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Record W4320036814 · doi:10.53350/pjmhs20221612610

Changes in Serum Adiponectin and Serum Leptin Levels Can Predict Pre-Eclampsia in Pregnant Women: A Prospective Study

2022· article· en· W4320036814 on OpenAlexaff
Zia Ullah, Farukh Bashir, Kalsum Fatima, Shoaib Ahmed, Bakhtiar Hassan Tahir, Faiza Irshad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsContinental (Canada)
Fundersnot available
KeywordsAdiponectinLeptinMedicineProspective cohort studyEclampsiaHormoneInternal medicinePreeclampsiaEndocrinologyAdipokineGestational ageCase-control studySignificant differencePregnancyObesityBiologyInsulin resistance

Abstract

fetched live from OpenAlex

Background: The study aims to investigate maternal adiponectin and leptin levels as prospective markers of preeclampsia, as well as the ratio of maternal adiponectin to leptin levels. Additionally, this research investigated the relationship between these two hormones. In addition to that, the purpose of this study was to analyze the connection that exists between the two hormones. Materials and Methods: This is a study that looks forward into the foreseeable future. The pregnant women who were willing to take part in the research were split into two groups: the first group, which was referred to as the study group, consisted of fifty women who had been diagnosed with pre-eclampsia, and the second group, which was referred to as the control group, consisted of fifty normotensive women of the same gestational age who did not have proteinuria. The levels of adiponectin, leptin, and their ratio were evaluated in the maternal serum of pre-eclamptic (the study group)and normotensive women(the control group).. The comparison's outcomes were analysed Results: There was a statistically significant difference in the levels of adiponectin found in the study group compared to the levels found in the control group. When comparing the levels of leptin in the study group with the control group, there was a statistically significant difference (p< 0.001) between the two groups. When compared to the control group, the ratio of adiponectin to leptin was considerably lower in the group that participated in the study (p<<0.001). When used as predictors of pre-eclampsia, serum leptin and the serum adiponectin/leptin ratio displayed a sensitivity of 90%, a specificity of 87.9%, a positive predictive value of 88.7%, and a negative predictive value of 73%. Additionally, the ratio of serum adiponectin to leptin exhibited a positive predictive value of 86.9 percent. Both the adiponectin/leptin ratio cutoff point and the leptin cutoff point were found to be optimal at 0.161. It was determined that a cutoff criterion of 24.1 ng/ml for serum leptin should be used. Both the adiponectin/leptin ratio and the serum leptin levels were significantly linked with significant degrees of accuracy (p <0.001) in the prediction of obesity. Conclusion: In research on pre-eclampsia, the examination of the maternal leptin level should be included, and a cut-off level of greater than 24.2 ng/ml should be employed as a diagnostic biomarker. This is due to the fact that pre-eclampsia is linked to hyperleptinemia, which is a condition that is characterised by dangerously high blood pressure in the mother. The ratio of adiponectin to leptin should be checked in every instance of preeclampsia, and the diagnostic threshold should be set to less than 0.153. This ratio is a potential biomarker for preeclampsia and should be examined in every case. Keywords: Leptin, adiponectin, the ratio of adiponectin to leptin, pre-eclampsia, and biomarkers

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.269
Teacher spread0.247 · 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 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

Citations2
Published2022
Admission routes1
Has abstractyes

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