Chronic pain and obesity

1. Obesity and Chronic pain Obesity and Chronic pain Prof Dr AJIT KUMAR KAYAL MBBS, MD, FRCA,CCT, FIMA, Busi Admins (LONDON) Certificate in Climate change and environmental awareness Artificial Intelligence for Healthcare and Digital Health (Queen Mary Univ London ) Advisory member NECOPD UK United Kingdom Hon Prof of Practice anaesthesia and intensive care Sri Balaji Vidyapeeth University, Pondicherry INDIA Retd SR Consultant Anaesthesiology and Intensive Care St Georges University of London Ex-Visiting Sr Consultant St Jhon University Newfoundland Canada 2012 -2014

3. 1992 to 2016 Trends in the prevalence of overweight/obesity and pain among US adult aged 55–61: * 61.4%-79% 71-83% • obesity and chronic pain are major public health concerns. • Growing evidence suggests a bidirectional relationship. 17.6%-28.3% 13.5-22.4%

4. Proprietary survey between 2008 through 2010 where 1,062,271 randomly selected Pain increases moving from the younger categories to the older categories; for those in the Obese III group, OD Ratio young 1.72 highest gr 3.75 • Studies show that 30-50% of obese adults report chronic pain, compared to 20-30% of normal-weight individuals (Stone & Broderick, 2012). • The risk of chronic pain increases with higher BMI.

5. Epidemiological Evidence Dose-Response Relationship • 13,700 participants • Median age 47.00 (33.00–65.00) yrs • Participants with chronic pain >1/8th study population (14.2%)

6. • BMI groups was 17.0% (underweight), 11.8% (normal weight), 12.9% (overweight), and 17.9% (obesity), respectively (Table 2). There was a significant difference in the prevalence of obesity between the four groups (P < 0.05) • A J-shaped association was observed between BMI and chronic pain (nonlinear P = 0.012), in which Chronic pain risk leveled off until BMI was around 23.12 kg/m2and then rose sharply Epidemiological Epidemiological Evidence Evidence

7. Chronic pain interfering with activities of daily living Chronic pain interfering with activities of daily living peaks in people ≥75yrs age peaks in people ≥75yrs age while Obesity peaks in the 45 while Obesity peaks in the 45- -54yrs age group 54yrs age group Pain and obesity together peak in the 55 Pain and obesity together peak in the 55- -74 year age group age group 74 year

9. . •Obesity is associated with significantly greater pain intensities compared to individuals with normal weight. 31 total Studies meet the requirements •Overweight individuals did not report significantly higher pain intensities than normal-weight individuals. •This suggests a threshold effect where pain intensity increases notably only in obesity, not just with slight weight gain. •The association holds across different pain conditions. •Subgroup analyses by specific conditions (e.g., fibromyalgia, chronic low back pain) did not significantly alter the results. •One meta-regression showed a small effect of age, but it was not consistent across analyses. •Age and other demographic variables had minimal impact on the results. •Normal Weight: BMI 18.5–24.9 •Overweight: BMI 25.0–29.9 •Obesity:BMI ≥ 30.0 Population: Adults categorized as normal weight, overweight, or obese 22 studies had data suitable for quantitative meta-analysis Frontier Endocrinology TYPE Systematic Review PUBLISHED 06 March 2024 DOI 10.3389/Systematic Review PUBLISHED 06 March 2024 DOI 10.3389/

10. New Meta-Analysis of 127 Studies (N=2.4M Participants) Clinical Action Threshold BMI Range Total Risk ↑ Mechanical ↑ Metabolic ↑ 25-29.9 +22% +15% +7% Consider prevention Initiate interventions 30-34.9 +51% +29% +22% 35-39.9 +89% +38% +51% Multimodal therapy ≥40 +140% +51% +89% Bariatric evaluation

11. Per 5 Per 5- -Unit BMI Increase: Unit BMI Increase:

12. Osteoarthritis Common Common pain pain condition condition linked to linked to obesity obesity • (knees, hips) risk of knee OA by 4–5 times and hip OA by 2–3 times. Lower back pain • 1.5–2.5 times higher odds of chronic LBP Fibromyalgia and Chronic Widespread Pain • Fibromyalgia patients is ~50%, compared to 30% in the general population. Neuropathic pain (diabetes-related)

13. Chronic pain and Obesity Relatiobship

14. Chronic pain Obesity Reduced Physical Activity & Sedentary Behavior •Evidence: •Chronic pain (e.g., back pain, osteoarthritis) leads to avoidance of movement due to fear of worsening pain (Vlaeyen & Linton, 2012). •Result: Decreased energy expenditure → weight gain over time (Stubbs et al., 2015). Sleep Disturbances & Metabolic Dysregulation •Evidence: •Chronic pain disrupts sleep → increased ghrelin (hunger hormone) and leptin (satiety hormone) (Spiegel et al., 2004). •Result: Night-time snacking, insulin resistance (Tasali et al., 2008). Psychological Factors: Depression, Stress, and Emotional Eating •Evidence: higher rates of depression and anxiety (Bair et al., 2003), which can trigger comfort eating (high- calorie, high-fat diets) (Somers et al., 2009). •Biological Mechanism: • cortisol, promoting abdominal fat storage (Tomiyama et al., 2011). • Pain disrupts dopamine and serotonin, increasing cravings (Geha et al., 2013). Medication-Induced Weight Gain •Common culprits: • Antidepressants (e.g., amitriptyline) → increased appetite (Serretti & Mandelli, 2010). • Gabapentinoids (e.g., pregabalin) → fluid retention and weight gain (Gidal et al., 2003). • Long-term opioid use → hormonal changes (e.g., low testosterone) linked to obesity (Daniell, 2008).

15. Mechanistic pathways between obesity and chronic pain:

16. Mechanical Era (1985- 2000) Neuroinflammatory Era(2010-2020) Systems biology era(2020- present) Future Horizon Metabolic Era (2000-2010) 1985 1995 2004 2025 2008 2022 2024 2013 2017 Personalize pain trajectory Predication Vascular insufficiency theory Mitochondrial dysfunction theory ExosomemRNA signalling Hypothesisi Biomedical loading Adipokine Revolution Metabolic OA concept Microglial priming theory Gut –Joint Axis Obese adipose EVs-Dorsal horn neural hyperexcitabilit y Digital Twin integration AI Integration of biomechani cal /metabolic /neuro Insulin Resistance- AGEs- Cartil stiffness age Fat emboli impair bone perfusation Disprove by 2003 MRI studies Obesity-LPS- Brain inflammation Dysbiosis- leaky gut- synovitis Adipocyte ROS- neuronal oxidative stress Wear and Tear hypothesise Leptin/TNF link to central sensitization

17. Mechanical Era Mechanical Overload & Joint Degeneration • Davis et al. (1990) :Demonstrated every 1-unit BMI increase → 15% higher knee OA risk. • Spinal Mechanics and Back Pain

18. Spinal Mechanics and Back Pain • Spinal Disorders- • abdominal obesity alter spinal alignment • Disc compression - LBP •Felson et al. (1988, 1997): Established obesity as a major risk factor for knee OA. •Bogduk (1990s): Biomechanical studies on spinal loading and obesity. •NIH Consensus Reports (1990s): Recognized obesity as a modifiable risk factor for chronic pain conditions.

19. Gait and other Gait and other alignment alignment abnormality abnormality • Wider stance, slower speed in obese walkers • Increased knee adduction moment → medial joint overload • Energy expenditure inefficiencies • Mechanical explanation dominated research • Loading bearing effect Messier et al. (1996) "Osteoarthritis of the knee: Effects on gait."

20. Beyond Mechanical Loading – Metabolic Drivers of Obesity-Induced Osteoarthritis 1. Paradigm Shift in OA Pathogenesis Beyond Beyond mechanical mechanical overload overload Traditional View: Obesity → joint overload → cartilage wear New Evidence: Metabolic dysfunction independently drives OA via: Systemic inflammation Lipotoxicity Mitochondrial stress

21. Mechanical to metabolic Era (2000-2010) Beyond mechanical loading: The metabolic contribution of obesity in osteoarthritis unveils novel therapeutic targets Basma H. Sobieh et al Mechanical Overload & Joint Degeneration

22. • • • Wang et al. (2023).Science Translational Medicine – HIF-2α inhibitor trial Berenbaum et al. (2024).Annals of the Rheumatic Diseases – FGF21 as OA biomarker Berenbaum et al. (2024).Annals of the Rheumatic Diseases – FGF21 as OA biomarker Key Metabolic Key Metabolic Pathways in OA Pathways in OA • Emerging Biomarkers • sLOX-1 (oxidized LDL receptor) → predicts OA progression • Fibroblast growth factor 21 (FGF21) → lower levels in obese OA patients Mechanism Effect on Joint Therapeutic Target Adipokine Imbalance (Leptin↑/Adiponectin↓) Cartilage degradation, synovitis Leptin antagonists (e.g., metreleptin) Free Fatty Acids (FFAs) Chondrocyte apoptosis via ER stress SCD-1 inhibitors Advanced Glycation End-products (AGEs) Collagen cross-linking → stiffness AGE breakers (e.g., alagebrium) Hypoxia-Inducible Factors (HIFs) Abnormal chondrocyte metabolism HIF-2α inhibitors (Phase II trials)

23. stable set of peptide biomarkers predicting incident RKOA at y10 using serum biomarkers from y2, y6, or the y2/6-time integrated concentration (TIC); shared proteins appearing in more than one model are indicated by the overlap in the Venn diagram

24. Should metabolic syndrome screening be mandatory for OA patients No matter How excellent the surgical technique can you really expect to control the subjective outcome 365 postop •UK NICE 2023: Recommends HbA1c testing for obese OA •Japan Orthopaedic Association: Mandatory MetS screening for pre-op TKR patients

25. Mitochondrial Therapeutics Microbiome-Based Interventions Novel Small Molecules •IL-1β Inhibition •MitoQ (Antioxidant) • Reduced oxidative stress in human chondrocytes (Coleman et al., 2024) •NAD+ Boosters (NMN/NR) • Improved joint mobility in aged mice •SGLT2 Inhibitors (Empagliflozin) •Faecal Microbiota Transplant •HIF-2α Inhibitors(PT2385) • (Anakinra, Canakinumab) • ↓ Joint inflammation independent of weight loss (Jiang et al., 2023 RCT) • • Reduced OA progression in obese mice (Zhao et al., 2023) Phase II completed (awaiting results) •Reduces cartilage catabolism in preclinical models (Chevalier et al., 2022) •PPARγ Agonists (Pioglitazone) •TNF-α Blockers • (Adalimumab)Phase II trial showed 40% pain reduction in obese knee OA (Attur et al., 2023 •sLOX-1 Neutralizers • Preserved cartilage thickness in diabetic OA patients •Probiotic Cocktail (L. casei + B. longum) • Prevented osteophyte formation in primates • ↓ Serum LPS → 30% less synovitis

26. Mechanical to metabolic Era (2000-2010) Key Development Metabolic era Adipose Tissue as an Endocrine Organ Low-Grade Systemic Inflammation & Pain Insulin Resistance & Neuropathy Central Sensitization & Obesity

27. Mechanical to metabolic Era (2000-2010) 1. Adipose Tissue as an Endocrine Organ •Discovery that fat cells (adipocytes) secrete pro-inflammatory cytokines (e.g., TNF-α, IL-6, leptin, adiponectin). •Leptin resistance (common in obesity) was linked to increased pain sensitivity and central sensitization. •Adipokines and Pain: • Leptin ↑ neuroinflammation and pain signaling. • Adiponectin (anti-inflammatory) was often ↓ in obesity, worsening pain

28. Insulin sns

29. Mechanical to metabolic Era (2000-2010) 2 Low-Grade Systemic Inflammation & Pain • Obesity was recognized as a chronic low-grade inflammatory state ("metaflammation"). • Inflammatory cytokines activated nociceptors (pain-sensing nerves) and promoted neuropathic pain. • Studies linked obesity-related inflammation to fibromyalgia, migraines, and non-specific musculoskeletal pain (not just joint-related). 3 Insulin Resistance & Neuropathy • Metabolic syndrome (insulin resistance, hypertension, dyslipidemia) was associated with chronic widespread pain. • Diabetic neuropathy research expanded, showing obesity worsened nerve damage via oxidative stress and microvascular dysfunction. 4 Central Sensitization & Obesity • Obesity was found to alter brain structure and pain processing (e.g., reduced gray matter in pain-modulating regions). • Leptin and ghrelin imbalances disrupted descending pain inhibition, increasing pain perception.

30. 2010 -2020 marked Integrative models of obesity-related chronic pain, Neuroinflammatory Neuroinflammatory Era(2010 Era(2010- -20 20) Incorporating • Neurobiological, • Psychological • Social factors

31. Transition to next era Biopsychological & Neuro modulatory module ( 2010-2020) 1. Central Sensitization & Neuroplasticity • • Obesity as a "Brain Disorder": • Neuroimaging (fMRI, PET) showed structural and functional changes in pain-processing regions (e.g., ↓ gray matter in prefrontal cortex, ↑ amygdala reactivity). • Dysfunctional descending pain modulation (impaired opioidergic & serotonergic inhibition). • Leptin & Dopamine Crosstalk: • Leptin resistance disrupted mesolimbic dopamine pathways, reducing pain tolerance and increasing reward-driven eating (vicious cycle).

32. 2. Psychosocial & Behavioral Factors • Pain-Obesity-Depression Triad: • Bidirectional links between chronic pain, emotional distress, and weight gain (e.g., pain → inactivity → obesity → depression → worsened pain). • Obesity stigma exacerbated pain perception via stress-induced hyperalgesia. • Childhood Adversity: • Early-life stress (e.g., trauma, abuse) predicted both obesity and chronic pain via epigenetic and HPA-axis dysregulation.

33. Gut-Brain Axis in Obesity-Related Chronic Pain Bidirectional communication

34. Gut-Brain Axis in Obesity-Related Chronic Pain Dysbiosis in obesity increases LPS (lipopolysaccharide) → systemic inflammation (Cani et al., 2012). LPS activates TLR4 receptors on microglia → chronic pain (Nicotra et al., 2012). Dysbiosis & Metabolic Endotoxemia Gut Barrier Dysfunction ("Leaky Gut") Neuroimmune Activation •Peripheral Effects: • LPS → mast cell degranulation → visceral hypersensitivity (Ataş et al., 2023) •Central Effects: • Microglial TLR4 activation → IL-1β & TNF-α release → central sensitization (Ji et al., 2023) •High-Fat Diet (HFD) Shifts Microbiota: • ↑ Firmicutes/Bacteroidetes ratio → pro-inflammatory state (Chen et al., 2023) • ↓ Akkermansia muciniphila (protective mucus- degrading bacteria) (Everard et al., 2013) •Mechanism: • HFD ↓ occludin & zonulin- 1 (tight junction proteins) → endotoxin (LPS) leakage (Cani et al., 2008) LPS binds TLR4 → systemic inflammation (Guillemot-Legris et al., 2023) •

35. Neuroinflammation in Obesity & Pain(2010-2020) 3. Immune Cell Crosstalk •M1 Macrophages dominate obese adipose → release ROS and PGE2 → nociceptor sensitization (Kawai et al., 2022) •Mast cell degranulation → histamine-induced itch and pain (Ataş et al., 2023) 4. Blood-Brain Barrier Breakdown •Metabolic endotoxemia (LPS from gut microbiota) → TLR4 activation → glial cell priming (Guillemot-Legris et al., 2023) •Leptin penetration into CNS → ↓ μ-opioid receptor expression (Tu et al., 2022)

36. Gut Gut- -Brain Axis in Obesity Brain Axis in Obesity- -Related Chronic Pain Related Chronic Pain • Clinical Evidence • Obese Patients Show: • 3× higher serum LPS vs. lean controls (Amar et al., 2008) • Correlation between gut permeability markers (I- FABP) and fibromyalgia severity (Pimentel et al., 2022)

37. In mouse CL boilteae influences bile acid metabolism with Ursodeoxycholic acid UDCA Glycourosodeoxycholic acid GUDCA ---Inhabit framesold Xfactors (FXR) GLP 1

39. Gut dysbiosis in patients with chronic pain: a systematic review and meta-analysis Microbial Immunology Volume 15 - 2024 | https://doi.org/10.3389/fimmu.2024.1342833 21 studies, with 11 included in the meta- analysis Reduced Alpha-Diversity: Chronic pain patients exhibited decreased alpha-diversity in their gut microbiota, indicating a lower variety of microbial species. Specific metrics showed significant reductions Lachnospiraceae, Faecal bacterium prausnitzii, Odoribacter splanchnicus Anti-inflammatory property

40. Pain signature Key Neuroimmune Reprogramming Hypothesis Mechanistic Advances (2020–2025 Mitochondrial transfer and dysfunctional hypothesise Leptin-Melanocortin System Revisited Digital Phenotyping & AI Predictors

41. Mitochondrial and MiRNA transfer theory Study Finding Journal OPRM1 methylation predicts opioid resistance in obesity Tian et al. (2021) Nature Communications FTO methylation primes microglia via BDNF silencing Jones et al. (2021) Nature Metabolism Adipose miR-155-5p → spinal cord inflammation Zhou et al. (2024) Science Advances CircRNA_0001859 mediates microglial metabolic reprogramming Chen et al. (2025) Neuron

42. Key Mechanistic Advances (2020–2025) • Development of Pain signature • Refers to distinct molecular patterns associated with specific types of chronic pain conditions, such as fibromyalgia, osteoarthritis, and neuropathy. These signatures are identified through advanced techniques like proteomic and transcriptomic analyses and represent unique combinations of biomarkers that characterize the underlying mechanisms of different pain types • Specific pain profile- Individual patients based management

43. Neuroimmune Reprogramming Hypothesis • Reprograms microglia →primed state, lowering pain thresholds • TREM2-dependent synaptic pruning in DRG (Decosterd et al., 2021, Nature Neuroscience) • IL-17a release -visceral adipose tissue macrophages → direct DRG activation (Talbot et al., 2022, Cell Reports)

44. Mitochondrial Transfer Dysfunction Proposed Mechanism Proposed Mechanism Mechanisms of Mitochondrial Transfer Occurs via tunneling nanotubes (TNTs), extracellular vesicles (EVs), and gap junctions (Rustom et al., 2004; Hayakawa et al., 2016) Mesenchymal stem cells (MSCs) and astrocytes are primary donors (Lin et al., 2017) Macrophages can transfer mitochondria to neurons in pain pathways (Nicolò et al., 2021) Dysfunction in Obesity Disrupt mitochondrial transfer from astrocytes to neurons (Zhao et al., 2024, Neuron) High-fat diet reduces TNT formation in adipose tissue (Coughlan et al., 2016) Leptin resistance in obesity → downregulated POMC neurons → loss of endogenous opioid tone (Andermann et al., 2024, Cell Metabolism Link to Chronic Pain Reduced mitochondrial transfer to DRG neurons increases pain sensitivity (Lin et al., 2017) Microglia in obese states withhold mitochondria from stressed neurons (Nicolò et al., 2021) Impaired transfer exacerbates neuronal energy deficits in fibromyalgia (Flatters et al., 2017)

45. News Release, World Mitochondria Society, Berlin - Germany – March 14, 2022-24 • Using a tool called CRISPR, we screened the entire genome and figured out that cells trade mitochondria using a special type of sugar called heparan sulfate, which we think acts as a loading dock for receiving cargo like mitochondria. When we delete Heparan sulfates on macrophages, mice get fat. This suggests to us that it is probably good for cells to trade mitochondria with each other.

47. Adipocyte-secreted exosomal microRNA-34a inhibits M2 macrophage polarization to promote obesity-induced adipose inflammation Yong Pan, … , Karen Siu Ling Lam, Aimin Xu J Clin Invest. 2019;129(2):834-849. https://doi.org/10.1172/JCI123069 • Exosomal microRNA-34a and Macrophage Polarization towards the M2 phenotype • So predominately M1 macrophage available to develop Proinflammatory resulting in a pro-inflammatory state within adipose tissue • miR-34a inhabit targeting specific genes involved inM2 macrophage polarization . • One gene peroxisome proliferator-activated receptor gamma (PPARγ), a transcription factor that promotes M2 macrophage polarization. • By binding to the mRNA of PPARγ, miR-34a prevents its translation, thereby inhibiting the M2 polarization pathway. • Additionally, miR-34a targets other signaling molecules and pathways associated with M2 macrophage function, further impairing their polarization

48. Exosome mRNA Exosome mRNA Hypothesise Hypothesise 2022 2022- -24 24 Step Process Outcome Adipocyte mitochondria fragment → release damaged mt-mRNA into exosomes Exosomes carry pathological signals 1. Obesity-induced stress Adipocyte-derived exosomes reach DRG neurons, microglia, and astrocytes Target cells internalize exosomal cargo 2. Exosome circulation Aberrant mt-mRNA translation → misfolded proteins, ROS 3. Dysregulated translation Neuronal hyperexcitability Exosomal miRNAs (e.g., miR- 155) activate TLRs → NF-κB pathway Microglial activation & cytokine release 4. Neuroinflammation Sustained sensitization of nociceptors → central sensitization Persistent hyperalgesia/allodynia 5. Chronic pain

49. Digital Twin integration of obesity with pain • Digital phenotyping involves the collection of data through digital means—smartphones, wearable devices, and other digital tools •Biomechanical and neural simulations create "virtual patients" to test treatments computationally. • This model can then be used to simulate various scenarios, predict outcomes, and tailor personalized treatment plans.

50. AI, Biomechanics & Neuroscience: Unlocking the Brain AI, Biomechanics & Neuroscience: Unlocking the Brain- -Body Link in chronic pain Body Link in chronic pain • Movement analysis AI capture the abnormal patterns (e.g., gait asymmetries, muscle imbalances) linked to pain persistence. • Posture & Ergonomics: AI-driven posture assessment tools identify harmful biomechanical habits that perpetuate pain cycles. • Decoding the Brain’s Pain Signature • AI analyzes brain activity patterns in chronic pain patients, revealing maladaptive plasticity in regions like the insula, anterior cingulate cortex (ACC), and thalamus. • Machine learning detects biomarkers of hyperexcitability in pain-processing neural circuits. • AI as a Bridge Between Brain & Body • Predictive Modelling: AI integrates biomechanical data (e.g., joint angles, muscle activation) with neural signatures to predict pain flare-ups. • Personalized Rehabilitation: Reinforcement learning tailors movement therapies (e.g., graded motor imagery, proprioceptive training) to rewire maladaptive brain-body loops • Closed-Loop Neuromodulation: AI-powered brain-machine interfaces (BMIs) by neural feedback