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J Gerontol A Biol Sci Med Sci. 2010 April; 65A(4): 407–413.
Published online 2009 November 23. doi:  10.1093/gerona/glp181
PMCID: PMC3004772

Patterns of Comorbid Inflammatory Diseases in Frail Older Women: The Women’s Health and Aging Studies I and II



Developing interventions to prevent frailty in older adults is a priority as it increases the risk for disability, institutionalization, and death. Single chronic inflammatory diseases are known to increase the risk of frailty. Identification of comorbid inflammatory diseases that synergistically might heighten this risk would provide further insight into therapeutic approaches to prevent frailty. The study aims were to characterize whether there are specific inflammatory disease pairs that are associated with frailty and to determine whether the risk of frailty is affected by synergistic interactions between these inflammatory diseases.


Data were from the Women's Health and Aging Studies I and II and complementary cohorts of community-dwelling women aged 70–79 years from Baltimore, Maryland (n = 620). Multivariable logistic regression analyses were performed to evaluate the relationships between these diseases and frailty.


Among the frail (11.3%), 15.2% had both depressive symptoms and anemia and 14.5% had pulmonary disease and anemia. The risk of frailty was synergistically increased in those with depressive symptoms and anemia (adjusted risk ratios = 11.93, 95% confidence interval [CI] 4.10–34.76) and those with pulmonary disease and anemia (risk ratios = 5.57, 95% CI 2.14–14.48), compared with those without either disease in each pair. The attributable proportions of frail cases due to interaction between the diseases of each pair were 0.56 (95% CI 0.07–1.05) and 0.61 (95% CI 0.18–1.05), respectively.


Synergistic interactions between specific inflammatory diseases may heighten the risk of frailty. These findings suggest that a common etiologic pathway may exist among co-occurring inflammatory diseases and that their improved comanagement may be an approach to reducing frailty.

Keywords: Frailty, Comorbidity, Inflammation

FRAILTY is a syndrome associated with falls, disability, institutionalization, and mortality in older adults (1,2). Frailty is thought to result from diminished physiological reserves and occurs in greater than 20% of community-dwelling adults who are greater than or equal to 85 years old, excluding those with selected chronic diseases, such as prior stroke and Parkinson’s disease, which may mimic frailty (2,3).

Elevation of proinflammatory mediators C-reactive protein (CRP) or interleukin-6 (IL-6) has each been demonstrated to be associated with frailty (4,5). Inflammation is also involved in many chronic diseases that have independent associations with frailty, including cardiovascular disease (CVD), anemia, diabetes mellitus (DM), and chronic kidney disease (CKD) (2,69). Furthermore, Chaves and colleagues (7) found a multiplicative synergistic interaction between the hemoglobin concentration (with anemia defined as less than 12 g/dL) and CVD status, relative to the risk of frailty, in the Women's Health and Aging Studies (WHAS) I and II. These findings raise the question of whether biologic interactions exist among these conditions to increase the risk of frailty.

To date, specific patterns of multimorbid chronic inflammatory diseases or conditions that commonly co-occur with frailty have not been elucidated. It is not known whether the proinflammatory state associated with a specific disease activates the etiologic cascade that has been postulated to result in frailty or whether this occurs through nondisease specific pathways. Identification of combinations of inflammatory diseases that act synergistically to heighten the risk of frailty would provide information about mechanistic pathways that induce a proinflammatory frail state. Our ability to comanage these diseases could be enhanced with such knowledge, potentially delaying frailty.

We hypothesized that specific combinations of comorbid chronic inflammatory diseases synergistically increase the likelihood of frailty. This heightened risk could occur from shared etiologic pathways or from the effects of biologic interactions among several inflammatory diseases. In this cross-sectional study, we tested our hypothesis by characterizing whether specific comorbid inflammatory disease pairs are associated with frailty and also by determining whether an association with frailty is affected by synergistic interactions between clustered inflammatory diseases in each pair.


Study Population

This cross-sectional study used data from WHAS I and II, two complementary longitudinal cohort studies of community-dwelling older women in Baltimore, Maryland. WHAS I was composed of 1,002 women, aged 65 years or older, who represented the one-third most disabled women in the Baltimore community. WHAS II consisted of 436 women, aged 70–79 years, who were recruited from among the two-thirds least disabled. Details about the sampling method and screening eligibility have been previously described (10). Standardized interviews, physical examination, and blood testing were performed in 71.1% women in WHAS I and in 49.5% women in WHAS II who fulfilled study eligibility criteria and consented to participate. Baseline data were assessed from November 1992 to February 1995 in WHAS I and from August 1994 to February 1996 in WHAS II. All participants gave written informed consent for their study participation. The research protocols were approved by the Johns Hopkins Medical Institutions Institutional Review Board.

The final analytic sample yielded 620 women, aged 70–79 years. Because cognitive impairment was associated with positive responses for some of the frailty constructs, participants (n = 57) with a Mini-Mental State Examination score less than 24 were excluded from the study (3). Participants were also excluded if they had missing data for frailty indicators (n = 6), a Geriatric Depression Scale (GDS) score (n = 4), serum creatinine (n = 78), hemoglobin concentration (n = 99), CRP (n = 87), and IL-6 (n = 84) or had blood drawn beyond 90 days of the first physical examination.

Laboratory Data

Nonfasting blood samples for assessment of hemoglobin concentration, serum creatinine, CRP, and IL-6 were collected via venipuncture, performed at participants’ homes in WHAS I or at the Johns Hopkins General Clinical Research Center in WHAS II by certified phlebotomists. IL-6 was assessed using the High-Sensitivity Quantikine Kit (R&D Systems, Minneapolis, MN), a commercial enzyme-linked immunosorbent assay. Blood specimens for other laboratory data were analyzed at Quest Diagnostics Laboratories (Teterboro, NJ).


Following validated criteria, study participants were classified as frail if they presented with three or more of the following measurable components: (a) unintentional weight loss of 10% or higher since age 60 years or a body mass index (BMI) less than 18.5; (b) slowness in gait, according to height; (c) weakness in dominant hand grip strength, according to BMI; (d) extreme exhaustion in the past month, as determined by self-reported low energy level, unusual fatigue, or unusual weakness; and (e) self-reported decreased energy expenditure based upon a modified Minnesota Leisure-time Physical Activity Scale (2,11).

Identification of Chronic Inflammatory Diseases

Decision-tree algorithms established by systematic physician review of every participant’s medical history validated the prevalence of 17 chronic diseases and conditions (10). Those classified with “possible disease” and “no disease” were considered free of the disease. Definite angina pectoris, myocardial infarction, and congestive heart failure were grouped together as CVD if 1 or more of these conditions was present (7). We also included three additional chronic diseases and conditions based upon previously developed metrics and definitions. Anemia was defined by the World Health Organization as a hemoglobin concentration of less than 12 g/dL (7,12). CKD was defined by a creatinine clearance of less than 60 mL/minute, calculated by the Modification of Diet in Renal Disease (MDRD) formula, and as previously defined by Matteini and colleagues in this study population (13,14). The presence of mild and severe depressive symptoms was defined by a GDS score of greater than 9 of 30 (15).

Eight chronic diseases and conditions were identified as inflammatory in etiology based upon sufficient evidence in the literature and an association with the highest CRP and/or IL-6 tertiles in preliminary analyses (data not shown). By these criteria, CKD, pulmonary disease, CVD, depressive symptoms, anemia, DM, peripheral artery disease (PAD), and rheumatoid arthritis (RA) were designated as inflammatory (8,1622).

Data Analysis

Two-way tabulations were employed to explore the distribution of sociodemographics and chronic diseases with frailty status at baseline. Pearson’s chi-squared tests were used to assess for differences between categorical variables. Multiple logistic regression analyses were conducted to evaluate the association between single inflammatory diseases and baseline frailty. Probability weights, referenced to WHAS community-dwelling older women, were incorporated into the analyses to adjust for age-stratified sampling and study refusals. Statistical significance was determined by an alpha level of less than or equal to .05.

Determination and Analyses of Chronic Inflammatory Disease Pairs

Twenty-five a priori chronic inflammatory disease pairs that were hypothesized to increase the risk of frailty were created. These disease pairs were formed from the eight inflammatory diseases and conditions based upon known comorbidity, clinical experience, and biologic plausibility. Pattern analyses were used as an adjunct tool for ranking and determining the frequency of the inflammatory disease pairs. The top 10 most frequently paired diseases were analyzed.

The association between each of these 10 disease pairs and frailty was examined using cross-tabulations and multiple logistic regression analyses. Age, race, and education were evaluated as potential confounders of the relationship between paired inflammatory diseases and frailty. Interaction terms, consisting of the product between the diseases in each pair, were created. Assessment for possible biologic interactions between the specific diseases of each pair was performed using the program developed by Andersson and colleagues (23) to measure effect modification. In this manner, the proportion of the association attributable to a disease pair interaction, above and beyond the association of each disease alone (the attributable proportion due to the interaction [AP]), was calculated. Model precision and parsimony were confirmed with hierarchical backward selection (p value > .1 for removal). Model fit was assessed with Akaike's information criteria and Bayesian information criteria, comparing unadjusted, partially adjusted, and fully adjusted models. Analyses were conducted using Stata 9.2 software (Stata Corp., College Station, TX).


Study Population

The major baseline characteristics of the study population by frailty status are presented in Table 1. About 11.3% (n = 67) were frail, and 88.7% (n = 553) were nonfrail. Multimorbidity was highly prevalent: 83.4% of the frail had greater than or equal to two of eight chronic inflammatory diseases and conditions compared with 40.8% in the nonfrail group (p value < .001). The frail, compared with the nonfrail, population was more likely to be African American (p value = .04). The frail group also had less education, lower cognitive function, decreased functional status, more medications, and more chronic inflammatory diseases (all p values < .05). Of the eight inflammatory diseases, depressive symptoms, CVD, anemia, PAD, and RA were remarkably more prevalent in the frail, compared with the nonfrail, population (all p values ≤ 0.01), except for CKD (54.3% vs 42.5% in the nonfrail, p value = .07), pulmonary disease (39.9% vs 28.7% in the nonfrail, p value = 0.07), and DM (17.0% vs 10.9% in the nonfrail, p value = .14).

Table 1.
Baseline Characteristics of WHAS I and II Study Participants Aged 70–79 Years

Single Inflammatory Diseases and Frailty

Results of multiple logistic regression analyses showing the association between each of the eight chronic inflammatory diseases and frailty are displayed in Table 2. The risk of frailty was increased, at statistically significant levels, for pulmonary disease, CVD, depressive symptoms, anemia, and RA (odds ratios = 1.78–7.37, all p values ≤ 0.04), adjusted for age, race, and education.

Table 2.
Odds of Being Frail vs Nonfrail by Inflammatory Diseases

Paired Inflammatory Diseases and Frailty

Ten of the most common inflammatory disease pairs in the study population and their prevalence rates are summarized in Table 3. Of the 10 disease pairs, 9 were more prevalent in the frail, compared with the nonfrail, population (all p values ≤ .01), except for CKD and pulmonary disease (p value = .08). Among the frail, CKD and depressive symptoms were the most prevalent disease pair (22.9%), followed by CVD and CKD (22.6%) and CVD and depressive symptoms (21.7%), respectively.

Table 3.
Baseline Prevalence of Inflammatory Disease Pairs in WHAS I and II Study Participants Aged 70–79 Years

How these inflammatory disease pairs are associated with the risk of frailty are displayed in Table 4. The adjusted risk of frailty was increased at statistically significant levels in all 10 disease pairs, compared with those without either disease in each pair. Statistically significant synergistic interactions were present in two of the disease pairs: (a) pulmonary disease and anemia and (b) depressive symptoms and anemia. Among women with both pulmonary disease and anemia, 61% of the frail cases were attributable to the interaction between the two diseases (AP = 0.61, 95% confidence interval [CI] 0.18–1.05). Among women with both depressive symptoms and anemia, 56% of the frail cases were attributable to the interaction between the two conditions (AP = 0.56, 95% CI 0.07–1.05). Also, there was evidence of potential synergistic interaction in disease pair CVD and pulmonary disease. Among those with both CVD and pulmonary disease, 46% of the frail cases were attributable to the interaction between both diseases (AP = 0.46, 95% CI −0.04 to 0.97).

Table 4.
The Risk of Frailty Due to Inflammatory Disease Pairs and Assessment of Potential Biologic Interactions Between Diseases


This study provides new evidence suggesting that specific disease clusters may synergistically increase the risk of frailty, compared with the risk from single chronic inflammatory diseases alone. The presence of coexisting inflammatory diseases could therefore be a risk factor for frailty. Chronic inflammatory diseases, such as CKD, may accelerate the progression of frailty by virtue of their end-stage pathophysiology alone. Notably, chronic inflammatory diseases and inflammation are risk factors for other causally related diseases that may hasten the progression of frailty, either individually or jointly.

In their proposed cycle of frailty, Fried and colleagues (24) hypothesize that some acute or chronic diseases can “potentially impact on every point in the cycle.” Beyond this, when vulnerable older adults develop multimorbid chronic diseases, the aggregate effect of a higher inflammatory disease burden could perpetuate a heightened proinflammatory state, which could elevate cortisol levels, as well as decrease their muscle mass, physiological reserves, and immunocompetence (24,25). Each of these effects alone, or in combination, could render them less capable of tolerating physiological stressors, more susceptible to new diseases, whether subclinical or clinical, and ultimately at greater risk of becoming frail. The implications regarding treatment of comorbid inflammatory diseases before the onset of frailty are therefore immense. These could include benefit from comanaging diseases effectively to minimize their severity and decreasing the financial burden often associated with multimorbid diseases. Further evidence is also needed as to whether clinical outcome would benefit from treating or preventing inflammation.

A second notable finding is the evidence suggesting additive biologic interactions between the diseases of specific inflammatory pairs. One theory that may explain statistically detectable synergistic interactions between paired conditions, such as depressive symptoms and anemia, is that multimorbidity could impact risk factors and physiological mechanisms, beyond clinically apparent diseases, that contribute to the risk of frailty. Researchers have recently begun investigating potential interactions between biochemical mediators and co-occurring impairments that present in geriatric syndromes, such as disability and frailty. For example, Cappola and colleagues (26) observed a synergistic effect between low insulin-like growth factor-1 and high IL-6 levels, which was associated with an increased risk of progressive functional decline and mortality in baseline physically disabled older women from WHAS I. Keller and colleagues (27) demonstrated in older outpatients that the synergistic interaction between vision and hearing impairments in the same individual has a greater effect on declining physical function than each sensory impairment by itself, independent of mental status and comorbidity. The identification of intersecting, or overlapping, physiological pathways between shared risk factors for frailty and other geriatric syndromes may offer new targets for developing clinical treatments in order to manage multimorbid inflammatory diseases aggressively and early on.


This study had several limitations. First, because its design was cross-sectional, we were unable to draw any conclusions regarding causality. Our study was also limited to a population-based sample of older women in Baltimore City and Baltimore County, Maryland. Therefore, the disease clusters identified as most prevalent may not be generalizable to older men because of gender-specific differences in body size and composition that may influence mortality rates from chronic diseases and the development of frailty (28). Furthermore, compared with an 8% black female older adult representation from the 1990 U.S. Census, approximately 25% of the screened WHAS population and 20.1% of our study sample consisted of black women aged 65 years or older (10). Because having an African American descent has been found to be associated with frailty, the higher representation of blacks in our study may render our results less generalizable to the overall U.S. older adult population, even though we adjusted for race in our analyses (3). However, these limitations in generalizability do not negate the internal validity of the study.

Second, there were inherent challenges in measuring the effects of multimorbidity as an exposure. We attempted to draw conclusions about the risk of frailty measured only by the effects of diagnosed chronic inflammatory diseases. The proinflammatory effects contributed by subclinical diseases on the risk of frailty could not be identified.

Next, CKD was determined based upon a calculated creatinine clearance using the MDRD formula. In a study by Froissart and colleagues (29), the MDRD formula demonstrated higher precision and accuracy than the Cockcroft–Gault equation in estimating the measured glomerular filtration rate (GFR) of all subpopulations based upon age and gender, except for women aged 65 years or older with a measured GFR less than 60 mL/minute per 1.73 m2. As a result, misclassification of CKD may have occurred. This phenomenon could have provided an inaccurate estimate of the number of women with impaired renal function and affected the assessment of additive interaction between disease pairs that include CKD. For example, of those with both CKD and anemia, 27% of the frail cases were attributable to the interaction between the two diseases, although not statistically significant (AP = 0.27, 95% CI -0.56–1.10).

Another factor that could influence the association between disease pair patterns and the likelihood of frailty is the socioeconomic profile of the community-based population from which the study participants were recruited. Educational attainment was lower in the screened Baltimore population than in the U.S. population of women aged 65 years or older. Of the screened Baltimore population, 42% had 12 or more years of education compared with 56% of U.S. women aged 65 years or older in 1991 (10). Approximately 20% of those who were screened for the study did not know or disclose their annual income and 70.4% reported an annual income of less than $35,000. It is possible that study participants with a lower socioeconomic status are more likely to have limited access to health care resources that promote preventive health and enhanced physical activity, which may modify their risk of developing disease-related outcomes, leading to frailty.

Finally, several previous findings, which showed the presence of synergistic interactions between different physiological biomarkers and disease risk factors in frail older adults, were cited to support our results that potential biologic interactions between inflammatory disease pairs exist (7,26). However, these studies were conducted on data from the same study cohort as ours. Similar findings from other study populations would further strengthen our conclusions about synergistic inflammatory disease pair interactions.


This study indicates that selected comorbid inflammatory disease pairs are associated with an elevated risk of frailty. Cross-validation of these disease pair patterns in frail older adults from other study populations would support the theory that specific co-occurring diseases, as a result of shared inflammatory pathways and biologic synergism, lead to this heightened risk. Our enhanced knowledge of the determinants of frailty and multimorbid chronic inflammatory disease patterns most predictive of frailty can affect how we treat these diseases before older adults become frail and may offer potential approaches to preventing frailty itself.


This work was supported by the National Institute on Aging (Contract N01 AG12112, Grant R01 AG11703, Grant R37 AG19905, Grant T32 AG00247 to S.S.C.), the National Institutes of Health-National Center for Research Resources, OutPatient General Clinical Research Center (Grant RR00722), the Robert Wood Johnson Foundation Amos Medical Faculty Development Program to C.O.W., and the Johns Hopkins Older Americans Independence Center (Grant P30-AG02133 to Q.X.).


This article was presented in abstract and poster form at the 2008 American Geriatrics Society Annual Scientific Meeting, Washington, DC.


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