Background/Objectives: Metabolic-obesity phenotypes are dynamic, yet little is known about how their longitudinal transitions affect cardiovascular disease (CVD) risk. We aimed to characterize these transitions and examine their associations with incident CVD in middle-aged and older Chinese adults. Methods: We included 4516 participants from the China Health and Retirement Longitudinal Study (CHARLS) who were free of CVD at the follow-up baseline (wave 3). Metabolic-obesity phenotypes (metabolically healthy non-obese [MHNO], metabolically unhealthy non-obese [MUNO], metabolically healthy obese [MHO], and metabolically unhealthy obese [MUO]) were assessed in 2011 and 2015, and six trajectory patterns were defined. Incident CVD (heart disease or stroke) was ascertained from 2015 to 2020. Cox proportional hazards models estimated associations between trajectory groups and incident CVD, and Kaplan–Meier curves compared cumulative incidence across groups. Results: Stable MHNO accounted for 35.96% of the cohort, whereas the stable non-MHNO group accounted for 28.06%. After multivariable adjustment, the small obesity recovery group (n = 89) showed an elevated CVD risk estimate compared with the stable MHNO (HR 2.32, 95% CI 1.33–4.05), while stable non-MHNO group were associated with higher CVD risk (HR 1.72, 95% CI 1.36–2.18). Incident obesity showed elevated but non-significant risk (HR 1.71, 95% CI 0.94–3.10), and metabolic decline showed borderline significance (HR 1.36, 95% CI 1.00–1.85). Sex-stratified analyses showed heterogeneous risk patterns. Conclusions: Metabolic-obesity phenotypes are dynamic, but unfavorable phenotypes often persist. The obesity recovery group, defined using BMI, may represent a heterogeneous group and does not necessarily indicate true cardiometabolic recovery; the observed association should not be interpreted as evidence that weight loss itself increases CVD risk. These findings highlight the importance of jointly considering longitudinal changes in metabolic health and obesity when examining their associations with incident CVD.

Background: The China Diet Balance Index 2022 (DBI-2022), released in 2024, is the latest dietary quality assessment tool developed in alignment with the updated Dietary Guidelines for Chinese Residents (2022). However, its association with hypertension in the Lingnan region—a geographic area distinguished by a unique dietary culture—has not been fully examined. Objective: This study aimed to systematically evaluate the dietary quality of Lingnan residents using DBI-2022 and explore its association with hypertension. Methods: We analyzed cross-sectional data from the 2015 China Adults Chronic Diseases and Nutrition Surveillance, focusing on 2982 Lingnan residents aged 45 years and older. Dietary information was collected via 3 consecutive days of 24-h dietary recalls and food frequency questionnaires, supplemented by standardized anthropometric measurements. We assessed the contribution of specific dietary components to overall quality and investigated the association between DBI-2022 indices and hypertension using multivariable regression models. Results: Among the 2982 participants, 821 (27.5%) were identified with hypertension. The primary dietary imbalances in the Lingnan population were characterized by moderate insufficient consumption (Low Bound Score [LBS] = 40.48) and moderate excessive intake (High Bound Score [HBS] = 22.58), with insufficient intake being the more prominent concern. Cereals, cooking oils, and salt emerged as key contributors to poor dietary quality, whereas soybeans, fruits, adequate water consumption, and dietary diversity were associated with better dietary quality. After adjusting for potential confounders, participants in the highest quartile of Diet Quality Distance (DQD) had significantly higher odds of hypertension (OR = 1.57, 95% CI: 1.05–2.35) compared to those in the lowest quartile. Similarly, the odds were elevated for those with high LBS (OR = 1.88, 95% CI: 1.24–2.87). Conclusions: Dietary insufficiency appears to be a more critical issue than excessive consumption among the Lingnan population. Poor dietary quality, particularly insufficient intake of protective foods, is significantly associated with elevated odds of hypertension. These findings support the applicability of DBI-2022 for regional dietary surveillance and highlight key priorities for targeted nutritional intervention strategies.

Background/Objectives: To identify major dietary patterns among adults aged ≥45 years and examine their associations with metabolic health–obesity phenotypes. Materials and Methods: We analyzed 32,091 adults aged ≥45 years from the 2015 China Adults National Chronic Disease and Nutrition Surveillance. Diet was assessed using three consecutive 24 h dietary recalls, with household condiment weighing used to improve estimation of cooking oil and salt intake. Dietary patterns were derived using principal component analysis and categorized into quartiles. Multinomial logistic regression adjusted for energy intake and key sociodemographic/lifestyle factors to estimate odds of metabolically unhealthy non-obesity (MUNO), metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) versus metabolically healthy non-obesity (MHNO). Results: Four patterns with geographic variation were identified: (1) the Rice–Vegetable–Pork pattern; (2) the Fruit–Egg–Dairy pattern; (3) the Red Meat–Offal–Snack pattern; and (4) the Soybeans–Tubers–Grains pattern. Compared with Q1, Q4 of Pattern 1 was associated with lower odds of MHO (OR = 0.42, 95% CI: 0.38–0.46) and MUO (OR = 0.40, 95% CI: 0.36–0.44). Pattern 2 and Pattern 3 were associated with higher odds of MHO (Q4 vs. Q1: OR = 1.42 and 1.21) and MUO (OR = 1.36 and 1.14, all p < 0.001). Pattern 4 was inversely associated with MHO (OR = 0.85, 95% CI: 0.79–0.92) but positively associated with MUNO (OR = 1.16, 95% CI: 1.08–1.24). Conclusions: The Rice–Vegetable–Pork pattern was associated with more favorable obesity-related metabolic phenotypes, whereas energy-dense, animal-derived patterns were associated with higher odds of obesity phenotypes; the Soybeans–Tubers–Grains pattern showed mixed associations.