López-Jiménez, Francisco

Background— There have been significant bidirectional changes in the prevalence of cardiovascular (CV) risk factors over time in the United States, making the net trend in risk for incident CV disease unknown. We assessed these trends by applying the Framingham Heart Study prediction model to national data. Methods and Results— The National Health and Nutrition Examination Survey (NHANES) II (1976–1980), NHANES III (1988–1994), and NHANES 1999–2004 are cross-sectional representative samples of the noninstitutionalized population of the United States. We excluded people with a history of CV disease, pregnant women, participants with missing CV risk factors data, and individuals outside the Framingham age range of 30 to 74 years. The Framingham risk function was used to estimate the 10-year risk for incident symptomatic CV disease. We calculated the slope of change or rate of change per year between NHANES II and III, and between NHANES III and 1999–2004. The difference between slopes was calculated and compared to zero. The average age-adjusted 10-year CV risk between NHANES II and III decreased from 10.0% to 7.9% between NHANES II and III, with a statistically significant slope ( P <0.001). However, the average age-adjusted CV risk decreased at a lesser magnitude between NHANES III and NHANES 1999–2004 from 7.9% to 7.4% ( P <0.001). These slopes were significantly different ( P <0.0001). In women and middle-aged participants, CV risk did not change between NHANES III and NHANES 1999–2004 ( P =0.40). Conclusions— The estimated net risk for CV disease in the US population decreased from 1976–1980 to 1988–1994 but has changed minimally from 1988–1994 to 1999–2004, particularly in women and middle-aged people.

The combination of metabolic syndrome and obstructive sleep apnea (OSA) has been termed “syndrome Z.” The prevalence of both OSA and metabolic syndrome is increasing worldwide, in part linked to the epidemic of obesity. Beyond their epidemiologic relationship, growing evidence suggests that OSA may be causally related to metabolic syndrome. We are only beginning to understand the potential mechanisms underlying the OSA–metabolic syndrome interaction. Although there is no clear consensus, there is growing evidence that alterations in the hypothalamic–pituitary axis, generation of reactive oxygen species (ROS) due to repetitive hypoxia, inflammation, and generation of adipokines may be implicated in the changes associated with both OSA and metabolic syndrome. Whether some or all of these metabolic alterations mechanistically link OSA to metabolic syndrome remains to be proven, but it is an area of intense scientific interest.

Recent reports suggest that IGF (insulin-like growth factor)-I and IGFBP-3 (IGF-binding protein-3) have independent and opposing mechanistic effects on insulin. The aim of the present study was to assess the relationship between the IGF-I/IGFBP-3 ratio and the metabolic syndrome. We examined 3281 subjects (1463 men and 1818 women, aged 20–49 years), otherwise healthy adults, who participated in NHANES III (Third National Health and Nutrition Examination Survey), which has released measurements of IGF-I and IGFBP-3. Insulin resistance was estimated using the computer HOMA2 (homoeostatic model assessment 2) model. The updated ATP-III (Adult Treatment Panel III) definition of the metabolic syndrome was used. We applied adjusted logistic and linear regression models. After adjusting for age and race, men and women in the lowest quartile of the IGF-I/IGFBP-3 ratio were 3-fold more likely to meet the ATP-III definition of the metabolic syndrome and twice as likely to be insulin-resistant. Mean values of the IGF-I/IGFBP-3 ratio decreased significantly as the number of metabolic syndrome components increased (P&lt;0.0001, as determined by ANOVA). The area under the ROC (receiver operating characteristic) curve for detecting insulin resistance using the IGF-I/IGFBP-3 ratio was 0.760, significantly improving upon either protein alone (P=0.01). In conclusion, the IGF-I/IGFBP-3 ratio is significantly associated with the metabolic syndrome. Calculating the ratio of these two proteins may provide insight into the metabolic syndrome clustering phenomenon.