Adipose Tissue–Central Nervous System Axis in Obesity: Molecular Mechanisms, Inflammation, and Nutritional and Technological Implications
Highlights
- The adipose tissue–CNS axis is a complex bidirectional network where adipokines, lipid mediators, metabolites, and neural circuits regulate energy balance, metabolism, and inflammation.
- Obesity disrupts this axis through chronic inflammation, neuroinflammation, oxidative stress, and altered lipid signaling, leading to central insulin/leptin resistance and metabolic dysfunction.
- Targeting nutrient-derived signals (e.g., fatty acids, SCFAs, polyphenols) and adipose–brain communication pathways offers promising strategies for preventing and treating obesity-related metabolic and neurological disorders.
- Innovative nutritional approaches and food technologies (e.g., functional foods, microbiota modulation, encapsulation systems) may improve metabolic resilience by restoring adipose–CNS axis homeostasis.
Abstract
1. Introduction
2. Methods
3. Neural and Endocrine Architecture of Adipose Tissue–CNS Communication
Role of Adipokines in the Adipose–Brain Axis
4. Neural Metabolic Alterations in Obesity
4.1. Oxidative Stress and Neuroinflammation in Obesity
4.2. Cellular Metabolic Alterations Induced by Nutritional Excess in the CNS
4.3. Trans-BBB Transport Dynamics, Spatial Heterogeneity, and Temporal Sequencing of Lipid Signaling
5. Nutrient-Derived and Bioactive Mediators of Adipose Tissue–Brain Communication
5.1. Lipid-Derived Mediators in Adipose–Brain Signaling
5.1.1. Fatty Acids and Lipid Sensing
5.1.2. Ceramides and Lipotoxic Signaling
5.1.3. DAGs, Oxylipins and S1
5.2. Nutrient-Sensing Receptors and Intracellular Signaling Pathways
5.3. Gut Microbiota-Derived Metabolites and Polyphenols as Modulators of the Adipose Tissue–CNS Axis
6. Metabolic Integration and Signaling Functions of White Adipose Tissue
6.1. Lipid and Carbohydrate Metabolism in Adipose Tissue
6.2. Role of Mitochondria and Cellular Bioenergetics
6.3. White Adipose Tissue as a Signaling Hub
6.4. Temporal Sequence and Spatial Heterogeneity in Adipose–CNS Crosstalk
7. Impact of Dietary Patterns on the CNS–Adipose Tissue Axis
7.1. Diet, Inflammation and Appetite Regulation
7.2. Nutritional Strategies for Improving Metabolic Health
7.3. Methodological Limitations of Experimental Models and Barriers to Clinical Translation
8. Food Matrix, Nutrient Bioavailability and Food Processing in the Obesity Context
8.1. The Food Matrix: Structure, Digestion, and Metabolic Signaling
8.2. Food Processing Effects on Food Properties in the Obesity Context
8.2.1. Thermal Processing
8.2.2. Mechanical Processing and Particle Size Reduction
8.2.3. Fermentation
8.3. Technological Innovations for Anti-Obesity Functional Foods
8.3.1. Nanoencapsulation and Microencapsulation Technologies
8.3.2. Precision Fermentation
8.3.3. Three-Dimensional Food Printing
9. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
| ACC1 | Acetyl-CoA carboxylase 1 |
| ACLY | ATP citrate lyase |
| Acrp30 | Adipocyte complement-related protein of 30 kDa |
| AD | Alzheimer’s disease |
| AdipoQ | Adiponectin |
| AdipoR1 | Adiponectin receptor 1 |
| AdipoR2 | Adiponectin receptor 2 |
| ADSCs | Adipose-derived stem cells |
| AGEs | Advanced glycation end products |
| AGPAT | 1-Acylglycerol-3-phosphate O-acyltransferase |
| AgRP | Agouti-related peptide |
| Akt | Protein kinase B |
| AMPK | AMP-activated protein kinase |
| APCs | Adipose progenitor cells |
| APJ | Apelin receptor |
| ARC | Arcuate nucleus |
| ATGL | Adipose triglyceride lipase |
| ATMs | Adipose tissue macrophages |
| BAMs | Border-associated macrophages |
| BAT | Brown adipose tissue |
| BBB | Blood–brain barrier |
| BCAAs | Branched-chain amino acids |
| BDNF | Brain-derived neurotrophic factor |
| cAMP | Cyclic adenosine monophosphate |
| CAT | Catalase |
| CD36 | Cluster of differentiation 36 |
| CerS6 | Ceramide synthase 6 |
| CHO | Carbohydrate |
| ChREBP | Carbohydrate-responsive element-binding protein |
| CNS | Central nervous system |
| CoA | Coenzyme A |
| COX | Cyclooxygenase |
| COX-2 | Cyclooxygenase-2 |
| CRP | C-reactive protein |
| CSF | Cerebrospinal fluid |
| DAGs | Diacylglycerols |
| DGAT | Diacylglycerol acyltransferase |
| DHA | Docosahexaenoic acid |
| DNA | Deoxyribonucleic acid |
| DNL | De novo lipogenesis |
| DPP-4 | Dipeptidyl peptidase-4 |
| DRG | Dorsal root ganglia |
| DRP1 | Dynamin-related protein 1 |
| ECM | Extracellular matrix |
| EPA | Eicosapentaenoic acid |
| ER | Endoplasmic reticulum |
| EVs | Extracellular vesicles |
| FABP4 | Fatty acid-binding protein 4 |
| FASN | Fatty acid synthase |
| FATP1 | Fatty acid transport protein 1 |
| FFAR1/GPR40 | Free fatty acid receptor 1/G protein-coupled receptor 40 |
| FFAR2/GPR43 | Free fatty acid receptor 2/G protein-coupled receptor 43 |
| FFAR3/GPR41 | Free fatty acid receptor 3/G protein-coupled receptor 41 |
| FFAR4/GPR120 | Free fatty acid receptor 4/G protein-coupled receptor 120 |
| FFARs | Free fatty acid receptors |
| FFAs | Free fatty acids |
| FIS1 | Mitochondrial fission 1 protein |
| FUNDC1 | FUN14 domain-containing protein 1 |
| FXR | Farnesoid X receptor |
| GIP | Glucose-dependent insulinotropic polypeptide |
| GLP-1 | Glucagon-like peptide-1 |
| GLUT1 | Glucose transporter 1 |
| GLUT4 | Glucose transporter 4 |
| GPAT | Glycerol-3-phosphate acyltransferase |
| GPCRs | G protein-coupled receptors |
| GPx | Glutathione peroxidase |
| Gsα | Stimulatory G protein alpha subunit |
| HFD | High-fat diet |
| HIFs | Hypoxia-inducible factors |
| HOMA-IR | Homeostatic model assessment of insulin resistance |
| HPA | Hypothalamic–pituitary–adrenal axis |
| HPP | High-pressure processing |
| hs-CRP | High-sensitivity C-reactive protein |
| HSL | Hormone-sensitive lipase |
| IFN-γ | Interferon gamma |
| IL-1β | Interleukin-1 beta |
| IL-6 | Interleukin-6 |
| ILC2s | Group 2 innate lymphoid cells |
| JNK | c-Jun N-terminal kinase |
| LDL | Low-density lipoprotein |
| lnc-BATE1 | Brown adipose tissue-enriched long non-coding RNA 1 |
| lnc-MALAT1 | Metastasis-associated lung adenocarcinoma transcript 1 |
| lncRNAs | Long non-coding RNAs |
| LOX | Lipoxygenase |
| LPA | Lysophosphatidic acid |
| LPAR1–6 | Lysophosphatidic acid receptors 1–6 |
| LXRs | Liver X receptors |
| M1 | Classically activated macrophage phenotype |
| M2 | Alternatively activated macrophage phenotype |
| MBH | Mediobasal hypothalamus |
| MCH | Melanin-concentrating hormone |
| mDIC | Mitochondrial dicarboxylate carrier |
| MetS | Metabolic syndrome |
| MFF | Mitochondrial fission factor |
| MFN1 | Mitofusin 1 |
| MFN2 | Mitofusin 2 |
| MiD49 | Mitochondrial dynamics protein of 49 kDa |
| MiD51 | Mitochondrial dynamics protein of 51 kDa |
| MIND | Mediterranean–Dietary Approaches to Stop Hypertension Intervention for Neurodegenerative Delay |
| miR-155 | MicroRNA-155 |
| miR-27a | MicroRNA-27a |
| miR-34a | MicroRNA-34a |
| miR-9-3p | MicroRNA-9-3p |
| miRNAs | MicroRNAs |
| MRI | Magnetic resonance imaging |
| mRNAs | Messenger RNAs |
| MRP4 | Multidrug resistance-associated protein 4 |
| mtDNA | Mitochondrial DNA |
| mTOR | Mechanistic target of rapamycin |
| mTORC1 | Mechanistic target of rapamycin complex 1 |
| MUFA | Monounsaturated fatty acid |
| NADPH | Nicotinamide adenine dinucleotide phosphate |
| NAD+ | Nicotinamide adenine dinucleotide |
| NE | Norepinephrine |
| NEIL1 | Nei-like DNA glycosylase 1 |
| NF-κB | Nuclear factor kappa B |
| NLCs | Nanostructured lipid carriers |
| NOTCH2 | Notch receptor 2 |
| NPY | Neuropeptide Y |
| Nrf2 | Nuclear factor erythroid 2-related factor 2 |
| Nscl-2 | Neuronal stem cell leukemia 2 |
| OAT3 | Organic anion transporter 3 |
| OGG1 | 8-Oxoguanine DNA glycosylase 1 |
| OMA1 | OMA1 zinc metallopeptidase |
| OPA1 | Optic atrophy protein 1 |
| P450 | Cytochrome P450 |
| PAI-1 | Plasminogen activator inhibitor-1 |
| PARPs | Poly(ADP-ribose) polymerases |
| PD | Parkinson’s disease |
| PET | Positron emission tomography |
| PGC-1α | Peroxisome proliferator-activated receptor gamma coactivator 1-alpha |
| PGE2 | Prostaglandin E2 |
| PKA | Protein kinase A |
| PKC | Protein kinase C |
| PKCζ | Protein kinase C zeta |
| PLIN1 | Perilipin 1 |
| PNPLA2 | Patatin-like phospholipase domain-containing protein 2 |
| POMC | Pro-opiomelanocortin |
| PON2 | Paraoxonase 2 |
| PP2A | Protein phosphatase 2A |
| PPARs | Peroxisome proliferator-activated receptors |
| PPARγ | Peroxisome proliferator-activated receptor gamma |
| PRDM16 | PR/SET domain 16 |
| Prx3 | Peroxiredoxin 3 |
| PUFAs | Polyunsaturated fatty acids |
| PVH | Paraventricular nucleus of the hypothalamus |
| PYY | Peptide YY |
| RAGE | Receptor for advanced glycation end products |
| RBP4 | Retinol-binding protein 4 |
| RCTs | Randomized controlled trials |
| RELA | RELA proto-oncogene, NF-κB subunit |
| RELM | Resistin-like molecule |
| Rho | Ras homolog family GTPase |
| RNA | Ribonucleic acid |
| ROCK | Rho-associated coiled-coil-containing protein kinase |
| ROS | Reactive oxygen species |
| S1P | Sphingosine-1-phosphate |
| S1PR1–S1PR5 | Sphingosine-1-phosphate receptors 1–5 |
| SAM | S-adenosylmethionine |
| SAT | Subcutaneous adipose tissue |
| SCFAs | Short-chain fatty acids |
| SCN | Suprachiasmatic nucleus |
| SFAs | Saturated fatty acids |
| SIRT1 | Sirtuin 1 |
| SIRT6 | Sirtuin 6 |
| SLNs | Solid lipid nanoparticles |
| SNS | Sympathetic nervous system |
| SOD | Superoxide dismutase |
| SPT | Serine palmitoyltransferase |
| STAT3 | Signal transducer and activator of transcription 3 |
| TAGs | Triacylglycerols |
| TCA | Tricarboxylic acid cycle |
| TGR5 | Takeda G protein-coupled receptor 5 |
| TH | Tyrosine hydroxylase |
| TLR4 | Toll-like receptor 4 |
| TNF-α | Tumor necrosis factor alpha |
| TNFRSF1A | Tumor necrosis factor receptor superfamily member 1A |
| UCP1 | Uncoupling protein 1 |
| VAT | Visceral adipose tissue |
| VLDL | Very-low-density lipoprotein |
| WAT | White adipose tissue |
| WNT | Wingless-related integration site |
| αMSH | Alpha-melanocyte-stimulating hormone |
| βARs | Beta-adrenergic receptors |
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| Authors (Year) | Study Type/Model | Dietary Exposure | Main Mechanism on the CNS–Adipose Tissue Axis | Main Outcomes | References |
|---|---|---|---|---|---|
| Estrada & Contreras (2019) | Review (mechanistic) | High SFA/sugars; Western-type diets | Systemic low-grade inflammation → microglial activation; disruption of immune–CNS homeostasis | ↑ Risk of neurological disorders in obesogenic contexts | [236] |
| Puente-Ruiz & Jais (2022) | Review | — | Bidirectional adipokine/cytokine signaling adipose ↔ CNS; sympathetic regulation of depots | Links obesity to metabolic dysfunction and neurodegeneration | [235] |
| Bruce-Keller et al. (2009) | Review (human + animal) | Diet-induced obesity | Oxidative stress & inflammation; impaired neuronal plasticity | ↑ CNS vulnerability, cognitive decline | [237] |
| Ryan, Woods & Seeley (2012) | Physiological review | Palatable high-fat diets | Elevation of “defended adiposity” via hypothalamic circuits (homeostatic set-point) | Greater difficulty maintaining weight loss | [241] |
| Kaiyala et al. (2000) | Experimental (dog model) | High-fat diet | ↓ Insulin transport into CSF/CNS → weaker negative feedback on adiposity | Increased adiposity and weight gain with reduced CNS insulin delivery | [242] |
| Jamar, Ribeiro & Pisani (2021) | Review | High-fat/high-sugar diets | Microbiota–gut–brain axis dysbiosis; microglial activation; hypothalamic inflammation | Obesity, metabolic syndrome, neuroinflammation | [239] |
| Yu et al. (2022) | Review | Diet-related dysbiosis | Crosstalk adipose tissue ↔ microbiota–gut–brain via microbial metabolites & immune cells | Systemic dysmetabolism impacting CNS signaling | [244] |
| Custers & Kiliaan (2022) | Review | Dietary lipids | BBB transport & brain lipid remodeling; altered lipid metabolism in neural tissue | Contribution to AD, PD, stroke mechanisms | [243] |
| Solas et al. (2017) | Review | Obesity/dietary patterns | Peripheral cytokines & vagal signaling; gut–brain–inflammation triad | Cognitive decline pathways and potential pharmacological targets | [240] |
| Frausto et al. (2021) | Review | Microbiota modulation | SCFAs, bile acids, Trp-derivatives → microglia/astroglia and amyloid pathways | Mechanistic links to Alzheimer’s disease | [245] |
| Godos et al. (2020) | Review | Antioxidant/omega-3-rich diets | ↓ Oxidative stress & neuroinflammation; neurotransmission & neurogenesis modulation | ↓ Depression/anxiety risk; mental health support | [247] |
| Granero & Guillazo-Blanch (2025) | Editorial (Special Issue) | Mediterranean/MIND dietary patterns | ↓ Inflammation; ↑ neuroplasticity and cognitive resilience | Association with ↓ risk of cognitive decline | [246] |
| Léon, Nadjar & Quarta (2021) | Perspective/Mechanistic review | Hypercaloric/high-fat diets | Maladaptive microglia–neuron crosstalk in hypothalamus; cytokines & fuel-sensing | Hypothalamic dysfunction → energy imbalance & obesity progression | [238] |
| Authors (Year) | Study Type/Model | Dietary Exposure | Main Mechanism on the CNS–Adipose Tissue Axis | Main Outcomes | References |
|---|---|---|---|---|---|
| Adepoju et al. (2026) | RCT (humans, obesity) | Almond consumption | ↓ systemic inflammation (cytokines) → indirect CNS signaling | ↓ IL-6, TNF-α; no effect on appetite | [248] |
| Åberg et al. (2025) | RCT (humans) | Wholegrain rye vs. refined wheat | Modulation of gut hormones (ghrelin, incretins) | ↓ ghrelin (postprandial), improved glycemia | [251] |
| Zhang et al. (2024) | RCT crossover (humans) | Eating frequency | No significant modulation of appetite/inflammation axis | No changes in ghrelin, leptin, CRP | [250] |
| Sanchez et al. (2024) | Animal study (mice) | High-fat diet (ω6/ω3 ratio) | Neuroinflammation (hypothalamus, hippocampus) | ↑ neuroinflammation, behavioral changes | [258] |
| Vujović et al. (2022) | RCT crossover (humans) | Meal timing (late vs. early eating) | Hormonal dysregulation (ghrelin/leptin) + adipose gene expression | ↑ hunger, ↓ energy expenditure | [252] |
| Reginato et al. (2021) | Systematic review | High-fat diet/fatty acids | Ceramide accumulation → hypothalamic inflammation → leptin resistance | Impaired energy balance regulation | [257] |
| Reimer et al. (2017) | RCT (humans) | Prebiotics + whey protein | Gut microbiota modulation → appetite signaling | ↓ hunger, altered microbiota | [254] |
| Juanola-Falgarona et al. (2014) | RCT (humans) | Low vs. high glycemic index diets | Metabolic signaling (insulin sensitivity) | ↓ weight, no strong effect on appetite/inflammation | [253] |
| Poulsen et al. (2014) | RCT crossover (humans) | Advanced glycation end products (AGEs) | Acute hormonal modulation (ghrelin) | ↑ ghrelin response; limited inflammation effects | [256] |
| Nilsson et al. (2013) | RCT crossover (humans) | Brown beans (fiber-rich meal) | SCFA production → gut–brain signaling | ↑ satiety hormones, ↓ IL-6 | [255] |
| Sofer et al. (2011) | RCT (humans) | Carbohydrate timing (evening intake) | Hormonal modulation (leptin, adiponectin) | ↓ hunger, ↓ inflammatory markers | [249] |
| Authors (Year) | Study Type/Model | Dietary Exposure | Proposed Mechanisms on the CNS–Adipose Tissue Axis | Main Outcomes | References |
|---|---|---|---|---|---|
| Asoudeh et al. (2023) | RCT, adolescents with MetS | Mediterranean diet | ↓ systemic inflammation may influence adipose–CNS signaling | ↓ IL-6, hs-CRP, improved HOMA-IR and lipid profile | [259] |
| Vitale et al. (2020) | RCT, overweight/obese adults | Mediterranean-like diet | ↑ SCFAs may contribute to gut–brain signaling and metabolic regulation | ↑ insulin sensitivity, ↓ postprandial glucose, microbiota shifts | [260] |
| Ulven et al. (2019) | RCT, MetS subjects | Nordic diet | Modulation of inflammatory gene expression may affect systemic–central signaling pathways | ↓ TNFRSF1A, ↑ RELA expression | [261] |
| Calvo-Malvar et al. (2021) | Cluster RCT, families | Atlantic diet | Improved dietary pattern may indirectly influence adiposity-related signaling to CNS | ↓ body weight, ↓ total cholesterol | [262] |
| Vetrani et al. (2016) | RCT, MetS subjects | Whole-grain diet | ↑ SCFAs (propionate) may modulate gut–brain axis and insulin signaling | ↑ propionate, ↓ postprandial insulin | [263] |
| Zhang et al. (2022) | RCT, overweight/obese adults | Avocado (MUFA + fiber) | Changes in lipid profile and inflammation may influence central metabolic regulation | ↓ CRP; modest effects on glycemic control | [264] |
| Pett et al. (2025) | RCT, overweight/obese adults | Mango intake | Antioxidant pathways (e.g., Nrf2) may indirectly affect metabolic and central signaling | ↑ insulin sensitivity (HOMA-IR), no significant change in inflammation | [265] |
| Palacios et al. (2019) | RCT, prediabetes adults | Almonds vs. CHO foods | Improved nutrient composition may have limited impact on CNS–adipose signaling | No significant effects on insulin sensitivity; improved nutrient profile | [266] |
| Technology | Main Application | Strengths | Limitations | Translational Potential |
|---|---|---|---|---|
| Single-cell RNA sequencing | Identification of adipocyte, immune and hypothalamic cell populations | High cellular resolution; identifies rare cell populations | Tissue dissociation, loss of spatial information, expensive | Biomarker discovery; precision medicine |
| Spatial transcriptomics | Maps gene expression within adipose tissue and hypothalamus | Preserves tissue architecture; identifies cell–cell interactions | High cost; limited availability | Identification of disease-specific niches |
| Single-cell multiomics | Integrates transcriptome, chromatin accessibility and epigenetics | Comprehensive molecular characterization | Computational complexity | Patient stratification |
| Extracellular vesicle profiling | Characterization of adipose-derived EVs reaching the CNS | Minimally invasive biomarkers | Isolation and standardization remain challenging | Early diagnosis; therapeutic monitoring |
| Advanced neuroimaging (MRI, PET) | Assessment of hypothalamic inflammation and brain connectivity | Applicable in humans; longitudinal studies | Indirect markers; expensive | High |
| Metabolomics/Lipidomics | Identification of circulating metabolic mediators | High-throughput; identifies metabolic signatures | Biological variability | Biomarker discovery |
| Chemogenetics | Functional manipulation of neuronal circuits | Demonstrates causality | Animal models only | Low (preclinical) |
| Optogenetics | Precise temporal control of neural circuits | Excellent mechanistic tool | Animal models only | Low |
| Organoids/Organ-on-chip | Human tissue modeling | Human-derived models; personalized approaches | Limited physiological complexity | Medium |
| Artificial intelligence integration | Integration of multiomics and imaging | Predictive models; precision nutrition | Requires large datasets | Very high |
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Castelli, S.; Aiello, G.; De Bruno, A.; Fratantonio, D.; Tripodi, G.; Aiello, V.; Lombardo, M.; Baldelli, S. Adipose Tissue–Central Nervous System Axis in Obesity: Molecular Mechanisms, Inflammation, and Nutritional and Technological Implications. Metabolites 2026, 16, 533. https://doi.org/10.3390/metabo16080533
Castelli S, Aiello G, De Bruno A, Fratantonio D, Tripodi G, Aiello V, Lombardo M, Baldelli S. Adipose Tissue–Central Nervous System Axis in Obesity: Molecular Mechanisms, Inflammation, and Nutritional and Technological Implications. Metabolites. 2026; 16(8):533. https://doi.org/10.3390/metabo16080533
Chicago/Turabian StyleCastelli, Serena, Gilda Aiello, Alessandra De Bruno, Deborah Fratantonio, Gianluca Tripodi, Vincenzo Aiello, Mauro Lombardo, and Sara Baldelli. 2026. "Adipose Tissue–Central Nervous System Axis in Obesity: Molecular Mechanisms, Inflammation, and Nutritional and Technological Implications" Metabolites 16, no. 8: 533. https://doi.org/10.3390/metabo16080533
APA StyleCastelli, S., Aiello, G., De Bruno, A., Fratantonio, D., Tripodi, G., Aiello, V., Lombardo, M., & Baldelli, S. (2026). Adipose Tissue–Central Nervous System Axis in Obesity: Molecular Mechanisms, Inflammation, and Nutritional and Technological Implications. Metabolites, 16(8), 533. https://doi.org/10.3390/metabo16080533

