Peripheral brain-derived neurotrophic factor levels in alzheimer's disease and mild cognitive impairment: A comprehensive systematic review and meta-analysis

Alzheimer's disease (AD) is becoming a growing global problem, and there is an urgent need to identify reliable blood biomarkers of the risk and progression of this condition. A potential candidate is the brain-derived neurotrophic factor (BDNF), which modulates major trophic effects in the brain. However, findings are apparently inconsistent regarding peripheral blood BDNF levels in AD patients vs. healthy people. We thus performed a systematic review and meta-analysis of the studies that have examined peripheral BDNF levels in patients with AD or mild cognitive impairment (MCI) and healthy controls. We searched articles through PubMed, EMBASE, and hand searching. Over a total pool of 2061 potential articles, 26 met all inclusion criteria (including a total of 1584 AD patients, 556 MCI patients, and 1294 controls). A meta-analysis of BDNF levels between early AD and controls showed statistically significantly higher levels (SMD [95 % CI]: 0.72 [0.31, 1.13]) with no heterogeneity. AD patients with a low (<20) mini-mental state examination (MMSE) score had lower peripheral BDNF levels compared with controls (SMD [95 % CI]: -0.33 [-0.60, -0.05]). However, we found no statistically significant difference in blood (serum/plasma) BDNF levels between all AD patients and controls (standard mean difference, SMD [95 % CI]: -0.16 [-0.4, 0.07]), and there was heterogeneity among studies (P < 0.0001, I 2 = 85.8 %). There were no differences in blood BDNF levels among AD or MCI patients vs. controls by subgroup analyses according to age, sex, and drug use. In conclusion, this meta-analysis shows that peripheral blood BDNF levels seem to be increased in early AD and decreased in AD patients with low MMSE scores respectively compared with their age- and sex-matched healthy referents. At present, however, this could not be concluded from individual studies.

Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis

Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.