Our findings suggest that at younger ages, death from coronary heart disease is influenced by genetic factors in both women and men. The results also imply that the genetic effect decreases at older ages.

Abstract. Zdravkovic S,

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.

Regions of the temporal and parietal lobes are particularly damaged in Alzheimer's disease (AD), and this leads to a predictable pattern of brain atrophy. In vivo quantification of subregional atrophy, such as changes in cortical thickness or structure volume, could lead to improved diagnosis and better assessment of the neuroprotective effects of a therapy. Toward this end, we have developed a fast and robust method for accurately quantifying cerebral structural changes in several cortical and subcortical regions using serial MRI scans. In 169 healthy controls, 299 subjects with mild cognitive impairment (MCI), and 129 subjects with AD, we measured rates of subregional cerebral volume change for each cohort and performed power calculations to identify regions that would provide the most sensitive outcome measures in clinical trials of disease-modifying agents. Consistent with regional specificity of AD, temporal-lobe cortical regions showed the greatest disease-related changes and significantly outperformed any of the clinical or cognitive measures examined for both AD and MCI. Global measures of change in brain structure, including whole-brain and ventricular volumes, were also elevated in AD and MCI, but were less salient when compared to changes in normal subjects. Therefore, these biomarkers are less powerful for quantifying disease-modifying effects of compounds that target AD pathology. The findings indicate that regional temporal lobe cortical changes would have great utility as outcome measures in clinical trials and may also have utility in clinical practice for aiding early diagnosis of neurodegenerative disease.

A substantial proportion of hospice patients who meet medical criteria for services choose to withdraw. Further research is needed to define more clearly the reasons for withdrawal and to investigate whether withdrawal is consistent with patient preferences.

The missing data are not missing completely at random in ADNI and likely conditional on certain features in addition to cognitive function. Missing data predictors vary across biomarkers and even MCI and AD groups do not share the same missing data pattern. Understanding the missing data structure may help in the design of future longitudinal studies and clinical trials in AD.

HHC use is common among elderly patients after discharge from acute care. A simple predictive model based on four risk factors can be used on admission to predict HHC use. This model may be useful for discharge planning and health care utilization planning for the elderly population.

The importance of some recognized risk factors on genetic influences for coronary heart disease (CHD) needs further clarification. The aim of the present study was therefore to study the impact of known risk factors on genetic influences for CHD-death. Both twin (correlated gamma-frailty) and non-twin models (univariate gamma-frailty) were utilized and compared regarding their suitability for genetic analyses. The study population consisted of twins born in Sweden between 1886 and 1925. As expected, our findings indicate that genetic influences are important for CHD-death. Inclusion of risk factors in the twin-model increased heritability estimates, primarily due to a substantial reduction in non-shared environmental variances. The genetic influences for CHD-death are only marginally mediated through the risk factors among males, but more so among females. Although the outcome phenotype used in the present study is not behavioral, the analyses demonstrate the potential of frailty models for quantitative genetic analyses of categorical phenotypes.