Bariatric Surgery and Neurologic Diagnoses in Adults With Obesity: A Retrospective Cohort Study

Bariatric Surgery and Neurologic Diagnoses in Adults With Obesity: A Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bariatric Surgery and Neurologic Diagnoses in Adults With Obesity: A Retrospective Cohort Study Jacob Lonergan, Caleb DeBass, Rawan Elkomi, Syed Fahad Gillani, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7919688/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background and Aims: Obesity, the gut–brain axis, and bariatric surgery may influence neurologic disease risk; however, population-level data are limited. This study compared the incidence of Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and epilepsy in adults with obesity who underwent bariatric surgery versus those who did not. Methods This was a retrospective cohort study using the TriNetX Global Collaborative Network. Adults with overweight/obesity (ICD-10 E66) were assigned to Surgical (RYGB or SG) or Non-Surgical cohorts. 1:1 propensity score matching was performed on demographic and clinical covariates. Risk ratios, risk difference, and hazard ratios were estimated for incident diagnoses (ICD-10: AD G30; PD G20; MS G35; epilepsy G40). Results In matched cohorts (n = 776,800; 388,400 per group), bariatric surgery was associated with a lower incidence of neurologic disease (0.6% vs 0.9%; RR 0.68, 95% CI 0.64–0.71; HR 0.71, 95% CI 0.68–0.75; p < 0.001). Risk reductions were also observed for Alzheimer’s disease (RR 0.64; HR 0.70), Parkinson’s disease (RR 0.80; HR 0.86), multiple sclerosis (RR 0.73; HR 0.76), and epilepsy (RR 0.64; HR 0.66). Relative risk reductions ranged from 20% to 36%, greatest for Alzheimer’s disease and epilepsy. Conclusion Among adults with obesity, bariatric surgery was associated with a lower recorded incidence of AD, PD, MS, and epilepsy. While these findings align with biologically plausible immunometabolic and gut–brain mechanisms, residual confoundings limit causal inference. Future research is needed to confirm causality. Key Points (1) Bariatric surgery was associated with lower recorded incidence of neurological disease. (2) Findings are based on a large, retrospective cohort analysis. (3) Results suggest potential gut–brain and immunometabolic mechanisms warranting further study. Introduction The gastrointestinal (GI) tract is the body’s largest interface with the external environment and hosts its most diverse microbial community, comprising over 2,000 bacterial species with genetic content that is approximately 150-fold that of the human genome [ 1 ]. This vast genetic diversity helps to regulate nutrient harvest, cholesterol metabolism, and even antimicrobial peptide production [ 2 ], underscoring their central role in host nutrition. Moreover, these commensal communities are also integral to host physiology: driving metabolite production [ 3 ], regulating mucosal immunity [ 4 ], and mediating signals with the central nervous system (CNS), along the gut–brain axis [ 5 ]. The brain–gut axis is a bidirectional network that coordinates digestion, immune tone, and neurobehavioral states via signals between the CNS and GI tract. It accomplishes this through three primary routes: neuronal, endocrine, and immune pathways [ 6 ], with the vagus nerve serving as the principal conduit for both afferent and efferent signals [ 7 ]. Through these channels, gut microbes modulate receptor activity, shape neurotransmission, and influence metabolite entry to the brain [ 8 ]. Signaling within this axis is driven by neurotransmitters and metabolites. Key messengers, including serotonin, dopamine, noradrenaline, and γ-aminobutyric acid (GABA), are generated in the CNS and peripherally, by enteroendocrine cells responding to intestinal peptides as well as by the microbiota itself [ 9 ]. Together, these chemical cues regulate digestion, immune homeostasis, mood, and cognition. When this cross-talk is disrupted (dysbiosis), intestinal and blood–brain barrier permeability can shift and neuroinflammation may ensue, linking microbial composition to GI, neurodegenerative, and psychiatric disease [ 10 ]. Within this framework, obesity, a major global health challenge, consistently reshapes gut community structure and function, impairing gut–brain signaling [ 11 , 12 ]. The resulting dysbiosis, marked by low-grade inflammation and metabolic derangement, contributes to cardiometabolic disease, including type 2 diabetes [ 13 ]. These disturbances extend to microbially derived messengers such as short-chain fatty acids (SCFAs) and bile acids, which regulate blood–brain barrier (BBB) integrity and influence neuroinflammation via receptors such as farnesoid X receptor and Takeda G protein-coupled receptor 5 [ 14 , 15 ]. When lifestyle and medical interventions fail, bariatric surgery is recommended to individuals with a BMI ≥ 40 or ≥ 35 with serious obesity-related medical problems, as it can produce durable weight loss by resecting or bypassing segments of the foregut [ 16 , 17 ]. Two principal operations include Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG). RYGB creates a small gastric pouch anastomosed to the jejunum (bypassing the stomach and duodenum) [ 18 ], whereas SG removes most of the greater-curvature stomach without intestinal bypass [ 19 ]. Beyond weight loss, both procedures have been shown to lead to various short- and long-term shifts in the microbiome [ 20 ]. Changes such as reduced adiposity, elevated circulating bile acids, altered gut hormones, and increased microbial tryptophan indoles [ 21 , 22 ] are observed. These effects ripple upstream to the brain, as microbiota-derived acetate can suppress hypothalamic Neuropeptide Y and Agouti-Related Peptide expression by dampening GABAergic neuroglial activity [ 23 ]. Consistent with this mechanism, RYGB has been linked to altered GABA signaling in extrahypothalamic circuits involved in memory and perception [ 24 ], directly implicating the gut–brain axis in central circuit modulation. Converging evidence indicates that microbiota profiles in Alzheimer’s disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS), autism spectrum disorder (ASD), and epilepsy closely mirror the ecological shifts seen post-RYGB and SG procedures [ 25 ]. However, evidence directly linking surgery-induced microbiota shifts to neurological disease incidence is limited. To address this gap, we used a demographically diverse patient cohort from the TriNetX Global Collaborative Network to conduct an unmatched retrospective cohort analysis to create comparable patient groups. We hypothesized that among adults with obesity, exposure to bariatric surgery (RYGB & SG) would be associated with altered incidence of major neurological diseases (AD, PD, MS, and epilepsy) relative to matched non-surgical controls. Methods Study Design and Data Source We conducted a retrospective cohort study using the TriNetX Global Collaborative Network. This network comprises 161 healthcare organizations and provides harmonized electronic health record (EHR) data, including diagnoses, procedures, medications, and encounters. The analysis, performed on October 14, 2025, compared the incidence of neurological disease (AD, PD, MS, and epilepsy) between a Surgical cohort and a Non-Surgical cohort. Cohort Selection Eligible patients were adults aged 18 years or older with overweight/obesity (ICD-10 E66). The Surgical cohort required evidence of bariatric surgery exposure by diagnosis or procedure, including CPT:1007385 (Bariatric Surgery Procedures), CPT:1007386 (Laparoscopic Bariatric Surgery Procedures), or ICD-10 Z98.84 (Bariatric surgery status). The Non-Surgical cohort required an absence of any bariatric surgery codes. Patients with prior diagnoses of Alzheimer’s disease (G30), Parkinson’s disease (G20), multiple sclerosis (G35), or Epilepsy and recurrent seizures (G40) were excluded. At query definition, 391,758 patients met Bariatric Surgery criteria, and 11,461,147 met Non-Surgery criteria. Matching and Covariate Adjustment To reduce confounding, a 1:1 propensity score matching (PSM) was performed. The propensity model included demographics and clinical factors: age at index; sex (male/female/unknown); race (White [2106-3], Black or African American [2054-5], Asian [2028-9], American Indian or Alaska Native [1002-5], Native Hawaiian or Other Pacific Islander [2076-8], other race [2131-1], unknown); ethnicity (Hispanic or Latino [2135-2], Not Hispanic or Latino [2186-5], unknown); hypertensive diseases (ICD-10 I10–I1A); diabetes mellitus (E08–E13); mental, behavioral, and neurodevelopmental disorders (F01-F99); and disorders of lipoprotein metabolism & other lipidemias (E78). Post-match balance was evaluated using standardized differences. Follow-Up Periods and Outcomes The index date was set as the earliest date a patient met the applicable cohort criteria. Outcomes were observed starting one day after the index date, with no end date specified. TriNetX restricted index events to those occurring within the previous 20 years. The primary neurological outcome comprised any incident diagnosis of Alzheimer’s disease (G30), Parkinson’s disease (G20), multiple sclerosis (G35), or epilepsy and recurrent seizures (G40). Time-to-event was analyzed using Kaplan–Meier curves with log-rank tests and Cox proportional hazards models to estimate hazard ratios (HRs) with 95% CIs. Statistical Analysis For all outcomes, we estimated risk (cumulative proportion), risk difference (RD), and risk ratio (RR) with 95% confidence intervals (CIs). We analyzed time-to-event using Kaplan-Meier curves, with a log-rank test to compare survival distributions between cohorts. We also used Cox proportional hazards models to estimate hazard ratios (HRs) with 95% CIs. Proportional-hazards diagnostics were performed to confirm the model's assumptions. A p-value of < 0.05 was considered statistically significant. Additionally, we performed number-of-instances analyses for each neurological outcome. This involved calculating the mean number of outcome instances per patient during follow-up, excluding patients with zero instances from the summary statistics. Two-sample t-tests were used to compare these mean counts between the cohorts. Results Primary Outcome: Composite Neurologic Diagnosis Among matched patients (n = 388,400 per cohort), a recorded diagnosis of any primary neurologic disease (Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, or epilepsy) occurred in 2,316 bariatric surgery patients (0.6%) versus 3,420 non-surgical patients (0.9%). The risk difference was − 0.3 percentage points (RD − 0.003; 95% CI, − 0.004 to − 0.002), the risk ratio was 0.68 (95% CI, 0.64–0.71), and the hazard ratio from the Cox model was 0.71 (95% CI, 0.68–0.75; p < 0.001). Kaplan–Meier analysis demonstrated higher neurologic event–free survival in the surgery cohort throughout follow-up; median time to event was not reached in either cohort, and the log-rank test was significant (p < 0.001). Secondary Outcomes: Alzheimer’s Disease (G30) Alzheimer’s disease occurred in 345/388,400 (0.09%) of surgical patients versus 538/388,400 (0.14%) of non-surgical patients. The RR was 0.64 (95% CI 0.56–0.73), and the HR was 0.70 (95% CI 0.61–0.80; p < 0.001). Mean diagnosis instances were 0.004 vs 0.007 (p < 0.001). Parkinson’s Disease (G20) Parkinson’s disease was diagnosed in 408 (0.11%) vs 509 (0.13%) patients, yielding an RR of 0.80 (95% CI 0.70–0.91) and HR 0.86 (95% CI 0.76–0.98; p = 0.023). Mean instances 0.007 vs 0.011 (p = 0.001). Secondary Outcomes: Multiple Sclerosis (G35) Multiple sclerosis occurred in 287 (0.07%) vs 393 (0.10%) patients, with an RR 0.73 (95% CI 0.63–0.85) and HR 0.76 (95% CI 0.66–0.89; p < 0.001). Mean instances 0.008 vs 0.009 (p = 0.39). Secondary Outcomes: Epilepsy (G40) Epilepsy was diagnosed in 1,325 (0.34%) vs 2,086 (0.54%) patients. The RR 0.64 (95% CI 0.59–0.68) and HR 0.66 (95% CI 0.62–0.71; p < 0.001) indicated a significant risk reduction. Mean instances 0.012 vs 0.021 (p < 0.001). The incidence and relative risk of each neurologic outcome are summarized in Table 1 . Number-of-Instances Analyses For the composite outcome, the mean number of diagnosis instances per patient was 5.11 ± 0.9 after surgery vs 5.43 ± 1.1 without surgery; the difference was not statistically significant (t = 1.05, p = 0.30). For individual diseases, only Alzheimer’s disease and epilepsy showed statistically significant reductions in mean instance frequency. Summary of Key Findings Across matched cohorts, bariatric surgery was consistently associated with lower risk and delayed onset of neurologic disease. Relative risk reductions ranged from 20% to 36%, with the most significant effects seen in Alzheimer’s disease and epilepsy. Differences in diagnosis frequency were minimal, suggesting results were not influenced by healthcare utilization. Discussion In this large, retrospective cohort of adults with obesity, exposure to bariatric surgery was associated with a roughly 32% lower relative risk and hazard of recorded diagnoses of AD, PD, MS, and epilepsy compared with matched non-surgical controls. Although absolute risk differences were minor, the consistency and direction of relative measures suggest a clinically meaningful signal that warrants closer interpretation. The resulting shift in gut ecology after bariatric surgery makes these findings notable. Mechanistically, lowered gastric acid, altered bile-acid delivery, and elevated oxygen content after surgery are recurrent drivers of change in commensal microbiota [ 26 ]. Research shows that, within weeks to months, bariatric surgery rapidly attenuates obesity-related systemic inflammation, significantly reducing pro-inflammatory cytokines (interleukin-6 ( IL-6) and tumor necrosis factor alpha (TNF-α)), C-reactive protein, and abdominal fat density [ 26 ]. In RYGB specifically, mouse models demonstrate reversal of hypothalamic toll-like receptor 4-linked neuroinflammation [ 27 ] and increases in the Akkermansia genus, which is associated with improved metabolism [ 28 ]. By reinforcing barrier function, dampening microglial activation, and improving insulin signaling [ 29 ], these post-operative changes provide a mechanistic frame for the disease-specific associations we observed. In AD, extracellular amyloid-β (Aβ) deposition and tau protein hyperphosphorylation lead to neurofibrillary tangle formation, contributing to synaptic loss and eventual neuronal death [ 30 ]. In line with this pathophysiology, obesity-associated neuroinflammation, reactive oxygen species (ROS) generation, and central insulin resistance promote Aβ/tau pathology and impair amyloid clearance. Excessive free fatty acids (FFAs) and BBB vulnerability further propagate this loop [ 31 , 32 ]. Post-surgical reductions in inflammatory tone, FFAs, and improvements in insulin sensitivity plausibly attenuate these drivers. Notably, SCFAs can both maintain BBB integrity and promote Aβ accumulation [ 33 ], underscoring that metabolite effects are not uniformly beneficial and may exert context-dependent effects. PD, characterized by motor symptoms (rigidity, tremor, bradykinesia) and common non-motor features (depression, sleep disturbance, constipation), is a progressive neurodegenerative condition associated with the deposition of aggregated α-synuclein [ 34 ]. Obesity-linked inflammation and lipid dysregulation intersect with α-synuclein pathology and dopaminergic signaling. After RYGB, evidence suggests normalization of dopamine receptor availability consistent with restoration of reward responsiveness and potentially favorable effects on basal ganglia networks [ 35 ], providing a mechanistic bridge to the lower PD incidence signal. SG, on the other hand, has been associated with post-operative increases in tauroursodeoxycholic acid, a secondary bile acid that reduces motor deficits in PD mouse models [ 36 ]. However, human post-operative bile-acid profiles remain heterogeneous, limiting firm mechanistic inferences [ 37 ]. MS is a chronic, immune-mediated disease of the CNS characterized by inflammatory demyelination and neurodegeneration [ 38 ]. While the exact cause remains unclear, obesity has been linked to the development of MS through the modulation of pro- and anti-inflammatory adipokines [ 39 ]. Surgery-induced immunometabolic re-balancing may reduce this risk, and animal data suggest altered bile-acid metabolism is associated with reduced neuroinflammation [ 40 ]. Given conflicting post-operative bile-acid findings, mechanistic mediation in MS should be tested explicitly. Epilepsy is a chronic neurological disorder characterized by the enduring predisposition to generate seizures [ 41 ]. Neuroinflammation is a common hallmark of the disease, implicating a mechanistic tie to immune-mediated gut–brain pathways [ 42 ]. Key inflammatory markers (IL-6, TNF-α, and high-mobility group box 1) exacerbate neuroinflammation and lower seizure threshold [ 43 ]. Obesity-related dysbiosis can propagate this state by increasing intestinal permeability and promoting lipopolysaccharide (LPS) translocation, which fuels metabolic endotoxemia and central hyperexcitability [ 43 , 44 ]. In this context, bariatric surgery may shift risk via restoration of barrier integrity and attenuation of low-grade inflammation, changes that would be expected to raise seizure threshold [ 45 , 46 ]. In addition, fecal increases in 5-HT, glutamate, and GABA reported after surgery point to gut-centric neuromodulation; however, plasma neurotransmitter levels do not consistently mirror these shifts, suggesting a primarily local GI signal that communicates with the brain through immune and vagal pathways [ 47 ]. Across conditions, surgery appears positioned to interrupt obesity-associated central insulin resistance, endotoxin-driven inflammation, and increased BBB permeability; through weight loss, inflammatory down-shifting, and microbial/metabolite re-patterning. That said, gut ecology is dynamic rather than fixed, and trajectories can diverge. Post-operative studies, in both RYGB and SG, exhibit procedure- and time-dependent enrichment of pro-inflammatory Proteobacteria and depletion of anti-inflammatory SCFA producers, patterns that may align with disease-specific risks [ 48 , 49 , 50 ]. This heterogeneity likely reflects variation in baseline microbiota and immune tone; procedure type and timing; diet/medication exposures; bile-acid pools/receptor signaling; and luminal vs mucosal sampling. Taken together, our findings fit a model in which bariatric surgery counteracts obesity-driven gut–immune perturbations that track with neuropathology across AD, PD, MS, and epilepsy. We suspect the lower relative risks reflect the interplay between post-operative microbiome and host neuroimmune susceptibility; however, these findings do not fully account for the complexity of the system. Prospective, microbiome-integrated studies are needed to further test causality and identify modifiable risks. Clinical implications This study signals a lower recorded incidence of AD, PD, MS, and epilepsy among adults with obesity who undergo bariatric surgery. Although absolute risk differences were small, the relative measures were consistently lower, which merits attention in routine care. Clinicians should not interpret these findings as a directive to recommend metabolic surgery primarily as a neuroprotective therapy. Instead, results support more intentional counseling about potential neurologic implications within standard shared decision making that prioritizes weight-loss efficacy, cardiometabolic benefit, surgical risk, nutritional demands and patient values. Practical steps include discussing the possibility that post-operative metabolic changes may influence long-term neurologic risk; incorporating baseline cognitive and neurologic screening and periodically thereafter for patients at elevated risk or with new symptoms; and monitoring maintenance of nutrition, supplementation, and medication to prevent deficiency-related neurologic complications. If a patient develops changes in cognitive, motor, sensory, or seizure symptoms, a timely referral to neurology or memory clinics is warranted, with perioperative planning coordinated among specialties. Health systems and bariatric programs can use these findings to build care pathways that integrate pre-operative counseling, post-operative neuro-focused surveillance, standardized laboratory and supplementation protocols, and pharmacy review for neuroactive regimens. Capturing procedure type, time since surgery, weight-loss trajectory, medication adjustments, and neurologic outcomes in routine documentation will improve interpretability for future real-world evaluations. Limitations The study’s primary limitations stem from its observational design. Although we used matching and covariate adjustment to improve comparability between surgical and non-surgical cohorts, residual confounding, particularly by indication, illness severity, health-seeking behavior, and surgeon/center factors, which cannot be fully excluded. Exposure ascertainment relied on electronic health record (EHR) procedure coding for RYGB and SG; coding errors, misclassification between procedures, and incomplete capture of out-of-network care are all possible. We could not account for operative technique, perioperative complications, or adherence to post-operative care, all of which might influence long-term neurologic outcomes. Similarly, our outcome identification depended on diagnosis codes for AD, PD, MS, and epilepsy, which vary in specificity across sites and may be influenced by differential health care utilization and follow-up intensity between groups. Despite efforts to align index dates, the possibility of immortal time bias remains, and competing risks were not fully modeled and could affect hazard estimates. Because absolute risk differences were small relative to cohort size, effect estimates may be sensitive to modest amounts of exposure or outcome misclassification. Important covariates are incompletely captured in structured EHR data. We lacked consistent measures of education, socioeconomic status, family history, APOE genotype, physical activity, sleep disorders, and detailed substance use, as well as granular medication data for neuroactive and metabolic agents that could confound neurologic risk. Nutritional status after surgery was not uniformly available, precluding adjustment for deficiency-related neurologic complications. Conclusion In this large, retrospective study of adults with obesity, exposure to bariatric surgery was associated with a significantly lower risk of a recorded diagnosis of AD, PD, MD, and epilepsy compared with matched non-surgical controls. This observation aligns with hypotheses that metabolic, inflammatory, and gut–brain remodeling after surgery may mitigate neuroinflammatory pathways, yet it coexists with heterogeneous post-operative microbiome findings and the possibility of residual confounding in routine care. Our results highlight the challenges of causal inference in real-world data, where procedure selection, health-seeking behavior, and long prodromal periods may be intertwined. While our data are not sufficient to establish a causal link, they suggest that the relationship between metabolic surgery and neurologic risk in clinical practice is promising but more nuanced than a uniformly protective narrative. Future research should include large, prospective studies with standardized neurologic assessments; paired microbiome, metabolite, and nutrition measures; and methods to estimate direct and indirect effects, accounting for competing risks. Such work will be necessary to reconcile mixed mechanistic signals, identify which patient profiles derive the greatest neurologic benefit, and align perioperative care and secondary prevention strategies with underlying biology. Declarations Declaration of Generative AI and AI-assisted technologies in the writing process During the preparation of this work the author(s) used ChatGPT (OpenAI) in order to assist in tasks such as language refinement, organization of author-drafted content, and formatting assistance. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication. IRB Statement This study was determined to be exempt from Howard University Hospitals Institutional Review Board (IRB) review as it involved the retrospective analysis of de-identified patient data obtained from a national research network. No identifiable private information was collected or recorded, and there was no direct interaction with patients. As such, the research qualifies for exemption under 45 CFR 46.104(d)(4). Author Contribution Author Contributions (ICMJE) J.L. designed the study and drafted the Introduction, Abstract, Methods, Discussion, and References. C.D. performed statistical analyses and authored the Results section. R.E. and S.F.G. collected and validated TriNetX data. C.E. wrote the Clinical Implications subsection. N.M. authored the Limitations subsection. M.M. supervised the project, provided mentorship, and performed the final review. All authors reviewed and approved the final manuscript. Data Availability Data Availability Statement:The data underlying this study were obtained from the TriNetX Global Collaborative Network, a federated real-world research platform that aggregates de-identified electronic health record data from participating healthcare organizations. These data are not publicly available due to institutional and patient privacy restrictions. Access to the TriNetX platform is available to researchers through a data use agreement with TriNetX, LLC (https://trinetx.com). The aggregated data supporting the findings of this study can be reproduced by other researchers with access to the TriNetX Global Network using the same inclusion/exclusion criteria and analytic parameters described in this manuscript. References Almeida A, Mitchell AL, Boland M, Forster SC, Gloor GB, Tarkowska A, et al. 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Frontiers in Molecular Neuroscience. 2018 Jan 23;11(10). doi:10.3389/fnmol.2018.00010 Vesga-Jiménez DJ, Martin C, Barreto GE, Aristizábal-Pachón AF, Pinzón A, González J. Fatty Acids: an Insight into the Pathogenesis of Neurodegenerative Diseases and Therapeutic Potential. International Journal of Molecular Sciences. 2022 Feb 25;23(5):2577. doi:10.3390/ijms23052577 Sittipo P, Choi J, Lee S, Lee YK. The Function of Gut Microbiota in immune-related Neurological disorders: a Review. Journal of Neuroinflammation. 2022 Jun 15;19(1). doi:10.1186/s12974-022-02510-1 Negi S, Khurana N, Duggal N. The Misfolding Mystery: α-syn and the Pathogenesis of Parkinson’s Disease. Neurochemistry International. 2024 Jul 1;177(105760). doi:10.1016/j.neuint.2024.105760 Hamilton J, Swenson S, Hajnal A, Thanos PK. Roux-en-Y Gastric Bypass Surgery Normalizes Dopamine D1, D2, and DAT Levels. Synapse. 2018 Jul 28;72(10):e22058. doi:10.1002/syn.22058 Rosa AI, Duarte-Silva S, Silva-Fernandes A, Nunes MJ, Carvalho AN, Rodrigues E, et al. Tauroursodeoxycholic Acid Improves Motor Symptoms in a Mouse Model of Parkinson’s Disease. Molecular Neurobiology. 2018 Apr 12;55(12):9139–55. doi:10.1007/s12035-018-1062-4 Khalaf K, Tornese P, Cocco A, Albanese A. Tauroursodeoxycholic acid: a Potential Therapeutic Tool in Neurodegenerative Diseases. Translational Neurodegeneration. 2022 Jun 4;11(1). doi:10.1186/s40035-022-00307-z Sandi D, Fricska-Nagy Z, Bencsik K, Vécsei L. Neurodegeneration in Multiple Sclerosis: Symptoms of Silent Progression, Biomarkers and Neuroprotective Therapy—Kynurenines Are Important Players. Molecules. 2021 Jun 5;26(11):3423. doi:10.3390/molecules26113423 Brigitta B. Pathophysiology of Depression. Dialogues in Clinical Neuroscience. 2002 Mar;4(1). doi:10.31887/DCNS.2002.4.1/bbondy Bhargava P, Smith MR, Mische L, Harrington E, Fitzgerald KC, Martin KE, et al. Bile Acid Metabolism Is Altered in Multiple Sclerosis and Supplementation Ameliorates Neuroinflammation. Journal of Clinical Investigation [Internet]. 2020 Jul 1 [cited 2023 Apr 21];130(7):3467–82. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324171/ doi:10.1172/JCI129401 WHO. Epilepsy [Internet]. World Health Organization. 2019 [cited 2025 Oct 10]. Available from: https://www.who.int/news-room/fact-sheets/detail/epilepsy Vezzani A. Epilepsy and Inflammation in the Brain: Overview and Pathophysiology. Epilepsy Currents. 2014 Jan;14(2_suppl):3–7. doi:10.5698/1535-7511-14.s2.3 Shokr MM, Eladawy RM, Azar YO, Al Raish SM. Probiotics and the Gut–Brain Axis: Emerging Therapeutic Strategies for Epilepsy and Depression Comorbidity. Foods. 2025 Aug 22;14(17):2926. doi:/10.3390/foods14172926 Tuomi K, Logomarsino JV. Bacterial Lipopolysaccharide, Lipopolysaccharide-Binding Protein, and Other Inflammatory Markers in Obesity and after Bariatric Surgery. Metabolic Syndrome and Related Disorders. 2016 Aug;14(6):279–88. doi:10.1089/met.2015.0170 Rhea EM, Salameh TS, Logsdon AF, Hanson AJ, Erickson MA, Banks WA. Blood-Brain Barriers in Obesity. The AAPS Journal. 2017 Apr 10;19(4):921–30. doi:10.1208/s12248-017-0079-3 Salas-Venegas V, Flores-Torres RP, Rodríguez-Cortés YM, Rodríguez-Retana D, Ramírez-Carreto RJ, Concepción-Carrillo LE, et al. The Obese Brain: Mechanisms of Systemic and Local Inflammation, and Interventions to Reverse the Cognitive Deficit. Frontiers in Integrative Neuroscience. 2022 Mar 29;16(798995). doi:10.3389/fnint.2022.798995 Prudencio AP, Fonseca DC, C. Cardinelli, Machado NM, Ferreira B, Sala P, et al. Increased Fecal Serotonin after Gastric Bypass Is Unrelated to Tryptophan Intake. Clinical Nutrition ESPEN. 2023 Mar 22;54(614). doi:10.1016/j.clnesp.2022.09.458 Park S, Wu X. Modulation of the Gut Microbiota in Memory Impairment and Alzheimer’s Disease via the Inhibition of the Parasympathetic Nervous System. International Journal of Molecular Sciences. 2022 Nov 5;23(21):13574–4. doi:10.3390/ijms232113574 Cui G, Liu S, Liu Z, Chen Y, Wu T, Lou J, et al. Gut Microbiome Distinguishes Patients with Epilepsy from Healthy Individuals. Frontiers in Microbiology. 2022 Jan 7;12(696632). doi:10.3389/fmicb.2021.696632 Fock E, Parnova R. Mechanisms of Blood–Brain Barrier Protection by Microbiota-Derived Short-Chain Fatty Acids. Cells [Internet]. 2023 Feb 18;12(4):657. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9954192/ doi:10.3390/cells12040657 Tables Table 1: Neurologic Outcomes After Bariatric Surgery vs Matched Non-Surgical Controls Outcome Risk % (Surgery) Risk % (No Surgery) Risk Ratio (95% CI) Hazard Ratio (95% CI) P-value Composite (AD, PD, MS, Epilepsy) 0.6 0.9 0.68 (0.64–0.71) 0.71 (0.68–0.75) <0.001 Alzheimer’s disease (G30) 0.09 0.14 0.64 (0.56–0.73) 0.70 (0.61–0.80) <0.001 Parkinson’s disease (G20) 0.11 0.13 0.80 (0.70–0.91) 0.86 (0.76–0.98) 0.023 Multiple sclerosis (G35) 0.07 0.1 0.73 (0.63–0.85) 0.76 (0.66–0.89) <0.001 Epilepsy (G40) 0.34 0.54 0.64 (0.59–0.68) 0.66 (0.62–0.71) <0.001 Table 1. The table summarizes the cumulative risk (%), risk ratio (RR) with 95% confidence intervals (CI), hazard ratio (HR) with 95% CI, and associated p-values for each neurologic outcome. RR and HR < 1.0 indicate reduced risk in the bariatric surgery cohort. All reported values were statistically significant, with 95% CIs excluding the null value of 1.0. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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07:43:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":776986,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7919688/v1/49475256-e980-4c56-8ca6-58d94f81f59f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bariatric Surgery and Neurologic Diagnoses in Adults With Obesity: A Retrospective Cohort Study","fulltext":[{"header":"Key Points","content":"\u003cp\u003e(1) Bariatric surgery was associated with lower recorded incidence of neurological disease.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(2) Findings are based on a large, retrospective cohort analysis.\u003c/p\u003e\n\u003cp\u003e(3) Results suggest potential gut\u0026ndash;brain and immunometabolic mechanisms warranting further study.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe gastrointestinal (GI) tract is the body\u0026rsquo;s largest interface with the external environment and hosts its most diverse microbial community, comprising over 2,000 bacterial species with genetic content that is approximately 150-fold that of the human genome [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This vast genetic diversity helps to regulate nutrient harvest, cholesterol metabolism, and even antimicrobial peptide production [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], underscoring their central role in host nutrition. Moreover, these commensal communities are also integral to host physiology: driving metabolite production [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], regulating mucosal immunity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and mediating signals with the central nervous system (CNS), along the gut\u0026ndash;brain axis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe brain\u0026ndash;gut axis is a bidirectional network that coordinates digestion, immune tone, and neurobehavioral states via signals between the CNS and GI tract. It accomplishes this through three primary routes: neuronal, endocrine, and immune pathways [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], with the vagus nerve serving as the principal conduit for both afferent and efferent signals [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Through these channels, gut microbes modulate receptor activity, shape neurotransmission, and influence metabolite entry to the brain [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Signaling within this axis is driven by neurotransmitters and metabolites. Key messengers, including serotonin, dopamine, noradrenaline, and γ-aminobutyric acid (GABA), are generated in the CNS and peripherally, by enteroendocrine cells responding to intestinal peptides as well as by the microbiota itself [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Together, these chemical cues regulate digestion, immune homeostasis, mood, and cognition. When this cross-talk is disrupted (dysbiosis), intestinal and blood\u0026ndash;brain barrier permeability can shift and neuroinflammation may ensue, linking microbial composition to GI, neurodegenerative, and psychiatric disease [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWithin this framework, obesity, a major global health challenge, consistently reshapes gut community structure and function, impairing gut\u0026ndash;brain signaling [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The resulting dysbiosis, marked by low-grade inflammation and metabolic derangement, contributes to cardiometabolic disease, including type 2 diabetes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These disturbances extend to microbially derived messengers such as short-chain fatty acids (SCFAs) and bile acids, which regulate blood\u0026ndash;brain barrier (BBB) integrity and influence neuroinflammation via receptors such as farnesoid X receptor and Takeda G protein-coupled receptor 5 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. When lifestyle and medical interventions fail, bariatric surgery is recommended to individuals with a BMI\u0026thinsp;\u0026ge;\u0026thinsp;40 or \u0026ge;\u0026thinsp;35 with serious obesity-related medical problems, as it can produce durable weight loss by resecting or bypassing segments of the foregut [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Two principal operations include Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG). RYGB creates a small gastric pouch anastomosed to the jejunum (bypassing the stomach and duodenum) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], whereas SG removes most of the greater-curvature stomach without intestinal bypass [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Beyond weight loss, both procedures have been shown to lead to various short- and long-term shifts in the microbiome [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Changes such as reduced adiposity, elevated circulating bile acids, altered gut hormones, and increased microbial tryptophan indoles [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] are observed. These effects ripple upstream to the brain, as microbiota-derived acetate can suppress hypothalamic Neuropeptide Y and Agouti-Related Peptide expression by dampening GABAergic neuroglial activity [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Consistent with this mechanism, RYGB has been linked to altered GABA signaling in extrahypothalamic circuits involved in memory and perception [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], directly implicating the gut\u0026ndash;brain axis in central circuit modulation.\u003c/p\u003e\u003cp\u003eConverging evidence indicates that microbiota profiles in Alzheimer\u0026rsquo;s disease (AD), Parkinson\u0026rsquo;s disease (PD), multiple sclerosis (MS), autism spectrum disorder (ASD), and epilepsy closely mirror the ecological shifts seen post-RYGB and SG procedures [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, evidence directly linking surgery-induced microbiota shifts to neurological disease incidence is limited. To address this gap, we used a demographically diverse patient cohort from the TriNetX Global Collaborative Network to conduct an unmatched retrospective cohort analysis to create comparable patient groups. We hypothesized that among adults with obesity, exposure to bariatric surgery (RYGB \u0026amp; SG) would be associated with altered incidence of major neurological diseases (AD, PD, MS, and epilepsy) relative to matched non-surgical controls.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eStudy Design and Data Source\u003c/h3\u003e\n\u003cp\u003eWe conducted a retrospective cohort study using the TriNetX Global Collaborative Network. This network comprises 161 healthcare organizations and provides harmonized electronic health record (EHR) data, including diagnoses, procedures, medications, and encounters. The analysis, performed on October 14, 2025, compared the incidence of neurological disease (AD, PD, MS, and epilepsy) between a Surgical cohort and a Non-Surgical cohort.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eCohort Selection\u003c/h2\u003e\u003cp\u003eEligible patients were adults aged 18 years or older with overweight/obesity (ICD-10 E66). The Surgical cohort required evidence of bariatric surgery exposure by diagnosis or procedure, including CPT:1007385 (Bariatric Surgery Procedures), CPT:1007386 (Laparoscopic Bariatric Surgery Procedures), or ICD-10 Z98.84 (Bariatric surgery status). The Non-Surgical cohort required an absence of any bariatric surgery codes. Patients with prior diagnoses of Alzheimer\u0026rsquo;s disease (G30), Parkinson\u0026rsquo;s disease (G20), multiple sclerosis (G35), or Epilepsy and recurrent seizures (G40) were excluded.\u003c/p\u003e\u003cp\u003eAt query definition, 391,758 patients met Bariatric Surgery criteria, and 11,461,147 met Non-Surgery criteria.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMatching and Covariate Adjustment\u003c/h3\u003e\n\u003cp\u003eTo reduce confounding, a 1:1 propensity score matching (PSM) was performed. The propensity model included demographics and clinical factors: age at index; sex (male/female/unknown); race (White [2106-3], Black or African American [2054-5], Asian [2028-9], American Indian or Alaska Native [1002-5], Native Hawaiian or Other Pacific Islander [2076-8], other race [2131-1], unknown); ethnicity (Hispanic or Latino [2135-2], Not Hispanic or Latino [2186-5], unknown); hypertensive diseases (ICD-10 I10\u0026ndash;I1A); diabetes mellitus (E08\u0026ndash;E13); mental, behavioral, and neurodevelopmental disorders (F01-F99); and disorders of lipoprotein metabolism \u0026amp; other lipidemias (E78). Post-match balance was evaluated using standardized differences.\u003c/p\u003e\n\u003ch3\u003eFollow-Up Periods and Outcomes\u003c/h3\u003e\n\u003cp\u003eThe index date was set as the earliest date a patient met the applicable cohort criteria. Outcomes were observed starting one day after the index date, with no end date specified. TriNetX restricted index events to those occurring within the previous 20 years. The primary neurological outcome comprised any incident diagnosis of Alzheimer\u0026rsquo;s disease (G30), Parkinson\u0026rsquo;s disease (G20), multiple sclerosis (G35), or epilepsy and recurrent seizures (G40).\u003c/p\u003e\u003cp\u003eTime-to-event was analyzed using Kaplan\u0026ndash;Meier curves with log-rank tests and Cox proportional hazards models to estimate hazard ratios (HRs) with 95% CIs.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eFor all outcomes, we estimated risk (cumulative proportion), risk difference (RD), and risk ratio (RR) with 95% confidence intervals (CIs). We analyzed time-to-event using Kaplan-Meier curves, with a log-rank test to compare survival distributions between cohorts. We also used Cox proportional hazards models to estimate hazard ratios (HRs) with 95% CIs. Proportional-hazards diagnostics were performed to confirm the model's assumptions. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003eAdditionally, we performed number-of-instances analyses for each neurological outcome. This involved calculating the mean number of outcome instances per patient during follow-up, excluding patients with zero instances from the summary statistics. Two-sample t-tests were used to compare these mean counts between the cohorts.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePrimary Outcome: Composite Neurologic Diagnosis\u003c/h2\u003e\u003cp\u003eAmong matched patients (n\u0026thinsp;=\u0026thinsp;388,400 per cohort), a recorded diagnosis of any primary neurologic disease (Alzheimer\u0026rsquo;s disease, Parkinson\u0026rsquo;s disease, multiple sclerosis, or epilepsy) occurred in 2,316 bariatric surgery patients (0.6%) versus 3,420 non-surgical patients (0.9%). The risk difference was \u0026minus;\u0026thinsp;0.3 percentage points (RD \u0026minus;\u0026thinsp;0.003; 95% CI, \u0026minus;\u0026thinsp;0.004 to \u0026minus;\u0026thinsp;0.002), the risk ratio was 0.68 (95% CI, 0.64\u0026ndash;0.71), and the hazard ratio from the Cox model was 0.71 (95% CI, 0.68\u0026ndash;0.75; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Kaplan\u0026ndash;Meier analysis demonstrated higher neurologic event\u0026ndash;free survival in the surgery cohort throughout follow-up; median time to event was not reached in either cohort, and the log-rank test was significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSecondary Outcomes: Alzheimer’s Disease (G30)\u003c/h3\u003e\n\u003cp\u003eAlzheimer\u0026rsquo;s disease occurred in 345/388,400 (0.09%) of surgical patients versus 538/388,400 (0.14%) of non-surgical patients.\u003c/p\u003e\u003cp\u003eThe RR was 0.64 (95% CI 0.56\u0026ndash;0.73), and the HR was 0.70 (95% CI 0.61\u0026ndash;0.80; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eMean diagnosis instances were 0.004 vs 0.007 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eParkinson\u0026rsquo;s Disease (G20)\u003c/p\u003e\u003cp\u003eParkinson\u0026rsquo;s disease was diagnosed in 408 (0.11%) vs 509 (0.13%) patients, yielding an RR of 0.80 (95% CI 0.70\u0026ndash;0.91) and HR 0.86 (95% CI 0.76\u0026ndash;0.98; p\u0026thinsp;=\u0026thinsp;0.023).\u003c/p\u003e\u003cp\u003eMean instances 0.007 vs 0.011 (p\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e\n\u003ch3\u003eSecondary Outcomes: Multiple Sclerosis (G35)\u003c/h3\u003e\n\u003cp\u003eMultiple sclerosis occurred in 287 (0.07%) vs 393 (0.10%) patients, with an RR 0.73 (95% CI 0.63\u0026ndash;0.85) and HR 0.76 (95% CI 0.66\u0026ndash;0.89; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eMean instances 0.008 vs 0.009 (p\u0026thinsp;=\u0026thinsp;0.39).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSecondary Outcomes: Epilepsy (G40)\u003c/h2\u003e\u003cp\u003eEpilepsy was diagnosed in 1,325 (0.34%) vs 2,086 (0.54%) patients.\u003c/p\u003e\u003cp\u003eThe RR 0.64 (95% CI 0.59\u0026ndash;0.68) and HR 0.66 (95% CI 0.62\u0026ndash;0.71; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) indicated a significant risk reduction.\u003c/p\u003e\u003cp\u003eMean instances 0.012 vs 0.021 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eThe incidence and relative risk of each neurologic outcome are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eNumber-of-Instances Analyses\u003c/h2\u003e\u003cp\u003eFor the composite outcome, the mean number of diagnosis instances per patient was 5.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 after surgery vs 5.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 without surgery; the difference was not statistically significant (t\u0026thinsp;=\u0026thinsp;1.05, p\u0026thinsp;=\u0026thinsp;0.30). For individual diseases, only Alzheimer\u0026rsquo;s disease and epilepsy showed statistically significant reductions in mean instance frequency.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSummary of Key Findings\u003c/h2\u003e\u003cp\u003eAcross matched cohorts, bariatric surgery was consistently associated with lower risk and delayed onset of neurologic disease. Relative risk reductions ranged from 20% to 36%, with the most significant effects seen in Alzheimer\u0026rsquo;s disease and epilepsy. Differences in diagnosis frequency were minimal, suggesting results were not influenced by healthcare utilization.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large, retrospective cohort of adults with obesity, exposure to bariatric surgery was associated with a roughly 32% lower relative risk and hazard of recorded diagnoses of AD, PD, MS, and epilepsy compared with matched non-surgical controls. Although absolute risk differences were minor, the consistency and direction of relative measures suggest a clinically meaningful signal that warrants closer interpretation.\u003c/p\u003e\u003cp\u003eThe resulting shift in gut ecology after bariatric surgery makes these findings notable. Mechanistically, lowered gastric acid, altered bile-acid delivery, and elevated oxygen content after surgery are recurrent drivers of change in commensal microbiota [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Research shows that, within weeks to months, bariatric surgery rapidly attenuates obesity-related systemic inflammation, significantly reducing pro-inflammatory cytokines (interleukin-6 ( IL-6) and tumor necrosis factor alpha (TNF-α)), C-reactive protein, and abdominal fat density [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In RYGB specifically, mouse models demonstrate reversal of hypothalamic toll-like receptor 4-linked neuroinflammation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and increases in the Akkermansia genus, which is associated with improved metabolism [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. By reinforcing barrier function, dampening microglial activation, and improving insulin signaling [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], these post-operative changes provide a mechanistic frame for the disease-specific associations we observed.\u003c/p\u003e\u003cp\u003eIn AD, extracellular amyloid-β (Aβ) deposition and tau protein hyperphosphorylation lead to neurofibrillary tangle formation, contributing to synaptic loss and eventual neuronal death [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In line with this pathophysiology, obesity-associated neuroinflammation, reactive oxygen species (ROS) generation, and central insulin resistance promote Aβ/tau pathology and impair amyloid clearance. Excessive free fatty acids (FFAs) and BBB vulnerability further propagate this loop [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Post-surgical reductions in inflammatory tone, FFAs, and improvements in insulin sensitivity plausibly attenuate these drivers. Notably, SCFAs can both maintain BBB integrity and promote Aβ accumulation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], underscoring that metabolite effects are not uniformly beneficial and may exert context-dependent effects.\u003c/p\u003e\u003cp\u003ePD, characterized by motor symptoms (rigidity, tremor, bradykinesia) and common non-motor features (depression, sleep disturbance, constipation), is a progressive neurodegenerative condition associated with the deposition of aggregated α-synuclein [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Obesity-linked inflammation and lipid dysregulation intersect with α-synuclein pathology and dopaminergic signaling. After RYGB, evidence suggests normalization of dopamine receptor availability consistent with restoration of reward responsiveness and potentially favorable effects on basal ganglia networks [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], providing a mechanistic bridge to the lower PD incidence signal. SG, on the other hand, has been associated with post-operative increases in tauroursodeoxycholic acid, a secondary bile acid that reduces motor deficits in PD mouse models [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, human post-operative bile-acid profiles remain heterogeneous, limiting firm mechanistic inferences [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMS is a chronic, immune-mediated disease of the CNS characterized by inflammatory demyelination and neurodegeneration [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. While the exact cause remains unclear, obesity has been linked to the development of MS through the modulation of pro- and anti-inflammatory adipokines [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Surgery-induced immunometabolic re-balancing may reduce this risk, and animal data suggest altered bile-acid metabolism is associated with reduced neuroinflammation [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Given conflicting post-operative bile-acid findings, mechanistic mediation in MS should be tested explicitly.\u003c/p\u003e\u003cp\u003eEpilepsy is a chronic neurological disorder characterized by the enduring predisposition to generate seizures [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Neuroinflammation is a common hallmark of the disease, implicating a mechanistic tie to immune-mediated gut\u0026ndash;brain pathways [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Key inflammatory markers (IL-6, TNF-α, and high-mobility group box 1) exacerbate neuroinflammation and lower seizure threshold [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Obesity-related dysbiosis can propagate this state by increasing intestinal permeability and promoting lipopolysaccharide (LPS) translocation, which fuels metabolic endotoxemia and central hyperexcitability [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In this context, bariatric surgery may shift risk via restoration of barrier integrity and attenuation of low-grade inflammation, changes that would be expected to raise seizure threshold [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In addition, fecal increases in 5-HT, glutamate, and GABA reported after surgery point to gut-centric neuromodulation; however, plasma neurotransmitter levels do not consistently mirror these shifts, suggesting a primarily local GI signal that communicates with the brain through immune and vagal pathways [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAcross conditions, surgery appears positioned to interrupt obesity-associated central insulin resistance, endotoxin-driven inflammation, and increased BBB permeability; through weight loss, inflammatory down-shifting, and microbial/metabolite re-patterning. That said, gut ecology is dynamic rather than fixed, and trajectories can diverge. Post-operative studies, in both RYGB and SG, exhibit procedure- and time-dependent enrichment of pro-inflammatory Proteobacteria and depletion of anti-inflammatory SCFA producers, patterns that may align with disease-specific risks [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This heterogeneity likely reflects variation in baseline microbiota and immune tone; procedure type and timing; diet/medication exposures; bile-acid pools/receptor signaling; and luminal vs mucosal sampling. Taken together, our findings fit a model in which bariatric surgery counteracts obesity-driven gut\u0026ndash;immune perturbations that track with neuropathology across AD, PD, MS, and epilepsy. We suspect the lower relative risks reflect the interplay between post-operative microbiome and host neuroimmune susceptibility; however, these findings do not fully account for the complexity of the system. Prospective, microbiome-integrated studies are needed to further test causality and identify modifiable risks.\u003c/p\u003e\u003cp\u003eClinical implications\u003c/p\u003e\u003cp\u003eThis study signals a lower recorded incidence of AD, PD, MS, and epilepsy among adults with obesity who undergo bariatric surgery. Although absolute risk differences were small, the relative measures were consistently lower, which merits attention in routine care.\u003c/p\u003e\u003cp\u003eClinicians should not interpret these findings as a directive to recommend metabolic surgery primarily as a neuroprotective therapy. Instead, results support more intentional counseling about potential neurologic implications within standard shared decision making that prioritizes weight-loss efficacy, cardiometabolic benefit, surgical risk, nutritional demands and patient values.\u003c/p\u003e\u003cp\u003ePractical steps include discussing the possibility that post-operative metabolic changes may influence long-term neurologic risk; incorporating baseline cognitive and neurologic screening and periodically thereafter for patients at elevated risk or with new symptoms; and monitoring maintenance of nutrition, supplementation, and medication to prevent deficiency-related neurologic complications. If a patient develops changes in cognitive, motor, sensory, or seizure symptoms, a timely referral to neurology or memory clinics is warranted, with perioperative planning coordinated among specialties.\u003c/p\u003e\u003cp\u003eHealth systems and bariatric programs can use these findings to build care pathways that integrate pre-operative counseling, post-operative neuro-focused surveillance, standardized laboratory and supplementation protocols, and pharmacy review for neuroactive regimens. Capturing procedure type, time since surgery, weight-loss trajectory, medication adjustments, and neurologic outcomes in routine documentation will improve interpretability for future real-world evaluations.\u003c/p\u003e\u003cp\u003eLimitations\u003c/p\u003e\u003cp\u003eThe study\u0026rsquo;s primary limitations stem from its observational design. Although we used matching and covariate adjustment to improve comparability between surgical and non-surgical cohorts, residual confounding, particularly by indication, illness severity, health-seeking behavior, and surgeon/center factors, which cannot be fully excluded. Exposure ascertainment relied on electronic health record (EHR) procedure coding for RYGB and SG; coding errors, misclassification between procedures, and incomplete capture of out-of-network care are all possible. We could not account for operative technique, perioperative complications, or adherence to post-operative care, all of which might influence long-term neurologic outcomes. Similarly, our outcome identification depended on diagnosis codes for AD, PD, MS, and epilepsy, which vary in specificity across sites and may be influenced by differential health care utilization and follow-up intensity between groups. Despite efforts to align index dates, the possibility of immortal time bias remains, and competing risks were not fully modeled and could affect hazard estimates. Because absolute risk differences were small relative to cohort size, effect estimates may be sensitive to modest amounts of exposure or outcome misclassification. Important covariates are incompletely captured in structured EHR data. We lacked consistent measures of education, socioeconomic status, family history, APOE genotype, physical activity, sleep disorders, and detailed substance use, as well as granular medication data for neuroactive and metabolic agents that could confound neurologic risk. Nutritional status after surgery was not uniformly available, precluding adjustment for deficiency-related neurologic complications.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this large, retrospective study of adults with obesity, exposure to bariatric surgery was associated with a significantly lower risk of a recorded diagnosis of AD, PD, MD, and epilepsy compared with matched non-surgical controls. This observation aligns with hypotheses that metabolic, inflammatory, and gut\u0026ndash;brain remodeling after surgery may mitigate neuroinflammatory pathways, yet it coexists with heterogeneous post-operative microbiome findings and the possibility of residual confounding in routine care. Our results highlight the challenges of causal inference in real-world data, where procedure selection, health-seeking behavior, and long prodromal periods may be intertwined. While our data are not sufficient to establish a causal link, they suggest that the relationship between metabolic surgery and neurologic risk in clinical practice is promising but more nuanced than a uniformly protective narrative. Future research should include large, prospective studies with standardized neurologic assessments; paired microbiome, metabolite, and nutrition measures; and methods to estimate direct and indirect effects, accounting for competing risks. Such work will be necessary to reconcile mixed mechanistic signals, identify which patient profiles derive the greatest neurologic benefit, and align perioperative care and secondary prevention strategies with underlying biology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of Generative AI and AI-assisted technologies in the writing process\u003c/h2\u003e\n\u003cp\u003eDuring the preparation of this work the author(s) used ChatGPT (OpenAI) in order to assist in tasks such as language refinement, organization of author-drafted content, and formatting assistance. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eIRB Statement\u003c/h2\u003e\n\u003cp\u003eThis study was determined to be exempt from Howard University Hospitals Institutional Review Board (IRB) review as it involved the retrospective analysis of de-identified patient data obtained from a national research network. No identifiable private information was collected or recorded, and there was no direct interaction with patients. As such, the research qualifies for exemption under 45 CFR 46.104(d)(4).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions (ICMJE) J.L. designed the study and drafted the Introduction, Abstract, Methods, Discussion, and References. C.D. performed statistical analyses and authored the Results section. R.E. and S.F.G. collected and validated TriNetX data. C.E. wrote the Clinical Implications subsection. N.M. authored the Limitations subsection. M.M. supervised the project, provided mentorship, and performed the final review. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData Availability Statement:The data underlying this study were obtained from the TriNetX Global Collaborative Network, a federated real-world research platform that aggregates de-identified electronic health record data from participating healthcare organizations. These data are not publicly available due to institutional and patient privacy restrictions. Access to the TriNetX platform is available to researchers through a data use agreement with TriNetX, LLC (https://trinetx.com). The aggregated data supporting the findings of this study can be reproduced by other researchers with access to the TriNetX Global Network using the same inclusion/exclusion criteria and analytic parameters described in this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlmeida A, Mitchell AL, Boland M, Forster SC, Gloor GB, Tarkowska A, et al. A New Genomic Blueprint of the Human Gut Microbiota. Nature. 2019 Feb 11;568(7753):499\u0026ndash;504. doi:10.1038/s41586-019-0965-1\u003c/li\u003e\n\u003cli\u003ePetrut SM, Bragaru AM, Munteanu AE, Moldovan AD, Moldovan CA, Rusu E. Gut over Mind: Exploring the Powerful Gut\u0026ndash;Brain Axis. Nutrients [Internet]. 2025 Feb 28;17(5):842\u0026ndash;2. Available from: https://www.mdpi.com/2072-6643/17/5/842 doi:10.3390/nu17050842\u003c/li\u003e\n\u003cli\u003eVernocchi P, Del Chierico F, Putignani L. Gut Microbiota Profiling: Metabolomics Based Approach to Unravel Compounds Affecting Human Health. 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JAMA. 2020 Sep 1;324(9):879. doi:10.1001/jama.2020.12567\u003c/li\u003e\n\u003cli\u003eLutz TA, Bueter M. The Physiology Underlying Roux-en-Y Gastric bypass: a Status Report. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology [Internet]. 2014 Dec 1;307(11):R1275\u0026ndash;91. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4315446/ doi:10.1152/ajpregu.00185.2014\u003c/li\u003e\n\u003cli\u003eChung AY, Thompson R, Overby DW, Duke MC, Farrell TM. Sleeve Gastrectomy: Surgical Tips. Journal of Laparoendoscopic \u0026amp; Advanced Surgical Techniques. 2018 Aug;28(8):930\u0026ndash;7. doi: 10.1089/lap.2018.0392\u003c/li\u003e\n\u003cli\u003eSeganfredo FB, Blume CA, Moehlecke M, Giongo A, Casagrande DS, Spolidoro JVN, et al. Weight-loss Interventions and Gut Microbiota Changes in Overweight and Obese patients: a Systematic Review. Obesity Reviews. 2017 May 19;18(8):832\u0026ndash;51. doi:10.1111/obr.12541\u003c/li\u003e\n\u003cli\u003eLiou AP, Paziuk M, Luevano JM ., Machineni S, Turnbaugh PJ, Kaplan LM. Conserved Shifts in the Gut Microbiota Due to Gastric Bypass Reduce Host Weight and Adiposity. Science Translational Medicine [Internet]. 2013 Mar 27;5(178):178ra41\u0026ndash;1. Available from: http://stm.sciencemag.org/content/5/178/178ra41.full doi:10.1126/scitranslmed.3005687\u003c/li\u003e\n\u003cli\u003eCornejo-Pareja I, Clemente-Postigo M, Tinahones FJ. Metabolic and Endocrine Consequences of Bariatric Surgery. Frontiers in Endocrinology. 2019 Sep 19;10(626). doi:10.3389/fendo.2019.00626\u003c/li\u003e\n\u003cli\u003eFrost G, Sleeth ML, Sahuri-Arisoylu M, Lizarbe B, Cerdan S, Brody L, et al. The short-chain Fatty Acid Acetate Reduces Appetite via a Central Homeostatic Mechanism. Nature communications [Internet]. 2014;5(3611):3611. Available from: https://www.ncbi.nlm.nih.gov/pubmed/24781306 doi:10.1038/ncomms4611\u003c/li\u003e\n\u003cli\u003ePatkar PP, Hao Z, Mumphrey MB, Townsend RL, Berthoud HR, Shin AC. Unlike Calorie restriction, Roux-en-Y Gastric Bypass Surgery Does Not Increase Hypothalamic AgRP and NPY in Mice on a high-fat Diet. International Journal of Obesity [Internet]. 2019 Feb 4 [cited 2025 Apr 21];43(11):2143\u0026ndash;50. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC6679822/ doi:10.1038/s41366-019-0328-x\u003c/li\u003e\n\u003cli\u003eAlmheiri RT, Hajjar B, Alkhaaldi SMI, Rabeh N, Aljoudi S, Abd-Elrahman KS, et al. Beyond Weight loss: Exploring the Neurological Ramifications of Altered Gut Microbiota post-bariatric Surgery. Journal of Translational Medicine. 2025 Feb 24;23(1). doi:10.1186/s12967-025-06201-2\u003c/li\u003e\n\u003cli\u003eTorriani M, Oliveira AL, Azevedo DC, Bredella MA, Yu EW. Effects of Roux-en-Y Gastric Bypass Surgery on Visceral and Subcutaneous Fat Density by Computed Tomography. Obesity Surgery. 2014 Nov 8;25(2):381\u0026ndash;5. doi:10.1007/s11695-014-1485-6 \u003c/li\u003e\n\u003cli\u003eChen J, Haase N, Sven-Bastiaan Haange, Sucher R, M\u0026uuml;nzker J, J\u0026auml;ger E, et al. Roux-en-Y Gastric Bypass Contributes to Weight loss-independent Improvement in Hypothalamic Inflammation and Leptin Sensitivity through gut-microglia-neuron-crosstalk. Molecular Metabolism. 2021 Jun 1;48(101214). doi:10.1016/j.molmet.2021.101214\u003c/li\u003e\n\u003cli\u003eYadav J, Liang T, Qin T, Nathan N, Katherine JP Schwenger, Pickel L, et al. Gut Microbiome Modified by Bariatric Surgery Improves Insulin Sensitivity and Correlates with Increased Brown Fat Activity and Energy Expenditure. Cell Reports of Medicine. 2023 May 1;4(5):101051\u0026ndash;1. doi:10.1016/j.xcrm.2023.101051\u003c/li\u003e\n\u003cli\u003eChakrabarti S, Chattopadhyay D. Unraveling the Connection: the Impact of Obesity on Neurological Diseases. Journal of Community Medicine and Public Health Reports. 2024 Jan 20;5(01). doi:10.38207/JCMPHR/2024/JAN05010315\u003c/li\u003e\n\u003cli\u003eJiang Jinglei, Tao YU, Qian Yulin, Meng W. Understanding the Role of Microglia in Alzheimer\u0026rsquo;s disease: Insights into mechanisms, acupuncture, and Potential Therapeutic targets. PubMed. 2025 Aug 1;45(4):922\u0026ndash;36. doi:10.19852/j.cnki.jtcm.20250327.002\u003c/li\u003e\n\u003cli\u003eTracey TJ, Steyn FJ, Wolvetang EJ, Ngo ST. Neuronal Lipid Metabolism: Multiple Pathways Driving Functional Outcomes in Health and Disease. Frontiers in Molecular Neuroscience. 2018 Jan 23;11(10). doi:10.3389/fnmol.2018.00010\u003c/li\u003e\n\u003cli\u003eVesga-Jim\u0026eacute;nez DJ, Martin C, Barreto GE, Aristiz\u0026aacute;bal-Pach\u0026oacute;n AF, Pinz\u0026oacute;n A, Gonz\u0026aacute;lez J. Fatty Acids: an Insight into the Pathogenesis of Neurodegenerative Diseases and Therapeutic Potential. International Journal of Molecular Sciences. 2022 Feb 25;23(5):2577. doi:10.3390/ijms23052577\u003c/li\u003e\n\u003cli\u003eSittipo P, Choi J, Lee S, Lee YK. The Function of Gut Microbiota in immune-related Neurological disorders: a Review. Journal of Neuroinflammation. 2022 Jun 15;19(1). doi:10.1186/s12974-022-02510-1\u003c/li\u003e\n\u003cli\u003eNegi S, Khurana N, Duggal N. The Misfolding Mystery: \u0026alpha;-syn and the Pathogenesis of Parkinson\u0026rsquo;s Disease. Neurochemistry International. 2024 Jul 1;177(105760). doi:10.1016/j.neuint.2024.105760\u003c/li\u003e\n\u003cli\u003eHamilton J, Swenson S, Hajnal A, Thanos PK. Roux-en-Y Gastric Bypass Surgery Normalizes Dopamine D1, D2, and DAT Levels. Synapse. 2018 Jul 28;72(10):e22058. doi:10.1002/syn.22058\u003c/li\u003e\n\u003cli\u003eRosa AI, Duarte-Silva S, Silva-Fernandes A, Nunes MJ, Carvalho AN, Rodrigues E, et al. Tauroursodeoxycholic Acid Improves Motor Symptoms in a Mouse Model of Parkinson\u0026rsquo;s Disease. Molecular Neurobiology. 2018 Apr 12;55(12):9139\u0026ndash;55. doi:10.1007/s12035-018-1062-4\u003c/li\u003e\n\u003cli\u003eKhalaf K, Tornese P, Cocco A, Albanese A. Tauroursodeoxycholic acid: a Potential Therapeutic Tool in Neurodegenerative Diseases. Translational Neurodegeneration. 2022 Jun 4;11(1). doi:10.1186/s40035-022-00307-z\u003c/li\u003e\n\u003cli\u003eSandi D, Fricska-Nagy Z, Bencsik K, V\u0026eacute;csei L. Neurodegeneration in Multiple Sclerosis: Symptoms of Silent Progression, Biomarkers and Neuroprotective Therapy\u0026mdash;Kynurenines Are Important Players. Molecules. 2021 Jun 5;26(11):3423. doi:10.3390/molecules26113423\u003c/li\u003e\n\u003cli\u003eBrigitta B. Pathophysiology of Depression. Dialogues in Clinical Neuroscience. 2002 Mar;4(1). doi:10.31887/DCNS.2002.4.1/bbondy\u003c/li\u003e\n\u003cli\u003eBhargava P, Smith MR, Mische L, Harrington E, Fitzgerald KC, Martin KE, et al. Bile Acid Metabolism Is Altered in Multiple Sclerosis and Supplementation Ameliorates Neuroinflammation. Journal of Clinical Investigation [Internet]. 2020 Jul 1 [cited 2023 Apr 21];130(7):3467\u0026ndash;82. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324171/ doi:10.1172/JCI129401\u003c/li\u003e\n\u003cli\u003eWHO. Epilepsy [Internet]. World Health Organization. 2019 [cited 2025 Oct 10]. Available from: https://www.who.int/news-room/fact-sheets/detail/epilepsy\u003c/li\u003e\n\u003cli\u003eVezzani A. Epilepsy and Inflammation in the Brain: Overview and Pathophysiology. Epilepsy Currents. 2014 Jan;14(2_suppl):3\u0026ndash;7. doi:10.5698/1535-7511-14.s2.3\u003c/li\u003e\n\u003cli\u003eShokr MM, Eladawy RM, Azar YO, Al Raish SM. Probiotics and the Gut\u0026ndash;Brain Axis: Emerging Therapeutic Strategies for Epilepsy and Depression Comorbidity. Foods. 2025 Aug 22;14(17):2926. doi:/10.3390/foods14172926\u003c/li\u003e\n\u003cli\u003eTuomi K, Logomarsino JV. Bacterial Lipopolysaccharide, Lipopolysaccharide-Binding Protein, and Other Inflammatory Markers in Obesity and after Bariatric Surgery. Metabolic Syndrome and Related Disorders. 2016 Aug;14(6):279\u0026ndash;88. doi:10.1089/met.2015.0170\u003c/li\u003e\n\u003cli\u003eRhea EM, Salameh TS, Logsdon AF, Hanson AJ, Erickson MA, Banks WA. Blood-Brain Barriers in Obesity. The AAPS Journal. 2017 Apr 10;19(4):921\u0026ndash;30. doi:10.1208/s12248-017-0079-3\u003c/li\u003e\n\u003cli\u003eSalas-Venegas V, Flores-Torres RP, Rodr\u0026iacute;guez-Cort\u0026eacute;s YM, Rodr\u0026iacute;guez-Retana D, Ram\u0026iacute;rez-Carreto RJ, Concepci\u0026oacute;n-Carrillo LE, et al. The Obese Brain: Mechanisms of Systemic and Local Inflammation, and Interventions to Reverse the Cognitive Deficit. Frontiers in Integrative Neuroscience. 2022 Mar 29;16(798995). doi:10.3389/fnint.2022.798995\u003c/li\u003e\n\u003cli\u003ePrudencio AP, Fonseca DC, C. Cardinelli, Machado NM, Ferreira B, Sala P, et al. Increased Fecal Serotonin after Gastric Bypass Is Unrelated to Tryptophan Intake. Clinical Nutrition ESPEN. 2023 Mar 22;54(614). doi:10.1016/j.clnesp.2022.09.458\u003c/li\u003e\n\u003cli\u003ePark S, Wu X. Modulation of the Gut Microbiota in Memory Impairment and Alzheimer\u0026rsquo;s Disease via the Inhibition of the Parasympathetic Nervous System. International Journal of Molecular Sciences. 2022 Nov 5;23(21):13574\u0026ndash;4. doi:10.3390/ijms232113574\u003c/li\u003e\n\u003cli\u003eCui G, Liu S, Liu Z, Chen Y, Wu T, Lou J, et al. Gut Microbiome Distinguishes Patients with Epilepsy from Healthy Individuals. Frontiers in Microbiology. 2022 Jan 7;12(696632). doi:10.3389/fmicb.2021.696632\u003c/li\u003e\n\u003cli\u003eFock E, Parnova R. Mechanisms of Blood\u0026ndash;Brain Barrier Protection by Microbiota-Derived Short-Chain Fatty Acids. Cells [Internet]. 2023 Feb 18;12(4):657. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9954192/ doi:10.3390/cells12040657\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Neurologic Outcomes After Bariatric Surgery vs Matched Non-Surgical Controls\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk % (Surgery)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk % (No Surgery)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk Ratio (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard Ratio (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComposite (AD, PD, MS, Epilepsy)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.68 (0.64\u0026ndash;0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.71 (0.68\u0026ndash;0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlzheimer\u0026rsquo;s disease (G30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.64 (0.56\u0026ndash;0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.70 (0.61\u0026ndash;0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParkinson\u0026rsquo;s disease (G20)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.80 (0.70\u0026ndash;0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.86 (0.76\u0026ndash;0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultiple sclerosis (G35)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.73 (0.63\u0026ndash;0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.76 (0.66\u0026ndash;0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEpilepsy (G40)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.64 (0.59\u0026ndash;0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e0.66 (0.62\u0026ndash;0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e \u003cem\u003eThe table summarizes the cumulative risk (%), risk ratio (RR) with 95% confidence intervals (CI), hazard ratio (HR) with 95% CI, and associated p-values for each neurologic outcome. RR and HR \u0026lt; 1.0 indicate reduced risk in the bariatric surgery cohort. All reported values were statistically significant, with 95% CIs excluding the null value of 1.0.\u003c/em\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7919688/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7919688/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aims:\u003c/h2\u003e\u003cp\u003eObesity, the gut\u0026ndash;brain axis, and bariatric surgery may influence neurologic disease risk; however, population-level data are limited. This study compared the incidence of Alzheimer\u0026rsquo;s disease, Parkinson\u0026rsquo;s disease, multiple sclerosis, and epilepsy in adults with obesity who underwent bariatric surgery versus those who did not.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis was a retrospective cohort study using the TriNetX Global Collaborative Network. Adults with overweight/obesity (ICD-10 E66) were assigned to Surgical (RYGB or SG) or Non-Surgical cohorts. 1:1 propensity score matching was performed on demographic and clinical covariates. Risk ratios, risk difference, and hazard ratios were estimated for incident diagnoses (ICD-10: AD G30; PD G20; MS G35; epilepsy G40).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn matched cohorts (n\u0026thinsp;=\u0026thinsp;776,800; 388,400 per group), bariatric surgery was associated with a lower incidence of neurologic disease (0.6% vs 0.9%; RR 0.68, 95% CI 0.64\u0026ndash;0.71; HR 0.71, 95% CI 0.68\u0026ndash;0.75; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Risk reductions were also observed for Alzheimer\u0026rsquo;s disease (RR 0.64; HR 0.70), Parkinson\u0026rsquo;s disease (RR 0.80; HR 0.86), multiple sclerosis (RR 0.73; HR 0.76), and epilepsy (RR 0.64; HR 0.66). Relative risk reductions ranged from 20% to 36%, greatest for Alzheimer\u0026rsquo;s disease and epilepsy.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eAmong adults with obesity, bariatric surgery was associated with a lower recorded incidence of AD, PD, MS, and epilepsy. While these findings align with biologically plausible immunometabolic and gut\u0026ndash;brain mechanisms, residual confoundings limit causal inference. Future research is needed to confirm causality.\u003c/p\u003e","manuscriptTitle":"Bariatric Surgery and Neurologic Diagnoses in Adults With Obesity: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 07:59:22","doi":"10.21203/rs.3.rs-7919688/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7087453e-5b0e-426a-ab92-43edd94752c4","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-11T22:23:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-19 07:59:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7919688","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7919688","identity":"rs-7919688","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}