Biomedical subjects

Mark E Glickman

Publications and source records attributed to Mark E Glickman.

5 recordsLinked to original sources

PubMed2006

The effect of age on hypertension control and management.

BACKGROUND: Despite guidelines recommending similar blood pressure (BP) treatment goals regardless of age, controversy exists regarding treating those > or = 80 years of age. Whether this affects current practice in terms of differences in BP control and number of prescribed antihypertensives by age is unknown. METHODS: This was a cross-sectional study of 59,207 outpatients with hypertension treated at 10 Veterans Health Administration sites. Outcome measures were BP control (< 140/90 mm Hg) and number of antihypertensive medications at the patient's last study visit. Uncontrolled BP was also categorized by whether systolic, diastolic, or both were elevated. RESULTS: Subjects 40 to 49 years and those 50 to 59 years of age had better BP control (adjusted odds ratios 1.35 [95% CI = 1.26 to 1.44] and 1.22 [CI = 1.17 to 1.28] respectively) compared with subjects 60 to 69 years of age; those 70 to 79 years of age and > or = 80 years had worse control (OR = 0.92 for both; respective CIs = 0.88 to 0.96 and 0.86 to 0.99). Antihypertensive medication use increased by successive decade to age 80 years, after which the trend reversed. Adjusted mean number of medications by age were: < 40 years, 2.60; 40 to 49, 2.82; 50 to 59, 2.91; 60 to 69, 3.01; 70 to 79, 3.03; > or = 80 years, 2.90 (P < .05 in pairwise comparisons). The trend of number of medications by age did not vary across hypertension categories, despite systolic hypertension increasing and diastolic hypertension decreasing with age. Subjects < 40 years of age were taking the fewest medications, followed by subjects > or = 80 years and then by those 40 to 49, 50 to 59, 70 to 79, and 60 to 69 years of age. CONCLUSIONS: The oldest hypertension patients, despite worse BP control, are being treated less aggressively with fewer medications than their younger counterparts (those 60 to 79 years of age). Our results suggest that current controversy in treating the oldest hypertensive patients is having an impact on actual practice.

Adolescent↗

PubMed2006

Sex differences in intellectual performance: analysis of a large cohort of competitive chess players.

Only 1% of the world's chess grandmasters are women. This underrepresentation is unlikely to be caused by discrimination, because chess ratings objectively reflect competitive results. Using data on the ratings of more than 250,000 tournament players over 13 years, we investigated several potential explanations for the male domination of elite chess. We found that (a) the ratings of men are higher on average than those of women, but no more variable; (b) matched boys and girls improve and drop out at equal rates, but boys begin chess competition in greater numbers and at higher performance levels than girls; and (c) in locales where at least 50% of the new young players are girls, their initial ratings are not lower than those of boys. We conclude that the greater number of men at the highest levels in chess can be explained by the greater number of boys who enter chess at the lowest levels.

Adolescent↗

PubMed2005

Apo-E genotypes and cardiovascular diseases: a sensitivity study using cross-validatory criteria.

The Apolipoprotein-E (Apo-E) gene, a gene that produces proteins which help to regulate lipid levels in the bloodstream, is of interest in the study of cardiovascular diseases. An approach to making inferences about the genetic effects of the Apo-E gene has been developed by Glickman and Gagnon (2002). The framework describes the role of genetic and risk factors on the onset ages of multiple diseases, and accounts for the possibility that an individual was censored for reasons related to the diseases of interest. The framework also allows for missing genetic information, so that subjects censored prior to genetic sampling, and therefore missing such information, may still be included in the analysis. We apply an extension to this framework to the original cohort of the Framingham Heart Study for measuring the effects of different Apo-E genotypes on the onset age of various cardiovascular disease events. In particular, we compare the fit of univariate versus multivariate onset age components to the model, whether to incorporate health covariates measured at baseline or at a point later in the study, and whether to assume a heritability model for Apo-E genotype frequencies. The results of the best fitting model are presented.

Age of Onset↗

PubMed2003

The analysis of survival data with a non-susceptible fraction and dual censoring mechanisms.

It is known that the ages of onset of many diseases are determined by both a genetic predisposition to disease as well as environmental risk factors that are capable of either triggering or hastening the onset of disease. Difficulties in modelling onset ages arise when a large fraction fail to inherit the disease-causing gene, and multiple reasons for censoring result in unobserved onset ages. We present a parametric Bayesian model that includes subjects with missing age information, non-susceptible subjects and allows for regression on risk factor information. The model is fit using Markov chain Monte Carlo simulation from the posterior distribution, and allows the simultaneous estimation of the proportion of the population at risk of disease, the mean onset age of disease, survival after disease onset, and the association of risk factors with susceptibility, onset age and survival after onset. An example employing Huntington's disease data is presented.

Age of Onset↗

PubMed2002

Modeling the effects of genetic factors on late-onset diseases in cohort studies.

Many late-onset diseases are caused by what appears to be a combination of a genetic predisposition to disease and environmental factors. The use of existing cohort studies provides an opportunity to infer genetic predisposition to disease on a representative sample of a study population, now that many such studies are gathering genetic information on the participants. One feature to using existing cohorts is that subjects may be censored due to death prior to genetic sampling, thereby adding a layer of complexity to the analysis. We develop a statistical framework to infer parameters of a latent variables model for disease onset. The latent variables model describes the role of genetic and modifiable risk factors on the onset ages of multiple diseases, and accounts for right-censoring of disease onset ages. The framework also allows for missing genetic information by inferring a subject's unknown genotype through appropriately incorporated covariate information. The model is applied to data gathered in the Framingham Heart Study for measuring the effect of different Apo-E genotypes on the occurrence of various cardiovascular disease events.

Adult↗