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Over the last decades, thyroid hormone metabolites (THMs) received marked attention as it has been demonstrated that they are bioactive compounds. Their concentrations were determined by immunoassay or mass-spectrometry methods. Among those metabolites, 3,5-diiodothyronine (3,5-T2), occurs at low nanomolar concentrations in human serum, but might reach tissue concentrations similar to those of T4 and T3, at least based on data from rodent models. However, the immunoassay-based measurements in human sera revealed remarkable variations depending on antibodies used in the assays and thus need to be interpreted with caution. In clinical experimental approaches in euthyroid volunteers and hypothyroid patients using the immunoassay as the analytical tool no evidence of formation of 3,5-T2 from its putative precursors T4 or T3 was found, nor was any support found for the assumption that 3,5-T2 might represent a direct precursor for serum 3-T1-AM generated by combined deiodination and decarboxylation from 3,5-T2, as previously documented for mouse intestinal mucosa. We hypothesized that lowered endogenous production of 3,5-T2 in patients requiring T4 replacement therapy after thyroidectomy or for treatment of autoimmune thyroid disease, compared to production of 3,5-T2 in individuals with intact thyroid glands might contribute to the discontent seen in a subset of patients with this therapeutic regimen. So far, our observations do not support this assumption. However, the unexpected association between high serum 3,5-T2 and elevated urinary concentrations of metabolites related to coffee consumption requires further studies for an explanation. Elevated 3,5-T2 serum concentrations were found in several situations including impaired renal function, chronic dialysis, sepsis, non-survival in the ICU as well as post-operative atrial fibrillation (POAF) in studies using a monoclonal antibody-based chemoluminescence immunoassay. Pilot analysis of human sera using LC-linear-ion-trap-mass-spectrometry yielded 3,5-T2 concentrations below the limit of quantification in the majority of cases, thus the divergent results of both methods need to be reconciliated by further studies. Although positive anti-steatotic effects have been observed in rodent models, use of 3,5-T2 as a muscle anabolic, slimming or fitness drug, easily obtained without medical prescription, must be advised against, considering its potency in suppressing the HPT axis and causing adverse cardiac side effects. 3,5-T2 escapes regular detection by commercially available clinical routine assays used for thyroid function tests, which may be seriously disrupted in individuals self-administering 3,5-T2 obtained over-the counter or from other sources.
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.
For the goal of individualized medicine, it is critical to have clinical phenotypes at hand which represent the individual pathophysiology. However, for most of the utilized phenotypes, two individuals with the same phenotype assignment may differ strongly in their underlying biological traits. In this paper, we propose a definition for individualization and a corresponding statistical operationalization, delivering thereby a statistical framework in which the usefulness of a variable in the meaningful differentiation of individuals with the same phenotype can be assessed. Based on this framework, we develop a statistical workflow to derive individualized phenotypes, demonstrating that under specific statistical constraints the prediction error of prediction scores contains information about hidden biological traits not represented in the modeled phenotype of interest, allowing thereby internal differentiation of individuals with the same assigned phenotypic manifestation. We applied our procedure to data of the population-based Study of Health in Pomerania to construct a refined definition of obesity, demonstrating the utility of the definition in prospective survival analyses. Summarizing, we propose a framework for the individualization of phenotypes aiding personalized medicine by shifting the focus in the assessment of prediction models from the model fit to the informational content of the prediction error.
Periodontitis is one of the most prevalent oral diseases worldwide and is caused by multifactorial interactions between host and oral bacteria. Altered cellular metabolism of host and microbes releases a number of intermediary end products known as metabolites. There is an increasing interest in identifying metabolites from oral fluids such as saliva to widen the understanding of the complex pathogenesis of periodontitis. It is believed that some metabolites might serve as indicators toward early detection and screening of periodontitis and perhaps even for monitoring its prognosis in the future. Because contemporary periodontal screening methods are deficient, there is an urgent need for novel approaches in periodontal screening procedures. To this end, we associated oral parameters (clinical attachment level, periodontal probing depth, supragingival plaque, supragingival calculus, number of missing teeth, and removable denture) with a large set of salivary metabolites (n = 284) obtained by mass spectrometry among a subsample (n = 909) of nondiabetic participants from the Study of Health in Pomerania (SHIP-Trend-0). Linear regression analyses were performed in age-stratified groups and adjusted for potential confounders. A multifaceted image of associated metabolites (n = 107) was revealed with considerable differences according to age groups. In the young (20 to 39 y) and middle-aged (40 to 59 y) groups, metabolites were predominantly associated with periodontal variables, whereas among the older subjects (≥60 y), tooth loss was strongly associated with metabolite levels. Metabolites associated with periodontal variables were clearly linked to tissue destruction, host defense mechanisms, and bacterial metabolism. Across all age groups, the bacterial metabolite phenylacetate was significantly associated with periodontal variables. Our results revealed alterations of the salivary metabolome in association with age and oral health status. Among our comprehensive panel of metabolites, periodontitis was significantly associated with the bacterial metabolite phenylacetate, a promising substance for further biomarker research.
Context: 3,5-Diiodo-<smlcap>L</smlcap>-thyronine (3,5-T<sub>2</sub>) is a thyroid hormone metabolite which exhibited versatile effects in rodent models, including the prevention of insulin resistance or hepatic steatosis typically forced by a high-fat diet. With respect to euthyroid humans, we recently observed a putative link between serum 3,5-T<sub>2</sub> and glucose but not lipid metabolism. Objective: The aim of the present study was to widely screen the urine metabolome for associations with serum 3,5-T<sub>2</sub> concentrations in healthy individuals. Study Design and Methods: Urine metabolites of 715 euthyroid participants of the population-based Study of Health in Pomerania (SHIP-TREND) were analyzed by <sup>1</sup>H-NMR spectroscopy. Multinomial logistic and multivariate linear regression models were used to detect associations between urine metabolites and serum 3,5-T<sub>2</sub> concentrations. Results: Serum 3,5-T<sub>2</sub> concentrations were positively associated with urinary levels of trigonelline, pyroglutamate, acetone and hippurate. In detail, the odds for intermediate or suppressed serum 3,5-T<sub>2</sub> concentrations doubled owing to a 1-standard deviation (SD) decrease in urine trigonelline levels, or increased by 29-50% in relation to a 1-SD decrease in urine pyroglutamate, acetone and hippurate levels. Conclusion: Our findings in humans confirmed the metabolic effects of circulating 3,5-T<sub>2</sub> on glucose and lipid metabolism, oxidative stress and enhanced drug metabolism as postulated before based on interventional pharmacological studies in rodents. Of note, 3,5-T<sub>2</sub> exhibited a unique urinary metabolic profile distinct from previously published results for the classical thyroid hormones.
Introduction: Patients who are overweight or obese have an increased risk of developing type 2 diabetes mellitus (T2DM). Weight loss can have a positive effect on glycemic control. Objective: We aimed to investigate glycemic control in patients with T2DM and overweight or obesity during a structured weight-loss program. Methods: This was a prospective, interventional study. We recruited 36 patients (14 men and 22 women) with a median age of 58.5 years and median body mass index (BMI) of 34.1, to a 15-week structured weight-loss program with a low-calorie (800 kcal) formula diet for 6 weeks. The primary end point, HbA<sub>1c</sub> level, and secondary end points, anthropometric data, medication, and safety, were assessed weekly. Laboratory values and quality of life were assessed at baseline and after 15 weeks. Results: HbA<sub>1c</sub> decreased from 7.3% at baseline to 6.5% at 15 weeks (p < 0.001), median body weight by 11.9 kg (p < 0.001), median BMI by 4.3 (p < 0.001) and median waist circumference by 11.0 cm (p < 0.001). Two participants discontinued insulin therapy, 4 could reduce their dosage of oral antidiabetic agents, and 6 completely discontinued their antidiabetic medication. Insulin dose decreased from 0.63 (0.38–0.89) to 0.39 (0.15–0.70) units/kg body weight (p < 0.001). No patient experienced hypoglycemic episodes or hospital emergency visits. Triglycerides and total cholesterol decreased as well as surrogate markers of liver function. However, the levels of high-density and low-density lipoprotein cholesterol (HDL-C and LDL-C) as well as uric acid remain unchanged. Regarding quality of life, the median physical health score increased from 44.5 (39.7–51.4) at baseline to 48.0 (43.1–55.3; p = 0.007), and the median mental health score decreased from 42.1 (36.1–46.7) to 37.4 (30.3–43.7; p = 0.004). Conclusions: A structured weight-loss program is effective in the short term in reducing HbA<sub>1c</sub>, weight, and antidiabetic medication in patients with T2DM who are overweight or obese. Levels of HDL-C and LDL-C were not affected by short-term weight loss. The decline in mental health and the long-term effects of improved glycemic control require further trials.