Predictive value of serum inflammatory factors combined with p-tau181 for anxiety and depression in Alzheimer’s disease patients

Abstract

BACKGROUND

Comorbid anxiety and depression are common among patients with Alzheimer’s disease (AD), significantly impacting these individuals’ disease trajectory and quality of life. Serum inflammatory markers and phosphorylated tau protein appear to be key predictors of these psychiatric disturbances emerging in our population of AD patients, although the mechanisms governing their combined predictive capacity have yet to be determined.

AIM

To examine the predictive effects of p-tau181 and serum inflammatory markers [interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α)] on anxiety and depression in AD patients.

METHODS

Retrospective data were collected from 125 patients diagnosed with AD, who were admitted to the neurology department of our center from January 2022 to December 2024. Based on the results of Hospital Anxiety and Depression Scale (HADS) anxiety and depression status, patients were stratified into two groups: Anxiety-depression group (HADS-A ≥ 8 or HADS-D ≥ 8, n = 56), and non-anxiety-depression group (HADS-A < 8 and HADS-D < 8, n = 69). The concentrations of serum IL-6, TNF-α and plasma p-tau181 were measured using enzyme-linked immunosorbent assay and single-molecule array technology. Receiver operating characteristic curve analysis and logistic regression were conducted to assess the predictive capacity of these biomarkers in anxiety and depression.

RESULTS

The study population included 125 AD patients (mean age 72.8 ± 8.6 years, males: Females ratio of 44.8%:55.2%). Out of the 125 patients, anxiety and/or depression developed in 56 (44.8%) during the observation period. The anxiety-depression group (n = 56) had significantly increased serum IL-6 levels (15.8 ± 4.2 pg/mL vs 9.6 ± 3.1 pg/mL, P < 0.001), TNF-α levels (28.4 ± 6.8 pg/mL vs 18.2 ± 5.4 pg/mL, P < 0.001) and p-tau181 concentrations (24.6 ± 5.8 pg/mL vs 16.4 ± 4.2 pg/mL, P < 0.001) compared with non-anxiety-depression group (n = 69). From multivariate logistic regression, three independent risk factors were identified: Increased IL-6 [odds ratio (OR) = 2.86, 95% confidence interval (CI): 1.42-5.76, P = 0.003], increased TNF-α (OR = 2.54, 95%CI: 1.28-5.04, P = 0.008) and increased p-tau181 (OR = 3.12, 95%CI: 1.56-6.24, P = 0.001). Overall prediction model performance was excellent (area under curve = 0.892, 95%CI: 0.834-0.950) with sensitivity of 82.1% and specificity of 85.5%, far better than that for predictors on an individual biomarker basis (P < 0.001).

CONCLUSION

We found that the combination of serum inflammatory markers (IL-6, TNF-α) as well as p-tau181 showed excellent predictive for anxiety and depression on AD patients. Conclusion: This multimarker panel shows promising preliminary predictive performance for identifying AD patients at higher risk for anxiety and depression; however, these findings are hypothesis-generating and require prospective external validation in multicenter cohorts before clinical implementation.

Key Words: Alzheimer disease; Anxiety; Depression; Interleukin-6; Tumor necrosis factor-alpha; Phosphorylated tau; Biomarkers; Retrospective study

Core Tip: Anxiety and depression are common neuropsychiatric complications that markedly worsen the prognosis, as well as the quality of life in patients with Alzheimer’s disease. This study uncovers that the predictive value for anxiety and depression is improved by combining serum inflammatory markers (interleukin-6 and tumor necrosis factor alpha) with p-tau181 instead of individual biomarkers. These findings highlight the importance of comprehensive biomarker assessment as part of standard of care in Alzheimer’s disease to identify at-risk patients for early treatment.

INTRODUCTION

Alzheimer’s disease (AD) is the most common type of dementia, impacting around 50 million people worldwide and serving as one of the top causes of disability in older adults. According to the World Health Organization, AD patients are expected to triple by 2050, putting great pressure on health systems around the world[1]. Dementia has become a major public health problem in China, with the prevalence rate of dementia around 6% for older people aged 60 and over, and trends of annual increase[2]. While there have been considerable improvements in diagnostic and therapeutic approaches which are associated with better identification and management of AD, patients often experience a number of neuropsychiatric complications, most commonly, anxiety and depression which are some of the more recognized psychiatric comorbidities that have received substantial clinical attention[3].

In AD patients, anxiety and depression present as mood disturbances that appear contemporaneous with or follows the onset of cognitive impairment and are characterized by a chronic state of worry together with low mood, loss of interest in pleasurable activities, and sleep disturbances[4]. Epidemiological investigations show a wide range of prevalence rates for anxiety and depression in AD patients across studies, with worldwide figures in the region of 30%-60%[5]. Incidence in early-stage AD ranges from 25% to 40%, but can reach up to as high as 40% to 70% in those with moderate to severe stages of the disease[6]. Anxiety and depression not only significantly reduce patients’ quality of life and social functioning but also accelerate cognitive decline, increased caregiver burden, prolonged hospital stays, higher healthcare costs, and may increase mortality[7]. Thus, a deeper understanding of the development and predictors of anxiety and depression in AD is essential for surmounting patients’ prognosis.

Current investigations focused on anxiety and depression pathogenesis in AD mainly target three domains: Neurobiological, neuroinflammatory, and neurodegenerative components[8]. Neurobiological cascades mainly include functional malfunctions in neurotransmitter systems including serotonin and norepinephrine, as well as hypothalamic-pituitary-adrenal axis activation[9]. Recent years have seen increasing appreciation for the role of neuroinflammation in the pathogenesis of AD-related psychiatric symptoms. Inflammatory mediators such as the interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α), which are important neuroinflammation mediators, may also affect depression and anxiety development through roles in neurotransmitter metabolism, neuroplasticity, and neuronal activity[10]. Meanwhile, phosphorylated tau protein at threonine 181 (p-tau181) represents a blood-based biomarker correlating with AD pathology and tau-induced neurodegeneration that may also contribute to psychiatric symptoms[11].

Neuroinflammation refers to a state of chronic low-grade inflammation in the central nervous system, which is one of the most common features associated with AD patients[12]. The neuroinflammatory hypothesis of depression has emerged as one of the major paradigms that used to explain mood disorders and it has been extensively validated in terms of its relation with depression or neurodegenerative[13]. However, studies exploring the effects of single inflammatory biomarkers on anxiety and depression in AD have reported inconclusive findings[14]. Some studies have associated increased IL-6 and TNF-α with the development of depression in AD, but others failed to find such relations[15]. Disparities such as these may be related to sample size, follow-up time or cutoff definitions for biomarkers. Importantly, previous studies mainly assessed individual biomarker predictive contributions without addressing the integrated predictive utility provided by inflammatory factors and tau pathology markers[16].

While previous evidence also reveals associations between inflammatory biomarkers and anxiety-depression incidence[16], combined prediction of multiple biomarkers has not been systematically investigated in AD patients[17]. This is especially true regarding the Chinese population, as similar research would be even less available[18]. Considering that Chinese AD patients may have different etiology, biomarker profiles, and treatment patterns in comparison with western populations, performing population-specific research holds high clinical significance[19]. On this basis, the current study adopts a retrospective cohort approach to investigate the associations between serum IL-6, TNF-α and p-tau181 at baseline with anxiety and depression in patients with AD, hoping to address three fundamental scientific questions: (1) Whether high levels of inflammatory factors or p-tau181 indicate an independent risk factor for developing subsequent anxiety-depression; (2) Whether there is a dose-response association between biomarker values and severity of anxiety-depression; and (3) Whether integrative effects exist when more than two biomarkers are combined rather than using individual ones[20]. This investigation aims to build a new theoretical basis for prevention and early identification of anxiety and depression in AD patients, provide a foundation for personalized prevention and treatment strategies, and ultimately improve overall prognosis and life quality for patients with AD.

MATERIALS AND METHODS

Study design

We used a retrospective design to explore the link between indices of anxiety/depression (clinician reported) and serum inflammatory markers (IL-6, TNF-α) and p-tau181 in AD using clinical data and biomarker measurements. All biomarker measurements (IL-6, TNF-α and p-tau181) were obtained at the time of patient enrolment when concurrent HADS assessment was performed; both procedures were completed in the same clinical encounter prior to any initiated treatment. The study plan was approved by the Medical Ethics Committee of our hospital (approval No. CZ20240228) and all data collection and utilization adhered to the principles of the Declaration of Helsinki.

Study population

All AD patients in whom this examination was performed in the neurology department in our institution from January 2022 to December 2024 were included as study participants. Inclusion criteria included the following: (1) Age from 55 to 90 years; (2) Diagnosis of probable AD using National Institute on Aging-Alzheimer’s Association criteria; (3) Mini-Mental State Examination (MMSE) scores of 10-26 points indicating mild to moderate dementia; (4) Availability of complete medical records including detailed past medical history, medication history and results of laboratory examinations; (5) Available serum samples for biomarker analysis; and (6) Ability to complete psychiatric assessment scales with help from caregivers.

Exclusionary criteria included: (1) The presence of any type besides AD (e.g., vascular dementia, frontotemporal dementia, Lewy body dementia etc.) or other central nervous system diseases that caused cognitive impairment; (2) Lifetime history of depressive disorder (s), generalized anxiety disorder and other major psychiatric disorders prior to AD stage; (3) Current treatment with antidepressant or anxiolytic medications within the past 3 months before screening for this study; (4) History of severe cardiac dysfunction, hepatic dysfunction, renal failure, malignant tumors or serious medical conditions other than those aforementioned as well as life-threatening diseases; (5) Infectious disease diagnoses in active state or chronic autoimmune disorders which may affect inflammatory markers’ levels; (6) Total aphasia before interaction that rendered the patient unable to cooperate during scale assessment and severely advanced dementia that not able to assess by any means; and (7) Documented details on either background medical history or biomarker data inadequately detailing to allow inclusion into analysis. Based on these criteria, 125 AD patients who were eligible participated.

Biomarker measurement and grouping

The following 3 biomarkers were evaluated in this study: (1) IL-6: Serum IL-6 concentrations were determined using enzyme-linked immunosorbent assay kits (R&D Systems, Minneapolis, MN, United States) with a detection range of 0.7-600 pg/mL and an intra-assay coefficient of variation < 5%; (2) TNF-α: Serum TNF-α concentrations were determined using enzyme-linked immunosorbent assay kits (R&D Systems, Minneapolis, MN, United States) with a detection range of 0.5-1000 pg/mL and an intra-assay coefficient of variation < 6%; and (3) Phosphorylated tau181 (p-tau181): Plasma p-tau181 concentrations were ascertained by single-molecule array technology (Simoa) (Quanterix, Billerica, MA, United States) with a detection limit of 0.019 pg/mL; the intra-assay coefficient of variation < 8%.

Assessment of anxiety and depression

Patients were assessed for anxiety and depression status at enrollment. Patients’ anxiety and depression status were evaluated using the Hospital Anxiety and Depression Scale (HADS). HADS is a well-established psychiatric assessment tool commonly used in clinical settings and medical research with proven reliability and validity among elderly individuals and those with limited cognition. The scale included 14 items that were classified into two subscales: The anxiety (HADS-A) and depression (HADS-D) both containing 7 items each, which is scored from 0 points to 3 points based on severity for each item.

Clinically significant symptoms of anxiety or depression were defined as a HADS-A score ≥ 8 and/or a HADS-D subscale score ≥ 8, that is being diagnosed with anxiety-depression. Based on related domestic and foreign studies and clinical guidelines, the diagnostic criterion has better clinical applicability. All scale assessments were performed by professionally trained neuropsychologists prior to which consistency training was done in order to ensure that the assessment was not dependent on others. All the assessment tools employed in this study consisting of HADS as anxiety-depression screening, MMSE for cognitive function, Neuropsychiatric Inventory to assess behavioral symptoms and Activities of Daily Living scale for functional status are internationally validated and widely used with established reliability and validity in Chinese AD population enabling finding reproducibility and cross-cultural comparability.

Statistical analysis

The data were analyzed using SPSS 26.0, and P < 0.05 was considered statistically significant. Continuous variables are expressed as mean ± SD or median (interquartile range) according to the result of normality test, and categorical variables are described as n (%). Between-group comparisons of continuous variables were performed using t-tests or Mann-Whitney U tests, and categorical variables were compared using χ² or Fisher’s exact tests. Spearman correlation was calculated to determine the relationships between biomarker levels and anxiety-depression scores. We used logistic regression analysis to identify associations between biomarkers and anxiety-depression, with univariate (P < 0.1) followed by multivariate modeling using the enter method to estimate odds ratios (ORs) and 95% confidence intervals (CIs). Individual and combined biomarker predictive performance was assessed using receiver operating characteristic (ROC) curve analysis.

RESULTS

Baseline characteristics of study subjects

A total of 125 AD subjects were enrolled in this study, including 56 males (44.8%) and 69 females (55.2%). Their ages range from 55 years to 88 years with a mean age of 72.8 ± 8.6 years old. No significant differences were found between the two groups in terms of age and gender composition (P > 0.05). The MMSE scores at the time of enrollment were between 10 points and 26 points, with a mean of 18.4 ± 4.2 points. For biomarker level composition, high IL-6 (> 10 pg/mL) was observed in 52.8% (66/125) of patients, elevated TNF-α > 20 pg/mL in 48.0% (60/125), and p-tau181 (> 18 pg/mL) in 55.2% (69/125). Biomarker levels were significantly elevated in the anxiety-depression group compared to the non-anxiety-depression group (all P < 0.001, Table 1).

Table 1 Baseline characteristics of study subjects, n (%)/mean ± SD.

Characteristics	Total (n = 125)	Non-anxiety-depression group (n = 69)	AD group (n = 56)	P value
Age (years)	72.8 ± 8.6	71.9 ± 8.2	73.9 ± 9.0	0.198
Male gender	56 (44.8)	32 (46.4)	24 (42.9)	0.696
MMSE score	18.4 ± 4.2	19.4 ± 4.2	17.2 ± 4.0	0.004
IL-6 (pg/mL)	12.4 ± 4.8	9.6 ± 3.1	15.8 ± 4.2	< 0.001
TNF-α (pg/mL)	22.8 ± 7.6	18.2 ± 5.4	28.4 ± 6.8	< 0.001
p-tau181 (pg/mL)	20.1 ± 6.2	16.4 ± 4.2	24.6 ± 5.8	< 0.001

AD: Anxiety-depression; MMSE: Mini-Mental State Examination; IL-6: Interleukin-6; TNF-α: Tumor necrosis factor-alpha; p-tau181: Phosphorylated tau-181.

Prevalence of anxiety and depression

Among the 125 patients with AD, 56 met criteria for anxiety and/or depression resulting in a total prevalence of 44.8%. HADS-A score range was between 0 and 18 points, with a mean of 7.2 ± 3.8 points. HADS-D scores were between 0 and 19 points with a mean of 7.8 ± 4.1 points. Of the 56 patients with anxiety-depression, 24 had isolated anxiety (HADS-A ≥ 8, HADS-D < 8), 18 had isolated depression (HADS-A < 8, HADS-D ≥ 8) and 14 had comorbid anxiety and depression (both ≥ 8). The mean HADS total score for anxiety-depression patients was 18.6 ± 5.4 points, which was significantly higher than non-anxiety-depression patients (8.2 ± 3.6 points, P < 0.001). The most common anxiety-depression symptoms developed were persistent worry (100%), sleep disturbances (91.1%), loss of interest (87.5%), fatigue (82.1%) and irritability were seen in 75.0% patients of anxiety-depression respectively (Figure 1A). The levels of biomarkers were significantly higher in anxiety -depression group (Figure 1B).

Figure 1 Comparison of different groups. A: Comparison of Hospital Anxiety and Depression Scale scores; B: Comparison of biomarker levels between groups. ^aP < 0.05. HADS: Hospital Anxiety and Depression Scale; IL-6: Interleukin-6; TNF-α: Tumor necrosis factor-alpha; p-tau181: Phosphorylated tau-181.

Association of biomarkers with anxiety-depression severity

A Spearman correlation analysis was conducted to evaluate the association between biomarker concentrations and severity scores for anxiety-depression. HADS total scores were all significantly positively correlated with all three biomarkers. IL-6 levels were moderately positively correlated with HADS total scores (r = 0.486, P < 0.001). Similarly, TNF-α concentrations were positively correlated (r = 0.512, P < 0.001). The strongest association with anxiety-depression severity was obtained using p-tau181 levels (r = 0.558, P < 0.001). When considered independently by subscale, all biomarkers correlated more strongly with the HADS-D than the HADS-A, indicating a greater association with depressive symptoms (Table 2).

Table 2 Correlation between biomarker levels and Hospital Anxiety and Depression Scale scores.

Biomarker	HADS-A (r)	P value	HADS-D (r)	P value	HADS-Total (r)
IL-6 (pg/mL)	0.418	< 0.001	0.524	< 0.001	0.486
TNF-α (pg/mL)	0.445	< 0.001	0.556	< 0.001	0.512
p-tau181 (pg/mL)	0.482	< 0.001	0.598	< 0.001	0.558

HADS-A: Hospital Anxiety and Depression Scale-Anxiety subscale; HADS-D: Hospital Anxiety and Depression Scale-Depression subscale; HADS: Hospital Anxiety and Depression Scale; IL-6: Interleukin-6; TNF-α: Tumor necrosis factor-alpha; p-tau181: Phosphorylated tau-181; r: Spearman correlation coefficient.

Univariate and multivariate analysis

Using univariate logistic regression analysis, several factors were associated with anxiety-depression occurrence. Compared with patients aged < 75 years, older patients (aged ≥ 75 years) had a higher risk of anxiety-depression (OR = 1.86, 95%CI: 0.92-3.76, P = 0.086). Females were more inclined towards anxiety-depression than males (OR = 1.72, 95%CI: 0.88-3.36, P = 0.112). MMSE score was significant as an important risk factor of current anxiety-depression, with patients having MMSE scores ≤ 18 being associated with a 2.24-fold higher risk than those having scores > 18 (95%CI: 1.12-4.48, P = 0.022). High IL-6 (> 10 pg/mL) was significantly associated with anxiety-depression occurrence (OR = 3.24, 95%CI: 1.58-6.65, P = 0.001). High level of TNF-α (> 20 pg/mL) also had strong association (OR = 2.92, 95%CI: 1.44-5.92, P = 0.003). Among the variables, p-tau181 (> 18 pg/mL) had the most significant association (OR = 3.56, 95%CI: 1.72-7.38, P < 0.001). Univariate analysis variables (P < 0.1) were entered into a multivariate logistic regression model, and finally found three independent risk factors for the occurrence of anxiety-depression (Table 3). Moreover, persistent elevation of IL-6 (+) was a significant independent risk factor for anxiety-depression (OR = 2.86, 95%CI: 1.42-5.76, P = 0.003), meaning that patients with IL-6 > 10 pg/mL had a risk of developing anxiety-depression which was almost or equal to three times higher than those whose levels were within normal range after adjustment for other confounding factors. Significantly elevated TNF-α was also an independent risk factor (OR = 2.54, 95%CI: 1.28-5.04, P = 0.008). The strongest independent correlate was elevated p-tau181 (OR = 3.12, 95%CI: 1.56-6.24, P = 0.001). Age and gender were not statistically significant in the multivariate analysis (P > 0.05). The model provided good fit (Hosmer-Lemeshow test, P = 0.682) with excellent discriminative ability (C-statistic = 0.842, Figure 2).

Figure 2 Forest plot of multivariate logistic regression analysis. Orange squares indicate significant associations; gray squares indicate non-significant associations. ^aP < 0.05. IL-6: Interleukin-6; TNF-α: Tumor necrosis factor-alpha; p-tau181: Phosphorylated tau-181; MMSE: Mini-Mental State Examination; OR: Odds ratio; CI: Confidence interval.

Table 3 Univariate analysis of risk factors for anxiety and depression.

Risk factor	Odds ratio	95%CI	P value
Age ≥ 75 years	1.86	0.92-3.76	0.086
Female gender	1.72	0.88-3.36	0.112
MMSE ≤ 18	2.24	1.12-4.48	0.022
Elevated IL-6 (> 10 pg/mL)	3.24	1.58-6.65	0.001
Elevated TNF-α (> 20 pg/mL)	2.92	1.44-5.92	0.003
Elevated p-tau181 (> 18 pg/mL)	3.56	1.72-7.38	< 0.001

CI: Confidence interval; MMSE: Mini-Mental State Examination; IL-6: Interleukin-6; TNF-α: Tumor necrosis factor-alpha; p-tau181: Phosphorylated tau-181.

ROC curve analysis

Individual and combined biomarker predictive performance was evaluated using ROC curve analysis. Analyzing individual biomarkers showed moderate predictive capability: IL-6 had an area under curve (AUC) of 0.782 (95%CI: 0.704-0.860), optimal cutoff of 12.4 pg/mL (sensitivity 75.0%, specificity 72.5%); TNF-α alone produced an AUC of 0.768 (95%CI: 0.688-0.848), with optimal cutoff of 22.6 pg/mL (sensitivity 71.4%, specificity 75.4%), and p-tau181 was associated with an AUC of 0.796 (95%CI: 0.720-0.872), optimal cutoff 19.8 pg/mL (sensitivity 78.6%, specificity 71.0%). The predictive model for all three combined achieved an excellent AUC of 0.892 (95%CI: 0.834-0.950), which was much higher than that of using each individual biomarker (DeLong test, all P < 0.001). The sensitivity and specificity of the combined model were 82.1% and 85.5%, respectively, positive predictive value was 82.1%, negative predictive value was 85.5% (Figure 3).

Figure 3 Receiver operating characteristic curve analysis for predictive value of biomarkers. The combined model achieved the highest area under curve (0.892). ROC: Receiver operating characteristic; IL-6: Interleukin-6; TNF-α: Tumor necrosis factor-alpha; p-tau181: Phosphorylated tau-181; AUC: Area under curve.

Risk stratification based on number of elevated biomarkers

To further investigate the cumulative effect of raised biomarkers on anxiety-depression risk, patients were stratified into four groups according to the number of raised biomarkers (0, 1, 2 or 3 raised). Results showed a clear dose-response relation with the number of elevated biomarkers and incidence of anxiety-depression. Incidence of anxiety-depression in patients without any elevated biomarker was lowest (12.5%) and highest (78.9%) when all three biomarkers were elevated. The ORs were graded for anxiety-depression, with the increment of risk by one elevated biomarker showing an increasing trend (P for trend < 0.001). Compared to patients without elevated biomarkers, those with one elevated biomarker had a 2.4-fold higher risk (OR = 2.42, 95%CI: 0.68-8.62), two elevated biomarkers underwent a 6.8-fold higher risk (OR = 6.84, 95%CI: 2.08-22.52) and the patients with all three elevation in the pertinent biomarkers have a statistic significantly increased risk of developing anxiety-depression (OR = 26.32, 95%CI: 7.24-95.68, Table 4).

Table 4 Risk stratification based on number of elevated biomarkers.

Number of elevated biomarkers	n	AD incidence (%)	OR (95%CI)	P value
0 (low risk)	24	12.5	Reference	-
1 (low-intermediate risk)	31	25.8	2.42 (0.68-8.62)	0.172
2 (intermediate-high risk)	32	50.0	6.84 (2.08-22.52)	0.002
3 (high risk)	38	78.9	26.32 (7.24-95.68)	< 0.001
P for trend	-	-	-	< 0.001

Elevated biomarkers defined as: IL-6 > 10 pg/mL, TNF-α > 20 pg/mL, p-tau181 > 18 pg/mL. AD: Anxiety-depression; OR: Odds ratio; CI: Confidence interval.

Sensitivity analysis

We performed several sensitivity analyses to check the stability of results. The combined model had very good predictive performance even when anxiety-depression diagnostic criteria based on HADS were modified to reflect moderate-severe symptoms (HADS-A ≥ 10 or HADS-D ≥ 10) (AUC = 0.876, 95%CI: 0.812-0.940). Specifically, in the gender-stratum analysis, the combined model showed a good AUC of 0.884 (95%CI: 0.802-0.966) for male patients (n = 56 total males in cohort) and an AUC of 0.898 (95%CI: 0.828-0.968) for female patients (n = 69 total females in cohort), while no significant interaction between genders was noticed (P = 0.724). When stratified by cognitive severity, the predictive value was greater in patients with moderate dementia (MMSE 10-18, AUC = 0.912) than those with mild dementia (MMSE 19-26, AUC = 0.856). Re-analysis removing patients with C-reactive protein > 10 mg/L, to eliminate the influence of systemic inflammation, also still supported the predictive value of the combined biomarker panel (AUC = 0.878, Table 5).

Table 5 Sensitivity analysis and subgroup analysis.

Analysis type	Subgroup	AUC (95%CI)	P value
Primary analysis	Overall (n = 125)	0.892 (0.834-0.950)	-
Stricter criteria	HADS ≥ 10	0.876 (0.812-0.940)	-
Gender-stratified	Male (n = 56)	0.884 (0.802-0.966)	0.724
Gender-stratified	Female (n = 69)	0.898 (0.828-0.968)	-
Cognitive severity	Moderate (MMSE 10-18)	0.912 (0.856-0.968)	0.048
Cognitive severity	Mild (MMSE 19-26)	0.856 (0.772-0.940)	-

AUC: Area under curve; CI: Confidence interval; HADS: Hospital Anxiety and Depression Scale; MMSE: Mini-Mental State Examination.

DISCUSSION

This retrospective study provides significant evidence for the relationship between serum inflammatory markers with p-tau181to predict anxiety and depression for AD patients, increasing our understanding of the complex pathophysiological mechanisms in neuropsychiatric complications that are observable in degenerative diseases. The results have significant ramifications for clinical care and underscore the need for broadening AD management strategies that can consider both cognitive and neuropsychiatric features of disease. The interrelationship of AD pathology and anxiety-depression is complex. Our results support three possible mechanistic models: First, where neuroinflammation and tau pathology serve as common upstream drivers of both impairments in cognition and disturbance of monoaminergic neurotransmission (predisposing to anxiety-depression). Chronic neuroinflammation and synaptic dysfunction driven by raised inflammatory markers may form a substrate that independently impacts both cognitive and emotional trajectories. Second, in observational studies, anxiety and depression have been associated with accelerated AD progression; chronic stress correlates with hippocampal atrophy, increased cortisol production, and heightened neuroinflammation - all of which are associated with neurodegeneration, though causality cannot be inferred from cross-sectional data. Third, neurodegeneration associated with AD could directly compromise limbic system structures, building a positive feedback loop that exacerbates both cognitive and emotional outcomes. The fact that we had shown elevated biomarkers and poorer cognitive function among our anxiety-depression patients provides some support for the idea that these outcomes were likely interrelated rather than two sides of a coin. Longitudinal studies with repeated measures are needed to establish the temporal sequence and directionality of these relationships, which may identify important intervention windows to disrupt this harmful cycle.

This observed association of inflammatory biomarkers, p-tau181 and anxiety-depression can be interpreted via several interrelated pathophysiological pathways. Background: Founded on the neuroinflammatory hypothesis of depression (major depressive disorder), chronic inflammation may disrupt neurotransmitter system and neuroplasticity mechanism, leading to mood disorders[21]. As such, this mechanism holds especially salient implication in AD-related anxiety-depression context due to the “double hit” of chronic neuroinflammation and neurodegenerative pathology on brain structure and function.

High IL-6 secretion promotes hypothalamic-pituitary-adrenal axis activation, including higher levels of cortisol, lower glucocorticoid receptor sensitivity and impaired negative feedback. These changes may be associated with dysregulation of stress responses in the limbic structures, most notably, the prefrontal circuits responsible for mood regulation. In addition, IL-6 induces indoleamine 2,3-dioxygenase upregulation, which reroutes tryptophan metabolism from serotonin towards the kynurenine pathway to produce metabolites with neurotoxic features. TNF-α mediates anxiety-depression by several convergent pathways. Consequently, chronically elevated TNF-α provokes blood-brain barrier breakdown through tight junction protein disruption and facilitates the infiltration of peripheral inflammatory signals into the central nervous system. Second, TNF-α plays a direct role in modulating serotonergic and dopaminergic neurotransmission through its influence on transporter action and receptor expression. Third, increased TNF-α trigger microglial activation and neuron apoptosis of especially hippocampal and prefrontal areas. Elevated p-tau181, reflecting tau pathology, is associated with psychiatric manifestations such as anxiety-depression; indirect inflammatory mechanisms may also contribute. Increased p-tau181 reflects tau pathology in limbic structures such as the entorhinal cortex and hippocampus, structures essential for memory function and emotional regulation. Therefore, tau accumulation ligates synaptic function, inhibits neuroplasticity and provokes neuroinflammatory responses that further saturate the mood dysregulation process.

Chronic neuroinflammation present in AD patients creates a pro-inflammatory environment, making such individuals susceptible to mood-disorders even pre-dementia[22]. The existence of a multitude of heightened biomarkers likely exacerbates this process with synergistic effects upon neurotransmitter systems, neuroplasticity processes and limbic circuit activity. Such mechanisms not only heighten anxiety-depression risk, but also diminish the brain’s resilience and recovery potential. Another important pathway was the inflammatory cascade connecting AD pathology to anxiety-depression. Elevated IL-6 and TNF-α were associated with chronic microglial activation, defined by continuous secretion of pro-inflammatory cytokines and reactive oxygen species[23]. This neuroinflammatory state enhances tau phosphorylation and aggregate formation; together, this forms a positive feedback loop sustaining both neurodegenerative and neuropsychiatric pathology[24]. These findings as an integrated biomarker panel also predict anxiety-depression providing strong recommendations for refining clinical treatment and patient management approaches across populations. The current AD care protocols focus mainly on cognitive aspects and behavioral symptoms but provide limited systematic attention to the early identification of anxiety-depression risk[25]. These findings imply that routine assessment of inflammatory markers and p-tau181 may represent a helpful biomarker for early identification of patients at high risk of developing anxiety-depression, and could inform early intervention strategies.

That the combined biomarker panel showed greater predictive value than either individual biomarkers emphasizes that multiple pathophysiologic pathways (e.g., neuroinflammation vs tau pathology) work together to influence neuropsychiatric outcomes. This result concurs with emerging neurodegenerative disease research evidence in which biomarker combinations have demonstrated enhanced diagnostic and prognostic accuracy. From a health care system perspective, early identification of high-risk patients during the AD disease trajectory may allow for more efficient allocation of mental health resources and targeted prevention approaches[26]. This method may be of particular benefit in health care environments that lack psychiatry resources, as risk stratification tools can help identify which patients would gain the most from early intervention[27].

Our results have immediate practical implications for AD management. Combined serum IL-6, TNF-α, and p-tau181 offer an implementable screening strategy via clinically applicable biomarker assessments. We suggest a risk stratification strategy based on the number of elevated biomarkers: Low (0 elevated), intermediate (1-2 elevated) and high-risk (3 elevated) status with increased intensity of interventions. Results: System-wide anxiety-depression monitoring at baseline, 3, 6, and 12 months should be performed on high-risk patients with subsequent proactive psychological counseling and prophylactic therapy. This approach could easily be incorporated into current AD care pathways, memory clinics and outpatient follow-up without placing a substantial resource burden on services. The combined prediction model generated in this study (AUC = 0.892) is able to discriminate very well for clinical decision support, which could justify integrating the model into an electronic health record system for automatic generation of risk alerts.

The overall incidence of anxiety-depression in this study is consistent with previous available meta-analyses, which report rates between 30% and 60%, depending on assessment used, diagnostic criteria or characteristics of the population. However, much of the prior work has focused on Western populations and little information exists specifically for the Chinese population, which may differ appreciably in terms of cultural factors, health care delivery patterns and disease profiles. The high levels of inflammatory markers detected in our study population speaks to the reliance on inflammation as a component of AD pathophysiology, that is progressively being acknowledged as an avenue for treatment. These results have important implications for AD management strategies, as anti-inflammatory approaches may serve a dual purpose involving both cognitive and neuropsychiatric outcomes.

There are several limitations of this study that need to be pointed out. Meanwhile, the exclusion of patients with prior psychiatric diagnoses in DOSE or current psychotropic medication use at baseline is methodologically sound to generate a clean sample but reduces generalizability to broader AD populations with these common comorbidities and concurrent treatments. We added a CONSORT style patient selection flow diagram with the number of patients screened, excluded at each criterion and ultimately enrolled so that readers can better assess potential selection bias (see Main Manuscript page 5 Lines 07-12). Second, the predictive model was built and internally validated in a single-center cohort of 125 patients, which did not include an independent validation dataset; thus, the reported AUC of 0.892 should be interpreted with caution.

The relationship between individual inflammatory biomarkers and anxiety-depression in AD has shown heterogeneous results in international studies. Whereas in some European studies IL-6 was identified as a significant predictor, other inflammatory markers like TNF-α presented stronger associations. These discrepancies could be due to variations in population genetics, environmental factors, healthcare systems, and biomarker measurement techniques. Our finding that combined biomarkers offer better prediction than individual markers may help resolve these apparent contradictions in the literature by highlighting the significance of comprehensive biomarker assessment. This has several important implications for future research. Prospective longitudinal studies with more frequent biomarker assessments could provide a clearer picture of the temporal dynamics of anxiety-depression development, facilitating the identification of critical intervention windows. Mechanistic studies probing the interaction between inflammation, tau pathology, and neurotransmitter systems could also clarify biological processes linking this biomarker to mood disorders[28,29].

The combined use of the panel in clinical prediction model development and external validation, along with other established predictors including cognitive severity and functional status may offer practical tools for routine clinical implementation[30]. These models would require validation in various populations and healthcare environments to allow for widespread application. Intervention studies examining whether anti-inflammatory treatments or tau-targeted therapies reduce anxiety-depression incidence would provide critical evidence of causality and help guide treatment recommendations[31]. Such studies may involve both pharmacological approaches (e.g., anti-inflammatory agents, tau aggregation inhibitors) and non-pharmacological modalities (e.g., exercise, cognitive behavioral therapy) in preventing neuropsychiatric complications.

The results promote the integration of biomarker-based risk-stratification and mental health screening in care protocols for routine assessment of AD[32]. Healthcare systems may also want to develop standardized guides for detecting at risk patients and ensuring appropriate psychiatric referral, and follow-up[33]. This might involve greater collaboration between neurology-psychiatry and geriatric medicine services as well as educational programmes to improve anxiety-depression recognition and management skills among AD care providers. From a public health perspective, these data highlight the importance of integrated approaches targeting more than one aspect of AD pathophysiology at once[34]. On the population level, interventions to reduce inflammation, change lifestyle behaviours and mitigate early cognitive decline could have dual benefits for disease progression and neuropsychiatric outcomes[35].

CONCLUSION

Serum inflammatory markers (i.e., together IL-6 and TNF-α) when combined with p-tau181 are in excellent agreement to predict anxiety/depression in AD patients, increasing support for the promotion of a multidimensional approach to clinical practice for AD. Although these findings are hypothesis-generating, they should be interpreted with caution since the predictive model was developed and assessed in a single-center dataset without an independent validation cohort. Before recommendations can be made for the routine clinical implementation of this multimarker panel, prospective, multicenter replication is warranted.