Background and ObjectivePeriodontal diseases are a major public health concern leading to tooth loss and have also been shown to be associated with several chronic systemic diseases. Smoking is a major risk factor for the development of numerous systemic diseases, as well as periodontitis. While it is clear that smokers have a significantly enhanced risk for developing periodontitis leading to tooth loss, the population varies regarding susceptibility to disease associated with smoking. This investigation focused on identifying differences in four broad sets of variables, consisting of: (i) host-response molecules; (ii) periodontal clinical parameters; (iii) antibody responses to periodontal pathogens and oral commensal bacteria; and (iv) other variables of interest, in a population of smokers with (n = 171) and without (n = 117) periodontitis. Material and MethodsBayesian network structured learning (BNSL) techniques were used to investigate potential associations and cross-talk between the four broad sets of variables. ResultsBNSL revealed two broad communities with markedly different topology between the populations of smokers, with and without periodontitis. Confidence of the edges in the resulting network also showed marked variations within and between the periodontitis and nonperiodontitis groups. ConclusionThe results presented validated known associations and discovered new ones with minimal precedence that may warrant further investigation and novel hypothesis generation. Cross-talk between the clinical variables and antibody profiles of bacteria were especially pronounced in the case of periodontitis and were mediated by the antibody response profile to Porphyromonas gingivalis.