Noncommunicable diseases have become an epidemic that poses devastating health consequences for individuals, families and communities globaly. Cardiovascular disease (CVD), cancer, chronic respiratory disease and diabetes are the leading causes of death affecting ~33.3 million people annually, ~80% due to CVD and cancer (54% and 28%, respectively; 2019). As such, prevention and control of these diseases is currently a major public health priority. In addition to preventive measures, early detection could dramatically improve disease outcomes, particularly in cancer, where precise diagnosis and staging of the disease are critical for successful treatment. However, current biomarkers have only limited success, largely due to low sensitivity and specificity. Diet can dramatically affect health, and different dietary habits have been associated with various human diseases such as cancer, cardiovascular diseases (CVD), type II diabetes, obesity, and hypertension. In particular, high consumption of red meat has been frequently suggested as a risk factor for human cancers and CVD. We aimed to develop biomarkers for cancer and CVD based on signatures of antibodies against glycans screened over nano-printed arrays, followed by deep-learning analysis. Such unique signatures of antibodies could be used to identify the disease at an early stage, and allow personalized dietary recommendations or a better therapeutic window. These novel sugar-based disease diagnostics could dramatically improve prognosis of cancer and CVD.