To assess the associations among several anthropometric measures, as well as BMI trajectories and colorectal cancer (CRC) risk in older women.

Prospective cohort study.

Forty clinical centres in the USA.

Totally, 79 034 postmenopausal women in the Women’s Health Initiative Observational Study.

During an average of 15·8 years of follow-up, 1514 CRC cases were ascertained. Five BMI trajectories over 18–50 years of age were identified using growth mixture model. Compared with women who had a normal BMI at age 18, women with obesity at age 18 had a higher risk of CRC (HR 1·58, 95 % CI 1·02, 2·44). Compared with women who kept relatively low normal body size during adulthood, women who progressed from normal to obesity (HR 1·29, 95 % CI 1·09, 1·53) and women who progressed from overweight to obesity (HR 1·37, 95 % CI 1·13, 1·68) had higher CRC risks. A weight gain > 15 kg from age 18 to 50 (HR 1·20, 95 % CI 1·04, 1·40) and baseline waist circumference > 88 cm (HR 1·33, 95 % CI 1·19, 1·49) were associated with higher CRC risks, compared with stable weight and waist circumference ≤ 88 cm, respectively.

Women who have a normal weight in early adult life and gain substantial weight later, as well as those who are persistently heavy over adulthood, demonstrated a higher risk of developing CRC. Our study highlights the importance of maintaining a healthy body weight over the life course for reducing the risk of developing CRC in women.

Childhood adversity and cannabis use are considered independent risk factors for psychosis, but whether different patterns of cannabis use may be acting as mediator between adversity and psychotic disorders has not yet been explored. The aim of this study is to examine whether cannabis use mediates the relationship between childhood adversity and psychosis.

Data were utilised on 881 first-episode psychosis patients and 1231 controls from the European network of national schizophrenia networks studying Gene–Environment Interactions (EU-GEI) study. Detailed history of cannabis use was collected with the Cannabis Experience Questionnaire. The Childhood Experience of Care and Abuse Questionnaire was used to assess exposure to household discord, sexual, physical or emotional abuse and bullying in two periods: early (0–11 years), and late (12–17 years). A path decomposition method was used to analyse whether the association between childhood adversity and psychosis was mediated by (1) lifetime cannabis use, (2) cannabis potency and (3) frequency of use.

The association between household discord and psychosis was partially mediated by lifetime use of cannabis (indirect effect coef. 0.078, s.e. 0.022, 17%), its potency (indirect effect coef. 0.059, s.e. 0.018, 14%) and by frequency (indirect effect coef. 0.117, s.e. 0.038, 29%). Similar findings were obtained when analyses were restricted to early exposure to household discord.

Harmful patterns of cannabis use mediated the association between specific childhood adversities, like household discord, with later psychosis. Children exposed to particularly challenging environments in their household could benefit from psychosocial interventions aimed at preventing cannabis misuse.

People with neuropsychiatric symptoms often experience delay in accurate diagnosis. Although cerebrospinal fluid neurofilament light (CSF NfL) shows promise in distinguishing neurodegenerative disorders (ND) from psychiatric disorders (PSY), its accuracy in a diagnostically challenging cohort longitudinally is unknown.

We collected longitudinal diagnostic information (mean = 36 months) from patients assessed at a neuropsychiatry service, categorising diagnoses as ND/mild cognitive impairment/other neurological disorders (ND/MCI/other) and PSY. We pre-specified NfL > 582 pg/mL as indicative of ND/MCI/other.

Diagnostic category changed from initial to final diagnosis for 23% (49/212) of patients. NfL predicted the final diagnostic category for 92% (22/24) of these and predicted final diagnostic category overall (ND/MCI/other vs. PSY) in 88% (187/212), compared to 77% (163/212) with clinical assessment alone.

CSF NfL improved diagnostic accuracy, with potential to have led to earlier, accurate diagnosis in a real-world setting using a pre-specified cut-off, adding weight to translation of NfL into clinical practice.

This paper used data from the Apathy in Dementia Methylphenidate Trial 2 (NCT02346201) to conduct a planned cost consequence analysis to investigate whether treatment of apathy with methylphenidate is economically attractive.

A total of 167 patients with clinically significant apathy randomized to either methylphenidate or placebo were included. The Resource Utilization in Dementia Lite instrument assessed resource utilization for the past 30 days and the EuroQol five dimension five level questionnaire assessed health utility at baseline, 3 months, and 6 months. Resources were converted to costs using standard sources and reported in 2021 USD. A repeated measures analysis of variance compared change in costs and utility over time between the treatment and placebo groups. A binary logistic regression was used to assess cost predictors.

Costs were not significantly different between groups whether the cost of methylphenidate was excluded (F(2,330) = 0.626, ηp 2 = 0.004, p = 0.535) or included (F(2,330) = 0.629, ηp 2 = 0.004, p = 0.534). Utility improved with methylphenidate treatment as there was a group by time interaction (F(2,330) = 7.525, ηp 2 = 0.044, p < 0.001).

Results from this study indicated that there was no evidence for a difference in resource utilization costs between methylphenidate and placebo treatment. However, utility improved significantly over the 6-month follow-up period. These results can aid in decision-making to improve quality of life in patients with Alzheimer’s disease while considering the burden on the healthcare system.

Apart from the psychiatric symptoms, cognitive deficits are also the core symptoms of schizophrenia. Brain network control theory provided information on the role of a specific brain region in the cognitive control process, helping understand the neural mechanism of cognitive impairment in schizophrenia.

To characterize the control properties of functional brain network in first-episode untreated patients with schizophrenia and the relationships between controllability and psychiatric symptoms, as well as exploring the predictive value of controllability in differentiating patients from healthy controls (HCs).

Average and modal controllability of brain networks were calculated and compared between 133 first-episode untreated patients with schizophrenia and 135 HCs. The associations between controllability and clinical symptoms were evaluated using sparse canonical correlation analysis. Support vector machine (SVM) and SVM-recursive feature elimination combined with the controllability were performed to establish the individual prediction model.

Compared to HCs, the patients with schizophrenia showed increased average controllability and decreased modal controllability in dorsal anterior cingulate cortex (dACC). Brain controllability predominantly in somatomotor, default mode, and visual networks was associated with the positive symptomatology of schizophrenia. The established model could identify patients with an accuracy of 0.68. Furthermore, the most discriminative features were located in dACC, medial prefrontal lobe, precuneus and superior temporal gyrus.

Altered controllability in dACC may play a critical role in the neuropathological mechanisms of cognitive deficit in schizophrenia, which could drive the brain function to different states to cope with varied cognitive tasks. As symptom-related biomarkers, controllability could be also beneficial to individual prediction in schizophrenia.

No significant relationships.

There is a growing consensus on brain networks that it is not immutable but rather a dynamic complex system for adapting environment. The neuroimaging research studying how brain regions work collaboratively with dynamic methods had demonstrated its effectiveness in revealing the neural mechanisms of schizophrenia.

To investigate altered dynamic brain functional topology in first-episode untreated schizophrenia patients (SZs) and establish classification models to find objective brain imaging biomarkers.

Resting-state-functional magnetic resonance data for SZs and matched healthy controls were obtained(Table1).

Power-264-template was used to extract nodes and sliding-window approach was carried out to establish functional connectivity matrices. Functional topology was assessed by eigenvector centrality(EC) and node efficiency and its time-fluctuating was evaluated with coefficient of variation(CV). Group differences of dynamic topology and correlation analysis between Positive and Negative Syndrome Scale(PANSS) scores and topology indices showing group differences, which also were used in establishing classification models, was examed.

The CV of node efficiency in angular and paracingulate gyrus was larger in SZs. There are 13 nodes assigned into several brain networks displaying altered CV of EC between groups(Figure1.A). Fluctuation of EC of the node in DMN, which was lower in SZs, showed negative correlation with PANSS total scores(Figure1.B). Dynamic functional topology of above nodes was used to train classification models and demonstrated 80% and 71% accuracy for support vector classification(SVC) and random forest(RF), respectively(Figure2).

Dynamic functional topology illustrated a capability in identifying SZs. Aberrated dynamics of DMN relevant to severity of patient’s symptoms could reveal the reason why it contributed to classification.

No significant relationships.

This study aimed to investigate the use of the head and neck cancer risk calculator version 2 in a primary care setting and to evaluate the impact of the risk calculator on the number of referrals stratified by urgency and cancer yield.

Referrals between April 2019 and August 2019, April 2020 and July 2020 (pre-risk calculator) and August 2020 and July 2021 (post-risk calculator) were analysed. Referral urgency, head and neck cancer risk calculator version 2 score, cancer diagnosis, cancer type and further investigations were recorded.

The 2023 patient encounters were analysed; there were 1110 (55 per cent) referrals before head and neck cancer risk calculator version 2 use and 913 (45 per cent) after head and neck cancer risk calculator version 2 use. A higher proportion of older (p < 0.001) and male (p < 0.013) patients were seen post-head and neck cancer risk calculator version 2 use. All cancer cases were seen on the urgent suspicion of cancer pathway post-head and neck cancer risk calculator version 2 use; however, a higher proportion of patients were seen as urgent suspicion of cancer (51.1 vs 83.5 per cent; p < 0.001). Overall, the cancer diagnosis rate increased from 2.7 to 4.1 per cent.

The head and neck cancer risk calculator version 2 had high sensitivity in cancer diagnosis. More studies are required to optimise the predicted versus actual cancer probability gap.