The association of specific and cumulative exposure to infectious agents with cognitive impairment in older Hispanic adults

Abstract

Background

Accumulating evidence implicates infectious pathogens as triggers of immune–inflammatory processes that contribute to neurodegeneration. Inflammation in both the brain and peripheral circulation is recognized as a critical factor in the development and progression of cognitive decline and neurodegenerative disorders, including Alzheimer's disease.

Objective

This retrospective case-control study investigated the association between cognitive impairment and presence of serum antibodies to seven pathogens in older adults.

Methods

One hundred sixty-five participants aged ≥ 65 years from the Panama Aging Research Initiative Health Disparities (PARI-HD) study were evaluated. Presence of IgG antibodies against Toxoplasma gondii, Herpes Simplex Virus Type 1 (HSV-1), Human Cytomegalovirus (HCMV), Helicobacter pylori, Chlamydia pneumoniae, Treponema pallidum, and Trypanosoma cruzi was measured. Participant demographics, inflammatory biomarkers and cognitive-functional factors were analyzed for associations with single/multiple pathogen-specific antibodies reactivity using multivariable regression models.

Results

Only C. pneumoniae seropositivity was significantly different between cognitively unimpaired and impaired groups (p = 0.02) and increasing TNF-α levels were directly associated with C. pneumoniae seropositivity (OR = 2.08, CI_95% 1.0–4.1, p = 0.04). Additionally, cumulative exposure to infectious agents increased the likelihood of cognitive impairment (OR = 1.51, CI_95% 1.01–2.26, p = 0.04) and was associated with slower processing speed as measured by TMT A test (OR = 17.43, CI_95% 2.32–32.53, p = 0.02). Notably, the presence of C. pneumoniae in multiple pathogen interactions further raised the likelihood of cognitive impairment (OR = 4.07, CI_95% 1.24–13.36, p = 0.03).

Conclusions

These results enhance our understanding of cognitive impairment in a Hispanic population and underscore the need for further studies on the role of C. pneumoniae and multi-pathogen infection in Alzheimer's disease.

Keywords

Alzheimer's disease antibodies cognitive impairment inflammation

Introduction

The prevalence of Alzheimer's disease (AD) in developing countries is expected to rise as a result of increases in life expectancy and population aging.¹ In Panama, the number of people over the age of 60 is expected to increase to 24% of the total population by 2050.² AD is a multifactorial condition influenced by numerous risk factors and characterized by a complex and heterogeneous pathogenesis, which complicates a full understanding of its etiology.³ The amyloid-based mechanism of AD pathogenesis is the most studied hypothesis.⁴ Some authors have identified antimicrobial properties of the amyloid-β (Aβ) protein,^5–8 suggesting that in some cases the expression of Aβ, and consequently the formation of its plaques, may be a response to the presence of an infectious organism. Substantial scientific evidence strongly indicates that neuroinflammation associated with infectious agents, contributes significantly to the onset of AD.⁹ Pathogens with and without neurotropism have been associated with cognitive impairment, dementia, and brain tissue degeneration.^10–13

During the aging process, there is an increased susceptibility to infection due to dysregulation of immune system activity and promotion of a chronic inflammatory state.^14–16 Latent infection or reactivation in the central nervous system (CNS) by neurotrophic agents can contribute to progression from an acute to a chronic inflammatory state.⁹ Chronic inflammation gives rise to intricate interactions that culminate in neuroinflammation and neurodegeneration.¹⁷ Pathogen infections have been shown to affect diverse pathological processes, such as Aβ aggregation, microglia activation, and presynaptic terminals loss.¹⁸ Several studies have found an association between the presence of pathogen-specific antibodies and AD pathogenesis. Anti-Helicobacter pylori antibodies are present in the cerebrospinal fluid of AD patients¹⁹ and are associated with AD mortality in men²⁰ and poor cognition among US adults.^21,22 Anti-Chlamydia pneumoniae antibodies have been detected in patients with AD and are associated with a five-fold increased risk of developing AD after infection.²³ Several studies suggest that Human Cytomegalovirus (HCMV) contributes to AD through inflammatory mechanisms; namely, infection induces the production of cytokines and acute phase proteins.²⁴ Similarly, Herpes Simplex Virus Type 1 (HSV-1) has been associated with lower Mini-Mental State Examination (MMSE) scores in older people,¹¹ and co-localized with Aβ plaques in AD patients.²⁵ In addition, there is evidence that Treponema pallidum forms aggregate during syphilis dissemination to the brain that mimic the Aβ plaques of AD patients.²⁶ Likewise, Toxoplasma gondii co-localizes with Aβ plaques in mouse brains.²⁷ T. gondii IgG antibody is associated with poorer executive function in adults,²⁸ and is considered a risk factor for the development of AD.²⁹ Trypanosoma cruzii is of interest because it is endemic in Panama but its association with AD has not been studied previously. Notably, although most research has focused on the contribution of single pathogens, some studies highlight the polymicrobial causality of multi-pathogen interactions in AD.³⁰

In the present study, we provide additional support for the establishment of the infectious and inflammatory hypotheses of AD as triggering factors of the disease. This is also relevant to the potential role of multi-pathogen infections in AD, as shown by investigations into the association between single and multiple exposures to common pathogens linked to the disease. Infection was defined by specific antibody responses. Correlations were also identified between inflammatory biomarkers, cognitive function, and the diagnosis of cognitive impairment in a cohort of older adults in Panama.

Methods

Study design, period, and participants

This was a descriptive, retrospective case-cohort analytical study, including participants (n = 165) aged ≥65 years from the Panama Aging Research Initiative-Health Disparities (PARI-HD). Participants were recruited from the Geriatrics Service of the Dr Arnulfo Arias Madrid Hospital Complex from September 2011 to May 2015. They were selected by purposive non-probability sampling. Participants were separated into two groups: cognitively impaired and cognitively unimpaired. The cutoff value established for this binary comparison for the cognitively unimpaired group required a MMSE score ≥24, a score of 1 or 2 in the Global Deterioration Scale and 10 points or less in the GDS-30. Study inclusion and exclusion criteria, clinical interview protocols, and physical and cognitive measures are described in Villarreal et al. (2019)³¹ Fasting blood draws were obtained on the same day as the interview with the participants, where the neuropsychological assessments were carried out. The study protocol had the approval of the Bioethics Committee, Scientific Committee, and Medical Education Subdivision of the Dr Arnulfo Arias Madrid Hospital Center (SDMdeDEI-CH-248–12).

Biological sample assessment

Specimens consist of serum samples from selected participants stored at −80°C. The sera were used for enzyme-linked immunosorbent assays (ELISA), using commercially available kits (Vircell, Spain) to determine the presence of specific IgG antibodies against Toxoplasma gondii, Trypanosoma cruzi, Herpes Simplex Virus Type 1 (HSV-1), Treponema pallidum, Human Cytomegalovirus, Helicobacter pylori and Chlamydia pneumoniae. Briefly, the specific antibodies present in the sample bind to the antigen attached on the surface of the polystyrene plate. Unbound immunoglobulins are removed by the washing step. Then the HRP-conjugated anti-human IgG secondary antibody reacts with the antigen-antibody complex, and those that do not specifically bind are removed by the washing step. Binding is visualized with the substrate (TMB), resulting in a blue color that changes to yellow when the stop solution is added. The absorbance was read on a spectrophotometer (Epoch, Bio Tek) at 450 nm. The definition of serologic status was determined based on the interpretation guide provided with each commercial kit. The results obtained were categorized as 0 = non-reactor, and 1 = reactor.

Serum samples were assayed in duplicate via a multiplex biomarker assay platform using Simoa^® electrochemiluminescence.³² The following blood proteins were quantified: α2M, CRP, Eotaxin-3, FABP3, I-309, IL-10, IL-18, IL-5, IL-1β, IL-6, IL-7, MIP-1α, SAA, sICAM-1, sVCAM-1; TARC, and TNF-α.

Cognitive evaluation test

For cognitive evaluations, the following cognitive tests were used: 30 item Spanish version of the Mini Mental State Examination³³ adjusted for age and education³⁴; Clock Drawing Test (CDT)³⁵; and the Trail Making Test form A (TMT A) and form B (TMT B).³⁶

Data analysis

To evaluate the single/multiple associations of serologic status against the seven pathogens with sociodemographic, inflammatory biomarkers, cognitive-functional evaluations, we performed descriptive analyses to examine frequencies and percentages for categorical variables and mean and standard deviation for continuous variables. To examine statistical significance, we conducted univariate analyses (ANOVA) for continuous variables and Pearson's X² tests for categorical variables. Also, multivariable models were fit using logistic or linear regression, including age, sex, years of education and income as covariates. Doubtful positives for infection of all diseases were conservatively reclassified as negatives in the dataset, and missing participant data were excluded. For each participant, the number of seropositive results was summed to determine the cumulative exposure present. A plot was created using the most common combinations of IgG qualitative results for the seven pathogens. All statistical analyses were conducted in Stata 18 (StataCorp LLC, College Station, TX, USA) and SPSS Statistics 25 (IBM, Armonk, New York, USA).

Results

Descriptive characteristics of the study sample

The study sample included 69 participants who were cognitively unimpaired (41.8%) and 96 cognitively impaired participants (58.2%) (see Table 1). Participants with cognitive impairment included subjects with mild cognitive impairment (26.7%, n = 44), with vascular dementia (12.1%, n = 20), with Alzheimer's disease (18.2%, n = 30), and with mixed dementia (1.2%, n = 2). Participants were 80 ± 7.7 years old on average and 67% of the study sample were women, with a mean of 7.6 ± 3.9 years of formal education. 42% of the participants had a body mass within the normal range, as indicated by BMI measurements (mean = 25.4 kg/m², SD = 5.2). Participants in the cognitively unimpaired group were younger (p = 0.004), with more years of education (p < 0.001), reported more physical activity (p = 0.028) and less multimorbidity (p = 0.041) than in the cognitively impaired group. As expected, a plurality of participants in the cognitively impaired group reported subjective memory impairment and worse global cognition scores compared to the cognitively unimpaired group (both p < 0.001).

Table 1.

Demographic, clinical, and cognitive characteristics of study participants.

Variables	Total (N = 165)	Cognitively unimpaired (n = 69)	Cognitively impaired (n = 96)
Variables	n (%)/M (SD)	n (%)/M (SD)	n (%)/M (SD)	p
Age (y)	80.04 (±7.73)	77.99 (±6.70)	81.51 (±8.11)	0.004
Age Group				0.025
65–74	41 (24.8%)	21 (30.4%)	20 (20.8%)
75–84	77 (46.7%)	36 (52.2%)	41 (42.7%)
≥85	47 (28.5%)	12 (17.4%)	35 (36.5%)
Sex, Female	110 (66.7%)	45 (65.2%)	65 (67.7%)	0.432
Marital Status				0.554
Not Partnered	87 (53.0%)	36 (52.9%)	51 (53.1)
Partnered	77 (47.0%)	32 (47.1%)	45 (46.9%)
Education (y)	7.64 (±3.88)	8.97 (±3.98)	6.69 (±3.52)	<0.001
Highest Education Attained				0.003
Incomplete primary school	6 (3.7%)	0 (0%)	6 (6.3%)
Completed primary school	77 (47.0%)	26 (37.7%)	51 (53.7%)
Completed secondary school	68 (41.5%)	33 (47.8%)	35 (36.8%)
University or higher	13 (7.9%)	10 (14.5%)	3 (3.2%)
Monthly income (USD)				0.647
<250	35 (22.2%)	12 (18.5%)	23 (24.7%)
250–500	42 (26.6%)	16 (24.6%)	26 (28.0%)
501–850	29 (18.4%)	14 (21.5%)	15 (16.1%)
>850	52 (32.9%)	23 (35.4%)	29 (31.2%)
Current physical activity (% yes)	53 (32.1%)	29 (42.0%)	24 (25.0%)	0.028
Body mass index (Kg/m2)	25.38 (±5.17)	25.93 (±4.82)	24.97 (±5.40)	0.275
BMI classification				0.241
Underweight	10 (7.2%)	2 (3.4%)	8 (10.1%)
Normal	58 (42.0%)	23 (39.0%)	35 (44.3%)
Overweight	47 (34.1%)	21 (35.6%)	26 (32.9%)
Obese	23 (16.7%)	13 (22.0%)	10 (12.7%)
Cancer (% yes)	9 (5.5%)	2 (2.9%)	7 (7.3%)	0.192
Chronic lung disease (% yes)	16 (9.7%)	8 (11.6%)	8 (8.3%)	0.596
Diabetes (% yes)	48 (29.1%)	18 (26.1%)	30 (31.3%)	0.493
Cardiovascular disease (% yes)	55 (33.3%)	18 (26.1%)	37 (38.5%)	0.132
Hypertension (% yes)	148 (89.7%)	64 (92.8)	84 (87.5%)	0.311
Stroke (% yes)	32 (19.4%)	8 (11.6%)	24 (25.0%)	0.045
Arthritis (% yes)	10 (6.1%)	4 (5.8%)	6 (6.3%)	1.000
Obesity	23 (16.7%)	13 (22.0%)	10 (12.7%)	0.169
Chronic health conditions (sum)				0.041
0–1	50 (30.3%)	22 (31.9%)	28 (29.2%)
2	50 (30.3%)	26 (37.7%)	24 (25.0%)
3	38 (23.0%)	16 (23.2%)	22 (22.9%)
≥4	27 (16.4%)	5 (7.2%)	22 (22.9%)
Global cognition	24.56 (±5.61)	28.57 (±1.67)	21.55 (±5.65)	<0.001
Subjective memory impairment (% yes)	75 (70.8%)	21 (47.7%)	54 (87.1%)	<0.001

M: Mean; SD: standard deviation; USD: U.S. dollars; BMI: body mass index.

Distribution of pathogen seropositivity among study groups

The percentage of reactivity for each of the pathogens between cognitively unimpaired and impaired groups was calculated (see Table 2). A significantly greater proportion of participants in the cognitively impaired group tested positive for IgG Chlamydia pneumoniae (p = 0.02). In addition, there was a significant difference in the cumulative exposures to infection agents between groups (Figure 1). Participants who were cognitively impaired had a greater frequency (39.2%) of seropositivity for five pathogens simultaneously relative to those who were cognitively unimpaired (20.6%) (p = 0.029).

Figure 1.

Frequency of exposure to multiple pathogens in cognitively unimpaired and impaired participants. Bars show relative frequency (%) of reactivity to IgG for 2/3, 4 or 5 pathogens simultaneously between cognitive unimpaired and impaired groups.

Table 2.

Seropositivity to study pathogens across groups.

Pathogen n (%)	Cognitively unimpaired	Cognitively impaired	p
Toxoplasma gondii	53 (76.81)	80 (83.33)	0.32
HSV-1	64 (94.12)	90 (96.77)	0.45
Helicobacter Pylori	30 (50.0)	46 (56.10)	0.49
HCMV	66 (95.65)	96 (100)	0.07
Treponema pallium	4 (5.80)	7 (7.29)	0.76
Trypanosoma cruzi	1 (1.45)	0 (0)	0.42
Chlamydia pneumoniae	40 (75.47)	73 (91.25)	0.02*

HSV-1: Herpes Simplex Virus Type 1, HCMV: Human Cytomegalovirus. *p < 0.05.

Inflammatory biomarker levels among study groups and the association of pathogen seropositivity with inflammation

The levels of inflammatory biomarkers measured from participantś serum, including: α2M, Adiponectin, CRP, Eotaxin3, FABP, I309, IL10, IL18, IL1β, IL5, IL6, IL7, MIP1α, SAA, ICAM1, VCAM1, TARC and TNFα were considered to examine differences between cognitively unimpaired and impaired groups (see Table 3). Biomarker values yielding extreme outliers (i.e., >4 standard deviations above the mean) were excluded from analyses. Mean levels of I-309 and TNF-α were significantly greater in the cognitively impaired group (p < 0.001 and p = 0.012, respectively).

Table 3.

Inflammatory biomarkers across groups.

Analyte (pg/mL)	Cognitively unimpaired (n = 65) M (SD)	Cognitively impaired (n = 95) M (SD)	p
α2microglobulina	2.20E + 09 (7.57E + 08)	2.26E + 09 (7.10E + 08)	0.57
Adiponectin	1.99E + 04 (1.33E + 04)	2.38E + 04 (1.73E + 04)	0.25
CRP	2.55E + 06 (1.23E + 07)	5.60E + 06 (2.41E + 07)	0.32
Eotaxin3	4.02E + 00 (1.27E + 01)	3.98E + 01 (3.98E + 01)	0.71
FABP	6.48E + 04 (4.25E + 04)	7.33E + 04 (6.35E + 04)	0.78
I-309	3.61E + 00 (2.58E + 00)	7.19E + 00 (5.87E + 00)	<0.001*
IL-10	2.64E + 00 (6.26E + 00)	8.66E + 00 (7.07E + 01)	0.07
IL-18	2.16E + 02 (1.47E + 02)	1.81E + 02 (8.67E + 01)	0.12
IL-1β	1.97E-01 (3.80E-01)	1.51E-01 (2.84E-01)	0.42
IL-5	1.55E + 00 (2.32E + 00)	1.16E + 00 (1.47E + 00)	0.72
IL-6	4.50E + 00 (9.04E + 00)	4.98E + 00 (1.78E + 01)	0.19
IL-7	1.25E + 01 (6.31E + 00)	1.11E + 01 (5.41E + 00)	0.12
MIP-1α	5.75E + 02 (2.13E + 03)	1.82E + 02 (2.21E + 02)	0.21
SAA	3.72E + 06 (1.98E + 07)	1.50E + 07 (6.63E + 07)	0.51
ICAM-1	1.04E + 05 (2.89E + 05)	1.87E + 05 (4.45E + 05)	0.56
sVCAM-1	2.01E + 05 (5.68E + 05)	3.62E + 05 (8.32E + 05)	0.26
TARC	3.43E + 02 (5.03E + 02)	2.97E + 02 (2.94E + 02)	0.95
TNF-α	2.03E + 00 (8.90E-01)	2.54E + 00 (1.39E + 00)	0.01*

α2M: alpha-2-macroglobulin; CRP: C-reactive protein; FABP: fatty acid binding protein; I-309: human cytokine I-309; IL-10: interleukin 10; IL-18: interleukin 18; IL-1β: interleukin 1β; IL-5: interleukin 5; IL-6: interleukin 6; IL-7: interleukin 7; MIP-1α: Macrophage Inflammatory Protein-1α; SAA: Serum Amyloid A Protein; ICAM-1: Intercellular Adhesion Molecule 1; sVCAM1: soluble vascular cellular adhesion molecule 1; TARC: Thymus and activation-regulated chemokine; TNF-α: tumor necrosis factor α; M: mean; SD: standard deviation. p for a trend over the two cognitive categories was calculated using univariate analyses, when *p < 0.05 was considered statistically significant.

Binary logistic regression analysis was performed to examine the association of the seropositivity to different pathogens and inflammatory biomarkers. Only TNF-α levels were directly associated with C. pneumoniae seropositivity (OR = 2.08, CI_95% 1.0–4.1, p = 0.04). Mean levels of TNFα are increased in C. pneumoniae IgG seropositive participants compared to C. pneumoniae non-reactors (p = 0.04) (see Figure 2). Other biomarkers, such as IL6, IL1β and CRP, were not significantly associated with C. pneumoniae or any of the other pathogens evaluated.

Figure 2.

Distribution of TNF-α levels among participants as a function of specific antibodies seroreactivity against Chlamydia pneumoniae. The dot plot shows TNF-α levels distributed according to the seropositivity to C. pneumoniae. The horizontal lines show the average per group and error bars the standard deviation. *p < 0.05.

Association of cumulative exposures to infection agents with cognitive diagnosis and function

Multivariable logistic regression adjusted for age, sex, education, and income, revealed that with each additional pathogen exposure (IgG reactive test), subjects were more likely (OR = 1.51, CI_95% 1.01–2.26, p = 0.04) to have a clinical diagnosis of cognitive impairment (Figure 3).

Figure 3.

Likelihood (odds) of cognitive impairment as a function of number of co-exposures. Plot shows predicted probability of cognitive impairment based on a logistic regression model with number of cumulative exposures to infection agents as a continuous variable, adjusted for age, sex, education, and income. Error bars represent 95% confidence intervals.

We evaluated the relationship between the number of cumulative exposures to infectious agents tested and different tests to evaluate cognitive function. The violin plot showing the distribution of MMSE scores across the sum of cumulative exposures to infection agents shows a wider range and overall decrease in MMSE scores as the number of co-exposures increases. The density distribution highlights the increased range of scores below the 50th percentile for co-exposures of 3, 4, and 5 infectious agents (Figure 4). Regarding the TMT A test, there was a significant difference in the TMT A score based on the number of co-exposures (F = 5.37, p = 0.01). Posthoc analysis revealed significant differences between 2–3 versus 4 co-exposures (p = 0.02) and 2–3 versus 5 co-exposures (p = 0.01). In addition, regression analysis revealed a significant association between increasing cumulative number of exposures to infectious agents and poorer performance on the TMT A test (OR = 17.43, CI_95% 2.32–32.53, p = 0.02).

Figure 4.

Distribution of MMSE score among cumulative number of exposures to infection agents. The violin plot shows the density distribution of MMSE scores as a function of the number of exposures to infection agents.

Association of specific pathogen seropositivity sets with cognitive diagnosis

To examine specific pathogen interactions, we created a plot of the most common IgG presence/absence combinations of seven infections (see Figure 5). The most common co-exposure patterns were for IgG antibody presence of T. gondii, HVS-1, CMV, H. pylori, and C. pneumoniae (Group #1), T. gondii, HVS-1, CMV and C. pneumoniae (Group #2) and HVS-1, CMV and C. pneumoniae (Group #3). Among the fifteen most common combinations, the most frequent infections comprising the coinfection patterns were CMV in 14 of the 15; followed by HVS-1 (11/15), C. pneumoniae (10/15), T. gondii (8/15), and H. pylori (7/15).

Figure 5.

Plot of the most common combinations of IgG titer presence/absence for seven infections. The fifteen most prevalent were plotted left to right from most common to least.

Regression analysis was performed using the three most common combinations sets to evaluate the relationship with cognitive impairment (see Table 4). The presence of C. pneumoniae in multiple infection interactions, significantly increased the odds of receiving a clinical diagnosis of cognitive impairment for group #1 (OR = 4.07, CI_95% 1.24–13.36, p = 0.021), group #2 (OR = 2.95, CI_95% 0.97–8.98, p = 0.057) and group #3 (OR = 3.05, CI_95% 1.0–9.29, p = 0.050).

Table 4.

Association between infection groupings and cognitive impairment.

	Odds Ratio	95% Confidence Interval	P
Group #1
Toxoplasma gondii	1.79	0.60–5.31	0.30
HSV-1	0.85	0.12–6.30	0.88
CMV	NE^
Helicobacter pylori	1.54	0.64–3.71	0.34
C. pneumoniae	4.07	1.24–13.36	0.021
Group #2
Toxoplasma gondii	1.51	0.56–4.02	0.42
HSV-1	2.06	0.37–11.58	0.41
CMV	NE^
C. pneumoniae	2.95	0.97–8.98	0.057
Group #3
HSV-1	2.30	0.42–12.64	0.34
CMV	NE^
C. pneumoniae	3.05	1.00–9.29	0.050

From three separate logistic regression models (one for each grouping) with cognitive impairment as the dependent variable and covariates including age, sex, years of education, and income; *p < 0.05, ^ NE: not estimable due to perfect prediction.

Discussion

This study examined the association between cognition and specific antibodies against a variety of common infectious agents and inflammatory biomarkers in Panamanian older people. We found increased seropositivity to C. pneumoniae in cognitively impaired participants compared to those who were not cognitively impaired. Moreover, we report increased serum TNF-α levels in participants who were seropositive to C. pneumoniae compared with those who were not exposed to these bacteria. In addition, cumulative exposure to infectious agents was associated with the diagnosis of cognitive impairment and with cognitive function, specifically with the incremental time it takes to complete visuospatial task. Lastly, the presence of C. pneumoniae in multiple pathogen interactions significantly increased the likelihood of a diagnosis of cognitive impairment.

There is accumulating evidence of an association between infections and neurodegenerative diseases. However, evidence of the association of pathogens with cognitive impairment or AD in Hispanic populations is limited. A case-cohort study in which approximately half of the sample was Hispanic, found an association between IgG levels to common periodontal microbiota and the risk of developing AD.³⁷ Another study, in which 70% of the sample was classified as Hispanic, showed an inverse association between the burden of infection with C. pneumoniae, H. pylori, CMV, HSV-1 and HSV-2 and executive function, and evidenced greater seroreactivity against C. pneumoniae in participants with cognitive impairment relative to no cognitive impairment.³⁸ In contrast, another study found a significantly higher seroprevalence of anti-HSV IgG in participants with AD compared to controls, but not for C. pneumoniae.³⁹ Likewise, H. pylori specific IgG antibody levels were found to be significantly increased in the CSF and serum of AD patients, especially in CSF samples where anti-H. pylori IgG antibodies correlated with the severity of the disease.¹⁹

Our results suggest that Chlamydia pneumoniae (C. pneumoniae) may play an important role in cognitive decline associated with infection diseases. C. pneumoniae is a Gram-negative, obligate intracellular bacteria commonly associated with respiratory infections⁴⁰ and chronic inflammatory diseases.⁴¹ Among the plausible bacterial pathogens associated with AD, C. pneumoniae emerges as a consistent and promising candidate. There is evidence of its detection in the brains^42–45 and cerebrospinal fluid.⁴⁶ of AD patients, also its association with typical AD features like amyloid deposits, and neurofibrillary tangles,⁴⁵ and as an AD risk factor^23,47,48 suggest that C. pneumoniae is a promising bacterial candidate to contribute to AD risk.^41,49,50 Although the specific characteristics of C. pneumoniae or the pathophysiology of its infection that make it more strongly related to cognitive impairment are not fully understood, there is evidence that C. pneumoniae infection can reach the CNS via the olfactory pathway and infect the trigeminal nerves, causing increased amyloid deposition, glial activation and neuroinflammation.⁵¹ In our results, we found an association between C. pneumoniae seropositivity and an increase in TNF-α serum levels. TNF-α is an inflammatory cytokine produced by immune cells during inflammation, infection, and cancer.⁵² Serafini et al. (2024)⁵³ found increased levels of TNF-α in patients with subjective cognitive impairment and Bu et al. (2015)⁴⁸ found higher levels of inflammatory cytokines, including TNF-α, in individuals exposed to four or five infectious pathogens, including C. pneumoniae. These findings suggest that the interaction between C. pneumoniae and cognitive impairment is related to the induction of an inflammatory process.

We examined the associations with pathogen seropositivity and found an increase in the frequency of clinically diagnosed cognitive impairment and poorer performance on processing speed measured by TMT A test, with cumulative exposures to infection agents. The escalating pattern leads us to consider the possible relationship between the incidence of cognitive impairment and the occurrence of cumulative exposures to multiple infectious agents over the life course. In this regard, a study reports the association of infectious disease burden with worse processing speed, and deficits in learning and memory domains of cognitive function.⁵⁴ In contrast to our results, several authors have reported the association of H. pylori antibodies with worse global cognition.^55,56 In addition, a significant association between CMV and functional impairment for BADL performance, even after controlling for age and sex, was found.⁵⁷ Moreover, CMV seropositivity was associated with an increased risk of AD and a faster rate of decline in global cognition.⁵⁸ We also have separately found associations of T.gondii infections with poorer performance on certain cognitive tests (unplublished data).

When examining specific IgG pathogen reactivity sets, the most common exposure pattern was between T. gondii, HSV-1, CMV, H. pylori, and C. pneumoniae. Notably, the presence of C. pneumoniae in multiple infection interactions significantly increases the likelihood of a clinical diagnosis of cognitive impairment by a factor of 4. The effect of cumulative viral and bacterial load on cognition was investigated by Strandberg et al. (2003),⁵⁹ who tested seropositivity for HSV-1, HSV-2, CMV, C. pneumoniae, and Mycoplasma pneumoniae in an elderly Finnish population. They found an inverse association between MMSE score and viral load. Similarly, Bu et al.⁴⁸ found an independent association of seropositive antibody load to CMV, HSV-1, Borrelia burgdorferi, C. pneumoniae and H. pylori with AD. In addition, this infection burden was inversely correlated with cognitive performance. A study published by Carter in 2017⁶⁰ reports that several host-pathogen interactomes are associated with AD genome-wide association study (GWAS) genes, the AD hippocampal transcriptome, and plaque or tangle proteins, supporting polymicrobial involvement in AD.

Our study had several limitations. First, our data are cross-sectional and thus did not, allow us to draw causal conclusions and examine the influence of pathogen infection on the progression of cognitive impairment and AD. In addition, although the covariates age, sex, years of study and income were used to control the regression analysis, other variables such as physical activity, BMI and other health conditions that were not considered during the analysis could have affected the results obtained. Also, since seropositivity to some pathogens tends to appear together, like CMV and HSV-1, we cannot exclude that overlapping infections could affect the results. However, strengths of this study include the ability to explore the association of inflammatory biomarkers and to examine the interaction of multiple pathogens, providing new insights into the relationship between various infectious diseases and neurological pathologies, and findings that draw attention to the possible critical contribution of C. pneumoniae.

Conclusion

This study examined the association between exposure to several common infectious diseases and cognitive impairment in a sample of older people in Panama. These findings contribute to the knowledge of the factors triggering cognitive decline in older adults. Our findings on the association of cognitive decline with cumulative exposure to infectious agents, including the specific role of C. pneumoniae in this interaction, justify further study of the multi-pathogen interface in neurodegenerative diseases and the underlying mechanisms. In addition, these findings could help open the way for further future research related to the implementation of intervention strategies such as better infection control or anti-inflammatory treatments in older adults.

Footnotes

Acknowledgements

All authors thank the PARI-HD study participants for their contribution to the research, and the past and present PARI-HD staff who assisted with data collection. We also acknowledge the literature review assistance of Kira Wiesinger.

ORCID iDs

Giselle A Rangel

Berta A Muñoz

Morgan Ramirez

Kristen Wroblewski

Alcibiades E Villarreal

Diana C Oviedo

Maria B Carreira

Rima McLeod

Gabrielle B Britton

Ethical considerations

This study was approved by the Bioethics Committee, Scientific Committee, and Medical Education Subdivision of the Dr Arnulfo Arias Madrid Hospital Center (SDMdeDEI-CH-248-12) on April 10, 2012. This research was conducted ethically in accordance with the World Medical Association Declaration of Helsinki.

Consent to participate

All participants provided written informed consent prior to enrolment in the study.

Author contributions

Giselle A Rangel: Conceptualization; Formal analysis; Formal analysis; Investigation; Methodology; Project administration; Writing – original draft; Writing – review & editing.

Berta A Muñoz: Formal analysis; Investigation; Resources; Writing – original draft.

Morgan Ramirez: Investigation; Software; Visualization; Writing – original draft.

Kristen Wroblewski: Formal analysis; Methodology; Software; Validation; Visualization; Writing – review & editing.

Alcibiades E Villarreal: Formal analysis; Writing – review & editing.

Diana C Oviedo: Formal analysis; Methodology; Writing – original draft; Writing – review & editing.

Maria B Carreira: Investigation; Writing – review & editing.

Rima McLeod: Conceptualization; Validation; Writing – review & editing.

Gabrielle B Britton: Conceptualization; Formal analysis; Formal analysis; Methodology; Supervision; Validation; Writing – review & editing.

Funding

This research was supported by INDICASAT-AIP (IGI-2021-006), Secretaría Nacional de Ciencia, Tecnología e Innovación (SENACYT), grant number [FID-22-092] and Sistema Nacional de Investigación (SNI) de Panamá by SENACYT, grant numbers [SNI-074-2022, SNI-044-2023, SNI-64-2021, SNI-063-2023 and SNI-66-2021]. All funding sources had no role in the study design or writing of the manuscript.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The data supporting the findings of this study are available in the article. Additional information is available upon request from the corresponding author.

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