Parentage analysis with genetic markers in natural populations. I. The expected proportion of offspring with unambiguous paternity.

Recent studies indicate that polymorphic genetic markers are potentially helpful in resolving genealogical relationships among individuals in a natural population. Genetic data provide opportunities for paternity exclusion when genotypic incompatibilities are observed among individuals, and the present investigation examines the resolving power of genetic markers in unambiguous positive determination of paternity. Under the assumption that the mother for each offspring in a population is unambiguously known, an analytical expression for the fraction of males excluded from paternity is derived for the case where males and females may be derived from two different gene pools. This theoretical formulation can also be used to predict the fraction of births for each of which all but one male can be excluded from paternity. We show that even when the average probability of exclusion approaches unity, a substantial fraction of births yield equivocal mother-father-offspring determinations. The number of loci needed to increase the frequency of unambiguous determinations to a high level is beyond the scope of current electrophoretic studies in most species. Applications of this theory to electrophoretic data on Chamaelirium luteum (L.) shows that in 2255 offspring derived from 273 males and 70 females, only 57 triplets could be unequivocally determined with eight polymorphic protein loci, even though the average combined exclusionary power of these loci was 73%. The distribution of potentially compatible male parents, based on multilocus genotypes, was reasonably well predicted from the allele frequency data available for these loci. We demonstrate that genetic paternity analysis in natural populations cannot be reliably based on exclusionary principles alone. In order to measure the reproductive contributions of individuals in natural populations, more elaborate likelihood principles must be deployed.

Estimation of the incidence of a rare genetic disease through a two-tier mutation survey.

Recent attempts to detect mutations involving single base changes or small deletions that are specific to genetic diseases provide an opportunity to develop a two-tier mutation-screening program through which incidence of rare genetic disorders and gene carriers may be precisely estimated. A two-tier survey consists of mutation screening in a sample of patients with specific genetic disorders and in a second sample of newborns from the same population in which mutation frequency is evaluated. We provide the statistical basis for evaluating the incidence of affected and gene carriers in such two-tier mutation-screening surveys, from which the precision of the estimates is derived. Sample-size requirements of such two-tier mutation-screening surveys are evaluated. Considering examples of cystic fibrosis (CF) and medium-chain acyl-CoA dehydrogenase deficiency (MCAD), the two most frequent autosomal recessive disease in Caucasian populations and the two most frequent mutations (delta F508 and G985) that occur on these disease allele-bearing chromosomes, we show that, with 50-100 patients and a 20-fold larger sample of newborns screened for these mutations, the incidence of such diseases and their gene carriers in a population may be quite reliably estimated. The theory developed here is also applicable to rare autosomal dominant diseases for which disease-specific mutations are found.

Multiple independent genetic factors at NOS1AP modulate the QT interval in a multi-ethnic population.

Extremes of electrocardiographic QT interval are associated with increased risk for sudden cardiac death (SCD); thus, identification and characterization of genetic variants that modulate QT interval may elucidate the underlying etiology of SCD. Previous studies have revealed an association between a common genetic variant in NOS1AP and QT interval in populations of European ancestry, but this finding has not been extended to other ethnic populations. We sought to characterize the effects of NOS1AP genetic variants on QT interval in the multi-ethnic population-based Dallas Heart Study (DHS, n = 3,072). The SNP most strongly associated with QT interval in previous samples of European ancestry, rs16847548, was the most strongly associated in White (P = 0.005) and Black (P = 3.6 x 10(-5)) participants, with the same direction of effect in Hispanics (P = 0.17), and further showed a significant SNP x sex-interaction (P = 0.03). A second SNP, rs16856785, uncorrelated with rs16847548, was also associated with QT interval in Blacks (P = 0.01), with qualitatively similar results in Whites and Hispanics. In a previously genotyped cohort of 14,107 White individuals drawn from the combined Atherosclerotic Risk in Communities (ARIC) and Cardiovascular Health Study (CHS) cohorts, we validated both the second locus at rs16856785 (P = 7.63 x 10(-8)), as well as the sex-interaction with rs16847548 (P = 8.68 x 10(-6)). These data extend the association of genetic variants in NOS1AP with QT interval to a Black population, with similar trends, though not statistically significant at P<0.05, in Hispanics. In addition, we identify a strong sex-interaction and the presence of a second independent site within NOS1AP associated with the QT interval. These results highlight the consistent and complex role of NOS1AP genetic variants in modulating QT interval.

EFFECTS OF INFORMATION DISPLAY ON THE CONSTRUCTION OF CLINICIAN MENTAL MODELS

Objective: To determine how a clinician’s background knowledge, their tasks, and displays of information interact to affect the clinician’s mental model. Design: Repeated Measure Nested Experimental Design Population, Sample, Setting: Populations were gastrointestinal/internal medicine physicians and nurses within the greater Houston area. A purposeful sample of 24 physicians and 24 nurses were studied in 2003. Methods: Subjects were randomized to two different displays of two different mock medical records; one that contained highlighted patient information and one that contained non-highlighted patient information. They were asked to read and summarize their understanding of the patients aloud. Propositional analysis was used to understand their comprehension of the patients. Findings: Different mental models were found between physicians and nurses given the same display of information. The information they shared was very minor compared to the variance in their mental models. There was additionally more variance within the nursing mental models than the physician mental models given different displays of the same information. Statistically, there was no interaction effect between the display of information and clinician type. Only clinician type could account for the differences in the clinician comprehension and thus their mental models of the cases. Conclusion: The factors that may explain the variance within and between the clinician models are clinician type, and only in the nursing group, the use of highlighting.

ELUCIDATING THE ROLE OF CD44 EXPRESSION ON MESENCHYMAL STEM CELLS WITHIN THE TUMOR MICROENVIRONMENT

The tumor microenvironment is comprised of a vast array of heterogeneous cells including both normal and neoplastic cells. The tumor stroma recruitment process has been exploited for an effective gene delivery technique using bone marrow derived MSC. Targeted migration of the MSC toward the tumor microenvironment, while successful, is not yet fully understood. This study was designed to assess the role of CD44 in the migration of MSC toward the tumor microenvironment and to determine the implications of CD44-deficient MSC within the tumor stroma. Inhibition of MSC migration was evaluated through a variety of methods in vitro and in vivo including CD44 receptor knockdown, CD44 antagonists, CD44 neutralizing antibodies and small molecule inhibitor of matrix metalloproteinases. Blocking CD44 signaling through MMP inhibition was characterized by lack of intracellular domain cleavage and lead to the decrease in Twist gene expression. A functional relationship between CD44 and Twist expression was confirmed by chromatin immunoprecipitation. Next, a series of murine tumor models were used to examine the role of CD44 deficient stroma within the tumor microenvironment. Labeled transgenic CD44 knockout (KO) MSC or wild type (WT) C57/B6 MSC were used to analyze the stromal incorporation within murine breast carcinomas (EO771 and 4T1). Subsequent tumors were analyzed for vessel formation (CD31), and the presence of tumor associated fibroblast (TAF) markers, α-smooth muscle actin (α-SMA), fibroblast activation protein (FAP), and fibroblast specific protein (FSP). The tumors with CD44KO MSC cells had less vessel formation than the tumors with WT MSC. The lack of fibroblastic TAF population as defined by FAP/FSP expression by the CD44KO MSC admixed tumors suggest that the bone marrow derived population of MSC were unable to contribute to the fibroblastic stromal population. Subsequently, a bone marrow transplantation experiment confirmed the endogenous migratory deficiencies of the CD44KO bone marrow derived stromal cells toward the tumor microenvironment in vivo. WT mice with CD44KO bone marrow had less CD44KOderived tumor stroma compared to mice with WT bone marrow. These results indicate that CD44 is crucial to stromal cell migration and incorporation to the tumor microenvironment as TAF.

A pure population of lung alveolar epithelial type II cells derived from human embryonic stem cells.

Alveolar epithelial type II (ATII) cells are small, cuboidal cells that constitute approximately 60% of the pulmonary alveolar epithelium. These cells are crucial for repair of the injured alveolus by differentiating into alveolar epithelial type I cells. ATII cells derived from human ES (hES) cells are a promising source of cells that could be used therapeutically to treat distal lung diseases. We have developed a reliable transfection and culture procedure, which facilitates, via genetic selection, the differentiation of hES cells into an essentially pure (>99%) population of ATII cells (hES-ATII). Purity, as well as biological features and morphological characteristics of normal ATII cells, was demonstrated for the hES-ATII cells, including lamellar body formation, expression of surfactant proteins A, B, and C, alpha-1-antitrypsin, and the cystic fibrosis transmembrane conductance receptor, as well as the synthesis and secretion of complement proteins C3 and C5. Collectively, these data document the successful generation of a pure population of ATII cells derived from hES cells, providing a practical source of ATII cells to explore in disease models their potential in the regeneration and repair of the injured alveolus and in the therapeutic treatment of genetic diseases affecting the lung.

The Role of Cell Sterilization in Population Based Studies of Radiogenic Second Cancers Following Radiation Therapy

Advances in radiotherapy have generated increased interest in comparative studies of treatment techniques and their effectiveness. In this respect, pediatric patients are of specific interest because of their sensitivity to radiation induced second cancers. However, due to the rarity of childhood cancers and the long latency of second cancers, large sample sizes are unavailable for the epidemiological study of contemporary radiotherapy treatments. Additionally, when specific treatments are considered, such as proton therapy, sample sizes are further reduced due to the rareness of such treatments. We propose a method to improve statistical power in micro clinical trials. Specifically, we use a more biologically relevant quantity, cancer equivalent dose (DCE), to estimate risk instead of mean absorbed dose (DMA). Our objective was to demonstrate that when DCE is used fewer subjects are needed for clinical trials. Thus, we compared the impact of DCE vs. DMA on sample size in a virtual clinical trial that estimated risk for second cancer (SC) in the thyroid following craniospinal irradiation (CSI) of pediatric patients using protons vs. photons. Dose reconstruction, risk models, and statistical analysis were used to evaluate SC risk from therapeutic and stray radiation from CSI for 18 patients. Absorbed dose was calculated in two ways: with (1) traditional DMA and (2) with DCE. DCE and DMA values were used to estimate relative risk of SC incidence (RRCE and RRMA, respectively) after proton vs. photon CSI. Ratios of RR for proton vs. photon CSI (RRRCE and RRRMA) were then used in comparative estimations of sample size to determine the minimal number of patients needed to maintain 80% statistical power when using DCE vs. DMA. For all patients, we found that protons substantially reduced the risk of developing a second thyroid cancer when compared to photon therapy. Mean RRR values were 0.052±0.014 and 0.087±0.021 for RRRMA and RRRCE, respectively. However, we did not find that use of DCE reduced the number of patents needed for acceptable statistical power (i.e, 80%). In fact, when considerations were made for RRR values that met equipoise requirements and the need for descriptive statistics, the minimum number of patients needed for a micro-clinical trial increased from 17 using DMA to 37 using DCE. Subsequent analyses revealed that for our sample, the most influential factor in determining variations in sample size was the experimental standard deviation of estimates for RRR across the patient sample. Additionally, because the relative uncertainty in dose from proton CSI was so much larger (on the order of 2000 times larger) than the other uncertainty terms, it dominated the uncertainty in RRR. Thus, we found that use of corrections for cell sterilization, in the form of DCE, may be an important and underappreciated consideration in the design of clinical trials and radio-epidemiological studies. In addition, the accurate application of cell sterilization to thyroid dose was sensitive to variations in absorbed dose, especially for proton CSI, which may stem from errors in patient positioning, range calculation, and other aspects of treatment planning and delivery.

Population genetics of VNTR loci and their applications in evolutionary studies

Variable number of tandem repeats (VNTR) are genetic loci at which short sequence motifs are found repeated different numbers of times among chromosomes. To explore the potential utility of VNTR loci in evolutionary studies, I have conducted a series of studies to address the following questions: (1) What are the population genetic properties of these loci? (2) What are the mutational mechanisms of repeat number change at these loci? (3) Can DNA profiles be used to measure the relatedness between a pair of individuals? (4) Can DNA fingerprint be used to measure the relatedness between populations in evolutionary studies? (5) Can microsatellite and short tandem repeat (STR) loci which mutate stepwisely be used in evolutionary analyses?^ A large number of VNTR loci typed in many populations were studied by means of statistical methods developed recently. The results of this work indicate that there is no significant departure from Hardy-Weinberg expectation (HWE) at VNTR loci in most of the human populations examined, and the departure from HWE in some VNTR loci are not solely caused by the presence of population sub-structure.^ A statistical procedure is developed to investigate the mutational mechanisms of VNTR loci by studying the allele frequency distributions of these loci. Comparisons of frequency distribution data on several hundreds VNTR loci with the predictions of two mutation models demonstrated that there are differences among VNTR loci grouped by repeat unit sizes.^ By extending the ITO method, I derived the distribution of the number of shared bands between individuals with any kinship relationship. A maximum likelihood estimation procedure is proposed to estimate the relatedness between individuals from the observed number of shared bands between them.^ It was believed that classical measures of genetic distance are not applicable to analysis of DNA fingerprints which reveal many minisatellite loci simultaneously in the genome, because the information regarding underlying alleles and loci is not available. I proposed a new measure of genetic distance based on band sharing between individuals that is applicable to DNA fingerprint data.^ To address the concern that microsatellite and STR loci may not be useful for evolutionary studies because of the convergent nature of their mutation mechanisms, by a theoretical study as well as by computer simulation, I conclude that the possible bias caused by the convergent mutations can be corrected, and a novel measure of genetic distance that makes the correction is suggested. In summary, I conclude that hypervariable VNTR loci are useful in evolutionary studies of closely related populations or species, especially in the study of human evolution and the history of geographic dispersal of Homo sapiens. (Abstract shortened by UMI.) ^

Models of DNA sequence evolution

Models of DNA sequence evolution and methods for estimating evolutionary distances are needed for studying the rate and pattern of molecular evolution and for inferring the evolutionary relationships of organisms or genes. In this dissertation, several new models and methods are developed.^ The rate variation among nucleotide sites: To obtain unbiased estimates of evolutionary distances, the rate heterogeneity among nucleotide sites of a gene should be considered. Commonly, it is assumed that the substitution rate varies among sites according to a gamma distribution (gamma model) or, more generally, an invariant+gamma model which includes some invariable sites. A maximum likelihood (ML) approach was developed for estimating the shape parameter of the gamma distribution $(\alpha)$ and/or the proportion of invariable sites $(\theta).$ Computer simulation showed that (1) under the gamma model, $\alpha$ can be well estimated from 3 or 4 sequences if the sequence length is long; and (2) the distance estimate is unbiased and robust against violations of the assumptions of the invariant+gamma model.^ However, this ML method requires a huge amount of computational time and is useful only for less than 6 sequences. Therefore, I developed a fast method for estimating $\alpha,$ which is easy to implement and requires no knowledge of tree. A computer program was developed for estimating $\alpha$ and evolutionary distances, which can handle the number of sequences as large as 30.^ Evolutionary distances under the stationary, time-reversible (SR) model: The SR model is a general model of nucleotide substitution, which assumes (i) stationary nucleotide frequencies and (ii) time-reversibility. It can be extended to SRV model which allows rate variation among sites. I developed a method for estimating the distance under the SR or SRV model, as well as the variance-covariance matrix of distances. Computer simulation showed that the SR method is better than a simpler method when the sequence length $L>1,000$ bp and is robust against deviations from time-reversibility. As expected, when the rate varies among sites, the SRV method is much better than the SR method.^ The evolutionary distances under nonstationary nucleotide frequencies: The statistical properties of the paralinear and LogDet distances under nonstationary nucleotide frequencies were studied. First, I developed formulas for correcting the estimation biases of the paralinear and LogDet distances. The performances of these formulas and the formulas for sampling variances were examined by computer simulation. Second, I developed a method for estimating the variance-covariance matrix of the paralinear distance, so that statistical tests of phylogenies can be conducted when the nucleotide frequencies are nonstationary. Third, a new method for testing the molecular clock hypothesis was developed in the nonstationary case. ^

Mouse models and quantitative trait loci of susceptibility to bleomycin- and radiation-induced pulmonary fibrosis

Radiotherapy involving the thoracic cavity and chemotherapy with the drug bleomycin are both dose limited by the development of pulmonary fibrosis. From evidence that there is variation in the population in susceptibility to pulmonary fibrosis, and animal data, it was hypothesized that individual variation in susceptibility to bleomycin-induced, or radiation-induced, pulmonary fibrosis is, in part, genetically controlled. In this thesis a three generation mouse genetic model of C57BL/6J (fibrosis prone) and C3Hf/Kam (fibrosis resistant) mouse strains and F1 and F2 (F1 intercross) progeny derived from the parental strains was developed to investigate the genetic basis of susceptibility to fibrosis. In the bleomycin studies the mice received 100 mg/kg (125 for females) of bleomycin, via mini osmotic pump. The animals were sacrificed at eight weeks following treatment or when their breathing rate indicated respiratory distress. In the radiation studies the mice were given a single dose of 14 or 16 Gy (Co$\sp{60})$ to the whole thorax and were sacrificed when moribund. The phenotype was defined as the percent of fibrosis area in the left lung as quantified with image analysis of histological sections. Quantitative trait loci (QTL) mapping was used to identify the chromosomal location of genes which contribute to susceptibility to bleomycin-induced pulmonary fibrosis in C57BL/6J mice compared to C3Hf/Kam mice and to determine if the QTL's which influence susceptibility to bleomycin-induced lung fibrosis in these progenitor strains could be implicated in susceptibility to radiation-induced lung fibrosis. For bleomycin, a genome wide scan revealed QTL's on chromosome 17, at the MHC, (LOD = 11.7 for males and 7.2 for females) accounting for approximately 21% of the phenotypic variance, and on chromosome 11 (LOD = 4.9), in male mice only, adding 8% of phenotypic variance. The bleomycin QTL on chromosome 17 was also implicated for susceptibility to radiation-induced fibrosis (LOD = 5.0) and contributes 7% of the phenotypic variance in the radiation study. In conclusion, susceptibility to both bleomycin-induced and radiation-induced pulmonary fibrosis are heritable traits, and are influenced by a genetic factor which maps to a genomic region containing the MHC. ^

Sequential updating via dynamic hierarchical models: A Bayesian approach to longitudinal data analysis

Most statistical analysis, theory and practice, is concerned with static models; models with a proposed set of parameters whose values are fixed across observational units. Static models implicitly assume that the quantified relationships remain the same across the design space of the data. While this is reasonable under many circumstances this can be a dangerous assumption when dealing with sequentially ordered data. The mere passage of time always brings fresh considerations and the interrelationships among parameters, or subsets of parameters, may need to be continually revised. ^ When data are gathered sequentially dynamic interim monitoring may be useful as new subject-specific parameters are introduced with each new observational unit. Sequential imputation via dynamic hierarchical models is an efficient strategy for handling missing data and analyzing longitudinal studies. Dynamic conditional independence models offers a flexible framework that exploits the Bayesian updating scheme for capturing the evolution of both the population and individual effects over time. While static models often describe aggregate information well they often do not reflect conflicts in the information at the individual level. Dynamic models prove advantageous over static models in capturing both individual and aggregate trends. Computations for such models can be carried out via the Gibbs sampler. An application using a small sample repeated measures normally distributed growth curve data is presented. ^

Behavioral and psychosocial factors associated with sexual risk in a population of crack cocaine users

The purpose of this dissertation was to examine the relationship between key psychosocial and behavioral components of the Transtheoretical Model and the Theory of Reasoned Action for sexual risk reduction in a population of crack cocaine smokers and sex workers, not in drug treatment. ^ The first study examined the results of an analysis of the association between two principal constructs in the Transtheoretical Model, the processes of change and the stages of change for condom use, in a high risk population. In the analysis of variance for all respondents, the overall F-test revealed that people in different stages have different levels of experiential process use, F(3,317) = 17.79, p = 0.0001 and different levels of behavioral process use, F(3,317) = 28.59, p = .0001. For the experiential processes, there was a significant difference between the precontemplation/contemplation stage, and both the action, and maintenance, stages.^ The second study explored the relationship between the Theory of Reasoned Action “beliefs” and the stages-of-change in the same population. In the analysis of variance for all participants, the results indicate that people in different stages did value the positive beliefs differently, F(3,502) = 15.38, p = .0001 but did not value the negative beliefs differently, F(3,502) = 2.08, p = .10. ^ The third study explored differences in stage-of-change by gender, partner type drug use, and HIV status. Three discriminant functions emerged, with a combined χ2(12) = 139.57, p = <.0001. The loading matrix of correlations between predictors and discriminant functions demonstrate that the strongest predictor for distinguishing between the precontemplation/contemplation stage and the preparation, action, and maintenance stages (first function) is partner type (.962). The loadings on the second discriminant function suggest that once partner type has been accounted for, ever having HIV/AIDS (.935) was the best predictor for distinguishing between the first three stages and the maintenance stage. ^ These studies demonstrate that behavioral change theories can contribute important insight to researchers and program planners attempting to alter HIV risk behavior in high-risk populations. ^

Bayesian generalized linear models for meta-analysis of diagnostic tests

With the recognition of the importance of evidence-based medicine, there is an emerging need for methods to systematically synthesize available data. Specifically, methods to provide accurate estimates of test characteristics for diagnostic tests are needed to help physicians make better clinical decisions. To provide more flexible approaches for meta-analysis of diagnostic tests, we developed three Bayesian generalized linear models. Two of these models, a bivariate normal and a binomial model, analyzed pairs of sensitivity and specificity values while incorporating the correlation between these two outcome variables. Noninformative independent uniform priors were used for the variance of sensitivity, specificity and correlation. We also applied an inverse Wishart prior to check the sensitivity of the results. The third model was a multinomial model where the test results were modeled as multinomial random variables. All three models can include specific imaging techniques as covariates in order to compare performance. Vague normal priors were assigned to the coefficients of the covariates. The computations were carried out using the 'Bayesian inference using Gibbs sampling' implementation of Markov chain Monte Carlo techniques. We investigated the properties of the three proposed models through extensive simulation studies. We also applied these models to a previously published meta-analysis dataset on cervical cancer as well as to an unpublished melanoma dataset. In general, our findings show that the point estimates of sensitivity and specificity were consistent among Bayesian and frequentist bivariate normal and binomial models. However, in the simulation studies, the estimates of the correlation coefficient from Bayesian bivariate models are not as good as those obtained from frequentist estimation regardless of which prior distribution was used for the covariance matrix. The Bayesian multinomial model consistently underestimated the sensitivity and specificity regardless of the sample size and correlation coefficient. In conclusion, the Bayesian bivariate binomial model provides the most flexible framework for future applications because of its following strengths: (1) it facilitates direct comparison between different tests; (2) it captures the variability in both sensitivity and specificity simultaneously as well as the intercorrelation between the two; and (3) it can be directly applied to sparse data without ad hoc correction. ^

Smokeless tobacco use in 2005 US Military population

U.S. Military personnel are more likely to use smokeless tobacco than civilians. The purpose of this study was to describe the relationship between smokeless tobacco use and sociodemographic, behavioral, and occupational variables, using data from the 2005 Department of Defense Survey of Health Related Behaviors among Active Duty Military Personnel. The DoD survey was comprised of representative active duty U.S. military members (N=16,146). In adjusted multivariate logistic regression models, this study found smokeless tobacco use to be more prevalent in younger age, males, whites, and enlisted-rank members. By service, higher rates were reported among members of the Army and Marine Corps than among the Air Force and Navy members. Smokeless tobacco use among those who also smoke or drink heavily was also much higher than among those who did not report smoking or heavy alcohol use. Results also showed increased prevalence of smokeless tobacco use among those who reported moderate or high impulsive behavior and among those who recently deployed. These findings contribute to improving the understanding of factors related to smokeless tobacco use in the military and may help design strategies to reduce the use of this potentially toxic substance and improve health for military members.^

The association between stress and physical fitness testing in the 2005 Department of Defense population survey

Objective. The purpose of this study was to examine the association of perceived stress and passing the fitness test in a cohort of Department of Defense active duty members. Reports of this association have been suggested in numerous articles. Methods. The 2005 DoD Survey of Health Related Behaviors Among Active Duty Military Personnel was used to examine the association between the participants’ perceived levels of stress from family and/or work related sources and the respondents’ last required fitness test taking into account potential confounder of the association. Measures of association were obtained from logistic regression models. Results. Participants who experienced “some” or “a lot” of stress either from work sources (OR 0.69, 95% CI: 0.58-0.87) or from personal/family sources (OR 0.70, 95% CI: 0.57-0.86) were less likely to pass the fitness test when compared to their counterparts who experienced “none” or “a little” stress. Additionally, those who reported “some” or “a lot” of stress either from work sources (OR 0.54, 95% CI: 0.41-0.70) or from personal/family sources (OR 0.54, 95% CI: 0.44-0.67) that interfered with their military duties were also less likely to pass the fitness test. The multivariate adjustment only slightly reduced the unadjusted association. Conclusions . An association exists between perceived stress levels and outcome of fitness testing. The higher the level of stress perceived, the less likely the person will be to pass the fitness test. Stress-related intervention might be useful to help the military members to achieve the level of fitness needed to perform their duties.^