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Povaliaeva A, Zhukov A, Bogdanov V, Bondarenko A, Senko O, Kuznetsova A, Kodryan M, Ioutsi V, Pigarova E, Rozhinskaya L, Mokrysheva N. Evaluation of the age-specific relationship between PTH and vitamin D metabolites. Bone Rep 2024; 22:101800. [PMID: 39281298 PMCID: PMC11399804 DOI: 10.1016/j.bonr.2024.101800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2024] [Revised: 08/22/2024] [Accepted: 08/25/2024] [Indexed: 09/18/2024] Open
Abstract
A commonly used method for determining vitamin D sufficiency is the suppression of excess PTH secretion. Conventionally, the main circulating vitamin D metabolite 25(OH)D is used for this assessment, however, the cut-off data for this parameter vary widely in the literature. The role of other metabolites as markers of vitamin D status is actively debated. The aim of our study was to assess the relationship between PTH, age and parameters characterizing vitamin D status, both "classical" - 25(OH)D3, and "non-classical" - 24,25(OH)2D3 and 25(OH)D3/24,25(OH)2D3 (vitamin D metabolite ratio, VMR). This prospective non-controlled cohort study included 162 apparently healthy Caucasian adult volunteers. When PTH was binarized according to the median value, at VMR < 14.9, 25(OH)D3 > 9.7 ng/mL and 24,25(OH)2D3 > 0.64 ng/mL there was a pronounced relationship between PTH and age (p = 0.001, p = 0.023 and p = 0.0134 respectively), with the prevalence of higher PTH levels in older individuals and vice versa. Moreover, at an age of <40.3 years, there was a pronounced relationship between PTH and VMR (p < 0.001), and similarly at an age of <54.5 years, there was a pronounced relationship between PTH and 25(OH)D3 (p = 0.002) as well as between PTH and 24,25(OH)2D3 (p = 0.0038): in younger people, higher PTH values prevailed only in the range of vitamin D insufficiency, while in the older age group this relationship was not demonstrated and PTH values were in general above the median. VMR controlled the correlation between PTH and age more strongly than metabolites 25(OH)D3 and 24,25(OH)2D3 (p = 0.0012 vs. p > 0.05 and p = 0.0385 respectively). The optimal threshold was found equal to 11.7 for VMR such that the relationship between PTH and age in the subset of participants with VMR < 11.7 was characterized by a correlation coefficient of ρ = 0.68 (p < 0.001), while the cohort with VMR > 11.7 was characterized by a very weak correlation coefficient of ρ = 0.12 (p = 0.218), which is non-significant. In summary, our findings suggest that the relationship between PTH and vitamin D is age-dependent, with a greater susceptibility to elevated PTH among older individuals even with preserved renal function, likely due to the resistance to vitamin D function. We propose VMR can be considered as a potential marker of vitamin D status. These findings require confirmation in larger population-based studies.
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Affiliation(s)
- Alexandra Povaliaeva
- Endocrinology Research Centre 11, Dmitriya Ul'yanova street, Moscow 117292, Russia
| | - Artem Zhukov
- Endocrinology Research Centre 11, Dmitriya Ul'yanova street, Moscow 117292, Russia
| | - Viktor Bogdanov
- Endocrinology Research Centre 11, Dmitriya Ul'yanova street, Moscow 117292, Russia
- Life Sciences Research Center, Moscow Institute of Physics and Technology, Dolgoprudniy, Russia
| | - Axenia Bondarenko
- Endocrinology Research Centre 11, Dmitriya Ul'yanova street, Moscow 117292, Russia
| | - Oleg Senko
- Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, Moscow, Russia
| | - Anna Kuznetsova
- Emanuel Institute of Biochemical Physics of Russian Academy of Sciences, Moscow, Russia
| | | | - Vitaliy Ioutsi
- Endocrinology Research Centre 11, Dmitriya Ul'yanova street, Moscow 117292, Russia
| | - Ekaterina Pigarova
- Endocrinology Research Centre 11, Dmitriya Ul'yanova street, Moscow 117292, Russia
| | - Liudmila Rozhinskaya
- Endocrinology Research Centre 11, Dmitriya Ul'yanova street, Moscow 117292, Russia
| | - Natalia Mokrysheva
- Endocrinology Research Centre 11, Dmitriya Ul'yanova street, Moscow 117292, Russia
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Zhuravlev YI, Ryazanov VV, Sen’ko OV, Dokukin AA, Vinogradov AP, Nelyubina EA, Stefanovskii DV. Using Hough-Like Transforms for Extracting Relevant Regularities from Big Applied Data. PATTERN RECOGNITION AND IMAGE ANALYSIS 2021. [DOI: 10.1134/s1054661821040313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Brusov OS, Senko OV, Kodryan MS, Kuznetsova AV, Matveev IA, Oleichik IV, Karpova NS, Faktor MI, Aleshenko AV, Sizov SV. [Application of machine learning for predicting the outcome of treatment of patients with schizophrenia according to the indicators of «Thrombodynamics» test]. Zh Nevrol Psikhiatr Im S S Korsakova 2021; 121:45-53. [PMID: 34481435 DOI: 10.17116/jnevro202112108145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
OBJECTIVE To identify relationships between thrombodynamic values and the severity of the condition in patients with schizophrenia spectrum disorders (SSD) before and after treatment. MATERIAL AND METHODS The study included 92 patients in an acute state of schizophrenia or schizotypal disorder, aged 16 to 57 years (median age [Q1; Q3] - 25 years). All patients received complex psychopharmacotherapy adequate to their psychopathological state. The PANSS was used to assess the severity of symptoms in patients. The coagulation parameters were determined by the thrombodynamics test, in which the growth of fibrin clots in platelet free plasma are observed from special activator. The patient population was divided into two groups with weak and strong response to treatment. Data analysis included machine learning (ML) techniques: logistic regression, random forests, decision trees, support vector machines with radial basis functions, statistically weighted syndromes, permutation method. RESULTS An analysis using permutation method revealed statistically significant different thrombodynamics values between groups of patients with weak and strong responses. There are significant differences between thrombodynamics values: T1D, T2D, T2Tlag and DTlag, and values characterizing the severity of positive symptoms before and after treatment (T1PposTot, T2PposTot), severity of psychopathological symptoms before treatment (T1Ppsy1, T1Ppsy6, T1Ppsy13). All ML techniques showed the relationship between thrombodynamics values and response to treatment. The best statistical significance was for statistically weighted syndromes method. CONCLUSION The combination of the results of different ML techniques at a high level of statistical significance identifies the thrombodynamic predictors of weak effect of treatment of SSD.
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Affiliation(s)
- O S Brusov
- Mental Health Research Center of the Ministry of Science and Higher Education, Moscow, Russia
| | - O V Senko
- Federal Research Center «Computer Science and Control» of Russian Academy of Science, Moscow, Russia
| | | | - A V Kuznetsova
- Emanuel Institute of Biochemical Physics of Russian Academy of Science, Moscow, Russia
| | - I A Matveev
- Federal Research Center «Computer Science and Control» of Russian Academy of Science, Moscow, Russia
| | - I V Oleichik
- Mental Health Research Center of the Ministry of Science and Higher Education, Moscow, Russia
| | - N S Karpova
- Mental Health Research Center of the Ministry of Science and Higher Education, Moscow, Russia
| | - M I Faktor
- Mental Health Research Center of the Ministry of Science and Higher Education, Moscow, Russia
| | - A V Aleshenko
- Emanuel Institute of Biochemical Physics of Russian Academy of Science, Moscow, Russia
| | - S V Sizov
- Mental Health Research Center of the Ministry of Science and Higher Education, Moscow, Russia
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Prognostic Model of Prehypertension Risk Based on Molecular Markers. Bull Exp Biol Med 2021; 170:689-692. [PMID: 33788117 DOI: 10.1007/s10517-021-05134-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Indexed: 10/21/2022]
Abstract
The prognostic models assessing the risk of prehypertension in coming 1-2-year period for 30-60-year-old subjects were developed with the help of computer recognition technology using 6 recognition methods. These models are based on the content of molecular markers in blood serum and the risk factors for the development of prehypertension in men and women who had "optimal" BP for last 3 years and in patients with newly diagnosed prehypertension. The models were compared for their prediction power. The most effective model was obtained with gradient boosting method based on the content of molecular markers. It is characterized with a high predictive power (ROC AUC=0.76), specificity (96.4%), and overall accuracy (86.6%) accompanied with close relationship between prognosis and actual symptoms of prehypertension (p=0.001).
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Zhuravlev YI, Sen’ko OV, Bondarenko NN, Ryazanov VV, Dokukin AA, Vinogradov AP. A Method for Predicting Rare Events by Multidimensional Time Series with the Use of Collective Methods. PATTERN RECOGNITION AND IMAGE ANALYSIS 2019. [DOI: 10.1134/s1054661819040217] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Zhuravlev YI, Ryazanov VV, Sen’ko OV, Dokukin AA, Afanas’ev PA. On Some Transformations of Features in Machine Learning in Medicine. PATTERN RECOGNITION AND IMAGE ANALYSIS 2018. [DOI: 10.1134/s1054661818040302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Kirilyuk IL, Kuznetsova AV, Sen’ko OV, Morozov AM. Method for detecting significant patterns in panel data analysis. PATTERN RECOGNITION AND IMAGE ANALYSIS 2017. [DOI: 10.1134/s1054661817010072] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Zhuravlev YI, Nazarenko GI, Vinogradov AP, Dokukin AA, Katerinochkina NN, Kleimenova EB, Konstantinova MV, Ryazanov VV, Sen’ko OV, Cherkashov AM. Methods for discrete analysis of medical data on the basis of recognition theory and some of their applications. PATTERN RECOGNITION AND IMAGE ANALYSIS 2016. [DOI: 10.1134/s105466181603024x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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