Citations

Purpose In March 1998, Common Toxicity Criteria (CTC) version 2.0 introduced the collection of attribution of adverse events (AEs) to study drug. We investigate whether attribution adds value to the interpretation of AE data. Patients and Methods Patients in the placebo arm of two phase III trials—North Central Cancer Treatment Group Trial 97-24-51 (carboxyamino-triazole v placebo in advanced non–small-cell lung cancer) and American College of Surgeons Oncology Group Trial Z9001 (imatinib mesylate v placebo after resection of primary gastrointestinal stromal tumors)—were studied. Attribution was categorized as unrelated (not related or unlikely) and related (possible, probable, or definite). Results In total, 398 patients (84 from Trial 97-24-51 and 314 from Trial Z9001) and 7,736 AEs were included; 47% and 50% of the placebo-arm AEs, respectively, were reported as related. When the same AE was reported in the same patient on multiple visits, the attribution category changed at least once 36% and 31% of the time. AE type and sex (Trial Z9001) and AE type and performance status (Trial 97-24-51) were associated with a higher likelihood of AEs being deemed related. Conclusion Nearly 50% of AEs were reported as attributed to study drug on the placebo arm of two randomized clinical trials. These data provide strong evidence that AE attribution is difficult to determine, unreliable, and of questionable value in interpreting AE data in randomized clinical trials.

Here, we review progress by the Penn Biomarker Core in the Alzheimer's Disease Neuroimaging Initiative (ADNI) toward developing a pathological cerebrospinal fluid (CSF) and plasma biomarker signature for mild Alzheimer's disease (AD) as well as a biomarker profile that predicts conversion of mild cognitive impairment (MCI) and/or normal control subjects to AD. The Penn Biomarker Core also collaborated with other ADNI Cores to integrate data across ADNI to temporally order changes in clinical measures, imaging data, and chemical biomarkers that serve as mileposts and predictors of the conversion of normal control to MCI as well as MCI to AD, and the progression of AD. Initial CSF studies by the ADNI Biomarker Core revealed a pathological CSF biomarker signature of AD defined by the combination of Aβ1‐42 and total tau (T‐tau) that effectively delineates mild AD in the large multisite prospective clinical investigation conducted in ADNI. This signature appears to predict conversion from MCI to AD. Data fusion efforts across ADNI Cores generated a model for the temporal ordering of AD biomarkers which suggests that Aβ amyloid biomarkers become abnormal first, followed by changes in neurodegenerative biomarkers (CSF tau, F‐18 fluorodeoxyglucose‐positron emission tomography, magnetic resonance imaging) with the onset of clinical symptoms. The timing of these changes varies in individual patients due to genetic and environmental factors that increase or decrease an individual's resilience in response to progressive accumulations of AD pathologies. Further studies in ADNI will refine this model and render the biomarkers studied in ADNI more applicable to routine diagnosis and to clinical trials of disease modifying therapies.

The Clinical Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI) has provided clinical, operational, and data management support to ADNI since its inception. This article reviews the activities and accomplishments of the core in support of ADNI aims. These include the enrollment and follow‐up of more than 800 subjects in the three original cohorts: healthy controls, amnestic mild cognitive impairment (now referred to as late MCI, or LMCI), and mild Alzheimer's disease (AD) in the first phase of ADNI (ADNI 1), with baseline longitudinal, clinical, and cognitive assessments. These data, when combined with genetic, neuroimaging, and cerebrospinal fluid measures, have provided important insights into the neurobiology of the AD spectrum. Furthermore, these data have facilitated the development of novel clinical trial designs. ADNI has recently been extended with funding from an NIH Grand Opportunities (GO) award, and the new ADNI GO phase has been launched; this includes the enrollment of a new cohort, called early MCI, with milder episodic memory impairment than the LMCI group. An application for a further 5 years of ADNI funding (ADNI 2) was recently submitted. This funding would support ongoing follow‐up of the original ADNI 1 and ADNI GO cohorts, as well as additional recruitment into all categories. The resulting data would provide valuable data on the earliest stages of AD, and support the development of interventions in these critically important populations.

Functions of the Alzheimer's Disease Neuroimaging Initiative (ADNI) magnetic resonance imaging (MRI) core fall into three categories: (1) those of the central MRI core laboratory at Mayo Clinic, Rochester, Minnesota, needed to generate high quality MRI data in all subjects at each time point; (2) those of the funded ADNI MRI core imaging analysis groups responsible for analyzing the MRI data; and (3) the joint function of the entire MRI core in designing and problem solving MR image acquisition, pre‐processing, and analyses methods. The primary objective of ADNI was and continues to be improving methods for clinical trials in Alzheimer's disease. Our approach to the present (“ADNI‐GO”) and future (“ADNI‐2,” if funded) MRI protocol will be to maintain MRI methodological consistency in the previously enrolled “ADNI‐1” subjects who are followed up longitudinally in ADNI‐GO and ADNI‐2. We will modernize and expand the MRI protocol for all newly enrolled ADNI‐GO and ADNI‐2 subjects. All newly enrolled subjects will be scanned at 3T with a core set of three sequence types: 3D T1‐weighted volume, FLAIR, and a long TE gradient echo volumetric acquisition for micro hemorrhage detection. In addition to this core ADNI‐GO and ADNI‐2 protocol, we will perform vendor‐specific pilot sub‐studies of arterial spin‐labeling perfusion, resting state functional connectivity, and diffusion tensor imaging. One of these sequences will be added to the core protocol on systems from each MRI vendor. These experimental sub‐studies are designed to demonstrate the feasibility of acquiring useful data in a multicenter (but single vendor) setting for these three emerging MRI applications.

A recently identified variant within the fat mass and obesity-associated ( FTO ) gene is carried by 46% of Western Europeans and is associated with an ~1.2 kg higher weight, on average, in adults and an ~1 cm greater waist circumference. With >1 billion overweight and 300 million obese persons worldwide, it is crucial to understand the implications of carrying this very common allele for the health of our aging population. FTO is highly expressed in the brain and elevated body mass index (BMI) is associated with brain atrophy, but it is unknown how the obesity-associated risk allele affects human brain structure. We therefore generated 3D maps of regional brain volume differences in 206 healthy elderly subjects scanned with MRI and genotyped as part of the Alzheimer's Disease Neuroimaging Initiative. We found a pattern of systematic brain volume deficits in carriers of the obesity-associated risk allele versus noncarriers. Relative to structure volumes in the mean template, FTO risk allele carriers versus noncarriers had an average brain volume difference of ~8% in the frontal lobes and 12% in the occipital lobes—these regions also showed significant volume deficits in subjects with higher BMI. These brain differences were not attributable to differences in cholesterol levels, hypertension, or the volume of white matter hyperintensities; which were not detectably higher in FTO risk allele carriers versus noncarriers. These brain maps reveal that a commonly carried susceptibility allele for obesity is associated with structural brain atrophy, with implications for the health of the elderly.

We executed a genome-wide association scan for age-related macular degeneration (AMD) in 2,157 cases and 1,150 controls. Our results validate AMD susceptibility loci near CFH ( P < 10 −75 ), ARMS2 ( P < 10 −59 ), C2/CFB ( P < 10 −20 ), C3 ( P < 10 −9 ), and CFI ( P < 10 −6 ). We compared our top findings with the Tufts/Massachusetts General Hospital genome-wide association study of advanced AMD (821 cases, 1,709 controls) and genotyped 30 promising markers in additional individuals (up to 7,749 cases and 4,625 controls). With these data, we identified a susceptibility locus near TIMP3 (overall P = 1.1 × 10 −11 ), a metalloproteinase involved in degradation of the extracellular matrix and previously implicated in early-onset maculopathy. In addition, our data revealed strong association signals with alleles at two loci ( LIPC , P = 1.3 × 10 −7 ; CETP , P = 7.4 × 10 −7 ) that were previously associated with high-density lipoprotein cholesterol (HDL-c) levels in blood. Consistent with the hypothesis that HDL metabolism is associated with AMD pathogenesis, we also observed association with AMD of HDL-c—associated alleles near LPL ( P = 3.0 × 10 −3 ) and ABCA1 ( P = 5.6 × 10 −4 ). Multilocus analysis including all susceptibility loci showed that 329 of 331 individuals (99%) with the highest-risk genotypes were cases, and 85% of these had advanced AMD. Our studies extend the catalog of AMD associated loci, help identify individuals at high risk of disease, and provide clues about underlying cellular pathways that should eventually lead to new therapies.

Objective: To determine apolipoprotein E (APOE)-ε4 and -ε2 frequencies and risk of mild cognitive impairment (MCI) and dementia in Shanghai, China. Methods: A total of 34 MCI and 34 dementia cases were recruited from an urban Memory Disorders Clinic and 32 controls were recruited from a residential community served by the clinic. Apolipoprotein E was genotyped using standard methods. Results: Among controls, frequencies were ε2, 0.11; ε3, 0.84; and ε4, 0.05; among MCI, 0.05, 0.77, and 0.18; and for dementia, 0.02, 0.84, and 0.15, respectively. In education-adjusted models, the odds ratio (OR) = 5.6 for dementia (95% CI = 1.09-29.3) and 4.7 for MCI (95% CI = 0.90-25.2) associated with any ε4 allele. The ε2 allele was inversely associated with dementia (OR = 0.12, 95% CI = 0.013-0.997) and MCI (OR = 0.38, 95% CI = 0.08-1.61). Conclusions: APOE-ε4 increases and -ε2 decreases the risk of dementia vs normal cognition. Similar trends were observed for amnestic mild cognitive impairment (aMCI).