CMV Infection, CD19+ B Cell Depletion, and Lymphopenia as Predictors for Unexpected Admission in the Institutionalized Elderly

Abstract

Background: Chronic infections played a detrimental role on health outcomes in the aged population, and had complex associations with lymphocyte subsets distribution. Our study aimed to explore the predictive roles of chronic infections, lymphopenia, and lymphocyte subsets on unexpected admission and mortality in the institutionalized elderly during 3 year follow-up period.
Results: There were 163 participants enrolled prospectively with median age of 87.3 years (IQR: 83.1-90.2), male of 88.3%, and being followed for 156.4 weeks (IQR: 136.9-156.4 weeks). The unexpected admission and mortality rates were 55.2% and 24.5% respectively. The Cox proportional hazards models demonstrated the 3rd quartile of cytomegalovirus IgG (OR: 3.26, 95% CI: 1.55- 6.84), lymphopenia (OR: 2.85, 95% CI: 1.2- 6.74), and 1st quartile of CD19⁺ B cell count (OR: 2.84, 95% CI: 1.29- 6.25) predicted elevated risks of unexpected admission after adjusting for potential confounders; while the 3rd quartile of CD3⁺ T cell indicated a reduced risk of mortality (OR: 0.19, 95% CI: 0.05- 0.71). Negative association between CMV IgG and CD19⁺ B cell count suggested that CMV infection might lead to B cell depletion rather than clonal expansion.
Conclusions: CMV infection, lymphopenia, and CD19⁺ B cell depletion might predict greater risk of unexpected admission, while more CD3⁺ T cell would suggest a reduced risk of mortality among the oldest-old population. A non-linear or U-shaped relationship was supposed between health outcomes and CMV infection, CD3⁺ T cell, or CD19⁺ B cell counts. Further prospective studies with more participants included would be needed to elucidate above findings.

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CMV Infection, CD19+ B Cell Depletion, and
Lymphopenia as Predictors for Unexpected
Admission in the Institutionalized Elderly
LiangYu Chen
Taipei Veterans General Hospital https://orcid.org/0000-0003-3539-6749
An-Chun Hwang
Taipei Veterans General Hospital
Chung-Yu Huang
Taipei Veterans General Hospital
Liang-Kung Chen
National Yang-Ming University
Fu-Der Wang
Taipei Veterans General Hospital
Yu-Jiun Chan (  yjchan@vghtpe.gov.tw )
Aging and Health Research Center
Research
Keywords: CD19+, chronic infection, cytomegalovirus, immunosenescence, lymphopenia, the elderly
DOI: https://doi.org/10.21203/rs.3.rs-106885/v1
License:   This work is licensed under a Creative Commons Attribution 4.0 International License. 
Read Full License

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Abstract
Background: Chronic infections played a detrimental role on health outcomes in the aged population, and
had complex associations with lymphocyte subsets distribution. Our study aimed to explore the
predictive roles of chronic infections, lymphopenia, and lymphocyte subsets on unexpected admission
and mortality in the institutionalized elderly during 3 year follow-up period.
Results: There were 163 participants enrolled prospectively with median age of 87.3 years (IQR: 83.1-
90.2), male of 88.3%, and being followed for 156.4 weeks (IQR: 136.9-156.4 weeks). The unexpected
admission and mortality rates were 55.2% and 24.5% respectively. The Cox proportional hazards models
demonstrated the 3rd quartile of cytomegalovirus IgG (OR: 3.26, 95% CI: 1.55- 6.84), lymphopenia (OR:
2.85, 95% CI: 1.2- 6.74), and 1st quartile of CD19+ B cell count (OR: 2.84, 95% CI: 1.29- 6.25) predicted
elevated risks of unexpected admission after adjusting for potential confounders; while the 3rd quartile of
CD3+ T cell indicated a reduced risk of mortality (OR: 0.19, 95% CI: 0.05- 0.71). Negative association
between CMV IgG and CD19+ B cell count suggested that CMV infection might lead to B cell depletion
rather than clonal expansion.
Conclusions: CMV infection, lymphopenia, and CD19+ B cell depletion might predict greater risk of
unexpected admission, while more CD3+ T cell would suggest a reduced risk of mortality among the
oldest-old population. A non-linear or U-shaped relationship was supposed between health outcomes and
CMV infection, CD3+ T cell, or CD19+ B cell counts. Further prospective studies with more participants
included would be needed to elucidate above ndings.
Background
In contrast to acute infections that trigger pro-inammatory cytokine storms, chronic infections indicate
an unique status of dynamic equilibrium between pathogens replication and host immune response.(1)
The global prevalence of chronic infections estimated > 90% for Varicella zoster virus (VZV), 83% for
cytomegalovirus (CMV), 5% for hepatitis B virus (HBV), and 2.5% for hepatitis C virus (HCV), and the
prevalence is expected to be higher for HBV and HCV in Asia-Pacic region.(1–3) The majority of chronic
infections are believed to be harmless that cause merely subclinical illness in immunocompetent hosts,
but opportunistic clinical diseases among immunocompromised persons, congenital abnormalities in
neonates, or tumor growth in specic target organs due to intrinsic oncogenicity.(1, 2)
Accumulating evidence suggests a detrimental role of chronic infections on physical function, cognition,
and other health outcomes in immunocompetent populations, especially in the critical-ill patients or the
older population. The Northern Manhattan Study reported that chronic infection burden had a negative
correlation with cognitive performance, predicted accelerated cognitive decline, dementia onset, and
stroke incidence.(4) Chronic herpesviral and
Chlamydia pneumoniae
(
C. pneumoniae
) infections were
also reported as risk factors of Alzheimer’s disease and cardiovascular mortality.(5, 6) The CMV infection
was proven a key indicator for cognitive impairment, frailty, also fatality in the Women’ Health and Aging

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studies I and II (WHAS I & II), the third National Health and Nutrition Examination Survey (NHANES III),
and other prospective cohorts.(7–9). And elevated CMV immunoglobulin (IgG) was considered as a
potentially surrogate marker for increased vulnerability and dysregulatory immunomodulation that driven
by repetitive antigen exposure from reactivation of latent infection.(10)
T cell immunity in both CD4+ and CD8+ subsets has complex interaction with physiological aging and
chronic infection.(11) T cell exhaustion of depletion in CD8+ and sometimes in CD4+ T cells had been
reported in HBV, HCV, and human immunodeciency virus infection.(11, 12) While T cell senescence of
antigen-specic CD8+ T cell clonal expansion was disclosed in CMV infection.(11, 13) Composed by
biological parameters of poor T cell mitogenicity, CMV seropositivity, inverted CD4+/CD8+, accumulated
CD8+ T cell count, and reduced CD19+ B cell count, the “immune risk prole” predicted mortality and
adverse health outcomes in the Swedish OCTO/NONA immune longitudinal studies in older persons.(10)
Moreover, lymphopenia was also taken as a surrogate marker for immunosenescence on predicting
infection incidence and mortality in both prospective and retrospective studies.(14, 15)
Although chronic infections were frequently associated with adverse health outcomes, accumulating
evidences still remained controversial because of complex interactions with lymphocyte subsets
distribution. Moreover, being more prevalent in Asia-Pacic region, the potential inuence of HBV and
HCV infections in the older generation was not fully investigated. Thus, our study aimed to answer the
possible impacts of chronic infections, lymphopenia, and lymphocyte subsets on unexpected admission
and mortality in 3-year follow-up period among the older population at the retirement communities for
veterans.
Results
There were 163 participants enrolled with a median age of 87.3years (IQR: 83.1–90.2years), male of
88.3%, and being followed for a median of 156.4 weeks (IQR: 136.9-156.4 weeks). The physical function
and cognitive status were relatively intact among participants, with median Barthel index score of 95
(IQR: 90–100) and Mini-Mental State Examination (MMSE) score of 26 (IQR: 23–29). Seropositivity was
99.4% of CMV (median IgG 236.8IU/mL, IQR: 174.1- 722.1IU/mL), 92% of VZV (median IgG 3.46S/CO,
IQR: 2.51–4.7S/CO), 57.1% of HBV surface antigen (HBsAg) (median IgG 17 mIU/mL, IQR: 3.8–50.8
mIU/mL), 7.4% of HCV (median IgG 0.07S/CO, IQR: 0.05–0.1S/CO), and 84.7% of
C. pneumoniae
(median IgG 1.49S/CO, IQR: 1.28–1.63S/CO) respectively. Five 5 participants (3.1%) moved out the
retirement communities without registration of nal health outcome. The unexpected admission rate was
55.2% (31.9% by infectious disease, 6.7% by cardiovascular disease), and the 3-year mortality rate was
24.5% (9.2% by infectious diseases, 3.7% by cardiovascular disease). Demographic characteristics,
chronic illness, geriatric syndromes, laboratory parameters in details were listed in Table1.

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Table 1
Baseline demographic characteristics of participants
Demographic and clinical characteristics Participants
N = 163 (100%)
Age, years 87.3 (83.1–90.2)
Male gender (%) 144 (88.3%)
Follow-up duration, weeks 156.4 (136.9- 156.4)
Move out from retirement communities 5 (3.1%)
Unexpected admission rate (%) 90 (55.2%)
Infectious diseases-related (%) 52 (31.9%)
Cardiovascular diseases-related (%) 11 (6.7%)
Mortality rate (%) 40 (24.5%)
Infectious diseases-related (%) 15 (9.2%)
Cardiovascular diseases-related (%) 6 (3.7%)
Body mass index, kgs/m224.2 (21.1–26)
Cigarette Smoking 
Ex-smoker (%) 17 (10.4%)
Active smoker (%) 20 (12.3%)
Alcohol consumption 
Ex-drinker (%) 10 (6.1%)
Active drinker (%) 28 (17.2%)
Education 
< 6years (%) 53 (32.5%)
6–9years (%) 51 (31.3%)
> 9years (%) 48 (29.4%)
Flu vaccine uptake (%) 109 (66.9%)
Abbreviations: BI: Barthel index; CD: cluster of differentiation; CMV: Cytomegalovirus; GDS-5: Geriatric
Depression Scale-5 items; HBsAg: hepatitis B virus surface antigen; HCV: hepatitis C virus; HDL-C: high
density lipoprotein-cholesterol; hs-CRP: highly sensitive C reactive protein; IgG: Immunoglobulin G;
JHFRAT: John Hopkins Fall Risk Assessment Tool; LDL-C: Low density lipoprotein-cholesterol; MMSE:
Mini-Mental Status Examination; MUST: Malnutrition Universal Screening Test; VZV: Varicella zoster
virus

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Demographic and clinical characteristics Participants
N = 163 (100%)
Underlying diseases 
Cerebrovascular disease (%) 9 (5.5%)
Chronic liver disease (%) 2 (1.2%)
Chronic kidney disease (%) 4 (2.5%)
Chronic lung disease (%) 24 (14.7%)
Congestive heart failure (%) 7 (4.3%)
Dementia (%) 7 (4.3%)
Depression (%) 8 (4.9%)
Diabetes mellitus (%) 45 (27.6%)
Hypertension (%) 118 (72.4%)
Peptic ulcer disease (%) 26 (16%)
Peripheral arterial disease (%) 32 (19.6%)
Malignancy (%) 11 (6.7%)
Charlson comorbidity index 1 (0–2)
Geriatric syndromes 
Physical function by BI score 95 (90–100)
Risk of falls by JHFRAT score 12 (10–16)
Polypharmacy (%) 83 (50.9%)
Types of medicine 5 (3–6)
Cognition by MMSE score 26 (23–29)
Depression by GDS-5 score 0 (0–1)
Malnutrition by MUST score 0 (0–0)
Visual impairment (%) 52 (31.9%)
Abbreviations: BI: Barthel index; CD: cluster of differentiation; CMV: Cytomegalovirus; GDS-5: Geriatric
Depression Scale-5 items; HBsAg: hepatitis B virus surface antigen; HCV: hepatitis C virus; HDL-C: high
density lipoprotein-cholesterol; hs-CRP: highly sensitive C reactive protein; IgG: Immunoglobulin G;
JHFRAT: John Hopkins Fall Risk Assessment Tool; LDL-C: Low density lipoprotein-cholesterol; MMSE:
Mini-Mental Status Examination; MUST: Malnutrition Universal Screening Test; VZV: Varicella zoster
virus

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Demographic and clinical characteristics Participants
N = 163 (100%)
Hearing impairment (%) 27 (16.6%)
Sleep disorder (%) 46 (28.2%)
Use of hypnotic agents (%) 36 (22.1%)
Constipation (%) 44 (27%)
Stool incontinence (%) 3 (1.8%)
Urinary incontinence (%) 5 (3.1%)
Laboratory tests 
White blood cell count, cells/cumm 5,900 (4,900- 6,700)
Hemoglobin, gm/dL 12.7 (11.6–13.6)
Platelet count, x1,000 cells/cumm 180.5 (148.7–221)
Neutrophil count, cells/cumm 3,157 (2,472- 3,914)
Lymphocyte count, cells/cumm 1,800 (1,500- 2,300)
CD3+ T cell count, cells/cumm 1,136 (903- 1,526)
CD4+ T cell count, cells/cumm 718 (557–926)
CD8+ T cell count, cells/cumm 390 (262–576)
CD19+ B cell count, cells/cumm 135 (81–205)
CD4+/CD8+1.81 (1.25–2.54)
Albumin, gm/dL 4 (3.8–4.2)
Cholesterol, mg/dL 163 (141–188)
Triglyceride, mg/dL 87 (62–122)
HDL-C, mg/dL 48 (36–57)
LDL-C, mg/dL 97 (77–117)
BUN, mg/dL 19.5 (16–25)
Abbreviations: BI: Barthel index; CD: cluster of differentiation; CMV: Cytomegalovirus; GDS-5: Geriatric
Depression Scale-5 items; HBsAg: hepatitis B virus surface antigen; HCV: hepatitis C virus; HDL-C: high
density lipoprotein-cholesterol; hs-CRP: highly sensitive C reactive protein; IgG: Immunoglobulin G;
JHFRAT: John Hopkins Fall Risk Assessment Tool; LDL-C: Low density lipoprotein-cholesterol; MMSE:
Mini-Mental Status Examination; MUST: Malnutrition Universal Screening Test; VZV: Varicella zoster
virus

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Demographic and clinical characteristics Participants
N = 163 (100%)
Serum creatinine, mg/dL 0.96 (0.79–1.24)
Aspartate amonitransferase, U/L 21 (17–25)
hs-CRP, mg/dL 0.14 (0.08–0.32)
Lymphopenia (%) 12 (7.4%)
CD4+/CD8+ < 1 18 (11%)
Chronic infection 
CMV seropositivity (%) 162 (99.4%)
VZV seropositivity (%) 150 (92%)
HBsAg seropositivity (%) 93 (57.1%)
HCV seropositivity (%) 12 (7.4%)
Chlamydia pneumoniae
seropositivity (%) 138 (84.7%)
CMV IgG, IU/mL 236.8 (174.1- 722.1)
VZV IgG, S/CO 3.46 (2.51–4.7)
HBsAg IgG, mIU/mL 17 (3.8–50.8)
HCV IgG, S/CO 0.07 (0.05–0.1)
Chlamydia pneumoniae
IgG, S/CO 1.49 (1.28–1.63)
Abbreviations: BI: Barthel index; CD: cluster of differentiation; CMV: Cytomegalovirus; GDS-5: Geriatric
Depression Scale-5 items; HBsAg: hepatitis B virus surface antigen; HCV: hepatitis C virus; HDL-C: high
density lipoprotein-cholesterol; hs-CRP: highly sensitive C reactive protein; IgG: Immunoglobulin G;
JHFRAT: John Hopkins Fall Risk Assessment Tool; LDL-C: Low density lipoprotein-cholesterol; MMSE:
Mini-Mental Status Examination; MUST: Malnutrition Universal Screening Test; VZV: Varicella zoster
virus
In the Cox proportional hazards model, lymphopenia was shown an important predictor for both
unexpected admission (OR: 2.97, 95% CI: 1.51–5.82) and mortality (OR: 2.32, 95% CI: 1- 5.33) in model 1,
remained signicant for unexpected admission (OR: 2.85, 95% CI: 1.2–6.74) but less signicant for
mortality (OR: 1.9, 95% CI: 0.66–5.4) in model 2. The 3rd quartile of CMV IgG (OR: 3.26, 95% CI: 1.55–5.2)
and the 1st quartile of CD19+ B cell count (OR: 2.84, 95% CI: 1.29–6.25) indicated greater risk of
unexpected admission, while the 3rd quartile of CD3+ T cell count had a reduced risk of mortality (OR:
0.19, 95% CI: 0.05–0.71) in model 2 (Table2).

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Table 2
The Cox proportional hazards model for mortality and unexpected admission
Variables Unexpected admission: OR (95% CI) Mortality: OR (95% CI)
Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2
CMV IgG, IU/mL
< 174.1 1 1 1 1 1 1
174.1- 236.8 1.23 (0.63,
2.39) 1.24 (0.62,
2.47) 2.05 (0.93,
4.51) 1.31 (0.52,
3.34) 1.37 (0.53,
3.5) 1.63 (0.57,
4.67)
236.8- 722.1 2.56 (1.41,
4.64)** 2.84 (1.56,
5.2)** 3.26 (1.55,
6.84)** 1.36 (0.54,
3.38) 1.15 (0.45,
2.89) 0.92 (0.31,
2.66)
> 722.1 1.65 (0.88,
3.12) 1.7 (0.89,
3.24) 2.02 (0.97,
4.18) 1.49 (0.6,
3.71) 1.17 (0.46,
2.95) 0.97 (0.35,
2.67)
HCV IgG, S/CO
< 0.0575 1 1 1 1 1 1
0.0575- 0.07 0.82 (0.45,
1.58) 0.96 (0.51,
1.83) 1.1 (0.5,
2.42) 0.41 (0.17–
0.97)* 0.51 (0.21,
1.26) 0.53 (0.18,
1.53)
0.07–0.1 1.03
(0.55–
1.91)
1.39 (0.72,
2.67) 1.62 (0.77,
3.39) 0.58 (0.25,
1.32) 0.85 (0.35,
2.06) 0.95 (0.34,
2.58)
> 0.1 1.63 (0.91,
2.91) 1.71 (0.92,
3.18) 1.84 (0.91,
3.72) 0.53 (0.22,
1.26) 0.65 (0.26,
1.59) 0.65 (0.23,
1.81)
Lymphopenia 3.43 (1.8,
6.5)*** 2.97 (1.51,
5.82)** 2.85 (1.2,
6.74)** 2.91 (1.29,
6.6)* 2.32 (1,
5.33)* 1.9 (0.66,
5.4)
CD3+ T cell count, cells/cumm
> 1,526 1 1 1 1 1 1
1,136- 1,526 0.97 (0.5,
1.88) 0.77 (0.38,
1.53) 0.43 (0.18,
1) 0.43 (0.13,
1.4) 0.21 (0.06,
0.73)* 0.19 (0.05,
0.71)*
903- 1,136 1.42 (0.77,
2.65) 0.98 (0.49,
1.92) 0.72 (0.34,
1.54) 1.12 (0.45,
2.76) 0.5 (0.19,
1.33) 0.59 (0.2,
1.73)
*
p
< 0.05, **
p
< 0.01, ***
p
< 0.001
Model 1: Adjusted by age, gender, body mass index, highly-sensitive C-reactive protein, and
multimorbidity by Charlson comorbidity index
Model 2: Adjusted by education status, cigarette smoking, alcohol consumption, albumin, low density
lipoprotein cholesterol, medical history of acute myocardial ischemia, congestive heart failure,
cerebrovascular accident, and diabetes mellitus, in addition to age, gender, body mass index, and
multimorbidity

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Variables Unexpected admission: OR (95% CI) Mortality: OR (95% CI)
Unadjusted Model 1 Model 2 Unadjusted Model 1 Model 2
< 903 2.22 (1.23,
4.02)** 1.54 (0.78,
3.03) 1.04 (0.47,
2.28) 2.07 (0.91,
4.7) 1 (0.4,
2.47) 0.94 (0.33,
2.7)
CD4+ T cell count, cells/cumm
> 926 1 1 1 1 1 1
557–718 1.34 (0.66,
2.69) 1.06 (0.51,
2.22) 0.63 (0.27,
1.46) 1.45 (0.46,
4.58) 1 (0.31,
3.18) 0.95 (0.27,
3.38)
718–926 2.15 (1.13,
4.11)* 1.55 (0.77,
3.12) 1.11 (0.52,
2.37) 2.23 (0.77,
6.42) 1.22 (0.42,
3.68) 1.31 (0.41,
4.13)
< 557 2.78 (1.46,
5.3)** 1.75 (0.84,
3.67) 1.57 (0.71,
3.46) 3.88 (1.42,
10.61)** 2.15 (0.75,
6.14) 2.55 (0.8,
8.06)
CD19+ B cell count, cells/cumm
> 205 1 1 1 1 1 1
81–135 2.07 (1.06,
4.04)* 1.76 (0.86,
3.6) 1.59 (0.73,
3.46) 1.46 (0.46,
4.62) 0.97 (0.3,
3.12) 0.99 (0.28,
3.5)
135–205 1.54 (0.77,
3.08) 1.24 (0.57,
2.71) 1.05 (0.45,
2.48) 2.42 (0.84,
6.98) 1.35 (0.44,
4.12) 1.11 (0.34,
3.64)
< 81 3.1 (1.64,
5.84)*** 2.24 (1.09,
4.63)* 2.84 (1.29,
6.25)** 3.67 (1.34,
10.03)* 1.99 (0.68,
5.81) 2.41 (0.79,
7.36)
*
p
< 0.05, **
p
< 0.01, ***
p
< 0.001
Model 1: Adjusted by age, gender, body mass index, highly-sensitive C-reactive protein, and
multimorbidity by Charlson comorbidity index
Model 2: Adjusted by education status, cigarette smoking, alcohol consumption, albumin, low density
lipoprotein cholesterol, medical history of acute myocardial ischemia, congestive heart failure,
cerebrovascular accident, and diabetes mellitus, in addition to age, gender, body mass index, and
multimorbidity
The CMV IgG positively correlated with age, u vaccine uptake, risks of fall, usage or hypnotic agents,
C.
pneumoniae
IgG, while negatively associated with education, cognition, and CD19+ B cell count (Table3).
For all lymphocyte, CD3+ T cell, and CD19+ B cell counts, there were positive associations with female
gender, body mass index (BMI), cognitive performance, hemoglobin, albumin, cholesterol, low density
lipoprotein-cholesterol, while negative correlations with age and u vaccine uptake. Other results of
Spearman correlation analysis were listed in Table3.

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Table 3
Results of Spearman correlation analysis between baseline characteristics
Indicators Variables Correlation coecient
p
value
CMV IgG Age 0.19 0.017*
Education -0.17 0.036*
Flu vaccine uptake 0.278 0.001**
JHFRAT score 0.219 0.006**
MMSE score -0.197 0.013*
Usage of hypnotic agents 0.169 0.043*
CD19+ B cell count -0.179 0.026*
Chlamydia pneumoniae
IgG 0.194 0.015*
Lymphocyte count Age -0.291 < 0.001***
Female Gender 0.226 0.004**
Body mass index 0.296 < 0.001***
Flu vaccine uptake -0.192 0.017*
Smoking history -0.194 0.014*
Diabetes mellitus 0.18 0.024*
JHFRAT -0.225 0.004**
MMSE score 0.227 0.004**
MUST score -0.259 0.001**
White blood cell count 0.5 < 0.001***
Hemoglobin 0.261 0.001**
Platelet count 0.185 0.018*
CD3+ T cell count 0.862 < 0.001***
CD4+ T cell count 0.757 < 0.001***
CD8+ T cell count 0.632 < 0.001***
*
p
< 0.05, **
p
< 0.01, ***
p
< 0.001
Abbreviations: CD: cluster of differentiation; CMV: cytomegalovirus; HDL-C: high density lipoprotein-
cholesterol; hs-CRP: highly sensitive C reactive protein; IgG: Immunoglobulin G; JHFRAT: John
Hopkins Fall Risk Assessment Tool; LDL-C: Low density lipoprotein-cholesterol; MMSE: Mini-Mental
Status Examination; MUST: Malnutrition Universal Screening Test; VZV: Varicella zoster virus

Page 11/19
Indicators Variables Correlation coecient
p
value
CD19+ B cell count 0.449 < 0.001***
Albumin 0.297 < 0.001***
Cholesterol 0.319 < 0.001***
Triglyceride 0.177 0.024*
LDL-C 0.234 0.003**
hs-CRP -0.186 0.018*
CD3+ T cell count Age -0.337 < 0.001***
Female Gender 0.304 < 0.001***
Body mass index 0.328 < 0.001***
Flu vaccine uptake -0.209 0.01*
Diabetes mellitus 0.195 0.015*
JHFRAT score -0.168 0.035*
MMSE score 0.193 0.015*
MUST score -0.224 0.005
White blood cell count 0.401 < 0.001***
Hemoglobin 0.263 0.001**
Platelet count 0.17 0.032*
Lymphocyte count 0.862 < 0.001***
CD4+ T cell count 0.835 < 0.001***
CD8+ T cell count 0.792 < 0.001***
CD19+ B cell count 0.423 < 0.001***
Albumin 0.239 0.003**
Cholesterol 0.364 < 0.001***
LDL-C 0.281 < 0.001***
*
p
< 0.05, **
p
< 0.01, ***
p
< 0.001
Abbreviations: CD: cluster of differentiation; CMV: cytomegalovirus; HDL-C: high density lipoprotein-
cholesterol; hs-CRP: highly sensitive C reactive protein; IgG: Immunoglobulin G; JHFRAT: John
Hopkins Fall Risk Assessment Tool; LDL-C: Low density lipoprotein-cholesterol; MMSE: Mini-Mental
Status Examination; MUST: Malnutrition Universal Screening Test; VZV: Varicella zoster virus

Page 12/19
Indicators Variables Correlation coecient
p
value
Triglyceride 0.206 0.01*
Chlamydia pneumoniae
IgG -0.17 0.033*
CD19+ B cell count Age -0.471 < 0.001***
Female gender 0.337 < 0.001***
Body mass index 0.265 0.001**
Flu vaccine uptake -0.278 0.001**
Alcohol consumption 0.215 0.007**
Chronic liver disease 0.184 0.02*
Barthel index score 0.23 0.004**
MMSE score 0.24 0.002**
White blood cell count 0.202 0.011*
Hemoglobin 0.232 0.003**
Lymphocyte count 0.449 < 0.001***
CD3+ T cell count 0.423 < 0.001***
CD4+ T cell count 0.587 < 0.001***
CD4+/CD8+0.343 < 0.001***
Albumin 0.192 0.016*
Cholesterol 0.299 < 0.001***
LDL-C 0.258 0.001**
Creatinine -0.168 0.035*
CMV IgG -0.179 0.026*
*
p
< 0.05, **
p
< 0.01, ***
p
< 0.001
Abbreviations: CD: cluster of differentiation; CMV: cytomegalovirus; HDL-C: high density lipoprotein-
cholesterol; hs-CRP: highly sensitive C reactive protein; IgG: Immunoglobulin G; JHFRAT: John
Hopkins Fall Risk Assessment Tool; LDL-C: Low density lipoprotein-cholesterol; MMSE: Mini-Mental
Status Examination; MUST: Malnutrition Universal Screening Test; VZV: Varicella zoster virus
Additional adjustment for associating factors was performed in nal model and still reported an elevated
risk of unexpected admission for the 3rd quartile of CMV IgG (OR: 2.66, 95% CI: 1.14–6.2). With
adjustment for correlating factors after removing white blood cell count, lymphocyte and subset counts,

Page 13/19
lymphopenia still indicated an increased risk of unexpected admission (OR: 3.59, 95% CI: 1.34–9.59), and
the 3rd quartile of CD3+ T cell remained a reduced risk of mortality (OR: 0.38, 95% CI: 0.15–0.95).
Discussion
Comparing with other chronic infections, CMV infection was a potentially surrogate predictor for
unexpected admission but not mortality, and the 3rd quartile of CMV IgG suggested an elevated risk of
unexpected admission among the older persons. For the CMV seroprevalence of 99.4% in our participants
and 91.7% of blood donors in Taiwan, the CMV seropositivity would not be a good surrogate markers on
outcomes prediction.(16) Increased frailty incidence had been disclosed in the elderly with the highest
CMV IgG in WHAS I & II, as well as poor survival possibility and rising incidence of cardiovascular disease
in the Sacramento Area Latino Study on Aging and the population-based European Prospective
Investigation of Cancer- Norfolk cohort study.(8, 17, 18) On the other hand, the BELFRAIL and another
cohort studies among the oldest-old population revealed a neutral role of CMV infection on frailty and
mortality.(19, 20) A non-linear or U-shaped relationship was supposed between CMV infection and
adverse health outcomes in the oldest-old population, which would lead to diverse ndings between
studies. To the best of our knowledge, this study would be the rst one to adopt unexpected admission
for outcome assessment, and to present a non-linear pattern of CMV infection on outcome prediction.
The bottom quartile of CD19+ B cell count showed a positive association with unexpected admission in 3-
year follow-up duration, while the effect weaned when adjusted for CMV IgG in nal model. CMV infection
was showed a better predictor for risk of unexpected admission after adjustment for competing factors
including CD19+ B cell count. Furthermore, a paradoxically negative correlation was disclosed between
the CMV IgG and CD19+ B cell count, which might indicate a CMV infection-accelerated B cell exhaustion
rather than a clonal expansion. Depletion in CD19+ B cell had been recognized an important indicator of
the “immune risk prole”, and lower CD19+ B cell count was reported an independent predictor of
mortality in patients under hemodialysis.(10, 21) Another observational study also revealed a decreased
percentage of CD19+ B cell in the elderly with frailty or poor physical function.(22, 23) However, due to
limited publications available at present, more studies would be needed for approaching the role of
CD19+ B cell on long-term health outcomes, and for elucidating the paradoxical relationship between
CMV infection and CD19+ B cell count.
Lymphopenia was shown an important risk factor of unexpected admission but not of mortality in our
participants. The NHANES study retrospectively recognized a positive association between lymphopenia
and mortality of either cardiovascular or non-cardiovascular causes in the general population.(14) The
Copenhagen General Population Study demonstrated lymphopenia as a risk factor of admission and
mortality due to infectious disease prospectively.(15) There were also prolonged length of stay and
elevated in-hospital mortality reported among the older inpatients with lymphopenia.(24) Even
lymphopenia predicts infection incidence, it failed to indicate an increased risk of mortality in the

Page 14/19
institutionalized elderly in our previous study.(25) Variation in lymphocyte subset distributions might be
the possible explanation for this equivocal ndings on predicting mortality by lymphopenia.(11)
Lowest risk of mortality in the elderly was reported in the 3rd quartile group of CD3+ T cell count in our
participants even after adjustment for all correlating factors. There had been a prospective study
revealing a higher CD3+, CD4+ T cell counts and CD4+/CD8+ among survivors in a 2-year follow-up in the
healthy Chinese elderly .(26) But our study failed to discover the ability by CD4+, CD8+ T cell counts or
CD4+/CD8+ on outcomes prediction in the 3-year period.
Several limitations were noticed in our study. First, the non-linear or U-shaped relationship between risk
factors and health outcomes might relate to the baseline characteristics of study participants at
enrollment. We had tried adjusting all possibly confounding factors of all-cause mortality as possible to
increase the strength of study, but more studies with larger sample size were still needed to prove our
ndings. Even limited participants were enrolled, with minimal drop-out rate of 3.1% and a follow-up
period of 3 years, we believed above ndings were convincing enough through adequate statistical
methods. Second, the proinammatory cytokines were not included at initial study design except highly
sensitive-C reactive protein, thus the complex interactions between chronic infection, lymphocyte subsets,
inammation, and health outcomes were not fully evaluated. Final, the current categories by ow
cytometry could not distinguish the naïve or well-differentiated cells from the same lymphocyte subsets.
Further studies to explore the possible inuence of naïve or well-differentiated cell distributions would be
needed for answering more questions.
Conclusions
CMV infection, lymphopenia, and CD19+ B cell depletion predicted greater risk of unexpected admission,
while more CD3+ T cell would suggest a reduced risk of mortality among the oldest-old population at
institutes. A non-linear or U-shaped relationship was supposed between health outcome and CMV
infection, CD3+ T cell, or CD19+ B cell counts. Further prospective studies with more participants included
would be needed to elucidate above ndings.
Methods
Participants
This prospective study enrolled residents with relatively intact physical and cognitive function at three
retirement communities for veterans in Taiwan from July 1st, 2016 to January 31st, 2017. Potential
candidates were invited for participation if they fullled the following criteria, (a) age ≥ 65 years, (b)
stayed at the retirement communities for more than 3 months, (c) free of acute illness 2 weeks prior to
study enrollment, and (d) with life expectancy for ≥ one year. Participants were excluded for those who
(a) could not complete the baseline assessment and laboratory tests, (b) would not like to be followed in
study period, or (c) would request for study withdrawal. All participants received baseline anthropometric

Page 15/19
measurements, comprehensive geriatric assessment, and venous blood sampling at enrollment by well-
trained staffs. Demographic characteristics, underlying diseases, and laboratory parameters were
collected, while a composite score of Charlson Comorbidity Index (CCI) was calculated for adjustment.
(27)
Measurements
Geriatric syndromes
Physical performance was approached by the Barthel index for activities of daily living. The higher
Barthel index score suggests a better physical function.(28) Risk of falls was evaluated by Johns
Hopkins Fall Risk Assessment Tool (JHFRAT) for home health care.(29) Scores of JHFRAT were
calculated from established items, including (1) age, (2) fall history ≤ 6 months, (3) bowel and urine
elimination, (4) usage of high risk medications, (5) patient care equipment, (6) mobility, and (7) cognition.
The more JHFRAT score indicated elevated risks of fall. Polypharmacy was identied if residents
concurrently took ≥ 5 types of medications.(30) Types of medications were counted and registered for
analysis.
Cognition was assessed by the MMSE, Chinese version. The lower MMSE score indicates a poor
cognition.(31) Depressive symptom was approached by the geriatric depression scale-5 item version
(GDS-5). Participants with higher GDS-5 had more possibility for depression.(32) Risk of undernutrition
was dened by Malnutrition Universal Screening Tool (MUST). Participants with higher MUST score faced
more risks of undernutrition.(33) Visual impairment, hearing diculty, sleep disorder, as well as urinary
and stool incontinence were also recorded.
Laboratory parameters
Peripheral venous blood samples were sampled via the antecubital vein in the morning after an overnight
fasting. Complete blood counts and differential counts were analyzed by the automated cellular analysis
system Beckman Coulter DxH 800 hematology analyzer (Beckman Coulter, Miami, FL). Biochemical
parameters of albumin, lipid prole, high sensitive-C reactive protein (hs-CRP), fasting glucose, aspartate
transaminase, blood urea nitrogen, and serum creatinine were measured by the ARCHITECT i2000SR
immunoassay analyzer (Abbott Diagnostics, Abbott Park, IL, USA). Lymphopenia was dened by absolute
lymphocyte count < 1,000/cumm.(25)
Lymphocyte subsets were determined ow-cytometric analysis system Beckman Coulter FC500 ow
cytometer (Beckman Coulter, Miami, FL) in freshly drawn peripheral blood after adequate processing as
the manufacturer’s instruction. Serum CMV IgG, VZV IgG, HBsAg IgG, HCV IgG, and
C. pneumoniae
IgG
were measured by a chemiluminescent immunoassay on the ARCHITECT i2000SR immunoassay
analyzer (Abbott Diagnostics, Abbott Park, IL, USA).
Outcome measurement

Page 16/19
Unexpected admission and mortality were recorded of each individual in 3-year follow-up period, and the
main etiology of each event was also registered. For those who lost to follow-up in study period would be
treated as censored cases, and time for follow-up were documented until the last available information.
Statistical analysis
Results for data analysis are expressed as median with interquartile range (IQR) for continuous variables,
and numbers (%) for categorical variables. Quartile grouping was performed on CMV IgG, VZV IgG, HBsAg
IgG, HCV IgG,
C. pneumoniae
IgG, CD3+ T cell count, CD4+ T cell count, CD8+ T cell count, CD19+ B cell
count, and CD4+/CD8+. Cox proportional hazard model was used to assess the possible impacts on
unexpected admission and mortality of lymphopenia and each quartile groups. Adjustment for age,
gender, BMI, hs-CRP, and CCI was performed in model 1, while further adjustments for confounders of all-
cause mortality including education status, cigarette smoking, alcohol consumption, serum albumin, low
density lipoprotein-cholesterol, congestive heart failure, cerebrovascular disease, and diabetes mellitus
were performed in model 2. Non-parametric method of Spearman correlation analysis was used for
comparison in case of possibly non-Gaussian distribution of numerical data. Another Cox proportional
hazard model with additional adjustment for associated factors was performed nally. All data analyses
were carried out with the Statistical Package for the Social Sciences for Windows version 20.0 (SPSS,
Chicago, IL, USA), and variables were considered as statically signicant if P < 0.05.
List Of Abbreviations
CD
cluster of differentiation; CMV:Cytomegalovirus;
C. pneumoniae
:
Chlamydia pneumoniae
; GDS-5:Geriatric
Depression Scale-5 items; HBsAg:hepatitis B virus surface antigen; HBV:hepatitis B virus; HCV:hepatitis C
virus; IgG:immunoglobulin G; hs-CRP:highly sensitive-C reactive protein; JHFRAT:John Hopkins Fall Risk
Assessment Tool; MMSE:Mini-Mental Status Examination; MUST:Malnutrition Universal Screening Test;
NHANES:the National Health and Nutrition Examination Survey; VZV:Varicella zoster virus; WHAS:the
Women’ Health and Aging studies
Declarations
Ethics approval and consent to participate
This study was approved by the Institutional Review Board of Taipei Veterans General Hospital, which
was conformed to the provisions of the World Medical Association’s Declaration of Helsinki (IRB-TPEVGH
No.: 2016-06-009A). The written informed consent for each participant was obtained before study
enrollment.
Availability of data and materials

Page 17/19
All collected data and analyses during the current study are available from the corresponding author on
reasonable request at email address: yjchan@vghtpe.gov.tw
Competing interest
The authors declare no conicts of interests.
Funding resources
This study was granted by MOHW 105-CDC-C-114-000115.
Authors’ contributions
LYC, ACH, and CYH designed the study protocol, collected the data, performed statistical analyses, and
drafted the manuscript. LKC, FDW, and YJC coordinated laboratory tests, modied study protocols,
rechecked statistical analyses, and provided critical suggestions before submission.
Acknowledgements
We thank all staffs’ assistances at the retirement communities for veterans.
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