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Record W2137881074 · doi:10.14740/jocmr1580w

Magnesium Replacement Does Not Improve Insulin Resistance in Patients With Metabolic Syndrome: A 12-Week Randomized Double-Blind Study

2014· article· en· W2137881074 on OpenAlexvenueno aff
Lima de Souza e Silva

Bibliographic record

VenueJournal of Clinical Medicine Research · 2014
Typearticle
Languageen
FieldNursing
TopicMagnesium in Health and Disease
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado da Bahia
KeywordsMedicineInsulin resistanceHypomagnesemiaInternal medicineMetabolic syndromeEndocrinologyPlaceboInsulinDiabetes mellitusMagnesiumChemistry

Abstract

fetched live from OpenAlex

BACKGROUND: To evaluate the effect of magnesium (Mg) replacement on insulin resistance and cardiovascular risk factors in women with metabolic syndrome (MS) without diabetes. METHODS: This 12-week clinical randomized double-blind study compared the effects of 400 mg/day of Mg with those of a placebo (n = 72) on fasting glucose, insulin, HOMA-IR, lipid profile and CRP. Mg was measured in serum (SMg) and in mononuclear cells (MMg). RESULTS: Hypomagnesemia (SMg < 1.7 mg/dL) was seen in 23.2% of patients and intracellular depletion in 36.1% of patients. The MMg means were lower in patients with obesity (0.94 ± 0.54 μg/mg vs. 1.19 ± 0.6 μg/mg, P = 0.04), and insulin resistance (0.84 ± 0.33 μg/mg vs. 1.14 ± 0.69 µg/mg, P < 0.05). Mg replacement did not alter SMg (1.82 ± 0.14 mg/dL vs. 1.81 ± 0.16 mg/dL, P = 0.877) and tended to increment MMg (0.90 ± 0.40 μg/mg vs. 1.21 ± 0.73 μg/mg, P = 0.089). HOMA-IR did not alter in interventions nor in placebo group (3.2 ± 2.0 to 2.8 ± 1.9, P = 0.368; 3.6 ± 1.9 to 3.2 ± 1.8, respectively), neither did other metabolic parameters. CONCLUSION: Serum and intracellular Mg depletion is common in patients with MS; however, Mg replacement in recommended dosage did not increase significantly Mg levels, neither reduced insulin resistance or metabolic control.

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)0.051
metaresearch head score (Gemma)0.029
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0510.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.109
GPT teacher head0.463
Teacher spread0.354 · 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 designRandomized trial
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

Citations44
Published2014
Admission routes1
Has abstractyes

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