Weight significantly impacts blood pressure, yet the link between their combined metabolic status and systolic blood pressure (SBP) trajectories remains unclear.

The impact of different body weight statuses and metabolic characteristics on the trajectory changes of SBP was investigated.

A total of 2 051 subjects who participated in health check-ups in Urumqi City, Xinjiang Uygur Autonomous Region from July 2019 to September 2021 were selected as study subjects, with baseline information collected. The dynamic changes of SBP and new-onset hypertension status were collected every 12 months at the Health Management Center of the First Affiliated Hospital of Xinjiang Medical University. Group-based trajectory modeling (GBTM) was used to determine the optimal number of trajectories for SBP among the subjects, grouping them based on SBP change trajectories. Restricted cubic spline (RCS) analysis was conducted to examine the potential dose-response relationship between BMI and SBP trajectories under different weight statuses and metabolic features. Multivariate Logistic regression analysis was employed to explore the impact of weight status and its metabolic characteristics on SBP change trajectories.

The average age of the subjects was (43.4±8.8) years old, including 883 males (43.1%) and 1 168 females. According to weight status and metabolic characteristics, participants were categorized into metabolically healthy normal weight (MHNW, n=915, 44.6%), metabolically healthy overweight/obese (MHO, n=608, 29.6%), metabolically unhealthy overweight/obese (MUO, n=379, 18.5%), and metabolically unhealthy normal weight (MUNW, n=149, 7.3%). During follow-up, a total of 362 (17.6%) new cases of hypertension were identified. GBTM categorized the subjects into three groups based on SBP trajectories: low-level group (n=870), mid-level group (n=1 067), and high-level group (n=114). There were statistically significant differences in gender, age, education below high school level, smoking and drinking proportions, BMI, SBP, diastolic blood pressure (DBP), fasting blood glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol among the three groups (P<0.05). Within the low-, mid-, and high-level SBP trajectory groups, there were statistically significant differences in gender distribution across the four metabolic phenotypes (P<0.05). RCS results showed that BMI was linearly positively correlated with SBP trajectory risk in MHNW, MUNW, and MUO phenotype populations (P_non-linear>0.05, P<0.001), while in the MHO phenotype population, this correlation was nonlinear (P_non-linear<0.05, P<0.001). Multivariate Logistic regression analysis revealed that compared with MHNW, MHO (OR=2.29, 95%CI=1.84-2.87), MUNW (OR=5.32, 95%CI=3.62-7.83), and MUO (OR=5.20, 95%CI=3.90-6.95) were independent risk factors for increased SBP trajectory risk levels (P<0.05), and as weight status and metabolic characteristic types changed, so did the SBP trajectory risk level increase (P_trend<0.001). Further stratified analysis by gender showed that MHO, MUNW, and MUO remained independent risk factors for SBP trajectory risk levels in both genders (P<0.05).

The SBP trajectories of the subjects can be divided into three groups: low-level, mid-level, and high-level. MUNW, MHO, and MUO may influence the long-term trend of SBP, suggesting that personalized blood pressure management strategies should be developed targeting different body weight statuses and metabolic characteristics.