According to the World Health Organization (World Health Organization, 2024), the global population aged 60 and over will reach about 1.4 billion by 2030 and about 2.1 billion by 2050. With the expanding middle-aged and older population, health concerns in this group are increasingly prominent. Middle-aged and older adults not only face chronic disease and physical decline but also brain and cognitive deterioration (Murman, 2015, Sun and Li, 2023, Zu et al., 2025). Cognitive functions, for example memory, attention, and executive function, are regarded as key to older adults’ ability to maintain independence and a good quality of life; good cognition enables daily self-care and social participation (Mograbi et al., 2014, Song et al., 2023). Cognitive decline produces a series of negative effects: it not only lowers daily functioning and independence but also weakens social participation and life satisfaction (Cunha et al., 2024), and in severe cases may progress to Alzheimer’s disease or other dementias (Murman, 2015). Therefore, preserving good cognitive ability is crucial for the quality of life of middle-aged and older adults.

Good cognitive function depends on brain health. Although age-related brain decline is common, brain health is also closely linked to various lifestyle factors (Röhr et al., 2022, Wen et al., 2025). In particular, substance-use behaviors matter. For example, long-term cigarette smoking has been associated with reduced gray-matter volume (Fritz et al., 2014). Similarly, chronic heavy alcohol use is considered directly neurotoxic—disrupting neurotransmitter balance, triggering neuroinflammation, and causing thiamine (vitamin B1) deficiency—which damages brain structure and function (De and Kril, 2014; Nutt et al., 2021). These observations indicate that unhealthy substance use may accelerate brain aging and thus threaten cognitive health in middle-aged and older adults. Recent data show substance use in this age group is common: over one-quarter of middle-aged and older adults remained persistent tobacco use (Du et al., 2022), and 61.5 % of U.S. adults aged 50 +  reported alcohol use in the past 12 months (Choi et al., 2024). Together, smoking and alcohol use are prevalent among middle-aged and older adults and pose potential threats to their cognitive health.

Additionally, attention should be paid to co-use of tobacco and alcohol. Co-use of tobacco and alcohol has a high prevalence among adults (Falk et al., 2006). In a study of hospitalized patients, about 51 % of individuals who smoked also used other substances, and among patients at risk for alcohol or drug use, 70 % smoked (Katz et al., 2008). These findings indicate that co-use of tobacco and alcohol is common. Compared with single-substance use, people who co-use of tobacco and alcohol have poorer health—showing more severe functional deficits and higher psychiatric and medical comorbidity (Bourgault et al., 2022). Co-use of tobacco and alcohol is also associated with worse mental and cognitive outcomes (Muhammad et al., 2021). For example, a study using a large Indian sample found the highest risk of low cognition among co-use of tobacco and alcohol, followed by alcohol only and tobacco only (Khan, 2022). Another study of specific cognitive domains reported that those who co-use tobacco and alcohol perform worse than those who only use one substance on complex social-cognitive tasks such as prospective memory (Marshall et al., 2016). Together, these results suggest co-use of tobacco and alcohol may exacerbate cognitive decline in middle-aged and older adults and even raise dementia risk.

However, most existing studies on co-use of tobacco and alcohol and cognitive decline are cross-sectional (Bourgault et al., 2022, Muhammad et al., 2021), making it difficult to determine whether long-term substance use causes cognitive deterioration or whether earlier cognitive deficits increase vulnerability to substance use. Substance use and cognitive decline may follow a complex, bidirectional dynamic. On one hand, chronic substance use may accelerate brain aging and cognitive decline (Bachi et al., 2017, Muhammad et al., 2021). On the other hand, cognitive decline—such as reduced executive function or memory—can weaken self-control and raise propensity for substance use (Bakhshani and Hossienbor, 2013). Some studies show that deficits in executive function, attention, and decision making promote onset and recurrence of substance use, producing a vicious cycle between cognitive impairment and substance use (Ramey and Regier, 2019). These reciprocal effects are dynamic, whereas traditional statistical methods typically assume unidirectional predictive paths and thus struggle to capture potential cyclical feedback between substance use and cognitive change.

In recent years, several studies have applied cross-lagged panel network (CLPN) analysis to longitudinally track and parse mental-health concerns in middle-aged and older adults (Liu et al., 2024, Ma et al., 2025, Sun et al., 2024). The cross-lagged approach can examine the dynamic relationships between multiple variables over time, providing evidence to help reveal the underlying predictive direction (Gong and Ren, 2025, Ma et al., 2025). For example, one study used a random intercept cross-lagged panel model (RI-CLPM) to analyze the bidirectional relationship between value decision-making processes and addictive behaviors and provided moderate predictive relationship (Kräplin et al., 2025). Unlike traditional latent-variable approaches such as structural equation modeling, which require a priori causal assumptions, network analysis uses a data-driven approach to represent the structure of intervariable relations (Wysocki et al., 2025). CLPN builds on this by incorporating longitudinal data to model bidirectional lagged effects between variables. For example, Sun et al. (2024) used CLPN to explore the prospective correlation between depressive symptoms and cognitive function in an elderly population. They revealed the time series relationship between depressive symptoms and cognitive function, providing a deeper understanding. Using CLPN, we can probe complex linkages between multiple substance-use behaviors and specific cognitive domains within a single model, helping to overcome cross-sectional studies’ limits for predictive relationship. Moreover, longitudinal network analysis can reveal time-varying interaction pathways between substance use and cognitive function, clarify which substances show stronger lagged effects on cognitive decline, and show how changes in cognition in turn influence subsequent substance use. These insights can inform cognitive-health intervention strategies for middle-aged and older adults.

Taken together, this study aims to examine the bidirectional predictive relationship between co-use of tobacco and alcohol (nicotine and alcohol) and cognitive-decline symptoms in middle-aged and older adults. We used a longitudinal design and cross-lagged panel network analysis to comprehensively map the relationship between tobacco and alcohol co-use and cognitive decline, and compared this relationship to that in people who use only one substance, aiming to provide new insights into the challenges of substance use and cognitive health in the context of an aging population.