Selasa, 27 Mei 2008

Health-Related Quality of Life Among Adults Who Experienced Maltreatment During Childhood

Objectives. We sought to assess the difference in a preference-based measure of health among adults reporting maltreatment as a child versus those reporting no maltreatment.
Methods. Using data from a study of adults who reported adverse childhood experiences and current health status, we matched adults who reported childhood maltreatment (n = 2812) to those who reported no childhood maltreatment (n=3356). Propensity score methods were used to compare the 2 groups. Health-related quality-of-life data (or "utilities") were imputed from the Medical Outcomes Study 36-Item Short Form Health Survey using the Short Form-6D preference-based scoring algorithm.
Results. The combined strata-level effects of maltreatment on Short Form-6D utility was a reduction of 0.028 per year (95% confidence interval=0.022, 0.034; P<.001). All utility losses for the childhood-maltreatment versus no-childhoodmaltreatment groups by age group were significantly different: 18-39 years, 0.042; 40-49 years, 0.038; 50-59 years, 0.023; 60-69 years, 0.016; 70 or more years, 0.025.
Conclusions. Persons who experienced childhood maltreatment had significant and sustained losses in health-related quality of life in adulthood relative to persons who did not experience maltreatment. These data are useful for asessing the cost-effectiveness of interventions designed to prevent child maltreatment in terms of cost per quality-adjusted life years saved. (Am J Public Health. 2008;98: 1094-1100. doi:10.2105/AJPH.2007.119826)
There is increasing evidence that exposure to childhood maltreatment can lead to greater susceptibility to lifelong physical and mental heath problems, including cardiovascular disease, hypertension, diabetes, anxiety disorders, depression, substance abuse, and perpetration of future violence.1-7 Childhood maltreatment can be defined as any act or series of acts of commission or omission by a parent or other caregiver, in the context of a relationship of responsibility, trust, or power, that results in harm, potential for harm, or threat of harm to a child's health, survival, development, or dignity.8,9
Childhood maltreatment poses a substantial risk for long-term health for many reasons. First, recurrent exposure to the stress associated with maltreatment can lead to potentially irreversible changes in the interrelated brain circuits and hormonal systems that regulate stress.10-12 Changes in these brain systems can lead to a premature physiological aging of the body that increases vulnerability to disease over the life course.11,12 Second, childhood maltreatment increases the risk of behavioral problems such as smoking, substance abuse, obesity, and sexual promiscuity. 1,13 Third, a related body of evidence indicates that early adverse childhood experiences have a profound effect on a range of cognitive, social, and emotional competencies that lay the foundation for successful learning, coping, and subsequent economic productivity. 13-16
This broad range of childhood maltreatment's impact on health suggests that it may also have an impact on victims' life expectancy and long-term health-related quality of life (HRQoL). When assessed together, these outcomes provide information on the effect that childhood maltreatment has on victims' remaining quality-adjusted life years (QALYs), which is a composite measure of health typically used in economic evaluations of health interventions such as costeffectiveness analyses.17-21
Assessment of the impact of childhood maltreatment on the first of the 2 components of the QALY-life expectancy-is relatively straightforward. It requires good epidemiological data on mortality outcomes associated with the acute and chronic phases of childhood maltreatment. Assessment of the impact of childhood maltreatment on the second component, HRQoL, is more complicated. When following national guidelines for conducting cost-effectiveness analyses,17,22,23 measures of HRQoL should reflect relative desirability of different health outcomes under consideration for the population of interest. Preference-based measures provide a summary value for a respondent's valuation of the quality of life of a particular health state, incorporating all positive and negative aspects of a health state into a single number.
A commonly used approach for valuing preferences in health is "utility." A utility weight is typically scaled between 1, representing perfect health, and 0, representing a health state judged equivalent to being dead. Decrements in HRQoL, as measured by utility weights on this scale, are then multiplied by length of life to estimate the QALYs associated with and without the intervention under consideration. These preferences, or utilities, can be directly elicited from the affected population or can be indirectly derived through the use of well-developed, generally accepted, and widely used generic HRQoL indexes whose valuation is based on general population samples.24-28
For health outcomes resulting from physical abuse, sexual abuse, psychological abuse, neglect, or any combination thereof, few if any studies have either directly or indirectly elicited utilities. The paucity of data, particularly for health states associated with childhood maltreatment, is most likely because of a variety of practical and methodological challenges.29 These include the difficulty in defining an average health state for acute or ongoing violent episodes, the cognitive challenges in eliciting preferences for health outcomes from children, proxy issues concerning parents or caregivers who are often the perpetrators of maltreatment, and other reasons associated with development of the field of childhood maltreatment prevention and priorities for research.30,31
Only a few studies have assessed the longterm impact of childhood maltreatment on HRQoL,32-35 but these have included summary measures of health that are not preference based. One summary measure of health, the Medical Outcomes Study 36-Item Short Form Health Survey (SF-36),36 is a commonly used health-state classification instrument. Edwards et al. compared self-reports of health on the SF-36 in an adult population to an index measure of the number of adverse exposures, including childhood maltreatment, experienced during childhood (the adverse childhood experiences [ACE] score).32 The authors found an inverse relationship between ACE score (on which the more adverse experiences, the higher the score) and the SF-36 overall summary measure. However, the summary measure derived from the SF-36 measures health on a scale from 0 (worst health) to 100 (best health) but does not explicitly incorporate preferences into its scoring algorithm and, therefore, cannot be used to obtain preference weights for constructing the QALY. Alternatively, preferencebased measures of HRQoL reflect relative desirability of a score (or index on a scale) based on tradeoffs that one would make on life expectancy to achieve better HRQoL.23
Fortunately, new methods have been developed that enable one to translate summary measures of HRQoL into preferencebased measures of HRQoL for use in costeffectiveness analyses. This represents an exciting advance in methodology, particularly as it is applied to health outcomes associated with violence that have received such little attention in terms of eliciting preference-based measures of HRQoL. We sought to derive preference-based values for childhood maltreatment outcomes derived from summary measures of health defined by adults self-reporting maltreatment outcomes during childhood. These results, when incorporated with epidemiological data on life expectancy, will provide a means for assessing lifetime losses in QALYs and for conducting cost-effectiveness analyses of interventions designed to prevent childhood maltreatment.

Study Population
Data were originally collected as part of the second survey wave of the Adverse Childhood Experiences Study at Kaiser Permanente's Health Appraisal Clinic in San Diego, California, between June and October 1997. Complete descriptions of the study population and several analyses of this large database are available elsewhere.1,32 Basic demographic information was collected from participants, as well as data on adverse events experienced during childhood, current health status as measured by the SF-36, health risk behaviors, and diseases past and present. Table 1 lists the questions used to measure adverse childhood experiences. Five categories of childhood maltreatment were included, with questions adapted from previously developed scales: physical abuse,37 sexual abuse,38 emotional abuse,37 physical neglect,39 and emotional neglect.39 An additional 5 categories of questions were asked regarding other adverse experiences during childhood, including household substance abuse, household mental illness, violent treatment of mother, household member in prison, and parental separation or divorce.
Data Analysis
Our main outcome measure of interest was a preference-based HRQoL measure, or utility, for 2 populations-adults who self-reported childhood maltreatment during the first 18 years of life (cases) and those who did not report maltreatment during childhood (controls).
Health utility measures were calculated using the Brazier algorithm (provided by Brazier) that transforms a summary measure of health into a preference-based measure of health. Brazier et al.40 first reduced a summary measure of health, the SF-36, into a 6-dimensional health state classification system, the Short Form-6D (SF-6D). The SF-6D includes physical functioning, role imitations, social functioning, pain, mental health, and vitality. Then they directly elicited preference- based measures of HRQoL, or utilities, for a variety of health states defined by the SF-6D from 165 health professionals and patients in the United Kingdom. Following positive outcomes from this pilot work, Brazier et al.41 refined the original models by using a representative sample of the general public (n=836). Several models were tested, with the fixed effects and random effects models being the most appropriate, with utility values as the dependent variable and personal characteristics and dummies for each level of the SF-6D as independent variables. Parameters were estimated from these models and then used for the population to estimate utility indices from the SF-6D. Subsequent studies have tested the validity and reliability of the transformation formula, and it is now seen as a promising method for deriving utilities or preference-based measures of health states from summary health data.41,42
Because our study relied on a large observational study with cases (the childhoodmaltreatment group) being assigned to experimental units without the benefits of randomization, systematic differences were likely to exist between individuals in the childhood-maltreatment and no-childhoodmaltreatment groups with respect to confounding covariates such as other adverse childhood experiences and socioeconomic status. Simple comparisons of HRQoL measures between childhood maltreatment and no childhood maltreatment are potentially misleading or biased in that the differences of health utility between the 2 groups could be explained by systematic between-group differences rather than as the effect of maltreatment per se.
Therefore, we use the method of stratification based on the propensity score, a scalar function of the covariates, to approximate a randomized controlled setting and to reduce bias in estimating marginal impacts of childhood maltreatment on predicted utility in an observational study.43,44 The method involved dividing units into 5 age groups and then dividing them into quintiles based on the propensity score within each age group (for a total of 25 strata). Health utility measures of childhood maltreatment and no childhood maltreatment were compared for those who fell into the same strata. An overall effect of childhood maltreatment on utility was estimated by using a weighted average of the within-strata estimates with the weights equal to the proportions of the population within the strata.
To assess the marginal impact of each type of childhood maltreatment on utility, logistic regression models were estimated with imputed health utility as the outcome variable and 5 types of maltreatment as predictors for all 25 strata. Similar to estimating the overall effect of childhood maltreatment on utility, the overall impact of each type of maltreatment on utility were weighted and combined across all 25 strata to determine the overall impact of that type of childhood maltreatment on utility.
To create the propensity score, which was defined as the predicted probability of being maltreated during childhood, we estimated a multiple logistic regression predicting childhood maltreatment by using a number of covariates as explanatory variables. These covariates included basic demographics (gender, age, age squared, race), family economic variables found to be related to childhood maltreatment in previous research (mother's years of education, log of number of residential moves in childhood, whether parent owned own home),45,46 and the other 5 categories of adverse childhood experiences described previously and in Table 1. The rationale for using the other adverse childhood experiences as covariates was to determine the marginal impact of childhood maltreatment on utility. The model, therefore, adjusted for exposure to other adverse childhood experiences as potential confounders.
Significance tests for all key variables were conducted between the childhood-maltreatment and no-childhood-maltreatment groups within each of the 25 strata for both before and after subclassification. We used an analysis of variance (ANOVA) to evaluate differences in prevalence of key variables that were continuous and a 2-sided Pearson ?2 test for variables that were categorical. A P value of less than .05 was considered significant in this analysis.
Of the 8667 respondents in the second survey wave of the Adverse Childhood Experiences Study, 7641 (88%) agreed to complete the SF-36, and 6815 (78.6%) completed all questions. An additional 647 respondents were excluded because they were missing information on childhood maltreatment (n=25) or on covariates used to develop the propensity score (n=622). Of the 6168 respondents who remained, the average age of participants was 55.4 years (SD=14.9), 53% were women, 76% were White, and 45.6% (n=2812) self-reported some form of maltreatment during childhood. Respondents that remained did not differ substantially on demographic characteristics from the original sample. For example, those respondents who remained in the analyses were similar in age (55.4 years vs 55.9 years) and were more likely to be men (by 1.1%) and White (by 2.1%) compared with the original sample. Therefore we feel that the respondents included in this analysis were representative of Kaiser Permanente's population.
Table 2 contains the prevalence of each individual form of childhood maltreatment, as well as the correlation between maltreatment types. Physical abuse had the highest prevalence of any of the abuse types (26%), whereas physical neglect was reported by the fewest participants (9%). Each maltreatment type was modestly to moderately correlated (P<.05), with the highest correlations between emotional abuse and emotional neglect (0.43), although physical abuse and emotional abuse were nearly as highly correlated (0.42).
A number of key variables were significantly different between the maltreated and nonmaltreated populations, as previously analyzed and reported by the Adverse Childhood Experience Study investigators.47,48 In particular, persons in all age groups who reported childhood maltreatment also reported significantly higher percentages of the other 5 measured adverse childhood experiences, compared with those who reported no childhood maltreatment. The measured economic variables were also significantly associated with childhood maltreatment. After we applied the stratified propensity score method, only 1 of the 25 strata had a significantly different mean propensity score, but the magnitude of the difference within this strata was slight (a score of 0.76 in the maltreated group vs 0.73 in the nonmaltreated group). Therefore, we concluded that the overall matching process was successful in reducing bias between the childhood-maltreatment and no-childhoodmaltreatment groups.43,44,49
Table 3 shows overall mean utility differences comparing the childhood-maltreatment group with the no-childhood-maltreatment group by age group and type of maltreatment. Overall, respondents who reported childhood maltreatment had a marginal utility difference (or disutility) of 0.028 (95% confidence interval [CI]=0.022, 0.034) compared with respondents who reported no childhood maltreatment. This result is in the range of what Walters and Brazier50 estimated as a minimally important difference (0.011 to 0.097) in utility for the SF-6D as measured in 11 studies. For every age group, the overall marginal difference in utilities for those reporting childhood maltreatment compared with those reporting no maltreatment were statistically significant at P<.05, with the largest difference occurring in the group aged 20 to 39 years and the smallest difference occurring in the group aged 60 to 69 years. Imputed utility scores by age group are provided for childhood-maltreatment and no-childhoodmaltreatment groups in Table 4.
Table 3 shows that, across all ages, emotional neglect had the strongest influence on the marginal disutility, followed by sexual abuse and physical abuse. Neither emotional abuse nor physical neglect significantly impacted the disutility across all age groups. However, type of maltreatment impacted the disutility differentially within each age group. For example, among those aged 19 to 49 years, physical abuse, sexual abuse, and emotional neglect significantly impacted disutility. Among those aged 50 to 59 years, however, only physical abuse significantly impacted disutility, and among those aged 60 to 69 years, only sexual abuse and emotional neglect significantly impacted disutility. Among those 70 years and older, only emotional abuse significantly impacted disutility. In fact, the influence of emotional abuse on disutility was only significant among those 70 years and older.
We found that persons who experienced maltreatment during childhood had significant and sustained losses in preference-based HRQoL in adulthood, as measured by health utilities, compared with persons who did not experience maltreatment during childhood. Overall, adults who self-reported any form of childhood maltreatment had a yearly loss of 0.03 QALYs, or 11 days per year. Physical abuse, sexual abuse, and emotional neglect alone significantly reduced HRQoL per year by 0.015, 0.016, and 0.026 QALYs, respectively; emotional abuse or physical neglect alone did not. Preference-based HRQoL, or utility, losses among the childhood-maltreatment group compared with the no-childhood-maltreatment group significantly differed for all age groups, with higher differential losses in utilities found among the youngest age group (0.04 QALYs, or 15 days per year). These differential losses diminished with increasing age up until age 70 years and older, at which time the marginal difference in utility losses between the childhood-maltreatment and no-childhoodmaltreatment groups increased.
Limitations and Potential Biases
The retrospective nature of the self-report data may be one explanation for the declining differences in utility as age increased, with the slight exception of the group 70 years and older. One might question the reliability of older age groups in self-reporting events that may have occurred, in some cases, more then a half century ago. However, there is accumulating evidence that suggests that the unreliability of retrospective reports of trauma is overstated.51,52 For example, in another analysis that used the Adverse Childhood Experiences Study data, researchers found that Cohen's ? was in the good-to-excellent range when a test-retest reliability of the ACE measure was conducted.53 In addition, other analyses from the Adverse Childhood Experiences Study have not found that the association between adverse childhood events and HRQoL decreases with age.32
The recollection of personally experienced events such as childhood maltreatment may have more to do with when the maltreatment occurred and other factors occurring during childhood than with the age of the respondent. Memories of events that occurred before age 3.5 years are very unlikely to be recalled and memories from the 3.5- to 6-year age range are also less likely to be recalled than those that occurred during a later age.54 Older age when the maltreatment ended, maternal support following the disclosure of maltreatment, and more-severe maltreatment have all been found to be associated with an increased likelihood of disclosure.55,56
Another probable source of bias in our study relating to retrospective self reports of childhood maltreatment was that some cases of maltreatment may not have been selfidentified. In a prospective study of women's memory of childhood sexual abuse, Williams57 found, for example, that about 38% of abused women did not recall abuse that had been confirmed 17 years earlier. This type of misclassification would bias our results toward the null. It could be that the effect of childhood maltreatment on HRQoL was mediated by the biological or psychological developmental stage of the individual, with certain types of maltreatment resulting in differential effects over time. Although these data suggested that this phenomenon might exist, more research in this area is warranted, particularly surrounding the effects on HRQoL of different combinations of abuse and other adverse outcomes experienced during childhood.
There were a number of other limitations with this study that should be considered. First, type of childhood maltreatment and other adverse exposures were defined by a limited number of survey questions. As such, there could exist wide exposure variance within each category that is not accounted for in the model. Second, the sample was not representative of the US population and included a group who had good health care coverage and access to health care. Thus, we cannot easily draw the conclusion that these utility losses would be higher or lower in other populations. However, we suspect that in populations with limited access to health care, and mental health services in particular, the marginal difference in utilities between cases and controls might be even greater. Third, we excluded respondents for whom complete SF-36 data (and therefore SF-6D data) were not available, and if these data were not missing at random, our results could be biased. To the best of our knowledge, there are no methods to impute missing values for the transformed SF-6D. Fourth, others have noted that traumatic events tend to be more memorable.58,59 Therefore, adult self-reports of the neglect subtypes from the Adverse Childhood Experiences Study data may be less reliable than reports of the other maltreatment subtypes that are more traumatic.
Public Health Implications
Despite these limitations, translated over a typical lifespan of an individual (aged 75 years, for example), these data suggest that persons who experienced childhood maltreatment have a marginal decrease in at least 2 years of undiscounted quality-adjusted life expectancy, compared with persons who did not experience childhood maltreatment. A cost-effectiveness analysis of an intervention designed to prevent childhood maltreatment, therefore, would include 2 QALYs saved for every case of childhood maltreatment prevented. These results represent a floor effect of the true impact of childhood maltreatment on QALYs for 3 reasons. First, these estimates did not include losses in life years that may be associated with childhood maltreatment because of its influence on key risk factors for suicide and drugor alcohol-related fatalities.60,61 Our estimates of QALYs lost in a maltreated population also did not account for differential mortality rates associated with chronic diseases found to be correlated with childhood maltreatment. And, of potential greater impact, our estimates did not include HRQoL losses incurred during the acute stage of the maltreatment.
These utility loss estimates were also conservative in that other adverse childhood exposures were controlled for in the estimation of the propensity score, thus making the utility losses estimated in this analysis marginal to any utility losses that could occur with co-existing adverse childhood exposures. Dong et al.48 found that the presence of 1 adverse childhood exposure resulted in significantly higher odds (between 2 and 17.7 times) of reporting additional adverse childhood exposures. As a reduction in SF-36 score by increasing number of self-reported adverse childhood exposures was shown in Edwards et al.,32 we would expect utility losses to also increase with an increasing number of adverse childhood exposures. The marginal effect of the other adverse childhood exposures may be less influential then the effect of childhood maltreatment on utility, however. To test this, we estimated utility losses by ACE score and found that individuals with 5 or more adverse childhood exposures had a marginal utility difference of 0.067. Compared with individuals with zero adverse childhood exposures, an individual with 5 or more exposures would have a marginal decrease of at least 5 years (over his or her lifespan) of undiscounted quality-adjusted life expectancy.
The results presented here are an important first step for developing the benefits measure for use in economic evaluations. Economic evaluations are critical for policymakers charged with making allocation decisions with scarce public health resources. Use of a composite measure, such as the QALY, allows the decisionmaker to consider effects of the intervention on length of life and quality of life simultaneously. Applications of cost-effectiveness analyses to interventions that prevent childhood maltreatment are ideal because of the impact on life expectancy previously suggested by the literature and on quality of life as indicated by these results. If cost-effectiveness analyses of interventions to prevent childhood maltreatment are to be successful, further research to estimate the impact of childhood maltreatment severity and duration on quality of life and differential mortality losses associated with victims of childhood maltreatment are essential. This would require a serious commitment to collecting and analyzing longitudinal data on these victimized children. Improvements in HRQoL assessment of children, both in defining the dimensions of health appropriate for this age group and in improving elicitation methods, are also needed. When short-term losses in HRQoL are coupled with the long-term losses in HRQoL presented here, analysts will have a complete accounting of QALYs that could be saved per case of childhood maltreatment prevented.
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[Afiliasi Pengarang]
Phaedra S. Corso, PhD, Valerie J. Edwards, PhD, Xiangming Fang, PhD, and James A. Mercy, PhD
[Afiliasi Pengarang]
About the Authors
At the time of the study, Phaedra S. Corso was with the Department of Health Policy and Management, College of Public Health, University of Georgia, Athens, and the Division of Violence Prevention, Centers for Disease Control and Prevention, Atlanta, GA. Xiangming Fang and James A. Mercy are with the Division of Violence Prevention, Centers for Disease Control and Prevention, Atlanta. Valerie J. Edwards is with the Division of Adult and Community Health, Centers for Disease Control and Prevention, Atlanta.
Requests for reprints should be sent to Phaedra Corso, Department of Health Policy and Management, College of Public Health, University of Georgia, N125 Paul Coverdell Center, Athens, GA 30602-7397 (e-mail:
This article was accepted October 24, 2007.
Note. The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.
P.S. Corso originated the study and supervised all aspects of its implementation, synthesized analyses, and led the writing. V.J. Edwards and X. Fang assisted with the study and completed the analyses. J.A. Mercy assisted with the synthesis of the analyses and the writing of the article. All authors helped to conceptualize ideas, interpret findings, and review and edit drafts of the article.
Human Participant Protection
No human participants were involved in this study.

Penyakit-penyakit Kardiovaskuler: Sleep-disordered Breathing and Cardiovascular Disease: An Outcome-based Definition of Hypopneas

Sleep-disordered Breathing and Cardiovascular Disease: An Outcome-based Definition of Hypopneas

Rationale: Epidemiologic studies on the consequences of sleepdisordered breathing invariably use the apnea-hypopnea index as the primary measure of disease severity. Although hypopneas constitute a majority of disordered breathing events, significant controversy remains about the best criteria used to define these events.

Objectives: The current investigation sought to assess the most appropriate definition for hypopneas that would be best correlated with cardiovascular disease.
Methods: A community sample of middle-aged and older adults was recruited as part of the Sleep Heart Health Study. Full-montage polysomnography was conducted and hypopneas were defined using different thresholds of oxyhemoglobin desaturation with and without arousals. Prevalent cardiovascular disease was assessed based on self-report. Logistic regression analysis was used to characterize the independent association between the hypopnea index and prevalent cardiovascular disease.

Measurements and Main Results: Using a sample of 6,106 adults with complete data on cardiovascular disease status and polysomnography, the current study found that hypopneas associated with an oxyhemoglobin desaturation of 4% or more were associated with prevalent cardiovascular disease independent of confounding covariates. The adjusted prevalent odds ratios for quartiles of the hypopnea index using a 4% desaturation criterion were as follows: 1.00 (<1.10>7.69 events/h). Hypopnea measures based on less than 4% oxyhemoglobin desaturation or presence of arousals showed no association with cardiovascular disease.

Conclusions: Hypopneas comprise a significant component of sleepdisordered breathing in the general community. By varying the criteria for defining hypopneas, this study demonstrates that hypopneas with a desaturation of at least 4% are independently associated with cardiovascular disease. In contrast, no association was observed between cardiovascular disease and hypopneas associated with milder desaturations or arousals.
Keywords: sleep-disordered breathing; cardiovascular disease; Sleep Heart Health Study

Epidemiologic studies have shown that sleep-disordered breathing (SDB) is associated with hypertension and cardiovascular disease (1-4). If left untreated, SDB increases the risk of fatal and nonfatal cardiovascular events that can be averted with continuous positive-pressure therapy (5). Across most of the published literature on the clinical implications of SDB, the magnitude of disease risk has been independent of confounding covariates and related to the aggregate frequency of apneas and hypopneas. Despite the wealth of empirical data linking SDB with adverse neurobehavioral and cardiovascular endpoints, the independent contribution of hypopneas to these outcomes remains to be determined. Properly addressing this issue begs the question of the most relevant definition of hypopnea that best correlates with one or more clinical consequences. In the absence of empirical evidence, there remains considerable controversy regarding the appropriate criteria for defining hypopneas. In the clinical and research arena, hypopneas are defined on the basis of several physiologic signals, including airflow, respiratory effort, oxyhemoglobin saturation, and the electroencephalogram (EEG).

Although a reduction in airflow that is associated with oxyhemoglobin desaturation of at least 4% has become the recommended criterion (6), there are no studies that have systematically explored the possibility of whether hypopneas based on an alternative oxyhemoglobin desaturation threshold (e.g., 2 or 3%) or those with an EEG arousal are also be associated with adverse cardiovascular effects. Information resulting from such exploration could result in an evidence-based definition of a hypopnea with enhanced ability to define the prevalence of SDB, optimize case finding, and stratify disease severity. Thus, the primary objective of this study was to examine the significance of varying levels of hypopnea-related oxyhemoglobin desaturation in the association between SDB and prevalent cardiovascular disease in a community cohort of middle-aged and older adults. It was hypothesized that increasing frequency of events characterized by nonapneic reduction in airflow may be independently associated with prevalent cardiovascular disease even when such events are accompanied by less severe oxyhemoglobin desaturation.


Study Sample
The specific aims and design of the Sleep Heart Health Study (SHHS) have been previously described (7). Briefly, the SHHS is a longitudinal cohort study of the cardiovascular consequences of SDB. Participants for the baseline cohort were recruited from ongoing epidemiologic studies of cardiovascular and respiratory disease. Participants from these ''parent'' studies were eligible if they were at least 40 years of age and were not being treated for SDB with positive-pressure therapy, oxygen, or tracheotomy. The SHHS cohort consists of 6,441 participants who completed the baseline examination, which included an overnight polysomnogram and several interview-administered questionnaires on sleep habits and medical history between November 1995 and January 1998. Informed consent was obtained from all participants, and the study protocol was approved by the institutional review board of each institution.


Unattended, home polysomnography was conducted using a portable monitor (P-Series; Compumedics, Abbotsville, Australia). The following physiologic variables were recorded: EEG (montage: C^sub 3^/A^sub 1^ and C^sub 4^/ A^sub 2^), right and left electrooculograms, a single bipolar electrocardiogram and a chin electromyogram, oxyhemoglobin saturation by pulse oximetry, chest and abdominal excursion by inductance plethysmography, airflow by an oronasal thermocouple, and body position by a mercury gauge. Recordings were stored in real time and sent to a central reading center for review and scoring. Details of polysomnographic equipment, hook-up procedures, failure rates, scoring, and quality assurance have been published (8). Apnea was identified if the airflow was absent or nearly absent for at least 10 seconds. Hypopnea was identified whenthere was at least a 30% reduction in airflow or thoracoabdominal movement below baseline values for at least 10 seconds. The apnea-hypopnea index (AHI) was defined as the number of apneas plus hypopneas per hour of sleep. The apnea index and hypopnea index were defined as the number of apneas and hypopneas, respectively, per hour of sleep. Sensitivity analyses were also conducted using different thresholds of oxyhemoglobin desaturation (≥4%, ≥3%, ≥2%, any desaturation) for apneas and hypopneas. Arousals were identified according to published criteria (9). Analyses examining the associations between the hypopnea index and cardiovascular disease were conducted with and without the inclusion of arousals in the definition of a hypopnea. Finally, an oxyhemoglobin desaturation index (ODI; events/h) was also constructed for varying thresholds (e.g., ≥2%, ≥3%, ≥4%) of drops in oxyhemoglobin saturation during sleep.

Ascertainment of Prevalent Cardiovascular Disease and Covariate Data
During the home visit, participants completed an interviewer-administered health questionnaire that included queries regarding physiciandiagnosed angina, history of heart failure, a previous heart attack, or stroke, as well as a history of bypass surgery or coronary angioplasty. Participants were allowed to provide an ''unsure'' response for each question. Prevalent cardiovascular disease was defined if the participant had any of the aforementioned cardiovascular conditions or procedures. Cardiovascular disease was classified as missing in the presence of unsure responses to any of the previous questions. Self-reported information related to other relevant exposures such as smoking was obtained. Smoking status was categorized as current, former, or never. Other measurements during the home visit included body weight, neck circumference, and three successive measurements of systolic and diastolic blood pressure. Covariate data included demographic variables (e.g., race), anthropometric variables (e.g., waist), and plasma lipids (total cholesterol and high-density lipoprotein [HDL] cholesterol). Race was classi- fied as white, African American, American Indian, Hispanic, or other.
Statistical Analysis

Unadjusted differences in continuous and categorical predictor variables across cardiovascular disease status were assessed for significance using t tests or χ[su[]2^ tests, as appropriate. Of the 6,441 participants in the baseline SHHS cohort, 335 (5.2%) were classified as having ''missing'' cardiovascular disease status. Thus, the sample size for the current analysis consisted of 6,106 subjects. Participants with missing cardiovascular disease status were younger and included a higher proportion of African Americans than those with prevalent cardiovascular disease data (Table E1 of the online supplement). To assess the associations between prevalent cardiovascular disease and SDB using different definitions, logistic regression analysis was used.

The independent variables included the AHI, the apnea index, the hypopnea index, and the ODI. Variables were categorized into four equal groups using quartiles for various oxyhemoglobin desaturation thresholds. In the development of the multivariable statistical models for prevalent cardiovascular disease, bivariate analyses were initially performed to determine the unadjusted relative odds ratios and the associated 95% confidence intervals for variables of interest comparing the second through fourth quartiles to the first quartile. Adjustments in these models included age, sex, race, body mass index, neck circumference, waist circumference, smoking status, total cholesterol, and HDL cholesterol. All statistical analyses were conducted using the SAS statistical software, version 9.1 (SAS Institute, Inc., Cary, NC).


Of the 6,106 SHHS participants with data on cardiovascular disease status, 16.8% (n = 1,025) reported prevalent cardiovascular disease. Table 1 shows the characteristics of the study cohort, including demographic and anthropometric variables by cardiovascular disease status. As expected, compared with participants without cardiovascular disease, those with cardiovascular disease were older and had a higher prevalence of other risk factors, including central obesity (i.e., larger waist and neck circumference), former or current tobacco use, prevalent hypertension, and lower HDL cholesterol levels. In addition, male sex and minority race were also associated with a higher prevalence of cardiovascular disease.

All bivariate and multivariable analyses were initially conducted with hypopneas or apneas using only the oxyhemoglobin desaturation criterion without an EEG arousal as part of the definition. At virtually every desaturation threshold by which hypopneas and apneas were defined, the unadjusted event frequency per hour of sleep was significantly higher in participants with cardiovascular disease (Table E2). To examine the degree of correlation between the apnea index and the hypopnea index, Pearson's correlation coefficients were computed (Table E3). As expected, regardless of the oxyhemoglobin desaturation threshold, the apnea index and the hypopnea index at varying oxyhemoglobin desaturation thresholds were modestly correlated (range of correlation coefficients, 0.15-0.40). These correlations indicate that, although apneas and hypopneas co-occur, there is moderate heterogeneity, which allows for an independent examination of the associations between the apnea index, the hypopnea index, and prevalent cardiovascular disease.

Multivariable logistic regression models were constructed for the apnea index and hypopnea index for each threshold of oxyhemoglobin desaturation (e.g., ≥4%, ≥3%, ≥2%) to determine their association with prevalent cardiovascular disease. In these multivariable models, the apnea index and the hypopnea index, each associated with a specific level of oxyhemoglobin desaturation, were included as an independent variable together with the following covariates: age, sex, race, body mass index, waist circumference, neck circumference, and other cardiovascular risk factors (i.e., smoking status, total cholesterol, andHDL cholesterol).

Sensitivity analyses showed that inclusion of prevalent hypertension as a covariate in each of the multivariable models constructed had no material impact on the reported odds ratios and thus it was not included as a covariate. Using a 4% desaturation criterion, the adjusted odds ratios for prevalent cardiovascular disease for quartile of the hypopnea index were as follows: 1.00 (quartile I: <1.01>7.69 events/h). Figure 1 shows that, for oxyhemoglobin desaturation thresholds of 4, 3, and 2%, the hypopnea index was independently associated with prevalent cardiovascular disease. In contrast, regardless of the oxyhemoglobin desaturation threshold, the apnea index was not associated with prevalent cardiovascular disease (Figure E1). Because the quartile cut points for the hypopnea index based on different desaturation thresholds are dissimilar, the odds ratios for prevalent cardiovascular disease cannot be compared across the different multivariable models shown in Figure 1.

Given that the association between cardiovascular disease and the hypopnea index defined using a given threshold of oxyhemoglobin desaturation (e.g.,≥2%,≥3%,≥4%) could be influenced by more severe oxyhemoglobin desaturations, further analyses were undertaken to determine whether hypopneas with oxyhemoglobin desaturations within a specific range (0-2%, 2-3%, 3- 4%) were associated with cardiovascular disease after accounting for hypopneas with oxyhemoglobin desaturation above the cut point (e.g., >2%, >3%, >4%). Hierarchical models were constructed with a stepwise addition of covariates as before. Table 2 shows that the frequency of hypopneas associated with a 4.0- 4.9% oxyhemoglobin desaturation was associated with prevalent cardiovascular disease after adjusting for the frequency of hypopneas with oxyhemoglobin desaturations above 5%. However, no significant associations were noted between prevalent cardiovascular disease and hypopnea-related oxyhemoglobin desaturations of less than 4%. Analyses were also conducted by including arousals as part of the hypopnea definition. The odds ratios relating prevalent cardiovascular disease to the quartiles of the hypopnea index on the basis of presence of oxyhemoglobin desaturation at various thresholds or occurrence of an arousal were either materially unchanged or lower in magnitude compared with those in Figure 1 (Figure E2). Thus, inclusion of an arousal in defining hypopneas did not improve the association between the hypopnea index and prevalent cardiovascular disease.
Additional analyses were also undertaken to characterize the association between prevalent cardiovascular disease and the ODI using different desaturation thresholds. The ODI is a frequency of desaturation events per hour of sleep that is determined independent of changes in airflow. Tables 3 and 4 show the adjusted odds ratio for cardiovascular disease for quartiles of ODI based on desaturations at or above a specific threshold (Table 3) and within specific range of oxyhemoglobin desaturation after adjusting for events with desaturation that exceeded the range (Table 4). These analyses revealed that the frequency of oxyhemoglobin desaturation in the 4.0-4.9% range is associated with prevalent cardiovascular disease even after adjusting for desaturations higher than 5%. However, the associations between prevalent cardiovascular disease and the ODI based on desaturation of less than 4% were not statistically significant.


The current investigation presents several unique findings. First, SDB in a community sample of middle-aged and older adults is characterized more by the occurrence of hypopneas than apneas. Second, independent of apnea index, the hypopnea index was associated with self-reported prevalent cardiovascular disease. Third, while keeping a fixed threshold of airflow reduction and regardless of an arousal criterion, the current study identified a clinically relevant hypopnea definition that best correlated with prevalent cardiovascular disease after accounting for several confounding covariates. Specifically, the frequency of hypopneas defined by a threshold of oxyhemoglobin desaturation of at least 4% was associated with cardiovascular disease. The strength or precision of association was not improved by reducing the desaturation threshold criterion to less than 4% or by including arousals in the definition. Similarly, examination of the ODI, which does not include the degree of airflow reduction, showed that desaturations based on a threshold of 4% or more were also associated with cardiovascular disease. Including less severe desaturation events showed no improvement in the association with cardiovascular disease compared with the 4% threshold.

Over the last decade, substantial evidence has accumulated linking SDB to excess morbidity and mortality. The risk of many clinical sequelae attributed to SDB appears to increase as the AHI increases. By including both apneas and hypopneas in this disease-defining metric, an implicit assumption is that these events are alike in their impact on clinical outcomes. Although this assumption may in fact be correct, there is a relative paucity of supporting evidence. Moreover, there are no empirical data indicating whether certain criteria for defining hypopneas are better associated with adverse SDB-related outcomes than others. A major challenge in defining the health-related implications of hypopneas is the inconsistency in defining these events. Differences in theamount of airflowreduction, degree of oxyhemoglobin desaturation, and the inclusion of arousal can lead to significant variability in hypopnea detection across different laboratories. Compounding this variability are the differences in the methods used to detect breathing abnormalities during sleep (e.g., thermistor vs. nasal pressure transducer). Thus, an outcome-based hypopnea definition is lacking and consensus recommendations are commonly used in research and clinical practice. For example, in a 2001 consensus report (10) by a task force of the American Academy of Sleep Medicine (AASM), it was recommended that a hypopnea be defined as an abnormal respiratory event characterized by a 30% or more reduction in airflow that is associated with an oxyhemoglobin desaturation of at least 4%. A 2005 update of those recommendations incorporated alternate criteria that included a discernible reduction in airflow associated with an oxyhemoglobin desaturation of at least 3% or an arousal from sleep (11). Most recently, the AASM published a comprehensive manual for scoring sleep and associated events in which both of the aforementioned definitions were also permitted (6). By allowing alternate criteria, there is an embedded recognition that the current level of evidence is insufficient for defining event criteria that are associated with adverse health outcomes. The work presented herein attempts to fill some of these gaps by carefully considering different thresholds of oxyhemoglobin desaturation and by including and excluding arousal from the definition of a hypopnea. Overall, our results suggest that hypopneas associated with an oxyhemoglobin desaturation of at least 4% are correlated with cardiovascular disease, whereas those associated with lesser degrees of hypoxemia or arousals show no association. Whether similar findings also emerge for other SDB-related outcomes, such as daytime sleepiness, neurocognitive dysfunction, and altered glucose homeostasis, remains to be determined.

The implications for using outcome-based thresholds for hypopnea definition in SDB are numerous. Although it is appealing to believe that using less stringent thresholds forSDBmay benefit the individual patient, there are reasons to believe that this may not be the case. First, the evidence supporting the use of less stringent criteria for SDB events is lacking. Such evidence should be based on rigorous analyses that test hypotheses on whether a threshold in oxyhemoglobin desaturation or the inclusion of arousals for defining hypopneas is associated with a clinical outcome in cross-sectional and longitudinal studies. A simple comparison of the AHI based on a 3% versus a 4% oxyhemoglobin desaturation threshold as a predictor of a clinical outcome is insufficient. Rather, analyses that examine a specific level of oxyhemoglobin desaturation for a hypopnea need to take into account events with oxyhemoglobin desaturation that are above the threshold. Moreover, because endpoints may vary with the type and severity of SDB events, the clinical value for different event definitions has to be individually determined. Second, lowering the criteria for detecting hypopneas will undoubtedly increase the number of patients who are diagnosed with SDB and started on treatment. Although serious side effects of positivepressure therapy are rare, the benefits of treatment may be small, particularly for those patients who received a diagnosis on less stringent criteria. Third, given the large reservoir of undiagnosed disease, priority should be initially placed on identifying and treating those patients who meet the most stringent and uncontroversial definition of SDB. Finally, lowering disease-defining thresholds without supporting evidence will raise the prevalence of disease and impose additional burden on already limited public heath resources. The foregoing considerations argue that adopting a particular set of criteria will require a concerted effort to define the clinical sequelae associated with varying definitions of SDB.

There are several important limitations in this study that merit discussion. The first limitation is that causal inferences are not possible given the cross-sectional nature of our analysis. Thus, although including arousals in the hypopnea definition did not augment the association with cardiovascular disease, sleep fragmentation cannot be excluded as a putative factor linking SDB to cardiovascular outcomes. The lack of an association with hypopnea-related arousals may merely reflect the poor reliability of scoring EEG arousals (12). Similarly, hypoxemia cannot be implicated as a causal factor because reverse causality (i.e., cardiovascular disease causing SDB) is also certainly possible. The second limitation is that assessments of breathing abnormalities during sleep were based on inductive plethysmography and an oronasal thermistor. A comprehensive survey of validity and reliability of scoring respiratory events concluded that the thermistor, as used in the SHHS, is far inferior in detecting hypopneas when compared with a nasal pressure device (13). However, the underdetection of hypopneas is likely to be similar in those with and without cardiovascular disease and thus any bias in the measures of association is probably small. The use of a thermistor to assess airflow also limited our ability to examine whether different levels of airflow reduction would be associated with prevalent cardiovascular disease. The third limitation comes from our use of self-reports to assess prevalent cardiovascular disease. Previous work from one of the SHHS parent cohorts has shown that proportions of confirmed self-reported myocardial infarction is 75.5 and 60.6% in men and women, respectively (14). For a diagnosis of heart failure, these estimates were 73.3 and 76.6%, respectively, confirming an underreporting of cardiovascular conditions. Nonetheless, in all probability, the degree of underreporting is likely to be unrelated to the abnormalities on the polysomnogram and thus would not lead to biased estimates of association. Finally, it is also important to recognize that the SHHS cohort is not representative of a population-based cohort. The older age of the sample, the recruitment of subjects from other epidemiologic cohorts with oversampling on snoring subjects, and the relatively low burden of SDB limit the generalizability of the reported results. These limitations notwithstanding, the current study also has several strengths. These include the use of full-montage polysomnography to characterize SDB with varying event definitions in a large community cohort. Furthermore, adjustments for cardiovascular risk factors and other demographic factors allowed for an unconfounded examination of how different hypopnea definitions correlate with prevalent cardiovascular disease. Finally, inclusion of hypopneas associated with severe oxyhemoglobin desaturation in multivariable models for a specific oxyhemoglobin desaturation threshold is also a major strength. Such adjustments are necessary to ensure that the measures of association derived for a specific threshold in oxyhemoglobin desaturation are not biased by events that are associated with severe degrees of oxyhemoglobin desaturation.

In summary, the results of this cross-sectional analysis of the SHHS data show that hypopneas with a 4% reduction in oxyhemoglobin saturation are associated with cardiovascular disease, even after accounting for events with greater degrees of desaturation. In contrast, there was no association between the frequency of hypopneas with less than 4% desaturation and cardiovascular disease. Additional research is needed to compare different event definitions in their association with other SDBrelated consequences in cross-sectional and longitudinal analyses. Without such evidence, expanding event definitions will certainly increase the number of patients with mild disease, but at the expense of identifying and adequately treating those that are severely affected but remain undiagnosed.

Conflict of Interest Statement: N.M.P. does not have a financial relationship with a commercial entity that has an interest in the subject of this manuscript. A.B.N. does not have a financial relationship with a commercial entity that has an interest in the subject of this manuscript. T.B.Y. does not have a financial relationship with a commercial entity that has an interest in the subject of this manuscript. H.E.R. does not have a financial relationship with a commercial entity that has an interest in the subject of this manuscript. M.H.S. is a consultant to Respironics, Inc., a coinventor of BiPAP, manufactured by Respironics, Inc., and has a financial interest in this brand and related devices manufactured by Respironics, Inc.; his immediate family and self own a noncontrolling number of shares in Respironics, Inc. M.H.S. received an honorarium from Respironics, Inc., for a lecture within the last 3 years; he was on an advisory board to Sanofi in the last 3 years.

Scientific Knowledge on the Subject
Sleep-disordered breathing has been associated with numerous health consequences. However, empirical data on an outcome-based definition of hypopneas are lacking.
What This Study Adds to the Field
Hypopneas with a 4% or more decrease in oxyhemoglobin saturation are associated with prevalent cardiovascular disease. Hypopneas with less than a 4% desaturation or those with an arousal are not associated with prevalent cardiovascular disease.

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[Afiliasi Pengarang]
Naresh M. Punjabi1, Anne B. Newman2, Terry B. Young3, Helaine E. Resnick4, and Mark H. Sanders5
1Division of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, Maryland; 2Center for Aging and Population Health, Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania; 3Population Health Sciences, University of Wisconsin; 4Institute for the Future of Aging Services, American Association of Homes and Services for the Aging, Washington, DC; and 5Division of Pulmonary and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
(Received in original form December 24,2007; accepted in final formFebruary 13,2008)
Supported by the National Heart, Lung, and Blood Institute through the following cooperative agreements: U01-HL53940 (University of Washington), U01-HL53941 (Boston University), U01-HL63463 (Case Western Reserve University), U01-HL53937 (Johns Hopkins University), U01-HL53938 (University of Arizona), U01-HL53916 (University of California, Davis), U01-HL53934 (University of Minnesota), U01-HL63429 (Missouri Breaks Research), U01-HL53931 (New York University).
The opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the Indian Health Service.
Correspondence and requests for reprints should be addressed to Naresh M. Punjabi, M.D., Ph.D., Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, 5501 Hopkins Bayview Circle, Baltimore, MD 21224. E-mail:
This article has an online supplement, which is accessible from this issue's table of contents at
Am J Respir Crit Care Med Vol 177. pp 1150-1155, 2008
Originally Published in Press as DOI: 10.1164/rccm.200712-1884OC on February 14, 2008

Internet address:

Estimation of LDL-Associated Apolipoprotein B from Measurements of Triglycerides and Total Apolipoprotein B

VLDL and chylomicrons may interfere with measurements of apolipoprotein B (apo B) on LDL particles. Ultracentrifugation of samples enriched in chylomicrons and VLDL and subsequent measurement of apo B in the infranate fraction [density (d) = 1.006] removes this interference. This apo B fraction is called "LDL-apo B."

METHODS: We retrospectively analyzed 64 895 measurements of triglycerides, total apo B, and LDL-apo B. Samples were ultracentrifuged, and 3 commercially available immunoassays that use different antibodies were used to measure LDL-apo B in the 1.006 infranate fraction.

RESULTS: After adjusting for triglyceride concentration, we found total apo B and LDL-apo B measurements to be strongly correlated. We derived a simple linear equation for calculating LDL-apo B concentration (in milligrams per deciliter) from measurements of total apo B and triglycerides: LDL-apo B = apo B - 10 mg/ dL - triglycerides/32. This equation accurately predicts LDL-apo B values within ± 12% of the measured value in 75% of cases.

CONCLUSIONS: Our equation provides a convenient means of estimating LDL-apo B from commonly available measurements of total apo B and triglycerides without the need for ultracentrifugation. LDL-apo B measurements were also independent of the different apo B antibodies in the 3 assays used in this study. An equation that predicts LDL-apo B particle number may be useful, regardless of the apo B assay used.

Measurements of plasma lipids (cholesterol, triglycerides) and HDL and LDL cholesterol subfractions (HDL-C, LDL-C) have been used to assess cardiovascular risk for several decades. Cholesterol, triglyceride, and HDL-C measurements have been readily available for quite some time, whereas LDL-C has been routinely estimated with the Friedewald equation. Although the reliability of this calculation decreases as the triglyceride concentration increases, the Friedewald equation is a useful clinical tool for estimating LDL-C in most cases, even with the current availability of assays that measure LDL-C directly.

Measurements of apolipoprotein B (apo B) are better for distinguishing and predicting cardiovascular disease risk than either measuring LDL-C directly (especially when triglycerides are high (1-6)) or calculating the LDL-C concentration via the Friedewald formula (7-11). Apo B measurement has been standardized, and current methods are highly precise and accurate. Apo B immunoassays use antibodies that recognize epitopes on LDL-C particles containing apo B-IOO, the main structural protein of these particles. Turbidity is a source of interference in apo B immunoassays of lipemic samples, and cross-reactivity of apo B-IOO antibodies with apo B-48 on chylomicrons can also produce interference. We propose that apo B values obtained for the 1.006 infranate fraction [density (if) = 1.006 kg/L)] of a sample after ultracentrifugation (hereby termed LDL-apo B) accurately estimate LDL particle numbers. Furthermore, we provide a simple equation for estimating LDL-apo B from measurements of triglycérides and apo B that is independent of the antibody used in the immunoassay.

In the beta-quantification reference method by which LDL-C is measured, a sample is ultracentrifuged through a solution of d = 1.006 kg/L. The supranate contains chylomicrons, VLDL1 and any beta-VLDL, whereas the infranate contains LDL, HDL, intermediate-density lipoprotein (IDL), and lipoprotein (a). Thus, ultracentrifugation can be used as a means to remove chylomicrons and VLDL from LDL and IDL. Apo B measured in the 1.006 infranate fraction is associated with LDL and IDL, which are considered atherogenic compared with VLDL particles. In addition, LDL-apo B has been associated with arteriographie changes in the FATS study and with decreased numbers of events and regression of coronary lesions in the SCRIP study (6, 12). LDL-apo B is likely to be a better marker of cardiovascular disease risk than total apo B. LDL-apo B has been measured >60 000 times at Berkeley HeartLab using 3 distinct commercially available assays with different specificities for apo B. In each measurement, 2 parts of an aqueous NaCl solution ( 11.5 g NaCl plus O. l g EDTA per 1 L water; d = 1.006) are added to 1 part patient serum, and the procedure is followed by ultracentrifugation in a Beckman 50.4 Ti fixed-angle rotor with a Beckman Coulter L8-80MR or L80-XP ultracentrifuge (Beckman Coulter) at 45 000 rpm for 11 h at 10 °C, resulting in 218 068& The top third of the centrifuged sample contains chylomicrons and VLDL and is removed. The apo B content in the bottom fraction (d > 1.006} is then measured with commercially available reagents in automated chemistry analyzers. We have used the following 3 methods to measure apo B in the 1.006 fraction: (a) Abbott reagents (cat. no. 9D93) with the Abbott Aeroset, (b) Roche reagents (cat. no. 03032639) with the Roche/Hitachi Module P-800, and (c) Kamiya K-Assay reagents (cat. no. KAI-024) with the Roche/ Hitachi Module P-800.

Perbesar 200%
Perbesar 400%

Fig. 1. Correlation between LDL-apo B and total apo B values at 210 mg/dL triglycerides (n = 1 000) (A) and at 700-950 mg/dL triglycerides (n = 1 000) (B).
The mean difference between apo B and LDL-apo B values is correlated with the triglyceride (TG) concentration (n = 8940) (C). Closed circles represent the mean difference at each triglyceride concentration; the line is a plot of the equation (in figure). Calculated and measured LDL-apo B values are strongly correlated (n = 1200, method a) (D).

We used deidentified data from 64 895 samples in our laboratory information system in a retrospective analysis in which we retrieved triglyceride, total apo B, and LDL-apo B data for each sample. LDL-apo B was measured with one of the 3 methods mentioned above. The data set was limited to samples with triglyceride concentrations >200 mg/dL,1 the threshold above which LDL-apo B was measured. This threshold was chosen because it is near the 95th percentile of triglyceride values for our laboratory's patient population and because our laboratory can accommodate routine measurement of LDL-apo B for this quantity of samples. Total apo B and LDL-apo B were strongly correlated, especially after adjusting for triglyceride concentration (Fig. 1, A and B). Plotting the difference between apo B and LDL-apo B values against triglyceride concentration revealed a linear relationship until the triglyceride concentration exceeded approximately 800 mg/dL (Fig. 1C). The following linear equation fits the data obtained with any of the 3 methods:
LDL-apo B = apo B - 10 mg/dL - triglycerides/32,1
where LDL-apo B, apo B, and tnglycerides are in milligrams per deciliter and apo B is the concentration of total apo B.

To demonstrate that this equation accurately predicts measured LDL-apo B values, we compared LDLapo B values calculated for subsets of data (n = 1200) with measured values obtained with each of the 3 methods (Table 1). The equation predicted measured LDL-apo B values to within ± 15% for all 3 methods in >85% of the cases (n = 3600). A comparison of all the LDL-apo B measurements (n = 64 895) with calculated LDL-apo B values demonstrated that the equation predicted the measured value to within ±15% in 84% of the cases. The correlations between measured and calculated LDL-apo B values were high (R^sup 2^ ≥ 0.88; Fig. 1D) and were independent of the method used. Thus, our equation appears to correctly predict measured LDL-apo B values. Although the performance criteria for apo B measurements have not been formally established, the total error for LDL-C has been established by the National Cholesterol Education Program to be ≤12% (13). Our equation was able to meet this 12% performance criterion with respect to measured LDL-apo B values in 75% of the cases (n = 64 895, all data for the 3 methods).

Other, more complex equations fit the data slightly better; however, the simplicity of our equation does not appear to compromise its predictive power compared with the more complex equations. Furthermore, its simplicity facilitates its use because it is easier for a clinician to remember. The triglycerides/32 term takes into consideration the VLDL contribution to apo B and is reminiscent of the triglycerides/5 term in the Friedewald equation. Because ultracentrifugation is not readily available in most clinical laboratories, our equation conveniently estimates LDL-apo B from commonly available measurements for total apo B and triglycerides. Furthermore, because our data suggest that the LDL-apo B values obtained with the 3 apo B assays are similar, this equation is likely to be broadly applicable and independent of the particular apo B assay used. Clinically, this equation can be used to estimate atherogenic apo B particle number without requiring specialized laboratory measurements. Further investigation is needed to determine whether this or a similar equation can be used universally for calculating LDL-apo B.

Grant/Funding Support: None declared.
Financial Disclosures: None declared.

[Catatan Kaki]
1 Editor's footnote: Although this journal has a policy of using SI nomenclature and expressing triglyceride and apo B concentrations in milligrams per liter, consideration of the likely clinical application of the equation presented herein requires these concentrations to be left expressed in milligrams per deciliter, the units in common clinical usage.

1. Walldius G, Jungner I, Holme I, Aastveit AH, Kolar W, Steiner E. High apolipoprotein B, low apolipoprotein A-I. and improvement in the prediction of fatal myocardial infarction (AMORIS study): a prospective study. Lancet 2001;358:2026-33.
2. Yusuf S, Hawken S, Ounpuu S, Dans T. Avezum A, Lanas F, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study, lancet 2004;364:937-52.
3. Gotto AM Jr, Whitney E, Stein EA, Shapiro DR, Clearlield M, Weis S, et al. Relation between baseline and on-treatment lipid parameters and first accurate major coronary events in the Air Force/Texas Coronary Atherosclerosis Prevention Study (AFCAPS/TexCAPS). Circulation 2000;101: 477-84.
4. Contois JH, McNamara JR, Lammi-Keefe CJ, Wilson PW, Massow T, Schaefer EJ. Reference interval for plasma apolipoprotein B determined with a standardized commercial immunoturbidimetric assay: results from the Framingham Offspring Study. Clin Giern 1996;42:515-23.
5. Sniderman AD. Apolipoprotein B and apolipoprotein A1 as predictors of coronary artery disease. Can J Cardiol 1988;4(Suppl A):24A-30A.
6. Brown G, Albers JJ, Fisher LD. Regression of coronary artery disease as a result of intensive lipid-lowering therapy in men with high levels of apolipoprotein B. N Engl J Med 1990;323:128998.
7. Marcovina SM, Albers JJ, Dati F, Ledue TB, Ritchie RF. International Federation of Clinical Chemistry standardization project for measurements of apolipoproteins A-I and B. Clin Chem 1991;37:167682.
8. Albers JJ. Marcovina SM, Kennedy H. International Federation of Clinical Chemistry standardization project for measurements of apolipoproteins A-I and B, II. Evaluation and selection of candidate reference materials. Clin Chem 1992; 38:658-62.
9. Marcovina SM, Albers JJ, Henderson LO, Hannon WH. International Federation of Clinical Chemistry standardization project for measurements of apolipoproteins A-I and B. III. Comparability of apolipoprotein A-I values by use of international reference materiai. Clin Chem 1993;39:773-81.
10. Marcovina SM, Albers JJ, Kennedy H, Mei JV, Henderson LO. Hannon WH. International Federation of Clinical Chemistry standardization project for measurements of apolipoproteins A-I and B. IV. Comparability of apolipoprotein B values by use of international reference material. Clin Chem 1994;40:586-92.
11. Jungner I, Marcovina SM, Walldius G, Holme I, Kolar W, Steiner E. Apolipoprotein B and A-I values in 147576 Swedish males and females, standardized according to the World Health Organization-International Federation of Clinical Chemistry First International Reference Materials. Clin Chem 1998;44:1641-9.
12. Haskell WL. Alderman EL, Fair JM, Superko HR, Maron DJ, Champagne MA, et al. Beneficial angiographie and clinical response to multifactor modification in the Stanford Coronary Risk Intervention Project (SCRIP). Circulation 1994;89: 975-90.
13. Myers GL, Cooper GR, Greinberg N, Kimberly MM, Waymack PP, Hassemer DJ. Standardization of iipid and lipoprotein measurements. In: Rifai N, Warnick G, Dominiczak MH, eds. Handbook of lipoprotein testing, 2nd ed. Washington, DC: AACC Press, 2000:726.
DOI: 10.1373/clinchem.2007.100941


Bentuk :
- Selinder
- Tidak bersegmen
Bagian Anterior
- Tanpa alat isap
- Tanpa kait-kait
- Mempunyai mulut
Rongga Badan
- Ada
Saluran Pencernaan
- mempunyai anus
- Terpisah jantan dan betina

Bentuk : Bentuk :
Seperti Pita Seperti Daun
Bersegmen Tidak Bersegmen
Bagian Anterior Bagian Anterior
Mempunyai alat isap Mempunyai alat isap
Kadang2 ada kait2 Tanpa kait-kait
Tanpa mulut Mempunyai mulut
Rongga Badan Rongga Badan
Tidak ada Tidak ada
Saluran Pencernaan Saluran Pencernaan
Tidak ada Ada tanpa anus
Kelamin Kelamin
Hermafrodit Umumnya Hermafrodit
kecuali Schistosoma

- Ascaris lumbricoides
- Trichuris trichiura
- Necator americanus
- Ancylostoma duodenale
- Strongyloides stercoralis
- Oxyuris vermicularis
- Trichinella spiralis
- Wuchereria bancrofti
- Brugia malayi
- Brugia timori
- Loa loa*
- Onchocerca volvulus*
- Dipetalonema perstans*
- Dipetalonema streptocerca*
- Mansonella ozzardi*
- Capillaria hepatica
- Toxocara cati
- Toxocara canis
- Gnathostoma spinigerum
*Tidak ada di Indonesia
Cestoda Trematoda
- Taenia saginata - Fasciolopsis buski
- Taenia solium - Echinostoma ilocanum
- Hymenolepis nana - Echinostoma malayanum
- Hymenolepis diminuta - Heterophyes heterohyes*
- Dipylidium caninum - Metagonimus yokogawai*
- Diphyllobothrium latum* - Gastrodiscoides hominis*
- Fasciola hepatica
- Clonorchis sinensis*
- Opisthorchis felineus*
- Opisthorchis viverrini*
- Dicrocoelium denditicum*
- Paragonimus westermani
- Schistostoma japoinicum
- Schistostoma mansoni*
- Schistostoma haematobium*
- Schistosoma mekongi*
Ascaris lumbricoides Roundworm, Cacing Gelang
Hospes : Manusia Penyakit : Askariasis
Habitat : Usus halus
Penyebaran geografik : Kosmopolit, terutama negara-negara tropik dan subtropik
Cacing dewasa : * bentuk bulat panjang (silindris)
* kedua ujung lebih kecil
* Pada mulut terdapat 3 bibir
* Jantan 15 – 31 cm
ekor melengkung ke ventral, mempunyai 2 spikula
* Betina 20 – 35 cm
ekor lurus

Telur tidak dibuahi
- Bentuk Lonjong
- 90 x 40 mikron
- Dinding diliputi lapisan luar
albuminoid tipis tidak teratur
- Lapisan hialin bening, tebal
- Lapisan vitellin tipis
- Isi : granula yang atropis
- Antara sel telur dan dinding
tidak ada rongga kosong
Telur dibuahi
- Bentuk agak bulat
- 60 x 45 mikron
- Lapisan luar albuminoid
agak tebal teratur
- Lapisan hialin bening, tebal
- Lapisan vitellin tipis
- Isi : Sel telur yang tidak
- Antara sel telur dan dinding
telur ada rongga kosong
berbentuk bulan sabit
Dalam tinja kadang-kadang ditemukan telur Ascaris lumbricoides yang dinding albuminoidnya tidak ada (telur dekortikasi)

Bila telur telah berisi larva disebut telur matang
Cacing dewasa jantan dan betina hidup dalam rongga usus halus manusia.
Cacing betina mengeluarkan telur 100.000 - 200.000 butir/hari terdiri dari telur yang dibuahi dan telur yang tidak dibuahi, telur-telur tersebut keluar bersama tinja penderita.
Dalam lingkungan yang sesuai (tanah liat, kelembaban tinggi dan suhu 25 – 30 °C), telur yang dibuahi berkembang menjadi telur matang (bentuk infektif) dalam waktu ± 3 minggu.
Telur matang bila tertelan oleh manusia, menetas di usus halus mengeluarkan larva, kemudian larva tersebut menembus dinding usus halus masuk ke pembuluh darah atau saluran limfe, dialirkan ke jantung kanan lalu ke paru.
Di paru larva menembus dinding pembuluh darah alveolus, masuk ke rongga alveolus, kemudian naik ke bronchiolus, bronchus, trachea sampai ke pharynx.
Dari pharynx larva tertelan ke dalam esofagus lalu menuju ke usus halus. Di usus halus larva berkembang menjadi cacing dewasa jantan dan betina.
Waktu yang diperlukan mulai telur matang tertelan sampai cacing dewasa betina mengeluarkan telur ± 2 bulan ( 8 - 10 minggu).
Makanan cacing dewasa adalah zat-zat makanan dalam rongga usus halus.
Cacing dewasa dapat hidup selama 1 – 1½ tahun dalam rongga usus halus
DAUR HIDUP Ascaris lumbricoides
Trichuris trichiura (Trichocephalus dispar, Whipworm, cacing cambuk)
Hospes : manusia Penyakit : Trikuriasis
Habitat : Usus besar terutama sekum
Penyebaran geografik : Kosmopolit, terutama negara tropik dan subtropik
* cacing dewasa berbentuk seperti cambuk
* 3/5 bagian anterior, halus seperti benang
* 2/5 bagian posterior, lebih gemuk
- Cacing jantan : * 3 - 4 cm
* Bagian posterior melingkar ke ventral > 360°,
mempunyai 1 spikulum

- Cacing betina : * 4 - 5 cm
* Bagian posterior, membulat tumpul,
melengkung < 360°

- Telur : * ± 50 x 32 m
* seperti tempayan, pada kedua kutub terdapat
tonjolan jernih
* dinding : - luar : kuning tengguli
- dalam : jernih
* Isi : sel telur

Cacing dewasa hidup di sekum dan kolon asendens dengan bagian anteriornya yang halus masuk kedalam mukosa usus.
Cacing betina mengeluarkan telur 3.000 – 10.000 butir/hari, telur tersebut keluar bersama tinja penderita.
Dalam lingkungan yang sesuai (tanah lembab, tempat teduh, suhu 25 – 30 °C) telur tersebut berkembang menjadi telur matang (bentuk infektif) dalam waktu 3 – 6 minggu.
Telur matang bila tertelan oleh manusia, menetas di usus halus mengeluarkan larva lalu menjadi cacing dewasa jantan dan betina. Setelah menjadi dewasa, cacing menuju ke sekum dan kolon asendens.
Waktu yang diperlukan mulai tertelannya telur sampai cacing betina mengeluarkan telur ± 30 – 90 hari (1 – 3 bulan).
Cacing dewasa dapat hidup 1-2 tahun.
DAUR HIDUP Trichuris trichiura
Pada Manusia :
- Necator americanus nekatoriasis
- Ancylostoma duodenale ankilostomiasis
Habitat : Usus halus (jejenum dan duodenum)
Penyebaran geografik :
Kosmopolit, terutama negara-negara tropik dan subtropik
Cacing dewasa : * Berbentuk silinder/selindrik
* Berwarna putih keabuan
- Cacing jantan : * 5 – 11 mm
* Ekor melebar (bursa kopulatriks)
* Mempunyai 2 spikula
- Cacing betina : * 9 – 13 mm
* Ekor lancip

Necator americanus : Bentuk badan seperti huruf S
Dalam mulut terdapat sepasang benda khitin
Ancylostoma duodenale : Bentuk badan seperti huruf C
Dalam mulut terdapat 2 pasang gigi sama besar

Telur : - Lonjong
- ± 60 x 40 mikron
- Dinding : tipis, bening, tidak berwarna
- Isi : tinja segar : embrio stadium morula 2 – 16 sel telur
tinja lama : larva

Larva rhabditiform : - ± 250 mikron
- esofagus mempunyai bulbus ( rhabditoid) 1/3 panjang badan
- mulut terbuka, panjang dan sempit
- genital premordial kecil

Larva filariform : - ± 700 mikron
- esofagus lurus (filariform), 1/4 panjang badan
- mulut tertutup
- ekor runcing
- mempunyai selubung (sarung)

* Larva filariform adalah bentuk infektif

Cacing dewasa hidup melekat pada usus halus. Cacing betina N. americanus bertelur ± 9.000 butir/hari, sedangkan A. duodenale ± 10.000 butir/hari. Telur-telur tersebut keluar bersama tinja penderita, setelah 1 – 1½ hari telur menetas mengeluarkan larva rhabditiform. Dalam waktu 3 – 5 hari larva rhabditiform tumbuh menjadi larva filariform (bentuk infektif) yang dapat menembus kulit dan dapat hidup selama 7 – 8 minggu ditanah (tanah yang baik untuk pertumbuhan larva adalah tanah gembur tercampur humus dan terlindung dari sinar matahari langsung, suhu untuk N. americanus 28 – 32 °C, sedangkan untuk A. duodenale 23 – 25 °C). Cara infeksi adalah larva filiriform menembus kulit masuk kapiler darah, mengikuti aliran darah ke jantung kanan lalu ke paru. Setelah sampai diparu larva filariform menembus dinding alveolus masuk ke alveolus kemudian naik ke bronchiolus, bronchus, trachea sampai ke pharynx. Dari pharynx larva tertelan masuk ke esofagus, lambung sampai usus halus. Setelah sampai di usus halus larva filariform berkembang menjadi cacing dewasa jantan dan betina yang melekat pada mukosa usus halus. Waktu yang diperlukan sejak larva filariform menembus kulit sampai menjadi cacing dewasa di usus halus 10 – 12 minggu. Cacing dewasa dapat hidup selama ± 5 tahun. Seekor cacing N. americanus dapat mengisap darah 0,05 – 0,1 cc/hari, sedangkan A. duodenale dapat mengisap darah 0,08 – 0,34 cc/hari.
DAUR HIDUP Hookworm (Cacing tambang)
Strongyloides stercoralisThreadworm, Cacing benang
Hospes : Manusia, kucing, anjing, kera, simpanse.
Habitat : Usus halus
Penyakit : Strongiloidiasis
Penyebaran geografik : Kosmopolit terutama daerah tropik dan subtropik
- Cacing dewasa ada 2 macam :
1. Cacing dewasa bentuk parasiter
* Hanya ditemukan cacing betina
* Panjang ± 2 mm
* Bentuk halus tidak berwarna
* Esofagus 1/3 panjang badan, bentuk filariform
* Uterus berisi telur
* Ekor berujung lancip

2. Cacing dewasa bentuk bebas
* Cacing jantan : - Panjang ± 0,75 mm
- Esofagus : mempunyai bulbus, pendek (bentuk rhabditoid), ¼ panjang badan
- Ekor : melengkung dgn 2 spikula
* Cacing betina : - Panjang ± 1 mm
- Esofagus bulbus, pendek (rhabditoid),
¼ panjang badan
- Uterus berisi telur
- Ekor berujung lancip

Telur : Mirip telur cacing tambang, jarang ditemukan oleh karena telurnya langsung pecah menghasilkan larva rhabditiform

Larva rabditiform
- Panjang ± 225 mikron
- Mulut terbuka, pendek dan lebar
- Esofagus mempunyai bulbus (rhabditoid)
¼ panjang badan
- Ekor berujung lancip
-Genital premordial besar
Larva filariform
- Panjang < 700 mikron
- Bentuknya lansing
- Tidak bersarung
- Mulut tertutup
- Esofagus lurus (filariform) ½ panjang badan
- Ekor ujungnya bercabang dua
(menyerupai huruf W)

* Larva filariform adalah bentuk infektif

Cacing dewasa betina bentuk parasitik hidup di mukosa usus halus (duodenum dan jejenum) berkembang biak secara partenogenesis, mengeluarkan telur beberapa lusin perhari; telur tersebut langsung menetas mengeluarkan larva rhabditiform yang masuk kedalam rongga usus halus lalu keluar bersama tinja penderita. S. stercoralis mempunyai 3 macam daur hidup.

• Daur hidup langsung
Larva rhabditiform yang keluar bersama tinja penderita setelah 2 - 3 hari di tanah/air bertumbuh menjadi larva filariform (bentuk infektif) yang dapat menembus kulit. Bila larva filariform tersebut menembus kulit manusia masuk ke kapiler darah, mengikuti aliran darah ke jantung kanan lalu ke paru. Setelah sampai di paru, larva filariform menembus dinding alveolus lalu masuk ke alveolus kemudian ke bronchiolus, bronchus, trachea dan pharynx. Dari pharynx larva tertelan masuk ke esofagus, lambung, usus halus lalu menjadi dewasa di usus halus. Waktu yang diperlukan saat larva filariform menembus kulit sampai cacing betina mengeluarkan telur kira-kira 28 hari. Daur hidup langsung sering terjadi di daerah beriklim dingin.
• Daur tidak langsung
Larva rhabditiform yang keluar bersama tinja penderita, ditanah akan bertumbuh menjadi cacing jantan dan cacing betina bentuk bebas. Kemudian cacing jantan akan membuahi cacing betina. Cacing betina mengeluarkan telur, kemudian telur tsb. menetas mengeluarkan larva rhabditiform lalu bertumbuh menjadi larva filariform yang infektif yang dapat menembus kulit atau bertumbuh lagi menjadi cacing dewasa bentuk bebas. Bila larva filariform dari bentuk bebas tersebut menembus kulit, maka proses selanjutnya seperti pada daur langsung, sampai menjadi cacing dewasa betina bentuk parasitik diusus halus. Daur hidup tidak langsung sering terjadi di daerah beriklim panas.

• Oto-infeksi
Pada oto-infeksi, larva rhabditiform berkembang menjadi larva filariform didalam usus halus atau disekitar anus (perianal). Bila larva filariform tersebut menembus mukosa usus halus atau kulit perianal penderita, maka proses selanjutnya seperti pada daur langsung, sampai menjadi cacing betina bentuk parasitik di usus halus.
Oto-infeksi tersebut penyebabnya belum diketahui. Adanya oto-infeksi dapat menyebabkan strongyloidiasis menahun pada seseorang.
DAUR HIDUP Strongyloides stercoralias
1. Daur hidup langsung
2. Daur hidup tidak langsung
3. Oto-infeksi
Oxyuris vermicularisEnterobius vermicularis Pinworm, Seatworm, Cacing kremi
Hospes : Manusia Penyakit : Oksiuriasis/enterobiasis

Habitat : Sekum
Penyebaran geografik : Kosmopolit
- Cacing dewasa : * Kecil berwarna putih, pada ujung anterior mempunyai
pelebaran kutikulum seperti sayap (cervical alae)
* Bulbus esofagus jelas sekali (rhabditoid)
* Cacing jantan :
- Panjang 2 – 5 mm
- Ekor melingkar sehingga bentuknya nampak
seperti tanda tanya (?) mempunyai 2 spikula
* Cacing betina
- Panjang 10 - 13 mm
- Ekor panjang dan runcing sehingga
nampak seperti jarum
- Uterus cacing betina yang gravid melebar dan
berisi telur

Telur :
- 50 – 60 mikron
- Bentuk asimetris
- Dinding tipis tidak berwarna
- Berisi larva

Cacing dewasa jantan dan betina hidup pada rongga sekum, usus besar dan usus halus yang berdekatan dengan sekum. Setelah cacing jantan membuahi cacing betina, maka cacing betina yang gravid bermigrasi ke daerah peri anal pada malam hari untuk mengeluarkan telurnya yang berjumlah 11.000 – 15.000 butir. Dalam waktu ± 6 jam setelah telur dikeluarkan oleh cacing betina, telur-telur tersebut menjadi matang (bentuk infektif). Cara infeksi adalah menelan telur matang atau bila larva dari telur yang menetas didaerah perianal bermigrasi kembali ke sekum.
Bila telur matang tersebut tertelan, telur akan menetas di usus halus mengeluarkan larva lalu menjadi cacing dewasa di sekum. Waktu yang diperlukan mulai telur tertelan sampai menjadi cacing dewasa ± 2 minggu sampai 2 bulan.
Bila telur matang pecah didaerah perianal maka keluar larva kemudian larva tersebut bermigrasi kembali ke sekum, melalui anus, rektum, kolon sigmoid, kolon desendens, kolon transversum dan kolon asendens. Proses tersebut disebut retrograde infeksi atau retro-infeksi atau oto-infeksi.
Cacing jantan mati setelah kopulasi (membuahi yang betina), sedangkan cacing betina mati setelah mengeluarkan telur-telurnya.
DAUR HIDUP Enterobius vermicularis
Taenia saginataBeef tapeworm, Cacing pita sapi
Hospes definitif : Manusia
Penyakit : Teniasis saginata
Hospes perantara : Sapi dan kerbau
Habitat : Cacing dewasa hidup dalam usus halus
Penyebaran geografis : Kosmopolit
Morfologi :
- Cacing dewasa : * Berbentuk pita terdiri atas :
- Kepala (skoleks)
- Leher (Collum)
- Badan (strobila) : proglotid immature
proglotid mature
proglotid gravida
- Panjang 4 – 12 m terdiri dari 1000 – 2000 proglotid
* Skoleks : - Bulat 1 – 2 mm
- Mempunyai 4 batil isap, tanpa rostelum dan

* Proglotid gravida: - Berbentuk segi empat, panjang > lebar
- Uterus mempunyai 15 – 30 cabang lateral
- Lubang genital di bagian lateral (unilateral)
- Lubang uterus tidak ada

Telur : - Bentuk agak bulat
- (30 – 40) x (20 – 30) mikron
- Dinding bergaris radial
- Isi heksakan embrio (embrio dengan 6 kait-kait)

Larva (sistiserkus bovis) : - Gelembung
- ½ - 1 cm
- Berisi cairan dan skoleks

DAUR HIDUP Taenia saginata
Taenia soliumPork tapeworm, Cacing pita babi
Hospes defenitif : Manusia
Hospes perantara : Babi dan kadang-kadang Manusia
Penyakit : - Cacing dewasa Teniasis solium
- Larva Sistiserkosis
Habitat : Cacing dewasa dalam rongga usus halus
Larva dalam otot, otak, mata, hati
Penyebaran geografis : Kosmopolit, terutama pada negara-negara
yang penduduknya suka makan daging babi
kurang matang
Morfologi :
* Cacing dewasa : * Berbentuk pita terdiri atas :
- Kepala (skoleks)
- Leher (collum)
- Badan (strobila) : proglotid immature
proglotid mature
proglotid grafida
- Panjang 2-4 m kadang-kadang 8 m
- Jumlah proglotid < 1000

* Skoleks : - Bulat, kecil
- ± 1 mm
- 4 batil isap dan rostelum dengan
2 baris kait-kait
* Proglotid gravida :
- Berbentuk segi empat, panjang > lebar
- Uterus mempunyai 7 – 12 cabang lateral
- Lubang genital di bagian lateral (unilateral)
- Lubang uterus tidak ada

Telur : - Bentuk agak bulat
- (30 – 40) x (20 – 30) mikron
- Dinding bergaris radial
- Isi heksakan embrio (embrio dengan 6 kait-kait)

Larva (sistiserkus selulose) : - Gelembung
- ½ - 1 cm
- Berisi cairan dan skoleks dengan kait-kait

DAUR HIDUP Taenia solium
Fasciolopsis buskiGiant Intestinal fluke
Hospes defenitif : Manusia, Babi, anjing, kucing
Penyakit : Fasciolopsiasis
Habitat : Usus halus
Hospes perantara pertama : Keong air tawar (Segmentina, Hippeutis)
Hospes perantara kedua : tumbuh-tumbuhan air (Eichornia grassipes,
Trapa natans, T. bicornis, Morning glory,
Elichoris tuberosa, Zizania)
Penyebaran geografis : China, Taiwan, Thailand, Malaysia, Laos, India
Vietnam dan Indonesia (Kalimantan Selatan)
Morfologi :
- Cacing dewasa :* Bentuk ovoid berwarna kemerahan
* Ukuran (20 – 75) x( 8 – 20) x (1 – 3) mm
* Batil isap mulut < batil isap perut
* Testes bercabang-cabang, atas bawah
* Ovarium bercabang-cabang
* Kelenjar vitalaria bagian lateral
* Sekum tidak bercabang
* Uterus berkelok kelok
Telur : * Bentuk lonjong
* Mempunyai operculum
* Dinding transparant
* Ukuran (130 – 140) x (80 – 85) mikron
* Isi sel telur (unembryonated)