RESUMEN

Childhood-onset type 1 diabetes (T1D) is associated with substantial psychiatric morbidity in later life, but it remains unknown whether these associations are due to common underlying biological mechanisms or the impacts of living with the condition and its treatment. Here, using Czech national register data, we identified children with T1D aged ≤14 years between 1994 and 2007 and estimated the risk of psychiatric disorders up to 24 years later. We found that children diagnosed with T1D had an elevated risk of developing substance use, mood, anxiety and personality disorders, and behavioral syndromes. Conversely, we found that children with T1D had a lower risk of developing psychotic disorders. In Mendelian randomization analysis, we found an association with schizophrenia, which, however, did not persist following multiple testing adjustment. The combined observational and Mendelian randomization evidence suggests that T1D diagnosis in childhood predisposes to far-reaching, extensive psychiatric morbidity, which is unlikely to be explicable by common underlying biological mechanisms. The findings of this study highlight that monitoring and addressing the mental health needs of children with T1D is imperative, whereas glucose dysregulation and/or inflammation implicated in schizophrenia pathogenesis warrants future research.

RESUMEN

The evidence on the impact of fruits and vegetable types on cardiovascular risk factors remains limited. Specifically, the utilisation of biomarkers to objectively measure dietary compliance and metabolic responses is emerging. This protocol and baseline characteristics of a pilot randomised controlled, crossover, dietary intervention study aimed to examine the effects of citrus fruits, cruciferous vegetables, or common fruits and vegetables on cardiovascular risk factors. A total of 39 volunteers with untreated prehypertension was recruited and consumed a standardised, provided diet with eight daily portions of citrus fruits and cruciferous vegetables, common fruits and vegetables, or a low fruit and vegetable diet (two portions/d, control diet) in a random order for 2 weeks each, separated by a wash-out week. A targeted cohort-based recruitment strategy was utilised and resulted in 74% of participants recruited by re-contacting preselected individuals from two cohort studies with a 15% average enrolment rate. Participants had an average age of 54.4 years (±6.1 years), BMI of 27.9 kg/m2, and BP of 135/81 mmHg and were mainly male (67%). The primary outcome was office blood pressure; secondary outcomes included arterial stiffness, lipid profiles, inflammation, cognitive function, and subjective mood. Biofluids, i.e., 24 h urine, stool, and blood samples, were collected for biomarker measurements with multiple metabolomic platforms and the gut microbial composition, together with traditional dietary biomarkers.

Asunto(s)

RESUMEN

Selective IgA deficiency (SIgAD) is the most common inborn error of immunity (IEI). Unlike many IEIs, evidence of a role for highly penetrant rare variants in SIgAD is lacking. Previous SIgAD studies have had limited power to identify common variants due to their small sample size. We overcame this problem first through meta-analysis of two existing GWAS. This identified four novel common-variant associations and enrichment of SIgAD-associated variants in genes linked to Mendelian IEIs. SIgAD showed evidence of shared genetic architecture with serum IgA and a number of immune-mediated diseases. We leveraged this pleiotropy through the conditional false discovery rate procedure, conditioning our SIgAD meta-analysis on large GWAS of asthma and rheumatoid arthritis, and our own meta-analysis of serum IgA. This identified an additional 18 variants, increasing the number of known SIgAD-associated variants to 27 and strengthening the evidence for a polygenic, common-variant aetiology for SIgAD.

RESUMEN

Understanding the genetic basis of neuro-related proteins is essential for dissecting the molecular basis of human behavioural traits and the disease aetiology of neuropsychiatric disorders. Here the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,000 individuals for 184 neuro-related proteins in human plasma. The analysis identified 125 cis-regulatory protein quantitative trait loci (cis-pQTL) and 164 trans-pQTL. The mapped pQTL capture on average 50% of each protein's heritability. At the cis-pQTL, multiple proteins shared a genetic basis with human behavioural traits such as alcohol and food intake, smoking and educational attainment, as well as neurological conditions and psychiatric disorders such as pain, neuroticism and schizophrenia. Integrating with established drug information, the causal inference analysis validated 52 out of 66 matched combinations of protein targets and diseases or side effects with available drugs while suggesting hundreds of repurposing and new therapeutic targets.

RESUMEN

BACKGROUND: Variation in thyroid function parameters within the normal range has been observationally associated with adverse health outcomes. Whether those associations reflect causal effects is largely unknown. METHODS: We systematically tested associations between genetic differences in thyrotropin (TSH) and free thyroxine (FT4) within the normal range and more than 1100 diseases and more than 6000 molecular traits (metabolites and proteins) in three large population-based cohorts. This was performed by combining individual and summary level genetic data and using polygenic scores and Mendelian randomization (MR) methods. We performed a phenome-wide MR study in the OpenGWAS database covering thousands of complex phenotypes and diseases. FINDINGS: Genetically predicted TSH or FT4 levels within the normal range were predominately associated with thyroid-related outcomes, like goitre. The few extra-thyroidal outcomes that were found to be associated with genetic liability towards high but normal TSH levels included atrial fibrillation (odds ratio = 0.92, p-value = 2.13 × 10-3), thyroid cancer (odds ratio = 0.57, p-value = 2.97 × 10-4), and specific biomarkers, such as sex hormone binding globulin (ß = -0.046, p-value = 1.33 × 10-6) and total cholesterol (ß = 0.027, p-value = 5.80 × 10-3). INTERPRETATION: In contrast to previous studies that have described the association with thyroid hormone levels and disease outcomes, our genetic approach finds little evidence of an association between genetic differences in thyroid function within the normal range and non-thyroidal phenotypes. The association described in previous studies may be explained by reverse causation and confounding. FUNDING: This research was funded by the Swiss National Science Foundation (P1BEP3_200041). The Fenland study (DOI 10.22025/2017.10.101.00001) is funded by the Medical Research Council (MC_UU_12015/1, MC_PC_13046 and MC_UU_00006/1). The EPIC-Norfolk study (DOI 10.22025/2019.10.105.00004) has received funding from the Medical Research Council (MR/N003284/1, MC-UU_12015/1, MC_PC_13048 and MC_UU_00006/1).

Asunto(s)

RESUMEN

For many diseases there are delays in diagnosis due to a lack of objective biomarkers for disease onset. Here, in 41,931 individuals from the United Kingdom Biobank Pharma Proteomics Project, we integrated measurements of ~3,000 plasma proteins with clinical information to derive sparse prediction models for the 10-year incidence of 218 common and rare diseases (81-6,038 cases). We then compared prediction models developed using proteomic data with models developed using either basic clinical information alone or clinical information combined with data from 37 clinical assays. The predictive performance of sparse models including as few as 5 to 20 proteins was superior to the performance of models developed using basic clinical information for 67 pathologically diverse diseases (median delta C-index = 0.07; range = 0.02-0.31). Sparse protein models further outperformed models developed using basic information combined with clinical assay data for 52 diseases, including multiple myeloma, non-Hodgkin lymphoma, motor neuron disease, pulmonary fibrosis and dilated cardiomyopathy. For multiple myeloma, single-cell RNA sequencing from bone marrow in newly diagnosed patients showed that four of the five predictor proteins were expressed specifically in plasma cells, consistent with the strong predictive power of these proteins. External replication of sparse protein models in the EPIC-Norfolk study showed good generalizability for prediction of the six diseases tested. These findings show that sparse plasma protein signatures, including both disease-specific proteins and protein predictors shared across several diseases, offer clinically useful prediction of common and rare diseases.

Asunto(s)

RESUMEN

Effective prevention strategies for post-COVID complications are crucial for patients, clinicians, and policy makers to mitigate their cumulative burden. This study evaluated the association of modifiable lifestyle factors (smoking, alcohol intake, BMI, physical activity, sedentary time, sleep duration, and dietary habits) with COVID-19 multisystem sequelae, death, and hospitalization in the UK Biobank cohort (n = 68,896). A favorable lifestyle (6-10 healthy factors; 46.4%) was associated with a 36% lower risk of multisystem sequelae (HR, 0.64; 95% CI, 0.58-0.69; ARR at 210 days, 7.08%; 95% CI, 5.98-8.09) compared to an unfavorable lifestyle (0-4 factors; 12.3%). Risk reductions spanned all 10 organ systems, including cardiovascular, coagulation, metabolic, gastrointestinal, kidney, mental health, musculoskeletal, respiratory disorders, and fatigue. This beneficial effect was largely attributable to direct lifestyle impacts independent of corresponding pre-infection comorbidities (71% for any sequelae). A favorable lifestyle was also related to the risk of post-COVID death (HR 0.59, 0.52-0.66) and hospitalization (HR 0.78, 0.73-0.84). These associations persisted across acute and post-acute infection phases, irrespective of hospitalization status, vaccination, or SARS-CoV-2 variant. These findings underscore the clinical and public health importance of adhering to a healthy lifestyle in mitigating long-term COVID-19 adverse impacts and enhancing future pandemic preparedness.

Asunto(s)

RESUMEN

BACKGROUND: Broad-capture proteomic technologies have the potential to improve disease prediction, enabling targeted prevention and management, but studies have so far been limited to very few selected diseases and have not evaluated predictive performance across multiple conditions. We aimed to evaluate the potential of serum proteins to improve risk prediction over and above health-derived information and polygenic risk scores across a diverse set of 24 outcomes. METHODS: We designed multiple case-cohorts nested in the EPIC-Norfolk prospective study, from participants with available serum samples and genome-wide genotype data, with more than 32 974 person-years of follow-up. Participants were middle-aged individuals (aged 40-79 years at baseline) of European ancestry who were recruited from the general population of Norfolk, England, between March, 1993 and December, 1997. We selected participants who developed one of ten less common diseases within 10 years of follow-up; we also subsampled a randomly drawn control subcohort, which also served to investigate 14 more common outcomes (n>70), including all-cause premature mortality (death before the age of 75 years; case numbers 71-437; controls 608-1556). Individuals were excluded from the current study owing to failed genotyping or proteomic quality control, relatedness, or missing information on age, sex, BMI, or smoking status. We used a machine learning framework to derive sparse predictive protein models for the onset of the the 23 individual diseases and all-cause premature mortality, and to derive a single common sparse multimorbidity signature that was predictive across multiple diseases from 2923 serum proteins. FINDINGS: Participants who developed one of ten less common diseases within 10 years of follow-up included 482 women and 507 men, with a mean age at baseline of 64·56 years (8·08). The random subcohort included 990 women and 769 men, with a mean age of 58·79 years (9·31). As few as five proteins alone outperformed polygenic risk scores for 17 of 23 outcomes (median dfference in concordance index [C-index] 0·13 [0·10-0·17]) and improved predictive performance when added over basic patient-derived information models for seven outcomes, achieving a median C-index of 0·82 (IQR 0·77-0·82). This included diseases with poor prognosis such as lung cancer (C-index 0·85 [+/- cross-validation error 0·83-0·87]), for which we identified unreported biomarkers such as C-X-C motif chemokine ligand 17. A sparse multimorbidity signature of ten proteins improved prediction across seven outcomes over patient-derived information models, achieving performances (median C-index 0·81 [IQR 0·80-0·82]) similar to those of disease-specific signatures. INTERPRETATION: We show the value of broad-capture proteomic biomarker discovery studies across multiple diseases of diverse causes, pointing to those that might benefit the most from proteomic approaches, and the potential to derive common sparse biomarker panels for prediction of multiple diseases at once. This framework could enable follow-up studies to explore the generalisability of proteomic models and to benchmark these against clinical assays, which are required to understand the translational potential of these findings. FUNDING: Medical Research Council, Health Data Research UK, UK Research and Innovation-National Institute for Health and Care Research, Cancer Research UK, and Wellcome Trust.

Asunto(s)

RESUMEN

AIMS: To assess the effects of non-diabetic hyperglycaemia (NDH, also known as pre-diabetes), including the impact of the NHS Diabetes Prevention Programme (NHS DPP), on COVID-19-related mortality during the pandemic. METHODS: This study included all 61,438,225 individuals registered with General Practices in England and alive on 1st March 2020. We assessed COVID-19-related mortality in the 2,290,280(3.7 %) individuals with diagnosed NDH between March 2020 and February 2022 compared to those without diagnosed NDH or diabetes using Cox regression to adjust for demographic factors and cardiovascular comorbidities. Individuals with diagnosed NDH were further sub-categorised based on their contact with the NHS DPP (N = 376,590). Analyses were stratified by age (years) (<50, 50-69 and ≥ 70). RESULTS: There were 158,070 COVID-19 deaths; 17,280(11 %) for people with diagnosed NDH. The adjusted hazard ratio (HR) was 0.95(0.93-0.96),p < 0.001 for those with diagnosed NDH compared to those without diagnosed diabetes or NDH. By age (years), HRs were, 2.53(2.23-2.88),p < 0.001 for < 50, 1.29(1.24-1.35),p < 0.001 for 50-69 and 0.87(0.85-0.89),p < 0.001 for ≥ 70. NHS DPP attendance was associated with lower COVID-19 mortality with a dose-response relationship with engagement. CONCLUSIONS: Younger people with diagnosed NDH were at higher relative risk of COVID-19 mortality. Attendance at the NHS DPP was associated with significantly lower COVID-19-related mortality.

Asunto(s)

RESUMEN

There is currently no medical therapy to prevent calcific aortic valve stenosis (CAVS). Multi-omics approaches could lead to the identification of novel molecular targets. Here, we perform a genome-wide association study (GWAS) meta-analysis including 14,819 cases among 941,863 participants of European ancestry. We report 32 genomic loci, among which 20 are novel. RNA sequencing of 500 human aortic valves highlights an enrichment in expression regulation at these loci and prioritizes candidate causal genes. Homozygous genotype for a risk variant near TWIST1, a gene involved in endothelial-mesenchymal transition, has a profound impact on aortic valve transcriptomics. We identify five genes outside of GWAS loci by combining a transcriptome-wide association study, colocalization, and Mendelian randomization analyses. Using cross-phenotype and phenome-wide approaches, we highlight the role of circulating lipoproteins, blood pressure and inflammation in the disease process. Our findings pave the way for the development of novel therapies for CAVS.

Asunto(s)

RESUMEN

BACKGROUND: Epidemiological evidence suggests that a potential association between dietary protein intake and cardiovascular disease (CVD) may depend on the protein source, that is, plant- or animal-derived, but past research was limited and inconclusive. OBJECTIVES: To evaluate the association of dietary plant- or animal-derived protein consumption with risk of CVD, and its components ischemic heart disease (IHD) and stroke. METHODS: This analysis in the European Prospective Investigation into Cancer and Nutrition (EPIC)-CVD case-cohort study included 16,244 incident CVD cases (10,784 IHD and 6423 stroke cases) and 15,141 subcohort members from 7 European countries. We investigated the association of estimated dietary protein intake with CVD, IHD, and stroke (total, fatal, and nonfatal) using multivariable-adjusted Prentice-weighted Cox regression. We estimated isocaloric substitutions of replacing fats and carbohydrates with plant- or animal-derived protein and replacing food-specific animal protein with plant protein. Multiplicative interactions between dietary protein and prespecified variables were tested. RESULTS: Neither plant- nor animal-derived protein intake was associated with incident CVD, IHD, or stroke in adjusted analyses without or with macronutrient-specified substitution analyses. Higher plant-derived protein intake was associated with 22% lower total stroke incidence among never smokers [HR 0.78, 95% confidence intervals (CI): 0.62, 0.99], but not among current smokers (HR 1.08, 95% CI: 0.83, 1.40, P-interaction = 0.004). Moreover, higher plant-derived protein (per 3% total energy) when replacing red meat protein (HR 0.52, 95% CI: 0.31, 0.88), processed meat protein (HR 0.39, 95% CI: 0.17, 0.90), and dairy protein (HR 0.54, 95% CI: 0.30, 0.98) was associated with lower incidence of fatal stroke. CONCLUSION: Plant- or animal-derived protein intake was not associated with overall CVD. However, the association of plant-derived protein consumption with lower total stroke incidence among nonsmokers, and with lower incidence of fatal stroke highlights the importance of investigating CVD subtypes and potential interactions. These observations warrant further investigation in diverse populations with varying macronutrient intakes and dietary patterns.

Asunto(s)

RESUMEN

BACKGROUND: Excess sedentary time (ST) is recognized as an important modifiable risk factor for coronary heart disease (CHD). However, whether the associations of genetic susceptibility with CHD incidence can be modified by replacing wearable-device-measured ST with physical activity (PA) is unknown. OBJECTIVES: To examine the associations of wearable-device-measured ST replaced by PA with incident CHD across strata of genetic susceptibility. METHODS: This study included 77,500 White British (57% female) with valid wrist-worn accelerometry and without prevalent CHD/stroke from UK Biobank. Genetic susceptibility to CHD was quantified through weighted polygenic risk scores for CHD based on 300 single-nucleotide polymorphisms. Wrist-worn accelerometer data were used to derive ST, light PA, and moderate-to-vigorous PA (MVPA). RESULTS: Reallocation of 60 min/day of ST into the same amount of MVPA was associated with approximately 9% lower relative risk of CHD for all participants and across strata of genetic risk: replacement of 1 min/day of ST associated with <1% lower relative risk of CHD. No evidence of interaction (p: 0.784) was found between genetic risk and ST for CHD risk. Reallocating 60 min/day of ST into the same MVPA time was associated with greater absolute CHD risk reductions at high genetic risk (0.27%) versus low genetic risk (0.15%). CONCLUSIONS: Replacing any amount of ST with an equal amount of MVPA time is associated with a lower relative risk of CHD, irrespective of genetic susceptibility to CHD. Reductions in CHD absolute risk for replacing ST with MVPA are greater at high genetic risk versus low genetic risk.

Asunto(s)

RESUMEN

BACKGROUND: It is unknown whether dietary intake of polyunsaturated fatty acids (PUFA) modifies the cardiovascular disease (CVD) risk associated with a family history of CVD. We assessed interactions between biomarkers of low PUFA intake and a family history in relation to long-term CVD risk in a large consortium. METHODS: Blood and tissue PUFA data from 40 885 CVD-free adults were assessed. PUFA levels ≤25th percentile were considered to reflect low intake of linoleic, alpha-linolenic, and eicosapentaenoic/docosahexaenoic acids (EPA/DHA). Family history was defined as having ≥1 first-degree relative who experienced a CVD event. Relative risks with 95% CI of CVD were estimated using Cox regression and meta-analyzed. Interactions were assessed by analyzing product terms and calculating relative excess risk due to interaction. RESULTS: After multivariable adjustments, a significant interaction between low EPA/DHA and family history was observed (product term pooled RR, 1.09 [95% CI, 1.02-1.16]; P=0.01). The pooled relative risk of CVD associated with the combined exposure to low EPA/DHA, and family history was 1.41 (95% CI, 1.30-1.54), whereas it was 1.25 (95% CI, 1.16-1.33) for family history alone and 1.06 (95% CI, 0.98-1.14) for EPA/DHA alone, compared with those with neither exposure. The relative excess risk due to interaction results indicated no interactions. CONCLUSIONS: A significant interaction between biomarkers of low EPA/DHA intake, but not the other PUFA, and a family history was observed. This novel finding might suggest a need to emphasize the benefit of consuming oily fish for individuals with a family history of CVD.

Asunto(s)

RESUMEN

SCOPE: To identify metabolites associated with habitual dairy consumption and investigate their associations with type 2 diabetes (T2D) risk. METHODS AND RESULTS: Metabolomics assays were conducted in the Fenland (n = 10,281) and EPIC-Norfolk (n = 1,440) studies. Using 82 metabolites assessed in both studies, we developed metabolite scores to classify self-reported consumption of milk, yogurt, cheese, butter, and total dairy (Fenland Study-discovery set; n = 6035). Internal and external validity of the scores was evaluated (Fenland-validation set, n = 4246; EPIC-Norfolk, n = 1440). The study assessed associations between each metabolite score and T2D incidence in EPIC-Norfolk (n = 641 cases; 16,350 person-years). The scores classified low and high consumers for all dairy types with internal validity, and milk, butter, and total dairy with external validity. The scores were further associated with lower incident T2D: hazard ratios (95% confidence interval) per standard deviation: milk 0.71 (0.65, 0.77); butter 0.62 (0.57, 0.68); total dairy 0.66 (0.60, 0.72). These associations persisted after adjustment for known dairy-fat biomarkers. CONCLUSION: Metabolite scores identified habitual consumers of milk, butter, and total dairy products, and were associated with lower T2D risk. These findings hold promise for identifying objective indicators of the physiological response to dairy consumption.

Asunto(s)

RESUMEN

AIMS: Better understanding of sex differences in cardiovascular disease (CVD) is essential in tailoring appropriate preventative strategies. Using a large population-based study with follow-up >25 years, we aimed to determine sex-specific lifetime risks of incident CVD and cardiovascular (CV) mortality amongst populations with and without prevalent CVD. METHODS AND RESULTS: Participants were drawn from the European Prospective Investigation into Cancer-Norfolk and followed up for a median of 26.2 years. Sex-specific lifetime risks were ascertained accounting for the competing risk of death. Models were adjusted for ethnicity and time-updated covariates: material deprivation, CV risk factors, lifestyle factors, comorbidities, and medication. A total of 23 859 participants [54.5% women; mean age (standard deviation) 59.2 (9.3) years at baseline] were included. Adjusted lifetime risks of incident CVD were higher in men than in women (69.1 vs. 57.7% at age 75): cause-specific hazard ratio (cHR) (99% confidence interval)-1.49 (1.41-1.57), while the risks of CV mortality at age 75 were 4.4% (men) and 3.1% (women): cHR-1.42 (1.31-1.54). Myocardial infarction was the predominant first presentation in men until the eighth decade. In women, the first CVD manifestations after their sixth decade were predominantly atrial fibrillation and stroke. The male-associated excess relative risks of incident CVD and CV mortality were halved in people with prevalent CVD. CONCLUSION: We characterized the sex-specific lifetime CV risks in a large cohort. Men had substantially higher risk of incident CVD and CV mortality than women, which was attenuated amongst people with prevalent CVD. Our findings provide an evidence base for sex-specific CV prevention.

In this population-based study, we aimed to understand the sex-specific lifetime trajectories of different heart and circulatory disorders and their relationship with death from heart disease. We included ∼24 000 participants in the analyses, who were followed up for >25 years. Men had a higher lifetime risk of heart and circulatory disorders compared with women. Heart attacks were the predominant first presentation in men until the eighth decade, while in women this was manifested as heart rhythm disorders and stroke after their sixth decade. The excess risk of death from heart disease observed in men with pre-existing heart disease was attenuated compared with those free of heart disease at baseline. In conclusion, men and women require tailored heart disease prevention efforts given the marked sex disparities in heart disease and death over the very long-term highlighted by our study.

Asunto(s)

RESUMEN

BACKGROUND: The association of fitness with cancer risk is not clear. METHODS: We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for risk of lung, colorectal, endometrial, breast, and prostate cancer in a subset of UK Biobank participants who completed a submaximal fitness test in 2009-12 (N = 72,572). We also investigated relationships using two-sample Mendelian randomisation (MR), odds ratios (ORs) were estimated using the inverse-variance weighted method. RESULTS: After a median of 11 years of follow-up, 4290 cancers of interest were diagnosed. A 3.5 ml O2⋅min-1⋅kg-1 total-body mass increase in fitness (equivalent to 1 metabolic equivalent of task (MET), approximately 0.5 standard deviation (SD)) was associated with lower risks of endometrial (HR = 0.81, 95% CI: 0.73-0.89), colorectal (0.94, 0.90-0.99), and breast cancer (0.96, 0.92-0.99). In MR analyses, a 0.5 SD increase in genetically predicted O2⋅min-1⋅kg-1 fat-free mass was associated with a lower risk of breast cancer (OR = 0.92, 95% CI: 0.86-0.98). After adjusting for adiposity, both the observational and genetic associations were attenuated. DISCUSSION: Higher fitness levels may reduce risks of endometrial, colorectal, and breast cancer, though relationships with adiposity are complex and may mediate these relationships. Increasing fitness, including via changes in body composition, may be an effective strategy for cancer prevention.

Asunto(s)

RESUMEN

AIMS/HYPOTHESIS: The identification of people who are at high risk of developing type 2 diabetes is a key part of population-level prevention strategies. Previous studies have evaluated the predictive utility of omics measurements, such as metabolites, proteins or polygenic scores, but have considered these separately. The improvement that combined omics biomarkers can provide over and above current clinical standard models is unclear. The aim of this study was to test the predictive performance of genome, proteome, metabolome and clinical biomarkers when added to established clinical prediction models for type 2 diabetes. METHODS: We developed sparse interpretable prediction models in a prospective, nested type 2 diabetes case-cohort study (N=1105, incident type 2 diabetes cases=375) with 10,792 person-years of follow-up, selecting from 5759 features across the genome, proteome, metabolome and clinical biomarkers using least absolute shrinkage and selection operator (LASSO) regression. We compared the predictive performance of omics-derived predictors with a clinical model including the variables from the Cambridge Diabetes Risk Score and HbA1c. RESULTS: Among single omics prediction models that did not include clinical risk factors, the top ten proteins alone achieved the highest performance (concordance index [C index]=0.82 [95% CI 0.75, 0.88]), suggesting the proteome as the most informative single omic layer in the absence of clinical information. However, the largest improvement in prediction of type 2 diabetes incidence over and above the clinical model was achieved by the top ten features across several omic layers (C index=0.87 [95% CI 0.82, 0.92], Δ C index=0.05, p=0.045). This improvement by the top ten omic features was also evident in individuals with HbA1c <42 mmol/mol (6.0%), the threshold for prediabetes (C index=0.84 [95% CI 0.77, 0.90], Δ C index=0.07, p=0.03), the group in whom prediction would be most useful since they are not targeted for preventative interventions by current clinical guidelines. In this subgroup, the type 2 diabetes polygenic risk score was the major contributor to the improvement in prediction, and achieved a comparable improvement in performance when added onto the clinical model alone (C index=0.83 [95% CI 0.75, 0.90], Δ C index=0.06, p=0.002). However, compared with those with prediabetes, individuals at high polygenic risk in this group had only around half the absolute risk for type 2 diabetes over a 20 year period. CONCLUSIONS/INTERPRETATION: Omic approaches provided marginal improvements in prediction of incident type 2 diabetes. However, while a polygenic risk score does improve prediction in people with an HbA1c in the normoglycaemic range, the group in whom prediction would be most useful, even individuals with a high polygenic burden in that subgroup had a low absolute type 2 diabetes risk. This suggests a limited feasibility of implementing targeted population-based genetic screening for preventative interventions.

Asunto(s)

RESUMEN

BACKGROUND: Little is known about whether the association between genetic susceptibility to high waist-to-hip ratio (WHR), a measure of abdominal obesity, and incident coronary heart disease (CHD) is modified by adherence to a healthy lifestyle. OBJECTIVES: To explore the interplay of genetic susceptibility to high WHR and adherence to a healthy lifestyle on incident CHD. METHODS: This study included 282,316 white British individuals from the UK Biobank study. Genetic risk for high WHR was estimated in the form of weighted polygenic risk scores (PRSs), calculated based on 156 single-nucleotide polymorphisms. Lifestyle scores were calculated based on 5 healthy lifestyle factors: regular physical activity, no current smoking, a healthy diet, <3 times/wk of alcohol consumption and 7-9 h/d of sleep. Incident CHD (n = 11,635) was accrued over a median 13.8 y of follow-up, and 12 individual cardiovascular disease risk markers assessed at baseline. RESULTS: Adhering to a favorable lifestyle (4-5 healthy factors) was associated with a 25% (hazard ratio: 0.75, 95% confidence interval: 0.70, 0.81) lower hazard of CHD compared with an unfavorable lifestyle (0-1 factor), independent of PRS for high WHR. Estimated 12-y absolute risk of CHD was lower for a favorable lifestyle at high genetic risk (1.73%) and medium genetic risk (1.67%) than for an unfavorable lifestyle at low genetic risk (2.08%). Adhering to a favorable lifestyle was associated with healthier levels of cardiovascular disease risk markers (except random glucose and high-density lipoprotein), independent of PRS for high WHR. CONCLUSIONS: Individuals who have high or medium genetic risk of abdominal obesity but adhere to a healthy lifestyle may have a lower risk of developing CHD, compared with those who have low genetic risk and an unhealthy lifestyle. Future clinical trials of lifestyle modification could be implemented for individuals at high genetic risk of abdominal obesity for the primary prevention of CHD events.

Asunto(s)

RESUMEN

BACKGROUND AND AIMS: Cardiovascular disease (CVD) remains the largest cause of death globally due to various risk factors. One novel potential contributor to CVD might be the metabolism of the essential amino acid tryptophan (Trp), which through many pathways can produce immunomodulatory metabolites such as kynurenine, indole-3-propionate and serotonin. We aim to identify the metabolites with the strongest association with cardiovascular disease, utilizing a substantial and diverse cohort of individuals. In our pursuit of this aim, our primary focus is to validate and reinforce the findings from previous cross-sectional studies. METHODS: We used the community-based EPIC-Norfolk cohort (46.3 % men, age 59.8 ± 9.0) with a median follow-up of 22.1 (17.6-23.3) years to study associations between the relative levels of Trp metabolites measured with untargeted metabolomics and incident development of CVD. Serum from n = 11,972 apparently healthy subjects was analysed, of which 6982 individuals had developed CVD at the end of follow-up. Cox proportional hazard models were used to study associations, adjusted for sex, age, conventional cardiovascular risk factors and CRP. All metabolites were Ln-normalised prior to analysis. RESULTS: Higher levels of Trp were inversely associated with mortality (HR 0.73; CI 0.64-0.83) and fatal CVD (HR 0.76; CI 0.59-0.99). Higher levels of kynurenine (HR 1.33; CI 1.19-1.49) and the [Kynurenine]/[Tryptophan]-ratio (HR 1.24; CI 1.14-1.35) were associated with a higher incident development of CVD. Serotonin was not associated with overall CVD, but we did find associations for myocardial infarction and stroke. Adjustment for CRP did not yield any discernible differences in effect size. CONCLUSIONS: Tryptophan levels were inversely correlated with CVD, while several of its major metabolites (especially kynurenine and serotonin) were positively correlated. These findings indicate that mechanistic studies are required to understand the role of Trp metabolism in CVD with the goal to identify new therapeutic targets.

Asunto(s)