Systematic review estimating the burden of dementia in the WHO Southeast Asia Region using Bayesian and frequentist approaches.

BACKGROUND: Rapid increase in life expectancy in low- and middle-income countries including the World Health Organization's Southeast Asia Region (SEAR) has resulted in an increase in the global burden of dementia, which is expected to become a leading cause of morbidity. Accurate burden estimates are key for informing policy and planning. Given the paucity of data, estimates were developed using both a Bayesian methodology and as well as a traditional frequentist approach to gain better insights into methodological approaches for disease burden estimates. METHODS: Seven databases were searched for studies published between 2010-2018 regarding dementia prevalence in SEAR, generating 8 relevant articles. A random-effects model (REM) and a Bayesian normal-normal hierarchical model (NNHM) were used to obtain the pooled prevalence estimate of dementia for people aged 60 and above in SEAR. The latter model was also developed to estimate age-specific dementia prevalence. Using UN population estimates for SEAR, total and age-specific projections of the burden of dementia in 2015, 2020 and 2030 were calculated. RESULTS: The prevalence of dementia in SEAR was found to be 3% (95% confidence interval (CI) = 2-6%) in those above age 60 based on REM, and 3.1% (95% credible interval = 1.5-5.0%) based on the NNHM. The estimated prevalence varies with age, increasing from 1.6% (95% credible interval = 0.8-2.5%) in people aged 60-69 to 12.4% (95% credible interval = 5.6-20%) in people above the age of 80. The risk of developing dementia increased exponentially with age. The number of people living with dementia in SEAR in 2015 was estimated at 5.51 million (95% credible interval = 2.66-8.82), with projections of 6.66 million (95% credible interval = 3.21-10.7) in 2020 and 9.6 million (95% credible interval = 4.62-15.36) in 2030. CONCLUSION: The burden of dementia in SEAR is substantial and will continue to increase rapidly by 2030. The lack of research focusing on dementia in SEAR points to a significant under-recognition of this disease. The projected rise in dementia cases in the future should prompt urgent governmental response to address this growing public health issue. We also argue that given the overall paucity of data for the region, the Bayesian approach offers a promising methodology for improved estimates of disease prevalence and burden and should continue to be explored.

Applying evolutionary genetics to developmental toxicology and risk assessment.

Evolutionary thinking continues to challenge our views on health and disease. Yet, there is a communication gap between evolutionary biologists and toxicologists in recognizing the connections among developmental pathways, high-throughput screening, and birth defects in humans. To increase our capability in identifying potential developmental toxicants in humans, we propose to apply evolutionary genetics to improve the experimental design and data interpretation with various in vitro and whole-organism models. We review five molecular systems of stress response and update 18 consensual cell-cell signaling pathways that are the hallmark for early development, organogenesis, and differentiation; and revisit the principles of teratology in light of recent advances in high-throughput screening, big data techniques, and systems toxicology. Multiscale systems modeling plays an integral role in the evolutionary approach to cross-species extrapolation. Phylogenetic analysis and comparative bioinformatics are both valuable tools in identifying and validating the molecular initiating events that account for adverse developmental outcomes in humans. The discordance of susceptibility between test species and humans (ontogeny) reflects their differences in evolutionary history (phylogeny). This synthesis not only can lead to novel applications in developmental toxicity and risk assessment, but also can pave the way for applying an evo-devo perspective to the study of developmental origins of health and disease.

Plant community change mediates the response of foliar δ(15)N to CO 2 enrichment in mesic grasslands.

Rising atmospheric CO2 concentration may change the isotopic signature of plant N by altering plant and microbial processes involved in the N cycle. CO2 may increase leaf δ(15)N by increasing plant community productivity, C input to soil, and, ultimately, microbial mineralization of old, (15)N-enriched organic matter. We predicted that CO2 would increase aboveground productivity (ANPP; g biomass m(-2)) and foliar δ(15)N values of two grassland communities in Texas, USA: (1) a pasture dominated by a C4 exotic grass, and (2) assemblages of tallgrass prairie species, the latter grown on clay, sandy loam, and silty clay soils. Grasslands were exposed in separate experiments to a pre-industrial to elevated CO2 gradient for 4 years. CO2 stimulated ANPP of pasture and of prairie assemblages on each of the three soils, but increased leaf δ(15)N only for prairie plants on a silty clay. δ(15)N increased linearly as mineral-associated soil C declined on the silty clay. Mineral-associated C declined as ANPP increased. Structural equation modeling indicted that CO2 increased ANPP partly by favoring a tallgrass (Sorghastrum nutans) over a mid-grass species (Bouteloua curtipendula). CO2 may have increased foliar δ(15)N on the silty clay by reducing fractionation during N uptake and assimilation. However, we interpret the soil-specific, δ(15)N-CO2 response as resulting from increased ANPP that stimulated mineralization from recalcitrant organic matter. By contrast, CO2 favored a forb species (Solanum dimidiatum) with higher δ(15)N than the dominant grass (Bothriochloa ischaemum) in pasture. CO2 enrichment changed grassland δ(15)N by shifting species relative abundances.

Fungal Community Responses to Past and Future Atmospheric CO2 Differ by Soil Type.

Soils sequester and release substantial atmospheric carbon, but the contribution of fungal communities to soil carbon balance under rising CO2 is not well understood. Soil properties likely mediate these fungal responses but are rarely explored in CO2 experiments. We studied soil fungal communities in a grassland ecosystem exposed to a preindustrial-to-future CO2 gradient (250 to 500 ppm) in a black clay soil and a sandy loam soil. Sanger sequencing and pyrosequencing of the rRNA gene cluster revealed that fungal community composition and its response to CO2 differed significantly between soils. Fungal species richness and relative abundance of Chytridiomycota (chytrids) increased linearly with CO2 in the black clay (P < 0.04, R(2) > 0.7), whereas the relative abundance of Glomeromycota (arbuscular mycorrhizal fungi) increased linearly with elevated CO2 in the sandy loam (P = 0.02, R(2) = 0.63). Across both soils, decomposition rate was positively correlated with chytrid relative abundance (r = 0.57) and, in the black clay soil, fungal species richness. Decomposition rate was more strongly correlated with microbial biomass (r = 0.88) than with fungal variables. Increased labile carbon availability with elevated CO2 may explain the greater fungal species richness and Chytridiomycota abundance in the black clay soil, whereas increased phosphorus limitation may explain the increase in Glomeromycota at elevated CO2 in the sandy loam. Our results demonstrate that soil type plays a key role in soil fungal responses to rising atmospheric CO2.