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SIGNIFICANT PSYCHOSOCIAL INFLUENCE IN FRAIL PEOPLE LIVING WITH HIV INDEPENDENT OF FRAILTY INSTRUMENT USED

 

S.A. Abdul-Aziz1, M.L. Chong2,3, M. McStea2, P.L. Wong2,3, S. Ponnampalavanar2,3, I. Azwa2,3, A. Kamarulzaman2,3, S.B. Kamaruzzaman2,3, R. Rajasuriar2,3

 

1. Faculty of Pharmacy, National University of Malaysia (UKM), Kuala Lumpur, Malaysia; 2. Centre of Excellence for Research in AIDS (CERiA), University of Malaya, Kuala Lumpur, Malaysia; 3. Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

Corresponding Author: Reena Rajasuriar, Department of Medicine, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia; Phone no: +60 3 7967 6686; Email: reena@um.edu.my

J Frailty Aging 2021;in press
Published online December 6, 2021, http://dx.doi.org/10.14283/jfa.2021.48

 


Abstract

Background: Antiretroviral therapy (ART) usage among people living with HIV (PLWH) has led to significant mortality declines and increasing lifespan. However, high incidence and early onset of aging-related conditions such as frailty, pose as a new threat to this population.
Objectives: We aimed to characterize frailty by comparing health domains consisting of psychosocial, functional and physical deficits between frail PLWH and matched uninfected controls; identify associated risk factors and the impact on negative health outcomes including mortality risk score, quality of life, healthcare utilization, functional disability and history of falls among virally suppressed PLWH.
Design: Cross-sectional study
Setting: Infectious disease clinic in a tertiary institution
Participants: Individuals aged >25 years, on ART > 12 months, not pregnant and without acute illness; multi-ethnic, Asian
Measurements: Frailty instruments included Frailty phenotype (FP), FRAIL scale (FS) and Frailty index (FI). FI health deficits were categorized into health domains (psychosocial, functional and physical) and used as standard comparator to characterize frailty. Health domains of frail PLWH were compared with frail matched, uninfected controls. Regression analyses were applied to explore associated risk factors and health-related frailty outcomes.
Results: We recruited 336 PLWH. Majority were male (83%), Chinese (71%) with CD4+ count 561 (397-738) cells/µl. Frailty prevalence among PLWH were 7% (FP); 16% (FS) and 22% (FI). Proportions of psychosocial, functional, and physical domains were similarly distributed among frail PLWH measured by different frailty instruments. When compared with matched controls, psychosocial dominance was significant among the PLWH, but not in functional and physical domains. Identified frailty risk factors included poor nutritional status, higher CD4+ count nadir, depression, metabolic syndrome, higher highly sensitive C-reactive protein (hsCRP) and history of AIDS-defining illness (ADI). Frailty influenced the risk for negative health outcomes including increased mortality risk scores, poor quality of life (QOL), frequent healthcare utilization and increased functional disability (p<0.05).
Conclusions: This study highlighted the importance of psychosocial influence in the development of frailty among treated PLWH in a multi-ethnic, Asian setting.

Key words: Non-AIDS illness, non-communicable disease, frailty instrument, health domain, frailty characteristics.


 

Introduction

Viral suppression and immune reconstitution with ART usage has prolonged life expectancy among PLWH. However, this led to changes in health-related concerns of this population where non-communicable diseases and geriatric conditions are increasing, adding burden to the underlying HIV management. Evidence indicate an increased risk for mortality associated with age-related diseases such as cardiovascular disease, liver impairment and osteoporosis in PLWH compared to uninfected individuals (1). Furthermore, earlier occurrence of age-related conditions has been observed among PLWH compared to the general population (2). Geriatric syndromes such as frailty have been reported prevalent among middle-aged PLWH, suggesting that age-related functional decline may occur independent of chronological age in this population (3-5). These findings suggested that HIV infection may accentuate the biological aging process (6, 7).
It is generally understood that frailty is a geriatric syndrome characterized by a combination of lower strength and malfunction of multiple physiologic systems which exposes an individual to vulnerability, dependency and mortality [8]. In the general population, frailty is more often observed with advanced aging (>65 years) (9). However, many studies evaluating age-related diseases in the PLWH population considers 50 years as the cut-off age to indicate older individuals. This is in view that non-infectious, old-age diseases such as cardiovascular diseases, malignancies, cognitive decline, and geriatric syndromes are increasingly prevalent at chronologically younger PLWH receiving combination ART compared to uninfected individuals (10, 11). A total of 90% of PLWH aged 50 and older (mean age, 59 years) had at least one chronic, non-HIV related comorbidity (12).
Frailty research among PLWH mainly used the frailty phenotype instrument (13). Reported frailty prevalence (using Frailty phenotype) ranged from 6 to 19% (10, 14, 15). However, direct comparison of these studies is debatable due to differences in population background and cut-offs used to define frailty. To our knowledge, research on frailty among Asians with HIV has only recently been explored and is still lacking (14, 16). Further investigation is needed, particularly in our setting where HIV treatment is often initiated during advanced stages of the disease.
Frailty in HIV infection involves a complex interplay of biological and non-biological constructs (17) and may differ depending on the socioeconomic and geographic region studied (18, 19). HIV-related risk factors previously shown to be associated with aging conditions such as severe immunodeficiency, chronic immune activation and inflammation, chronic co-infections and exposure to adverse effects from older ART are common in resource-limited settings. This suggests that the characteristics, determinants and impact associated with frailty in PLWH are likely to differ across various economic and cultural regions.
Thus, our study objectives were to compare health domains characterizing frailty determined by multiple frailty instruments between frail PLWH and frail matched controls; identify associated risk factors and the impact of frailty on negative health outcomes in predominantly young PLWH receiving ART.

 

Methods

Study population

Study participants were recruited from the Malaysian HIV and Aging cohort, as previously described (5). Briefly, individuals on routine clinical follow-up fulfilling the following criteria were approached; age at least 25 years, on stable combination ART, with HIV RNA levels <50 copies/mL for at least 12 months, not pregnant and no acute illness at the point of recruitment. Uninfected participants of the same age range were recruited among attendees to community-based healthcare screening programmes, community-dwellers responding to study fliers, visitors to a tertiary institution and accompanying companions of the PLWH participants. All participants provided informed consent and the study protocol was approved by the institutional review board (MEC 20151-937).

 

Data collection

Enrolment started in January 2014 and ended in August 2016. All participants made two consecutive visits to a research clinic within 6 months of recruitment to complete all assessments. Each participant answered a structured questionnaire, underwent physical assessments, and had biochemical screening performed by trained personnel.
Demographics, history of smoking, alcohol consumption, comorbid conditions, current medications (prescription and non-prescription), healthcare utilization – indicated by the number of visits to any healthcare facility within the past 12 months, history of falls within the past 12 months and unintentional weight loss within the past year were self-reported. Validated questionnaires used to measure exhaustion, physical activity, nutritional status, social support, functional ability, quality of life and depression are listed in Supplementary Table S1.
Hospital records were accessed to obtain relevant clinical and HIV-related parameters including CD4+ count nadir, HIV RNA, history of ADI, treatment regimen since diagnosis and presence of other health conditions. Exposure to nucleoside reverse transcriptase inhibitors (NRTIs) previously shown to be associated with mitochondrial toxicity (didanosine, stavudine and zidovudine) were categorized as D-drugs (20).
Biochemical screening on peripheral blood sample included current CD4+ count, CD4+/CD8+ ratio, CMV serology (IgG), hsCRP, D-dimer, fasting glucose level, fasting lipid profile, full blood count, HbA1C, hepatitis C surface antigen (Ag), hepatitis C antibody, HIV RNA (viral load), insulin, liver function test, renal function test, rapid plasma regain (RPR), assay and Treponema pallidum particle agglutination (TPPA) for syphilis, serum homocysteine, thyroid function test, 25-hydroxy-vitamin D, kynurenine-to-tryptophan (KT) ratio and soluble CD14 (sCD14). Urine sample was tested for urine full examination microscopic examination (UFEME). Presence of hyperglycaemia, hyperlipidaemia, hypertension and obese waist circumference were included as criteria used to measure metabolic syndrome (21).
Physical assessments were performed to measure anthropometric measurements (height, weight, hip and waist circumference), blood pressure and electrocardiogram (ECG), walking speed (4.5 meters), grip strength using Jamar® Plus+ dynamometer (Sammons Preston, Rolyon, Bolingbrook, IL) and distribution of fat and muscle mass was measured using bioimpedance analyzer (Bodystat Ltd, Isle of Man, British Isles). Lowest 20th percentile of the HIV-uninfected controls was used as cut-offs to determine low values for walking speed, grip strength and muscle mass.

Frailty assessments

Definitions of three frailty instruments developed for the general elderly population were used as done in prior studies (22). They include the Frailty phenotype (FP) (23), FRAIL scale (FS) (8, 24) and Frailty index (FI) (25). Each frailty instrument measures frailty differently. The FP is a scoring system consisting of five physical-related components: exhaustion, unintentional weight loss, weakness (grip strength), slowness (walking speed) and low physical activity (Supplementary Table S2). Each present component scores as one (total = 5). The FRAIL scale (FS) consisted of five components: exhaustion, resistance, ambulation, illnesses (>5) and loss of weight (Supplementary Table S3). All components are self-reports which scores one point when present. The total scoring of FP and FS were similarly categorized as frail (score 3-5), pre-frail (score 1-2) and robust (score 0). The Frailty index (FI) is a ratio of presenting health deficits (Supplementary Table S4), where 35 items representing various deficits were considered (26). For ease of comparison with the two former frailty instruments, index score was stratified into categories of increasing severity, >0.21 (frail), 0.13 to 0.21 (pre-frail) and <0.13 robust ((27, 28). Gender-based lowest 20th percentile of grip strength and walking speed among age-comparable HIV-uninfected were established among all uninfected controls (N=277) recruited in this study.

Assessment of health domains characterizing frailty

To determine patterns of frailty characteristics in the total PLWH group, FI deficits were categorized into three health domains (physical, functional and psychosocial) (Supplementary Table S2) (29), FI was used as a standard comparator because deficits in the index reflected domains of health concerns in frailty. The proportions of accumulated FI deficits in each domain were identified and stratified against frailty in each instrument (30). To calculate proportions of deficits within each level of severity, the number of deficits accumulated in each domain was the numerator while the total number of accumulated deficits in all domains was used as the denominator. To establish differences in the prevalence of frailty and frailty domains between PLWH and uninfected controls, both groups were matched based on gender, ethnicity, and age within the nearest 1-year range (herein known as “matched subset”).

Assessment of negative health-related outcomes

Health-related outcomes included mortality risk scores, quantified using the Veterans Aging Cohort Study (VACS) index (31) where increasing scores denote increasing risk for all-cause mortality; QOL, assessed using four domains of control, autonomy, self-realisation and pleasure with Control, Autonomy, Self-realization and Pleasure-12 (CASP-12) (32) where lower scores depict lower quality of life; self-reported healthcare utilisation, indicated by the number of visits to any healthcare facility within the past 12 months; functional disability, defined as needing assistance in at least one of seven specific activities assessed using Lawton’s Instrumental Activities of Daily Living (IADL) (33); and self-reported history of falls within the past 12 months.

Assessment of frailty risk factors

Potential frailty risk factors were identified from evidence reported in the general elderly population as well as in PLWH (13, 34). These include self-reported socio-demographics (age, gender, ethnicity, education level, employment status, drinking status, smoking status, weight, waist-hip-ratio, social support, nutritional status and physical activity); HIV-related parameters (CD4+ count, duration of known HIV infection, history of ADI, duration on ART, types of ART received as treatment; biochemical markers (hsCRP, hydroxyvitamin D, total protein, gamma-glutamyl transferase (GGT), albumin, cytomegalovirus antibody (CMV IgG), hepatitis C, KT ratio and sCD14) and multimorbidities (defined as more than five non-communicable chronic disease), depression, diabetes, stroke, metabolic syndrome and hypertension).

Statistical analysis

Prior to comparing health domains in frailty between the three instruments, frailty prevalence was firstly identified. Consistency between instruments in determining frailty were compared using coefficient correlation. Secondly, FI was used as a standard comparator because deficits in the index reflected health concerns associated with frailty and allowed assessment of predominant factors. Thus, to determine patterns of frailty characteristics, we expressed proportions of accumulated FI deficits according to three categories of health-related frailty domains which were functional, physical, and psychosocial and stratified these against severities of frailty status identified by all three instruments. To calculate proportions of deficits within each level of severity, the number of deficits accumulated in each domain was set as the numerator while the total number of accumulated deficits in all domains was used as the denominator. Descriptively, all absolute values were reported as median (interquartile range, IQR). Risk factor analyses were applied only on FP and FS in view that FI was the standard comparator. Associations with risk factors and health outcomes were investigated using logistic and linear regression, respectively. Entry criterion for multivariate logistic regression was P-value less than 0.2. Regression for health outcomes were adjusted for age, gender and ethnicity except for mortality risk scores which was not adjusted for age given that this parameter is a component of the VACS Index. Models were constructed by stepwise regression for inclusion and exclusion at P value of 0.05, with consideration on interactions, collinearity, total prediction and over specification. Statistical analyses were conducted using StataSE 13 (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, Texas, USA: StataCorp LP).

 

Results

Characteristics of the study population

A total of 344 PLWH participants were recruited. Of these, 336 completed all assessments in the study and were included in the analysis. The majority were male (83%) and Chinese (71%) with a median age of 44 (37-51) years. CD4+ count nadir was 111 (35-246) cells/µl while current CD4+ count was 561 (397-738) cells/µl. The duration on ART was 6 (3-10) years with nearly half (44%) reporting history of ADI (Table 1). A total of 172 controls were paired with PLWH based on age, gender and ethnicity as matched subset to compare the prevalence of frailty and frailty domains between PLWH and uninfected controls. Characteristics of PLWH in the matched subset were comparable to the larger PLWH group.

Table 1. Comparison of characteristics among the PLWH participants and matched subsets

Abbreviations: BMI – Body mass index (*WHO categorization), MoCA – Montreal Cognitive Assessment, ADI – AIDS-defining illnesses, ART – antiretroviral therapy, D-drugs – Zidovudine, Didanosine & Stavudine, HDL – high-density lipoprotein, LDL – low-density lipoprotein, eGFR – estimated glomerular filtration rate, GGT – gamma-glutamyl transferase, CMV IgG – cytomegalovirus immunoglobulin, KT – Kynurenin-tryptophan, sCD14 – soluble; Symbols: # includes hyperglycemia, hyperlipidemia, hypertension & obese waist circumference; a. comparison between total PLWH and matched PLWH subset; b. comparison between matched PLWH and HIV-uninfected subset; $ – chronic, non-communicable diseases

 

Frailty identification among instruments used

Among the PLWH (N=336), frailty identified by each instrument were highest in FI (22%), followed by FS (16%) and least by FP (7%) while the percentage of pre-frail individuals were highest in FP (56%), followed by FS (53%) and least in FI (38%). In each instrument, a large percentage of frail individuals were younger than 50 years of age with 48% in FP; 61% in FS and 60% in FI. The FI scores among the PLWH were 0.14 (0.08 – 0.20). Consistency between frailty measurements as assessed by correlation coefficients were low with only fair to moderate correlations identified between instruments (FP vs FS, r=0.281, p<0.001; FP vs FI, r=0.241, p<0.001; FS vs FI, r=0.492, p<0.001). Only 10 PLWH were consistently identified as frail by all 3 instruments. In the matched subset, there were no differences in frailty prevalence between the paired groups (PLWH vs control) except in FI (15 (9%) vs 9 (5%), p=0.172 (FP); 36 (21%) vs 34 (20%), p=0.400 (FS) and 45 (26%) vs 22 (13%), p<0.001 (FI), respectively).

Significant psychosocial deficits among PLWH compared to controls

We established characteristics of frailty among frail PLWH according to three domains. In all three instruments, acquired physical, functional- and psychosocial-related domains were almost equally proportioned in frail individuals (Figure 1). Accumulation of psychosocial-related deficits was highest among those identified frail by FI (35%). Meanwhile, accumulation of functional-related deficits was highest among those determined by FS (35%) while physical related deficits were similar in proportion in all three instruments. Overall, health-related deficits in each domain were almost equally characterized among frail PLWH, reflecting comparable dominance between them. Next, similar investigations were applied on frail matched subset, comparing PLWH with controls (Figure 2). Overall, proportions of physical-related deficits were consistently higher than functional- and psychosocial-related deficits in both subset groups. Interestingly, proportions of psychosocial-related deficits were consistently higher among frail PLWH compared to frail controls regardless of frailty instrument used. These results collectively show that psychosocial factors largely characterize frailty among the PLWH compared to matched controls.

Figure 1. Distribution of health-related domains among frail PLWH

Figure 2. Comparison of health-related domains between frail PLWH* and matched controls*

Symbol: *Nfrail PLWH = 15 (Frailty phenotype), 36 (FRAIL Scale) and 45 (Frailty Index); Nfrail controls = 9 (Frailty phenotype), 34 (FRAIL scale) and 22 (Frailty index)).

 

Risk factors for frailty among PLWH

We explored associations between frailty with socio-demographics and clinical-related variables among the PLWH (n=336) for FP and FS (Table 2). Frailty assessed by FP was significantly associated with poor nutritional status (at risk of malnutrition and malnourished vs. normal; (OR 14.50, 95% CI 4.09-51.40) and (OR 2.29, 95% CI 1.11-4.72), respectively; P<0.001.), higher CD4+ count nadir (OR 1.38, 95% CI 1.12-1.69, P=0.002), depression (OR 2.00, 95% CI 1.18-3.39, P=0.01) and metabolic syndrome (OR 1.97, 95% CI 1.09-3.58, P=0.026). In contrast, frailty assessed by FS was significantly associated with higher hsCRP levels (OR 1.06 95% CI 1.02-1.09, P=0.001) and history of AIDS-defining illnesses (OR 1.26 95% CI 0.80-1.97, P=0.001).

Table 2. Risk factors and negative health outcomes related to frailty among PLWH (N=366)

Abbreviations: BMI – Body mass index (*WHO categorization), MoCA – Montreal Cognitive Assessment, ADI – AIDS-defining illnesses, ART –antiretroviral therapy, D-drugs – Zidovudine, Didanosine & Stavudine, GGT – gamma-glutamyl transferase, CMV IgG – cytomegalovirus immunoglobulin, KT – Kynurenin-tryptophan, sCD14 – soluble; Symbols: £Linear regression #includes hyperglycemia, hyperlipidemia, hypertension and obese waist circumference $chronic, non-communicable diseases; &Logistic regression. Notes: Multivariate model for FP was adjusted for age, gender and ethnicity; P-value level of significance is p<0.05.

 

Negative health outcomes associated with frailty among PLWH

The relationship between negative health outcomes and frailty was assessed among the PLWH (Table 2). Frailty in both FP and FS were independently associated with increasing mortality risk scores (Coef. 2.55, 95% CI 0.24-4.87, P=0.031 and Coef. 2.30, 95% CI 0.06-4.53, P=0.044), respectively, and utilization of healthcare (Coef. 0.99, 95% CI 0.04-1.94, P=0.041 and Coef. 1.50, 95% CI 0.74-2.25, P<0.001), respectively. Other associated negative health outcomes identified include poor quality of life (FP), (Coef. -1.92, 95% CI (-2.99) -(-0.85), P<0.001) and functional disability (FS), (Coef. 1.80, 95% CI 1.12-2.90, P=0.015).

 

Discussion

Frailty, a condition common to the elderly, was prevalent in this predominantly young PLWH study, comparable to previous findings from various settings (14, 15, 30). To our knowledge, this is the first study to compare frailty instruments among virally suppressed PLWH receiving treatment in a developing country with multi-ethnic background. Of clinical significance is the identification of consistent psychosocial influence among frail PLWH compared to uninfected controls. Our findings imply that current interventions established in the general elderly population for frailty may not be optimal for the HIV setting.
Psychosocial factors significantly characterized frail PLWH in this study, regardless of frailty instrument used, as observed in larger proportions of psychosocial deficits in matched PLWH compared to matched controls. These psychosocial deficits include depression, stress, anxiety, declining memory status and lack of social support. In addition, depression and trends of social isolation among frail PLWH were higher compared to non-frail individuals in this study. This implies that psychosocial well-being of PLWH in this setting were distinctly compromised. It is established that cognitive issues and lack of social support are interlinked with HIV infection, which is further influenced by social stigma and discrimination (35-37). The burden of these factors among PLWH population supports our finding that psychosocial factors influence the risk for frailty. Interestingly, frailty characterized in our study presented differently than that reported elsewhere (28). Thus, we believe external factors may play a large role in aggravating frailty risk. Examples of external factors include lack of facilities providing social support such as community counselling or helpline, cultural influences, religious believes and stigma (38, 39). These factors were not investigated here, thus, further exploration is warranted to develop appropriate interventional approach against frailty among those at risk.
At least half of those found frail in our study were younger than 50 years of age (48-61% depending on frailty instrument). However, there were no significant difference in frailty prevalence between matched PLWH and controls subsets. We postulate this was influenced by relatively poor health status of the controls who displayed high percentages for multimorbidity, overweight and wide waist circumference, exaggerating the risk for frailty in this group. Indeed, Malaysia reported among the highest obesity rates (10%) relative to other populations living in the Asian and Southeast Asian region (2-11%) (40-43). Weight-related metabolic syndrome is a frailty risk factor among our PLWH participants. Worldwide, the prevalence of metabolic syndrome is increasing due to the growth of unhealthy or unbalanced diet and sedentary lifestyle. Even though studies on metabolic syndrome among PLWH in Asia is limited, the presence of several clinical abnormalities rendering higher risk for diabetes mellitus and cardiovascular diseases, have all been reported prevalent among PLWH in this region (21). These clinical abnormalities include abnormal waist circumference, dyslipidaemia, hypertension and raised fasting plasma glucose. Adipose tissue is a dynamic source of pro-inflammatory markers (44) and a two-way relationship is known to exist between inflammation and immune activation (45). Consistently, evidence from the general elderly population suggest that metabolic syndrome moderates the relationship between frailty and cognitive function [46]. Furthermore, ART are also associated with metabolic derangements including dyslipidaemia and fat redistribution (47).
Additionally, an interplay of various other factors is suggested to be involved in the pathophysiology of HIV-related metabolic abnormalities, including HIV-induced immune activation, genetics and lifestyle factors (48). Higher levels of the clinical inflammatory marker, hsCRP, was observed among our PLWH participants compared to controls. This is congruent with previous evidence linking frailty and metabolic disruption as well as immune activation in older PLWH men aged 59 (56 – 65 years) (30).
Interestingly, both nutritional related conditions i.e. malnutrition and metabolic syndrome, were identified as risk factors for frailty in our study. Each condition represents distinct pathogenic processes that can lead to frailty in the general elderly population (49). However, it is unknown how HIV infection interacts with these pathways in the pathogenic development of frailty. Longitudinal studies are recommended to investigate the role of metabolic syndrome and influence of Asian heterogeneity, lifestyle, and behaviour on frailty as these factors are affected by rapid socioeconomic changes across the region.
There are several limitations to our study. First, our study was a cross-sectional study which limits causality identifications. Secondly, our HIV participants were biased to only regular clinic attendees with stable virologic suppression. Ongoing viremia and immune deterioration have been associated with frailty and thus our inclusion criteria may have inadvertently led to an underestimation of frailty among PLWH participants in our setting.

 

Conclusion

Despite virally suppressive ART, frailty was prevalent among predominantly young PLWH. This is of concern since frailty is an established risk factor for negative health outcomes affecting functional ability and independence. In addition, our findings suggest increasing risk of frailty due to exaggerated inflammation of varying causes. Furthermore, the significant influence of psychosocial decline involving depression and lack of social support on frailty in this highly stigmatized population suggest the need to review current interventional approaches for optimal management.

 

Acknowledgements: The authors would like to thank all the study participants; investigators of the Malaysian Elderly Longitudinal Research Study (MELOR); Nor Syuhada AB, Rishanantini S, Sivaraj N, Sheron G and Erica L who helped with data collection and coordination of health assessments.

Funding: This work was funded by the High Impact Research Grant (HIR/MOHE; H-20001-E000001, UM.0000099/HIR.C3) received by SK, AK, SP, and R.R; and the University Malaya Postgraduate Research Fund (PG266-2016A) received by RR and SA.

Conflicts of Interest: None to declare.

Ethical standards: Participants provided consent for the utilization of collected data for research purposes.

 

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