The effect of mindfulness-based interventions on biomarkers in cancer patients and survivors: A systematic review.
Alessio MatizBruna ScaggianteCiro ConversanoAngelo GemignaniGaetano PascolettiFranco FabbroCristiano CrescentiniPublished in: Stress and health : journal of the International Society for the Investigation of Stress (2024)
Various reviews and meta-analyses have shown the positive effects of mindfulness-based interventions (MBIs) on the mental health of cancer patients and survivors. Some studies have also investigated the impact of MBIs on physiological markers of health in oncology, but a systematic review has not been conducted in this field. The current paper aims to fill this gap in the literature. Following preferred reporting items for systematic reviews and meta-analyses 2020 guidelines, data were obtained from the databases of Pubmed, Scopus, Web of Science in May 2022. Twenty-five studies were included. Globally, 35 biomarkers were employed in these studies and were categorized 8 groups (cortisol; blood pressure (BP), heart rate, and respiratory rate; C-reactive protein; telomere length and telomerase activity (TA); genetic signature; cytokines and hormones; leucocyte activation; leucocyte count and cell subpopulation analysis). In seven of these categories of biomarkers, positive effects of MBIs were observed. The most promising results were obtained for cortisol, BP, TA and pro-inflammatory gene expression. However, the generally low number of studies per single biomarker limits the possibility to draw reliable conclusions. The present review presents a comprehensive state-of-the-art for MBIs in oncology on biomarkers, confirming MBIs' potential for improving physiological health in cancer patients and survivors besides those already shown in literature on psychological well-being.
Keyphrases
- meta analyses
- systematic review
- heart rate
- mental health
- blood pressure
- gene expression
- public health
- case control
- healthcare
- young adults
- randomized controlled trial
- palliative care
- heart rate variability
- physical activity
- emergency department
- big data
- type diabetes
- genome wide
- deep learning
- risk assessment
- machine learning
- clinical practice
- peripheral blood
- adverse drug
- mesenchymal stem cells
- electronic health record
- sleep quality
- weight loss
- blood glucose