Article

Predicting Barnes and Lee risk of dementia in older adults: the late-life dementia risk index

Department of Psychiatry, University of California, San Francisco, CA 94121, USA.
Neurology (Impact Factor: 8.29). 05/2009; 73(3):173-9. DOI: 10.1212/WNL.0b013e3181a81636
Source: PubMed

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.

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Available from: Deborah Barnes, Nov 15, 2014
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    • "The data source includes longitudinal data on a wide range of potential risk factors, including demographic factors, lifestyle, heath status measurements, medical history/diagnoses, and drugs. We had power to consider a wide range of potentially important risk factors, in comparison to cohort studies with smaller samples1011121314151617181920. In those aged 60–79 years, we had good recording of data for most factors, and for missing data at baseline we used robust multiple imputation techniques utilizing the entire patient record, taking into account the longitudinal records rather than relying solely on baseline parameters. "
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    ABSTRACT: Background: Existing dementia risk scores require collection of additional data from patients, limiting their use in practice. Routinely collected healthcare data have the potential to assess dementia risk without the need to collect further information. Our objective was to develop and validate a 5-year dementia risk score derived from primary healthcare data. Methods: We used data from general practices in The Health Improvement Network (THIN) database from across the UK, randomly selecting 377 practices for a development cohort and identifying 930,395 patients aged 60–95 years without a recording of dementia, cognitive impairment or memory symptoms at baseline. We developed risk algorithm models for two age groups (60–79 and 80–95 years). An external validation was conducted by validating the model on a separate cohort of 264,224 patients from 95 randomly chosen THIN practices that did not contribute to the development cohort. Our main outcome was 5-year risk of first recorded dementia diagnosis. Potential predictors included sociodemographic, cardiovascular, lifestyle and mental health variables. Results: Dementia incidence was 1.88 (95 % CI, 1.83–1.93) and 16.53 (95 % CI, 16.15–16.92) per 1000 PYAR for those aged 60–79 (n = 6017) and 80–95 years (n = 7104), respectively. Predictors for those aged 60–79 included age, sex, social deprivation, smoking, BMI, heavy alcohol use, anti-hypertensive drugs, diabetes, stroke/TIA, atrial fibrillation, aspirin, depression. The discrimination and calibration of the risk algorithm were good for the 60–79 years model; D statistic 2.03 (95 % CI, 1.95–2.11), C index 0.84 (95 % CI, 0.81–0.87), and calibration slope 0.98 (95 % CI, 0.93–1.02). The algorithm had a high negative predictive value, but lower positive predictive value at most risk thresholds. Discrimination and calibration were poor for the 80–95 years model. Conclusions: Routinely collected data predicts 5-year risk of recorded diagnosis of dementia for those aged 60–79, but not those aged 80+. This algorithm can identify higher risk populations for dementia in primary care. The risk score has a high negative predictive value and may be most helpful in ‘ruling out’ those at very low risk from further testing or intensive preventative activities. Keywords: Dementia, Primary care, Risk assessment, Routinely collected data
    Full-text · Article · Dec 2016 · BMC Medicine
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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. "
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    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)
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    • "However, it should be noted that previous studies have also obtained conflicting results. Kivipelto et al. [23] found that obesity was a significant risk factor for dementia over a 20 year follow-up period, whereas Barnes et al. [24] 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. [27] [28], 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. "

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