emcardioНеотложная кардиология и кардиоваскулярные рискиEmergency Cardiology and Cardiovascular Risks journal2616-633XБелорусский государственный медицинский университет10.51922/2616-633X.2026.10.1.2798emcardio-346Research ArticleОРИГИНАЛЬНЫЕ НАУЧНЫЕ ПУБЛИКАЦИИORIGINAL SCIENTIFIC PUBLICATIONSФенотипы постковидного синдрома по результатам кластерного анализа и их связь с метаболическими маркерамиPhenotypes of Post-COVID syndrome identified by cluster analysis and their association with metabolic markersРепинаЮ. В.RepinaYu. V.

Минск

Minsk

yulikost@mail.ruДоценкоЭ. А.DotsenkoE. A.

Минск

Minsk

ШолковаМ. В.SholkavaM. V.

Минск

Minsk

УО «Белорусский государственный медицинский университет»БеларусьBelarusian State Medical UniversityBelarus20262107202610127982809Copyright © Репина Ю.В., Доценко Э.А., Шолкова М.В., 20262026Репина Ю.В., Доценко Э.А., Шолкова М.В.Repina Y.V., Dotsenko E.A., Sholkava M.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://emcardio.bsmu.by/jour/article/view/346Введение

Введение. Постковидный синдром поражает около 36% лиц, перенёсших коронавирусную инфекцию COVID-19, и характеризуется клинической гетерогенностью. Кардиоваскулярные проявления постковидного синдрома (миокардит, нарушения ритма, тромбоэмболические осложнения) и дислипидемия сохраняются до 3 лет после острой инфекции, однако фенотипическая структура постковидного синдрома и её связь с метаболическими маркерами риска у молодых пациентов изучены недостаточно.

Цель – выделить основные фенотипы пациентов с постковидным синдромом в возрастной группе 18–44 лет методом кластерного анализа симптомов и оценить их связь с метаболическими маркерами (липидный профиль, индексы системного воспаления, показатели гемостаза), потенциально ассоциированными с кардиоваскулярным риском.

Материалы и методы

Материалы и методы. Одноцентровое поперечное исследование (n = 251, возраст 18–44 лет). Фенотипирование выполнялось методом кластерного анализа k-средних по 9 бинарным симптомным признакам. Оптимальное число кластеров определялось по коэффициенту силуэта. Оценивались липидный профиль (холестерин-липопротеиды низкой плотности, общий холестерин, триглицериды, холестерин-липопротеиды высокой плотности), индексы системного воспаления (нейтрофильно-лимфоцитарный индекс, тромбоцитарно-лимфоцитарный индекс, индекс системного иммунного воспаления), показатели гемостаза (D-димер, фибриноген), уровни ферритина и С-реактивного белка. Статистический анализ включал критерий Краскела–Уоллиса, тест Манна–Уитни с поправкой Бонферрони, критерий χ².

Результаты

Результаты. Выделены три фенотипа: малосимптомный (Фенотип 1, n = 100; 39,8%), нейрокогнитивно-психоэмоциональный (Фенотип 2, n = 83; 33,1%) и мультисистемный (Фенотип 3, n = 68; 27,1%). Фенотипы достоверно различались по синдромной нагрузке (медиана 4, 8 и 12 баллов; H = 160,8; p < 0,001), полу (χ² = 15,9; p = 0,0004), возрасту (H = 18,2; p = 0,0001) и частоте избыточной массы тела (p = 0,003). Метаболическим маркером с межфенотипическими различиями оказался ферритин (p = 0,030): наибольший уровень – Фенотип 1 (медиана 64,8 нг/мл), что может отражать субклиническое воспаление. По показателям липидного профиля, индексам системного воспаления и гемостаза значимых различий между фенотипами не выявлено (все p > 0,10). Вместе с тем в когорте молодых пациентов с постковидным синдромом зафиксирована высокая распространённость дислипидемии: холестерин-липопротеиды низкой плотности > 3,0 ммоль/л отмечен у 52,0%, холестерин-липопротеиды низкой плотности > 2,6 ммоль/л – у 70,5%.

Заключение

Заключение. Мультисистемный фенотип несет наибольшее кардиоваскулярное бремя (наибольший возраст, преобладание женщин, высокая частота артериальной гипертензии в анамнезе и избыточной массы тела) и представляет приоритетную группу для углубленного кардиологического наблюдения. Высокая распространенность дислипидемии в выборке, независимая от фенотипа, обосновывает рутинное исследование липидного профиля у молодых лиц с ПКС в первичном звене здравоохранения.

Introduction

Introduction. Post-COVID syndrome (PCS, long COVID, ICD-10 code U09.9) affects approximately 36% of individuals who have had COVID-19 and is characterized by clinical heterogeneity spanning cardiorespiratory, neurocognitive, musculoskeletal, and psychoemotional domains. Cardiovascular manifestations of PCS (myocarditis, arrhythmias, thromboembolic events), and dyslipidaemia may persist for up to three years after acute infection; however, the phenotypic structure of PCS and its association with metabolic risk markers in young patients remain insufficiently studied.

Aim

Aim. To identify the main PCS phenotypes in patients aged 18–44 years using symptom-based cluster analysis (k-means) and to assess their association with metabolic markers (lipid profile, systemic inflammation indices, haemostasis parameters).

Materials and methods

Materials and methods. A single-centre cross-sectional study (n = 251, aged 18–44 years). Phenotyping was performed by k-means clustering using 9 binary symptom variables. The optimal number of clusters was determined by silhouette coefficient. Assessments included lipid profile, systemic inflammation indices (NLR, PLR, SII), haemostasis markers (D-dimer, fibrinogen), ferritin, and CRP. Statistics: Kruskal–Wallis test, Mann–Whitney test with Bonferroni correction, chi-square test.

Results

Results. Three phenotypes were identified: low-symptom (P1, n = 100; 39.8%), neurocognitive–psychoemotional (P2, n = 83; 33.1%), and multisystem (P3, n = 68; 27.1%). Phenotypes differed significantly in symptom burden (medians 4, 8, and 12 points; H = 160.8; p < 0.001), sex (χ² = 15.9; p = 0.0004), age (H = 18.2; p = 0.0001), and prevalence of overweight (p = 0.003). Ferritin proved to be the only metabolic marker with significant inter-phenotype differences (p = 0.030), with the highest level in P1 (median 64.8 ng/mL), possibly reflecting subclinical inflammatory activation in the presence of minimal symptoms. No significant differences between phenotypes were found for lipid profile (LDL, TC, TG, HDL), systemic inflammation indices (NLR, PLR, SII), or haemostasis markers (fibrinogen, D-dimer) (all p > 0.10). Nevertheless, dyslipidaemia was highly prevalent across the entire PCS cohort: LDL > 3.0 mmol/L in 52.0% and LDL > 2.6 mmol/L in 70.5% of participants.

Conclusion

Conclusion. The multisystem phenotype carries the greatest cumulative cardiovascular burden (oldest age, female predominance, highest prevalence of hypertension history and overweight) and represents a priority group for indepth cardiological follow-up. The high prevalence of dyslipidaemia across all phenotypes supports routine lipid profiling in young PCS patients in primary care.

постковидный синдроммашинное обучениекластерный анализдислипидемиякардиоваскулярный рискмаркеры воспалениямолодые взрослыеферритинpost-acute sequelae of SARS-CoV-2 infectionmachine learningcluster analysisdyslipidemiascardiovascular risk factorsinflammation biomarkersyoung adultsferritinРабота выполнена в рамках ГНТП «Научно-техническое обеспечение качества и доступности медицинских услуг» на 2023-2025 годы: «Разработать метод определения вероятности постковидного синдрома (U09,9 – состояние после COVID-19)» Рег. № НИОКТР 20231573.The study was carried out within the framework of the State Scientific and Technical Programme “Scientific and technical support for the quality and accessibility of medical services” for 2023–2025: “Development of a method for determining the probability of post-COVID syndrome (U09.9 – condition following COVID-19)”, Reg. 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The authors declare that there are no conflicts of interest present.