Catherine J. Field’s research while affiliated with University of Alberta and other places

Background Dysbiosis of the gut microbiota has been demonstrated in neurodevelopmental disorders but the underlying mechanisms that may explain these associations are poorly understood. Gut secretory immunoglobulin A (SIgA) binds pathogenic microbes, preventing mucosal penetration. Gut microbes also influence SIgA production and its binding characteristics through short-chain fatty acid (SCFA) metabolites, allowing them to regulate the immune response. Serum IgA deficiency has been noted in children with autism spectrum disorders (ASD). In this study, we aimed to determine whether SIgA level in infancy is associated with gut microbiota taxonomy and metabolites, and neurodevelopmental outcomes in preschool children. Methods For a subsample of 178 children from the Canadian CHILD Cohort Study, gut microbiota of fecal samples collected at 3–4 months and 12 months was profiled using 16S rRNA sequencing. Gut bacterial metabolites levels and SIgA level were measured by nuclear magnetic resonance (NMR) based metabolomics and SIgA enzyme-linked immunosorbent assay at 3–4 months, respectively. Bayley-III Scale of Infant Development was assessed at 12 and 24 months. We evaluated direct relationships in multiple linear regression models and putative causal relationships in statistical mediation models. Results Propionate and butyrate levels at 3–4 months were associated with decreased Bayley cognitive score at 24 months (p-values: 0.01 and 0.02, respectively) in adjusted multiple linear regression models, but when we investigated an indirect relationship mediated by decreased SIgA level at 3–4 months, it did not reach statistical significance (p-values: 0.18 and 0.20, respectively). Lactate level at 3–4 months was associated with increased Bayley cognitive score at 24 months in adjusted multiple linear regression models (p-value: 0.01), but the statistical model mediated by increased SIgA level at 3–4 months did not reach statistical significance neither (p-value: 0.20). Conclusions Our study contributes to growing evidence that neurodevelopment is influenced by the infant gut microbiota and that it might involve SIgA level, but larger studies are required.

Context While increased fiber intake may benefit appetite and metabolism in the general population, its effects in individuals with Prader–Willi Syndrome (PWS), a condition characterized by hyperphagia, obesity and metabolic dysregulation, remain to be explored. Objectives This study assessed the effects of a fiber intervention on hyperphagia, metabolic health, and gut microbiota in individuals with PWS, and explored associations between changes in health markers and shifts in microbiota. Methods Participants received either a high-dose fiber intervention (35g/day) or a control for 3 weeks. Following a washout period of 4 to 8 weeks, participants switched treatments for another 3 weeks. Fecal (bacterial 16S ribosomal RNA) and blood (immunometabolic markers, targeted metabolomics) samples were collected before and after each treatment. Results Fourteen participants (with a median age of 13.6 years, 8 [57.1%] were female) reported high tolerance to the fiber intervention. While it did not significantly alter hyperphagia or key metabolic markers, the fiber intervention led to shifts in gut microbiota diversity and increased the abundance of beneficial bacteria, such as Bifidobacterium longum and Faecalibacterium prausnitzii. Additionally, it altered fecal and serum metabolites, including a decrease in branched-chain fatty acids and an increase in serum C4-OH acylcarnitine. Conclusion While this study did not observe significant changes in primary or secondary endpoints, it suggests that a short-term high-fiber intervention may induce beneficial shifts in gut microbiota and microbial metabolites in individuals with PWS. Further research is warranted to investigate the long-term effects and potential therapeutic applications of fiber interventions in PWS.

Transverse ( T 2 ) relaxation and J‐coupling evolution of oleic, linoleic, and linolenic acid allylic protons are investigated at 9.4 T as an indirect means of assessing fat composition. We hypothesize that the relative proximity of one allylic proton group to the methyl end of the fatty acid impacts the measures. A Point RESolved Spectroscopy (PRESS) sequence was used for apparent (includes J‐coupling effects) T 2 estimation. Spectra were acquired from oleic, linoleic, and linolenic acid, from edible oils, and from intraperitoneal adipose tissue of two groups of mice ( n = 4 per group) randomized to a high or low ω‐3 diet. Apparent T 2 values of 62, 106, and 220 ms were obtained for oleic, linoleic, and linolenic acid allylic protons, respectively. For oils containing negligible linolenic acid, apparent T 2 values correlated with linoleic acid contribution to total unsaturation ( R ² = 1). For oils containing linolenic acid, a correlation with linolenic acid contribution to total unsaturation was found ( R ² = 0.91). In animal experiments, the mean allylic apparent T 2 in the group fed the high ω‐3 diet was 72.3 ms compared to 66.1 ms measured from the low ω‐3 diet group. Signal yield with an echo time (TE) of 130 ms results in an inverse correlation with relative linolenic acid content ( R ² = 0.99) for the oils.

Purpose To report the effects of a 12-week high-intensity interval training (HIIT) program on cardiometabolic biomarkers in patients with prostate cancer on active surveillance (AS) from the Exercise During Active Surveillance for Prostate Cancer (ERASE) Trial. Methods Fifty-two men with prostate cancer on AS were randomized to either an exercise (HIIT; n = 26) or usual care (UC; n = 26) group. The HIIT intervention consisted of progressive, supervised, aerobic HIIT at an intensity of 85 to 95% VO2peak for 28 to 40 min per session performed three times/week for 12 weeks. Blood samples were collected at baseline and postintervention to analyze cardiometabolic biomarkers. Analysis of covariance was used to examine between-group mean differences. Results Blood data were obtained from 49/52 (94%) participants at postintervention. Participants were aged 63.4 ± 7.1 years and 40% were obese. The HIIT group attended 96% of the planned exercise sessions. No significant between-group changes in weight were observed after the intervention. Compared to UC, HIIT significantly improved total cholesterol (−0.40 mmol/L; 95% confidence interval[CI], −0.70 to −0.10; p = 0.011), non-high-density lipoprotein-c (−0.35 mmol/L; 95% CI, −0.60 to −0.11; p = 0.006), insulin (−13.6 pmol/L; 95% CI, −25.3 to −1.8; p = 0.025), insulin-like growth factor (IGF)-1 (−15.0 ng/mL; 95% CI, −29.9 to −0.1; p = 0.048), and IGF binding protein (IGFBP)-3 (152.3 ng/mL; 95% CI, 12.6 to 292.1; p = 0.033). No significant differences were observed for fasting glucose, HbA1c, other lipid markers, IGFBP-1, adiponectin, and leptin. Conclusions The ERASE Trial showed that a 12-week aerobic HIIT program improved several cardiometabolic biomarkers in patients with prostate cancer on AS that may contribute to cardiovascular health benefits and potentially influence signaling pathways in the progression of prostate cancer. Further research is needed to confirm the effects of exercise on cardiometabolic markers in men with prostate cancer on AS and determine if these effects are associated with improved long-term clinical outcomes.

Background: Although adolescents with obesity have an increased risk of cardiometabolic disease, a subset maintains a healthy cardiometabolic profile. Unhealthy lifestyle behaviors may determine cardiometabolic risk. We aimed to characterize the lifestyle behaviors of adolescents with obesity, compare differences between metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO), and assess associations between lifestyle behaviors and cardiometabolic profiles. Methods: Participants aged 10-18 years with body mass index (BMI) ≥ 95th percentile were included. Dietary intake (DI) was estimated from 3-day food records, and diet quality (DQ) was assessed using the Healthy Eating Index-Canadian Adaptation. Physical activity (PA), body composition, anthropometrics, blood markers, and blood pressure (BP) were objectively measured. MUO was defined as having high triglycerides, BP, glucose, or low high-density lipoprotein. Regression analyses were performed between lifestyle behaviors and cardiometabolic markers. Results: Thirty-nine participants (BMI z-score 2.8 [2.5-3.5], age 12.5 [10.9-13.5] years, 56.4% female) were included. A high proportion of participants failed to meet lifestyle recommendations, particularly for DQ (94.7%, n = 36), fiber (94.7%, n = 36), and PA (90.9%, n = 30). No differences in lifestyle behaviors were found between MUO (59.0%, n = 22) and MHO (41.0%, n = 16). Protein intake was negatively associated with BMI and waist circumference z-scores, fat mass index, insulin resistance, low-density lipoprotein, and C-reactive protein, whereas higher DQ was associated with lower C-reactive protein. Higher light PA levels were associated with lower total cholesterol and triglycerides. Conclusion: Adolescents with either MUO or MHO displayed low adherence to DQ, DI, and PA recommendations; no differences in lifestyle behaviors were found. Protein intake, DQ, and PA were associated with a healthier cardiometabolic profile.