Associations between kisspeptin hormone level and its genetic polymorphisms with polycystic ovary syndrome
Notice bibliographique
Résumé
Abstract Background Polycystic ovary syndrome (PCOS) is among the most common endocrine and metabolic disorders. Kisspeptin, a neuropeptide, which has been implicated in enhancing hypothalamic–pituitary–ovarian (HPO) axis activity, might play a role in the pathogenesis of PCOS. However, previous studies have had inconsistent results. Objectives In this study, we conducted meta‐analyses to assess the possible association between kisspeptin hormone and its genetic aspect with PCOS. Additionally, we performed a Mendelian randomization (MR) analysis to clarify the cause–effect relationship of kisspeptin level and the risk of developing PCOS. Search Strategy We obtained summary data from published clinical studies and genome‐wide association study (GWAS) consortia studies to perform meta‐analyses and an MR study. A literature search for eligible studies published until December 2023 was performed. Exposure SNPs for kisspeptin level were derived from a GWAS dataset from the deCODE database, while the genetic associations for PCOS were obtained from the FinnGen study and the largest GWAS dataset. Selection Criteria Studies that evaluated the association between the kisspeptin level and/or KiSS‐1 metastasis suppressor ( KISS1 ) polymorphisms and PCOS were included for meta‐analyses. Data Collection and Analysis Data extraction was performed, and the quality of the studies was assessed using the Newcastle–Ottawa Scale independently by two reviewers. A random‐effect model was used to estimate outcomes, and effects were reported as standardized mean difference (SMD) and their 95% confidence interval (CI). The I 2 was used to assess heterogeneity. The causal associations between kisspeptin level and PCOS were assessed using MR methods. The reliability, potential biases, and heterogeneity of the results were assessed through inverse variance weighting, MR‐Egger regression, Mendelian randomization pleiotropy residual sum and outlier, as well as leave‐one‐out analysis. Main Results The meta‐analyses incorporated 30 studies that met the criteria. A total of 1932 PCOS patients and 1641 controls were included to analyze the relationship between kisspeptin levels and PCOS. Compared with controls, patients with PCOS showed significantly increased kisspeptin levels (SMD = 0.67, 95% CI [0.36, 0.97], P < 0.001). The cumulative meta‐analysis suggested the result was robust. A total of 872 PCOS and 934 controls were included to assess the association between KISS1 polymorphism and the risk of PCOS. The polymorphisms of rs4889, rs12998, and rs372790354 were significantly associated with PCOS susceptibility. Concretely, rs4889 KISS1 gene polymorphism was related to PCOS for the comparisons of allele, homozygote, heterozygote, dominant, and recessive models. The rs12998 KISS1 gene polymorphism was associated with the risk of PCOS for the comparisons of homozygote and recessive models. Furthermore, the rs372790354 KISS1 gene polymorphism was associated with the risk of PCOS for the comparisons of allele, homozygote, and recessive models. The inverse variance weighted (IVW) method results showed that a higher kisspeptin level was causally related to the risk of PCOS (Finngen) (OR IVW = 1.12, 95% CI [1.03, 1.22], P = 0.0065). Finally, the leave‐one‐out sensitivity analysis corroborated the robustness of the MR findings. Conclusion There is a causal relationship between higher kisspeptin levels and an increased risk of PCOS. rs4889, rs12998, and rs372790354 KISS1 gene polymorphisms are associated with the risk of PCOS to varying degrees.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».