Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer’s disease

www.nature.com/scientificreports OPEN Received: 31 March 2017 Accepted: 16 June 2017 Published online: 28 July 2017 Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer’s disease Dragan Gamberger1, Nada Lavrač2, Shantanu Srivatsa3, Rudolph E. Tanzi4 & P. Murali Doraiswamy3,5 The heterogeneity of Alzheimer’s disease contributes to the high failure rate of prior clinical trials. We analyzed 5-year longitudinal outcomes and biomarker data from 562 subjects with mild cognitive impairment (MCI) from two national studies (ADNI) using a novel multilayer clustering algorithm. The algorithm identified homogenous clusters of MCI subjects with markedly different prognostic cognitive trajectories. A cluster of 240 rapid decliners had 2-fold greater atrophy and progressed to dementia at almost 5 times the rate of a cluster of 184 slow decliners. A classifier for identifying rapid decliners in one study showed high sensitivity and specificity in the second study. Characterizing subgroups of at risk subjects, with diverse prognostic outcomes, may provide novel mechanistic insights and facilitate clinical trials of drugs to delay the onset of AD. Alzheimer’s disease is a major public health concern worldwide and the leading cause of dementia in late life. There are no therapies to slow progression or delay its onset. Consequently, there is an urgent need to develop accurate prognostic tests and effective disease modifying therapies. The 99% failure rate of clinical drug trials over the past two decades1 points to both our incomplete knowledge of pathology and prognostics. Both clinical experience and research outcome study data have shown that AD is a heterogeneous condition with high individual variability in age of onset, rate of clinical decline as well as degree of underlying pathology2–5. Characterizing subgroups of at risk subjects, with homogenous but diverse prognostic outcomes, may provide novel mechanistic insights and facilitate clinical trials of drug to delay AD onset. Of the nearly 5 million people affected by AD dementia in the US, it has been estimated that 60% are women. In addition to individual heterogeneity, the study of potential sex differences in AD epidemiology, biology and therapeutics has been a relatively neglected area of research (reviewed in refs 6 and 7. The reported higher prevalence of Alzheimer’s disease (AD) in women had been attributed previously to longer female life expectancy or a detection bias but some, but not all, recent findings suggest that older women may be at greater risk for AD than men6, 7. For example, one study found that the age-specific risk of AD was almost two-fold greater in women than men (17.2% versus 9.1% at age 65 years and 28.5% versus 10.2% at age 75 years)8 and some other studies find that sex-differences become most prominent among eighty year olds7. Potential mechanisms to explain such differences include greater effects of the Apolipoprotein E4 allele in women, sex hormones (such as estrogen), lower cognitive reserve, and differences in occupational or educational attainment (reviewed in refs 6 and 7. Sex differences in immune system responsivity, MRI brain atrophy rates9 and effects of plaque-tangle pathology10 have also been reported. In contrast, other studies report a higher risk for men to develop verbal memory loss, incident MCI6 and cerebrovascular disease6. Overall these studies argue for a more definitive examination of sex differences in the vulnerability to AD. AD may have a prolonged preclinical and prodromal phase and there is great interest in characterizing these phases using biomarkers. Mild cognitive impairment (MCI) is a risk factor for AD and is clinically characterized by mild cognitive deficits but relatively normal everyday functioning and the absence of dementia11, 12. Prior studies have documented that MCI subjects have an intermediate phenotype between AD and cognitively healthy 1 Ruđer Bošković Institute, Zagreb, Croatia. 2Jožef Stefan Institute, Ljubljana, Slovenia. 3Duke Institute for Brain Sciences, Duke University Health System, Durham, USA. 4Genetics and Aging Research Unit and Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, USA. 5Neurocognitive Disorders Program, Division of Translational Neuroscience, Department of Psychiatry, Duke University Health System, Durham, USA. Correspondence and requests for materials should be addressed to P.M.D. (email: murali. ) Scientific Reports | 7: 6763 | DOI:10.1038/s41598-017-06624-y 1 www.nature.com/scientificreports/ Female MCI MCI Mean (SD) Mean (SD) Male MCI Mean (SD) Significance N 562 218 344 — Age 74.0 (7.5) 72.8 (7.6) 74.8 (7.3) <0.01 Education 15.9 (2.9) 15.4 (2.8) 16.2 (3.0) <0.01 CDR-SB 1.6 (0.9) 1.6 (0.9) 1.6 (1.0) — ADAS-11 11.5 (4.6) 11.2 (4.8) 11.8 (4.4) — MMSE 27.2 (1.8) 27.1 (1.8) 27.2 (1.8) — RAVLT-immediate 31.3 (9.5) 33.9 (10.6) 29.7 (8.4) <0.001 APOE4 + (%) 54% 58% 52% — F/U (months) 34.2 (14.2) 34.3 (14.1) 34.1 (14.3) — Table 1. Baseline Demographic and Clinical Variables. ADAS = Alzheimer’s Disease Assessment Scale Cognitive Subscale Total Score; CDR-SB = Clinical dementia Rating Sum of Boxes; MMSE = Mini-Mental State Exam total score; RAVLT = Rey Auditory Verbal Learning Test; F/u = follow up duration averaged between the two studies; P-values are for comparison of male versus female subjects. subjects with regards to cognition, hippocampal atrophy, neuronal metabolism and cortical fibrillary amyloid pathology (determined) (reviewed in refs 11 and 12. While MCI has been divided into amnestic, non-amnestic and multi-domain MCI, even amnestic MCI is not homogenous13. Approximately 10–15% of such subjects may progress to dementia on an annualized basis but there is considerable variability from study to study and within the group – many MCI subjects remain cognitively stable for years and some even revert to normal cognitive states. Thus, identifying subgroups within MCI remains a priority13. A number of baseline factors have been linked to such variability. For example, in one 36-month study, the annualized rate of conversion from amnestic MCI to AD dementia was higher in amyloid-positive versus amyloid negative MCI subjects14. Such results have led to attempts to further subgroup amnestic MCI based on pathological or neuronal loss biomarkers to improve the homogeneity and accuracy of predicting prognostic outcome15. While studies have shown that combining multiple baseline markers does improve prediction, there is no consensus on the best combination of predictive markers and no biomarker has been fully validated and approved for predicting future dementia risk. These findings are not surprising due to a high degree of randomness in the MCI data as a consequence of the fact that cognitive impairment can have different causes and different manifestations and be affected by multiple biological (...truncated)