Literature Review: Diagnosing Types of Dementia Using Biomarkers

Purpose This literature review attempts to determine the most effective biomarkers to diagnose the most common types of dementia. Methods Information from articles in various databases (PubMed, ProQuest, NIH, etc.) were compiled to form a list of biomarkers for the most common types of dementia. Results Biomarkers specific to Alzheimer’s disease (AD) include increased p-tau & t-tau, decreased Aβ42, increased GFAP, CHIT1, & YKL-40, and increased glutamic acid, hypoxanthine, & anthranilic acid. Frontotemporal dementia (FTD) biomarkers include decreased p-tau & t-tau, increased Aβ42/40, increased GFAP, CHIT1 & YKL-40, fastest change in NPTX2 & neurofilament light chain, and MRI anterior vs. posterior index. Biomarkers for vascular dementia (VaD) include decreased brevican & neurocan peptides (compared to AD) and increased MR-proANP & CT-proET-1. Finally, Lewy body dementia (LBD) biomarkers include decreased CSF ⍺-synuclein (compared to AD) and increased GFAP. Conclusion Understanding the specific biomarkers for each type of dementia is crucial in establishing an early and definitive diagnosis that can determine the appropriate course of treatment. Each of the biomarkers outlined in this literature review vary in their clinical applicability. Although some of them have already been incorporated into clinical practice alongside manifestations and symptoms characteristic to each type of dementia, other biomarkers (REPS1, etc.) still require further research and studies before they are put to use

Single QTL effects, epistasis, and pleiotropy account for two-thirds of the phenotypic F(2) variance of growth and obesity in DU6i x DBA/2 mice

Genes influencing body weight and composition and serum concentrations of leptin, insulin, and insulin-like growth factor I (IGF-I) in nonfasting animals were mapped in an intercross of the extreme high-growth mouse line DU6i and the inbred line DBA/2. Significant loci with major effects (F > 7.07) for body weight, obesity, and muscle weight were found on chromosomes 1, 4, 5, 7, 11, 12, 13, and 17, for leptin on chromosome 14, for insulin on chromosome 4, and for IGF-I on chromosome 10 at the Igf1 gene locus itself and on chromosome 18. Significant interaction between different quantitative trait loci (QTL) positions was observed (P < 0.01). Evidence was found that loci having small direct effect on growth or obesity contribute to the obese phenotype by gene–gene interaction. The effects of QTLs, epistasis, and pleiotropy account for 64% and 63% of the phenotypic variance of body weight and fat accumulation and for over 32% of muscle weight and serum concentrations of leptin, and IGF-I in the F2 population of DU6i x DBA/2 mice. [The quantitative trait loci described in this paper have been submitted to the Mouse Genome Database.

Rapid and robust association mapping of expression quantitative trait loci

We applied a simple and efficient two-step method to analyze a family-based association study of gene expression quantitative trait loci (eQTL) in a mixed model framework. This two-step method produces very similar results to the full mixed model method, with our method being significantly faster than the full model. Using the Genetic Analysis Workshop 15 (GAW15) Problem 1 data, we demonstrated the value of data filtering for reducing the number of tests and controlling the number of false positives. Specifically, we showed that removing non-expressed genes by filtering on expression variability effectively reduced the number of tests by nearly 50%. Furthermore, we demonstrated that filtering on genotype counts substantially reduced spurious detection. Finally, we restricted our analysis to the markers and transcripts that were closely located. We found five times more signals in close proximity (cis-) to transcripts than in our genome-wide analysis. Our results suggest that careful pre-filtering and partitioning of data are crucial for controlling false positives and allowing detection of genuine effects in genetic analysis of gene expression

Examination of Resistance Settings Based on Body Weight for the 3-Minute All-Out Critical Power Test

International Journal of Exercise Science 11(4): 585-597, 2018. There are conflicting suggestions regarding the most valid resistance (3-5% of body weight) to use for the critical power (CP) 3-min all-out (CP3min) test to estimate CP and anaerobic work capacity (AWC). The purpose of this study was to determine if the CP and AWC estimates from the CP3min test were affected by the percentage of body weight used to set the resistance on a Monark cycle ergometer. Ten recreationally trained participants (mean ± SD: Age: 22.2 ± 2.2 yrs.) completed the CP3min test at resistances of 4.5% (CP4.5%) and 3% (CP3%) of body weight to determine the CP and AWC. There were no significant differences between the CP4.5% (167 ± 34 W) and CP3% (156 ± 36 W) estimates. The AWC3% (5.6 ± 2.5 kJ) estimates were significantly lower than the AWC4.5% (9.0 ± 4.0 kJ).The CP and AWC estimates from the CP4.5% were consistent with values reported in the literature, however, the AWC estimate from the CP3% was lower than typically reported. These findings suggested that a resistance set at 3% of body weight for the CP3min test may be too low to accurately estimate AWC, but 3% and 4.5% resulted in the same estimation of CP. Thus, the principal finding of this study was that a resistance of 4.5% of body weight for CP3-min in recreationally trained participants resulted in more accurate estimates of AWC, compared to a resistance of 3%, and supports the use of 4.5% body weight resistance to measure both CP and AWC