Background-The association between concentrations of uric acid and the metabolic syndrome in children and adolescents remains incompletely understood. The objective of this study was to examine how these 2 were associated in a nationally representative sample of US children and adolescents. Methods and Results-We performed a cross-sectional analysis of 1370 males and females aged 12 to 17 years using data from the National Health and Nutrition Examination Survey 1999 -2002. The prevalence of the metabolic syndrome was Ͻ1% among participants in the lowest quartile of serum concentration of uric acid, 3.7% in the second quartile, 10.3% in the third quartile, and 21.1% in the highest quartile. Compared with the lowest 2 quartiles of uric acid together (Յ291.5 mol/L), the odds ratios were 5.80 (95% confidence interval, 3.22 to 10.46) for those in the third quartile (Ͼ291.5 to Յ339 mol/L or Ͼ4.9 to Յ5.7 mg/dL) and 14.79 (95% confidence interval, 7.78 to 28.11) for those in the top quartile (Ͼ339 mol/L) after adjustment for age, sex, race or ethnicity, and concentrations of C-reactive protein.Starting with the lowest quartile of concentration of uric acid, mean concentrations of serum insulin were 66.2, 66.7, 79.9, and 90.9 pmol/L for ascending quartiles, respectively (P for trend Ͻ0.001). Conclusions-Among US children and adolescents, serum concentrations of uric acid are strongly associated with the prevalence of the metabolic syndrome and several of its components.

Impaired fasting glucose and IGT are associated with modest increases in the risk for cardiovascular disease.

OBJECTIVE—Impaired fasting glucose (IFG) and/or impaired glucose tolerance (IGT) are considered to constitute “pre-diabetes.” We estimated the prevalence of IFG, IGT, and pre-diabetes among U.S. adolescents using data from a nationally representative sample. RESEARCH DESIGN AND METHODS—We analyzed data from participants aged 12–19 years in the National Health and Nutrition Examination Survey 2005–2006. We used fasting plasma glucose and 2-h glucose during an oral glucose tolerance test to assess the prevalence of IFG, IGT, and pre-diabetes and used the log-binomial model to estimate the prevalence ratios (PRs) and 95% CIs. RESULTS—The unadjusted prevalences of IFG, IGT, and pre-diabetes were 13.1, 3.4, and 16.1%, respectively. Boys had a 2.4-fold higher prevalence of pre-diabetes than girls (95% CI 1.3–4.3). Non-Hispanic blacks had a lower rate than non-Hispanic whites (PR 0.6, 95% CI 0.4–0.9). Adolescents aged 16–19 years had a lower rate than those aged 12–15 years (0.6, 0.4–0.9). Overweight adolescents had a 2.6-fold higher rate than those with normal weight (1.3–5.1). Adolescents with two or more cardiometabolic risk factors had a 2.7-fold higher rate than those with none (1.5–4.8). Adolescents with hyperinsulinemia had a fourfold higher prevalence (2.2–7.4) than those without. Neither overweight nor number of cardiometabolic risk factors was significantly associated with pre-diabetes after adjustment for hyperinsulinemia. CONCLUSIONS—Pre-diabetes was highly prevalent among adolescents. Hyperinsulinemia was independently associated with pre-diabetes and may account for the association of overweight and clustering of cardiometabolic risk factors with pre-diabetes.

Objective. To determine the prevalence of metabolic syndrome among patients with gout and to examine the association between the 2 conditions in a nationally representative sample of US adults. Methods. Using data from 8,807 participants age >20 years in the Third National Health and Nutrition Examination Survey (1988 -1994), we determined the prevalence of metabolic syndrome among individuals with gout and quantified the magnitude of association between the 2 conditions. We used both the revised and original National -4.61), respectively. With the original NCEP/ATP criteria, the corresponding prevalences were slightly lower, whereas the corresponding odds ratios were slightly higher. The stratified prevalences of metabolic syndrome by major associated factors of gout (i.e., body mass index, hypertension, and diabetes) remained substantially and significantly higher among those with gout than those without gout (all P values <0.05). Conclusion. These findings indicate that the prevalence of metabolic syndrome is remarkably high among individuals with gout. Given the serious complications associated with metabolic syndrome, this frequent comorbidity should be recognized and taken into account in long-term treatment and overall health of individuals with gout.

OBJECTIVEWe sought to assess the associations of testosterones and sex hormone–binding globulin (SHBG) with metabolic syndrome and insulin resistance in men.RESEARCH DESIGN AND METHODSWe defined metabolic syndrome according to the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Among men aged ≥20 years who participated in the Third National Health and Nutrition Examination Survey (n = 1,226), the Cox proportional hazards model was used to estimate the prevalence ratio and 95% CI of metabolic syndrome according to circulating concentrations of testosterones and SHBG.RESULTSAfter adjustment for age, race/ethnicity, smoking status, alcohol intake, physical activity level, LDL cholesterol, C-reactive protein, and insulin resistance, men in the first quartile (lowest) (prevalence ratio 2.16 [95% CI 1.53–3.06]) and second quartile of total testosterone (2.51 [1.86–3.37]) were more likely to have metabolic syndrome than men in the fourth quartile (highest, referent group) (P < 0.001 for linear trend). Similarly, men in the first quartile of SHBG (2.17 [1.32–3.56]) were more likely to have metabolic syndrome than men in the fourth quartile (P = 0.02 for linear trend). No significant associations of calculated free testosterone (P = 0.31 for linear trend) and bioavailable testosterone (P = 0.11 for linear trend) with metabolic syndrome were detected after adjustment for all possible confounders.CONCLUSIONSLow concentrations of total testosterone and SHBG were strongly associated with increased likelihood of having metabolic syndrome, independent of traditional cardiovascular risk factors and insulin resistance.

Background:  Conditions that affect erythrocyte turnover affect HbA1c concentrations. Although many forms of anemia are associated with lowering of HbA1c, iron deficiency tends to increase HbA1c. We examined the effect of iron and hemoglobin (Hb) status on HbA1c and on the relationship between concentrations of fasting glucose and HbA1c in a national sample of adults in the US. Methods:  Cross‐sectional data from 8296 adults aged ≥20 years who participated in NHANES 1999–2002 were used. Results:  The prevalence of low Hb (defined as <120 and <118 g/L in women aged 20–69 and ≥70 years, respectively, and <137, <133, and <124 g/L in men aged 20–49, 50–69, and ≥70 years, respectively) was 5.5%. There was a significant positive correlation between Hb concentrations and HbA1c concentrations after adjusting for age, gender, and race or ethnicity, with HbA1c rising from a mean of 5.28% among participants with Hb <100 g/L to 5.72% among participants with Hb ≥170 g/L. The adjusted mean concentrations of HbA1c were 5.56% and 5.46% among participants with and without iron deficiency, respectively (P = 0.095). However, there was no evidence of differences in the relationship between fasting glucose and HbA1c when groups of anemic and non‐anemic individuals with and without iron deficiency were examined individually. Conclusions:  Caution should be used when diagnosing diabetes and prediabetes among people with high or low Hb when the HbA1c level is near 6.5% or 5.7%, respectively, as changes in erythrocyte turnover may alter the test result. However, the trend for HbA1c to increase with iron deficiency does not appear to require screening for iron deficiency in ascertaining the reliability of HbA1c in the diagnosis of diabetes and prediabetes in a given individual.

Among US adults, hypertriglyceridemia is common. Until the benefits of treating hypertriglyceridemia that is not characterized by extreme elevations of TG concentration with medications are incontrovertible, therapeutic lifestyle change remains the preferred treatment.

Peak bone mass is genetically determined, but little is known about the heritability of bone loss. Inbred mice were ovariectomized at 16 weeks of age and killed at three time-points after surgery. We found that the variation in estrogen deficit-related cortical bone loss is genetically determined.Introduction: Variability in adult bone morphology and composition among three inbred mouse strains-A/J, C57BL/6J (B6), and C3H/HeJ (C3H)-suggests that they gain bone in different ways during growth. In this study, we tested the hypothesis that these strains would also lose bone differently after estrogen deprivation. Materials and Methods: Female A/J, B6, and C3H mice (N ‫ס‬ 70/strain) were either ovariectomized (OVX) or sham-operated at 16 weeks of age and killed at 4, 8, and 16 weeks after surgery. Cortical bone histomorphometry was performed on right femoral mid-diaphyseal cross-sections. Mechanical properties were determined by loading left femoral mid-diaphyses to failure in four-point bending. Results: Both OVX-A/J and OVX-B6 mice showed a 7-8% decrease in cortical area and width because of an 8-10% marrow expansion at 16 weeks after OVX. This bone loss did not affect mechanical properties in OVX-A/J femurs, but maximum load and stiffness in OVX-B6 decreased slightly (9%) at 4 and 8 weeks, and markedly (14-19%) at 16 weeks after OVX. In contrast, OVX-C3H showed a significant decrease in cortical area and width (6-7%) at 4 weeks after OVX and a slight decrease in the subperiosteal area (4%) at 8 weeks after OVX, although marrow area remained unchanged. Surprisingly, intracortical resorption spaces, which were present in sham-C3H mice, were greatly increased (+195%) in OVX-C3H mice at 8 weeks after OVX. Bone strength and stiffness in OVX-C3H mice decreased markedly (12-14%) at 4 weeks but slightly (8-10%) at 8 weeks after OVX. All indices except intracortical pore area in OVX-C3H mice returned to sham levels at 16 weeks after OVX. Conclusions: The magnitude, timing, and location of cortical bone loss after OVX varied significantly among A/J, B6, and C3H mice. The subsequent changes in mechanical properties after OVX depended on the variable bone patterns as well as the size and shape of the adult bone. Our results suggest that patterns of estrogen deficit-associated cortical bone loss are genetically determined.