0
We're unable to sign you in at this time. Please try again in a few minutes.
Retry
We were able to sign you in, but your subscription(s) could not be found. Please try again in a few minutes.
Retry
There may be a problem with your account. Please contact the AMA Service Center to resolve this issue.
Contact the AMA Service Center:
Telephone: 1 (800) 262-2350 or 1 (312) 670-7827  *   Email: subscriptions@jamanetwork.com
Error Message ......
Original Article |

A Longitudinal Twin Study of Fears From Middle Childhood to Early Adulthood:  Evidence for a Developmentally Dynamic Genome FREE

Kenneth S. Kendler, MD; Charles O. Gardner, PhD; Peter Annas, PhD; Michael C. Neale, PhD; Lindon J. Eaves, PhD, DSc; Paul Lichtenstein, PhD
[+] Author Affiliations

Author Affiliations: Virginia Institute for Psychiatric and Behavioral Genetics (Drs Kendler, Gardner, Neale, and Eaves) and Departments of Psychiatry (Drs Kendler, Gardner, Neale, and Eaves) and Human Genetics (Drs Kendler, Neale, and Eaves), Medical College of Virginia/Virginia Commonwealth University, Richmond; Department of Psychology, Uppsala University, Uppsala, Sweden (Dr Annas); and Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden (Dr Lichtenstein).


Arch Gen Psychiatry. 2008;65(4):421-429. doi:10.1001/archpsyc.65.4.421.
Text Size: A A A
Published online

Context  While the nature of common fears changes over development, we do not know whether genetic effects on fear-proneness are developmentally stable or developmentally dynamic.

Objective  To determine the temporal pattern of genetic and environmental effects on the level of intensity of common fears.

Design  Prospective, 4-wave longitudinal twin study. Structural modeling was performed with Mx.

Setting  General community.

Participants  Two thousand four hundred ninety twins and their parents from the Swedish Twin Study of Child and Adolescent Development.

Main Outcome Measure  The level of parent- and/or self-reported fears obtained at ages 8 to 9, 13 to 14, 16 to 17, and 19 to 20 years.

Results  Thirteen questionnaire items formed 3 distinct fear factors: situational, animal, and blood/injury. For all 3 fears, the best-fit model revealed developmentally dynamic effects and, in particular, evidence for both genetic attenuation and innovation. That is, genetic factors influencing fear intensity at age 8 to 9 years decline substantially in importance over time. Furthermore, new sets of genetic risk factors impacting fear intensity “come on line” in early adolescence, late adolescence, and early adulthood. As the twins aged, the influence of the shared environment declined and unique environment increased. No sex effects were found for situational fears while for animal and blood/injury fears, genetic factors in males and females were correlated but not identical. Shared environmental factors were both more important and more stable for animal fears than for situational or blood/injury fears.

Conclusions  Genetic effects on fear are developmentally dynamic from middle childhood to young adulthood. As children age, familial-environmental influences on fears decline in importance.

Figures in this Article

When considering the role of individual differences in the etiology of excessive fears and phobias, emphasis is typically given to traits that are conceptualized as being temporally stable, such as behavioral inhibition, autonomic lability, neuroticism, cognitive bias (for threatening stimuli), and disgust sensitivity.14 However, fears demonstrate a dynamic progression through development3—what Marks5 has termed an ontogenetic parade. Furthermore, epidemiological investigations show consistent differences in the ages at onset of individual phobia subtypes.6,7 Realistic etiologic models for excessive fears and phobias will require an understanding of the developmentally dynamic processes that underlie risk.

Cross-sectional studies in children,810 adolescents,11 and adults6,1215 consistently show genetic contributions to excessive fears and phobias. While developmentally dynamic genetic effects have been demonstrated for a range of phenotypes, including antisocial behavior,16,17 cognitive abilities,18 plasma lipids,19 and weight,20,21 to our knowledge, no study to date has examined fears in a longitudinal and genetically informative sample to elucidate the patterns of temporal stability or change in genetic influences. Therefore, in this report, we examine the development of genetic and environmental risk factors for situational, animal, and blood/injury fears in a population-based cohort of Swedish twins. Because the twins were assessed 4 times between the ages of 8 and 20 years, they all passed through puberty, a developmental period of particular interest given prior evidence for the impact of gonadal hormones on fear mechanisms.2224

The primary goal of the study was to discriminate between 2 hypotheses about the developmental pattern of genetic risk factors for fears. The “developmentally stable” hypothesis predicts that a single set of genetic risk factors impacts the level of fears at age 8 years and these same genes constitute the only genetic influences on fear-proneness throughout development. By contrast, the “developmentally dynamic” hypothesis predicts that genetic effects on fear-proneness will vary over time. This variability might be manifested through genetic innovation, in which new genes become active that previously were without effect on fear intensity, or genetic attenuation, in which genes that impact at one developmental age decline in influence at later periods.

These analyses have 2 additional goals: (1) To determine the nature and stability of shared and unique environmental influences on fears from childhood to young adulthood. (2) To explore whether the magnitude or nature of the genetic and environmental risk factors for fears differs in males and females.

SAMPLE

The sample was obtained from the population-based Swedish Twin Registry, which contains information on all twins born in Sweden since 1886.25 As detailed elsewhere, this sample—called the Swedish Twin Study of Child and Adolescent Development (or T-CHAD)—began with all twin pairs born in Sweden between May 1985 and December 1986 where both twins were alive and residing in Sweden in 1994.26 To date, this sample has been assessed 4 times for their level of fears: at the age of 8 to 9 years by a mailed questionnaire to parents (n = 1109 or 75% response); age 13 to 14 years with a mailed questionnaire to parents (n = 1063, 73%) and children (n = 2263, 78%); age 16 to 17 years with a mailed questionnaire to parents (n = 1067, 74%) and children (n = 2369 children, 82%); and age 19 to 20 years with a questionnaire solely to the twins (n = 1705, 59%). Each of the questionnaires was approved by the Ethics Committee of the Karolinska Institute, Stockholm, Sweden. No specific informed consent was required as response to the questionnaire is seen in Sweden as constituting consent.

ZYGOSITY DETERMINATION

Zygosity determination was based on well-validated questions to both twins and parents chosen from a discriminant analysis of 106 same-sex pairs from this sample that had their zygosity determined by typing 16 polymorphic DNA markers.26 Those with uncertain scores were classified as zygosity unknown.

MEASURES

Using a questionnaire developed by Fredrikson et al,27 the children were asked to rate their own fear intensity and the parents were asked to rate each child's fear intensity for the specific objects or situations on a scale ranging from 0 (no fear) to 10 (maximal fear). Social fears were not added until the assessment at age 13 to 14 years and so are not included herein. Because the initial scale contained only 2 items to assess animal fears (snakes and spiders), 3 additional items (rats, dogs, and wasps) were added from those most commonly listed in an open-ended question to respondents. These items were added for the age 13 to 14 years and all subsequent assessments. The exact wording (in English translation) of the items used in these analyses is seen in Table 1. The present study is a follow-up of a previous report from the assessment at age 8 to 9 years.8

Table Graphic Jump LocationTable 1. Factor Loadings (Promax28) of Individual Fears Assessed by Self-Report at Age 16 to 17 Yearsa
DATA ANALYSIS

For these analyses of fears, we began with 2717 twin individuals, of whom 28 had missing data and 199 had unknown zygosity. The remaining 2490 individuals came from 1237 complete pairs and 16 single twins. Of these pairs, 246 were female monozygotic (MZ); 184, female dizygotic (DZ); 243, male MZ; 177, male DZ; and 387, opposite-sex DZ pairs.

The model used in these analyses is presented in Figure 1, which illustrates for a single individual the sources of liability for only additive genetic effects. The model has 4 major features. First, it contains 4 latent fear scores (T1-T4) that reflect the “true” level of fear at ages 8 to 9, 13 to 14, 16 to 17, and 19 to 20 years, herein called, for simplicity, times 1, 2, 3, and 4. Second, these latent variables are indexed by ratings of fear either by parental report (P) or self-report (S). Both reports are available for times 2 and 3, while only the parent report is available for time 1 and only self-report at time 4. The degree to which the parent- and self-reported fear ratings index the latent fear level is reflected by the paths λP and λS. Third, the genetic and environmental influences on the latent fear scores at times 1, 2, 3, and 4 are modeled as a Cholesky decomposition. Taking genes as an example, this developmentally informative approach divides up genetic risk into 4 factors (F1-F4). The first (F1) begins in childhood (age 8-9 years) and is continuously active over the entire developmental period. The strength of its effect at each of the 4 ages is reflected, respectively, in the path coefficients f11, f12, f13, and f14. The second factor begins in early adolescence (age 13-14 years) and impacts times 2, 3, and 4 via paths f22, f23, and f24. The third factor starts in late adolescence (age 16-17 years) and impacts times 3 and 4 via paths f33 and f34. The fourth and final factor acts only at time 4, young adulthood (age 19-20 years), via path f44. The “developmentally stable” hypothesis predicts that all of the genetic liability to fears will be captured by the first factor with no evidence for genetic innovation later in development. By contrast, the “developmentally dynamic” hypothesis predicts such innovation—that new genetic variation that impacts fear levels will be seen in early and late adolescence and young adulthood. Furthermore, consistent with the “developmentally dynamic” hypothesis would be evidence for genetic attenuation—that the impact of the first and perhaps second genetic factors acting early in development decline over time. Fourth, the model contains 2 reporter-specific common factors, one each for parent and self, as well as rater- and time-specific residuals. To identify the f44 path, it was necessary to constrain the 3 self-report– specific loadings to equality.

Place holder to copy figure label and caption
Figure 1.

The model used in these analyses presented for 1 source of liability, such as additive genetic effects. The model contains 4 latent fear scores (T1-T4) reflecting the true level of fear at time 1 (age 8-9 years), time 2 (age 13-14 years), time 3 (age 16-17 years), and time 4 (age 19-20 years). These latent variables are indexed by ratings of fear by parental report (P) (available for times 1-3) and by self-report (S) (available for times 2-4). The degree to which the parent- and self-reported fear ratings index the latent fear level is reflected by the paths λP and λS. The genetic and environmental influences on the latent fear scores are modeled as a Cholesky decomposition. See the text of this article for more details. F indicates the 4 genetic risk factors; f, the path from the genetic factors to the latent fear scores at each of the 4 ages; R, residual effects.

Graphic Jump Location

Our analyses focus on the latent measures of fear that were reported by both parents and children because these measures are most likely to be valid given that they reflect both the subjective and objective manifestations of these fears. The reporter-specific factors—reported by one but not the other rater—are required in the model but are of less interest to us and are not focused on herein.

Estimates of heritability and shared environmental effects for fears estimated from this model are not directly comparable with those obtained from standard twin models and would be expected to be higher. In standard twin models, errors of measurement contribute to and are confounded with individual-specific environment, thereby reducing estimates of heritability or shared environment. However, through the use of multiple raters, this model unconfounds the true individual-specific environment that impacts the latent fear scores (T1-T4) from the errors of measurement that contribute to the rater-specific effects (P1-P3 and S2-S4).

For each factor, we examined both qualitative and quantitative sex effects. Qualitative sex effects arise when genetic factors that influence a trait are not entirely the same in males and females and are measured by the genetic correlation rg. rg can vary from zero (ie, entirely distinct sets of genes in the 2 sexes) to unity (ie, identical genetic factors impacting males and females). Quantitative sex effects arise when the same genetic factors impact to different degrees in males and females.

Analyses were performed using the Mx software package.29 Levels of fear were treated as a continuous variable. Because the sum scores for the items identified by factor analyses had a skewed distribution, we did a square root transformation followed by standardization separately for each sex, age, and zygosity group.

Given the complexity of these models, aside from evaluating sex effects, we did not attempt further simplification. For evaluating fits, we used the Bayesian Information Criterion (BIC),30 which performs well with complex models.31 The lower the BIC value, the better the balance of explanatory power and parsimony.

To determine the factor structure of the fear items prior to our genetic analyses, we conducted an oblique Promax factor analysis,28 identifying the number of factors using a traditional eigenvalue criterion.

FACTOR ANALYSIS

Starting, arbitrarily, with self-report data at age 16 to 17 years, our Promax rotation28 (Table 1) produced 3 readily interpretable factors: situational (fears of closed places, heights, flying, dark, and lightening), animal (fears of rats, snakes, spiders, wasps, and dogs) and blood/injury (fears of dentists, injections, and blood). Only fear of dogs loaded less than +0.40 on its expected factor. This factor structure was very similar to that seen with the other time-rater combinations at which the full list of fears was assessed: parents, age 13 to 14 years; self, ages 13 to 14 years; parents, age 16 to 17 years; and self, age 19 to 20 years. The mean (SD) of the 10 congruency coefficients (a measure of factorial similarity)32 between these 5 occasions of measurement for each of the factors were, respectively, situational, 0.979 (0.009); animal, 0.981 (0.008); and blood/injury 0.972 (0.197).

MEAN FEAR INTENSITIES

Table 2 presents mean (SE) levels of reported fear intensity as a function of the type of fear, the sex of the twin, the informant (self vs parent), and age. Four patterns are of interest. First, with only one exception (animal fear by parental report in females increased slightly from ages 8-9 to 13-14 years), across both sexes and both informants and all fears, mean fear levels declined with age. Second, across both informants, all ages, and all fear factors, females had greater fear intensity than males. Third, at both ages where we had reports from parent and child, for both sexes, and across all fear factors, self-report fear intensity exceeded parent-reported fear intensity. Fourth, fear intensities were generally greater for animal than for situational or blood/injury stimuli.

Table Graphic Jump LocationTable 2. Mean (SE) per Item Rating of Fear Intensity as a Function of Type of Fear, Sex, Informant, and Age
TWIN ANALYSIS
Situational Fears

The correlation matrix across raters and across time for situational fears, seen in Table 3, has 3 noteworthy patterns. First, correlations within time between parents and self-ratings are moderately high (about +0.50). Second, correlations within rater across time (eg, self, age 13-14 years to self, age 16-17 years of +0.61) are somewhat higher than cross-rater cross-time (eg, parent, age 13-14 years to self, age 16-17 years of +0.40). Third, cross-time correlations tend to decline as the interval between ratings increase. eTable 1 depicts the twin correlations for situational fears within and across time for the 5 zygosity groups (female MZ, female DZ, male MZ, male DZ, and male-female DZ) separately for parental and self-rating.

Table Graphic Jump LocationTable 3. Pearson Correlations Between Raters and Across Time for a Standardized Measure of Situational Feara

For situational fears, the best BIC value was obtained for a model with no qualitative or quantitative sex effects (Table 4). Parameter estimates for this best-fit model are seen in Table 5 and results for the genetic factors illustrated in Figure 2A, where purple represents the first genetic factor starting at age 8 to 9 years; blue, the second genetic factor starting at age 13 to 14 years; yellow, the third genetic factor starting at age 16 to 17 years; and green, the fourth genetic factor acting only at age 19 to 20 years. Seven results are noteworthy. First, genetic factors play a strong role in influencing the situational fears as indexed by self-ratings and parent ratings. Heritability was estimated at 50%, 68%, 69%, and 59% across the 4 periods. Second, consistent with the predictions of the “developmentally dynamic” hypothesis, we find substantial evidence for genetic innovation as seen in the loadings on genetic factors 2, 3, and 4. That is, in addition to the temporally stable genetic influences that begin at age 8 to 9 years, as illustrated in Figure 2A, the model demonstrated substantial new genetic influences on situational fears that emerged at ages 13 to 14, 16 to 17, and 19 to 20 years. Stable genetic influences on situational fears constitute a minority of the total genetic effect after age 8 to 9 years. Third, we also see evidence for genetic attenuation. In particular, the first genetic factor accounts for a total of 50% of the phenotypic variance in the intensity of situational fears at age 8 to 9 years but declines steeply in influence and by age 19 to 20 years accounts for only 4% of phenotypic variance. Fourth, except at age 8 to 9 years, shared environmental effects on the liability to situational fears were relatively modest accounting for 41%, 5%, 4%, and 12% of the variance across the 4 periods. Unlike the genetic factors, shared environmental influences on situational fears had modest temporal stability. Fifth, unique environmental factors accounted for 9%, 26%, 26%, and 29% of the variance in situational fears as reported by both parent and child. These unique environmental effects had somewhat greater temporal stability than did the shared environmental effects. Sixth, at the 2 points for which the estimates were meaningful (because we had reports from both parent and child), the λP path was somewhat higher than the λS path, suggesting that parental ratings were a better index of fears than self-report. Seventh, parameter estimates for the parent- and self-report factors for situational fears (and other examined fears) are seen in eTable 2. More consistent reporter-specific genetic factors were seen for self-ratings than for parent ratings.

Place holder to copy figure label and caption
Figure 2.

The proportion of total variance in fears accounted for by genetic factors through development. The y-axis represents the total phenotypic variance so the sum of all the factors equals the total heritability. Purple represents the first genetic factor starting at age 8 to 9 years. Blue represents the second genetic factor starting at age 13 to 14 years. Yellow represents the third genetic factor starting at age 16 to 17 years and green represents the fourth genetic factor acting only at age 19 to 20 years. A, Results for situational fears, the exact parameter estimates of which are seen in Table 5. B, Results for animal fears, the exact parameter estimates of which are seen in Table 6. C, Results for blood/injury fears, the exact parameter estimates of which are seen in Table 7.

Graphic Jump Location
Table Graphic Jump LocationTable 4. BIC Scores for Qualitative and Quantitative Sex Effects for Animal, Blood/Injury, and Situational Fearsa
Table Graphic Jump LocationTable 5. Parameter Estimates for the Best-Fit Model for Situational Fearsa
Other Fears

The correlation matrices across raters and across time for animal and blood/injury fears are seen in eTable 3 and eTable 4 and are very similar to that seen for situational fears (Table 2). eTable 5 and eTable 6 depict the twin correlations for animal and blood/injury fears within and across time for the 5 zygosity groups separately for parental and self-ratings. As seen in Table 4, we did not find evidence for quantitative sex effects for any of the other fear dimensions. However, for animal and blood/injury fears, thebest-fit model contained qualitative sex effects, with estimates of rg of +0.34 and +0.50, respectively.

Parameter estimates for the best-fit models for animal and blood/injury fears are seen in Table 6 and Table 7 and Figure 2B and C. The results for animal and blood/injury fears resembled those found for situational fears in strongly supporting the “developmentally dynamic” rather than the “developmentally stable” hypothesis for genetic effects. More specifically, our modeling results provided evidence for both genetic innovation and genetic attenuation for the intensity of animal and blood/injury fears. As with situational fears, the role of shared environment generally declined with increasing age for both animal and blood/injury fears.

Table Graphic Jump LocationTable 6. Parameter Estimates for the Best-Fit Model for Animal Fearsa
Table Graphic Jump LocationTable 7. Parameter Estimates for the Best-Fit Model for Blood/Injury Fearsa
MAJOR FINDINGS

The major goal of this report was to discriminate between a “developmentally stable” and a “developmentally dynamic” hypothesis of genetic risk for the intensity of common fears from childhood to young adulthood. For all 3 fears examined, our model results unequivocally supported the “developmentally dynamic” hypothesis. For situational, animal, and blood/injury fears, our parameter estimates showed evidence for 2 dynamic processes: genetic innovation and attenuation. We identified 1 set of genetic risk factors that act in childhood and have a steep decline in influence with age. Furthermore, we see evidence for new sets of genetic risk factors “coming on line” in early adolescence, late adolescence, and early adulthood.

This article had 2 subsidiary goals, the first of which was to clarify the developmental impact of environmental factors on fears. For all 3 fears, as the twins aged, the influence of the shared environment declined and unique environment increased. This is an expected pattern given that adolescence is a time of declining influence of the home environment as individuals spend less time with family and progressively make their own world, spending more time with friends.33

Shared environmental influences on fears could result from 2 previously articulated mechanisms of fear acquisition.3436 Most likely they arise because twins learn to fear particular stimuli from parental instruction or through social learning by exposure to parental fears. Twins also could have been correlated for exposure to fear-inducing experiences (eg, rats in the basement or a particularly unsympathetic dentist). Shared environmental influences for both situational and blood/injury fears were not very stable over time. By contrast, animal fears demonstrated an enduring and substantial effect of a single set of familial-environmental factors from age 8 to 9 years through young adulthood. Unfortunately, our data provide no insight into why shared environmental experiences would have a more transitory effect on situational and blood/injury than on animal fears.

Across all 4 ages of assessment, the shared environment was substantially more important for animal than for situational or blood/injury fears. Perhaps twins were more exposed to parental fear reactions or warnings about dangerous animals than about situational or blood/injury stimuli. Alternatively, as they were growing up, twins might have been particularly correlated in their exposure to rats, snakes, spiders, and wasps in their home or neighborhood.

Individual-specific environmental influences on fears were generally more temporally stable than familial-environmental effects. This pattern would be predicted if fears arising from individual exposure to frightening stimuli had a more enduring impact on fear levels than did instruction or observation of parental fears.

The second subsidiary goal of these analyses was to clarify sex differences in the genetic and environmental risk factors for fears in childhood and adolescence. Consistent with earlier results with this fear scale,27 mean levels of fear intensity were higher in females than in males across all 3 fear factors, both raters, and all 4 ages. While we found strong evidence for qualitative sex effects for animal and blood/injury but not for situational fears, we found little support for quantitative genetic effects for any of the fear dimensions examined. Consistent with evidence in rodents for the impact of gonadal hormones on fear conditioning,22,24 sex appears to be an important modifier of genetic risk factors in humans for animal and blood/injury fears. By contrast, our results suggest that after controlling for mean differences in fears between the sexes, the magnitude and developmental progression of genetic and environmental effects on fears were similar in males and females.

RELATIONSHIP WITH PRIOR STUDIES

Our findings can be best appreciated when put in the context of prior genetically informative studies of fears and phobias. Our results are consistent with prior investigations in toddlers,37 children,810 adolescents,11 and adults6,1215 in suggesting genetic effects on fears and phobias. The heritability estimates obtained in this sample are comparable with those found in several other studies of children and adolescents10,37,38 but generally higher than those reported for fears and phobias in adults.12,15,39,40 Evidence of shared environmental influences on fears have been found in some12,37 but not all studies of fears and phobias.15,39,40 Our results would suggest that family-environmental influences should be much more likely detected for fears and phobias in younger samples, although the literature is not entirely consistent in this regard. One prior study specifically examined for sex effects on situational, blood/injury, and animal fears and phobias in adult twins from the Virginia twin register.40 No quantitative sex effects were found for any of these fears/phobias while qualitative sex effects were found for situational and blood/injury but not for animal fear/phobias.

LIMITATIONS

These results should be considered in the context of 6 potentially important methodological limitations. First, since we had no measures of fear from subjects younger than 8 years, we could not examine the development of those specific fears (eg, of strangers) that emerge and typically disappear in early childhood. Second, our list of fears was abbreviated at age 8 to 9 years, especially for animal fears. We reran our analyses of animal fears using only the 2 items (snakes and spiders) available at all ages. The pattern of results was very similar to that seen in Table 6 with one exception—the genetic loading on the fears in young adulthood were largely shared with those seen at early periods rather than specific to that age.

Third, prior studies suggest a hierarchical structure of fears,41 and twin studies in both adults6,14,15 and children8 have suggested that genetic risk factors for individual fears and phobias are substantially intercorrelated. Therefore, the ideal approach to this data would have been a multivariate model that would examine genetic and environmental factors common to all 3 fears as well as fear-specific factors. However, given the problems of developing, fitting, and interpreting such a complex model, herein, we chose the simpler approach of examining the fears one at a time.

Fourth, our analyses examined the full range of the intensity of fear in response to phobic stimuli, from none to maximal. Our results are therefore not necessarily the same as those that would be obtained if we examined only the extreme high scorers, many of whom might have met criteria for a clinical phobia.

Fifth, some of our findings might have arisen artifactually because we had only parental reports at age 8 to 9 years and only self-reports at age 19 to 20 years. To examine this question, we developed an alternative model that did not combine parent and twin ratings but instead contained the same 4 genetic and environmental factors seen in Figure 1 fitted directly to our 6 measures of fear: parental report at ages 8 to 9, 13 to 14, and 16 to 17 years and self-report at ages 13 to 14, 16 to 17, and 19 to 20 years. Reassuringly, this less elegant model showed all the major trends reported earlier (results available on request). For example, the sharp decline in shared environmental effects could result from our reliance solely on parental report for fears at age 8 to 9 years compared with the later measures, which all included self-report data. However, looking only at parental reports, we see exactly the same trends. For example, for situational fears, shared environment in parental ratings accounts for 30% of variance at age 8 to 9 years, declining to 9% at age 13 to 14 years and 5% at age 16 to 17 years. We also saw strong support for our “developmentally dynamic” hypothesis using this model. For example, for situational fears, the second genetic factor had robust effects on both parental ratings (with loadings at ages 13-14 and 16-17 years of 0.32 and 0.39, respectively) and self-ratings (with loadings of 0.50, 0.52, and 0.46 at ages 13-14, 16-17, and 19-20 years, respectively). When we examine only parental reports or only twin reports, we still have strong evidence for developmentally dynamic genetic effects on fears.

Sixth, it was not feasible to examine jointly developmental changes in levels of fear reported by both parent and child and those specific to parental or child report. Our analyses focused on the former because of their greater validity. As seen in eTable 1, significant genetic effects were found that were unique to individual reporters. These genetic effects tended to be higher for child than parental report and higher for animal than for blood/injury or situational fears.

Most approaches to psychiatric genetics and especially those involving gene finding assume a static genome, one in which the impact of individual genetic variants on risk is stable over time and through periods of development. This assumption may be appropriate for some phenotypes over certain periods, such as major depression in adulthood.42 That this is not uniformly the case is demonstrated by prior studies of a range of behavioral and physiological phenotypes.1621

For some phenotypes, the genome is likely to be particularly dynamic during childhood and adolescence. It cannot therefore be assumed that genes that influence a trait during one age period will be entirely the same as those that impact the same trait in a later developmental phase. To capture the temporal variation in gene action underlying some phenotypes, gene-finding studies will need to use longitudinal designs.

Our study provides no direct insight into the nature of the dynamic changes in the genetic influences on fear. However, because we studied subjects from ages 8 to 20 years, we of necessity captured the impact of their pubertal transition on genetic and environmental risk factors for fear. This may be of significance because both animal22,24 and human studies23 suggest that fear mechanisms can be altered by the hormonal changes occurring during puberty. Future work will need to clarify whether the genetic effects on these developmental changes in fear intensity can be best understood at the level of mental processes, such as changes in cognitive biases or disgust sensitivity,2 and/or at the level of neurobiology, for example, altered functioning of brain fear circuitry in structures such as the amygdala and medial prefrontal cortex.43,44

What evolutionary forces might be responsible for the developmental complexity of genetic risk factors for fears? Genetic influences on fear-proneness may have arisen through selection during evolution because moderate levels of innate fears for dangerous stimuli such as snakes, spiders, heights, and blood have been more adaptive than either no fear or cripplingly high levels of fear.45,46 In such a situation—where intermediate levels of a trait are maximally fit—an evolutionary process termed stabilizing selection47 occurs, which produces substantial additive genetic variation.47,48 However, the adaptiveness of specific fears is probably age dependent. Stimuli that are particularly hazardous for a child are likely to differ from those that pose danger to a late adolescent. If this is the case, selective forces over evolutionary time are likely to sculpt a temporally dynamic set of genetic risk factors with expression tied to developmental stage.

Correspondence: Kenneth S. Kendler, MD, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University Medical School, Box 980126, 800 E Leigh St, Room 1-123, Richmond, VA 23298-0126 (kendler@vcu.edu).

Submitted for Publication: July 1, 2007; final revision received October 2, 2007; accepted October 3, 2007.

Financial Disclosure: None reported.

Funding/Support: This study was supported in part by National Institutes of Health grants MH-068643 and MH-65322, the Swedish Council for Working Life and Social Research (project 2004-0383), and the Swedish Research Council (project 2004-1415).

Muris  PMerckelbach  H The etiology of childhood specific phobia: a multifactorial model. Vasey  MWDadds  MRThe Developmental Psychopathology of Anxiety. Oxford, NY Oxford University Press2001;355- 385
Armfield  JM Cognitive vulnerability: a model of the etiology of fear. Clin Psychol Rev 2006;26 (6) 746- 768
PubMed Link to Article
Silverman  WKMoreno  J Specific phobia. Child Adolesc Psychiatr Clin N Am 2005;14 (4) 819- 843
PubMed Link to Article
Fyer  AJ Current approaches to etiology and pathophysiology of specific phobia. Biol Psychiatry 1998;44 (12) 1295- 1304
PubMed Link to Article
Marks  IM Fears, Phobias, and Rituals.  New York, NY Oxford University Press1987;
Kendler  KSNeale  MCKessler  RCHeath  ACEaves  LJ The genetic epidemiology of phobias in women: the interrelationship of agoraphobia, social phobia, situational phobia, and simple phobia. Arch Gen Psychiatry 1992;49 (4) 273- 281
PubMed Link to Article
Becker  ESRinck  MTurke  VKause  PGoodwin  RNeumer  SMargraf  J Epidemiology of specific phobia subtypes: findings from the Dresden Mental Health Study. Eur Psychiatry 2007;22 (2) 69- 74
PubMed Link to Article
Lichtenstein  PAnnas  P Heritability and prevalence of specific fears and phobias in childhood. J Child Psychol Psychiatry 2000;41 (7) 927- 937
PubMed Link to Article
Rose  RJDitto  WB A developmental-genetic analysis of common fears from early adolescence to early adulthood. Child Dev 1983;54 (2) 361- 368
PubMed Link to Article
Stevenson  JBatten  NCherner  M Fears and fearfulness in children and adolescents: a genetic analysis of twin data. J Child Psychol Psychiatry 1992;33 (6) 977- 985
PubMed Link to Article
Nelson  ECGrant  JDBucholz  KKGlowinski  AMadden  PAFReich  WHeath  AC Social phobia in a population-based female adolescent twin sample: co-morbidity and associated suicide-related symptoms. Psychol Med 2000;30 (4) 797- 804
PubMed Link to Article
Phillips  KFulker  DWRose  RJ Path analysis of seven fear factors in adult twin and sibling pairs and their parents. Genet Epidemiol 1987;4 (5) 345- 355
PubMed Link to Article
Torgersen  S The nature and origin of common phobic fears. Br J Psychiatry 1979;134343- 351
PubMed Link to Article
Kendler  KSMyers  JPrescott  CANeale  MC The genetic epidemiology of irrational fears and phobias in men. Arch Gen Psychiatry 2001;58 (3) 257- 265
PubMed Link to Article
Sundet  JMSkre  IOkkenhaug  JJTambs  K Genetic and environmental causes of the interrelationships between self-reported fears: a study of a non-clinical sample of Norwegian identical twins and their families. Scand J Psychol 2003;44 (2) 97- 106
PubMed Link to Article
Jacobson  KCPrescott  CAKendler  KS Sex differences in the genetic and environmental influences on the development of antisocial behavior. Dev Psychopathol 2002;14 (2) 395- 416
PubMed Link to Article
Silberg  JLRutter  MTracy  KMaes  HHEaves  L Etiological heterogeneity in the development of antisocial behavior: the Virginia Twin Study of Adolescent Behavioral Development and the Young Adult Follow-Up. Psychol Med 2007;37 (8) 1193- 1202
PubMed Link to Article
Cardon  LRFulker  DWDeFries  JCPlomin  R Continuity and change in general cognitive ability from 1 to 7 years of age. Dev Psychol 1992;28 (1) 64- 73
Link to Article
Middelberg  RPMartin  NGWhitfield  JB Longitudinal genetic analysis of plasma lipids. Twin Res Hum Genet 2006;9 (4) 550- 557
PubMed Link to Article
Fischbein  SMolenaar  PCMBoomsma  DI Simultaneous genetic-analysis of longitudinal means and covariance structure using the simplex model—application to repeatedly measured weight in a sample of 164 female twins. Acta Genet Med Gemellol (Roma) 1990;39 (2) 165- 172
PubMed
Fabsitz  RRCarmelli  DHewitt  JK Evidence for independent genetic influences on obesity in middle age. Int J Obes Relat Metab Disord 1992;16 (9) 657- 666
PubMed
Koshibu  KLevitt  P Gene × environment effects: stress and memory dysfunctions caused by stress and gonadal factor irregularities during puberty in control and TGF-alpha hypomorphic mice. Neuropsychopharmacology 2008;33 (3) 557- 565
PubMed Link to Article
Killgore  WDOki  MYurgelun-Todd  DA Sex-specific developmental changes in amygdala responses to affective faces. Neuroreport 2001;12 (2) 427- 433
PubMed Link to Article
Toufexis  DJMyers  KMDavis  M The effect of gonadal hormones and gender on anxiety and emotional learning. Horm Behav 2006;50 (4) 539- 549
PubMed Link to Article
Lichtenstein  Pde Faire  UFloderus  BSvartengren  MSvedberg  PPedersen  NL The Swedish Twin Registry: a unique resource for clinical, epidemiological and genetic studies. J Intern Med 2002;252 (3) 184- 205
PubMed Link to Article
Lichtenstein  PTuvblad  CLarsson  HCarlstrom  E The Swedish Twin study of CHild and Adolescent Development: the TCHAD-Study. Twin Res Hum Genet 2007;10 (1) 67- 73
PubMed Link to Article
Fredrikson  MAnnas  PFischer  HWik  G Gender and age differences in the prevalence of specific fears and phobias. Behav Res Ther 1996;34 (1) 33- 39
PubMed Link to Article
SAS Institute Inc, SAS OnlineDoc Version 9.1.3.  Cary, NC SAS Institute Inc2002-2005;NC State University SCDoS, editor. 2005. Cary, NC
Neale  MCBoker  SMXie  GMaes  HH Mx: Statistical Modeling. 6th ed. Richmond Virginia Commonwealth University Medical School2003;
Schwarz  G Estimating the dimension of a model. Ann Stat 1978;6461- 464
Link to Article
Markon  KEKrueger  RF An empirical comparison of information-theoretic selection criteria for multivariate behavior genetic models. Behav Genet 2004;34 (6) 593- 610
PubMed Link to Article
Derogatis  LRSerio  JCCleary  PA An empirical comparison of three indices of factorial similarity. Psychol Rep 1972;30791- 804
Link to Article
Larson  RRichards  MH Daily companionship in late childhood and early adolescence—changing developmental contexts. Child Dev 1991;62 (2) 284- 300
PubMed Link to Article
Rachman  S The conditioning theory of fear-acquisition: a critical examination. Behav Res Ther 1977;15 (5) 375- 387
PubMed Link to Article
Ost  LG Ways of acquiring phobias and outcome of behavioral treatments. Behav Res Ther 1985;23 (6) 683- 689
PubMed Link to Article
Kendler  KSMyers  JPrescott  CA The etiology of phobias: an evaluation of the stress-diathesis model. Arch Gen Psychiatry 2002;59 (3) 242- 248
PubMed Link to Article
Eley  TCBolton  DO'Connor  TGPerrin  SSmith  PPlomin  R A twin study of anxiety-related behaviours in pre-school children. J Child Psychol Psychiatry 2003;44 (7) 945- 960
PubMed Link to Article
Bolton  DEley  TCO'Connor  TGPerrin  SRabe-Hesketh  SRijsdijk  FSmith  P Prevalence and genetic and environmental influences on anxiety disorders in 6-year-old twins. Psychol Med 2006;36 (3) 335- 344
PubMed Link to Article
Hettema  JMNeale  MCKendler  KS A review and meta-analysis of the genetic epidemiology of anxiety disorders. Am J Psychiatry 2001;158 (10) 1568- 1578
PubMed Link to Article
Kendler  KSJacobson  KCMyers  JPrescott  CA Sex differences in genetic and environmental risk factors for irrational fears and phobias. Psychol Med 2002;32 (2) 209- 217
PubMed Link to Article
Taylor  S The hierarchic structure of fears. Behav Res Ther 1998;36 (2) 205- 214
PubMed Link to Article
Kendler  KSNeale  MCKessler  RCHeath  ACEaves  LJ A longitudinal twin study of 1-year prevalence of major depression in women. Arch Gen Psychiatry 1993;50 (11) 843- 852
PubMed Link to Article
Pezze  MAFeldon  J Mesolimbic dopaminergic pathways in fear conditioning. Prog Neurobiol 2004;74 (5) 301- 320
PubMed Link to Article
Cannistraro  PARauch  SL Neural circuitry of anxiety: evidence from structural and functional neuroimaging studies. Psychopharmacol Bull 2003;37 (4) 8- 25
PubMed
Marks  INesse  RM Fear and fitness: an evolutionary analysis of anxiety disorders. Ethol Sociobiol 1994;15247- 261
Link to Article
Seligman  MEP Phobias and preparedness. Behav Ther 1971;2307- 320
Link to Article
Hartl  DL Principles of Population Genetics.  Sunderland, MA Sinauer1980;
Lande  R Natural selection and random genetic drift in phenotypic evolution. Evolution Int J Org Evolution 1976;30 (2) 314- 334
Link to Article

Figures

Place holder to copy figure label and caption
Figure 1.

The model used in these analyses presented for 1 source of liability, such as additive genetic effects. The model contains 4 latent fear scores (T1-T4) reflecting the true level of fear at time 1 (age 8-9 years), time 2 (age 13-14 years), time 3 (age 16-17 years), and time 4 (age 19-20 years). These latent variables are indexed by ratings of fear by parental report (P) (available for times 1-3) and by self-report (S) (available for times 2-4). The degree to which the parent- and self-reported fear ratings index the latent fear level is reflected by the paths λP and λS. The genetic and environmental influences on the latent fear scores are modeled as a Cholesky decomposition. See the text of this article for more details. F indicates the 4 genetic risk factors; f, the path from the genetic factors to the latent fear scores at each of the 4 ages; R, residual effects.

Graphic Jump Location
Place holder to copy figure label and caption
Figure 2.

The proportion of total variance in fears accounted for by genetic factors through development. The y-axis represents the total phenotypic variance so the sum of all the factors equals the total heritability. Purple represents the first genetic factor starting at age 8 to 9 years. Blue represents the second genetic factor starting at age 13 to 14 years. Yellow represents the third genetic factor starting at age 16 to 17 years and green represents the fourth genetic factor acting only at age 19 to 20 years. A, Results for situational fears, the exact parameter estimates of which are seen in Table 5. B, Results for animal fears, the exact parameter estimates of which are seen in Table 6. C, Results for blood/injury fears, the exact parameter estimates of which are seen in Table 7.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Factor Loadings (Promax28) of Individual Fears Assessed by Self-Report at Age 16 to 17 Yearsa
Table Graphic Jump LocationTable 2. Mean (SE) per Item Rating of Fear Intensity as a Function of Type of Fear, Sex, Informant, and Age
Table Graphic Jump LocationTable 3. Pearson Correlations Between Raters and Across Time for a Standardized Measure of Situational Feara
Table Graphic Jump LocationTable 4. BIC Scores for Qualitative and Quantitative Sex Effects for Animal, Blood/Injury, and Situational Fearsa
Table Graphic Jump LocationTable 5. Parameter Estimates for the Best-Fit Model for Situational Fearsa
Table Graphic Jump LocationTable 6. Parameter Estimates for the Best-Fit Model for Animal Fearsa
Table Graphic Jump LocationTable 7. Parameter Estimates for the Best-Fit Model for Blood/Injury Fearsa

References

Muris  PMerckelbach  H The etiology of childhood specific phobia: a multifactorial model. Vasey  MWDadds  MRThe Developmental Psychopathology of Anxiety. Oxford, NY Oxford University Press2001;355- 385
Armfield  JM Cognitive vulnerability: a model of the etiology of fear. Clin Psychol Rev 2006;26 (6) 746- 768
PubMed Link to Article
Silverman  WKMoreno  J Specific phobia. Child Adolesc Psychiatr Clin N Am 2005;14 (4) 819- 843
PubMed Link to Article
Fyer  AJ Current approaches to etiology and pathophysiology of specific phobia. Biol Psychiatry 1998;44 (12) 1295- 1304
PubMed Link to Article
Marks  IM Fears, Phobias, and Rituals.  New York, NY Oxford University Press1987;
Kendler  KSNeale  MCKessler  RCHeath  ACEaves  LJ The genetic epidemiology of phobias in women: the interrelationship of agoraphobia, social phobia, situational phobia, and simple phobia. Arch Gen Psychiatry 1992;49 (4) 273- 281
PubMed Link to Article
Becker  ESRinck  MTurke  VKause  PGoodwin  RNeumer  SMargraf  J Epidemiology of specific phobia subtypes: findings from the Dresden Mental Health Study. Eur Psychiatry 2007;22 (2) 69- 74
PubMed Link to Article
Lichtenstein  PAnnas  P Heritability and prevalence of specific fears and phobias in childhood. J Child Psychol Psychiatry 2000;41 (7) 927- 937
PubMed Link to Article
Rose  RJDitto  WB A developmental-genetic analysis of common fears from early adolescence to early adulthood. Child Dev 1983;54 (2) 361- 368
PubMed Link to Article
Stevenson  JBatten  NCherner  M Fears and fearfulness in children and adolescents: a genetic analysis of twin data. J Child Psychol Psychiatry 1992;33 (6) 977- 985
PubMed Link to Article
Nelson  ECGrant  JDBucholz  KKGlowinski  AMadden  PAFReich  WHeath  AC Social phobia in a population-based female adolescent twin sample: co-morbidity and associated suicide-related symptoms. Psychol Med 2000;30 (4) 797- 804
PubMed Link to Article
Phillips  KFulker  DWRose  RJ Path analysis of seven fear factors in adult twin and sibling pairs and their parents. Genet Epidemiol 1987;4 (5) 345- 355
PubMed Link to Article
Torgersen  S The nature and origin of common phobic fears. Br J Psychiatry 1979;134343- 351
PubMed Link to Article
Kendler  KSMyers  JPrescott  CANeale  MC The genetic epidemiology of irrational fears and phobias in men. Arch Gen Psychiatry 2001;58 (3) 257- 265
PubMed Link to Article
Sundet  JMSkre  IOkkenhaug  JJTambs  K Genetic and environmental causes of the interrelationships between self-reported fears: a study of a non-clinical sample of Norwegian identical twins and their families. Scand J Psychol 2003;44 (2) 97- 106
PubMed Link to Article
Jacobson  KCPrescott  CAKendler  KS Sex differences in the genetic and environmental influences on the development of antisocial behavior. Dev Psychopathol 2002;14 (2) 395- 416
PubMed Link to Article
Silberg  JLRutter  MTracy  KMaes  HHEaves  L Etiological heterogeneity in the development of antisocial behavior: the Virginia Twin Study of Adolescent Behavioral Development and the Young Adult Follow-Up. Psychol Med 2007;37 (8) 1193- 1202
PubMed Link to Article
Cardon  LRFulker  DWDeFries  JCPlomin  R Continuity and change in general cognitive ability from 1 to 7 years of age. Dev Psychol 1992;28 (1) 64- 73
Link to Article
Middelberg  RPMartin  NGWhitfield  JB Longitudinal genetic analysis of plasma lipids. Twin Res Hum Genet 2006;9 (4) 550- 557
PubMed Link to Article
Fischbein  SMolenaar  PCMBoomsma  DI Simultaneous genetic-analysis of longitudinal means and covariance structure using the simplex model—application to repeatedly measured weight in a sample of 164 female twins. Acta Genet Med Gemellol (Roma) 1990;39 (2) 165- 172
PubMed
Fabsitz  RRCarmelli  DHewitt  JK Evidence for independent genetic influences on obesity in middle age. Int J Obes Relat Metab Disord 1992;16 (9) 657- 666
PubMed
Koshibu  KLevitt  P Gene × environment effects: stress and memory dysfunctions caused by stress and gonadal factor irregularities during puberty in control and TGF-alpha hypomorphic mice. Neuropsychopharmacology 2008;33 (3) 557- 565
PubMed Link to Article
Killgore  WDOki  MYurgelun-Todd  DA Sex-specific developmental changes in amygdala responses to affective faces. Neuroreport 2001;12 (2) 427- 433
PubMed Link to Article
Toufexis  DJMyers  KMDavis  M The effect of gonadal hormones and gender on anxiety and emotional learning. Horm Behav 2006;50 (4) 539- 549
PubMed Link to Article
Lichtenstein  Pde Faire  UFloderus  BSvartengren  MSvedberg  PPedersen  NL The Swedish Twin Registry: a unique resource for clinical, epidemiological and genetic studies. J Intern Med 2002;252 (3) 184- 205
PubMed Link to Article
Lichtenstein  PTuvblad  CLarsson  HCarlstrom  E The Swedish Twin study of CHild and Adolescent Development: the TCHAD-Study. Twin Res Hum Genet 2007;10 (1) 67- 73
PubMed Link to Article
Fredrikson  MAnnas  PFischer  HWik  G Gender and age differences in the prevalence of specific fears and phobias. Behav Res Ther 1996;34 (1) 33- 39
PubMed Link to Article
SAS Institute Inc, SAS OnlineDoc Version 9.1.3.  Cary, NC SAS Institute Inc2002-2005;NC State University SCDoS, editor. 2005. Cary, NC
Neale  MCBoker  SMXie  GMaes  HH Mx: Statistical Modeling. 6th ed. Richmond Virginia Commonwealth University Medical School2003;
Schwarz  G Estimating the dimension of a model. Ann Stat 1978;6461- 464
Link to Article
Markon  KEKrueger  RF An empirical comparison of information-theoretic selection criteria for multivariate behavior genetic models. Behav Genet 2004;34 (6) 593- 610
PubMed Link to Article
Derogatis  LRSerio  JCCleary  PA An empirical comparison of three indices of factorial similarity. Psychol Rep 1972;30791- 804
Link to Article
Larson  RRichards  MH Daily companionship in late childhood and early adolescence—changing developmental contexts. Child Dev 1991;62 (2) 284- 300
PubMed Link to Article
Rachman  S The conditioning theory of fear-acquisition: a critical examination. Behav Res Ther 1977;15 (5) 375- 387
PubMed Link to Article
Ost  LG Ways of acquiring phobias and outcome of behavioral treatments. Behav Res Ther 1985;23 (6) 683- 689
PubMed Link to Article
Kendler  KSMyers  JPrescott  CA The etiology of phobias: an evaluation of the stress-diathesis model. Arch Gen Psychiatry 2002;59 (3) 242- 248
PubMed Link to Article
Eley  TCBolton  DO'Connor  TGPerrin  SSmith  PPlomin  R A twin study of anxiety-related behaviours in pre-school children. J Child Psychol Psychiatry 2003;44 (7) 945- 960
PubMed Link to Article
Bolton  DEley  TCO'Connor  TGPerrin  SRabe-Hesketh  SRijsdijk  FSmith  P Prevalence and genetic and environmental influences on anxiety disorders in 6-year-old twins. Psychol Med 2006;36 (3) 335- 344
PubMed Link to Article
Hettema  JMNeale  MCKendler  KS A review and meta-analysis of the genetic epidemiology of anxiety disorders. Am J Psychiatry 2001;158 (10) 1568- 1578
PubMed Link to Article
Kendler  KSJacobson  KCMyers  JPrescott  CA Sex differences in genetic and environmental risk factors for irrational fears and phobias. Psychol Med 2002;32 (2) 209- 217
PubMed Link to Article
Taylor  S The hierarchic structure of fears. Behav Res Ther 1998;36 (2) 205- 214
PubMed Link to Article
Kendler  KSNeale  MCKessler  RCHeath  ACEaves  LJ A longitudinal twin study of 1-year prevalence of major depression in women. Arch Gen Psychiatry 1993;50 (11) 843- 852
PubMed Link to Article
Pezze  MAFeldon  J Mesolimbic dopaminergic pathways in fear conditioning. Prog Neurobiol 2004;74 (5) 301- 320
PubMed Link to Article
Cannistraro  PARauch  SL Neural circuitry of anxiety: evidence from structural and functional neuroimaging studies. Psychopharmacol Bull 2003;37 (4) 8- 25
PubMed
Marks  INesse  RM Fear and fitness: an evolutionary analysis of anxiety disorders. Ethol Sociobiol 1994;15247- 261
Link to Article
Seligman  MEP Phobias and preparedness. Behav Ther 1971;2307- 320
Link to Article
Hartl  DL Principles of Population Genetics.  Sunderland, MA Sinauer1980;
Lande  R Natural selection and random genetic drift in phenotypic evolution. Evolution Int J Org Evolution 1976;30 (2) 314- 334
Link to Article

Correspondence

CME
Also Meets CME requirements for:
Browse CME for all U.S. States
Accreditation Information
The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
Note: You must get at least of the answers correct to pass this quiz.
Your answers have been saved for later.
You have not filled in all the answers to complete this quiz
The following questions were not answered:
Sorry, you have unsuccessfully completed this CME quiz with a score of
The following questions were not answered correctly:
Commitment to Change (optional):
Indicate what change(s) you will implement in your practice, if any, based on this CME course.
Your quiz results:
The filled radio buttons indicate your responses. The preferred responses are highlighted
For CME Course: A Proposed Model for Initial Assessment and Management of Acute Heart Failure Syndromes
Indicate what changes(s) you will implement in your practice, if any, based on this CME course.
Submit a Comment

Multimedia

Some tools below are only available to our subscribers or users with an online account.

Web of Science® Times Cited: 24

Related Content

Customize your page view by dragging & repositioning the boxes below.

Articles Related By Topic
Related Collections