Continuous outcome logistic regression for analyzing body mass index distributions.
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ABSTRACT:
SUBMITTER: Lohse T
PROVIDER: S-EPMC5721934 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:This paper details the design, evaluation, and implementation of a framework for detecting and modeling nonlinearity between a binary outcome and a continuous predictor variable adjusted for covariates in complex samples. The framework provides familiar-looking parameterizations of output in terms of linear slope coefficients and odds ratios. Estimation methods focus on maximum likelihood optimization of piecewise linear free-knot splines formulated as B-splines. Correctly specifying the optimal number and positions of the knots improves the model, but is marked by computational intensity and numerical instability. Our inference methods utilize both parametric and nonparametric bootstrapping. Unlike other nonlinear modeling packages, this framework is designed to incorporate multistage survey sample designs common to nationally representative datasets. We illustrate the approach and evaluate its performance in specifying the correct number of knots under various conditions with an example using body mass index (BMI; kg/m2) and the complex multi-stage sampling design from the Third National Health and Nutrition Examination Survey to simulate binary mortality outcomes data having realistic nonlinear sample-weighted risk associations with BMI. BMI and mortality data provide a particularly apt example and area of application since BMI is commonly recorded in large health surveys with complex designs, often categorized for modeling, and nonlinearly related to mortality. When complex sample design considerations were ignored, our method was generally similar to or more accurate than two common model selection procedures, Schwarz's Bayesian Information Criterion (BIC) and Akaike's Information Criterion (AIC), in terms of correctly selecting the correct number of knots. Our approach provided accurate knot selections when complex sampling weights were incorporated, while AIC and BIC were not effective under these conditions.
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Project description:Background Patients with a single ventricle who experience early life growth failure suffer high morbidity and mortality in the perisurgical period. However, long-term implications of poor infant growth, as well as associations between body mass index (BMI) and outcome in adulthood, remain unclear. We aimed to model BMI trajectories of patients with a single ventricle undergoing a Fontan procedure to determine trajectory-based differences in baseline characteristics and long-term clinical outcomes. Methods and Results We performed a retrospective analysis of medical records from patients in the Australia and New Zealand Fontan Registry receiving treatment at the Royal Children's Hospital, The Children's Hospital at Westmead, Royal Melbourne Hospital, and Royal Prince Alfred Hospital from 1981 to 2018. BMI trajectories were modeled in 496 patients using latent class growth analysis from 0 to 6 months, 6 to 60 months, and 5 to 16 years. Trajectories were compared regarding long-term incidence of severe Fontan failure (defined as mortality, heart transplantation, Fontan takedown, or New York Heart Association class III/IV heart failure). Three trajectories were found for male and female subjects at each age group-lower, middle, higher. Subjects in the lower trajectory at 0 to 6 months were more likely to have an atriopulmonary Fontan and experienced increased mortality long term. No association was found between higher BMI trajectory, current BMI, and long-term outcome. Conclusions Poor growth in early life correlates with increased long-term severe Fontan failure. Delineation of distinct BMI trajectories can be used in larger and older cohorts to find optimal BMI targets for patient outcome.
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Project description:Depression and obesity are complex global health problems. Recent studies suggest that a genetic predisposition to obesity might be accentuated in people with depression, but these analyses are prone to bias. Here, we tested the hypothesis that depression accentuates genetic susceptibility to obesity and applied negative control experiments to test whether any observed interactions were real or driven by confounding and statistical biases. We used data from up to 378 000 Europeans in UK Biobank, a 73 variant body mass index (BMI) genetic risk score, two depression measures [depression symptoms (DS), major depression (MD)] and an antidepressant usage variable available. We tested whether (i) depression and (ii) antidepressant treatment accentuated genetic susceptibility to obesity. Finally, we performed negative control experiments by sampling individuals at random so that they had BMI distributions identical to depression cases and controls. Depression was associated with an accentuation of an individual's genetic risk of obesity with evidence of interactions for both DS and MD (Pinteraction = 7 × 10-4 and 7 × 10-5 respectively). Antidepressant usage within DS cases accentuated genetic obesity risk (Pinteraction = 9 × 10-4), but not for MD (Pinteraction = 0.13). Negative control experiments suggested that the observed interactions for MD (empirical-P = 0.067) may be driven by statistical biases or confounding factors but were not possible with the larger DS groups. Antidepressant usage interaction also appears to be driven by statistical artefacts (empirical-P = 0.510 using MD and 0.162 using DS). We have highlighted the importance of running negative experiments to confirm putative interactions in gene-environment studies. We provide some tentative evidence that depression accentuates an individual's genetic susceptibility to higher BMI but demonstrated that the BMI distributions within cases and controls might drive these interactions.
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Project description:ObjectiveTo determine whether the obesity paradox exists in patients who undergo carotid artery stenting (CAS) or carotid endarterectomy (CEA) for symptomatic carotid artery stenosis.MethodsWe combined individual patient data from 2 randomized trials (Endarterectomy vs Angioplasty in Patients with Symptomatic Severe Carotid Stenosis and Stent-Protected Angioplasty vs Carotid Endarterectomy) and 3 centers in a third trial (International Carotid Stenting Study). Baseline body mass index (BMI) was available for 1,969 patients and classified into 4 groups: <20, 20-<25, 25-<30, and ≥30 kg/m2. Primary outcome was stroke or death, investigated separately for the periprocedural and postprocedural period (≤120 days/>120 days after randomization). This outcome was compared between different BMI strata in CAS and CEA patients separately, and in the total group. We performed intention-to-treat multivariable Cox regression analyses.ResultsMedian follow-up was 2.0 years. Stroke or death occurred in 159 patients in the periprocedural (cumulative risk 8.1%) and in 270 patients in the postprocedural period (rate 4.8/100 person-years). BMI did not affect periprocedural risk of stroke or death for patients assigned to CAS (ptrend = 0.39) or CEA (ptrend = 0.77) or for the total group (ptrend = 0.48). Within the total group, patients with BMI 25-<30 had lower postprocedural risk of stroke or death than patients with BMI 20-<25 (BMI 25-<30 vs BMI 20-<25; hazard ratio 0.72; 95% confidence interval 0.55-0.94).ConclusionsBMI is not associated with periprocedural risk of stroke or death; however, BMI 25-<30 is associated with lower postprocedural risk than BMI 20-<25. These observations were similar for CAS and CEA.
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Project description:Novel drug targets for sustained reduction in body mass index (BMI) are needed to curb the epidemic of obesity, which affects 650 million individuals worldwide and is a causal driver of cardiovascular and metabolic disease and mortality. Previous studies reported that the Arg95Ter nonsense variant of GPR151, an orphan G protein-coupled receptor, is associated with reduced BMI and reduced risk of Type 2 Diabetes (T2D). Here, we further investigate GPR151 with the Pakistan Genome Resource (PGR), which is one of the largest exome biobanks of human homozygous loss-of-function carriers (knockouts) in the world. Among PGR participants, we identify eleven GPR151 putative loss-of-function (plof) variants, three of which are present at homozygosity (Arg95Ter, Tyr99Ter, and Phe175LeufsTer7), with a cumulative allele frequency of 2.2%. We confirm these alleles in vitro as loss-of-function. We test if GPR151 plof is associated with BMI, T2D, or other metabolic traits and find that GPR151 deficiency in complete human knockouts is not associated with clinically significant differences in these traits. Relative to Gpr151+/+ mice, Gpr151-/- animals exhibit no difference in body weight on normal chow and higher body weight on a high-fat diet. Together, our findings indicate that GPR151 antagonism is not a compelling therapeutic approach to treatment of obesity.
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Project description:Under the logistic regression framework, we propose a forward-backward method, SODA, for variable selection with both main and quadratic interaction terms. In the forward stage, SODA adds in predictors that have significant overall effects, whereas in the backward stage SODA removes unimportant terms to optimize the extended Bayesian Information Criterion (EBIC). Compared with existing methods for variable selection in quadratic discriminant analysis, SODA can deal with high-dimensional data in which the number of predictors is much larger than the sample size and does not require the joint normality assumption on predictors, leading to much enhanced robustness. We further extend SODA to conduct variable selection and model fitting for general index models. Compared with existing variable selection methods based on the Sliced Inverse Regression (SIR) (Li, 1991), SODA requires neither linearity nor constant variance condition and is thus more robust. Our theoretical analysis establishes the variable-selection consistency of SODA under high-dimensional settings, and our simulation studies as well as real-data applications demonstrate superior performances of SODA in dealing with non-Gaussian design matrices in both logistic and general index models.
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Project description:ObjectivesBeneficial effects of overweight and obesity on mortality after acute myocardial infarction (AMI) have been described as "Body Mass Index (BMI) paradox". However, the effects of BMI is still on debate. We analyzed the association between BMI and 1-year clinical outcomes after AMI.MethodsAmong 13,104 AMI patients registered in Korea Acute Myocardial Infarction Registry-National Institute of Health (KAMIR-NIH) between November 2011 and December 2015, 10,568 patients who eligible for this study were classified into 3 groups according to BMI (Group 1; < 22 kg/m2, 22 ≤ Group 2 < 26 kg/m2, Group 3; ≥ 26 kg/m2). The primary end point was all cause death at 1 year.ResultsOver the median follow-up of 12 months, the event of primary end point occurred more frequently in the Group 1 patients than in the Group 3 patients (primary endpoint: adjusted hazard ratio [aHR], 1.537; 95% confidence interval [CI] 1.177 to 2.007, p = 0.002). Especially, cardiac death played a major role in this effect (aHR, 1.548; 95% confidence interval [CI] 1.128 to 2.124, p = 0.007).ConclusionsHigher BMI appeared to be good prognostic factor on 1-year all cause death after AMI. This result suggests that higher BMI or obesity might confer a protective advantage over the life-quality after AMI.
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Project description:ImportanceBody mass index (BMI) is used to diagnose obesity in adolescents worldwide, despite evidence that weight does not scale with height squared in adolescents. To account for this, health care providers diagnose obesity using BMI percentiles for each age (BMI z scores), but this does not ensure that BMI is accurate in adolescents.ObjectiveTo compare the accuracy of BMI vs other body fat indices of the form body mass divided by heightn in estimating body fat levels in adolescents.Design, setting, and participantsCross-sectional data from the 1999 to 2006 US National Health and Nutrition Examination Survey were analyzed between September 2015 and December 2016.Main outcomes and measuresDual-energy x-ray absorptiometry and anthropometric data were used to determine changes in body fat levels, body proportions, and the scaling relationships among body mass, height, and percent body fat. To assess the merits of each adiposity index, 3 criteria were used: stability with age, accuracy in estimating percent body fat, and accuracy in classifying adolescents as overweight vs normal weight.ResultsParticipants included 2285 non-Hispanic white participants aged 8 to 29 years. Percent body fat varied with both age and height during adolescence, invalidating the standard weight-to-height regression as the way of finding the optimal body fat index. Because the correct regression model (percent body fat is proportional to mass divided by heightn) suggested that percent body fat scales to height with an exponent closer to 3, we therefore focused on the tri-ponderal mass index (TMI; mass divided by height cubed) as an alternative to BMI z scores. For ages 8 to 17 years, TMI yielded greater stability with age and estimated percent body fat better than BMI (R2?=?0.64 vs 0.38 in boys and R2?=?0.72 vs 0.66 in girls). Moreover, TMI misclassified adolescents as overweight vs normal weight less often than BMI z scores (TMI, 8.4%; 95% CI, 7.3%-9.5% vs BMI, 19.4%; 95% CI, 17.8%-20.0%; P?<?.001) and performed equally as well as updated BMI percentiles derived from the same data set (TMI, 8.4%; 95% CI, 7.3%-9.5% vs BMI, 8.0%; 95% CI, 6.9%-9.1%; P?=?.62).Conclusions and relevanceThe tri-ponderal mass index estimates body fat levels more accurately than BMI in non-Hispanic white adolescents aged 8 to 17 years. Moreover, TMI diagnoses adolescents as overweight more accurately than BMI z scores and equally as well as updated BMI percentiles but is much simpler to use than either because it does not involve complicated percentiles. Taken together, it is worth considering replacing BMI z scores with TMI to estimate body fat levels in adolescents.
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Project description:The relation between obesity and stroke outcome has been disputed. This study was aimed to determine the association of body mass index (BMI) with mortality and functional outcome in patients with acute ischemic stroke. Data were from a national, multi-centre, prospective, hospital-based register: the ChinaQUEST (Quality Evaluation of Stroke Care and Treatment) study. Of 4782 acute ischemic stroke patients, 282 were underweight (BMI?<?18.5?kg/m2), 2306 were normal-weight (BMI 18.5 to?<?24?kg/m2), 1677 were overweight (BMI 24 to <28?kg/m2) and 517 were obese (BMI???28?kg/m2). The risks of death at 12 months and death or high dependency at 3 and 12 months in overweight (HR: 0.97, 95% CI: 0.78-1.20; OR: 0.93, 95% CI: 0.80-1.09; OR: 0.95, 95% CI: 0.81-1.12) and obese patients (HR: 1.07, 95% CI: 0.78-1.48; OR: 0.96, 95% CI: 0.75-1.22; OR: 1.06, 95% CI: 0.83-1.35) did not differ from normal-weight patients significantly after adjusting for baseline characteristics. Underweight patients had significantly increased risks of these three outcomes. In ischemic stroke patients, being overweight or obese was not associated with decreased mortality or better functional recovery but being underweight predicted unfavourable outcomes.
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Project description:Many diseases such as cancer and heart diseases are heterogeneous and it is of great interest to study the disease risk specific to the subtypes in relation to genetic and environmental risk factors. However, due to logistic and cost reasons, the subtype information for the disease is missing for some subjects. In this article, we investigate methods for multinomial logistic regression with missing outcome data, including a bootstrap hot deck multiple imputation (BHMI), simple inverse probability weighted (SIPW), augmented inverse probability weighted (AIPW), and expected estimating equation (EEE) estimators. These methods are important approaches for missing data regression. The BHMI modifies the standard hot deck multiple imputation method such that it can provide valid confidence interval estimation. Under the situation when the covariates are discrete, the SIPW, AIPW, and EEE estimators are numerically identical. When the covariates are continuous, nonparametric smoothers can be applied to estimate the selection probabilities and the estimating scores. These methods perform similarly. Extensive simulations show that all of these methods yield unbiased estimators while the complete-case (CC) analysis can be biased if the missingness depends on the observed data. Our simulations also demonstrate that these methods can gain substantial efficiency compared with the CC analysis. The methods are applied to a colorectal cancer study in which cancer subtype data are missing among some study individuals.
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