 10−5) at other loci (MPP2/MPP3/BCAM/DBF4B in 17q21.31 region, P = 2.67×10−7; CHGB, P = 5.54×10−6; 10p12.33, P = 6.61×10−6). Adjusting for sex and APOE e4 dosage, notable signals included chromosome 17p12, P = 6.42×10−7; MGC34034, P = 6.51×10−7; OR8A1, P = 1.08×10−6; RAD21, P = 3.00×10−6; MPP2/MPP3, P = 4.02×10−6; 10p12.33 region, P = 5.00×10−6; DCC, P = 8.51×10−6). In AAO analyses among LOAD cases, we confirmed associations of the variation near 19q13.32 (APOE region) with genome-wide statistical significance and detected strong associations near the AD candidate gene VDR, after adjustment for sex and APOE e4 dosage. Another locus with associations in the main AAO and dichotomized analyses, RAD21, disrupts chromatin loop structure and may be involved in causing significant changes in apolipoprotein expression (Mishiro et al (2009)).
Background Nutrition-sensitive interventions supporting enhanced household food production have potential to improve child dietary quality. However, heterogeneity in market access may cause systematic differences in program effectiveness depending on household wealth and child age. Identifying these effect modifiers can help development agencies specify and target their interventions. Objective This study investigates mediating effects of household wealth and child age on links between farm production and child diets, as measured by production and intake of nutrient-dense food groups. Methods Two rounds (2013 and 2014) of nationally representative survey data (n = 5,978 observations) were used to measure production and children's dietary intake, as well as a household wealth index and control variables, including breastfeeding. Novel steps used include measuring production diversity in terms of both species grown and food groups grown, as well as testing for mediating effects of family wealth and age of child. Results We find significant associations between child dietary diversity and agricultural diversity in terms of diversity of food groups and of species grown, especially for older children in poorer households, and particularly for fruits and vegetables, dairy and eggs. With each additional food group produced, log-odds of meeting minimum dietary diversity score (≥4) increase by 0.25 (p = 0.01) for children aged 24–59 months. For younger children aged 18–23 months there is a similar effect size but only in the poorest two quintiles of household wealth, and for infants 6–18 months we find no correlation between production and intake in most models. Conclusions Child dietary intake is associated with the composition of farm production, most evident among older preschool children and in poorer households. To improve the nutrition of infants, other interventions are needed; and for relatively wealthier households, own farm production may displace market purchases, which could attenuate the impact of household production on child diets.
"Leaving no one behind" is at the heart of the agenda of the Sustainable Development Goals, requiring that health systems be vigilant to how interventions can be accessed equitably by all, including population subgroups that face exclusion. In the World Health Organization (WHO) South-East Asia Region, inequalities can be found across and within countries but there has been a growing commitment to examining and starting to tackle them. Over the past decade in particular, WHO has been developing an armamentarium of tools to enable analysis of health inequalities and action on health equity. Tools include the Health Equity Assessment Toolkit in built-in database and upload database editions, as well as the Innov8 tool for reorientation of national health programmes. Countries across the region have engaged meaningfully in the development and application of these tools, in many cases aligning them with, or including them as part of, ongoing efforts to examine inequities in population subgroups domestically. This paper reflects on these experiences in Bangladesh, India, Indonesia, Nepal, Sri Lanka and Thailand, where efforts have ranged from workshops to programme reorientation; the creation of assemblies and conferences; and collation of evidence through collaborative research, reviews/synthesis and conferences. This promising start must be maintained and expanded, with greater emphasis on building capacity for interpretation and use of evidence on inequalities in policy-making. This may be further enhanced by the use of innovative mixed methodologies and interdisciplinary approaches to refine and contextualize evidence, with a concomitant shift in attention, developing solutions to redress inequities and anchor health reform within communities. There are many lessons to be learnt in this region, as well as mounting political and popular will for change.
Abstract Introduction African‐American (AA) individuals have a higher risk for late‐onset Alzheimer's disease (LOAD) than Americans of primarily European ancestry (EA). Recently, the largest genome‐wide association study in AAs to date confirmed that six of the Alzheimer's disease (AD)‐related genetic variants originally discovered in EA cohorts are also risk variants in AA; however, the risk attributable to many of the loci (e.g., APOE, ABCA7) differed substantially from previous studies in EA. There likely are risk variants of higher frequency in AAs that have not been discovered. Methods We performed a comprehensive analysis of genetically determined local and global ancestry in AAs with regard to LOAD status. Results Compared to controls, LOAD cases showed higher levels of African ancestry, both globally and at several LOAD relevant loci, which explained risk for AD beyond global differences. Discussion Exploratory post hoc analyses highlight regions with greatest differences in ancestry as potential candidate regions for future genetic analyses.
Late-onset Alzheimer's disease (LOAD) is a highly heritable neurological disease with several known genetic risk loci (APOE, CR1, etc). With the exception of APOE, these loci were identified using GWAS in large datasets and have small effects on risk (e.g., odds ratios ∼= 1.1). However, phenotypic heterogeneity may obscure the true effects of these and other loci. To investigate this concern we performed a GWAS between autopsy-confirmed cases (ACC) and controls and compared the results to a GWAS of clinically identified cases (CIC) and controls. Samples were derived from 12 cohorts that are part of the Alzheimer's Disease Genetics Consortium. Genotyping was performed on high-density genotyping chips and imputed to a 1000 Genome Project map. Logistic regression was performed within each cohort and METAL was used to meta-analyze across cohorts. Datasets consisted of 4,854 neuropathology confirmed cases and 8,264 controls (ACC set) and 5,190 clinically identified cases and 7,400 controls (CIC set). Both control sets included a mix of clinically identified and neuropathology confirmed controls. Most known LOAD loci were confirmed in both the ACC and CIC analyses with at least nominal association (P<0.05); the lone exception being EPHA1, whose effect trended in the reported direction, but did not reach statistical significant association in the autopsy dataset (P-value=0.20). There were no novel associations that met a genome-wide correction for multiple-testing in either analysis. There were however several loci with strong association (P<5x10–6) in the ACC analysis that were not associated in the CIC analysis. Notably, common variants in the amyloid precursor protein (APp) were associated with autopsy cases (rs4817090, OR=0.85, P = 2.4x10–6), but not with clinic-based cases (OR=0.98, P = 0.55). While it is known that rare variation in APp can lead to early-onset forms of AD, this study is the first to implicate APp in a large case-control study. It suggests common variation in APp may influence LOAD pathology in a way that a more heterogeneous clinic-based sample may not be adequately powered to detect, and confirms the utility of an autopsy-confirmed dataset. To further follow-up we are examining association with more detailed neuropathology features, and investigating the utility of autopsy-confirmed controls.
Increasing household income may increase the purchase of micronutrient-dense fruits, vegetables, and animal foods in low income countries, but evidence supporting this link is limited. Using data from 4,286 households (HH) assessed in a national survey of 21 Village Development Committees across 3 agro-ecological zones in Nepal (May-Jul 2013), we assessed associations between HH wealth (socioeconomic status quintiles) and expenditure on specific foods. Results were stratified by zone, sex of head of HH and maternal education, tested by the Wilcoxon rank sum test. Reported median (IQR) monthly food expenditure was USD$60 (34-104), with $17 (4-35) spent on staples; $10 (3-20) on meat; $8 (3-16) on fruits & vegetables; $5 (3-8) on oils; $3 (0-7) on legumes; $2 (0.7-4) on snacks; $0 (0-1) on eggs; $0 (0-5) on dairy; $4 (2-10) on alcohol, soda, juice, sugar, and tea, inclusive. In HHs above the highest vs lowest wealth quintiles, staples expenditure was higher in HHs headed by males (p=.0006), located in the mountains (p<.01), and with women with no (p<.01) or secondary education (p=.02). Expenditure on fruits & vegetables, meats, dairy, and eggs was higher in HHs above the highest vs below the lowest quintile, across each stratum of zone, sex of head of HH and education. While changes in staple purchases between wealth groups varied by zone and HH factors; fruits, vegetables, dairy, and meats were more likely to be purchased by HHs of greatest vs least wealth in all zones and by all factors, suggesting a need for ways to enable poorer HHs to access nutritious foods via market purchases. Supported by USAID through the Nutrition Innovation Lab (prime Tufts Univ) and a Borlaug Fellowship through Purdue University's Center for Global Food Security.
Rare mutations in the gene encoding for tau (MAPT, microtubule-associated protein tau) cause frontotemporal dementia-spectrum (FTD-s) disorders, including FTD, progressive supranuclear palsy (PSP) and corticobasal syndrome, and a common extended haplotype spanning across the MAPT locus is associated with increased risk of PSP and Parkinson's disease.We identified a rare tau variant (p.A152T) in a patient with a clinical diagnosis of PSP and assessed its frequency in multiple independent series of patients with neurodegenerative conditions and controls, in a total of 15 369 subjects.Tau p.A152T significantly increases the risk for both FTD-s (n 5 2139, OR 5 3.0, CI: 1.6 -5.6, P 5 0.0005) and Alzheimer's disease (AD) (n 5 3345, OR 5 2.3, CI: 1.3 -4.2, P 5 0.004) compared with 9047 controls.Functionally, p.A152T (i) decreases the binding of tau to microtubules and therefore promotes microtubule assembly less efficiently; and (ii) reduces the tendency to form abnormal fibers.However, there is a pronounced increase in the formation of tau oligomers.Importantly, these findings suggest that other regions of the tau protein may be crucial in regulating normal function, as the p.A152 residue is distal to the domains considered responsible for microtubule interactions or aggregation.These data provide both the first genetic evidence and functional studies supporting the role of MAPT p.A152T as a rare risk factor for both FTD-s and AD and the concept that rare variants can increase the risk for relatively common, complex neurodegenerative diseases, but since no clear significance threshold for rare genetic variation has been established, some caution is warranted until the findings are further replicated.
