Psychosocial factors, health behaviors and risk of cancer incidence: Testing interaction and effect modification in an individual participant data meta-analysis.

Depression, anxiety and other psychosocial factors are hypothesized to be involved in cancer development. We examined whether psychosocial factors interact with or modify the effects of health behaviors, such as smoking and alcohol use, in relation to cancer incidence. Two-stage individual participant data meta-analyses were performed based on 22 cohorts of the PSYchosocial factors and CAncer (PSY-CA) study. We examined nine psychosocial factors (depression diagnosis, depression symptoms, anxiety diagnosis, anxiety symptoms, perceived social support, loss events, general distress, neuroticism, relationship status), seven health behaviors/behavior-related factors (smoking, alcohol use, physical activity, body mass index, sedentary behavior, sleep quality, sleep duration) and seven cancer outcomes (overall cancer, smoking-related, alcohol-related, breast, lung, prostate, colorectal). Effects of the psychosocial factor, health behavior and their product term on cancer incidence were estimated using Cox regression. We pooled cohort-specific estimates using multivariate random-effects meta-analyses. Additive and multiplicative interaction/effect modification was examined. This study involved 437,827 participants, 36,961 incident cancer diagnoses, and 4,749,481 person years of follow-up. Out of 744 combinations of psychosocial factors, health behaviors, and cancer outcomes, we found no evidence of interaction. Effect modification was found for some combinations, but there were no clear patterns for any particular factors or outcomes involved. In this first large study to systematically examine potential interaction and effect modification, we found no evidence for psychosocial factors to interact with or modify health behaviors in relation to cancer incidence. The behavioral risk profile for cancer incidence is similar in people with and without psychosocial stress.

Estimating Additive Interaction in Two-Stage Individual Participant Data Meta-Analysis.

Individual participant data (IPD) meta-analysis provides important opportunities to study interaction and effect modification for which individual studies often lack power. While previous meta-analyses have commonly focused on multiplicative interaction, additive interaction holds greater relevance for public health and may in certain contexts better reflect biological interaction. Methodological literature on interaction in IPD meta-analysis does not cover additive interaction for models including binary or time-to-event outcomes. We aimed to describe how the Relative Excess Risk due to Interaction (RERI) and other measures of additive interaction or effect modification can be validly estimated within two-stage IPD meta-analysis. First, we explain why direct pooling of study-level RERI estimates may lead to invalid results. Next, we propose a three-step procedure to estimate additive interaction: 1) estimate effects of both exposures and their product term on the outcome within each individual study; 2) pool study-specific estimates using multivariate meta-analysis; 3) estimate an overall RERI and 95% confidence interval based on the pooled effect estimates. We illustrate this procedure by investigating interaction between depression and smoking and risk of smoking-related cancers using data from the PSYchosocial factors and Cancer (PSY-CA) consortium. We discuss implications of this procedure, including the application in meta-analysis based on published data.

Depressive Symptoms, Socioeconomic Position, and Mortality in Older People Living With and Beyond Cancer.

OBJECTIVE: Evidence shows that higher depressive symptoms are associated with mortality among people living with and beyond cancer (LWBC). However, prior studies have not accounted for a wider range of potential confounders, and no study has explored whether socioeconomic position (SEP) moderates the association. This study aimed to examine the association between depressive symptoms and mortality among people LWBC, and moderation by SEP. METHODS: Participants from the English Longitudinal Study of Aging, diagnosed with cancer and with a measure of depressive symptoms within 4 years after their diagnosis, were included. Elevated depressive symptoms were indicated by a score of ≥3 on the eight-item Center for Epidemiologic Studies Depression Scale. Cox regression models examined associations with all-cause mortality. Competing risk regression examined associations with cancer mortality. RESULTS: In 1352 people LWBC (mean age = 69.6 years), elevated depressive symptoms were associated with a 93% increased risk of all-cause mortality (95% confidence interval = 1.52-2.45) within the first 4 years of follow-up and a 48% increased risk within a 4- to 8-year follow-up (95% confidence interval = 1.02-2.13) after multivariable adjustment. Elevated depressive symptoms were associated with a 38% increased risk of cancer mortality, but not after excluding people who died within 1 year after baseline assessments. There were no interactions between depressive symptoms and SEP. CONCLUSIONS: Elevated depressive symptoms are associated with a greater risk of all-cause mortality among people LWBC within an 8-year follow-up period. Associations between depressive symptoms and cancer mortality might be due to reverse causality.

The mediating role of health behaviors in the association between depression, anxiety and cancer incidence: an individual participant data meta-analysis.

BACKGROUND: Although behavioral mechanisms in the association among depression, anxiety, and cancer are plausible, few studies have empirically studied mediation by health behaviors. We aimed to examine the mediating role of several health behaviors in the associations among depression, anxiety, and the incidence of various cancer types (overall, breast, prostate, lung, colorectal, smoking-related, and alcohol-related cancers). METHODS: Two-stage individual participant data meta-analyses were performed based on 18 cohorts within the Psychosocial Factors and Cancer Incidence consortium that had a measure of depression or anxiety (N = 319 613, cancer incidence = 25 803). Health behaviors included smoking, physical inactivity, alcohol use, body mass index (BMI), sedentary behavior, and sleep duration and quality. In stage one, path-specific regression estimates were obtained in each cohort. In stage two, cohort-specific estimates were pooled using random-effects multivariate meta-analysis, and natural indirect effects (i.e. mediating effects) were calculated as hazard ratios (HRs). RESULTS: Smoking (HRs range 1.04-1.10) and physical inactivity (HRs range 1.01-1.02) significantly mediated the associations among depression, anxiety, and lung cancer. Smoking was also a mediator for smoking-related cancers (HRs range 1.03-1.06). There was mediation by health behaviors, especially smoking, physical inactivity, alcohol use, and a higher BMI, in the associations among depression, anxiety, and overall cancer or other types of cancer, but effects were small (HRs generally below 1.01). CONCLUSIONS: Smoking constitutes a mediating pathway linking depression and anxiety to lung cancer and smoking-related cancers. Our findings underline the importance of smoking cessation interventions for persons with depression or anxiety.

Depression, anxiety, and the risk of cancer: An individual participant data meta-analysis.

BACKGROUND: Depression and anxiety have long been hypothesized to be related to an increased cancer risk. Despite the great amount of research that has been conducted, findings are inconclusive. To provide a stronger basis for addressing the associations between depression, anxiety, and the incidence of various cancer types (overall, breast, lung, prostate, colorectal, alcohol-related, and smoking-related cancers), individual participant data (IPD) meta-analyses were performed within the Psychosocial Factors and Cancer Incidence (PSY-CA) consortium. METHODS: The PSY-CA consortium includes data from 18 cohorts with measures of depression or anxiety (up to N = 319,613; cancer incidences, 25,803; person-years of follow-up, 3,254,714). Both symptoms and a diagnosis of depression and anxiety were examined as predictors of future cancer risk. Two-stage IPD meta-analyses were run, first by using Cox regression models in each cohort (stage 1), and then by aggregating the results in random-effects meta-analyses (stage 2). RESULTS: No associations were found between depression or anxiety and overall, breast, prostate, colorectal, and alcohol-related cancers. Depression and anxiety (symptoms and diagnoses) were associated with the incidence of lung cancer and smoking-related cancers (hazard ratios [HRs], 1.06-1.60). However, these associations were substantially attenuated when additionally adjusting for known risk factors including smoking, alcohol use, and body mass index (HRs, 1.04-1.23). CONCLUSIONS: Depression and anxiety are not related to increased risk for most cancer outcomes, except for lung and smoking-related cancers. This study shows that key covariates are likely to explain the relationship between depression, anxiety, and lung and smoking-related cancers. PREREGISTRATION NUMBER: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=157677.

Excess body weight and specific types of depressive symptoms: Is there a mediating role of systemic low-grade inflammation?

OBJECTIVES: Obesity is associated with an increased risk of depression. Systemic low-grade inflammation, a plausible consequence of obesity, has also been linked to depression. However, the potential mediating effects of systemic low-grade inflammation on the association between excess body weight and specific symptom domains of depression remain uncertain. This study examined whether systemic low-grade inflammation mediated the associations of excess body weight (overweight and obesity) with subsequent overall, cognitive-affective, and somatic depressive symptoms. DESIGN: This study used a prospective cohort design. METHODS: The final analytical sample included 4,942 adults aged ≥50 years drawn from the English Longitudinal Study of Ageing (ELSA). Body mass index (BMI) and covariates were ascertained at baseline (wave 4, 2008/09). Continuous BMI scores were divided into four categories: 'normal weight' (18.5 ≤ BMI <25 kg/m2); 'overweight' (25 ≤ BMI <30 kg/m2); 'obesity' (BMI ≥30 kg/m2); in addition to 'excess body weight' ('overweight' and 'obesity' combined). Covariates included sociodemographic variables, behavioural factors, and chronic physical conditions. Serum concentrations of CRP were measured at wave 6 (2012/13). Depressive symptoms were assessed at baseline and ten years later (wave 9, 2018/19), using the 8-item Centre for Epidemiological Studies Depression (CES-D) Scale. Two symptom domains were constructed, distinguishing between cognitive-affective (depressed mood, loneliness, sadness, enjoyment in life, and happiness) and somatic (sleep problems, low energy levels, and fatigue) symptoms. Mediation analyses were performed to examine whether CRP statistically mediated the associations between BMI categories and depressive symptoms. RESULTS: In multivariable-adjusted analyses, excess body weight was associated with elevated somatic (OR = 1.231, 95% CI: 1.029, 1.473), but not cognitive-affective or overall depressive symptoms at follow-up. Higher CRP was associated with elevated somatic (OR = 1.156, 95% CI: 1.061, 1.259), but not cognitive-affective or overall depressive symptoms. CRP acted as a partial mediator (14.92%) of the association between excess body weight and elevated somatic, but not cognitive-affective, or overall depressive symptoms. CONCLUSION: Systemic low-grade inflammation may partially explain the association of excess body weight with somatic depressive symptoms, but not the associations with cognitive-affective or overall depressive symptoms.

Systemic inflammation and subsequent risk of amyotrophic lateral sclerosis: Prospective cohort study.

BACKGROUND: While systemic inflammation has been implicated in the etiology of selected neurodegenerative disorders, its role in the development of amyotrophic lateral sclerosis (ALS), a condition with high case-fatality, is untested. Accordingly, we quantified the relationship of C-reactive protein (CRP), an acute-phase reactant and marker of systemic inflammation, with subsequent ALS occurrence. METHODS: We used data from UK Biobank, a prospective cohort study of 502,649 participants who were aged 37 to 73 years when examined at research centers between 2006 and 2010. Venous blood was collected at baseline in the full cohort and assayed for CRP, and repeat measurement was made 3-7 years later in a representative subgroup (N = 14,514) enabling correction for regression dilution. ALS was ascertained via national hospitalization and mortality registries until 2021. We computed multivariable hazard ratios with accompanying 95% confidence intervals for log-transformed CRP expressed as standard deviation and tertiles. RESULTS: In an analytical sample of 400,884 initially ALS-free individuals (218,203 women), a mean follow-up of 12 years gave rise to 231 hospitalizations and 223 deaths ascribed to ALS. After adjustment for covariates which included health behaviors, comorbidity, and socio-economic status, a one standard deviation higher log-CRP was associated with elevated rates of both ALS mortality (hazard ratios; 95% confidence intervals: 1.32; 1.13, 1.53) and hospitalizations (1.20; 1.00, 1.39). There was evidence of dose-response effects across tertiles of CRP for both outcomes (p for trend ≤ 0.05). Correction for regression dilution led to a strengthening of the relationship with CRP for both mortality (1.62; 1.27, 2.08) and hospitalizations (1.37; 1.05, 1.76). CONCLUSIONS: Higher levels of CRP, a blood-based biomarker widely captured in clinical practice, is associated with moderately increased future risk of amyotrophic lateral sclerosis.

Butterfly Transforms for Efficient Representation of Spatially Variant Point Spread Functions in Bayesian Imaging.

Bayesian imaging algorithms are becoming increasingly important in, e.g., astronomy, medicine and biology. Given that many of these algorithms compute iterative solutions to high-dimensional inverse problems, the efficiency and accuracy of the instrument response representation are of high importance for the imaging process. For efficiency reasons, point spread functions, which make up a large fraction of the response functions of telescopes and microscopes, are usually assumed to be spatially invariant in a given field of view and can thus be represented by a convolution. For many instruments, this assumption does not hold and degrades the accuracy of the instrument representation. Here, we discuss the application of butterfly transforms, which are linear neural network structures whose sizes scale sub-quadratically with the number of data points. Butterfly transforms are efficient by design, since they are inspired by the structure of the Cooley-Tukey fast Fourier transform. In this work, we combine them in several ways into butterfly networks, compare the different architectures with respect to their performance and identify a representation that is suitable for the efficient representation of a synthetic spatially variant point spread function up to a 1% error. Furthermore, we show its application in a short synthetic example.

Overweight, obesity, and individual symptoms of depression: A multicohort study with replication in UK Biobank.

OBJECTIVES: Obesity is associated with increased risk of depression, but the extent to which this association is symptom-specific is unknown. We examined the associations of overweight and obesity with individual depressive symptoms. METHODS: We pooled data from 15 population-based cohorts comprising 57,532 individuals aged 18 to 100 years at study entry. Primary analyses were replicated in an independent cohort, the UK Biobank study (n = 122,341, age range 38 to 72). Height and weight were assessed at baseline and body mass index (BMI) was computed. Using validated self-report measures, 24 depressive symptoms were ascertained once in 16 cross-sectional, and twice in 7 prospective cohort studies (mean follow-up 3.2 years). RESULTS: In the pooled analysis of the primary cohorts, 22,045 (38.3 %) participants were overweight (BMI between 25 and 29.9 kg/m2), 12,025 (20.9 %) class I obese (BMI between 30 and 34.9 kg/m2), 7,467 (13.0 %) class II-III obese (BMI ≥ 35 kg/m2); and 7,046 (12.3 %) were classified as depressed. After multivariable adjustment, obesity class I was cross-sectionally associated with 1.11-fold (95 % confidence interval 1.01-1.22), and obesity class II-III with 1.31-fold (1.16-1.49) higher odds of overall depression. In symptom-specific analyses, robust associations were apparent for 4 of the 24 depressive symptoms ('could not get going/lack of energy', 'little interest in doing things', 'feeling bad about yourself, and 'feeling depressed'), with confounder-adjusted odds ratios of having 3 or 4 of these symptoms being 1.32 (1.10-1.57) for individuals with obesity class I, and 1.70 (1.34-2.14) for those with obesity class II-III. Elevated C-reactive protein and 21 obesity-related diseases explained 23 %-31 % of these associations. Symptom-specific associations were confirmed in longitudinal analyses where obesity preceded symptom onset, were stronger in women compared with men, and were replicated in UK Biobank. CONCLUSIONS: Obesity is associated with a distinct set of depressive symptoms. These associations are partially explained by systemic inflammation and obesity-related morbidity. Awareness of this obesity-related symptom profile and its underlying biological correlates may inform better targeted treatments for comorbid obesity and depression.

The assessment of left ventricular mechanical dyssynchrony from gated 99mTc-tetrofosmin SPECT and gated 18F-FDG PET by QGS: a comparative study.

BACKGROUND: Due to partly conflicting studies, further research is warranted with the QGS software package, with regard to the performance of gated FDG PET phase analysis as compared to gated MPS as well as the establishment of possible cut-off values for FDG PET to define dyssynchrony. METHODS: Gated MPS and gated FDG PET datasets of 93 patients were analyzed with the QGS software. BW, Phase SD, and Entropy were calculated and compared between the methods. The performance of gated PET to identify dyssynchrony was measured against SPECT as reference standard. ROC analysis was performed to identify the best discriminator of dyssynchrony and to define cut-off values. RESULTS: BW and Phase SD differed significantly between the SPECT and PET. There was no significant difference in Entropy with a high linear correlation between methods. There was only moderate agreement between SPECT and PET to identify dyssynchrony. Entropy was the best single PET parameter to predict dyssynchrony with a cut-off point at 62%. CONCLUSION: Gated MPS and gated FDG PET can assess LVMD. The methods cannot be used interchangeably. Establishing reference ranges and cut-off values is difficult due to the lack of an external gold standard. Further prospective research is necessary.

Geometric Variational Inference.

Efficiently accessing the information contained in non-linear and high dimensional probability distributions remains a core challenge in modern statistics. Traditionally, estimators that go beyond point estimates are either categorized as Variational Inference (VI) or Markov-Chain Monte-Carlo (MCMC) techniques. While MCMC methods that utilize the geometric properties of continuous probability distributions to increase their efficiency have been proposed, VI methods rarely use the geometry. This work aims to fill this gap and proposes geometric Variational Inference (geoVI), a method based on Riemannian geometry and the Fisher information metric. It is used to construct a coordinate transformation that relates the Riemannian manifold associated with the metric to Euclidean space. The distribution, expressed in the coordinate system induced by the transformation, takes a particularly simple form that allows for an accurate variational approximation by a normal distribution. Furthermore, the algorithmic structure allows for an efficient implementation of geoVI which is demonstrated on multiple examples, ranging from low-dimensional illustrative ones to non-linear, hierarchical Bayesian inverse problems in thousands of dimensions.

Probabilistic Autoencoder Using Fisher Information.

Neural networks play a growing role in many scientific disciplines, including physics. Variational autoencoders (VAEs) are neural networks that are able to represent the essential information of a high dimensional data set in a low dimensional latent space, which have a probabilistic interpretation. In particular, the so-called encoder network, the first part of the VAE, which maps its input onto a position in latent space, additionally provides uncertainty information in terms of variance around this position. In this work, an extension to the autoencoder architecture is introduced, the FisherNet. In this architecture, the latent space uncertainty is not generated using an additional information channel in the encoder but derived from the decoder by means of the Fisher information metric. This architecture has advantages from a theoretical point of view as it provides a direct uncertainty quantification derived from the model and also accounts for uncertainty cross-correlations. We can show experimentally that the FisherNet produces more accurate data reconstructions than a comparable VAE and its learning performance also apparently scales better with the number of latent space dimensions.

Dynamical Field Inference and Supersymmetry.

Knowledge on evolving physical fields is of paramount importance in science, technology, and economics. Dynamical field inference (DFI) addresses the problem of reconstructing a stochastically-driven, dynamically-evolving field from finite data. It relies on information field theory (IFT), the information theory for fields. Here, the relations of DFI, IFT, and the recently developed supersymmetric theory of stochastics (STS) are established in a pedagogical discussion. In IFT, field expectation values can be calculated from the partition function of the full space-time inference problem. The partition function of the inference problem invokes a functional Dirac function to guarantee the dynamics, as well as a field-dependent functional determinant, to establish proper normalization, both impeding the necessary evaluation of the path integral over all field configurations. STS replaces these problematic expressions via the introduction of fermionic ghost and bosonic Lagrange fields, respectively. The action of these fields has a supersymmetry, which means there exists an exchange operation between bosons and fermions that leaves the system invariant. In contrast to this, measurements of the dynamical fields do not adhere to this supersymmetry. The supersymmetry can also be broken spontaneously, in which case the system evolves chaotically. This affects the predictability of the system and thereby makes DFI more challenging. We investigate the interplay of measurement constraints with the non-linear chaotic dynamics of a simplified, illustrative system with the help of Feynman diagrams and show that the Fermionic corrections are essential to obtain the correct posterior statistics over system trajectories.

Correction to: Persistent physical symptoms reduction intervention: a system change and evaluation in secondary care (PRINCE secondary) - a CBT-based transdiagnostic approach: study protocol for a randomised controlled trial.

An amendment to this paper has been published and can be accessed via the original article.

Systemic low-grade inflammation and subsequent depressive symptoms: Is there a mediating role of physical activity?

OBJECTIVE: Systemic low-grade inflammation has been associated with the onset of depression, but the exact mechanisms underlying this relationship remain elusive. This study examined whether physical activity (PA) explained the association between elevated serum levels of inflammatory markers and subsequent depressive symptoms. DESIGN: Prospective cohort design. METHOD: The sample consisted of 3809 non-depressed men and women (aged 50+) recruited from the English Longitudinal Study of Ageing (ELSA). Serum levels of inflammatory markers (C-reactive protein (CRP), fibrinogen) and covariates (age, sex, education, wealth, body mass index, smoking, cholesterol, triglycerides) were measured at baseline (wave 4, 2008/09). Self-reported weekly moderate/vigorous (high) PA versus no weekly moderate/vigorous (low) PA was examined at a four-year follow-up (wave 6, 2012/13), using a single-item question. Depressive symptoms were assessed at baseline, four years (wave 6, 2012/13) and six years post baseline (wave 7, 2014/15), using the 8-item version of the Centre for Epidemiological Studies Depression Scale (CES-D). RESULTS: Participants with higher baseline concentrations of inflammatory markers were significantly more likely to report low PA levels four years later (CRP: OR: 1.25; 95% CI, 1.05-1.48; fibrinogen: OR: 1.18; 95% CI, 1.05-1.39). Moreover, low PA was associated with higher odds of elevated depressive symptoms at follow-up (OR: 1.59; 95% CI, 1.15-2.19). Mediation analyses revealed that low PA explained a total of 36.71% of the relationship between high CRP and elevated depressive symptoms, and 33.26% between higher levels of fibrinogen and elevated depressive symptoms six years later. No direct association was found between systemic low-grade inflammation and future depressive symptoms. CONCLUSION: These results suggest that low PA is a significant partial mediator of the relationship between systemic low-grade inflammation and subsequent elevated depressive symptoms in a nationally representative cohort of older adults.

Persistent physical symptoms reduction intervention: a system change and evaluation in secondary care (PRINCE secondary) - a CBT-based transdiagnostic approach: study protocol for a randomised controlled trial.

BACKGROUND: Persistent physical symptoms (PPS), also known as medically unexplained symptoms (MUS), affect approximately 50% of patients in secondary care and are often associated with disability, psychological distress and increased health care costs. Cognitive behavioural therapy (CBT) has demonstrated both short- and long-term efficacy with small to medium effect sizes for PPS, with larger treatment effects for specific PPS syndromes, including non-cardiac chest pain, irritable bowel syndrome (IBS) and chronic fatigue syndrome (CFS). Research indicates that PPS conditions share similar cognitive and behavioural responses to symptoms, such as avoidance and unhelpful beliefs. This suggests that a transdiagnostic approach may be beneficial for patients with PPS. METHODS: A randomised controlled trial (RCT) will be conducted to evaluate the efficacy and cost-effectiveness of a transdiagnostic CBT-based intervention for PPS. 322 participants with PPS will be recruited from secondary care clinics. Participants stratified by clinic and disability level will be randomised to CBT plus standard medical care (SMC) versus SMC alone. The intervention consists of 8 CBT sessions delivered by a qualified therapist over a period of 20 weeks. Outcomes will be assessed at 9, 20, 40- and 52-weeks post randomisation. Efficacy will be assessed by examining the difference between arms in the primary outcome Work and Social Adjustment Scale (WSAS) at 52 weeks after randomisation. Secondary outcomes will include mood, symptom severity and clinical global impression at 9, 20, 40 and 52 weeks. Cost-effectiveness will be evaluated by combining measures of health service use, informal care, loss of working hours and financial benefits at 52 weeks. DISCUSSION: This trial will provide a powered evaluation of the efficacy and cost-effectiveness of a transdiagnostic CBT approach versus SMC for patients with PPS. It will also provide valuable information about potential healthcare pathways for patients with PPS within the National Health Service (NHS). TRIAL REGISTRATION: ClinicalTrials.gov NCT02426788. Registered 27 April 2015. Overall trial status: Ongoing; Recruitment status: No longer recruiting.

Spectral Detector Computed Tomography Pulmonary Angiography: Improved Diagnostic Assessment and Automated Estimation of Window Settings Angiography of Pulmonary Arteries From Novel Spectral Detector Computed Tomography Provides Improved Image Quality if Settings are Adjusted.

OBJECTIVE: This study aimed to evaluate image quality (IQ) of virtual monoenergetic images (VMIs) from novel spectral detector computed tomography angiography of the pulmonary arteries and to identify appropriate window settings for each kiloelectron volt level. MATERIALS: Forty consecutive patients were included in this institutional review board-approved, Health Insurance Portability and Accountability Act-compliant study.Signal- and contrast-to-noise ratios were calculated within the pulmonary trunk, and pulmonary/lobar/segmental arteries were calculated. The IQ and diagnostic certainty were rated by 2 radiologists on 5-point scales. In addition, they recorded appropriate window settings (center/width) that were linearly modeled against attenuation within the pulmonary trunk to generate generable results. RESULTS: Signal- and contrast-to-noise ratios, IQ, and diagnostic certainty are significantly increased in low-kiloelectron volt VMIs (≤60 keV). Interrater agreement was excellent (ĸ = 0.89). We developed 2 linear models (R: 0.91-0.97 and R: 0.43-0.91, respectively, P ≤ 0.01), that suggest appropriate window settings. CONCLUSIONS: The VMIs from spectral detector computed tomography improve objective and subjective IQ in angiography of the pulmonary arteries, if window settings are adjusted; they can be automatically estimated using reported linear models.

Biodistribution and radiation dosimetry of (68)Ga-PSMA HBED CC-a PSMA specific probe for PET imaging of prostate cancer.

PURPOSE: Positron emission tomography (PET) agents targeting the prostate-specific membrane antigen (PSMA) are currently under broad clinical and scientific investigation. (68)Ga-PSMA HBED-CC constitutes the first (68)Ga-labelled PSMA-inhibitor and has evolved as a promising agent for imaging PSMA expression in vivo. The aim of this study was to evaluate the whole-body distribution and radiation dosimetry of this new probe. METHODS: Five patients with a history or high suspicion of prostate cancer were injected intravenously with a mean of 139.8 ± 13.7 MBq of (68)Ga-PSMA HBED-CC (range 120-158 MBq). Four static skull to mid-thigh scans using a whole-body fully integrated PET/MR-system were performed 10 min, 60 min, 130 min, and 175 min after the tracer injection. Time-dependent changes of the injected activity per organ were determined. Mean organ-absorbed doses and effective doses (ED) were calculated using OLINDA/EXM. RESULTS: Injection of a standard activity of 150 MBq (68)Ga-PSMA HBED-CC resulted in a median effective dose of 2.37 mSv (Range 1.08E-02 - 2.46E-02 mSv/MBq). The urinary bladder wall (median absorbed dose 1.64E-01 mGv/MBq; range 8.76E-02 - 2.91E-01 mGv/MBq) was the critical organ, followed by the kidneys (median absorbed dose 1.21E-01 mGv/MBq; range 7.16E-02 - 1.75E-01), spleen (median absorbed dose 4.13E-02 mGv/MBq; range 1.57E-02 - 7.32E-02 mGv/MBq) and liver (median absorbed dose 2.07E-02 mGv/MBq; range 1.80E-02 - 2.57E-02 mGv/MBq). No drug-related pharmacological effects occurred. CONCLUSION: The use of (68)Ga-PSMA HBED-CC results in a relatively low radiation exposure, delivering organ doses that are comparable to those of other (68)Ga-labelled PSMA-inhibitors used for PET-imaging. Total effective dose is lower than for other PET-agents used for prostate cancer imaging (e.g. (11)C- and (18)F-Choline).

Systemic inflammation, lifestyle behaviours and dementia: A 10-year follow-up investigation.

Objectives: Lifestyle behaviours have been linked to dementia incidence, but their cumulative impact on dementia and the underlying mechanisms remain poorly understood. This study investigated the association of co-occurring lifestyle behaviours with dementia incidence and the mediating role of systemic inflammation in this association. Methods: The sample comprised 3131 participants (55.2% female) from the English Longitudinal Study of Ageing aged 52-92 years at baseline (2008/09). Self-reported baseline lifestyle behaviours (alcohol intake, fruit and vegetable consumption, smoking, physical activity, sleep duration, social engagement, and cognitive activity) were summed to derive an index of lifestyle behaviours, ranging from 0 to 7, with higher scores denoting a higher number of health-risk behaviours. Incident dementia cases (n = 130, 4.2%) were identified through doctor-diagnosed dementia, informant interviews, and health records between 2014/15 and 2018/19. Systemic inflammation was measured through fasting plasma concentrations of C-reactive protein in 2012/13. Results: Binary logistic regression models indicated that the odds of subsequent dementia increased by 1.19 for each additional health-risk behaviour (95% confidence intervals: 1.04, 1.37, p = 0.014) after adjusting for age, sex, ethnicity, wealth, education, marital status, body mass index, coronary heart disease, hypertension, stroke, and depression. However, this association was not mediated by C-reactive protein. Conclusions: Co-occurring health-risk behaviours were associated with higher dementia incidence up to 10 years later, underscoring the importance of modifying health-risk behaviours for the prevention of dementia. Systemic inflammation did not explain the association between behaviours and dementia.