scholarly journals A study protocol for the development and internal validation of a multivariable prognostic model to determine lower extremity muscle injury risk in elite football (soccer) players, with further exploration of prognostic factors

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Tom Hughes ◽  
Richard Riley ◽  
Jamie C. Sergeant ◽  
Michael J. Callaghan
Keyword(s):  
Logistic Regression ◽  
Prognostic Factors ◽  
Clinical Reasoning ◽  
Prognostic Model ◽  
Model Development ◽  
Health Examination ◽  
Prevention Strategies ◽  
Internal Validation ◽  
Risk Estimates ◽  
Multivariable Logistic Regression

Abstract Background Indirect muscle injuries (IMIs) are a considerable burden to elite football (soccer) teams, and prevention of these injuries offers many benefits. Preseason medical, musculoskeletal and performance screening (termed periodic health examination (PHE)) can be used to help determine players at risk of injuries such as IMIs, where identification of PHE-derived prognostic factors (PF) may inform IMI prevention strategies. Furthermore, using several PFs in combination within a multivariable prognostic model may allow individualised IMI risk estimation and specific targeting of prevention strategies, based upon an individual’s PF profile. No such models have been developed in elite football and the current IMI prognostic factor evidence is limited. This study aims to (1) develop and internally validate a prognostic model for individualised IMI risk prediction within a season in elite footballers, using the extent of the prognostic evidence and clinical reasoning; and (2) explore potential PHE-derived PFs associated with IMI outcomes in elite footballers, using available PHE data from a professional team. Methods This is a protocol for a retrospective cohort study. PHE and injury data were routinely collected over 5 seasons (1 July 2013 to 19 May 2018), from a population of elite male players aged 16–40 years old. Of 60 candidate PFs, 15 were excluded. Twelve variables (derived from 10 PFs) will be included in model development that were identified from a systematic review, missing data assessment, measurement reliability evaluation and clinical reasoning. A full multivariable logistic regression model will be fitted, to ensure adjustment before backward elimination. The performance and internal validation of the model will be assessed. The remaining 35 candidate PFs are eligible for further exploration, using univariable logistic regression to obtain unadjusted risk estimates. Exploratory PFs will also be incorporated into multivariable logistic regression models to determine risk estimates whilst adjusting for age, height and body weight. Discussion This study will offer insights into clinical usefulness of a model to predict IMI risk in elite football and highlight the practicalities of model development in this setting. Further exploration may identify other relevant PFs for future confirmatory studies and model updating, or influence future injury prevention research.

scholarly journals Prediction of Shoulder Pain in Youth Competitive Swimmers: The Development and Internal Validation of a Prognostic Prediction Model

2020 ◽  
Vol 49 (1) ◽  
pp. 154-161
Author(s):  
Stef Feijen ◽  
Thomas Struyf ◽  
Kevin Kuppens ◽  
Angela Tate ◽  
Filip Struyf
Keyword(s):  
Logistic Regression ◽  
Shoulder Pain ◽  
Muscle Endurance ◽  
Internal Validation ◽  
Competitive Swimming ◽  
Final Model ◽  
Shoulder Muscle ◽  
Multivariable Logistic Regression ◽  
Posterior Shoulder ◽  
Competitive Swimmers

Background: Knowledge of predictors for shoulder pain in swimmers can assist professionals working with the athlete in developing optimal prevention strategies. However, study methodology and limited available data have constrained a comprehensive understanding of which factors cause shoulder pain. Purpose: To investigate risk factors and develop and internally validate a multivariable prognostic model for the prediction of shoulder pain in swimmers. Study Design: Cohort study; Level of evidence, 2. Methods: A total of 201 pain-free club- to international-level competitive swimmers were followed for 2 consecutive seasons. The cohort consisted of 96 male (mean ± SD age, 13.9 ± 2.2 years) and 105 female (13.9 ± 2.2 years) swimmers. Demographic, sport-specific, and musculoskeletal characteristics were assessed every 6 months. Swim-training exposure was observed prospectively. Shoulder pain interfering with training was the primary outcome. Multiple imputation was used to cope with missing data. The final model was estimated using multivariable logistic regression. We applied bootstrapping to internally validate the model and correct for overoptimism. Results: A total of 42 new cases of shoulder pain were recorded during the study. Average duration of follow-up was 1.1 years. Predictors included in the final model were acute:chronic workload ratio (odds ratio [OR], 4.31; 95% CI, 1.00-18.54), competitive level (OR, 0.19; 95% CI, 0.06-0.63), shoulder flexion range of motion, posterior shoulder muscle endurance (OR, 0.96; 95% CI, 0.92-0.99), and hand entry position error (OR, 0.37; 95% CI, 0.16-0.91). After internal validation, this model maintained good calibration and discriminative power (area under the receiver operating characteristic curve, 0.71; 95% CI, 0.60-0.94). Conclusion: Our model consists of parameters that are readily measurable in a swimming setting, allowing the identification of swimmers at risk for shoulder pain. Multivariable logistic regression showed the strongest predictors for shoulder pain were regional competitive swimming level, acute:chronic workload ratio, posterior shoulder muscle endurance, and hand entry error.

scholarly journals Pre-season screening currently has no value for injury prediction: The development and internal validation of a multivariable prognostic model to predict indirect muscle injury risk in elite football (soccer) players

2019 ◽  
Author(s):  
Tom Hughes ◽  
Richard D. Riley ◽  
Michael J. Callaghan ◽  
Jamie C. Sergeant
Keyword(s):  
Prognostic Factors ◽  
Muscle Injury ◽  
Injury Risk ◽  
Prognostic Model ◽  
Missing Values ◽  
Prediction Models ◽  
Health Examination ◽  
List Type ◽  
Muscle Injuries ◽  
Parsimonious Model

ABSTRACTBackgroundIn elite football (soccer), periodic health examination (PHE) could provide prognostic factors to predict injury risk.ObjectiveTo develop and internally validate a prognostic model to predict individual indirect (non-contact) muscle injury (IMI) risk during a season in elite footballers, only using PHE-derived candidate prognostic factors.MethodsRoutinely collected preseason PHE and injury data were used from 119 players over 5 seasons (1st July 2013 to 19th May 2018). Ten candidate prognostic factors (12 parameters) were included in model development. Multiple imputation was used to handle missing values. The outcome was any time-loss, index indirect muscle injury (I-IMI) affecting the lower extremity. A full logistic regression model was fitted, and a parsimonious model developed using backward-selection to remove non-significant factors. Predictive performance was assessed through calibration, discrimination and decision-curve analysis, averaged across all imputed datasets. The model was internally validated using bootstrapping and adjusted for overfitting.ResultsDuring 317 participant-seasons, 138 I-IMIs were recorded. The parsimonious model included only age and frequency of previous IMIs; apparent calibration was perfect but discrimination was modest (C-index = 0.641, 95% confidence interval (CI): 0.580 to 0.703), with clinical utility evident between risk thresholds of 37-71%. After validation and overfitting adjustment, performance deteriorated (C-index = 0.580; calibration-in-the-large =-0.031, calibration slope =0.663).ConclusionThe selected PHE data were insufficient prognostic factors from which to develop a useful model for predicting IMI risk in elite footballers. Further research should prioritise identifying novel prognostic factors to improve future risk prediction models in this field.Trial registration numberNCT03782389KEY POINTSFactors measured through preseason screening generally have weak prognostic strength for future indirect muscle injuries and further research is needed to identify novel, robust prognostic factors.Because of sample size restrictions, and until the evidence base improves, it is likely that any further attempts at creating a prognostic model at individual club level would also suffer from poor performance.The value of using preseason screening data to make injury predictions or to select bespoke injury prevention strategies remains to be demonstrated, so screening should only be considered as useful for detection of salient pathology or for rehabilitation/ performance monitoring purposes at this time.

scholarly journals An Externally Validated Dynamic Nomogram for Predicting Unfavorable Prognosis in Patients With Aneurysmal Subarachnoid Hemorrhage

2021 ◽  
Vol 12 ◽  
Author(s):  
Ping Hu ◽  
Yang Xu ◽  
Yangfan Liu ◽  
Yuntao Li ◽  
Liguo Ye ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Prognostic Factors ◽  
Subarachnoid Hemorrhage ◽  
Validation Cohort ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
External Validation ◽  
Brier Score ◽  
Web Based ◽  
Training Cohort ◽  
Multivariable Logistic Regression

Background: Aneurysmal subarachnoid hemorrhage (aSAH) leads to severe disability and functional dependence. However, no reliable method exists to predict the clinical prognosis after aSAH. Thus, this study aimed to develop a web-based dynamic nomogram to precisely evaluate the risk of poor outcomes in patients with aSAH.Methods: Clinical patient data were retrospectively analyzed at two medical centers. One center with 126 patients was used to develop the model. Least absolute shrinkage and selection operator (LASSO) analysis was used to select the optimal variables. Multivariable logistic regression was applied to identify independent prognostic factors and construct a nomogram based on the selected variables. The C-index and Hosmer–Lemeshow p-value and Brier score was used to reflect the discrimination and calibration capacities of the model. Receiver operating characteristic curve and calibration curve (1,000 bootstrap resamples) were generated for internal validation, while another center with 84 patients was used to validate the model externally. Decision curve analysis (DCA) and clinical impact curves (CICs) were used to evaluate the clinical usefulness of the nomogram.Results: Unfavorable prognosis was observed in 46 (37%) patients in the training cohort and 24 (29%) patients in the external validation cohort. The independent prognostic factors of the nomogram, including neutrophil-to-lymphocyte ratio (NLR) (p = 0.005), World Federation of Neurosurgical Societies (WFNS) grade (p = 0.002), and delayed cerebral ischemia (DCI) (p = 0.0003), were identified using LASSO and multivariable logistic regression. A dynamic nomogram (https://hu-ping.shinyapps.io/DynNomapp/) was developed. The nomogram model demonstrated excellent discrimination, with a bias-corrected C-index of 0.85, and calibration capacities (Hosmer–Lemeshow p-value, 0.412; Brier score, 0.12) in the training cohort. Application of the model to the external validation cohort yielded a C-index of 0.84 and a Brier score of 0.13. Both DCA and CIC showed a superior overall net benefit over the entire range of threshold probabilities.Conclusion: This study identified that NLR on admission, WFNS grade, and DCI independently predicted unfavorable prognosis in patients with aSAH. These factors were used to develop a web-based dynamic nomogram application to calculate the precise probability of a poor patient outcome. This tool will benefit personalized treatment and patient management and help neurosurgeons make better clinical decisions.

scholarly journals Development and validation of nomograms for prediction of overall survival and cancer-specific survival of patients of colorectal cancer

2019 ◽  
Vol 50 (3) ◽  
pp. 261-269
Author(s):  
Jieyun Zhang ◽  
Yue Yang ◽  
Xiaojian Fu ◽  
Weijian Guo
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Prognostic Model ◽  
Cox Regression ◽  
External Validation ◽  
Cancer Prognosis ◽  
Cancer Specific Survival ◽  
Internal Validation ◽  
Development And Validation

Abstract Purpose Nomograms are intuitive tools for individualized cancer prognosis. We sought to develop a clinical nomogram for prediction of overall survival and cancer-specific survival for patients with colorectal cancer. Methods Patients with colorectal cancer diagnosed between 1988 and 2006 and those who underwent surgery were retrieved from the Surveillance, Epidemiology, and End Results database and randomly divided into the training (n = 119 797) and validation (n = 119 797) cohorts. Log-rank and multivariate Cox regression analyses were used in our analysis. To find out death from other cancer causes and non-cancer causes, a competing-risks model was used, based on which we integrated these significant prognostic factors into nomograms and subjected the nomograms to bootstrap internal validation and to external validation. Results The 1-, 3-, 5- and 10-year probabilities of overall survival in patients of colorectal cancer after surgery intervention were 83.04, 65.54, 54.79 and 38.62%, respectively. The 1-, 3-, 5- and 10-year cancer-specific survival was 87.36, 73.44, 66.22 and 59.11%, respectively. Nine independent prognostic factors for overall survival and nine independent prognostic factors for cancer specific survival were included to build the nomograms. Internal and external validation CI indexes of overall survival were 0.722 and 0.721, and those of cancer-specific survival were 0.765 and 0.766, which was satisfactory. Conclusions Nomograms for prediction of overall survival and cancer-specific survival of patients with colorectal cancer. Performance of the model was excellent. This practical prognostic model may help clinicians in decision-making and design of clinical studies.

scholarly journals Predicting patient-reported and objectively measured functional outcome 6 months after ankle fracture in people aged 60 years or over in the UK: prognostic model development and internal validation

BMJ Open ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. e029813 ◽  
Author(s):  
David J Keene ◽  
Karan Vadher ◽  
Keith Willett ◽  
Dipesh Mistry ◽  
Matthew L Costa ◽  
...  
Keyword(s):  
Data Model ◽  
Ankle Fracture ◽  
Prognostic Model ◽  
Predictive Accuracy ◽  
Model Development ◽  
Internal Validation ◽  
Link Type ◽  
Ankle Function ◽  
Objectively Measured

ObjectiveTo predict functional outcomes 6 months after ankle fracture in people aged ≥60 years using post-treatment and 6-week follow-up data to inform anticipated recovery, and identify people who may benefit from additional monitoring or rehabilitation.DesignPrognostic model development and internal validation.Setting24 National Health Service hospitals, UK.MethodsParticipants were the Ankle Injury Management clinical trial cohort (n=618) (ISRCTN04180738), aged 60–96 years, 459/618 (74%) female, treated surgically or conservatively for unstable ankle fracture. Predictors were injury and sociodemographic variables collected at baseline (acute hospital setting) and 6-week follow-up (clinic). Outcome measures were 6-month postinjury (primary) self-reported ankle function, using the Olerud and Molander Ankle Score (OMAS), and (secondary) Timed Up and Go (TUG) test by blinded assessor. Missing data were managed with single imputation. Multivariable linear regression models were built to predict OMAS or TUG, using baseline variables or baseline and 6-week follow-up variables. Models were internally validated using bootstrapping.ResultsThe OMAS baseline data model included: alcohol per week (units), postinjury EQ-5D-3L visual analogue scale (VAS), sex, preinjury walking distance and walking aid use, smoking status and perceived health status. The baseline/6-week data model included the same baseline variables, minus EQ-5D-3L VAS, plus five 6-week predictors: radiological malalignment, injured ankle dorsiflexion and plantarflexion range of motion, and 6-week OMAS and EQ-5D-3L. The models explained approximately 23% and 26% of the outcome variation, respectively. Similar baseline and baseline/6 week data models to predict TUG explained around 30% and 32% of the outcome variation, respectively.ConclusionsPredictive accuracy of the prognostic models using commonly recorded clinical data to predict self-reported or objectively measured ankle function was relatively low and therefore unlikely to be beneficial for clinical practice and counselling of patients. Other potential predictors (eg, psychological factors such as catastrophising and fear avoidance) should be investigated to improve predictive accuracy.Trial registration numberISRCTN04180738; Post-results.

Predictors of perioperative atrial fibrillation in non cardiac elective surgery: the Hart score

EP Europace ◽  
2021 ◽  
Vol 23 (Supplement_3) ◽  
Author(s):  
G Stronati ◽  
C Mondelli ◽  
S Principi ◽  
M Silenzi ◽  
A Urbinati ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Logistic Regression ◽  
High Risk ◽  
Risk Score ◽  
Primary Endpoint ◽  
Thyroid Dysfunction ◽  
Previous History ◽  
Internal Validation ◽  
Old Patients ◽  
Multivariable Logistic Regression

Abstract Funding Acknowledgements Type of funding sources: None. BACKGROUND  Postoperative atrial fibrillation (POAF) is defined as de novo onset of atrial fibrillation in the post operative period, in patients with no previous history of atrial fibrillation. It affects almost 3% of all over 45 year old patients undergoing non cardiovascular surgery and is associated with a higher risk of stroke and mortality. PURPOSE  To assess independent predictors of POAF in elective non-cardiac surgery.  METHODS  Retrospective observational database including all patients attending a cardiological preoperative assessment from the 1st of January 2016 to the 31st of December 2019. The primary endpoint was the occurrence of POAF. Individual predictors of the primary endpoint were tested through a series of multivariable logistic regression models, including only those variables with a p<.01 in the univariable models. Independent predictors were entered into a score, and assigned points according to their odd ratios. Performance of the risk score was tested with the receiver operating characteristic  curve. A bootstrap procedure was employed for internal validation of both the multivariable logistic regression model and the risk score, using 10000 bootstrap samples and bias-corrected and accelerated confidence intervals. RESULTS  A total of 2048 patients were enrolled (1350 men, age 71 ± 12 years). Fourty-four patients experienced POAF (2.1%) – median 3 days (1st-3rd quartile 2-3 days). Age (OR 1.03 for each year; 95% CI 1.01-1.07), hypertension (OR 3.43; 95% CI 1.22-9.63), thyroid dysfunction (OR 2.70; 95% CI 1.35-5.42) and intermediate or high risk surgery (OR 18.28; 95% CI 2.51-33.09) resulted as independent predictors of POAF (all p < 0.05). We therefore created the HART score according to Table 1 (OR 2.59 for each point; 95% CI 1.79-3.75; p > 0.001). A cut-off score ≥ 5 has a 70% sensitivity and a 72% specificity in detecting POAF in our population (AUC 0.74). Bootstrapping for internal validation confirmed the overall results. CONCLUSIONS A four items point-based risk score such as the HART score, could be effective in implementing effective POAF screening and improve management. The HART score Variable Points H Hypertension 1 point A Age (65-74 years) 1 point Age (75+ years) 2 points R Intermediate Risk surgery 3 points High Risk surgery 3 points T Thyroid dysfunction 1 point

scholarly journals Effect of prostatic apex shape (Lee types) and urethral sphincter length in preoperative MRI on very early continence rates after radical prostatectomy

2021 ◽  
Author(s):  
Mike Wenzel ◽  
Felix Preisser ◽  
Matthias Mueller ◽  
Lena H. Theissen ◽  
Maria N. Welte ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Radical Prostatectomy ◽  
Regression Models ◽  
Urethral Sphincter ◽  
Anatomical Characteristics ◽  
Logistic Regression Models ◽  
Pad Test ◽  
Type D ◽  
Multivariable Logistic Regression ◽  
Type C

Abstract Purpose To test the effect of anatomic variants of the prostatic apex overlapping the membranous urethra (Lee type classification), as well as median urethral sphincter length (USL) in preoperative multiparametric magnetic resonance imaging (mpMRI) on the very early continence in open (ORP) and robotic-assisted radical prostatectomy (RARP) patients. Methods In 128 consecutive patients (01/2018–12/2019), USL and the prostatic apex classified according to Lee types A–D in mpMRI prior to ORP or RARP were retrospectively analyzed. Uni- and multivariable logistic regression models were used to identify anatomic characteristics for very early continence rates, defined as urine loss of ≤ 1 g in the PAD-test. Results Of 128 patients with mpMRI prior to surgery, 76 (59.4%) underwent RARP vs. 52 (40.6%) ORP. In total, median USL was 15, 15 and 10 mm in the sagittal, coronal and axial dimensions. After stratification according to very early continence in the PAD-test (≤ 1 g vs. > 1 g), continent patients had significantly more frequently Lee type D (71.4 vs. 54.4%) and C (14.3 vs. 7.6%, p = 0.03). In multivariable logistic regression models, the sagittal median USL (odds ratio [OR] 1.03) and Lee type C (OR: 7.0) and D (OR: 4.9) were independent predictors for achieving very early continence in the PAD-test. Conclusion Patients’ individual anatomical characteristics in mpMRI prior to radical prostatectomy can be used to predict very early continence. Lee type C and D suggest being the most favorable anatomical characteristics. Moreover, longer sagittal median USL in mpMRI seems to improve very early continence rates.

scholarly journals Comparison of laparoscopic and hand-assisted laparoscopic nephrectomy for inflammatory renal disease: which is the preferred approach?

2021 ◽  
Vol 13 ◽  
pp. 175628722098404
Author(s):  
Xudong Guo ◽  
Hanbo Wang ◽  
Yuzhu Xiang ◽  
Xunbo Jin ◽  
Shaobo Jiang
Keyword(s):  
Logistic Regression ◽  
Renal Disease ◽  
Complication Rate ◽  
Adverse Outcomes ◽  
Laparoscopic Nephrectomy ◽  
Multivariable Logistic Regression Analysis ◽  
Invasive Approach ◽  
Intraoperative Complication ◽  
Multivariable Logistic Regression ◽  
Significant Difference

Aims: Management of inflammatory renal disease (IRD) can still be technically challenging for laparoscopic procedures. The aim of the present study was to compare the safety and feasibility of laparoscopic and hand-assisted laparoscopic nephrectomy in patients with IRD. Patients and methods: We retrospectively analyzed the data of 107 patients who underwent laparoscopic nephrectomy (LN) and hand-assisted laparoscopic nephrectomy (HALN) for IRD from January 2008 to March 2020, including pyonephrosis, renal tuberculosis, hydronephrosis, and xanthogranulomatous pyelonephritis. Patient demographics, operative outcomes, and postoperative recovery and complications were compared between the LN and HALN groups. Multivariable logistic regression analysis was conducted to identify the independent predictors of adverse outcomes. Results: Fifty-five subjects in the LN group and 52 subjects in the HALN group were enrolled in this study. In the LN group, laparoscopic nephrectomy was successfully performed in 50 patients (90.9%), while four (7.3%) patients were converted to HALN and one (1.8%) case was converted to open procedure. In HALN group, operations were completed in 51 (98.1%) patients and conversion to open surgery was necessary in one patient (1.9%). The LN group had a shorter median incision length (5 cm versus 7 cm, p < 0.01) but a longer median operative duration (140 min versus 105 min, p < 0.01) than the HALN group. There was no significant difference in blood loss, intraoperative complication rate, postoperative complication rate, recovery of bowel function, and hospital stay between the two groups. Multivariable logistic regression revealed that severe perinephric adhesions was an independent predictor of adverse outcomes. Conclusion: Both LN and HALN appear to be safe and feasible for IRD. As a still minimally invasive approach, HALN provided an alternative to IRD or when conversion was needed in LN.

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p &lt; 0,0001), education (p &lt; 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

scholarly journals The influence of comorbid depression and overweight status on peripheral inflammation and cortisol levels

2021 ◽  
pp. 1-8
Author(s):  
Anna P. McLaughlin ◽  
Naghmeh Nikkheslat ◽  
Caitlin Hastings ◽  
Maria A. Nettis ◽  
Melisa Kose ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Significant Risk ◽  
Normal Weight ◽  
Cortisol Awakening Response ◽  
Peripheral Inflammation ◽  
Comorbid Depression ◽  
Diurnal Cortisol ◽  
Overweight Status ◽  
Multivariable Logistic Regression ◽  
Cortisol Levels

Abstract Background Depression and overweight are each associated with abnormal immune system activation. We sought to disentangle the extent to which depressive symptoms and overweight status contributed to increased inflammation and abnormal cortisol levels. Methods Participants were recruited through the Wellcome Trust NIMA Consortium. The sample of 216 participants consisted of 69 overweight patients with depression; 35 overweight controls; 55 normal-weight patients with depression and 57 normal-weight controls. Peripheral inflammation was measured as high-sensitivity C-Reactive Protein (hsCRP) in serum. Salivary cortisol was collected at multiple points throughout the day to measure cortisol awakening response and diurnal cortisol levels. Results Overweight patients with depression had significantly higher hsCRP compared with overweight controls (p = 0.042), normal-weight depressed patients (p < 0.001) and normal-weight controls (p < 0.001), after controlling for age and gender. Multivariable logistic regression showed that comorbid depression and overweight significantly increased the risk of clinically elevated hsCRP levels ⩾3 mg/L (OR 2.44, 1.28–3.94). In a separate multivariable logistic regression model, overweight status contributed most to the risk of having hsCRP levels ⩾3 mg/L (OR 1.52, 0.7–2.41), while depression also contributed a significant risk (OR 1.09, 0.27–2). There were no significant differences between groups in cortisol awakening response and diurnal cortisol levels. Conclusion Comorbid depression and overweight status are associated with increased hsCRP, and the coexistence of these conditions amplified the risk of clinically elevated hsCRP levels. Overweight status contributed most to the risk of clinically elevated hsCRP levels, but depression also contributed to a significant risk. We observed no differences in cortisol levels between groups.

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