The potential relevance of docosahexaenoic acid and eicosapentaenoic acid to the etiopathogenesis of childhood neuropsychiatric disorders

Tesei, Alessandra; Crippa, Alessandro; Ceccarelli, Silvia; Mauri, Maddalena; Molteni, Massimo; Agostoni, Carlo; Nobile, Maria

doi:10.1007/s00787-016-0932-4

Cited by 27 publications

(18 citation statements)

References 91 publications

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“…The FAs that are used to best identify ADHD seem to be linoleic acid, AA, EPA, EPA + DHA, and the total amount of PUFA. All of these components were extensively linked to ADHD in previous studies ( 40 , 41 ). Our machine-learning method reached greater accuracy (72%) when we used, as a single-domain dataset, the cerebral hemodynamic responses (both oxygenated hemoglobin and deoxygenated hemoglobin) measured by NIRS during a spatial working memory task.…”

Section: Discussionmentioning

confidence: 84%

The Utility of a Computerized Algorithm Based on a Multi-Domain Profile of Measures for the Diagnosis of Attention Deficit/Hyperactivity Disorder

et al. 2017

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The current gold standard for diagnosis of attention deficit/hyperactivity disorder (ADHD) includes subjective measures, such as clinical interview, observation, and rating scales. The significant heterogeneity of ADHD symptoms represents a challenge for this assessment and could prevent an accurate diagnosis. The aim of this work was to investigate the ability of a multi-domain profile of measures, including blood fatty acid (FA) profiles, neuropsychological measures, and functional measures from near-infrared spectroscopy (fNIRS), to correctly recognize school-aged children with ADHD. To answer this question, we elaborated a supervised machine-learning method to accurately discriminate 22 children with ADHD from 22 children with typical development by means of the proposed profile of measures. To assess the performance of our classifier, we adopted a nested 10-fold cross validation, where the original dataset was split into 10 subsets of equal size, which were used repeatedly for training and testing. Each subset was used once for performance validation. Our method reached a maximum diagnostic accuracy of 81% through the combining of the predictive models trained on neuropsychological, FA profiles, and deoxygenated-hemoglobin features. With respect to the analysis of a single-domain dataset per time, the most discriminant neuropsychological features were measures of vigilance, focused and sustained attention, and cognitive flexibility; the most discriminating blood FAs were linoleic acid and the total amount of polyunsaturated fatty acids. Finally, with respect to the fNIRS data, we found a significant advantage of the deoxygenated-hemoglobin over the oxygenated-hemoglobin data in terms of predictive accuracy. These preliminary findings show the feasibility and applicability of our machine-learning method in correctly identifying children with ADHD based on multi-domain data. The present machine-learning classification approach might be helpful for supporting the clinical practice of diagnosing ADHD, even fostering a computer-aided diagnosis perspective.

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Section: Discussionmentioning

confidence: 84%

The Utility of a Computerized Algorithm Based on a Multi-Domain Profile of Measures for the Diagnosis of Attention Deficit/Hyperactivity Disorder

et al. 2017

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“…Other micronutrient deficiencies have been reported in autism, including magnesium, zinc, selenium, and vitamins A, D, E, and B complex . Brain deficiency of essential PUFAs, especially DHA, also deserves consideration, as noted above , and one review observed that “the majority of studies on attention‐deficit/hyperactivity disorder (ADHD) and autism found a significant decrease in DHA levels” . Whether brain deficiency of carnitine or PUFA might relate to the etiology of ADHD is beyond the scope of this discussion, but we note that ADHD also affects far more males than females.…”

Section: Non‐mendelian Non‐dysmorphic (Nomend) Autism and The Brainmentioning

confidence: 83%

Brain carnitine deficiency causes nonsyndromic autism with an extreme male bias: A hypothesis

Beaudet

2017

BioEssays

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Could 10–20% of autism be prevented? We hypothesize that nonsyndromic or “essential” autism involves extreme male bias in infants who are genetically normal, but they develop deficiency of carnitine and perhaps other nutrients in the brain causing autism that may be amenable to early reversal and prevention. That brain carnitine deficiency might cause autism is suggested by reports of severe carnitine deficiency in autism and by evidence that TMLHE deficiency – a defect in carnitine biosynthesis – is a risk factor for autism. A gene on the X chromosome (SLC6A14) likely escapes random X-inactivation (a mixed epigenetic and genetic regulation) and could limit carnitine transport across the blood-brain barrier in boys compared to girls. A mixed, common gene variant-environment hypothesis is proposed with diet, minor illnesses, microbiome, and drugs as possible risk modifiers. The hypothesis can be tested using animal models and by a trial of carnitine supplementation in siblings of probands. Perhaps the lack of any Recommended Dietary Allowance for carnitine in infants should be reviewed.

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“…Similarly, medication-naïve first-episode psychotic patients also showed erythrocyte DHA deficits compared with healthy controls [ 25 , 26 ], and a recent meta-analysis of 18 case-control studies reported significant DHA deficits in patients with SCZ [ 27 ], the presence of reduced concentrations of ω-3 PUFAs has been reported in children with ASD [ 28 ]. Further confirming the key role played by ω-3 PUFAs in neurodevelopment, data from observational studies consistently indicated lower ω-3 PUFAs blood levels (especially DHA) in children with ADHD compared with their typically developing peers [ 29 , 30 ]. Several studies accordingly reported a significant negative relationship between blood ω-3 PUFAs status and ADHD symptoms: low blood levels of DHA and EPA have been shown to be associated with ADHD symptoms such as inattention and hyperactivity [ 31 , 32 , 33 ].…”

Section: Introductionmentioning

confidence: 88%

The Role of Omega-3 Fatty Acids in Developmental Psychopathology: A Systematic Review on Early Psychosis, Autism, and ADHD

Agostoni

Nobile

Ciappolino

et al. 2017

IJMS

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In this systematic review, we will consider and debate studies that have explored the effects of ω-3 polyunsaturated fatty acids (PUFAs) in three major, and somehow related, developmental psychiatric disorders: Autism, Attention Deficit and Hyperactivity disorder and Psychosis. The impact of ω-3 PUFAs on clinical symptoms and, if possible, brain trajectory in children and adolescents suffering from these illnesses will be reviewed and discussed, considering the biological plausibility of the effects of omega-3 fatty acids, together with their potential perspectives in the field. Heterogeneity in study designs will be discussed in the light of differences in results and interpretation of studies carried out so far.

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The potential relevance of docosahexaenoic acid and eicosapentaenoic acid to the etiopathogenesis of childhood neuropsychiatric disorders

Cited by 27 publications

References 91 publications

The Utility of a Computerized Algorithm Based on a Multi-Domain Profile of Measures for the Diagnosis of Attention Deficit/Hyperactivity Disorder

The Utility of a Computerized Algorithm Based on a Multi-Domain Profile of Measures for the Diagnosis of Attention Deficit/Hyperactivity Disorder

Brain carnitine deficiency causes nonsyndromic autism with an extreme male bias: A hypothesis

The Role of Omega-3 Fatty Acids in Developmental Psychopathology: A Systematic Review on Early Psychosis, Autism, and ADHD

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