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.
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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. "
[Show abstract][Hide abstract] 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. (
Journal of the International Neuropsychological Society 02/2015; 21(2):1-10. DOI:10.1017/S1355617715000028 · 2.96 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. "
"We hypothesized that a multidomain model that includes a combination of MRI and cognitive and functional measures would predict conversion from MCI to AD with good accuracy. Finally, point-based risk prediction tools have proven to be useful in other settings for stratification of individuals into high-and low-risk groups   . Thus, a pointbased risk-stratification tool may be useful in research settings to identify individuals with MCI who are at high risk of conversion. "
[Show abstract][Hide abstract] ABSTRACT: Background
Our objective in this study was to develop a point-based tool to predict conversion from amnestic mild cognitive impairment (MCI) to probable Alzheimer's disease (AD).
Subjects were participants in the first part of the Alzheimer's Disease Neuroimaging Initiative. Cox proportional hazards models were used to identify factors associated with development of AD, and a point score was created from predictors in the final model.
The final point score could range from 0 to 9 (mean 4.8) and included: the Functional Assessment Questionnaire (2‒3 points); magnetic resonance imaging (MRI) middle temporal cortical thinning (1 point); MRI hippocampal subcortical volume (1 point); Alzheimer's Disease Cognitive Scale—cognitive subscale (2‒3 points); and the Clock Test (1 point). Prognostic accuracy was good (Harrell's c = 0.78; 95% CI 0.75, 0.81); 3-year conversion rates were 6% (0‒3 points), 53% (4‒6 points), and 91% (7‒9 points).
A point-based risk score combining functional dependence, cerebral MRI measures, and neuropsychological test scores provided good accuracy for prediction of conversion from amnestic MCI to AD.
Alzheimer's & dementia: the journal of the Alzheimer's Association 02/2014; 10(6). DOI:10.1016/j.jalz.2013.12.014 · 12.41 Impact Factor