Background
==========

The development of practice guidelines
--------------------------------------

In clinical medicine, variations exist that do not appear to be related to variations in the clinical presentation and severity of disease \[[@B1]-[@B3]\]. In response, practice guidelines have been developed in an attempt to reduce the wide practice variations and, through this process, to increase the appropriateness and quality of medical care and to reduce health care costs \[[@B4]-[@B8]\].

Despite the publication and dissemination of practice guidelines \[[@B9]\], there has been relatively little evaluation of the application and impact of clinical practice guidelines \[[@B9]-[@B14]\]. Some of the difficulty in the evaluation of these guidelines relates to the methods that were used to develop them \[[@B9]\]. Guidelines have often have been developed before adequate data have been available to assess the relationship between clinical practice patterns and desired clinical outcomes. Nevertheless, there have been some reviews of practice guideline evaluation \[[@B15],[@B16]\].

While epidemiological designs are commonly used to evaluate the effectiveness of health care interventions, never has this been discussed in the context of outcomes research. We propose the use of a methodological framework for outcomes research to evaluate practice guidelines.

Methodological issues with the measurement of practice variations
-----------------------------------------------------------------

In the debate about reasons to promote the development of practice guidelines, few have questioned whether the variations are real, or alternatively, whether they are simply a function of methodological flaws in the measurement of medical practices themselves, the result of variations in practice patterns across groups of patients with a similar diagnosis, or both. Furthermore, few studies have addressed whether practice variations, in fact, lead to outcome variations. Finally, little attention has been paid to the identification and measurement of initial conditions, that is, the potentially confounding factors and effect modifiers of the practice patterns outcomes relationship.

Measurement of practice pattern variation
-----------------------------------------

The measurement of medical practice patterns is susceptible to error. Measurement error may affect the validity of medical practice measurement in three major ways. First, it may lead to selection bias, in that subjects are selected to belong to a certain group based on an erroneous diagnosis. Secondly, it may lead to misclassification of exposure (information bias), in that patients treated with a specific practice pattern are classified in the wrong diagnostic group. Thirdly, it may lead to misclassification of outcomes, in that patients with a given outcome are classified in the wrong diagnostic group.

Potential problems with the measurement of practice variations relate to the mechanisms that underlie the choice of groups that are compared in studies of practice variations. These mechanisms must be defined clearly to minimize selection bias. In many studies of practice variations, populations are arbitrarily divided according to hospitals, regions, counties, or countries. Little information is available about the factors that lead these groups to go to a particular hospital, live in a particular region, go to a particular doctor, etc. The population base from which each comparison group is derived should, in principle, be quite similar for all groups. Basically, if the groups are drawn from a similar population, unmeasurable and potentially confounding variables are more likely to be equally distributed between groups.

In addition, the measurement of practice variations cannot be valid without information on relevant \"initial conditions\". Initial conditions are all confounding factors and effect modifiers, other than the treatment/practice patterns, that may cause or influence the clinical outcomes of interest. These factors may explain practice variations among groups that do not share similar initial conditions. To evaluate practice patterns-outcomes associations, potential confounders must be identified and controlled for in the analysis.

Aside from clinical presentation and severity of illness; the initial conditions to be identified and characterized as completely as possible include physician, patient, and practice environment factors (Table [1](#T1){ref-type="table"}). Measurement of such factors is essential to minimize the chance of a systematic error following confounding biases and effect modification (Figure [1](#F1){ref-type="fig"}).

![](1472-6963-2-7-1){#F1}

::: {#T1 .table-wrap}
::: {.caption}
######

Initial conditions to be taken into account when making inferences about practice patterns-outcomes associations
:::

  **Patient factors**                   **Physician factors**                                         **Environmental factors**
  ------------------------------------- ------------------------------------------------------------- -------------------------------------------------------------
  Demographics Severity of illness      Physicians\' beliefs and interpretation of medical evidence   Medical insurance status (fee-for-service vs. managed care)
  Heterogeneity of disease expression   Medical uncertainty                                           Availability of needed procedure
  Patient preference                    Specialist vs. generalist Physicians\' skills                 Proximity of medical centers with appropriate expertise
  Patient attitude towards disease      Inappropriate use Nature of group
:::

Identification and measurement of outcomes of interest
------------------------------------------------------

Limitations to the development and evaluation of practice guidelines also include the absence of a clear concept of the targeted outcomes and the paucity of outcomes data to support these guidelines \[[@B17]\]. There appears to be only a weak relationship between the purpose of guidelines and many of the outcomes usually measured in clinical research, that is, the source of evidence for guideline development (evidence-based). The initial goals of establishing practice guidelines -- to reduce costs and enhance the quality and appropriateness of treatment -- are, in fact, rarely the basis for guideline development, since little data is available for these outcomes. To some degree, the development of guidelines has been driven by the availability of data on clinical outcomes, such as morbidity and mortality, rather than those outcomes related to the primary goals of the guidelines.

The evaluation of practice guidelines
-------------------------------------

Throughout the development of practice guidelines, the major deficiency has been the lack of an evaluative method \[[@B18]-[@B27]\]. Thus, we suggest a methodological framework for outcomes research to be applied to evaluate practice guidelines. Outcomes research evaluates practice patterns as they occur in actual clinical settings. This type of research can describe practice patterns, evaluate their divergence from practice guidelines and determine the effect of practice variations on outcomes. Outcomes research is necessarily observational in nature and, although observational studies have been used to evaluate health care interventions, the proposed methodological framework has yet to be applied to outcomes research.

Why should outcomes research be used to evaluate and validate practice guidelines? The primary goal of practice guidelines is the consistent adherence by physicians to practice patterns that achieve the \"best\" outcomes at the lowest cost. Outcomes research evaluates practice patterns as they occur in actual clinical settings, and is thus the logical method to evaluate practice guidelines. In fact, outcomes research and practice guidelines are connected through concepts that relate to efficacy and effectiveness research (Figure [2](#F2){ref-type="fig"}). Efficacy studies, which normally complement practice guideline development, are those performed in highly selected groups of patients to investigate if a particular intervention works under controlled conditions set by the study investigators. In contrast, outcomes research evaluates practice as it occurs in actual clinical settings \[[@B28]\]. Research in these settings is called effectiveness research because the investigators have limited control over the conditions that qualify the practice settings. The difference between efficacy and effectiveness research can be summarized as follows: does it work at all (efficacy) or does it work in the real world (effectiveness)? Thus, there exists a dynamic process in which evidence from both effectiveness and efficacy studies feeds into the development and evaluation of practice guidelines, as depicted in Figure [2](#F2){ref-type="fig"}.

![Relationship between outcomes research and practice guidelines](1472-6963-2-7-2){#F2}

Most practice guidelines are derived from efficacy studies rather than effectiveness studies. Therefore, it is not surprising that practice guidelines are not fully applicable in actual clinical practice. We suggest that effectiveness studies be used not only as a method to evaluate practice guidelines but also as a basis for their development. These could include both observational studies and effectiveness trials. Outcomes research better reflects practice in the real world and may make guidelines more likely to be applied. However, to date, little attention has been paid to the epidemiological underpinnings of the methods used to conduct outcomes research.

Discussion
==========

We will first propose a methodological framework for outcomes research. Then, we will show how it can be used to evaluate practice guidelines. Finally, we will address the limitations of the proposed methodological framework.

Generic epidemiological issues in outcomes research
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In the proposed methodological framework, the generic issues related to outcomes research will be discussed in sequential order. In outcomes research, the first step is to identify the study population and the groups (hospitals, providers, regions, etc.) that will be compared. The next step is the measurement of practice patterns and outcomes. After groups are compared on the basis of the treatment they receive and outcomes of interest, associations are sought between practice patterns and the various measures of outcome. This step of the methodological framework raises issues of confounding bias because not all factors that can confound these associations are measured and controlled or even known. The presence or absence of confounding bias can be affected by the other sources of bias namely selection and information biases. Lastly, we discuss the issue of temporal trends. In the evaluation of practice guidelines, the measurement of practice patterns may not be contemporaneous with the publication of practice guidelines. This may explain and even lead to the frequently observed discrepancy between the actual practice and what the guidelines state that it should be. Finally, two particularities of outcomes research 1) the presence of ecological exposures in individual level studies and 2) the common use of large administrative databases are discussed.

Specification of the model
--------------------------

### Definition of the elements of the proposed epidemiological model for outcomes research

In the proposed model for outcomes research designed to evaluate practice guidelines, the outcome of interest can be a disease (Table [2](#T2){ref-type="table"}). For example, if the practice patterns that are being studied pertain to coronary revascularization, complications such as mortality and reinfarction after acute myocardial infarction may constitute the outcome of interest. Finally, the consequences of different practice patterns on medical resources (cost, quality and appropriateness) may be another possible outcome of interest.

::: {#T2 .table-wrap}
::: {.caption}
######

Epidemiological model for outcomes research to evaluate practice guidelines
:::

  Outcome =                                                               Exposure +            Confounders +                                                                                Error
  ----------------------------------------------------------------------- --------------------- ----------------------------------------------------------------------------------------------------- ------------------------------------------------------------------
  • Clinical outcomes (for example, disease development, complications)   • Practice patterns   • Extraneous factors that can influence practice patterns and outcomes, that is, initial conditions   • Misclassification of diagnosis, practice patterns and outcomes
  • Quality                                                                                • Error in measurement of practice patterns and outcomes
  • Cost
  • Appropriateness                                                                                • Unmeasured factors
:::

In the studies of outcome research, practice patterns, (which constitute the exposure in the proposed model), range from the use of medication, diagnostic tests and therapeutic procedures to the length of hospital stay, transfer to other facilities and/or scheduled physicians visits. The primary goal of outcomes research is the evaluation of the effects of the selected practice patterns on the outcomes of interest. Consequently, any inference made about this association must be evaluated as a function of the potential selection, information (measurement error) and confounding biases. A limitation of outcomes research as it is most often performed is the lack of attention given to the measurement of each of the elements of the epidemiological model shown in Table 3. The basis of the proposed methodological framework will be the identification of generic sources of potential bias that relate to each element of the proposed model.

Selection bias
--------------

Since outcomes research is observational in nature, the choice of the study population and of the compared groups is highly susceptible to selection bias. As applied to outcomes research, selection bias is defined as a [distortion]{.ul} in the estimate of the practice patterns outcomes association due to the [way]{.ul} that subjects [are selected for inclusion]{.ul} in the study population and in the different groups to be compared \[[@B29]\]. A major consequence of selection bias is the potential confounding of inferences made about practice patterns-outcomes associations. This occurs when some characteristics of the subjects related to practice patterns or clinical outcomes influence the selection or exclusion of individual subjects, groups of subjects or practice environments.

The selection process should be such that patients included in the study population come from the same target population \[[@B30]\]. Furthermore, patients or study members should have a similar probability of being selected and included in the actual population. Inclusion and exclusion criteria must be clearly defined in order to characterize the actual population as precisely as possible. Judging the internal validity of a study is more feasible when there is a detailed account of how the individuals were selected to become members of the actual population. Finally, the study population, also needs to be carefully characterized so that the inferences derived from the analysis of the study population can be evaluated for both internal validity (based on the data analyzed in the study) and external validity (the extent to which results obtained from the data analyzed in a particular study can be generalized to populations outside of the study). Any systematic differences between those actually studied and the source (target) population could result in biased estimates of the impact of a practice pattern on a clinical outcome.

In many studies of outcomes research, groups exposed to different practice patterns are compared. The identification of such groups of patients is sought to assess the impact of different practice patterns on various outcomes in actual clinical settings and, as previously mentioned, can be used to assess practice guidelines. Because of such study design, it becomes unclear as to what the target population precisely is. Is it the group (the set of patients in a given environment) or is it the individuals receiving the various practice patterns within each group? For example, in a study of regional variations in the treatment of acute myocardial infarction in the U.S., the treatment of patients (practice patterns) was compared across different regions of the U.S. In this study, one wishes to generalize the findings about practice patterns-outcomes associations to all individuals with acute myocardial infarction (individual level). One also wishes to generalize the effect of the exposure, which is in this case practice patterns, to those prevalent in a given region (ecological level).

The presence of these two levels, the individual and the ecological levels, introduces an added level of complexity in terms of the assessment of the effect of the exposure on outcome. When comparing practice patterns across regions using individual data, there is a certain degree of correlation brought about by the clustering of practice patterns that needs to be taken into account. Such a correlation is very difficult to quantify. In contrast, when assessing the effect of the exposure at the individual level, there are ecological factors (initial conditions particular to a given region) that need to be taken into account. The data originating from studies with mixed design, which are often the design of outcomes research studies, need to be analyzed with special attention to the degree of correlation between the individual covariates and to the presence of ecological exposure variables.

Another potential source of selection bias is the choice of the groups to be compared, which depends on the criteria used to divide the groups. Individuals included in the groups to be compared should have the same probability of being included in these groups. Not infrequently in outcomes research, geographic criteria (such as country, regions, hospitals) are used because such criteria allow the identification of clinically comparable groups that receive very different treatments, whose resulting outcomes can then be assessed. However, such a process must be scrutinized for the possibility of selection bias other than the treatments that are being evaluated. Such selection bias would make groups not comparable as to clinical and other factors that could affect outcomes.

The presence of a biased selection process could lead to confounding bias when practice patterns-outcomes associations are assessed. Such a situation may occur when the study groups are not comparable with regard to some characteristics of the subjects related to practice patterns or clinical outcomes that influenced the selection or exclusion of individual subjects, groups of subjects or practice environments. For example, in the same study of regional variations in the treatment of acute myocardial infarction, census regions of the U.S. were arbitrarily chosen as a basis for comparison. In this example, patients with similar risk of developing the outcome of interest, which is defined here as a complication after acute myocardial infarction, may not have had the same probability of being included in the different groups to be compared. Confounders may then bias the practice patterns/outcomes association if the selection of different risk groups is related to practice patterns.

Selection bias can also affect the assessment of outcomes. Potential sources of this bias include loss to follow-up or missing data. Follow-up data is difficult to obtain in outcomes research studies, which often rely on administrative databases for data acquisition. Linkage, either of different databases or of the same database over time, is often performed \[[@B31]\]. A failure to link the databases for a number of individuals presents a problem equivalent to having data missing for these individuals.

Information bias
----------------

The second step in outcomes research studies is the measurement of practice patterns and of the outcomes of interest. Here, issues of information bias must be considered. Information bias can be defined as a [distortion]{.ul} of the potential practice patterns outcomes association due to [misclassification]{.ul} of subjects with regard to practice patterns, outcome measures or both, or due to measurement error \[[@B29]\].

There are two major ways in which practice patterns can be misclassified. They relate to the sensitivity and specificity of the tests that are used for the diagnosis for which practice patterns are being evaluated and for the classification of the outcomes of interest. The measurement of the different practice patterns and their related outcomes largely depend on the identification of a group of patients who have a given diagnosis and require a given treatment. The characteristics that make a diagnosis more amenable to outcomes research are the following: 1) a precise diagnostic definition, 2) a diagnostic test with high sensitivity and specificity, 3) reproducibility among different individuals and locations, 4) easily coded, 5) related to a procedure, and 6) common and costly, so that it is likely to be collected in large, administrative databases frequently used in outcomes research. Because of such requirements, only a limited number of clinical conditions are amenable to outcomes research. Acute myocardial infarction is an example of a diagnosis that can be made with a high level of certainty because it has a precise diagnostic definition and well-defined diagnostic criteria, which, when taken together, have high sensitivity and specificity for the correct classification of patients. Therefore, it is easy to identify a study population that, in fact, has this disease and to describe their treatment. Thus, in order to minimize the misclassification of relevant practice patterns, the methods used to classify the disease and the outcomes that relate to the practice patterns under investigation must have high sensitivity and specificity \[[@B29],[@B31],[@B32]\].

Given the principles underlying the measurement of practice patterns and outcomes, how are the measurements generally made in outcomes research studies? The measurement of the exposure (practice patterns) in outcomes research is valid only if it corresponds to the \"true\" practice as performed in the clinical setting. Again, practice can only be \"true\" if the diagnosis is correct. The identification of both patients with the disease of interest and their treatment requires a source of information that has the features of a diagnostic test.

In outcomes research, administrative databases are often used as an information source to identify a study population and to obtain data on exposure. The database coding of diagnoses and procedures can be used as a \"diagnostic test\" to identify the clinical condition for which practice patterns will be described and to classify the practice patterns themselves and the outcomes of interest. Such a \"diagnostic test\" will have higher sensitivity and specificity values for some diagnoses than for others.

For example, administrative database coding will have higher sensitivity and specificity for procedure-related diagnoses (such as hip fracture) because the diagnostic code is related to a major operation and is likely to be recorded for administrative purposes. In contrast, a diagnostic criterion for osteoarthritis can be quite vague and administrative coding is likely to have very low sensitivity and specificity for this diagnosis.

The use of databases as a diagnostic test must be validated in all outcomes research studies, especially those using administrative databases. Methods to validate these databases include chart reviews, *a priori* coding systems or both. These validation methods ensure that coding is as accurate and reproducible as possible, thus allowing the database to be used as a diagnostic test to identify the study population and the practice patterns and the outcomes in outcomes research. However, these validation methods are rarely used.

Finally, appropriate measures of outcomes that will serve to evaluate practice guidelines must be identified. This presents a problem because most practice guidelines aim to reduce practice variations, which will, in turn, lead to improved appropriateness and quality of care. However, how appropriateness and quality of care are measured is controversial and will not be discussed here \[[@B33]-[@B97]\]. Nevertheless, defining the outcomes that will be used to evaluate practice guidelines is a crucial step in this process.

Quality of life and functional status measures constitute another group of outcome measures that should be included for the evaluation of practice guidelines. These dimensions of outcomes have received more attention from health providers, while consumers have become more concerned about outcomes of care. However, these outcomes also are difficult to measure, because they rely heavily on patient interviews and questionnaires. They are likely to vary with patient expectations, culture, and climate and are thus potentially to be measured with error and be misclassified. A few reliable, valid instruments have been developed to assess health-related quality of life \[[@B91],[@B92]\], but such instruments are not easily used to collect this information from large databases. There is a need to develop instruments to measure these types of outcomes, whether they are conversion factors for existing databases (such using length of stay as a proxy for cost) or new measures that could easily be integrated in administrative databases. Such measures could include estimates of functional class or severity of illness.

At present, many outcomes research studies measure mortality and disease-specific morbidity. The validity of the measurement of these outcomes is limited by the type of database that is used. For example, using death registries to obtain causes for death is a notoriously invalid source for this type of information. There are many examples of poor correlation between cause of death as established by death registries versus disease registries. Death certificates in New York City during 1992 were assessed to determine the accuracy and frequency of reporting tuberculosis as a cause of death. Of 310 persons who died with active tuberculosis in 1992 (based on a disease-specific registry), only 34% had tuberculosis listed on their death certificate. Thus, in this example, as in many others like it, using death certificates led to an inaccurate measure of disease burden \[[@B98]\].

Confounding bias
----------------

In outcomes research terms, confounding bias is present when the effect of the practice variations on the outcomes of interest is distorted because of the effects of [extraneous variables]{.ul} (variables that are causally associated with the practice variations and the outcomes of interest) \[[@B29]\]. This issue is crucial in outcomes research because, while outcomes research shares the purpose of a clinical trial (to evaluate different treatments), it primarily uses observational methods -- investigators conducting outcomes research have limited control over potentially confounding factors ([the initial conditions of individual groups of patients]{.ul}). Because outcomes research builds on existing practice variations and analyses the natural ongoing experiment, there is ample opportunity for confounding bias to invalidate any inference made about practice patterns outcome associations \[[@B99]\]. For example, variations in practice patterns could reflect variation not only in the use of a given procedure but also in the severity of disease. Assignment of patients to certain procedures on the basis of the severity of illness makes sense clinically, but in outcomes research, it is a common and important source of confounding if the procedure is either efficacious or particularly harmful in high-risk patients. Many indices have been developed to measure the severity of illness when using existing databases to correct for such confounding, but one can never be sure that this type of confounding has been entirely controlled \[[@B100],[@B101]\]. This presents an intrinsic limitation of outcomes research.

Avoidance of confounding bias is limited by the source of data used to describe practice patterns, particularly when observational data, such as the large Medicare administrative databases, are used to compare outcomes among patients who receive different treatments. The potential for confounding bias arises because many factors other than the treatment under evaluation may affect patient outcomes. These factors include comorbid diseases, severity of illness, and patient, physician and environmental factors. Such factors are likely to influence treatment decisions but are difficult to capture fully in recorded data. Researchers cannot adjust for imbalances in prognostic factors that are unmeasured or poorly categorized and administrative data, in particular, may lack the precise and accurate coverage of clinical details needed to permit full and fair adjustments. Further data collection might solve this issue, but it is not always possible to collect additional information. Standard statistical modeling can attempt to adjust for the known differences between the groups, but this might not be sufficient for unmeasured differences.

Several alternative methods have been suggested. One method is subgroup analysis \[[@B102]\] to adjust for unmeasured differences between groups of individuals who differ on known risk factors. Another method consists of the use of instrumental variables \[[@B103],[@B104]\]. Instrumental variables are observable factors that influence treatments but do not directly affect patient outcomes. This approach uses the so-called instrumental variables to mimic a randomization of patients to different likelihoods of receiving alternative treatments. McClellan *et al.*\[[@B103]\] applied this methodology to assess whether more aggressive use of invasive cardiac procedures improved outcomes in the elderly. In this study, the instrumental variable was the distance of the patient\'s residence from the nearest hospital with on-site angiography. The authors noted lower mortality among elderly individuals who received more aggressive treatment than among those treated more conservatively.

Temporal trend bias
-------------------

We propose a bias called a \"temporal trend bias\" that is particular to the use of outcomes research to evaluate practice guidelines. This bias results from the inability to control for secular trends. It reflects the fact that by the time practice guidelines are published and disseminated, new treatments and technology are being incorporated into clinical practice. Thus, it is difficult to identify a pure application of a practice guideline whose application is not undermined by recent advances in medicine and technology. For example, we evaluated the effect of a specific set of guidelines on return to work after acute myocardial infarction. The use of these guidelines had been successful in a university setting; this study assessed their use in a community setting. During the 5 years that elapsed between these two studies, practices changed. The use of guidelines was less successful in the community not only because they did not influence practice but also because usual care had grown closer to the proposed guidelines \[[@B105]\].

Ecological exposure in individual level studies
-----------------------------------------------

A frequently encountered particularity of outcomes research study design is the presence of both ecological exposure and individual level covariates in the same analysis. Because the unit of analysis is a group, but inferences are made about the impact of a given practice pattern on individual outcomes, many outcomes research analyses have elements of both individual and ecological analyses \[[@B106]\]. In our study of regional variations in the treatment of acute myocardial infarction, measures describing practice patterns at the regional level, ecological exposure, (proportion of patients receiving angiography, angioplasty, and coronary artery bypass surgery) were linked to the outcome measures of mortality adjusting for individual level variables that measured severity of disease. Then, inferences were made about the use of these procedures at the patient level. Although the unit of analysis is the region, which would demand an ecological analysis, there are individual level covariates, which are likely to be correlated within each region, that need to be taken into account.

When group measures are used that contain individual-level variability with some degree of correlatedness (within region) and aggregate-level variability (between regions), specific analytic tools must be used. It has been suggested that hierarchical logistic regression modeling be used to examine the interplay between sources of variation in the use of health-care services, that is, between ecological-level and individual-level sources. This type of modeling is designed to separate true variability across areas from observed variability. An application of this method is the work by Gatsonis *et al.*\[[@B107]\] who found that practice variations across regions of the U.S. in the use of angiography after acute myocardial infarction were largely explained by differences in patient characteristics and geographic region. However, states that had more on-site availability of angiography still tended to have higher angiography rates after accounting for between-region and within-region variability. After analysis for sources of variability, more reliable inferences about the associations between practice patterns and outcomes can be made.

Sources of data
---------------

The application of the proposed methodological framework for outcomes research largely depends on the sources of data that are used to evaluate the effect of the practice variations on outcomes \[[@B56]\]. Most commonly, the study design is a retrospective cohort analysis and the dataset that is used has been obtained either for administrative purposes (discharge databases) or for a randomized clinical trial that addressed a different question \[[@B108]\]. Less often, a prospective cohort study is designed to evaluate a particular set of practice guidelines \[[@B109]\]. Although a prospective design provides more control in data collection than a retrospective analysis, both designs are subject to selection, information and confounding biases.

The ideal database to use for the evaluation of practice guidelines is one that allows the precise measurement of the practice patterns (exposure) and outcomes (disease) as well as the measurement of potential confounders (severity of illness, precision of diagnosis, socioeconomic characteristics). Unfortunately, such a database probably does not exist. The strength of administrative databases, such as that of Medicare is that they allow the observation of large numbers of patients for which practice patterns can be evaluated as they occur in actual clinical practice. Furthermore, administrative databases allow the observation of practice patterns outcomes associations in large numbers of unselected patients.

However, the limitations of such databases include the missing information about potential confounding factors, such as severity of illness, and the limited ability to measure exposure and outcome accurately. Many databases that are not designed for clinical research either mismeasure patient outcomes or fail to capture outcomes that are important to both physicians and patients (such as quality of life and functional status). The control of these biases was the basis of the methodological framework for outcomes research proposed in this chapter.

The application of outcomes research methods to practice guideline evaluation
-----------------------------------------------------------------------------

The application of outcomes research methods to practice guideline evaluation can accomplish several goals. One important goal is the evaluation of practice guidelines, that is, to determine to what extent the guidelines accomplished their primary goals after their dissemination. We have suggested the model of chronic disease epidemiology as the methodological framework for outcomes research to evaluate practice guidelines.

The steps to evaluate practice guidelines using outcomes research when the basic design is a retrospective cohort study are summarized in Figure [3](#F3){ref-type="fig"} Some limitations to the application of this model exist. The reasons for the inability of the proposed methodological framework to deal completely with the intrinsic biases in outcomes research are listed in Figure [4](#F4){ref-type="fig"}. They relate mostly to the databases usually used in studies of outcomes research.

![Steps to evaluate practice guidelines using outcomes research](1472-6963-2-7-3){#F3}

![Reasons for the inability of the proposed methodological framework to deal with biases in outcomes research](1472-6963-2-7-4){#F4}

Summary
=======

The proposed methodological framework for outcomes research to evaluate practice guidelines reflects the selection, information and confounding biases inherent in its observational nature which must be accounted for in both the design and the analysis phases of any outcomes research study. Indeed, a major limitation of outcomes research is the inability to account for unobserved heterogeneity that directly correlates with practice patterns and/or health outcomes. This may lend bias to any inferences made about practice variations and outcomes. \"Researchers cannot correct for the subtle reason doctors choose one treatment over another for a particular patient. That bias, in turn, can undermine the entire premise of outcomes research\" \[[@B110]\]. These are intrinsic properties of outcomes research that can be dealt with only in part, by applying the principles of chronic disease epidemiology. Thus, this proposed methodology can serve as a framework for the conduct of outcomes research in the evaluation of practice guidelines but its application will be limited.

Competing interests
===================

none declared

Pre-publication history
=======================

The pre-publication history for this paper can be accessed here:

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Background
==========

Macrophages, and their circulating form monocytes, are potent defenders of the integrity of our body by mediating crucial physiological and protective functions. Just to mention some : they are central actors in innate immunity and inflammatory reactions; they process and present foreign antigens either by themselves, or through their lineage descendents; they are also dead cells scavengers. Strikingly, their role as front line defense against myriad of potentially pathogenic infectious agents in the outside environment, is essential in many species from insects to humans. Macrophages use various sets of receptor proteins to react to these agents, a central role being plaid by the Toll family molecules \[[@B1],[@B2]\]. Such receptors recognize microbial lipids \[[@B3]\], lipoproteins \[[@B4],[@B5]\], microbial carbohydrates \[[@B6]\] and bacterial DNA specific patterns \[[@B7]\]. Whatever the specific type of Toll like receptors involved, their engagements induce macrophages activation. This results in the production and release of a broad variety of specialized molecules aimed at limiting the multiplication and propagation of the infectious agents (innate immunity). The front line effector molecules produced by activated macrophages are reactive oxygen species (ROS) and reactive nitrogen species (RNS), highly diffusible molecules which have strong cytotoxic activities, including against macrophages themselves \[[@B8]-[@B10]\]. Such toxic effects are potentially dangerous since extensive macrophage destructions in the body can lead to the development of the fatal complication known as septic shock. Most of the time however, auto-protective redox buffering mechanisms prevent extensive destruction of activated macrophages. The aim of the present work was to elucidate by which mechanisms macrophages stimulated by bacterial products, manage to avoid massive auto-destruction caused by RNS? Experiments were performed with cells of the murine macrophage cell line (RAW 264.7) stimulated *in vitro* with lipopolysaccharide (LPS) and/or interferon (IFN-γ), two stimuli which induce strong endogenous NO production \[[@B11]\]. Our results directly establish that macrophage resistance is tightly regulated by the expression of definite sets of auto-protective redox buffering molecules.

Results
=======

LPS and/or IFN-γ *in vitro*, stimulate cells of the murine RAW264.7 line along the differentiation pathway of secreting macrophages : induction of TNFα and NO production
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------

We first made a detailed kinetics analysis of the response of RAW 264.4 cells to *in vitro* stimulation by LPS, or IFN, or both. We confirmed that these products stimulated the differentiation of the cells toward the phenotype of activated macrophages releasing NO and secreting TNFα. In our experiments, RAW 264.7 cells were exposed *in vitro* to either 50 units/ml of IFN-γ (IFN~50~), or 5 μg/ml LPS (LPS~5~), or a mixture of both stimulating products. Cell free culture supernatants were harvested at 0, 6, 24 and 48 H and assessed for the production of TNFα, nitrites/nitrates and citrullin. Titrating TNFα production by ELISA, the strongest stimulation was observed with either LPS or LPS + IFN, leading to abundant secretions of TNFα as early as 6 H after the beginning of the culture, with a subsequent plateau at 24 and 48 H. The quantities of TNFα released by 0.25 × 10^6^ cells initially seeded were in the range of 1400--1700 pg/ml at 24 and 48 H. Stimulation by IFN alone was less efficient inducing roughly only half of the above TNFα production (table [2](#T2){ref-type="table"}). In the same supernatants, the production of NO was evaluated by quantifying citrulline and the total nitrite/nitrate with the Griess reagent. Both products were titrated in quantity (15--30 μM range and 30--70 μM range respectively) in 24 and 48 H culture supernatants of RAW 264.7 cells stimulated by either LPS or LPS+IFN. Smaller quantities were titrated in supernatants of cells stimulated by IFN alone (Table [2](#T2){ref-type="table"}). Parallel experiments were performed to monitor NOs2 (iNOs) mRNA induction and NOs2 (iNOs) protein synthesis in RAW 264.7 cells exposed *in vitro* to either 50 units/ml of IFN-γ (IFN~50~), or 5 μg/ml LPS (LPS~5~), or a mixture of both stimulating products. Cells were pelleted at 0, 6, 24 H and 48 H. NOs2 (iNOs) mRNA was evaluated by semi-quantitative RT-PCR (Figure [1B](#F1){ref-type="fig"}), and NOs2 (iNOs) protein intracellular level by immunoblot (Figure [1C](#F1){ref-type="fig"}). NOs2 (iNOs) was undetectable both at the mRNA and protein levels in unstimulated RAW 264.7 cells. It was strongly induced at times 6, 24 and 48 H with the three stimulation protocols (Figure [1A](#F1){ref-type="fig"} &[1B](#F1){ref-type="fig"}). Only slight variations were noted depending of the stimulations used; IFN-γ alone being as previously, the weakest inducing stimulus compared to LPS and LPS + IFN-γ.

![**A/ Growth rate and survival of RAW 264.7 cells cultured in medium, or medium supplemented with LPS or IFN-γ or LPS+IFN-γ.** RAW 264.7 cells were cultured (4 × 10^6^ cells in 20 ml) in medium (• œ) or medium supplemented with either IFN-γ (50 U/ml -.), or LPS (5 μg/ml -) or a mixture of IFN-γ (50 U/ml) + LPS (5 μg/ml -). The number of live cells recovered at the indicated times was estimated using trypan blue exclusion. Similar results were obtained estimating the cell survival either by 3H-thymidine incorporation, or by the MTT reduction test. **B/ NOS2 (iNOs) mRNA induction in RAW 264.7 cultured with LPS or IFN-γ or LPS+IFN-γ.** RAW 264.7 cells seeded into six-well plates (2.5 × 10^6^ cells/well) were treated with the different stimuli as indicated above for the indicated times and analyzed by RT-PCR with specific primers for murine NOS2 (iNOs) (Table [1](#T1){ref-type="table"}). HPRT was used as internal control for semi-quantitative estimation. **C/ NOS2 (iNOs) protein in RAW 264.7 cultured with LPS or IFN-ã or LPS+IFN-γ.** RAW 264.7 cells were treated with the different stimuli as indicated above for various times and cell pellets (1 × 10^6^ cells) were lysed with lysis buffer. Protein concentrations in samples were adjusted and electrophoresed on 15% SDS-polyacrylamide gel, then transferred to nitro-cellulose membrane and Western bloted using polyclonal rabbit anti-murine NOS2 (iNOs), as described in Materials and Methods.](1471-2172-3-3-1){#F1}

::: {#T2 .table-wrap}
::: {.caption}
######

Differentiation of RAW 264 7 cells toward secreting-activated macrophages following culture with LPS, IFN-γ or LPS+IFN-γ
:::

  ----------------- ---------------------------- ---------------------------- -------------------- ------------ ----------------------------- --------- ---------- ---------- --------- --------- ---------- ----------

                                                                                **nitric oxide metabolism**

                    **TNF production (pg/ml)**   **nitrites/nitrates (μM)**   **citruline (μM)**

  **Sample type**   **0 h**                      **6 h**                      **24 h**             **48 h**     **0 h**                       **6 h**   **24 h**   **48 h**   **0 h**   **6 h**   **24 h**   **48 h**

  **medium**        156+/ 12                     215+/ 18                     332+/ 168            462 +/ 250   ND                            ND        ND         ND         20+/ 4    22+/ 5    25+/ 7     33+/ 8
  **LPS**           156+/ 12                     1480+/ 187                   1646+/ 266           1733+/ 284   ND                            ND        26+/ 5     38+/ 18    20+/ 4    35+/ 12   47+/ 11    86+/ 31
  **IFN**           156+/ 12                     402+/ 28                     681 +/ 42            819+/ 126    ND                            ND        4+/ 2      12+/ 3     20+/ 4    25+/ 5    34+/ 9     42+/ 8
  **LPS/IFN**       156+/ 12                     1392+/ 224                   1502+/ 335           1754+/ 201   ND                            ND        13+/ 9     33+/ 15    20+/ 4    37+/ 12   43+/ 7     72+/ 22
  ----------------- ---------------------------- ---------------------------- -------------------- ------------ ----------------------------- --------- ---------- ---------- --------- --------- ---------- ----------

ND: not detectable RAW 264.7 cells (250 000 cells/well) were cultured with medium or medium supplemented with either IFN-γ (50 U/ml), or LPS (5 μg/ml) or a mixture of IFN-γ (50 U/ml) + LPS (5 μg/ml). Cell-free culture supernatants were harvested at 0, 6, 24, and 48 hours. The concentrations of TNF-α (measured using an ELISA kit), nitrites/nitrates (measured using the Griess reagent) and citrulline (measured using colorimetric reaction), were evaluated in each culture supernatant as described in Methods. Data represent means +/- SEM of five independent experiments.
:::

Death induction of RAW 264.7 cells stimulated by IFN-γ and/or LPS *in vitro*, with selection of a fraction of surviving activated RAW 264.7
-------------------------------------------------------------------------------------------------------------------------------------------

*In vitro* exposure of cells of the murine macrophage cell line RAW 264.7 to either 5 μg/ml LPS (LPS~5~), or 50 units/ml of IFN-γ (IFN~50~), or a mixture of both stimulating products, led to a substantial reduction of the growth rate and number of surviving cells after 24 H or 48 H in culture (Figure [1A](#F1){ref-type="fig"}). The strongest deleterious effects were observed culturing the cells with either LPS alone or both IFN and LPS, resulting in the recovery of only approximatively 20% viable cells as estimated by MTT assay (or trypan blue test) at 24 H, and 13% at 48 H. IFN alone induced less cytoxicity with 54% and 50% viable cells recovered at 24 H and 48 H respectively (Figure [1A](#F1){ref-type="fig"}). The surviving cells subsequently recovered an *in vitro* growth rate comparable to untreated parental RAW 264.7 cells in culture (not shown). Additional experiments indicated however that those surviving activated macrophages had acquired peculiar resistance capacity to exogenous oxydative and nitrosative stress. More experiments were performed to characterize this resistance phenotype and the differentiation induced cellular metabolic events responsible for this resistance.

Nitrosative stress resistance of RAW 264.7 macrophages surviving LPS, or IFN, or LPS+ IFN, stimulation
------------------------------------------------------------------------------------------------------

We next established that the selected population of activated macrophages surviving *in vitro* stimulation by either LPS, or IFN, or LPS+ IFN, had acquired a nitrosative stress resistance phenotype. RAW 264.7 cells, recovered from 24 H stimulated cultures with LPS, or IFN, or LPS+ IFN, or from unstimulated cultures (control parental cells), were washed and seeded in fresh culture medium in the presence of 1 mM of the NO releasing compound diethylenetriamine nitric oxide (DETA-NO). This concentration had been established previously to reproducibly induce the apoptotic death of about 50% unstimulated parental RAW 264.7 cells after 48 H *in vitro* (Figure [2A](#F2){ref-type="fig"}). The susceptibility of LPS, or IFN, or LPS+ IFN activated macrophages to the NO releasing compound DETA-NO, was evaluated *in vitro* by measuring the number of metabolically active cells (reducing Formazan = MTT assay) or trypan blue negative cells, recovered at the indicated time. We established previously that the MTT test provides a reproducible quantitative estimate of cell viability giving comparable results with the trypan blue dye exclusion method. As can be seen in Figure [2B](#F2){ref-type="fig"}, unselected parental RAW 264.7 cells were susceptible to DETA-NO death induction *in vitro*, with only 55% cells surviving after 24 H, and 45% after 48 hours. By contrast, LPS, or IFN, or LPS+ IFN activated RAW 264.7 macrophages were less susceptible to DETA-NO death induction *in vitro*, with variations depending upon the stimulation protocol.(Fig [2B](#F2){ref-type="fig"}). Activated-macrophages recovered from cultures stimulated with LPS alone were almost completely resistant to the nitrosative stress. Activated-macrophages recovered from IFN, or IFN + LPS stimulated cultures, exhibited an intermediate NO resistance, with about 70% and 60% metabolically active cells, or trypan blue negative cells, recovered after 24 or 48 H in culture with DETA-NO.

![**A / Susceptibility of RAW 264.7 undifferentiated cells to various doses of the NO donor compound DETA-NO in culture**. RAW 264.7 cells seeded at 5.10^4^ per well in 96 wells plates were cultured in medium alone (●) or medium supplemented with 125 μM (◇), 250 μM (△), 500 μM (□), 1000 μM (O) DETA-NO. The number of live cells recovered at the indicated times was estimated using the MTT reduction test. Similar results were obtained estimating the cell survival either by 3H-thymidine incorporation, or by trypan blue exclusion. **B/ Susceptibility of RAW 264.7 either undifferentiated, or differentiated cells, to 1 mM dose of the NO donor compound DETA-NO in culture.** RAW 264.7 cells were cultured (10 × 10^6^ cells in 50 ml) in medium alone (○) or medium supplemented with either IFN-γ (50 U/ml -- ◆), or LPS (5 μg/ml -- ■) or a mixture of IFN-γ (50 U/ml) + LPS (5 μg/ml -- ▲). After 24 h, the cells were recovered, washed, seeded at 5.10^4^ cells per well in 96 wells plate, and exposed to 1 mM dose of the NO donor compound DETA-NO for the indicated times. The percentage of living cells was assessed after 48 hours exposure to the NO donor compound using the MTT assay. The experiments were performed in triplicate and the results presented are representative of four separate experiments.](1471-2172-3-3-2){#F2}

Upregulation of redox protection/detoxification systems in LPS-differentiated NO resistant RAW 264.7 cells
----------------------------------------------------------------------------------------------------------

LPS (and/or IFN) induced NO resistance of differentiated RAW 264.7 macrophages could result from an up-regulation of their cellular redox protection/detoxification systems. We therefore evaluated the LPS (and/or IFN) induced modulation of a number of redox proteins, critical in the three major systems protecting cells against oxydative and nitrosative stress : the superoxide dismutase/catalase system (Cu/Zn SOD, Mn SOD and catalase); the glutathione system (glutathione (GSH), γ-glutamyl cystein synthase (γ-GCS), glutathione peroxidase (Gpx), gluthatione reductase (GR); and the thioredoxin system (thioredoxin (Trx), thioredoxin reductase (TR). The transcriptional modulation of several of these redox proteins was evaluated by semi-quantitative RT-PCR on 24 hours cell extracts, using the specific primers described in table [1](#T1){ref-type="table"}. As shown in Figure [3](#F3){ref-type="fig"}, Cu/Zn SOD, Mn SOD, and catalase, were significantly upregulated (130 to 200 % increased) in NO resistant activated RAW 264.7 macrophages obtained following 24 hours stimulation with LPS (and/or IFN). By contrast, γ-glutamyl cystein synthase (γ-GCS) and glutathion peroxidase (Gpx) were only marginally increased with IFN, while glutathione reductase (GR), thioredoxin (Trx) and thioredoxin reductase (TR) mRNA were not significantly affected. This gene transcription study was further documented by measuring cellular concentrations of glutathione (GSH) in lysates of activated RAW 264.7 macrophages, recovered 6, 24 or 48 hours after LPS (and/or IFN) stimulations. As can be seen in Figure [4](#F4){ref-type="fig"}, glutathione level was approximatively 3.5 folds increased at 6 hours in LPS stimulated RAW 264.7 cells, compared to basal level in unstimulated control RAW 264.7 cells. This LPS induced high glutathione level was still detected at 24 and 48 hours (2.6 and 5.2 fold increased respectively). IFN~50~ and IFN~50~/LPSs stimulations also increased intra cellular glutathione levels in RAW 264.7 cells, with the same kinetics than LPS alone, although with variable efficiency (Figure [4](#F4){ref-type="fig"}). Additional experiments were performed to quantify the cellular specific activity of several other redox protection proteins in lysates of RAW 264.7 macrophages, recovered 6, 24 or 48 hours after LPS (and/or IFN) stimulations. As can be seen in table [3](#T3){ref-type="table"}, catalase activity was approximately two folds increased at 6 and 24 hours after LPS stimulation of RAW 264.7 cells, compared to basal level in unstimulated cells, (but not after IFN~50~ or LPS~5~/IFN~50~ treatments), returning to basal level at 48 H. A sharp seven fold increased SOD activity was observed at 6 H in RAW 264.7 cells stimulated with IFN~50~ and IFN~50~/LPS~5~, which was not observed at any other time point, nor with the other stimulation protocols. No significant changes in Gpx activity was observed in stimulated cells.

![**Transcriptional modulation of important redox proteins in RAW 264.7 either undifferentiated, or differentiated cells**. RAW 264.7 cells were cultured (2.5 × 10^6^ cells/well) in medium, or medium supplemented with either LPS (5 μg/ml -- hached boxes), or IFN-γ (50 U/ml -- green boxes) or a mixture of IFN-γ (50 U/ml) + LPS (5 μg/ml) (black boxes). After 24 h, the cells were recovered and washed. Total cellular RNA were extracted and used for semi-quantitative RT-PCR evaluation of the redox proteins indicated. Vertical axis indicate the ratio of the redox protein mRNA in cells cultured in medium with the three stimulating conditions, to the same redox protein mRNA in cells cultured in medium alone. HPRT mRNA was used as internal standard.](1471-2172-3-3-3){#F3}

![**Glutathione concentrations in RAW 264.7 either undifferentiated, or differentiated cells**. Cells were cultured for the indicated times in medium, or medium supplemented with 5 μg/ml LPS, 50 U/ml IFN-γ or a mixture of IFN-γ (50 U/ml) + LPS (5 μg/ml). Total glutathione was quantified using \"Glutathione Cellular Assay\" kit (specific evaluation of glutathione-SH groups). Results are expressed as means +/- SEM of five independent experiments.](1471-2172-3-3-4){#F4}

::: {#T1 .table-wrap}
::: {.caption}
######

Pairs of synthetic primers used in RT-PCR to amplify the different molecular systems studied.
:::

  **Gene studied**   **Sens sequence**                          **Anti-sens sequence**                      **size (pb)**
  ------------------ ------------------------------------------ ------------------------------------------- ---------------
  HPRT               5\'TGG AAT CCT GTG GCA TCC ATG AAA C 3\'   5\' TAA AAC GCA GCT CAG TAA CAG TCC G 3\'   348
  catalase           5\' GCA GAT ACC TGT GAA CTG TC 3\'         5\' GTA GAA TGT CCG CAC CTG AG 3\'          229
  GCS                5\' CCT TCT GGC ACA GCA CGT TG 3\'         5\' TAA GAC GGC ATC TCG CTC CT 3\'          346
  Gpx                5\' CCT CAA GTA CGT CCG ACC TG 3\'         5\' CAA TGT CGT TGC GGC ACA CC 3\'          197
  GR                 5\' AGC CGC CTG AAC ACC ATC TA 3\'         5\' CCG TCT GAA TGC CCA CTT TA 3\'          601
  NOS(2)             5\' ACG CTT CAC TTC CAA TGC AAC 3\'        5\' TGA GGG CTG ACA CAA GGC CTC 3\'         511
  SOD(Cu/Zn)         5\' AAG GCC GTG TGC GTG CTG AA 3\'         5\' CAG GTC TCC AAC ATG CCT CT 3\'          246
  SOD(Mn)            5\' GCA CAT TAA CGC GCA GAT CA 3\'         5\' AGC CTC CAG CAA CTC TCC TT 3\'          241
  Trx                5\' CCC TTC TTC CAT TCC CTC TG 3\'         5\' AAC TCC CCC ACC TTT TGA CC 3\'          149
  TR                 5\' TCC TCT TTT TCT ACC CAC TG 3\'         5\' GTA TTC CTT GCT GTC ATC CA 3\'          464
:::

::: {#T3 .table-wrap}
::: {.caption}
######

Activities of the redox enzymes catalase, Gpx and SOD, in differentiated NO resistant RAW 264 7 cells
:::

  -------------------- ------------ ------------ ---------- ----------- ---------- ---------- ------------ ----------- -----------
  **time (hours)**     **6**        **24**       **48**     **6**       **24**     **48**     **6**        **24**      **48**

  **control**          873+/-100    761+/-128    767+/-96   257+/-116   379+/-95   300+/-32   249+/-111    263+/-47    326+/-76
  **LPS~5~**           1563+/-408   1036+/-670   738+/-90   335+/-206   249+/-55   323+/-52   454+/-174    205+/-136   231+/-169
  **IFN~50~**          598+/81      555+/ 92     612+/76    420+/ 30    218+/46    248+/17    1637+/512    288+/ 85    66+/37
  **LPS~5~/IFN~50~**   778+/67      698+/167     849+/ 33   280+/13     213+/61    268+/ 18   1833+/1601   284+/ 190   236+/115
  -------------------- ------------ ------------ ---------- ----------- ---------- ---------- ------------ ----------- -----------

RAW 264.7 cells were cultured (10 × 10^6^ cells in 50 ml) with medium or medium supplemented with either IFN-γ (50 U/ml), or LPS (5 μg/ml) or a mixture of IFN-γ (50 U/ml) + LPS (5 μg/ml). Cells were harvested at 6, 24, and 48 hours. Activities of the enzymes : catalase (decomposition of H~2~O~2~), Gpx (oxidation of NADPH) and SOD (reaction with chromophore) were determined on cell lysates as described in Methods. Results are expressed as means +/- SEM of five independent experiments.
:::

LPS induced NO resistance of differentiated RAW 264.7 macrophages is abrogated by chemical inhibitors affecting the intra-cellular redox protection/detoxification systems
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------

The causal relationship between the acquisition of NO resistance and the upregulation of a selective set of redox protection/detoxification systems in LPS differentiated RAW 264.7 macrophages was established using chemical inhibitors targeting those redox systems. The compounds used were relatively specific inhibitors affecting the SOD/catalase system and the glutathione system, the two redox protection/detoxification biochemical sets significantly up-regulated in LPS-differentiated macrophages. DETC is an inhibitor of both Mn SOD and Cu/Zn SOD, ATZ is a catalase inhibitor, and BSO reduces the intra-cellular level of glutathione by inhibiting the γ-glutamyl cysteine synthase. In our experiments, RAW 264.7 cells were first differentiated *in vitro*, as previously, by stimulation with an optimal dose of LPS (5 μg/ml) for 48 h. Selected-surviving-LPS-differentiated RAW 264.7 macrophages (and control unstimulated RAW 264.7 cells) were washed and incubated for an additional 2 hours period in the presence of the redox inhibitors (or not). Subsequently, nitrosative stress resistance of the treated cells was evaluated by culturing them in the presence of 1 mM DETA-NO and assessing cell viability after 24 hours (number of metabolically active cells reducing formazan = MTT test). The results are presented in Figure [5](#F5){ref-type="fig"}. LPS differentiated macrophages were 90--100% resistant to the toxic effects of NO released by 1 mM DETA-NO *in vitro*, while unstimulated cells were susceptible to NO, with 45% cells only surviving to the same *in vitro* treatment. Noteworthy, incubation of LPS differentiated cells in the presence of the redox inhibitors abrogated their NO resistance and restored their susceptibility to the toxic effects of DETA-NO. The SOD inhibitor DETC was the most effective compound. It completely reversed the NO resistance status of LPS-differentiated macrophages : only 10% of DETC-treated LPS-differentiated macrophages were recovered at 48 hours following exposure to DETA-NO; compared to 90% recovery of LPS-differentiated macrophages cultured without DETC (Figure [5C](#F5){ref-type="fig"}). The two others redox inhibitors BSO (γ-GCS inhibitor) and ATZ (catalase inhibitor), also very significantly abrogated NO resistance in LPS-differentiated macrophages (with only 35% and 30% viable cells recovery following DETA-NO exposure of LPS-differentiated macrophages treated with these two compounds, respectively) (Figure [5A](#F5){ref-type="fig"} &[5B](#F5){ref-type="fig"}). In addition we noted that the SOD inhibitor DETC substantially sensitized unstimulated RAW 264.7 cells to the toxic effects of DETA-NO : only 10% DETC treated unstimulated RAW 264.7 cells survived NO exposure, compared to 40% control unstimulated RAW 264.7 not incubated with DETC (Figure [5C](#F5){ref-type="fig"}).

![**LPS induced NO resistance in differentiated RAW 264.7 macrophages is abrogated by chemical inhibitors affecting the intra-cellular redox protection/detoxification systems.** RAW 264.7 cells were first treated *in vitro*, as previously, culturing them in medium (undifferentiated cells), or medium supplemented with 5 μg/ml LPS. After for 24 hours, surviving-LPS-differentiated RAW 264.7 macrophages, and control undifferentiated RAW 264.7 cells, were washed and incubated for an additional 2 hours period in the presence of the redox inhibitors BSO (an inhibitor of γ-glutamyl cysteine synthase), or ATZ (a catalase inhibitor), or DETC (an inhibitor of both Mn SOD and Cu/Zn SOD). Subsequently, the resistance of the treated cells to exogenous NO was evaluated by culturing them (or not) in the presence of 1 mM DETA-NO (a dose previously established to be toxic for undifferentiated RAW 264.7 cells, Figure [2A](#F2){ref-type="fig"}). The % viability of cell exposed to exogenous NO was assessed after 48 hours as the percent of metabolically active cells reducing formazan (MTT test) compared to control (number of RAW 264.7 cells undifferentiated, not exposed to exogeneous NO, surviving at the end of the culture period = 100% cell viability).](1471-2172-3-3-5){#F5}

Discussion
==========

Macrophages mediate crucial functions protecting the organism against pathogenic infectious agents. Upon encounter with micro-organisms or stimulation by cytokines, they produce and release various sets of effector molecules aimed at destroying the foreign agents. Reactive oxygen species (ROS) and reactive nitrogen species (RNS) are their front line effector molecules \[[@B13],[@B14]\]. These highly diffusible products exert strong cytotoxic activities against micro-organisms and many cells, including against macrophages themselves. However, a fraction of activated macrophages exposed to redox stress manage to escape destruction. Our results establish that nitrosative resistance results from the adapatative tuning of the redox buffers in these cells.

Reactive oxygen species and nitrogen monoxide are the earliest cytotoxic defense molecules produced by activated murine macrophages after contact with the infectious agents. Elevated oxygen production in macrophages (and polymorpho nuclear cells) relies upon the enzyme NaDPH oxydase \[[@B15]\]. Elevated and sustained production of nitric oxide (NO), a short-lived radical molecule generated from the guanido nitrogen group of L-arginine, is controled by an inducible NO synthase isoform (iNOS) \[[@B15]\]. This iNOS enzyme is tightly regulated in macrophages at variance with the two additional constitutive isoforms of NO synthase (cNOS) which are expressed in different tissues and function in basal physiological conditions. These later enzymes produce low levels of NO involved respectively in neurotransmission, and vascular relaxation \[[@B16]-[@B19]\]. Elevated production of NO in activated macrophages by the inducible NOS isoform following cytokines and/or bacterial lipopolysaccharide (LPS) challenge has been recognized as an essential anti-bacterial defense mechanism in rodent \[[@B14]\]. NO is not a strong oxidant by itself. However, NO can react with transition metals and reactive oxygen species (ROS) leading to the secondary generation of very toxic and reactive products like peroxynitrite (ONOO-) \[[@B20]\]. All these products, NO and its related nitrosocompounds, mediate toxicity in bacterial assailants. They also mediate growth arrest and apoptosis in normal mammalian cells \[[@B13]\] (including autocrine and paracrine toxicity for activated macrophages) and tumor cells \[[@B21]-[@B23]\].

Mammalian cells are equiped with intracellular protection systems, " redox buffer ", which protect them from endogen oxygen radicals produced by the respiration in mitochondria \[[@B24],[@B25]\]. Three biochemical sytems are the most important redox buffers. A predominant protection is by the Cu/Zn-dependent or Mn-dependent superoxide dismutase enzymes (SOD) which convert superoxide ions to hydrogen peroxide, then detoxified by catalase \[[@B26],[@B27]\]. A second protection is by gluthatione (GSH), an intracellular peptidic thiol molecule with both oxidant scavenger and redox regulating capacities \[[@B28]-[@B30]\]. Thiols on gluthatione combine with NO and form the less reactive product S-nitrosoglutathione (GSNO). A third protection is performed by thioredoxine (Trx) which can buffer ROS and RNS through oxidation of its intra chain disulfide bridge, reduced thioredoxine being then regenerated by the enzyme thioredoxine reductase (TR) \[[@B31]-[@B33]\]. The respective roles of these intracellular antioxidant autoprotective systems against ROS and/or RNS-mediated auto-injury in activated macrophages have not been defined in details. Using a murine macrophage cell line (RAW 264.7), abundantly producing NO upon stimulation with lipopolysaccharide (LPS) and/or interferon (IFN-γ), we examined changes in those intracellular redox homeostatic systems and, using relatively specific inhibitors, identified the most important redox protection systems.

Our experiments indicate that macrophage stimulation induce an up-regulation of the two major intracellular redox buffering systems : the enzymatic SOD-catalase system and the RNS acceptor-neutralizer molecule glutathione. Such differentiated macrophages are resistant to exogenous NO This activation-induced new redox status is stable since the resistant phenotype of RAW 264.7 cells persisted after induction for at least one week in culture (data not shown). The regulation is both at the transcriptional and protein levels for SOD and catalase. The increased glutathione content in cells following stimulation is not explained by an increased transcription of the enzyme γ-glutamylcysteine synthase controling its synthesis; nor by an increased transcription of the regenerating enzyme glutathione reductase. It might result from the post translational stress-induced allosteric activation of the glutathione generating enzyme γ-glutamylcysteine synthase, or (and) from intra-cellular mobilization of an undefined inactive pool \[[@B34]\]. We did not observed any modulation of the third redox buffering system thioredoxine-thioredoxine reductase. This is at variance with our own previous results with THP1 monocytic human cells which heavily relied upon the thioredoxine system to maintain their redox homeostasis \[[@B33]\]. Such variation might represent inter species difference. The present data with RAW 264.7 cells are in good agreement with previous work from Brockaus & Brune group who reported that stably transfected RAW 264.7 cells overexpressing CuZn SOD were more resistant to endogenous or exogenous NO \[[@B35]\].

Using relatively specific inhibitors, we were able to establish a causal relationship between the upregulation of the intra cellular redox buffering systems mentioned above and the acquisition of their nitrosative stress resistant phenotype. The SOD inhibitor DETC almost completely reversed the NO resistance status of LPS-differentiated macrophages. It also substantially sensitized unstimulated RAW 264.7 cells to the toxic effects of DETA-NO. The two others inhibitors BSO (γ-GCS inhibitor) and ATZ (catalase inhibitor), also very significantly abrogated NO resistance in LPS-differentiated macrophages. Indeed the activation-induced resistance of macrophages has been studied by many groups (also designated as endotoxin-tolerized macrophages). Their results linked the resistance of macrophages to several, non exclusive, mechanisms such as : complex impaired expression and/or function of common signaling pathways \[[@B36]\] increased expression of Bcl-xL anti-apototic proteins \[[@B37]\], increased expression of Bfl-1 anti-apototic gene and simultaneous down regulation of caspase-8 mRNA \[[@B38]\], alteration of the Fas-Fas ligand transduction pathway \[[@B39]\], over-expression of CD14 receptor \[[@B40]\], autocrine cytokine regulatory network between IL-12 and IL-10 \[[@B41],[@B42]\]. The multiplicity and complexity of the macrophage responses was recently documented using the gene array technology which detected a broad spectrum of genes overexpressed in response to *S. typhimurium* and *S. typhimurium* LPS in RAW 264.7 cells \[[@B43]\]. We also observed in our experiments an increased expression of Bcl~2~ and Bcl-~XL~ and decreased expression of Bax and Bad in NO-resistant activated macrophages (not shown). Nevertheless, our results clearly demonstrated the crucial auto-protective importance of the up-regulation of the two major redox buffering systems SOD-catalase and glutathione, for nitrosative stress resistance of activated macrophage. A schematic representation of the many factors regulating survival of activated macrophages is presented in Figure [6](#F6){ref-type="fig"}.

![Schematic representation of the many factors regulating nitrosative stress resistance and survival of activated macrophage.](1471-2172-3-3-6){#F6}

Conclusion
==========

Activated macrophages exposed to endogenous NO are submited to a redox nitrosative stress which acts as a strong selection process allowing the survival of the fraction of cells up-regulating key sets of autoprotective redox buffering molecules. What are the cascade of events and the mechanisms leading to the transcriptional activation of SOD genes and increased glutathione availability in activated macrophages, remain to be determined. Such findings might help define new strategies aimed at improving the induction of nitrosative stress resistant macrophages which could be the best efficient defenders of the organisms against infection and possibly cancer.

Materials and Methods
=====================

Materials
---------

The monoclonal antibody (MoAb) against NOs2 (iNOs) was purchased from Pharmingen (San Diego, CA). The horseradish peroxydase-conjugated goat-anti-rabbit antibody was purchased from BIO-RAD (Bio-Rad laboratories, California). The NO donor compound 2,2\'(hydroxynitrosohydrazino)bis-ethanamine (DETA/NO) was purchased from Calbiochem. Purified LPS from *Escherichia Coli*, Leupeptin, pepstatin, phenylmethylsulfonyl fluoride (PMSF), EDTA, Hepes, CHAPS, 3-(4,5-dimethylthiazol-2-yl)-2-5-diphenyltetrazolium bromide (MTT), ATZ (aminotriazol), BSO (Buthionine sulfoximine), DETC (Diethylthiocarbamate) and DTT, were from Sigma. ECL chemoluminescence enhancer reagents were from Amersham.

Cell cultures
-------------

Cells of the mouse macrophage-RAW 264.7 line were obtained from the American Type Culture Collection \[[@B11]\] and cultured in RPMI 1640 medium supplemented with glutamax 1 (GIBCO/BRL/Life Technologies), 7% (v/v) heat-inactivated foetal calf serum (HyClone), 100 units/ml penicillin and 100 μg/ml streptomycin, at 37°C in a humidified air/CO~2~ (5%) atmosphere. Standart bulk cultures were with 10 × 10^6^ cells in 150 cm^2^/50 ml vials (Corning, polystyrene) in culture medium, or culture medium supplemented with either 5 μg/ml *Escherichia Coli* LPS (serotype O127:B8, from Sigma), or 50 units/ml recombinant mouse IFN-γ (Genzyme), or the combinaison of both (LPS 5 μg/ml plus IFN-γ 50 units/ml).

Titration of macrophage-derived pro-inflammatory products
---------------------------------------------------------

TNF-α were measured in undiluted supernatants using ELISA kit purchased from Medgenix Diagnostics (Rungis, France) according to manufacter\'s instructions, in triplicates for each sample. The total amount of nitrite/nitrate, the stable products of NO, was determined in supernatants using the Griess reagent. Briefly, 50 μl of culture supernatants collected at 6, 24 and 48 hours were mixed with 150 μl of Griess reagent (1% sulfanilamide/0,1% naphtylethylenediamine-dihydrochloride) at RT for 30 s. The absorbance at 543 nm was immediately determined on Dynex Revelation F 3.21 microplate reader. Nitrite concentrations in each sample were determined by extrapolation from a sodium nitrite standart curve.

L-citrulline levels were determined by the colorimetric reaction of carbamido groups with diacetyl monoxime in acid solution \[[@B10]\]. Briefly, 30 μl of urease (25 U/ml) was added to 300 μl of supernatant, and incubated for 1 hour at 37°C. After addition of 37.5 μl trichloroacetic acid (TCA, 59% vol/vol), the precipitated proteins were pelleted by 5 minutes centrifugation at 12 000 g. 250 μl of supernatant was harvested and mixed with 300 μl of a 1:1 (vol/vol) mixture of 240 mmol diacetylmonoxime and a solution of phenazone (3 g in 104 ml H~2~O reacted with 12 mg FeSO~4~ and 21 ml H~2~SO~4~ 36N, and incubated 15 minutes at 90°C in dark. 200 μl of the reaction mixture was transferred to a microtitration plate for measurement of the optical density at 492 nm using Dynex Revelation F 3.21 microplate reader. Citrulline concentrations were determined by extrapolation from a citrulline standart curve.

RNA isolation and RT-PCR analysis
---------------------------------

Following *in vitro* stimulation for the indicated times with the indicated stimulus, total cellular RNAs were extracted from 2.5 × 10^6^ RAW 264.7 cells using TRI reagent (Euromedex, Souffelweyersheim, France) according to manufacturer\'s instructions. cDNAs were synthesised in 40 μl reaction mixtures containing 2 μg of RNA, 8 μl of 5X buffer (250 mM TRIS-HCL, 250 mM KCL, 50 mM MgCl~2~, 50 mM DTT and 2.5 mM spermidine), 2 μl of each dNTP 25 mM, 0.4 μl of 50 mM oligo-dT primers, 0.4 μl of RNAsin at 22 000 U/ml and 0.6 μl of Avian Myeloblastosis Virus-reverse transcriptase (Appligene, Strasbourg, France) at 42°C for 2 hours. PCR was performed using 1/20 of the cDNA reaction mixture in a 100 μl reaction volume containing 10 μl of 10X buffer (10 mM TRIS-HCL, 50 mM KCL, 1.5 mM MgCl~2~, 0.1% Triton X 100 and 0.2 mg/ml gelatine), 0.5 μl of each dNTP 25 mM, 0.4 μl of Taq Polymerase 5 000 U/ml (Thermus aquaticus, Appligene) and 1 μl of each primer 20 μM. The RT-PCR products were subjected to electrophoresis on agarose gel, stained with ethidium bromide, scanned and quantified by densitometry. The sequences of sense and anti-sense PCR-primers used for amplification and the predicted size of products are described on Table [1](#T1){ref-type="table"}. HPRT mRNA was used as internal standard. Modulation of mRNA expression in RAW 264.7 cells either undifferentiated, or differentiated, was standardized as follow. Bands were scanned and quantified by densitometry. The ratios : redox protein mRNA band intensity / HPRT mRNA band intensity were calculated for each stimulation conditions. The ratio values were then standardized taking the relevant mRNA band intensity ratio in undifferentiated RAW 264.7 cells as reference value 1.

Immunoblot analysis
-------------------

Pellets of 1 × 10^6^ cells were lysed in lysis buffer (25 mM HEPES, 0.5% Nonidet P40, 0.1% SDS, 0.5 M NaCl, 5 mM EDTA, 0.1 mM sodium deoxycholate, 1 mM PMSF, and 0.1 mg/ml leupeptin/pepstatin, pH 7.8) at 4°C for 30 min. Nuclei and membranes were removed by centrifugation at 12 000 g for 15 min. The amount of protein in each lysate was measured using the BSA microbiuret assay from Pierce. Loading buffer (42 mM tris-HCl, pH 6.8, 10% glycerol, 2.3% SDS, 5% 2-mercaptoethanol, and 0.002% bromophenol blue) was added to each lysate, which was subsequently boiled for 3 min and electrophoresed on an 15% SDS-polyacrylamide gel. Proteins were transferred to nitro-cellulose membrane. The membrane was saturated with TBS (150 mM NaCl, 10 mM tris HCl, pH 7.5) containing 10% skim milk overnight, washed twice 15 min with TTBS 0.1% (150 mM NaCl, 10 mM tris HCl, 0.1% Tween 20, pH 7.5), and incubated with a polyclonal rabbit-anti-murine NOS2 (iNOs) (1/1000 dilution), in TTBS 0.1% containing 3% skimmed milk for 1 h. The membrane was subsequently washed twice 15 min with TTBS 0.1%, and incubated for 45 min with the second antibody, peroxidase conjugated goat-anti-rabbit (1/5000). Bound antibodies were visualised by chemiluminescence using an ECL Western Immunoblotting Kit (Amersham).

Susceptibility-resistance of RAW 264.7 cells to NO-mediated cell injury
-----------------------------------------------------------------------

The susceptibility-resistance of RAW 264.7 cells exposed *in vitro* to exogenous NO was evaluated on cells distributed in 96 wells plates (4.10^4^ per well). Diethylene triamine-nitric oxide (DETA-NO) 1 mM was added to each well at t0 and incubation processed for the indicated times. This 1 mM dose induced substantial cytotoxicity of undifferentiated RAW 264.7 cells after 24 h (Figure [2A](#F2){ref-type="fig"}). After various incubation times, cell viability was assessed using 3 methods: trypan blue dye exclusion test, 3H thymidine incorporation and MTT assay (mitochondria-dependant reduction of 3-(4,5-dimethylthiazol-2-yl) 2,5-diphenyltetrazolium bromide (MTT) to formazan. The 3 tests give highly concordant results in every experiments, with or without NO-donor molecules. Results with the MTT assay only are presented. It was performed as follow : 20 μl of MTT (5 mg/ml in PBS 1X) was added in each wells and incubated at 37°C for 4 hours. Then, 150 μl of medium was removed from each well, and 100 μl of 0,5% HCl/propanol-1 was added to dissolve cristals. The net absorbance ratio (ratio A~550~-A~630~) in each well was immediately recorded using an ELISA microplate reader. The net absorbance in wells containing cells cultured in control medium was taken as the 100% viability value. The percent of viable cells exposed to DETA-NO was calculated by comparison.

Determination of cellular content of glutathion
-----------------------------------------------

The cellular concentration of glutathion was evaluated using the reagent kit Bioxytech GSH-400 (Bioproducts, Gagny, France) according to manufacturer instructions\', a glutathione colorimetric quantification test based upon specific evaluation of glutathione-SH groups.

Activities of redox enzymes
---------------------------

*In vitro* cultures of RAW 264.7 cells, in medium or medium supplemented with the differentiating agents, were interruped at the indicated times. Cells were pelleted, washed three times and lysed by three successive freezing-thawing cycles. The enzymatic activities were evaluated on cell lysates recovered after centrifugation at 12 000 g for 15 mn. The catalase activity was assayed by monitoring the decreased absorbance at 240 nm resulting from the decomposition of H~2~O~2~ for 1 min at 37°C. The assay mixture consisted of 2.98 ml of H~2~O~2~ solution from a stock solution of 0,1 ml of 30% (w/v) H~2~O~2~ diluted in 50 ml of sodium phosphate buffer, pH 7, and 20 μl of the incubation solution (0.025 mg of enzyme/ml of buffer). All assays were performed in triplicate. Activity is expressed in comparision with control activitis determined for each incubation time set as 100% value \[[@B12]\]. SOD and Gpx activities were determined using the reagent kits SOD-525 and Gpx-340 kits (from Alexi and Calbiochem respectively) following manufacturer\'s instructions.

Statistical analysis
--------------------

Results are reported as mean ± SD except where indicated, and differences between groups are calculated by means of the nonpaired Student\'s t-test. Experiments were performed in triplicate.

Abbreviations
=============

NO : nitric oxide

GSH : reduced glutathione

GSSG : oxidized glutathione

LPS : lipopolysaccharide

IFN-γ : interferon γ

INOs : inducible isoform of nitric-oxide synthase

GCS : γ-glutamylcysteine synthase

GR : glutathione reductase

GPx : glutathione peroxydase

Trx : thioredoxin

TR : thioredoxin reductase

Cu/Zn SOD : Cu/Zn-dependent superoxide dismutase

Mn SOD: Mn-dependent superoxide dismutase

DETC: Diethylthiocarbamate

ATZ: Aminotriazol

BSO: Buthionine sulfoximine

Acknowledgements
================

This work was supported by the Institut National de la Sante et de la Recherche Medicale, the Association pour la Recherche contre le Cancer (Grants n° 9974 and n° 5483), the Ministère de l\'Education Nationale de la Recherche et de la Technologie. PJF has received a research grant from the Association Française pour la Recherche Thérapeutique.