OBJECTIVE—Adiponectin, a plasma protein exclusively synthesized and secreted by adipose tissue, has recently been shown to have anti-inflammatory, antiatherogenic properties in vitro and beneficial metabolic effects in animals. Lower plasma levels of adiponectin have been documented in human subjects with metabolic syndrome and coronary artery disease. We investigated whether the level of this putative protective adipocytokine could be increased by treatment with a peroxisome proliferator-activated receptor-γ (PPAR-γ) agonist in diabetic patients. RESEARCH DESIGN AND METHODS—Type 2 diabetic patients (30 in the treatment group and 34 in the placebo group) were recruited for a randomized double-blind placebo-controlled trial for 6 months with the PPAR-γ agonist rosiglitazone. Blood samples were collected and metabolic variables and adiponectin levels were determined in all patients before initiation of the study. RESULTS—In the rosiglitazone group, mean plasma adiponectin level was increased by more than twofold (P < 0.0005), whereas no change was observed in the placebo group. Multivariate linear regression analysis showed that whether rosiglitazone was used was the single variable significantly related to the changes of plasma adiponectin. The amount of variance in changes of plasma adiponectin level explained by the treatment was ∼24% (r2 = 0.24) after adjusting for age, sex, and changes in fasting plasma glucose, HbA1c, insulin resistance index, and BMI. CONCLUSIONS—Rosiglitazone increases plasma adiponectin levels in type 2 diabetic subjects. Whether this may contribute to the antihyperglycemic and putative antiatherogenic benefits of PPAR-γ agonists in type 2 diabetic patients warrants further investigation.

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In molecular biology, the issue of quantifying the similarity between two biological sequences is very important. Past research has shown that word-based search tools are computationally efficient and can find some new functional similarities or dissimilarities invisible to other algorithms like FASTA. Recently, under the independent model of base composition, Wu, Burke, and Davison (1997, Biometrics 53, 1431 1439) characterized a family of word-based dissimilarity measures that defined distance between two sequences by simultaneously comparing the frequencies of all subsequences of n adjacent letters (i.e., n-words) in the two sequences. Specifically, they introduced the use of Mahalanobis distance and standardized Euclidean distance into the study of DNA sequence dissimilarity. They showed that both distances had better sensitivity and selectivity than the commonly used Euclidean distance. The purpose of this article is to extend Mahalanobis and standardized Euclidean distances to Markov chain models of base composition. In addition, a new dissimilarity measure based on Kullback-Leibler discrepancy between frequencies of all n-words in the two sequences is introduced. Applications to real data demonstrate that Kullback-Leibler discrepancy gives a better performance than Euclidean distance. Moreover, under a Markov chain model of order kQ for base composition, where kQ is the estimated order based on the query sequence, standardized Euclidean distance performs very well. Under such a model, it performs as well as Mahalanobis distance and better than Kullback-Leibler discrepancy and Euclidean distance. Since standardized Euclidean distance is drastically faster to compute than Mahalanobis distance, in a usual workstation/PC computing environment, the use of standardized Euclidean distance under the Markov chain model of order kQ of base composition is generally recommended. However, if the user is very concerned with computational efficiency, then the use of Kullback-Leibler discrepancy, which can be computed as fast as Euclidean distance, is recommended. This can significantly enhance the current technology in comparing large datasets of DNA sequences.