  Currently emerging "big data" techniques are reshaping medical science into a
data science. Medical claims data allow assessing an entire nation's health
state in a quantitative way, in particular with regard to the occurrences and
consequences of chronic and pandemic diseases like diabetes.
  We develop a quantitative, statistical approach to test for associations
between the incidence of type 1 or type 2 diabetes and any possible other
disease as provided by the ICD10 diagnosis codes using a complete set of
Austrian inpatient data. With a new co-occurrence analysis the relative risks
for each possible comorbidity are studied as a function of patient age and
gender, a temporal analysis investigates whether the onset of diabetes
typically precedes or follows the onset of the other disease. The samples is
always of maximal size, i.e. contains all patients with that comorbidity within
the country. The present study is an equivalent of almost 40,000 studies, all
with maximum patient number available. Out of more than thousand possible
associations, 123 comorbid diseases for type 1 or type 2 diabetes are
identified at high significance levels.
  Well known diabetic comorbidities are recovered, such as retinopathies,
hypertension, chronic kidney diseases, etc. This validates the method.
Additionally, a number of comorbidities are identified which have only been
recognized to a lesser extent, for example epilepsy, sepsis, or mental
disorders. The temporal evolution, age, and gender-dependence of these
comorbidities are discussed. The new statistical-network methodology developed
here can be readily applied to other chronic diseases.
