Jessica Tyrrell

Background: Heavy menstrual bleeding (HMB) and menstrual pain (MP) are among the most common menstrual cycle related symptoms with substantial impacts on quality of life. Despite this, population-level evidence on their prevalence across the life course is scarce. Methods: We conducted descriptive analyses of six longitudinal population-based cohorts from the United Kingdom, United States, Norway, and Australia. We estimated cumulative prevalence (across all available timepoints) and age-specific point prevalence of HMB and MP. Cumulative prevalence estimates were stratified by sociodemographic factors, where data permitted. We also examined HMB and MP incidence in adolescent cohorts with repeated measurements. Findings: HMB and MP are commonly reported, with cumulative prevalence across all the cohorts and timepoints (i.e., ever reported) ranging from 36% to 89% for HMB and 46% to 72% for MP. In adolescence, MP (point-prevalence: 33% to 47%) is more commonly reported than HMB (16% to 24%), whereas in later ages (45 years and above), HMB (47% to 70%) is more prevalent than MP (24% to 40%). We found no consistent evidence that symptoms were more likely to co-occur than occur independently; patterns varied across life stages and cohorts. Symptoms were more common in those from lower socioeconomic backgrounds and minoritised ethnic groups. Incidence of both HMP and MP peaked in the years immediately following menarche. Interpretation: Most women experience heavy and/or painful periods at some point. MP is more common in adolescence and HMB in later reproductive life. Generating robust life-course evidence on menstrual symptom patterns is essential to inform clinical guidelines, reduce diagnostic delays, and address inequalities in menstrual health.

Schizophrenia (SCZ) is a highly polygenic disorder and is associated with several comorbidities, including depression, cardiovascular disease, and diabetes; yet the biological pathways underlying this clustering remain poorly understood. This study examined associations between a polygenic risk score for SCZ (PRS-SCZ) and circulating protein levels in 44,661 participants of the UK Biobank. We assessed 1,459 proteins (with ≤ 10% missingness) spanning neurological, inflammatory, cardiometabolic, and oncology panels. After adjustment for technical (population structure, genotyping chip), individual (age, sex, lifestyle), and clinical (comorbidities and medication) variables and multiple testing correction, the standardised PRS-SCZ was significantly negatively associated with C2, RNASET2, BTN2A1, CDSN, HLAE, LTA, MICA-MICB, CNTN3, FCER2, INHBC and CPVL concentrations, and positively with ICAM3, BTN3A2, IL5RA, SLAM7 concentrations. These protein biomarkers may provide insights into the biological pathways underlying schizophrenia and/or its comorbidities.

Background Major depressive disorder (MDD) is a highly heterogeneous condition that is frequently co-morbid with type 2 diabetes (T2D), and yet the biological mechanisms linking these diseases remain unclear. We aim to identify distinct biological pathways in depression that may modify T2D risk. Methods Using Clustered Mendelian randomisation (MR-Clust), we analysed 621 genome-wide significant MDD variants to identify clusters of variants with similar causal effects on T2D. These clusters were validated, and their causal effects were comprehensively tested against glycaemic traits, depression subtypes, T2D risk factors, and cardiometabolic biomarkers using external GWAS data and the UK Biobank. Functional annotation of these clusters was performed using FUMA. Results MR-Clust identified three distinct clusters of MDD-associated variants. Two clusters (MDD1 and MDD2) were causal for higher T2D and its related risk factors, adverse glycaemic and cardiometabolic profiles. Functional annotation implicated brain expression that overlapped strongly with depression-related traits such as smoking and neuroticism. By contrast, MDD3 was causal for lower T2D risk, more favourable glycaemic and cardiometabolic biomarker profiles, and was enriched for gene sets linked to fatty acid metabolism and steroid biosynthesis. MDD1 and MDD2 clusters were associated with atypical-like depression symptoms, whereas MDD3 was associated with melancholic depression symptoms. Conclusion Our findings demonstrate a heterogenous genetic architecture for depression, with distinct biological pathways conferring opposing effects on cardiometabolic health. Understanding this heterogeneity could help tailor prevention and treatment strategies for people with depression at greatest metabolic risk.

Major depressive disorder (MDD) and type 2 diabetes (T2D) represent two global health challenges with a high degree of co-occurrence. Here, we aim to investigate the causal relationship between MDD and T2D in diverse ancestries using Mendelian randomization (MR) in GWAS summary statistic and individual level (UK Biobank (UKB)) data. We assessed the bi-directional causal relationship between: (a) MDD and T2D and (b) MDD and glycaemic biomarkers (e.g. TG:HDL-C ratio, a measure of insulin resistance, fasting glucose) in non-diabetic individuals. In UKB we also tested the role of T2D on treatment resistant depression (TRD). We used multivariable MR (MVMR) to assess the role of body mass index (BMI) in the MDD to T2D relationship. Our results demonstrated that a doubling in MDD genetic liability was associated with 1.14 higher odds of T2D (95% CI:1.09, 1.19), whilst a doubling in T2D genetic liability associated with 1.02 higher odds of MDD (95% CI:1.01, 1.03). Consistent effect estimates were observed in the UKB when stratifying by sex and suggested a role for T2D in TRD. T2D GWAS derived clusters of genetic variants highlighted the importance of specific pathways in the MDD relationship, including variants raising T2D risk via body fat (OR:1.04; 95% CI:1.02, 1.06), obesity mediated insulin resistance (OR:1.06; 95% CI:1.04, 1.09) and residual glycaemic (OR: 1.02; 95% CI:1.00, 1.04) pathways. MVMR with BMI attenuated the bidirectional relationship between MDD and T2D, particularly from MDD to T2D. Genetic liability to MDD was also associated with higher TG:HDL-C ratio in individuals without T2D (β:0.11; 95% CI:0.08, 0.14). We provide evidence of bidirectional causal association between MDD and T2D, with MDD strongly predicting insulin resistance and T2D. T2D predicted both MDD and TRD and highlighted the importance of obesity and body fat pathways in the T2D to MDD relationship.

When collecting marker-based motion capture data from clinical populations, speed of collection and comfort for the participant is a priority. This could be achieved by attaching markers to motion capture Velcro suits, as opposed to the skin. This study aimed to ascertain the reliability of sagittal-plane gait parameters estimated using Plug-in Gait (PiG) and Conventional Gait Model 2 (CGM2) marker sets from data collected in Suited and Non-suited (markers placed onto skin) conditions. For ten participants, markers were placed based on PiG and CGM2 models and data captured during a 2-min treadmill walk. Trials were repeated in suited and non-suited conditions. PiG ankle flexion/extension measurements had poor/moderate reliability (Non-suited ICC = 0.531, Suited ICC = 0.435). CGM2 ankle flexion/extension measurements had good/excellent reliability (Non-suited ICC = 0.916, Suited ICC = 0.900). There were significant differences in minimal detectable change (MDC) between conditions at the ankle for PiG (Non-suited MDC = 2.32°, Suited MDC = 18.90°), but not for CGM2 (Non-suited MDC = 0.63°, Suited MDC = 0.95°). When using CGM2, knee (Non-suited ICC = 0.878, Suited ICC = 0.855) and hip (Non-suited ICC = 0.897, Suited ICC = 0.948) showed good/excellent reliability in both conditions. A motion capture suit is not a reliable solution when collecting joint angle data using the PiG model but is reliable enough to consider when using the CGM2 model.

Aims: Glycated Hemoglobin A _1c (HbA1c) is widely used for the diagnosis and management of type 2 diabetes mellitus (T2D), with regular testing in primary care recommended every three to six months. We aimed to identify distinct, long-term HbA1c trajectories following a T2D diagnosis and investigate how these glycaemic control trajectories were associated with health-related traits and T2D complications. Methods: A cohort of 12,435 unrelated individuals of European ancestry with T2D was extracted from the UK Biobank data linked to primary care records. Latent class growth mixture modelling was applied to identify classes with similar HbA1c trajectories over the 10-years following T2D diagnosis. We tested for associations of HbA1c class membership with sociodemographic factors, biomarkers, polygenic scores, and T2D-related outcomes, using logistic regression and Cox proportional hazards models. Results: Six HbA1c trajectory classes were identified. The largest class (76.8%) maintained low and stable HbA1c levels over time. The other five classes demonstrated higher and more variable trajectories and included: two with parabolic shapes (starting low and distinguished by the height of their peaks), two with high initial HbA1c levels that declined over time (one rapidly, one slowly), and one class with a rapid increase in HbA1c five years after diagnosis. Younger age at T2D diagnosis, higher fasting glucose levels, higher random glucose levels, and higher body mass index polygenic score were associated with membership of these five classes. These classes were also more likely to be prescribed glucose-lowering medication at diagnosis and had fewer primary care visits in the month and year prior to diagnosis. Relative to the low and stable class, these five showed increased risks of T2D complications, including stroke (HR=1.55 [1.31-1.84]), kidney disease (HR=1.39 [1.27-1.53]), all-cause mortality (HR=1.36 [1.23-1.51]), and progression to combination therapy (HR=3.22 [3.04-3.41]) or insulin (HR=3.21 [2.89-3.55]). Conclusion: Individuals with T2D who show higher and more variable HbA1c trajectories are at increased risk of developing T2D-related complications. Early identification of patients at risk, based on factors such as age at diagnosis and previous healthcare utilisation could improve patient outcomes.

Disordered eating and self-harm commonly co-occur in young people suggesting potential for shared underlying causes. Body image dissatisfaction (BID) has been recognised as a psychological correlate of body size, associated with both disordered eating and self-harm. However, the investigation into etiological pathways early in the lifecourse to provide detail on how body size and BID may foster disordered eating and self-harm remains largely unexplored. Employing data from two large population-based cohorts, the UK Biobank and the Avon Longitudinal Study of Parents And Children (ALSPAC), we conducted bidirectional Mendelian randomization (MR) to determine the causal direction of effect between genetically predicted prepubertal body size and two measures of BID indicating (i) desire to be smaller, and (ii) desire to be larger. We then used multivariable regression followed by counterfactual mediation analyses. Bidirectional MR indicated robust evidence that increased genetically predicted prepubertal body size increased desire to be smaller and decreased desire to be larger. Evidence for the reverse causal direction was negligible. These findings remained very similar across sensitivity analyses. In females and males, multivariable regression analyses demonstrated that being overweight increased the risk of disordered eating (risk ratio (RR), 95% confidence interval (CI): 1.19, 1.01 to 1.40 and 1.98, 1.28 to 3.05, respectively) and self-harm (RR, 95% CI: 1.35, 1.04 to 1.77 and 1.55, 0.86 to 2.81, respectively), while being underweight was protective against disordered eating (RR, 95% CI: 0.57, 0.40 to 0.81 and 0.81, 0.38 to 1.73, respectively). There was weak evidence of an increase in the risk of self-harm among underweight individuals. Mediation analyses indicated that the relationship between being overweight and subsequent disordered eating was largely mediated by the desire to be smaller. Our research carries important public health implications, suggesting distinct risk profiles for self-harm and disordered eating in relation to weight and body image. In addition, a better understanding of genetically predicted prepubertal BID may be valuable in the prevention and treatment of disordered eating and self-harm in adolescence.