Incident depression in people with comorbid type 2 diabetes and chronic gastritis/duodenitis: a large-scale cohort study

Incident depression in people with comorbid type 2 diabetes and chronic gastritis/duodenitis: a large-scale 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 Article Incident depression in people with comorbid type 2 diabetes and chronic gastritis/duodenitis: a large-scale cohort study Ying Yu, Bo Hu, Yu-Ling Cui, Yan-Yu Li, Xinwen Yu, Hao Xie, Lijuan Du, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8141277/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 Objectives Type 2 diabetes (T2D) and chronic gastritis/duodenitis (CGD) are both strongly associated with the onset of depression. However, the impact of T2D-CGD comorbidity on incident depression remains unclear. Methods This prospective cohort study utilized data from 387 149 participants in the UK Biobank to examine the relationship between T2D-CGD comorbidity and incident depression. Results Patients with T2D exhibited a significantly higher likelihood of developing CGD than those without T2D (odds ratio = 2.10, 95% CI = [1.97, 2.24]). Both T2D and CGD were independently associated with an increased risk of incident depression, with their comorbidity demonstrating the strongest associations (adjusted hazard ratio = 2.29, 95% CI = [1.84, 2.85]). Notably, the comorbidity was linked to an elevated risk of depression within 15 years of disease onset. White matter hyperintensity, particularly near the cerebral ventricles, partially mediated the relationship between T2D-CGD comorbidity and incident depression. Conclusions Integrated screening and long-term monitoring strategies should be prioritized for population with the comorbidity of T2D and CGD, as it significantly elevates the risk of incident depression. White matter hyperintensity can serve as an imaging biomarker for detecting the risk of depression in patients with T2D-CGD comorbidity. Health sciences/Diseases/Endocrine system and metabolic diseases/Diabetes/Diabetes complications Biological sciences/Neuroscience/Diseases of the nervous system clinical trial cohort study database research diabetes complications Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Major depression has emerged as a clinically significant comorbidity strongly linked to type 2 diabetes (T2D). Extensive evidence demonstrates that patients with T2D face a 1.5 to 2.0-fold increased risk of developing depression compared to non-diabetic individuals[ 1 , 2 ]. Moreover, both depression and subthreshold depressive symptoms have been consistently associated with higher risks of dementia onset and all-cause mortality[ 3 , 4 ]. Chronic gastritis is a persistent gastric mucosal inflammation caused by H. pylori infection, bile reflux, drugs, or alcohol, leading to atrophy/metaplasia and potential dysplasia. Due to shared etiology, clinical features, therapeutic approaches, and anatomical continuity between gastritis and duodenitis, these two conditions are often considered collectively in clinical practice and referred to as Chronic gastritis or duodenitis (CGD)[ 5 ]. CGD is prevalent in T2D primarily due to high H. pylori susceptibility in T2D patients[ 6 – 8 ], which worsens glycemic control and insulin resistance in reverse[ 9 , 10 ]. Emerging evidence also links CGD to depression via gut-brain axis dysfunction[ 11 , 12 ], suggesting a close relationship between CGD and depressive disorders. Despite these plausible connections, the interplay between T2D-CGD comorbidity and depression incidence remains understudied, highlighting a critical gap in current research. This study utilized the UK Biobank database to conduct a retrospective cohort analysis, investigating the association between T2D-CGD comorbidity and incident depression. Building on prior research that suggests the relationship between T2D, CGD, and depression risk may be mediated by microvascular dysfunction, neurodegenerative pathology, and neuroinflammation[ 1 , 13 , 14 ]—typically observed as white matter hyperintensities (WMH) on brain MRI—this study further explored the potential mediating role of WMH in the connection between T2D-CGD comorbidity and depression. 2. Materials and Methods 2.1 Study design, participants inclusion, and groups This study utilized data from the UK Biobank, a large-scale, ongoing prospective cohort study encompassing over 500 000 participants in the United Kingdom. Between 2006 and 2010, all participants underwent comprehensive baseline assessments[ 15 ]. Written informed consent was obtained from all participants, and ethical approval was granted by the North West Multicenter Research Ethics Committee. As the data were fully anonymized, no additional institutional review was required for this analysis. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. This prospective cohort study included 502 128 subjects. To minimize biases related to comorbid conditions, 14 069 participants with dementia, schizophrenia, bipolar disorder, brain tumors, stroke, type 1 diabetes, malnutrition-related diabetes, gestational diabetes, and acute gastritis were excluded. To ensure temporal precedence and reduce confounding, 100 910 individuals with baseline depression or those who developed T2D or CGD during the follow-up period were excluded. Finally, 387 149 participants were included in the final analysis (Fig. 1 ) and were divided into four cohorts: (1) Patients without T2D or CGD (Control cohort); (2) Patients with T2D alone (T2D cohort); (3) Patients with CGD alone (CGD cohort); (4) Patients with T2D-CGD comorbidity (COM cohort). 2.2 Definition of T2D, CGD, and depression The UK Biobank captures disease onset dates through multiple linked data sources, including hospital episode statistics, primary care records, death register linkage, and others. Patients with T2D were identified using four methods: 1) ICD-10 codes E11 (non-insulin-dependent diabetes mellitus); 2) blood glucose ≥ 11.1 mmol/L at baseline; 3) HbA1c ≥ 6.5% (48 mmol/mol) at baseline; and 4) self-reported T2D diagnosis and dates. Patients with CGD were identified based on ICD-10 codes K29 (gastritis and duodenitis), excluding K29.0 (acute hemorrhagic gastritis) and K29.1 (other acute gastritis). Incident depression cases were identified using ICD-10 codes F32 (depressive episode) and F33 (recurrent depressive disorder). 2.3 Potential risk factors A total of 18 potential risk factors were identified: 1) five disease-related factors, including T2D status, CGD status, comorbidity of both, T2D duration, and CGD duration; 2) three demographic factors, including age, sex, and body mass index (BMI); 3) two environmental factors, including the Townsend deprivation index (TDI) and urbanization level ( Supplementary material 1 ); and 4) eight modifiable lifestyle factors, including sleep duration, fruit and vegetable intake, meat intake, bread and cereal intake, tea intake, coffee intake, smoking frequency, and alcohol consumption frequency ( Supplementary material 2 ). 2.4 Primary outcomes The primary outcome of this study was incident depression. Participants who did not reach either the primary outcome were right-censored at the date of March 31, 2025, which marked the latest update from the UK Biobank. 2.5 Imaging data T2-weighted fluid-attenuated inversion recovery (FLAIR) data were acquired using a Siemens Skyra 3T running scanner with a standard Siemens 32-channel RF receive head coil. Two indicators of WMH were used: 1) Peri-ventricular WMH (pvWMH), which are located within 10 mm from the lateral ventricle wall, primarily involving the radiations of the corpus callosum and the medial part of the centrum semiovale; 2) Deep WMH (deepWMH), situated more than 10 mm away from the ventricle in deep white matter regions, typically in the frontal, parietal, and temporal lobes, as well as the corona radiata above the basal ganglia. 2.6 Statistical analysis All statistical analyses were performed using in-house codes on the MATLAB R2018a platform (MathWorks, Natick, MA). Baseline demographic characteristics were summarized as percentages for categorical variables and means with standard deviations (SDs) for continuous variables. Participants with missing data were excluded from corresponding analysis. The odds ratio (OR) was calculated to quantify the association between T2D and CGD. Kaplan-Meier (K-M) survival curves were used to estimate cumulative incidence of depression across groups. Multivariable Cox regression analyses assessed the risks of incident depression linked to the comorbidity of T2D and CGD, compared to the Control cohort. Five models adjusted for different confounders were used: model 1 (unadjusted), model 2 (T2D and CGD status and durations), model 3 (model 2 plus age, sex, and BMI), model 4 (model 3 plus TDI and urbanization level), and model 5 (model 4 plus eight modifiable lifestyle factors). The Fine and Gray method accounted for competing risks of death. Hazard ratios (HRs) with 95% confidence intervals (CIs) were reported to assess statistical significance. The proportional hazard assumption was tested using Schoenfeld residuals [ 16 ], which were satisfied. Relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (SI) [ 17 ] were used to determine whether the combined effect of T2D and CGD on the risk of incident depression exceeded the simple additive effects of the individual conditions ( supplementary material 3 ). The bootstrap method with 10 000 resamples was employed to estimate 95% CIs for these three indicators. To further explore underlying mechanisms, cerebral WMH was used for mediation analysis in the relationship between T2D-CGD comorbidity and incident depression, adjusting for age, sex, and BMI as covariates. The indirect effect (mediation effect) was calculated, and the bootstrap method with 10 000 resamples was applied to estimate 95% CIs. 3. Results 3.1 Demographic information and the association between T2D and CGD The demographic information is summarized in Table 1 . A significant positive association was found between T2D and CGD, with an OR of 2.10 (95% CI: [1.97, 2.24]), indicating that individuals with T2D had 2.1 times the odds of developing CGD compared to those without T2D. Table 1 Demographic, clinical, and lifestyle information. Control cohort N = 358 501 T2D cohort N = 11 876 CGD cohort N = 15 679 COM cohort N = 1 093 Age (years) 55.93 ± 8.16 58.85 ± 7.48 58.73 ± 7.52 61.55 ± 6.27 Female Sex 197 804 (55.2%) 4 612 (38.8%) 8 243 (52.6%) 398 (36.4%) BMI (kg/m 2 ) 26.87 ± 4.40 30.96 ± 5.86 27.56 ± 4.57 31.25 ± 5.69 Depression Cases 13 607 (3.8%) 621 (5.2%) 1 141 (7.3%) 107 (9.8%) Death Cases 29 834 (8.3%) 2 471 (20.8%) 2 126 (13.6%) 393 (36.0%) T2D duration (years) — 5.70 ± 7.59 — 5.43 ± 5.52 CGD duration (years) — — 6.61 ± 5.59 6.43 ± 5.53 Townsend Deprivation Index -1.49 ± 2.98 -0.64 ± 3.36 -0.98 ± 3.19 0.12 ± 3.48 Urbanization Level Very Low 7 822 (2.2%) 189 (1.6%) 211 (1.3%) 5 (0.4%) Low 18 908 (5.3%) 457 (3.8%) 682 (4.3%) 22 (2.0%) Moderate 24 587 (6.9%) 682 (5.7%) 1,050 (6.7%) 72 (6.6%) High 307 184 (85.7%) 10 548 (88.8%) 13 736 (87.6%) 994 (90.9%) Sleep Duration (hours/day) 7.15 ± 1.02 7.18 ± 1.27 7.10 ± 1.22 7.19 ± 1.50 Smoking Frequency Level 0.16 ± 0.52 0.18 ± 0.54 0.23 ± 0.61 0.21 ± 0.59 Alcohol Drinking Frequency Level 3.16 ± 1.48 2.54 ± 1.64 2.87 ± 1.59 2.15 ± 1.68 Coffee Intake (cups/day) 2.01 ± 2.02 2.03 ± 2.16 1.89 ± 2.09 1.91 ± 2.11 Tea Intake (cups/day) 3.37 ± 2.79 3.28 ± 3.14 3.67 ± 3.06 3.47 ± 3.09 Vegetable and Fruit Intake (Normalized value) 0.15 ± 0.09 0.16 ± 0.09 0.15 ± 0.09 0.16 ± 0.10 Meat Intake (Normalized value) 2.24 ± 0.65 2.33 ± 0.69 2.27 ± 0.64 2.30 ± 0.68 Bread and Cereal Intake (Normalized value) 0.12 ± 0.06 0.13 ± 0.06 0.12 ± 0.06 0.13 ± 0.06 T2D: Type 2 diabetes; CGD: Chronic gastritis or duodenitis; COM: Patients with T2D-CGD comorbidity; BMI: Body mass index; TDI: Townsend deprivation index. 3.2 K-M analysis and multivariable Cox regression analysis of incident depression The K-M survival curves in Fig. 2 illustrate that COM group exhibited the highest incidence of depression, followed by the CGD group, T2D group, and Control group (log-rank test P < 0.0001). Multivariable Cox regression analysis revealed that T2D-CGD comorbidity was significantly associated with a higher risk of incident depression across five models: model 1 (univariable; HR = 2.56, 95% CI = [2.12, 3.09]), model 2 (adjusted for T2D and CGD status and duration; aHR = 2.90, 95% CI = [2.37, 3.58]), model 3 (model 2 plus age, sex, and BMI; aHR = 2.76, 95% CI = [2.24, 3.41]), model 4 (model 3 plus TDI and urbanization level; aHR = 2.48, 95% CI = [2.00, 3.06]), and model 5 (model 4 plus eight modifiable lifestyle factors; aHR = 2.29, 95% CI = [1.84, 2.85]). All aHRs and 95% CIs for model 5 are presented in Fig. 3 ( Supplementary Fig. 1 for other models). The Fine and Gray method was applied for competing risk analysis to account for bias from competing risks of death, resulting in an aHR for model 5 of 1.95 (95% CI = [1.48, 2.56]). 3.3 Analysis of synergistic effect The synergistic effect of T2D and CGD on depression risk did not exceed the sum of their individual effects (RERI = 0.38, 95% CI = [-0.20, 1.04]; AP = 0.13, 95% CI = [-0.09, 0.30]; SI = 1.26, 95% CI = [0.86, 1.72]). This result suggests that the combined effect of T2D and CGD on incident depression did not exceed the sum of their individual effects. 3.4 Stratified analysis by follow-up duration​​ When the follow-up time was stratified as 10 years, the adjusted HRs (aHRs) and 95% CIs for the association between T2D-CGD comorbidity and depression (model 5) were as follows: aHR = 2.34 (95% CI = [1.57, 3.50]), aHR = 2.54 (95% CI = [1.83, 3.53]), and aHR = 1.22 (95% CI = [0.80, 1.86]) for each time interval, respectively ( Supplementary Fig. 2 ). In summary, T2D-CGD comorbidity was linked to the risk of depression within 15 years following disease onset (with baseline disease durations of approximately 5 years). 3.5 Stratified analysis by sex​​ When we manually divided all participants into two group according to sex, the aHRs and 95% CIs for the association between T2D-CGD comorbidity and depression (model 5) were aHR = 2.26 (95% CI = [1.64–3.13]) in female group, and aHR = 2.29 (95% CI = [1.71–3.08]) in male group ( Supplementary Fig. 3 ). In brief, the risk of T2D-CGD comorbidity for depression was similar between female and male groups. 3.6 Mediation analysis Mediation analyses were conducted to explore whether cerebral WMH volume mediated the association between T2D-CGD comorbidity and incident depression. A total of 54 060 participants who underwent MRI scans were included in this analysis ( Supplementary material 4 ). The mediation analysis revealed significant indirect effects via both WMH subtypes: pvWMH showed an indirect effect of 1.9% (95% CI: 0.8%–3.2%), while deepWMH demonstrated an indirect effect of 0.7% (95% CI: 0.1%–1.4%), indicating that the association between T2D-CGD comorbidity and incident depression was partially mediated by WMH, particularly pvWMH (Fig. 4 ). 3.7 Sensitive analysis To assess the robustness of these findings, this study tested a scenario where only diagnostic data (ICD-10 codes) were used to define T2D, excluding participants based on other criteria (blood biochemical data and self-reported diagnosis). This analysis included 9 101 participants (76.6%) and yielded results consistent with the original findings ( supplementary Fig. 4 ). Additionally, depression was defined to include both depressive episodes and recurrent depressive disorders. By including only participants with depressive episodes (15 474 participants, 98.7%) and excluding others, the results remained consistent with the original findings ( supplementary Fig. 5 ). 4. Discussion Few studies have examined the comorbidity of T2D and CGD, with no research on its impact on depression incidence. This cohort study found that patients with T2D are more likely to develop CGD compared to those without T2D. The T2D-CGD comorbidity was identified as a risk factor for incident depression. The T2D-CGD comorbidity was associated with an increased risk of depression within 15 years of disease onset. Mediation analysis revealed that WMH, particularly those near the cerebral ventricle, partially mediated the association between T2D-CGD comorbidity and incident depression. 4.1 The relationship between T2D and CGD Although the relationship between T2D and CGD remains underexplored, substantial evidence links T2D to H. pylori infection[ 6 – 8 , 10 , 18 – 20 ]. Most studies report OR or relative risks (RR) ranging from 1.08 to 2.0 for H. pylori infection in patients with T2D, although some findings contradict this association[ 21 ] or even suggest a protective effect of H. pylori against T2D[ 22 ]. These inconsistencies may result from population heterogeneity or unresolved causal mechanisms. While H. pylori -induced insulin resistance, chronic inflammation, and metabolic syndrome are widely considered drivers of T2D[ 20 , 23 , 24 ], other studies indicate that hyperglycemia in patients with T2D may impair H. pylori eradication efficacy[ 25 ]. Furthermore, dietary irregularities associated with T2D could also exacerbate CGD incidence. These findings suggest a bidirectional, multifactorial interaction between T2D and CGD rather than a unidirectional causal link. 4.2 The relationship between T2D and depression The association between T2D and depression has been well established[ 26 – 29 ], with shared pathogenic mechanisms including genetic predisposition, insulin resistance, microvascular damage, chronic inflammation, and gut-brain axis dysregulation[ 13 , 30 – 33 ]. Notably, while CGD is more strongly associated with depression than T2D in this study, research on CGD-related depression remains limited. Most existing studies focus on gut-brain interaction disorders[ 11 , 12 , 34 ], which only partially overlap with CGD and require distinct therapeutic approaches. Importantly, CGD and H. pylori infection are distinct yet overlapping conditions—not all patients with CGD are H. pylori -positive, nor do all H. pylori carriers develop CGD. This distinction underscores the critical need for direct investigation into T2D-CGD comorbidity to guide evidence-based clinical management strategies. 4.3 The relationship between T2D, depression, and WMH The association between WMH and depression observed in our study is consistent with established neuroimaging evidence[ 35 – 37 ]. Diffusion MRI studies consistently demonstrate a correlation between depression and impaired white matter microstructural integrity[ 38 , 39 ]. Our findings specifically highlight the mediating role of pvWMH in the relationship between T2D-CGD comorbidity and depression, supporting the results of previous studies[ 37 , 40 ]. Mechanistically, this study proposes that T2D-CGD comorbidity contributes to depression via cerebrovascular dysfunction characterized by: (1) endothelial cell damage impairing the blood-brain barrier, and (2) venous outflow obstruction resulting in interstitial fluid accumulation and periventricular edema[ 41 – 43 ]. Notably, this impairment in cerebrospinal fluid-interstitial fluid exchange shares pathophysiological features with Alzheimer's disease, suggesting potential overlapping neurodegenerative mechanisms in these comorbid conditions[ 44 ]. Several limitations must be considered. As noted in the discussion section, the causal relationships between T2D, CGD, and depression remain complex and unresolved in this observational study. Further mechanistic research is required to clarify these relationships. Additionally, some factors may not have been accounted for, potentially confounding or driving the associations observed. For example, the regularity of dietary patterns and medication use over extended periods cannot be analyzed in the current sample. Selection bias may also be a concern in the UK Biobank, primarily due to its reliance on hospital data, primary care records, and death registration. Thus, patients who were ill but did not seek medical treatment may have been missed, potentially underestimating the effect size of the exposure association or delaying diagnosis. In conclusion, integrated screening and long-term monitoring strategies should be prioritized for population with the comorbidity of T2D and CGD, as it significantly elevates the risk of incident depression. White matter hyperintensity can serve as an imaging biomarker for detecting the risk of depression in patients with T2D-CGD comorbidity. Declarations Author Contributions Statement B.H., Y.Y.L., and H.X. were involved in the conception, design, and conduct of the study and the analysis and interpretation of the results. B.H. wrote the first draft of the manuscript, and all authors edited, reviewed, and approved the final version of the manuscript. B.G., G.B.C and Y.Y. had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. X.W.Y., A.L.Y., Y.X.J., and S.R.L. provided clinical expertise and validation. L.J.D. and X.Y.B. contributed to data verification and visualization. M.H.N. and L.F.Y. performed validation analysis. Data availability statement: All data are available in UK Biobank. Ethics Approval Statement: Ethical approval was granted by the North West Multicenter Research Ethics Committee. As the data were fully anonymized, no additional institutional review was required for this analysis. Patient Consent Statement: Written informed consent was obtained from all individuals. Prior Presentation: This study has been posted on medRxiv: https://doi.org/10.1101/2025.08.17.25333765 Funding statement BH has received funding from the National Natural Science Foundation of China [grant number 82302148]. YY has received funding from the Key Science and Technology Program of Shaanxi Province [grant number 2023-YBSF-331] and Fourth Military Medical University [grant number 2023XC045]. GBC has received funding from the National Natural Science Foundation of China [grant number 82471936]. Conflict of interest disclosure All authors declare no competing interests. References van Sloten TT, Sedaghat S, Carnethon MR, Launer LJ, Stehouwer CDA (2020) Cerebral microvascular complications of type 2 diabetes: stroke, cognitive dysfunction, and depression. lancet Diabetes Endocrinol 8:325–336. https://doi.org/10.1016/s2213-8587(19)30405-x Nouwen A, Winkley K, Twisk J, Lloyd CE, Peyrot M, Ismail K et al (2010) Type 2 diabetes mellitus as a risk factor for the onset of depression: a systematic review and meta-analysis. Diabetologia 53:2480–2486. https://doi.org/10.1007/s00125-010-1874-x Katon W, Pedersen HS, Ribe AR, Fenger-Grøn M, Davydow D, Waldorff FB et al (2015) Effect of depression and diabetes mellitus on the risk for dementia: a national population-based cohort study. JAMA psychiatry 72:612–619. https://doi.org/10.1001/jamapsychiatry.2015.0082 Lo DF, Zia H, Zia T, Visaria A (2022) Investigating mortality in the association between depression and diabetes. Diabetologia 65:2172–2173. https://doi.org/10.1007/s00125-022-05807-1 Organization WH (2010) ICD-10. International Statistical Classification of Diseases and Related Health Problems. Acta Chir Iugosl 56:65 Zhou X, Zhang C, Wu J, Zhang G (2013) Association between Helicobacter pylori infection and diabetes mellitus: a meta-analysis of observational studies. Diabetes Res Clin Pract 99:200–208. https://doi.org/10.1016/j.diabres.2012.11.012 Kountouras J, Polyzos SA, Doulberis M, Zeglinas C, Artemaki F, Vardaka E et al (2018) Potential impact of Helicobacter pylori-related metabolic syndrome on upper and lower gastrointestinal tract oncogenesis. Metabolism 87:18–24. https://doi.org/10.1016/j.metabol.2018.06.008 Han X, Li Y, Wang J, Liu B, Hu H, Li X et al (2016) Helicobacter pylori infection is associated with type 2 diabetes among a middle- and old-age Chinese population. Diab/Metab Res Rev 32:95–101. https://doi.org/10.1002/dmrr.2677 Tsay FW, Hsu PI (2018) H. pylori infection and extra-gastroduodenal diseases. J Biomed Sci 25:65. https://doi.org/10.1186/s12929-018-0469-6 Sahoo OS, Mitra R, Bhattacharjee A, Kar S, Mukherjee O (2023) Is Diabetes Mellitus a Predisposing Factor for Helicobacter pylori Infections? Current diabetes reports. 23:195–205. https://doi.org/10.1007/s11892-023-01511-5 Fairlie T, Shah A, Talley NJ, Chey WD, Koloski N, Yeh Lee Y et al (2023) Overlap of disorders of gut-brain interaction: a systematic review and meta-analysis. lancet Gastroenterol Hepatol 8:646–659. https://doi.org/10.1016/s2468-1253(23)00102-4 Josefsson A, Hreinsson JP, Simrén M, Tack J, Bangdiwala SI, Sperber AD et al (2022) Global Prevalence and Impact of Rumination Syndrome. Gastroenterology 162 731 – 42.e9. https://doi.org/10.1053/j.gastro.2021.11.008 Steens ILM, Schram MT, Houben A, Berendschot T, Jansen JFA, Backes WH et al (2025) Type 2 diabetes and depression via microvascular dysfunction, neurodegeneration, inflammation, advanced glycation end products (AGEs), and arterial stiffness. Diabetes Obes Metab 27:4847–4858. https://doi.org/10.1111/dom.16527 Geraets AFJ, van Agtmaal MJM, Stehouwer CDA, Sörensen BM, Berendschot T, Webers CAB et al (2020) Association of Markers of Microvascular Dysfunction With Prevalent and Incident Depressive Symptoms: The Maastricht Study. Hypertension (Dallas, Tex: 1979). 76:342–349. https://doi.org/10.1161/hypertensionaha.120.15260 Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J et al (2015) UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12:e1001779. https://doi.org/10.1371/journal.pmed.1001779 David S (1982) Partial residuals for the proportional hazards regression model. Biometrika. :239 – 41 Knol MJ, VanderWeele TJ, Groenwold RH, Klungel OH, Rovers MM, Grobbee DE (2011) Estimating measures of interaction on an additive scale for preventive exposures. Eur J Epidemiol 26:433–438. https://doi.org/10.1007/s10654-011-9554-9 Shi Y, Duan JY, Liu DW, Qiao YJ, Han QX, Pan SK et al (2018) Helicobacter pylori Infection is Associated with Occurrence of Proteinuria in Type 2 Diabetes Patients: A Systemic Review and Meta-Analysis. Chin Med J 131:2734–2740. https://doi.org/10.4103/0366-6999.245269 Mansori K, Dehghanbanadaki H, Naderpour S, Rashti R, Moghaddam AB, Moradi Y (2020) A systematic review and meta-analysis of the prevalence of Helicobacter pylori in patients with diabetes. Diabetes Metab Syndr 14:601–607. https://doi.org/10.1016/j.dsx.2020.05.009 Li J, Yuan W, Liu J, Yang B, Xu X, Ren X et al (2024) Association between Helicobacter pylori infection and type 2 diabetes mellitus: a retrospective cohort study and bioinformatics analysis. BMC Endocr disorders 24:168. https://doi.org/10.1186/s12902-024-01694-2 Man S, Ma Y, Jin C, Lv J, Tong M, Wang B et al (2020) Association between Helicobacter pylori Infection and Diabetes: A Cross-Sectional Study in China. J Diabetes Res 2020:7201379. https://doi.org/10.1155/2020/7201379 Zhou M, Liu J, Qi Y, Wang M, Wang Y, Zhao F et al (2018) The association between Helicobacter pylori seropositivity and risk of new-onset diabetes: a prospective cohort study. Diabetologia 61:300–307. https://doi.org/10.1007/s00125-017-4465-2 Santos MLC, de Brito BB, da Silva FAF, Sampaio MM, Marques HS, Oliveira ESN et al (2020) Helicobacter pylori infection: Beyond gastric manifestations. World J Gastroenterol 26:4076–4093. https://doi.org/10.3748/wjg.v26.i28.4076 Chen Y, Yang C, You N, Zhang J (2023) Relationship between Helicobacter pylori and glycated hemoglobin: a cohort study. Front Cell Infect Microbiol 13:1196338. https://doi.org/10.3389/fcimb.2023.1196338 Song X, Cai C, Jin Q, Chen X, Yu C (2021) The efficacy of Helicobacter pylori eradication in diabetics and its effect on glycemic control: A systematic review and meta-analysis. Helicobacter 26:e12781. https://doi.org/10.1111/hel.12781 Sommer J, Borgmann SO, Gontscharuk V, Zaharia OP, Maalmi H, Herder C et al (2024) Depression symptoms, wellbeing, health-related quality of life, and diabetes-related distress in novel subtypes of recent-onset diabetes in Germany: a 5-year observational follow-up study. lancet Diabetes Endocrinol 12:890–903. https://doi.org/10.1016/s2213-8587(24)00234-1 Wojujutari Ajele K, Sunday Idemudia E (2025) The role of depression and diabetes distress in glycemic control: A meta-analysis. Diabetes Res Clin Pract 221:112014. https://doi.org/10.1016/j.diabres.2025.112014 van Gennip ACE, Schram MT, Köhler S, Kroon AA, Koster A, Eussen S et al (2023) Association of type 2 diabetes according to the number of risk factors within the recommended range with incidence of major depression and clinically relevant depressive symptoms: a prospective analysis. lancet Healthy Longev 4:e63–e71. https://doi.org/10.1016/s2666-7568(22)00291-4 Xu H, Chen Q (2025) The bidirectional influence between type 2 diabetes mellitus and the state of depression and anxiety. J Affect Disord 386:119467. https://doi.org/10.1016/j.jad.2025.119467 Maina JG, Balkhiyarova Z, Nouwen A, Pupko I, Ulrich A, Boissel M et al (2023) Bidirectional Mendelian Randomization and Multiphenotype GWAS Show Causality and Shared Pathophysiology Between Depression and Type 2 Diabetes. Diabetes Care 46:1707–1714. https://doi.org/10.2337/dc22-2373 Khawagi WY, Al-Kuraishy HM, Hussein NR, Al-Gareeb AI, Atef E, Elhussieny O et al (2024) Depression and type 2 diabetes: A causal relationship and mechanistic pathway. Diabetes Obes Metab 26:3031–3044. https://doi.org/10.1111/dom.15630 Yao J, Zhu CQ, Sun Y, Huang YW, Li QH, Liao HM et al (2025) Insulin resistance: The role in comorbid type 2 diabetes mellitus and depression. Neurosci Biobehav Rev 175:106218. https://doi.org/10.1016/j.neubiorev.2025.106218 Carr AL, Sluiman AJ, Grecian SM, Forster R, McLachlan S, Strachan MWJ et al (2021) Depression as a risk factor for dementia in older people with type 2 diabetes and the mediating effect of inflammation. Diabetologia 64:448–457. https://doi.org/10.1007/s00125-020-05301-6 Sperber AD, Freud T, Aziz I, Palsson OS, Drossman DA, Dumitrascu DL et al (2022) Greater Overlap of Rome IV Disorders of Gut-Brain Interactions Leads to Increased Disease Severity and Poorer Quality of Life. Clin Gastroenterol Hepatol 20:e945–e56. https://doi.org/10.1016/j.cgh.2021.05.042 Markus HS, de Leeuw FE (2023) Cerebral small vessel disease: Recent advances and future directions. Int J Stroke 18:4–14. https://doi.org/10.1177/17474930221144911 Pearcy LB, Karim HT, Butters MA, Krafty R, Boyd BD, Banihashemi L et al (2025) White matter hyperintensities and relapse risk in late-life depression. J Affect Disord 383:298–305. https://doi.org/10.1016/j.jad.2025.04.116 Zhou R, Cai Q, Liu C, Hui J, Kang M, Gou Y et al (1991) Association between white matter hyperintensity and anxiety/depression. Cerebral cortex (New York, NY: 2024;34. https://doi.org/10.1093/cercor/bhae149 Fleischman DA, Arfanakis K, Leurgans SE, Zhang S, Lamar M, Han SD et al (2023) Late-life depressive symptoms and white matter structural integrity within older Black adults. Front Aging Neurosci 15:1138568. https://doi.org/10.3389/fnagi.2023.1138568 Kieseppä T, Mäntylä R, Luoma K, Rikandi E, Jylhä P, Isometsä E (2022) White Matter Hyperintensities after Five-Year Follow-Up and a Cross-Sectional FA Decrease in Bipolar I and Major Depressive Patients. Neuropsychobiology 81:39–50. https://doi.org/10.1159/000516234 Zhang F, Ping Y, Jin X, Hou X, Song J (2023) White matter hyperintensities and post-stroke depression: A systematic review and meta-analysis. J Affect Disord 320:370–380. https://doi.org/10.1016/j.jad.2022.09.166 Cai J, Sun J, Chen H, Chen Y, Zhou Y, Lou M et al (2022) Different mechanisms in periventricular and deep white matter hyperintensities in old subjects. Front Aging Neurosci 14:940538. https://doi.org/10.3389/fnagi.2022.940538 Black S, Gao F, Bilbao J (2009) Understanding white matter disease: imaging-pathological correlations in vascular cognitive impairment. Stroke 40:S48–52. https://doi.org/10.1161/strokeaha.108.537704 Hamilton NB, Kolodziejczyk K, Kougioumtzidou E, Attwell D (2016) Proton-gated Ca(2+)-permeable TRP channels damage myelin in conditions mimicking ischaemia. Nature 529:523–527. https://doi.org/10.1038/nature16519 Xu X, Yang X, Zhang J, Wang Y, Selim M, Zheng Y et al (2025) Choroid plexus free-water correlates with glymphatic function in Alzheimer's disease. Alzheimer's Dement J Alzheimer's Assoc 21:e70239. https://doi.org/10.1002/alz.70239 Additional Declarations There is NO conflict of interest to disclose Supplementary Files supplementarymaterials.pdf SUPPLEMENTAL MATERIA 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8141277","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":555813997,"identity":"db0127dd-2752-4382-bcdb-ca84083c4a38","order_by":0,"name":"Ying Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYFACxgeMDRVyEDYPcVqYDRgbzhiTqqWxjRQtBjeSmT/OnGcQrTsjgfHB2zYGeXMitLBJbtxmkLvtRgKz4dw2BsOdDQS15B9jfLjtD0gLmzRvG0OCwQFiHPZwDtgW9t/EamGQ3NgA1sLGTJQWyTOP2SRnHANqOfOwWXLOOQnDDYS08B0HOqynBqjlePLBD2/KbOQJ2qJwIQHGZGwAEhIE1AOBfD8hQ0fBKBgFo2AUAABTaUZ9s148cwAAAABJRU5ErkJggg==","orcid":"","institution":"Tangdu Hospital, Fourth Military Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ying","middleName":"","lastName":"Yu","suffix":""},{"id":555813998,"identity":"e6d43101-f294-4ea2-8761-410228864c6a","order_by":1,"name":"Bo Hu","email":"","orcid":"https://orcid.org/0000-0002-1135-0669","institution":"Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Hu","suffix":""},{"id":555813999,"identity":"5e8d441d-9d31-4535-bb93-e16f1ff476cb","order_by":2,"name":"Yu-Ling Cui","email":"","orcid":"","institution":"Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu-Ling","middleName":"","lastName":"Cui","suffix":""},{"id":555814000,"identity":"3c6c2c64-0916-4f47-8158-a0b4f936d5fb","order_by":3,"name":"Yan-Yu Li","email":"","orcid":"","institution":"Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yan-Yu","middleName":"","lastName":"Li","suffix":""},{"id":555814001,"identity":"b09d6a66-aaaf-4cec-93ed-e8d8af7c674f","order_by":4,"name":"Xinwen Yu","email":"","orcid":"","institution":"Xijing Hospital, Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinwen","middleName":"","lastName":"Yu","suffix":""},{"id":555814002,"identity":"3059bd94-69fc-4c3c-8a83-d497555cf1ad","order_by":5,"name":"Hao Xie","email":"","orcid":"","institution":"Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Xie","suffix":""},{"id":555814003,"identity":"cb6d0ea4-0473-473d-b1d3-f12564916b64","order_by":6,"name":"Lijuan Du","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lijuan","middleName":"","lastName":"Du","suffix":""},{"id":555814004,"identity":"17cfe22f-7b60-408b-910f-884adcc38ab6","order_by":7,"name":"Xiaoyan Bai","email":"","orcid":"","institution":"Tangdu Hospital, Air Force Medical Univerisity","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Bai","suffix":""},{"id":555814005,"identity":"5f3b008a-b657-49f3-9e34-ea28aa398489","order_by":8,"name":"Min-Hua Ni","email":"","orcid":"","institution":"Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Min-Hua","middleName":"","lastName":"Ni","suffix":""},{"id":555814006,"identity":"8c58b2d7-e863-4fe1-9ee9-297ff6f72325","order_by":9,"name":"Ai-Li Yang","email":"","orcid":"","institution":"Tangdu Hospital, Air Force Medical University (Fourth Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Ai-Li","middleName":"","lastName":"Yang","suffix":""},{"id":555814007,"identity":"a5a3bc7c-6d4b-4867-8220-88c594ee7744","order_by":10,"name":"Yu-Xin Jin","email":"","orcid":"","institution":"Tangdu Hospital, Air Force Medical University (Fourth Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Yu-Xin","middleName":"","lastName":"Jin","suffix":""},{"id":555814008,"identity":"e462edb4-2ecb-47a8-8302-9c962c6a99ba","order_by":11,"name":"Shengru Liang","email":"","orcid":"","institution":"Xijing Hospital, Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shengru","middleName":"","lastName":"Liang","suffix":""},{"id":555814009,"identity":"318cf1ea-8f9c-45a5-9a5f-c2901ad4b299","order_by":12,"name":"Lin-Feng Yan","email":"","orcid":"","institution":"Tangdu Hospital, Air Force Medical University (Fourth Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Lin-Feng","middleName":"","lastName":"Yan","suffix":""},{"id":555814010,"identity":"eb0bba95-6e41-42a2-b3ee-81fddd19c209","order_by":13,"name":"Bin Gao","email":"","orcid":"","institution":"Tangdu Hospital, Air Force Medical University (Fourth Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Gao","suffix":""},{"id":555814011,"identity":"a5ca4392-c710-4a42-aff2-6569545986e5","order_by":14,"name":"Guang-Bin Cui","email":"","orcid":"https://orcid.org/0000-0002-7935-9803","institution":"Tangdu Hospital, Air Force Medical University (Fourth Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Guang-Bin","middleName":"","lastName":"Cui","suffix":""}],"badges":[],"createdAt":"2025-11-18 05:45:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8141277/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8141277/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97721368,"identity":"ae2a8ac8-98ae-4725-8ec6-696694bf2599","added_by":"auto","created_at":"2025-12-08 15:38:31","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":127442,"visible":true,"origin":"","legend":"","description":"","filename":"20251110maintext.docx","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/993d0b4859cd36a01dbf5933.docx"},{"id":97721434,"identity":"aedf4600-eb92-47ee-b7ff-61eeecdcb07c","added_by":"auto","created_at":"2025-12-08 15:38:35","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1020040,"visible":true,"origin":"","legend":"","description":"","filename":"Fig1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/774e99b09512bd41335b784b.tif"},{"id":97721437,"identity":"6d4d5252-3993-47d9-a5dd-f9371c9e2f8e","added_by":"auto","created_at":"2025-12-08 15:38:35","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":612704,"visible":true,"origin":"","legend":"","description":"","filename":"Fig2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/ca25279dfa16ed21dd91c22a.tif"},{"id":97721415,"identity":"2d8b67aa-9d11-427f-8547-27da6aa83165","added_by":"auto","created_at":"2025-12-08 15:38:34","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":625296,"visible":true,"origin":"","legend":"","description":"","filename":"Fig3.tif","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/05df3e1863643de0f37fbb8d.tif"},{"id":97721389,"identity":"bb19ac97-b810-4eb8-8b27-880a8ae26da8","added_by":"auto","created_at":"2025-12-08 15:38:33","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":528724,"visible":true,"origin":"","legend":"","description":"","filename":"Fig4.tif","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/aceec32ef089a93df6e6c593.tif"},{"id":97721461,"identity":"a68f89ae-1f7e-4bff-9978-31032ad89d0e","added_by":"auto","created_at":"2025-12-08 15:38:36","extension":"json","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":15643,"visible":true,"origin":"","legend":"","description":"","filename":"2025NUTD00737.json","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/375c536153664f19ac33a506.json"},{"id":97721385,"identity":"2dd1aaaf-1c46-4c4d-8340-3717eae57b5a","added_by":"auto","created_at":"2025-12-08 15:38:33","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":962035,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/eecdbc51f52b0dc1950e520c.pdf"},{"id":97721442,"identity":"62f671e9-e88b-473e-8fd3-cd4f0e8c880f","added_by":"auto","created_at":"2025-12-08 15:38:35","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":121933,"visible":true,"origin":"","legend":"","description":"","filename":"2025NUTD007370enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/65f0bfc1bb5dc7a246c96356.xml"},{"id":97721435,"identity":"24735ca6-f79a-4778-807b-17e42a33d5c6","added_by":"auto","created_at":"2025-12-08 15:38:35","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1020040,"visible":true,"origin":"","legend":"","description":"","filename":"Fig1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/e511c4ca0ead78550b7f26a5.tif"},{"id":97721393,"identity":"0d80d52c-8ced-4de7-8aa7-1e2b8d55acc8","added_by":"auto","created_at":"2025-12-08 15:38:33","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":612704,"visible":true,"origin":"","legend":"","description":"","filename":"Fig2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/91162c5b7b77936656cf9995.tif"},{"id":97894913,"identity":"5c27e566-2960-48e8-94c1-c97c75dd995d","added_by":"auto","created_at":"2025-12-10 15:33:13","extension":"tif","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":625296,"visible":true,"origin":"","legend":"","description":"","filename":"Fig3.tif","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/3da9e63752f6f26cf0abc3de.tif"},{"id":97721404,"identity":"f1774a8b-81f3-4cf3-a07f-19d263b82705","added_by":"auto","created_at":"2025-12-08 15:38:34","extension":"tif","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":528724,"visible":true,"origin":"","legend":"","description":"","filename":"Fig4.tif","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/0b47de33b0e13aa199afb86e.tif"},{"id":97721429,"identity":"06eef5d0-b748-4405-b9e7-c397e30731ef","added_by":"auto","created_at":"2025-12-08 15:38:35","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":329784,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/7b425d41ac213595d2ce54ba.png"},{"id":97721474,"identity":"8b53d882-8962-4c6b-98cc-1887ec6a799d","added_by":"auto","created_at":"2025-12-08 15:38:36","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":113472,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/c8767cd3e17e74ec9b17f715.png"},{"id":97721459,"identity":"2b4a3148-4e4d-4555-8f48-06480d5070a1","added_by":"auto","created_at":"2025-12-08 15:38:36","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":172014,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/049a4dceda26cfae32d0c8d9.png"},{"id":97894167,"identity":"9a3c51b3-bcbb-4eeb-9bcf-35270022010e","added_by":"auto","created_at":"2025-12-10 15:31:59","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":179016,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFig4.png","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/f5f0d0fa382077f2cf388a95.png"},{"id":97721423,"identity":"e6b864d3-c41d-4c95-973a-1747798463b1","added_by":"auto","created_at":"2025-12-08 15:38:35","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":119846,"visible":true,"origin":"","legend":"","description":"","filename":"2025NUTD007370structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/fab895b1d9aaef55a18b5d59.xml"},{"id":97721425,"identity":"d5c00745-a2bd-4ab8-86ea-2c5c840524ff","added_by":"auto","created_at":"2025-12-08 15:38:35","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":131297,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/af71183ac75bdb12f1616950.html"},{"id":97895634,"identity":"a200ab79-d814-4b58-b377-868a9ffbc700","added_by":"auto","created_at":"2025-12-10 15:34:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":16211285,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart depicting participant selection and subgrouping\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/83ab4ff309c2dc99fb660884.png"},{"id":97895935,"identity":"6e307b45-2a62-4532-84ca-2ff09a9f66eb","added_by":"auto","created_at":"2025-12-10 15:35:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3468074,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier analysis comparing depression incidence among patients with T2D-CGD comorbidity (COM cohort), T2D alone (T2D cohort), CGD alone (CGD cohort), and no conditions (Control cohort).\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/d7d5bd7f7ecca50a5117f699.png"},{"id":97721373,"identity":"141c93cb-0554-4ebb-9dc0-2b7b6d0a1611","added_by":"auto","created_at":"2025-12-08 15:38:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3149066,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of T2D-CGD comorbidity on depression incidence after adjusting for clinical, demographic, environmental, and modifiable lifestyle risk factors. Red box: risk factors; Blue box: protective factors.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/581b19444b411e3a1475f56e.png"},{"id":97721411,"identity":"9ce3cc9d-c433-4894-829a-c896721b58d5","added_by":"auto","created_at":"2025-12-08 15:38:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2286617,"visible":true,"origin":"","legend":"\u003cp\u003eDirect, indirect, and prime effect and significance in the mediation analysis\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/cdce321e1e34789150727ea1.png"},{"id":104399544,"identity":"b3d075a4-a82a-4bda-9a37-60648078a43b","added_by":"auto","created_at":"2026-03-11 12:06:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":31099483,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/0feb7162-72fa-4d5d-ae2b-f0247e421b1c.pdf"},{"id":97721392,"identity":"69331136-35d5-48be-9616-11441eb50e88","added_by":"auto","created_at":"2025-12-08 15:38:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":962035,"visible":true,"origin":"","legend":"SUPPLEMENTAL MATERIA","description":"","filename":"supplementarymaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8141277/v1/c4c8e0aaa5e08a099971b3aa.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Incident depression in people with comorbid type 2 diabetes and chronic gastritis/duodenitis: a large-scale cohort study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMajor depression has emerged as a clinically significant comorbidity strongly linked to type 2 diabetes (T2D). Extensive evidence demonstrates that patients with T2D face a 1.5 to 2.0-fold increased risk of developing depression compared to non-diabetic individuals[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Moreover, both depression and subthreshold depressive symptoms have been consistently associated with higher risks of dementia onset and all-cause mortality[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eChronic gastritis is a persistent gastric mucosal inflammation caused by \u003cem\u003eH. pylori\u003c/em\u003e infection, bile reflux, drugs, or alcohol, leading to atrophy/metaplasia and potential dysplasia. Due to shared etiology, clinical features, therapeutic approaches, and anatomical continuity between gastritis and duodenitis, these two conditions are often considered collectively in clinical practice and referred to as Chronic gastritis or duodenitis (CGD)[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. CGD is prevalent in T2D primarily due to high \u003cem\u003eH. pylori\u003c/em\u003e susceptibility in T2D patients[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], which worsens glycemic control and insulin resistance in reverse[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Emerging evidence also links CGD to depression via gut-brain axis dysfunction[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], suggesting a close relationship between CGD and depressive disorders. Despite these plausible connections, the interplay between T2D-CGD comorbidity and depression incidence remains understudied, highlighting a critical gap in current research.\u003c/p\u003e\u003cp\u003eThis study utilized the UK Biobank database to conduct a retrospective cohort analysis, investigating the association between T2D-CGD comorbidity and incident depression. Building on prior research that suggests the relationship between T2D, CGD, and depression risk may be mediated by microvascular dysfunction, neurodegenerative pathology, and neuroinflammation[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u0026mdash;typically observed as white matter hyperintensities (WMH) on brain MRI\u0026mdash;this study further explored the potential mediating role of WMH in the connection between T2D-CGD comorbidity and depression.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study design, participants inclusion, and groups\u003c/h2\u003e\u003cp\u003eThis study utilized data from the UK Biobank, a large-scale, ongoing prospective cohort study encompassing over 500 000 participants in the United Kingdom. Between 2006 and 2010, all participants underwent comprehensive baseline assessments[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Written informed consent was obtained from all participants, and ethical approval was granted by the North West Multicenter Research Ethics Committee. As the data were fully anonymized, no additional institutional review was required for this analysis. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.\u003c/p\u003e\u003cp\u003eThis prospective cohort study included 502 128 subjects. To minimize biases related to comorbid conditions, 14 069 participants with dementia, schizophrenia, bipolar disorder, brain tumors, stroke, type 1 diabetes, malnutrition-related diabetes, gestational diabetes, and acute gastritis were excluded. To ensure temporal precedence and reduce confounding, 100 910 individuals with baseline depression or those who developed T2D or CGD during the follow-up period were excluded. Finally, 387 149 participants were included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and were divided into four cohorts: (1) Patients without T2D or CGD (Control cohort); (2) Patients with T2D alone (T2D cohort); (3) Patients with CGD alone (CGD cohort); (4) Patients with T2D-CGD comorbidity (COM cohort).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Definition of T2D, CGD, and depression\u003c/h2\u003e\u003cp\u003eThe UK Biobank captures disease onset dates through multiple linked data sources, including hospital episode statistics, primary care records, death register linkage, and others. Patients with T2D were identified using four methods: 1) ICD-10 codes E11 (non-insulin-dependent diabetes mellitus); 2) blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L at baseline; 3) HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5% (48 mmol/mol) at baseline; and 4) self-reported T2D diagnosis and dates. Patients with CGD were identified based on ICD-10 codes K29 (gastritis and duodenitis), excluding K29.0 (acute hemorrhagic gastritis) and K29.1 (other acute gastritis). Incident depression cases were identified using ICD-10 codes F32 (depressive episode) and F33 (recurrent depressive disorder).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Potential risk factors\u003c/h2\u003e\u003cp\u003eA total of 18 potential risk factors were identified: 1) five disease-related factors, including T2D status, CGD status, comorbidity of both, T2D duration, and CGD duration; 2) three demographic factors, including age, sex, and body mass index (BMI); 3) two environmental factors, including the Townsend deprivation index (TDI) and urbanization level (\u003cb\u003eSupplementary material 1\u003c/b\u003e); and 4) eight modifiable lifestyle factors, including sleep duration, fruit and vegetable intake, meat intake, bread and cereal intake, tea intake, coffee intake, smoking frequency, and alcohol consumption frequency (\u003cb\u003eSupplementary material 2\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Primary outcomes\u003c/h2\u003e\u003cp\u003eThe primary outcome of this study was incident depression. Participants who did not reach either the primary outcome were right-censored at the date of March 31, 2025, which marked the latest update from the UK Biobank.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Imaging data\u003c/h2\u003e\u003cp\u003eT2-weighted fluid-attenuated inversion recovery (FLAIR) data were acquired using a Siemens Skyra 3T running scanner with a standard Siemens 32-channel RF receive head coil. Two indicators of WMH were used: 1) Peri-ventricular WMH (pvWMH), which are located within 10 mm from the lateral ventricle wall, primarily involving the radiations of the corpus callosum and the medial part of the centrum semiovale; 2) Deep WMH (deepWMH), situated more than 10 mm away from the ventricle in deep white matter regions, typically in the frontal, parietal, and temporal lobes, as well as the corona radiata above the basal ganglia.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using in-house codes on the MATLAB R2018a platform (MathWorks, Natick, MA). Baseline demographic characteristics were summarized as percentages for categorical variables and means with standard deviations (SDs) for continuous variables. Participants with missing data were excluded from corresponding analysis. The odds ratio (OR) was calculated to quantify the association between T2D and CGD. Kaplan-Meier (K-M) survival curves were used to estimate cumulative incidence of depression across groups. Multivariable Cox regression analyses assessed the risks of incident depression linked to the comorbidity of T2D and CGD, compared to the Control cohort. Five models adjusted for different confounders were used: model 1 (unadjusted), model 2 (T2D and CGD status and durations), model 3 (model 2 plus age, sex, and BMI), model 4 (model 3 plus TDI and urbanization level), and model 5 (model 4 plus eight modifiable lifestyle factors). The Fine and Gray method accounted for competing risks of death. Hazard ratios (HRs) with 95% confidence intervals (CIs) were reported to assess statistical significance. The proportional hazard assumption was tested using Schoenfeld residuals [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], which were satisfied.\u003c/p\u003e\u003cp\u003eRelative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (SI) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] were used to determine whether the combined effect of T2D and CGD on the risk of incident depression exceeded the simple additive effects of the individual conditions (\u003cb\u003esupplementary material 3\u003c/b\u003e). The bootstrap method with 10 000 resamples was employed to estimate 95% CIs for these three indicators.\u003c/p\u003e\u003cp\u003eTo further explore underlying mechanisms, cerebral WMH was used for mediation analysis in the relationship between T2D-CGD comorbidity and incident depression, adjusting for age, sex, and BMI as covariates. The indirect effect (mediation effect) was calculated, and the bootstrap method with 10 000 resamples was applied to estimate 95% CIs.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Demographic information and the association between T2D and CGD\u003c/h2\u003e\u003cp\u003eThe demographic information is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A significant positive association was found between T2D and CGD, with an OR of 2.10 (95% CI: [1.97, 2.24]), indicating that individuals with T2D had 2.1 times the odds of developing CGD compared to those without T2D.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic, clinical, and lifestyle information.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControl cohort\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;358 501\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eT2D cohort\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;11 876\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCGD cohort\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;15 679\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCOM cohort\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;1 093\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.93\u0026thinsp;\u0026plusmn;\u0026thinsp;8.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.85\u0026thinsp;\u0026plusmn;\u0026thinsp;7.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58.73\u0026thinsp;\u0026plusmn;\u0026thinsp;7.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61.55\u0026thinsp;\u0026plusmn;\u0026thinsp;6.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale Sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e197 804 (55.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 612 (38.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 243 (52.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e398 (36.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.87\u0026thinsp;\u0026plusmn;\u0026thinsp;4.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.96\u0026thinsp;\u0026plusmn;\u0026thinsp;5.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.56\u0026thinsp;\u0026plusmn;\u0026thinsp;4.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.25\u0026thinsp;\u0026plusmn;\u0026thinsp;5.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression Cases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 607 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e621 (5.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 141 (7.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e107 (9.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeath Cases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 834 (8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 471 (20.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 126 (13.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e393 (36.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2D duration (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.70\u0026thinsp;\u0026plusmn;\u0026thinsp;7.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.43\u0026thinsp;\u0026plusmn;\u0026thinsp;5.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCGD duration (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.61\u0026thinsp;\u0026plusmn;\u0026thinsp;5.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.43\u0026thinsp;\u0026plusmn;\u0026thinsp;5.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTownsend Deprivation Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;2.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;3.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;3.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrbanization Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVery Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 822 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e189 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e211 (1.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (0.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 908 (5.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e457 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e682 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 587 (6.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e682 (5.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,050 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72 (6.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e307 184 (85.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 548 (88.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 736 (87.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e994 (90.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep Duration (hours/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking Frequency Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol Drinking Frequency Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoffee Intake (cups/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.01\u0026thinsp;\u0026plusmn;\u0026thinsp;2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.89\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTea Intake (cups/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.28\u0026thinsp;\u0026plusmn;\u0026thinsp;3.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.47\u0026thinsp;\u0026plusmn;\u0026thinsp;3.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetable and Fruit Intake\u003c/p\u003e\u003cp\u003e(Normalized value)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeat Intake (Normalized value)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBread and Cereal Intake (Normalized value)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eT2D: Type 2 diabetes; CGD: Chronic gastritis or duodenitis; COM: Patients with T2D-CGD comorbidity; BMI: Body mass index; TDI: Townsend deprivation index.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 K-M analysis and multivariable Cox regression analysis of incident depression\u003c/h2\u003e\u003cp\u003eThe K-M survival curves in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrate that COM group exhibited the highest incidence of depression, followed by the CGD group, T2D group, and Control group (log-rank test P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Multivariable Cox regression analysis revealed that T2D-CGD comorbidity was significantly associated with a higher risk of incident depression across five models: model 1 (univariable; HR\u0026thinsp;=\u0026thinsp;2.56, 95% CI = [2.12, 3.09]), model 2 (adjusted for T2D and CGD status and duration; aHR\u0026thinsp;=\u0026thinsp;2.90, 95% CI = [2.37, 3.58]), model 3 (model 2 plus age, sex, and BMI; aHR\u0026thinsp;=\u0026thinsp;2.76, 95% CI = [2.24, 3.41]), model 4 (model 3 plus TDI and urbanization level; aHR\u0026thinsp;=\u0026thinsp;2.48, 95% CI = [2.00, 3.06]), and model 5 (model 4 plus eight modifiable lifestyle factors; aHR\u0026thinsp;=\u0026thinsp;2.29, 95% CI = [1.84, 2.85]). All aHRs and 95% CIs for model 5 are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e for other models). The Fine and Gray method was applied for competing risk analysis to account for bias from competing risks of death, resulting in an aHR for model 5 of 1.95 (95% CI = [1.48, 2.56]).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Analysis of synergistic effect\u003c/h2\u003e\u003cp\u003eThe synergistic effect of T2D and CGD on depression risk did not exceed the sum of their individual effects (RERI\u0026thinsp;=\u0026thinsp;0.38, 95% CI = [-0.20, 1.04]; AP\u0026thinsp;=\u0026thinsp;0.13, 95% CI = [-0.09, 0.30]; SI\u0026thinsp;=\u0026thinsp;1.26, 95% CI = [0.86, 1.72]). This result suggests that the combined effect of T2D and CGD on incident depression did not exceed the sum of their individual effects.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Stratified analysis by follow-up duration​​\u003c/h2\u003e\u003cp\u003eWhen the follow-up time was stratified as \u0026lt;\u0026thinsp;5 years, 5\u0026ndash;10 years, and \u0026gt;\u0026thinsp;10 years, the adjusted HRs (aHRs) and 95% CIs for the association between T2D-CGD comorbidity and depression (model 5) were as follows: aHR\u0026thinsp;=\u0026thinsp;2.34 (95% CI = [1.57, 3.50]), aHR\u0026thinsp;=\u0026thinsp;2.54 (95% CI = [1.83, 3.53]), and aHR\u0026thinsp;=\u0026thinsp;1.22 (95% CI = [0.80, 1.86]) for each time interval, respectively (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eIn summary, T2D-CGD comorbidity was linked to the risk of depression within 15 years following disease onset (with baseline disease durations of approximately 5 years).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Stratified analysis by sex​​\u003c/h2\u003e\u003cp\u003eWhen we manually divided all participants into two group according to sex, the aHRs and 95% CIs for the association between T2D-CGD comorbidity and depression (model 5) were aHR\u0026thinsp;=\u0026thinsp;2.26 (95% CI = [1.64\u0026ndash;3.13]) in female group, and aHR\u0026thinsp;=\u0026thinsp;2.29 (95% CI = [1.71\u0026ndash;3.08]) in male group (\u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eIn brief, the risk of T2D-CGD comorbidity for depression was similar between female and male groups.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Mediation analysis\u003c/h2\u003e\u003cp\u003eMediation analyses were conducted to explore whether cerebral WMH volume mediated the association between T2D-CGD comorbidity and incident depression. A total of 54 060 participants who underwent MRI scans were included in this analysis (\u003cb\u003eSupplementary material 4\u003c/b\u003e). The mediation analysis revealed significant indirect effects \u003cem\u003evia\u003c/em\u003e both WMH subtypes: pvWMH showed an indirect effect of 1.9% (95% CI: 0.8%\u0026ndash;3.2%), while deepWMH demonstrated an indirect effect of 0.7% (95% CI: 0.1%\u0026ndash;1.4%), indicating that the association between T2D-CGD comorbidity and incident depression was partially mediated by WMH, particularly pvWMH (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Sensitive analysis\u003c/h2\u003e\u003cp\u003eTo assess the robustness of these findings, this study tested a scenario where only diagnostic data (ICD-10 codes) were used to define T2D, excluding participants based on other criteria (blood biochemical data and self-reported diagnosis). This analysis included 9 101 participants (76.6%) and yielded results consistent with the original findings (\u003cb\u003esupplementary Fig.\u0026nbsp;4\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, depression was defined to include both depressive episodes and recurrent depressive disorders. By including only participants with depressive episodes (15 474 participants, 98.7%) and excluding others, the results remained consistent with the original findings (\u003cb\u003esupplementary Fig.\u0026nbsp;5\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eFew studies have examined the comorbidity of T2D and CGD, with no research on its impact on depression incidence. This cohort study found that patients with T2D are more likely to develop CGD compared to those without T2D. The T2D-CGD comorbidity was identified as a risk factor for incident depression. The T2D-CGD comorbidity was associated with an increased risk of depression within 15 years of disease onset. Mediation analysis revealed that WMH, particularly those near the cerebral ventricle, partially mediated the association between T2D-CGD comorbidity and incident depression.\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.1 The relationship between T2D and CGD\u003c/h2\u003e\u003cp\u003eAlthough the relationship between T2D and CGD remains underexplored, substantial evidence links T2D to \u003cem\u003eH. pylori\u003c/em\u003e infection[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Most studies report OR or relative risks (RR) ranging from 1.08 to 2.0 for \u003cem\u003eH. pylori\u003c/em\u003e infection in patients with T2D, although some findings contradict this association[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] or even suggest a protective effect of \u003cem\u003eH. pylori\u003c/em\u003e against T2D[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These inconsistencies may result from population heterogeneity or unresolved causal mechanisms. While \u003cem\u003eH. pylori\u003c/em\u003e-induced insulin resistance, chronic inflammation, and metabolic syndrome are widely considered drivers of T2D[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], other studies indicate that hyperglycemia in patients with T2D may impair \u003cem\u003eH. pylori\u003c/em\u003e eradication efficacy[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Furthermore, dietary irregularities associated with T2D could also exacerbate CGD incidence. These findings suggest a bidirectional, multifactorial interaction between T2D and CGD rather than a unidirectional causal link.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.2 The relationship between T2D and depression\u003c/h2\u003e\u003cp\u003eThe association between T2D and depression has been well established[\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], with shared pathogenic mechanisms including genetic predisposition, insulin resistance, microvascular damage, chronic inflammation, and gut-brain axis dysregulation[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Notably, while CGD is more strongly associated with depression than T2D in this study, research on CGD-related depression remains limited. Most existing studies focus on gut-brain interaction disorders[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], which only partially overlap with CGD and require distinct therapeutic approaches. Importantly, CGD and \u003cem\u003eH. pylori\u003c/em\u003e infection are distinct yet overlapping conditions\u0026mdash;not all patients with CGD are \u003cem\u003eH. pylori\u003c/em\u003e-positive, nor do all \u003cem\u003eH. pylori\u003c/em\u003e carriers develop CGD. This distinction underscores the critical need for direct investigation into T2D-CGD comorbidity to guide evidence-based clinical management strategies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.3 The relationship between T2D, depression, and WMH\u003c/h2\u003e\u003cp\u003eThe association between WMH and depression observed in our study is consistent with established neuroimaging evidence[\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Diffusion MRI studies consistently demonstrate a correlation between depression and impaired white matter microstructural integrity[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Our findings specifically highlight the mediating role of pvWMH in the relationship between T2D-CGD comorbidity and depression, supporting the results of previous studies[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Mechanistically, this study proposes that T2D-CGD comorbidity contributes to depression \u003cem\u003evia\u003c/em\u003e cerebrovascular dysfunction characterized by: (1) endothelial cell damage impairing the blood-brain barrier, and (2) venous outflow obstruction resulting in interstitial fluid accumulation and periventricular edema[\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Notably, this impairment in cerebrospinal fluid-interstitial fluid exchange shares pathophysiological features with Alzheimer's disease, suggesting potential overlapping neurodegenerative mechanisms in these comorbid conditions[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral limitations must be considered. As noted in the discussion section, the causal relationships between T2D, CGD, and depression remain complex and unresolved in this observational study. Further mechanistic research is required to clarify these relationships. Additionally, some factors may not have been accounted for, potentially confounding or driving the associations observed. For example, the regularity of dietary patterns and medication use over extended periods cannot be analyzed in the current sample. Selection bias may also be a concern in the UK Biobank, primarily due to its reliance on hospital data, primary care records, and death registration. Thus, patients who were ill but did not seek medical treatment may have been missed, potentially underestimating the effect size of the exposure association or delaying diagnosis.\u003c/p\u003e\u003cp\u003eIn conclusion, integrated screening and long-term monitoring strategies should be prioritized for population with the comorbidity of T2D and CGD, as it significantly elevates the risk of incident depression. White matter hyperintensity can serve as an imaging biomarker for detecting the risk of depression in patients with T2D-CGD comorbidity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB.H., Y.Y.L., and H.X. were involved in the conception, design, and conduct of the study and the analysis and interpretation of the results. B.H. wrote the first draft of the manuscript, and all authors edited, reviewed, and approved the final version of the manuscript. B.G., G.B.C and Y.Y. had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. X.W.Y., A.L.Y., Y.X.J., and S.R.L. provided clinical expertise and validation. L.J.D. and X.Y.B. contributed to data verification and visualization. M.H.N. and L.F.Y. performed validation analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u0026nbsp;\u003c/strong\u003eAll data are available in UK Biobank.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval Statement:\u0026nbsp;\u003c/strong\u003eEthical approval was granted by the North West Multicenter Research Ethics Committee. As the data were fully anonymized, no additional institutional review was required for this analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Consent Statement:\u0026nbsp;\u003c/strong\u003eWritten informed consent was obtained from all individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrior Presentation:\u0026nbsp;\u003c/strong\u003eThis study has been posted on medRxiv: https://doi.org/10.1101/2025.08.17.25333765\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBH has received funding from the National Natural Science Foundation of China [grant number 82302148]. YY has received funding from the\u0026nbsp;Key Science and Technology Program of Shaanxi Province\u0026nbsp;[grant number 2023-YBSF-331] and Fourth Military Medical University [grant number 2023XC045]. GBC has received funding from the National Natural Science Foundation of China [grant number 82471936].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003evan Sloten TT, Sedaghat S, Carnethon MR, Launer LJ, Stehouwer CDA (2020) Cerebral microvascular complications of type 2 diabetes: stroke, cognitive dysfunction, and depression. lancet Diabetes Endocrinol 8:325\u0026ndash;336. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s2213-8587(19)30405-x\u003c/span\u003e\u003cspan address=\"10.1016/s2213-8587(19)30405-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNouwen A, Winkley K, Twisk J, Lloyd CE, Peyrot M, Ismail K et al (2010) Type 2 diabetes mellitus as a risk factor for the onset of depression: a systematic review and meta-analysis. Diabetologia 53:2480\u0026ndash;2486. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00125-010-1874-x\u003c/span\u003e\u003cspan address=\"10.1007/s00125-010-1874-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaton W, Pedersen HS, Ribe AR, Fenger-Gr\u0026oslash;n M, Davydow D, Waldorff FB et al (2015) Effect of depression and diabetes mellitus on the risk for dementia: a national population-based cohort study. JAMA psychiatry 72:612\u0026ndash;619. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jamapsychiatry.2015.0082\u003c/span\u003e\u003cspan address=\"10.1001/jamapsychiatry.2015.0082\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLo DF, Zia H, Zia T, Visaria A (2022) Investigating mortality in the association between depression and diabetes. Diabetologia 65:2172\u0026ndash;2173. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00125-022-05807-1\u003c/span\u003e\u003cspan address=\"10.1007/s00125-022-05807-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrganization WH (2010) ICD-10. International Statistical Classification of Diseases and Related Health Problems. Acta Chir Iugosl 56:65\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou X, Zhang C, Wu J, Zhang G (2013) Association between Helicobacter pylori infection and diabetes mellitus: a meta-analysis of observational studies. Diabetes Res Clin Pract 99:200\u0026ndash;208. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.diabres.2012.11.012\u003c/span\u003e\u003cspan address=\"10.1016/j.diabres.2012.11.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKountouras J, Polyzos SA, Doulberis M, Zeglinas C, Artemaki F, Vardaka E et al (2018) Potential impact of Helicobacter pylori-related metabolic syndrome on upper and lower gastrointestinal tract oncogenesis. Metabolism 87:18\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.metabol.2018.06.008\u003c/span\u003e\u003cspan address=\"10.1016/j.metabol.2018.06.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHan X, Li Y, Wang J, Liu B, Hu H, Li X et al (2016) Helicobacter pylori infection is associated with type 2 diabetes among a middle- and old-age Chinese population. Diab/Metab Res Rev 32:95\u0026ndash;101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/dmrr.2677\u003c/span\u003e\u003cspan address=\"10.1002/dmrr.2677\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTsay FW, Hsu PI (2018) H. pylori infection and extra-gastroduodenal diseases. J Biomed Sci 25:65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12929-018-0469-6\u003c/span\u003e\u003cspan address=\"10.1186/s12929-018-0469-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSahoo OS, Mitra R, Bhattacharjee A, Kar S, Mukherjee O (2023) Is Diabetes Mellitus a Predisposing Factor for Helicobacter pylori Infections? Current diabetes reports. 23:195\u0026ndash;205. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11892-023-01511-5\u003c/span\u003e\u003cspan address=\"10.1007/s11892-023-01511-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFairlie T, Shah A, Talley NJ, Chey WD, Koloski N, Yeh Lee Y et al (2023) Overlap of disorders of gut-brain interaction: a systematic review and meta-analysis. lancet Gastroenterol Hepatol 8:646\u0026ndash;659. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s2468-1253(23)00102-4\u003c/span\u003e\u003cspan address=\"10.1016/s2468-1253(23)00102-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJosefsson A, Hreinsson JP, Simr\u0026eacute;n M, Tack J, Bangdiwala SI, Sperber AD et al (2022) Global Prevalence and Impact of Rumination Syndrome. Gastroenterology 162 731\u0026thinsp;\u0026ndash;\u0026thinsp;42.e9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1053/j.gastro.2021.11.008\u003c/span\u003e\u003cspan address=\"10.1053/j.gastro.2021.11.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSteens ILM, Schram MT, Houben A, Berendschot T, Jansen JFA, Backes WH et al (2025) Type 2 diabetes and depression via microvascular dysfunction, neurodegeneration, inflammation, advanced glycation end products (AGEs), and arterial stiffness. Diabetes Obes Metab 27:4847\u0026ndash;4858. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/dom.16527\u003c/span\u003e\u003cspan address=\"10.1111/dom.16527\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeraets AFJ, van Agtmaal MJM, Stehouwer CDA, S\u0026ouml;rensen BM, Berendschot T, Webers CAB et al (2020) Association of Markers of Microvascular Dysfunction With Prevalent and Incident Depressive Symptoms: The Maastricht Study. Hypertension (Dallas, Tex: 1979). 76:342\u0026ndash;349. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/hypertensionaha.120.15260\u003c/span\u003e\u003cspan address=\"10.1161/hypertensionaha.120.15260\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J et al (2015) UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12:e1001779. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pmed.1001779\u003c/span\u003e\u003cspan address=\"10.1371/journal.pmed.1001779\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDavid S (1982) Partial residuals for the proportional hazards regression model. Biometrika. :239\u0026thinsp;\u0026ndash;\u0026thinsp;41\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKnol MJ, VanderWeele TJ, Groenwold RH, Klungel OH, Rovers MM, Grobbee DE (2011) Estimating measures of interaction on an additive scale for preventive exposures. Eur J Epidemiol 26:433\u0026ndash;438. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10654-011-9554-9\u003c/span\u003e\u003cspan address=\"10.1007/s10654-011-9554-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShi Y, Duan JY, Liu DW, Qiao YJ, Han QX, Pan SK et al (2018) Helicobacter pylori Infection is Associated with Occurrence of Proteinuria in Type 2 Diabetes Patients: A Systemic Review and Meta-Analysis. Chin Med J 131:2734\u0026ndash;2740. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4103/0366-6999.245269\u003c/span\u003e\u003cspan address=\"10.4103/0366-6999.245269\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMansori K, Dehghanbanadaki H, Naderpour S, Rashti R, Moghaddam AB, Moradi Y (2020) A systematic review and meta-analysis of the prevalence of Helicobacter pylori in patients with diabetes. Diabetes Metab Syndr 14:601\u0026ndash;607. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.dsx.2020.05.009\u003c/span\u003e\u003cspan address=\"10.1016/j.dsx.2020.05.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi J, Yuan W, Liu J, Yang B, Xu X, Ren X et al (2024) Association between Helicobacter pylori infection and type 2 diabetes mellitus: a retrospective cohort study and bioinformatics analysis. BMC Endocr disorders 24:168. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12902-024-01694-2\u003c/span\u003e\u003cspan address=\"10.1186/s12902-024-01694-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMan S, Ma Y, Jin C, Lv J, Tong M, Wang B et al (2020) Association between Helicobacter pylori Infection and Diabetes: A Cross-Sectional Study in China. J Diabetes Res 2020:7201379. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2020/7201379\u003c/span\u003e\u003cspan address=\"10.1155/2020/7201379\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou M, Liu J, Qi Y, Wang M, Wang Y, Zhao F et al (2018) The association between Helicobacter pylori seropositivity and risk of new-onset diabetes: a prospective cohort study. Diabetologia 61:300\u0026ndash;307. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00125-017-4465-2\u003c/span\u003e\u003cspan address=\"10.1007/s00125-017-4465-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSantos MLC, de Brito BB, da Silva FAF, Sampaio MM, Marques HS, Oliveira ESN et al (2020) Helicobacter pylori infection: Beyond gastric manifestations. World J Gastroenterol 26:4076\u0026ndash;4093. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3748/wjg.v26.i28.4076\u003c/span\u003e\u003cspan address=\"10.3748/wjg.v26.i28.4076\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Y, Yang C, You N, Zhang J (2023) Relationship between Helicobacter pylori and glycated hemoglobin: a cohort study. Front Cell Infect Microbiol 13:1196338. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcimb.2023.1196338\u003c/span\u003e\u003cspan address=\"10.3389/fcimb.2023.1196338\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong X, Cai C, Jin Q, Chen X, Yu C (2021) The efficacy of Helicobacter pylori eradication in diabetics and its effect on glycemic control: A systematic review and meta-analysis. Helicobacter 26:e12781. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/hel.12781\u003c/span\u003e\u003cspan address=\"10.1111/hel.12781\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSommer J, Borgmann SO, Gontscharuk V, Zaharia OP, Maalmi H, Herder C et al (2024) Depression symptoms, wellbeing, health-related quality of life, and diabetes-related distress in novel subtypes of recent-onset diabetes in Germany: a 5-year observational follow-up study. lancet Diabetes Endocrinol 12:890\u0026ndash;903. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s2213-8587(24)00234-1\u003c/span\u003e\u003cspan address=\"10.1016/s2213-8587(24)00234-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWojujutari Ajele K, Sunday Idemudia E (2025) The role of depression and diabetes distress in glycemic control: A meta-analysis. Diabetes Res Clin Pract 221:112014. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.diabres.2025.112014\u003c/span\u003e\u003cspan address=\"10.1016/j.diabres.2025.112014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan Gennip ACE, Schram MT, K\u0026ouml;hler S, Kroon AA, Koster A, Eussen S et al (2023) Association of type 2 diabetes according to the number of risk factors within the recommended range with incidence of major depression and clinically relevant depressive symptoms: a prospective analysis. lancet Healthy Longev 4:e63\u0026ndash;e71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s2666-7568(22)00291-4\u003c/span\u003e\u003cspan address=\"10.1016/s2666-7568(22)00291-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu H, Chen Q (2025) The bidirectional influence between type 2 diabetes mellitus and the state of depression and anxiety. J Affect Disord 386:119467. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jad.2025.119467\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2025.119467\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaina JG, Balkhiyarova Z, Nouwen A, Pupko I, Ulrich A, Boissel M et al (2023) Bidirectional Mendelian Randomization and Multiphenotype GWAS Show Causality and Shared Pathophysiology Between Depression and Type 2 Diabetes. Diabetes Care 46:1707\u0026ndash;1714. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2337/dc22-2373\u003c/span\u003e\u003cspan address=\"10.2337/dc22-2373\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhawagi WY, Al-Kuraishy HM, Hussein NR, Al-Gareeb AI, Atef E, Elhussieny O et al (2024) Depression and type 2 diabetes: A causal relationship and mechanistic pathway. Diabetes Obes Metab 26:3031\u0026ndash;3044. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/dom.15630\u003c/span\u003e\u003cspan address=\"10.1111/dom.15630\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYao J, Zhu CQ, Sun Y, Huang YW, Li QH, Liao HM et al (2025) Insulin resistance: The role in comorbid type 2 diabetes mellitus and depression. Neurosci Biobehav Rev 175:106218. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neubiorev.2025.106218\u003c/span\u003e\u003cspan address=\"10.1016/j.neubiorev.2025.106218\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarr AL, Sluiman AJ, Grecian SM, Forster R, McLachlan S, Strachan MWJ et al (2021) Depression as a risk factor for dementia in older people with type 2 diabetes and the mediating effect of inflammation. Diabetologia 64:448\u0026ndash;457. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00125-020-05301-6\u003c/span\u003e\u003cspan address=\"10.1007/s00125-020-05301-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSperber AD, Freud T, Aziz I, Palsson OS, Drossman DA, Dumitrascu DL et al (2022) Greater Overlap of Rome IV Disorders of Gut-Brain Interactions Leads to Increased Disease Severity and Poorer Quality of Life. Clin Gastroenterol Hepatol 20:e945\u0026ndash;e56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cgh.2021.05.042\u003c/span\u003e\u003cspan address=\"10.1016/j.cgh.2021.05.042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarkus HS, de Leeuw FE (2023) Cerebral small vessel disease: Recent advances and future directions. Int J Stroke 18:4\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/17474930221144911\u003c/span\u003e\u003cspan address=\"10.1177/17474930221144911\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePearcy LB, Karim HT, Butters MA, Krafty R, Boyd BD, Banihashemi L et al (2025) White matter hyperintensities and relapse risk in late-life depression. J Affect Disord 383:298\u0026ndash;305. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jad.2025.04.116\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2025.04.116\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou R, Cai Q, Liu C, Hui J, Kang M, Gou Y et al (1991) Association between white matter hyperintensity and anxiety/depression. Cerebral cortex (New York, NY: 2024;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/cercor/bhae149\u003c/span\u003e\u003cspan address=\"10.1093/cercor/bhae149\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFleischman DA, Arfanakis K, Leurgans SE, Zhang S, Lamar M, Han SD et al (2023) Late-life depressive symptoms and white matter structural integrity within older Black adults. Front Aging Neurosci 15:1138568. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnagi.2023.1138568\u003c/span\u003e\u003cspan address=\"10.3389/fnagi.2023.1138568\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKiesepp\u0026auml; T, M\u0026auml;ntyl\u0026auml; R, Luoma K, Rikandi E, Jylh\u0026auml; P, Isomets\u0026auml; E (2022) White Matter Hyperintensities after Five-Year Follow-Up and a Cross-Sectional FA Decrease in Bipolar I and Major Depressive Patients. Neuropsychobiology 81:39\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1159/000516234\u003c/span\u003e\u003cspan address=\"10.1159/000516234\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang F, Ping Y, Jin X, Hou X, Song J (2023) White matter hyperintensities and post-stroke depression: A systematic review and meta-analysis. J Affect Disord 320:370\u0026ndash;380. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jad.2022.09.166\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2022.09.166\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCai J, Sun J, Chen H, Chen Y, Zhou Y, Lou M et al (2022) Different mechanisms in periventricular and deep white matter hyperintensities in old subjects. Front Aging Neurosci 14:940538. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fnagi.2022.940538\u003c/span\u003e\u003cspan address=\"10.3389/fnagi.2022.940538\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlack S, Gao F, Bilbao J (2009) Understanding white matter disease: imaging-pathological correlations in vascular cognitive impairment. Stroke 40:S48\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/strokeaha.108.537704\u003c/span\u003e\u003cspan address=\"10.1161/strokeaha.108.537704\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHamilton NB, Kolodziejczyk K, Kougioumtzidou E, Attwell D (2016) Proton-gated Ca(2+)-permeable TRP channels damage myelin in conditions mimicking ischaemia. Nature 529:523\u0026ndash;527. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature16519\u003c/span\u003e\u003cspan address=\"10.1038/nature16519\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu X, Yang X, Zhang J, Wang Y, Selim M, Zheng Y et al (2025) Choroid plexus free-water correlates with glymphatic function in Alzheimer's disease. Alzheimer's Dement J Alzheimer's Assoc 21:e70239. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/alz.70239\u003c/span\u003e\u003cspan address=\"10.1002/alz.70239\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"clinical trial, cohort study, database research, diabetes complications","lastPublishedDoi":"10.21203/rs.3.rs-8141277/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8141277/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eObjectives\u003c/b\u003e\u003c/p\u003e\u003cp\u003eType 2 diabetes (T2D) and chronic gastritis/duodenitis (CGD) are both strongly associated with the onset of depression. However, the impact of T2D-CGD comorbidity on incident depression remains unclear.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis prospective cohort study utilized data from 387 149 participants in the UK Biobank to examine the relationship between T2D-CGD comorbidity and incident depression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePatients with T2D exhibited a significantly higher likelihood of developing CGD than those without T2D (odds ratio\u0026thinsp;=\u0026thinsp;2.10, 95% CI = [1.97, 2.24]). Both T2D and CGD were independently associated with an increased risk of incident depression, with their comorbidity demonstrating the strongest associations (adjusted hazard ratio\u0026thinsp;=\u0026thinsp;2.29, 95% CI = [1.84, 2.85]). Notably, the comorbidity was linked to an elevated risk of depression within 15 years of disease onset. White matter hyperintensity, particularly near the cerebral ventricles, partially mediated the relationship between T2D-CGD comorbidity and incident depression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIntegrated screening and long-term monitoring strategies should be prioritized for population with the comorbidity of T2D and CGD, as it significantly elevates the risk of incident depression. White matter hyperintensity can serve as an imaging biomarker for detecting the risk of depression in patients with T2D-CGD comorbidity.\u003c/p\u003e","manuscriptTitle":"Incident depression in people with comorbid type 2 diabetes and chronic gastritis/duodenitis: a large-scale cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 15:38:12","doi":"10.21203/rs.3.rs-8141277/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":"28b469dd-db69-450c-97e3-8ef389662ec4","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59156395,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases/Diabetes/Diabetes complications"},{"id":59156396,"name":"Biological sciences/Neuroscience/Diseases of the nervous system"}],"tags":[],"updatedAt":"2026-02-27T16:46:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 15:38:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8141277","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8141277","identity":"rs-8141277","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}