Socio-affective skills and early academic disengagement in higher education: a sequential mediation model of emotion regulation and academic self-efficacy.

With the acceleration of global population aging, cognitive impairment has emerged as a defining public health challenge of the 21st century, placing a heavy burden on both individuals and society [, ]. Alzheimer’s disease and related dementias (ADRD)—including vascular, frontotemporal, and mixed etiologies—account for severe cognitive impairment in ~10% of US adults aged ≥ 65 years [, , ]. An additional 15%–22% live with mild cognitive impairment (MCI), a state of measurable cognitive decline with preserved activities of daily living that does not meet diagnostic criteria for dementia [, , ]. Cognitive function involves domains including complex attention, executive function, learning and memory, language, perceptual‐motor function, and social cognition. Decline in these domains (i.e., cognitive decline) is a core feature of ADRD and MCI []. The identification of modifiable risk factors and an in‐depth understanding of the mechanisms of their association are fundamental to the early prevention and delayed onset of diseases. Among numerous risk factors, a strong association between depression and cognitive decline has been well‐established by extensive epidemiological studies [–]. Individuals with depressive symptoms face a significantly elevated risk of cognitive decline, an association that remains independent of traditional confounders such as age, sex, educational attainment, and vascular risk factors [–, , ]. However, a critical limitation pervades the existing literature, as the vast majority of evidence derives from cross‐sectional designs or single‐occasion depression assessments. Depression is intrinsically fluctuating and often chronic [, ], and its neuropathological impact on cognition—hippocampal atrophy, prefrontal hypofunction, and sustained neuroinflammation—accumulates progressively across the illness trajectory [, ]. A single snapshot can neither distinguish cumulative high‐intensity symptom burden nor capture the dynamic course of mood fluctuations over time; consequently, it may yield a biased—and potentially underestimated—assessment of cognitive decline risk. Integrating cumulative exposure intensity, high‐exposure duration, and dynamic change patterns of depressive symptoms may therefore offer a potentially useful framework for identifying the cognitive decline risk.