В обзоре приведены основные свойства, формы, метаболизм, фармакокинетика кокаина, возможные результаты его взаимодействия с другими веществами и лекарственными средствами, различные аспекты влияния кокаина на сердечно-сосудистую систему. Подробно рассмотрены механизмы действия кокаина, лежащие в основе развития основных широко распространенных патологических процессов в сердце и сосудах. Проанализированы данные большого числа опубликованных исследований, посвященных влиянию кокаина на развитие различных нарушений проводимости и ритма сердца, артериальной гипертензии, эндотелиальной дисфункции, расслоение крупных артерий и аорты. Также обсуждены особенности действия кокаина в разных группах пациентов и в зависимости от длительности его употребления. Приведены рекомендации по диагностике и выбору терапии при ряде патологических состояний, связанных с употреблением кокаина. Особое внимание уделено спорным вопросам лечения и влияния кокаина на сердце и сосуды, диагностическим возможностям различных методов исследования. The review presents the main properties, forms, metabolism, pharmacokinetics of cocaine, possible results of its interaction with other substances and medicines, various aspects of cocaine effect on the cardiovascular system. The mechanisms of cocaine action underlying the development of the main widespread pathological processes in the heart and blood vessels, are considered in detail. The data of a large number of published studies on the cocaine influence on the development of various disorders of conduction and heart rhythm, arterial hypertension, endothelial dysfunction, and dissection of large arteries and aorta are analyzed. The particulars of action with the duration of cocaine use as well as in different groups of patients are discussed. Recommendations are given for the diagnostics and choice of therapy for a number of pathological conditions associated with cocaine use. Particular attention is paid to controversial issues of treatment and effect of cocaine on the heart and blood vessels, the diagnostic capabilities of various diagnostic methods.
Background:Systemic lupus erythematosus (SLE) has a significant genetic predisposition. Many genetic variants of susceptibility to SLE have been published and analyzed, but the clinical and functional significance of the various genotypes has not yet been clearly defined [1].Objectives:To estimate the association between some of non-HLA gene polymorphisms such as STAT4 rs7574865, RUNX1 rs9979383, IL6 rs1800795, IL6R rs2228145, IL6R rs4845618 and susceptibility to SLE in Belarusian population as well as some disease manifestations.Methods:We examined 383 healthy blood donors and 54 SLE patients (18-72 years old, median age 35) classified according to the 1997 American College of Rheumatology (ACR) revised classification criteria [2]. Deoxyribonucleic acid was extracted from peripheral blood samples by phenol-chloroform method. Genotyping was performed by real-time PCR with fluorescent probes. Differences of distribution of all the single nucleotide polymorphism (SNP) genotypes and their associations with secondary antiphospholipid syndrom (APS) and lupus arthritis were analyzed using Pearson χ2 (χ2) and two-way Fisher exact test (F, p2-t). Diagnostic odds ratio (dOR), likelihood ratio of positive (LR +) and negative (LR –) tests and corresponding 95% confidence intervals (CI) were also calculated.Results:We revealed significant difference in STAT4 rs7574865 genotypes in SLE patients and healthy donors (χ2=8,27, р=0,016) with significant increase of ТТ genotype frequency in SLE patients vs healthy donors (χ2=6.83 p=0.009; p2-t =0.020; dOR=3.78 (CI95% 1.36-10.55); LR+ =3.44 (CI95% 1.35-8.71); LR– =0.91 (CI95% 0.83-0.98)). Lupus arthritis was more common in risk TT-genotype SLE carriers than in other SLE patients (χ2=5.902 p=0.015; p2-t =0.027).We revealed significant increase of СТ genotype (RUNX1 rs9979383) in healthy donors vs SLE patients (χ2=4.14; p=0.042; dOR=0.53 (CI95% 0.29-0.98); LR+ =0.69 (CI95% 0.45-0.99); LR– =1.3 (CI95% 1.01-1,56)). Lupus arthritis was more common in SLE СТ-genotype carriers than in other SLE patients (χ2=4.66 p=0.031; p2-t =0.058).Significant differences in IL6 rs1800795, IL6R rs2228145 and IL6R rs4845618 genotypes distribution between studied groups were not found (χ2, p=0.427, p=0.559 and p=0.407, correspondingly) but GG-genotype (IL6 rs1800795) carriership in SLE patients was associated with increased APS frequency (χ2=4.45, p=0.035; dOR=0.19 (CI95% 0.04-0.9); LR+ =0.28 (CI95% 0.07-0.93); LR– =1.41 (CI95% 1.03-1.64).Conclusion:Our data suggest the susceptibility to SLE in ТТ genotype of STAT4 rs7574865 polymorphism, protective role of СТ genotype of RUNX1 rs9979383 for SLE and association between GG-genotype of IL6 rs1800795 and APS in SLE patients in Belarusian population. Lupus arthritis was associated with ТТ genotype of STAT4 rs7574865 and СТ genotype of RUNX1 rs9979383.References:[1]Chen L, Morris DL, Vyse TJ. Genetic advances in systemic lupus erythematosus: an update. Curr Opin Rheumatol 2017;29:423–33.[2]Hochberg MC. Updating the American College of Rheumatology Revised Criteria for the classification of Systemic Lupus Erythematosus. Arthritis Rheum 1997;40:1725.Disclosure of Interests:None declared
BackgroundSecondary amyloidosis (SAM) including renal amyloidosis (RAM) is a common severe outcome in rheumatoid arthritis (RA) patients. Some clinical markers were published as risk factors for SA [1].ObjectivesTo reveal simple clinical and genentic markers of SAM/RAM in RA patients and to estimate their prognostic value.MethodsIn the observational trial of RA patients (n=590: 482 women, 108 men) histologically confirmed secondary amyloidosis (SAM) was revealed in 5.8%, renal amyloidosis (RAM) – in 3.6%. This SAM/RAM frequency was considered as pretest probability. We investigated associations between SAM/RAM and a number of genetic markers (erythrocytic antigens of AB0, MN and Rh0 blood groups, HLA antigens of A, B, C, DR, DQ locuses) and clinical markers (sex, triggers, age of RA onset, early RA variant, RF during the first year of RA, extraarticular manifestations, comorbidity and rapid RA progress. Statistical significance of the revealed association was estimated by Fisher exact test (F). Likelihood ratio of positive (LR+) and negative (LR–) tests and prognostic odds ratio (pOR) as well as SAM/RAM post-test probability (Ppost) were calculated.ResultsWe revealed no significant difference in SAM /RAM frequency between RA men and women (F, p2-t>0.1). SAM Ppost in RF+ patients during the first 3 years of RA increases 2.5 times vs RF- patients. SAM Ppost in patients with rapid RA progress during the first 3 years (RA severity progression index (DSPI)>+1SD[2]) increases 2.4 times vs patients with DSPI<–1SD. Some co-morbidities were associated with SAM: coronary/cerebral atherosclerosis, gastroduodenal ulcer, cholelithiasis and chronic hepatitis (see table), but they shouldn't be considered as independent SAM predictors because of their moderate pairwise correlation (RG=0.37-0.68). Severe anemia and chronic renal insufficiency increase SAM Ppost up to 35.1% and 55.2% correspondingly. SAM 2.8 times more frequent registered in patients with NSAID adverse drug reactions (ADRs) and 7.7 times more frequent – in patients with glucocorticoids (GK) ADRs vs patients without NSAID/GK ADRs. We revealed distinct negative correlation between methotrexate use and SAM (RG=-0.57, p=0.0002), as well as RAM (RG=-0.79, p<0.0001). SAM Ppost increases 3.3, 2.8, and 2.5 times in the presence of N blood group, HLA B27 and HLA B18 correspondingly.Table 1.Operational parameters of the revealed SAM predictorsMarkerpORLR+LR−F, p2.tRF+4.02.80.700.0279Rapid RA progress3.92.60.650.0135Gastroduodenal ulcer3.72.90.790.0023Cholelithiasis3.73.20.850.0080Severe anemia9.88.80.90<0.0001Chronic renal insufficiency54.720.00.37<0.0001NSAID ADRs3.01.40.490.0122GK ADRs8.82.30.26<0.0001Methotrexate0.280.662.40.0113N blood group10.34.50.440.0046HLA-B274.33.10.720.0069HLA-B184.02.70.670.0117N+/HLA-B18+phenotype increases SAM Ppost up to 46.2%, N+/HLA-B27+ and N+/HLA-B27+/HLA-B18+phenotypes – up to 42.8% and 69.9% correspondingly. N–/HLA-B27–/HLA-B18–phenotype decreases SAM Ppost up to 1.3%. HLA-B27 increases RAM Ppost up to 12.1% (pOR=6.0, LR+=3.7, LR–=0.62) ...
BackgroundAdverse drug reactions (ADRs) associated with glucocorticoid (GK) use are registered in 30-60% cases of GK therapy in rheumatoid arthritis (RA) patients when the prescribed daily doses exceed 10 mg during the period more than month. In the case of continuous GK therapy with the daily doses above 20 mg during the period more than 6 months GK dependence (GKD) develops. GK withdrawal syndrome is the main GKD criterion.ObjectivesTo reveal genetic markers of GK ADRs as GKD in RA patients.MethodsIn the observational trial of RA patients (n=522: 423 women, 99 men) detailed pharmacological anamnesis/catamnesis was collected. We investigated a number of genetic markers: erythrocytic antigens of AB0, Rh0, MN, P and Lewis blood groups, haptoglobin phenotypes, HLA antigens of A, B, C, DR, DQ locuses and Bw4-6 and DR51-53 super-types. Statistical significance was estimated by Fisher exact test (F). We selected the prognostic markers by means of Gamma correlation coefficient (RG). Likelihood ratio of positive (LR+) and negative (LR–) tests and prognostic odds ratio (pOR) of revealed markers as well as GK ADRs and GKD pre-test (Ppre) and post-test (Ppost) probabilities were calculated. Ppost was calculated by means of formula based on the Bayes theorem (Kullback information estimate).ResultsWe revealed GK ADRs in 50.9% RA patients (CI95 46.3-55.5%), in 54.6% women and in 34.9% men (F, p2-t=0,0015). GK ADRs as GKD was revealed in 45.5% RA patients (CI95 41.0-50.2%): in 48.8% women and in 31.3% men (p2-t=0.0048). Ppre=50% in women and Ppre=30% in men. Thus female sex is a predictor of GK ADRs and GKD. We revealed no association between AB0, Rh0, MN, P and Lewis blood groups and GKD (pi) for GKD presence and absence were calculated for 3 ranges: JHp1-1=0.252, JHp2-1=0.058, JHp2-2=0.204; total Ji=0.514 was above the significant level (Jxi≥0.5). Diagnostic coefficients (DC) used for the GKD prognosis in women were -4.7, -1.1 and 1.9 correspondingly, so Hp1-1 phenotype is GKD protective factor, and Hp2-2 phenotype is GKD risk factor.Some HLA antigens associated with GKD (table 1). Their informative values for the GKD prognosis were calculated (table 2).Table 1.Significant associations between HLA antigens and GKDHLA antigenRGZppcor1A190.512.90.00360.0284B120.362.90.00350.0546DR10.502.70.00620.0426DQ10.452.80.00470.01871p value after the Bonferroni correction.Table 2.Information value of HLA antigens for the GKD prognosisHLA antigenPhenotypeJxijJxiDCA19A19+0.280.672.1A19–0.39−2.9DR1DR1+0.340.642.6DR1–0.30−2.2DQ1DQ1+0.370.612.9DQ1–0.24−1.9Total Kullback information estimates for HLA A19, DR1 and DQ1 antigens were above the significant level (Jxi≥0.5).ConclusionsHLA A19, DR1 and DQ1 antigens could be used as GKD predictors in the case of indefinite prognosis.Disclosure of InterestNone declared
Background Adverse drug reactions (ADR) associated with metothrexate (MTX) use in rheumatoid arthritis (RA) patients frequently lead to different complications and MTX withdrawal [1] especially without folate supplementation. Objectives To reveal genetic markers associated with MTX adverse reactions in RA patients and to estimate their prognostic value. Methods In the observational trial of RA patients (n=500: 405 women, 95 men) treated with disease modifying antirheumatic drug MTX was used in 30.6% (153/500) of patients. MTX withdrawal due to the adverse events occurred in 41.2% (63/153; CI95 33.7-49.1%) of them. This level of ADR frequency associated with WTX use was considered as MTX adverse reactions pretest probability (Ppre). Association between MTX ADR and a number of the genetic markers (AB0, Rh0, MN, P1blood groups; haptoglobin; HLA-A, -B, -C, -DR, -DQ locuses, supertypes HLA-Bw4, HLA-Bw6, HLA-DR51-53; sensitivity to phenylthiocarbamide; dermatoglyphic characteristics) was investigated. All markers were dichotomic (marker+, marker-). Statistical significance of the revealed association was estimated by Fisher exact test. Likelihood ratio of positive (LR+) and negative (LR-) tests and prognostic odds ratio (pOR) as well as post-test probability (Ppost) of MTX adverse reactions were calculated. Results Gastrointestinal ADR and hepatotoxicity were registered in 13.1% (20/153) of patients, skin and mucous ADR in 12.4%, haematological abnormalities in 10.5% and infections in 9.8% of patients. MTX adverse reactions were significantly more common in the case of following phenotypes: P1– vs P1+ 75.0% (9/12) and 12.5% (1/8), p2-t=0.0198; HLA-A10+ vs HLA-A10- 58.6% (17/29) and 32.1% (25/78), p2-t=0.0152; HLA-C2– vs HLA-C2+ 46.7% (28/60) and 0.0% (0/7), p2-t=0.0359 with adjustment of zero frequency by J. Haldane; HLA-DR3+ vs HLA-DR3–68.8% (11/16) and 33.3% (15/45), p2-t=0.0194; HLA-DQ2+ vs HLA-DQ2– 61.9% (13/21) and 32.5% (13/40), p2-t=0.0333. On the basis of these findings operational parameters of the revealed markers were determined as predictors of A1 outcome (MTX ADR+) and A2 outcome (MTX ADR-): P1: pOR=21.0; P1–: LR+=3.0, Ppost=61.7%; P1+: LR–=0.14 Ppost= 8.9%; HLA-A10: pOR=3.0; A10+: LR+=2.2, Ppost=60.7%; A10–: LR-=0.73, Ppost= 33.8%; HLA-C2: pOR=13.1; C2–: LR+=1.2, Ppost=45.7%; C2+: LR–=0.09, Ppost= 5.9%; HLA-DR3: pOR=4.4; DR3+: LR+=3.0, Ppost=75.5%; DR3–: LR–=0.67, Ppost= 31.9%; HLA-DQ2: pOR=3.0; DQ2+: LR+=2.0, Ppost=58.4%; DQ2–: LR-=0.67, Ppost= 31.9%. There was no association between sensitivity to phenylthiocarbamide or dermatoglyphic characteristics and MTX adverse reactions. Conclusions Revealed phenotypes are useful as supplementary predictors of MTX adverse reactions in RA patients in the case of uncertain prognosis on the basis of clinical variables [2]. Availability of several independent predictors leads to considerable increase of the post-test probability of prediction of ADR, associated with MTX, oversteping the significant threshold levels of prediction: Ppost≥95% for the approval of t...
Background:Rheumatoid arthritis (RA), associated with Chlamydial Infection, has some clinical and immunological particulars that interfere with the early diagnosis and require significant changes in treatment strategy [1].Objectives:To estimate the distribution of some non-HLA genetic markers such as STAT4 rs7574865, IL6 rs1800795, IL6R rs2228145 and rs4845618 in Chlamydia positive and negative RA patients and healthy controls.Methods:We examined 380 healthy blood donors and 187 RA patients classified according to the ACR/EULAR 2010 criteria for RA [2]. Twenty-three of the RA patients were positive for Chlamidia trachomatis (n=17) or Chlamidia pneumonia (n=6) persistence. DNA from peripheral blood samples was extracted by phenol-chloroform method. SNPs were genotyped by the real-time PCR with fluorescent probes. Statistical significance of SNPs’ frequency was estimated by two-way Fisher exact test (F, p2-t) with Bonferroni correction for multiple comparisons (pcor). Moreover, diagnostic odds ratio (dOR), the likelihood ratio of positive (LR+) and negative (LR–) tests and corresponding confidence intervals (CI) were calculated.Results:We revealed statistically significant increase of genotype СС frequency (IL6 rs1800795) in Chlamydia-associated RA (60.9%) vs healthy donors (20.7%): p2-t=0.000065; pcor=0.00026; dOR=5.95 (CI95%2.53-13.94); LR+=2.94 (CI95%1.90-3.29); LR–=0.49 (CI95%0.28-0.75) as well as in Chlamydia-associated RA (60.9%) vs Chlamydia-negative RA (23.9%): p2-t=0.00051; pcor=0.002; dOR=4.99 (CI95%2.04-12.16); LR+=2.56 (CI95%1.60-3.57); LR–=0.51 (CI95%0.29-0.78). Significant differences in STAT4 rs7574865, IL6R rs2228145 and IL6R rs4845618 distribution between studied groups were not found.Conclusion:Our data suggest the association between СС genotype of IL6 rs1800795 and Chlamydia-associated RA.References:[1]Soroka N.F. Rheumatoid Arthritis, associated with Chlamydial infection // Healthcare 2009; 1: 5-9.[2]Aletaha D. et al. 2010 Rheumatoid arthritis classification criteria// Arthritis Rheum 2010; 62 (9): 2569-81.Disclosure of Interests:Tatiana Zybalova: None declared, Viktor Yagur: None declared, Roza Goncharova: None declared, Nikolaj Soroka Grant/research support from: JSC BIOCAD, Natalia Dostanko: None declared, Valery Apanasovich: None declared, Anastasiya Tushina: None declared
Background Severity status of rheumatoid arthritis (RA) patient is more extensive characteristic than disease severity as it includes concomitant diseases burden (total comorbidity) as well as decrease of organ functional reserves which steady decline with age. Objectives To develop Functional Reserves Decline Index (FRDI) including assessment of 5 key organ systems against age: cardiovascular system (cardiac output), respiratory system (maximum breathing capacity), nervous system (nerve conduction velosity), urinary tract (glomerular filtration rate) and tissue metabolism (cellular water) using of currently available nomograms [1] and to apply it to the severity status of RA patient. Methods For the calculation of FRDI we have used nomograms by J.F. Fries [1] which present the dependence of organ functional reserves decline with age for 5 organ systems in the age range 20-90. The nomograms were approximated by means of 5 linear functions: Y1=-0.64×X+77.98 (R2=1), where Y1-5– reserve capacity of the mentioned systems, X – age, years, R2 – coefficient of determination; Y2=-0.97×X+100.87 (R2=1); Y3=-X+105.71 (R2=1); Y4=-0.70×X+89.91 (R2=1); Y5=-0.68×X+100.66 (R2=1). Results The functional reserve capacities of each organ system for the age of 20, 30, 40 etc. to 90 years were calculated by the presented formulas and summarized for each range of age and the sum was divided by 5. Then a new summarized linear function of the mentioned parameters was plotted against age (see picture below) and approximated by formula: Ysum = 120 – X, where X – age, years. Functional reserve capacity at 20 years was assumed to be 100%. We have suggested to determine FRDI (%) by formula: 100-(120-X) and to indicate the level of FRDI in points as follows: ≤25% – 0 points, 26% to 50% – 1 points, 51% to 75% – 2 points, ≥76% – 3 points. Example: for the age of 75 FRDI is 100-(120-75)=55%, i.e. 2 points. Image/graph Conclusions The suggested approach allows taking into account the patient’s age as FRDI (0-3 points) along with disease severity (0-3 points) and total comorbidity assessed by DUSOI (0-3 points) in the severity status of RA patients. The severity status of RA patient can be determined as the sum of these three indices and graded as follows: minimal (1-3 points), moderate (4-6 points), severe (7-8 points) and critical (9 points). The ranges of severity status are refining in the group of 590 RA patients (2625 follow-ups at present). References Fries, J.F.//The Milbank Memorial Fund Quarterly.1983;1:397-419. Disclosure of Interest None Declared
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