Biomedical subjects

Changyu Shen

Publications and source records attributed to Changyu Shen.

12 recordsLinked to original sources

PubMed2007

A mixed-effects model for cognitive decline with non-monotone non-response from a two-phase longitudinal study of dementia.

Most longitudinal studies of elderly are characterized by substantial drop-out due to death and many other factors beyond the control of the investigators. In a two-phase longitudinal study of dementia, subjects with cognitive impairment skip the first phase survey in the next follow-up, leading to intermittent missing variables measured in that phase. In the context of analysing pre-dementia cognitive decline in an elderly population, both of the two causes of non-response can potentially be informative in the sense that the missingness is dependent on the unobserved outcome. To take these factors into account, mixed-effects models are constructed to allow the outcome and the multiple causes of missing values to share the same 'random parameter' or random effect. The crucial assumption of our model is that the random effects of the model for the outcome and that of the model for the missing-data indicators are linked in a deterministic manner. It can be thought of as an approximation of a more general and realistic situation, in which the two models have distinct, yet dependent, random effects. We conduct a simulation study to investigate possible deviations of the estimates under such a scenario. A second simulation illustrates the magnitude of the bias in estimating the difference of decline rate between two groups when the random effects are linked in different manners for the two groups.

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PubMed2006

A statistical framework to discover true associations from multiprotein complex pull-down proteomics data sets.

Experimental processes to collect and process proteomics data are increasingly complex, and the computational methods to assess the quality and significance of these data remain unsophisticated. These challenges have led to many biological oversights and computational misconceptions. We developed an empirical Bayes model to analyze multiprotein complex (MPC) proteomics data derived from peptide mass spectrometry detections of purified protein complex pull-down experiments. Using our model and two yeast proteomics data sets, we estimated that there should be an average of about 20 true associations per MPC, almost 10 times as high as was previously estimated. For data sets generated to mimic a real proteome, our model achieved on average 80% sensitivity in detecting true associations, as compared with the 3% sensitivity in previous work, while maintaining a comparable false discovery rate of 0.3%. Cross-examination of our results with protein complexes confirmed by various experimental techniques demonstrates that many true associations that cannot be identified by previous approach are identified by our method.

Algorithms↗

PubMed2006

A copula model for repeated measurements with non-ignorable non-monotone missing outcome.

A normal copula-based selection model is proposed for continuous longitudinal data with a non-ignorable non-monotone missing-data process. The normal copula is used to combine the distribution of the outcome of interest and that of the missing-data indicators given the covariates. Parameters in the model are estimated by a pseudo-likelihood method. We first use the GEE with a logistic link to estimate the parameters associated with the marginal distribution of the missing-data indicator given the covariates, assuming that covariates are always observed. Then we estimate other parameters by inserting the estimates from the first step into the full likelihood function. A simulation study is conducted to assess the robustness of the assumed model under different missing-data processes. The proposed method is then applied to one example from a community cohort study to demonstrate its capability to reduce bias.

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PubMed2006

Treating postprandial hyperglycemia does not appear to delay progression of early type 2 diabetes: the Early Diabetes Intervention Program.

OBJECTIVE: Postprandial hyperglycemia characterizes early type 2 diabetes. We investigated whether ameliorating postprandial hyperglycemia with acarbose would prevent or delay progression of diabetes, defined as progression to frank fasting hyperglycemia, in subjects with early diabetes (fasting plasma glucose [FPG] <140 mg/dl and 2-h plasma glucose > or =200 mg/dl). RESEARCH DESIGN AND METHODS: Two hundred nineteen subjects with early diabetes were randomly assigned to 100 mg acarbose t.i.d. or identical placebo and followed for 5 years or until they reached the primary outcome (two consecutive quarterly FPG measurements of > or =140 mg/dl). Secondary outcomes included measures of glycemia (meal tolerance tests, HbA(1c), annual oral glucose tolerance tests [OGTTs]), measures of insulin resistance (homeostasis model assessment [HOMA] of insulin resistance and insulin sensitivity index from hyperglycemic clamps), and secondary measures of beta-cell function (HOMA-beta, early- and late-phase insulin secretion, and proinsulin-to-insulin ratio). RESULTS: Acarbose significantly reduced postprandial hyperglycemia. However, there was no difference in the cumulative rate of frank fasting hyperglycemia (29% with acarbose and 34% with placebo; P = 0.65 for survival analysis). There were no significant differences between groups in OGTT values, measures of insulin resistance, or secondary measures of beta-cell function. In a post hoc analysis of subjects with initial FPG <126 mg/dl, acarbose reduced the rate of development of FPG > or =126 mg/dl (27 vs. 50%; P = 0.04). CONCLUSIONS: Ameliorating postprandial hyperglycemia did not appear to delay progression of early type 2 diabetes. Factors other than postprandial hyperglycemia may be greater determinants of progression of diabetes. Alternatively, once FPG exceeds 126 mg/dl, beta-cell failure may no longer be remediable.

Acarbose↗

PubMed2006

Mining Alzheimer disease relevant proteins from integrated protein interactome data.

Huge unrealized post-genome opportunities remain in the understanding of detailed molecular mechanisms for Alzheimer Disease (AD). In this work, we developed a computational method to rank-order AD-related proteins, based on an initial list of AD-related genes and public human protein interaction data. In this method, we first collected an initial seed list of 65 AD-related genes from the OMIM database and mapped them to 70 AD seed proteins. We then expanded the seed proteins to an enriched AD set of 765 proteins using protein interactions from the Online Predicated Human Interaction Database (OPHID). We showed that the expanded AD-related proteins form a highly connected and statistically significant protein interaction sub-network. We further analyzed the sub-network to develop an algorithm, which can be used to automatically score and rank-order each protein for its biological relevance to AD pathways(s). Our results show that functionally relevant AD proteins were consistently ranked at the top: among the top 20 of 765 expanded AD proteins, 19 proteins are confirmed to belong to the original 70 AD seed protein set. Our method represents a novel use of protein interaction network data for Alzheimer disease studies and may be generalized for other disease areas in the future.

Algorithms↗

PubMed2005

Alzheimer disease and mortality: a 15-year epidemiological study.

BACKGROUND: Alzheimer disease (AD) is considered a leading cause of death, but few studies have examined the contribution of AD to mortality based on follow-up of representative US cohorts. OBJECTIVE: To examine mortality rates, duration of survival, causes of death, and the contribution of AD to the risk of mortality in an aging community-based cohort, controlling for other predictors. DESIGN: Fifteen-year prospective epidemiological study. Mortality rates per 1000 person-years and the population-attributable risk of mortality were determined. Cox proportional hazards models were used to estimate relative risk of mortality due to AD, adjusting for relevant covariates. Death certificates were abstracted for listed causes of death. SETTING: A largely blue-collar rural community in southwestern Pennsylvania. PARTICIPANTS: A community-based cohort of 1670 adults 65 years and older at study enrollment. MAIN OUTCOME MEASURE: Mortality. RESULTS: In the overall cohort, AD was a significant predictor of mortality, with a hazard ratio of 1.4 after adjusting for covariates. The population-attributable risk of mortality from AD was 4.9% based on the same model. Examining the sexes separately, AD increased mortality risk only among women. Death certificates of AD subjects were more likely to list dementia/AD, other brain disorders, pneumonia, and dehydration, and less likely to include cancer. CONCLUSIONS: Alzheimer disease was responsible for 4.9% of the deaths in this elderly cohort. Alzheimer disease increased the risk of mortality 40% in the cohort as a whole and separately in women but not in men. The mean (SD) duration of survival with AD was 5.9 (3.7) years, and longer with earlier age at onset.

Age Factors↗

PubMed2005

Application of pattern-mixture models to outcomes that are potentially missing not at random using pseudo maximum likelihood estimation.

In this work, we fit pattern-mixture models to data sets with responses that are potentially missing not at random (MNAR, Little and Rubin, 1987). In estimating the regression parameters that are identifiable, we use the pseudo maximum likelihood method based on exponential families. This procedure provides consistent estimators when the mean structure is correctly specified for each pattern, with further information on the variance structure giving an efficient estimator. The proposed method can be used to handle a variety of continuous and discrete outcomes. A test built on this approach is also developed for model simplification in order to improve efficiency. Simulations are carried out to compare the proposed estimation procedure with other methods. In combination with sensitivity analysis, our approach can be used to fit parsimonious semi-parametric pattern-mixture models to outcomes that are potentially MNAR. We apply the proposed method to an epidemiologic cohort study to examine cognition decline among elderly.

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PubMed2005

Discover true association rates in multi-protein complex proteomics data sets.

Experimental processes to collect and process proteomics data are increasingly complex, while the computational methods to assess the quality and significance of these data remain unsophisticated. These challenges have led to many biological oversights and computational misconceptions. We developed a complete empirical Bayes model to analyze multi-protein complex (MPC) proteomics data derived from peptide mass spectrometry detections of purified protein complex pull-down experiments. Our model considers not only bait-prey associations, but also prey-prey associations missed in previous work. Using our model and a yeast MPC proteomics data set, we estimated that there should be an average of 28 true associations per MPC, almost ten times as high as was previously estimated. For data sets generated to mimic a real proteome, our model achieved on average 80% sensitivity in detecting true associations, as compared with the 3% sensitivity in previous work, while maintaining a comparable false discovery rate of 0.3%.

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PubMed2004

Mild cognitive impairment, amnestic type: an epidemiologic study.

OBJECTIVE: To estimate the prevalence and examine the course of mild cognitive impairment (MCI), amnestic type, using current criteria, within a representative community sample. METHODS: Retroactive application of MCI criteria to data collected during a prospective epidemiologic study was performed. The subjects were drawn from voter registration lists, composing a cohort of 1,248 individuals with mean age of 74.6 (5.3) years, who were nondemented at entry and who were assessed biennially over 10 years of follow-up. The Petersen amnestic MCI criteria were operationalized as 1) impaired memory: Word List Delayed Recall score of <1 SD below mean; 2) normal mental status: Mini-Mental State Examination score of 25+; 3) normal daily functioning: no instrumental impairments; 4) memory complaint: subjective response to standardized question; 5) not demented: Clinical Dementia Rating Scale score of <1. RESULTS: At the five assessments, amnestic MCI criteria were met by 2.9 to 4.0% of the cohort. Of 40 persons with MCI at the first assessment, 11 (27%) developed dementia over the next 10 years. Over each 2-year interval, MCI persons showed increased risk of dementing (odds ratio = 3.9, 95% CI = 2.1 to 7.2); 11.1 to 16.7% progressed to Alzheimer disease and 0 to 5.0% progressed to other dementias. Over the same intervals, 11.1 to 21.2% of those with MCI remained MCI; of 33.3 to 55.6% who no longer had MCI, half had reverted to normal. CONCLUSIONS: In this community-based sample, 3 to 4% of nondemented persons met MCI operational criteria; despite increased risk of progressing to dementia, a substantial proportion also remained stable or reverted to normal during follow-up. Amnestic MCI as currently defined is a high-risk but unstable and heterogeneous group.

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PubMed2004

Predictors of nursing facility admission: a 12-year epidemiological study in the United States.

OBJECTIVES: To identify predictors of institutionalization in a community-based cohort of older adults. DESIGN: Prospective, longitudinal. After initial assessment at study entry, surviving participants were reassessed in a series of approximately biennial waves until October 2001; baseline for the current analysis was Wave 2 (1989-91). SETTING: Largely rural, blue-collar community in the mid-Monongahela Valley of southwestern Pennsylvania. PARTICIPANTS: A population-based cohort of 1,147 adults, aged 66 and older (mean 74.1) at baseline, who were not already institutionalized and who had complete data on all variables of interest. MEASUREMENTS: Cox proportional hazards models were used to identify predictors of institutionalization from among selected variables measured at baseline, including age, sex, education, marital status, living arrangements, ability to perform instrumental activities of daily living (IADLs), depressive symptoms, number of prescription medications (as an index of overall morbidity), self-reported social support, hospitalization during the preceding year, and cognitive functioning. Dementia was defined according to the operational criteria of the Diagnostic and Statistical Manual of Mental Disorders, Third Edition, Revised, and by a Clinical Dementia Rating of 0.5 or greater, based on a standardized clinical assessment. The outcome variable was institutionalization, defined as entry into in a nursing home. RESULTS: Significant predictors of institutionalization were dementia (hazard ratio (HR)=5.09, 95% confidence interval (CI)=2.92-8.84), measured as a time-dependent variable; older age (HR=1.06, 95% CI=1.03-1.10); IADL disability (HR=1.31, 95% CI=1.15-1.50); worse/less social support (HR=1.27, 95% CI=1.10-1.46); and number of prescription medications (HR=1.21, 95% CI=1.11-1.32), measured at baseline. The interaction between number of prescription drugs and dementia was also significant, suggesting that prescription medication count had less effect on institutionalization in those with dementia than in those without. CONCLUSION: Dementia emerged as the most potent risk factor for institutionalization in this 12-year community-based epidemiological study. Medical burden conferred greater vulnerability to institutionalization in nondemented persons than in those with dementia.

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PubMed2003

Functional transitions and active life expectancy associated with Alzheimer disease.

CONTEXT: The concept of active life expectancy, the number of years a person can expect to live without disability, is used for the first time, to our knowledge, to examine the effect of Alzheimer disease (AD) on total life expectancy with different degrees of disability. OBJECTIVES: To estimate and compare total life expectancy and average duration lived with different degrees of disability, between persons with and without AD. DESIGN: Ten-year prospective epidemiologic study. SETTING: A largely blue-collar rural community in Southwestern Pennsylvania. PARTICIPANTS: A population-based cohort of 1201 subjects (at the beginning of follow-up) with a mean age of 75 years. MAIN OUTCOME MEASURES: At age 70 and every 2 years thereafter, among persons with AD and nondemented persons, (1) the total expectancy of remaining life and (2) the duration lived with different numbers of impaired instrumental activities of daily living (IADLs), grouped as 0 to 1, 2 to 5, and 6 to 7 impairments. RESULTS: Alzheimer disease greatly shortened the total life expectancy to a similar extent in men and women, with the most pronounced reduction among those who were younger. Besides their shorter survival, men and women with AD spent more absolute years, and also a greater proportion of their remaining lives, with 6 to 7 IADL impairments than did their nondemented age peers. Nondemented women spent more years with 2 to 5 IADL impairments than nondemented men, while women with AD spent more years with 6 to 7 IADL impairments than men with AD. CONCLUSION: The concept of active life expectancy adds a useful new dimension to the study of outcomes in AD.

Activities of Daily Living↗

PubMed2003

Serum anticholinergic activity in a community-based sample of older adults: relationship with cognitive performance.

BACKGROUND: Serum anticholinergic activity (SAA), as measured by a radioreceptor assay, quantifies a person's overall anticholinergic burden caused by all drugs and their metabolites. In several small geriatric patient groups, SAA has been associated with cognitive impairment or frank delirium. To our knowledge, there has not yet been any systematic study of the prevalence of SAA and its effect on cognition in a community-based population. METHODS: Serum anticholinergic activity was measured in 201 subjects who were randomly selected among the participants in an epidemiological community study, based on their age and sex. Cognitive performance was assessed with use of the Mini-Mental State Examination. The association between SAA and cognitive performance was examined using a univariate analysis and a multiple logistic regression model, adjusting for age, sex, educational level, and number of medications. RESULTS: Serum anticholinergic activity was detectable in 180 (89.6%) participants (range, 0.50-5.70 pmol/mL). Univariate testing showed a significant association between SAA and Mini-Mental State Examination scores. Logistic regression analysis indicated that subjects with SAA at or above the sample's 90th percentile (ie, SAA >/=2.80 pmol/mL) were 13 times (odds ratio, 1.08-152.39) more likely than subjects with undetectable SAA to have a Mini-Mental State Examination score of 24 (the sample's 10th percentile) or below. CONCLUSIONS: To our knowledge, this is the largest analysis of SAA and the first to examine its extent and relationship with cognitive performance in a community sample. Its results suggest that SAA can be detected in most older persons in the community and confirm that even low SAA is associated with cognitive impairment.

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