Prachi Singh

Normal-Weight Central Obesity: Implications for Total and Cardiovascular Mortality

Context It is uncertain whether measures of central obesity, such as waist-to-hip ratio (WHR), provide additional information beyond body mass index (BMI) in defining mortality risks associated with obesity. Contribution This population-based cohort study found that normal BMI and central obesity (defined by WHR) were associated with the worst long-term survival compared with individuals with normal fat distribution regardless of BMI category. Caution Information on body fat distribution was based on anthropometric indicators alone. Implication Normal weight with central obesity may represent an important group for targeted lifestyle modifications and other preventive strategies. Obesity defined by body mass index (BMI) or measures of central obesity, such as waist-to-hip ratio (WHR) and waist circumference, is associated with increased total and cardiovascular mortality (13). However, a recent meta-analysis (4) showed that being overweight according to BMI was actually associated with lower total mortality, challenging the paradigm that BMI is linked to increased mortality. Further, whether measures of fat distribution provide any incremental risk information beyond BMI alone has been a major source of controversy (2, 3, 58). Indeed, the 2013 American Heart Association/American College of Cardiology/The Obesity Society guideline for the management of obesity (9) does not recommend measuring WHR and assumes that persons with normal BMI are not exposed to any obesity-related cardiovascular risk in view of the limited available data proving otherwise. A recent large study (6) showed that incorporating waist circumference information in prediction models did not increase the prognostic value already provided by BMI; however, for a given BMI category, subgroups of waist circumference or WHR were also associated with increased mortality risk. Other studies showed that measures of central obesity, such as WHR, waist-to-height ratio (10, 11), or waist circumference alone, may provide additional information beyond BMI on mortality risk among middle-aged adults, provided there is no adjustment for obesity-related cardiovascular risk factors (2, 3, 7). Further, a study (12) has shown that measures of central obesity are more strongly associated with total and cardiovascular disease mortality rates than BMI. Another study (13) showed that persons with normal body weight measured by BMI, but who had increased body fat measured by bioimpedance, have higher total and cardiovascular mortality rates and a higher prevalence of metabolic syndrome and its components than those with normal BMI and body fat content. In addition, a recent meta-analysis of individual-patient data in those with coronary artery disease (12) has shown that persons with normal BMI who are in the top tertile of central obesity measures had the highest total mortality rate. These results have been attributed to several factors. First, the diagnostic accuracy of BMI for obesity is not optimal, especially in persons with greater body fat percentages and normal or intermediate BMI (14). Second, those with normal body weight and higher body fat have less muscle mass, which is a factor associated with higher mortality risk and metabolic dysregulation (15, 16). Third, animal and human studies (17) have recently shown that adipose tissue in the legs and buttocks may have a favorable effect on glucose metabolism. Finally, persons with a central distribution of fat, particularly when measured with WHR, have less adipose tissue in the lower extremities (17). To our knowledge, no studies in the general U.S. population have specifically focused on assessing the mortality risk in persons with normal BMI and central obesity compared with those who are overweight or obese according to BMI. Thus, we hypothesized that persons with normal BMI and central obesity would have greater mortality risk than those who have any other combination of BMI and central obesity. We investigated the total and cardiovascular mortality risks associated with different patterns of body adiposity in a large cohort of participants in NHANES III (Third National Health and Nutrition Examination Survey) (1988 to 1994). Because hip circumference was not measured in the most recent National Health and Nutrition Examination Surveys (conducted yearly from 1999 to 2010) and WHR data to define central obesity were crucial to our primary hypothesis, we have used data from NHANES III instead. Methods Study Design and Participants NHANES III is a cross-sectional survey that produces generalizable health estimates for the U.S. population using a stratified, multistage, probability sampling design. From a sample of 39695 persons, 33994 were interviewed and 30818 were examined at mobile examination centers. The examination consisted of extensive anthropometric, physiologic, and laboratory testing. Waist and hip circumferences were measured by a trained examiner with a measuring tape positioned at the high point of the iliac crest for the waist and at the greatest circumference of the buttocks. The design and methods for the survey are available elsewhere (18). In our study, 16124 adults aged 18 years or older had WHRs available. Because extremely thin persons and those with a history of nonskin cancer have a higher mortality risk, we further restricted our analysis to persons with a BMI greater than 18.5 kg/m2 and those without a history of nonskin cancer. The resulting sample was 15184 participants (7249 men and 7935 women). Total and Cardiovascular Mortality Assessment Identifier data were matched to the National Death Index to determine mortality status, with mortality follow-up from the date of the NHANES survey through 31 December 2006. A complete description of the methodology to link baseline NHANES III data to the National Death Index can be found elsewhere (19). International Classification of Diseases, Ninth Revision (ICD-9), codes from 1986 to 1998 and International Classification of Diseases, 10th Revision (ICD-10), codes from 1999 to 2000 were used to ascertain the underlying cause of death. Cardiovascular deaths were defined as those with ICD-9 codes 390 to 398, 402, and 404 to 429 and ICD-10 codes I00 to I09, I11, I13, and I20 to I51 (NHANES III codes 53 to 75). Statistical Analysis The overarching analytic goal was to estimate the influence of various magnitudes of central obesity and BMI on total mortality. To do this, we conducted weighted survival modeling that enabled the estimation of the relative risk for mortality, quantified by the hazard ratio (HR), and the expected survival for NHANES III participants. The NHANES III survey design and sampling weights were incorporated into the statistical analysis to calculate weighted means, SEs for continuous variables, and weighted percentages for categorical variables. To determine the total mortality associated with the different patterns of adiposity, we created multivariable Cox proportional hazards models and adjusted for potential confounders previously shown to be associated with obesity and mortality (4). These variables were age at examination, sex, education level, and smoking history. Although we considered adjusting for obesity-related cardiovascular risk factors in the models, we did not adjust our final estimates for these factors. Epidemiologic obesity research has shown that it might be inappropriate to control for factors in the causal pathway between obesity and death, such as diabetes, atherogenic dyslipidemia, and hypertension. In testing for differences in mortality risk for combinations of central obesity (defined by WHR) and BMI, we considered higher-order interactions of WHR with BMI and other covariates. The association pattern of WHR and BMI was found to be different for men versus women (that is, statistically significant higher-order interaction terms), so the final modeling was conducted using sex-stratified data to more clearly present the findings. The interaction terms allowed the potential quadratic risks (U-shaped risks) of values for these variables. The estimated model contains polynomial functions of BMI and WHR (for example, BMI2 and BMI2WHR2). These terms allow for relationships that are more sensitive to change in risk for mortality based on unique combinations (profiles) of WHR and BMI. The estimated risk function, when other covariates in the model are controlled for, will resemble a saddle with high- and low-risk areas. When possible, covariates were the grand mean centered to lessen the collinearity induced from quadratic effects. Likelihood ratio tests were used to determine whether these higher-order model terms could be removed. The proportional hazards assumption for all variables was assessed and satisfied for the final models. After these models were established, we estimated HRs with estimated model variables for different combinations of WHR and BMI stratified by sex. For these comparisons, we chose a BMI of 22 kg/m2 to represent persons with normal BMI, 27.5 kg/m2 to represent overweight persons, and 33 kg/m2 to represent obese persons. For WHR, we chose 0.89 and 1.00 for men and 0.80 and 0.92 for women as a measure of central obesity. Each of these sets of values were chosen either to reflect the approximate midpoint of standard clinical interpretationsto avoid issues with values at common thresholds (for example, BMI values at 30 kg/m2)or to be clinical targets we sought to better understand. Wald-based (or large sample-based) HR estimates and their SEs were assessed to provide significance tests among these representative patient profiles (20). Once the fitted Cox model was deemed satisfactory, we sought to estimate measures of absolute risk by means-adjusted 5- and 10-year survival estimates (21). In this analysis, we replicated observations to standardize (reweight) them and ensure balance across sex, age, WHR, and BMI categories. The expected survival was computed as a weighted estimate

Normal-weight central obesity

Context It is uncertain whether measures of central obesity, such as waist-to-hip ratio (WHR), provide additional information beyond body mass index (BMI) in defining mortality risks associated with obesity. Contribution This population-based cohort study found that normal BMI and central obesity (defined by WHR) were associated with the worst long-term survival compared with individuals with normal fat distribution regardless of BMI category. Caution Information on body fat distribution was based on anthropometric indicators alone. Implication Normal weight with central obesity may represent an important group for targeted lifestyle modifications and other preventive strategies. Obesity defined by body mass index (BMI) or measures of central obesity, such as waist-to-hip ratio (WHR) and waist circumference, is associated with increased total and cardiovascular mortality (13). However, a recent meta-analysis (4) showed that being overweight according to BMI was actually associated with lower total mortality, challenging the paradigm that BMI is linked to increased mortality. Further, whether measures of fat distribution provide any incremental risk information beyond BMI alone has been a major source of controversy (2, 3, 58). Indeed, the 2013 American Heart Association/American College of Cardiology/The Obesity Society guideline for the management of obesity (9) does not recommend measuring WHR and assumes that persons with normal BMI are not exposed to any obesity-related cardiovascular risk in view of the limited available data proving otherwise. A recent large study (6) showed that incorporating waist circumference information in prediction models did not increase the prognostic value already provided by BMI; however, for a given BMI category, subgroups of waist circumference or WHR were also associated with increased mortality risk. Other studies showed that measures of central obesity, such as WHR, waist-to-height ratio (10, 11), or waist circumference alone, may provide additional information beyond BMI on mortality risk among middle-aged adults, provided there is no adjustment for obesity-related cardiovascular risk factors (2, 3, 7). Further, a study (12) has shown that measures of central obesity are more strongly associated with total and cardiovascular disease mortality rates than BMI. Another study (13) showed that persons with normal body weight measured by BMI, but who had increased body fat measured by bioimpedance, have higher total and cardiovascular mortality rates and a higher prevalence of metabolic syndrome and its components than those with normal BMI and body fat content. In addition, a recent meta-analysis of individual-patient data in those with coronary artery disease (12) has shown that persons with normal BMI who are in the top tertile of central obesity measures had the highest total mortality rate. These results have been attributed to several factors. First, the diagnostic accuracy of BMI for obesity is not optimal, especially in persons with greater body fat percentages and normal or intermediate BMI (14). Second, those with normal body weight and higher body fat have less muscle mass, which is a factor associated with higher mortality risk and metabolic dysregulation (15, 16). Third, animal and human studies (17) have recently shown that adipose tissue in the legs and buttocks may have a favorable effect on glucose metabolism. Finally, persons with a central distribution of fat, particularly when measured with WHR, have less adipose tissue in the lower extremities (17). To our knowledge, no studies in the general U.S. population have specifically focused on assessing the mortality risk in persons with normal BMI and central obesity compared with those who are overweight or obese according to BMI. Thus, we hypothesized that persons with normal BMI and central obesity would have greater mortality risk than those who have any other combination of BMI and central obesity. We investigated the total and cardiovascular mortality risks associated with different patterns of body adiposity in a large cohort of participants in NHANES III (Third National Health and Nutrition Examination Survey) (1988 to 1994). Because hip circumference was not measured in the most recent National Health and Nutrition Examination Surveys (conducted yearly from 1999 to 2010) and WHR data to define central obesity were crucial to our primary hypothesis, we have used data from NHANES III instead. Methods Study Design and Participants NHANES III is a cross-sectional survey that produces generalizable health estimates for the U.S. population using a stratified, multistage, probability sampling design. From a sample of 39695 persons, 33994 were interviewed and 30818 were examined at mobile examination centers. The examination consisted of extensive anthropometric, physiologic, and laboratory testing. Waist and hip circumferences were measured by a trained examiner with a measuring tape positioned at the high point of the iliac crest for the waist and at the greatest circumference of the buttocks. The design and methods for the survey are available elsewhere (18). In our study, 16124 adults aged 18 years or older had WHRs available. Because extremely thin persons and those with a history of nonskin cancer have a higher mortality risk, we further restricted our analysis to persons with a BMI greater than 18.5 kg/m2 and those without a history of nonskin cancer. The resulting sample was 15184 participants (7249 men and 7935 women). Total and Cardiovascular Mortality Assessment Identifier data were matched to the National Death Index to determine mortality status, with mortality follow-up from the date of the NHANES survey through 31 December 2006. A complete description of the methodology to link baseline NHANES III data to the National Death Index can be found elsewhere (19). International Classification of Diseases, Ninth Revision (ICD-9), codes from 1986 to 1998 and International Classification of Diseases, 10th Revision (ICD-10), codes from 1999 to 2000 were used to ascertain the underlying cause of death. Cardiovascular deaths were defined as those with ICD-9 codes 390 to 398, 402, and 404 to 429 and ICD-10 codes I00 to I09, I11, I13, and I20 to I51 (NHANES III codes 53 to 75). Statistical Analysis The overarching analytic goal was to estimate the influence of various magnitudes of central obesity and BMI on total mortality. To do this, we conducted weighted survival modeling that enabled the estimation of the relative risk for mortality, quantified by the hazard ratio (HR), and the expected survival for NHANES III participants. The NHANES III survey design and sampling weights were incorporated into the statistical analysis to calculate weighted means, SEs for continuous variables, and weighted percentages for categorical variables. To determine the total mortality associated with the different patterns of adiposity, we created multivariable Cox proportional hazards models and adjusted for potential confounders previously shown to be associated with obesity and mortality (4). These variables were age at examination, sex, education level, and smoking history. Although we considered adjusting for obesity-related cardiovascular risk factors in the models, we did not adjust our final estimates for these factors. Epidemiologic obesity research has shown that it might be inappropriate to control for factors in the causal pathway between obesity and death, such as diabetes, atherogenic dyslipidemia, and hypertension. In testing for differences in mortality risk for combinations of central obesity (defined by WHR) and BMI, we considered higher-order interactions of WHR with BMI and other covariates. The association pattern of WHR and BMI was found to be different for men versus women (that is, statistically significant higher-order interaction terms), so the final modeling was conducted using sex-stratified data to more clearly present the findings. The interaction terms allowed the potential quadratic risks (U-shaped risks) of values for these variables. The estimated model contains polynomial functions of BMI and WHR (for example, BMI2 and BMI2WHR2). These terms allow for relationships that are more sensitive to change in risk for mortality based on unique combinations (profiles) of WHR and BMI. The estimated risk function, when other covariates in the model are controlled for, will resemble a saddle with high- and low-risk areas. When possible, covariates were the grand mean centered to lessen the collinearity induced from quadratic effects. Likelihood ratio tests were used to determine whether these higher-order model terms could be removed. The proportional hazards assumption for all variables was assessed and satisfied for the final models. After these models were established, we estimated HRs with estimated model variables for different combinations of WHR and BMI stratified by sex. For these comparisons, we chose a BMI of 22 kg/m2 to represent persons with normal BMI, 27.5 kg/m2 to represent overweight persons, and 33 kg/m2 to represent obese persons. For WHR, we chose 0.89 and 1.00 for men and 0.80 and 0.92 for women as a measure of central obesity. Each of these sets of values were chosen either to reflect the approximate midpoint of standard clinical interpretationsto avoid issues with values at common thresholds (for example, BMI values at 30 kg/m2)or to be clinical targets we sought to better understand. Wald-based (or large sample-based) HR estimates and their SEs were assessed to provide significance tests among these representative patient profiles (20). Once the fitted Cox model was deemed satisfactory, we sought to estimate measures of absolute risk by means-adjusted 5- and 10-year survival estimates (21). In this analysis, we replicated observations to standardize (reweight) them and ensure balance across sex, age, WHR, and BMI categories. The expected survival was computed as a weighted estimate

Leptin Induces C-Reactive Protein Expression in Vascular Endothelial Cells

Objective—There is increasing evidence of an association between leptin and increased cardiovascular risk. Higher leptin levels are associated with increased levels of C-reactive protein (CRP), which itself elicits proatherogenic effects in the vascular endothelium. We tested the hypothesis that leptin induces CRP expression in human coronary artery endothelial cells (HCAECs). Methods and Results—We confirmed the presence of both long and short isoforms of the leptin receptor in cultured HCAECs. Leptin but not IFN&agr;A/D nor tumor necrosis factor (TNF) &agr;, induced expression of CRP. A dose dependent increase of CRP mRNA and protein was observed with increasing concentration of leptin (0 to 400 ng/mL). This increased CRP expression was attenuated in the presence of anti-leptin receptor antibodies and also by inhibition of ERK1/2 by PD98059 (20 to 40 &mgr;mol/L). Time (0 to 60 minutes) and leptin concentration (0 to 200 ng/mL)-dependence of ERK1/2 phosphorylation were evident in response to leptin treatment. Leptin also elicited ROS generation. Inhibition of ROS by catalase (200 &mgr;g/mL) prevented ERK1/2 phosphorylation and CRP mRNA transcription. Conclusion—Leptin induces CRP expression in HCAECs via activation of the leptin receptor, increased ROS production, and phosphorylation of ERK1/2. These studies suggest a mechanism for the proatherogenic effects of leptin.

Modest Visceral Fat Gain Causes Endothelial Dysfunction in Healthy Humans

OBJECTIVES The aim of this study was to determine the impact of fat gain and its distribution on endothelial function in lean healthy humans. BACKGROUND Endothelial dysfunction has been identified as an independent predictor of cardiovascular events. Whether fat gain impairs endothelial function is unknown. METHODS A randomized controlled study was conducted to assess the effects of fat gain on endothelial function. Forty-three normal-weight healthy volunteers were recruited (mean age 29 years; 18 women). Subjects were assigned to gain weight (approximately 4 kg) (n=35) or to maintain weight (n=8). Endothelial function (brachial artery flow-mediated dilation [FMD]) was measured at baseline, after fat gain (8 weeks), and after weight loss (16 weeks) for fat gainers and at baseline and follow-up (8 weeks) for weight maintainers. Body composition was measured by dual-energy X-ray absorptiometry and abdominal computed tomographic scans. RESULTS After an average weight gain of 4.1 kg, fat gainers significantly increased their total, visceral, and subcutaneous fat. Blood pressure and overnight polysomnography did not change after fat gain or loss. FMD remained unchanged in weight maintainers. FMD decreased in fat gainers (9.1+/-3% vs. 7.8+/-3.2%, p=0.003) but recovered to baseline when subjects shed the gained weight. There was a significant correlation between the decrease in FMD and the increase in visceral fat gain (rho=-0.42, p=0.004), but not with subcutaneous fat gain (rho=-0.22, p=0.15). CONCLUSIONS In normal-weight healthy young subjects, modest fat gain results in impaired endothelial function, even in the absence of changes in blood pressure. Endothelial function recovers after weight loss. Increased visceral rather than subcutaneous fat predicts endothelial dysfunction. (Fat Gain and Cardiovascular Disease Mechanisms; NCT00589498).

Relationships between leptin and C-reactive protein with cardiovascular disease in the adult general population

Background Leptin could be a key regulator of C-reactive protein (CRP) levels, which serve as a marker of systemic inflammation. Both leptin and CRP are predictors of cardiovascular disease (CVD). However, the interactions between leptin and CRP, and their association with CVD, remain unclear. We therefore studied them in a large, multiethnic population.Methods We analyzed leptin and CRP levels, anthropometric variables and cardiovascular risk factor data from 6,251 participants from the Third National Health and Nutrition Examination Survey (NHANES III). Logistic regression was used to estimate the association between leptin, CRP and CVD (defined as history of myocardial infarction or stroke). Receiver operating characteristic curves were created to study the additional value of leptin and CRP for the association with CVD.Results The mean age was 44.4 ± 0.21 years (52.5% women). After adjustment for age, race, dyslipidemia, hypertension, diabetes, smoking, obesity and CRP, high levels of leptin were significantly associated with CVD in men (odds ratio 2.47, 95% CI 1.19–5.19) and in women (odds ratio 3.30, 95% CI 1.47–7.99). After adjustment for leptin, CRP was not associated with CVD. There was a significant correlation between levels of leptin and CRP (Spearman correlation ρ = 0.22 in men and ρ = 0.32 in women, both P < 0.0001). The area under the curve, representing the association between cardiovascular risk factors and CVD, increased after the addition of high levels of both leptin and CRP together.Conclusion High leptin levels are independently associated with CVD even after adjustment for CRP; elevated CRP levels are not associated with CVD after adjustment for leptin. However, increased concentrations of both leptin and CRP confer the highest risk for CVD.

Leptin upregulates the expression of plasminogen activator inhibitor-1 in human vascular endothelial cells

A prothrombotic state in obesity may be partially responsible for the higher incidence of atherosclerotic complications. However the factors responsible for this prothrombotic state, linked with high levels of plasminogen activator inhibitor-1 (PAI-1), are not fully known. Leptin is elevated in obesity and studies have shown a positive correlation between leptin and PAI-1 levels in human subjects, along with a negative correlation with tissue-type plasminogen activator (tPA). We tested the hypothesis that leptin induces PAI-1 and inhibits tPA expression using human coronary artery endothelial cells (HCAEC) in culture as these cells play an important role in atherosclerosis. We demonstrate that leptin induces the transcription and translation of PAI-1 in HCAEC. The leptin dependent upregulation of PAI-1 mRNA and protein was comparable to insulin-induced PAI-1 expression. We show leptin concentration (0-150 ng/ml) dependent increases in PAI-1 mRNA and protein after 6 and 12h of leptin administration, respectively. Increased intracellular PAI-1 expression correlates with increased PAI-1 activity in conditioned media and inhibition of specific ERK1/2 pathway by treatment with PD98059 (20-40 microM) inhibits leptin dependent PAI-1 expression. However no changes in tPA expression were seen with time or increasing concentrations of leptin. Also leptin treatment did not alter total tPA concentration or tPA activity in conditioned media. In conclusion, our study shows that leptin upregulates the expression of PAI-1 in vascular endothelial cells via activation of ERK1/2 but does not regulate tPA expression. These studies demonstrate a novel mechanism for the prothrombotic role of leptin in development of atherosclerosis.

Experimental Sleep Restriction Causes Endothelial Dysfunction in Healthy Humans

Background Epidemiologic evidence suggests a link between short sleep duration and cardiovascular risk, although the nature of any relationship and mechanisms remain unclear. Short sleep duration has also been linked to an increase in cardiovascular events. Endothelial dysfunction has itself been implicated as a mediator of heightened cardiovascular risk. We sought to determine the effect of 8 days/8 nights of partial sleep restriction on endothelial function in healthy humans. Methods and Results Sixteen healthy volunteers underwent a randomized study of usual sleep versus sleep restriction of two‐thirds normal sleep time for 8 days/8 nights in a hospital‐based clinical research unit. The main outcome was endothelial function measured by flow‐mediated brachial artery vasodilatation (FMD). Those randomized to sleep restriction slept 5.1 hours/night during the experimental period compared with 6.9 hours/night in the control group. Sleep restriction was associated with significant impairment in FMD (8.6±4.6% during the initial pre‐randomization acclimation phase versus 5.2±3.4% during the randomized experimental phase, P=0.01) whereas no change was seen in the control group (5.0±3.0 during the acclimation phase versus 6.73±2.9% during the experimental phase, P=0.10) for a between‐groups difference of −4.40% (95% CI −7.00 to −1.81%, P=0.003). No change was seen in non‐flow mediated vasodilatation (NFMD) in either group. Conclusion In healthy individuals, moderate sleep restriction causes endothelial dysfunction. Clinical Trial Registration URL: ClinicalTrials.gov. Unique identifier: NCT01334788.

Patients With Obstructive Sleep Apnea Exhibit Impaired Endothelial Function After Myocardial Infarction

BACKGROUND Impaired brachial flow-mediated dilation (FMD) is associated with risk for subsequent cardiovascular events in patients after myocardial infarction (MI). These patients often have obstructive sleep apnea (OSA). We tested the hypothesis that patients with OSA post MI will exhibit more severe impairment in FMD. METHODS We studied 64 patients with MI admitted to our hospital. OSA was determined using polysomnography. FMD was measured using high-resolution ultrasonography, with researchers blind to the OSA diagnosis. RESULTS The mean age was 60 ± 11 years, and the mean BMI was 29 (26, 32 kg/m(2)), 84% of patients were men, 39% had moderate to severe OSA (apnea-hypopnea index [AHI] > 15), and 31% of the patients had mild OSA (5 ≤ AHI < 15). FMD was severely impaired in patients with moderate to severe OSA (0.8% ± 0.7%) as compared with patients without OSA (4.7% ± 0.8%, P = .001) and with mild OSA (3.9% ± 0.8%, P = .015). Linear regression showed that FMD was associated with log nocturnal nadir oxygen saturation (minSaO(2)) (β = 31.17, P = .0001), age (β = -0.11, P = .006). MinSaO(2) was an independent predictor of FMD after adjustment for possible confounders (β = 26.15, P = .001). CONCLUSIONS FMD is severely impaired in patients with moderate to severe OSA post MI, which may be partially related to nocturnal hypoxemia. Patients with OSA may, therefore, be at higher risk for subsequent cardiovascular events after an MI. Identifying and treating OSA may have important implications in the long-term prognosis of patients post MI. Further studies are necessary to determine if the presence of OSA would affect the long-term occurrence of cardiovascular events after an MI.

Effects of weight gain and weight loss on regional fat distribution

BACKGROUND Normal-weight adults gain lower-body fat via adipocyte hyperplasia and upper-body subcutaneous (UBSQ) fat via adipocyte hypertrophy. OBJECTIVES We investigated whether regional fat loss mirrors fat gain and whether the loss of lower-body fat is attributed to decreased adipocyte number or size. DESIGN We assessed UBSQ, lower-body, and visceral fat gains and losses in response to overfeeding and underfeeding in 23 normal-weight adults (15 men) by using dual-energy X-ray absorptiometry and abdominal computed tomography scans. Participants gained ∼5% of weight in 8 wk and lost ∼80% of gained fat in 8 wk. We measured abdominal subcutaneous and femoral adipocyte sizes and numbers after weight gain and loss. RESULTS Volunteers gained 3.1 ± 2.1 (mean ± SD) kg body fat with overfeeding and lost 2.4 ± 1.7 kg body fat with underfeeding. Although UBSQ and visceral fat gains were completely reversed after 8 wk of underfeeding, lower-body fat had not yet returned to baseline values. Abdominal and femoral adipocyte sizes, but not numbers, decreased with weight loss. Decreases in abdominal adipocyte size and UBSQ fat mass were correlated (ρ = 0.76, P = 0.001), as were decreases in femoral adipocyte size and lower-body fat (ρ = 0.49, P = 0.05). CONCLUSIONS UBSQ and visceral fat increase and decrease proportionately with a short-term weight gain and loss, whereas a gain of lower-body fat does not relate to the loss of lower-body fat. The loss of lower-body fat is attributed to a reduced fat cell size, but not number, which may result in long-term increases in fat cell numbers.

Leptin upregulates caveolin-1 expression: Implications for development of atherosclerosis

OBJECTIVE To determine the role of hyperleptinemia on caveolin-1 expression and leptin signaling. METHODS Endothelial cells are critical to atherosclerosis development; therefore we investigated hyperleptinemia in cultured vascular endothelial cells. Dose-dependent effect of leptin on caveolin-1 expression was determined by Western blot analysis. Also, the consequence of increased caveolin-1 expression on leptin signaling was investigated by adenovirus mediated caveolin-1 overexpression. The effect of increased caveolin-1 expression on leptin-dependent activation of ERK1/2 and eNOS was determined by Western blot analysis. RESULTS Leptin upregulates caveolin-1 protein expression in a dose dependent manner and increased caveolin-1 expression impairs leptin signaling. CONCLUSIONS Leptin increases caveolin-1 protein expression which impairs leptin signaling in vascular endothelial cells. Our study identifies an additional leptin mediated proatherogenic mechanism and a novel caveolin-1 dependent leptin feedback mechanism which may have implications for development of peripheral leptin resistance in the endothelium.