Perspectives on the Implications of Depression on Alzheimer’s Disease: Shared Pathophysiology,Epidemiological Challenges and Therapeutic Management

Abstract

Alzheimer’s disease (AD) and depression are interrelated neuropsychiatric disorders that share underlying mechanisms such as neuroinflammation, dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, and imbalances in neurotransmitters. This review analyses the reciprocal relationship between AD and depression, emphasising standard pathological mechanisms, epidemiological patterns, preclinical models and emerging treatment strategies. A structured narrative review of the literature was performed using PubMed, Scopus and Web of Science. Articles published between 2000 and 2025 were chosen based on their relevance to animal models and therapeutic approaches connecting AD and depression. Depression significantly raises the likelihood of developing AD, while the progression of AD exacerbates depressive symptoms. Standard mechanisms involve increased levels of Interleukin-6 (IL-6) and Tumour Necrosis Factor Alpha (TNF-α), decreased brain-derived neurotrophic factor (BDNF) and disruptions in the HPA axis. Therapeutic strategies targeting inflammation and neuroendocrine regulation have shown encouraging outcomes. A comprehensive understanding of the overlapping pathophysiology of AD and depression is crucial for early identification and effective treatment. Future investigations should focus on biomarker-driven diagnostics, modulation of the gut-brain axis, and personalised treatment options.

Keywords

Alzheimer’s disease depression neuroinflammation amyloid-beta & tau pathology biomarkers treatment strategies

Introduction

Alzheimer’s disease (AD) is a neurodegenerative condition that mainly affects the cerebral cortex and hippocampus, resulting in gradual memory loss, cognitive impairment and changes in behaviour. The disease’s pathology is characterised by the accumulation of amyloid-β (Aβ) and the hyperphosphorylation of tau, which is influenced by genetic mutations in APP, PSEN-1 and PSEN-2, as well as the presence of the APOE ε4 allele, often seen alongside conditions such as diabetes and cardiovascular disease.¹ This review intends to investigate the reciprocal relationship between depression and AD, emphasising their shared pathophysiological mechanisms, common biomarkers, and preclinical models, while also showcasing new therapeutic approaches for their co-occurrence. Depression has multifarious implications regarding AD when viewed from the preclinical, epidemiologic and therapeutic perspectives.^2,3 Preclinically, depression is both a risk factor for and potentially an early manifestation of AD neuropathology. Specifically, depressive symptoms often appear before cognitive impairment; therefore, depressive symptoms may be one of the first non-cognitive symptoms presented during the course of AD, and are associated with changes in neurobiology related to amyloid pathology, tau pathology, and neurogenesis. About epidemiology, studies have found a positive association between depression, in particular late-life depression or recurrent depression, and risk of dementia and AD. Studies have also found that the risk associated with depression depends on the timing of the depression episode, the severity of the depression episode, and the number of recurrent episodes. The association between depression and dementia suggests that depression has a potential causal role in disease initiation or may be a prodromal syndrome. Regarding therapy, antidepressant treatment may reduce this risk by stimulating neurogenesis, reducing amyloid aggregation, lessening tau pathology, and ameliorating inflammatory processes, which are part of the AD process. For these reasons, early recognition and treatment of depressive symptoms (including mild and subclinical forms) are viewed as necessary preventive measures in reducing or delaying the development of AD. Consequently, treatment of depression is beginning to be examined to see if it can be integrated into a dementia risk reduction framework, as a potential strategy to reduce cognitive decline or dementia burden.⁴

Materials and Methods

A comprehensive literature search was conducted across multiple databases, including PubMed, Scopus, Web of Science and Google Scholar. The search was guided by specific keywords and Boolean operators, including ‘Alzheimer’s disease’, ‘GSK3’, ‘HPA axis’ and ‘SCFAs’, to locate pertinent studies that explore the common pathophysiology and treatment methods associated with AD and depression.

The review employed a comprehensive search strategy combining terms related to depression, AD, and therapeutic or neuroprotective mechanisms. The following Boolean string was used: (‘depression’ OR ‘major depressive disorder’ OR ‘mood disorder’) AND (‘Alzheimer’s disease’ OR ‘dementia’ OR ‘cognitive decline’) AND (‘pathophysiology’ OR ‘mechanism’ OR ‘inflammation’ OR ‘amyloid’ OR ‘tau’) AND (‘therapy’ OR ‘treatment’ OR ‘management’ OR ‘antidepressant’ OR ‘neuroprotection’). Literature searches were conducted in PubMed, Scopus, Web of Science, ScienceDirect, and Google Scholar, yielding 60, 50, 30, 20 and 10 records, respectively. Subsequently, automatic and manual deduplication was performed to remove duplicates. A total of 100 unique articles were screened for relevance, of which 55 were excluded at the title/abstract level because of an irrelevant focus, lack of a clear mechanistic linkage between depression and AD, or non-English language. The remaining 45 articles were assessed in full text, and all were retained for inclusion in the qualitative synthesis. Risk-of-bias assessment for each included study considered sample size, blinding, conflict of interest declaration, and appropriateness of statistical methods. The overall strength of evidence was graded based on study design (randomised controlled trials, observational and mechanistic studies), consistency of findings, and reproducibility.

Criteria for Inclusion and Exclusion

Studies from January 2000 to December 2025 were considered. Only peer-reviewed articles, clinical trials, and reviews that centred on therapeutic strategies, molecular mechanisms, and novel treatment options were included. Articles not subjected to peer review, outdated materials, or those lacking relevance to the overlapping conditions of AD and depression were excluded.

Extraction of Data and Quality Evaluation

Information was extracted concerning therapeutic targets, molecular mechanisms, clinical implications and innovative approaches. The quality of each article was evaluated using standard critical appraisal instruments, such as checklists tailored to study types.

Data Synthesis

The information collected was thematically organised into therapeutic targets, emerging strategies and current or developing therapies. This synthesis offered a structured overview of the trajectory of ongoing research, emphasising the combination of pathophysiological insights with clinical advancements in the management of AD and depression.

Global and National Prevalence of AD and Depression

It is anticipated that the burden of AD and depression will escalate considerably in LMICs because of ageing populations, urbanisation and changing lifestyles.⁵ However, LMICs face significant systemic barriers to the diagnosis and management of neuropsychiatric disorders. Limited access to specialists, the absence of neuroimaging, and insufficient laboratory facilities all contribute to difficulties in making a prompt and accurate diagnosis.^6,7 Most LMICs have a severe shortage of trained neurologists, geriatric psychiatrists, and neuropsychologists, which increases delays in patient management. Cultural stigmas and beliefs about mental illness and cognitive impairment also limit families’ willingness to seek out professional help.^8,9 Table 1 illustrates the prevalence of AD.

Table 1.

Prevalence and Key Epidemiological Features of Alzheimer’s Disease Globally and in India.

Sl. No	Category	Global Statistics	Indian Statistics	References
1	Total prevalence	33.9 million cases as per 2024 reports	4 million people are affected by AD in India	¹⁰
2	Annual new cases	AD accounts for 60%–70% new cases each year globally	India reports 0.5 million estimated cases each year	¹¹
3	Age distribution	Primarily affects people age 65+, 74% cases are 75+	In India, prevalence increases sharply after age 65, with 7% prevalence in people aged 75+	¹²
4	Gender distribution	Nearly 2/3 of global AD cases are women	Women in India have a higher prevalence, likely due to longer life expectancy and caregiving burden	¹³
5	Mortality and disability rank	The 7th leading cause of death worldwide, a major contributor to disability in the elderly	Increasing cause of mortality and dependency; underdiagnosed and underreported in rural areas	¹⁴

Note: AD = Alzheimer’s disease.

Aetiology, Pathophysiology and Biomarkers of AD

The development of AD requires the interaction between hereditary risk factors along with external elements that drive disease progression. A patient’s AD classification depends on their age, as well as their family’s history of medical conditions. Most AD cases manifest past age 65, which medical professionals identify as late-onset AD, but a lesser segment of individuals develop early-onset AD during their younger years. The initial factors that lead to AD development consist of amyloid plaques and NFTs, and these occur with genetic mutations in APP, PSEN1 and PSEN2, together with findings from both inflammation theory and cholinergic theory.^15,16 The key pathological mechanisms involved in AD are illustrated in Figure 1.

Figure 1.

Source: BioRender software.

AD patients may also have elevated neurofilament light polypeptide in their CSF. Plasma NF-L levels are elevated in Aβ-positive AD patients and those with mild cognitive impairment, showing a strong correlation with the severity of cognitive impairment.^17,18 The enhanced activity of other biomarkers, such as β-secretase-1, monoamine oxidase B, and phospholipase A2, has been reported in the platelets and brains of individuals with AD. Figure 2 illustrates the aetiology, pathophysiology and biomarkers of AD. There are numerous logistical and economic hurdles to implementing biomarker-based diagnostics in LMICs. For example, CSF sampling and PET scan imaging are not practical for many patients due to low uptake and the high costs incurred by health systems, as well as the need for specialised building infrastructure. Therefore, the evaluation of minimally invasive plasma biomarkers that are cost-effective, such as plasma Aβ42/40 ratios and neurofilament light chain, is being pursued. Blood-based markers, if shown to have high sensitivity and specificity in validating disease progression for early detection in low-resource settings, would offer substantial improvements in how patients with dementia and MCI are diagnosed and cared for. For example, portable blood assays could be coupled with mobile cognitive assessments or algorithmic diagnostic models driven by machine learning AI, specifically targeting LMIC contexts.¹⁹ Biomarkers of AD are given in Table 2.

Figure 2.

Source: BioRender software.

Table 2.

Common Tests Used to Detect Alzheimer’s Disease Changes in the Brain.²⁰

Biomarker	Biological Medium	Detection Method	AD-associated Finding	Clinical Utility
Aβ42	CSF	Immunoassays (ELISA, MS)	Decreased levels in AD	Core diagnostic marker
Aβ42/Aβ40 ratio	CSF	Immunoassays	Strong concordance with amyloid PET	Reliable surrogate for PET imaging
Plasma Aβ	Plasma	Mass spectrometry	Correlates with cerebral amyloidosis	Less invasive, emerging biomarker
Amyloid PET tracers ([18F] florbetapir, [18F] flutemetamol, [18F] florbetaben)	Brain imaging	PET scan	Detect Aβ plaques	FDA/EMA approved
PiB (11C-labelled thioflavin-T)	Brain imaging	PET scan	Increased cortical uptake in AD	Research tool; limited by the short 11C half-life
Phosphorylated tau (P-tau)	CSF	ELISA	Elevated in AD vs. controls & other tauopathies	Most specific biomarker for AD

Note: AD = Alzheimer’s disease; Aβ = Amyloid-beta; Aβ42 = Amyloid-beta 42 peptide; Aβ40 = Amyloid-beta 40 peptide; CSF = Cerebrospinal fluid; PET = Positron emission tomography; PiB = Pittsburgh Compound B; P-tau = Phosphorylated tau; ELISA = Enzyme-linked immunosorbent assay; MS = Mass spectrometry; FDA = Food and Drug Administration (USA); EMA = European Medicines Agency; [18F]-Fluorine = 18 radioisotope; 11C = Carbon-11 radioisotope.

Recent Preclinical Reports on Amyloid-beta (Aβ) and APP Hypothesis

Scientists have discovered essential elements that drive the progression of AD. Research evidence shows that AD brains containing damaged neurons attract both reactive astrocytes and microglia cells to gather near Aβ plaques. Neuronal growth defence mechanisms rely on APP metabolism through α- and γ-secretase to produce Aβ proteins. Secretase enzymes α and γ produce non-amyloidogenic cleavage of APP while maintaining its structurally complete composition. When β-secretase processes APP, followed by γ-secretase, it forms harmful 40- to 42-amino acid amyloid peptides. The natural aggregation of Aβ peptides develops into oligomers that eventually solidify as insoluble beta-sheet crystalline fibres, which form senile plaques. Cells that produce an aggregation-prone Aβ42 variant lead to abnormal Aβ42 accumulation in the body. Elevated blood levels of Aβ42 occur when normal APP gene activities become dysfunctional because of genetic changes. PSEN1, together with PSEN2 mutations, forces cells to produce greater amounts of Aβ42 while decreasing the levels of Aβ40. Aβ42 oligomers trigger destructive oxidative processes, hyperphosphorylating tau proteins, and lead to damaging impacts on brain cells, including mitochondrial dysfunction.²¹ Researchers have extensively studied how Aβ42 causes damage in AD progression. Studies indicate that the accumulation of soluble Aβ oligomers, which develop earlier and are more damaging to brain cells, precedes the formation of plaques. Research using mice with mutations in the APP genes shows AD-like symptoms but does not reproduce the meaningful brain damage seen in AD-affected humans. Further research is needed to clarify if APP/Aβ plays a vital role in dementia development while recognising possible species-related errors.²² The pathophysiological mechanisms of familial AD are illustrated in Figure 3. Overlapping features in AD and depression are given in Table 3.

Figure 3.

Source: BioRender software.

Table 3.

Similarities and Differences Between Alzheimer’s Disease and Depression.

Sl. No	Domain	Feature	AD	Depression	Overlap/Notes	References
1	Mood & affect	Persistent sadness, apathy	Common in the early and middle stages	Core symptom	Apathy overlaps with depressive anhedonia	²²
2	Cognitive function	Memory, attention, executive dysfunction	Central feature (progressive cognitive decline)	Often impaired (esp. in major depression)	Cognitive symptoms overlap but differ in cause	²⁴
3	Sleep	Insomnia, disrupted sleep-wake cycle	Very common (especially sundowning)	Very common	Sleep disruption may worsen both conditions	²⁵
4	Neurotransmitters	Reduced acetylcholine, serotonin, and dopamine	Core pathological feature	Serotonin, dopamine, and norepinephrine deficits	Monoaminergic dysfunction is shared	²⁶
5	Inflammation	Chronic neuroinflammation	Microglial activation in the brain	Elevated cytokines (e.g., IL-6, TNF-α)	Shared inflammatory pathways	²⁷
6	Neurotrophic factors	Reduced BDNF	Decrease in AD brains	Decrease in depression	BDNF is a common target for neuroplasticity	²⁸

Note: AD = Alzheimer’s disease; BDNF = Brain-derived neurotrophic factor; IL-6 = Interleukin-6; TNF-α = Tumour necrosis factor alpha.

Recent preclinical investigations of Aβ and the APP hypotheses have provided mechanistic discoveries and genetic associations with familial AD, thus enhancing excitement for targeted therapies. Nevertheless, this promise has faced severe translational problems when applied to clinical outcomes in humans: importantly, nearly 30 years of clinical trials targeting amyloid, including immunotherapy, have often failed to provide a meaningful cognitive benefit in most patients with sporadic, late-onset AD. The identified translational gaps include the lack of relevant animal experimental models of familial AD, a lack of knowledge regarding the physiological roles of APP and Aβ in humans, and a lack of knowledge regarding the pathogenesis of AD, which includes unresolved controversies and repeated trends of failed attempts to translate preclinical targets into human therapies. These translational gaps must be addressed explicitly to help stimulate more clinically relevant drug research in AD.²³

Neurofibrillary Tau Tangles

Cells containing neurofibrillary tangles (NFTs) develop improper bend regulation of microtubules because they accumulate excessive phosphorylated tau proteins. Excessive phosphorylation of tau proteins leads to the formation of paired helical filaments (PHFs), which break down brain neuronal support structures and impair cell formation. Small tau fibril assemblies known as PHFs start from the entorhinal cortex in the brain and progress through the limbic system, then move into the neocortex at Stages III–IV. The neocortex receives complete tangle invasion during Stages V and VI. Research has shown that these NFTs appear in targeted brain areas of people around the age of twenty, accompanied by progressive frequency increases noted in 85% of subjects approaching 65 years old within Stage I–III boundaries. Data indicate that NFT development appears in 98% of the telencephalon tissue of typical human subjects by the age of 80. During AD, patients develop abnormal tau that displays a heightened 4R tau to 3R tau ratio, primarily resulting from MAPT protein splicing events, primarily through the retention of exon 10. Tau undergoes splicing to produce two main isoforms: one with three (3Rtau) and another with four (4Rtau) microtubule-binding repeat domains. The unequal tau expression disrupts Wnt signalling transcription, while impairing axonal transportation and elevating 4R tau levels to accelerate neurodegenerative processes. When modified by mutations, tau loses its function to interact with microtubules yet shows enhanced capacity to assemble filamentous structures. Recent research demonstrates that tau functions as an essential factor in neurodegeneration and cognitive decline during AD by possibly collaborating with Aβ pathogenesis.²⁹

Aetiology, Pathophysiology and Biomarkers of Depression

HPA Axis

The hypothalamic-pituitary-adrenal (HPA) axis within the brain contains elements that regulate cortisol levels as a stress hormone. The relationship between abnormal regulation of this system and both depression and AD has been confirmed through research. When the HPA axis does not work correctly in depression, it makes the brain release too much cortisol. Stress hormones, and the most common one is cortisol, which has been found to cause neurotoxicity, especially in the hippocampus.³⁰

Likewise, the HPA axis dysregulation is present in AD; stress, as well as exposure to glucocorticoids, increases neuronal damage in the brain. The hypophyseal portal circulation is altered in AD patients, and cortisol levels are increased, which could exacerbate hippocampal changes. They occur together with important AD features, which include both brain deposits of amyloid-beta protein outside cells and abnormal clumps of phosphorylated tau proteins inside cells. The synchrony of these mechanisms demonstrates that depression and AD have a common ‘pathophysiological aetiology’ with chronic stress and persistent HPA axis activation implicated in their onset and progression. These aspects show that stress, HPA axis dysfunction, and neurodegeneration are closely related, and therefore, this system should be the target of these therapies. Hence, since cortisol hormones of stress bring about damage to the brain, it would be possible to counter the advancement of not only depression but also AD.³¹ Key features and applications of commonly used animal models in depression research are summarised in Table 4. Mechanisms of neuroinflammation and neuroendocrine dysfunction associated with depression are depicted in Figure 4.

Table 4.

Key Features and Applications of Commonly Used Animal Models in Depression Research.

Sl. No	Model	Species	Key Features	Applications	References
1	Chronic unpredictable stress (CUS)	Rat, mouse	Induces behavioural despair and anhedonia	• Mimics chronic stress-induced depression	³²
2	Forced swim test	Rat, mouse	Measures immobility as a depression marker	• Used for antidepressant screening	³³
3	Learned helplessness model	Rat, mouse	Repeated inescapable stress exposure	• Studies stress-related depression	³⁴
4	Chronic mild stress (CMS)	Rat, mouse	Induces anhedonia and neuroinflammation	• Evaluates antidepressant response	³⁵

Figure 4.

Source: BioRender software.

Role of Inflammation and Cytokines

Inflammation and cytokine imbalances are significant in the aetiology of depression and AD and are a key linkage between both conditions. It is also worth noting that cytokines, the small proteins that play a role in regulating immune responses, can affect brain function if the balance is altered. Research shows people with depression have larger levels of IL-6, IL-1β and TNF-α, pro-inflammatory cytokines in their system. These cytokines affect NT metabolism by modulating serotonin, dopamine and norepinephrine signalling, disrupting neuroendocrine physiology through disruption of the HPA axis, and inhibiting neural plasticity by decreasing BDNF concentration.³⁶ These effects add up to core features of depression, which include mood changes, cognitive malfunction and emotional instability.³⁷

Gut-brain Axis and Depression

Through the gut-brain axis connection, the CNS and the intestines form an active communication pathway. Multiple studies prove GBA’s importance in depression growth, and recent studies show how gut microbes now affect disease progression. This discovery is revolutionising how we conceive digestive health, primarily affecting neurological and psychological health. A review reveals that all previous research with rodents exposed to SWR has linked dysbiosis, or an alteration in the gut microbiota composition, to depressive signs. Imbalances in the gut microbiota affect the brain through mechanisms that include altering neurotransmitter production, such as serotonin and GABA. Some of these neurotransmitters play central roles in controlling mood and cognition. However, the epigenetic changes also suggest a role in immune regulation via cytokine production to perpetuate or reduce systemic inflammation, a predisposing factor for depression.³⁸ Dysbiosis also affects the synthesis of neuroactive chemicals, such as SCFAs, which are necessary for directing healthy brain functions and maintaining the strength of neurons. Short-chain fatty acids (SCFAs), such as acetate, propionate and butyrate, are key metabolites produced by gut microbiota through the fermentation of dietary fibres.³⁹

Shared Mechanisms and Epidemiology Between AD and Depression

AD and depression are comorbid, as both might have common biological substrates of synergistic interaction between the two disorders. Depression is not only an essential secondary diagnosis in AD but can also be considered a precocious risk factor of the disease and shares the exact pathological mechanisms. Hypofunction and blunted cortisol regulation of the HPA axis lead to prolonged cortisol exposure, with consequential synaptic atrophy of the hippocampus, which affects both disorders. Furthermore, an imbalance of neurotransmitters, a reduced production of serotonin, dopamine and acetylcholine, is thought to interfere with communication within the brain and exacerbate behavioural and cognitive symptoms. The notion of cognitive reserve provides a convincing account for the variability in clinical presentation of patients who share a comparable burden of neuropathology.^40,41

More educated people, who hold intelligent occupations and are socially connected, may tolerate more pathology before exhibiting symptoms. However, depression, especially if persistent or recurrent, may diminish cognitive reserve via neurotoxic mechanisms such as hippocampal atrophy, disrupted neurogenesis and synaptic loss through inflammation. Having depressive symptoms in midlife was related to more rapid cognitive decline and earlier dementia. Thus, promoting activities that contribute to cognitive reserve, such as lifelong learning, learning a second language, engaging in creative arts, and participating in community activities, may provide a nonpharmacological buffer against neurodegeneration and its associated psychological comorbidities.^42,43 Widespread pathways include lower BDNF levels together with higher oxidative stress and broken mitochondria, which disrupt how neurons grow and function. The same protein, amyloid-beta, is implicated in AD and depression; stress and depressive states may increase the rate of deposition of amyloid-beta. Neural underpinnings, including hippocampal reduction and changes in connectivity of elaborate networks that include the hippocampus, reinforce the relationship between these disorders. These overlapping pathways not only account for this co-occurrence but also may serve as potential targets for the treatment.^44,45 The pathogenic loop between AD and depression is given in Figure 5.

Figure 5.

Source: BioRender software.

Biomarker-driven Prevention Strategies

Biomarker-driven preventive approaches to address the consequences of depression in AD centre on the identification of shared pathophysiological vulnerabilities (through precise molecular and imaging markers) at the earliest possible stage. Through the identification of several important biomarkers, including CSF Aβ42/tau ratio as well as p-tau181/217 in plasma and neuroimaging via amyloid/tau PET and/or measurements of hippocampal volume, individuals suffering from late-life depression can now be stratified as being at high risk for AD progression, as in many cases depressive states precede the accumulation of amyloid and the formation of NFTs. Targeted intervention using either anti-amyloid monoclonal antibodies (e.g., lecanemab or donanemab) in preclinical stages, coupled with SSRIs or lifestyle changes (i.e., exercise or Mediterranean diet) to inhibit inflammation and HPA axis dysregulation, has the potential to interrupt the continuum of depression leading to dementia by addressing common mechanisms of action, such as oxidative stress and synaptic loss, before clinical signs of AD develop. The validation of this precision medicine approach in various cohorts, such as ADNI, reinforces that the best way to combat the rise in AD among those suffering from depression is through early and regular monitoring of a biomarker-based approach to potential therapeutics, thereby reducing the incidence of AD in this population.⁴⁴

Limitations, Conflicting Findings and Translational Gaps

Some limitations include an overreliance on observational data that are subject to confounding factors, such as reverse causation, where depression late in life may be a result of AD in the prodromal stage and not an independent risk factor and also the difficulties of diagnosing cognitive decline and depression due to the overlap of symptoms (apathy, anhedonia, executive dysfunction) creates high comorbidity rates and makes it difficult to determine prevalence rates. In addition, heterogeneity in the design of studies, small sample sizes in mechanistic studies, and no consistent method of assessing depression (GDS vs. DSM criteria) all further limit the ability to generalise findings from a study with a predominantly white population to populations that are underrepresented in most research.

Additionally, conflicting data points to disagreement regarding the timing of the depression. Meta-analyses indicate that midlife depression is associated with a 1.8–2-fold increased risk of AD due to common pathways (i.e., dysregulation of the HPA axis and inflammation); however, depression at a late age typically appears to be inversely associated with AD development, possibly as a behavioural marker of neurodegeneration rather than as a causal factor for AD development. Furthermore, differences exist between men and women concerning the risk of developing AD due to depression: in men, odds of developing AD are higher if depressed; in women, depressive symptoms correlate with the rate of cognitive decline. Of note, amyloid/tau studies conducted thus far indicate mixed results regarding whether depression exacerbates AD neuropathology, independent of a person’s APOE status.⁴⁵

Future Prospects

There are nonpharmacological interventions that have been tested, including cognitive behavioural therapy, brief psychotherapies, emotion-oriented therapies (reminiscence, validation therapy), and sensory stimulation therapies (music, art, pet therapy), that show some evidence of utility, especially in mild-moderate depression within older adults with AD. These nonpharmacological therapies have fewer side effects, are often delivered in conjunction with caregiver training, and adapt well to limited resources. Examples of lifestyle changes, such as regular physical activity, matter because they have been shown to reduce depressive symptoms through various mechanisms, such as mitigating hippocampus atrophy, and can be a low-cost and widely available approach. Community-based interventions and supportive approaches for caregivers are realistic strategies for healthcare systems with limited funding aimed at reducing caregivers’ cognitive anxiety, depression, and burnout. Additionally, greater training of primary healthcare professionals and increased public awareness can enhance their awareness and knowledge of mental illness morbidity, thereby improving a clinician’s ability to diagnose broadly and manage resources in low-resource settings. Pharmacological treatments should still be considered for the treatment of depression; however, the financial burden and access for antidepressants, targeted inhibitors, hormones, or similar approaches may still be higher than generics for reasons related to the complexities of drug administration and claims for access to pharmacological interventions. All previously discussed nonpharmacological and cost-effective approaches for the treatment of depression adhere to ongoing monitoring for adverse side effects, and whether from a person’s access and hospital environment around services or cost of access.

Conclusion

AD and depression have intricate biological, neurochemical, and environmental connections that can profoundly affect cognitive function and overall quality of life. Key factors, such as neuroinflammation, dysregulation of the HPA axis, and neurotransmitter imbalances, are critical in both illnesses, underscoring the importance of integrated diagnostic and therapeutic approaches. Progress in biomarker research and multi-omics technologies presents encouraging opportunities for early identification and focused interventions. An all-encompassing strategy that incorporates anti-inflammatory therapies, stress reduction, and lifestyle changes may enhance outcomes and improve overall well-being.

Footnotes

Acknowledgements

The authors express heartfelt gratitude to the Principal, JSS college of Pharmacy, JSS Academy of Higher Education & Research, Mysore for providing all the obligatory facilities for the completion of this review work.

Authors’ Contributions

All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agreed to be accountable for all aspects of the work. All the authors are eligible to be authors as per the International Committee of Medical Journal Editors (ICMJE) requirements/guidelines.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Data Availability

All the data are available with the authors and shall be provided upon request.

Declaration of Conflicting Interests

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

Ethics Approval

This study does not involve experiments on animals or human subjects.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Informed Consent

Not applicable.

Publisher’s Note:

All claims expressed in this article are solely those of the authors and do not necessarily represent those of the publisher, the editors and the reviewers. This journal remains neutral with regard to jurisdictional claims in published institutional affiliation.

Use of Artificial Intelligence-assisted Technology:

The authors declare that they have not used artificial intelligence (AI) tools for writing and editing of the manuscript, and no images were manipulated using AI.

Abbreviations

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