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Record W2794653065 · doi:10.1093/schbul/sby017.629

F98. HYPOVITAMINOSIS D IN SCHIZOPHRENIA: ASSOCIATED CARDIOVASCULAR RISK

2018· article· en· W2794653065 on OpenAlexaboutno aff
R. Nefzi, Amine Larnaout, H. Ben Ammar, Emira Khelifa, Amina Aissa, Z. El Hechmi

Bibliographic record

VenueSchizophrenia Bulletin · 2018
Typearticle
Languageen
FieldMedicine
TopicElectrolyte and hormonal disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSchizophrenia (object-oriented programming)Metabolic syndromeVitamin D and neurologyvitamin D deficiencyHypovitaminosisInternal medicineVitaminRetrospective cohort studyPediatricsPsychiatryObesity

Abstract

fetched live from OpenAlex

Vitamin D modulate the course of many neurologic diseases and conditions. Moreover, the prevalence of vitamin D deficiency might be higher in psychiatric patients, in particular with schizophrenia. Likewise, there is an inverse relationship between vitamin D levels and several cardiovascular risk factors, including the metabolic syndrome, that patients with schizophrenia are predisposed to develop. It is within this framework that this study aims to explore the relationship between vitamin D levels in a cohort of Tunisian patients with schizophrenia and to determine the cardiovascular risk according to whether they had hypovitaminosis D or not. A cross-sectional and retrospective descriptive study was conducted at the “F” psychiatry department at the Razi Hospital, Manouba over a twelve-month period from June 1st, 2015 to May 31st, 2016, including 80 patients with schizophrenia in period of clinical remission. The evaluation focused on anthropometric parameters and cardiovascular risk factors. A dosage of vitamin D was performed. The patients had an average age of 42.5 years and 70% were male. 25 patients had metabolic syndrome. 49% of patients had vitamin D insufficiency and 51% had vitamin D deficiency. Vitamin D levels had not been affected by the clinical characteristics of the disease. However, there was no significant association between vitamin D levels and metabolic syndrome. A significant negative correlation was found between the total sum of the various cardiovascular risk factors and the vitamin D deficiency (p <0.001). In our study, all patients had vitamin D levels below the recommended levels. 25 patients (31%) met the criteria for metabolic syndrome. All our patients had at least one cardiovascular risk factor. The majority (33% and 27%) had respectively three or four FRCV. 10% had more than five concurrent FRCVs. This result has been described in many studies. Indeed, in patients with schizophrenia, the cardiometabolic risk seems to increase continuously. Several European studies have reported a prevalence of metabolic syndrome ranging from 28% to 37% in patients with schizophrenia. Higher rates of 43% and 46% were reported respectively in the United States and Canada. Moreover, with schizophrenia have an increased risk of sudden death and are 2 to 4 times more likely to die prematurely compared to the general population. These results have been explained with a multicausal model focusing on genetics, lifestyle, smoking, diet and sedentary behavior as well as by the side effects of antipsychotics known to induce weight gain and aggravate symptoms. risk factors for cardiometabolic disease, although studies in naïve patients reflect various abnormalities early on. However, several studies confirm that certain metabolic abnormalities may occur in schizophrenic patients naive to any antipsychotic treatment. This result is consistent with current literature data that highlight increased metabolic and cardiovascular risk in vitamin D deficiency. Indeed, in the general population, vitamin D deficiency is an important risk factor for cardiometabolic disease. The majority of cohort studies have reported an increase in the incidence of cardiovascular disease in people with low vitamin D levels.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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