Hair cortisol-a method to detect chronic cortisol levels in patients with Prader-Willi syndrome

Prader Willi Syndrome ◽

Hair Cortisol ◽

Hair Samples ◽

Abstract Background: Prader-Willi syndrome (PWS) is a multisymptomatic, rare, genetic, neurodevelopmental disorder in adults mainly characterized by hyperphagia, cognitive dysfunction, behavioral problems and risk of morbid obesity. Although endocrine insufficiencies are common, hypocortisolism is rare and knowledge on long-term cortisol concentrations is lacking. The aim of this study was to evaluate long-term cortisol levels in PWS by measurements of hair cortisol.Methods: Twenty-nine adults with PWS, 15 men and 14 women, median age 29 years, median BMI 27 kg/m2, were included. Scalp hair samples were analyzed for cortisol content using liquid-chromatography tandem-mass spectrometry. In addition, a questionnaire on auxology, medication and stress were included. For comparison, 105 age- and sex-matched participants from the population-based Lifelines Cohort study were included as controls. The mean hair cortisol between the groups were compared and associations between BMI and stress were assessed by a generalized linear regression model.Results: In the PWS group large variations in hair cortisol was seen. Mean hair cortisol was 12.8±25.4 pg/mg compared to 3.8±7.3 pg/mg in controls (p=0.001). The linear regression model similarly showed higher cortisol levels in patients with PWS, which remained consistent after adjusting for BMI and stress (p=0.023). Furthermore, hair cortisol increased with BMI (p=0.012) and reported stress (p=0.014).Conclusion: Long-term cortisol concentrations were higher in patients with PWS compared to controls and increased with BMI and stress, suggesting an adequate cortisol response to chronic stress. Hair cortisol demonstrate promising applications in the context of PWS treatment and disease management.

Hair cortisol-a method to detect chronic cortisol levels in patients with Prader-Willi syndrome

BMC Endocrine Disorders ◽

10.1186/s12902-020-00646-w ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Hasanain Hamid Shukur ◽

Yolanda B. de Rijke ◽

Elisabeth F. C. van Rossum ◽

Laith Hussain-Alkhateeb ◽

Charlotte Höybye

Keyword(s):

Linear Regression ◽

Regression Model ◽

Behavioral Problems ◽

Linear Regression Model ◽

Prader Willi Syndrome ◽

Hair Cortisol ◽

Hair Samples ◽

Abstract Background Prader-Willi syndrome (PWS) is a multisymptomatic, rare, genetic, neurodevelopmental disorder in adults mainly characterized by hyperphagia, cognitive dysfunction, behavioral problems and risk of morbid obesity. Although endocrine insufficiencies are common, hypocortisolism is rare and knowledge on long-term cortisol concentrations is lacking. The aim of this study was to evaluate long-term cortisol levels in PWS by measurements of hair cortisol. Methods Twenty-nine adults with PWS, 15 men and 14 women, median age 29 years, median BMI 27 kg/m2, were included. Scalp hair samples were analyzed for cortisol content using liquid-chromatography tandem-mass spectrometry. In addition, a questionnaire on auxology, medication and stress were included. For comparison, 105 age- and sex-matched participants from the population-based Lifelines Cohort study were included as controls. The mean hair cortisol between the groups were compared and associations between BMI and stress were assessed by a generalized linear regression model. Results In the PWS group large variations in hair cortisol was seen. Mean hair cortisol was 12.8 ± 25.4 pg/mg compared to 3.8 ± 7.3 pg/mg in controls (p = 0.001). The linear regression model similarly showed higher cortisol levels in patients with PWS, which remained consistent after adjusting for BMI and stress (p = 0.023). Furthermore, hair cortisol increased with BMI (p = 0.012) and reported stress (p = 0.014). Conclusion Long-term cortisol concentrations were higher in patients with PWS compared to controls and increased with BMI and stress, suggesting an adequate cortisol response to chronic stress. Hair cortisol demonstrate promising applications in the context of PWS treatment and disease management.

Hair cortisol- a novel method to explore Prader-Willi syndrome.

10.21203/rs.3.rs-31626/v1 ◽

2020 ◽

Author(s):

Hasanain Hamid Shukur ◽

Yolanda B de Rijke ◽

Elisabeth FC van Rossum ◽

Laith Hussain-Alkhateeb ◽

Charlotte Höybye

Keyword(s):

Linear Regression ◽

Regression Model ◽

Behavioral Problems ◽

Linear Regression Model ◽

Prader Willi Syndrome ◽

Hair Cortisol ◽

Hair Samples ◽

Abstract Background: Prader-Willi syndrome (PWS) is a multisymptomatic, rare, genetic, neurodevelopmental disorder in adults mainly characterized by hyperphagia, cognitive dysfunction, behavioral problems and risk of morbid obesity. Although endocrine insufficiencies are common, hypocortisolism is rare and knowledge on long-term cortisol concentrations is lacking. The aim of this study was to evaluate long-term cortisol levels in PWS by measurements of hair cortisol. Methods: Twenty-nine adults with PWS, 15 men and 14 women, median age 29 years, median BMI 27 kg/m2, were included. Scalp hair samples were analyzed for cortisol content using liquid-chromatography tandem-mass spectrometry. In addition, a questionnaire on auxology, medication and stress were included. For comparison, 105 age- and sex-matched participants from the population-based Lifelines Cohort study were included as controls. The mean hair cortisol between the groups were compared and associations between BMI and stress were assessed by a generalized linear regression model. Results: In the PWS group large variations in hair cortisol was seen. Mean hair cortisol was 12.8±25.4 pg/mg compared to 3.8±7.3 pg/mg in controls (p=0.001). The linear regression model similarly showed higher cortisol levels in patients with PWS, which remained consistent after adjusting for BMI and stress (p=0.023). Furthermore, hair cortisol increased with BMI (p=0.012) and reported stress (p=0.014). Conclusion: Long-term cortisol concentrations were higher in patients with PWS compared to controls and increased with BMI and stress, suggesting an adequate cortisol response to chronic stress. Hair cortisol demonstrate promising applications in the context of PWS treatment and disease management.

Hair cortisol-a method to detect chronic cortisol levels in patients with Prader-Willi syndrome

10.21203/rs.3.rs-31626/v4 ◽

2020 ◽

Author(s):

Hasanain Hamid Shukur ◽

Yolanda B de Rijke ◽

Elisabeth FC van Rossum ◽

Laith Hussain-Alkhateeb ◽

Charlotte Höybye

Keyword(s):

Linear Regression ◽

Regression Model ◽

Behavioral Problems ◽

Linear Regression Model ◽

Prader Willi Syndrome ◽

Hair Cortisol ◽

Hair Samples ◽

Abstract Background: Prader-Willi syndrome (PWS) is a multisymptomatic, rare, genetic, neurodevelopmental disorder in adults mainly characterized by hyperphagia, cognitive dysfunction, behavioral problems and risk of morbid obesity. Although endocrine insufficiencies are common, hypocortisolism is rare and knowledge on long-term cortisol concentrations is lacking. The aim of this study was to evaluate long-term cortisol levels in PWS by measurements of hair cortisol. Methods: Twenty-nine adults with PWS, 15 men and 14 women, median age 29 years, median BMI 27 kg/m2, were included. Scalp hair samples were analyzed for cortisol content using liquid-chromatography tandem-mass spectrometry. In addition, a questionnaire on auxology, medication and stress were included. For comparison, 105 age- and sex-matched participants from the population-based Lifelines Cohort study were included as controls. The mean hair cortisol between the groups were compared and associations between BMI and stress were assessed by a generalized linear regression model. Results: In the PWS group large variations in hair cortisol was seen. Mean hair cortisol was 12.8±25.4 pg/mg compared to 3.8±7.3 pg/mg in controls (p=0.001). The linear regression model similarly showed higher cortisol levels in patients with PWS, which remained consistent after adjusting for BMI and stress (p=0.023). Furthermore, hair cortisol increased with BMI (p=0.012) and reported stress (p=0.014). Conclusion: Long-term cortisol concentrations were higher in patients with PWS compared to controls and increased with BMI and stress, suggesting an adequate cortisol response to chronic stress. Hair cortisol demonstrate promising applications in the context of PWS treatment and disease management.

Hair cortisol-a method to detect chronic cortisol levels in patients with Prader-Willi syndrome

10.21203/rs.3.rs-31626/v5 ◽

2020 ◽

Author(s):

Hasanain Hamid Shukur ◽

Yolanda B de Rijke ◽

Elisabeth FC van Rossum ◽

Laith Hussain-Alkhateeb ◽

Charlotte Höybye

Keyword(s):

Linear Regression ◽

Regression Model ◽

Behavioral Problems ◽

Linear Regression Model ◽

Prader Willi Syndrome ◽

Hair Cortisol ◽

Hair Samples ◽

Abstract Background: Prader-Willi syndrome (PWS) is a multisymptomatic, rare, genetic, neurodevelopmental disorder in adults mainly characterized by hyperphagia, cognitive dysfunction, behavioral problems and risk of morbid obesity. Although endocrine insufficiencies are common, hypocortisolism is rare and knowledge on long-term cortisol concentrations is lacking. The aim of this study was to evaluate long-term cortisol levels in PWS by measurements of hair cortisol.Methods: Twenty-nine adults with PWS, 15 men and 14 women, median age 29 years, median BMI 27 kg/m2, were included. Scalp hair samples were analyzed for cortisol content using liquid-chromatography tandem-mass spectrometry. In addition, a questionnaire on auxology, medication and stress were included. For comparison, 105 age- and sex-matched participants from the population-based Lifelines Cohort study were included as controls. The mean hair cortisol between the groups were compared and associations between BMI and stress were assessed by a generalized linear regression model.Results: In the PWS group large variations in hair cortisol was seen. Mean hair cortisol was 12.8±25.4 pg/mg compared to 3.8±7.3 pg/mg in controls (p=0.001). The linear regression model similarly showed higher cortisol levels in patients with PWS, which remained consistent after adjusting for BMI and stress (p=0.023). Furthermore, hair cortisol increased with BMI (p=0.012) and reported stress (p=0.014).Conclusion: Long-term cortisol concentrations were higher in patients with PWS compared to controls and increased with BMI and stress, suggesting an adequate cortisol response to chronic stress. Hair cortisol demonstrate promising applications in the context of PWS treatment and disease management.

Performance of Multiple Linear Regression Model for Long-term PM10 Concentration Prediction Based on Gaseous and Meteorological Parameters

Journal of Applied Sciences ◽

10.3923/jas.2012.1488.1494 ◽

2012 ◽

Vol 12 (14) ◽

pp. 1488-1494 ◽

Cited By ~ 12

Author(s):

Ahmad Zia Ul-Saufie ◽

Ahmad Shukri Yahaya ◽

NorAzam Ramli ◽

Hazrul Abdul Hamid

Keyword(s):

Linear Regression ◽

Regression Model ◽

Multiple Linear Regression ◽

Linear Regression Model ◽

Meteorological Parameters ◽

Multiple Linear Regression Model ◽

Pm10 Concentration ◽

Concentration Prediction

Using Bayesian Networks to Predict Long-Term Health-Related Quality of Life and Comorbidity after Bariatric Surgery: A Study Based on the Scandinavian Obesity Surgery Registry

Journal of Clinical Medicine ◽

10.3390/jcm9061895 ◽

2020 ◽

Vol 9 (6) ◽

pp. 1895

Author(s):

Yang Cao ◽

Mustafa Raoof ◽

Eva Szabo ◽

Johan Ottosson ◽

Ingmar Näslund

Keyword(s):

Bariatric Surgery ◽

Linear Regression ◽

Bayesian Networks ◽

Regression Model ◽

Linear Regression Model ◽

Health Related Quality ◽

Related Quality ◽

Health Related

Previously published literature has identified a few predictors of health-related quality of life (HRQoL) after bariatric surgery. However, performance of the predictive models was not evaluated rigorously using real world data. To find better methods for predicting prognosis in patients after bariatric surgery, we examined performance of the Bayesian networks (BN) method in predicting long-term postoperative HRQoL and compared it with the convolution neural network (CNN) and multivariable logistic regression (MLR). The patients registered in the Scandinavian Obesity Surgery Registry (SOReg) were used for the current study. In total, 6542 patients registered in the SOReg between 2008 and 2012 with complete demographic and preoperative comorbidity information, and preoperative and postoperative 5-year HROoL scores and comorbidities were included in the study. HRQoL was measured using the RAND-SF-36 and the obesity-related problems scale. Thirty-five variables were used for analyses, including 19 predictors and 16 outcome variables. The Gaussian BN (GBN), CNN, and a traditional linear regression model were used for predicting 5-year HRQoL scores, and multinomial discrete BN (DBN) and MLR were used for 5-year comorbidities. Eighty percent of the patients were randomly selected as a training dataset and 20% as a validation dataset. The GBN presented a better performance than the CNN and the linear regression model; it had smaller mean squared errors (MSEs) than those from the CNN and the linear regression model. The MSE of the summary physical scale was only 0.0196 for GBN compared to the 0.0333 seen in the CNN. The DBN showed excellent predictive ability for 5-year type 2 diabetes and dyslipidemia (area under curve (AUC) = 0.942 and 0.917, respectively), good ability for 5-year hypertension and sleep apnea syndrome (AUC = 0.891 and 0.834, respectively), and fair ability for 5-year depression (AUC = 0.750). Bayesian networks provide useful tools for predicting long-term HRQoL and comorbidities in patients after bariatric surgery. The hybrid network that may involve variables from different probability distribution families deserves investigation in the future.

Measurement of cortisol in human Hair as a biomarker of systemic exposure

Clinical & Investigative Medicine ◽

10.25011/cim.v30i5.2894 ◽

2007 ◽

Vol 30 (5) ◽

pp. 183 ◽

Cited By ~ 294

Author(s):

Brittany Sauvé ◽

Gideon Koren ◽

Grace Walsh ◽

Sonya Tokmakejian ◽

Stan HM Van Uum

Keyword(s):

Reference Range ◽

Systemic Exposure ◽

Hair Color ◽

Hair Cortisol ◽

Serum Samples ◽

Endogenous Production ◽

Hair Samples ◽

Obese Subjects ◽

Purpose: Current methods for measuring long-term endogenous production of cortisol can be challenging due to the need to take multiple urine, saliva or serum samples. Hair grows approximately 1 centimeter per month, and hair analysis accurately reflects exposure to drug abuse and environmental toxins. Here we describe a new assay for measurement of cortisol in hair, and determined a reference range for non-obese subjects. Methods: For measurement of cortisol in hair we modified an immunoassay originally developed for measuring cortisol in saliva. We compared hair samples obtained from various parts of the head, and assessed the effect of hair dying. We analyzed hair samples from non-obese subjects, in whom we also obtained urine, saliva and blood samples for cortisol measurements. Results: The mean extraction recovery for hair cortisol standards of 100 ng/ml, 50 ng/ml and 2 ng/ml (n=6) was 87.9%, 88.9% and 87.4%, respectively. Hair cortisol levels were not affected by hair color or by dying hair samples after they were obtained. Cortisol levels were decreased in hair that was artificially colored before taking the sample. The coefficient of variation was high for cortisol levels in hair from different sections of the head (30.5 %), but was smaller when comparing between hair samples obtained from the vertex posterior (15.6%). The reference range for cortisol in hair was 17.7-153.2 pg/mg of hair (median 46.1 pg/mg). Hair cortisol levels correlated significantly with cortisol in 24-hour urine (r=0.33; P=0.041). Conclusion: The correlation of hair cortisol with 24-hour urine cortisol supports its relevance as biomarker for long-term exposure.

Predicting Long-Term Health-Related Quality of Life after Bariatric Surgery Using a Conventional Neural Network: A Study Based on the Scandinavian Obesity Surgery Registry

Journal of Clinical Medicine ◽

10.3390/jcm8122149 ◽

2019 ◽

Vol 8 (12) ◽

pp. 2149 ◽

Cited By ~ 2

Author(s):

Yang Cao ◽

Mustafa Raoof ◽

Scott Montgomery ◽

Johan Ottosson ◽

Ingmar Näslund

Keyword(s):

Bariatric Surgery ◽

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Health Related Quality ◽

Related Quality ◽

Health Related ◽

Better Than

Severe obesity has been associated with numerous comorbidities and reduced health-related quality of life (HRQoL). Although many studies have reported changes in HRQoL after bariatric surgery, few were long-term prospective studies. We examined the performance of the convolution neural network (CNN) for predicting 5-year HRQoL after bariatric surgery based on the available preoperative information from the Scandinavian Obesity Surgery Registry (SOReg). CNN was used to predict the 5-year HRQoL after bariatric surgery in a training dataset and evaluated in a test dataset. In general, performance of the CNN model (measured as mean squared error, MSE) increased with more convolution layer filters, computation units, and epochs, and decreased with a larger batch size. The CNN model showed an overwhelming advantage in predicting all the HRQoL measures. The MSEs of the CNN model for training data were 8% to 80% smaller than those of the linear regression model. When the models were evaluated using the test data, the CNN model performed better than the linear regression model. However, the issue of overfitting was apparent in the CNN model. We concluded that the performance of the CNN is better than the traditional multivariate linear regression model in predicting long-term HRQoL after bariatric surgery; however, the overfitting issue needs to be mitigated using more features or more patients to train the model.

Psychological and physiological effects of caring for patients with treatment-resistant depression

Psychological Medicine ◽

10.1017/s0033291711003035 ◽

2012 ◽

Vol 42 (9) ◽

pp. 1825-1833 ◽

Cited By ~ 12

Author(s):

L. J. Rane ◽

A. Fekadu ◽

A. S. Papadopoulos ◽

S. C. Wooderson ◽

L. Poon ◽

...

Keyword(s):

Patient Outcome ◽

Linear Regression ◽

Regression Model ◽

Hpa Axis ◽

Linear Regression Model ◽

Treatment Resistant Depression ◽

Treatment Resistant ◽

Carer Burden ◽

Resistant Depression

BackgroundCarers of patients with psychiatric disorders show high levels of anxiety and depression, possibly mediated through disruption of the hypothalamo–pituitary–adrenal (HPA) axis. Among carers of patients with treatment-resistant depression (TRD), we set out to determine the psychological and physiological (HPA axis) consequences of caring, and the association of these consequences with long-term outcome in patients.MethodThirty-five informal carers of patients with severe TRD requiring in-patient treatment were recruited and compared with 23 controls. HPA-axis activity was assessed by measuring post-awaking salivary cortisol. The Involvement Evaluation Questionnaire (IEQ) and the General Health Questionnaire-12 (GHQ-12) were administered to measure carer burden and psychiatric caseness respectively. Independent t tests were used to compare differences between carers and controls and a linear regression model was used to determine the association of post-awakening cortisol with carer status while controlling for confounding variables. Data on long-term patient outcome (12 to 83 months), measured using the Hamilton Depression Rating Scale (HAMD), were also obtained and linear regression was used to determine the association between cortisol output in carers and remission status in patients.ResultsCarers experienced high carer burden and high psychiatric caseness. Carers showed reduced cortisol output after awakening, calculated as the area under the curve with respect to ground (AUCg), which remained significant after controlling for potential confounders. In a linear regression model, non-remission in patients was associated with reduced cortisol output in carers.ConclusionsCaring for patients with TRD is associated with adverse psychological and physiological changes suggesting hypocortisolism post-awakening. These changes are associated with poor patient outcome.