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      Validating the role of the Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI) and a genetic risk score in progression to cognitive impairment in a population-based cohort of older adults followed for 12 years

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          Abstract

          Background

          The number of people living with dementia is expected to exceed 130 million by 2050, which will have serious personal, social and economic implications. Employing successful intervention and treatment strategies focused on disease prevention is currently the only available approach that can have an impact on the projected rates of dementia, with risk assessment being a key component of population-based risk reduction for identification of at-risk individuals. We evaluated a risk index comprising lifestyle, medical and demographic factors (the Australian National University Alzheimer’s Disease Risk Index [ANU-ADRI]), as well as a genetic risk score (GRS), for assessment of the risk of progression to mild cognitive impairment (MCI).

          Methods

          The ANU-ADRI was computed for the baseline assessment of 2078 participants in the Personality and Total Health (PATH) Through Life project. GRSs were constructed on the basis of 25 single-nucleotide polymorphisms previously associated with Alzheimer’s disease (AD). Participants were assessed for clinically diagnosed MCI and dementia as well as psychometric test-based MCI (MCI-TB) at 12 years of follow-up. Multi-state models were used to estimate the odds of transitioning from cognitively normal (CN) to MCI, dementia and MCI-TB over 12 years according to baseline ANU-ADRI and GRS.

          Results

          A higher ANU-ADRI score was associated with increased risk of progressing from CN to both MCI and MCI-TB (HR 1.07 [95% CI 1.04–1.11]; 1.07 [1.04–1.09]). The GRS was associated with transitions from CN to dementia (HR 4.19 [95% CI 1.72–10.20), but not to MCI or MCI-TB (HR 1.05 [95% CI 0.86–1.29]; 1.03 [0.87–1.21]). Limitations of our study include that the ethnicity of participants in the PATH project is predominately Caucasian, potentially limiting the generalisability of the results of this study to people of other ethnicities. Biomarkers of AD were not available to define MCI attributable to AD. Not all the predictive variables for the ANU-ADRI were available in the PATH project.

          Conclusions

          In the general population, the ANU-ADRI, comprising lifestyle, medical and demographic factors, is associated with the risk of progression from CN to MCI, whereas a GRS comprising the main AD risk genes was not associated with this risk. The ANU-ADRI may be used for population-level risk assessment and screening.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13195-017-0240-3) contains supplementary material, which is available to authorized users.

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          Most cited references22

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          Validity of the Brief Patient Health Questionnaire Mood Scale (PHQ-9) in the general population.

          The aim of this study was to assess the validity of the Patient Health Questionnaire depression module (PHQ-9). It has been subject to studies in medical settings, but its validity as a screening for depression in the general population is unknown. A representative population sample (2,066 subjects, 14-93 years) filled in the PHQ-9 for diagnosis [major depressive disorder, other depressive disorder, depression screen-positive (DS+) and depression screen-negative (DS-)] and other measures for distress (GHQ-12), depression (Brief-BDI) and subjective health perception (EuroQOL; SF-36). A prevalence rate of 9.2% of a current PHQ depressive disorder (major depression 3.8%, subthreshold other depressive disorder 5.4%) was identified. The two depression groups had higher Brief-BDI and GHQ-12 scores, and reported lower health status (EuroQOL) and health-related quality of life (SF-36) than did the DS- group (P's < .001). Strong associations between PHQ-9 depression severity and convergent variables were found (with BDI r = .73, with GHQ-12 r = .59). The results support the construct validity of the PHQ depression scale, which seems to be a useful tool to recognize not only major depression but also subthreshold depressive disorder in the general population.
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            Higher risk of progression to dementia in mild cognitive impairment cases who revert to normal.

            To estimate rates of progression from mild cognitive impairment (MCI) to dementia and of reversion from MCI to being cognitively normal (CN) in a population-based cohort.
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              Current Developments in Dementia Risk Prediction Modelling: An Updated Systematic Review

              Background Accurate identification of individuals at high risk of dementia influences clinical care, inclusion criteria for clinical trials and development of preventative strategies. Numerous models have been developed for predicting dementia. To evaluate these models we undertook a systematic review in 2010 and updated this in 2014 due to the increase in research published in this area. Here we include a critique of the variables selected for inclusion and an assessment of model prognostic performance. Methods Our previous systematic review was updated with a search from January 2009 to March 2014 in electronic databases (MEDLINE, Embase, Scopus, Web of Science). Articles examining risk of dementia in non-demented individuals and including measures of sensitivity, specificity or the area under the curve (AUC) or c-statistic were included. Findings In total, 1,234 articles were identified from the search; 21 articles met inclusion criteria. New developments in dementia risk prediction include the testing of non-APOE genes, use of non-traditional dementia risk factors, incorporation of diet, physical function and ethnicity, and model development in specific subgroups of the population including individuals with diabetes and those with different educational levels. Four models have been externally validated. Three studies considered time or cost implications of computing the model. Interpretation There is no one model that is recommended for dementia risk prediction in population-based settings. Further, it is unlikely that one model will fit all. Consideration of the optimal features of new models should focus on methodology (setting/sample, model development and testing in a replication cohort) and the acceptability and cost of attaining the risk variables included in the prediction score. Further work is required to validate existing models or develop new ones in different populations as well as determine the ethical implications of dementia risk prediction, before applying the particular models in population or clinical settings.
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                Author and article information

                Contributors
                +61 2 612 56338 , shea.andrews@anu.edu.au
                Ranmalee.Eramudugolla@anu.edu.au
                jorge.velez@anu.edu.au
                Nicolas.Cherbuin@anu.edu.au
                Simon.Easteal@anu.edu.au
                Kaarin.Anstey@anu.edu.au
                Journal
                Alzheimers Res Ther
                Alzheimers Res Ther
                Alzheimer's Research & Therapy
                BioMed Central (London )
                1758-9193
                4 March 2017
                4 March 2017
                2017
                : 9
                : 16
                Affiliations
                [1 ]ISNI 0000 0001 2180 7477, GRID grid.1001.0, John Curtin School of Medical Research, , Australian National University, ; Canberra, Australia
                [2 ]Centre for Research on Ageing, Health and Wellbeing, Research School of Population Health Australian National University, The Australian National University Florey, Building 54, Mills Road, Acton ACT 2601, Canberra, Australia
                [3 ]ISNI 0000 0004 0486 8632, GRID grid.412188.6, , Universidad del Norte, ; Barranquilla, Colombia
                [4 ]ISNI 0000 0000 8882 5269, GRID grid.412881.6, Neuroscience Research Group, , University of Antioquia, ; Medellin, Colombia
                Author information
                http://orcid.org/0000-0002-1921-9470
                Article
                240
                10.1186/s13195-017-0240-3
                5336661
                28259165
                fa3d0a97-0e3e-47e4-adaa-f6292b6aff85
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 10 October 2016
                : 1 February 2017
                Funding
                Funded by: FundRef http://dx.doi.org/http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;
                Award ID: 973302, 179805, and 1002160, 1002560
                Funded by: FundRef http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;
                Award ID: 12010227
                Award ID: 1002560
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2017

                Neurology
                alzheimer’s disease,cognitive aging,mild cognitive impairment (mci),cohort studies,risk factors in epidemiology,multi-state models

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