Metreleptin therapy lowers plasma angiopoietin-like protein 3 in patients with generalized lipodystrophy.

BACKGROUND: Reduced triglyceride clearance due to impaired lipoprotein lipase-mediated lipolysis contributes to severe hypertriglyceridemia in lipodystrophy. Angiopoietin-like protein 3 (ANGPTL3) and 4 (ANGPTL4) impair clearance of triglycerides by inhibiting lipoprotein lipase. Whether circulating ANGPTL3/4 levels are altered in lipodystrophy and the effects of leptin replacement on these ANGPTLs are unknown. OBJECTIVE: To examine if ANGPTL3/4 levels are elevated in patients with generalized lipodystrophy and assess the effects of leptin replacement on these ANGPTLs. METHODS: Preleptin treatment plasma levels of ANGPTLs in patients with generalized lipodystrophy (n = 22) were compared with healthy controls (n = 39) using a post hoc case-control study design. In a prospective open-label study, we studied the effects of metreleptin therapy (16-32 weeks) on plasma ANGPTL3/4 in patients with generalized lipodystrophy. RESULTS: Plasma ANGPTL3 (geometric mean [95% confidence interval]; 223 [182-275] vs 174 ng/mL [160-189], P = .02) but not ANGPTL4 levels (55 [37-81] vs 44 ng/mL [37-52], P = .26) were higher in patients with lipodystrophy compared with healthy controls. There was a significant decrease in total cholesterol, triglycerides, and glycosylated hemoglobin (A1C) levels following metreleptin therapy. After metreleptin, ANGPTL3 concentrations decreased significantly (223 [182-275] vs 175 ng/mL [144-214], P = .01) with no change in ANGPTL4 (55 [37-81] vs 48 ng/mL [32-73], P = .11). CONCLUSIONS: These findings suggest that elevated plasma levels of ANGPTL3 in leptin-deficient states is attenuated with leptin therapy.

Plasma serpinB1 is related to insulin sensitivity but not pancreatic ß-Cell function in non-diabetic adults.

Pancreatic ß-cell dysfunction because of reduced ß-cell mass and function is a primary determinant in the progression of diabetes. Increase in ß-cell mass and compensatory hyperinsulinaemia is frequently associated with insulin-resistant states. Although the humoral factors mediating this compensatory response are unknown, serpinB1, a protease inhibitor, has recently been proposed to be one such factor. In this study, we examine the relationships between plasma serpinB1, insulin sensitivity, and pancreatic ß-cell function in non-diabetic individuals. 117 subjects (women, n = 50, men, n = 67; age= 37.6 ± 10.8; BMI=31.1 ± 7.7 kg/m2) underwent an insulin-modified frequently sampled intravenous glucose tolerance test (FSIVGTT) at the NIH Clinical Research Center. Acute insulin response (AIR) and insulin sensitivity index (SI) were obtained from the FSIVGTT with MINMOD analysis. The Quantitative Insulin Sensitivity Check Index (QUICKI) was calculated from fasting insulin and glucose values. Plasma serpinB1 levels were measured using an ELISA assay. Simple linear correlation analyses were performed to evaluate the relationship between serpinB1 and measures of insulin sensitivity and ß-cell function. Circulating serpinB1 levels were unrelated to age, sex, race, BMI, or percent body fat. SI but not AIR significantly correlated with circulating serpinB1 levels (r = 0.23, P < 0.05). QUICKI tended to positively correlate with serpinB1 (r = 0.16, P = 0.09). Circulating serpinB1 is directly associated with insulin sensitivity but not ß-cell function in non-diabetic adults. Whether this modest association plays a role in insulin sensitivity in humans remains to be clarified.

Smoking cessation and the risk of diabetes mellitus and impaired fasting glucose: three-year outcomes after a quit attempt.

UNLABELLED: Weight gain after smoking cessation may increase diabetes mellitus and impaired fasting glucose (IFG) risk. This study evaluated associations between smoking cessation and continued smoking with incident diabetes and IFG three years after a quit attempt. The 1504 smokers (58% female) were mean (standard deviation) 44.7 (11.1) years old and smoked 21.4 (8.9) cigarettes/day. Of 914 participants with year 3 data, the 238 abstainers had greater weight gain, increase in waist circumference, and increase in fasting glucose levels than the 676 continuing smokers (p ≤ 0.008). In univariate analyses, Year 3 abstinence was associated with incident diabetes (OR = 2.60, 95% CI 1.44-4.67, p = .002; 4.3% absolute excess) and IFG (OR = 2.43, 95% CI 1.74-3.41, p<0.0001; 15.6% absolute excess). In multivariate analyses, incident diabetes was associated independently with older age (p = 0.0002), higher baseline body weight (p = 0.021), weight gain (p = 0.023), baseline smoking rate (p = 0.008), baseline IFG (p<0.0001), and baseline hemoglobin A1C (all p<0.0001). Smoking more at baseline predicted incident diabetes among eventual abstainers (p<0.0001); weighing more at baseline predicted incident diabetes among continuing smokers (p = 0.0004). Quitting smoking is associated with increased diabetes and IFG risk. Independent risk factors include older age, baseline body weight, baseline glycemic status, and heavier pre-quit smoking. These findings may help target smokers for interventions to prevent dysglycemia. TRIAL REGISTRATION: Clinicaltrials.gov NCT00332644.

Electrocardiographic changes associated with smoking and smoking cessation: outcomes from a randomized controlled trial.

INTRODUCTION: Cardiovascular disease (CVD) can be detected and quantified by analysis of the electrocardiogram (ECG); however the effects of smoking and smoking cessation on the ECG have not been characterized. METHODS: Standard 12-lead ECGs were performed at baseline and 3 years after subjects enrolled in a prospective, randomized, placebo-controlled clinical trial of smoking cessation pharmacotherapies. ECGs were interpreted using the Minnesota Code ECG Classification. The effects of (i) smoking burden on the prevalence of ECG findings at baseline, and (ii) smoking and smoking cessation on ECG changes after 3 years were investigated by multivariable and multinomial regression analyses. RESULTS: At baseline, 532 smokers were (mean [SD]) 43.3 (11.5) years old, smoked 20.6 (7.9) cigarettes/day, with a smoking burden of 26.7 (18.6) pack-years. Major and minor ECG criteria were identified in 87 (16.4%) and 131 (24.6%) of subjects, respectively. After adjusting for demographic data and known CVD risk factors, higher pack-years was associated with major ECG abnormalities (p = 0.02), but current cigarettes/day (p = 0.23) was not. After 3 years, 42.9% of subjects were abstinent from smoking. New major and minor ECG criteria were observed in 7.2% and 15.6% of subjects respectively, but in similar numbers of abstinent subjects and continuing smokers (p>0.2 for both). Continuing smokers showed significant reduction in current smoking (-8.4 [8.8] cigarettes/day, p<0.001) compared to baseline. CONCLUSIONS: In conclusion, major ECG abnormalities are independently associated with lifetime smoking burden. After 3 years, smoking cessation was not associated with a decrease in ECG abnormalities, although cigarettes smoked/day decreased among continuing smokers.

Long-term effects of smoking and smoking cessation on exercise stress testing: three-year outcomes from a randomized clinical trial.

BACKGROUND: The long-term effects of smoking and smoking cessation on markers of cardiovascular disease (CVD) prognosis obtained during treadmill stress testing (TST) are unknown. The purpose of this study was to evaluate the long-term effects of smoking cessation and continued smoking on TST parameters that predict CVD risk. METHODS: In a prospective, double-blind, randomized, placebo-controlled trial of 5 smoking cessation pharmacotherapies, symptom-limited TST was performed to determine peak METs, rate-pressure product (RPP), heart rate (HR) increase, HR reserve, and 60-second HR recovery, before and 3 years after the target smoking cessation date. Relationships between TST parameters and treatments among successful abstainers and continuing smokers were evaluated using multivariable analyses. RESULTS: At baseline, the 600 current smokers (61% women) had a mean age of 43.4 (SD 11.5) years and smoked 20.7 (8.4) cigarettes per day. Their exercise capacity was 8.7 (2.3) METs, HR reserve was 86.6 (9.6)%, HR increase was 81.1 (20.9) beats/min, and HR recovery was 22.3 (11.3) beats. Cigarettes per day and pack-years were independently and inversely associated with baseline peak METs (P < .001), RPP (P < .01, pack-years only), HR increase (P < .05), and HR reserve (P < .01). After 3 years, 168 (28%) had quit smoking. Abstainers had greater improvements than continuing smokers (all P < .001) in RPP (2,055 mm Hg beats/min), HR increase (5.9 beats/min), and HR reserve (3.7%), even after statistical adjustment (all P < .001). CONCLUSIONS: Smokers with a higher smoking burden have lower exercise capacity, lower HR reserve, and a blunted exercise HR response. After 3 years, TST improvements suggestive of improved CVD prognosis were observed among successful abstainers.

Effects of smoking intensity and cessation on inflammatory markers in a large cohort of active smokers.

BACKGROUND: Cigarette smoking has been associated with increases in C-reactive protein (CRP) and leukocyte counts (white blood cell [WBC]); however, the effects of smoking intensity and smoking cessation on inflammatory markers have not been evaluated prospectively in a large, modern cohort of current smokers. METHODS: White blood cell count and high-sensitivity CRP were measured in current smokers enrolled in a randomized, prospective clinical trial of 5 smoking cessation pharmacotherapies. Smoking intensity parameters included cigarettes per day, pack-years, Fagerström Test of Nicotine Dependence score, and carbon monoxide levels. C-reactive protein also was measured after 1 year with assessment of abstinence status. RESULTS: The 1,504 current smokers (58% female) were (mean [SD]) 44.7 (11.1) years old, smoked 21.4 (8.9) cigarettes per day, and had a smoking burden of 29.4 (20.4) pack-years. Log(CRP) was not associated with any marker of smoking intensity, except for a weak correlation with pack-years (r = 0.05, P = .047). In contrast, statistically significant correlations were observed between all 4 markers of smoking intensity and WBC count (all P ≤ .011). In multivariable models, waist circumference (P < .001) and triglycerides (P < .05), but no markers of smoking intensity, were associated with log(CRP). However, pack-years (P = .002), cigarettes per day (P = .013), carbon monoxide (P < .001), and Fagerström Test of Nicotine Dependence (P < .001) were independently associated with WBC count. After 1 year, log(CRP) (P = .296) and changes in log(CRP) (P = .455) did not differ between abstainers and continuing smokers. CONCLUSIONS: Smoking intensity is associated with increased WBC count, but not CRP levels. Smoking cessation does not reduce CRP. The relationship between CRP and smoking intensity may be masked by CRP's stronger relationship with adiposity.