Combining polygenic risk scores to understand genetic liability to physical-mental health multimorbidity in UK Biobank

Neurodevelopmental copy-number variants increase risk of internalizing and cardiometabolic multimorbidity: Findings from the UK Biobank

Genetic Risk Prediction for CKD: A Journey of a Thousand Miles

Background. Higher body mass index (BMI) is associated with poor survival after breast cancer diagnosis. BMI is a heritable trait. A polygenic risk score (PRS) for BMI has been associated with cardiometabolic traits and found to modify weight loss interventions. Whether BMI genetic scores affect survival in women with breast cancer is unknown. Methods. This analysis was conducted in the Cancer Prevention Study II Nutrition cohort. Women diagnosed with non-metastatic breast cancer between 1992 and 2017 were included in the analysis if they had genotype data. Analyses were restricted to unrelated, postmenopausal women at the time of diagnosis who were of European ancestry (N=3,566). Deaths through 2020 were identified through linkage with the National Death Index. Primary cause of death was based on the International Classification of Disease codes. Pre-diagnosis BMI was self-reported (median 1.3 years from BMI measurement to diagnosis, interquartile range (IQR): 0.6, 1.9). We constructed a PRS using findings from a published meta-analysis of BMI genome wide association studies (GWAS) that included ~700,000 individuals and the PRSice tool.Hazard ratios (HR) and 95% confidence intervals (CI) between the PRS and all-cause mortality were estimated using Cox proportional hazards regression and Fine and Gray models for cause-specific mortality to account for competing risks. Models were adjusted for age and GWAS-specific statistically significant (P<0.05) principal components for population stratification. Mediation was estimated using the mediation R package. Results. The median age at diagnosis was 71.5 years (IQR: 66.3, 76.8). Most women were overweight (33.5%) or obese (18.4%) and were diagnosed with localized stage (63%) and estrogen receptor positive (65%) breast cancer. During a median follow-up of 14.5 years (IQR: 9.7-19.8), there were 1,825 (51.2%) deaths, including 301 breast cancer and 337 cardiovascular disease (CVD) specific deaths. In multivariable models, a 5 Kg/m2 increase in BMI was associated with increased risks of all-cause mortality (HR=1.09, 95% CI: 1.04, 1.15), breast cancer-specific mortality (HR=1.27, 95% CI: 1.13, 1.42), and CVD-specific mortality (HR=1.25, 95% CI: 1.11, 1.41). The PRS was highly predictive of pre-diagnostic BMI (P<0.001). A 1-standard deviation increase in the PRS was associated with statistically increased risk of all-cause mortality (HR=1.06, 95% CI: 1.02, 1.11). Risks of breast cancer (HR=1.07, 95% CI: 0.96, 1.19) or CVD (HR=1.01, 95% CI: 0.90, 1.12) specific mortality did not reach statistical significance. We estimated that 27% of the PRS and all-cause mortality association was mediated by BMI (95% CI: 12%, 91%; P=0.004). Conclusions. Women with breast cancer predisposed to higher BMI were at increased risk of all-cause mortality. A BMI-related PRS may be a useful tool to identify women with breast cancer in need of additional interventions and/or surveillance. Citation Format: Clara Bodelon, Adriana Lori, Mariah Landry, James Hodge, Parichoy Pal Choudhury, Ying Wang, Lauren E. McCullough, Alpa V. Patel, Lauren R. Teras. Genetic predisposition to obesity and survival in women with breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3425.

Large-scale biobanks provide important opportunities for mental health research, but selection biases raise questions regarding the comparability of individuals with those in clinical research settings. To compare the genetic liability to psychiatric disorders in individuals with schizophrenia in the UK Biobank with individuals in the Psychiatric Genomics Consortium (PGC) and to compare genetic liability and phenotypic features with participants recruited from clinical settings. This cross-sectional study included participants from the population-based UK Biobank and schizophrenia samples recruited from clinical settings (CLOZUK, CardiffCOGS, Cardiff F-Series, and Cardiff Affected Sib-Pairs). Data were collected between January 1993 and July 2021. Data analysis was conducted between July 2021 and June 2023. A genome-wide association study of UK Biobank schizophrenia case-control status was conducted, and the results were compared with those from the PGC via genetic correlations. To test for differences with the clinical samples, polygenic risk scores (PRS) were calculated for schizophrenia, bipolar disorder, depression, and intelligence using PRS-CS. PRS and phenotypic comparisons were conducted using pairwise logistic regressions. The proportions of individuals with copy number variants associated with schizophrenia were compared using Firth logistic regression. The sample of 517 375 participants included 1438 UK Biobank participants with schizophrenia (550 [38.2%] female; mean [SD] age, 54.7 [8.3] years), 499 475 UK Biobank controls (271 884 [54.4%] female; mean [SD] age, 56.5 [8.1] years), and 4 schizophrenia research samples (4758 [28.9%] female; mean [SD] age, 38.2 [21.0] years). Liability to schizophrenia in UK Biobank was highly correlated with the latest genome-wide association study from the PGC (genetic correlation, 0.98; SE, 0.18) and showed the expected patterns of correlations with other psychiatric disorders. The schizophrenia PRS explained 6.8% of the variance in liability for schizophrenia case status in UK Biobank. UK Biobank participants with schizophrenia had significantly lower schizophrenia PRS than 3 of the clinically ascertained samples and significantly lower rates of schizophrenia-associated copy number variants than the CLOZUK sample. UK Biobank participants with schizophrenia had higher educational attainment and employment rates than the clinically ascertained schizophrenia samples, lower rates of smoking, and a later age of onset of psychosis. Individuals with schizophrenia in the UK Biobank, and likely other volunteer-based biobanks, represent those less severely affected. Their inclusion in wider studies should enhance the representation of the full spectrum of illness severity.

Asthma is associated with adverse cardiovascular outcomes, but little is known about its role in the development of cardiometabolic multimorbidity (CMM). We aimed to examine the associations of asthma with both incident and coexisting cardiometabolic diseases (CMDs), characterizing their patterns and transitions to CMM in men and women. This prospective cohort study, based on the UK Biobank, included 51,335 participants with asthma and 395,890 without asthma at baseline in 2006–2010. Participants were followed for the development of CMDs, including type 2 diabetes, coronary heart disease, and stroke, using primary care records, hospital admission and death register data, and self-reported medical information up to December 31, 2022. CMM was defined as the coexistence of two or more CMDs. We used Cox proportional hazards models and multi-state models to assess the associations of asthma with the incidence and transitions to CMDs and CMM among participants free of CMDs. During a median follow-up of 13.8 years, 60,033 participants (13.4%) developed CMD, of whom 7,048 (1.6%) progressed to CMM. Asthma was associated with increased risks of all incident CMDs and CMM (hazard ratio [HR] = 1.54, 95% confidence interval = 1.44–1.64), as well as CMD counts and CMM patterns (e.g., HR = 1.60 [1.50–1.71] for 2 CMDs, and HR = 1.70 [1.56–1.84] for comorbid type 2 diabetes and coronary heart disease). For the transitions from no CMD to first CMD, from first CMD to CMM, and from no CMDs to death, the hazard ratios were 1.29 (1.26–1.33), 1.20 (1.12–1.28), and 1.14 (1.09–1.18), respectively. All these associations were more pronounced in women. In summary, individuals with asthma were at increased risk of developing cardiometabolic diseases and progressing to cardiometabolic multimorbidity. Early prevention and management of asthma, with integration into cardiometabolic risk assessment, may be crucial for mitigating future cardiometabolic multimorbidity.

The prevalence of multiple long-term conditions (MLTC) is increasing. It is essential to develop strategies to prevent and manage MLTC; however, the biological mechanisms underlying MLTC are not yet clearly understood. We used UK Biobank data as part of the ADMISSION research collaborative to identify genetic drivers for MLTC. We used the UK Biobank (UKBB) self-reported illness data to characterise MLTC (defined as two or more long-term conditions) using 51 common disease labels. A genome-wide association study (GWAS) was conducted for MLTC and complex MLTC (complex MLTC was defined as having three or more diseases from the 51 self-reported diseases, with these three diseases additionally belonging to different body systems), and post-GWAS analyses were conducted to explore the genomic loci associated with MLTC. We then undertook a factor analysis on the individual-level disease data to identify the factors contributing to MLTC. We investigated the genomics of these factors using single disease polygenic risk score (PRS) and GWAS. The prevalence of simple MLTC was 33.0% (n = 111,184) and complex MLTC was 11.2% (n = 37,650). The majority (81.3%) of significant SNPs from MLTC GWAS were located in chromosome 6 with most of them in the HLA region. The 'T cell activation' pathway and apoptosis signalling pathways were identified in gene-based pathway analysis. Five latent factors were identified through factor analysis with the following underlying characteristics: Factor 1, metabolic disease; Factor 2, mental ill health; Factor 3, cancer; Factor 4, musculoskeletal and inflammation-related traits; Factor 5, digestive system-related diseases. The GWAS and PRS-based analysis validated the characteristics of these factors. The MLTC GWAS, complex MLTC GWAS and factor-based GWAS analyses highlighted the association between HLA genes and MLTC. Further research is needed to disentangle the association between MLTC and the HLA genes, along with the integration of multi-omics data.

1261-OR: Molecular Risk Scores Enhance Type 2 Diabetes (T2D) Prediction beyond Hemoglobin A1c (HbA1c) and Body Mass Index (BMI) across Diverse Clinical Contexts

Cardiometabolic multimorbidity (CMM) poses a mounting health challenge worldwide. Seven surrogate indexes of insulin resistance (IR)-the triglyceride-glucose index (TyG), TyG-body mass index (TyG-BMI), TyG-waist circumference (TyG-WC), Chinese visceral adiposity Index (CVAI), Metabolic score for IR (METS-IR), Atherogenic index of plasma (AIP), and estimated glucose disposal rate (eGDR)-are well-established. However, comparative studies evaluating their predictive capacity for CMM incidence in Chinese middle-aged and older adults remain scarce. This study aimed to assess the associations between these seven IR indexes and CMM risk within this population and determine their relative predictive abilities. Utilizing the China Health and Retirement Longitudinal Study (CHARLS) 2011-2020 datasets, this prospective cohort investigation assessed Chinese participants aged ≥ 45years. We applied Kaplan-Meier curve plotting, multivariable Cox proportional hazards models, and restricted cubic splines (RCS) to quantify associations of insulin resistance surrogate indexes with CMM risk. Predictive accuracy was appraised via time-dependent receiver operating characteristic (ROC) curves, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Subgroup evaluations further verified findings robustness. During a median follow-up of 9years, 1043 (14.49%) of the 7197 participants developed CMM. After adjusting for potential confounders, we observed that each standard deviation (SD) increase in eGDR was associated with a reduced risk of CMM, with an adjusted hazard ratio (HR) of 0.834 [95% confidence interval (CI): 0.781-0.891]. In contrast, each SD increase in TyG, TyG-BMI, TyG-WC, METS-IR, AIP and CVAI were associated with an increased risk of CMM. Restricted cubic spline analyses showed that TyG-BMI, TyG-WC, METS-IR, and eGDR were nonlinear associated with incident new-onset CMM (P-nonlinearity < 0.05). eGDR showed a L-shaped association (P-nonlinearity < 0.05), with CMM risk decreasing until the inflection point at 11.82 (HR = 0.758; 95% CI: 0.702-0.818). Conversely, TyG-WC displayed a U-shaped relationship (inflection point: 572.14). Moreover, the time-dependent AUC analysis revealed that eGDR exhibited superior predictive discrimination (AUC 0.68-0.76) during early follow-up and across all intervals. The optimal cut-off value for eGDR was determined to be 7.64. All seven insulin resistance surrogate indexes independently demonstrated elevated CMM risk. In the context of Chinese middle-aged and elderly cohorts, eGDR demonstrated a notable predictive capacity for CMM. ROC-derived cut-offs (eGDR < 7.64) are utilised for identifying high-risk individuals requiring intervention. Spline-derived thresholds (eGDR = 11.82) serve as therapeutic targets for risk reduction.

Background and Aims: Chronic Kidney Disease (CKD) typically co-exists with multiple long-term conditions (LTCs). The impact of CKD combined with multiple LTCs on hospitalisation rates is not known. We hypothesised that hospitalisation rates would be high in people with multiple LTCs, particularly in those with CKD. We also hypothesised that the association between multiple LTCs and hospitalisation would be greatest in subgroups and with certain patterns of LTCs. Method: Two cohorts were studied in parallel: UK Biobank (2006-2019) and Secure Anonymised Information Linkage Databank (SAIL: 2011-2018, Wales, UK). UK Biobank is a prospective research cohort. SAIL is a routine care database. Participants were included if their kidney function was measured at baseline. LTCs were obtained from self-report (UK Biobank) and primary care read codes (SAIL). Participants were categorised into zero, one, two, three and four or more LTCs with and without CKD. CKD was defined as estimated glomerular filtration rate less than 60 ml/min/1.73m2 (single blood test for UK Biobank, two blood tests three months apart for SAIL). Hospitalisation events were obtained from linked hospital records. Results: Among 469,344 of 502,503 UK Biobank participants, those without CKD had a median age of 58 and a median of 1 LTC. Those with CKD had a median age of 64 and a median of 2 LTCs. Among 1,620,490 of 2,768,862 SAIL participants, those without CKD had a median age of 50 and a median of 1 LTC. Those with CKD had a median age of 79 and a median of 4 LTCs. Participants with four or more LTCs had high event rates (Rate Ratios (RRs) 5.35 (95% CI 5.20-5.51)/3.77 (95% CI 3.71-3.82)) with higher rates in CKD (RRs 8.99 (95% CI 8.47-9.54)/9.92 (95% CI 9.75-10.09)). Amongst those with CKD, the association between each increase in LTC count and hospitalisation was greatest in those under the age of 50 (RRs 1.93 (95% CI 1.73-2.16)/1.35(95% CI 1.29-1.41)). Event rates were highest in those with eGFR&amp;lt;30ml/min/1.73m2, but the impact of multiple LTCs was weaker in these participants compared to those with higher eGFRs. Event rates were high in certain patterns of LTCs: cardiometabolic LTCs (RRs 4.45 (95% CI 4.02-4.92)/2.81 (95% CI 2.71-2.91)), complex patterns (RRs 3.60 (95% CI 3.26-3.96)/2.91 (95% CI 2.81-3.01)) and physical/mental LTCs (RRs 3.30 (95% CI 2.86-3.80)/3.18 (95% CI 3.06-3.30)). Conclusion: People with multiple LTCs have high rates of hospitalisation and the rates are augmented in those with CKD. The impact of multiple LTCs is greatest in younger patients and in those with certain patterns of LTCs. Strategies should be developed to prevent hospitalisations in these high-risk groups. Hospitalisation Events by Chronic Kidney Disease (CKD) status and number of Long-term conditions (LTCs) in UK Biobank Hospitalisation Events by Chronic Kidney Disease (CKD) status and number of Long-term conditions (LTCs) in SAIL

Introduction: The aromatic amino acids tyrosine and phenylalanine may increase type 2 diabetes risk, but few studies have examined whether these critical molecular precursors exert additional phenotypic effects and whether these effects are independent of insulin resistance. We evaluated associations between two aromatic amino acids - tyrosine and phenylalanine - with a broad range of phenotypic categories in the UK Biobank using polygenic risk score (PRS) instrumental variables that are robust to confounding and reverse causation and tested for mediation by glycated hemoglobin (HbA1c) as a measure of insulin resistance. Hypothesis: We hypothesized that tyrosine and phenylalanine would show broad phenotypic effects and that many of these effects would be mediated by HbA1c. Methods: We constructed PRS (Crosspred) using all nominally significant and common (minor allele frequency &gt;5%) variants from genome-wide association studies (SAIGE) of unrelated European ancestry participants in the UK Biobank. We evaluated associations with 273 curated phenotypes spanning 20 categories, including endocrine, circulatory, and cancer related traits. We corrected for multiple comparisons using false discovery rate &lt;0.05. Mediation by HbA1c was estimated using the CMAverse package in R, controlling for age, sex, center, ancestral principal components, body mass index, and socioeconomic status. Results: A total of 108,554 participants had tyrosine or phenylalanine measured at baseline (mean age=57 years; 54% (58,880/108,554) female). PRS were predictive of tyrosine (R 2 =0.28) and phenylalanine (R 2 =0.26). Tyrosine and phenylalanine PRS showed significant associations with 121/273 (44%) and 124/273 (45%) of phenotypes, respectively, including chronic kidney disease, serum testosterone, lymphocyte count, and myocardial infarction (MI; n=3,349 cases, mean years of follow-up=11). For incident MI, every one standard deviation increase in the tyrosine PRS increased the odds of MI by 4.4% (total effect odds ratio (OR) = 1.044 (95% confidence interval (CI): 1.002, 1.089). The direct effect of the tyrosine PRS on incident MI was slightly decreased (OR= 1.040 (95% CI: 0.997, 1.084) when extending the statistical model to examine mediation by HbA1c, with an estimated indirect effect of 1.004 (95% CI: 1.003, 1.006) and an estimated percent mediated by HbA1c of 9.95% (95% CI: -0.04%, 19.90%). Findings were consistent for phenylalanine, where the percent of the total effect mediated by HbA1c was 8.94% (95% CI: -1.93, 19.80%). Conclusions: The effect of tyrosine and phenylalanine on incident MI may primarily operate through pathways other than glucose dysregulation.

IntroductionPolygenic risk scores (PRSs) provide a personalised individual level estimate of genetic liability to a disease. Since most genome wide association studies (GWAS) have been conducted in populations of White...

Although humans contain the same genes, the sequence within these DNA sites can vary from person to person. These small variations, also known as genetic variants, can increase the risk of developing certain diseases. While each variant will only have a weak effect, if multiple variations are present the odds of developing the disease becomes significantly higher. To determine which variants are linked to a disease, researchers carry out genome-wide association studies which involve analyzing the genomes of individuals with and without the condition and comparing their genetic codes. This data is then used to calculate how different combinations of variants impact a person’s chance of getting the disease, also known as a polygenic risk score. Currently, most genome-wide association studies only incorporate genetic data from people with European ancestry. Consequently, polygenic risk scores performed using this information may not accurately predict the risk of developing the disease for individuals with other ethnicities, such as people with Asian ancestry. Here, Ho et al. evaluated how well previously calculated polygenic risk scores for the four most common cancers (breast, colorectal, prostate and lung) worked on individuals of East Asian descent. The scores were tested on a dataset containing the genetic sequence, medical history, diet and activity levels of over 21,000 people living in Singapore in the 1990s. Ho et al. found that the polygenic risk scores for breast, prostate and colorectal cancer were able to predict disease risk. However, the score for lung cancer did not perform as well. The polygenic risk score for breast cancer was the most accurate, and was able to stratify individuals into distinct risk bands at an earlier age than other scores. These findings shed light on which existing polygenic risk scores will be effective at assessing cancer risk in individuals with East Asian ancestry. Indeed, Ho et al. have already incorporated the polygenic risk score for breast cancer into a pilot study screening individuals in a comparable population in Singapore. However, the polygenic risk scores tested still performed better on individuals with European ancestry, highlighting the need to address the lack of Asian representation in genome-wide association studies.

Ultra-processed food, genetic risk, and the risk of cardiometabolic diseases and cardiometabolic multimorbidity: A prospective study

Objective To investigate the familial clustering of postpartum haemorrhage in the Swedish population, and to quantify the relative contributions of genetic and environmental effects.Design Register based cohort study.Setting Swedish population...

Increased Obesity Is Causal for Increased Inflammation—A Mendelian Randomisation Study