A neurobiological model of memory impairment in late-life major depressive disorder Davide Bruno1, Jay Nierenberg2, Michel Grothe3, Domenico Pratico’ 4, Anilkumar Pillai5, John Csernansky6, Henry Sershen2, Charles Marmar7, Kenji Hashimoto8, Yadong Huang9, Stefan Teipel3, Berislav Zlokovic10, Henrik Zetterberg11, Kaj Blennow11, and Nunzio Pomara2 1 Liverpool John Moores University, UK; 2 Nathan Kline Institute, USA; 3 German Center for Neurodegenerative Diseases, Germany; 4 Temple University, USA; 5 Augusta University, USA; 6 Northwestern University, USA; 7 New York University, USA; 8 Chiba University, Japan; 9 University of California, San Francisco, USA; 10 University of Southern California, USA; 11 University of Gothenburg, Sweden BACKGROUND Links between depressive symptoms and dementia, including Alzheimer’s disease (AD), have been reported. However, the nature of this association remains debated: depression may constitute a risk factor for dementia, a prodromal condition, or both. These considerations are especially important for late-life depression. In this study, we aimed to examine which cerebro-spinal fluid (CSF) neurobiological markers were associated with evidence of memory decline in a population of older individuals with major depressive disorder (MDD). The ultimate goal of this research is to generate hypotheses about the possible mechanisms linking late-life MDD and AD. RESULTS Bivariate Spearman correlations were carried out to identify biomarkers associated with memory scores and hippocampal volume; p values were adjusted with false discovery rate. Rr was the only outcome affected by biomarkers in MDD. All reported significant correlations were positive (see Figure on the right). No significant associations with memory or hippocampal volume were found in the controls. ρ = 0.593 (.290-.870) ρ = 0.692 (.300-.880) ρ = 0.654 (.136-.906) ρ = 0.678 (.191-932) ρ = 0.924 (.809-.994) ρ = 0.898 (.797-.942) ρ = 0.636 (.118-.883) Demographic and Memory Characteristics of Study Participants by MDD diagnosis Characteristic Comparison Group MDD Group   (N=19) (N=28) p values Age (years) 68.1 ± 7.3 66.5 ± 5.4 0.41 Education (years)a 16.7 ± 2.7 16.5 ± 2.7 0.79 21-item HAM-D 1.2 ± 1.9 14.9 ± 8.8 <0.001 MMSE 29.5 ± 0.5 29.8 ± 0.6 0.13 Total recall rating 64.4 ± 12.3 64.9 ± 13.9 0.91 Delayed recall rating 8.5 ± 2.8 9.5 ± 2.5 0.22 Hippocampal volume 5.2 ± 0.4 5.2 ± 0.5 0.78 Females (n) 12 (63%) 10 (36%) 0.12 Recency ratio 1.3 ± 0.6 1.2 ± 0.6 0.81 The data are the mean ± standard deviation 21-item HAM-D: 21-item Hamilton Depression Rating Scale, MMSE: Mini-Mental State Examination. METHODS We examined a set of diverse CSF biomarker data from a sample of 47 participants, 28 with late-life MDD and 19 controls; all participants were cognitively intact at the time of testing, and aged 60 or above. Demographics table on the right. Memory performance was measured with the BSRT (total recall, delayed recall, and the recency ratio; Rr); in addition, hippocampal volume (hip/TIV) was measured. Eight conceptual biomarker categories were identified a priori as potential predictors. These were: synaptic dysfunction (measured by neurogranin), ApoE fragments, inflammation (IL6 and Il8), blood-brain barrier (BBB) breakdown (cyclophilin-A, albumin quotient), tau (t and p), amyloid beta (40 and 42), neurotransmitters (glutamine, glutamate, and GABA), and oxidative stress (f2-isoprostanes). CONCLUSIONS These preliminary findings provide some support to the notion that memory decline in late-life MDD may emerge as a consequence of the deleterious effects of soluble, rather than aggregated, amyloid beta; this in turn may lead to BBB disruption and increased tau pathology. Alternatively, and following the two-hit hypothesis, BBB breakdown may be the initial source of the decline. These findings also confirm the utility of Rr as a cognitive marker of decline.

What is the Recency ratio? Individuals with Alzheimer’s disease present a reduction of the primacy effect, while the recency effect is intact or exaggerated when tested immediately after learning; however, after a delay, recency suffers the largest drop in performance For this reason, we have proposed a ratio between recency performance in an immediate memory task and recency performance in a delayed task, the recency ratio (Rr), as a measure of risk of cognitive decline Rr can be computed from most neuropsychological tests of memory (e.g., AVLT), especially when using lists of unrelated items Take recency performance (e.g., last four words) from trial 1, and divide by recency performance in the delayed trial – to avoid missing data, add 1 to each term Trial 1 recency + 1 / Delayed trial recency + 1 It predicts conversion to aMCI and is sensitive to CSF levels of glutamate Contact: d.bruno@ljmu.ac.uk References: Bruno D, Reichert C, Pomara N. The recency ratio as an index of cognitive performance and decline in elderly individuals. Journal of clinical and experimental neuropsychology. 2016 Oct 20;38(9):967-73 / Bruno D, Nierenberg J, Cooper TB, Marmar CR, Zetterberg H, Blennow K, Hashimoto K, Pomara N. The recency ratio is associated with reduced CSF glutamate in late-life depression. Neurobiology of Learning and Memory. 2017 May 31;141:14-8