Major depressive disorder (MDD) is a highly prevalent mental disorder and the third leading cause of the global burden of mental illness. From 2010 to 2021, the age-standardized disability-adjusted life year rate for depressive symptoms exhibited the most substantial increase, with a 16.4% rise (95% eISSN 1976-3026 OPEN ACCESS confidence interval [CI]: 11.9% to 21.3%) among the 25 leading Tier 3 causes [1]. Individuals grappling with depressive symptoms often manifest symptoms such as persistent low moods, diminished interest in activities they typically find pleasurable, and a reduced capacity to engage in daily activities [2]. These symptoms can have significant health implications, including a decline in physical well-being, and hinder an individual’s ability to function optimally in their professional life. Research has demonstrated a correlation between depressive symptoms and suboptimal performance on various cognitive functioning assessments. Even when controlling for confounding variables, depressive symptoms show a significant association with lower scores in language, processing speed, and memory [3]. Cognitive impairment (CoI) is a common feature of MDD, and even after depressive symptoms subside, patients may still experience problems with memory, executive functioning, and attention [4]. It is worth noting that depressive symptoms is more prevalent in the older adult population, with a prevalence of up to 13.2% in later life [5,6]. In addition, studies have shown a significant correlation between depressive symptoms and CoI [7]. Individuals with depressive symptoms have been observed to have a higher risk of dementia, in part due to the higher prevalence of CoI in this population [8].

Diabetes mellitus (DM) is a metabolic disease characterized by elevated blood glucose levels and has traditionally been thought to involve primarily abnormalities in glucose metabolism. In recent years, the medical community has recognized that diabetes is a multifaceted disease with potential implications that go far beyond the simple regulation of blood sugar [9]. A mounting body of research has underscored the potential impact of diabetes on cognitive function, as the global incidence of the disease continues to rise [10]. Clinical practice has shown that DM is often associated with a variety of complications, including cardiovascular disease, neuropathy, and retinopathy [11]. However, a less explored aspect is its potential association with cognitive performance. The potential for CoI to complicate the management of disease in older patients with diabetes has been demonstrated, with observed difficulties in following treatment regimens. This, in turn, has been shown to correlate with glycemic control, increase the risk of complications, and ultimately compromise the patient’s overall health and quality of life [12]. Given the established association between diabetes and cognitive function, it is imperative to undertake a comprehensive exploration of the relationship between diabetes and CoI.

CoI is typified by a decline in one or more cerebral functions, including memory, learning, attention, decision-making, and executive functioning. The severity of this decline can range from mild to severe [13]. On a global scale, the prevalence of CoI is increasing, particularly among individuals over the age of 65. CoI is a prevalent feature among patients diagnosed with CoI. Although it does not fully align with the diagnostic criteria for dementia, its prevalence increases significantly with age, reaching as high as 19.7% in certain cases [14]. If CoI persists and worsens, patients are often diagnosed with dementia, which is currently the fifth leading cause of death worldwide [15]. This condition significantly impairs patients’ ability to care for themselves, resulting in a substantial disease burden. The financial burden on patients and society is significant, with healthcare expenditures for individuals with low cognitive function exceeding $300 billion [16]. Therefore, ascertaining the factors associated with CoI and how they interact is imperative for the development and implementation of effective interventions that may help reduce the incidence of CoI and enhance patient quality of life [17-19].

Although DM and depressive symptoms are each well-established as independent risk factors for CoI, the elusive nature of their synergistic interplay and underlying mechanisms remains poorly characterized. This study postulates that the co-occurrence of DM and depressive symptoms shows statistical associations with CoI in older adults, and that these two conditions may influence each other. To rigorously evaluate this interaction hypothesis, we harnessed data from two nationally representative cohorts spanning Eastern and Western populations—the China Health and Retirement Longitudinal Study (CHARLS) and the National Health and Nutrition Examination Survey (NHANES)—conducting a comprehensive cross-cohort analysis.

This study is the first to examine the role of diabetes and depressive symptoms on CoI in older adults using a highly representative prospective cohort database in East Asia and North America. The results showed a significant synergistic effect of diabetes and depressive symptoms on CoI in older adults.

Evidence has demonstrated that individuals diagnosed with diabetes exhibit substantial damage to both white and grey matter, with these alterations exhibiting a robust correlation with a deterioration in cognitive function [30,31]. Although some studies have explored the effects of obesity, medication, and other factors on cognitive function in diabetic patients, the exact mechanisms are unclear [32]. After controlling for confounders, this study found that diabetes was significantly associated with CoI.

In fully adjusted models, a significant correlation was identified between depressive symptoms and CoI. Several studies have indicated that individuals may show CoI during the initial episode of depressive symptoms. Furthermore, patients with recurrent depressive symptoms may have a higher likelihood of CoI compared to those experiencing a single episode of depressive symptoms [33]. Despite the administration of effective antidepressant medication, CoI remains a persistent issue among patients [34]. Depressive symptoms have been identified as a significant factor associated with CoI in the older adult population [35]. In older persons suffering from moderate to severe depressive symptoms, CoI may be a reliable indicator of dementia [13]. Structural magnetic resonance imaging studies have demonstrated volume reductions in the cerebellar vermis and the inferior temporal gyrus in patients with depressive symptoms. Furthermore, a comprehensive investigation of the brain’s structure and function has revealed significant atrophy in additional regions, including the temporal lobe, superior temporal gyrus, inferior frontal gyrus, amygdala, hippocampus, thalamus, and cingulate gyrus. The atrophy of these brain regions may indicate the presence of information-processing deficits in patients, reducing their ability to participate in mental and social activities. These activities are considered risk factors for COI and MDD [36,37]. Furthermore, the presence of white matter hyperintensity has been implicated as a potential causative factor contributing to the increased prevalence of neuropsychiatric conditions, including depressive symptoms [38]. In diabetic patients, Moran et al. [39] reported grey mat-ter loss in multiple brain regions, including the medial temporal lobe, anterior cingulate gyrus, and medial frontal lobe, in a large cross-sectional study. Additionally, the study observed white matter loss in frontal and temporal lobe regions. The study suggests that these changes in brain volume are strongly associated with poorer performance on cognitive tests in people with type 2 diabetes. Overall, both depressive symptoms and diabetes showed negative effects on brain structure and showed similar brain region changes in imaging studies.

In addition to CoI, diabetes is associated with mood changes that often co-occur with depressive symptoms in diabetics. Factors such as dysregulation of the hypothalamic-pituitaryadrenal (HPA) axis and overactivity of the sympathetic nervous system are closely associated with the development of diabetes and depressive symptoms. Pathological mechanisms such as chronic inflammation, atherosclerosis, and abnormal cytokine secretion may play an important role in the interrelationship between these two conditions [21,40]. These physiological mechanisms may be linked to mood changes in diabetes, which may in turn correlate with depressive symptoms. In older adults, diabetes and depressive symptoms are risk factors for CoI in earlier research. The underlying mechanisms are intricate, though. According to the present study, having diabetes and depressive symptoms together is associated with a significantly higher odds of CoI when compared to having either condition alone. The mechanisms underlying this association are not fully understood but may be complex. Given the observed association with cognition, this highlights the need for healthcare providers to work together to treat older adults with depressive symptoms and diabetes. For older individuals with diabetes or depressive symptoms, screening for additional illnesses may be advised to stop future CoIs.

This study revealed a potential interaction between diabetes and depressive symptoms that differed across cohorts. In both cohorts, diabetes or depressive symptoms (alone or in combination) were associated with a significantly higher risk of CoI, but an additive interaction was observed only in the CHARLS cohort. These findings share both similarities and differences with the results of previous studies. For instance, while Downer et al. [41] investigated the impacts of diabetes and depressive symptoms on CoI, they did not detect a similar interaction effect. In contrast, Liu et al. [42] demonstrated that diabetic patients have a significantly elevated risk of MDD but failed to evaluate the synergistic effect of these two conditions on CoI.

The results of the mediation analyses suggest that depressive symptom severity plays only a limited bridging role in the observed relationship between diabetes and CoI and that there is heterogeneity across populations. The bidirectional patterns of the mediators in the CHARLS cohort may be related to the assessment methods of depressive symptoms in Chinese older adults, differences in diabetes management, cultural factors, or environmental stress levels [43,44]. Several factors may contribute to the observed heterogeneity between the two cohorts. First, healthcare system differences may play a role: in China, diabetes management often relies on fragmented primary care services, whereas the U.S. has more structured chronic disease management programs, which may influence the timing and effectiveness of metabolic and mental health interventions [45,46]. Second, social support structures differ substantially between the two populations. Chinese older adults traditionally rely on family-based care, whereas Western populations often depend on formal social services and community support networks; variations in social support availability may modify the impact of comorbid conditions on cognitive health [47,48]. Third, cultural factors may influence both the expression and reporting of depressive symptoms. In Chinese culture, mental health symptoms are often somatized or underreported due to stigma, which may affect the detection and management of depression in older adults [49]. Fourth, differences in health behaviors, including dietary patterns, physical activity levels, and adherence to medical recommendations, may also contribute to the divergent interaction patterns observed [50,51]. Finally, genetic susceptibility and biological mechanisms—such as variations in inflammatory markers or HPA axis regulation across populations—may further explain the differential synergistic effects [52]. Although the small proportion of mediating effects suggests that additional biological (e.g., inflammatory processes, vascular lesions) and psychosocial mechanisms remain to be explored, this study emphasizes the importance of simultaneously monitoring depressive symptom severity in diabetes management. Future studies should adopt longitudinal designs, incorporating neuroimaging and biomarkers, to validate the potential protective effect of depressive symptoms-focused interventions on cognitive outcomes in patients with diabetes. The significant nonlinear interaction between PHQ-9 scores and diabetes in the NHANES cohort suggests that the impact of diabetes on CoI is partially attenuated at lower levels of depressive symptoms but becomes markedly potentiated once PHQ-9 scores exceed approximately four points. This threshold is clinically significant, as it falls well below the standard diagnostic cutoff of 10, suggesting that even subthreshold depressive symptoms may amplify the adverse cognitive effects of diabetes in older adults. This nonlinear pattern may reflect threshold effects in neuroinflammatory pathways or hyperactivation of the hypothalamic-pituitary-adrenal axis [53,54]. By contrast, no significant interaction between CES-D-10 scores and diabetes was observed in the CHARLS cohort, possibly owing to population differences in genetic susceptibility, social support structures, or diabetes management practices between Eastern and Western settings [55,56].

This study has two core strengths. First, the cross-cohort consistency of our findings strengthens the external validity of our conclusions by leveraging large-scale databases (NHANES and CHARLS) from both China and the United States, with samples spanning both Eastern and Western populations that differ significantly in ethnicity, culture, and lifestyle. Second, NHANES employs a multistage probability sampling design to ensure broad representativeness of the U.S. population, while CHARLS relies on a nationwide random sample from China, collectively enhancing the generalizability of the results.

This study has the following limitations. First, its cross-sectional design cannot establish causality, necessitating longitudinal studies to further validate these associations. Second, limited data timeliness may affect interpretability: the NHANES dataset covers only 2011-2014, with cognitive assessments restricted to samples from that cycle; CHARLS has a relatively short follow-up period, potentially impairing the detection of long-term associations. Third, reliance on self-reported data for core variables introduces susceptibility to recall bias or social desirability bias. Additionally, divergences between the two cohorts in racial composition, cultural background, and healthcare systems warrant caution when generalizing results across populations.