Association of Serum Trimethylamine-N-Oxide Concentration from Childhood to Early Adulthood with Age and Sex.

BACKGROUND: Primary prevention is the cornerstone of cardiometabolic health. In the randomized, controlled Special Turku Coronary Risk Factor Intervention Project (STRIP), dietary counseling intervention was given to children from infancy to 20 years of age and a follow-up was completed at age 26 years. We investigated the associations of age, sex, gut microbiome, and dietary intervention with the gut metabolite and the cardiac biomarker trimethylamine-N-oxide (TMAO). METHODS: Overall, 592 healthy participants (females 46%) from STRIP were investigated. Compared to the control group, the intervention group had received dietary counseling between ages 7 months and 20 years focused on low intakes of saturated fat and cholesterol and the promotion of fruit, vegetable, and whole-grain consumption. TMAO serum concentrations were measured by a liquid chromatography-tandem mass spectrometry method at ages 11, 13, 15, 17, 19, and 26 years. Microbiome composition was assessed using 16S rRNA gene sequencing at 26 years of age. RESULTS: TMAO concentrations increased from age 11 to 26 years in both sexes. At all measurement time points, males showed significantly higher serum TMAO concentrations compared to females, but concentrations were similar between the intervention and control groups. A direct association between TMAO concentrations and reported fiber intake was found in females. Gut microbiome analysis did not reveal associations with TMAO. CONCLUSIONS: TMAO concentration increased from childhood to early adulthood but was not affected by the given dietary intervention. In females, TMAO concentrations could be directly associated with higher fiber intake suggesting sex-specific differences in TMAO metabolism.

Exploring machine learning strategies for predicting cardiovascular disease risk factors from multi-omic data.

BACKGROUND: Machine learning (ML) classifiers are increasingly used for predicting cardiovascular disease (CVD) and related risk factors using omics data, although these outcomes often exhibit categorical nature and class imbalances. However, little is known about which ML classifier, omics data, or upstream dimension reduction strategy has the strongest influence on prediction quality in such settings. Our study aimed to illustrate and compare different machine learning strategies to predict CVD risk factors under different scenarios. METHODS: We compared the use of six ML classifiers in predicting CVD risk factors using blood-derived metabolomics, epigenetics and transcriptomics data. Upstream omic dimension reduction was performed using either unsupervised or semi-supervised autoencoders, whose downstream ML classifier performance we compared. CVD risk factors included systolic and diastolic blood pressure measurements and ultrasound-based biomarkers of left ventricular diastolic dysfunction (LVDD; E/e' ratio, E/A ratio, LAVI) collected from 1,249 Finnish participants, of which 80% were used for model fitting. We predicted individuals with low, high or average levels of CVD risk factors, the latter class being the most common. We constructed multi-omic predictions using a meta-learner that weighted single-omic predictions. Model performance comparisons were based on the F1 score. Finally, we investigated whether learned omic representations from pre-trained semi-supervised autoencoders could improve outcome prediction in an external cohort using transfer learning. RESULTS: Depending on the ML classifier or omic used, the quality of single-omic predictions varied. Multi-omics predictions outperformed single-omics predictions in most cases, particularly in the prediction of individuals with high or low CVD risk factor levels. Semi-supervised autoencoders improved downstream predictions compared to the use of unsupervised autoencoders. In addition, median gains in Area Under the Curve by transfer learning compared to modelling from scratch ranged from 0.09 to 0.14 and 0.07 to 0.11 units for transcriptomic and metabolomic data, respectively. CONCLUSIONS: By illustrating the use of different machine learning strategies in different scenarios, our study provides a platform for researchers to evaluate how the choice of omics, ML classifiers, and dimension reduction can influence the quality of CVD risk factor predictions.

Umbilical cord blood DNA methylation in children who later develop type 1 diabetes.

AIMS/HYPOTHESIS: Distinct DNA methylation patterns have recently been observed to precede type 1 diabetes in whole blood collected from young children. Our aim was to determine whether perinatal DNA methylation is associated with later progression to type 1 diabetes. METHODS: Reduced representation bisulphite sequencing (RRBS) analysis was performed on umbilical cord blood samples collected within the Finnish Type 1 Diabetes Prediction and Prevention (DIPP) Study. Children later diagnosed with type 1 diabetes and/or who tested positive for multiple islet autoantibodies (n = 43) were compared with control individuals (n = 79) who remained autoantibody-negative throughout the DIPP follow-up until 15 years of age. Potential confounding factors related to the pregnancy and the mother were included in the analysis. RESULTS: No differences in the umbilical cord blood methylation patterns were observed between the cases and controls at a false discovery rate <0.05. CONCLUSIONS/INTERPRETATION: Based on our results, differences between children who progress to type 1 diabetes and those who remain healthy throughout childhood are not yet present in the perinatal DNA methylome. However, we cannot exclude the possibility that such differences would be found in a larger dataset.

Enterovirus-associated changes in blood transcriptomic profiles of children with genetic susceptibility to type 1 diabetes.

AIMS/HYPOTHESIS: Enterovirus infections have been associated with the development of type 1 diabetes in multiple studies, but little is known about enterovirus-induced responses in children at risk for developing type 1 diabetes. Our aim was to use genome-wide transcriptomics data to characterise enterovirus-associated changes in whole-blood samples from children with genetic susceptibility to type 1 diabetes. METHODS: Longitudinal whole-blood samples (356 samples in total) collected from 28 pairs of children at increased risk for developing type 1 diabetes were screened for the presence of enterovirus RNA. Seven of these samples were detected as enterovirus-positive, each of them collected from a different child, and transcriptomics data from these children were analysed to understand the individual-level responses associated with enterovirus infections. Transcript clusters with peaking or dropping expression at the time of enterovirus positivity were selected as the enterovirus-associated signals. RESULTS: Strong signs of activation of an interferon response were detected in four children at enterovirus positivity, while transcriptomic changes in the other three children indicated activation of adaptive immune responses. Additionally, a large proportion of the enterovirus-associated changes were specific to individuals. An enterovirus-induced signature was built using 339 genes peaking at enterovirus positivity in four of the children, and 77 of these genes were also upregulated in human peripheral blood mononuclear cells infected in vitro with different enteroviruses. These genes separated the four enterovirus-positive samples clearly from the remaining 352 blood samples analysed. CONCLUSIONS/INTERPRETATION: We have, for the first time, identified enterovirus-associated transcriptomic profiles in whole-blood samples from children with genetic susceptibility to type 1 diabetes. Our results provide a starting point for understanding the individual responses to enterovirus infections in blood and their potential connection to the development of type 1 diabetes. DATA AVAILABILITY: The datasets analysed during the current study are included in this published article and its supplementary information files ( www.btk.fi/research/computational-biomedicine/1234-2 ) or are available from the Gene Expression Omnibus (GEO) repository (accession GSE30211).

Subclinical atherosclerosis in young adults predicting cardiovascular disease: The Cardiovascular Risk in Young Finns Study.

BACKGROUND AND AIMS: Atherosclerosis is accompanied by pre-clinical vascular changes that can be detected using ultrasound imaging. We examined the value of such pre-clinical features in identifying young adults who are at risk of developing atherosclerotic cardiovascular disease (ASCVD). METHODS: A total of 2641 individuals free of ASCVD were examined at the mean age of 32 years (range 24-45 years) for carotid artery intima-media thickness (IMT) and carotid plaques, carotid artery elasticity, and brachial artery flow-mediated endothelium-dependent vasodilation (FMD). The average follow-up time to event/censoring was 16 years (range 1-17 years). RESULTS: Sixty-seven individuals developed ASCVD (incidence 2.5%). The lowest incidence (1.1%) was observed among those who were estimated of having low risk according to the SCORE2 risk algorithm (<2.5% 10-year risk) and who did not have plaque or high IMT (upper decile). The highest incidence (11.0%) was among those who were estimated of having a high risk (≥2.5% 10-year risk) and had positive ultrasound scan for carotid plaque and/or high IMT (upper decile). Carotid plaque and high IMT remained independently associated with higher risk in multivariate models. The distributions of carotid elasticity indices and brachial FMD did not differ between cases and non-cases. CONCLUSIONS: Screening for carotid plaque and high IMT in young adults may help identify individuals at high risk for future ASCVD.

Interactions of y+LAT1 and 4F2hc in the y+l amino acid transporter complex: consequences of lysinuric protein intolerance-causing mutations.

Lysinuric protein intolerance (LPI) is an inherited aminoaciduria caused by recessive mutations in the SLC7A7 gene encoding y+L amino acid transporter 1 (y+LAT1), which combines with 4F2hc to generate an active transporter responsible for the system y+L amino acid transport. We have previously shown that the y+LAT1 proteins with point mutations are expressed in the plasma membrane, while those with frameshift mutations are retained in the cytoplasm. This finding has prompted us to study whether the difference in localization is due to the inability of the structurally altered mutant y+LAT1 proteins to heteromerize with 4F2hc. For this purpose, we utilized FACS technique to reveal fluorescence resonance energy transfer (FRET) in cells expressing wild type or LPI-mutant CFP-tagged y+LAT1 and YFP-tagged 4F2hc. The heteromerization of y+LAT1 and 4F2hc within the cell is not disrupted by any of the tested LPI mutations. In addition, the expression rate of the LPI mutant y+LAT1 proteins was significantly lower and cellular mortality was markedly increased than that of the wild type y+LAT1 in transfected samples. Our results indicate that the FACS-FRET method provides an alternative approach for screening of potential protein associations.

Association of human bocavirus 1 infection with respiratory disease in childhood follow-up study, Finland.

Human bocavirus 1 (HBoV1) DNA is frequently detected in the upper airways of young children with respiratory symptoms. Because of its persistence and frequent co-detection with other viruses, however, its etiologic role has remained controversial. During 2009-2011, using HBoV1 IgM, IgG, and IgG-avidity enzyme immunoassays and quantitative PCR, we examined 1,952 serum samples collected consecutively at 3- to 6-month intervals from 109 constitutionally healthy children from infancy to early adolescence. Primary HBoV1 infection, as indicated by seroconversion, appeared in 102 (94%) of 109 children at a mean age of 2.3 years; the remaining 7 children were IgG antibody positive from birth. Subsequent secondary infections or IgG antibody increases were evident in 38 children and IgG reversions in 10. Comparison of the seroconversion interval with the next sampling interval for clinical events indicated that HBoV1 primary infection, but not secondary immune response, was significantly associated with acute otitis media and respiratory illness.

Exploring the transcriptomic variation caused by the Finnish founder mutation of lysinuric protein intolerance (LPI).

Lysinuric protein intolerance (LPI) is an autosomal recessive disorder caused by mutations in cationic amino acid transporter gene SLC7A7. Although all Finnish patients share the same homozygous mutation, their clinical manifestations vary greatly. The symptoms range from failure to thrive, protein aversion, anemia and hyperammonaemia, to immunological abnormalities, nephropathy and pulmonary alveolar proteinosis. To unravel the molecular mechanisms behind those symptoms not explained directly by the primary mutation, gene expression profiles of LPI patients were studied using genome-wide microarray technology. As a result, we discovered 926 differentially-expressed genes, including cationic and neutral amino acid transporters. The functional annotation analysis revealed a significant accumulation of such biological processes as inflammatory response, immune system processes and apoptosis. We conclude that changes in the expression of genes other than SLC7A7 may be linked to the various symptoms of LPI, indicating a complex interplay between amino acid transporters and various cellular processes.

Interoperability design of personal health information import service.

Availability of personal health information for individual use from professional patient records is an important success factor for personal health information management (PHIM) solutions such as personal health records. In this paper we focus on this crucial part of personal wellbeing information management splutions and report the interoperability design of personal information import service. Key requirements as well as design factors for interfaces between PHRs and EPRs are discussed. Open standards, low implementation threshold and the acknowledgement of local market and conventions are emphasized in the design.

Method for modeling social care processes for national information exchange.

Finnish social services include 21 service commissions of social welfare including Adoption counselling, Income support, Child welfare, Services for immigrants and Substance abuse care. This paper describes the method used for process modeling in the National project for IT in Social Services in Finland (Tikesos). The process modeling in the project aimed to support common national target state processes from the perspective of national electronic archive, increased interoperability between systems and electronic client documents. The process steps and other aspects of the method are presented. The method was developed, used and refined during the three years of process modeling in the national project.

Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values.

Abnormal blood pressure is strongly associated with risk of high-prevalence diseases, making the study of blood pressure a major public health challenge. Although biological mechanisms underlying hypertension at the single omic level have been discovered, multi-omics integrative analyses using continuous variations in blood pressure values remain limited. We used a multi-omics regression-based method, called sparse multi-block partial least square, for integrative, explanatory, and predictive interests in study of systolic and diastolic blood pressure values. Various datasets were obtained from the Finnish Twin Cohort for up to 444 twins. Blocks of omics-including transcriptomic, methylation, metabolomic-data as well as polygenic risk scores and clinical data were integrated into the modeling and supported by cross-validation. The predictive contribution of each omics block when predicting blood pressure values was investigated using external participants from the Young Finns Study. In addition to revealing interesting inter-omics associations, we found that each block of omics heterogeneously improved the predictions of blood pressure values once the multi-omics data were integrated. The modeling revealed a plurality of clinical, transcriptomic, and metabolomic factors consistent with the literature and that play a leading role in explaining unit variations in blood pressure. These findings demonstrate (1) the robustness of our integrative method to harness results obtained by single omics discriminant analyses, and (2) the added value of predictive and exploratory gains of a multi-omics approach in studies of complex phenotypes such as blood pressure.

Permutation-based significance analysis reduces the type 1 error rate in bisulphite sequencing data analysis of human umbilical cord blood samples.

DNA methylation patterns are largely established in-utero and might mediate the impacts of in-utero conditions on later health outcomes. Associations between perinatal DNA methylation marks and pregnancy-related variables, such as maternal age and gestational weight gain, have been earlier studied with methylation microarrays, which typically cover less than 2% of human CpG sites. To detect such associations outside these regions, we chose the bisulphite sequencing approach. We collected and curated clinical data on 200 newborn infants; whose umbilical cord blood samples were analysed with the reduced representation bisulphite sequencing (RRBS) method. A generalized linear mixed-effects model was fit for each high coverage CpG site, followed by spatial and multiple testing adjustment of P values to identify differentially methylated cytosines (DMCs) and regions (DMRs) associated with clinical variables, such as maternal age, mode of delivery, and birth weight. Type 1 error rate was then evaluated with a permutation analysis. We discovered a strong inflation of spatially adjusted P values through the permutation analysis, which we then applied for empirical type 1 error control. The inflation of P values was caused by a common method for spatial adjustment and DMR detection, implemented in tools comb-p and RADMeth. Based on empirically estimated significance thresholds, very little differential methylation was associated with any of the studied clinical variables, other than sex. With this analysis workflow, the sex-associated differentially methylated regions were highly reproducible across studies, technologies, and statistical models.

Long-term tracking and population characteristics of lipoprotein (a) in the Cardiovascular Risk in Young Finns Study.

BACKGROUND AND AIMS: Lipoprotein (a) (Lp(a)) is a causal risk factor for cardiovascular diseases and its levels are under strict genetic control. Therefore, it is hypothesized that the concentration of Lp(a) remains stable throughout life. Finns have lower Lp(a) levels than central Europeans, but it is unknown whether there are differences within Finland, especially between the eastern and western parts of the country with known genetic duality and persistent differences in cardiovascular disease rates. We have examined the long-term stability of Lp(a) levels over 25 years in the Cardiovascular Risk in Young Finns Study (YFS), and the characteristics of individuals with different Lp(a) levels, including their geographical origin within Finland. METHODS: In YFS, the first large baseline examination was conducted in 1980 (baseline age, 3-18 years). Several follow-ups during the past 40 years have been conducted to investigate the determinants of cardiometabolic health. Lp(a) levels have been measured in study years 1986 (N = 2464, ages 9-24 years), 2001 (N = 2281, ages 24-39 years), 2007 (N = 2204, ages 35-45 years) and 2011 (N = 2044, ages 39-49 years). Tracking of Lp(a) was estimated by calculating Spearman's rank order correlations between the study years, and by cross-tabulating how many individuals diagnosed with either elevated or non-elevated Lp(a) levels in 1986, 2001 and 2007 remained in the same category in the latest follow-up in 2011. RESULTS: Spearman's correlation coefficients varied between r = 0.84-0.96. Most individuals (87-94%) who had a high Lp(a) level (>30 mg/dl) in any of the previous study years had a high level also in 2011. On average, the median Lp(a) levels were consistently ∼20% higher in the individuals originating from eastern Finland compared to those from western Finland, but there were no differences in the distribution of known genetic determinants between eastern and western Finns that would have explained the observed difference. CONCLUSIONS: These data confirm that Lp(a) levels remain very stable over the life-course. In line with the genetic duality between eastern and western parts of Finland, we observed about 20% higher Lp(a) levels in individuals originating from eastern Finland compared to those originating from western Finland.

Glycoprotein Acetyls: A Novel Inflammatory Biomarker of Early Cardiovascular Risk in the Young.

Background Low-grade inflammation in the young may contribute to the early development of cardiovascular disease. We assessed whether circulating levels of glycoprotein acetyls (GlycA) were better able to predict the development of adverse cardiovascular disease risk profiles compared with the more commonly used biomarker high-sensitivity CRP (C-reactive protein). Methods and Results A total of 3306 adolescents and young adults from the Avon Longitudinal Study of Parents and Children (mean age, 15.4±0.3; n=1750) and Cardiovascular Risk in Young Finns Study (mean age, 32.1±5.0; n=1556) were included. Baseline associations between inflammatory biomarkers, body composition, cardiovascular risk factors, and subclinical measures of vascular dysfunction were assessed cross-sectionally in both cohorts. Prospective risk of developing hypertension and metabolic syndrome during 9-to-10-year follow-up were also assessed as surrogate markers for future cardiovascular risk. GlycA showed greater within-subject correlation over 9-to-10-year follow-up in both cohorts compared with CRP, particularly in the younger adolescent group (r=0.36 versus 0.07). In multivariable analyses, GlycA was found to associate with multiple lifestyle-related cardiovascular disease risk factors, cardiometabolic risk factor burden, and vascular dysfunction (eg, mean difference in flow-mediated dilation=-1.2 [-1.8, -0.7]% per z-score increase). In contrast, CRP levels appeared predominantly driven by body mass index and showed little relationship to any measured cardiovascular risk factors or phenotypes. In both cohorts, only GlycA predicted future risk of both hypertension (risk ratio [RR], ≈1.1 per z-score increase for both cohorts) and metabolic syndrome (RR, ≈1.2-1.3 per z-score increase for both cohorts) in 9-to-10-year follow-up. Conclusions Low-grade inflammation captured by the novel biomarker GlycA is associated with adverse cardiovascular risk profiles from as early as adolescence and predicts future risk of hypertension and metabolic syndrome in up to 10-year follow-up. GlycA is a stable inflammatory biomarker which may capture distinct sources of inflammation in the young and may provide a more sensitive measure than CRP for detecting early cardiovascular risk.

Mapping the Finnish National EHR to the LOINC.

The unified content of EHRs promote shared understanding of patient data, information exchange between information systems and health care organizations, data retrieval from EHR and reuse of data for administrative purposes, statistical analysis or clinical research. The purpose of this study was to analyze to what extent Finnish national headings can be coded with the current version LOINC. Ten (37%) of national headings can be mapped to LOINC terms in clinical class. There were LOINC terms in other classes which correspond to headings. Furthermore, inconsistency exists in the names of headings. The need for mapping national headings to all terms in LOINC is needed.

Reference architecture of application services for personal wellbeing information management.

Personal information management has been proposed as an important enabler for individual empowerment concerning citizens' wellbeing and health information. In the MyWellbeing project in Finland, a strictly citizen-driven concept of "Coper" and related architectural and functional guidelines have been specified. We present a reference architecture and a set of identified application services to support personal wellbeing information management. In addition, the related standards and developments are discussed.

Task analysis and interoperable application services for service event management.

In addition to the information specifications for electronic health records, functional and behavioral capabilities need to be agreed to achieve interoperability. In this paper, we present results from task analysis and specification of software services to support the management of service events. The work has been performed to support the management of the nationally shared EPR in Finland. The results support the specification of information sharing and composition in relation to healthcare workflows and activities. The specification of a functional reference model and software services for the management of service events and encounters promotes the integration of shared EHR and systems adaptability for migration towards interoperable electronic health records in healthcare networks.

Evaluating the direct effects of childhood adiposity on adult systemic metabolism: a multivariable Mendelian randomization analysis.

BACKGROUND: Individuals who are obese in childhood have an elevated risk of disease in adulthood. However, whether childhood adiposity directly impacts intermediate markers of this risk, independently of adult adiposity, is unclear. In this study, we have simultaneously evaluated the effects of childhood and adulthood body size on 123 systemic molecular biomarkers representing multiple metabolic pathways. METHODS: Two-sample Mendelian randomization (MR) was conducted to estimate the causal effect of childhood body size on a total of 123 nuclear magnetic resonance-based metabolic markers using summary genome-wide association study (GWAS) data from up to 24 925 adults. Multivariable MR was then applied to evaluate the direct effects of childhood body size on these metabolic markers whilst accounting for adult body size. Further MR analyses were undertaken to estimate the potential mediating effects of these circulating metabolites on the risk of coronary artery disease (CAD) in adulthood using a sample of 60 801 cases and 123 504 controls. RESULTS: Univariable analyses provided evidence that childhood body size has an effect on 42 of the 123 metabolic markers assessed (based on P < 4.07 × 10-4). However, the majority of these effects (35/42) substantially attenuated when accounting for adult body size using multivariable MR. We found little evidence that the biomarkers that were potentially influenced directly by childhood body size (leucine, isoleucine and tyrosine) mediate this effect onto adult disease risk. Very-low-density lipoprotein markers provided the strongest evidence of mediating the long-term effect of adiposity on CAD risk. CONCLUSIONS: Our findings suggest that childhood adiposity predominantly exerts its detrimental effect on adult systemic metabolism along a pathway that involves adulthood body size.

Childhood exposure to parental smoking and life-course overweight and central obesity.

OBJECTIVE: To evaluate the association between childhood parental smoking exposure and the risk of overweight/obesity from childhood to adulthood. METHODS: This study leverages the data from two longitudinal population based cohort studies, the Cardiovascular Risk in Young Finns Study between years 1980-2011/2012 (YFS; N = 2,303; baseline age 3-18 years) and the Special Turku Coronary Risk Factor Intervention Project between years 1989-2009/2010 (STRIP; N = 632; baseline age 7 months). Weight, height and waist circumference were measured from childhood to adulthood. Overweight/obesity was defined as body mass index ≥25 kg/m2 in adults and using the Cole criteria in children. Central obesity was defined as waist circumference > 100/90 cm in men/women and as a waist-to-height ratio > 0.50 in children. Statistical analyses were adjusted for age, sex, socioeconomic status, smoking, birth weight, parental ages, diet and physical activity. RESULTS: Childhood parental smoking exposure was associated with increased risk for life-course overweight/obesity (YFS: RR1.13, 95%CI 1.02-1.24; STRIP: RR1.57, 95%CI 1.10-2.26) and central obesity (YFS: RR1.18, 95%CI 1.01-1.38; STRIP: RR1.45, 95%CI 0.98-2.15). CONCLUSIONS: Childhood exposure to parental smoking is associated with increased risk of overweight/obesity over the life-course. KEY MESSAGES Exposure to parental smoking in childhood was associated with increased risk of overweight/obesity, central obesity and adiposity measured by skinfold thickness from childhood to adulthood.