Previous evidence on the associations of dairy intake with risk of cardiometabolic diseases has been inconsistent with studies showing inverse, null, or positive associations. We aimed to assess these associations in China, where dairy consumption level is low and cardiometabolic disease patterns differ from those in the West. The China Kadoorie Biobank is a prospective cohort study with ∼512,000 adult participants recruited from 10 diverse localities in China during 2004-2008. At baseline and periodic resurveys, information on the consumption frequency of major food groups was collected using a validated interviewer-administered laptop-based questionnaire. During ∼ 5.4 million person-years of follow-up, 18,306 diabetes, 33,946 ischemic heart diseases [IHD, including 3888 acute myocardial infarction (MI)], 33,670 ischemic stroke, 7191 intracerebral hemorrhage (ICH) cases, and 13,241 cardiovascular deaths were recorded. Cox regression was used to calculate adjusted hazard ratios (HRs) relating dairy intake to cardiometabolic disease risk. At baseline, 10.7% of participants regularly consumed (i.e., ≥4 d/wk) dairy products, whereas 70.0% reported never or rare consumption. After adjusting for potential confounders including body mass index, dairy consumption was significantly and positively associated with IHD but inversely associated with risks of acute MI, ICH and cardiovascular death, with HRs for regular consumers compared with nonconsumers being 1.09 (95% CI: 1.06, 1.12), 0.88 (0.80, 0.98), 0.69 (0.62, 0.76), and 0.82 (0.77, 0.87), respectively, but not with diabetes and IS. These associations were largely independent of systolic blood pressure. In Chinese adults, higher dairy consumption was associated with lower risks of acute MI, ICH, and cardiovascular death. Future studies are warranted to further elucidate these relationships and their causality.

Computational approaches are transforming nutrition science by integrating data from wearables, digital health platforms, and multiomics technologies to unravel complex diet-health interactions. Traditional statistical models cannot adequately capture the temporal, nonlinear, and individual variability inherent in such data. Computational nutrition, integrating data science, machine learning, and systems modeling, has therefore emerged as a distinct and rapidly developing field. Landmark studies have demonstrated its potential to improve dietary assessment, predict metabolic responses, and design personalized interventions. From an early-career perspective, however, the rise of computational nutrition also exposes structural and educational gaps. Early-career researchers often encounter fragmented training, limited mentorship, and restricted access to interoperable data and computational infrastructure. Empowering early-career researchers through integrated curricula, equitable data access, and recognition of interdisciplinary contributions will be essential for ensuring that computational nutrition evolves into a transparent, reproducible, and inclusive discipline capable of advancing both personalized and population-level nutrition.