Although pulmonary artery banding remains a useful palliation in bi-ventricular shunting lesions, single-stage repair holds several advantages. We investigate outcomes of the former approach in high-risk patients.

Retrospective cohort study including all pulmonary artery banding procedures over 9 years, excluding single ventricle physiology and left ventricular training.

Banding was performed in 125 patients at a median age of 41 days (2–294) and weight of 3.4 kg (1.8–7.32). Staged repair was undertaken for significant co-morbidity in 81 (64.8%) and anatomical complexity in 44 (35.2%). The median hospital stay was 14 days (interquartile range 8–33.5) and 14 patients (11.2%) required anatomical repair before discharge. Nine patients died during the initial admission (hospital mortality 7.2 %) and five following discharge (inter-stage mortality 4.8%). Of 105 banded patients who survived, 19 (18.1%) needed inter-stage re-admission and 18 (14.4%) required unplanned re-intervention. Full repair was performed in 93 (74.4%) at a median age of 13 months (3.1–49.9) and weight of 8.5 kg (3.08–16.8). Prior banding, 54% were below the 0.4th weight centile, but only 28% remained so at repair. Post-repair, 5/93 (5.4%) developed heart block requiring permanent pacemaker, and 11/93 (11.8%) required unplanned re-intervention. The post-repair mortality (including repairs during the initial admission) was 6/93 (6.5%), with overall mortality of the staged approach 13.6% (17/125).

In a cohort with a high incidence of co-morbidity, pulmonary artery banding is associated with a significant risk of re-intervention and mortality. Weight gain improves after banding, but heart block, re-intervention, and mortality remain frequent following repair.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

The application of digital monitoring biomarkers in health, wellness and disease management is reviewed. Harnessing the near limitless capacity of these approaches in the managed healthcare continuum will benefit from a systems-based architecture which presents data quality, quantity, and ease of capture within a decision-making dashboard.

A framework was developed which stratifies key components and advances the concept of contextualized biomarkers. The framework codifies how direct, indirect, composite, and contextualized composite data can drive innovation for the application of digital biomarkers in healthcare.

The de novo framework implies consideration of physiological, behavioral, and environmental factors in the context of biomarker capture and analysis. Application in disease and wellness is highlighted, and incorporation in clinical feedback loops and closed-loop systems is illustrated.

The study of contextualized biomarkers has the potential to offer rich and insightful data for clinical decision making. Moreover, advancement of the field will benefit from innovation at the intersection of medicine, engineering, and science. Technological developments in this dynamic field will thus fuel its logical evolution guided by inputs from patients, physicians, healthcare providers, end-payors, actuarists, medical device manufacturers, and drug companies.

Obesity has been shown to be associated with depression and it has been suggested that higher body mass index (BMI) increases the risk of depression and other common mental disorders. However, the causal relationship remains unclear and Mendelian randomisation, a form of instrumental variable analysis, has recently been employed to attempt to resolve this issue.

To investigate whether higher BMI increases the risk of major depression.

Two instrumental variable analyses were conducted to test the causal relationship between obesity and major depression in RADIANT, a large case–control study of major depression. We used a single nucleotide polymorphism (SNP) in FTO and a genetic risk score (GRS) based on 32 SNPs with well-established associations with BMI.

Linear regression analysis, as expected, showed that individuals carrying more risk alleles of FTO or having higher score of GRS had a higher BMI. Probit regression suggested that higher BMI is associated with increased risk of major depression. However, our two instrumental variable analyses did not support a causal relationship between higher BMI and major depression (FTO genotype: coefficient −0.03, 95% CI −0.18 to 0.13, P = 0.73; GRS: coefficient −0.02, 95% CI −0.11 to 0.07, P = 0.62).

Our instrumental variable analyses did not support a causal relationship between higher BMI and major depression. The positive associations of higher BMI with major depression in probit regression analyses might be explained by reverse causality and/or residual confounding.