Background The current study explored the association between green space and depression in a deprived, multiethnic sample of pregnant women, and examined moderating and mediating variables. Method 7547 women recruited to the ‘Born in Bradford’ cohort completed a questionnaire during pregnancy. A binary measure of depressive symptoms was calculated using a validated survey. Two green space measures were used: quintiles of residential greenness calculated using the normalised difference vegetation index for three neighbourhood sizes (100, 300 and 500 m buffer zones around participant addresses); access to major green spaces estimated as straight line distance between participant address and nearest green space (>0.5 hectares). Logistic regression analyses examined relationships between green space and depressive symptoms, controlling for ethnicity, demographics, socioeconomic status (SES) and health behaviours. Multiplicative interactions explored variations by ethnic group, SES or activity levels. Mediation analysis assessed indirect effects via physical activity. Results Pregnant women in the greener quintiles were 18–23% less likely to report depressive symptoms than those in the least green quintile (for within 100 m of green space buffer zone). The green space-depressive symptoms association was significant for women with lower education or who were active. Physical activity partially mediated the association of green space, but explained only a small portion of the direct effect. Conclusions Higher residential greenness was associated with a reduced likelihood of depressive symptoms. Associations may be stronger for more disadvantaged groups and for those who are already physically active. Improving green space is a promising intervention to reduce risk of depression in disadvantaged groups.

Background: The occurrence of a range of health outcomes following myocardial infarction (MI) is unknown. Therefore, this study aimed to determine the long-term risk of major health outcomes following MI and generate sociodemographic stratified risk charts in order to inform care recommendations in the post-MI period and underpin shared decision making. Methods and findings: This nationwide cohort study includes all individuals aged ≥18 years admitted to one of 229 National Health Service (NHS) Trusts in England between 1 January 2008 and 31 January 2017 (final follow-up 27 March 2017). We analysed 11 non-fatal health outcomes (subsequent MI and first hospitalisation for heart failure, atrial fibrillation, cerebrovascular disease, peripheral arterial disease, severe bleeding, renal failure, diabetes mellitus, dementia, depression, and cancer) and all-cause mortality. Of the 55,619,430 population of England, 34,116,257 individuals contributing to 145,912,852 hospitalisations were included (mean age 41.7 years (standard deviation [SD 26.1]); n = 14,747,198 (44.2%) male). There were 433,361 individuals with MI (mean age 67.4 years [SD 14.4)]; n = 283,742 (65.5%) male). Following MI, all-cause mortality was the most frequent event (adjusted cumulative incidence at 9 years 37.8% (95% confidence interval [CI] [37.6,37.9]), followed by heart failure (29.6%; 95% CI [29.4,29.7]), renal failure (27.2%; 95% CI [27.0,27.4]), atrial fibrillation (22.3%; 95% CI [22.2,22.5]), severe bleeding (19.0%; 95% CI [18.8,19.1]), diabetes (17.0%; 95% CI [16.9,17.1]), cancer (13.5%; 95% CI [13.3,13.6]), cerebrovascular disease (12.5%; 95% CI [12.4,12.7]), depression (8.9%; 95% CI [8.7,9.0]), dementia (7.8%; 95% CI [7.7,7.9]), subsequent MI (7.1%; 95% CI [7.0,7.2]), and peripheral arterial disease (6.5%; 95% CI [6.4,6.6]). Compared with a risk-set matched population of 2,001,310 individuals, first hospitalisation of all non-fatal health outcomes were increased after MI, except for dementia (adjusted hazard ratio [aHR] 1.01; 95% CI [0.99,1.02];p = 0.468) and cancer (aHR 0.56; 95% CI [0.56,0.57];p < 0.001). The study includes data from secondary care only—as such diagnoses made outside of secondary care may have been missed leading to the potential underestimation of the total burden of disease following MI. Conclusions: In this study, up to a third of patients with MI developed heart failure or renal failure, 7% had another MI, and 38% died within 9 years (compared with 35% deaths among matched individuals). The incidence of all health outcomes, except dementia and cancer, was higher than expected during the normal life course without MI following adjustment for age, sex, year, and socioeconomic deprivation. Efforts targeted to prevent or limit the accrual of chronic, multisystem disease states following MI are needed and should be guided by the demographic-specific risk charts derived in this study.

Elexacaftor/Tezacaftor/Ivacaftor (ETI) modulator therapy is often associated with increased body mass index (BMI) in people with cystic fibrosis (CF). This is thought to reflect improved clinical stability and increased appetite and nutritional intake. We explored the change in BMI and nutritional intake following ETI modulator therapy in adults with CF. Dietary intake, measured with myfood24®, and BMI were collected from adults with CF at baseline and follow-up as part of an observational study. Changes in BMI and nutritional intake in participants who commenced ETI therapy between time points were assessed. To contextualize findings, we also assessed changes in BMI and nutritional intake between study points in a group on no modulators. In the pre and post ETI threapy group (n = 40), BMI significantly increased from 23.0 kg/m² (IQR 21.4, 25.3) at baseline to 24.6 kg/m² (IQR 23.0, 26.7) at follow-up (p<0.001), with a median of 68 weeks between time points (range 20-94 weeks) and median duration of ETI therapy was 23 weeks (range 7-72 weeks). There was a significant decrease in energy intake from 2551 kcal/day (IQR 2107, 3115) to 2153 kcal/day (IQR 1648, 2606), p0.05), a median of 28 weeks apart (range 20-76 weeks). These findings tentatively suggest that the increase in BMI with ETI therapy may not simply be attributable to an increase in oral intake. Further exploration into the underlying aetiology of weight gain with ETI therapy is needed.

To review methodological approaches for automated segmentation of subcutaneous adipose tissue, visceral adipose tissue, and skeletal muscle from abdominal cross-sectional imaging for body composition analysis. Four databases were searched for publications describing automated segmentation of subcutaneous adipose tissue, visceral adipose tissue, and/or skeletal muscle from abdominal CT or MR imaging between 2019 and 2023. Included reports were evaluated to assess how imaging modality, cohort size, vertebral level, model dimensionality, and use of a volume or single slice affected segmentation accuracy and/or clinical utility. Exclusion criteria included reports not in English language, manual or semi-automated segmentation methods, reports prior to 2019 or solely of paediatric patients, and those not describing the use of abdominal CT or MR. After exclusions, 172 reports were included in the review. CT imaging was utilised approximately four times as often as MRI, and segmentation accuracy did not significantly differ between the two modalities. Cohort size had no significant effect on segmentation accuracy. There was little evidence to refute the current practice of extracting body composition metrics from the third lumbar vertebral level. There was no clear benefit of using a 3D model to perform segmentation over a 2D approach. Automated segmentation of intra-abdominal soft tissues for body composition analysis is an intense area of research activity. Segmentation accuracy is not affected by cross-sectional imaging modality. Extracting metrics from a single slice at the third lumbar vertebral level is a common approach, however, extracting metrics from a volumetric slab surrounding this level may increase the resilience of the technique, which is important for clinical translation. A paucity of publicly available datasets led to most reports using different data sources, preventing direct comparison of segmentation techniques. Future efforts should prioritise creating a standardised dataset to facilitate benchmarking of different algorithms and subsequent clinical adoption.

In the UK, the majority of cancers are diagnosed following referral from the GP after presenting with symptoms, signs, or test results associated with undiagnosed cancer.¹ In England and Wales, an urgent referral pathway for patients presenting to GPs with ‘red flag’ symptoms was introduced in 2000, whereby all patients should be seen by a specialist within 2 weeks of referral (known as the Two Week Wait [TWW] pathway). These guidelines were updated in 2015 by incorporating more non-specific symptoms and lowering the threshold of risk of cancer from 5% to 3%.² The TWW pathway is a high-volume referral pathway and the number of referrals has increased substantially over the last decade; in 2019/2020, 2.4 million patients were referred via this pathway.³ In addition to monitoring TWW referral rates, other key outcome indicators are the conversion rate (percentage of TWW referrals resulting in a cancer diagnosis) and the detection rate (percentage of new cancer cases treated resulting from a TWW referral) (Box 1). Improving the detection rate is a key priority as this means that patients may be diagnosed more promptly rather than by other routes such as emergency presentations, which have poorer survival.¹ However, obtaining the right balance between high detection rate and low conversion rate is difficult and complex, involving many stakeholders.⁴ The full impact of increasing referrals on conversion and detection rates, as well as the wider impact on patients and the health system, is not well understood.⁴ There is a growing body of published data on cancer diagnostics publicly available including data by cancer pathway.