Prospective Population Cohort Research Articles

Arrhythmic Gut Microbiome Signatures for Risk Profiling of Type-2 Diabetes

Lifestyle, obesity and the gut microbiome are among the most important risk factors for the development of metabolic disorders. We demonstrate in 1,976 subjects of a prospective population cohort (KORA) that specific gut microbiota members show 24-hour oscillations in their relative abundance and identified 13 taxa with disrupted rhythmicity in T2D. Cross-validated prediction models based on this signature classified T2D with an AUC of 73%. BMI as independent factor improved classification of T2D. The validity of this arrhythmic signature was confirmed in 699 KORA subjects five years after initial sampling, being most effective in combination with BMI and microbiota (AUC=78%). Shotgun metagenomic analysis functionally linked 26 metabolic pathways to the diurnal oscillation of gut bacteria. In summary, we determined a cohort-specific risk pattern of arrhythmic taxa which significantly contributes to the classification and prediction of T2D, suggesting a functional link between circadian rhythmicity and the gut microbiome in metabolic diseases.

Consumption of Meat and Dairy Products Is Not Associated with the Risk for Rheumatoid Arthritis among Women: A Population-Based Cohort Study.

Diet has gained attention as a risk factor for the development of rheumatoid arthritis (RA), especially with regards to food of animal origin, such as meat and dairy products. By using data from national patient registers and dietary data from a large prospective population cohort, the Swedish Mammography Cohort, we aimed to investigate whether the consumption of meat and dairy products had any impact on the risk of subsequent development of RA. During 12 years of follow-up (January 2003–December 2014; 381, 456 person-years), 368 patients with a new diagnosis of RA were identified. No associations between the development of RA and the consumption of meat and meat products (hazard ratio [HR] for the fully adjusted model: 1.08 [95% CI: 0.77–1.53]) or the total consumption of milk and dairy products (HR for the fully adjusted model: 1.09 [95% CI: 0.76–1.55]) were observed. In conclusion, in this large prospective cohort of women, no associations were observed between dietary intake of meat and dairy products and the risk of RA development.

The Reciprocal Association between Problem Gambling and Mental Health Symptoms/Substance Use: Cross-Lagged Path Modelling of Longitudinal Cohort Data.

To date, studies have highlighted cross-sectional and unidirectional prospective relationships between problem gambling and mental health symptoms or substance use. The current study aims to: (1) examine the reciprocal relationships between problem gambling and mental health symptoms (depression, generalized anxiety)/substance use variables (hazardous alcohol use, daily tobacco use, and drug use) using cross-lagged path models in a prospective general population cohort sample; and (2) determine whether these associations are moderated by age and gender. This study involved secondary data analysis from 1109 respondents who provided data during Wave 2 or 3 (12-months apart) of the Tasmanian Longitudinal Gambling Study (Australia). Depression (odds ratio (OR) = 2.164) and generalized anxiety (OR = 2.300) at Wave 2 were found to have cross-lagged associations with the subsequent development of any-risk gambling (low-risk, moderate-risk, or problem gambling) at Wave 3. Hazardous alcohol use, daily tobacco use, and drug use at Wave 2 were not associated with the development of any-risk gambling at Wave 3. Any-risk gambling at Wave 2 was not associated with the subsequent development of any mental health symptoms or substance use variables at Wave 3. Age and gender failed to be significant moderators in the associations between any-risk gambling and mental health symptoms or substance use variables. Future longitudinal and event-level research is required to further substantiate these prospective relationships, with a view to developing targeted preventions and interventions.

Adverse childhood experiences and adult mood problems: evidence from a five-decade prospective birth cohort.

Retrospectively recalled adverse childhood experiences (ACEs) are associated with adult mood problems, but evidence from prospective population cohorts is limited. The aims of this study were to test links between prospectively ascertained ACEs and adult mood problems up to age 50, to examine the role of child mental health in accounting for observed associations, and to test gender differences in associations. The National Child Development Study is a UK population cohort of children born in 1958. ACEs were defined using parent or teacher reports of family adversity (parental separation, child taken into care, parental neglect, family mental health service use, alcoholism and criminality) at ages 7-16. Children with no known (n = 9168), single (n = 2488) and multiple (n = 897) ACEs were identified in childhood. Adult mood problems were assessed using the Malaise inventory at ages 23, 33, 42 and 50 years. Associations were examined separately for males and females. Experiencing single or multiple ACEs was associated with increased rates of adult mood problems after adjustment for childhood psychopathology and confounders at birth [2+ v. 0 ACEs - men: age 23: odds ratio (OR) 2.36 (95% confidence interval (CI) 1.7-3.3); age 33: OR 2.40 (1.7-3.4); age 42: OR 1.85 (1.4-2.4); age 50: OR 2.63 (2.0-3.5); women: age 23: OR 2.00 (95% CI 1.5-2.6); age 33: OR 1.81 (1.3-2.5); age 42: OR 1.59 (1.2-2.1); age 50: OR 1.32 (1.0-1.7)]. Children exposed to ACEs are at elevated risk for adult mood problems and a priority for early prevention irrespective of the presence of psychopathology in childhood.

Independent effect of physical activity and resting heart rate on the incidence of atrial fibrillation in the general population

While physical activity (PA) may influence resting heart rate (RHR), and a low RHR may be a risk factor for atrial fibrillation (AF), controversy exists regarding the association between PA and development of AF. Using data from a Korean, prospective population cohort, we investigated the independent effect of PA and RHR on the incidence of AF in the general population. A total of 8,811 participants aged 40–69 years were analyzed. Total PA assessed based on questionnaires was divided into quartiles, with the lowest to the highest being Q1, Q2, Q3, and Q4. During a median follow-up of 139 months, AF developed in 167 participants (1.9%). Q3 of total PA was associated with a significantly lower risk of AF than Q1 even after adjusting for RHR as a covariate, but Q4 was not. The risk of AF was higher in participants with RHR < 60 bpm than in those with RHR 70–85 bpm, and the significance persisted after adjusting for PA as a covariate. This study showed that a moderate amount of total PA was associated with a lower risk of incident AF independent of RHR and that low RHR was an independent risk factor for AF in the general Korean population.

Validation of the Systematic COronary Risk Evaluation - Older Persons (SCORE-OP) in the EPIC-Norfolk prospective population study

BackgroundThe Systematic COronary Risk Evaluation – Older Persons (SCORE-OP) algorithm is developed to assess 10-year risk of death due to cardiovascular disease (CVD) in individuals aged ≥65 years. We studied the performance of SCORE-OP in the European Prospective Investigation of Cancer Norfolk (EPIC-Norfolk) prospective population cohort. Methods10-year CVD mortality as predicted by SCORE-OP was compared with observed CVD mortality among individuals in the EPIC-Norfolk cohort. Persons aged 65–79 years without known CVD were included in the analysis. CVD mortality was defined as death due to ischemic heart disease, cardiac failure, cerebrovascular disease, peripheral-artery disease or aortic aneurysm. Predicted 10-year CVD mortality was calculated by the SCORE-OP algorithm, and compared to observed mortality rates. The area under the receiver operator characteristics curve (AUROC) was calculated to evaluate discriminative power. Calibration was evaluated by calculating ratios of predicted vs observed mortality and by Hosmer-Lemeshow tests. ResultsA total of 6590 individuals (45.8% men), mean age 70.2 years (standard deviation 3.3) were included. The predicted mortality by SCORE-OP was 9.84% (95% confidence interval (CI) 9.76–9.92) and observed mortality was 10.2% (95% CI 9.52–11.04), ratio 0.96. AUROC was 0.63 (95% CI 0.60–0.65), and X2 was 3.3 (p = 0.92). ConclusionSCORE-OP overall accurately estimates the rate of CVD mortality in a general population aged 65–79 years. However, while calibration is excellent, the discriminative power of the SCORE-OP is limited, and as such cannot be readily implemented in clinical practice for this population.

Machine Learning Approaches for the Estimation of Biological Aging: The Road Ahead for Population Studies.

In recent years, different machine learning algorithms have been developed for the estimation of Biological Age (BA), defined as the hypothetical underlying age of an organism. BA can be computed based on different circulating and non-circulating biomarkers. In this perspective, identifying biomarkers with a prominent influence on BA and developing reliable models for its estimation is of fundamental importance for monitoring healthy aging, and could provide new tools to screen health status and the risk of clinical events in the general population. Here, we briefly review the different machine learning (ML) approaches used for BA estimation, focusing on those methods with potential application to the Moli-sani study, a prospective population-based cohort study of 24,325 subjects (35–99 years). In particular, we discuss the potential of BA estimation based on blood biomarkers, which likely represents the easiest and most immediate way to compute organismal BA. Similarly, we describe ML methods for the estimation of brain age based on structural neuroimaging features. For each method, we discuss the relation with epidemiological variables (e.g., mortality), genetic and environmental factors, and common age-related diseases (e.g., Alzheimer disease), to examine the potential as aging biomarker in the general population. Finally, we hypothesize new approaches for BA estimation, both at the single organ and at the whole organism level. Overall, here we trace the road ahead in the Big Data era for our and other prospective general population cohorts, presenting ways to exploit the notable amount of data available nowadays.

263 – Gut Microbiota Profiling in a Prospective Population Cohort in Relation to Metabolic Health

263 – Gut Microbiota Profiling in a Prospective Population Cohort in Relation to Metabolic Health

Postmenopausal breast cancer and occupational exposure to chemicals.

Objectives The aim of this study was to investigate if exposure to chemicals in the workplace was associated with an increased risk of postmenopausal breast cancer. Methods The study comprised women born 1923-1950 living in Malmö city, Sweden, 1991-1996, and enrolled for a prospective population cohort study. Occupational exposure to various chemicals was assessed from job-exposure matrices. An extensive set of individual data on hormonal breast cancer risk factors were collected via a baseline questionnaire and used for confounding control. First time diagnoses of invasive breast cancer were identified through the Swedish Cancer Registry until end of follow-up on 31 December 2013. Results Of 16 084 women, 1011 were diagnosed with breast cancer. Women exposed to chemicals in their occupational environment had a statistically significant increased risk [adjusted hazard ratio (HR adj) 1.26, 95% confidence interval (CI) 1.02-1.54] of breast cancer, and the risk correlated with duration of exposure. Investigation of risk in association with specific chemicals showed a non-significantly elevated risk after exposure to organic solvents. More than ten years of exposure to diesel exhaust was associated with an increased risk (HR adj1.69, 95% CI 1.01-2.82). Occupational chemical exposures account for 2% of the breast cancer cases in this population. Conclusions Occupational exposure to chemicals in general was associated with an elevated risk of breast cancer. A slight elevation of risk was seen after exposure to organic solvents. A statistically significant elevation of risk after >10 years of exposure to diesel exhaust was an unexpected finding.

Prediction of premature all-cause mortality: A prospective general population cohort study comparing machine-learning and standard epidemiological approaches.

BackgroundPrognostic modelling using standard methods is well-established, particularly for predicting risk of single diseases. Machine-learning may offer potential to explore outcomes of even greater complexity, such as premature death. This study aimed to develop novel prediction algorithms using machine-learning, in addition to standard survival modelling, to predict premature all-cause mortality.MethodsA prospective population cohort of 502,628 participants aged 40–69 years were recruited to the UK Biobank from 2006–2010 and followed-up until 2016. Participants were assessed on a range of demographic, biometric, clinical and lifestyle factors. Mortality data by ICD-10 were obtained from linkage to Office of National Statistics. Models were developed using deep learning, random forest and Cox regression. Calibration was assessed by comparing observed to predicted risks; and discrimination by area under the ‘receiver operating curve’ (AUC).Findings14,418 deaths (2.9%) occurred over a total follow-up time of 3,508,454 person-years. A simple age and gender Cox model was the least predictive (AUC 0.689, 95% CI 0.681–0.699). A multivariate Cox regression model significantly improved discrimination by 6.2% (AUC 0.751, 95% CI 0.748–0.767). The application of machine-learning algorithms further improved discrimination by 3.2% using random forest (AUC 0.783, 95% CI 0.776–0.791) and 3.9% using deep learning (AUC 0.790, 95% CI 0.783–0.797). These ML algorithms improved discrimination by 9.4% and 10.1% respectively from a simple age and gender Cox regression model. Random forest and deep learning achieved similar levels of discrimination with no significant difference. Machine-learning algorithms were well-calibrated, while Cox regression models consistently over-predicted risk.ConclusionsMachine-learning significantly improved accuracy of prediction of premature all-cause mortality in this middle-aged population, compared to standard methods. This study illustrates the value of machine-learning for risk prediction within a traditional epidemiological study design, and how this approach might be reported to assist scientific verification.

Stability of the Manual Ability Classification System in young children with cerebral palsy.

To examine the stability over time of the Manual Ability Classification System (MACS) levels in children with cerebral palsy (CP) aged 18 to 60months. This was a prospective longitudinal population-based study of 252 Australian children (160 males [63%] 92 females [37%]; mean age [SD] 41.7mo [14], range 17.2mo-69.2mo) with CP. Children were classified at 18months (n=70), 24 months (n=131), 30months (n=173), 36months (n=209), 48months (n=226), and 60months (n=221) of age. Stability of the MACS was examined using the proportion of specific positive agreement and transition proportions, which are measures of agreement. There were 1030 unique observations, with each of the 252 participants seen between two and six occasions (median=4). Average specific positive agreement over the study period was 76% for MACS level I, 67% for level II, 50% for level III, 51% for level IV, and 83% for level V. MACS levels I and V have the highest degree of stability, while levels III and IV have the lowest. We show how this may be explained by the proportion of children in each MACS level. Using measures of agreement rather than measures of reliability provides accurate information when measuring stability over time of an ordinal classification system. The relative stability of MACS levels can be explained by the proportion of children in each level. Children classified in Manual Ability Classification System (MACS) levels III and IV change levels at next assessment about 50% of the time. Children should be assessed with the MACS regularly, particularly those in levels III and IV. Stability within ordinal classification level categories can be predicted using a measurement error model. Transition proportions or specific agreement is recommended for reporting stability of ordinal classification systems.

Type 2 diabetes in patients with end-stage kidney disease: influence on cardiovascular disease-related mortality risk.

To examine the association between type 2 diabetes mellitus, with and without diabetic nephropathy, and cardiovascular disease-related mortality in dialysis-dependent patients with end-stage kidney disease (ESKD); to determine whether this association is affected by the age of the patient. Prospective population cohort analysis of Australia and New Zealand Dialysis and Transplant Registry data for all patients with incident ESKD who commenced dialysis in Australia or New Zealand during 1980-2014. Primary outcome: cardiovascular disease-related mortality; secondary outcome: all-cause mortality. Of 56 552 patients followed for a median 2.5 years (total, 193 549 person-years), 15 829 (28.0%) had type 2 diabetes and diabetic nephropathy; 4993 (8.8%) had type 2 diabetes and non-diabetic nephropathy. Cardiovascular disease-related mortality during the first 10 years of dialysis was significantly higher for patients with diabetes/diabetic nephropathy (277 deaths per 1000 patients; 95% CI, 270-284) or diabetes/non-diabetic nephropathy (220 deaths per 1000 patients; 95% CI, 208-231) than for patients without type 2 diabetes (136 deaths per 1000 patients; 95% CI, 133-140). The risk of cardiovascular disease-related mortality was greater for patients with diabetes/diabetic nephropathy (adjusted hazard ratio [aHR], 1.63; 95% CI, 1.56-1.72) or diabetes/non-diabetic nephropathy (aHR, 1.31; 95% CI, 1.23-1.41) than for patients without diabetes. The excess risk associated with having diabetes was greater for younger than for older patients. Mortality risk is higher for patients with incident ESKD commencing dialysis who also have type 2 diabetes than for patients without diabetes, particularly among patients under 50 years of age, and the risk was more pronounced in patients for whom ESKD was attributed to diabetic nephropathy.

Androgen Receptor Activity and Radiotherapeutic Sensitivity in African-American Men with Prostate Cancer: A Large Scale Gene Expression Analysis and Meta-Analysis of RTOG Trials

Population data suggests that African-American (AfA) men have an increased mortality from prostate cancer (PCa) compared to White (C) men. Socioeconomic variables contribute to this disparity, yet intrinsic biological differences remain plausible. Herein, we investigate the interplay of androgen receptor activity (AR-A) and radiotherapeutic sensitivity to provide a molecular rationale to help explain the disparity in outcomes for AfA men with PCa. Transcriptome-wide expression profiles of FFPE tumor samples from 5,831 localized PCa patients were used. Tissue was obtained from a prospective population cohort (n=5,239) and two retrospective cohorts with long-term outcomes (n=592). Predicted radiation sensitivity was measured using the 24-gene post-operative radiation therapy (RT) outcome score (PORTOS). AR-A was defined from the pooled expression of nine canonical AR-target genes. Clinical radiosensitivity was validated using individual patient data from four RTOG trials (92-02, 94-08, 94-13, and 99-10), comprised of 6,011 patients (18.5% AfA). Competing risk adjustments were used for all survival analyses for biochemical recurrence (BCR) and distant metastases (DM). In men treated by surgery from the prospective cohort, low AR-A tumors were significantly more likely to develop DM (10-year rate: 37% vs 17%, p=0.008). On multivariable analysis after adjusting for Gleason grade, T-stage, PSA, margin status, and lymph node invasion, low AR-A remained independently prognostic for DM (p=0.03). After generating a matched cohort of AfA and C patients, AfA tumors were more likely to have low AR-A (p<0.001). However, AfA tumors had decreased double strand break repair pathway expression (p<0.001) and increased predicted RT sensitivity (p<0.001). This suggests that AfA men may have improved outcomes with RT. To clinically test whether AfA tumors are more radiosensitive, we leveraged four large RTOG trials of men treated with RT. On both unadjusted and propensity weighted cohorts (adjusting for age, performance status, PSA, Gleason grade, T-stage, N-stage, and use/duration of hormone therapy), AfA men had significantly improved outcomes compared to C men (BCR: HR 0.82; 95% CI 0.74-0.92; p = 0.0005 and DM: HR 0.70; (95% CI 0.57-0.86; p = 0.0008). Our data suggest that there are population-level differences in AR signaling and DNA repair in AfA and C men’s PCa, which transcriptionally suggest that AfA men may harbor more radiosensitive tumors. To our knowledge, this is the first report demonstrating that AfA men may have improved outcomes compared to C men treated with RT, which is consistent with our hypothesis regarding diversity in AR-A, and warrants further investigation.

Application of Data Science Technology on Research of Circulatory System Disease Prediction Based on a Prospective Cohort

Chronic diseases represented by circulatory diseases have gradually become the main types of diseases affecting the health of our population. Establishing a circulatory system disease prediction model to predict the occurrence of diseases and controlling them is of great significance to the health of our population. This article is based on the prospective population cohort data of chronic diseases in China, based on the existing medical cohort studies, the Kaplan–Meier method was used for feature selection, and the traditional medical analysis model represented by the Cox proportional hazards model was used and introduced. Support vector machine research methods in machine learning establish circulatory system disease prediction models. This paper also attempts to introduce the proportion of the explanation variation (PEV) and the shrinkage factor to improve the Cox proportional hazards model; and the use of Particle Swarm Optimization (PSO) algorithm to optimize the parameters of SVM model. Finally, the experimental verification of the above prediction models is carried out. This paper uses the model training time, Accuracy rate(ACC), the area under curve (AUC)of the Receiver Operator Characteristic curve (ROC) and other forecasting indicators. The experimental results show that the PSO-SVM-CSDPC disease prediction model and the S-Cox-CSDPC circulation system disease prediction model have the advantages of fast model solving speed, accurate prediction results and strong generalization ability, which are helpful for the intervention and control of chronic diseases.

Heart Failure Stimulates Tumor Growth by Circulating Factors.

Heart failure (HF) survival has improved, and nowadays, many patients with HF die of noncardiac causes, including cancer. Our aim was to investigate whether a causal relationship exists between HF and the development of cancer. HF was induced by inflicting large anterior myocardial infarction in APCmin mice, which are prone to developing precancerous intestinal tumors, and tumor growth was measured. In addition, to rule out hemodynamic impairment, a heterotopic heart transplantation model was used in which an infarcted or sham-operated heart was transplanted into a recipient mouse while the native heart was left in situ. After 6 weeks, tumor number, volume, and proliferation were quantified. Candidate secreted proteins were selected because they were previously associated both with (colon) tumor growth and with myocardial production in post-myocardial infarction proteomic studies. Myocardial gene expression levels of these selected candidates were analyzed, as well as their proliferative effects on HT-29 (colon cancer) cells. We validated these candidates by measuring them in plasma of healthy subjects and patients with HF. Finally, we associated the relation between cardiac specific and inflammatory biomarkers and new-onset cancer in a large, prospective general population cohort. The presence of failing hearts, both native and heterotopically transplanted, resulted in significantly increased intestinal tumor load of 2.4-fold in APCmin mice (all P<0.0001). The severity of left ventricular dysfunction and fibrotic scar strongly correlated with tumor growth ( P=0.002 and P=0.016, respectively). We identified several proteins (including serpinA3 and A1, fibronectin, ceruloplasmin, and paraoxonase 1) that were elevated in human patients with chronic HF (n=101) compared with healthy subjects (n=180; P<0.001). Functionally, serpinA3 resulted in marked proliferation effects in human colon cancer (HT-29) cells, associated with Akt-S6 phosphorylation. Finally, elevated cardiac and inflammation biomarkers in apparently healthy humans (n=8319) were predictive of new-onset cancer (n=1124) independently of risk factors for cancer (age, smoking status, and body mass index). We demonstrate that the presence of HF is associated with enhanced tumor growth and that this is independent of hemodynamic impairment and could be caused by cardiac excreted factors. A diagnosis of HF may therefore be considered a risk factor for incident cancer.

Associations of Asthma and Asthma Control With Atrial Fibrillation Risk

Asthma, a chronic inflammatory airway disease, and atrial fibrillation (AF) share several common pathophysiological mechanisms. Research on the association between asthma and atrial fibrillation is lacking, and to our knowledge, no previous studies have assessed the dose-response association between levels of asthma control and AF. To assess the association between asthma, levels of asthma control, and AF. This prospective population cohort analyzed data on adults from a second and third iteration of the survey-based Nord-Trøndelag Health Study (HUNT) in Norway. All included participants were free from AF at baseline. Atrial fibrillation was ascertained by linking HUNT data with hospital records from the 2 hospitals in Nord-Trøndelag County. Data analysis was completed from May 2017 to November 2017. Self-reported asthma was categorized into 3 groups: those who had ever had asthma, those who self-report being diagnosed with asthma, and those who had active asthma. Asthma control was defined according to Global Initiative for Asthma guidelines and was categorized into controlled, partly controlled, and uncontrolled cases. Atrial fibrillation. A total of 54 567 adults were included (of whom 28 821 [52.8%] were women). Of these, 5961 participants (10.9%) reported ever having asthma, 3934 participants (7.2%) reported being diagnosed with asthma, and 2485 participants (4.6%) reported having active asthma. During a mean (SD) follow-up of 15.4 (5.8) years, 2071 participants (3.8%) developed AF. Participants with physician-diagnosed asthma had an estimated 38% higher risk of developing AF (adjusted hazard ratio, 1.38 [95% CI, 1.18-1.61]) compared with participants without asthma. There was a dose-response association between levels of asthma control and risk of AF with the highest risk for AF in participants with uncontrolled asthma (adjusted hazard ratio, 1.74 [95% CI, 1.26-2.42]; P for trend < .001). Asthma and lack of asthma control were associated with moderately increased risks of AF in a dose-response manner. Further studies are needed to explore the underlying mechanisms and clarify causal pathways between asthma and AF.

O1.1 ALTERED COMPLEMENT PATHWAY PROTEIN EXPRESSION IS ASSOCIATED WITH PSYCHOTIC EXPERIENCES AT AGE 11 WHICH PERSIST AT AGE 18

BackgroundThe identification of early biomarkers of psychotic disorder is important because early treatment is associated with improved outcome. We have previously shown that altered complement and coagulation pathway associated proteins are associated pathway with psychotic disorder at age 18. In the current study we test the hypothesis that altered complement pathway proteins are associated with persisting psychotic experiences from age 11 to age 18.MethodsThe Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective general population cohort, and a rich resource of demographic, environmental, and clinical data on the individuals involved. We studied a subsample of the cohort who participated in psychiatric assessment interviews at age 11 and 18, and who provided plasma samples at age 11. Semi-targeted proteomic profiling was used to specifically assess the complement pathway proteins in age 11 children who experienced psychotic experiences (but not disorder) at age 11 and age 18 (n=39) compared to age 11 children who only experienced psychotic experiences at age 11.Results11 of 34 proteins assessed were significantly differentially expressed at p<0.05 and of these 8 remained significant following correction for multiple comparisons. Protein changes were in keeping with increased proteins expression of most complement pathway proteins. Several protein changes represented specific replications of the changes observed in age 11 samples prior to psychotic disorder at age 18, namely increased plasminogen, complement factor H, and complement factor 1r.DiscussionOur findings implicate the blood complement system in the persistence of psychotic experiences from age 11 to age 18. Considering that psychotic experiences are predictive of many psychiatric disorders our findings implicate the complement system not just in psychotic disorders, but more broadly in the vulnerability to a range of adult psychiatric disorders.

32.4 ALTERED COMPLEMENT PATHWAY PROTEIN EXPRESSION IS ASSOCIATED WITH PSYCHOTIC EXPERIENCES AT AGE 11 WHICH PERSIST AT AGE 18

BackgroundThe identification of early biomarkers of psychotic disorder is important because early treatment is associated with improved outcome. We have previously shown that altered complement and coagulation pathway associated proteins are associated pathway with psychotic disorder at age 18. In the current study we test the hypothesis that altered complement pathway proteins are associated with persisting psychotic experiences from age 11 to age 18.MethodsThe Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective general population cohort, and a rich resource of demographic, environmental, and clinical data on the individuals involved. We studied a subsample of the cohort who participated in psychiatric assessment interviews at age 11 and 18, and who provided plasma samples at age 11. Semi-targeted proteomic profiling was used to specifically assess the complement pathway proteins in age 11 children who experienced psychotic experiences (but not disorder) at age 11 and age 18 (n=39) compared to age 11 children who only experienced psychotic experiences at age 11.Results11 of 34 proteins assessed were significantly differentially expressed at p<0.05 and of these 8 remained significant following correction for multiple comparisons. Protein changes were in keeping with increased proteins expression of most complement pathway proteins. Several protein changes represented specific replications of the changes observed in age 11 samples prior to psychotic disorder at age 18, namely increased plasminogen, complement factor H, and complement factor 1r.DiscussionOur findings implicate the blood complement system in the persistence of psychotic experiences from age 11 to age 18. Considering that psychotic experiences are predictive of many psychiatric disorders our findings implicate the complement system not just in psychotic disorders, but more broadly in the vulnerability to a range of adult psychiatric disorders.

Evaluation of non-HDL cholesterol as a predictor of non-fatal cardiovascular events in a prospective population cohort

IntroductionNon-HDL cholesterol (non-HDL-C) is becoming relevant both in its participation in cardiovascular risk assessment and as a therapeutic target. The objective of the present study was to assess the independent predictive capacity of both non-HDL-C and LDL-C (the main priority in dyslipidemias to reduce cardiovascular risk), in cardiovascular morbidity in a population-based sample. MethodsA prospective cohort study involving 1186 individuals in the non-HDL-C group and 1177 in the LDL-C group, followed for 10.7years (SD=2.2), who had not had any previous cardiovascular event. The predictor variables included in the adjustment were: gender, age, arterial hypertension, diabetes mellitus, smoker status and non-HDL-C in one group. In the other group, consisting of patients presenting TG levels of 400mg/dl, non-HDL-C was replaced by LDL-C. Survival curves (Kaplan–Meier) were calculated and two Cox regression models were applied, one for each group. ResultsNon-HDL-C group presented 6.2% of non-fatal cardiovascular episodes during follow-up and the LDL-C group 6.0%. After adjustment, for each 30mg/dl increase in non-HDL-C, the incidence of new non-fatal cardiovascular events increased by 31% (HR=1.31, 95% CI: 1.06–1.61; p=0.018) and in the LDL-C group by 27% (HR=1.27, 95% CI: 0.97–1.61, p=0.068). ConclusionsAfter a follow-up of 10.7years, non-HDL-C has been shown in our population as a prognostic factor of non-fatal cardiovascular disease, but not LDL-C, although its HR is close to statistical significance.

Early population-based outcomes of infants born with congenital diaphragmatic hernia

PurposeThis study aims to describe short-term outcomes of live-born infants with congenital diaphragmatic hernia (CDH) and to identify prognostic factors associated with early mortality.DesignA prospective population cohort study was undertaken...