Relationship Between Lipoprotein(a), Renal Function Indicators, and Chronic Kidney Disease: Evidence From a Large Prospective Cohort Study
Notice bibliographique
Résumé
BACKGROUND: Chronic kidney disease (CKD) poses a significant global public health challenge. While lipoprotein(a) (Lp[a]) has been established as a significant factor in cardiovascular disease, its connection to CKD risk remains a topic of debate. Existing evidence indicates diverse risks of kidney disease among individuals with various renal function indicators, even when within the normal range. OBJECTIVE: This study aims to investigate the joint associations between different renal function indicators and Lp(a) regarding the risks of incident CKD in the general population. METHODS: The analysis involved a cohort of 329,415 participants without prior CKD who were enrolled in the UK Biobank between 2006 and 2010. The participants, with an average age of 56 (SD 8.1) years, included 154,298/329,415 (46.84%) males. At baseline, Lp(a) levels were measured using an immunoturbidimetric assay and classified into 2 groups: low (<75 nmol/L) and high (≥75 nmol/L). To assess participants' baseline renal function, we used the baseline urine albumin-to-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR). The relationship between Lp(a), renal function indicators, and the risk of CKD was evaluated using multivariable Cox regression models. These models were adjusted for various factors, including sociodemographic variables, lifestyle factors, comorbidities, and laboratory measures. RESULTS: A total of 6003 incident CKD events were documented over a median follow-up period of 12.5 years. The association between elevated Lp(a) levels and CKD risk did not achieve statistical significance among all participants, with a hazard ratio (HR) of 1.05 and a 95% CI ranging from 0.98 to 1.13 (P=.16). However, a notable interaction was identified between Lp(a) and UACR in relation to CKD risk (P for interaction=.04), whereas no significant interaction was observed between Lp(a) and eGFR (P for interaction=.96). When compared with the reference group with low Lp(a) and low-normal UACR (<10 mg/g), the group with high Lp(a) and low-normal UACR exhibited a nonsignificant association with CKD risk (HR 0.98, 95% CI 0.90-1.08; P=.74). By contrast, both the low Lp(a) and high-normal UACR (≥10 mg/g) group (HR 1.16, 95% CI 1.08-1.24; P<.001) and the high Lp(a) and high-normal UACR group (HR 1.32, 95% CI 1.19-1.46; P<.001) demonstrated significant associations with increased CKD risks. In individuals with high-normal UACR, elevated Lp(a) was linked to a significant increase in CKD risk, with an HR of 1.14 and a 95% CI ranging from 1.03 to 1.26 (P=.01). Subgroup analyses and sensitivity analyses consistently produced results that were largely in line with the main findings. CONCLUSIONS: The analysis revealed a significant interaction between Lp(a) and UACR in relation to CKD risk. This implies that Lp(a) may act as a risk factor for CKD even when considering UACR. Our findings have the potential to provide valuable insights into the assessment and prevention of CKD, emphasizing the combined impact of Lp(a) and UACR from a public health perspective within the general population. This could contribute to enhancing public awareness regarding the management of Lp(a) for the prevention of CKD.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».