Archives of Gerontology and Geriatrics 59 (2014) 636–641
Contents lists available at ScienceDirect
Archives of Gerontology and Geriatrics journal homepage: www.elsevier.com/locate/archger
Understanding red blood cell parameters in the context of the frailty phenotype: interpretations of the FIBRA (Frailty in Brazilian Seniors) study Joa˜o Carlos Silva a, Ze´lia Vieira de Moraes a, Conceic¸a˜o Aparecida da Silva a, Silvia de Barros Mazon b, Maria Elena Guariento a, Anita Liberalesso Neri a, Andre´ Fattori a,* a b
Department of Internal Medicine, Faculty of Medical Sciences, Campinas State University-Unicamp, Brazil Department of Clinical Pathology, Faculty of Medical Sciences, Campinas State University-Unicamp, Brazil
A R T I C L E I N F O
A B S T R A C T
Article history: Received 14 September 2013 Received in revised form 23 July 2014 Accepted 25 July 2014 Available online 14 August 2014
Frailty and anemia in the elderly appear to share a common pathophysiology associated with chronic inflammatory processes. This study uses an analytical, cross-sectional, population-based methodology to investigate the probable relationships between frailty, red blood cell parameters and inflammatory markers in 255 community-dwelling elders aged 65 years or older. The frailty phenotype was assessed by non-intentional weight loss, fatigue, low grip strength, low energy expenditure and reduced gait speed. Blood sample analyses were performed to determine hemoglobin level, hematocrit and reticulocyte count, as well as the inflammatory variables IL-6, IL-1ra and hsCRP. In the first multivariate analysis (model I), considering only the erythroid parameters, Hb concentration was a significant variable for both general frailty status and weight loss: a 1.0 g/dL drop in serum Hb concentration represented a 2.02-fold increase (CI 1.12–3.63) in an individual’s chance of being frail. In the second analysis (model II), which also included inflammatory cytokine levels, hsCRP was independently selected as a significant variable. Each additional year of age represented a 1.21-fold increase in the chance of being frail, and each 1-unit increase in serum hsCRP represented a 3.64-fold increase in the chance of having the frailty phenotype. In model II reticulocyte counts were associated with weight loss and reduced metabolic expenditure criteria. Our findings suggest that reduced Hb concentration, reduced RetAbs count and elevated serum hsCRP levels should be considered components of frailty, which in turn is correlated with sarcopenia, as evidenced by weight loss. ß 2014 Elsevier Ireland Ltd. All rights reserved.
Keywords: Frailty Elderly Anemia (red blood cell distribution width) RDW Reticulocytes
1. Introduction Because of the significant increase in the elderly population and consequent new demands for care, many studies have sought to establish effective, easily applicable criteria for identifying frail seniors (Morley, Kim, Haren, Kevorkian, & Banks, 2005; Morley et al., 2013; Walston et al., 2006). The operationalized frailty criteria based on strictly biological characteristics proposed by Fried et al. (2001) have been extensively used and corroborate the
* Corresponding author at: Rua Tessa´lia Vieira de Camargo, 126, Cidade Universita´ria Zeferino Vaz, Campinas, Sa˜o Paulo Zip Code: 13083-887, Brazil. Tel.: +55 19 35217878; fax: +55 19 35217878. E-mail addresses: [email protected], [email protected] (A. Fattori). http://dx.doi.org/10.1016/j.archger.2014.07.014 0167-4943/ß 2014 Elsevier Ireland Ltd. All rights reserved.
relationship between frailty and physical vulnerability, as well as decreased physiological reserves (Fried et al., 2001). In parallel, studies have shown the importance of anemia in the context of the elderly both because of its relationship with functional loss and reduced survival and because it has been identified as an additional factor in the development of the frailty phenotype (Artz, 2008; Bross, Soch, & Smith-Knuppel, 2010; Denny, Kuchibhatla, & Cohen, 2006; Landi et al., 2007; Penninx et al., 2004). In fact, frailty and anemia among older people appear to share a common pathophysiology associated with chronic inflammatory processes (Chang, Weiss, Xue, & Fried, 2012; Ferrucci & Balducci, 2008; Leng, Chaves, Koenig, & Walston, 2002; Maccio & Madeddu, 2012). Diseases associated with aging, such as atherosclerosis, diabetes, kidney disease, age-associated cardiovascular changes and osteo-degenerative diseases, progress with chronic inflammation and their clinical impact may be
J.C. Silva et al. / Archives of Gerontology and Geriatrics 59 (2014) 636–641
aggravated if they occur concomitantly with anemia (Ferrucci & Balducci, 2008). In light of this, it is difficult to establish a cause-and-effect relationship between frailty parameters and anemia, the downward-spiral model proposed by Fried being the most plausible for an understanding of these processes. The present study can be expected to help in the identification of probable relationships between frailty, red blood cell parameters and inflammatory markers, which, in the context of frailty, are equivalent to sarcopenia.
637
Assay Kit for automated analysis in an Immulite 1000 Immunoassay System (Erlanger, Germany) by chemiluminescent immunometric assay. hsCRP assays were carried out by nephelometry with the CardioPhase hsCRP commercial kit in a BN ProSpec system (Erlanger, Germany). IL-6 and IL-1RA were quantified with the Human IL-6 and IL-1RA/IL-1F3 Quantikine ELISA kits (R&D Systems, Minneapolis, USA). This work was supported by Fundac¸a˜o de Amparo a` Pesquisa do Estado de Sa˜o Paulo [2009/53113-1]. 2.2. Statistical analysis
2. Methods 2.1. Participants and methodology The study data were collected from a sample of residents in urban Campinas as part of the FIBRA multicenter population-based study. Before the data were collected, participants were informed of the objectives of the study and the type of data that would be collected. They signed a voluntary informed-consent form approved by the Committee for Ethics in Research at the School of Medical Sciences, State University of Campinas (Unicamp). The sample consisted of 255 community-dwelling elders recruited in their homes in various census areas for the municipality of Campinas, Brazil. The inclusion criteria were: being 65 years of age or older; being able to understand the instructions; agreeing to participate; and being a permanent resident at the address given in the last census. The recruiters were instructed to use the following exclusion criteria: having a clinical picture of dementia, as evidenced by problems related to memory, attention, spatial orientation, temporal orientation and communication; being bedridden or a wheelchair user; having severe sequelae of stroke, with localized loss of strength and/or aphasia; having advanced or unstable Parkinson’s disease with severe motor or speech impairments or negative affectivity; having severe auditory or visual deficits that made communication very difficult; and being an end-stage patient. Sociodemographic, anthropometric, frailty-criteria, blood-pressure and cognitive-screening measures were collected for all the study population. Poor cognitive performance, measured by MMSE, was used as an exclusion criterion for participation in a second stage involving the collection of the following measures: physical and mental health, functional capacity, care expectations and perceived social support (Folstein, Folstein, & McHugh, 1975). The frailty phenotype was determined using the criteria in the Cardiovascular Health Study and the Women’s Health and Aging Study (WHAS), which were described and validated in previous publications; the cut-off points for frailty variables adjusted for the Brazilian population were published previously by our group (Fattori, Santimaria, Alves, Guariento, & Neri, 2013; Fried et al., 2001). Blood samples were collected by venipuncture using sterile material and stored away from light in dry collection tubes and EDTA tubes. The samples were sent to the Hematology Laboratory at Unicamp University Hospital for complete blood counts and reticulocyte counts using flow cytometry and the SLS-hemoglobin method with an XE-2100 hematology analyzer (Sysmex Corporation, Kobe, Japan). The serum was centrifuged and frozen at -80 8C. The parameters used to define anemia were those proposed by the WHO (World Health Organization), i.e., the presence of anemia is indicated by a hemoglobin (Hb) level below 13 g/dL in men and 12 g/dL in women. Inflammatory cytokine assays were performed at the Physiology and Immunology Laboratory in the Department of Clinical Pathology, Hospital das Clı´nicas, Unicamp, and at the Metabolism Laboratory in the School of Medical Sciences, Unicamp. Serum erythropoietin was quantified with the EPO
To describe the profile of the sample in terms of the study variables, frequency tables showing the absolute frequency (n) and percentage (%) for each categorical variable (gender, age group, anemia, frailty and education) were drawn up, together with descriptive statistics (mean, standard deviation and minimum and maximum values) of the numeric variables (age, individual income and hematologic data). The categorical variables were compared using the chi-square test or Fisher’s exact test (for expected values less than 5). As the numeric variables did not have a normal distribution, the Mann– Whitney test was used to compare these between two groups, and the Kruskal–Wallis test was used for three or more groups. A significance level of 5% (p < 0.05) was used for all the statistical tests. Exploratory analyses were performed using PCA with orthogonal varimax rotation; the variables Hb, MCV, RDW, WBC, RetAbs, EPO, hsCRP, IL-6, IL-1ra and all frailty criteria were included in the model. The PCA components were selected when each of them explained at least 10% of the total variance. Univariate and multivariate stepwise logistic regression analysis of the data was also performed. Gender, age and BMI (body mass index) were defined as antecedent variables, and two analytical models were then developed: (I) a model using the number of comorbidities and red cell indices individually as independent variables and (II) a model that also included inflammatory cytokine and erythropoietin levels. In both models we used the frailty classification and each of the defining criteria as outcome, and a significance level of 5% (p < 0.05) was used with a 95% confidence interval.
3. Results Our findings show a greater prevalence of frailty among women (9.2%) than among men (2.4%) and a greater proportion of robust individuals in the male population (51.8%) than in the female population (39.6%). Anemia was more prevalent in women than in men, although the difference was not statistically significant (10.3% and 8.6%, respectively) (data not shown). As the main objective of this study was to compare red cell indices in frail and non-frail individuals and as physiological differences were expected between the genders, association analyses for each red cell parameter were carried out for the three frailty statuses and each criterion separately for males and females. Table 1 shows that there was a statistical association between weight loss in males and lower serum Hb levels as well as lower absolute reticulocyte count. This result was also observed among elderly females; however, unlike in men, low Hb levels in women were also associated with reduced grip strength and gait time, as well as with the general frailty classification (Table 2). Analysis of RDW, which measures the variation in red blood cell size, revealed an association between higher values of this parameter and elderly women who were frail according to the criteria fatigue and gait time as well as the frailty status per se.
J.C. Silva et al. / Archives of Gerontology and Geriatrics 59 (2014) 636–641
638
Table 1 Comparison of red cell parameters for seniors for each frailty criterion and frailty status (males, n = 82). FIBRA study. Mean SD
RBC
Hb
HCT
MCV
RDW
RetAbs
Negative Positive pa
4.77 0.32 4.79 0.66 0.553
14.48 0.86 13.63 0.77 0.003
42.98 2.25 41.55 2.25 0.064
90.33 3.92 87.79 8.82 0.757
13.53 0.69 13.48 0.64 0.782
54.99 16.91 44.87 10.51 0.055
Fatigue
Negative Positive pa
4.76 0.38 4.82 0.45 0.831
14.34 0.91 14.51 0.70 0.516
42.75 2.33 42.90 1.85 0.868
90.08 4.96 89.41 5.36 0.658
13.57 0.66 13.13 0.74 0.143
52.56 16.40 62.22 14.41 0.077
Reduced grip strength
Negative Positive pa
4.77 0.38 4.72 0.38 0.843
14.33 0.89 14.55 0.89 0.461
42.68 2.23 43.61 2.72 0.168
89.73 5.08 92.54 2.99 0.060
13.50 0.68 13.75 0.65 0.405
54.44 16.58 46.71 13.71 0.217
Reduced physical activity
Negative Positive pa
4.77 0.36 4.75 0.46 0.617
14.36 0.87 14.34 0.97 0.959
42.69 2.31 43.04 2.23 0.613
89.72 4.57 91.02 6.21 0.103
13.52 0.63 13.56 0.85 0.699
51.67 15.72 60.10 17.39 0.070
Gait speed
Negative Positive pa
4.76 0.38 4.81 0.38 0.530
14.30 0.88 14.70 0.93 0.131
42.60 2.28 43.81 2.08 0.064
89.80 5.10 91.33 4.01 0.335
13.53 0.69 13.50 0.61 0.548
53.10 16.68 56.84 14.76 0.402
Frailty
Robust Pre-frail Frail pb
4.73 0.30 4.83 0.44 4.31 0.40 0.191
14.38 0.88 14.35 0.91 13.80 1.13 0.710
42.55 2.28 43.11 2.25 41.05 3.04 0.269
90.01 4.03 89.72 5.89 95.30 1.70 0.165
13.55 0.60 13.51 0.71 13.25 0.49 0.746
51.67 16.06 55.85 16.82 50.20 19.06 0.586
Male Weight loss
a b
p value when the results for the frailty criteria (negative vs. positive) were compared using the Mann–Whitney test. p value when the variables for the three frailty groups were compared using the Kruskall–Wallis test. Significant differences (Dunn post hoc test, p < 0.05).
Table 2 Comparison of red cell parameters for seniors for each frailty criterion and frailty status (females, n = 173). FIBRA study. Mean SD
RBC
Hb
HCT
MCV
RDW
RetAbs
Negative Positive pa
4.50 0.36 4.33 0.39 0.032
13.34 1.02 12.86 1.17 0.103
40.23 2.90 39.07 3.27 0.197
89.49 4.36 90.36 4.28 0.272
13.56 0.82 13.76 1.27 0.783
56.66 15.14 46.00 15.22 0.025
Fatigue
Negative Positive pa
4.44 0.36 4.65 0.41 0.024
13.23 1.07 13.55 0.98 0.195
39.92 3.05 40.97 2.58 0.122
89.96 4.18 88.26 5.02 0.150
13.53 0.91 13.90 0.82 0.014
51.27 15.39 50.90 14.14 0.954
Reduced grip strength
Negative Positive pa
4.50 0.38 4.34 0.34 0.025
13.36 1.05 12.83 1.08 0.019
40.28 3.00 38.99 3.01 0.041
89.61 4.61 89.87 2.71 0.908
13.57 0.84 13.73 1.16 0.680
51.01 15.26 5.43 14.81 0.382
Reduced physical activity
Negative Positive pa
4.50 0.39 4.37 0.33 0.082
13.34 1.04 12.99 1.13 0.062
40.22 2.98 39.48 3.165 0.182
89.50 4.44 90.32 3.81 0.401
13.54 0.82 13.78 1.14 0.409
50.84 14.83 53.26 16.31 0.344
Gait speed
Negative Positive pa
4.50 0.38 4.40 0.37 0.122
13.38 1.03 12.89 1.13 0.012
40.29 2.92 39.32 3.26 0.126
89.80 4.43 89.32 3.95 0.830
13.49 0.38 13.95 1.16 0.010
51.36 15.12 51.41 15.46 0.934
Frailty
Robust Pre-frail Frail pb
4.50 0.37 4.48 0.39 4.29 0.35 0.113
13.40 1.00 13.30 1.07 12.46 1.09 0.017 (A)
40.38 2.99 40.11 2.93 38.38 3.39 0.139
89.81 4.06 89.64 4.70 89.39 3.18 0.754
13.43 0.78 13.62 0.82 14.24 1.43 0.024 (B)
52.53 15.26 50.70 15.05 50.11 15.97 0.556
Female Weight loss
a
p value when the results for the frailty criteria (negative vs. positive) were compared using the Mann–Whitney test. p value when the variables for the three frailty groups were compared using the Kruskall-Wallis test. Significant differences (Dunn post hoc test, p < 0.05: (A) nonfrail 6¼ frail, pre-frail 6¼ frail, (B) non-frail 6¼ frail). b
The PCA model accounted for 57.17% of the variance and was composed of a component formed by WBC, RetAbs, EPO, hsCRP, IL1ra and IL-6, all directly related (inflammatory component); the second component was formed by males with a positive correlation with Hb and EPO (predictable physiological component); the third by age, which inversely correlated with Hb and RetAbs and directly correlated with the presence of weight loss, EPO and reduced gait speed (sarcopenia component); the fourth by age with a direct correlation with RetAbs, impairment of grip strength and decreased physical activity; and finally, a component consisting of the direct correlation between IL-6, IL-1ra and the presence of fatigue.
The data were subjected to univariate and multivariate analysis using stepwise selection of the variables to correct for confounding factors. In the univariate analysis, female gender and age were statistically associated with frailty. Univariate analysis (Table 3) of red cell indices showed that the presence of anemia (as a categorical variable based on the WHO classification) and parameters related to the red blood cell population (expressed numerically as serum Hb, absolute number of red blood cells and RDW) were also associated with the presence of frailty. In multivariate regression model I (Table 4), which included only red cell indices, the general frailty classification was related to age and serum Hb level. Absolute reticulocyte count (RetAbs) was
J.C. Silva et al. / Archives of Gerontology and Geriatrics 59 (2014) 636–641 Table 3 Univariate analysis of variables of interest associated with frailty. FIBRA Study (n = 255).
Table 5 Multivariate analysis of variables of interest associated with frailty status (Model II). FIBRA Study (n = 255).
Variable
ORa
(95% CI OR)
p value*
Variable
Female gender Age RBC Hb HTC MCV RDW RetAbs Serum EPO Serum hsCRP Serum IL-1RA Serum IL-6
4.94 0.84 0.13 0.40 0.77 1.00 2.15 0.99 1.01 4.83 1.00 1.02
1.081–22.580 0.744–0.970 0.032–0.542 0.259–0.650 0.660–0.913 0.900–1.127 1.263–3.683 0.958–1.026 0.998–1.034 1.776–13.15 1.000–1.003 0.910–1.143
0.039
Contents lists available at ScienceDirect
Archives of Gerontology and Geriatrics journal homepage: www.elsevier.com/locate/archger
Understanding red blood cell parameters in the context of the frailty phenotype: interpretations of the FIBRA (Frailty in Brazilian Seniors) study Joa˜o Carlos Silva a, Ze´lia Vieira de Moraes a, Conceic¸a˜o Aparecida da Silva a, Silvia de Barros Mazon b, Maria Elena Guariento a, Anita Liberalesso Neri a, Andre´ Fattori a,* a b
Department of Internal Medicine, Faculty of Medical Sciences, Campinas State University-Unicamp, Brazil Department of Clinical Pathology, Faculty of Medical Sciences, Campinas State University-Unicamp, Brazil
A R T I C L E I N F O
A B S T R A C T
Article history: Received 14 September 2013 Received in revised form 23 July 2014 Accepted 25 July 2014 Available online 14 August 2014
Frailty and anemia in the elderly appear to share a common pathophysiology associated with chronic inflammatory processes. This study uses an analytical, cross-sectional, population-based methodology to investigate the probable relationships between frailty, red blood cell parameters and inflammatory markers in 255 community-dwelling elders aged 65 years or older. The frailty phenotype was assessed by non-intentional weight loss, fatigue, low grip strength, low energy expenditure and reduced gait speed. Blood sample analyses were performed to determine hemoglobin level, hematocrit and reticulocyte count, as well as the inflammatory variables IL-6, IL-1ra and hsCRP. In the first multivariate analysis (model I), considering only the erythroid parameters, Hb concentration was a significant variable for both general frailty status and weight loss: a 1.0 g/dL drop in serum Hb concentration represented a 2.02-fold increase (CI 1.12–3.63) in an individual’s chance of being frail. In the second analysis (model II), which also included inflammatory cytokine levels, hsCRP was independently selected as a significant variable. Each additional year of age represented a 1.21-fold increase in the chance of being frail, and each 1-unit increase in serum hsCRP represented a 3.64-fold increase in the chance of having the frailty phenotype. In model II reticulocyte counts were associated with weight loss and reduced metabolic expenditure criteria. Our findings suggest that reduced Hb concentration, reduced RetAbs count and elevated serum hsCRP levels should be considered components of frailty, which in turn is correlated with sarcopenia, as evidenced by weight loss. ß 2014 Elsevier Ireland Ltd. All rights reserved.
Keywords: Frailty Elderly Anemia (red blood cell distribution width) RDW Reticulocytes
1. Introduction Because of the significant increase in the elderly population and consequent new demands for care, many studies have sought to establish effective, easily applicable criteria for identifying frail seniors (Morley, Kim, Haren, Kevorkian, & Banks, 2005; Morley et al., 2013; Walston et al., 2006). The operationalized frailty criteria based on strictly biological characteristics proposed by Fried et al. (2001) have been extensively used and corroborate the
* Corresponding author at: Rua Tessa´lia Vieira de Camargo, 126, Cidade Universita´ria Zeferino Vaz, Campinas, Sa˜o Paulo Zip Code: 13083-887, Brazil. Tel.: +55 19 35217878; fax: +55 19 35217878. E-mail addresses: [email protected], [email protected] (A. Fattori). http://dx.doi.org/10.1016/j.archger.2014.07.014 0167-4943/ß 2014 Elsevier Ireland Ltd. All rights reserved.
relationship between frailty and physical vulnerability, as well as decreased physiological reserves (Fried et al., 2001). In parallel, studies have shown the importance of anemia in the context of the elderly both because of its relationship with functional loss and reduced survival and because it has been identified as an additional factor in the development of the frailty phenotype (Artz, 2008; Bross, Soch, & Smith-Knuppel, 2010; Denny, Kuchibhatla, & Cohen, 2006; Landi et al., 2007; Penninx et al., 2004). In fact, frailty and anemia among older people appear to share a common pathophysiology associated with chronic inflammatory processes (Chang, Weiss, Xue, & Fried, 2012; Ferrucci & Balducci, 2008; Leng, Chaves, Koenig, & Walston, 2002; Maccio & Madeddu, 2012). Diseases associated with aging, such as atherosclerosis, diabetes, kidney disease, age-associated cardiovascular changes and osteo-degenerative diseases, progress with chronic inflammation and their clinical impact may be
J.C. Silva et al. / Archives of Gerontology and Geriatrics 59 (2014) 636–641
aggravated if they occur concomitantly with anemia (Ferrucci & Balducci, 2008). In light of this, it is difficult to establish a cause-and-effect relationship between frailty parameters and anemia, the downward-spiral model proposed by Fried being the most plausible for an understanding of these processes. The present study can be expected to help in the identification of probable relationships between frailty, red blood cell parameters and inflammatory markers, which, in the context of frailty, are equivalent to sarcopenia.
637
Assay Kit for automated analysis in an Immulite 1000 Immunoassay System (Erlanger, Germany) by chemiluminescent immunometric assay. hsCRP assays were carried out by nephelometry with the CardioPhase hsCRP commercial kit in a BN ProSpec system (Erlanger, Germany). IL-6 and IL-1RA were quantified with the Human IL-6 and IL-1RA/IL-1F3 Quantikine ELISA kits (R&D Systems, Minneapolis, USA). This work was supported by Fundac¸a˜o de Amparo a` Pesquisa do Estado de Sa˜o Paulo [2009/53113-1]. 2.2. Statistical analysis
2. Methods 2.1. Participants and methodology The study data were collected from a sample of residents in urban Campinas as part of the FIBRA multicenter population-based study. Before the data were collected, participants were informed of the objectives of the study and the type of data that would be collected. They signed a voluntary informed-consent form approved by the Committee for Ethics in Research at the School of Medical Sciences, State University of Campinas (Unicamp). The sample consisted of 255 community-dwelling elders recruited in their homes in various census areas for the municipality of Campinas, Brazil. The inclusion criteria were: being 65 years of age or older; being able to understand the instructions; agreeing to participate; and being a permanent resident at the address given in the last census. The recruiters were instructed to use the following exclusion criteria: having a clinical picture of dementia, as evidenced by problems related to memory, attention, spatial orientation, temporal orientation and communication; being bedridden or a wheelchair user; having severe sequelae of stroke, with localized loss of strength and/or aphasia; having advanced or unstable Parkinson’s disease with severe motor or speech impairments or negative affectivity; having severe auditory or visual deficits that made communication very difficult; and being an end-stage patient. Sociodemographic, anthropometric, frailty-criteria, blood-pressure and cognitive-screening measures were collected for all the study population. Poor cognitive performance, measured by MMSE, was used as an exclusion criterion for participation in a second stage involving the collection of the following measures: physical and mental health, functional capacity, care expectations and perceived social support (Folstein, Folstein, & McHugh, 1975). The frailty phenotype was determined using the criteria in the Cardiovascular Health Study and the Women’s Health and Aging Study (WHAS), which were described and validated in previous publications; the cut-off points for frailty variables adjusted for the Brazilian population were published previously by our group (Fattori, Santimaria, Alves, Guariento, & Neri, 2013; Fried et al., 2001). Blood samples were collected by venipuncture using sterile material and stored away from light in dry collection tubes and EDTA tubes. The samples were sent to the Hematology Laboratory at Unicamp University Hospital for complete blood counts and reticulocyte counts using flow cytometry and the SLS-hemoglobin method with an XE-2100 hematology analyzer (Sysmex Corporation, Kobe, Japan). The serum was centrifuged and frozen at -80 8C. The parameters used to define anemia were those proposed by the WHO (World Health Organization), i.e., the presence of anemia is indicated by a hemoglobin (Hb) level below 13 g/dL in men and 12 g/dL in women. Inflammatory cytokine assays were performed at the Physiology and Immunology Laboratory in the Department of Clinical Pathology, Hospital das Clı´nicas, Unicamp, and at the Metabolism Laboratory in the School of Medical Sciences, Unicamp. Serum erythropoietin was quantified with the EPO
To describe the profile of the sample in terms of the study variables, frequency tables showing the absolute frequency (n) and percentage (%) for each categorical variable (gender, age group, anemia, frailty and education) were drawn up, together with descriptive statistics (mean, standard deviation and minimum and maximum values) of the numeric variables (age, individual income and hematologic data). The categorical variables were compared using the chi-square test or Fisher’s exact test (for expected values less than 5). As the numeric variables did not have a normal distribution, the Mann– Whitney test was used to compare these between two groups, and the Kruskal–Wallis test was used for three or more groups. A significance level of 5% (p < 0.05) was used for all the statistical tests. Exploratory analyses were performed using PCA with orthogonal varimax rotation; the variables Hb, MCV, RDW, WBC, RetAbs, EPO, hsCRP, IL-6, IL-1ra and all frailty criteria were included in the model. The PCA components were selected when each of them explained at least 10% of the total variance. Univariate and multivariate stepwise logistic regression analysis of the data was also performed. Gender, age and BMI (body mass index) were defined as antecedent variables, and two analytical models were then developed: (I) a model using the number of comorbidities and red cell indices individually as independent variables and (II) a model that also included inflammatory cytokine and erythropoietin levels. In both models we used the frailty classification and each of the defining criteria as outcome, and a significance level of 5% (p < 0.05) was used with a 95% confidence interval.
3. Results Our findings show a greater prevalence of frailty among women (9.2%) than among men (2.4%) and a greater proportion of robust individuals in the male population (51.8%) than in the female population (39.6%). Anemia was more prevalent in women than in men, although the difference was not statistically significant (10.3% and 8.6%, respectively) (data not shown). As the main objective of this study was to compare red cell indices in frail and non-frail individuals and as physiological differences were expected between the genders, association analyses for each red cell parameter were carried out for the three frailty statuses and each criterion separately for males and females. Table 1 shows that there was a statistical association between weight loss in males and lower serum Hb levels as well as lower absolute reticulocyte count. This result was also observed among elderly females; however, unlike in men, low Hb levels in women were also associated with reduced grip strength and gait time, as well as with the general frailty classification (Table 2). Analysis of RDW, which measures the variation in red blood cell size, revealed an association between higher values of this parameter and elderly women who were frail according to the criteria fatigue and gait time as well as the frailty status per se.
J.C. Silva et al. / Archives of Gerontology and Geriatrics 59 (2014) 636–641
638
Table 1 Comparison of red cell parameters for seniors for each frailty criterion and frailty status (males, n = 82). FIBRA study. Mean SD
RBC
Hb
HCT
MCV
RDW
RetAbs
Negative Positive pa
4.77 0.32 4.79 0.66 0.553
14.48 0.86 13.63 0.77 0.003
42.98 2.25 41.55 2.25 0.064
90.33 3.92 87.79 8.82 0.757
13.53 0.69 13.48 0.64 0.782
54.99 16.91 44.87 10.51 0.055
Fatigue
Negative Positive pa
4.76 0.38 4.82 0.45 0.831
14.34 0.91 14.51 0.70 0.516
42.75 2.33 42.90 1.85 0.868
90.08 4.96 89.41 5.36 0.658
13.57 0.66 13.13 0.74 0.143
52.56 16.40 62.22 14.41 0.077
Reduced grip strength
Negative Positive pa
4.77 0.38 4.72 0.38 0.843
14.33 0.89 14.55 0.89 0.461
42.68 2.23 43.61 2.72 0.168
89.73 5.08 92.54 2.99 0.060
13.50 0.68 13.75 0.65 0.405
54.44 16.58 46.71 13.71 0.217
Reduced physical activity
Negative Positive pa
4.77 0.36 4.75 0.46 0.617
14.36 0.87 14.34 0.97 0.959
42.69 2.31 43.04 2.23 0.613
89.72 4.57 91.02 6.21 0.103
13.52 0.63 13.56 0.85 0.699
51.67 15.72 60.10 17.39 0.070
Gait speed
Negative Positive pa
4.76 0.38 4.81 0.38 0.530
14.30 0.88 14.70 0.93 0.131
42.60 2.28 43.81 2.08 0.064
89.80 5.10 91.33 4.01 0.335
13.53 0.69 13.50 0.61 0.548
53.10 16.68 56.84 14.76 0.402
Frailty
Robust Pre-frail Frail pb
4.73 0.30 4.83 0.44 4.31 0.40 0.191
14.38 0.88 14.35 0.91 13.80 1.13 0.710
42.55 2.28 43.11 2.25 41.05 3.04 0.269
90.01 4.03 89.72 5.89 95.30 1.70 0.165
13.55 0.60 13.51 0.71 13.25 0.49 0.746
51.67 16.06 55.85 16.82 50.20 19.06 0.586
Male Weight loss
a b
p value when the results for the frailty criteria (negative vs. positive) were compared using the Mann–Whitney test. p value when the variables for the three frailty groups were compared using the Kruskall–Wallis test. Significant differences (Dunn post hoc test, p < 0.05).
Table 2 Comparison of red cell parameters for seniors for each frailty criterion and frailty status (females, n = 173). FIBRA study. Mean SD
RBC
Hb
HCT
MCV
RDW
RetAbs
Negative Positive pa
4.50 0.36 4.33 0.39 0.032
13.34 1.02 12.86 1.17 0.103
40.23 2.90 39.07 3.27 0.197
89.49 4.36 90.36 4.28 0.272
13.56 0.82 13.76 1.27 0.783
56.66 15.14 46.00 15.22 0.025
Fatigue
Negative Positive pa
4.44 0.36 4.65 0.41 0.024
13.23 1.07 13.55 0.98 0.195
39.92 3.05 40.97 2.58 0.122
89.96 4.18 88.26 5.02 0.150
13.53 0.91 13.90 0.82 0.014
51.27 15.39 50.90 14.14 0.954
Reduced grip strength
Negative Positive pa
4.50 0.38 4.34 0.34 0.025
13.36 1.05 12.83 1.08 0.019
40.28 3.00 38.99 3.01 0.041
89.61 4.61 89.87 2.71 0.908
13.57 0.84 13.73 1.16 0.680
51.01 15.26 5.43 14.81 0.382
Reduced physical activity
Negative Positive pa
4.50 0.39 4.37 0.33 0.082
13.34 1.04 12.99 1.13 0.062
40.22 2.98 39.48 3.165 0.182
89.50 4.44 90.32 3.81 0.401
13.54 0.82 13.78 1.14 0.409
50.84 14.83 53.26 16.31 0.344
Gait speed
Negative Positive pa
4.50 0.38 4.40 0.37 0.122
13.38 1.03 12.89 1.13 0.012
40.29 2.92 39.32 3.26 0.126
89.80 4.43 89.32 3.95 0.830
13.49 0.38 13.95 1.16 0.010
51.36 15.12 51.41 15.46 0.934
Frailty
Robust Pre-frail Frail pb
4.50 0.37 4.48 0.39 4.29 0.35 0.113
13.40 1.00 13.30 1.07 12.46 1.09 0.017 (A)
40.38 2.99 40.11 2.93 38.38 3.39 0.139
89.81 4.06 89.64 4.70 89.39 3.18 0.754
13.43 0.78 13.62 0.82 14.24 1.43 0.024 (B)
52.53 15.26 50.70 15.05 50.11 15.97 0.556
Female Weight loss
a
p value when the results for the frailty criteria (negative vs. positive) were compared using the Mann–Whitney test. p value when the variables for the three frailty groups were compared using the Kruskall-Wallis test. Significant differences (Dunn post hoc test, p < 0.05: (A) nonfrail 6¼ frail, pre-frail 6¼ frail, (B) non-frail 6¼ frail). b
The PCA model accounted for 57.17% of the variance and was composed of a component formed by WBC, RetAbs, EPO, hsCRP, IL1ra and IL-6, all directly related (inflammatory component); the second component was formed by males with a positive correlation with Hb and EPO (predictable physiological component); the third by age, which inversely correlated with Hb and RetAbs and directly correlated with the presence of weight loss, EPO and reduced gait speed (sarcopenia component); the fourth by age with a direct correlation with RetAbs, impairment of grip strength and decreased physical activity; and finally, a component consisting of the direct correlation between IL-6, IL-1ra and the presence of fatigue.
The data were subjected to univariate and multivariate analysis using stepwise selection of the variables to correct for confounding factors. In the univariate analysis, female gender and age were statistically associated with frailty. Univariate analysis (Table 3) of red cell indices showed that the presence of anemia (as a categorical variable based on the WHO classification) and parameters related to the red blood cell population (expressed numerically as serum Hb, absolute number of red blood cells and RDW) were also associated with the presence of frailty. In multivariate regression model I (Table 4), which included only red cell indices, the general frailty classification was related to age and serum Hb level. Absolute reticulocyte count (RetAbs) was
J.C. Silva et al. / Archives of Gerontology and Geriatrics 59 (2014) 636–641 Table 3 Univariate analysis of variables of interest associated with frailty. FIBRA Study (n = 255).
Table 5 Multivariate analysis of variables of interest associated with frailty status (Model II). FIBRA Study (n = 255).
Variable
ORa
(95% CI OR)
p value*
Variable
Female gender Age RBC Hb HTC MCV RDW RetAbs Serum EPO Serum hsCRP Serum IL-1RA Serum IL-6
4.94 0.84 0.13 0.40 0.77 1.00 2.15 0.99 1.01 4.83 1.00 1.02
1.081–22.580 0.744–0.970 0.032–0.542 0.259–0.650 0.660–0.913 0.900–1.127 1.263–3.683 0.958–1.026 0.998–1.034 1.776–13.15 1.000–1.003 0.910–1.143
0.039
