Exercise reduces systemic immune inflammation index (SII) in childhood cancer patients

The Predictive Role of Hematological Parameters in Hypertension

Hypertension (HT) is a common chronic disease that often causes target-organ damage and severe complications, contributing to cardiovascular morbidity and mortality worldwide. Accumulating evidence suggests that inflammation plays a prominent role in the initiation and progression of HT. Multiple inflammatory biomarkers have been proposed to predict HT. Several new hematological parameters can reflect the inflammatory response and platelet activation. The major advantage of hematological parameters over conventional inflammatory markers is that they are relatively inexpensive and easily obtained from routine blood tests. Numerous studies have investigated several hematological parameters for their utility as predictive biomarkers for the diagnosis and prognosis of HT. Among them, the neutrophil to lymphocyte ratio (NLR), monocyte to high density lipoprotein cholesterol ratio (MHR), red cell distribution width (RDW), platelet to lymphocyte ratio (PLR), mean platelet volume (MPV), platelet distribution width (PDW), and systemic immune-inflammation index (SII) have recently received attention. We searched PubMed and Embase databases (up to September 18, 2022) to assess the relationships between hematological parameters and HT. This review discusses the diagnostic and prognostic value of these hematological parameters in HT, providing an important basis for early screening, risk stratification, and optimal management of hypertensive patients.

Systemic immune-inflammation index combined with pediatric appendicitis score in assessing the severity and prognosis for paediatric appendicitis

BACKGROUND

Pediatric appendicitis is a common cause of abdominal pain in children and is recognized as a significant surgical emergency. A prompt and accurate diagnosis is essential to prevent complications such as perforation and peritonitis.

AIM

To investigate the predictive value of the systemic immune-inflammation index (SII) combined with the pediatric appendicitis score (PAS) for the assessment of disease severity and surgical outcomes in children aged 5 years and older with appendicitis.

METHODS

Clinical data of 104 children diagnosed with acute appendicitis were analyzed. The participants were categorized into the acute appendicitis group and chronic appendicitis group based on disease presentation and further stratified into the good prognosis group and poor prognosis group based on prognosis. The SII and PAS were measured, and a joint model using the combined SII and PAS was constructed to predict disease severity and surgical outcomes.

RESULTS

Significant differences were observed in the SII and PAS parameters between the acute appendicitis group and chronic appendicitis group. Correlation analysis showed associations among the SII, PAS, and disease severity, with the combined SII and PAS model demonstrating significant predictive value for assessing disease severity [aera under the curve (AUC) = 0.914] and predicting surgical outcomes (AUC = 0.857) in children aged 5 years and older with appendicitis.

CONCLUSION

The study findings support the potential of integrating the SII with the PAS for assessing disease severity and predicting surgical outcomes in pediatric appendicitis, indicating the clinical utility of the combined SII and PAS model in guiding clinical decision-making and optimizing surgical management strategies for pediatric patients with appendicitis.

Effect of physical activity on patients of NSCLC

OBJECTIVE

The purpose of this study is to assess the impact of physical activity on both therapeutic efficacy and immune-related adverse events (irAEs) during immunotherapy for non-small cell lung cancer (NSCLC).

METHODS

Physical activity was divided into three groups: light physical activity (LPA), moderate physical activity (MPA), and vigorous physical activity (VPA) for laboratory indexes, efficacy, and irAEs. A multivariate logistic regression was employed to analyze the relationship between sedentary behavior with efficacy and irAEs.

RESULTS

The study included 121 patients. The three levels of physical activity were not significantly associated with efficacy or irAEs. However, noteworthy disparities were observed in base-hemoglobin levels (F = 3.4, P = 0.037) and base-lymphocyte levels (χ2 = 6.13, P = 0.047) among the three groups. After treatment, we identified statistically significant variations in albumin levels (P = 0.012) and lymphocyte counts (P = 0.035). Furthermore, a negative correlation emerged between pre-treatment sedentary behavior duration and immune-efficacy (β: -0.005, P = 0.027).

CONCLUSIONS

In summary, within the cohort of NSCLC patients undergoing single immunotherapy or a combination of immunotherapy and chemotherapy, physical activity is closely related to immune and inflammatory indicators in patients, and prolonged sitting will reduce the therapeutic effect.

Kinetics of Immune Cell Mobilization during Acute Aerobic Exercise in Healthy Adults

While pre-post differences in immune cell mobilization after acute aerobic exercise are well investigated, less is known about when and to what extent immune cells are mobilized during acute aerobic exercise. This experimental trial aimed to investigate the detailed kinetics of circulating immune cells in twelve healthy adults (n=6 females) who completed a 40-min aerobic exercise bout at 60% of the participants' V̇O2peak on a bicycle ergometer. Cellular inflammation markers and sex-dependent differences in circulating immune cells were analyzed. Blood samples were taken immediately before, after warm-up, during exercise after 5 min, 10 min, 15 min, 30 min, 40 min (cessation), and 60 min post exercise. Significant increases in leukocytes (p<0.001), lymphocytes (p<0.001), neutrophils (p=0.003) and platelets (p=0.047) can be observed after 5 min of exercise. The cellular inflammation markers show significant alterations only post exercise. Significant sex differences were observed for neutrophils (p=0.049) and neutrophil-to-lymphocyte ratio (p=0.007) one hour post exercise. These results indicate that i) leukocytes are already mobilized after 5 min of moderate-to-vigorous aerobic exercise, ii) the magnitude of exercise induced leukocyte mobilization is dependent on exercise duration, iii) integrative cellular inflammation markers are only altered after exercise cessation, and iv) the observed effects might be sex-dependent.

Associations of lifestyle characteristics with circulating immune markers in the general population based on NHANES 1999 to 2014

Lifestyles maybe associated with the immune and inflammatory state of human body. We aimed to comprehensively explore the relationship between lifestyles and circulating immune-inflammatory markers in the general population. Data from NHANES 1999-2014 was used. Lifestyle factors included leisure-time physical activity (LTPA), diet quality (Healthy Eating Index-2015, HEI-2015), alcohol consumption, cigarettes smoking, sleep hour and sedentary time. Immune makers included C-reactive protein (CRP), neutrophil-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), platelet-lymphocyte ratio (PLR) and monocyte-lymphocyte ratio (MLR). Generalized linear regression models were used to adjust confounders. Regressions of restricted cubic splines were utilized to evaluate the potentially non-linear relationships between exposures and outcomes. As results, HEI was negatively associated with CRP (P < 0.001), SII (P < 0.001), and NLR (P < 0.001). Cigarettes per day was positively associated with CRP (P < 0.001), SII (P < 0.001), and NLR (P = 0.008). Alcohol consumption was negatively associated with CRP (P < 0.001), but positively associated with PLR (P = 0.012) and MLR (P < 0.001). Physical activity was negatively associated with CRP (P < 0.001), SII (P = 0.005), and NLR (P = 0.002), but positively associated with PLR (P = 0.010). Participants with higher healthy lifestyle score had significantly lower CRP, SII and NLR (all P values < 0.05). Most of the sensitivity analyses found similar results. In conclusion, we found significant associations between lifestyles and immune markers in the general population, which may reflect a systemic inflammatory response to unhealthy lifestyles.

Inverse association of the systemic immune-inflammation index with serum anti-ageing protein Klotho levels in individuals with osteoarthritis: A cross-sectional study

BACKGROUND

The association between the systemic immune-inflammation index (SII) and the serum soluble-Klotho concentration (pg/ml) in osteoarthritis (OA) patients is unknown. This study aimed to investigate the relationship between the SII and serum soluble-Klotho levels in OA patients.

METHODS

All study data were obtained from the National Health and Nutrition Examination Survey (NHANES) database (n = 1852 OA patients; age range = 40-79 years). The SII and serum Klotho measurement data are from the NHANES mobile examination centre. The SII values were divided into quartiles (Q1-4: 0.02-3.36, 3.36-4.78, 4.79-6.70, and 6.70-41.75). A multivariate linear regression model was constructed to evaluate the association between the SII and serum Klotho levels in OA patients; interaction tests were conducted to test the stability of the statistical results.

RESULTS

Multivariate linear regression revealed a negative linear relationship between the SII and serum Klotho concentration in OA patients (β = -6.05; 95% CI: -9.72, -2.39). Compared to Q1, Q4 was associated with lower serum Klotho concentrations (β = -59.93; 95% CI: -96.57, -23.28). Compared with that of Q1, the β value of Q2-Q4 showed a downwards trend as the SII increased (Ptrend <0.001). The stratified analysis results indicated that the SII had a greater sensitivity in predicting serum Klotho concentrations in OA patients aged 60-79 years (Pinteraction = 0.028).

CONCLUSIONS

There was a significant negative linear correlation between the SII and serum Klotho concentration in OA patients. The SII can serve as a predictive indicator of serum Klotho concentrations in OA patients. Klotho may be a potential anti-inflammatory drug for OA treatment.

Synergistic Interactions of Insufficient Physical Activity and a High Systemic Immune-Inflammation Index on Psychological Problems in Indonesians With Type 2 Diabetes Mellitus

BACKGROUND

High-grade inflammation represents a critical contribution to the onset of depression and might be manageable by physical activity (PA). Nevertheless, no study has examined synergistic interactions of insufficient PA and high values of the systemic immune-inflammation index (SII) on psychological problems.

OBJECTIVE

We investigated independent and synergistic interactions of insufficient PA and high SII levels on stress, anxiety, and depression in T2DM patients.

METHODS

A cross-sectional research design with 294 T2DM patients was conducted. An XP-100 automated hematology analyzer was used to evaluate inflammatory biomarkers. Depression, Anxiety, and Stress Scale-21 items and a standardized questionnaire about PA were respectively used to measure psychological problems and metabolic equivalent of task (MET)-h/week.

RESULTS

A multiple linear regression demonstrated that patients with insufficient PA were significantly more likely to have higher stress (β = 1.84, 95% confidence interval (CI) = 1.03-2.65), anxiety (β = 1.88, 95% CI = 1.81-2.96), and depression (β = 2.53, 95% CI = 0.82-4.24) than those with active PA. A high SII level was a key predictor and was most strongly associated with stress (β = 2.61, 95% CI = 2.02-3.20), anxiety (β = 3.16, 95% CI = 2.37-3.94), and depression (β = 3.72, 95% CI = 2.49-4.96) compared to those who had low SII levels. Notably, additive interaction results showed that combining insufficient PA and a high SII level had a significantly escalated 1.71-fold risk of stress, 1.82-fold risk of anxiety, and 2.69-fold risk of depression.

CONCLUSIONS

Active PA and a low SII had a positive synergistic effect of decreasing psychological problems.

Explainable machine learning model for predicting skeletal muscle loss during surgery and adjuvant chemotherapy in ovarian cancer

BACKGROUND

Skeletal muscle loss during treatment is associated with poor survival outcomes in patients with ovarian cancer. Although changes in muscle mass can be assessed on computed tomography (CT) scans, this labour-intensive process can impair its utility in clinical practice. This study aimed to develop a machine learning (ML) model to predict muscle loss based on clinical data and to interpret the ML model by applying SHapley Additive exPlanations (SHAP) method.

METHODS

This study included the data of 617 patients with ovarian cancer who underwent primary debulking surgery and platinum-based chemotherapy at a tertiary centre between 2010 and 2019. The cohort data were split into training and test sets based on the treatment time. External validation was performed using 140 patients from a different tertiary centre. The skeletal muscle index (SMI) was measured from pre- and post-treatment CT scans, and a decrease in SMI ≥ 5% was defined as muscle loss. We evaluated five ML models to predict muscle loss, and their performance was determined using the area under the receiver operating characteristic curve (AUC) and F1 score. The features for analysis included demographic and disease-specific characteristics and relative changes in body mass index (BMI), albumin, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). The SHAP method was applied to determine the importance of the features and interpret the ML models.

RESULTS

The median (inter-quartile range) age of the cohort was 52 (46-59) years. After treatment, 204 patients (33.1%) experienced muscle loss in the training and test datasets, while 44 (31.4%) patients experienced muscle loss in the external validation dataset. Among the five evaluated ML models, the random forest model achieved the highest AUC (0.856, 95% confidence interval: 0.854-0.859) and F1 score (0.726, 95% confidence interval: 0.722-0.730). In the external validation, the random forest model outperformed all ML models with an AUC of 0.874 and an F1 score of 0.741. The results of the SHAP method showed that the albumin change, BMI change, malignant ascites, NLR change, and PLR change were the most important factors in muscle loss. At the patient level, SHAP force plots demonstrated insightful interpretation of our random forest model to predict muscle loss.

CONCLUSIONS

Explainable ML model was developed using clinical data to identify patients experiencing muscle loss after treatment and provide information of feature contribution. Using the SHAP method, clinicians may better understand the contributors to muscle loss and target interventions to counteract muscle loss.

Association of markers of inflammation on attention and neurobehavioral outcomes in survivors of childhood acute lymphoblastic leukemia

Background

Survivors of childhood acute lymphoblastic leukemia (ALL) are at-risk of developing cognitive impairment and neurobehavioral symptoms. Inflammation induced by a compromised health status during cancer survivorship is proposed as a pathophysiological mechanism underlying cognitive impairment in cancer survivors.

Objectives

To evaluate the associations of biomarkers of inflammation with attention and neurobehavioral outcomes in survivors of childhood ALL, and to identify clinical factors associated with biomarkers of inflammation in this cohort.

Methods

We recruited patients who were diagnosed with ALL at ≤ 18 years old and were currently ≥5 years post-cancer diagnosis. The study outcomes were attention (Conners Continuous Performance Test) and self-reported behavioral symptoms (Adult Self-Report [ASR] checklist). Using a commercial screening kit, survivors' plasma (5ml) was assayed for 17 cytokines/chemokine cell-signaling molecules that are associated with neurodegenerative diseases. The final panel of the targeted markers included interleukin (IL)-8, IL-13, interferon-gamma (IFN-γ), monocyte chemoattractant protein-1 (MCP-1), macrophage inflammatory protein-1β, and tumor necrosis factor-α. Biomarker levels were rank-ordered into tertiles based on the sample distribution. Multivariable general linear modeling was used to test for associations between biomarkers and study outcomes in the overall cohort and stratified by gender.

Results

This study included 102 survivors (55.9% males, mean[SD] age 26.2[5.9] years; 19.3[7.1] years post-diagnosis). Survivors within top tertiles of IFN-γ (Estimate =6.74, SE=2.26; P=0.0037) and IL-13 (Estimate =5.10, SE=2.27; P=0.027) demonstrated more inattentiveness. Adjusting for age, gender and treatment, more self-reported thought (Estimate=3.53, SE=1.78; P=0.050) and internalizing problems (Estimate =6.52, SE=2.91; P=0.027) correlated with higher IL-8. Higher levels of IL-13 (RR = 4.58, 95% CI: 1.01-11.10) and TNF-α (RR = 1.44, 95% CI: 1.03-4.07) were observed in survivors had developed chronic health conditions (n=26, 25.5%). The stratified analysis showed that association of IFN-γ with attention was stronger in male survivors than in female survivors.

Conclusion

Inflammation due to cancer-related late effects may potentially be mechanistic mediators of neurobehavioral problems in pediatric ALL survivors. Markers of inflammation can potentially be applied to assess or monitor the effectiveness of interventions, particularly behavioral interventions, in improving cognitive outcomes in survivors. Future work includes understanding the underlying gender-specific pathophysiology behind functional outcomes in the population.

Feasibility and benefits of a videoconferencing-based home exercise programme for paediatric cancer survivors during the coronavirus disease 2019 pandemic

Objective

We aimed to determine the feasibility and benefits of a videoconferencing‐based home exercise programme to improve health‐related quality of life (HRQOL), posttraumatic growth and physical strength levels of paediatric cancer survivors during the coronavirus disease 2019 (COVID‐19).

Methods

Eight paediatric cancer survivors participated in the intervention for 8 weeks. The programme comprised home exercise sessions administered using Zoom, a videoconferencing platform. The supervised exercise sessions were performed two times per week; the participants were taught to perform joint exercises at home for the remaining 5 days of the week. HRQOL, posttraumatic growth and physical strength levels were assessed at baseline and after the intervention.

Results

The rates of recruitment, retention and attendance were 52.9%, 88.9% and 98.4%, respectively. There were no cases of adverse events. The programme significantly improved flexibility (z = −2.21, p = 0.03), muscle strength (z = −2.67, p = 0.01) and power (z = −2.41, p = 0.02) among five domains of physical fitness measured using a physical activity promotion system and also improved total physical strength (z = −2.67, p = 0.01). Posttraumatic growth decreased slightly, whereas HRQOL improved slightly; however, the change was not statistically significant.

Conclusion

The study findings present preliminary evidence of the feasibility and benefits of this videoconferencing‐based home exercise programme among paediatric cancer survivors.