New Insights into the Genetic Etiology of Alzheimer’s Disease and Related Dementias

Characterization of the genetic landscape of Alzheimer’s disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/‘proxy’ AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele

The importance of engaging in physical activity in older adulthood for transitions between cognitive status categories and death: A coordinated analysis of 14 longitudinal studies

Background: Given increasing incidence of cognitive impairment and dementia, further understanding of modifiable factors contributing to increased healthspan is crucial. Extensive literature provides evidence that physical activity (PA) delays the onset of cognitive impairment; however, it is unclear whether engaging in PA in older adulthood is sufficient to influence progression through cognitive status categories. Method: Applying a coordinated analysis approach, this project independently analyzed 14 longitudinal studies (NTotal = 52 039; mean baseline age across studies = 69.9-81.73) from North America and Europe using multistate survival models to estimate the impact of engaging in PA on cognitive status transitions (nonimpaired, mildly impaired, severely impaired) and death. Multinomial regression models were fit to estimate life expectancy (LE) based on American PA recommendations. Meta-analyses provided the pooled effect sizes for the role of PA on each transition and estimated LEs. Results: Controlling for baseline age, sex, education, and chronic conditions, analyses revealed that more PA is significantly associated with decreased risk of transitioning from nonimpaired to mildly impaired cognitive functioning and death, as well as substantially longer LE. Results also provided evidence for a protective effect of PA after onset of cognitive impairment (eg, decreased risk of transitioning from mild-to-severe cognitive impairment; increased likelihood of transitioning backward from severe-to-mild cognitive impairment), though between-study heterogeneity suggests a less robust association. Conclusions: These results yield evidence for the importance of engaging in PA in older adulthood for cognitive health, and a rationale for motivating older adults to engage consistently in PA

Developmental expression of non-coding RNAs in Chlamydia trachomatis during normal and persistent growth

Chlamydia trachomatis is an obligate intracellular bacterium that exhibits a unique biphasic developmental cycle that can be disrupted by growth in the presence of IFN-γ and β-lactams, giving rise to an abnormal growth state termed persistence. Here we have examined the expression of a family of non-coding RNAs (ncRNAs) that are differentially expressed during the developmental cycle and the induction of persistence and reactivation. ncRNAs were initially identified using an intergenic tiling microarray and were confirmed by northern blotting. ncRNAs were mapped, characterized and compared with the previously described chlamydial ncRNAs. The 5′- and 3′-ends of the ncRNAs were determined using an RNA circularization procedure. Promoter predictions indicated that all ncRNAs were expressed from σ66 promoters and eight ncRNAs contained non-templated 3′-poly-A or poly-AG additions. Expression of ncRNAs was studied by northern blotting during (i) the normal developmental cycle, (ii) IFN-γ-induced persistence and (iii) carbenicillin-induced persistence. Differential temporal expression during the developmental cycle was seen for all ncRNAs and distinct differences in expression were seen during IFN-γ and carbenicillin-induced persistence and reactivation. A heterologous co-expression system was used to demonstrate that one of the identified ncRNAs regulated the expression of FtsI by inducing degradation of ftsI mRNA

Arterial hypertension and β-amyloid accumulation have spatially overlapping effects on posterior white matter hyperintensity volume: a cross-sectional study

Background: White matter hyperintensities (WMH) in subjects across the Alzheimer’s disease (AD) spectrum with minimal vascular pathology suggests that amyloid pathology—not just arterial hypertension—impacts WMH, which in turn adversely influences cognition. Here we seek to determine the effect of both hypertension and Aβ positivity on WMH, and their impact on cognition. Methods: We analysed data from subjects with a low vascular profile and normal cognition (NC), subjective cognitive decline (SCD), and amnestic mild cognitive impairment (MCI) enrolled in the ongoing observational multicentre DZNE Longitudinal Cognitive Impairment and Dementia Study (n = 375, median age 70.0 [IQR 66.0, 74.4] years; 178 female; NC/SCD/MCI 127/162/86). All subjects underwent a rich neuropsychological assessment. We focused on baseline memory and executive function—derived from multiple neuropsychological tests using confirmatory factor analysis—, baseline preclinical Alzheimer’s cognitive composite 5 (PACC5) scores, and changes in PACC5 scores over the course of three years (ΔPACC5). Results: Subjects with hypertension or Aβ positivity presented the largest WMH volumes (pFDR < 0.05), with spatial overlap in the frontal (hypertension: 0.42 ± 0.17; Aβ: 0.46 ± 0.18), occipital (hypertension: 0.50 ± 0.16; Aβ: 0.50 ± 0.16), parietal lobes (hypertension: 0.57 ± 0.18; Aβ: 0.56 ± 0.20), corona radiata (hypertension: 0.45 ± 0.17; Aβ: 0.40 ± 0.13), optic radiation (hypertension: 0.39 ± 0.18; Aβ: 0.74 ± 0.19), and splenium of the corpus callosum (hypertension: 0.36 ± 0.12; Aβ: 0.28 ± 0.12). Elevated global and regional WMH volumes coincided with worse cognitive performance at baseline and over 3 years (pFDR < 0.05). Aβ positivity was negatively associated with cognitive performance (direct effect—memory: − 0.33 ± 0.08, pFDR < 0.001; executive: − 0.21 ± 0.08, pFDR < 0.001; PACC5: − 0.29 ± 0.09, pFDR = 0.006; ΔPACC5: − 0.34 ± 0.04, pFDR < 0.05). Splenial WMH mediated the relationship between hypertension and cognitive performance (indirect-only effect—memory: − 0.05 ± 0.02, pFDR = 0.029; executive: − 0.04 ± 0.02, pFDR = 0.067; PACC5: − 0.05 ± 0.02, pFDR = 0.030; ΔPACC5: − 0.09 ± 0.03, pFDR = 0.043) and WMH in the optic radiation partially mediated that between Aβ positivity and memory (indirect effect—memory: − 0.05 ± 0.02, pFDR = 0.029). Conclusions: Posterior white matter is susceptible to hypertension and Aβ accumulation. Posterior WMH mediate the association between these pathologies and cognitive dysfunction, making them a promising target to tackle the downstream damage related to the potentially interacting and potentiating effects of the two pathologies. Trial registration: German Clinical Trials Register (DRKS00007966, 04/05/2015)

Micrometeorological methods for greenhouse gas measurement

Micrometeorological techniques are useful if greenhouse gas (GHG) emissions from larger areas (i.e. entire fields) should be integrated. The theory and the various techniques such as flux-gradient, aerodynamic, and Bowen ratio as well as Eddy correlationmethods are described and discussed. Alternativemethods also used areEddy correlation, mass balance techniques, and tracer-based methods.The analytical techniques with current state-of-the-art approaches as well as the calculation procedures are presented

Greenhouse gases from agriculture

The rapidly changing global climate due to increased emission of anthropogenic greenhouse gases (GHGs) is leading to an increased occurrence of extreme weather events such as droughts, floods, and heatwaves. The three major GHGs are carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). The major natural sources of CO2 include ocean-atmosphere exchange, respiration of animals, soils (microbial respiration) and plants, and volcanic eruption; while the anthropogenic sources include burning of fossil fuel (coal, natural gas, and oil), deforestation, and the cultivation of land that increases the decomposition of soil organic matter and crop and animal residues. Natural sources of CH4 emission include wetlands, termite activities, and oceans. Paddy fields used for rice production, livestock production systems (enteric emission from ruminants), landfills, and the production and use of fossil fuels are the main anthropogenic sources of CH4. Nitrous oxide, in addition to being a major GHG, is also an ozone-depleting gas. N2O is emitted by natural processes from oceans and terrestrial ecosystems. Anthropogenic N2O emissions occur mostly through agricultural and other land-use activities and are associated with the intensification of agricultural and other human activities such as increased use of synthetic fertiliser (119.4 million tonnes of N worldwide in 2019), inefficient use of irrigation water, deposition of animal excreta (urine and dung) from grazing animals, excessive and inefficient application of farm effluents and animal manure to croplands and pastures, and management practices that enhance soil organic N mineralisation and C decomposition. Agriculture could act as a source and a sink of GHGs. Besides direct sources, GHGs also come from various indirect sources, including upstream and downstream emissions in agricultural systems and ammonia (NH3) deposition from fertiliser and animal manure

Lifetime and current depression in the German National Cohort (NAKO)

OBJECTIVES: The present study introduces the assessment of depression and depressive symptoms in the German National Cohort (NAKO), a population-based mega cohort. Distribution of core measures, and associations with sociodemographic factors are examined. METHODS: The current analysis includes data from the first 101,667 participants (NAKO data freeze 100,000). Depression and depressive symptoms were assessed using a modified version of the depression section of the Mini-International Neuropsychiatric Interview (MINI), self-reported physician's diagnosis of depression, and the depression scale of the Patient Health Questionnaire (PHQ-9). RESULTS: A lifetime physician's diagnosis of depression was reported by 15.0% of participants. Of those, 47.6% reported having received treatment for depression within the last 12 months. Of the subset of 26,342 participants undergoing the full depression section of the modified MINI, 15.9% were classified by the MINI with a lifetime depressive episode. Based on the PHQ-9, 5.8% of the participants were classified as currently having a major or other depression by the diagnostic algorithm, and 7.8% according to the dimensional assessment (score = 10). Increased frequency of depression measures and higher depression scores were observed in women and participants with lower education level or a family history of depression. CONCLUSIONS: The observed distributions of all depression measures and their associations with sociodemographic variables are consistent with the literature on depression. The NAKO represents a valuable epidemiologic resource to investigate depression, and the range of measures for lifetime and current depression allows users to select the most suitable instrument for their specific research question

Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution;(B) incorporating demographics & clinical covariates;(C) the impact of the size of the training data set;(D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of sample predictions. The highest performance for A 42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status

Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution;(B) incorporating demographics & clinical covariates;(C) the impact of the size of the training data set;(D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of sample predictions. The highest performance for A 42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status

Dietary patterns are related to cognitive functioning in elderly enriched with individuals at increased risk for Alzheimer's disease

PURPOSE: To investigate cross-sectional associations between dietary patterns and cognitive functioning in elderly free of dementia. METHODS: Data of 389 participants from the German DELCODE study (52% female, 69 ± 6 years, mean Mini Mental State Score 29 ± 1) were included. The sample was enriched with elderly at increased risk for Alzheimer's disease (AD) by including participants with subjective cognitive decline, mild cognitive impairment (MCI) and siblings of AD patients. Mediterranean and MIND diets were derived from 148 Food Frequency Questionnaire items, and data-driven patterns by principal component analysis (PCA) of 39 food groups. Associations between dietary patterns and five cognitive domain scores were analyzed with linear regression analyses adjusted for demographics (model 1), and additionally for energy intake, BMI, other lifestyle variables and APOe4-status (model 2). For PCA-derived dietary components, final model 3 included all other dietary components. RESULTS: In fully adjusted models, adherence to Mediterranean and MIND diet was associated with better memory. The 'alcoholic beverages' PCA component was positively associated with most cognitive domains. Exclusion of MCI subjects (n = 60) revealed that Mediterranean and MIND diet were also related to language functions; associations with the alcoholic beverages component were attenuated, but most remained significant. CONCLUSION: In line with data from elderly population samples, Mediterranean and MIND diet and some data-derived dietary patterns were related to memory and language function. Longitudinal data are needed to draw conclusions on the putative effect of nutrition on the rate of cognitive decline, and on the potential of dietary interventions in groups at increased risk for AD