Predicting risk of dementia in older adults The late-life dementia risk index
ABSTRACT To develop a late-life dementia risk index that can accurately stratify older adults into those with a low, moderate, or high risk of developing dementia within 6 years.
Subjects were 3,375 participants in the Cardiovascular Health Cognition Study without evidence of dementia at baseline. We used logistic regression to identify those factors most predictive of developing incident dementia within 6 years and developed a point system based on the logistic regression coefficients.
Subjects had a mean age of 76 years at baseline; 59% were women and 15% were African American. Fourteen percent (n = 480) developed dementia within 6 years. The final late-life dementia risk index included older age (1-2 points), poor cognitive test performance (2-4 points), body mass index <18.5 (2 points), > or =1 apolipoprotein E epsilon4 alleles (1 point), cerebral MRI findings of white matter disease (1 point) or ventricular enlargement (1 point), internal carotid artery thickening on ultrasound (1 point), history of bypass surgery (1 point), slow physical performance (1 point), and lack of alcohol consumption (1 point) (c statistic, 0.81; 95% confidence interval, 0.79-0.83). Four percent of subjects with low scores developed dementia over 6 years compared with 23% of subjects with moderate scores and 56% of subjects with high scores.
The late-life dementia risk index accurately stratified older adults into those with low, moderate, and high risk of developing dementia. This tool could be used in clinical or research settings to target prevention and intervention strategies toward high-risk individuals.
Full-textDOI: · Available from: Deborah Barnes, Nov 15, 2014
- SourceAvailable from: Avron Spiro
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- "Some of the health conditions associated with MetS are included in well-established risk factor indices, such as the Framingham Stroke Risk Profile (FSRP) or Dementia Risk Indices (Barnes & Yaffe, 2009). These indices show that higher health risks (measured, for example, by differences between the first and fourth quartile of FSRP) are associated with decreases in brain structure (e.g., reduction in total cerebral brain volume ratio) or brain function (e.g., poor performance on cognitive tests) (Barnes et al., 2009). Evidence describing neural changes associated with MetS when considered as a constellation of risk factors, however, is scarce. "
ABSTRACT: This study explored effects of the metabolic syndrome (MetS) on language in aging. MetS is a constellation of five vascular and metabolic risk factors associated with the development of chronic diseases and increased risk of mortality, as well as brain and cognitive impairments. We tested 281 English-speaking older adults aged 55–84, free of stroke and dementia. Presence of MetS was based on the harmonized criteria (Alberti et al., 2009). Language performance was assessed by measures of accuracy and reaction time on two tasks of lexical retrieval and two tasks of sentence processing. Regression analyses, adjusted for age, education, gender, diabetes, hypertension, and heart disease, demonstrated that participants with MetS had significantly lower accuracy on measures of lexical retrieval (action naming) and sentence processing (embedded sentences, both subject and object relative clauses). Reaction time was slightly faster on the test of embedded sentences among those with MetS. MetS adversely affects the language performance of older adults, impairing accuracy of both lexical retrieval and sentence processing. This finding reinforces and extends results of earlier research documenting the negative influence of potentially treatable medical conditions (diabetes, hypertension) on language performance in aging. The unanticipated finding that persons with MetS were faster in processing embedded sentences may represent an impairment of timing functions among older individuals with MetS. ( JINS , 2015, 21 , 116–125)Journal of the International Neuropsychological Society 02/2015; 21(2):1-10. DOI:10.1017/S1355617715000028 · 3.01 Impact Factor
- "However, it should be noted that previous studies have also obtained conflicting results. Kivipelto et al.  found that obesity was a significant risk factor for dementia over a 20 year follow-up period, whereas Barnes et al.  found that low body mass index (BMI) was a risk factor for the development of dementia over 6 years. As pointed out by Anstey et al.  , high cholesterol and high BMI in mid-life are both significant risk factors for AD, but neither is a risk factor when it appears in late-life. "
Dataset: Further Evaluation of DRA
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- "Moreover, some risk factors may also be affected by the disease itself. Weight loss typically precedes AD by about 6 years, and hence low BMI may show up as a risk factor for AD in a study of older adults (e.g., Barnes et al. 2009) when it is actually as symptom of AD (Knopman et al. 2007). It is notable that the two previously published risk indices for dementia described above found that opposite categories of BMI were associated with increased risk of dementia. "
ABSTRACT: Alzheimer’s disease (AD) affects approximately 35 million people worldwide. Increasing evidence suggests that many risk factors for AD are modifiable. AD pathology develops over decades. Hence risk reduction interventions require very long follow-ups to show effects on AD incidence. Focussing on AD risk, instead of diagnosis, provides a more realistic target for prevention strategies. We developed a novel methodology that yields a global approach to risk assessment for AD for use in population-based settings and interventions. The methodology was used to develop a risk assessment tool that can be updated as more evidence becomes available. First, a systematic search strategy identified risk and protective factors for AD. Eleven risk factors and four protective factors for AD were identified for which odds ratios were published or could be calculated (age, sex, education, body mass index, diabetes, depression, serum cholesterol, traumatic brain injury, smoking, alcohol intake, social engagement, physical activity, cognitive activity, fish intake, and pesticide exposure). An algorithm was developed to combine the odds ratios into an AD risk score. The approach allows for interactions among risk factors which provides for their varying impact over the life-course as current evidence suggests midlife is a critical period for some risk factors. Finally, a questionnaire was developed to assess the risk and protective factors by self-report. Compared with developing risk indices on single cohort studies, this approach allows for more risk factors to be included, greater generalizeability of results, and incorporation of interactions based on findings from different stages of the lifecourse. Electronic supplementary material The online version of this article (doi:10.1007/s11121-012-0313-2) contains supplementary material, which is available to authorized users.Prevention Science 01/2013; 14(4). DOI:10.1007/s11121-012-0313-2 · 2.63 Impact Factor