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Poor sleep quality or sleep deprivation may be related to decreased bone mineral density. We aimed to assess whether associations of sleep characteristics and bone turnover or strength are present in adults from the general population and whether these are independent of common risk factors such as sex, age, and obesity. A total of 1037 participants from the Study of Health in Pomerania-TREND underwent laboratory-based polysomnography and quantitative ultrasound measurements at the heel. Of these participants, 804 completed standardised questionnaires to assess daytime sleepiness, insomnia, and sleep quality. Serum concentrations of two bone turnover markers, intact amino-terminal propeptide of type 1 procollagen (P1NP) and carboxy-terminal telopeptide of type 1 collagen (CTX) were measured. Cross-sectional associations of polysomnography variables (total sleep time, sleep efficiency, time spent wake after sleep onset, oxygen desaturation index, apnea–hypopnea index, and obstructive sleep apnea [OSA]), as well as sleep questionnaire scores with the bone turnover markers and the ultrasound-based stiffness index were assessed in linear regression models. In adjusted models, higher insomnia scores and lower sleep quality scores were related to a higher bone turnover in women but not in men. However, associations between polysomnography variables or questionnaire scores and the stiffness index were absent. Our study provides limited evidence for relationships between sleep characteristics and bone turnover and strength independent of common risk factors for OSA and osteoporosis. Nevertheless, women reporting poor sleep or insomnia in combination with risk factors for osteoporosis might benefit from an evaluation of bone health.
Abstract
Background
Twenty five‐hydroxy vitamin D (25OHD) levels have been proposed to protect against periodontitis based on in vitro and observational studies but evidence from long‐term randomized controlled trials (RCTs) is lacking. This study tested whether genetically proxied 25OHD is associated with periodontitis using Mendelian randomization (MR).
Methods
Genetic variants strongly associated with 25OHD in a genome‐wide association study (GWAS) of 417,580 participants of European ancestry were used as instrumental variables, and linked to GWAS summary data of 17,353 periodontitis cases and 28,210 controls. In addition to the main analysis using an inverse variance weighted (IVW) model, we applied additional robust methods to control for pleiotropy. We also undertook sensitivity analyses excluding single nucleotide polymorphisms (SNPs) used as instruments with potential pleiotropic effects and used a second 25OHD GWAS for replication. We identified 288 SNPs to be genome‐wide significant for 25OHD, explaining 7.0% of the variance of 25OHD levels and providing ≥90% power to detect an odds ratio (OR) of ≤ 0.97.
Results
MR analysis suggested that a 1 standard deviation increase in natural log‐transformed 25OHD was not associated with periodontitis risk (IVW OR = 1.04; 95% confidence interval (CI): 0.97–1.12; P‐value = 0.297). The robust models, replication, and sensitivity analyses were coherent with the primary analysis.
Conclusions
Collectively, our findings suggest that 25OHD levels are unlikely to have a substantial effect on the risk of periodontitis, but large long‐term RCTs are needed to derive definitive evidence on the causal role of 25OHD in periodontitis.
Objective
This study provides a comprehensive overview of the associations of five adipokines (adiponectin, chemerin, galectin‐3, leptin, and resistin) with fat deposits, behavioral risk factors, and metabolic phenotypes.
Methods
Using multivariable linear and logistic regression models, cross‐sectional data from 4,116 participants of the population‐based Study of Health in Pomerania were analyzed.
Results
Participants with obesity showed higher chemerin, galectin‐3, and leptin but showed lower adiponectin concentrations. Independently of other fat compounds, liver fat content, visceral adipose tissue, and subcutaneous adipose tissue (SAT) were inversely associated with adiponectin. Independent positive associations of liver fat content and SAT with chemerin as well as of SAT with galectin‐3 and leptin were observed. Physically inactive participants had higher chemerin and leptin concentrations. Smokers had higher chemerin and galectin‐3 as well as lower leptin. Alcohol consumption was associated with adiponectin (positive) and resistin (inverse). All adipokines were associated with at least one lipid marker. Associations with glucose metabolism were seen for adiponectin, chemerin, galectin‐3, and leptin.
Conclusions
High adiponectin concentrations were related to favorable metabolic conditions, whereas high chemerin, galectin‐3, and leptin were associated with an unfavorable metabolic profile. High leptin seems to be primarily indicative of obesity, whereas high adiponectin and chemerin are associated with a broader range of metabolic phenotypes.
Abstract
Metabolomics studies now approach large sample sizes and the health characterization of the study population often include complete blood count (CBC) results. Upon careful interpretation the CBC aids diagnosis and provides insight into the health status of the patient within a clinical setting. Uncovering metabolic signatures associated with parameters of the CBC in apparently healthy individuals may facilitate interpretation of metabolomics studies in general and related to diseases. For this purpose 879 subjects from the population‐based Study of Health in Pomerania (SHIP)‐TREND were included. Using metabolomics data resulting from mass‐spectrometry based measurements in plasma samples associations of specific CBC parameters with metabolites were determined by linear regression models. In total, 118 metabolites significantly associated with at least one of the CBC parameters. Strongest associations were observed with metabolites of heme degradation and energy production/consumption. Inverse association seen with mean corpuscular volume and mean corpuscular haemoglobin comprised metabolites potentially related to kidney function. The presently identified metabolic signatures are likely derived from the general function and formation/elimination of blood cells. The wealth of associated metabolites strongly argues to consider CBC in the interpretation of metabolomics studies, in particular if mutual effects on those parameters by the disease of interest are known.