Aging‐Driven Inter‐Organ Crosstalk in Postmenopausal Osteoporosis: From Immunometabolic Drift to Multisystem Frailty

ABSTRACT

Postmenopausal osteoporosis (PMOP) is increasingly recognized as an aging‐associated, multisystem vulnerability state in which estrogen withdrawal amplifies immune and metabolic drift across bone marrow, muscle, adipose tissue, the gut, vasculature, and neural circuits. We synthesize evidence that key control nodes including RANKL–RANK–OPG imbalance, Th17/Treg disequilibrium, loss of regulatory B cell IL‐10 restraint, inflammatory myeloid polarization, and expansion of bone marrow adipose tissue encode persistent osteoclastogenic tone and impaired formation. We map how microbiota‐derived metabolites and barrier dysfunction tune osteoimmunity, and how exercise‐responsive myokines and metabolites can counteract drift. Extracellular vesicles emerge as bidirectional couriers that propagate senescence and inflammation or support repair, but clinical translation requires ISEV‐aligned methodological rigor and robust manufacturing, biodistribution, and safety frameworks. Building on these inter‐organ axes, we propose a phenotype‐aware “network reset” roadmap that integrates antifracture therapy with functional restoration, falls prevention, cardiometabolic risk control, and inflammatory monitoring, prioritizing composite endpoints and real‐world implementation infrastructure. This systems framing shifts PMOP management from bone‐only correction toward coordinated restoration of whole‐body resilience.

Postmenopausal osteoporosis (PMOP) is increasingly recognized as an aging‐associated, multisystem state of fragility, in which estrogen deficiency exacerbates immune and metabolic drift across the bone marrow, skeletal muscle, adipose tissue, gut, vascular system, and neural circuits. This metabolic and immune dysregulation further amplifies bone resorption, ultimately driving postmenopausal osteoporosis.

Abbreviations

AI

artificial intelligence

BAIBA

β‐aminoisobutyric acid

BCAAs

branched‐chain amino acids

BM

bone marrow

BMAT

bone marrow adipose tissue

BMD

bone mineral density

Bregs

regulatory B cells

C3

complement component 3

CCL2

chemokine (C‐C motif) ligand 2

CNS

central nervous system

CV

cardiovascular

CXCL12

C‐X‐C motif chemokine ligand 12

CXCR4

C‐X‐C chemokine receptor 4

EV

extracellular vesicle

EVs

extracellular vesicles

FGF23

fibroblast growth factor 23

FLS

fracture liaison service

FXR

Farnesoid X receptor

GPCR

G protein‐coupled receptor

GWAS

genome‐wide association study

HSC

hematopoietic stem cell

HSCs

hematopoietic stem cells

IL‐10

interleukin‐10

IL‐15

interleukin‐15

IL‐17

interleukin‐17

IL‐17A

interleukin‐17A

IL‐6

interleukin‐6

IL‐7

interleukin‐7

ISEV

International Society for Extracellular Vesicles

K2

vitamin K2

L‐BAIBA

L‐β‐aminoisobutyric acid

M1

classically activated macrophages

MCP‐1

monocyte chemoattractant protein‐1

MDSCs

Myeloid‐derived suppressor cells

METRNL

meteorin‐like protein

miRNA

microRNA

OPG

osteoprotegerin

OVX

ovariectomy

PMOP

postmenopausal osteoporosis

POI

primary ovarian insufficiency

POI/POF

primary ovarian insufficiency/primary ovarian failure

PTH

parathyroid hormone

RANK

receptor activator of nuclear factor κB

RANKL

receptor activator of nuclear factor κB ligand

RANKL/OPG

receptor activator of nuclear factor κB ligand/osteoprotegerin

ROS

reactive oxygen species

RWE

real‐world evidence

SCFAs

short‐chain fatty acids

TGF‐β

transforming growth factor‐β

TGR5

Takeda G protein‐coupled receptor 5

Th17

T helper 17 cell

TNF‐α

tumor necrosis factor‐α

Treg

regulatory T cell

Tregs

regulatory T cells

ZO‐1

zonula occludens‐1

1. Introduction

Postmenopausal osteoporosis (PMOP) has classically been framed as an estrogen‐deficiency–driven loss of bone mass and microarchitectural deterioration that culminates in fragility fractures [1]. However, fractures are only the visible “tip” of a broader aging‐related syndrome in which reduced skeletal resilience co‐evolves with declines in muscle, metabolic flexibility, and functional reserve, thereby amplifying disability risk and accelerating dependence [2]. In population‐based analyses, osteoporosis frequently clusters with sarcopenia‐ and frailty‐related traits in older women, supporting a shift from a single‐organ to a multisystem vulnerability paradigm [3].

Aging is increasingly understood as an integrated, network‐level process in which “altered intercellular communication” is a hallmark that links chronic inflammation and metabolic dysregulation to end‐organ decline [4, 5]. In PMOP, estrogen deficiency intersects with osteoimmunity, reshaping immune cell composition and cytokine tone in ways that bias remodeling toward osteoclastogenesis and impaired osteoblast support [6]. We use “immunometabolic drift” to describe the age‐ and menopause‐associated coupling of inflammaging with disordered nutrient sensing, adipose dysfunction, and microbiome‐related signals that collectively destabilize bone homeostasis [5, 7, 8]. This framing elevates PMOP from a localized skeletal disorder to a systems‐biology problem in which the direction and magnitude of inter‐organ signals determine whether the organism lands in resilience versus frailty [5]. Figure 1 illustrates the conceptual shift from a bone‐centric remodeling imbalance model to an osteoimmune framework in which immune‐cell dysregulation and the RANKL–RANK–OPG axis drive PMOP progression (Figure 1).

FIGURE 1.

Paradigm shift from traditional bone‐centric model to osteoimmune framework in PMOP. The left panel illustrates the conventional view of PMOP as a consequence of imbalanced bone formation and resorption between osteoblasts and osteoclasts. The right panel represents the emerging “osteoimmune” paradigm. Immune cell subsets, including Th17 cells, Tregs, Bregs, memory B cells, and macrophages, modulate bone turnover through cytokines such as IL‐17A, TNF‐α, IL‐10, and OPG. The RANKL–RANK–OPG axis serves as a central conduit for immune–bone crosstalk, highlighting how immune dysregulation after menopause accelerates bone loss.

Bone and muscle show longitudinally coupled trajectories with aging, consistent with shared endocrine, mechanical, and inflammatory constraints that can propagate functional decline [9]. Adipose tissue contributes additional endocrine and immunometabolic inputs, especially under metabolically unhealthy states, that may increase skeletal fragility despite higher body mass [7]. The gut microbiome is another upstream regulator, with recent evidence emphasizing bone‐relevant microbial metabolites and immune pathways as plausible mediators of the gut–bone axis [8]. Within this inter‐organ framework, exercise is treated as a programmable perturbation that reshapes systemic signaling via myokines and post‐transcriptional regulators, which is relevant to bone remodeling and functional capacity [10]. We further emphasize extracellular vesicles (EVs) as mobile carriers of proteins and RNAs that can transmit state information across organs, potentially explaining how local tissue stress becomes systemic dysfunction during aging [11]. EV biology is also directly pertinent to PMOP because exosomes/EVs modulate osteoblast–osteoclast communication and are being explored as therapeutic platforms in osteoporosis models [12].

This narrative review was informed by searches of PubMed, Embase, and Web of Science from database inception to 31 October 2025, using combinations of keywords and controlled vocabulary terms related to postmenopausal osteoporosis/estrogen deficiency; osteoimmunology (e.g., RANKL–RANK–OPG, Th17/Treg, regulatory B cells, macrophage programs); immunometabolism/inflammaging; bone marrow microenvironment and marrow adiposity; gut microbiome and microbial metabolites (e.g., short‐chain fatty acids, bile acids, indoles); extracellular vesicles/exosomes; exercise and myokines; and frailty/osteosarcopenia with vascular and neurobehavioral comorbid axes. We screened titles/abstracts and prioritized English‐language, full‐text peer‐reviewed human evidence (clinical trials and observational cohorts) together with guideline‐ and review‐level syntheses; conference abstracts and case reports were excluded, and preprints were considered only when they provided unique mechanistic insights and are clearly identified as such. Animal (e.g., ovariectomy models) and cellular mechanistic studies were included to support biological plausibility and pathway mapping. Evidence was synthesized qualitatively (no meta‐analysis) and explicitly distinguished by tier (human vs. animal vs. mechanistic) in the narrative and tables; reference lists of key articles were also hand‐searched to capture additional relevant studies.

2. Immunometabolic Drift as a Systems Trigger

PMOP can be reframed as a systems‐level consequence of endocrine withdrawal superimposed on aging biology, where immune–metabolic “set points” drift in parallel across marrow, muscle, adipose, and vascular compartments. Estrogen deficiency amplifies osteoclastogenic cues and perturbs immune cell behavior, while aging adds immunosenescence and inflammaging, jointly shifting the cytokine–metabolite milieu toward chronic, low‐grade inflammation that favors net bone resorption [6]. Immunometabolic drift is therefore not only a local skeletal event but a network trigger that couples marrow niche remodeling, mitochondrial/redox stress, and reduced metabolic flexibility into a self‐reinforcing loop [13]. In this context, the “starting point” of PMOP is best conceptualized as an inter‐organ coordination failure: marrow immune–hematopoietic niches become biased, adipose depots become more inflammatory and ectopic, muscle bioenergetics become less adaptable, and systemic cardiometabolic risk rises, with each change feeding back into skeletal fragility and frailty trajectories [14]. Figure 2 summarizes how osteoimmune homeostasis restrains osteoclastogenesis under physiological conditions and how inflammatory cues disrupt this balance to accelerate postmenopausal bone loss (Figure 2). Specifically, the schematic contrasts anti‐resorptive immune cues (e.g., Breg/Treg‐derived IL‐10 and OPG restraint) with inflammatory cytokines/chemokines (e.g., IL‐1, IL‐6, IL‐17, TNF‐α, CXCL1/2, and CCL20) that amplify RANKL–RANK signaling and activate NF‐κB/NFATc1 to drive osteoclast differentiation.

FIGURE 2.

The role of osteoimmunity in osteoporosis under normal and inflammatory conditions.

2.1. Estrogen Deficiency Plus Aging: Inflammaging, Immunosenescence, and Reduced Metabolic Flexibility

Estrogen withdrawal removes anti‐inflammatory and bone‐protective constraints, permitting higher tonic activation of innate and adaptive immune pathways that increase osteoclastogenesis and impair osteoblast–osteoclast coupling [6]. Aging concurrently drives immunosenescence (loss of naïve T‐cell pools, restricted repertoire diversity, and altered innate sensing) and inflammaging (persistent, low‐grade cytokine production), creating a permissive baseline for exaggerated inflammatory responses to metabolic or microbial stimuli [13]. In PMOP, these processes intersect to skew T‐cell and myeloid cell phenotypes toward more osteoclastogenic programs, thereby translating systemic immune aging into skeletal catabolism [15]. Reduced metabolic flexibility is a key amplifier: the ability to switch between lipid and glucose oxidation becomes less efficient with age and menopause, increasing susceptibility to insulin resistance and oxidative stress; these states, in turn, sustain inflammatory signaling [16]. Human work in postmenopausal women indicates that hormone replacement can modulate parameters linked to metabolic flexibility and mitochondrial function, supporting a mechanistic connection between sex steroid milieu and whole‐body substrate handling [17]. Conceptually, this dual‐hit (endocrine loss + immune/metabolic aging) lowers the threshold for chronic inflammation, disrupts mitochondrial substrate switching, and increases redox burden; these conditions are repeatedly implicated as upstream drivers of both frailty phenotypes and osteoporotic risk [18].

2.2. Bone Marrow Microenvironment Remodeling: Altered Hematopoietic–Immune Niches and Uncoupled Remodeling

PMOP progression is tightly linked to bone marrow (BM) niche remodeling, where changes in stromal, vascular, and immune components reshape hematopoiesis and osteoimmune crosstalk [19]. Aging and estrogen deficiency alter the balance of osteogenic versus adipogenic differentiation within mesenchymal progenitor pools and modify endothelial–perivascular signaling, thereby perturbing the spatial organization and trophic support of both hematopoietic stem cells (HSCs) and osteolineage cells [20]. Bone marrow adipocytes are increasingly recognized as active niche elements that can regulate immune cell output and inflammatory tone; mechanistic evidence supports a role for BM adiposity in fostering pro‐inflammatory myeloid programs that are relevant to bone loss [21]. Recent syntheses further emphasize that BM adipocytes participate in vascular niche function and can influence marrow cellular trafficking and metabolic signaling, providing an anatomical substrate for “niche‐to‐network” propagation of immunometabolic drift [22]. This niche remodeling matters for remodeling balance: osteoclastogenic cytokines and growth factors originating from immune cells and stromal cells (and shaped by niche architecture) can uncouple resorption from formation, while altered HSC niche function can bias immune cell production toward phenotypes that reinforce inflammation and osteoclast activation [23]. Thus, BM microenvironment remodeling acts as a relay station, translating systemic endocrine and aging cues into durable shifts in osteoimmune set points.

2.3. Key Immunometabolic Nodes: RANKL/OPG, Th17/Treg Skewing, Macrophage Polarization, Mitochondria, and Redox Balance

At the pathway level, immunometabolic drift converges on a limited set of high‐leverage nodes that integrate endocrine, immune, and metabolic signals into osteoclast/osteoblast outputs. The RANKL–RANK signaling axis is central to osteoclast differentiation and activation and is modulated by immune cells (including B‐ and T‐lineage cells) and stromal cues; dysregulation in the balance between pro‐osteoclastogenic RANKL and anti‐resorptive constraints (e.g., via OPG) is repeatedly highlighted as a mechanistic backbone of inflammatory bone loss [24]. Adaptive immune skewing, particularly toward Th17 dominance with relative Treg insufficiency, provides a cytokine logic for sustained osteoclastogenesis via IL‐17 and related inflammatory programs in PMOP [25]. Human observational evidence in postmenopausal women supports concurrent immune perturbations alongside disease‐associated shifts in microbial/immune features, consistent with a systems model rather than a bone‐only phenotype [26]. In parallel, macrophage polarization states exert context‐dependent control over bone remodeling: pro‐inflammatory (M1‐like) programs can reinforce osteoclastogenesis and inhibit reparative bone formation, whereas pro‐resolving programs can support tissue repair, making macrophage states a tractable “switch” linking inflammation resolution to skeletal outcomes [27, 28]. Finally, mitochondrial dysfunction and redox imbalance are not merely downstream damage signals; they act as upstream immunometabolic amplifiers by increasing ROS, perturbing inflammasome/cytokine outputs, and impairing osteoblast bioenergetics [29]. Emerging work on mitochondrial quality control further integrates mitophagy and mitochondrial dynamics into osteoporosis pathobiology, providing a mechanistic bridge between aging hallmarks and osteoimmune dysregulation [30]. Collectively, these nodes form a compact control architecture through which immunometabolic drift becomes “encoded” into persistent remodeling imbalance. Figure 3 depicts the osteoimmune cell network in PMOP, highlighting Th17/Treg skewing, pro‐resorptive myeloid programs, and gut‐metabolite modulation converging on cytokine and RANKL signaling to shape osteoclast activity (Figure 3). A consolidated map of these immunometabolic control nodes, together with representative readouts, actionable leverage points, and the underlying evidence context, is provided in Table 1.

FIGURE 3.

Immune cell subset network driving bone homeostasis and resorption in PMOP. This schematic illustrates the multicellular regulatory network among immune cell subsets and bone‐resorbing cells. Th17 cells secrete IL‐17A to stimulate RANKL expression, while Tregs and Bregs produce IL‐10 and TGF‐β to inhibit osteoclastogenesis. Memory B cells and M1 macrophages contribute to pro‐resorptive inflammation via RANKL and TNF‐α, respectively. External regulatory input from the gut microbiota and its metabolites (e.g., SCFAs) is also shown, acting through Th17 suppression and Breg expansion. The figure highlights spatial and functional heterogeneity across immune lineages in shaping postmenopausal bone remodeling.

TABLE 1.

Key immunometabolic nodes encoding osteoimmune drift in PMOP.

Node/pathway	Mechanistic role in PMOP (match to review text)	Representative readouts/biomarkers	Translational leverage points	Evidence context	References
RANKL–RANK–OPG osteoimmune licensing	Osteoclastogenesis is “licensed” by RANKL signaling and constrained by OPG; osteoimmune imbalance increases pro‐resorptive tone and uncouples remodeling	Circulating/locally inferred RANKL:OPG ratio; osteoclast precursor frequency; bone resorption markers (CTX)	Antiresorptive targeting (RANKL blockade), upstream immune‐tone reduction, niche‐level modulation	Review/synthesis of osteoimmunology in osteoporosis contexts	[31, 32]
Th17/Treg disequilibrium (IL‐17 axis)	Adaptive immune skewing toward Th17 with relative Treg insufficiency sustains osteoclastogenic cytokine programs; gut–immune cues can shift this balance	Th17/Treg ratio; IL‐17A; IL‐10/TGF‐β; inflammatory composite indices	Phenotype‐guided immune rebalancing; microbiota/metabolite interventions that reduce Th17 and expand Tregs	Preclinical + translational synthesis	[33, 34]
Bregs/IL‐10 restraint on osteoclasts	Regulatory B cells restrain osteoclast differentiation via IL‐10; reduction in OVX models links peripheral immune organs to bone loss	Breg abundance (flow); IL‐10; ex vivo osteoclastogenesis assays	Strategies that expand Bregs/IL‐10 signaling; gut‐directed immunomodulation	OVX/preclinical mechanistic evidence	[35]
Macrophage polarization and inflammatory myeloid programs	Pro‐inflammatory macrophage states reinforce osteoclastogenesis and inhibit repair; aging/metabolic stress can lock in M1‐like programs and propagate inflammaging	M1/M2 markers; TNF‐α/IL‐6; monocyte subset shifts	Resolution‐promoting reprogramming; vesicle‐based modulation; anti‐inflammatory adjuncts	System‐level EV–macrophage coupling and inflammaging synthesis	[36, 37, 38]
Bone marrow adipose tissue (BMAT) expansion/niche rewiring	BMAT is metabolically active; expands with aging/estrogen deficiency and rewires marrow cytokine gradients, stromal support, and immune outputs toward catabolic remodeling	BMAT imaging/biopsy surrogates; adipokines; marrow inflammatory chemokines (e.g., CCL2)	Target marrow adiposity/inflammation; metabolic risk clustering–guided screening and intervention	Reviews/syntheses on BMAT as niche and inflammatory mediator	[31, 39, 40, 41]
Gut metabolites as endocrine‐like osteoimmune modulators (SCFAs, bile acids, indoles, BCAAs)	Microbiota‐derived metabolites signal via intestinal receptors and systemic circulation to tune osteoimmunity and osteoblast/osteoclast energetics; barrier dysfunction increases endotoxin load and inflammation	SCFAs; bile acid profiles; barrier markers (ZO‐1/occludin); LPS‐related inflammation	“Metabolites + barrier + immunity” triad targeting; diet/pre/probiotics; receptor pathways (FXR/TGR5)	Human associations + mechanistic framing + model evidence	[34, 42, 43, 44]
Bone–vascular shared mineral/inflammation logic (“calcification paradox”)	Bone demineralization and vascular mineralization can co‐progress via shared regulators (OPG/RANKL, Wnt inhibitors, vesicle‐mediated mineralization) under chronic inflammation/metabolic risk	Vascular calcification markers; vitamin K status proxies; lipid oxidation/inflammation indices	Integrated fracture + CV risk management; nutrient and lipid‐centric risk control; vesicle pathway targeting	Reviews linking osteoporosis–CVD coupling and vesicle mineralization	[45, 46, 47]

2.4. Systems‐Level “Drift Indicators”: Low‐Grade Inflammation, Insulin Resistance, Ectopic Fat, Sarcopenia, and Frailty Coupling

Clinically and translationally, immunometabolic drift is best captured using coupled indicators rather than single biomarkers. Multiple studies in postmenopausal women report associations between circulating inflammatory markers or composite inflammatory/nutritional indices and reduced BMD, supporting low‐grade inflammation as an accessible systemic readout of PMOP‐related biology [48]. Metabolic dysfunction is equally integral: systematic evidence links insulin resistance indices to bone outcomes, including fracture risk and/or BMD associations, implying that cardiometabolic drift and skeletal fragility can share upstream drivers [49]. Large‐scale population analyses further support connections between insulin resistance measures and BMD patterns, strengthening the rationale for integrating glycemic/insulin metrics into PMOP risk models [50]. Ectopic fat deposition and altered adipose endocrine function after menopause contribute to systemic inflammation and insulin resistance and may interact with BM adiposity to reinforce osteoimmune activation [51]. The musculoskeletal frailty axis provides an additional convergence point: osteosarcopenia is increasingly framed as an integrated syndrome in which bone and muscle deterioration synergize, sharing inflammatory and metabolic mechanisms and jointly increasing adverse outcomes [52]. Empirically, osteoporotic patients frequently exhibit sarcopenia and self‐reported frailty markers, supporting the clinical relevance of combined screening and mechanistically coherent endpoints [53]. Cohort and cross‐sectional studies also show that frailty relates to osteoporosis and that muscle strength and balance can mediate this relationship, reinforcing the concept that “drift” manifests as coupled deterioration across bone, muscle function, and systemic resilience [54].

3. Bone as an Endocrine‐Immune Hub: Outbound Signals and Systemic Circuits

Beyond serving as a structural tissue, bone is increasingly conceptualized as an endocrine and immune‐regulatory organ that broadcasts molecular cues to coordinate whole‐body physiology [55]. Osteocytes, osteoblast‐lineage cells, and bone‐resorbing osteoclasts operate within a marrow ecosystem that also functions as a primary lymphoid site, thereby embedding bone remodeling within hematopoietic and immune circuits [31]. In this framework, postmenopausal bone loss is not only a consequence of local remodeling imbalance but also a potential amplifier of inter‐organ communication failures that manifest as metabolic dysfunction, sarcopenia, vascular vulnerability, and neurobehavioral decline [55]. Canonical osteokines (e.g., FGF23, sclerostin, osteocalcin) and osteoimmune mediators (e.g., RANKL/OPG‐related signaling) constitute a shared language linking skeletal cells with kidney, adipose tissue, muscle, cardiovascular systems, and the central nervous system [56, 57]. This outbound signaling view provides a mechanistic scaffold for interpreting PMOP as a node in aging‐driven, multi‐system frailty networks [58].

3.1. Osteocyte/Osteoblast/Osteoclast Secretomes: Osteokines, Cytokines, and Chemokines

Osteocytes are positioned as long‐lived mechanosensory “broadcast cells” whose secreted factors regulate both local remodeling and distant organ physiology [59]. A prototypical example is osteocyte‐derived FGF23, which acts hormonally to control phosphate and vitamin D metabolism and is also discussed in the context of cardiovascular pathology when chronically elevated [60]. Osteocytes also produce Wnt pathway antagonists such as sclerostin, classically restraining osteoblast activity while increasingly being examined for extra‐skeletal associations with metabolic phenotypes [61]. In adult bone remodeling, osteocytes are a major source of membrane‐bound RANKL that licenses osteoclastogenesis, thereby linking mechanical loading, senescence‐associated cues, and immune mediators to resorption intensity [31]. Osteoblast‐lineage cells contribute both coupling signals (e.g., OPG as a decoy receptor for RANKL) and immune‐niche factors (e.g., IL‐7‐related support of lymphoid progenitors) that integrate bone formation with hematopoiesis [31]. At the system level, the RANKL/OPG axis illustrates how “bone molecules” simultaneously participate in skeletal turnover and immune organ biology, underscoring why PMOP should be interpreted as an osteoimmune network disorder rather than a single‐tissue disease [31].

3.2. Bone Marrow Adipose Tissue: How Marrow Adiposity Rewires Bone–Peripheral Communication

Bone marrow adipose tissue (BMAT) expands with aging and is frequently enriched in conditions associated with skeletal fragility, positioning it as a local and systemic modulator of PMOP trajectories [39]. Contemporary syntheses emphasize that BMAT is not merely “space filling” but metabolically active, with nutrient and endocrine responsiveness that differs from classical white adipose depots [40]. BMAT‐derived cytokines and chemokines, such as TNF‐α, IL‐6, and CCL2/MCP‐1, are highlighted as plausible mediators of inflammation‐driven bone loss, linking marrow adiposity to osteoclastogenic tone and impaired osteogenesis [41]. Mechanistically, BMAT expansion may reshape the marrow immune–hematopoietic niche by altering the balance of stromal support signals and inflammatory cues, thereby biasing immune cell outputs that can secondarily affect peripheral organs [31]. In parallel, osteoimmune reviews note that adult sources of RANKL relevant to trabecular homeostasis can extend beyond classical osteoblast‐lineage populations, reinforcing the concept that “fatty marrow” participates directly in osteoclast licensing [31]. Framed as an inter‐organ crosstalk node, BMAT provides a credible conduit through which estrogen deficiency and aging translate into coupled phenotypes of bone loss, metabolic inflexibility, and frailty risk [39].

3.3. Systemic Effects of Bone‐Derived Signals: Muscle, Metabolism, Brain, and Cardiovascular Homeostasis

Osteocalcin remains a central candidate osteokine in bone‐to‐periphery signaling models, with endocrine‐relevant actions proposed across glucose handling, muscle function, and neurobiology [62]. Within the bone–muscle axis, recent integrative discussions emphasize that bone‐derived factors can influence muscle performance and adaptation beyond purely mechanical loading, supporting the concept of bidirectional endocrine crosstalk in osteosarcopenic states [63]. For metabolism, sclerostin has been reviewed not only as a local inhibitor of Wnt signaling in bone but also as a circulating factor associated with adiposity and indices of metabolic function while acknowledging ongoing debate regarding its endocrine versus predominantly paracrine nature [61]. In the cardiovascular domain, FGF23 is repeatedly positioned as a bone‐derived hormone with kidney‐directed physiological roles and with documented associations to cardiac disease phenotypes in observational and synthesis‐level literature [60]. Recent reviews further highlight the FGF23–klotho axis as a mechanistically important interface connecting mineral metabolism dysregulation to cardiovascular risk, particularly in chronic kidney disease contexts that commonly coexist with aging‐related multimorbidity [64]. These multi‐organ signal effects provide a plausible biological substrate for why PMOP frequently clusters with cardiometabolic and neuro‐musculoskeletal decline in aging populations [58].

3.4. The “Bone–Immune–Metabolic” Closed Loop: From Local Microenvironment Drift to Systemic Frailty

Osteoimmunology frameworks emphasize that bone remodeling is inseparable from immune cell differentiation and trafficking within the marrow, making local perturbations capable of propagating system‐wide inflammatory consequences [31]. In population‐level and mechanistic‐leaning analyses, systemic inflammation and frailty phenotypes are independently linked to osteoporotic traits, and frailty can partially mediate the association between inflammatory burden and osteoporosis‐related outcomes [65]. Clinical studies further report positive associations between osteoporosis and frailty syndrome in women, reinforcing the need to interpret PMOP as a multisystem vulnerability state rather than an isolated skeletal endpoint [66]. Large cross‐sectional evidence likewise supports a robust correlation between osteoporosis and frailty after covariate adjustment, aligning epidemiology with the biological expectation of shared inflammatory and metabolic drivers [67]. Conceptual reviews describing the convergence of sarcopenia, osteoporosis, and frailty provide a mechanistic narrative in which bone‐derived endocrine/immune outputs, muscle decline, and adipose‐driven inflammation reinforce one another, accelerating functional deterioration [52]. Thus, a closed‐loop model in which marrow niche remodeling drives immune and metabolic drift, which then promotes musculoskeletal weakening, ultimately leading to reduced activity and further marrow and adipose inflammation, offers a coherent explanation for the transition from PMOP to multisystem frailty [58].

4. Inter‐Organ Axes in PMOP: From Local Remodeling to Multisystem Frailty

PMOP increasingly presents as a network disorder in which estrogen deficiency and aging‐related immunometabolic drift couple skeletal fragility to sarcopenia, adipose inflammation, gut dysbiosis, vascular dysfunction, and neurobehavioral changes that collectively shape a frailty phenotype. In this section, we organize “bone‐centered” inter‐organ axes as bidirectional loops, where endocrine factors, immune cell trafficking, microbial metabolites, and extracellular vesicles (EVs)/matrix vesicle biology transmit signals across compartments, thereby converting local remodeling imbalance into systemic vulnerability. The major bone‐centered inter‐organ axes, along with representative bidirectional mediators and clinically actionable levers/endpoints, are summarized in Table 2.

TABLE 2.

Major inter‐organ axes linking bone fragility to multisystem frailty in PMOP.

Axis	Bidirectional mediators (examples)	Core mechanism in PMOP (match to review text)	Clinical correlates/endpoints to capture	Practical modifiable levers	References
Bone–muscle (osteosarcopenia)	Osteocalcin; sclerostin tone; myokines (irisin, IL‐6 family); exercise metabolites	Coupled decline of bone and muscle via shared inflammatory/endocrine/mechanical constraints; reduced activity amplifies drift	BMD + muscle mass/strength; gait speed; falls; frailty indices	Progressive resistance + balance ± aerobic; protein adequacy; phenotype‐aware combined therapy	[68, 69]
Bone–adipose (systemic fat + BMAT)	Adipokines; IL‐6/TNF‐α; BMAT cytokines/chemokines; EVs	Metaflammation and BMAT expansion bias mesenchymal fate away from osteogenesis and increase osteoclastogenic tone; “metabolic bone fragility”	Central adiposity; IR/dyslipidemia clustering; bone quality (beyond BMD)	Weight/metabolic risk control; inflammation reduction; consider BMAT‐related risk subtyping	[39, 70]
Bone–gut (microbiota–metabolites–barrier–immunity)	SCFAs; bile acids (FXR/TGR5); indoles; barrier integrity; endotoxin load	Metabolites act via intestinal signaling and circulation to modulate osteoimmunity; barrier disruption increases inflammation and bone resorption	Microbiome/metabolite panels; SCFAs/bile acids; permeability markers; inflammatory tone	Diet/pre/probiotics; barrier‐restoring strategies; metabolite‐informed stratification	[34, 42, 44, 71]
Bone–vascular (calcification paradox)	OPG/RANKL; Wnt inhibitors; vitamin K2/MGP axis; mineralization EVs	Shared immunometabolic substrates drive parallel bone loss and vascular calcification; vesicle pathways central to ectopic mineralization	Vascular calcification; cardiometabolic markers; fracture + CV composite outcomes in high‐risk	Integrated CV risk management; nutrient adequacy; lipid/inflammation control	[46, 72, 73]
Bone–brain/neural	Osteocalcin; RANKL/RANK neuroimmune signaling; sleep/circadian disruption; activity loops	Neurobehavioral state (sleep, pain, mood) and activity feedback to loading and inflammation; endocrine/immune links provide mechanistic plausibility	Sleep quality; circadian rhythm metrics; pain/mood; cognition/function as covariates	Exercise; sleep interventions; include neuro endpoints in network‐reset trials	[74, 75, 76]
Bone–immune organs (spleen/lymphoid tissues)	Bregs (IL‐10); Th17/Treg shifts across sites; complement; MDSCs	Peripheral immune compartments can be rewired (e.g., probiotics) to shift osteoclastogenic balance; systemic innate/adaptive immunity regulates bone loss	Immune phenotyping (Bregs, Th17/Treg); cytokines; monocyte subsets	Immunomodulatory microbiota strategies; target systemic innate pathways where justified	[77, 78, 79]
Bone–ovarian aging (POI/POF spectrum)	Early estrogen deprivation; immune–metabolic comorbidity clustering	Earlier reproductive aging accelerates lifetime skeletal vulnerability and may align with broader multisystem aging phenotypes	Early BMD screening; long‐term fracture risk; metabolic/immune covariates	Earlier surveillance and prevention; phenotype‐based risk management	[80, 81, 82]

4.1. Bone–Muscle Axis (Bone–Muscle): Osteosarcopenia, Myokines, and Mechanical Signal Decay

Osteosarcopenia is increasingly recognized as a co‐morbid geriatric syndrome in which low bone mass/quality and reduced muscle mass/strength reinforce each other through shared inflammatory, endocrine, and mechanical pathways, amplifying falls and fracture risk beyond either condition alone [68]. Skeletal muscle communicates with bone via myokines and exercise‐induced factors, including irisin, IL‐6 family signals, and myostatin/activin‐related cues, that modulate osteoblast–osteoclast coupling and osteocyte signaling, thereby linking physical activity patterns to remodeling set‐points [69]. Conversely, bone acts as an endocrine organ that shapes muscle energetics and exercise adaptation, with osteocalcin emerging as a key osteokine connecting skeletal remodeling status to whole‐body performance and metabolic flexibility [83, 84]. Aging and menopause attenuate mechanical loading inputs (reduced activity, altered gait, and decreased muscle power), which can shift osteocyte programs toward anti‐anabolic signaling (e.g., higher sclerostin tone) and blunt normal anabolic responses to loading, thereby accelerating both bone loss and muscle decline [85, 86]. At the systems level, irisin/FNDC5 has been proposed as a muscle‐derived endocrine mediator that can couple exercise to coordinated benefits in bone and the central nervous system, supporting physical activity as a multi‐organ “signal amplifier.” [87, 88] Therapeutically, this axis supports integrated interventions (progressive resistance training plus nutritional optimization) evaluated with dual endpoints, skeletal fragility risk and physical function, rather than bone density alone [89, 90].

4.2. Bone–Adipose Axis (Bone–Adipose): Adipose Inflammation, Adipokines, and Marrow Adiposity

The bone–adipose axis in PMOP reflects both systemic adipose inflammation (metaflammation) and marrow adipose tissue (BMAT) expansion, which together can favor adipogenic allocation of mesenchymal stromal cells over osteoblastogenesis and amplify osteoclastogenic inflammatory cues within the marrow niche [91, 92]. Adipokines and adipose‐derived cytokines exert endocrine/paracrine control over bone remodeling, and obesity‐associated low‐grade inflammation can impair bone strength and material properties even when areal BMD is not reduced, supporting the concept of obesity‐related skeletal fragility [70]. Bone–adipose crosstalk is bidirectional: bone functions as an endocrine organ that contributes to systemic metabolic regulation, while marrow/adipose‐derived factors can reshape osteoblast/osteocyte programs and the marrow microenvironment in ways that influence hematopoiesis and immune tone [55, 93]. BMAT is increasingly recognized as an active endocrine‐like compartment that expands with aging and menopause/estrogen deficiency, alters local lipid and cytokine landscapes, and is associated with impaired bone formation and microarchitectural deterioration [94, 95]. Clinically, insulin resistance and type 2 diabetes in postmenopausal women are linked to elevated fragility‐fracture risk (often disproportionate to BMD), arguing for combined metabolic and skeletal risk stratification in PMOP management [49, 96].

4.3. Bone–Gut Axis (Bone–Gut): Microbiota–Metabolites–Immunity and Barrier‐Driven Endotoxin Load

Human observational studies indicate that postmenopausal osteopenia/osteoporosis is associated with altered gut microbiome composition and functional pathway signatures that relate to BMD and bone metabolic indices [97, 98]. Additional cohort analyses have linked microbiome patterns and accompanying metabolic features (including amino‐acid–related signals) to inter‐individual variation in BMD among postmenopausal women, supporting a menopause‐sensitive gut–bone phenotype [99, 100]. Microbiome‐derived metabolites, particularly short‐chain fatty acids (SCFAs), can restrain osteoclastogenesis and modulate bone mass through GPCR‐linked immune–metabolic reprogramming, thereby positioning these metabolites as endocrine‐like mediators of the gut–bone axis [42, 101]. Bile‐acid signaling is also implicated: circulating bile‐acid profiles associate with BMD in postmenopausal women, and pharmacologic co‐activation of FXR and TGR5 has demonstrated anti‐osteoporotic efficacy in preclinical models [43, 102]. Barrier integrity represents a second critical lever: sex‐steroid deficiency can increase gut permeability and inflammatory cytokine output, while microbiota‐directed interventions such as fecal microbiota transplantation (FMT) restore tight‐junction proteins (e.g., ZO‐1 and occludin), suppress pro‐osteoclastogenic cytokines, and mitigate OVX‐induced bone loss [44, 103]. Consistent with this, controlled microbiota‐modulating interventions show bone‐relevant signals across models and humans, and natural compounds that target the gut–bone axis (e.g., nodakenin) have been reported to improve intestinal‐barrier/microbiota readouts alongside OVX bone outcomes in preclinical studies [104, 105]. Collectively, translational framing in PMOP increasingly emphasizes an integrated triad of metabolites, barrier function, and immunity, rather than microbiome composition alone, as a practical target set for intervention design [42, 106]. Figure 4 summarizes this gut–bone axis model, highlighting microbial metabolites (e.g., SCFAs, bile‐acid derivatives, and indole compounds) acting via intestinal signaling and systemic circulation to modulate osteoimmunity.

FIGURE 4.

Regulatory roles of the bone–gut axis in postmenopausal osteoporosis. The gut microbiota produces multiple metabolites, including short‐chain fatty acids (SCFAs), secondary bile acids, branched‐chain amino acids (BCAAs), and indoles. After entering the intestinal lumen, these metabolites act through two major routes: First, they engage diverse receptors on intestinal villus epithelial cells to elicit systemic effects, thereby modulating osteoimmune processes involved in the pathogenesis of postmenopausal osteoporosis; second, they can be directly absorbed into the bloodstream, where they exert systemic regulatory functions.

4.4. Bone–Vascular Axis (Bone–Vascular): Calcification, Endothelium, and Shared Immunometabolic Substrates

Osteoporosis and cardiovascular disease frequently co‐occur in aging women and share major risk factors (physical inactivity, diabetes, dyslipidemia, and smoking) as well as chronic low‐grade inflammation and oxidative stress [45, 107]. The “calcification paradox” framework highlights that bone demineralization and vascular calcification can progress in parallel through convergent disturbances in mineral metabolism and inflammation‐linked osteogenic signaling [108, 109]. Key molecular bridges include the RANK/RANKL/OPG axis and Wnt‐pathway modulation (including sclerostin), both of which have been implicated in vascular calcification biology as well as skeletal remodeling control [110, 111]. Vitamin K2 biology provides a clinically relevant example of bone–vascular coupling because vitamin K–dependent activation of matrix Gla protein is a recognized inhibitor of vascular calcification, while vitamin K–dependent pathways also intersect with skeletal mineralization and bone health [112]. Dyslipidemia‐associated inflammation and lipid oxidation can promote osteogenic programs in the vasculature and are also linked to adverse skeletal outcomes, supporting lipid‐centric risk assessment when PMOP clusters with cardiometabolic features [113, 114]. Extracellular vesicles (including matrix vesicles) are mechanistically central to ectopic mineralization, and vesicle‐mediated calcification pathways have been proposed as a cross‐tissue “carrier” mechanism contributing to the bone–vascular calcification coupling [115, 116]. Clinically, this axis argues for integrated management, including fracture prevention and vascular risk control, especially in patients with metabolic syndrome features where dual‐organ endpoints may be most informative.

4.5. Bone–Brain/Neural Axis (Bone–Brain): Neuroinflammation, Activity Feedback Loops, and Sleep/Circadian Disruption

Bone‐derived endocrine signals can influence brain function, and osteocalcin has been highlighted as a hormone linking skeletal remodeling to cognition, stress‐related behaviors, and broader neurophysiological regulation [62, 117]. In parallel, RANKL/RANK signaling, best known for osteoclast biology, also has described roles in the central nervous system and neuroinflammatory regulation, including pathways relevant to microglial function [118]. Menopausal transition is associated with increased frailty prevalence and is also viewed as a neurological transition state with prominent sleep/circadian, affective, and cognitive symptom burdens in a subset of women, supporting the concept that estrogen loss can concurrently destabilize musculoskeletal integrity and neurophysiological resilience [119, 120]. Sleep and circadian disruption are increasingly linked to osteoporosis risk through neuroendocrine and inflammatory mechanisms that regulate bone remodeling rhythms, and sleep disorders may therefore act as both consequence and driver within the bone–brain–frailty loop [74, 121]. Exercise‐related endocrine factors such as irisin have been proposed as messengers in multi‐organ crosstalk (including bone and brain), and irisin can directly promote osteoblast differentiation, implying that reduced physical activity may remove a coordinated protective signal across these systems [122]. Translationally, this axis supports capturing pain, sleep, mood, and physical activity as remodeling‐relevant covariates in PMOP studies while motivating mechanistic work on endocrine and neuroimmune mediators that connect CNS state to skeletal turnover.

4.6. Bone–Immune Organs Axis (Bone–Immune Organs): Trafficking, Inflammatory Memory, and Rewiring of Systemic Immunity

PMOP is increasingly interpreted through an osteoimmunology framework in which the bone marrow and secondary lymphoid organs (e.g., spleen and lymph nodes) jointly shape osteoclastogenesis and systemic inflammatory tone [123, 124]. Regulatory B cells (Bregs) can suppress osteoclast differentiation through IL‐10, and ovariectomy‐associated reductions in Bregs provide a spleen‐linked mechanism contributing to estrogen‐deficiency bone loss in vivo [35]. Complement signaling participates in osteoimmune remodeling, and C3 deficiency has been shown to inhibit osteoclast differentiation and protect against ovariectomy‐induced osteoporosis [77]. Microbiome‐directed immunomodulation demonstrates multi‐site immune rewiring: Lactobacillus rhamnosus administration has been reported to decrease Th17 cells and increase Tregs across bone marrow and peripheral lymphoid tissues (including spleen, lymph nodes, and Peyer's patches) while attenuating ovariectomy‐related bone loss [33]. Aging‐related expansion and functional reprogramming of suppressive myeloid compartments (including MDSC subsets) can enhance osteoclastogenic potential, providing a route by which systemic immune aging contributes to skeletal deterioration [125]. Human single‐cell profiling further supports osteoimmune remodeling in PMOP by identifying an imbalance in circulating monocyte subsets consistent with a reshaped osteoclast‐precursor landscape [126].

4.7. Bone–Ovarian Aging Axis (Bone–Ovarian Aging): POI/POF as a Shared Bone–Immune–Metabolic Comorbidity Spectrum

Premature ovarian insufficiency/failure (POI/POF) is a clinically informative extension of PMOP because earlier menopause/estrogen deprivation is associated with lower bone mass and increased long‐term risk of osteoporosis and fractures [80, 127]. Systematic reviews and meta‐analyses indicate that women with POI have significantly reduced BMD compared with controls, supporting earlier surveillance and proactive prevention rather than waiting for postmenopausal thresholds [128]. Mechanistically, POI is frequently accompanied by adverse cardiometabolic profiles (e.g., dyslipidemia/insulin resistance) and autoimmune comorbidity patterns, suggesting that bone loss can occur within a broader immune–metabolic phenotype rather than an isolated endocrine consequence [129, 130]. Consistent with an “accelerated aging/multimorbidity” view of premature estrogen loss, ovarian aging can be framed as an estrogen‐sensitive amplifier of inter‐organ dysfunction, strengthening the rationale to position PMOP within a linked immune–metabolic–musculoskeletal network and earlier frailty trajectories [131, 132].

Across the bone–muscle, bone–adipose, bone–gut, bone–vascular, bone–brain, and bone–immune organ axes, estrogen withdrawal and aging‐associated inflammaging repeatedly converge on core osteoimmune control nodes, most notably RANKL/OPG imbalance and adaptive immune skewing, which sustain a pro‐osteoclastogenic tone. In parallel, inflammatory myeloid programming/macrophage polarization and immunometabolic “gain controls” (mitochondria/redox stress) amplify cytokine signaling and impair anabolic coupling, thereby linking skeletal remodeling imbalance to adipose dysfunction, vascular vulnerability, and neurobehavioral feedback loops. Recurrent mobile mediators, including gut‐derived metabolites and extracellular vesicles/matrix vesicles, provide shared “carrier” mechanisms that translate barrier and metabolic states into marrow and peripheral inflammatory set points across organs. Priorities for intervention and validation should therefore include phenotype‐stratified combination trials that co‐target these shared nodes using composite endpoints, and prospective standardization/validation of metabolite‐ and EV‐based biomarkers to test causal mediation and enable treatment monitoring.

5. Mediators: Myokines/Metabolites and Extracellular Vesicles

Exercise reprograms inter‐organ communication by reshaping soluble exerkines (myokines and metabolites) and by releasing circulating extracellular vesicles (EVs), thereby linking musculoskeletal loading to systemic immunometabolic homeostasis [133, 134]. In PMOP, estrogen deficiency promotes pro‐inflammatory and pro‐osteoclastogenic signaling, and aging‐associated chronic low‐grade inflammation (“inflammageing”) further raises baseline inflammatory tone linked to functional decline [135, 136]. Because the anti‐inflammatory shift induced by exercise training is context dependent, postmenopausal women, especially those with obesity or metabolic comorbidities, may show variable or attenuated improvements in inflammatory biomarkers, potentially reducing net “beneficial” signal gain at the network level [137, 138]. Conceptually, myokines and exercise‐derived metabolites provide rapid, concentration‐dependent endocrine tuning, whereas EVs deliver protected multi‐omic cargo that can encode tissue state and mediate inter‐organ crosstalk under physiological and pathological conditions.

5.1. Exercise‐Induced Factors: Representative Myokines, Metabolites, and Integrative Actions

Skeletal muscle functions as an endocrine organ, and exercise induces a coordinated secretome that includes canonical myokines (e.g., IL‐6, IL‐15, irisin, METRNL, myostatin) together with small metabolites that act as signaling mediators across adipose tissue, liver, immune compartments, and bone [139, 140]. Aging alters the exercise‐responsive profile of multiple myokines, with several anabolic/regenerative factors tending to decline while catabolic or inflammatory cues tend to rise, providing a mechanistic basis for reduced musculoskeletal adaptability in older adults [141]. Irisin has been reviewed as a candidate osteoprotective mediator with reported actions on osteoblast/osteoclast pathways and oxidative/inflammatory signaling relevant to osteoporosis prevention [142]. The exercise‐induced metabolite β‐aminoisobutyric acid (BAIBA) exerts bone‐relevant biology, including protection of osteocytes against ROS‐associated injury via receptor‐mediated signaling, and reduced BAIBA signaling capacity has been proposed to contribute to diminished skeletal resilience with aging [143]. Recent work further links BAIBA to osteocyte endocrine outputs (e.g., FGF23 induction), supporting the idea that exercise metabolites can reshape phosphate/mineral handling as part of systemic adaptation [144]. In an ovariectomy (OVX) PMOP‐context model, exercise‐associated increases in circulating L‐BAIBA and/or exogenous L‐BAIBA supplementation suppress osteoclastogenesis and mitigate OVX‐associated bone loss, implicating BAIBA as one molecular bridge between muscle contraction and antiresorptive signaling [145].

5.2. EVs as a “System Courier”: Sources, Cargo, and Targeting Logic

EVs are heterogeneous lipid‐bilayer particles released by most cell types and are increasingly framed as a conserved mechanism for intercellular and inter‐organ information transfer [146, 147]. Their cargo repertoire (proteins, RNAs, lipids, and metabolites) can reflect parental‐cell state and modulate recipient‐cell phenotype via surface interactions and/or internalization‐dependent delivery of functional payloads [147, 148]. Mechanistically, EV uptake can proceed through multiple endocytic routes (e.g., clathrin/caveolin‐dependent pathways, macropinocytosis, phagocytosis), and heterogeneity in vesicle surfaces and recipient‐cell programs is a key determinant of functional “tropism.” [149] For PMOP‐relevant skeletal compartments, EVs released by osteoblasts/osteocytes/osteoclasts and marrow stromal/immune cells regulate osteogenesis–osteoclastogenesis coupling and encode marrow inflammatory states relevant to osteoporosis [150, 151]. Beyond the skeleton, muscle‐ and adipose‐derived EVs can enter the circulation and influence distant metabolic and immune programs, providing plausible vehicles for exporting exercise‐ or metaflammation‐linked signals [152, 153]. Endothelial‐derived EVs participate in vascular inflammation and related remodeling pathways, supporting their inclusion in bone–vascular comorbidity logic [154]. Cross‐kingdom EVs (including bacterial EVs/OMVs) can be detected systemically in settings of barrier dysfunction and can modulate host immune tone, expanding the gut–immune–bone communication space [155, 156]. Because natural EV targeting may be insufficient for therapy, engineering approaches (parental‐cell genetic engineering, surface modification, and enhanced loading strategies) are actively being developed to improve specificity and payload control [157, 158].

5.3. EVs in PMOP: A Double‐Edged System and Aging‐Associated EV Remodeling

Within osteoimmunology, EVs can either amplify inflammatory/osteoclastogenic programs or support osteogenesis and resolution pathways, depending on cellular origin, cargo composition, and recipient context [150, 159]. Osteoporosis‐focused syntheses further describe EV cargos (notably regulatory RNAs and proteins) as modulators of osteoblast–osteoclast coupling and as potential conduits linking marrow microenvironment stress to broader systemic vulnerability [150, 151]. In PMOP, estrogen deficiency is accompanied by immune activation and inflammaging that favors osteoclastogenic cytokine networks, creating a milieu in which EV signaling is more likely to carry pro‐resorptive inflammatory cues [135, 160]. Cellular senescence can increase EV release and reshape EV cargo (senescence‐associated EVs), providing a mechanistic route for transmitting inflammatory and tissue‐dysfunction signals beyond the initiating niche [161, 162]. In obesity/metabolic‐stress contexts that often overlap with aging, senescence and nutrient overload have been linked to EV‐mediated crosstalk that sustains chronic low‐grade inflammation, offering conceptual parallels for marrow adiposity–associated immunometabolic drift in PMOP [163, 164]. At the effector level, adipocyte‐derived EVs/exosomes can skew macrophage polarization and reinforce adipose inflammation, illustrating how adipose stress signals can be packaged into vesicular messages that propagate inflammaging [165, 166].

5.4. EVs and Microenvironment Coupling: Marrow Niche, Gut Barrier, Adipose Inflammation, and Neurovascular Links

At the marrow level, extracellular vesicles (EVs) are increasingly recognized as regulators of hematopoietic stem/progenitor‐cell behavior and immune‐cell programming within niche microenvironments, linking local tissue states to systemic immune tone [167]. In parallel, adipose‐derived EVs act as endocrine‐like effectors that influence peripheral metabolic homeostasis (including liver and skeletal muscle) and can promote pathological inflammation in obesity‐related states, providing a plausible vesicular route by which adiposity reshapes bone–immune–metabolic loops [164, 168]. More generally, EVs are established modulators of macrophage phenotypes and inflammatory signaling, supporting a systems‐level view in which vesicular traffic can help lock in (or potentially reverse) pro‐inflammatory states relevant to inflammaging and frailty trajectories [169, 170]. Along the gut axis, EVs from host cells and the microbiota are increasingly incorporated into models of intestinal inflammation and barrier regulation, consistent with the idea that vesicles can tune mucosal immunity and thereby influence distal inflammatory burdens that affect bone turnover [171, 172]. Microbiota‐derived EVs have been reviewed as bioactive carriers capable of host immune modulation, offering a conceptual template for microbiota‐to‐host vesicular communication that could intersect with gut–bone endocrine–immune pathways [172]. Extending to the brain axis, bacterial EVs have been systematically reviewed as candidate mediators capable of reaching the central nervous system, including evidence for blood–brain barrier passage. EVs more broadly are implicated in neuroinflammation, together supporting a vesicle‐enabled gut–brain inflammatory communication route that may indirectly modulate activity, pain, sleep, and thus mechanical loading signals relevant to PMOP‐associated frailty [173, 174].

5.5. Biomarkers and Methodological Cautions: Standards, Quantification, Function, and Clinical Feasibility

Because EV preparations are inherently heterogeneous and isolation can co‐enrich contaminants, community standards (MISEV2018) emphasize rigorous reporting of pre‐analytics, isolation workflows, particle/protein characterization, and appropriate functional attribution controls [146]. Methodologically, multiple isolation approaches (e.g., differential ultracentrifugation, size‐exclusion chromatography, precipitation, immunoaffinity, and hybrid workflows) are used, but scalability and reproducibility remain central translational constraints for both biomarkers and therapeutics [146, 175]. For EV‐based therapeutic development (including engineered EV delivery concepts), rigorous definition of cargo‐loading strategy, potency/functional assays, biodistribution, and safety/toxicology is repeatedly highlighted as essential for credible translation [146, 169]. From a clinical translation viewpoint, circulating (blood‐derived) EVs are attractive for biomarker development because of accessibility, but clinical deployment still requires standardized pre‐analytics, robust analytical validation, and careful control of confounders (comorbidities, medications, and batch effects) aligned with minimal‐information standards [146, 175]. Accordingly, claims that specific EV cargos (e.g., miRNA signatures) support diagnosis, risk stratification, or treatment monitoring should be anchored to harmonized protocols and prospective validation rather than single‐cohort discovery signals [146, 175].

6. Translation and Intervention Strategies: From “Single‐Target Calcium Supplementation” to “System‐Level Network Reset”

In PMOP framed as aging‐driven inter‐organ crosstalk, interventions should be evaluated not only for antifracture efficacy, but also for their ability to re‐tune the coupled bone–muscle–adipose–immune–metabolic network that underlies frailty trajectories [176]. The practical implication is a shift from “bone‐only” endpoints toward integrated outcomes spanning skeletal strength, falls risk, muscle performance, cardiometabolic risk, and inflammatory burden, with treatment selection and sequencing guided by baseline fracture risk, comorbidity, and functional reserve [176]. Importantly, “reset” does not imply one universal therapy; rather, it implies stratified combinations of lifestyle, pharmacotherapy, and emerging immunometabolic or delivery approaches aligned to distinct phenotypes (e.g., osteosarcopenic, metabolically unhealthy, inflammaging‐dominant) [52].

6.1. Lifestyle and Exercise Prescriptions: Layered Benefits Across the Bone–Muscle–Metabolism–Inflammation Network

Exercise is a first‐line “network intervention” because it targets mechanical loading, neuromuscular control, and systemic immunometabolic regulation simultaneously [176]. Contemporary guidance supports resistance training to improve strength and BMD‐relevant loading, balance training to reduce falls risk, and aerobic components to support cardiometabolic health, implemented with safety constraints and progressive overload [176]. A recent synthesis emphasizes that multicomponent programs (resistance + balance, with or without impact/aerobic elements) are best positioned to address the osteosarcopenia–frailty continuum, where muscle weakness and instability often mediate fracture risk as strongly as BMD [177]. In osteosarcopenia, targeting muscle power and gait/balance can be conceptualized as “closing the loop” in which pain, fear of falling, and reduced activity accelerate both bone loss and systemic deconditioning [52]. Exercise may also attenuate chronic low‐grade inflammation and improve metabolic flexibility, thereby indirectly reducing osteoclastogenic signaling and frailty‐related vulnerability, although effect sizes vary by adherence, intensity, and baseline phenotype [65]. Implementation should therefore be phenotype‐aware: prioritize supervised resistance/balance for frail or high‐fall‐risk individuals, and integrate aerobic conditioning for those with cardiometabolic clustering, while monitoring symptoms and functional gains [176].

6.2. Pharmacotherapy and Combination Strategies: Antifracture Efficacy Plus “Extra‐Skeletal” Monitoring

Evidence‐based pharmacologic therapy remains indispensable for moderate‐to‐high fracture risk, with bisphosphonates typically recommended as initial options and denosumab as an alternative/second‐line when bisphosphonates are unsuitable [178]. Large comparative syntheses support antifracture benefits across major drug classes, and reinforce that anabolic agents (e.g., PTH receptor agonists, romosozumab) can provide stronger fracture protection in appropriate high‐risk patients, with sequencing (anabolic first, then antiresorptive) often favored to consolidate gains [179]. Because PMOP in this review is framed as multisystem drift, “bone‐outcome success” should be accompanied by structured surveillance of extra‐skeletal signals, particularly cardiometabolic and vascular risk, when choosing agents such as romosozumab [180]. Randomized‐trial–based assessments have quantified major adverse cardiovascular event considerations for romosozumab and remain central to risk–benefit discussions in patients with high baseline CV risk [181]. Meanwhile, emerging human data suggest that antiresorptives may modulate systemic inflammatory markers in some contexts, which is conceptually aligned with an immunometabolic framing but should not be overinterpreted as an anti‐inflammatory therapy without outcomes confirmation [182]. Real‐world evidence is increasingly used to complement trial data for persistence, safety, and sequencing decisions (e.g., long‐term denosumab strategies), but methodology and confounding require careful appraisal [183].

6.3. Targeting Immunometabolism: Anti‐Inflammation, Metabolic Reprogramming, Microbiome‐Derived Metabolites, and Precision Phenotyping

A “network reset” agenda prioritizes interventions that simultaneously reduce inflammaging, restore metabolic flexibility, and improve musculoskeletal function, while acknowledging that most immunometabolic strategies remain investigational in PMOP [65]. Proof‐of‐concept human work is emerging for senescence‐targeting approaches in early postmenopausal women, supporting the plausibility that upstream aging biology can be therapeutically manipulated to influence systemic biomarkers relevant to musculoskeletal aging [184]. Metabolic repurposing is also being explored; for example, clinical trial evidence in midlife women has evaluated metformin with endpoints relevant to aging‐related functional trajectories, supporting continued investigation of metabolic modulators as adjuncts in frailty‐prone phenotypes [185]. The gut–bone axis provides an additional immunometabolic lever: current reviews synthesize how microbiota composition and metabolites (including SCFAs and bile‐acid–linked pathways) interface with immune tone, barrier function, and bone remodeling, making microbiome‐informed stratification and targeted dietary/prebiotic/probiotic strategies biologically credible, though clinical standardization is incomplete [186]. Practically, this argues for phenotype‐based subtyping (e.g., inflammaging‐high, insulin‐resistant, osteosarcopenic) to rationalize combinations (exercise + antiresorptive/anabolic ± metabolic/microbiome adjuncts) and to avoid “one‐size‐fits‐all” escalation [52].

6.4. EVs and Delivery Systems: Engineered EVs, Biomimetic Nanocarriers, Tissue‐Directed Delivery, and Risk

EV‐based and EV‐inspired delivery is attractive for PMOP‐as‐network‐disease because it can, in principle, package multi‐omic cargo and retarget signals across organs while reducing systemic off‐target exposure [187]. Recent translational reviews outline engineering strategies including cargo optimization, surface functionalization, and delivery‐platform integration (e.g., hydrogels or scaffolds) to improve retention and controlled release at skeletal sites, concepts that could be adapted to bone–muscle–immune nodes in PMOP [187]. EV‐functionalized biomaterials are increasingly discussed as a way to localize vesicle activity and support sustained therapeutic signaling in bone contexts, highlighting design logic applicable to osteoporosis‐related bone repair and remodeling modulation [188]. However, moving EVs toward clinical PMOP management requires strict methodological rigor: isolation, characterization, and reporting should align with ISEV consensus guidance to ensure reproducibility, comparability, and mechanistic credibility [146]. Updated ISEV guidance further emphasizes standardized nomenclature and minimal information expectations, which is critical when claims involve inter‐organ targeting or cargo‐based mechanism attribution [189]. Risk assessment must include biodistribution uncertainty, immunogenicity/clearance, batch variability, and manufacturing scale‐up constraints, which are often the real bottlenecks rather than biological plausibility [187]. Figure 5 highlights how 3D‐printed, immunomodulatory hydrogels can localize pro‐regenerative cues and reprogram osteoimmune microenvironments as an in vivo adjunct to osteoporosis therapy (Figure 5). The figure further specifies the key design variables, namely a GelMA‐based 3D‐bioprinted hydrogel incorporating Sr‐CSH and the resulting local release of Sr, Ca, and Si ions, as well as the downstream cellular readouts, including macrophage polarization from an M0 to an M2 phenotype, enhanced BMSC and osteoblast activity, and reduced osteoclast activity.

FIGURE 5.

3D‐printed hydrogels modulate osteoimmunity for in vivo therapy.

6.5. Clinical Translation Roadmap: Stratification, Endpoints, Real‐World Evidence, and Follow‐Up Architecture

A pragmatic translation pathway begins with patient stratification that integrates skeletal risk with functional and immunometabolic context, consistent with modern guideline emphasis on individualized fracture prevention [176]. For a PMOP “multisystem frailty” framing, stratification should explicitly include muscle mass/strength, falls risk, cardiometabolic markers, and inflammatory burden alongside BMD and prior fracture history, because these dimensions frequently co‐aggregate and predict outcomes [52]. Endpoint selection should therefore move toward composite outcomes, such as fracture and physical performance or frailty metrics and, when relevant, cardiovascular or cognitive endpoints, to test whether interventions truly “reset” network risk rather than only improving BMD [52]. Implementation science is equally central: fracture liaison services (FLS) are repeatedly supported as a systems‐level strategy to close diagnosis–treatment gaps, improve secondary prevention, and create longitudinal data pipelines for outcome tracking [190]. Recent work further evaluates FLS effectiveness and cost‐effectiveness, supporting health‐system adoption as a foundational platform for integrated PMOP care [191]. Real‐world evidence is increasingly formalized in osteoporosis research, with consensus recommendations emphasizing transparent design, bias control, and reporting standards so that RWE can credibly inform sequencing, adherence, and safety in heterogeneous older populations [192]. Finally, follow‐up architecture should be pre‐specified (e.g., monitoring fracture/falls, function, and cardiometabolic safety signals) and aligned to the chosen intervention class and patient phenotype to enable adaptive, data‐driven management over the aging trajectory [192]. Figure 6 outlines an immune‐subtype–guided precision‐medicine roadmap linking biomarker panels to stratified therapeutic matching and implementation pathways (registries and AI‐enabled prediction) in PMOP (Figure 6).

FIGURE 6.

Precision medicine roadmap for immune‐subtype–guided therapy in PMOP. This roadmap illustrates how immune subtype profiling can be translated into individualized treatment. Biomarkers such as Th17/Treg ratio, Breg abundance, IL‐6, leptin, and microbial metabolites improve fracture‐risk prediction beyond conventional models. Stratified phenotypes enable tailored interventions, including IL‐17 inhibitors, RANKL blockade (denosumab), and microbiota‐targeted strategies (probiotics, SCFAs). A therapy‐matching matrix supports rational combinations (e.g., IL‐17 ± RANKL blockade with microbiota modulation), with romosozumab as a potential lead‐in followed by biomarker‐guided consolidation. Implementation strategies, including real‐world registries, AI‐based prediction tools, and cost‐effectiveness evaluation, ensure effective clinical translation toward precision osteoimmunology.

Minimal clinical assessment set for implementation (clinic‐feasible 5‐item panel). For real‐world uptake, we recommend a minimal set of measures that captures skeletal fragility, neuromuscular function, inflammatory burden, and endocrine–metabolic context: (A) DXA‐based BMD (hip/spine) plus fracture history and FRAX (or equivalent 10‐year risk estimate), with vertebral fracture assessment when indicated. (B) Falls history in the prior 12 months plus a simple mobility test (gait speed or Timed Up‐and‐Go). (C) Muscle strength via handgrip dynamometry (or chair‐stand time if grip testing is unavailable). (D) Systemic inflammatory burden via high‐sensitivity CRP (or IL‐6 where available). (E) Serum 25‐hydroxyvitamin D plus one cardiometabolic marker reflecting metabolic drift (HbA1c or fasting glucose). Together, this minimal set maps directly onto a multisystem “network reset” strategy by pairing antifracture therapy (skeletal risk) with targeted exercise and falls prevention (function/falls), while monitoring and addressing inflammaging and endocrine–metabolic contributors that can reinforce osteoimmune drift.

7. Summary and Outlook

PMOP is increasingly best understood as an aging‐driven, inter‐organ network disorder rather than a bone‐only consequence of estrogen withdrawal. In this review, we integrated evidence across marrow niche biology, osteoimmunity, metabolism, muscle function, and gut‐derived signaling into an “immunometabolic drift” framework, in which menopausal endocrine loss amplifies inflammaging and reduced metabolic flexibility, lowering the threshold for remodeling uncoupling and for progression toward multisystem frailty [5, 6, 58]. This reframing matters clinically because fractures are often preceded and predicted by parallel declines in strength, balance, cardiometabolic health, and inflammatory restraint, implying that skeletal fragility is frequently a readout of broader resilience loss [52, 54].

Mechanistically, several high‐leverage nodes repeatedly translate systemic state into skeletal outcomes. Osteoimmune reprogramming, most prominently via RANKL/OPG imbalance and adaptive immune skewing (e.g., Th17/Treg disequilibrium), provides a coherent logic for sustained osteoclastogenesis under chronic, low‐grade inflammation [24, 25]. Bone marrow microenvironment remodeling further amplifies this drift: stromal–vascular alterations and expansion of metabolically active bone marrow adipose tissue can bias mesenchymal fate away from osteogenesis and reshape immune output in ways that reinforce catabolic tone [19, 39]. In parallel, mitochondrial dysfunction and redox stress act as upstream “gain controls” that couple nutrient‐sensing failure to inflammatory signaling and impaired osteoblast bioenergetics [29, 30]. Finally, mobile mediators, including myokines/metabolites and extracellular vesicles (EVs), provide plausible carrier mechanisms for how local tissue stress becomes systemic vulnerability, but their bidirectionality demands rigorous attribution of source, cargo, and function [146].

A central translational implication is that PMOP interventions should be judged by network‐level benefit, not solely by changes in areal bone mineral density (BMD). Exercise and lifestyle prescriptions can be conceptualized as programmable, multi‐target perturbations that simultaneously improve loading, neuromuscular control, and systemic immunometabolic regulation, aligning them with both fracture prevention and frailty mitigation goals [176, 177]. Pharmacotherapy remains indispensable for moderate‐to‐high fracture risk, and a systems framing argues for deliberate sequencing plus “extra‐skeletal” monitoring (e.g., cardiometabolic and vascular risk where relevant) rather than assuming bone efficacy implies whole‐body neutrality [179, 180]. At the health‐system level, fracture liaison services and well‐designed real‐world evidence pipelines can provide the longitudinal infrastructure needed to operationalize this multisystem approach in heterogeneous older populations [190, 192].

Looking forward, four priorities are most likely to accelerate progress. (1) Precision phenotyping: longitudinal cohorts should co‐measure bone strength/architecture, muscle performance, cardiometabolic markers, microbiome/metabolites, and inflammatory/EV signatures to define reproducible PMOP subtypes and early “drift indicators” that precede first fracture [52]. (2) Causal validation across axes: strengthen the association→causality chain by pairing human multi‐omics with controlled perturbations in animal models and cell systems, while explicitly mapping evidence tiers for each claim [193]. (3) Endpoint modernization: trials should more routinely include composite outcomes capturing fractures, falls, physical function, and mechanistically informed biomarkers to directly test “network reset” rather than bone‐only improvement [52, 194]. (4) Standards‐driven EV and delivery development: translation will require ISEV‐aligned reporting, scalable manufacturing, and biodistribution/safety packages matched to intended clinical use, especially for engineered EVs or biomimetic carriers [146, 187, 189]. In summary, reframing PMOP as aging‐driven inter‐organ crosstalk shifts the goal from single‐pathway correction to precision “network reset,” with success defined by both skeletal integrity and whole‐body resilience. Figure 7 integrates single‐cell and spatial transcriptomics with cross‐omics modeling to show how osteoclastogenic immune niches and risk signatures can be derived for precision osteoimmunology (Figure 7).

FIGURE 7.

Multi‐omics approaches in decoding osteoimmunity. This schematic illustrates how different omics layers contribute to understanding immune‐mediated bone loss in postmenopausal osteoporosis. (A) Single‐cell transcriptomics identifies high‐risk immune clusters (Mac_OLR1, Tem_CCL4) and their osteoclastogenic signaling. (B) Spatial transcriptomics maps inflammatory osteoclast niches such as CXCL12–CXCR4 microdomains. (C) Multi‐omics integration across GWAS, transcriptomics, and methylomics enables the derivation of diagnostic signatures, including a validated 9‐gene score for risk stratification. (D) Persistent challenges, including small cohorts, lack of fracture endpoints, and limited data harmonization, highlight the need for large‐scale longitudinal immune–bone datasets. Together, these layers provide a systems‐level framework for precision osteoimmunology.

Author Contributions

X.R. and X.C.: writing – original draft, conceptualization and writing – review and editing.

Funding

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

This study did not generate any new data.