A common variant in the adiponectin gene on weight loss and body composition under sibutramine therapy in obesity

Pharmacogenomics of Medication-Induced Weight Gain and Antiobesity Medications

Obesity is a chronic, multifactorial disease associated with a large number of comorbidities. The clinical management of obesity involves a stepwise integrated approach, beginning with behavioral and lifestyle modification, followed by antiobesity medications, endobariatric procedures, and bariatric surgery. Weight gain and subsequent obesity are common side effects of medications, such as prednisone or antipsychotics. In this era of precision medicine, it is essential to identify patients at the highest risk of weight gain as a result of medication use. Pharmacogenomics could play an important role in obesity management by optimizing use of antiobesity medications as well as minimizing adverse weight gain. This review aims to provide a comprehensive analysis of the current literature on the role of pharmacogenomics in obesity and medication-induced weight gain. In summary, there are more robust studies of medication associated with weight gain and pharmacogenomics, and more studies are needed to understand the role of pharmacogenomics in antiobesity medications.

Effect of orlistat on periostin, adiponectin, inflammatory markers and ultrasound grades of fatty liver in obese NAFLD patients

Orlistat is recommended in the treatment of obesity, which is an independent risk factor for nonalcoholic fatty liver disease (NAFLD). The reported findings of orlistat in NAFLD are divisive. Recently, periostin is identified as an important regulatory molecule in the pathogenesis of obesity-induced fatty liver. Therefore, this study aimed to evaluate the potential effects of orlistat in the treatment of NAFLD. A 16-week prospective observational study was conducted, with obese NAFLD patient (n=77) receiving orlistat (120 mg capsules, three times a day) with hypocaloric diet or hypocaloric diet only. Grades of fatty liver were determined using ultrasound (US) echogenicity of liver; serum levels of periostin, adiponectin, tumor necrosis factor (TNF)-α and interleukin-6 were determined using ELISA kits at 0 and 16 weeks. Correlations of US grades of fatty liver with these biomarkers were also determined. Orlistat significantly reversed the US grades of fatty liver (P=0.016), decreased serum levels of periostin (P=0.030) and TNF-α (P=0.040), and increased serum adiponectin levels (P<0.001) when compared with hypocaloric diet only. Serum interleukin-6 levels were not found to be significantly different in both groups after the treatment. In the orlistat group, the degree of reduction in grades of fatty liver was found to be positively correlated with the changes in serum levels of periostin (rs=0.306, P=0.041) and adiponectin (rs=0.314, P=0.036), whereas the associations were insignificant with the change in serum levels of TNF-α (rs=0.053, P=0.729). Mild gastrointestinal side effects (20%) were reported in the orlistat group. In conclusion, orlistat is effective in the treatment of NAFLD patients without fibrosis. This study demonstrated a positive association between the reduction of fatty infiltration in the liver and the changes in serum levels of periostin and adiponectin in obese NAFLD patients.

Clinical and genetic predictors of weight gain in patients diagnosed with breast cancer

Background:

Post-diagnosis weight gain in breast cancer patients has been associated with increased cancer recurrence and mortality. Our study was designed to identify risk factors for this weight gain and create a predictive model to identify a high-risk population for targeted interventions.

Methods:

Chart review was conducted on 459 breast cancer patients from Northwestern Robert H. Lurie Cancer Centre to obtain weights and body mass indices (BMIs) over an 18-month period from diagnosis. We also recorded tumour characteristics, demographics, clinical factors, and treatment regimens. Blood samples were genotyped for 14 single-nucleotide polymorphisms (SNPs) in fat mass and obesity-associated protein (FTO) and adiponectin pathway genes (ADIPOQ and ADIPOR1).

Results:

In all, 56% of patients had >0.5 kg m^–2 increase in BMI from diagnosis to 18 months, with average BMI and weight gain of 1.9 kg m^–2 and 5.1 kg, respectively. Our best predictive model was a primarily SNP-based model incorporating all 14 FTO and adiponectin pathway SNPs studied, their epistatic interactions, and age and BMI at diagnosis, with area under receiver operating characteristic curve of 0.85 for 18-month weight gain.

Conclusion:

We created a powerful risk prediction model that can identify breast cancer patients at high risk for weight gain.