Tea and coffee consumption in relation to depression and anxiety symptoms: findings from the multicentric LIPOKAP study.

Depression is one of the most prevalent mental disorders worldwide. According to the World Mental Health Report 2022, an estimated 280 million people were affected by depression in 2019. Based on years of healthy life lost to disability (YLDs), depressive disorders rank as the second leading cause of disease burden globally, accounting for 5.6% of all YLDs in 2019. In China, a national epidemiological survey conducted between 2013 and 2015 reported a prevalence of 6.8%, exceeding the global average. Although depression affects individuals across all age groups, college students are at high risk of depression. Studies have shown that, compared with their non-college peers, college students exhibit poor mental health and face higher risks of depression and anxiety. The global burden of depression among young adults is particularly concerning, as the Global Burden of Disease Study consistently identifies depressive disorders as a leading cause of disability in this age group worldwide. A systematic review of studies worldwide further indicated that the pooled prevalence of depression among university students ranges from 30.6% to 33.6%, highlighting a major public health challenge for higher education institutions globally. Depression is widely recognized as a multi-factorial disorder resulting from the complex interplay of biological, psychological and social factors. Numerous cross-sectional and longitudinal studies have demonstrated strong associations between depression or depressive symptoms among college students and a range of factors, including gender, interpersonal relationships, academic pressure, self-rated health, family relationships, socioeconomic status, childhood experiences, growth environment, and negative life events. However, the relative importance of these factors remains inconclusive. Hence, identifying and screening the key determinants of depressive symptoms among college students is crucial for building effective predictive models.