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Record W3171607606 · doi:10.1093/pch/pxab032

Micronutrient deficiencies in autism spectrum disorder: A macro problem?

2021· article· en· W3171607606 on OpenAlexafffund
Laura M. Kinlin, Catherine S. Birken

Bibliographic record

VenuePaediatrics & Child Health · 2021
Typearticle
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsInstitute for Work & HealthInstitute for Clinical Evaluative SciencesSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsAutism spectrum disorderMicronutrientAutismMacroMedicinePsychiatryComputer sciencePathology

Abstract

fetched live from OpenAlex

A 7-year-old boy with autism spectrum disorder (ASD) was referred for outpatient paediatric assessment because of food selectivity and a limited food repertoire. His diet consisted exclusively of yogurt drink, pudding, and chicken nuggets. His parents had tried to give him a children’s multivitamin and encourage a more varied diet, without success. A review of systems was positive for gingival bleeding and fatigue. The physical exam was notable for gingival swelling and perifollicular petechiae in the bilateral lower extremities. The patient’s blood work identified a microcytic anemia (hemoglobin 75 g/L, mean corpuscular volume 58 fL). Hypochromia, microcytosis, and poikilocytosis were present on blood smear. Ferritin was <1 µg/L. Ascorbic acid (vitamin C) level was ultimately reported as <5 µmol/L. The patient was diagnosed with both iron deficiency anemia and scurvy. A 10-year-old boy with ASD presented to the emergency department following a generalized tonic-clonic seizure (his first known seizure). Initial blood work identified an ionized calcium level of 0.79 mmol/L. He was admitted to hospital for ongoing management of hypocalcemia, including calcium infusion. His diet was found to consist exclusively of rice, banana, and canned chicken. He had refused dairy products since early childhood and was not receiving supplemental vitamin D or calcium. In the context of the patient’s limited dietary repertoire and parental concerns regarding eye pain, an urgent ophthalmological assessment was arranged. Findings were consistent with xerophthalmia, and he was treated urgently with oral vitamin A, as per World Health Organization guidelines (1). Further blood work showed vitamin D deficiency (25-hydroxy vitamin D <5 nmol/L), elevated alkaline phosphatase (440 U/L) and elevated parathyroid hormone (132 ng/L). On x-ray, there was no radiographic evidence of rickets. As suspected, the patient’s vitamin A level was very low (0.2 µmol/L). The patient had both xerophthalmia (secondary to nutritional vitamin A deficiency), and symptomatic hypocalcemia (secondary to severe vitamin D deficiency). ASD is a neurodevelopmental disorder with onset in childhood, characterized by (i) impairments in social communication and (ii) restricted, repetitive patterns of behaviours, interests or activities (2). ASD affects approximately 1 in 66 Canadian children and youth from 5 to 17 years of age (3). Feeding problems are common in children and youth with ASD (4). Food refusal, limited dietary repertoire, and high frequency single food intake, in particular, may be seen in ASD (5). The origins of these problems are not completely understood, but likely relate, in part, to insistence on sameness and sensory differences. Restricted diet—resulting from food refusal, limited dietary repertoire and high frequency single food intake—can lead to micronutrient deficiencies. There are numerous case reports of children and youth with ASD and the following micronutrient deficiencies (6–8): Vitamin A deficiency, causing xerophthalmia (the spectrum of ophthalmologic disease caused by vitamin A deficiency) Vitamin C deficiency, causing scurvy (the disease resulting from severe vitamin C deficiency) Vitamin D deficiency, causing vitamin D-deficiency rickets (a defect in mineralization of newly formed bone) Iron deficiency, causing iron-deficiency anemia (a state of insufficient total body iron, such that normal physiologic processes, like hematopoiesis, are not maintained) Micronutrient deficiencies can result in significant morbidity, which may be compounded by invasive investigations, prolonged hospital admission and delayed diagnosis, due in part to the perceived rarity of these conditions (e.g., scurvy [9–11]). The incidence of micronutrient deficiencies in Canadian children and youth with ASD is unknown. Furthermore, very little is understood about the clinical characteristics, use of healthcare resources, and significant health complications associated with these micronutrient deficiencies. A Canadian Paediatric Surveillance Program (CPSP) study on micronutrient deficiencies in children and youth with ASD began in January 2020 and is ongoing (8). CPSP participants are being asked to report all children and youth less than 18 years of age with ASD and a new diagnosis of one or more of the following: vitamin A deficiency/xerophthalmia; scurvy; severe, symptomatic vitamin D deficiency; and severe iron-deficiency anemia. Detailed case definitions (included in the study protocol [8]) were developed to capture cases of biochemical micronutrient deficiency associated with clinical sequelae, and not biochemical deficiency alone. The primary goal of this CPSP study is to understand the burden of serious micronutrient deficiencies better in Canadian children and youth with ASD, in order to inform anticipatory guidance, screening, and prevention strategies in this population. Funding: Laura Kinlin is supported by a Fellowship Award from the Canadian Institutes of Health Research (CIHR). Potential Conflicts of Interest: All authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.266
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations3
Published2021
Admission routes2
Has abstractno

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