Search

Background: Lactation has been widely associated with optimal neurocognitive development, but the underlying mechanism remains unknown. Human milk oligosaccharides (HMOs) are complex sugars that support brain development, but previous studies examining their associations with cognition have yielded inconsistent findings., Objectives: This study aimed to provide a broader understanding of how HMOs jointly influence cognition., Methods: We used data from an ongoing longitudinal cohort of Latino mother-infant dyads. Human milk samples from 1 mo (n = 157) and 6 mo (n = 107) postpartum were assessed for the 19 most abundant HMOs. Cognitive performance was assessed at 2 y using the Bayley Scale of Infant and Toddler Development. A partial least squares model identified HMO combinations predictive of cognitive scores., Results: At 1 mo, the combination of higher concentrations of lacto-N-neotetraose (LNnT), lacto-N-tetraose (LNT), lacto-N-fucopentaose (LNFP)-III, 6'-sialyllactose, and 2'-fucosyllactose (FL) with lower concentrations of sialyllacto-N-tetraose (LST) b, LNFP-II, fucodisialyllacto-N-hexaose, and 3-FL significantly predicted higher cognitive scores (β: 0.61; 95% confidence interval [CI]: 0.30, 0.92), explaining an additional 8% of the variance over a model with only nuisance covariates (11%). Additional analyses revealed that the combination of higher LNFP-III and lower LSTb alone explained 5% more of the variation in cognitive scores (β: 0.66; 95% CI: 0.24, 1.09). At 6 mo (n = 107), higher LNnT, LNT, and LNFP-III and lower 3FL and LSTb concentrations explained an extra 6% of the variance in cognitive scores (β: 0.43; 95% CI: 0.12, 0.75)., Conclusions: This study highlights specific HMO combinations in early life influencing cognitive performance at 2 y., (Copyright © 2024 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.)

Background and Aims: The brain age gap (BAG), calculated as the difference between a machine learning model-based predicted brain age and chronological age, has been increasingly investigated in psychiatric disorders. Tobacco and alcohol use are associated with increased BAG; however, no studies have compared global and regional BAG across substances other than alcohol and tobacco. This study aimed to compare global and regional estimates of brain age in individuals with substance use disorders and healthy controls., Design: This was a cross-sectional study., Setting: This is an Enhancing Neuro Imaging through Meta-Analysis Consortium (ENIGMA) Addiction Working Group study including data from 38 global sites., Participants: This study included 2606 participants, of whom 1725 were cases with a substance use disorder and 881 healthy controls., Measurements: This study used the Kaufmann brain age prediction algorithms to generate global and regional brain age estimates using T1 weighted magnetic resonance imaging (MRI) scans. We used linear mixed effects models to compare global and regional (FreeSurfer lobestrict output) BAG (i.e. predicted minus chronological age) between individuals with one of five primary substance use disorders as well as healthy controls., Findings: Alcohol use disorder (β = -5.49, t = -5.51, p < 0.001) was associated with higher global BAG, whereas amphetamine-type stimulant use disorder (β = 3.44, t = 2.42, p = 0.02) was associated with lower global BAG in the separate substance-specific models., Conclusions: People with alcohol use disorder appear to have a higher brain-age gap than people without alcohol use disorder, which is consistent with other evidence of the negative impact of alcohol on the brain., (© 2024 The Author(s). Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.)

Growing evidence indicates that human milk oligosaccharides (HMOs) are important bioactive compounds that enhance health and developmental outcomes in breastfed babies. Maternal dietary intake likely contributes to variation in HMO composition, but studies identifying diet-HMO relationships are few and inconsistent. This study aimed to investigate how the maternal intake of macronutrients and micronutrients-specifically proteins, fats, vitamins, and minerals-associated with HMOs at 1 month (n = 210), 6 months (n = 131), and 12 months postpartum (n = 84). Several associations between maternal dietary factors and HMO profiles were identified utilizing partial correlation analysis. For example, maternal free sugar (rho = -0.02, p < 0.01), added sugar (rho = -0.22, p < 0.01), and sugary sweetened beverage (rho = -0.22, p < 0.01) intake were negatively correlated with the most abundant HMO, 2'-fucosyllactose (2'-FL), at 1 month, suggesting that higher sugar consumption was associated with reduced levels of 2'-FL. Further, vitamins D, C, K, and the minerals zinc and potassium were positively correlated with 2'-FL at 1 month (p All < 0.05). For the longitudinal analysis, a mixed-effects linear regression model revealed significant associations between maternal vitamin intake and HMO profiles over time. For example, for each unit increase in niacin intake, there was a 31.355 nmol/mL increase in 2'-FL concentration ( p = 0.03). Overall, the results provide additional evidence supporting a role for maternal nutrition in shaping HMO profiles, which may inform future intervention strategies with the potential of improving infant growth and development through optimal HMO levels in mothers' milk.

Objective: This study explores the impact of maternal pre-pregnancy BMI on infant neurodevelopment at 24 months in low-income Latino families. It also investigates whether infant diet mediates this relationship., Methods: Latino mother-infant pairs (n = 163) were enrolled at 1 month post partum and were followed for 2 years, with assessments at 6-month intervals. Maternal pre-pregnancy anthropometrics were self-reported at baseline, and child neurodevelopment was assessed at 24 months using the Bayley Scales of Infant Development. Diet quality of infants was measured using the Healthy Eating Index (HEI)-2015 and HEI-Toddlers-2020 scores at multiple time points. Mediation and regression models that adjust for maternal factors were used to examine the associations., Results: Pre-pregnancy BMI showed significant negative associations with child cognitive scores (β = -0.1, 95% CI: -0.2 to -0.06, p < 0.001) and language scores (β = -0.1, 95% CI: -0.2 to -0.03, p = 0.01) at 24 months. Infant HEI-2015 scores at 24 months partly mediated these associations, explaining 23% and 30% of the total effect on cognitive and language subscales, respectively. No specific dietary components in infants mediated the relationship, except for the total HEI-2015 score., Conclusions: Managing maternal obesity pre-pregnancy is crucial for improving infant neurodevelopmental outcomes, especially in low-income Latino families. Promoting healthy weight and enhancing infant diet quality can enhance neurodevelopment in these populations., (© 2024 The Obesity Society.)

Objectives: Chronic stress induces preclinical changes in the metabolic, cardiovascular, and immune systems. This phenomenon, known as allostatic load (AL), can impair executive functions (EF), which may be even more affected in individuals with excess weight due to their characteristic inflammatory state and cardiometabolic changes. Adverse childhood experiences (ACEs) contribute to AL and may influence executive functioning presumably via alterations within the hypothalamic-pituitary axis, including epigenetic modifications. We assess the relationship between AL and EF in youth with and without excess weight, and the effect ACEs on executive functioning., Methods: One hundred eighty-two adolescents and young adults (85 with normal weight and 97 with overweight/obesity; 10-21 years) were recruited. The estimated AL index included the following: systolic and diastolic blood pressure, glycated hemoglobin, high- and low-density lipoprotein cholesterol, triglycerides, high-sensitivity C-reactive protein, fibrinogen, and cortisol. ACEs were measured using the Juvenile Victimization Questionnaire. The neuropsychological evaluation included the assessment of inhibition, working memory, and cognitive flexibility processes., Results: AL was not significantly associated with executive functioning, and this relationship did not depend on body-weight status. ACEs, available for 57 of 182 participants, were significantly associated with poorer executive functioning., Conclusions: Our study shows that AL is not associated with executive functioning in adolescents and young adults. Since the current sample was young, we hypothesize that a longer exposure to AL might be required for its negative effects to surface. Nevertheless, exposure to early adversity seems to be associated with poorer executive functioning in youth., (© 2024 The Authors. American Journal of Human Biology published by Wiley Periodicals LLC.)

Background and Aims: Recently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies., Design and Setting: Cross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14-22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17-22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22-37 years)., Cases: Cases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected., Measurements: Graph theory metrics of segregation and integration were used to summarize SCN., Findings: Mirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = -0.029, P = 0.002], lower modularity (AUC = -0.14, P = 0.004), lower average shortest path length (AUC = -0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = -0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar., Conclusion: Structural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology., (© 2023 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.)

Introduction: There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance., Methods: We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method., Results: The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = -0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments., Discussion: Our findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples., Competing Interests: RS has served on the scientific advisory board of Embera Neuro-therapeutics. DS has received research grants and/or consultancy honoraria from Lundbeck and Sun. MY has received funding from several law firms in relation to expert witness reports. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Cao, McCabe, Callas, Cupertino, Ottino-González, Murphy, Pancholi, Schwab, Catherine, Hutchison, Cousijn, Dagher, Foxe, Goudriaan, Hester, Li, Thompson, Morales, London, Lorenzetti, Luijten, Martin-Santos, Momenan, Paulus, Schmaal, Sinha, Solowij, Stein, Stein, Uhlmann, van Holst, Veltman, Wiers, Yücel, Zhang, Conrod, Mackey, Garavan and the ENIGMA Addiction Working Group.)

Background: Excessive body weight has been related to lower cognitive performance. One of the mechanisms through which excess body weight may affect cognition is inflammation., Hypothesis: Our hypothesis is that both body mass index (BMI) and circulating levels of inflammatory biomarkers will be negatively related to cognitive performance., Design: Cross-sectional study., Setting: Users of the public health centres of the Consorci Sanitari de Terrassa (Terrassa, Spain) between 2010 and 2017 aged 12-21 years., Participants: One hundred and five adolescents (46 normoweight, 18 overweight, 41 obese)., Measurements: Levels of high sensitivity C-reactive protein, interleukin 6, tumour necrosis factor α (TNFα) and fibrinogen were determined from blood samples. Cognitive performance was evaluated and six cognitive composites were obtained: working memory, cognitive flexibility, inhibitory control, decision-making, verbal memory, and fine motor speed. A single multivariate general lineal model was used to assess the influence of the four inflammatory biomarkers, as well as participants' BMI, sex, and age on the 6 cognitive indexes., Results: An inverse relationship between BMI and inhibitory control (F = 5.688, p = .019; β = -0.212, p = .031), verbal memory (F = 5.404, p = .022; β = -0.255, p = .009) and fine motor speed (F = 9.038, p = .003; β = -0.319, p = .001) was observed. Levels of TNFα and fibrinogen were inversely related to inhibitory control (F = 5.055, p = .027; β = -0.226, p = .021) and verbal memory (F = 4.732, p = .032; β = -0.274, p = .005), respectively., Limitations: The cross-sectional nature of the study, the use of cognitive tests designed for clinical purposes, and the use of BMI as a proxy for adiposity are limitations of our study that must be taken into account when interpreting results., Conclusions: Our data indicate that some components of executive functions, together with verbal memory, are sensitive to specific obesity-related inflammatory agents at early ages., Competing Interests: Declaration of Competing Interest None., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Obesity is characterized by cardiometabolic and neurocognitive changes. However, how these two factors relate to each other in this population is unknown. We tested the association that cardiometabolic measures may have with impulse behaviors and white matter microstructure in adolescents with and without an excess weight. One hundred and eight adolescents (43 normal-weight and 65 overweight/obesity; 11-19 years old) were medically and psychologically (Temperament Character Inventory Revised, Three-Factor Eating Questionnaire-R18, Conners' Continuous Performance Test-II, Stroop Color and Word Test, Wisconsin Card Sorting Test, Kirby Delay Discounting Task) evaluated. A subsample of participants (n = 56) underwent a brain magnetic resonance imaging acquisition. In adolescents, higher triglycerides and having a body mass index indicative of overweight/obesity predicted a more impulsive performance in Conners' Continuous Performance Test-II (higher commission errors). In addition, higher glucose and diastolic blood pressure values predicted increments in the Three-Factor Eating Questionnaire-R18 emotional eating scale. Neuroanatomically, cingulum fractional anisotropy showed a negative relationship with glycated hemoglobin. The evaluation of the neurocognitive differences associated with obesity, usually based on body mass index, should be complemented with cardiometabolic measures., (© 2023. The Author(s).)

Background and Aims: Graph theoretic analysis of structural covariance networks (SCN) provides an assessment of brain organization that has not yet been applied to alcohol dependence (AD). We estimated whether SCN differences are present in adults with AD and heavy-drinking adolescents at age 19 and age 14, prior to substantial exposure to alcohol., Design: Cross-sectional sample of adults and a cohort of adolescents. Correlation matrices for cortical thicknesses across 68 regions were summarized with graph theoretic metrics., Setting and Participants: A total of 745 adults with AD and 979 non-dependent controls from 24 sites curated by the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA)-Addiction consortium, and 297 hazardous drinking adolescents and 594 controls at ages 19 and 14 from the IMAGEN study, all from Europe., Measurements: Metrics of network segregation (modularity, clustering coefficient and local efficiency) and integration (average shortest path length and global efficiency)., Findings: The younger AD adults had lower network segregation and higher integration relative to non-dependent controls. Compared with controls, the hazardous drinkers at age 19 showed lower modularity [area-under-the-curve (AUC) difference = -0.0142, 95% confidence interval (CI) = -0.1333, 0.0092; P-value = 0.017], clustering coefficient (AUC difference = -0.0164, 95% CI = -0.1456, 0.0043; P-value = 0.008) and local efficiency (AUC difference = -0.0141, 95% CI = -0.0097, 0.0034; P-value = 0.010), as well as lower average shortest path length (AUC difference = -0.0405, 95% CI = -0.0392, 0.0096; P-value = 0.021) and higher global efficiency (AUC difference = 0.0044, 95% CI = -0.0011, 0.0043; P-value = 0.023). The same pattern was present at age 14 with lower clustering coefficient (AUC difference = -0.0131, 95% CI = -0.1304, 0.0033; P-value = 0.024), lower average shortest path length (AUC difference = -0.0362, 95% CI = -0.0334, 0.0118; P-value = 0.019) and higher global efficiency (AUC difference = 0.0035, 95% CI = -0.0011, 0.0038; P-value = 0.048)., Conclusions: Cross-sectional analyses indicate that a specific structural covariance network profile is an early marker of alcohol dependence in adults. Similar effects in a cohort of heavy-drinking adolescents, observed at age 19 and prior to substantial alcohol exposure at age 14, suggest that this pattern may be a pre-existing risk factor for problematic drinking., (© 2022 Society for the Study of Addiction.)

Background: Nicotine and illicit stimulants are very addictive substances. Although associations between grey matter and dependence on stimulants have been frequently reported, white matter correlates have received less attention., Methods: Eleven international sites ascribed to the ENIGMA-Addiction consortium contributed data from individuals with dependence on cocaine (n = 147), methamphetamine (n = 132) and nicotine (n = 189), as well as non-dependent controls (n = 333). We compared the fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) of 20 bilateral tracts. Also, we compared the performance of various machine learning algorithms in deriving brain-based classifications on stimulant dependence., Results: The cocaine and methamphetamine groups had lower regional FA and higher RD in several association, commissural, and projection white matter tracts. The methamphetamine dependent group additionally showed lower regional AD. The nicotine group had lower FA and higher RD limited to the anterior limb of the internal capsule. The best performing machine learning algorithm was the support vector machine (SVM). The SVM successfully classified individuals with dependence on cocaine (AUC = 0.70, p < 0.001) and methamphetamine (AUC = 0.71, p < 0.001) relative to non-dependent controls. Classifications related to nicotine dependence proved modest (AUC = 0.62, p = 0.014)., Conclusions: Stimulant dependence was related to FA disturbances within tracts consistent with a role in addiction. The multivariate pattern of white matter differences proved sufficient to identify individuals with stimulant dependence, particularly for cocaine and methamphetamine., (Copyright © 2021. Published by Elsevier B.V.)

Some eating patterns, such as restrained eating and uncontrolled eating, are risk factors for eating disorders. However, it is not yet clear whether they are associated with neurocognitive differences. In the current study, we analyzed whether eating patterns can be used to classify participants into meaningful clusters, and we examined whether there are neurocognitive differences between the clusters. Adolescents ( n = 108; 12 to 17 years old) and adults ( n = 175, 18 to 40 years old) completed the Three Factor Eating Questionnaire, which was used to classify participants according to their eating profile using k means clustering. Participants also completed personality questionnaires and a neuropsychological examination. A subsample of participants underwent a brain MRI acquisition. In both samples, we obtained a cluster characterized by high uncontrolled eating patterns, a cluster with high scores in restrictive eating, and a cluster with low scores in problematic eating behaviors. The clusters were equivalent with regards to personality and performance in executive functions. In adolescents, the cluster with high restrictive eating showed lower cortical thickness in the inferior frontal gyrus compared to the other two clusters. We hypothesize that this difference in cortical thickness represents an adaptive neural mechanism that facilitates inhibition processes.