To determine cognitive and metabolic decline patterns according to genetic risk, Small et al. (2000) investigated cerebral metabolic rates using PET in middle-aged and older nondemented persons with normal memory performance.
Participants were right-handed and in the 50- to 84-year-age range. Of the 54 participants with mild memory complaints, 27 were apo E-e4 carriers and 27 were noncarriers. A single copy of the apo E-e4 allele was associated with lowered inferior parietal, lateral temporal, and posterior cingulate metabolism, which predicted cognitive decline after 2 years of longitudinal follow-up. For the 20 nondemented participants followed longitudinally, memory-performance scores did not decline significantly, but cortical metabolic rates did. In apo E-e4 carriers, a 4% left posterior cingulate metabolic decline was observed, and inferior parietal and lateral temporal regions demonstrated the greatest magnitude (5%) of metabolic decline after 2 years. These results have practical implications for clinical trials of dementiaprevention treatments. The right lateral temporal metabolism for apo E-e4 carriers at baseline and 2-year follow-up yielded an estimated power under the most conservative scenario (i.e., assuming that the points are connected exactly in reverse order) of 0.9, to detect a 1-unit decline from baseline to follow-up using a one-tailed test. A sample size of only 20 participants, therefore, would be needed in each treatment arm (i.e., active drug or placebo) to detect a drug-effect size of 0.8 (a = 0.05, power = 0.8).
Thus, a clinical trial of a novel intervention to prevent cerebral metabolic decline would require only 40 participants over a 2-year treatment period. Such findings are consistent with previous PET studies showing stable and replicable results (Andreasen, Arndt, Cizadlo, et al., 1996), and suggest that combining PET and AD genetic-risk measures will allow investigators to use relatively small sample sizes when testing antidementia treatments in preclinical AD stages.
These results indicate that the combination of cerebral metabolic rates and genetic-risk factors provides a means for preclinical AD detection that will assist in response monitoring during experimental treatments.
Participants were right-handed and in the 50- to 84-year-age range. Of the 54 participants with mild memory complaints, 27 were apo E-e4 carriers and 27 were noncarriers. A single copy of the apo E-e4 allele was associated with lowered inferior parietal, lateral temporal, and posterior cingulate metabolism, which predicted cognitive decline after 2 years of longitudinal follow-up. For the 20 nondemented participants followed longitudinally, memory-performance scores did not decline significantly, but cortical metabolic rates did. In apo E-e4 carriers, a 4% left posterior cingulate metabolic decline was observed, and inferior parietal and lateral temporal regions demonstrated the greatest magnitude (5%) of metabolic decline after 2 years. These results have practical implications for clinical trials of dementiaprevention treatments. The right lateral temporal metabolism for apo E-e4 carriers at baseline and 2-year follow-up yielded an estimated power under the most conservative scenario (i.e., assuming that the points are connected exactly in reverse order) of 0.9, to detect a 1-unit decline from baseline to follow-up using a one-tailed test. A sample size of only 20 participants, therefore, would be needed in each treatment arm (i.e., active drug or placebo) to detect a drug-effect size of 0.8 (a = 0.05, power = 0.8).
Thus, a clinical trial of a novel intervention to prevent cerebral metabolic decline would require only 40 participants over a 2-year treatment period. Such findings are consistent with previous PET studies showing stable and replicable results (Andreasen, Arndt, Cizadlo, et al., 1996), and suggest that combining PET and AD genetic-risk measures will allow investigators to use relatively small sample sizes when testing antidementia treatments in preclinical AD stages.
These results indicate that the combination of cerebral metabolic rates and genetic-risk factors provides a means for preclinical AD detection that will assist in response monitoring during experimental treatments.
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