Depression is a widespread psychological disorder that significantly affects personal wellbeing, social dynamics, and economic productivity across the globe (1). Following the COVID-19 epidemic, there has been a sharp rise in the worldwide occurrence of depression, disproportionately affecting middle-aged and older populations (2). A systematic review revealed that 35.1% of older adults worldwide suffer from depression, highlighting an urgent need for targeted interventions (3).

Current treatment options for depression encompass psychotherapy, pharmacotherapy, electrotherapy, and combination therapies, yet many of these approaches have limitations, mainly for the older population. Antidepressant medications are frequently prescribed and show clinical efficacy; however, they are often linked to adverse effects, including increased risks of falls, fractures, and other complications, which may limit their long-term use, particularly in older adults (4).

The pathogenesis of depression is multifactorial, shaped by a complex interplay of genetic, psychological, and environmental factors, as well as biological dysregulations. Genetic factors, including abnormalities in receptor systems, play a critical role in the onset of depression (5). Additionally, psychological stressors and immune system dysfunction, particularly inflammation, have been shown to significantly contribute to the development and progression of depressive symptoms (6). Recent studies have increasingly examined the connection between metabolic health and mental wellbeing, proposing that both may share common pathophysiological pathways (7–9).

Meta-analyses consistently demonstrate that individuals with impaired metabolic health are at a significantly heightened risk of developing depressive symptoms (10). Studies have shown that individuals experiencing depressive symptoms often exhibit distinct alterations in their lipid profiles. Specifically, depression has been associated with lower levels of high-density lipoprotein (HDL) cholesterol and elevated levels of very-low-density lipoprotein cholesterol, triglycerides, and total cholesterol. These lipid profile changes may be indicative of underlying metabolic dysregulation in depression, potentially contributing to the increased risk of cardiometabolic diseases in depressed individuals (10, 11). These findings indicate a robust link between lipid metabolism dysregulation and depressive symptoms, highlighting the potential for metabolic markers as indicators of mental health risk.

Consequently, markers that accurately represent fluctuations in lipid levels could be valuable biomarkers for diagnosing depressive disorders.

The cardiometabolic index (CMI) is figured outperforming the multiplication of the triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio with waist-to-height ratio [WHtR; (12)], indicates both the level of obesity and the individual's lipid metabolic state. As a novel indicator, CMI provides a more comprehensive assessment of cardiometabolic health compared to traditional measures like BMI, as demonstrated in various studies (9, 13, 14), and has already been demonstrated to be useful in identifying diabetes (12) and erectile dysfunction (15), as well as and predicting the progression of atherosclerosis (16) and renal function (17). Although the relationship between CMI and depression has been investigated among US populations (8, 9), evidence from other demographic groups, particularly in non-Western contexts, remains scarce. Our study applies data from the China Health and Retirement Longitudinal Study (CHARLS) to investigate the relationship between CMI and depressive symptoms among adult Chinese individuals who are in their middle age or older.

In this cross-sectional study utilizing data from the 2011–2012 wave of the CHARLS cohort (N = 7,800), a robust association emerged between CMI and depressive symptoms in fully adjusted models. Notably, this association persisted after propensity score matching to address baseline confounding and was further validated in an independent CHARLS subsample, reinforcing result reliability. Subgroup stratification revealed differential vulnerability, with the highest odds ratios (ORs) for the CMI-depression link observed in females, rural residents, ever-alcohol consumers, never-smokers, and individuals without hypertension or diabetes. The amplified association in women may originate from sex-specific biological pathways. Estrogen's dual regulation of lipid metabolism and neuroinflammation (31) could create a synergistic pathway linking metabolic dysregulation to mood disorders. Mechanistically, postmenopausal estrogen decline correlates with visceral adiposity and microglial activation (32), thereby potentiating neuroinflammatory cascades that may drive depression pathogenesis in high-CMI individuals. Concurrently, gender-related psychosocial factors—including disproportionate caregiving responsibilities and culturally reinforced emotional suppression norms (26)—may compound physiological stress responses and subjective symptom reporting in women. Geographic disparities further modulated risk stratification. Rural residents exhibited heightened susceptibility, likely reflecting systemic barriers such as insufficient access to preventive metabolic screenings and mental health services (33). These structural gaps may delay early interventions, enabling subclinical metabolic disturbances to evolve into overt depression.

The substantial variations in sample sizes between subgroups may explain these differences, thus, propensity score matching was employed to equalize the baseline disparities.

As we all known, this is the foremost investigation of the association between CMI and depressive symptoms within the Chinese community of those in their middle to senior years. A cross-sectional study using NHANES 2011–2018 data reported findings similar to ours (9), demonstrating a prominent association between CMI and the possibility of depression [OR = 1.36, 95% CI: 1.16–1.59]. It is noteworthy that our study included participants aged 45 years or over, while the NHANES study incorporated those aged 20 years or over. However, its subgroup analysis revealed a stronger association between CMI and depression in participants aged 60 and above [OR = 1.48, 95% CI: 1.17–1.89].

A cross-sectional study using NHANES 2011–2014 data also yielded findings consistent with ours (8), showing a prominent association between CMI and the likelihood of depression [OR = 1.36, 95% CI: 1.16–1.59]. Subgroup analysis indicated a stronger association between CMI and depression in participants < 60 years of age [OR = 2.49, 95% CI: 1.92–3.23], although the association remained significant in participants over 60 years [OR = 2.21, 95% CI: 1.51–3.25], with no significant interaction between age groups (P = 0.666). The curvilinear relationship between CMI and depression perceived in this study aligns with our findings. However, we observed a saturation effect in the relationship between CMI and depression at CMI ≥ 0.594, where participants with CMI ≥ 0.594 had a 162% greater likelihood of depression in comparison to those with CMI < 0.594 [OR = 2.62, 95% CI: 2.36–2.91]. The saturation effect was not reported in this study, and the inflection points occurred at CMI = 0.9522 and CMI = 1.58, with ORs of 1.00, 1.87, and 2.05 for CMI ranges of < 0.9522, 0.9522–1.58, and ≥1.58, respectively. The CHARLS cohort specifically targeted middle-aged and older Chinese adults (aged ≥ 45 years), contrasting with the NHANES population that included participants as young as 20 years. Age-related metabolic alterations—particularly elevated visceral adiposity and insulin resistance (34)—could reduce the CMI threshold required to identify depression risk in aging populations. Ethnic disparities in metabolic profiles likely contribute to these variations. East Asian individuals, for example, demonstrate greater visceral fat accumulation at comparable BMI levels relative to Western populations (35, 36), a physiological distinction that might heighten the clinical relevance of lower CMI values in this demographic. Divergent comorbidity patterns and lifestyle behaviors further distinguish the cohorts. Notably, rural residents and female participants in our study displayed heightened CMI-depression correlations, suggesting subgroup-specific metabolic susceptibilities not observed in the NHANES cohort. Methodological distinctions also warrant consideration. The CHARLS assessment relied on the CESD-10 scale, whereas NHANES adopted the PHQ-9. This difference holds implications for threshold determination, as the CESD-10's emphasis on somatic manifestations (e.g., fatigue, appetite changes) overlaps with metabolic dysregulation phenotypes (37), potentially conflating biological and psychological endpoints. Furthermore, analytical discrepancies arose from covariate selection. Our models accounted for residence type (rural/urban) and social engagement—variables absent in NHANES datasets—introducing heterogeneity in spline-derived inflection points between studies.

A study involving nine cohorts from the Netherlands demonstrated an association between depression and metabolic changes (11). The study used proton nuclear magnetic resonance metabolomics to compare metabolic markers between depressed and non-depressed individuals, concluding that inferior HDL-C levels and superior triglyceride levels were prominently correlated with an increasing likelihood of depression (p < 0.05). The CMI, a recently introduced measure, is figured out by multiplying the WHtR by the TG/HDL-C (8, 9, 12), reflects both obesity and lipid levels. Studies have shown to be useful in identifying diabetes (7), erectile dysfunction (15), and predicting the progression of atherosclerosis (38).

There is a complex interplay between obesity, lipid metabolism disorders, and depression (39, 40). CMI, as an integrated measure of metabolic health, captures both visceral adiposity and lipid abnormalities, both of which are strongly associated with an increased risk of depression. A meta-analysis and systematic review found that individuals with abdominal obesity had a prominently greater possibility of depression in comparison to non-obese participants, with a relative risk (RR) of 1.38 (41). Additionally, a prospective cohort study in a male population reported that both overall obesity, evaluated by BMI, and abdominal obesity, measured by Visceral Adipose Tissue (VAT) area, were prominently associated with a greater occurrence of depressive symptoms. However, after multivariate modification for BMI and VAT, only VAT remained statistically significant in predicting the likelihood of developing depression in men. Lipid dysregulation also plays a gender-specific role in depression. In men, elevated LDL cholesterol levels are linked to an increased likelihood of depression, while in women, reduced HDL cholesterol levels are associated with a higher prevalence of depression (42, 43). Genetic factors may contribute to this relationship, as interactions between LDL cholesterol levels and serotonin transporter gene polymorphisms have been observed in men, further increasing their risk of depression (44). These findings underscore the critical role of lipid metabolism in the mental health of the older adults. A longitudinal study revealed that severe depression or anxiety symptoms predicted lower HDL cholesterol levels and increased abdominal obesity (45), both of which were not associated with symptom improvement. Among older patients, higher levels of LDL, total cholesterol, and triglycerides were prominently observed in those with depressive symptoms compared to their non-depressed counterparts (46). The prevailing view is that individuals suffering from depression experience elevated glucocorticoid levels due to chronic dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, which plays a pivotal role in stress response and metabolic regulation (47–49). Prolonged exposure to elevated glucocorticoids not only disrupts insulin's glycemic control but also induces insulin resistance, leading to persistent hyperglycemia and excessive fat accumulation. This metabolic dysregulation further contributes to chronic inflammation, promoting the release of pro-inflammatory cytokines such as IL-6 and TNF-α, which have been implicated in both depression onset and severity (50). Furthermore, cortisol-induced metabolic disturbances can impair neuroplasticity by affecting hippocampal structure and function, thereby exacerbating depressive symptoms (51). Studies suggest that persistent glucocorticoid elevation is associated with reduced hippocampal volume (52), synaptic dysfunction, and neurotransmitter imbalances, particularly involving serotonin and dopamine (53). These changes not only reinforce depressive symptoms but also create a bidirectional relationship between metabolic dysregulation and mood disorders, where depression itself perpetuates metabolic abnormalities (50). This pathophysiological interplay highlights the potential of metabolic-targeted interventions in disrupting the self-perpetuating cycle between elevated CMI and depression progression. As metabolic dysregulation, particularly lipid abnormalities, plays a critical role in depression risk, nutritional strategies, such as the Mediterranean diet and other anti-inflammatory diets, have demonstrated robust evidence in mitigating depression risk (54). A prospective cohort study reported that higher adherence to the Dutch Healthy Diet correlated with a notably reduced incidence of depressive symptoms (55). In rural Chinese populations, diets high in vegetables, fruits, and lean animal proteins were associated with a lower risk of late-life depression, whereas pro-inflammatory dietary patterns exacerbated susceptibility (56). Comprehensive meta-analyses consistently indicate that structured exercise programs substantially ameliorate depressive symptoms across various demographics (57). The antidepressant properties of exercise are mediated via multifaceted pathways, including neurotransmitter modulation (serotonin, dopamine, and GABA) and BDNF upregulation, fostering neuroplasticity and stress resilience (58, 59). Both aerobic training and mind-body practices, performed 3–5 times weekly at moderate intensity over 4–16 weeks, demonstrate consistent efficacy in alleviating depressive symptoms (60). Metformin, a first-line antidiabetic agent, has exhibited neuroprotective and antidepressant potential by attenuating depression-like phenotypes and metabolic impairments in preclinical models (61). A nationwide cohort study revealed that long-term metformin therapy was associated with a decreased risk of incident depression (62). The antidepressant-like actions of metformin are hypothesized to involve AMP-activated protein kinase (AMPK) activation in the brain, a central regulator of energy metabolism and neuroinflammation (63). These mechanisms highlight the importance of addressing both metabolic and psychological aspects of health in older adults. Monitoring metabolic health, including CMI, should be a routine part of depression screening in clinical settings. Early identification of individuals with elevated CMI could lead to more effective intervention strategies targeting both metabolic dysregulation and depressive symptoms.

A major strength of our study is that it represents the first large-scale cross-sectional analysis based on the CHARLS cohort. However, several limitations must be acknowledged. Due to the cross-sectional design, causal relationships between CMI and depression cannot be established, highlighting the need for well-conducted prospective cohort studies to address this limitation. While our multivariate models adjusted for known confounders such as smoking status, alcohol use, and urban/rural residence, residual confounding may persist due to unmeasured factors, including dietary patterns, physical activity, sleep disorders, and chronic exposure to environmental pollutants. Furthermore, given that the CHARLS database primarily targets individuals aged 45 and older, the findings of this study should be interpreted and generalized with caution.

Additionally, as this study relied on publicly available data from the CHARLS database, there are limitations in the medication data. Participants were only asked about antidepressant use if they self-reported having “emotional or psychiatric problems” or if a doctor had previously informed them of such issues. Consequently, only a small proportion of participants (49) reported using antidepressants, and only one participant in the final sample of 7,800 was recorded as using antidepressants. This may not fully reflect the true extent of antidepressant use in individuals with depressive symptoms. Moreover, since the CHARLS database does not provide detailed medication data, we were unable to account for other medications, such as fenofibrate or omega-3, that may influence the relationship between CMI and depression. The absence of detailed medication data limits our ability to explore the potential effects of pharmacological treatments on the study outcomes. Future research with more detailed medication data could help clarify these potential effects.

In summary, our research suggests that higher CMI levels are strongly linked to elevated CESD-10 scores and a greater possibility of depression among Chinese people of middle age and beyond. This relationship is particularly evident among women, rural dwellers, former drinkers, non-smokers, and those without hypertension or diabetes. Nevertheless, further extensive prospective research is required to validate these findings.