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Research paper
Models for predicting risk of dementia: a systematic review
  1. Xiao-He Hou1,
  2. Lei Feng2,
  3. Can Zhang3,
  4. Xi-Peng Cao4,
  5. Lan Tan1,
  6. Jin-Tai Yu1,4
  1. 1Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
  2. 2Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
  3. 3Genetics and Aging Research Unit, Mass General Institute for Neurodegenerative Disease (MIND), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
  4. 4Clinical Research Center, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
  1. Correspondence to Dr Jin-Tai Yu, Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao 266071, China; yu-jintai{at}


Background Information from well-established dementia risk models can guide targeted intervention to prevent dementia, in addition to the main purpose of quantifying the probability of developing dementia in the future.

Methods We conducted a systematic review of published studies on existing dementia risk models. The models were assessed by sensitivity, specificity and area under the curve (AUC) from receiver operating characteristic analysis.

Results Of 8462 studies reviewed, 61 articles describing dementia risk models were identified, with the majority of the articles modelling late life risk (n=39), followed by those modelling prediction of mild cognitive impairment to Alzheimer’s disease (n=15), mid-life risk (n=4) and patients with diabetes (n=3). Age, sex, education, Mini Mental State Examination, the Consortium to Establish a Registry for Alzheimer’s Disease neuropsychological assessment battery, Alzheimer’s Disease Assessment Scale-cognitive subscale, body mass index, alcohol intake and genetic variables are the most common predictors included in the models. Most risk models had moderate-to-high predictive ability (AUC>0.70). The highest AUC value (0.932) was produced from a risk model developed for patients with mild cognitive impairment.

Conclusion The predictive ability of existing dementia risk models is acceptable. Population-specific dementia risk models are necessary for populations and subpopulations with different characteristics.

  • dementia
  • alzheimer’s disease
  • risk model
  • prediction
  • systematic review

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  • Contributors LT, J-TY and LF conceived the study. X-HH, X-PC and LF selected reports and extracted the data. X-HH and CZ analysed and interpreted the data. X-HH and J-TY wrote the first draft of the manuscript. All the authors critically revised the manuscript for intellectual content and approved the final version. LT and J-TY are guarantors.

  • Funding This work was supported by grants from the National Key R&D Program of China (2016YFC1305803), the National Natural Science Foundation of China (81471309), Taishan Scholars Program of Shandong Province (ts201511109 and tsqn20161079), Qingdao Key Health Discipline Development Fund, Qingdao Outstanding Health Professional Development Fund, and Shandong Provincial Collaborative Innovation Center for Neurodegenerative Disorders.

  • Competing interests None declared.

  • Patient consent Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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