Search

Background Although the onset of inflammatory cascades may profoundly influence the nature of antibody responses, the interplay between inflammatory and humoral (antibody) immune markers remains unclear. Thus, we explored the reciprocity between the humoral immune system and inflammation and assessed how external socio-demographic factors may influence these interactions. From the AMORIS cohort, 5513 individuals were identified with baseline measurements of serum humoral immune [immunoglobulin G, A & M (IgG, IgA, IgM)] and inflammation (C-reactive protein (CRP), albumin, haptoglobin, white blood cells (WBC), iron and total iron-binding capacity) markers measured on the same day. Correlation analysis, principal component analysis and hierarchical clustering were used to evaluate biomarkers correlation, variation and associations. Multivariate analysis of variance was used to assess associations between biomarkers and educational level, socio-economic status, sex and age. Results Frequently used serum markers for inflammation, CRP, haptoglobin and white blood cells, correlated together. Hierarchical clustering and principal component analysis confirmed the interaction between these main biological responses, showing an acute response component (CRP, Haptoglobin, WBC, IgM) and adaptive response component (Albumin, Iron, TIBC, IgA, IgG). A socioeconomic gradient associated with worse health outcomes was observed, specifically low educational level, older age and male sex were associated with serum levels that indicated infection and inflammation. Conclusions These findings indicate that serum markers of the humoral immune system and inflammation closely interact in response to infection or inflammation. Clustering analysis presented two main immune response components: an acute and an adaptive response, comprising markers of both biological pathways. Future studies should shift from single internal marker assessment to multiple humoral and inflammation serum markers

Background Although the onset of inflammatory cascades may profoundly influence the nature of antibody responses, the interplay between inflammatory and humoral (antibody) immune markers remains unclear. Thus, we explored the reciprocity between the humoral immune system and inflammation and assessed how external socio-demographic factors may influence these interactions. From the AMORIS cohort, 5513 individuals were identified with baseline measurements of serum humoral immune [immunoglobulin G, A & M (IgG, IgA, IgM)] and inflammation (C-reactive protein (CRP), albumin, haptoglobin, white blood cells (WBC), iron and total iron-binding capacity) markers measured on the same day. Correlation analysis, principal component analysis and hierarchical clustering were used to evaluate biomarkers correlation, variation and associations. Multivariate analysis of variance was used to assess associations between biomarkers and educational level, socio-economic status, sex and age. Results Frequently used serum markers for inflammation, CRP, haptoglobin and white blood cells, correlated together. Hierarchical clustering and principal component analysis confirmed the interaction between these main biological responses, showing an acute response component (CRP, Haptoglobin, WBC, IgM) and adaptive response component (Albumin, Iron, TIBC, IgA, IgG). A socioeconomic gradient associated with worse health outcomes was observed, specifically low educational level, older age and male sex were associated with serum levels that indicated infection and inflammation. Conclusions These findings indicate that serum markers of the humoral immune system and inflammation closely interact in response to infection or inflammation. Clustering analysis presented two main immune response components: an acute and an adaptive response, comprising markers of both biological pathways. Future studies should shift from single internal marker assessment to multiple humoral and inflammation serum markers

Background Although the onset of inflammatory cascades may profoundly influence the nature of antibody responses, the interplay between inflammatory and humoral (antibody) immune markers remains unclear. Thus, we explored the reciprocity between the humoral immune system and inflammation and assessed how external socio-demographic factors may influence these interactions. From the AMORIS cohort, 5513 individuals were identified with baseline measurements of serum humoral immune [immunoglobulin G, A & M (IgG, IgA, IgM)] and inflammation (C-reactive protein (CRP), albumin, haptoglobin, white blood cells (WBC), iron and total iron-binding capacity) markers measured on the same day. Correlation analysis, principal component analysis and hierarchical clustering were used to evaluate biomarkers correlation, variation and associations. Multivariate analysis of variance was used to assess associations between biomarkers and educational level, socio-economic status, sex and age. Results Frequently used serum markers for inflammation, CRP, haptoglobin and white blood cells, correlated together. Hierarchical clustering and principal component analysis confirmed the interaction between these main biological responses, showing an acute response component (CRP, Haptoglobin, WBC, IgM) and adaptive response component (Albumin, Iron, TIBC, IgA, IgG). A socioeconomic gradient associated with worse health outcomes was observed, specifically low educational level, older age and male sex were associated with serum levels that indicated infection and inflammation. Conclusions These findings indicate that serum markers of the humoral immune system and inflammation closely interact in response to infection or inflammation. Clustering analysis presented two main immune response components: an acute and an adaptive response, comprising markers of both biological pathways. Future studies should shift from single internal marker assessment to multiple humoral and inflammation serum markers

Background Although the onset of inflammatory cascades may profoundly influence the nature of antibody responses, the interplay between inflammatory and humoral (antibody) immune markers remains unclear. Thus, we explored the reciprocity between the humoral immune system and inflammation and assessed how external socio-demographic factors may influence these interactions. From the AMORIS cohort, 5513 individuals were identified with baseline measurements of serum humoral immune [immunoglobulin G, A & M (IgG, IgA, IgM)] and inflammation (C-reactive protein (CRP), albumin, haptoglobin, white blood cells (WBC), iron and total iron-binding capacity) markers measured on the same day. Correlation analysis, principal component analysis and hierarchical clustering were used to evaluate biomarkers correlation, variation and associations. Multivariate analysis of variance was used to assess associations between biomarkers and educational level, socio-economic status, sex and age. Results Frequently used serum markers for inflammation, CRP, haptoglobin and white blood cells, correlated together. Hierarchical clustering and principal component analysis confirmed the interaction between these main biological responses, showing an acute response component (CRP, Haptoglobin, WBC, IgM) and adaptive response component (Albumin, Iron, TIBC, IgA, IgG). A socioeconomic gradient associated with worse health outcomes was observed, specifically low educational level, older age and male sex were associated with serum levels that indicated infection and inflammation. Conclusions These findings indicate that serum markers of the humoral immune system and inflammation closely interact in response to infection or inflammation. Clustering analysis presented two main immune response components: an acute and an adaptive response, comprising markers of both biological pathways. Future studies should shift from single internal marker assessment to multiple humoral and inflammation serum markers

Background Although the onset of inflammatory cascades may profoundly influence the nature of antibody responses, the interplay between inflammatory and humoral (antibody) immune markers remains unclear. Thus, we explored the reciprocity between the humoral immune system and inflammation and assessed how external socio-demographic factors may influence these interactions. From the AMORIS cohort, 5513 individuals were identified with baseline measurements of serum humoral immune [immunoglobulin G, A & M (IgG, IgA, IgM)] and inflammation (C-reactive protein (CRP), albumin, haptoglobin, white blood cells (WBC), iron and total iron-binding capacity) markers measured on the same day. Correlation analysis, principal component analysis and hierarchical clustering were used to evaluate biomarkers correlation, variation and associations. Multivariate analysis of variance was used to assess associations between biomarkers and educational level, socio-economic status, sex and age. Results Frequently used serum markers for inflammation, CRP, haptoglobin and white blood cells, correlated together. Hierarchical clustering and principal component analysis confirmed the interaction between these main biological responses, showing an acute response component (CRP, Haptoglobin, WBC, IgM) and adaptive response component (Albumin, Iron, TIBC, IgA, IgG). A socioeconomic gradient associated with worse health outcomes was observed, specifically low educational level, older age and male sex were associated with serum levels that indicated infection and inflammation. Conclusions These findings indicate that serum markers of the humoral immune system and inflammation closely interact in response to infection or inflammation. Clustering analysis presented two main immune response components: an acute and an adaptive response, comprising markers of both biological pathways. Future studies should shift from single internal marker assessment to multiple humoral and inflammation serum markers

Lifestyle-related risk factors such as hyperglycemia and dyslipidemia have been associated with several cancers. However, studies exploring their link with prostate cancer (PCa) clinicopathological characteristics are sparse and inconclusive. Here, we investigated the associations between serum metabolic markers and PCa clinicopathological characteristics. The study comprised 14,294 men from the Swedish Apolipoprotein MOrtality RISk (AMORIS) cohort who were diagnosed with PCa between 1996 and 2011. Univariate and multivariable logistic regression were used to investigate the relation between glucose, triglycerides and total cholesterol and PCa risk categories, PSA, Gleason score, and T-stage. Mean age at time of PCa diagnosis was 69 years. Men with glucose levels >6.9 mmol/L tend to have PSA<4 mu g/L, while those with glucose levels of 5.6-6.9 mmol/L had a greater odds of PSA>20 mu g/L compared to PSA 4.0-9.9 mu g/L. Hypertriglyceridemia was also positively associated with PSA>20 mu g/L. Hyperglycemic men had a greater odds of intermediate-and high-grade PCa and advanced stage or metastatic PCa. Similarly, hypertriglyceridemia was positively associated with high-grade PCa. There was also a trend toward an increased odds of intermediate risk localized PCa and advanced stage PCa among men with hypertriglyceridemia. Total cholesterol did not have any statistically significant association with any of the outcomes studied. Our findings suggest that high serum levels of glucose and triglycerides may influence PCa aggressiveness and severity. Further investigation on the role of markers of glucose and lipid metabolism in influencing PCa aggressiveness and severity is needed as this may help define important targets for intervention.

Lifestyle-related risk factors such as hyperglycemia and dyslipidemia have been associated with several cancers. However, studies exploring their link with prostate cancer (PCa) clinicopathological characteristics are sparse and inconclusive. Here, we investigated the associations between serum metabolic markers and PCa clinicopathological characteristics. The study comprised 14,294 men from the Swedish Apolipoprotein MOrtality RISk (AMORIS) cohort who were diagnosed with PCa between 1996 and 2011. Univariate and multivariable logistic regression were used to investigate the relation between glucose, triglycerides and total cholesterol and PCa risk categories, PSA, Gleason score, and T-stage. Mean age at time of PCa diagnosis was 69 years. Men with glucose levels >6.9 mmol/L tend to have PSA<4 mu g/L, while those with glucose levels of 5.6-6.9 mmol/L had a greater odds of PSA>20 mu g/L compared to PSA 4.0-9.9 mu g/L. Hypertriglyceridemia was also positively associated with PSA>20 mu g/L. Hyperglycemic men had a greater odds of intermediate-and high-grade PCa and advanced stage or metastatic PCa. Similarly, hypertriglyceridemia was positively associated with high-grade PCa. There was also a trend toward an increased odds of intermediate risk localized PCa and advanced stage PCa among men with hypertriglyceridemia. Total cholesterol did not have any statistically significant association with any of the outcomes studied. Our findings suggest that high serum levels of glucose and triglycerides may influence PCa aggressiveness and severity. Further investigation on the role of markers of glucose and lipid metabolism in influencing PCa aggressiveness and severity is needed as this may help define important targets for intervention.

Lifestyle-related risk factors such as hyperglycemia and dyslipidemia have been associated with several cancers. However, studies exploring their link with prostate cancer (PCa) clinicopathological characteristics are sparse and inconclusive. Here, we investigated the associations between serum metabolic markers and PCa clinicopathological characteristics. The study comprised 14,294 men from the Swedish Apolipoprotein MOrtality RISk (AMORIS) cohort who were diagnosed with PCa between 1996 and 2011. Univariate and multivariable logistic regression were used to investigate the relation between glucose, triglycerides and total cholesterol and PCa risk categories, PSA, Gleason score, and T-stage. Mean age at time of PCa diagnosis was 69 years. Men with glucose levels >6.9 mmol/L tend to have PSA<4 mu g/L, while those with glucose levels of 5.6-6.9 mmol/L had a greater odds of PSA>20 mu g/L compared to PSA 4.0-9.9 mu g/L. Hypertriglyceridemia was also positively associated with PSA>20 mu g/L. Hyperglycemic men had a greater odds of intermediate-and high-grade PCa and advanced stage or metastatic PCa. Similarly, hypertriglyceridemia was positively associated with high-grade PCa. There was also a trend toward an increased odds of intermediate risk localized PCa and advanced stage PCa among men with hypertriglyceridemia. Total cholesterol did not have any statistically significant association with any of the outcomes studied. Our findings suggest that high serum levels of glucose and triglycerides may influence PCa aggressiveness and severity. Further investigation on the role of markers of glucose and lipid metabolism in influencing PCa aggressiveness and severity is needed as this may help define important targets for intervention.

Lifestyle-related risk factors such as hyperglycemia and dyslipidemia have been associated with several cancers. However, studies exploring their link with prostate cancer (PCa) clinicopathological characteristics are sparse and inconclusive. Here, we investigated the associations between serum metabolic markers and PCa clinicopathological characteristics. The study comprised 14,294 men from the Swedish Apolipoprotein MOrtality RISk (AMORIS) cohort who were diagnosed with PCa between 1996 and 2011. Univariate and multivariable logistic regression were used to investigate the relation between glucose, triglycerides and total cholesterol and PCa risk categories, PSA, Gleason score, and T-stage. Mean age at time of PCa diagnosis was 69 years. Men with glucose levels >6.9 mmol/L tend to have PSA<4 mu g/L, while those with glucose levels of 5.6-6.9 mmol/L had a greater odds of PSA>20 mu g/L compared to PSA 4.0-9.9 mu g/L. Hypertriglyceridemia was also positively associated with PSA>20 mu g/L. Hyperglycemic men had a greater odds of intermediate-and high-grade PCa and advanced stage or metastatic PCa. Similarly, hypertriglyceridemia was positively associated with high-grade PCa. There was also a trend toward an increased odds of intermediate risk localized PCa and advanced stage PCa among men with hypertriglyceridemia. Total cholesterol did not have any statistically significant association with any of the outcomes studied. Our findings suggest that high serum levels of glucose and triglycerides may influence PCa aggressiveness and severity. Further investigation on the role of markers of glucose and lipid metabolism in influencing PCa aggressiveness and severity is needed as this may help define important targets for intervention.

Lifestyle-related risk factors such as hyperglycemia and dyslipidemia have been associated with several cancers. However, studies exploring their link with prostate cancer (PCa) clinicopathological characteristics are sparse and inconclusive. Here, we investigated the associations between serum metabolic markers and PCa clinicopathological characteristics. The study comprised 14,294 men from the Swedish Apolipoprotein MOrtality RISk (AMORIS) cohort who were diagnosed with PCa between 1996 and 2011. Univariate and multivariable logistic regression were used to investigate the relation between glucose, triglycerides and total cholesterol and PCa risk categories, PSA, Gleason score, and T-stage. Mean age at time of PCa diagnosis was 69 years. Men with glucose levels >6.9 mmol/L tend to have PSA<4 mu g/L, while those with glucose levels of 5.6-6.9 mmol/L had a greater odds of PSA>20 mu g/L compared to PSA 4.0-9.9 mu g/L. Hypertriglyceridemia was also positively associated with PSA>20 mu g/L. Hyperglycemic men had a greater odds of intermediate-and high-grade PCa and advanced stage or metastatic PCa. Similarly, hypertriglyceridemia was positively associated with high-grade PCa. There was also a trend toward an increased odds of intermediate risk localized PCa and advanced stage PCa among men with hypertriglyceridemia. Total cholesterol did not have any statistically significant association with any of the outcomes studied. Our findings suggest that high serum levels of glucose and triglycerides may influence PCa aggressiveness and severity. Further investigation on the role of markers of glucose and lipid metabolism in influencing PCa aggressiveness and severity is needed as this may help define important targets for intervention.