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 Table of Contents  
ORIGINAL ARTICLE
Year : 2015  |  Volume : 2  |  Issue : 3  |  Page : 147-154

Effects of diet-induced weight loss on the cardiometabolic markers in obese African American and white women with prediabetes


1 University of Cincinnati, College of Nursing, Proctor Hall, Cincinnati, OH, USA
2 The Ohio State University, Wexner Medical Center, Division of Endocrinology, Diabetes and Metabolism, McCampbell Hall, 1581 Dodd Drive, Columbus, Ohio, USA

Date of Submission14-Mar-2015
Date of Decision07-Jul-2015
Date of Acceptance02-Jun-2015
Date of Web Publication6-Aug-2015

Correspondence Address:
Trudy Gaillard
University of Cincinnati, College of Nursing, Procter Hall #238, 3110 Vine Street, P. O. Box 210038, Cincinnati, Ohio
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2347-9906.162335

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  Abstract 

Objective: The objective was to investigate the effects of a 6 month diet-induced weight loss (DIWL) on glucose homeostasis, cardiometabolic variables, and high density lipoproteins (HDL) associated functionality paraoxonase1 (PON1) enzyme in overweight/obese African American (AA) and White American (WA) women, with prediabetes. Methods: We recruited 108 obese women (67 AA and 41 WA) with prediabetes hemoglobin A 1 C (HbA 1 C; 5.7-6.4%). Metabolic studies, fasting cardiometabolic markers (lipids/lipoproteins, inflammatory markers), and PON1 were performed at 0, 3, and 6 months. Insulin sensitivity (Si), glucose effectiveness (Sg), acute insulin response to glucose (AIRg), and disposition index (DI) were obtained (Bergman's Minmod). The DIWL program consists of approximately 1200 kcal/day for 6 months. Results: The mean body mass index was greater in AA than WA (38 ± 8 vs. 34 ± 8 kg/m 2 ). DIWL resulted in a mean 7.5% and 10.3% (8kg vs. 10.3 kg) weight loss in AA versus WA. Mean fasting and 2 h serum glucose, insulin, and c-peptide levels decreased significantly during 3 and 6 versus 0 month. The mean Si, Sg, AIRg, and DI did not improve in either AA or WA. DIWL had no significant effect on the cardiometabolic markers or PON1 in the present study. Conclusions: We found ethnic differences in the magnitude of the weight loss. However, modest weight loss had no impact on the glucose homeostasis, cardiovascular markers, and HDL functionality in prediabetic AA and WA women.

Keywords: Apolipoprotein A1, blacks and whites, high-density lipoproteins,  CRP, insulin resistance, triglycerides, paraoxonase1 enzyme, prediabetes


How to cite this article:
Gaillard T, Osei K. Effects of diet-induced weight loss on the cardiometabolic markers in obese African American and white women with prediabetes. J Obes Metab Res 2015;2:147-54

How to cite this URL:
Gaillard T, Osei K. Effects of diet-induced weight loss on the cardiometabolic markers in obese African American and white women with prediabetes. J Obes Metab Res [serial online] 2015 [cited 2019 Jul 17];2:147-54. Available from: http://www.jomrjournal.org/text.asp?2015/2/3/147/162335


  Introduction Top


African Americans (AA) are disproportionately affected by obesity and type 2 diabetes and the associated morbidity and mortality is significant when compared to White Americans (WA). [1],[2] The latter in the AA group occur despite a favorable lipid profile (higher high-density lipoproteins [HDL]/lower triglyceride, more buoyant low-density lipoprotein [LDL] cholesterol particle size) [3],[4],[5] and a greater insulin resistance (IR) in AA than in WA. [6],[7],[8],[9],[10] We have recently proposed that other nontraditional cardiovascular disease (CVD) risk factors (e.g., oxidative stress burden, proinflammatory state, etc.) may also contribute to the mortality and morbidity found in AA than in WA with and without type 2 diabetes. [10] In this regard, we [7],[8],[9],[10] and others [2],[5] have demonstrated that nondiabetic AA have a higher oxidative stress burden (oxidized LDL [ox-LDL]), [11],[12] inflammation C-reactive protein (CRP), [13],[14],[15] lower paraoxonase 1 (PON1) enzyme, [16],[17] and lower adiponectin levels [18],[19] when compared with WA. These ethnic propensity in CVD risk factors in blacks also extends to black South Africans, [20] South Asians, [21] and Afro-Caribbean immigrants in United Kingdom [22] when compared to their White counterparts.

Recent evidence suggests that PON1 is responsible, in part, for the anti-oxidant [23],[24],[25],[26] and anti-inflammatory [27] as well as anti-apoptotic [28] properties of HDL. Serum PON1 is co-associated with HDL and apolipoprotein A1 (Apo-A1) in the circulation. [25],[26] Surprisingly, extensive metabolic studies on the potential role of HDL and PON1 in AA as CVD risk factors have not been performed to be the best of our knowledge. PON1 is modulated by several modalities such as genetic [16],[17] as well as the environmentally acquired factors (aerobic exercise, statins, and weight loss). [29],[30],[31] Thus, intervention modalities to improve HDL functionality (PON1 activity) in high-risk population with and without diabetes and metabolic syndrome as well as coronary artery disease are urgently needed. [32],[33],[34],[35] In this regard, weight loss using lifestyle behavior modification and caloric restriction, and physical activity is often recommended for subjects with obesity, metabolic syndrome, hyperlipidemia, prediabetes, and type 2 diabetes. [14],[30],[31],[32],[33],[34] Recent studies have shown that lifestyle modification reduced the risk of diabetes and improved glycemic control in several populations. [32],[33],[34] AA is more overweight/obese and manifest higher prevalence of type 2 diabetes and hypertension than their WA counterparts. Therefore, we performed comprehensive studies to examine the effects of 6 months of diet-induced weight loss (DIWL) on (1) glucose homeostasis and insulin sensitivity (Si), (2) quantitative lipids and lipoproteins, (3) qualitative HDL functionality (PON1), and (4) cardiovascular biomarkers (ox-LDL, IL-6, adiponectin, and CRP) in overweight/obese AA and WA women with prediabetes.


  Methods Top


Participants were recruited from local a newspaper advertisement and our research population database. Each subject signed a written consent form approved by the Institutional Review Board of the Ohio State University Wexner Medical Center. After screening, we recruited 108 obese women with prediabetes (67 AA and 41 WA, (mean age; 46.5 ± 3.6 years, body mass index (BMI); 37.8 ± 6.3 kg/m 2 ). Prediabetes was defined as (1) impaired fasting glucose; 100-125 mg/dl, (2) impaired glucose tolerance; 2 h glucose 140-199 mg/dl, and (3) hemoglobin A1c (Hb A1c); 5.7-6.4%. The following were excluded (1) patients with severe liver, heart, lung, and kidney diseases, (2) participating in endurance exercise or regular competitive sport, (3) participating in weight reduction program within the past 6 months, (4) smokers, and (5) those taking oral multivitamin supplements containing antioxidants or need for such supplements.

Clinical and anthropometric measurements

Subjects reported to the Center for Clinical and Translational Science (CCTS)/Clinical Research Center (CRC) after 10-12 h overnight fast. Blood pressure (BP) was measured 3 times, at 10 min intervals with the subject in supine position. The average of the 3 BP readings was taken as the mean basal BP. Height (cm), weight (kg), were used to calculate the BMI (kg/m 2 ); waist and hip circumference (cm) were used to calculate waist-hip-ratio in each subject. A whole body dual energy X-ray absorptiometry scan was conducted using the Lunar DPX Pro 2001 (Lunar Corp., Madison, WI) with the subject lying supine on a flat surface. In addition, bioelectrical impedance analyzer was used to estimate the total body fat and lean body mass in each subject (RJL system, Inc.). All the subjects completed a physical activity and online National Institutes of Health's (NIH's) Food Frequency Questionnaire.

Metabolic studies

Baseline fasting blood was obtained for hormonal assays; insulin, c-peptide, glucose, serum lipids/lipoproteins (total cholesterol, triglyceride, HDL-C, LDL-C, Apo-A1, ApoB) inflammatory markers (adiponectin, IL-6, CRP, and ox-LDL), and PON1.

Patients underwent oral glucose tolerance test and frequently sampled intravenous tolerance test (FSIVGTT) on two separate days at the CRC.

Oral glucose tolerance test

Each subject was instructed to ingest at least 250 g of carbohydrate in their regular meals for at least 3 days before the test as previously described. [6],[7] Blood samples were drawn for serum glucose, insulin, and c-peptide levels. The subjects then ingested 75 g of oral glucose load (Glucola, Baltimore, MD) over a 2 min period. Blood samples were drawn at t = 0, 30, 60, 90, and 120 min for serum 9 glucose, insulin, and c-peptide levels.

Frequently sampled intravenous glucose tolerance

With the subject in a supine position, two intravenous needles were inserted into the forearm veins and kept patent with 0.9% normal saline infusion as previously described. [6],[7] Briefly, four blood samples were obtained at t = −20, −10, −5, and 0 min for basal serum glucose, c-peptide, and insulin concentrations. The average of the four samples was considered the basal level. Thereafter, 0.3 g/kg glucose (50 ml of 50% dextrose water) was infused over a 1-min period. At t = 19 min, intravenous insulin (0.05 units/kg, Humulin; Eli Lilly, Indianapolis, IN) dissolved in 30 ml of 0.9% normal saline was infused over 60 s. Blood samples were obtained at frequent intervals (t = 2, 3, 4, 5, 6, 8, 10, 12, 16, 19, 22, 24, 25, 27, 30, 40, 60, 70, 90, 120, 140, 150, 160, and 180 min) for serum glucose, c-peptide, and insulin levels.

Dietary regimen and composition

The obese and prediabetic participants were enrolled in a supervised diet DIWL program for 6 months at our Comprehensive Weight Loss Management Center. The supervised dietary program consisted of meal replacements with slim-fast 3-2-1 (www.slimfast.com) (360 kcal) meal plan, 3 snacks, 2 cans of slim-fast (breakfast and lunch), and one balanced mixed meal at dinner approximately 440-500 kcal. This slim-fast formula consisted of 50% carbohydrate, 30% fat, and 20% protein in energy content.

Longitudinal follow-up procedures

The subjects attended weekly lifestyle and educational classes that focused on healthy nutrition, physical activity, and lifestyle modification instructions. Classes focused on weight loss and maintenance and how to prevent recidivism. Each subject was instructed on the slim-fast 3-2-1 and individualized diet to lose at least 7% or more of initial body weight. A detailed nutrition survey and a 3 day food record were used as the basis of formulating the DIWL program. Subjects were asked to maintain their routine exercise and physical activity during the study. The subjects completed total health, nutrition, and physical activity questionnaires at monthly intervals during the study.

Monitoring/adherence

We asked the subjects to bring all the empty cans of slim-fast for inspection by the dietary team. Dietary logs and weight were monitored weekly by the registered dietitian according to the study protocol. We achieved 80% compliance with the slim-fast replacements for those who completed the study.

Calculations

The body mass index (kg/m 2 ) was used to define body weight as follows: Overweight = 25-29.9 kg/m 2 , Grade 1 (Mild) = 30-35 kg/m 2 , Grade 2 (35.1-39.9 kg/m 2 ), Grade 3 (Morbid obesity) = ≥40 kg/m 2 . Nutritional analysis was performed using commercial NIH Nutrition Software Program (VioCare, Inc., Princeton, NJ http://www.viocare.com/index.aspx). IR and beta cell function were also calculated using the homeostasis model assessment (HOMA). HOMA-IR index was calculated as: Fasting insulin (μU/ml) × fasting plasma glucose (mmol/ml)/22.5. HOMA-%B (β-cell function) was calculated as: 20 × fasting insulin (μU/ml)/fasting glucose (mmol/ml) -3.5. [8],[9],[10] In addition, Si, glucose effectiveness (Sg), acute insulin response to glucose (AIRg), DI, and GEZI were calculated using Bergman's Minmod Millennium 6.1 software program was used to measure various aspects of insulin dynamics. [6],[7] Si was defined as insulin-mediated glucose disposal). Sg was defined as the ability of glucose to mediate its own disposal at basal insulin level. GEZI was defined as glucose-mediated glucose disposal at theoretically zero insulin concentration. GEZI = Sg-BI, where BI is basal insulin effect. AIRg was defined as the incremental area under the curve for glucose-mediated insulin release from t = 0-10 min during FSIVGTT. Disposition index (DI) was calculated as Six AIRg. DI reflects the ability of beta cell secretion to compensate for the prevailing peripheral IR.

Analytical methods

All the blood samples were centrifuged at -4°C, the supernatant collected and stored at -20° and -80°C. All the metabolic assays of each subject were run in a single batch to minimize the inter-assay variability. Serum glucose levels were measured by glucose oxidase method (Model 2300, Yellow Spring Instrument, Antioch, Ohio, USA). Serum insulin and c-peptide levels were measured by standard radioimmunoassay techniques. The coefficients of variation (CV) were 6% and 10%, respectively. The lower limit of the c-peptide assay was 0.1 ng/ml and the intra- and inter-assay CV were 7% and 13%, respectively. The HbA1c was measured by the cationic, microcolumn chromatographic technique (Bayer, Inc.). The normal reference range was 4.0-5.6% (20.2-37.7 mmol/mol). The serum cholesterol, HDL-C, and triglycerides were measured using enzymatic methods. LDL-cholesterol was calculated using Friedwald's equation: LDL-C = total cholesterol - HDL-C - triglyceride/5, for serum triglycerides <400 mg/dl. The Apo Al and Apo Bl00 were measured using nuclear magnetic resonance (LipoScience, Raleigh, NC). Adiponectin (Quintikine, R and D, Mineapolis, MN) and oxidized LDL (Mercodia, NC) were measured using ELISA. CRP was measured using nephelometry (Synchron Lx, Systems, Beckman Synchron LX System, Brea, CA).

Measurement of paraoxonase enzyme activity

Paraoxonase 1 was measured as previous described in our lab. [10]

Procedure

In this assay, arylesterase/paraoxonase catalyzes the cleavage of phenyl acetate resulting in phenol formation. The rate of formation of phenol was measured by monitoring the increase in the absorbance at 410 nm at 25°C. The working reagent consisted of 20 mM Tris/HCl buffer, pH 8.0, containing 1 mM CaCl 2, and 4 mM phenyl acetate as the substrate. Samples diluted 1:50 in buffer were added and the change in absorbance was recorded following a 20 s lag time. One unit of arylesterase activity was equal to 1 μM of phenol formed per minute. The activity was expressed in U/L, based on the extinction coefficient of phenol of 1310/M/cm at 410 nm, pH 8.0, and 25°C.

Statistical analyses

Results are expressed as mean ± standard deviation, unless stated otherwise. Statistical analyses were performed using SAS 9.1. The nonparametric data are analyzed using Chi-square and Mann-Whitney Ranked test. Student's paired, unpaired t-test, and multiple t-tests are used to analyze the data within and between the groups. Spearman's univariate linear regression was used to determine the relationships between BMI and Si, HOMA-IR and AIRg, PON1, oxidized LDL, and CRP. Multiple regression analyses were performed using linear square regression models to examine the relationships between BMI and Si and cardiometabolic markers after adjusting for fasting glucose, insulin, age, gender, and ethnicity. P < 0.05 was considered statistically significant.


  Results Top


Baseline clinical characteristics of obese AA and WA women with prediabetes

The mean age was not significantly different in our obese prediabetic AA and WA (46.3 ± 10 vs. 49.7 ± 11 years). We found that prediabetic AA were more obese [Table 1], (BMI: 38.2 ± 8.2 vs. 34.0 ± 8 kg/m 2 , P = 0.05), with higher % body fat (47 ± 3 vs. 46 ± 2%, P = 0.04). AA had lower lean body mass when compared to WA. We found no statistically significant differences between the systolic and diastolic B P values in AA and WA women. Modest weight loss did not change the systolic B P values in AA.
Table 1: Clinical characteristics of obese/overweight AA and WA at 0, 3, and 6 months of DIWL


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Longitudinal weight loss

The AA lost approximately 3.9 ± 3.7 kg and 5.8 ± 4.8 kg, while WA lost 7.8 ± 5.7 kg and 9.5 ± 7.8 kg at 3 months and 6 months, respectively. This weight loss was sustained for the 6 months study period in both groups. DIWL reduced their percent body weight by 12% and 22% of the initial body weight ln AA and WA, respectively at 6 months [Table 1].

As shown in [Table 2], mean fasting and 2 h post challenge serum glucose, insulin, and c-peptide levels were not significantly different at baseline in obese AA and WA women. Fasting and 2 h serum insulin levels were significantly reduced in AA and WA during weight loss at 3 and 6 months when compared with baseline (0 month). However, there were no differences between the AA and WA. In addition, corresponding serum c-peptide profiles were significantly lower in AA and WA at 3 and 6 months compared to baseline.
Table 2: OGTT parameters in obese/overweight AA and WA at 0, 3, and 6 months of DIWL


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As shown in [Table 3], Si, AIRg, and DI nor Sg were not significantly different in AA and WA women at baseline. While, Si did not change, AIRg and DI increased in both AA and WA during weight loss. The mean HOMA-IR was not significantly different in AA versus WA groups. Thus, similar to Si, HOMA-IR did not significantly change during the intervention [Table 3].
Table 3: Si, inflammation, oxidation in obese/overweight AA and WA at 0, 3, and 6 months of DIWL


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[Table 3] also shows the inflammatory markers and HDL functionality parameters measured in our study. We assessed HDL functionality PON1 and its surrogate markers, PON1 (0.55 ± 0.30 vs. 0.52 ± 0.33 U/L, P = 0.26) and ox-LDL (41 ± 13 vs. 51 ± 18 u/l, P = 0.321) were not significantly different in the obese AA and WA women at baseline. Modest weight loss was not associated with changes in PON1 in AA and WA. Mean CRP tended to be higher (30%), but not significantly different AA than WA at baseline (8.6 ± 9.0 vs. 6.0 ± 6.4 mg/L, P = 0.119). During the weight loss, CRP decreased by 20% and 30% in AA and WA. We found that the mean adiponectin levels were significantly lower in AA versus WA (P = 0.03). During weight loss, adiponectin slightly increased at 6 months in AA and WA. Finally, mean IL-6 levels were not different at baseline in AA than WA and were not changed with modest weight loss in AA and WA women.

Lipid and lipoprotein levels in obese African American and White American with prediabetes

We found no significant differences in total serum cholesterol, LDL-C, HDL-C, and non-HDL-C levels at baseline. Modest weight loss did not significantly change these lipid/lipoprotein levels. In addition, Apo A1 and Apo B100 were also not different at baseline and did not change during the dietary intervention in AA than WA. However, the mean serum triglycerides were significantly lower in obese AA versus WA at baseline (84 ± 47 vs. 118 ± 58 mg/dl, P = 0.01). Serum triglycerides levels were decreased at 3 and 6 months versus baseline in both AA and WA women [Table 4].
Table 4: Lipids and lipoproteins in obese/overweight AA and WA at 0, 3, and 6 months of DIWL


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Correlation coefficients and linear regression

We determined the relationships of weight change with inulin sensitivity and cardiometabolic markers in the AA and WA with prediabetes. Weight change significantly correlated with insulin (r = 0.454, P = 0.007) and Si (r = 0.41), P = 0.05). However, weight change did not correlate with PON1 and lipids/lipoproteins at 3 and 6 months, even after adjusting for age and race/ethnicity.


  Discussion Top


African Americans have greater prevalent rates of obesity and the associated comorbid conditions such as type 2 diabetes and hypertension than their WA counterparts (1-3). Therefore, the goal of our DIWL was to promote at least 7% of the initial weight in 6 months in both groups. Our DIWL achieved approximately 5 kg weight loss in AA (12%) and 9 kg weight loss in WA (22%) from the baseline weight at 6 months similar to other studies on low- and very-low caloric diets. [36]

Another major primary goal of the present study was to determine the effects of DIWL on glucose homeostasis in the prediabetic, obese AA and WA women. The modest weight loss was associated with modest improvement in the fasting and postprandial serum glucose, insulin, and c-peptide responses. However, unlike our previous studies, [6],[7] we found that Si and DI, as well as HOMA-IR and HOMA-B% were not significantly different in modestly obese AA versus WA at baseline. We should note that Mason et al. [35] reported decreased HOMA-IR in patients with mild obesity (BMI: 30 kg/m 2 ) during DIWL. However, unlike Si and HOMA-IR, AIRg, Sg, and GEZI were significantly higher in AA than WA. This is consistent with our previous report in nondiabetic AA and WA. [6],[7] However, Sg and GEZI did not significantly change during the DIWL in AA and WA. The significance of this observation deserves further investigation.

Characteristically, obesity is associated with low HDL-C and high serum triglycerides in several populations. Despite marked obesity, we observed normal fasting serum total cholesterol, LDL-C, and non-HDL-C at baseline and remained unchanged during DIWL at 6 months in our prediabetic AA and WA women. However, while we found similar serum ApoA1 and HDL-C, the serum triglycerides were significantly lower in obese AA versus WA at baseline. Furthermore, the weight loss slightly reduced serum triglycerides in both AA and WA. Indeed, the lower serum triglycerides in AA women occurred, despite identical IR indices (Si and HOMA-IR) at baseline and 3 and 6 months. Thus, AA retained the previously reported paradoxical relationships of Si and HDL-C, ApoA1 and triglycerides in obese AA women during weight loss. [30],[31],[32],[33],[34],[35],[36] The mechanism of this paradoxical relation of Si and HDL-C and triglycerides in AA deserves further elucidation.

Previous studies reported that AA have HDL dysfunctionality as assessed by PON1, ox-LDL, and CRP in nondiabetic, postmenopausal AA than WA (31). In contrast with our previous studies, [10] HDL-functionality as assessed by PON1, was not significantly different at baseline in obese prediabetic AA and WA women. Surprisingly, PON1 remained unchanged during the 6 month of DIWL in obese prediabetic AA and WA. In this context, Aicher et al. [30] demonstrated that 6 month, DIWL was associated with a decreased HDL-C, but had no effect on PON1 in multiethnic (mostly blacks), overweight/obese women with and without type 2 diabetes. However, the average weight loss in their study was 2.2 ± 3.9 kg which was much lower than that found in AA and WA in the present study. In this context, racial/ethnic differences in HDL functionality have been investigated in nonblack ethnic populations. Dodani, et al. [21] have reported that HDL is dysfunctional in South Asian immigrants, a population with an extraordinary propensity to develop coronary artery disease.

Dysglycemia worsens atherosclerosis in humans. The potential mechanisms that increase the risk of atherosclerosis including higher oxidative stress burden, [11],[12],[24],[25] subclinical inflammation (CRP), [13],[14],[15] and endothelial tissue injury [27],[28] as well as tissue glycation (i.e., advanced glycation end-products). [37] In this context, recent studies have shown that AA have increased oxidative stress burden (higher ox-LDL) [11],[12],[24],[25] and higher subclinical inflammation (CRP) [13],[14],[15],[20] than WA. In the current study, we found that modest weight loss was not associated with significant changes in ox-LDL and CRP in AA when compared with WA. Finally, adiponectin, a potent endogenous insulin sensitizer has been implicated in the pathogenesis of CAD, type 2 diabetes, and metabolic syndrome. Serum adiponectin is lower in blacks than whites and patients with obesity and type 2 diabetes. In the present study, the mean adiponectin levels were significantly lower in AA versus WA, similar to other observations. [18],[19] However, during modest weight loss, serum adiponectin levels were not significantly changed in obese AA versus WA at 6 months. The reasons are unclear and may require more severe weight loss in obese AA.

Limitations of the present study

Although our current study has major strengths, we also recognize some limitations. We experienced a high attrition or dropout rates in our study, especially in the obese AA than WA (30% and 20%, respectively). This affected the sample size and the statistical power of the study. We were surprised that the slim-fast 3-2-1, achieved more than expected weight reduction goals in the present program. We should note that, we did not provide complementary or concurrent exercise program as part of the DIWL program in our study.

In summary, overweight/obese AA experienced lower weight loss than WA with prediabetes during DIWL with hypocaloric regimen. We observed slight improvements in serum glucose/insulin/c-peptide responses in WA than AA during weight loss. However, marked obese AA and WA women had similar Si values, which surprisingly, did not change during modest weight loss. The modest weight loss had no or only minimal effects on lipids and lipoproteins in obese AA versus WA. However, unlike WA, the IR in AA was paradoxically associated with normal HDL-C, ApoA1, and lower serum triglycerides. Furthermore, HDL functionality assessed by PON1 and cardiometabolic markers (ox-LDL, CRP, IL-6) and adiponectin levels were not significant changed in our obese, AA versus WA during dietary weight loss intervention.


  Conclusions Top


There are ethnic differences in response to modest, DIWL with respect to the magnitude of weight loss in AA and WA. However, DIWL had only mild or no impact on glucose homeostasis, HDL functionality PON1, and several cardiometabolic risk factors in obese AA and WA with prediabetes. Whether a more intensive weight loss program would significantly improve glucose homeostasis, cardiometabolic parameters, and HDL functionality PON1 in overweight/obese, AA, and WA with prediabetes remain to be investigated.

Acknowledgments

The project described was supported by Award Number UL1RR025755 from the National Center for Research Resources, funded by the Office of the Director, National Institutes of Health (OD) and supported by the NIH Roadmap for Medical Research. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Center for Research Resources or the National Institutes of Health.

Financial support and sponsorship

The study was supported by an Americans Diabetes Association for Clinical and Translational Award Number: 0-11-CT-39. We thank the nursing and staff of the CCTS/CRC.

Conflicts of interest

There are no conflicts of interest.

 
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