Dataset Information

Overweight, Obesity, and Survival After Stroke in the Framingham Heart Study.

ABSTRACT:

Background

We investigated how body weight affects survival after stroke, leveraging the availability of multiple prestroke body mass index (BMI) measurements and using a nested case-control design in a community-based sample.

Methods and results

We compared all-cause mortality in participants stratified by prestroke weight. Separate analyses were performed for ischemic stroke and all stroke and for age-, sex-, and BMI category-matched stroke-free controls. Participants were grouped into BMI categories and followed for up to 10 years. Differences in survival were tested for interaction by case status. In sensitivity analysis, to exclude those with prestroke weight loss, we restricted the reference group to participants with 2 consistently normal BMI measurements within 10 years before stroke/matching. There were 782 stroke cases (age 71±9, 51% female participants, 87% ischemic stroke) and 2346 controls (age 72±9, 51% female participants). Overweight participants with ischemic stroke had a lower mortality compared with those with normal weight (hazard ratio [HR]=0.70, 95%CI 0.55-0.90, P=0.005). The association of reduced mortality with BMI ?25, compared with normal-weight BMI 18.5 to <25, was pronounced among ischemic stroke cases but diminished with inclusion of hemorrhagic strokes (case-control interaction P=0.051 and P=0.130, respectively). Compared with participants with stable normal weight, moderately increased weight was protective after ischemic stroke (overweight HR=0.72, 95%CI 0.53-0.99, P=0.041).

Conclusions

Overweight and mildly obese participants had better 10-year survival after ischemic stroke compared with normal-weight participants, even after excluding persons with recent prestroke weight loss. There may be unknown protective factors associated with a moderately increased body weight before stroke.

SUBMITTER: Aparicio HJ

PROVIDER: S-EPMC5669145 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Publications

Overweight, Obesity, and Survival After Stroke in the Framingham Heart Study.

Aparicio Hugo J HJ Himali Jayandra J JJ Beiser Alexa S AS Davis-Plourde Kendra L KL Vasan Ramachandran S RS Kase Carlos S CS Wolf Philip A PA Seshadri Sudha S

Journal of the American Heart Association 20170624 6

<h4>Background</h4>We investigated how body weight affects survival after stroke, leveraging the availability of multiple prestroke body mass index (BMI) measurements and using a nested case-control design in a community-based sample.<h4>Methods and results</h4>We compared all-cause mortality in participants stratified by prestroke weight. Separate analyses were performed for ischemic stroke and all stroke and for age-, sex-, and BMI category-matched stroke-free controls. Participants were group ...[more]

PMID: 28647687

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Project description:BackgroundStatistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome.MethodsIn this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy, and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times.ResultsSimulation results demonstrate that the Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. The Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years.ConclusionsTraditional methods for modeling longitudinal and survival data, such as the time dependent covariate method, that use the observed longitudinal data, tend to provide downwardly biased estimates. The two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance.

Project description:PurposeThe insulin-signaling pathway plays a pivotal role in cancer biology; however, evidence of genetic alterations in human studies is limited. This case-control study nested within the Framingham Heart Study (FHS) examined the association between inherited genetic variation in the insulin receptor (INSR) gene and obesity-related cancer risk.MethodsThe study sample consisted of 1,475 controls and 396 cases from the second familial generation of the FHS. Participants who provided consent were genotyped. Nineteen single-nucleotide polymorphisms (SNPs) in the INSR gene were investigated in relation to risk of obesity-related cancers combined and breast, prostate and colorectal cancers. Generalized estimation equation models controlling for familial correlations and include age, sex, smoking and body mass index as covariates, assuming additive models, were used.ResultsThree SNPs, rs2059807, s8109559 and rs919275, were significantly associated with obesity-related cancers (p value < 0.02) with the most significantly associated SNP being rs2059807 (p value = 0.008). Carriers of two copies of SNP rs2059807 risk allele T were significantly less prevalent among subjects with obesity-related cancers [f(TT)cases = 14 vs. f(TT)controls = 18 %; OR 1.23]. In exploratory analyses evaluating site-specific cancers, the INSR rs2059807 association with these cancers was consistent with that observed for the main outcome (ORs colorectal cancer = 1.5, breast cancer = 1.29, prostate = 1.06). There was no statistically significant interaction between the INSR-SNP and blood glucose in relation to obesity-related cancer.ConclusionsThe INSR gene is implicated in obesity-related cancer risk, as 3 of 19 SNPs were nominally associated, after false discovery rate (FDR) correction, with the main outcome. Risk allele homozygotes (rs2059807) were less prevalent among subjects with obesity-related cancer. These results should be replicated in other populations to confirm the findings.

Dataset Information

Overweight, Obesity, and Survival After Stroke in the Framingham Heart Study.

Background

Methods and results

Conclusions

Publications

Overweight, Obesity, and Survival After Stroke in the Framingham Heart Study.

Similar Datasets

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