Background
The flu is a contagious respiratory illness caused by influenza viruses that infects the nose, throat, and lungs causing mild to severe illness. Seasonal influenza still causes significant morbidity and mortality in older adults [
1,
2]. The best preventive strategy generally accepted worldwide is a yearly influenza vaccination [
3]. However, this preventive strategy is not equally effective across the lifespan and therefore it is still debated in older adults beyond age 65 [
4‐
6]. A large scale retrospective review of influenza vaccine studies performed by Goodwin et al. concluded that influenza-specific antibody responses were reduced in older compared to young adults [
7]. However, Mosterin Hopping et al. suggest that the age-associated decline in antibody responses may be an effect of repeated annual influenza vaccination rather than age [
8]. In addition to age, multiple chronic conditions, age-related gradual functional decline of the immune system (termed immunosenescence) and frailty may also contribute to the risk of influenza [
9‐
11]. Diminished specific antibody responses observed in older persons may increase the risk of infections and thereby limit the effectiveness of vaccines (i.e. percentage reduction of disease in a vaccinated group of people compared to an unvaccinated group) with fluctuating rates which can drop from 50% to 11% depending on the clinical settings, the vaccine formulation and the circulating virus strains [
12,
13]. Hence, despite the promotion of preventive measures including influenza vaccination, a typical influenza season usually results in a significant health care burden and in thousands of deaths particularly in vulnerable older persons. Between 1976 and 2007, an average of 23′000 influenza-associated deaths per year have been reported in United States in persons with underlying respiratory and circulatory causes, with 89% of fatal cases in adults over age 65 [
2]. Therefore, influenza associated morbidity and mortality remain a major public health challenge in western societies and they may further increase as a consequence of the demographic change we are facing with the population above 60 expected to double in size by 2050 [
14].
Noteworthy, the older population is heterogeneous in terms of its overall health condition comprising psycho-social and medical dimensions which may interfere with their immune competence and vaccine response. This heterogeneity complicates efforts that aim at the improvement of preventive strategies while the latter should be adapted specifically to the needs of sub-populations that are at risk of vaccination failure. Recently the presence or absence of frailty was proposed as a predictor of vaccine response which would be superior to chronological age as the frailty concept reflects the overall functional status of an older individual [
15,
16]. Frailty is a geriatric syndrome characterized by a cumulative decline in physiological functions that causes an increased vulnerability to internal and external stressors, e.g. infections [
17]. Two diagnostic tools have gained wide recognition among scientists and physicians so far, the first being the Frailty Index of Rockwood et al. [
18] and the second being the frailty criteria developed by Fried et al. [
19]. Interestingly, Ridda et al. showed that the Frailty Index was a good predictor of the immune response to pneumococcal vaccine in hospitalized older persons [
15]. Then, Yao et al. reported similar results in community-dwelling older adults during the 2007–2008 season for influenza vaccination applying the Fried frailty criteria for the first time [
16]. Moreover, it is also known that from year to year vaccine effectiveness as measured based on laboratory confirmed influenza cases in a randomized influenza vaccine trial, may fluctuate particularly in older persons. This variability in vaccine effectiveness when comparing different influenza seasons, is largely attributable to the circulating influenza subtype. It is frequently reported that vaccine effectiveness reaches lowest level for influenza A subtype H3N2 than influenza B subtype as shown in the last interim report estimates of 2016–17 seasonal influenza vaccine effectiveness from United States Centers for Disease Control and Prevention [
20]. We believe that additional studies are needed to validate the diagnosis of frailty and the intermediate state of pre-frailty with regard to its predictive power of vaccine response. The aim of the present study was to assess the relevance of frailty status based on the Fried frailty criteria on influenza vaccine antibody response during the 2014–2015 seasonal flu vaccine campaign in community-dwelling German older persons. Importantly, we also studied the relevance of individual frailty criteria on each virus subtype antibody titer.
Discussion
Previous studies have reported that frailty had a significant impact on influenza [
16] and pneumococcal vaccine responses [
15]. It has even been suggested that the frailty status could be a stronger predictor of vaccine response than age [
16]. In the present study, our data have not provided evidence for a weaker antibody response after influenza vaccination in frail individuals when compared to prefrail individuals. Nevertheless, our study corroborates recent results drawn from secondary outcomes of larger vaccine studies where the frailty criteria of Rockwood were slightly adapted and applied [
21,
22]. In a large-scale efficacy and immunogenicity trial of standard-dose versus high-dose influenza vaccines DiazGranados et al. did not find an interaction between frailty status and HAI vaccine antibody titers [
21]. In addition, the frailty status did not influence the incidence of influenza-like illnesses in older adults above 65 years of age [
21]. Talbot et al. analyzed whether frailty status confounds influenza vaccine effectiveness estimates in a so-called case positive test-negative study design [
22]. Adults above 50 years of age hospitalized with respiratory symptoms between 2006 and 2012 where tested for influenza, assessed for frailty and asked for their vaccination history. In this study despite a higher prevalence of hospitalizations for respiratory symptoms in prefrail and frail individuals, influenza vaccine effectiveness estimates were not significantly different between frail and non-frail older persons. The authors concluded that frailty was not a significant confounder in vaccine effectiveness studies. Neither the present study that applied the Fried frailty criteria nor previous studies using the Frailty Index of Rockwood found a significant impact of the frailty syndrome on vaccine response based on measures of antibody titers and effectiveness based on incidence of influenza. Nevertheless, limitations in the study by Talbot et al. were discussed recently with regard to vaccine effectiveness estimates [
22]. Indeed, the applied strategy for statistical analysis was challenged based on the existing bias that frailty can still influence decision to vaccinate as well as risk of hospitalization and death from influenza [
23].
Our results also highlight the difficulty to discriminate immune competence between prefrail and frail individuals. Indeed, differentiating robust (no parameter), prefrail (1–2 parameters) and frail (3–5 parameters) persons may require additional standardization and possibly alternative approaches. Although the diagnosis of pre-frailty and frailty were not associated with vaccine response standard readouts as HAI titers, rate of seroconversion and seroprotection, we further analyzed potential associations with individual frailty criteria by exploring the immunogenicity of the influenza vaccine according to the participants’ baseline individual frailty criteria: weakness, slowness, low level of physical activity, self-reported exhaustion, and unintentional weight loss. We could show differences for associations of the different Fried criteria with the vaccine response. Our analysis suggests that among the five parameters used to diagnose frailty, low physical activity may be regarded as a relevant predictor of a weak humoral response to seasonal influenza vaccination in prefrail and frail individuals particularly for influenza B type and A (H3N2) subtype. Variability in vaccine effectiveness when comparing different influenza seasons is linked to the circulating influenza subtype. During H3N2 endemic winter seasons an increase in all-cause mortality is often observed. Viral epidemiology studies suggest that circulation of influenza virus, in particular with the virus type A subtype H3N2, is the main seasonal driver of excess mortality among the elderly [
24]. This has been shown in the United States [
25] and recently confirmed in Europe [
26]. Our observation would suggest that lack of exercise can be consider as a risk factor in elderly during H3N2 endemic winter season.
We know that exercise is a powerful preventive strategy for several aspects of health in older adults including the immune system (for review [
27]). However, most of the knowledge in this field has been established for younger adults and athletes. It may also be suggested that older adults may benefit from physical exercise programs. Indeed, beyond general health improvement and reduction of frailty status, currently recommended exercise programs may also help to improve vaccine immunogenicity and reduce infection rates in older persons. In that line, a recent work of de Araujo et al. showed that exercise promotes strong and long-lasting immune responses to influenza vaccine in older persons [
28].
We should acknowledge some important limitations to our study. It is worth notice that in many studies, an important confounder like nutritional status was not systematically assessed and this may explain different outcomes. In our study, malnutrition was absent according to the MNA. It may be suggested that we enrolled a population that may be more immunocompetent than the prefrail and frail populations that were included in other studies. Moreover, it should also be noticed that the participants of the present study were drawn from regions with high vaccination coverage. This may be relevant as a recent report from Mosterin Hopping et al. showed that numbers of vaccinations and age are confounders of vaccination effectiveness, suggesting that repetitive vaccinations mask the differences in influenza vaccine responses observed in aged population compared to younger adults [
8]. Of note, about 77% of our study participants had received a seasonal influenza vaccination the year prior to our trial. Also, due to their age range, it is reasonable to assume that they had multiple seasonal influenza shots and/or exposure to various influenza strains over the years prior to the current study. Indeed, the participants had relatively high pre-vaccination antibody titers and even seroprotection rates to the influenza vaccine strains, especially to the H1N1 and H3N2 strains. The respective percentages were very similar to the results reported in other studies with healthy older robust volunteers [
7]. These characteristics of our study participants may thus be regarded as a limitation of our study. To clarify this issue a study may be considered with robust, prefrail and frail participants who present with a naïve vaccination status. However, such a study may not be seen as realistic for Western European populations, nor ethical.
Our study included individuals with a well-defined diagnosis of frailty based on the Fried frailty criteria and the participants were community-dwelling older individuals. Classical phenotypical characteristics of frail subjects like reduction in quality of life, cognitive and mobility scores were observed as compared to prefrail subjects and can be attributed to age (with a mean of 3 year old difference) instead of frailty per se. However, it did not impact the vaccine response in our study. Also, we do not exclude that institutionalized elderly may behave differently regarding frailty status and vaccine response at the same age.
Unfortunately, we could not compare our results in prefrail and frail older individuals to results obtained in non-frail (i.e. robust) older adults as only individuals diagnosed as prefrail or frail were included in the present study which represent a sub-analysis of the placebo arm of an intervention trial. It is worth noticing that although Yao et al. observed some differences in influenza vaccine response between prefrail and frail individuals, the more consistent and significant changes were reported when the frail versus robust study participants were compared [
16]. Herein, frailty according to the Fried frailty criteria was overall not associated with reduction in vaccine response in community dwelling older persons. Thus, our results do not support the use of these criteria as a whole to predict influenza vaccine response.
Methods
Study design, ethics and trial registration
The dataset analyzed in the present article focuses exclusively on the participants who received the placebo treatment in a multi-center, prospective, randomized, double-blind, placebo-controlled, parallel clinical trial conducted in prefrail and frail older people ≥70 years of age in Germany. The full data set will be documented separately. The original trial was designed to demonstrate that, relative to placebo, supplementation with an investigational compound would improve immune response in prefrail/frail older persons. The study protocol was approved by the following German Ethics Committees: Muenster (Muenster), Saarland (Saarbruecken), Medical Chamber of Nordrhein (Duesseldorf), State Medical Chamber of Baden (Stuttgart), Geschäftsstelle (Berlin), and Ärztekammer Niedersachsen (Hannover). The trial was registered on
clinicaltrials.gov (Identifier: NCT02262091) and conducted according to the guidelines laid down in the Declaration of Helsinki. Written informed consent was obtained from all study participants after the nature and possible consequences of the studies had been fully explained and before they were screened for eligibility criteria.
Study participants
Prior to the influenza season of 2014–2015, community-dwelling older volunteers ≥70 years were recruited through adverts in print media and the internet, flyers in GPs offices and by word of mouth with their GP. Inclusion criteria were: prefrail and frail subjects, as determined by the Fried frailty criteria, who were 70 years and older, willing to get a seasonal influenza vaccination and who did not meet any of the exclusion criteria. Exclusion criteria included: rapid deteriorating health status, including terminal, severe or uncontrolled acute as well as chronic diseases (e.g. diabetes, carcinoma, renal diseases), allergy to eggs or influenza vaccine components, any vaccination in the last 3 months, current use of immune modulating medication (including use of steroids, immune suppressive treatment), use of antibiotics within last 2 months, regular consumption of prebiotics or probiotics, yogurts or other dietary supplements, blood transfusion or donation within the last 4 weeks, family history of Guillain-Barre syndrome, body mass index (BMI) > 35 kg/m2, likelihood to be non-compliant with study procedure, current participation or participation in another clinical trial in the previous 4 weeks. Screening also included a medical history (co-morbidities, allergies, medication, and influenza vaccination during the previous year), clinical examination (anthropometric measures, blood pressure, pulse rate), geriatric questionnaires (Mini-mental state examination: MMSE, short form of the mini nutritional assessment: MNA, instrumental activities of daily living: IADL, and quality of life questionnaire: EQ-5D-5L™) and routine laboratory tests (blood chemistry and hematology). Baseline characteristics were collected at enrollment.
Fried frailty criteria
Prefrail and frail older people were diagnosed according to the five criteria developed by Fried and colleagues [
19], i.e. weight loss, exhaustion, grip strength, walk time and physical activity. The cut-offs values applied for each criteria were the same as in the original study that defined the frailty phenotype [
19]. Participants were diagnosed as frail if three or more of these five criteria were assessed as positive and those with one or two positive criteria were considered to be prefrail [
19].
Influenza vaccination and measurement of specific antibody response
Subjects meeting eligibility criteria received a standard dose of a seasonal, trivalent, inactivated, split-virus influenza vaccine (Influsplit SSW® 2014–2015, Lot N°:AFLUA833CA) administrated by intramuscular injection according to the manufacturer’s recommendations at day 30 after the baseline visit. The 2014–2015 influenza seasonal vaccine contained 15 mcg of hemagglutinin of each of the following viral strains: A/California/7/2009 (H1N1), A/Texas/50/2012 (H3N2) and B/Massachusetts/2/2012. Blood samples were collected at the baseline visit (V0, day 0), at visit 1 (day 30) prior to administration of the vaccine, and at visit 2 (day 60). Sera were stored at −80 °C and sent on dry ice to the Pasteur Institute (Paris, France) for blinded analysis of vaccine-specific antibody titer measurements. Serum antibody titers against the three viral strains of the vaccine were measured by hemagglutination inhibition (HAI) assay with guinea pig red blood cells, according to standard protocols (Manuel for the laboratory diagnosis and virological surveillance of influenza, World Health Organization 2011). Titers which were <10 (below the detection limit) were expressed as 5, to represent half of the detection threshold. Three standard measures of vaccine response were studied for each vaccine strain: 1) geometric mean titer (GMT) of HAI antibodies, 2) seroconversion rate defined as percentage of subjects with an increase from <1:10 to ≥1:40 or a ≥ 4-fold increase in HAI titers, 3) seroprotection rate defined as percentage of subjects with HAI titers ≥40.
Statistical analysis
Data for demographic characteristics were expressed as means (± SD) for continuous variables and counts (%) for categorical variables. Three demographic data were shown to have statistically significant differences between the frail and prefrail subgroups; age, BMI and overall quality of life score. Change from baseline of HAI titer data were evaluated with the use of a linear model taking as fixed effects the baseline titer value and the frailty criteria. Logarithmic transformation was applied on the HAI titer data to produce the following model: log(HAIafter/HAIbefore) = Intercept +
β
1
log(HAIbefore) +
β
2
Frailty Status +
ε, where HAI
after
refers to the titer count after vaccination and HAI
before
refers to the titer count before vaccination. The intercept represents a general mean with the prefrail group as a reference and β
1
pertains to the coefficient of how baseline titer count impacts the dependent variable and β
2
on how the Frail group differs from the Prefrail group. The term log(HAIafter/HAIbefore) is equivalent to log(HAI
after
) – log(HAI
before
) via mathematical property of the logarithmic function. Seroprotection and seroconversion rates were analyzed using Fisher’s exact test since number of incidences in most cases are lower than 10. Both SAS ver. 9.3 and R ver. 3.2.3 were used for the statistical analyses. The subgroup of subjects belonging to the placebo group was big enough to detect a 0.7 or higher effect size (a difference in GMT ratio of 70% of the standard deviation between the frail and prefrail) with the conventional 80% power. No adjustment for multiplicity (adjustment of the type-I error; α) was applied to the statistical analyses mentioned above due to the exploratory nature of this subgroup analysis.
Acknowledgements
We are thankful to all the study participants and the staff from the eight participating sites who made this study possible. We acknowledge the sponsor’s study team for their dedication to the project. We are also thankful to Annick Mercenier for reviewing the manuscript.