Sydney Memory & Ageing Study

1. Sydney Memory & Ageing Study Henry Brodaty Brain and Ageing Research Program University of New South Wales www.brainage.med.unsw.edu.au

2. What is Sydney MAS? • A longitudinal epidemiological study of people aged 70-90 • Funded through an NHMRC Program Grant to Sachdev, Brodaty & Andrews www.brainage.med.unsw.edu.au

3. Why MAS? Objectives • To examine clinical characteristics of mild cognitive impairment • Rate of change in cognitive function over time www.brainage.med.unsw.edu.au

4. Who are MAS and partners? Sydney Memory and Ageing Study Brain and Ageing Research Program www.brainage.med.unsw.edu.au

5. Wei Wen et al – Neuroimaging • Stephen Lord, Jasmine Menant, Kim Delbaere – Falls, gait, balance • Julian Trollor, Bernhard Baume – Inflammaging • Katherine Samaras, Eveline Berglund-Smith – Metabolic, diabetes, lipids • Melissa Slavin – Subjective Cognitive Complaints • Nicole Kochan – fMRI & normative neuropsych • Simone Reppermund – IADLs, depression • Lee-Fay Low – CALD and MCI • Karen Mathers – Genetics • Anne Poljack – Proteomics • Sharon Ong - Nutrition

6. Sydney MAS: Method • Population based sample • 70-90 yos , Electoral Roll, Eastern Sydney • Community dwelling • Excluded if: • dementia, psychiatric disorders • neurological disorders • developmental disability • active malignancy • insufficient English to test

7. Sydney MAS: Method • Comprehensive assessment of 1037 Ss and informants including • detailed neuropsychological tests • MRI (52.3%) • bloods, genetics (>90%) • Follow up: at 12, 36 months by telephone and at 2, 4 yrs with comprehensive assessment Sachdev et al. (2010). International Psychogeriatrics, 22:8, 1248–1264

8. MCI criteria • Petersen criteria revised1,2 • Not normal, not dementia • Self and/or informant complaint • Generally intact global cognition • MMSE > 24 • Preserved basic ADLs and minimal impairment of complex function 1 Petersen et al, Arch Neurol 1999;56:303–308 2 Winblad et al, J Intern Med 2004;256:240–246

9. MCI criteria • Impairment on objective cognitive tasks and/or • Evidence of decline over time on objective cognitive tasks • Domain impairment defined as • Performance 1.5 SD below published normative values (age, education matched where possible) 1 Petersen et al, Arch Neurol 1999;56:303–308 2 Winblad et al, J Intern Med 2004;256:240–246

10. Results: Sample • Sample • 8914 mailed  1772 responded • 1037 eligible (MMSE > 24) • Demographics • Mean Age: 78.8 (SD = 4.8) • Mean Education: 11.6 (SD = 3.5) • Sex: Men = 465 (44.8%) Sachdev et al. (2010). International Psychogeriatrics, 22:8, 1248–1264

11. Results: MCI Prevalence • Baseline MCI prevalence 34.8% • Amnestic MCI = 10.5% • Amnestic multi-domain MCI = 8.8% • Nonamnestic MCI = 12.8% • Nonamnestic multi-domain MCI = 2.7% • Most published MCI rates range 3-25% Sachdev et al. (2010). International Psychogeriatrics, 22:8, 1248–1264

12. Results: MCI Incidence • 104.6 per 1000 person years1 • Higher than other published rates • 51-76.8 per 1000 person yrs2,3 • Higher in men (156.8) than women (70.3) • Amnestic MCI: 47.7 (single) & 7.9 (multi) • Nonamnestic MCI: 45.0 (single) & 3.9 (multi) 1Brodaty et al. (2011). “Mild Cognitive Impairment: rates of incidence, progression and reversion over two years”, submitted. 2Luck et al. (2010). Dement Geriatr Cogn Disord;29(2):164-175. 3Luck et al. (2010). J Am Geriatr Soc; 58(10):1903-1910.

13. Results: Progression to Dementia • Progression MCI to dementia = 4.8% • Similar to 2.6% (annual) reported for long term studies1 • Progression NCI to dementia = 1.2% • Slightly lower than estimated 1.8% for similarly aged persons2 1Mitchell & Shiri-Feshki (2009). Acta Psychiatr Scand;119(4):252-265. 2 Petersen et al (2001). Neurology; 56(9):1133-1142

14. Results: MCI Stability • 28.2% of those with MCI at baseline reverted to NCI at follow-up • Similar to published rates 31-37%1,2,3 • Multidomain subtypes more likely to progress to dementia (vs. NCI) than single domain 1Manly et al. (2008). Ann Neurol;63(4):494-506. 2Ravaglia et al. (2008). J Am Geriatr Soc; 56(1):51-58. 3Artero et al. (2008). J Neurol Neurosurg Psychiatry; 79(9):979-984

15. Sensitivity analysis • Increased stringency for domain impairment from ≥ 1 tests to ≥ 2 tests • Calculated impairment against sample norms

16. MCI stability: Published norms Participants (%) Baseline classification

17. MCI stability: Sample norms Participants (%) Baseline classification

18. MCI Prevalence Risk Factors • N = 757, English speaking background • Baseline regression analyses: • Young-old (70-80 years) vs. old-old (80-90 years) • Female vs. male

19. Results: Risk Factors • Increased MCI risk: • APOE4 • High homocysteine • Heart disease • Reduced MCI risk: • Better odour identification • Visual acuity • Mental activity

20. Results: Risk Factors • 70-79yrs: slow 6-m walk, poor odour ID, high homocysteine • Females MCI risk factors: • history of depression • less mental activity • slower 6-m walk • poorer visual acuity • higher homocysteine

21. Results: Risk Factors • 70-79yrs • Males MCI risk factors • poor odour ID • higher homocysteine • 80-90yrs • only significant factor was poor visual acuity in males

22. Instrumental Activities of Daily Living (IADL)(Simone Reppermund)

23. IADLs • Restrictions in IADL are present in MCI • Detailed IADL assessments are important (gold standard = performance-based IADL measures) • Assistance with more demanding tasks • IADL can be differentiated between high and low cognitive demand • High cognitively demanding items depend on memory & executive function and associatedwith all cognitive domains

24. IADLs • Low cognitive demand IADLs examples • managing everyday activities • preparing food • personal hygiene • High cognitive demand IADLs examples • oping with unfamiliar situations • performing a task when under pressure • describing what he/she has just seen/ heard • High cognitive demand activities > frequently affected in MCI • Men generally have more difficulties

25. Depression and cognition(Simone Reppermund)

26. Depression & cognitive impairment • Depression • Current depressive symptoms and • Past depression • 800 participants (57% F, mean age 78.6 yrs) • Clinically significant symptoms of depression (GDS score ≥ 6) = 6.1% • 16.6% reported a history of depression

27. Participants with depressive Sx performed worse on memory and executive function Depression & cognitive impairment

28. Depression, cognitive impairment & IADLS • No differences on the Bayer IADL total score between participants with or without • current depressive symptoms • past depression • Current and past depressive episodes are associated with poorer cognitive performance but not with functional abilities

29. CALD and cognitionLee-Fay Low

30. Can MCI be accurately diagnosed in English speakers from linguistic minorities? • We found 2- to 3-fold higher prevalence of MCI in participants from NESBs compared with those from ESB, due to... • ...higher rates of objective cognitive impairment in NESB participants (tested in English) • Rates of functional impairment & SCC similar • No difference found in MCI incidence

31. Can MCI be accurately diagnosed in English speakers from linguistic minorities? • The association between MCI prevalence and NESB status was related to the proportion of time the participant spoke English and the proportion of life they had lived in Australia, but not to age, gender and education. • CONCLUSION: Difficult to diagnose MCI in people from linguistic minority groups, even when proficient in English. Consider English language and acculturation during diagnosis

32. NeuroimagingWei Wen

33. Imaging studies • The link between two nodes (cortical regions) represents the existence/absence and strength of the white matter fibres connecting the two cortical regions.

34. Global efficiency v Age: considering connectivity of the whole brain Brain Ageing Program University of New South Wales

35. Cortical surface morphometry: g-SI g-SI for each hemisphere was measured as the ratio of the total sulcal area and the outer cortical area. 2 participants with (A) a high and (B) a low g-SI.

36. Cortical surface morphometry: sulcal span (width and depth)

37. Cortical surface morphometry (g-SI) • Lower g-SI (global sulcal index) for older age. • Men had lower g-SI and faster decline rate of g-SI

38. fMRINicole Kochan

39. fMRI • Default mode network (DMN) significantly disrupted in MCI • In healthy individuals, DMN active during rest and deactivated during task performance • Posteromedial cortex (PMC) – consisting of medial precuneus, posterior cingulate and retrosplenial cortex = major node of the DMN • Varied reported alterations in task-induced deactivation in regions of the PMC during performance of memory tasks

40. fMRI & Functional Decline • Posteromedial cortex (PMC) is one of the earliest affected regions in AD • Under high working memory load, greater deactivation of PMC in MCI1 • Can this deactivation predict functional decline?2 1Kochan et al. (2011). Dement Geriatr Cogn Disord, in press. 2Kochan et al. (2011). Cortical Responses to a Graded Working Memory Challenge Predict Functional Decline in Mild Cognitive Impairment.

41. Method: fMRI • N = 30 MCI, fMRI data acquired • while Ps performed Working Memory task • WM load increased during the task • Manipulated by altering number of targets • WM Load = low, medium, high • calibrated for each P so that • medium = 75-85% accuracy • high = 60-70% accuracy Kochan et al. (2011). Cortical Responses to a Graded Working Memory Challenge Predict Functional Decline in Mild Cognitive Impairment.

42. Kochan et al. (2011). Cortical Responses to a Graded Working Memory Challenge Predict Functional Decline in Mild Cognitive Impairment

43. Results: fMRI • Individuals with MCI followed for 2 yrs • Results: • greater PMC deactivation predicted greater decline in IADL Kochan et al. (2011). Cortical Responses to a Graded Working Memory Challenge Predict Functional Decline in Mild Cognitive Impairment.

44. Kochan et al. (2011). Cortical Responses to a Graded Working Memory Challenge Predict Functional Decline in Mild Cognitive Impairment.

45. Kochan et al. (2011). Cortical responses to a graded working memory challenge predict functional decline in MCI

46. Subjective Cognitive ComplaintsSCCMelissa Slavin

47. SCC Wave 1 • 95.5% of participants or their informants endorsed SCC • Participants more likely to endorse a memory complaint overall • Informants seemed more accurate • endorse complaint when objective cognitive impairment also present

48. SCC Wave 1 • SCC related to mood (depression, anxiety) and personality (neuroticism & inversely with openness and conscientiousness) • At Wave 1 psychological factors explained the number of complaints more than cognitive performance

49. SCC – Longitudinal Data, W1W2 • Participant memory complaints • Predicted MCI • ? greater sensitivity of self-observation • Informant memory complaints • Predicted dementia • ? greater specificity of decline observed by others • Non-memory complaints did not predict MCI or dementia

50. Neuropsychiatric Symptoms(NPS)Megan Heffernan