Depression and incidence of inflammation-related physical health conditions: a cohort study in UK Biobank

Depression and incidence of inflammation-related physical health conditions: a cohort study in UK Biobank | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Depression and incidence of inflammation-related physical health conditions: a cohort study in UK Biobank View ORCID Profile Shuvajit Saha , View ORCID Profile Regina Prigge , View ORCID Profile Caroline A Jackson , View ORCID Profile Bruce Guthrie , Kelly J Fleetwood doi: https://doi.org/10.1101/2025.01.16.25320668 Shuvajit Saha 1 Usher Institute, University of Edinburgh , Edinburgh, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shuvajit Saha For correspondence: v1ssaha{at}ed.ac.uk Regina Prigge 1 Usher Institute, University of Edinburgh , Edinburgh, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Regina Prigge Caroline A Jackson 1 Usher Institute, University of Edinburgh , Edinburgh, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Caroline A Jackson Bruce Guthrie 2 Advanced Care Research Centre, Usher Institute, University of Edinburgh , Edinburgh, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Bruce Guthrie Kelly J Fleetwood 1 Usher Institute, University of Edinburgh , Edinburgh, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background Depression is associated with multiple physical health conditions, and inflammation is a mechanism commonly proposed to explain this association. We aimed to investigate the association between depression and the incidence of physical health conditions thought to have an inflammatory etiological component, including coronary heart disease, peripheral arterial disease, type 2 diabetes, inflammatory bowel disease, inflammatory arthritis and Parkinson’s Disease. Methods We conducted a cohort study using UK Biobank (UKB) data linked to primary care, hospital admission and death data. We ascertained depression at baseline using primary care and hospital records, and self-report at the UKB baseline assessment. We identified incident physical health conditions during follow-up using primary care, hospital admission and death data. We used Cox proportional hazards models to determine hazard ratios of each incident inflammation-related condition in those with versus without depression at baseline, serially adjusting for sociodemographic factors, lifestyle factors and baseline count of morbidities. Result We included 172,556 UKB participants who had continuous primary care records. Of these, 30,770 (17.8%) had a history of depression at baseline. After excluding participants with missing data, 168,641 (98%) were included in analysis. Median follow-up was 7.1 years (IQR: 6.3, 8.0). In the model adjusted for age and sex, depression was significantly associated with a higher hazard of all inflammation-related conditions. After additionally accounting for differences in country, ethnicity and deprivation, the association between depression and each condition generally attenuated but remained statistically significant, with effect estimates ranging from a 30% increased hazard of inflammatory bowel disease (HR 1.30, 95% CI 1.06 to 1.58) to a 53% increased hazard of Parkinson’s Disease (HR 1.53, 95% CI 1.25 to 1.87). After further adjusting for lifestyle factors and comorbidity count, the association persisted only for Parkinson’s Disease (HR 1.45, 95% CI 1.18 to 1.79). Conclusions Our study found that depression is consistently associated with multiple inflammation-related physical health conditions, although associations did not persist after adjustment for lifestyle factors and baseline physical condition count. Further research is needed to explore underlying mechanisms, including inflammatory biomarkers and modifiable lifestyle factors on the causal pathway. Background Depression is a common mental health condition, affecting an estimated 300 million people worldwide [ 1 ]. The Global Burden of Disease Study 2019 reported an age-standardized prevalence of depressive disorders of 3440 per 100,000 people, with depressive disorders ranked 13 th for disease burden, worldwide [ 2 ]. As well as diminishing human capital and reducing quality of life [ 1 ], there is growing evidence that depression may be an independent risk factor for the development of some physical health conditions. Evidence from meta-analyses indicate that depression is associated with an 81% higher risk of developing coronary heart disease (CHD) [ 3 ], a 60% increased risk of developing type 2 diabetes (T2D) [ 4 ] and an increased risk of stroke [ 5 ], inflammatory bowel disease (IBD) [ 6 ] and Parkinson’s disease (PD) [ 7 ]. The link between depression and physical health conditions is likely to be multifactorial, but evidence suggests that chronic low-grade inflammation plays a crucial role in this complex association [ 8 ]. In a recent case-control study of UK Biobank (UKB) participants, individuals with depression versus without were found to have higher systemic inflammation levels [ 9 ]. Moreover, evidence indicates that depression may precede and augment pro-inflammatory cytokines, including interleukin-6 (IL-6) and C-reactive protein (CRP), potentially contributing to the development of cardiometabolic and other age-related diseases in healthy older adults [ 10 ]. Studies have also shown that anti-inflammatory agents can positively affect depressive symptoms [ 11 ]. Depression itself may therefore have an underlying inflammatory aetiological component and/or may lead to an altered inflammatory profile, each of which could help to explain a link with physical health conditions. If the link between depression and physical health conditions is at least partly due to an inflammatory mechanism, then we would expect to observe an association between depression and the incidence of various inflammation-related conditions. However, the data supporting depression as a risk factor for some inflammation-related conditions, particularly peripheral arterial disease (PAD) and inflammatory arthritis (IA), is less robust, primarily due to the lack of large cohort studies. Moreover, to our knowledge, no study has examined associations between depression and incidence of multiple physical health conditions associated with inflammation within a single population. Therefore, we aimed to investigate the association between depression and the incidence of various conditions thought to have an inflammatory aetiological component among mid-aged participants within the UKB cohort. Methods Study design and participants We conducted a cohort study using UKB data. The UKB is a cohort of half a million mid-aged adults with information on a wide range of health conditions [ 12 ]. Individuals aged 40-69 years and registered with a general practitioner in England, Scotland or Wales were invited. Participants attended baseline assessments between 2006 and 2010, which involved a touch-screen questionnaire, verbal interview, and physical measurements [ 12 , 13 ]. Participants provided written informed consent to follow-up through linkage to national datasets including primary care, hospital, cancer registry and death records. UKB has ethical approval from the NHS North West Research Ethics Committee (reference: 21/NW/0157). The present study population comprised UKB participants with linked primary care data who had continued consent to participation. Methods are summarised here, and described in detail in a previous paper [ 14 ]. Linked primary care data was available from Scotland, Wales and practices in England that used either the SystmOne or Vision practice management systems. We included individuals with a continuous primary care record (no gaps of more than 90 days between practice registrations) from at least one year prior to their baseline assessment and extending to at least one day after the baseline assessment [ 15 ]. We excluded the small proportion of primary care records from the UKB extract of the Vision practice management system in England because this linked dataset is missing records from people who died before data extraction. Linked electronic health records We ascertained the presence of conditions at baseline using information provided by the participants during their baseline assessment as well as from primary care, hospital, and cancer registry records. We identified conditions from primary care records using Read V2 and Clinical Terms V3 (CTV3) codes, from hospital records using ICD-10 codes and OPCS-4 procedure codes, and from death records using ICD-10 codes. All code lists are available in our project GitHub repository ( https://github.com/rprigge-uoe/mltc-codelists ). When people relocate within the UK, their primary care records are transferred between practices, and thus their records should capture their entire medical history. Hence, we used all primary care records up to and including the date of the participant’s baseline assessment to define conditions at baseline. Cancer registry and hospital records were available from different dates for England, Wales, and Scotland, with a minimum of eight years of records prior to the baseline assessments. To maintain consistency across secondary care data sources and the three countries, conditions at baseline were defined for each participant using cancer registry and hospital records spanning eight years up to and including their baseline assessment date. We ascertained the occurrence of incident physical health conditions during follow-up from primary care, hospital and death records. Health record follow-up varied by country, with primary care and cancer registry records available up to at least 2016, while hospital and death records were complete to 2022. Participants were therefore followed up until the earliest of death, the end of continuous primary care records, or the end of cancer registry follow-up (Additional file 1: Table S1). Depression A history of depression at baseline was identified if a participant had a prior diagnosis of depression in their linked primary care or hospital records, or if they self-reported a history of depression in response to the baseline assessment question, “Has a doctor ever told you that you have had any other serious medical conditions or disabilities?” Inflammation-related physical health conditions We identified incident physical health conditions associated with inflammation (amongst people without each specific condition at baseline). Two clinicians [SS, BG] selected six conditions/groups of conditions associated with inflammation from a list of 80 long-term health conditions (appendix 2): coronary heart disease (CHD), peripheral arterial disease (PAD), type 2 diabetes (T2D), inflammatory bowel disease (IBD), Parkinson’s Disease (PD), and inflammatory arthritis and related conditions (IA). CHD was defined as including myocardial infarction, stable angina, unstable angina and CHD not otherwise specified. IA and related conditions comprised ankylosing spondylitis, juvenile arthritis, lupus erythematosus (local and systemic), polymyalgia rheumatica, post infective and reactive arthropathies, psoriatic arthropathy, rheumatoid arthritis, and systemic sclerosis (henceforth just called IA). Covariates Age at baseline assessment and sex were determined from recruitment data and optionally updated by participants at the baseline assessment. Self-reported ethnicity was categorized into three groups: White, South Asian, and other ethnic minority groups. Country of residence (England, Wales, or Scotland) and area-based deprivation, assessed through the Townsend Deprivation Index [ 16 ], were derived from participants’ home addresses at baseline. The Townsend Deprivation Index was divided into deciles across the entire UKB cohort, ranging from 1 (least deprived) to 10 (most deprived). Information on smoking, alcohol intake frequency, sleep disturbance, and physical activity was collected from the baseline assessment touchscreen questionnaire. Body mass index (BMI) was obtained from measurements taken during the baseline assessment. BMI was classified according to World Health Organization (WHO) guidelines, including the following categories: BMI <25 kg/m², BMI 25-29.9 kg/m², BMI 30-34.9 kg/m², and BMI ≥35 kg/m². We defined smoking status as current, previous and never smokers [ 17 ]. Participants were asked about their alcohol consumption frequency, with responses ranging from daily or almost daily to never. Sleep disturbance was determined from responses to the question ‘Do you have trouble falling asleep at night or do you wake up in the middle of the night?’ with responses of never/rarely, sometimes or usually. Additionally, low physical activity was included as a dichotomous variable, defined as engaging in no activity or light activity with a frequency of once per week or less [ 18 ]. For each participant, we counted the number of long-term health conditions (Additional file 1: Table S2) at baseline including 69 physical health conditions and 10 mental health conditions. Conditions were identified from the baseline assessment and from primary care, hospital and cancer registry records, as described above. Statistical analysis For each of the six outcome conditions, we used Cox proportional hazard models to estimate the hazard ratios (HRs) with 95% confidence intervals (CIs) for time to incidence of the inflammation-related condition(s) by depression status at baseline. For each of the six outcome condition, individuals with that condition at baseline were excluded from the analysis. We obtained crude (unadjusted) estimates, with subsequent models adjusting for covariates as follows: model 1 adjusted for age at baseline and sex; model 2 additionally adjusted for other baseline socio-demographic factors, specifically country of residence, ethnicity, and Townsend Deprivation Index; and model 3 additionally adjusted for lifestyle factors (smoking, alcohol intake frequency, physical activity, sleep disturbance, and BMI) and baseline count of morbidities. Age and number of conditions at baseline were included in the models as continuous variables, each with a linear term and a quadratic term. Age and number of conditions at baseline were included in the models as continuous variables, scaled by subtracting their respective means. For each of these variables, we included the linear term, and we additionally included a quadratic term if it improved the fit of the model. The proportional hazards assumption was checked for all variables included in the study by using log cumulative hazard plots. The analysis was conducted using R version 4.3.2 [ 19 ]. Each covariate had less than 1% missing data ( table 1 ) and 98% of participants had complete data. We therefore conducted a complete-case analysis rather than performing multiple imputation. View this table: View inline View popup Table 1: Baseline characteristics for participants Results Among the 172,556 participants who met the inclusion criteria, 30,770 (17.8%) had a history of depression at baseline ( table 1 ). After excluding participants with missing data, 168,641 (98%) were included in statistical models ( figure 1 ). Median follow-up was 7.1 years (IQR: 6.3, 8.0). The mean age at baseline was 57 years. Most participants were white and from England, with no difference in depression prevalence by ethnicity. Participants with depression were more likely to be female and more commonly lived in areas with high deprivation. Current and previous smoking, low physical activity, sleep disturbance, and obesity were more common in people with versus without depression. Individuals with depression were more likely not to drink alcohol or to drink alcohol only occasionally compared to those without depression. At baseline, people with depression had a median of one more health condition compared to those without depression, and had higher baseline prevalence of CHD, PAD, T2D, IBD, PD and IA. Download figure Open in new tab Figure 1: Flow diagram of UK Biobank sample selection * Excluding participants who withdrew permission for their data to be included in research before 13 October 2023 Among individuals free of each inflammation-related condition at baseline, 4.6%, 3.7%, and 1.7% of participants developed CHD, T2D, and IA, respectively. However, fewer than 1% of participants developed PAD, IBD, and PD. Kaplan-Meier curves illustrate that participants with depression developed each condition at a faster rate than participants without depression ( figure 2 ). Download figure Open in new tab Figure 2: Proportion free of each inflammation-related condition over time, by depression status at baseline In the unadjusted model, a history of depression was associated with a statistically significantly higher hazard of all of the inflammation-related conditions ( figure 3 ). After adjustment for age and sex, compared to people without a history of depression, people with a history of depression had a 36% increased hazard of CHD (HR 1.36, 95% CI 1.28–1.44), a 58% increased hazard of PAD (HR 1.58, 95% CI 1.37–1.83), a 42% increased hazard of T2D (HR 1.42, 95% CI 1.34–1.52), a 34% increased hazard of IBD (HR 1.34, 95% CI 1.10–1.63), a 52% increased hazard of PD (HR 1.52, 95% CI 1.25–1.86), and 37% increased hazard of IA (HR 1.37, 95% CI 1.25-1.50). Further accounting for country, ethnicity and deprivation made little difference to the estimated associations between depression and each condition. Following further adjustment for number of baseline comorbidities and lifestyle factors, there were no statistically significant associations between depression and five of the six conditions, the exception being PD (HR 1.45, 95% CI 1.18-1.79; figure 3 ). Download figure Open in new tab Figure 3: Unadjusted and adjusted hazard ratios (with 95% confidence intervals) for associations between depression and inflammation-related physical health conditions * n= Number of participants developing the condition during follow-up; N= Number of participants without the condition at baseline Model 1: Adjusted for age and sex Model 2: Further adjusted for ethnicity, country, and Townsend deprivation index Model 3: Further adjusted for number of the conditions, smoking, alcohol intake, low physical activity, sleep disturbance, and BMI Discussion We found that a history of depression at baseline was associated with an increased incidence of inflammation-related physical health conditions in middle and early old age. This association persisted after adjusting for age, sex and other sociodemographic factors. After additionally adjusting for baseline comorbidities and lifestyle factors the association persisted for PD only. Previous research documents associations between depression and incidence of CHD [ 20 – 22 ], T2D [ 4 , 23 – 25 ], IA [ 25 – 28 ], IBD [ 6 ] and PD [ 7 ]. Most studies on IA have focused solely on rheumatoid arthritis, whereas our study included a broader set of IA conditions. Although few studies have investigated the association between depression and incident PAD, a recent UKB cohort study found that self-reported frequency of depressive symptoms prior to the baseline assessment in the previous two-week period were associated with a higher risk of developing PAD [ 29 ]. After adjusting for age, sex, and other sociodemographic factors, our findings are consistent with previous studies that made similar adjustments, reporting an increased risk of incident CHD [ 30 – 36 ], PAD [ 29 , 37 ], T2D [ 38 – 40 ], and PD [ 41 , 42 ] among people with depression. While the strength of the associations between depression and CHD [ 30 , 31 , 33 – 36 ], PAD [ 29 , 37 ], and T2D [ 38 – 40 ] was similar in some previous studies, others observed much higher HRs of 2.22 for CHD [ 32 ], and 3.24 [ 41 ] and 3.13 [ 42 ] for PD. Potential explanations for the observed differences in these studies include the use of varying methods to measure depression, such as the Centre for Epidemiologic Studies Depression Scale (CESD) [ 32 ], International Classification of Primary Care (ICPC) codes [ 42 ], and diagnoses made by psychiatrists [ 41 ]. A further potential reason for differences between studies is the use of varying adjustment sets. Our current study found that the association between depression and incident CHD, PAD, T2D, IBD, and IA did not persist after additional adjustments for number of baseline comorbidities, disrupted sleep, and lifestyle factors, aligning with some previous studies [ 25 , 33 , 34 , 37 , 40 , 43 – 49 ] but contrasting with others [ 4 , 20 – 22 , 24 – 28 , 50 ]. In line with previous studies [ 51 , 52 ], in our study the association between depression and PD persisted after adjustment for all covariates. However, reverse causation could partially explain these results; that is, patients with undiagnosed PD may develop depressive symptoms years before the motor symptoms become apparent [ 53 ]. In our study, the difference in effect estimates following conservative versus wider adjustment for covariates might lead to the conclusion that the association between depression and inflammation-related physical health conditions is simply due to confounding rather than depression being an independent risk factor. However, the final model adjusts for factors that may play a mediating rather than a confounding role. For example, depression might lead to low physical activity, high BMI and disturbed sleep, each of which are thought to promote chronic low-grade systemic inflammation [ 54 – 58 ], which in turn might mechanistically increase the risk of various chronic diseases, including those examined in this study. While our study is not inconsistent with the hypothesis that inflammation is the mechanism linking depression and many physical health conditions, it does not preclude the possibility that depression and inflammation-related physical conditions may share a common mechanism. For example, a dysregulated hypothalamic-pituitary-adrenal axis may separately lead to both depression and cardiometabolic disease, contributing to the association between depression and cardiometabolic diseases [ 59 , 60 ]. Alternatively, behavioural pathways, including smoking, may be additional or alternative mechanisms accounting for associations between depression and CHD [ 59 ] and PAD [ 61 ], whilst physical inactivity may partly explain links with T2D and CHD [ 59 ]. There is also some evidence [ 62 ] suggesting shared genetic variations between depression and a range of physical health conditions including IA and PD. Our study has several strengths. The large study population, long follow-up period and breadth of data collected in the UKB allowed us to examine the links between depression and multiple inflammation-related physical health conditions within a single cohort. Our study further benefits from the use linked primary care data, as well as hospital records, to ascertain incident physical health conditions. Many of these outcomes, particularly T2D and IBD, are under-ascertained when relying solely on hospital admission data. Our study has some limitations. Firstly, there are some missing values in the socio-demographic characteristics and lifestyle-related variables, but the proportion of missing values for these variables was less than 1%, and only about 2% of participants have any missing data. Therefore, a complete case analysis is unlikely to have introduced significant bias. Secondly, in our current study, we did not account for CRP, a well-established marker of chronic inflammation, as repeated measures of CRP data were unavailable. However, CRP levels can fluctuate markedly due to acute infection and other transient factors, meaning that single measurements of CRP may not reliably measure chronic inflammation. Finally, the UKB cohort primarily consists of individuals with white ethnicity and is subject to the healthy cohort effect [ 63 ], which may limit the generalizability of the findings to other populations. Despite this, the UKB is a large cohort with 500,000 participants, and we included approximately 170,000 participants in the current study, ensuring sufficient heterogeneity to detect associations between baseline characteristics and health outcomes [ 12 ]. Finally, whilst we adjusted for various demographic characteristics and lifestyle factors, as with any observational study there may be residual confounding through misclassification of included confounders and unknown confounders. In the current study, the association between depression and incident inflammation-related conditions did not persist after fully adjusting for age, sex, other sociodemographic factors, baseline comorbidities, and lifestyle factors. However, some of these factors likely play a mediating rather than a confounding role, detailed exploration of which was beyond the scope of the current study. These findings underscore the need for lifestyle interventions among individuals with depression to mitigate risk factors for cardiometabolic diseases and other physical health conditions. Health practitioners should prioritize screening for risk factors of physical illnesses in people with depression, with particular attention to conditions with potential inflammatory causes. A more integrated approach to patient care combining mental health evaluations with proactive management of physical health risks could improve outcomes and promote holistic well-being. Furthermore, future studies should adopt methodologies such as mediation analysis to investigate and untangle the underlying mechanisms linking depression and inflammation-related physical health conditions, including analysis of inflammatory biomarkers. Exploring genetic and environmental interactions could also offer a deeper understanding of individual susceptibility to these conditions, paving the way for precision medicine approaches in mental health care. Conclusions Our study found that depression is consistently associated with multiple inflammation-related physical health conditions, although associations did not persist after adjustment for lifestyle factors and baseline physical condition count, except for PD. Further research is needed to better understand the underlying mechanisms, including formal mediation analyses with a specific focus on inflammatory biomarkers and the contribution of modifiable lifestyle factors that may lie on the causal pathway. Data Availability This study was conducted using data from the UK Biobank. Researchers can apply to access the UK Biobank data for health research in the public interest. The code lists used in this study are available from https://github.com/rprigge-uoe/mltc-codelists. Declarations Ethics approval and consent to participate UKB has ethical approval from the NHS North West Research Ethics Committee (reference:21/NW/0157). All participants provided written and informed consent for data collection, analysis, and record linkage. Consent for publication Not Applicable Availability of data and materials This study was conducted using data from the UK Biobank. Researchers can apply to access the UK Biobank data for health research in the public interest. The code lists used in this study are available from https://github.com/rprigge-uoe/mltc-codelists . Competing interests The authors declare that they have no competing interests. Funding This study was funded by Medical Research Council/National Institute for Health Research (MC/S028013). Authors’ contributions BG was responsible for the conception of the study and all authors contributed to the study design. RP and KF derived the analysis dataset. SS performed the analyses, and all authors contributed to interpretation of the findings. SS wrote the first draft of the manuscript, and all authors commented on manuscript drafts. The authors read and approved the final manuscript. Acknowledgements The study was conducted using the UK Biobank Resource under application number 57213. The authors would like to thank the UK Biobank participants and the UK Biobank staff for their contributions to this study. List of abbreviations BMI Body Mass Index CHD Coronary Heart Disease CI Confidence Interval CRP C-Reactive Protein CTV3 Clinical Terms V3 HRs Hazard Ratios IA Inflammatory Arthritis and Related Conditions IBD Inflammatory Bowel Disease IL-6 Interleukin-6 PAD Peripheral Arterial Disease PD Parkinson’s Disease T2D Type 2 Diabetes UKB UK Biobank WHO World Health Organization References 1. ↵ Patel V , Chisholm D , Parikh R , Charlson FJ , Degenhardt L , Dua T , et al. 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Share Depression and incidence of inflammation-related physical health conditions: a cohort study in UK Biobank Shuvajit Saha , Regina Prigge , Caroline A Jackson , Bruce Guthrie , Kelly J Fleetwood medRxiv 2025.01.16.25320668; doi: https://doi.org/10.1101/2025.01.16.25320668 Share This Article: Copy Citation Tools Depression and incidence of inflammation-related physical health conditions: a cohort study in UK Biobank Shuvajit Saha , Regina Prigge , Caroline A Jackson , Bruce Guthrie , Kelly J Fleetwood medRxiv 2025.01.16.25320668; doi: https://doi.org/10.1101/2025.01.16.25320668 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Public and Global Health Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4421) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15212) Forensic Medicine (30) Gastroenterology (1121) Genetic and Genomic Medicine (6581) Geriatric Medicine (667) Health Economics (996) Health Informatics (4520) Health Policy (1366) Health Systems and Quality Improvement (1611) Hematology (539) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15906) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (667) Neurology (6580) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1141) Occupational and Environmental Health (956) Oncology (3324) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5433) Public and Global Health (9212) Radiology and Imaging (2193) Rehabilitation Medicine and Physical Therapy (1368) Respiratory Medicine (1194) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ff4f9178e2509d6',t:'MTc3OTM4MTEwMw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();