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Central sensitization syndromes as a neuropsychiatric risk state for dementia: Clinical and epidemiologic perspectives

Published online by Cambridge University Press:  29 July 2026

Kevin V. Hackshaw Open the ORCID record for Kevin V. Hackshaw [Opens in a new window]
Kevin V. Hackshaw*
Affiliation:
Division of Rheumatology, Department of Medicine, Dell Medical School at The University of Texas at Austin , USA
*
Corresponding author: Kevin V. Hackshaw;Email: kevin.hackshaw@austin.utexas.edu
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Abstract

Dementia prevention increasingly targets modifiable midlife risk states contributing to neurodegenerative vulnerability. While vascular and metabolic factors are established, a substantial proportion of risk remains unexplained. Central sensitization syndromes (CSS)—chronic conditions characterized by neural amplification—are highly prevalent, co-occur with depression and sleep disturbance, and involve neuroimmune and network-level mechanisms implicated in early Alzheimer’s disease. To synthesize epidemiologic evidence linking CSS with incident dementia and evaluate competing causal explanations, including shared vulnerability, reverse causation, and medication-related confounding. A structured narrative review was conducted of longitudinal cohort and meta-analytic studies assessing incident dementia across CSS-related conditions. Multiple CSS—including migraine, fibromyalgia, irritable bowel syndrome, restless legs syndrome, and PTSD disorder—are associated with modest-to-moderate increases in dementia risk. Multisite chronic pain demonstrates dose–response relationships with hippocampal atrophy and incident dementia. However, many conditions lack dedicated longitudinal studies. Interpretation is limited by residual confounding, reverse causation, healthcare utilization bias, and medication effects. CSS are associated with increased dementia risk but causality remains unproven. Central sensitization may represent a clinically identifiable vulnerability state integrating pain, affective burden, and sleep disruption. This highlights a potentially high-risk population that may not be fully captured by traditional dementia risk models. Biomarker-integrated longitudinal studies and advanced causal modeling are needed to clarify mechanisms.

Keywords

Dementia riskcentral sensitizationchronic painneuropsychiatrydepressionsleep disturbancemidlife riskcognitive decline

Information

Type
Review
Information
CNS Spectrums , Volume 31 , Issue 1 , 2026 , e29
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2026. Published by Cambridge University Press

Introduction

Alzheimer’s disease and related dementias develop over decades and are increasingly understood as the downstream manifestation of cumulative biological vulnerability.Reference Jack, Bennett and Blennow 1 –Reference Jack, Therneau and Weigand 8 Contemporary prevention frameworks emphasize the identification of modifiable midlife risk states that influence long-term neurodegenerative trajectories. While vascular and metabolic factors are well established, they do not fully account for the heterogeneity of cognitive aging observed across populations.Reference Vos, Xiong and Visser 9 –Reference Kivipelto, Mangialasche and Snyder 17 This gap has prompted increasing interest in systemic conditions that may contribute to cumulative central nervous system stress and reduced resilience over time. From a neuropsychiatric perspective, central sensitization syndromes (CSS) may represent a convergent clinical phenotype in which pain, mood, sleep, and cognitive symptoms co-occur, reflecting shared network-level vulnerability relevant to neurodegenerative risk.

Interpretation of dementia risk in CSS is complicated by extensive pharmacologic exposure. Individuals with these conditions frequently receive long-term pharmacotherapy targeting pain amplification, sleep disturbance, mood symptoms, and autonomic dysregulation.Reference Livingston, Huntley and Sommerlad 15 –Reference Kivipelto, Mangialasche and Snyder 17 Many of these medications—including anticholinergic agents, benzodiazepines, sedative-hypnotics, gabapentinoids, and certain antidepressants—have potential cognitive effects or have been associated with dementia risk in observational studies of older adults. Consequently, associations between CSS and dementia may reflect not only underlying disease biology but also medication exposure patterns, polypharmacy, and confounding by indication. Clarifying these relationships is therefore essential for interpreting epidemiologic findings and for guiding safer pharmacologic management of chronic pain in aging populations.

CSS are chronic conditions characterized by amplified central nervous system processing of sensory and interoceptive input.Reference Woolf 18 –Reference Ji, Nackley, Huh, Terrando and Maixner 22 Though historically framed within pain medicine, CSS extend beyond nociceptive amplification and encompass fatigue, cognitive complaints, sleep disturbance, autonomic dysregulation, and affective symptoms.Reference Clauw 23 –Reference Bair, Robinson, Katon and Kroenke 27 Core CSS include fibromyalgia, migraine, irritable bowel syndrome, temporomandibular disorders, interstitial cystitis/bladder pain syndrome, vulvodynia, and chronic multisite pain. Additional conditions such as post-traumatic stress disorder (PTSD), restless legs syndrome, endometriosis, dysmenorrhea, PCOS, and multiple chemical sensitivity may exhibit sensitization-related mechanisms but occupy a less established position within the CSS construct.Reference Yunus 28 –Reference Miller, Rodriguez-Saona and Hackshaw 33

CSS share convergent biological themes: neuroimmune priming, glial activation, altered salience and default mode network connectivity, hypothalamic–pituitary–adrenal (HPA) axis dysregulation, metabolic disturbance, and chronic sleep disruption.Reference Grace, Hutchinson, Maier and Watkins 34 –Reference Clauw 36 These mechanisms overlap substantially with emerging models of early AD vulnerability.Reference Heneka, Golenbock and Latz 37 –Reference Ju, Lucey and Holtzman 39

Epidemiologic studies increasingly report elevated incident dementia risk among individuals with several CSS.Reference Tzeng, Chung and Liu 40 –Reference Chen, Li and Tsai 47 However, interpretation is complicated by overlapping comorbidities, medication exposure, and potential reverse causation.Reference Prince, Ali, Guerchet, Prina, Albanese and Wu 48 –Reference Petersen, Lopez and Armstrong 51 Recent work in fibromyalgia has further emphasized the interconnected nature of central sensitization, depressive burden, functional impairment, and neurobehavioral vulnerability. Data-driven phenotyping studies demonstrate stable symptom clusters characterized by varying levels of pain, depression, and central sensitization, suggesting that sensitization-related disorders may reflect broader neurobehavioral states rather than isolated pain syndromes. This conceptual framework supports investigation of central sensitization as a clinically identifiable vulnerability phenotype relevant to long-term brain health.Reference Hackshaw, Osuna-Diaz and Sebastian 52 –Reference Hackshaw, Osuna-Diaz and Sebastian 54 This review synthesizes epidemiologic evidence linking CSS with incident dementia and evaluates competing causal explanations using a structured framework. Clinical and research implications are outlined relevant to dementia risk stratification and prevention.

Methods

A structured narrative review was conducted informed by epidemiologic principles of causal inference and bias assessment of the epidemiologic and mechanistic literature. Searches of PubMed/MEDLINE and Scopus were performed through January 2026 using condition-specific terms combined with “dementia,” “Alzheimer’s disease,” “incident,” “longitudinal,” and “cohort. Searches incorporated combinations of controlled vocabulary terms (Medical Subject Headings [MeSH] where applicable) and free-text keywords. Representative PubMed search syntax included combinations of (“fibromyalgia” OR “central sensitization” OR “migraine” OR “irritable bowel syndrome” OR “restless legs syndrome” OR “post-traumatic stress disorder”) AND (“dementia” OR “Alzheimer disease” OR “cognitive decline”) AND (“cohort” OR “longitudinal” OR “incident”). Equivalent database-specific syntax was used for Scopus. English-language studies were included. Reference lists of eligible articles and relevant reviews were manually screened to identify additional studies. Included are prospective cohorts, retrospective population-based cohorts, nested case–control analyses, and meta-analyses reporting incident dementia. Cross-sectional cognitive studies were excluded from the incidence synthesis. Studies evaluating cognitive decline, mild cognitive impairment, neuroimaging markers, hippocampal atrophy, or dementia biomarkers without incident dementia outcomes were not included in evidence grading but were reviewed separately as mechanistic or supportive evidence. All-cause dementia and Alzheimer disease-specific outcomes were recorded separately whenever reported.

Adjusted hazard ratios (HR), relative risks (RR), or odds ratios (OR) were extracted where available. Alzheimer’s disease–specific endpoints were prioritized when reported.

Confounding by indication refers to bias arising when medications are prescribed because of disease severity or characteristics that themselves influence dementia risk. Protopathic bias occurs when treatment is initiated for symptoms that are actually early manifestations of an undiagnosed disease.

Strength of Evidence Classification:

  1. 1. Moderate-to-high (replicated cohorts or meta-analyses)

  2. 2. Moderate (single large cohort)

  3. 3. Evidence gap (absence of dedicated longitudinal data)

    Literature screening and data extraction were conducted by the author. Eligible studies were reviewed in full text. Data extracted included study design, population, follow-up duration, dementia outcome definition, adjustment variables, and effect estimates. Evidence classifications assigned to each condition are summarized in Table 1. A structured synthesis of epidemiologic studies, including effect estimates, adjustment variables, and evidence ratings, is provided in Table 2.

Table 1.

Evidence Classification of Dementia Risk Across Central Sensitization Syndromes

A two-column table classifying dementia risk evidence across fifteen central sensitization syndromes, ranging from moderate-high evidence to evidence gaps. See long description.
Table 1. Long description

The table consists of two columns: Condition and Evidence Category. The data is organized as follows:

* Multisite Chronic Pain: Moderate-High

* Migraine: Moderate-High

* P T S D: Moderate-High

* Fibromyalgia: Moderate

* I B S: Moderate

* R L S: Moderate

* M E / C F S: Evidence Gap

* T M D: Evidence Gap

* C R P S: Evidence Gap

* I C / B P S: Evidence Gap

* Vulvodynia: Evidence Gap

* Endometriosis: Evidence Gap

* P C O S: Evidence Gap

* Dysmenorrhea: Evidence Gap

* M C S: Evidence Gap

Moderate-to-high evidence indicates support from replicated cohort studies or meta-analyses. Moderate evidence indicates support from large observational cohorts with limited replication. Evidence gap indicates an absence of dedicated longitudinal studies.

Evidence classifications were assigned based on the quantity, consistency, and methodological quality of available longitudinal studies. Moderate-to-high evidence indicates support from replicated cohort studies and/or meta-analyses. Moderate evidence indicates support from one or more large observational cohorts with consistent findings but limited replication. Evidence gap indicates absence of dedicated longitudinal studies evaluating incident dementia outcomes.

Table 2.

Epidemiologic Evidence Linking Central Sensitization Syndromes to Incident Dementia

A table summarizing epidemiologic evidence linking central sensitization syndromes to incident dementia across various conditions. See long description.
Table 2. Long description

The table contains 11 columns: Condition, Key Studies, Design, N, Outcome, Effect Estimate, Depression Adjusted, Sleep Adjusted, Medication Adjusted, Lag Period Used, and Evidence Rating.

* Fibromyalgia: Studies by Chang 2016, Lin 2017, Tzeng 2018; National cohort; N greater than 40,000; Outcome: All-cause dementia; H R 1.45 to 2.77; Depression: Partial; Sleep: No; Medication: Limited; Lag: Rare; Rating: Moderate.

* Migraine: Zhang 2021 meta-analysis; N greater than 1 million; Outcome: All-cause dementia; R R approximately 1.30; Depression: Variable; Sleep: Rarely; Medication: Rarely; Lag: Variable; Rating: Moderate-High.

* I B S: Lee 2016, Tsai 2017, Fang 2022; Cohort/meta-analysis; N greater than 100,000; Outcome: Dementia/A D; H R 1.20 to 1.50; Depression: Partial; Sleep: No; Medication: No; Lag: Rare; Rating: Moderate.

* R L S: Yoo 2020, Kim 2020; National cohort; N greater than 50,000; Outcome: All-cause dementia; H R 1.20 to 1.80; Depression: Variable; Sleep: Limited; Medication: No; Lag: Rare; Rating: Moderate.

* P T S D: Yaffe 2010, Zhang 2018; Cohort/meta-analysis; N greater than 200,000; Outcome: Dementia; R R 1.50 to 2.00; Depression: Usually; Sleep: Limited; Medication: Limited; Lag: Variable; Rating: Moderate-High.

* Multisite Chronic Pain: Veronese 2017, Wang 2019, Shen 2021, Zhao 2023; Prospective cohort; N greater than 400,000; Outcome: Dementia plus imaging; H R 1.30 to 2.00; Depression: Yes; Sleep: Variable; Medication: Rarely; Lag: Some cohorts; Rating: Moderate-High.

* The following conditions are listed as Evidence Gap with no study data: M E / C F S, T M D, C R P S, I C / B P S, Vulvodynia, Endometriosis, P C O S, Dysmenorrhea, and M C S.

Summary of representative longitudinal cohort studies and meta-analyses evaluating incident dementia risk across central sensitization syndromes and related conditions. Effect estimates represent adjusted hazard ratios (HR), relative risks (RR), or odds ratios (OR) reported in the literature. Adjustment columns indicate whether studies accounted for depression, sleep-related factors, or medication exposure. Lag period refers to exclusion periods used to reduce reverse-causation bias. Evidence ratings reflect overall strength of the available epidemiologic evidence.

Causal inference framework

Observed associations between CSS and incident dementia should be interpreted within explicit causal frameworks rather than as inherently directional effects. One conceptual model proposes that CSS may be associated with sustained neuroimmune activation, which could contribute to network-level dysfunction and pathways relevant to AD vulnerability. Chronic neuroimmune activation may alter synaptic plasticity, promote maladaptive glial signaling, disrupt salience-default mode network interactions, impair hippocampal function, and reduce neural resilience. These processes overlap with biological pathways implicated in early neurodegenerative vulnerability, although direct longitudinal mediation remains unproven. In this model, persistent peripheral and central sensitization amplify glial activation and inflammatory signaling, lowering the threshold for synaptic dysfunction and accelerating vulnerability within memory-relevant neural circuits. This pathway is biologically plausible and aligns with converging evidence linking chronic inflammation, synaptic dysregulation, and neurodegenerative progressionReference Ji, Nackley, Huh, Terrando and Maixner 22 , Reference Grace, Hutchinson, Maier and Watkins 34 , Reference Heneka, Golenbock and Latz 37 (Figure 1).

Figure 1.

Directed acyclic graph. Directed acyclic graph (DAG) illustrating hypothesized relationships between central sensitization syndromes (CSS) and incident dementia. Pathways include direct neurobiological effects, mediation through depression, sleep disturbance, and chronic pain, and confounding by shared vulnerability factors. Reverse causation, medication-related effects, and healthcare utilization bias are also depicted, highlighting competing causal explanations.

A directed acyclic graph mapping the causal pathways between shared vulnerabilities, central sensitization syndromes, and incident dementia. See long description.
Figure 1. Long description

A flowchart consisting of six colored boxes connected by directional arrows.

* At the top left, a light green box labeled Shared upstream vulnerability includes Genetics, Inflammation, Metabolic risk, and Socioeconomic status. Arrows point from this box to Central sensitization syndromes and Incident dementia.

* In the center, a dark green box labeled Central sensitization syndromes has arrows pointing to Incident dementia, Healthcare utilization forward slash surveillance, and Medication exposure.

* At the top right, a dark blue box represents the primary outcome: Incident dementia (A D preferred; non-vascular).

* At the bottom left, a red box labeled Healthcare utilization forward slash surveillance has an arrow pointing back to Central sensitization syndromes.

* At the bottom center, an orange box labeled Medication exposure (gabapentinoids, anticholinergics, sedatives, opioids) has an arrow pointing to Incident dementia.

* At the bottom right, a purple box labeled Prodromal A D (reverse causation) > pain forward slash sleep forward slash affect changes has arrows pointing to Central sensitization syndromes, Healthcare utilization forward slash surveillance, and Incident dementia.

However, several alternative causal structures must also be considered. One possibility is shared upstream vulnerability: systemic inflammation, cardiometabolic risk, genetic susceptibility, or stress-related neuroendocrine dysregulation could independently increase the likelihood of both CSS and dementia, creating a non-causal association driven by common antecedents. A second explanation is reverse causation, whereby prodromal AD—long before overt cognitive symptoms emerge—alters pain processing networks and increases pain sensitivity, thereby manifesting clinically as a sensitization phenotype. Third, differential healthcare utilization may introduce surveillance bias in observed relationships; individuals with chronic pain conditions often have more frequent medical contact, increasing the probability of earlier cognitive assessment and diagnosis. Finally, medication-mediated pathways must be addressed, as commonly prescribed agents for chronic pain, mood disturbance, or sleep disruption may themselves influence cognitive trajectories either positively or negatively.Reference Sperling, Karlawish and Johnson 12 , Reference Heneka, Carson and El Khoury 14 Without study designs capable of distinguishing among these competing pathways—through temporally anchored exposures, biomarker integration, and advanced causal modeling—firm etiologic inferences regarding CSS as a driver of dementia risk cannot be justified.Reference Hernán and Robins 50 These considerations highlight that associations between chronic sensitization states and dementia risk should be interpreted within an explicit life-course framework in which exposures, mediators, and prodromal disease processes evolve dynamically over time. Framing these relationships within psychiatric epidemiology emphasizes that chronic pain, mood disorders, and sleep dysregulation should be modeled jointly rather than as isolated exposures in dementia risk research.

Condition-specific evidence

The overall strength of evidence across CSS is summarized in Table 1, while key epidemiologic studies and methodological characteristics are summarized in Table 2.

Fibromyalgia

Population-based cohort studies report elevated dementia risk among individuals with fibromyalgia, with adjusted HR generally ranging from approximately 1.4 to 2.0 depending upon cohort characteristics and outcome definitions.Reference Clauw 23 , Reference Häuser, Ablin and Fitzcharles 24 , Reference Bair, Robinson, Katon and Kroenke 27 , Reference Tzeng, Chung and Liu 40 , Reference Chen, Li and Tsai 47 , Reference Chang, Hsu and Muo 55 –Reference Whitmer, Sidney, Selby, Johnston and Yaffe 57 Taiwanese nationwide studies reported adjusted HRs ranging from 1.45 to 2.77 for incident dementia during follow-up periods of approximately 5–10 years. Some analyses demonstrate AD-specific associations after multivariable adjustment.Reference Tzeng, Chung and Liu 40 , Reference Chen, Li and Tsai 47 , Reference Chang, Hsu and Muo 55 However, FM populations exhibit high prevalence of depression, sleep disturbance, metabolic risk, and psychotropic medication exposure. Few studies incorporate extended lag periods or biomarker-confirmed AD endpoints.Reference Jack, Bennett and Blennow 1 , Reference Jack, Knopman and Jagust 5 , Reference Tzeng, Chung and Liu 40 , Reference Chang, Hsu and Muo 55 Consequently, whether FM represents a causal contributor, shared vulnerability marker, or early manifestation of prodromal AD remains unresolved. Evidence rating: Moderate.

Migraine

Meta-analyses demonstrate modest but consistent increased dementia risk among migraineurs.Reference Zhang, Parikh and Kamel 58 –Reference Wang, Huang and Lin 61 Vascular and inflammatory mechanisms may mediate risk, but antidepressant exposure and healthcare utilization confound interpretation.Reference Sacco, Ornello and Ripa 62 –Reference Lin, Chou and Lai 65 Time-varying models and AD biomarker integration are needed. Meta-analytic estimates suggest pooled RR in the range of 1.25–1.40 for incident dementia. Evidence rating: Moderate-to-high.

Irritable bowel syndrome

IBS cohorts demonstrate elevated dementia incidence, including AD-specific signals.Reference Fang, Wang and Zhou 66 –Reference Tsai, Lin, Lee and Kao 68 Gut–brain axis dysregulation provides mechanistic plausibility; however, psychiatric comorbidity and metabolic clustering remain substantial confounders.Reference Cryan, O’Riordan and Cowan 69 , Reference Fond, Loundou and Hamdani 70 Cohort studies generally report adjusted HRs between 1.2 and 1.5. Evidence rating: Moderate.

Restless legs syndrome

RLS is associated with increased dementia risk in retrospective cohorts.Reference Yoo, Han and Shin 71 –Reference Yeh, Wang and Lin 73 Chronic sleep fragmentation offers a biologically plausible pathway, yet dopaminergic medication exposure complicates interpretation.Reference Mander, Winer and Walker 74 , Reference Roe, Anderson and Spivack 75 Available retrospective cohorts report adjusted HRs ranging from approximately 1.2 to 1.8. Evidence rating: Moderate.

Post-traumatic stress disorder

Meta-analytic evidence supports elevated dementia risk in PTSD.Reference Zhang, Wang, Li, Li and Zhou 76 –Reference Smith, de Araujo Junior and Moreira 79 Chronic glucocorticoid exposure and hippocampal vulnerability are plausible mediators, but reverse causation cannot be excluded.Reference Lupien, McEwen, Gunnar and Heim 80 –Reference White, Fripp and Hutmacher 82 Meta-analyses generally report pooled risk estimates between 1.5 and 2.0. Evidence rating: Moderate.

Chronic multisite pain

Prospective cohorts demonstrate dose–response relationships between number of pain sites and dementia risk, strengthening causal plausibility under Hill criteria.Reference Wang, Xiao and Liu 83 –Reference Shen, Zhang and Jiang 85 Neuroimaging analyses further demonstrate associations with hippocampal atrophy and accelerated cognitive decline. Nevertheless, shared inflammatory or metabolic vulnerability may underlie both. The UK Biobank analysis by Zhao et al. demonstrated a dose–response relationship between increasing pain-site burden and incident dementia risk. Individuals reporting four or more pain sites exhibited significantly elevated dementia incidence compared with participants without chronic pain. Associations persisted after adjustment for demographic characteristics, vascular risk factors, depression, and socioeconomic variables. Neuroimaging analyses further demonstrated smaller hippocampal volumes and accelerated cognitive decline among individuals with greater multisite pain burden. Evidence rating: Moderate-to-high.

ME/CFS, CRPS, TMD, vulvodynia, MPS, IC/BPS, dysmenorrhea, PCOS, endometriosis, MCS

For several CSS—including temporomandibular disorders, complex regional pain syndrome, myalgic encephalomyelitis/chronic fatigue syndrome, vulvodynia, myofascial pain syndrome, interstitial cystitis/painful bladder syndrome, primary dysmenorrhea, polycystic ovarian syndrome, endometriosis, and multiple chemical sensitivity—dedicated longitudinal dementia-incidence studies are sparse or absent. Nevertheless, these conditions share biological themes with better-studied CSS, including neuroimmune activation, autonomic imbalance, sleep disturbance, metabolic dysfunction, and high psychiatric comorbidity. Although mechanistic overlap provides biologic plausibility, there is currently insufficient longitudinal evidence to infer elevated dementia risk for these conditions. Accordingly, these disorders should be viewed as evidence gaps rather than established dementia-risk phenotypes.Reference Maixner, Fillingim, Williams, Smith and Slade 31 , Reference Wang, Sun and Zheng 86 –Reference Tirlapur, Kuhrt, Chaliha, Ball, Meads and Khan 94

Limitations of the current evidence

Depression as a major confounding and mediating factor

Depression represents a particularly important potential confounder in studies examining associations between CSS and dementia risk.Reference Livingston, Sommerlad and Orgeta 16 , Reference Dotson, Beydoun and Zonderman 95 –Reference Geerlings, Schoevers and Beekman 97 Major depressive disorder occurs at substantially higher prevalence among individuals with many CSS, including fibromyalgia, migraine, irritable bowel syndrome, and chronic multisite pain. Shared biological mechanisms—including chronic stress exposure, HPA axis dysregulation, neuroimmune activation, altered monoaminergic signaling, and sleep disturbance—likely contribute to this comorbidity.Reference Sluka and Clauw 21 , Reference Bair, Robinson, Katon and Kroenke 27 , Reference Katon 98 –Reference Miller and Raison 101 As a result, depression may represent both a shared vulnerability state and a downstream consequence of chronic sensitization.

Independently of chronic pain conditions, depression itself has been consistently associated with increased risk of incident dementia in large population-based cohorts and meta-analyses.Reference Ownby, Crocco, Acevedo, John and Loewenstein 102 –Reference Gao, Huang and Zhao 106 Individuals with a history of major depressive episodes demonstrate significantly elevated risk of subsequent cognitive decline and Alzheimer’s disease compared with non-depressed controls. Several mechanisms have been proposed to explain this relationship, including chronic glucocorticoid exposure leading to hippocampal atrophy, sustained neuroinflammatory signaling, reduced neurotrophic support, vascular injury, and behavioral mediators such as reduced physical activity and social engagement.Reference Chen, Hu, Wei, Qin, McCracken and Copeland 107 –Reference Krishnan and Nestler 111

Importantly, depression may operate in multiple causal roles within CSS–dementia associations. First, depression may act as a confounder, increasing the likelihood of both sensitization-related disorders and later dementia. Second, depression may function as a mediator, arising as a consequence of chronic pain and contributing to downstream neurodegenerative vulnerability. Third, depressive symptoms may represent an early manifestation of prodromal neurodegeneration, further complicating causal interpretation.Reference Saczynski, Beiser, Seshadri, Auerbach, Wolf and Au 112 –Reference Gerrits, van Oppen, van Marwijk, Penninx and van der Horst 115 Without longitudinal study designs that incorporate time-varying measures of mood symptoms and adequate lag periods, disentangling these pathways remains difficult. Future studies examining sensitization-related dementia risk should therefore incorporate detailed assessment of depressive symptom trajectories and consider analytic approaches capable of distinguishing confounding from mediation.

Sleep disturbance and sleep-disordered breathing as shared risk pathways

Sleep disturbance represents another important potential confounder in studies examining associations between CSS and dementia risk. Disrupted sleep architecture is highly prevalent across many CSS, including fibromyalgia, migraine, irritable bowel syndrome, restless legs syndrome, and PTSD.Reference Sluka and Clauw 21 , Reference Clauw 36 , Reference Ju, Lucey and Holtzman 39 , Reference Ramar and Olson 116 , Reference Sateia 117 Chronic insomnia, fragmented sleep, circadian disruption, and sleep-disordered breathing may therefore represent shared biological vulnerabilities rather than downstream consequences of sensitization alone.

Accumulating evidence suggests that impaired sleep contributes directly to neurodegenerative processes relevant to Alzheimer’s disease.Reference Xie, Kang and Xu 118 –Reference Lim, Kowgier, Yu, Buchman and Bennett 121 Experimental and longitudinal studies demonstrate that reduced slow-wave sleep and sleep fragmentation impair glymphatic clearance of amyloid-β and tau from the interstitial space, potentially accelerating accumulation of pathogenic proteins within the brain.Reference Iliff, Wang and Liao 122 –Reference Lucey, McCullough and Landsness 124 Obstructive sleep apnea further introduces intermittent hypoxia, oxidative stress, endothelial dysfunction, and systemic inflammation—mechanisms that may amplify neurodegenerative vulnerability.Reference Yaffe, Laffan and Harrison 125 –Reference Kerner and Roose 128

Notably, sleep disturbance may operate in several causal roles within CSS–dementia associations. First, poor sleep may function as a confounder, increasing the likelihood of both chronic sensitization states and later dementia.Reference Finan, Goodin and Smith 129 , Reference Blackwell, Yaffe and Laffan 130 Second, sleep disruption may serve as a mediator, arising as a consequence of chronic pain or autonomic dysregulation and subsequently promoting neurodegenerative processes.Reference Haack, Simpson, Sethna, Kaur and Mullington 131 , Reference Irwin 132 Third, alterations in sleep architecture may represent an early manifestation of prodromal Alzheimer’s disease, further complicating causal interpretation.Reference Musiek and Holtzman 133 , Reference Bubu, Brannick and Mortimer 134 Without longitudinal studies incorporating objective sleep measurements and extended lag periods, disentangling these pathways remains challenging.

Future studies examining sensitization-related dementia risk should therefore incorporate detailed characterization of sleep architecture, sleep-disordered breathing, and circadian disruption. Integration of polysomnography, actigraphy, and sleep biomarkers may help clarify whether disturbed sleep represents a causal pathway linking sensitization states to neurodegenerative risk or instead reflects shared upstream vulnerability.

Most studies rely on administrative data with ICD-coded exposure and outcome definitions. Exposure and outcome misclassification are likely. Few datasets incorporate biomarker-confirmed AD endpoints.

Medication exposure and pharmacoepidemiologic confounding

Medication exposure introduces a fundamental challenge in interpreting associations between CSS and dementia risk, as it operates within complex, time-varying causal structures. Pharmacologic treatments for chronic pain, sleep disturbance, and affective symptoms are not randomly assigned but are instead closely linked to disease severity, comorbidity burden, and healthcare utilization. As a result, observed associations between medication use and dementia may reflect confounding by indication, reverse causation, or cumulative disease burden rather than direct pharmacologic effects. From a causal inference perspective, medication exposure should therefore be conceptualized as a dynamic mediator-confounder within longitudinal pathways linking sensitization states to neurodegenerative outcomes, requiring analytic approaches that explicitly account for time-dependent confounding.Reference Barker, Greenwood, Jackson and Crowe 135 –Reference Campbell, Boustani, Skopelja, Gao, Unverzagt and Murray 138 Active-comparator designs compare treatments prescribed for similar indications, helping reduce confounding by indication.

Gabapentinoids, including gabapentin and pregabalin, are widely prescribed for neuropathic and centralized pain states. Although generally considered cognitively neutral in short-term clinical trials, emerging pharmacoepidemiologic studies have raised concerns regarding possible associations between chronic gabapentinoid exposure and increased dementia incidence in some populations.Reference Wiffen, Derry and Bell 139 –Reference Taipale, Hamina and Karttunen 141 Interpretation of these findings remains challenging because gabapentinoids are preferentially prescribed to individuals with severe chronic pain, multimorbidity, and polypharmacy—factors that themselves may confer elevated dementia risk. Distinguishing medication-related effects from underlying disease severity therefore requires analytic approaches capable of addressing confounding by indication.

Anticholinergic medications represent another important potential confounder in this literature. Drugs with anticholinergic properties—including tricyclic antidepressants, certain bladder antispasmodics, first-generation antihistamines, and several psychotropic agents—are commonly used in patients with chronic pain, sleep disturbance, and autonomic symptoms. A substantial body of epidemiologic research has demonstrated associations between cumulative anticholinergic exposure and increased risk of cognitive decline and dementia.Reference Gray, Anderson and Dublin 142 –Reference Coupland, Hill, Dening, Morriss, Moore and Hippisley-Cox 145 Because many CSS patients receive medications with moderate or strong anticholinergic burden, medication exposure may contribute to observed associations between sensitization syndromes and dementia risk independent of the underlying disorder.

Additional medication classes frequently prescribed in CSS populations may also influence cognitive trajectories. Benzodiazepines and sedative-hypnotics are commonly used for insomnia and anxiety but have been linked to cognitive impairment and increased dementia risk in some observational studies.Reference Islam, Iqbal and Walther 146 –Reference Shash, Kurth and Bertrand 148 Opioids, although less central to contemporary management of centralized pain, may contribute to cognitive dysfunction through sedation, sleep disruption, and altered neuroinflammatory signaling.Reference Kamboj, Tookman, Jones and Curran 149 –Reference Valentino and Van Bockstaele 151 Antidepressants—particularly those with strong anticholinergic properties—may similarly influence cognitive outcomes, although evidence remains mixed and may reflect underlying depression severity rather than medication-specific effects.

Polypharmacy further complicates interpretation of epidemiologic findings. Individuals with chronic multisymptom conditions frequently receive multiple medications affecting central nervous system signaling, autonomic function, and sleep regulation. Interactions among these agents may produce cumulative cognitive effects that are difficult to disentangle in administrative datasets lacking detailed dosage and duration information.

Future studies examining sensitization-related dementia risk should incorporate pharmacoepidemiologic strategies capable of addressing these complexities. New-user cohort designs, active comparator analyses, cumulative exposure modeling, and time-varying medication covariates can help mitigate confounding by indication and immortal time bias. Without such methodological approaches, medication exposure will remain an important source of residual confounding in studies linking CSS with dementia risk.

Residual confounding remains pervasive. Sleep architecture, inflammatory burden, socioeconomic status, and physical activity are rarely measured with sufficient precision. Protopathic bias is insufficiently addressed. Short exclusion windows do not mitigate decades-long preclinical AD pathology. Repeated longitudinal measures of sensitization severity and neurodegenerative biomarkers are largely absent.

Accordingly, future studies should prioritize pharmacoepidemiologic designs capable of disentangling these effects, including new-user cohorts, active comparator strategies, and marginal structural models with time-varying exposures. Without such approaches, medication use will remain an important source of residual bias, limiting the ability to distinguish whether observed dementia risk is attributable to underlying sensitization biology, treatment effects, or their interaction over the life course.

Future directions

From a psychiatric epidemiology perspective, these findings highlight the importance of integrating chronic pain, affective disorders, and sleep dysregulation into unified models of neurodegenerative risk, rather than treating these domains as independent contributors. This integrated perspective aligns with contemporary models of multimorbidity in mental health, where interacting biological and behavioral domains jointly influence long-term cognitive outcomes. Rather than acting as isolated risk domains, chronic pain, depressive symptoms, sleep disturbance, and medication burden may represent interacting components of a shared vulnerability network. Future studies should model these domains simultaneously rather than treating them as independent covariates. Clarifying etiologic directionality between chronic sensitization states and dementia risk will require methodologic designs that move beyond traditional observational associations. First, future cohorts should integrate blood-based Alzheimer’s disease biomarkers—such as plasma Aβ42/40 ratios, phosphorylated tau (p-tau181 or p-tau217), and neurofilament light chain—to distinguish preclinical neurodegeneration from downstream cognitive outcomes.Reference Palmqvist, Janelidze and Quiroz 152 –Reference Mattsson, Cullen, Andreasson, Zetterberg and Blennow 154 Embedding these biomarkers within pain and sensitization cohorts will allow investigators to determine whether heightened sensitization precedes measurable neurodegenerative pathology or instead reflects early central nervous system vulnerability. Second, extended lag analyses of at least 5–10 years are essential to mitigate reverse causation, particularly given the long prodromal phase of Alzheimer’s disease and related dementias.Reference Sperling, Aisen and Beckett 2 , Reference Jack, Knopman and Jagust 5 , Reference Bateman, Xiong and Benzinger 10 , Reference Villemagne, Burnham and Bourgeat 11 By excluding events occurring within early follow-up windows, investigators can better assess whether sensitization phenotypes represent antecedent risk states rather than early manifestations of neurodegeneration.

Quasi-experimental epidemiologic approaches will also be critical. Target trial emulation designs can explicitly define eligibility criteria, exposure assignment, follow-up windows, and outcome ascertainment in a manner that mimics randomized trials using observational data, thereby reducing immortal time and selection biases. Marginal structural models can address time-varying confounding, especially where pain severity, medication exposure, mood disorders, and healthcare utilization evolve dynamically over time and may both influence and be influenced by dementia risk. Active-comparator pharmacoepidemiologic strategies—comparing therapeutics with similar indications but differing mechanistic pathways—can further isolate medication effects from confounding by indication. Finally, repeated longitudinal assessment of sensitization severity using validated quantitative sensory testing, symptom indices, and biomarker panels will be necessary to model trajectories rather than static exposures. Capturing within-person change over time will help determine whether escalating sensitization predicts subsequent neurodegenerative biomarker conversion or cognitive decline, thereby strengthening causal inference under a life-course framework.

Clinical implications for prescribing in older adults

Although causality has not been established, several CSS are consistently associated with increased dementia risk in observational studies. These associations highlight the importance of careful interpretation of cognitive risk signals in patients with chronic pain who often require long-term pharmacologic treatment. Particular attention should be given to minimizing cumulative anticholinergic burden, as numerous medications commonly used in these populations—including certain antidepressants, antihistamines, and bladder antispasmodics—have been associated with increased risk of cognitive decline and dementia in older adults. Similarly, prolonged benzodiazepine and sedative-hypnotic use should be approached cautiously given observational evidence linking these agents to adverse cognitive outcomes. Gabapentinoids remain widely prescribed for centralized pain states and are often considered cognitively neutral; however, emerging epidemiologic studies suggest that long-term exposure warrants continued evaluation, particularly in older patients with multimorbidity and polypharmacy. In clinical practice, these considerations support a cautious prescribing approach that prioritizes minimizing unnecessary central nervous system–active medications and incorporating periodic cognitive monitoring in patients with chronic sensitization syndromes receiving long-term pharmacotherapy.

Taken together, the available evidence supports conceptualizing CSS not as isolated clinical entities but as a composite neuropsychiatric vulnerability state. This perspective aligns with emerging models of multimorbidity in which interacting domains—pain, mood, sleep, and cognition—jointly influence long-term brain health. Framing sensitization in this manner may facilitate integration into life-course dementia risk models and guide more comprehensive clinical assessment strategies.

Conclusions

This review positions central sensitization as a potential midlife vulnerability construct within the life-course epidemiology of dementia. Rather than representing a single causal exposure, sensitization-related conditions likely reflect interacting biological and behavioral processes that collectively shape neurodegenerative risk.

Observed associations may arise from shared vulnerability, medication effects, healthcare utilization patterns, or early manifestations of prodromal disease. Clarifying these pathways will require biomarker-integrated longitudinal studies and rigorous causal modeling. Until such evidence is available, central sensitization should be considered a clinically relevant risk phenotype rather than a confirmed causal determinant of dementia.

Data availability statement

Not applicable.

Author contribution

Conceptualization: K.V.H.; Data curation: K.V.H.; Formal analysis: K.V.H.; Funding acquisition: K.V.H.; Investigation: K.V.H.; Methodology: K.V.H.; Resources: K.V.H.; Software: K.V.H.; Supervision: K.V.H.; Validation: K.V.H.; Visualization: K.V.H.; Writing - original draft: K.V.H.; Writing - review & editing: K.V.H.

Financial support

This research was funded by the National Institutes of Health, grant number NIH R61/R33NS117211.

Ethical approval

Not applicable. This article is a narrative review of previously published studies and does not involve human participants or identifiable patient data.

Disclosures

The author declares no conflicts of interest.

Use of artificial intelligence tools

Artificial intelligence-assisted editorial tools were used during manuscript preparation to support language refinement, organizational review, and editorial consistency. All literature searches, study selection, interpretation of findings, scientific content, conclusions, and final manuscript decisions were performed solely by the author. The author assumes full responsibility for the accuracy, integrity, and originality of the manuscript.

References

Jack, CR, Bennett, DA, Blennow, K, et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 2018;14(4):535–562. doi:10.1016/j.jalz.2018.02.018Google Scholar
Sperling, RA, Aisen, PS, Beckett, LA, et al. Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging‐Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7(3):280–292. doi:10.1016/j.jalz.2011.03.003Google Scholar
Dubois, B, Feldman, HH, Jacova, C, et al. Preclinical Alzheimer’s disease: Definition, natural history, and diagnostic criteria. Alzheimers Dement. 2016;12(3):292–323. doi:10.1016/j.jalz.2016.02.002Google Scholar
Rafii, MS, Aisen, PS, eds. Tau accumulation in autosomal dominant Alzheimer’s disease: a longitudinal [(18)F]flortaucipir study. Alzheimer’s Res Ther. 2023;15(1). doi:10.1186/s13195-023-01234-5Google Scholar
Jack, CR, Knopman, DS, Jagust, WJ, et al. Tracking pathophysiological processes in Alzheimer’s disease: An updated hypothetical model of dynamic biomarkers. Lancet Neurol. 2013;12(2):207–216. doi:10.1016/S1474-4422(12)70291-0Google Scholar
Lilford, P, Hughes, JC Biomarkers and the diagnosis of preclinical dementia. BJPsych Adv. 2018;24(6):422–430. doi:10.1192/bja.2018.12Google Scholar
Aisen, PS, Cummings, J, Jack, CR, et al. Early-stage Alzheimer disease: getting trial-ready. Nat Rev Neurol. 2022;18(7):389–399. doi:10.1038/s41582-022-00645-6Google Scholar
Jack, CR, Therneau, TM, Weigand, SD, et al. Prevalence of biologically vs clinically defined Alzheimer spectrum entities using the National Institute on Aging–Alzheimer’s Association research framework. JAMA Neurol. 2019;76(10):1174–1183. doi:10.1001/jamaneurol.2019.1971Google Scholar
Vos, SJB, Xiong, C, Visser, PJ, et al. Preclinical Alzheimer’s disease and its outcome: A longitudinal cohort study. Lancet Neurol. 2013;12(10):957–965. doi:10.1016/S1474-4422(13)70194-7Google Scholar
Bateman, RJ, Xiong, C, Benzinger, TLS, et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med. 2012;367(9):795–804. doi:10.1056/NEJMoa1202753Google Scholar
Villemagne, VL, Burnham, S, Bourgeat, P, et al. Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: A prospective cohort study. Lancet Neurol. 2013;12(4):357–367. doi:10.1016/S1474-4422(13)70044-9Google Scholar
Sperling, RA, Karlawish, J, Johnson, KA. Preclinical Alzheimer disease—the challenges ahead. Nat Rev Neurol. 2013;9(1):54–58. doi:10.1038/nrneurol.2012.241Google Scholar
Hampel, H, Hardy, J, Blennow, K, et al. The amyloid-β pathway in Alzheimer’s disease. Mol Psychiatry. 2021;26(10):5481–5503. doi:10.1038/s41380-021-01249-0Google Scholar
Heneka, MT, Carson, MJ, El Khoury, J, et al. Neuroinflammation in Alzheimer’s disease. Lancet Neurol. 2015;14(4):388–405. doi:10.1016/S1474-4422(15)70016-5Google Scholar
Livingston, G, Huntley, J, Sommerlad, A, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020;396(10248):413–446. doi:10.1016/S0140-6736(20)30367-6Google Scholar
Livingston, G, Sommerlad, A, Orgeta, V, et al. Dementia prevention, intervention, and care. Lancet. 2017;390(10113):2673–2734. doi:10.1016/S0140-6736(17)31363-6Google Scholar
Kivipelto, M, Mangialasche, F, Snyder, HM, et al. World-Wide FINGERS Network: A global approach to risk reduction and prevention of dementia. Alzheimers Dement. 2020;16(7):1078–1094. doi:10.1002/alz.12123Google Scholar
Woolf, CJ. Central sensitization: Implications for the diagnosis and treatment of pain. Pain. 2011;152(3 suppl):S2–S15. doi:10.1016/j.pain.2010.09.030Google Scholar
Latremoliere, A, Woolf, CJ. Central sensitization: A generator of pain hypersensitivity by central neural plasticity. J Pain. 2009;10(9):895–926. doi:10.1016/j.jpain.2009.06.012Google Scholar
Kosek, E, Cohen, M, Baron, R, et al. Do we need a third mechanistic descriptor for chronic pain states? Pain. 2016;157(7):1382–1386. doi:10.1097/j.pain.0000000000000507Google Scholar
Sluka, KA, Clauw, DJ. Neurobiology of fibromyalgia and chronic widespread pain. Neuroscience. 2016;338:114–129. doi:10.1016/j.neuroscience.2016.06.006Google Scholar
Ji, RR, Nackley, A, Huh, Y, Terrando, N, Maixner, W. Neuroinflammation and central sensitization in chronic and widespread pain. Anesthesiology. 2018;129(2):343–366. doi:10.1097/ALN.0000000000002130Google Scholar
Clauw, DJ. Fibromyalgia. JAMA. 2014;311(15):1547–1555. doi:10.1001/jama.2014.3266Google Scholar
Häuser, W, Ablin, J, Fitzcharles, MA, et al. Fibromyalgia. Nat Rev Dis Primers. 2015;1(1):15022. doi:10.1038/nrdp.2015.22Google Scholar
Yunus, MB. Editorial review (thematic issue: an update on central sensitivity syndromes and the issues of nosology and psychobiology). Curr Rheumatol Rev. 2015;11(2):70–85. doi:10.2174/157339711102150702112236Google Scholar
Martinez-Lavin, M. Fibromyalgia and small fiber neuropathy: The plot thickens! Clin Rheumatol. 2018;37(12):3167–3171. doi:10.1007/s10067-018-4300-2Google Scholar
Bair, MJ, Robinson, RL, Katon, W, Kroenke, K. Depression and pain Comorbidity. Arch Intern Med. 2003;163(20):2433–2445. doi:10.1001/archinte.163.20.2433Google Scholar
Yunus, MB. Fibromyalgia and overlapping disorders: The unifying concept of central sensitivity syndromes. Semin Arthritis Rheum. 2007;36(6):339–356. doi:10.1016/j.semarthrit.2006.12.009Google Scholar
Clauw, DJ. Diagnosing and treating chronic musculoskeletal pain based on the underlying mechanism(s). Best Pract Res Clin Rheumatol. 2015;29(1):6–19. doi:10.1016/j.berh.2015.04.024Google Scholar
Diatchenko, L, Fillingim, RB, Smith, SB, Maixner, W. The phenotypic and genetic signatures of common musculoskeletal pain conditions. Nat Rev Rheumatol. 2013;9(6):340–350. doi:10.1038/nrrheum.2013.43Google Scholar
Maixner, W, Fillingim, RB, Williams, DA, Smith, SB, Slade, GD. Overlapping chronic pain conditions: Implications for diagnosis and classification. J Pain. 2016;17(9 suppl):T93–T107. doi:10.1016/j.jpain.2016.06.002Google Scholar
Aaron, LA, Buchwald, D. A review of the evidence for overlap among unexplained clinical conditions. Ann Intern Med. 2001;134(9_Part_2 pt 2):868–881. doi:10.7326/0003-4819-134-9_Part_2-200105011-00011Google Scholar
Miller, JS, Rodriguez-Saona, L, Hackshaw, KV. Metabolomics in Central Sensitivity Syndromes. Meta. 2020;10(4):164. doi:10.3390/metabo10040164Google Scholar
Grace, PM, Hutchinson, MR, Maier, SF, Watkins, LR. Pathological pain and the neuroimmune interface. Nat Rev Immunol. 2014;14(4):217–231. doi:10.1038/nri3621Google Scholar
Napadow, V, LaCount, L, Park, K, As-Sanie, S, Clauw, DJ, Harris, RE. Intrinsic brain connectivity in fibromyalgia is associated with chronic pain intensity. Arthritis Rheum. 2010;62(8):2545–2555. doi:10.1002/art.27497Google Scholar
Clauw, DJ. Fibromyalgia and related conditions. Mayo Clin Proc. 2015;90(5):680–692. doi:10.1016/j.mayocp.2015.03.014Google Scholar
Heneka, MT, Golenbock, DT, Latz, E. Innate immunity in Alzheimer’s disease. Nat Immunol. 2015;16(3):229–236. doi:10.1038/ni.3102Google Scholar
Buckner, RL, Andrews-Hanna, JR, Schacter, DL. The brain’s default Network. Ann N Y Acad Sci. 2008;1124(1):1–38. doi:10.1196/annals.1440.011Google Scholar
Ju, YS, Lucey, BP, Holtzman, DM. Sleep and Alzheimer disease pathology—a bidirectional relationship. Nat Rev Neurol. 2014;10(2):115–119. doi:10.1038/nrneurol.2013.269Google Scholar
Tzeng, NS, Chung, CH, Liu, FC, et al. Fibromyalgia and risk of dementia: A nationwide, population-based cohort study. Am J Med Sci. 2018;355(2):153–161. doi:10.1016/j.amjms.2017.09.002Google Scholar
Chen, YC, Tseng, CH, Wang, SJ, et al. Migraine and risk of dementia: A nationwide retrospective cohort study. Cephalalgia. 2018;38(1):124–132. doi:10.1177/0333102416683913Google Scholar
Lee, YC, Hu, HY, Huang, N, et al. Rapid Screening of MDR-TB in cases of extra pulmonary tuberculosis using geno type MTBDRplus. PLoS One. 2016;11(7):e0159651. doi:10.1371/journal.pone.0159651Google Scholar
Kang, JH, Chen, YH, Lin, HC. Comorbidities and dementia risk in patients with restless legs syndrome: A population-based study. J Clin Psychiatry. 2013;74(7):e651–e657. doi:10.4088/JCP.12m08273Google Scholar
Yaffe, K, Vittinghoff, E, Lindquist, K, et al. Posttraumatic stress disorder and risk of dementia among US veterans. Arch Gen Psychiatry. 2010;67(6):608–613. doi:10.1001/archgenpsychiatry.2010.61Google Scholar
Zhao, W, Zhao, L, Chang, X, Lu, X, Tu, Y. Elevated dementia risk, cognitive decline, and hippocampal atrophy in multisite chronic pain. Proc Natl Acad Sci USA. 2023;120(9):e2215192120. doi:10.1073/pnas.2215192120Google Scholar
Taipale, H, Karttunen, N, Koponen, M, et al. Serum caffeine and metabolites are reliable biomarkers of early Parkinson disease. Neurology. 2018;90(5):e404–e411. doi:10.1212/WNL.0000000000004888Google Scholar
Chen, MH, Li, CT, Tsai, CF, et al. Facial muscle movements encoding pain—a systematic review. Pain. 2019;160(3):535–549. doi:10.1097/j.pain.0000000000001424Google Scholar
Prince, M, Ali, GC, Guerchet, M, Prina, AM, Albanese, E, Wu, YT. Recent global trends in the prevalence and incidence of dementia, and survival with dementia. Alzheimer’s Res Ther. 2016;8(1):23. doi:10.1186/s13195-016-0188-8Google Scholar
Richardson, K, Fox, C, Maidment, I, et al. Anticholinergic drugs and risk of dementia: Case-control study. BMJ. 2018;361:k1315. doi:10.1136/bmj.k1315Google Scholar
Hernán, MA, Robins, JM. Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol. 2016;183(8):758–764. doi:10.1093/aje/kwv254Google Scholar
Petersen, RC, Lopez, O, Armstrong, MJ, et al. Practice guideline update summary: Mild cognitive impairment [RETIRED]: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology. Neurology. 2018;90(3):126–135. doi:10.1212/WNL.0000000000004826Google Scholar
Hackshaw, KV, Osuna-Diaz, MM, Sebastian, KR, et al. Depression, pain, and functional impairment across fibromyalgia, rheumatoid arthritis, and systemic lupus erythematosus: A cross-condition analysis and mediation model. J Psychosom Res. 2026;209:112897. doi:10.1016/j.jpsychores.2026.112897Google Scholar
Hackshaw, KV, Osuna, MM, Sebastian, KL, et al. Longitudinal stability of data-driven fibromyalgia phenotypes across repeated clinical assessments. Pain Rep. doi:10.1097/PR9.0000000000001472Google Scholar
Hackshaw, KV, Osuna-Diaz, MM, Sebastian, KR, et al. Psychiatric comorbidity patterns across disability-based fibromyalgia phenotypes. CNS Spectr. 2026;31(1):e7. doi:10.1017/S1092852926100893Google Scholar
Chang, KH, Hsu, CC, Muo, CH, et al. Impact of type of surgery on survival outcome in patients with early gallbladder cancer in the era of minimally invasive surgery: oncologic safety of laparoscopic surgery. Medicine (Baltimore). 2016;95(22):e3675. doi:10.1097/MD.0000000000003675Google Scholar
Lin, CS, Lin, CC, Yeh, CC, et al. Quality of life, functioning, and depressive symptom severity in older adults with major depressive disorder treated with citalopram in the star*d study. J Clin Psychiatry. 2017;78(7):897–903. doi:10.4088/JCP.16m11335Google Scholar
Whitmer, RA, Sidney, S, Selby, J, Johnston, SC, Yaffe, K. Midlife cardiovascular risk factors and risk of dementia in late life. Neurology. 2005;64(2):277–281. doi:10.1212/01.WNL.0000149519.47454.F2Google Scholar
Zhang, Y, Parikh, NS, Kamel, H, et al. Migraine and risk of dementia: a systematic review and meta-analysis. Cephalalgia. 2021;41(4):503–512. doi:10.1177/0333102420983293Google Scholar
Chuang, CS, Lin, CL, Lin, MC, Sung, FC, Kao, CH. Primary headaches, attention deficit disorder and learning disabilities in children and adolescents. J Headache Pain. 2013;14(1):54. doi:10.1186/1129-2377-14-54Google Scholar
Lee, SY, Lim, JS, Oh, DJ, Kong, IG, Choi, HG. Rheumatoid factor as predictor of response to treatment with anti-TNF alpha drugs in patients with rheumatoid arthritis: Results of A cohort study. Medicine (Baltimore). 2019;98(5):e14181. doi:10.1097/MD.0000000000014181Google Scholar
Wang, HK, Huang, CY, Lin, SH, et al. The patient-reported outcome of chronic pain after the harvest of anterior iliac bone for anterior cervical arthrodesis. J Clin Neurosci. 2017;36:102–107. doi:10.1016/j.jocn.2016.10.016Google Scholar
Sacco, S, Ornello, R, Ripa, P, et al. Migraine and risk of ischaemic heart disease: A systematic review and meta-analysis of observational studies. Eur J Neurol. 2015;22(6):1001–1011. doi:10.1111/ene.12701Google Scholar
Kurth, T, Gaziano, JM, Cook, NR, Logroscino, G, Diener, HC, Buring, JE. Migraine and risk of cardiovascular disease in women. JAMA. 2006;296(3):283–291. doi:10.1001/jama.296.3.283Google Scholar
Campbell, NL, Maidment, I, Fox, C, Khan, B, Boustani, M. Response Letter to Lakin and Doe. J Am Geriatr Soc. 2013;61(2):313–314. doi:10.1111/jgs.12108Google Scholar
Lin, HC, Chou, P, Lai, SW, et al. The relative contribution of cognition and symptomatic remission to functional outcome following treatment of a first episode of psychosis. J Clin Psychiatry. 2014;75(06):e566–e572. doi:10.4088/JCP.13m08606Google Scholar
Fang, X, Wang, J, Zhou, C, et al. Association between irritable bowel syndrome and risk of dementia: A systematic review and meta-analysis. Front Med (Lausanne). 2022;9:875235. doi:10.3389/fmed.2022.875235Google Scholar
Chen, CH, Lin, CL, Kao, CH. Docetaxel rechallenge in a heavily pretreated patient with castration-resistant prostate cancer: a case report and review of literature. Medicine (Baltimore). 2016;95(14):e2754. doi:10.1097/MD.0000000000002754Google Scholar
Tsai, MC, Lin, CL, Lee, PH, Kao, CH. Pharmacologic cardioversion with intravenous amiodarone is likely safe in neurocritically Ill patients. J Clin Neurosci. 2017;39:59–61. doi:10.1016/j.jocn.2017.01.014Google Scholar
Cryan, JF, O’Riordan, KJ, Cowan, CSM, et al. The microbiota–gut–brain axis. Physiol Rev. 2019;99(4):1877–2013. doi:10.1152/physrev.00018.2018Google Scholar
Fond, G, Loundou, A, Hamdani, N, et al. Anxiety and depression comorbidities in irritable bowel syndrome (IBS): A systematic review and meta-analysis. Eur Arch Psychiatry Clin Neurosci. 2014;264(8):651–660. doi:10.1007/s00406-014-0502-zGoogle Scholar
Yoo, JE, Han, K, Shin, DW, et al. Altered cortical gray matter volume and functional connectivity after transcutaneous spinal cord direct current stimulation in idiopathic restless legs syndrome. Sleep Med. 2020;74:254–261. doi:10.1016/j.sleep.2020.07.026Google Scholar
Kim, HB, Myung, SK, Lee, SM, Park, YC. Effects of repetitive transcranial magnetic stimulation in subjects with sleep disorders. Sleep Med. 2020;71:113–121. doi:10.1016/j.sleep.2020.01.028Google Scholar
Yeh, CC, Wang, YC, Lin, CL, et al. Trinucleotide repeat expansion of TATA-binding protein gene associated with Parkinson’s disease: A Thai multicenter study. Parkinsonism Relat Disord. 2016;28:146–149. doi:10.1016/j.parkreldis.2016.05.008Google Scholar
Mander, BA, Winer, JR, Walker, MP. Sleep and human aging. Neuron. 2017;94(1):19–36. doi:10.1016/j.neuron.2017.02.004Google Scholar
Roe, CM, Anderson, MJ, Spivack, B. Use of anticholinergic medications by older adults with dementia. J Am Geriatr Soc. 2002;50(5):836–842. doi:10.1046/j.1532-5415.2002.50208.xGoogle Scholar
Zhang, L, Wang, L, Li, X, Li, L, Zhou, J. Alcohol withdrawal hallucinations in the general population, an epidemiological study. Psychiatry Res. 2018;262:129–134. doi:10.1016/j.psychres.2018.02.021Google Scholar
Flatt, JD, Gilsanz, P, Quesenberry, CP, Albers, KB, Whitmer, RA. Post-traumatic stress disorder and risk of dementia among members of a health care delivery system. Alzheimers Dement. 2018;14(1):28–34. doi:10.1016/j.jalz.2017.04.014Google Scholar
Qureshi, SU, Kimbrell, T, Pyne, JM, et al. Greater prevalence and incidence of dementia in older veterans with posttraumatic stress disorder. J Am Geriatr Soc. 2010;58(9):1627–1633. doi:10.1111/j.1532-5415.2010.02977.xGoogle Scholar
Smith, AL, de Araujo Junior, DA, Moreira, JD, et al. The association between post-traumatic stress disorder (PTSD) and cognitive impairment: A systematic review of neuroimaging findings. J Psychiatr Res. 2023;164:259–269. doi:10.1016/j.jpsychires.2023.06.016Google Scholar
Lupien, SJ, McEwen, BS, Gunnar, MR, Heim, C. Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nat Rev Neurosci. 2009;10(6):434–445. doi:10.1038/nrn2639Google Scholar
Small, GW, Kepe, V, Ercoli, LM, et al. PET of brain amyloid and tau in mild cognitive impairment. N Engl J Med. 2006;355(25):2652–2663. doi:10.1056/NEJMoa054625Google Scholar
White, SR, Fripp, J, Hutmacher, F, et al. The effect of plasma cortisol on hippocampal atrophy and clinical progression in mild cognitive impairment. Alzheimers Dement (Amst). 2023;15(3):e12463. doi:10.1002/dad2.12463Google Scholar
Wang, T, Xiao, S, Liu, Y, et al. Combating Elder Mistreatment: Still Muddling—Not Yet Transformed. J Am Geriatr Soc. 2019;67(6):1117–1119. doi:10.1111/jgs.15874Google Scholar
Veronese, N, Stubbs, B, Solmi, F, et al. Burning pain: axonal dysfunction in erythromelalgia. Pain. 2017;158(5):900–911. doi:10.1097/j.pain.0000000000000856Google Scholar
Shen, X, Zhang, M, Jiang, H, et al. Issue Information. Alzheimers Dement. 2020;16(S7):1107–1116. doi:10.1002/alz.12263Google Scholar
Wang, Z, Sun, Z, Zheng, H, et al. Association between chronic pain and dementia: a systematic review and meta-analysis. Eur J Ageing. 2024;21(1):17. doi:10.1007/s10433-024-00812-2Google Scholar
Innes, KE, Sambamoorthi, U. The potential contribution of chronic pain and common chronic pain conditions to subsequent cognitive decline, new onset cognitive impairment, and incident dementia: a systematic review and conceptual model for future research. J Alzheimer’s Dis. 2020;78(3):1177–1195. doi:10.3233/JAD-200960Google Scholar
Schrepf, A, Maixner, W, Fillingim, R, et al. The chronic overlapping pain condition screener. J Pain. 2024;25(1):265–272. doi:10.1016/j.jpain.2023.08.009Google Scholar
Bansal, AS, Seton, KA, Brooks, JCW, Carding, SR. Cognitive dysfunction in myalgic encephalomyelitis/chronic fatigue syndrome-aetiology and potential treatments. Int J Mol Sci. 2025;26(5):1896. doi:10.3390/ijms26051896Google Scholar
Ruiz-Marrara, J, Antunes, LG, Bataglion, C, Fernandes, MH, Melchior, MO, Magri, LV. Cognitive domain impairments in chronic painful temporomandibular disorders: associations with pain intensity, hypervigilance and catastrophising: a cross-sectional analysis. J Oral Rehabil. 2026;53(1):76–88. doi:10.1111/joor.70064Google Scholar
Pszczołowska, M, Walczak, K, Miśków, W, et al. Association between female reproductive factors and risk of dementia. J Clin Med. 2024;13(10):2983. doi:10.3390/jcm13102983Google Scholar
Nijs, J, Van Houdenhove, B, Oostendorp, RA. Recognition of central sensitization in patients with musculoskeletal pain: Application of pain neurophysiology in manual therapy practice. Man Ther. 2010;15(2):135–141. doi:10.1016/j.math.2009.12.001Google Scholar
Williams, DA, Clauw, DJ. Understanding fibromyalgia: Lessons from the broader pain research community. J Pain. 2009;10(8):777–791. doi:10.1016/j.jpain.2009.06.001Google Scholar
Tirlapur, SA, Kuhrt, K, Chaliha, C, Ball, E, Meads, C, Khan, KS. The ‘evil twin syndrome’ in chronic pelvic pain: A systematic review of prevalence studies of bladder pain syndrome and endometriosis. Int J Surg. 2013;11(3):233–237. doi:10.1016/j.ijsu.2013.02.003Google Scholar
Dotson, VM, Beydoun, MA, Zonderman, AB. Recurrent depressive symptoms and the incidence of dementia and mild cognitive impairment. Neurology. 2010;75(1):27–34. doi:10.1212/WNL.0b013e3181e62124Google Scholar
Cherbuin, N, Kim, S, Anstey, KJ. Dementia risk estimates associated with measures of depression: A systematic review and meta-analysis. BMJ Open. 2015;5(12):e008853. doi:10.1136/bmjopen-2015-008853Google Scholar
Geerlings, MI, Schoevers, RA, Beekman, ATF, et al. Depression and risk of cognitive decline and Alzheimer’s disease. Br J Psychiatry. 2000;176(6):568–575. doi:10.1192/bjp.176.6.568Google Scholar
Katon, W. The comorbidity of diabetes mellitus and depression. Am J Med 2008;121(11 Suppl 2):S8–S15. doi:10.1016/j.amjmed.2008.09.008Google Scholar
Young, SN. How to increase serotonin in the human brain without drugs. J Psychiatry Neurosci. 2007;32(6):394–399. doi:10.1139/jpn.0738Google Scholar
Maletic, V, Robinson, M, Oakes, T, Iyengar, S, Ball, SG, Russell, J. Neurobiology of depression: An integrated view of key findings. Int J Clin Pract. 2007;61(12):2030–2040. doi:10.1111/j.1742-1241.2007.01602.xGoogle Scholar
Miller, AH, Raison, CL. The role of inflammation in depression: From evolutionary imperative to modern treatment target. Nat Rev Immunol. 2016;16(1):22–34. doi:10.1038/nri.2015.5Google Scholar
Ownby, RL, Crocco, E, Acevedo, A, John, V, Loewenstein, D. Depression and risk for Alzheimer disease: Systematic review, meta-analysis, and metaregression analysis. Arch Gen Psychiatry. 2006;63(5):530–538. doi:10.1001/archpsyc.63.5.530Google Scholar
Diniz, BS, Butters, MA, Albert, SM, Dew, MA, Reynolds, CF. Late-life depression and risk of vascular dementia and Alzheimer’s disease: Systematic review and meta-analysis of community-based cohort studies. JAMA Psychiatry. 2013;70(7):714–722. doi:10.1001/jamapsychiatry.2013.1953Google Scholar
Byers, AL, Yaffe, K. Depression and risk of developing dementia. Nat Rev Neurol. 2011;7(6):323–331. doi:10.1038/nrneurol.2011.60Google Scholar
Barnes, DE, Yaffe, K, Byers, AL, McCormick, M, Schaefer, C, Whitmer, RA. Midlife vs late-life depressive symptoms and risk of dementia: Differential effects for Alzheimer disease and vascular dementia. Arch Gen Psychiatry. 2012;69(5):493–498. doi:10.1001/archgenpsychiatry.2011.1481Google Scholar
Gao, Y, Huang, C, Zhao, K, et al. Depression as a risk factor for dementia and mild cognitive impairment: A meta-analysis of longitudinal studies. Int J Geriatr Psychiatry. 2013;28(5):441–449. doi:10.1002/gps.3845Google Scholar
Chen, R, Hu, Z, Wei, L, Qin, X, McCracken, C, Copeland, JR. Severity of depression and risk for subsequent dementia: Cohort studies in China and the UK. Br J Psychiatry. 2008;193(5):373–377. doi:10.1192/bjp.bp.107.044974Google Scholar
Wilson, RS, Barnes, LL, Mendes de Leon, CF, et al. Depressive symptoms, cognitive decline, and risk of AD in older persons. Neurology. 2002;59(3):364–370. doi:10.1212/WNL.59.3.364Google Scholar
Sheline, YI, Sanghavi, M, Mintun, MA, Gado, MH. Depression duration but not age predicts hippocampal volume loss in medically healthy women with recurrent major depression. J Neurosci. 1999;19(12):5034–5043. doi:10.1523/JNEUROSCI.19-12-05034.1999Google Scholar
Raison, CL, Capuron, L, Miller, AH. Cytokines sing the blues: Inflammation and the pathogenesis of depression. Trends Immunol. 2006;27(1):24–31. doi:10.1016/j.it.2005.11.006Google Scholar
Krishnan, V, Nestler, EJ. The molecular neurobiology of depression. Nature. 2008;455(7215):894–902. doi:10.1038/nature07455Google Scholar
Saczynski, JS, Beiser, A, Seshadri, S, Auerbach, S, Wolf, PA, Au, R. Depressive symptoms and risk of dementia: The Framingham Heart Study. Neurology. 2010;75(1):35–41. doi:10.1212/WNL.0b013e3181e62138Google Scholar
Brommelhoff, JA, Gatz, M, Johansson, B, McArdle, JJ, Fratiglioni, L, Pedersen, NL. Depression as a risk factor or prodromal feature for dementia? Findings in a population-based sample of Swedish twins. Psychol Aging. 2009;24(2):373–384. doi:10.1037/a0015713Google Scholar
Pan, A, Sun, Q, Okereke, OI, Rexrode, KM, Hu, FB. Depression and risk of stroke morbidity and mortality: A meta-analysis and systematic review. JAMA. 2011;306(11):1241–1249. doi:10.1001/jama.2011.1282Google Scholar
Gerrits, MMJG, van Oppen, P, van Marwijk, HWJ, Penninx, BWJH, van der Horst, HE. Pain and the onset of depressive and anxiety disorders. Pain. 2014;155(1):53–59. doi:10.1016/j.pain.2013.09.005Google Scholar
Ramar, K, Olson, EJ. Management of common sleep disorders. Am Fam Physician. 2013;88(4):231–238.Google Scholar
Sateia, MJ. International classification of sleep disorders—third Edition. Chest. 2014;146(5):1387–1394. doi:10.1378/chest.14-0970Google Scholar
Xie, L, Kang, H, Xu, Q, et al. Sleep drives metabolite clearance from the adult brain. Science. 2013;342(6156):373–377. doi:10.1126/science.1241224Google Scholar
Spira, AP, Gamaldo, AA, An, Y, et al. Self-reported sleep and β-amyloid deposition in community-dwelling older adults. JAMA Neurol. 2013;70(12):1537–1543. doi:10.1001/jamaneurol.2013.4258Google Scholar
Shokri-Kojori, E, Wang, GJ, Wiers, CE, et al. β-Amyloid accumulation in the human brain after one night of sleep deprivation. Proc Natl Acad Sci USA. 2018;115(17):4483–4488. doi:10.1073/pnas.1721694115Google Scholar
Lim, ASP, Kowgier, M, Yu, L, Buchman, AS, Bennett, DA. Sleep fragmentation and the risk of incident Alzheimer’s disease and cognitive decline in older persons. Sleep. 2013;36(7):1027–1032. doi:10.5665/sleep.2802Google Scholar
Iliff, JJ, Wang, M, Liao, Y, et al. A paravascular pathway facilitates CSF flow through the brain parenchyma and the clearance of interstitial solutes, including amyloid β. Sci Transl Med. 2012;4(147):147ra111. doi:10.1126/scitranslmed.3003748Google Scholar
Ju, YE, McLeland, JS, Toedebusch, CD, et al. Sleep quality and preclinical Alzheimer disease. JAMA Neurol. 2013;70(5):587–593. doi:10.1001/jamaneurol.2013.2334Google Scholar
Lucey, BP, McCullough, A, Landsness, EC, et al. Reduced non–rapid eye movement sleep is associated with tau pathology in early Alzheimer’s disease. Sci Transl Med. 2019;11(474):eaau6550. doi:10.1126/scitranslmed.aau6550Google Scholar
Yaffe, K, Laffan, AM, Harrison, SL, et al. Sleep-disordered breathing, hypoxia, and risk of mild cognitive impairment and dementia in older women. JAMA. 2011;306(6):613–619. doi:10.1001/jama.2011.1115Google Scholar
Osorio, RS, Gumb, T, Pirraglia, E, et al. Sleep-disordered breathing advances cognitive decline in the elderly. Neurology. 2015;84(19):1964–1971. doi:10.1212/WNL.0000000000001566Google Scholar
Rosenzweig, I, Glasser, M, Polsek, D, Leschziner, GD, Williams, SCR, Morrell, MJ. Sleep apnoea and the brain: A complex relationship. Lancet Respir Med. 2015;3(5):404–414. doi:10.1016/S2213-2600(15)00090-9Google Scholar
Kerner, NA, Roose, SP. Obstructive sleep apnea is linked to depression and cognitive impairment: Evidence and potential mechanisms. Am J Geriatr Psychiatry. 2016;24(6):496–508. doi:10.1016/j.jagp.2016.01.134Google Scholar
Finan, PH, Goodin, BR, Smith, MT. The association of sleep and pain: An update and a path forward. J Pain. 2013;14(12):1539–1552. doi:10.1016/j.jpain.2013.08.007Google Scholar
Blackwell, T, Yaffe, K, Laffan, A, et al. Associations of objectively and subjectively measured sleep quality with subsequent cognitive decline in older community-dwelling men: The MrOS Sleep Study. Sleep. 2014;37(4):655–663. doi:10.5665/sleep.3562Google Scholar
Haack, M, Simpson, N, Sethna, N, Kaur, S, Mullington, JM. Sleep deficiency and chronic pain: Potential underlying mechanisms and clinical implications. Neuropsychopharmacology. 2020;45(1):205–216. doi:10.1038/s41386-019-0439-zGoogle Scholar
Irwin, MR. Sleep and inflammation: Partners in sickness and in health. Nat Rev Immunol. 2019;19(11):702–715. doi:10.1038/s41577-019-0190-zGoogle Scholar
Musiek, ES, Holtzman, DM. Mechanisms linking circadian clocks, sleep, and neurodegeneration. Science. 2016;354(6315):1004–1008. doi:10.1126/science.aah4968Google Scholar
Bubu, OM, Brannick, M, Mortimer, J, et al. Sleep, cognitive impairment, and Alzheimer’s disease: A systematic review and meta-analysis. Sleep. 2017;40(1):zsw032. doi:10.1093/sleep/zsw032Google Scholar
Barker, MJ, Greenwood, KM, Jackson, M, Crowe, SF. Cognitive effects of long-term benzodiazepine Use. CNS Drugs. 2004;18(1):37–48. doi:10.2165/00023210-200418010-00004Google Scholar
de Gage S, B, Moride, Y, Ducruet, T, et al. Benzodiazepine use and risk of Alzheimer’s disease: Case-control study. BMJ. 2014;349(sep09 2):g5205. doi:10.1136/bmj.g5205Google Scholar
de Gage S, B, Begaud, B, Bazin, F, et al. Benzodiazepine use and risk of dementia: Prospective population-based study. BMJ. 2012;345(sep27 4):e6231. doi:10.1136/bmj.e6231Google Scholar
Campbell, NL, Boustani, MA, Skopelja, EN, Gao, S, Unverzagt, FW, Murray, MD. Use of anticholinergics and the risk of cognitive impairment in an African American population. Neurology. 2010;75(2):152–159. doi:10.1212/WNL.0b013e3181e7f2abGoogle Scholar
Wiffen, PJ, Derry, S, Bell, RF, et al. Gabapentin for chronic neuropathic pain in adults. Cochrane Database Syst Rev. 2020;2020(2):CD007938. doi:10.1002/14651858.CD007938.pub4Google Scholar
Moore, RA, Wiffen, PJ, Derry, S, Toelle, T, Rice, ASC. Pregabalin for neuropathic pain in adults. Cochrane Database Syst Rev. 2019;1(1):CD007076. doi:10.1002/14651858.CD007076.pub3Google Scholar
Taipale, H, Hamina, A, Karttunen, N, et al. Incident gabapentin use and risk of adverse neurological outcomes including cognitive impairment: A population-based cohort study. Eur J Clin Pharmacol. 2020;76(4):579–587. doi:10.1007/s00228-019-02828-2Google Scholar
Gray, SL, Anderson, ML, Dublin, S, et al. Cumulative use of strong anticholinergics and incident dementia: A prospective cohort study. JAMA Intern Med. 2015;175(3):401–407. doi:10.1001/jamainternmed.2014.7663Google Scholar
Fox, C, Richardson, K, Maidment, ID, et al. Anticholinergic medication use and cognitive impairment in the older population: The Medical Research Council Cognitive Function and Ageing Study. J Am Geriatr Soc. 2011;59(8):1477–1483. doi:10.1111/j.1532-5415.2011.03491.xGoogle Scholar
Carrière, I, Fourrier-Reglat, A, Dartigues, JF, et al. Drugs with anticholinergic properties, cognitive decline, and dementia in an elderly general Population. Arch Intern Med. 2009;169(14):1317–1324. doi:10.1001/archinternmed.2009.229Google Scholar
Coupland, CAC, Hill, T, Dening, T, Morriss, R, Moore, M, Hippisley-Cox, J. Anticholinergic drug exposure and the risk of dementia: A nested case-control study. JAMA Intern Med. 2019;179(8):1084–1093. doi:10.1001/jamainternmed.2019.0677Google Scholar
Islam, MM, Iqbal, U, Walther, B, et al. Benzodiazepine use and risk of dementia in the elderly population: A systematic review and meta-analysis. Neuroepidemiology. 2017;47(3–4):181–191. doi:10.1159/000454881Google Scholar
Gray, SL, Dublin, S, Yu, O, et al. Benzodiazepine use and risk of incident dementia or cognitive decline: Prospective population based study. BMJ. 2016;352:i90. doi:10.1136/bmj.i90Google Scholar
Shash, D, Kurth, T, Bertrand, M, et al. Benzodiazepine, psychotropic medication, and dementia: A population-based cohort study. Alzheimers Dement. 2016;12(5):604–613. doi:10.1016/j.jalz.2015.10.006Google Scholar
Kamboj, SK, Tookman, A, Jones, L, Curran, HV. The effects of immediate-release morphine on cognitive functioning in patients receiving chronic opioid therapy in palliative care. Pain. 2005;117(3):388–395. doi:10.1016/j.pain.2005.06.022Google Scholar
Mogri, M, Desai, H, Webster, L, Grant, BJ, Mador, MJ. Hypoxemia in patients on chronic opiate therapy with and without sleep apnea. Sleep Breath. 2009;13(1):49–57. doi:10.1007/s11325-008-0208-4Google Scholar
Valentino, RJ, Van Bockstaele, E. Endogenous opioids: The downside of opposing stress. Neurobiol Stress. 2015;1:23–32. doi:10.1016/j.ynstr.2014.09.006Google Scholar
Palmqvist, S, Janelidze, S, Quiroz, YT, et al. Discriminative accuracy of plasma phospho-tau217 for Alzheimer disease vs other neurodegenerative disorders. JAMA. 2020;324(8):772–781. doi:10.1001/jama.2020.12134Google Scholar
Schindler, SE, Bollinger, JG, Ovod, V, et al. High-precision plasma β-amyloid 42/40 predicts current and future brain amyloidosis. Neurology. 2019;93(17):e1647–e1659. doi:10.1212/WNL.0000000000008081Google Scholar
Mattsson, N, Cullen, NC, Andreasson, U, Zetterberg, H, Blennow, K. Association between longitudinal plasma neurofilament light and neurodegeneration in patients with Alzheimer disease. JAMA Neurol. 2019;76(7):791–799. doi:10.1001/jamaneurol.2019.0765Google Scholar
Figure 0

Table 1. Evidence Classification of Dementia Risk Across Central Sensitization SyndromesTable 1. long description.

Figure 1

Table 2. Epidemiologic Evidence Linking Central Sensitization Syndromes to Incident DementiaTable 2. long description.

Figure 2

Figure 1. Directed acyclic graph. Directed acyclic graph (DAG) illustrating hypothesized relationships between central sensitization syndromes (CSS) and incident dementia. Pathways include direct neurobiological effects, mediation through depression, sleep disturbance, and chronic pain, and confounding by shared vulnerability factors. Reverse causation, medication-related effects, and healthcare utilization bias are also depicted, highlighting competing causal explanations.Figure 1. long description.