9 research outputs found

    The national child odontology registry (SCOR): a valuable resource for odontological and public health research

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    Abstract Background Since 1972 The National Child Odontology Registry has collected data on the oral health of most of all Danish children and adolescents. However, comprehensive information on the registry has not previously been available, making it difficult to approach and use the registry for research purposes. Methods By combining historical documentation and simple descriptive statistics we provide an overview of major events in the timeline of The National Child Odontology Registry and discuss how they impact the available data. We provide a broad overview of the dental variables in the registry, and how the registration criteria for some of the core dental variables (gingivitis, periodontitis, and dental caries) have changed over time. We then provide examples of how aggregate variables for the core dental diseases, allowing for comparison across registration criteria, can be created. Results Most of the Danish population born during or after 1965 have a least one entry in the National Child Odontology Registry, with 68% having entries spanning their entire childhood and adolescence. The prevalence of gingivitis and periodontitis seem to increase significantly in the years immediately following changes in how registration criteria for these variables, raising questions as to whether these diseases are generally underreported, or subject to overreporting in the years following the registration changes. The mandatory ages of registration instituted in 2003, do not appear to have had a strong impact on the ages at which registrations are made. For variables not directly comparable across datasets due to changes in registration criteria aggregate variables of measurements can be computed in most cases. Conclusions The National Child Odontology Registry provides a unique opportunity to study the impact of childhood oral health on life trajectories, but using the registry is not without issues, and we strongly recommend consulting with experts in the field of odontology to ensure the best use of available data

    Association of general health and lifestyle factors with the salivary microbiota – Lessons learned from the ADDITION-PRO cohort

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    Introduction: Previous research indicates that the salivary microbiota may be a biomarker of oral as well as systemic disease. However, clarifying the potential bias from general health status and lifestyle-associated factors is a prerequisite of using the salivary microbiota for screening. Materials & Methods: ADDDITION-PRO is a nationwide Danish cohort, nested within the Danish arm of the Anglo-Danish-Dutch Study of Intensive treatment in People with Screen-Detected Diabetes in Primary Care. Saliva samples from n=746 individuals from the ADDITION-PRO cohort were characterized using 16s rRNA sequencing. Alpha- and beta diversity as well as relative abundance of genera was examined in relation to general health and lifestyle-associated variables. Permutational multivariate analysis of variance (PERMANOVA) was performed on individual variables and all variables together. Classification models were created using sparse partial-least squares discriminant analysis (sPLSDA) for variables that showed statistically significant differences based on PERMANOVA analysis (p < 0.05). Results: Glycemic status, hemoglobin-A1c (HbA1c) level, sex, smoking and weekly alcohol intake were found to be significantly associated with salivary microbial composition (individual variables PERMANOVA, p < 0.05). Collectively, these variables were associated with approximately 5.8% of the observed differences in the composition of the salivary microbiota. Smoking status was associated with 3.3% of observed difference, and smoking could be detected with good accuracy based on salivary microbial composition (AUC 0.95, correct classification rate 79.6%). Conclusions: Glycemic status, HbA1c level, sex, smoking and weekly alcohol intake were significantly associated with the composition of the salivary microbiota. Despite smoking only being associated with 3.3% of the difference in overall salivary microbial composition, it was possible to create a model for detection of smoking status with a high correct classification rate. However, the lack of information on the oral health status of participants serves as a limitation in the present study. Further studies in other cohorts are needed to validate the external validity of these findings.ISSN:2235-298

    Genetics of Plasma Bilirubin and Associations between Bilirubin and Cardiometabolic Risk Profiles in Danish Children and Adolescents

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    Bilirubin is the end product of heme catabolism, mainly produced by the breakdown of mature red blood cells. Due to its anti-inflammatory, antioxidant, antidiabetic, and antilipemic properties, circulating bilirubin concentrations are inversely associated with the risk of cardiovascular disease, type 2 diabetes, and all-cause mortality in adults. Some genetic loci associated with circulating bilirubin concentrations have been identified by genome-wide association studies in adults. We aimed to examine the relationship between circulating bilirubin, cardiometabolic risk factors, and inflammation in children and adolescents and the genetic architecture of plasma bilirubin concentrations. We measured fasting plasma bilirubin, cardiometabolic risk factors, and inflammatory markers in a sample of Danish children and adolescents with overweight or obesity (n = 1530) and in a population-based sample (n = 1820) of Danish children and adolescents. Linear and logistic regression analyses were performed to analyze the associations between bilirubin, cardiometabolic risk factors, and inflammatory markers. A genome-wide association study (GWAS) of fasting plasma concentrations of bilirubin was performed in children and adolescents with overweight or obesity and in a population-based sample. Bilirubin is associated inversely and significantly with a number of cardiometabolic risk factors, including body mass index (BMI) standard deviation scores (SDS), waist circumference, high-sensitivity C-reactive protein (hs-CRP), homeostatic model assessment for insulin resistance (HOMA-IR), hemoglobin A1c (HbA1c), low-density lipoprotein cholesterol (LDL-C), triglycerides, and the majority of measured inflammatory markers. In contrast, bilirubin was positively associated with fasting plasma concentrations of alanine transaminase (ALT), high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SDS), and the inflammatory markers GH, PTX3, THBS2, TNFRSF9, PGF, PAPPA, GT, CCL23, CX3CL1, SCF, and TRANCE. The GWAS showed that two loci were positively associated with plasma bilirubin concentrations at a p-value threshold of −8 (rs76999922: ÎČ = −0.65 SD; p = 4.3 × 10−8, and rs887829: ÎČ = 0.78 SD; p = 2.9 × 10−247). Approximately 25% of the variance in plasma bilirubin concentration was explained by rs887829. The rs887829 was not significantly associated with any of the mentioned cardiometabolic risk factors except for hs-CRP. Our findings suggest that plasma concentrations of bilirubin non-causally associates with cardiometabolic risk factors in children and adolescents

    DataSheet_1_Association of general health and lifestyle factors with the salivary microbiota – Lessons learned from the ADDITION-PRO cohort.zip

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    IntroductionPrevious research indicates that the salivary microbiota may be a biomarker of oral as well as systemic disease. However, clarifying the potential bias from general health status and lifestyle-associated factors is a prerequisite of using the salivary microbiota for screening.Materials & MethodsADDDITION-PRO is a nationwide Danish cohort, nested within the Danish arm of the Anglo-Danish-Dutch Study of Intensive treatment in People with Screen-Detected Diabetes in Primary Care. Saliva samples from n=746 individuals from the ADDITION-PRO cohort were characterized using 16s rRNA sequencing. Alpha- and beta diversity as well as relative abundance of genera was examined in relation to general health and lifestyle-associated variables. Permutational multivariate analysis of variance (PERMANOVA) was performed on individual variables and all variables together. Classification models were created using sparse partial-least squares discriminant analysis (sPLSDA) for variables that showed statistically significant differences based on PERMANOVA analysis (p ResultsGlycemic status, hemoglobin-A1c (HbA1c) level, sex, smoking and weekly alcohol intake were found to be significantly associated with salivary microbial composition (individual variables PERMANOVA, p ConclusionsGlycemic status, HbA1c level, sex, smoking and weekly alcohol intake were significantly associated with the composition of the salivary microbiota. Despite smoking only being associated with 3.3% of the difference in overall salivary microbial composition, it was possible to create a model for detection of smoking status with a high correct classification rate. However, the lack of information on the oral health status of participants serves as a limitation in the present study. Further studies in other cohorts are needed to validate the external validity of these findings.</p

    Table_1_Multiomics signatures of type 1 diabetes with and without albuminuria.xlsx

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    Aims/hypothesisTo identify novel pathophysiological signatures of longstanding type 1 diabetes (T1D) with and without albuminuria we investigated the gut microbiome and blood metabolome in individuals with T1D and healthy controls (HC). We also mapped the functional underpinnings of the microbiome in relation to its metabolic role.MethodsOne hundred and sixty-one individuals with T1D and 50 HC were recruited at the Steno Diabetes Center Copenhagen, Denmark. T1D cases were stratified based on levels of albuminuria into normoalbuminuria, moderate and severely increased albuminuria. Shotgun sequencing of bacterial and viral microbiome in stool samples and circulating metabolites and lipids profiling using mass spectroscopy in plasma of all participants were performed. Functional mapping of microbiome into Gut Metabolic Modules (GMMs) was done using EggNog and KEGG databases. Multiomics integration was performed using MOFA tool.ResultsMeasures of the gut bacterial beta diversity differed significantly between T1D and HC, either with moderately or severely increased albuminuria. Taxonomic analyses of the bacterial microbiota identified 51 species that differed in absolute abundance between T1D and HC (17 higher, 34 lower). Stratified on levels of albuminuria, 10 species were differentially abundant for the moderately increased albuminuria group, 63 for the severely increased albuminuria group while 25 were common and differentially abundant both for moderately and severely increased albuminuria groups, when compared to HC. Functional characterization of the bacteriome identified 23 differentially enriched GMMs between T1D and HC, mostly involved in sugar and amino acid metabolism. No differences in relation to albuminuria stratification was observed. Twenty-five phages were differentially abundant between T1D and HC groups. Six of these varied with albuminuria status. Plasma metabolomics indicated differences in the steroidogenesis and sugar metabolism and circulating sphingolipids in T1D individuals. We identified association between sphingolipid levels and Bacteroides sp. abundances. MOFA revealed reduced interactions between gut microbiome and plasma metabolome profiles albeit polar metabolite, lipids and bacteriome compositions contributed to the variance in albuminuria levels among T1D individuals.ConclusionsIndividuals with T1D and progressive kidney disease stratified on levels of albuminuria show distinct signatures in their gut microbiome and blood metabolome.</p

    DataSheet_1_Multiomics signatures of type 1 diabetes with and without albuminuria.docx

    No full text
    Aims/hypothesisTo identify novel pathophysiological signatures of longstanding type 1 diabetes (T1D) with and without albuminuria we investigated the gut microbiome and blood metabolome in individuals with T1D and healthy controls (HC). We also mapped the functional underpinnings of the microbiome in relation to its metabolic role.MethodsOne hundred and sixty-one individuals with T1D and 50 HC were recruited at the Steno Diabetes Center Copenhagen, Denmark. T1D cases were stratified based on levels of albuminuria into normoalbuminuria, moderate and severely increased albuminuria. Shotgun sequencing of bacterial and viral microbiome in stool samples and circulating metabolites and lipids profiling using mass spectroscopy in plasma of all participants were performed. Functional mapping of microbiome into Gut Metabolic Modules (GMMs) was done using EggNog and KEGG databases. Multiomics integration was performed using MOFA tool.ResultsMeasures of the gut bacterial beta diversity differed significantly between T1D and HC, either with moderately or severely increased albuminuria. Taxonomic analyses of the bacterial microbiota identified 51 species that differed in absolute abundance between T1D and HC (17 higher, 34 lower). Stratified on levels of albuminuria, 10 species were differentially abundant for the moderately increased albuminuria group, 63 for the severely increased albuminuria group while 25 were common and differentially abundant both for moderately and severely increased albuminuria groups, when compared to HC. Functional characterization of the bacteriome identified 23 differentially enriched GMMs between T1D and HC, mostly involved in sugar and amino acid metabolism. No differences in relation to albuminuria stratification was observed. Twenty-five phages were differentially abundant between T1D and HC groups. Six of these varied with albuminuria status. Plasma metabolomics indicated differences in the steroidogenesis and sugar metabolism and circulating sphingolipids in T1D individuals. We identified association between sphingolipid levels and Bacteroides sp. abundances. MOFA revealed reduced interactions between gut microbiome and plasma metabolome profiles albeit polar metabolite, lipids and bacteriome compositions contributed to the variance in albuminuria levels among T1D individuals.ConclusionsIndividuals with T1D and progressive kidney disease stratified on levels of albuminuria show distinct signatures in their gut microbiome and blood metabolome.</p

    The gut microbiota contributes to the pathogenesis of anorexia nervosa in humans and mice

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    International audienceAbstract Anorexia nervosa (AN) is an eating disorder with a high mortality. About 95% of cases are women and it has a population prevalence of about 1%, but evidence-based treatment is lacking. The pathogenesis of AN probably involves genetics and various environmental factors, and an altered gut microbiota has been observed in individuals with AN using amplicon sequencing and relatively small cohorts. Here we investigated whether a disrupted gut microbiota contributes to AN pathogenesis. Shotgun metagenomics and metabolomics were performed on faecal and serum samples, respectively, from a cohort of 77 females with AN and 70 healthy females. Multiple bacterial taxa (for example, Clostridium species) were altered in AN and correlated with estimates of eating behaviour and mental health. The gut virome was also altered in AN including a reduction in viral–bacterial interactions. Bacterial functional modules associated with the degradation of neurotransmitters were enriched in AN and various structural variants in bacteria were linked to metabolic features of AN. Serum metabolomics revealed an increase in metabolites associated with reduced food intake (for example, indole-3-propionic acid). Causal inference analyses implied that serum bacterial metabolites are potentially mediating the impact of an altered gut microbiota on AN behaviour. Further, we performed faecal microbiota transplantation from AN cases to germ-free mice under energy-restricted feeding to mirror AN eating behaviour. We found that the reduced weight gain and induced hypothalamic and adipose tissue gene expression were related to aberrant energy metabolism and eating behaviour. Our ‘omics’ and mechanistic studies imply that a disruptive gut microbiome may contribute to AN pathogenesis
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