Differential effects of leptin on adiponectin expression with weight gain versus obesity.

BACKGROUND/OBJECTIVE: Adiponectin exerts beneficial effects by reducing inflammation and improving lipid metabolism and insulin sensitivity. Although the adiponectin level is lower in obese individuals, whether weight gain reduces adiponectin expression in humans is controversial. We sought to investigate the role of weight gain, and consequent changes in leptin, on altering adiponectin expression in humans. METHODS/RESULTS: Forty-four normal-weight healthy subjects were recruited (mean age 29 years; 14 women) and randomized to either gain 5% of body weight by 8 weeks of overfeeding (n=34) or maintain weight (n=10). Modest weight gain of 3.8±1.2 kg resulted in increased adiponectin level (P=0.03), whereas weight maintenance resulted in no changes in adiponectin. Further, changes in adiponectin correlated positively with changes in leptin (P=0.0085). In-vitro experiments using differentiated human white preadipocytes showed that leptin increased adiponectin mRNA and protein expression, whereas a leptin antagonist had opposite effects. To understand the role of leptin in established obesity, we compared adipose tissue samples obtained from normal-weight versus obese subjects. We noted, first, that leptin activated cellular signaling pathways and increased adiponectin mRNA in the adipose tissue from normal-weight participants, but did not do so in the adipose tissue from obese participants. Second, we noted that obese subjects had increased caveolin-1 expression, which attenuates leptin-dependent increases in adiponectin. CONCLUSIONS: Modest weight gain in healthy individuals is associated with increases in adiponectin levels, which correlate positively with changes in leptin. In vitro, leptin induces adiponectin expression, which is attenuated by increased caveolin-1 expression. In addition, the adipose tissue from obese subjects shows increased caveolin-1 expression and impaired leptin signaling. This leptin signal impairment may prevent concordant increases in adiponectin levels in obese subjects despite their high levels of leptin. Therefore, impaired leptin signaling may contribute to low adiponectin expression in obesity and may provide a target for increasing adiponectin expression, hence improving insulin sensitivity and cardio-metabolic profile in obesity.

Obstructive sleep apnea and acute coronary syndromes: etiology, risk, and management.

Obstructive sleep apnea (OSA) is characterized by upper airway collapse and airflow reduction despite respiratory effort, resulting in intermittent hypoxia and arousals, leading to a cascade of hemodynamic, autonomic, inflammatory, and metabolic effects, responsible for its adverse cardiovascular effect. OSA is an independent risk factor for cardiovascular disease, and its prevalence in patients presenting with acute coronary syndromes is up to 69%. Furthermore, OSA has been associated with increased risk of adverse events after an acute coronary syndrome. Continuous positive airway pressure is considered the mainstay of treatment of OSA and has been shown to reduce the risk of cardiovascular events. However, the proper time to start treatment in the acute setting is unknown. A prospective randomized clinical trial is currently underway to answer this question.

Diagnostic performance of body mass index to identify obesity as defined by body adiposity: a systematic review and meta-analysis.

OBJECTIVE: We performed a systematic review and meta-analysis of studies that assessed the performance of body mass index (BMI) to detect body adiposity. DESIGN: Data sources were MEDLINE, EMBASE, Cochrane, Database of Systematic Reviews, Cochrane CENTRAL, Web of Science, and SCOPUS. To be included, studies must have assessed the performance of BMI to measure body adiposity, provided standard values of diagnostic performance, and used a body composition technique as the reference standard for body fat percent (BF%) measurement. We obtained pooled summary statistics for sensitivity, specificity, positive and negative likelihood ratios (LRs), and diagnostic odds ratio (DOR). The inconsistency statistic (I2) assessed potential heterogeneity. RESULTS: The search strategy yielded 3341 potentially relevant abstracts, and 25 articles met our predefined inclusion criteria. These studies evaluated 32 different samples totaling 31 968 patients. Commonly used BMI cutoffs to diagnose obesity showed a pooled sensitivity to detect high adiposity of 0.50 (95% confidence interval (CI): 0.43-0.57) and a pooled specificity of 0.90 (CI: 0.86-0.94). Positive LR was 5.88 (CI: 4.24-8.15), I (2)=97.8%; the negative LR was 0.43 (CI: 0.37-0.50), I (2)=98.5%; and the DOR was 17.91 (CI: 12.56-25.53), I (2)=91.7%. Analysis of studies that used BMI cutoffs >or=30 had a pooled sensitivity of 0.42 (CI: 0.31-0.43) and a pooled specificity of 0.97 (CI: 0.96-0.97). Cutoff values and regional origin of the studies can only partially explain the heterogeneity seen in pooled DOR estimates. CONCLUSION: Commonly used BMI cutoff values to diagnose obesity have high specificity, but low sensitivity to identify adiposity, as they fail to identify half of the people with excess BF%.

Accuracy of body mass index in diagnosing obesity in the adult general population.

BACKGROUND: Body mass index (BMI) is the most widely used measure to diagnose obesity. However, the accuracy of BMI in detecting excess body adiposity in the adult general population is largely unknown. METHODS: A cross-sectional design of 13 601 subjects (age 20-79.9 years; 49% men) from the Third National Health and Nutrition Examination Survey. Bioelectrical impedance analysis was used to estimate body fat percent (BF%). We assessed the diagnostic performance of BMI using the World Health Organization reference standard for obesity of BF%>25% in men and>35% in women. We tested the correlation between BMI and both BF% and lean mass by sex and age groups adjusted for race. RESULTS: BMI-defined obesity (> or =30 kg m(-2)) was present in 19.1% of men and 24.7% of women, while BF%-defined obesity was present in 43.9% of men and 52.3% of women. A BMI> or =30 had a high specificity (men=95%, 95% confidence interval (CI), 94-96 and women=99%, 95% CI, 98-100), but a poor sensitivity (men=36%, 95% CI, 35-37 and women=49%, 95% CI, 48-50) to detect BF%-defined obesity. The diagnostic performance of BMI diminished as age increased. In men, BMI had a better correlation with lean mass than with BF%, while in women BMI correlated better with BF% than with lean mass. However, in the intermediate range of BMI (25-29.9 kg m(-2)), BMI failed to discriminate between BF% and lean mass in both sexes. CONCLUSIONS: The accuracy of BMI in diagnosing obesity is limited, particularly for individuals in the intermediate BMI ranges, in men and in the elderly. A BMI cutoff of> or =30 kg m(-2) has good specificity but misses more than half of people with excess fat. These results may help to explain the unexpected better survival in overweight/mild obese patients.