Overweight and obesity, defined as abnormal or excessive fat accumulation, have become increasingly prevalent across the globe (1). They have emerged as critical public health challengesand are strongly associated with the global burden of non-communicable diseases and premature mortality (2, 3). In China, these conditions have surged in recent decades, driven by rapid urbanization, shifts toward high-energy diets, and sedentary lifestyles (4). Projections suggest that by 2030, approximately 70.5% of Chinese adults will be classified as overweight or obesity (5). This rising prevalence has serious implications for public health, particularly due to its strong association with cardiovascular and metabolic disorders (6, 7). According to the 2023 Report on Cardiovascular Health and Diseases in China, cardiovascular diseases (CVDs)—often linked to obesity and its metabolic complications—accounted for 47.35% of all urban and 48.98% of all rural deaths in 2021 (4). These trends underscore the urgency of understanding obesity’s association with the presence of cardiometabolic disorders. Given their unique occupational exposures and lifestyle patterns, oilfield employees represent a key but understudied population for investigating the early manifestations of obesity-related metabolic risk.

Cardiometabolic risk factors (CRFs) refer to a constellation of measurable physiological indicators—including elevated blood pressure, impaired fasting glucose, high triglycerides, high low-density lipoprotein cholesterol (LDL-C), and low high-density lipoprotein cholesterol (HDL-C)—that individually or collectively increase the odds of CVDs (8). These factors are strongly associated with indicators of atherosclerotic progression, endothelial dysfunction, and systemic inflammation (9). Increasing epidemiological evidence highlights that metabolic abnormalities have been associated with early emergence and co-occurrence of cardiometabolic risk factors—often in individuals without diagnosed disease—posing a significant health burden, especially in working populations undergoing routine health surveillance (10–13). Moreover, in the Chinese population, growing evidence suggests that cardiometabolic risk has been observed comparatively lower body mass index (BMI) thresholds, due to increased visceral adiposity and unique fat distribution patterns (14). This ethnic susceptibility highlights the need for population-specific research on the interplay between excess adiposity and CRFs, particularly in non-clinical and working populations.

Despite the well-established relationship between obesity and cardiometabolic disorders, limited data exist on how excess weight, as defined by BMI, relates to CRFs in occupational populations in China. Oilfield workers, who are often exposed to physically demanding work, irregular schedules, and suboptimal dietary patterns, may show different patterns of metabolic abnormalities. However, few studies have focused on this group or systematically evaluated how BMI categories—normal weight, overweight, and obesity—correspond to CRFs in this setting.

To address this gap, the present study aims to evaluate the associations between BMI and multiple CRFs among oilfield employees in China. By positioning BMI—a widely accepted anthropometric indicator of adiposity—as the primary exposure, and CRFs as clinically relevant early metabolic abnormalities, this study provides occupation-specific evidence on how overweight and obesity are associated with the presence of metabolic risk. Focusing on a working population undergoing routine health examinations allows for tailored insights into the role of overweight and obesity in early cardiometabolic disruption, thereby informing targeted prevention strategies and workplace health management in high-risk labor groups.

A key strength of our study lies in the robustness of its findings, which were confirmed through a sensitivity analysis using the WHO-recommended BMI cutoffs. This finding supports the appropriateness of our initial choice to use the Chinese-specific BMI standards, which are more tailored to the physiological characteristics of our study population. The consistent results across both classification systems highlight the relevance of screening for excess weight in this population, regardless of the classification criteria applied. Although occupational exposures were not the primary focus of this study, conducting the analysis within an oilfield workforce remains meaningful. The homogeneous work setting and shared lifestyle patterns of oilfield employees provide a stable context for examining BMI-CRF associations, reducing variability introduced by mixed occupational populations. Thus, the occupational background enhances—rather than weakens—the relevance of our findings to this specific workforce.

This study reaffirms BMI—as a practical, validated anthropometric measure of adiposity—as the primary exposure variable associated with multiple CRFs in an occupational population. The consistent and robust associations observed across overweight and obesity categories underscore BMI’s relevance for identifying individuals with metabolic abnormalities among Chinese oilfield workers.

Specifically, both overweight and obesity were significantly linked to higher odds of hypertension, diabetes, and dyslipidemia—including high TC, high TG, high LDL-C, and low HDL-C—with obesity being associated with the greatest overall cardiometabolic burden. Supporting these findings, RCS analyses revealed clear dose–response pattern between BMI and all CRFs, with evidence of potential nonlinear associations for high TG and low HDL-C. Collectively, these results highlight the relevance of considering excess adiposity in workplace health assessments to manage metabolic abnormalities in occupational populations.

Consistent with previous large-scale studies (21, 22), overweight and obesity were significantly associated with higher odds of most CRFs such as hypertension and dyslipidemia. However, the association between overweight and diabetes was attenuated and became non-significant after full adjustment, which contrasts with previous observational findings that reported positive associations between overweight and diabetes (23, 24). This discrepancy may be partly accounted for by and the fact that central adiposity—which is common in Asian populations—is more strongly associated with the odds of diabetes than BMI alone. A national cross-sectional study in Chinese adults demonstrated that abdominal obesity was independently linked to type 2 diabetes, regardless of BMI level (25), suggesting that central adiposity may capture additional metabolic burden beyond that reflected by BMI. While BMI remains a practical and widely used measure in population-based studies, incorporating waist circumference or other indicators of fat distribution may enhance association assessment in future research. Supporting this, sensitivity analysis using WHO BMI cutoffs recommended for Asian populations yielded slightly stronger association estimates for diabetes and high TG, underscoring concerns that universal BMI thresholds may inadequately capture ethnic differences in fat distribution and insulin sensitivity. Notably, the OR for low HDL-C increased after adjustment, suggesting negative confounding by lifestyle factors such as smoking and drinking, which may mask the underlying association in unadjusted models (26, 27). This emphasizes the importance of controlling for confounders when assessing dyslipidemia burden. However, these interpretations should be made cautiously, as the cross-sectional design precludes causal inference.

While prior research has predominantly focused on general or clinical populations, relatively few studies have examined the cardiometabolic implications of excess adiposity in occupational groups. This represents an important gap, as working populations—particularly those engaged in physically intensive, high-stress, or environmentally hazardous occupations—may carry distinct association profiles. For example, studies among coal miners, transportation workers, and industrial employees have reported variable associations between BMI and metabolic disorders, with some noting higher prevalence patterns, potentially related to factors such as shift work, chemical exposure, or chronic stress (28–30). However, findings remain inconsistent across occupational sectors and are often limited by small sample sizes or narrow exposure assessment. By analyzing a large, well-characterized sample of Chinese oilfield workers, our study provides context-specific confirmation of the well-documented associations between BMI and cardiometabolic risk factors. These findings underscore the importance of monitoring cardiometabolic profiles in industrial labor populations and offer occupation-specific descriptive data that may inform future surveillance research in similar high-risk workforces.

RCS demonstrated significant nonlinear associations between BMI and lipid abnormalities, particularly high TG and low HDL-C, with steeper increases in odds observed at higher BMI levels. These patterns are consistent with prior evidence showing that higher adiposity is associated with greater metabolic dysregulation, which are known to disproportionately impair lipid regulation in individuals with obesity (31). The BMI values at which the spline curves showed turning points were exploratory and data-driven, and therefore should not be interpreted as clinical thresholds. Confirmation in independent cohorts is needed.

In subgroup analyses, the associations between BMI and six CRFs were generally consistent across age, sex, night-shift work, smoking status, and chemical exposure, supporting the robustness of the main findings. Notably, stronger associations were observed for hypertension among current smokers, suggesting that smoking may be associated with obesity-related vascular odds through synergistic effects on systemic inflammation and endothelial injury (32). The BMI-diabetes association was more pronounced in older participants, which may reflect cumulative metabolic burden and which is consistent with age-related physiological changes reported in prior literature (33). In addition, sex significantly modified the association between BMI and low HDL-C, with male exhibiting a greater adverse association, consistent with evidence that sex hormones and fat distribution patterns influence HDL metabolism (34, 35). By contrast, no significant association modification was observed for night-shift work or chemical exposure. As multiple subgroup and interaction comparisons were performed, these results may be prone to chance findings and should therefore be interpreted cautiously as exploratory.

These findings should be interpreted with caution. The absence of statistically significant interactions does not necessarily imply that occupational exposures play a negligible role. Rather, the binary and self-reported nature of exposure variables likely introduced non-differential misclassification, which tends to bias interaction estimates toward the null. Therefore, the present study may not have had sufficient resolution to detect the potential contribution of occupational hazards. More refined exposure assessment—incorporating quantitative intensity, duration, and cumulative dose—is needed to clarify how occupational factors may jointly influence metabolic health alongside adiposity. Nevertheless, given prior evidence linking chemical exposures to metabolic disturbances, further longitudinal research is warranted to clarify potential synergistic effects between adiposity and workplace hazards (36). Although ethnic differences in adiposity and fat distribution may influence metabolic risk in China, our subgroup analyses were limited to age, sex, chemical exposure, night-shift work, and smoking status. Future studies with larger and more diverse samples are needed to examine potential ethnic heterogeneity and to develop culturally tailored workplace health strategies.

This study has several limitations that should be acknowledged. First, the cross-sectional design precludes any inference about causal relationships between BMI and CRFs. Additionally, because several CRFs in this population are relatively common (>30%), the ORs estimated from logistic regression may overestimate the true prevalence ratios and should be interpreted as measures of association rather than direct risk ratios. Future studies using log-binomial or modified Poisson regression models could provide more accurate estimates of prevalence ratios. Longitudinal studies are also needed to confirm temporal associations and clarify potential causal pathways. Second, a total of 23.7% of participants were excluded from the primary analysis due to missing covariate information, although missingness for the primary exposure (BMI, 0.05%) and outcomes (CRFs, 2.3%) was minimal. Complete-case analysis was considered appropriate as the main analytic approach, and sensitivity analyses using multiple imputation for missing covariates yielded broadly consistent results. Third, BMI cannot differentiate fat from lean mass or fully capture visceral fat—which may be more relevant to metabolic risk, especially in Asian populations. However, it remains a practical and standardized measure of adiposity in large occupational cohorts. Future studies could integrate additional indicators to better assess fat distribution. Despite its limitations, our findings support the relevance of BMI as an indicator for identifying individuals with cardiometabolic abnormalities. Fourth, although we adjusted for a comprehensive set of covariates, residual confounding due to unmeasured or imprecisely measured variables cannot be ruled out. In particular, key factors such as detailed dietary intake, psychosocial stress, and genetic susceptibility were not captured by the health examination system. Moreover, several occupational exposures—including chemical exposure, night-shift work, noise, and dust—were obtained through self-reported binary classifications without quantitative information on duration, frequency, or intensity. This may have reduced the precision of subgroup analyses and limited our ability to evaluate dose–response effects. Finally, the study population was drawn from a single oilfield-affiliated hospital in northwest China, limiting the generalizability of our findings to other occupational sectors or the general Chinese population. The use of Chinese-specific BMI criteria may further constrain comparability with studies based on WHO standards. Therefore, caution is warranted when extrapolating our results to non-Asian populations or settings with different demographic and occupational characteristics.

The findings of this study should be interpreted with caution, particularly given the limitations of its cross-sectional design. First, although the results underscore the importance of monitoring excess weight in occupational populations—where work routines and environmental conditions may influence metabolic health—future longitudinal studies are required to clarify temporal relationships. Second, the stronger associations observed when applying WHO BMI thresholds suggest that current national criteria may underestimate cardiometabolic susceptibility in certain subgroups, but confirmation in prospective and nationally representative cohorts is necessary. Third, although an interaction between BMI and chemical exposure was detected, the absence of consistent associations across occupational exposures indicates that further mechanistic and longitudinal evidence is needed before drawing firm conclusions. Overall, rather than supporting immediate intervention strategies, these findings primarily highlight the need for continued surveillance and future cohort studies to better understand how adiposity and workplace factors jointly relate to cardiometabolic health in industrial workforces.

In this occupational population, BMI was independently associated with increased odds of hypertension, diabetes, and dyslipidemia, with obesity exhibiting the strongest associations across CRFs. The observed dose–response pattern and subgroup differences highlight the importance of excess adiposity as a modifiable factor, underscoring the need for targeted screening and prevention strategies in industrial workforces.