Systemic immune-inflammation index and its relation to blood pressure and dyslipidemia in adults: A retrospective study.
Ghadeer S AljuraibanFahad J AlharbiAli O AljohiAbdullah Z AlmeshariAbdulaziz S AlsahliBader Saad AlotaibiManal AbudawoodWaad AlfawazMahmoud AbulmeatyPublished in: Medicine (2024)
High blood pressure (BP) and dyslipidemia are major risk factors for cardiovascular disease mortality. The systemic immune-inflammation index (SII) has been suggested as a predictive tool to identify those at risk for chronic diseases, however, its use for predicting high BP and dyslipidemia has not been thoroughly investigated. This study aimed to examine the association between SII and high BP as well as lipid markers. Retrospective hospital data from a large cohort (n = 3895) of Saudi adults aged ≥18 years were analyzed. Lipid markers (cholesterol, high-density lipoprotein, low-density lipoprotein [LDL]), systolic BP, and diastolic BP measures were extracted. When the sample was divided into quartiles of SII, cholesterol, triglycerides, and LDL were higher in those with a higher SII than in those with a lower SII (P < .01). After adjusting for potential confounders, higher SII was significantly associated with higher odds of hypertension (odds ratio: 1.12, 95% confidence interval: 1.04-1.21) and elevated LDL (odds ratio: 1.07, 95% CI: 1.02-1.14), but not with elevated cholesterol. Across quartiles of SII, there was a significant trend between higher SII and the odds of hypertension in people with diabetes and those aged ≥65 years. The SII could be an economical predictive measure for identifying individuals at risk of hypertension and some aspects of dyslipidemia. Longitudinal studies are needed to confirm this relationship.
Keyphrases
- low density lipoprotein
- blood pressure
- cardiovascular disease
- hypertensive patients
- high density
- heart rate
- oxidative stress
- type diabetes
- left ventricular
- cross sectional
- climate change
- fatty acid
- heart failure
- emergency department
- mass spectrometry
- machine learning
- big data
- risk factors
- skeletal muscle
- deep learning
- high resolution
- insulin resistance
- acute care