OBJECTIVE

Elevated lipopolysaccharide-binding protein (LBP), a marker of subclinical endotoxemia, may be involved in the pathogenesis of obesity and metabolic risk. We aimed to investigate the association between plasma LBP and metabolic disorders in apparently healthy Chinese.

RESEARCH DESIGN AND METHODS

A population-based study including 559 overweight/obese (BMI ≥24.0 kg/m2) and 500 normal-weight (18.0 ≤ BMI <24.0 kg/m2) subjects aged 35–54 years was conducted in Shanghai, China. Fasting plasma glucose, lipid profile, LBP, high-sensitivity C-reactive protein, interleukin-6, high-molecular-weight (HMW) adiponectin, leptin, hepatic enzymes, and body composition were measured. Metabolic syndrome was defined by the updated National Cholesterol Education Program Adult Treatment Panel III criterion for Asian Americans.

RESULTS

LBP levels were significantly higher in overweight/obese individuals than in normal-weight individuals (geometric mean 27.6 [95% CI 25.2–30.3] vs. 10.0 [9.1–11.1] μg/ml; P < 0.001). After multiple adjustments including BMI, the odds ratios were 3.54 (95% CI 2.05–6.09) and 5.53 (95% CI 2.64–11.59) for metabolic syndrome and type 2 diabetes, respectively, comparing the highest with the lowest LBP quartile. Further adjustments for inflammatory markers almost abolished the significant association of LBP with metabolic syndrome but not that with type 2 diabetes, and controlling for adipokines and hepatic enzymes did not substantially alter the results.

CONCLUSIONS

Elevated circulating LBP was associated with obesity, metabolic syndrome, and type 2 diabetes in apparently healthy Chinese. These findings suggested a role of lipopolysaccharide via initiation of innate immune mechanism(s) in metabolic disorders. Prospective studies are needed to confirm these results.

Obesity, a major risk factor for metabolic disorders, is reaching epidemic proportions worldwide with 1.6 billion overweight/obese adults in 2005 (1). An unhealthy diet and lifestyle-associated obesogenic environment, along with genetic predisposition, are the main recognized drivers of the global epidemic of obesity and associated metabolic disorders. However, increasing evidence supports the fact that chronic low-grade inflammation is one of the key mechanisms underlying the pathogenesis of obesity-related metabolic disorders (2). However, the agents responsible for initiating and sustaining this low-grade inflammatory signal have yet to be identified.

A link between chronic infection and atherosclerosis has been postulated, in which lipopolysaccharide (LPS) derived from various Gram-negative bacteria might play a pivotal role (3). Animal studies (4,5) and human evidence (6) suggested that subclinical endotoxemia, indicated by low to moderately elevated LPS, may be involved in the pathogenesis of metabolic disorders. Lowering LPS concentrations through antibiotic (4) or rosiglitazone therapy (6) could improve metabolic outcomes. LPS is a potential factor in triggering the innate immune response and modulating the inflammatory cascade via stimulation of the nuclear factor-κB pathway and transcription of proinflammatory genes (7). However, the short half-life of LPS (8) and the difficulty of removing interference in blood (9) limit the utility of LPS testing in clinical or research settings.

Lipopolysaccharide-binding protein (LBP), an acute-phase protein synthesized in liver, initiates recognition and monomerization of LPS and amplifies host responses to LPS (10). Having a relatively long half-life, LBP delivers LPS to membrane and soluble forms of CD14 and consequently interacts with Toll-like receptor 4, triggering a downstream signaling cascade that leads to the upregulation of proinflammatory cytokines (7,10). Therefore, the presence of LBP could reflect an “effective” LPS level and innate immune response triggered by LPS (11,12).

Limited data have suggested that elevated LBP levels were observed in patients with nonalcoholic fatty liver disease (11) and were associated with unfavorable effects on metabolic traits in glucose-intolerant men (13) or with the increased prevalence of coronary artery disease (12). However, the role of LBP on obesity-related metabolic disorders has not been explored. Therefore, we aimed to investigate whether plasma LBP concentrations were associated with metabolic disorders such as metabolic syndrome, insulin resistance, and type 2 diabetes and also to what extent the associations were explained by adiposity, adipokines, hepatic enzymes, and inflammation in apparently healthy Chinese.

The Gut Microbiota and Obesity Study was a population-based case-control study among noninstitutionalized residents aged 35–54 years in Shanghai, China. This study investigated the effects of gut microbiota and environmental factors on obesity and related metabolic disorders. Two urban districts (Luwan and Zhabei) were chosen to represent people with high and low socioeconomic status in urban Shanghai. Participants were enrolled through their response to an advertisement. The fieldwork was conducted simultaneously in both districts from November 2007 through January 2008. Five hundred pairs of age- and sex-matched subjects (overweight/obesity) and control subjects (normal-weight) were planned to be recruited. This sample size presumably could provide 80% power to detect a 25% difference in gut microbiota composition between groups (14). Overweight/obesity and normal weight were defined as BMI ≥24.0 kg/m2 and 18.0 ≤ BMI <24.0 kg/m2, respectively, which was modified from the recommendation by the Working Group on Obesity in China (15).

Eligible candidates were adult residents who have lived in Shanghai for at least 10 years. Exclusion criteria included 1) diarrhea for 3 consecutive days within the previous 3 months, 2) heavy alcohol consumption (≥40 g/day ethanol for men and ≥20 g/day for women), 3) diagnosed diabetes, taking oral antidiabetic agents or insulin, cancer, coronary heart disease, myocardial infarction, stroke, or severe kidney or liver diseases, 4) infectious diseases including tuberculosis, AIDS, and hepatitis, 5) severe psychological disorders or physical disabilities, 6) antibiotics used for 3 consecutive days within the previous 3 months, 7) gastrointestinal surgery within 1 year, or 8) pregnancy or lactation in women. The protocol was approved by the Institutional Review Board of the Institute for Nutritional Sciences and written informed consent was obtained from all participants. A total of 1,059 eligible participants (559 overweight/obese and 500 normal-weight) were successfully recruited.

Of these individuals, 960 (90.7%) completed a dual-energy X-ray absorptiometry (DEXA) scan. Baseline characteristics (BMI, waist circumference, family history of chronic diseases, smoking, alcohol drinking, and educational attainment) were similar between those with and without DEXA scans. However, individuals who did not participate in the DEXA scan were younger (43.0 vs. 46.2 years; P < 0.001) and tended to be male (57.6 vs. 36.7%, P < 0.001) compared with those who had the scan.

Data collection

Home interviews were conducted by trained physicians or public health workers from the local Centers for Disease Control and Prevention and community clinics. Information on demographic variables, health status, and behavior was collected using a standardized questionnaire. Smoking was defined as never, current, and former. Alcohol drinking was defined as “yes” or “no.” Physical activity data were collected by using the International Physical Activity Questionnaire (short last 7-day format, http://www.ipaq.ki.se/scoring.pdf), and the level for each individual was calculated as a sum of MET-minute/week score and then classified as low and high by sex-specific total MET median. Educational attainment was categorized into three groups (0 to 9, 10 to 12, and ≥13 years of education). Family history of chronic diseases was positive if a parent or sibling had coronary heart disease, stroke, or type 2 diabetes.

After a home interview, all participants had a physical examination after overnight fasting. Body weight and height were measured to the nearest 0.1 kg and 0.1 cm, respectively, with the participants in light indoor clothing without shoes. BMI was calculated as weight in kilograms divided by the square of height in meters. Waist circumference was obtained at the midpoint between the lowest rib and the iliac crest to the nearest 0.1 cm, after inhalation and exhalation. Blood pressure was measured by using an electronic blood pressure monitor (Omron HEM-705CP; OMRON Healthcare, Vernon Hills, IL) on the right arm of the participant in a comfortable sitting position after at least a 5-min rest. Three measurements were taken and the mean of the last two measurements was used for the analyses. Whole-body densitometry was conducted with the participant in light clothing and without carrying any metal objects by using a Hologic DXA (QDR-4500; Hologic, Waltham, MA).

Laboratory methods

Fasting peripheral venous EDTA blood samples were collected and centrifuged at 4°C and 3,000 rpm for 15 min. After being frozen, the samples were shipped in dry ice to the Institute for Nutritional Sciences and stored at −80°C until analyses. Total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, glucose, aspartate aminotransferase (AST), alanine aminotransferase (ALT), and γ-glutamyltransferase (GGT) were measured enzymatically on an automatic analyzer (Hitachi 7080, Tokyo, Japan) with reagents purchased from Wako Pure Chemical Industries (Osaka, Japan). Plasma high-sensitivity (hs) C-reactive protein (CRP) was measured by a particle-enhanced immunoturbidimetric assay (Roche Diagnostics, Indianapolis, IN). A1C was quantified from resolved erythrocytes with an automated immunoassay (Roche Diagnostics). Insulin was measured with a completely homologous radioimmunoassay (Linco Research, St. Charles, MO), which had <0.2% cross-reactivity with proinsulin. The insulin resistance index (homeostasis model assessment of insulin resistance [HOMA-IR]) was calculated using updated homeostasis model assessment methods (http://www.dtu.ox.ac.uk/).

Plasma LBP levels were determined by a sandwich ELISA (USCN Life Science & Technology, Missouri City, TX). Plasma samples were diluted at least 200 times and assayed according to the manufacturer's instructions. The assay has a sensitivity of 0.2 ng/ml and a measurable concentration range of 0.78–50 ng/ml. The intra-assay and interassay coefficients of variation were <5 and <10%, respectively. Enzyme immunoassays were used to measure plasma interleukin (IL)-6, leptin (R&D Systems, Minneapolis, MN), and high-molecular-weight (HMW)-adiponectin (Millipore, St. Charles, MO). The interassay coefficients of variation were 9.6 and 6.5% for IL-6 at 0.49 and 5.65 pg/ml, 5.4 and 3.5% for leptin at 65.7 and 581 pg/ml, and 8.1 and 3.8% for HMW-adiponectin at 21.23 and 61.50 ng/ml, respectively. Hepatitis B surface antigen (HBsAg) was detected by a Murex HBsAg ELISA kit (Murex Biotech, Dartford, Kent, U.K.).

Definition of diseases

Metabolic syndrome was defined according to the updated National Cholesterol Education Program Adult Treatment Panel III criteria for Asian-Americans (16), including at least three of the following components: 1) waist circumferences ≥90 cm in men or ≥80 cm in women, 2) triglycerides ≥1.7 mmol/l, 3) HDL cholesterol <1.03 mmol/l in men or <1.30 mmol/l in women, 4) blood pressure ≥130/85 mmHg or current use of antihypertensive medications, and 5) fasting plasma glucose ≥5.6 mmol/l. Type 2 diabetes was defined as fasting plasma glucose ≥7.0 mmol/l or 2-h postload plasma glucose ≥11.1 mmol/l during an oral glucose tolerance test. The selection procedure for the oral glucose tolerance test is described in supplementary Fig. A1 (available in an online appendix at http://care.diabetesjournals.org/cgi/content/full/dc10-0340/DC1). Elevated hepatic enzymes were defined as the highest quartiles of one or more hepatic enzymes: AST ≥30 IU/l, ALT ≥33 IU/l, or GGT ≥42 IU/l.

Statistical analyses

Log transformations were performed for LBP, triglycerides, insulin, HOMA-IR, inflammatory makers, adipokines, and hepatic enzymes to approximate normality. ANCOVA for continuous variables and logistic regression models for categorical variables were applied for the comparison across obesity status. Multiple comparison corrections were performed using the Benjamini and Hochberg procedure (17). Partial Spearman correlation coefficients between LBP and various parameters were calculated by adjustment for age, sex, and BMI. Individuals with presumably acute inflammation (hs-CRP >10 mg/l) were excluded (n = 18, 1.7%), leaving 1,041 subjects in the analysis. Of these, 942 subjects completed a DEXA scan.

Participants were classified into four groups according to their LBP quartiles in the whole sample. Multivariate logistic regression models were used to estimate the odds ratios (ORs) for metabolic syndrome, type 2 diabetes, and insulin resistance. Potential confounding variables include age, sex, lifestyle factors, education level, family history of chronic diseases, and BMI (or total body fat). In addition, we adjusted for inflammatory markers, adipokines, and elevated hepatic enzymes. Data management and statistical analyses were performed using Stata 9.2 (StataCorp, College Station, TX). Statistical tests were two-sided and P < 0.05 was considered statistically significant.

General characteristics

The prevalence of metabolic syndrome and newly defined type 2 diabetes was 42.0% (n = 445) and 13.8% (n = 146), respectively. The mean BMI values were 28.0 ± 2.7 kg/m2 in overweight/obese subjects and 21.0 ± 1.4 kg/m2 in normal-weight subjects (P < 0.001) (Table 1).

Table 1

Characteristics of participants according to obese status

Normal weight (18.0 ≤ BMI <24.0 kg/m2)Overweight/obesity (BMI ≥24.0 kg/m2)P value*
n 500 559  
Age (years) 45.8 ± 5.5 46.0 ± 5.4 0.604 
BMI (kg/m221.0 ± 1.4 28.0 ± 2.7 <0.001 
Men 175 (35.0) 234 (41.9) 0.022 
Physical inactivity 249 (49.8) 281 (50.3) 0.869 
Education levels   0.002 
    0–9 years 114 (22.8) 175 (31.3)  
    10–12 years 273 (54.6) 281 (50.3)  
    >12 years 113 (22.6) 103 (18.4)  
Current smoker (yes) 117 (23.4) 158 (28.3) 0.767 
Alcohol drinker (yes) 183 (36.6) 204 (36.5) 0.245 
Family history of chronic diseases 200 (40.0) 222 (39.7) 0.939 
Newly defined type 2 diabetes 36 (7.2) 110 (19.7) <0.001 
Metabolic syndrome 51 (10.2) 394 (70.5) <0.001 
Waist circumference (cm) 75.9 ± 6.1 93.2 ± 8.2 <0.001 
Systolic blood pressure (mmHg) 118.4 ± 15.3 130.9 ± 17.6 <0.001 
Diastolic blood pressure (mmHg) 74.7 ± 9.8 84.0 ± 11.6 <0.001 
Glucose (mmol/l) 5.81 ± 1.13 6.30 ± 1.54 <0.001 
A1C (%) 5.58 ± 0.62 5.79 ± 0.78 <0.001 
Insulin (μU/ml) 7.3 (7.0−7.7) 11.3 (10.9−11.8) <0.001 
HOMA-IR 0.85 (0.82−0.89) 1.33 (1.27−1.38) <0.001 
Total cholesterol (mmol/l) 5.16 ± 1.14 5.33 ± 1.18 0.014 
LDL cholesterol (mmol/l) 3.14 ± 0.93 3.41 ± 0.99 <0.001 
HDL cholesterol (mmol/l) 1.53 ± 0.44 1.23 ± 0.34 <0.001 
Triglycerides (mmol/l) 1.01 (0.96−1.06) 1.61 (1.53−1.69) <0.001 
DEXA scan 456 (91.2) 504 (90.2) 0.697 
Total body fat mass (kg) (n = 960)    
    Men 11.8 ± 3.3 20.7 ± 4.3 <0.001 
    Women 15.7 ± 2.8 25.7 ± 5.1 <0.001 
Total body fat (%) (n = 960)    
    Men 19.0 ± 4.3 25.6 ± 3.4 <0.001 
    Women 29.4 ± 3.8 36.1 ± 3.6 <0.001 
Trunk fat mass (kg) (n = 960)    
    Men 6.3 ± 2.1 12.1 ± 2.7 <0.001 
    Women 7.7 ± 1.8 13.7 ± 3.0 <0.001 
HBsAg carriage 44 (8.8) 50 (8.9) 0.916 
Elevated hepatic enzymes 141 (28.2) 294 (52.6) <0.001 
AST (IU/l) 23.6 (22.9–24.3) 26.3 (25.4–27.2) <0.001 
ALT (IU/l) 19.1 (18.2–20.0) 27.8 (26.5–29.2) <0.001 
GGT (IU/l) 22.5 (21.3–23.8) 33.1 (31.2–35.0) <0.001 
hs-CRP (mg/l) 0.61 (0.57–0.66) 1.38 (1.28–1.49) <0.001 
IL-6 (pg/ml) 1.19 (1.13–1.26) 1.67 (1.59–1.76) <0.001 
HMW-adiponectin (μg/ml) 3.16 (2.93–3.41) 1.88 (1.73–2.04) <0.001 
Leptin (ng/ml) 3.99 (3.70–4.31) 8.94 (8.40–9.51) <0.001 
LBP (μg/ml) 10.0 (9.1–11.1) 27.6 (25.2–30.3) <0.001 
Normal weight (18.0 ≤ BMI <24.0 kg/m2)Overweight/obesity (BMI ≥24.0 kg/m2)P value*
n 500 559  
Age (years) 45.8 ± 5.5 46.0 ± 5.4 0.604 
BMI (kg/m221.0 ± 1.4 28.0 ± 2.7 <0.001 
Men 175 (35.0) 234 (41.9) 0.022 
Physical inactivity 249 (49.8) 281 (50.3) 0.869 
Education levels   0.002 
    0–9 years 114 (22.8) 175 (31.3)  
    10–12 years 273 (54.6) 281 (50.3)  
    >12 years 113 (22.6) 103 (18.4)  
Current smoker (yes) 117 (23.4) 158 (28.3) 0.767 
Alcohol drinker (yes) 183 (36.6) 204 (36.5) 0.245 
Family history of chronic diseases 200 (40.0) 222 (39.7) 0.939 
Newly defined type 2 diabetes 36 (7.2) 110 (19.7) <0.001 
Metabolic syndrome 51 (10.2) 394 (70.5) <0.001 
Waist circumference (cm) 75.9 ± 6.1 93.2 ± 8.2 <0.001 
Systolic blood pressure (mmHg) 118.4 ± 15.3 130.9 ± 17.6 <0.001 
Diastolic blood pressure (mmHg) 74.7 ± 9.8 84.0 ± 11.6 <0.001 
Glucose (mmol/l) 5.81 ± 1.13 6.30 ± 1.54 <0.001 
A1C (%) 5.58 ± 0.62 5.79 ± 0.78 <0.001 
Insulin (μU/ml) 7.3 (7.0−7.7) 11.3 (10.9−11.8) <0.001 
HOMA-IR 0.85 (0.82−0.89) 1.33 (1.27−1.38) <0.001 
Total cholesterol (mmol/l) 5.16 ± 1.14 5.33 ± 1.18 0.014 
LDL cholesterol (mmol/l) 3.14 ± 0.93 3.41 ± 0.99 <0.001 
HDL cholesterol (mmol/l) 1.53 ± 0.44 1.23 ± 0.34 <0.001 
Triglycerides (mmol/l) 1.01 (0.96−1.06) 1.61 (1.53−1.69) <0.001 
DEXA scan 456 (91.2) 504 (90.2) 0.697 
Total body fat mass (kg) (n = 960)    
    Men 11.8 ± 3.3 20.7 ± 4.3 <0.001 
    Women 15.7 ± 2.8 25.7 ± 5.1 <0.001 
Total body fat (%) (n = 960)    
    Men 19.0 ± 4.3 25.6 ± 3.4 <0.001 
    Women 29.4 ± 3.8 36.1 ± 3.6 <0.001 
Trunk fat mass (kg) (n = 960)    
    Men 6.3 ± 2.1 12.1 ± 2.7 <0.001 
    Women 7.7 ± 1.8 13.7 ± 3.0 <0.001 
HBsAg carriage 44 (8.8) 50 (8.9) 0.916 
Elevated hepatic enzymes 141 (28.2) 294 (52.6) <0.001 
AST (IU/l) 23.6 (22.9–24.3) 26.3 (25.4–27.2) <0.001 
ALT (IU/l) 19.1 (18.2–20.0) 27.8 (26.5–29.2) <0.001 
GGT (IU/l) 22.5 (21.3–23.8) 33.1 (31.2–35.0) <0.001 
hs-CRP (mg/l) 0.61 (0.57–0.66) 1.38 (1.28–1.49) <0.001 
IL-6 (pg/ml) 1.19 (1.13–1.26) 1.67 (1.59–1.76) <0.001 
HMW-adiponectin (μg/ml) 3.16 (2.93–3.41) 1.88 (1.73–2.04) <0.001 
Leptin (ng/ml) 3.99 (3.70–4.31) 8.94 (8.40–9.51) <0.001 
LBP (μg/ml) 10.0 (9.1–11.1) 27.6 (25.2–30.3) <0.001 

Data are arithmetic mean ± SD, n (%), or geometric mean (95% CI). n = 1,059. Percentages may not sum to 100 because of rounding.

*P value was calculated after adjustment for age and sex; P value for body fat comparison was not adjusted for sex.

†Data not adjusted for itself.

‡Benjamini and Hochberg–corrected statistical significance (17).

Compared with normal-weight subjects, overweight/obese individuals were more likely to have lower educational attainment and higher prevalence of metabolic syndrome and type 2 diabetes (all P < 0.01) (Table 1). They also had higher values for waist circumference, blood pressure, glucose, A1C, insulin, HOMA-IR, total cholesterol, LDL cholesterol, and triglycerides and lower HDL cholesterol concentration (all P < 0.05). Meanwhile, they exhibited higher levels of total body fat mass/percentage and trunk fat mass (all P < 0.001), hepatic enzymes (AST, ALT, and GGT), inflammatory markers (hs-CRP and IL-6) and leptin, accompanied by lower concentrations of HMW-adiponectin (all P < 0.001). Importantly, plasma LBP levels were significantly higher in overweight/obese participants than in normal-weight participants (geometric mean 27.6 [95% CI 25.2–30.3] vs. 10.0 [9.1–11.1] μg/ml; P < 0.001). In addition, LBP levels were higher in subjects with impaired fasting glucose than in those with normal fasting glucose (16.7 [15.0–18.6] vs. 13.8 [12.2–15.6] μg/ml; P = 0.041) and higher in subjects with impaired glucose tolerance than in those with normal glucose tolerance (31.2 [24.3–40.1] vs. 14.4 [12.8–16.3] μg/ml; P < 0.001), after adjustment for age and sex.

Correlation between LBP concentrations and metabolic parameters

LBP was positively correlated with BMI, waist circumference (all P < 0.001) (supplementary Table A1, available in an online appendix), blood pressure, total cholesterol, LDL cholesterol, triglycerides, glucose, insulin, HOMA-IR, hepatic enzymes, and leptin and negatively correlated with HDL cholesterol and HMW-adiponectin (all P < 0.05, data not shown), after adjustment for age and sex. Plasma LBP was also highly correlated with inflammatory markers (hs-CRP r = 0.93 and IL-6 r = 0.50, both P < 0.001) after adjustment for age and sex. These correlation coefficients were attenuated but remained statistically significant after further adjustment for BMI (supplementary Table A1). When the analyses were stratified by obesity status, significant correlations with the metabolic traits were more pronounced among overweight/obese subjects than among normal-weight individuals. In addition, LBP was strongly correlated with total body fat mass/percentage and trunk fat mass in sex-stratified correlation models (all P < 0.001) (data not show).

Associations of LBP concentrations with metabolic syndrome, type 2 diabetes, and insulin resistance

The risk for metabolic syndrome in the whole study sample increased progressively across the LBP quartiles (Ptrend < 0.001) (Table 2) and those in the highest LBP quartile had an OR of 3.54 (95% CI 2.05–6.09) compared with those in the lowest quartile (model 2), after adjustment for age, sex, lifestyle factors, family history of chronic diseases, and BMI. Similar trends were also observed for the metabolic syndrome components. Further adjustment for inflammatory markers (model 3) abolished the significant associations for metabolic syndrome and most of its components except for hypertriglyceridemia and low HDL cholesterol.

Table 2

ORs (95% CI) for metabolic syndrome, type 2 diabetes, and insulin resistance according to quartiles of LBP

Quartile of LBP
Ptrend
Q1 (LBP ≤6.5 μg/ml)Q2 (6.5 < LBP ≤15.8 μg/ml)Q3 (15.8 < LBP ≤42.0 μg/ml)Q4 (LBP >42.0 μg/ml)
Metabolic syndrome (n = 1,041) 35/260 91/260 132/261 176/260  
    Model 1 3.45 (2.22–5.37) 6.25 (4.05–9.65) 12.90 (8.28–20.10) <0.001 
    Model 2 2.78 (1.64–4.72) 3.04 (1.81–5.12) 3.54 (2.05–6.09) <0.001 
    Model 3 2.56 (1.50–4.38) 2.38 (1.36–4.15) 1.80 (0.84–3.88) 0.053 
Central obesity 59/260 110/260 164/261 204/260  
    Model 1 2.50 (1.71–3.66) 5.92 (4.01–8.72) 12.80 (8.42–19.47) <0.001 
    Model 2 1.64 (0.85–3.16) 2.54 (1.30–4.97) 2.53 (1.18–5.43) 0.005 
    Model 3 1.49 (0.77–2.90) 1.88 (0.90–3.92) 1.09 (0.35–3.35) 0.304 
Elevated blood pressure 66/260 89/260 119/261 157/260  
    Model 1 1.51 (1.02–2.23) 2.24 (1.53–3.28) 4.19 (2.85–6.15) <0.001 
    Model 2 1.16 (0.76–1.76) 1.32 (0.87–2.00) 1.73 (1.11–2.69) 0.013 
    Model 3 1.09 (0.71–1.67) 1.08 (0.69–1.68) 0.94 (0.50–1.75) 0.982 
Hypertriglyceridemia 25/260 70/260 102/261 130/260  
    Model 1 3.61 (2.18–5.99) 5.82 (3.56–9.52) 9.34 (5.72–15.25) <0.001 
    Model 2 3.11 (1.85–5.24) 4.05 (2.43–6.76) 4.98 (2.94–8.43) <0.001 
    Model 3 3.04 (1.80–5.12) 3.77 (2.23–6.38) 4.10 (2.14–7.85) <0.001 
Low HDL cholesterol 65/260 82/260 116/261 118/260  
    Model 1 1.43 (0.97–2.11) 2.64 (1.81–3.86) 2.77 (1.89–4.04) <0.001 
    Model 2 1.17 (0.78–1.75) 1.72 (1.15–2.58) 1.39 (0.90–2.15) 0.056 
    Model 3 1.18 (0.78–1.76) 1.76 (1.15–2.68) 1.54 (0.87–2.72) 0.027 
Hyperglycemia 140/260 157/260 174/261 188/260  
    Model 1 1.28 (0.90–1.82) 1.64 (1.14–2.35) 2.14 (1.48–3.09) <0.001 
    Model 2 1.18 (0.82–1.69) 1.34 (0.92–1.97) 1.49 (0.99–2.27) 0.047 
    Model 3 1.08 (0.75–1.56) 1.00 (0.66–1.52) 0.60 (0.32–1.13) 0.382 
Type 2 diabetes (n = 1,041) 10/260 22/260 43/261 67/260  
    Model 1 2.29 (1.06–4.95) 4.71 (2.30–9.61) 8.30 (4.15–16.59) <0.001 
    Model 2 1.99 (0.91–4.33) 3.53 (1.69–7.37) 5.53 (2.64–11.59) <0.001 
    Model 3 1.93 (0.88–4.21) 3.06 (1.45–6.46) 3.23 (1.38–7.58) 0.003 
Quartile of LBP
Ptrend
Q1 (LBP ≤6.5 μg/ml)Q2 (6.5 < LBP ≤15.8 μg/ml)Q3 (15.8 < LBP ≤42.0 μg/ml)Q4 (LBP >42.0 μg/ml)
Metabolic syndrome (n = 1,041) 35/260 91/260 132/261 176/260  
    Model 1 3.45 (2.22–5.37) 6.25 (4.05–9.65) 12.90 (8.28–20.10) <0.001 
    Model 2 2.78 (1.64–4.72) 3.04 (1.81–5.12) 3.54 (2.05–6.09) <0.001 
    Model 3 2.56 (1.50–4.38) 2.38 (1.36–4.15) 1.80 (0.84–3.88) 0.053 
Central obesity 59/260 110/260 164/261 204/260  
    Model 1 2.50 (1.71–3.66) 5.92 (4.01–8.72) 12.80 (8.42–19.47) <0.001 
    Model 2 1.64 (0.85–3.16) 2.54 (1.30–4.97) 2.53 (1.18–5.43) 0.005 
    Model 3 1.49 (0.77–2.90) 1.88 (0.90–3.92) 1.09 (0.35–3.35) 0.304 
Elevated blood pressure 66/260 89/260 119/261 157/260  
    Model 1 1.51 (1.02–2.23) 2.24 (1.53–3.28) 4.19 (2.85–6.15) <0.001 
    Model 2 1.16 (0.76–1.76) 1.32 (0.87–2.00) 1.73 (1.11–2.69) 0.013 
    Model 3 1.09 (0.71–1.67) 1.08 (0.69–1.68) 0.94 (0.50–1.75) 0.982 
Hypertriglyceridemia 25/260 70/260 102/261 130/260  
    Model 1 3.61 (2.18–5.99) 5.82 (3.56–9.52) 9.34 (5.72–15.25) <0.001 
    Model 2 3.11 (1.85–5.24) 4.05 (2.43–6.76) 4.98 (2.94–8.43) <0.001 
    Model 3 3.04 (1.80–5.12) 3.77 (2.23–6.38) 4.10 (2.14–7.85) <0.001 
Low HDL cholesterol 65/260 82/260 116/261 118/260  
    Model 1 1.43 (0.97–2.11) 2.64 (1.81–3.86) 2.77 (1.89–4.04) <0.001 
    Model 2 1.17 (0.78–1.75) 1.72 (1.15–2.58) 1.39 (0.90–2.15) 0.056 
    Model 3 1.18 (0.78–1.76) 1.76 (1.15–2.68) 1.54 (0.87–2.72) 0.027 
Hyperglycemia 140/260 157/260 174/261 188/260  
    Model 1 1.28 (0.90–1.82) 1.64 (1.14–2.35) 2.14 (1.48–3.09) <0.001 
    Model 2 1.18 (0.82–1.69) 1.34 (0.92–1.97) 1.49 (0.99–2.27) 0.047 
    Model 3 1.08 (0.75–1.56) 1.00 (0.66–1.52) 0.60 (0.32–1.13) 0.382 
Type 2 diabetes (n = 1,041) 10/260 22/260 43/261 67/260  
    Model 1 2.29 (1.06–4.95) 4.71 (2.30–9.61) 8.30 (4.15–16.59) <0.001 
    Model 2 1.99 (0.91–4.33) 3.53 (1.69–7.37) 5.53 (2.64–11.59) <0.001 
    Model 3 1.93 (0.88–4.21) 3.06 (1.45–6.46) 3.23 (1.38–7.58) 0.003 
Q1 (LBP ≤5.9 μg/ml)Q2 (5.9 < LBP ≤12.7 μg/ml)Q3 (12.7 < LBP ≤37.8 μg/ml)Q4 (LBP >37.8 μg/ml)Ptrend
Insulin resistance (n = 899) 27/224 38/225 72/225 87/225  
    Model 1 1.49 (0.87–2.54) 3.48 (2.13–5.70) 4.64 (2.85–7.55) <0.001 
    Model 2 1.12 (0.64–1.98) 1.95 (1.14–3.32) 1.90 (1.10–3.28) 0.005 
    Model 3 1.14 (0.64–2.01) 1.97 (1.14–3.41) 1.95 (0.97–3.92) 0.014 
Q1 (LBP ≤5.9 μg/ml)Q2 (5.9 < LBP ≤12.7 μg/ml)Q3 (12.7 < LBP ≤37.8 μg/ml)Q4 (LBP >37.8 μg/ml)Ptrend
Insulin resistance (n = 899) 27/224 38/225 72/225 87/225  
    Model 1 1.49 (0.87–2.54) 3.48 (2.13–5.70) 4.64 (2.85–7.55) <0.001 
    Model 2 1.12 (0.64–1.98) 1.95 (1.14–3.32) 1.90 (1.10–3.28) 0.005 
    Model 3 1.14 (0.64–2.01) 1.97 (1.14–3.41) 1.95 (0.97–3.92) 0.014 

Model 1: adjusted for age and sex. Model 2: further adjusted for smoking (current, yes or no), alcohol drinking (yes or no), physical activity (low or high by the sex-specific MET/week median), education (0–9, 10–12, and ≥13 years), family history of chronic diseases (yes or no), and BMI. Model 3: further adjusted for hsCRP and IL-6.

For those with newly defined type 2 diabetes, the OR in the highest quartile was 5.53 (95% CI 2.64–11.59) compared with that in the lowest LBP quartile (Ptrend < 0.001) in the multivariable model (Table 2, model 2). A positive association between LBP and insulin resistance, represented by the highest quartile of HOMA-IR, was also observed in nondiabetic participants (OR 1.90 [95% CI 1.10–3.28]). The ORs for diabetes and insulin resistance were slightly attenuated but remained statistically significant after further adjustment for hs-CRP and IL-6 (model 3).

The associations between LBP and metabolic disorders were not attenuated by additionally controlling for HMW-adiponectin, leptin, and elevated hepatic enzymes (as categorical or continuous variables) in model 2 (data not shown). Replacing BMI with total body fat mass in the multiple regression models also did not materially change the magnitude of the associations. The results remained similar after exclusion of HBsAg-positive subjects (n = 91, 8.7%) or subjects with 18.0 ≤ BMI <18.5 kg/m2 (n = 14, 1.3%) (data not shown).

We further conducted joint classification analyses to examine whether obesity, trunk fat mass, HMW-adiponectin, and elevated hepatic enzymes modified the associations of LBP with metabolic syndrome and type 2 diabetes (Fig. 1). No significant interactions were observed between LBP and these factors (P > 0.05 for all interaction tests).

Figure 1

ORs for metabolic disorders according to joint classification of LBP and obesity status (A and B), trunk fat (sex and obesity-stratified tertile [T], C and D), HMW-adiponectin (E and F), and hepatic enzymes (G and H). A–D: Modified metabolic syndrome was defined as having two or more components of metabolic syndrome without central obesity. Adjusted for age, sex, smoking, alcohol drinking, physical activity, education, and family history of chronic diseases. E–H: Adjusted for age, sex, smoking, alcohol drinking, physical activity, education, family history of chronic diseases, and BMI.

Figure 1

ORs for metabolic disorders according to joint classification of LBP and obesity status (A and B), trunk fat (sex and obesity-stratified tertile [T], C and D), HMW-adiponectin (E and F), and hepatic enzymes (G and H). A–D: Modified metabolic syndrome was defined as having two or more components of metabolic syndrome without central obesity. Adjusted for age, sex, smoking, alcohol drinking, physical activity, education, and family history of chronic diseases. E–H: Adjusted for age, sex, smoking, alcohol drinking, physical activity, education, family history of chronic diseases, and BMI.

Close modal

Our data showed significant associations between elevated LBP concentrations and the risk for metabolic syndrome, insulin resistance, and type 2 diabetes independent of conventional cardiovascular risk factors in an apparently healthy Chinese population. Further adjustment for inflammatory factors, but not for adipokines and elevated hepatic enzymes, substantially attenuated the associations for metabolic syndrome and most of its components, suggesting that chronic inflammation may mediate the effects of innate immune response induced by LPS-LBP.

In a previous human study, Ghanim et al. (18) reported that increased plasma LPS and LBP and also expression of Toll-like receptors and suppressor of cytokine signaling-3 in mononuclear cells (MNCs) could be induced by consuming a meal with a high-fat, high-carbohydrate content, but not with isocaloric fruit and fiber, implying a potential role of the LPS-LBP pathway in postprandial inflammation and related metabolic disorders. In addition, positive correlations between LBP and metabolic traits such as BMI, diastolic blood pressure, fasting glucose, insulin, and triglycerides were observed in 60 men with glucose intolerance (13). Moreover, a higher LBP level was associated with increased prevalence of coronary artery disease independent of established cardiovascular risk factors in 247 male patients (12). With a relatively large sample size of apparently healthy men and women, our study provides more convincing evidence about the relationship between LBP and metabolic abnormalities.

In recent years, the effects of microbiota on health have attracted increasing attention, and low-grade endotoxemia or LPS was found to link to various metabolic consequences. However, most studies have been performed in mice and few in human populations. Studies in mice demonstrated that two- to threefold increased circulating LPS induced by a high-fat diet or LPS infusion led to increased levels of fasting glucose and insulin and body weight gain (4,5). The occurrences of a metabolic response could be counteracted by a CD14 mutant (5) or improved by changing gut microbiota (4). In humans, a high LPS concentration was found in individuals with type 2 diabetes (6). The administration of rosiglitazone, a hypoglycemic agent with a potential anti-inflammatory effect (19), could reduce LPS levels (6). However, the short half-life of LPS (8) and the disadvantages associated with its assay (9) have limited its potential applications in routine clinical settings and large-scale studies. Having a relatively long half-life and reliable measurements, LBP might serve as a marker reflecting an “effective” LPS level and innate immune response triggered by LPS (11,12). Therefore, circulating LBP levels can be considered a promising early biomarker and intervention target for inflammatory conditions.

We observed a stronger correlation between LBP and inflammatory markers than that between LBP and adipokines after adjustment for BMI. Moreover, adjusting for hs-CRP and IL-6 almost eliminated the associations of LBP with metabolic syndrome and most of its traits (Table 2, model 3), whereas controlling for HMW-adiponectin and leptin had little impact on the associations. A potential mechanism is that LPS-LBP–triggered immune response activates the nuclear factor-κB pathway, stimulates formation of IL-6, IL-1, and tumor necrosis factor-α (7), and upregulates CRP synthesis in hepatocytes. However, it is noteworthy that low plasma HMW-adiponectin contributed additional risk for metabolic syndrome and type 2 diabetes under the high LBP condition (Fig. 1 E and F). In fact, existing data showed that LPS increased plasma leptin levels and downregulated adiponectin receptor mRNA in healthy volunteers (20).

We also examined whether adjustment for hepatic enzymes altered the associations, because LBP is synthesized in liver and LPS could cause hepatocyte damage by inducing upregulation of tumor necrosis factor-α expression in Kupffer cells (7). Previous prospective studies also suggested that elevated hepatic enzymes predicted risk of metabolic syndrome and type 2 diabetes (21). However, no material change was observed when we controlled for elevated hepatic enzymes or excluded individuals having hepatitis B infection. Collectively, our findings indicate that the inflammatory cascade initiated by LPS-LBP triggered an innate immune response, which is a major mechanism linking LBP to the pathogenesis of metabolic disorders.

Interestingly, unlike the case for metabolic syndrome risk, controlling for inflammatory markers in current study did not alter the significant associations of LBP with dyslipidemia, insulin resistance, and type 2 diabetes (Table 2). The minor effect of inflammation on the association between LBP and dyslipidemia might be explained by a sequence identity shared between LBP and lipid transfer proteins that could modify lipid homeostasis during subclinical endotoxemia (22). Previous studies demonstrated that hyperinsulinemia might affect immune competence, including functions of neutrophils (13) and hepatic Kupffer cells (23), which subsequently influenced LPS clearance and LBP synthesis. Nevertheless, endotoxemia might induce insulin resistance, whereas CD14 mutant mice showed hypersensitivity to insulin (5). MNCs were also suggested to be a target of insulin action and involved in the interaction between inflammation and insulin resistance (24). The increased suppressor of cytokine signaling-3 expression in MNCs, accompanied by increased LPS and LBP concentrations after a high-fat, high-carbohydrate meal, might also interfere with insulin signaling and play a role in insulin resistance (18,24). Our data suggested that LBP might exert its influence directly on insulin action in addition to stimulating a downstream pathway generating inflammatory cytokines. Moreover, the LBP gene was recently found to be genetically susceptible to type 2 diabetes in Japanese (25), which may provide one possible reason that the conventionally inflammatory cytokine showed little impact on the association between LBP and type 2 diabetes.

To our knowledge, this is the first study that systematically investigated the associations of LBP concentrations with the risk of obesity-related metabolic disorders. Admittedly, the cross-sectional nature of this study does not allow for a causal inference. Nonetheless, we tried to eliminate potential effects of acute inflammation and other potential confounders by applying strict exclusion criteria and also included a relatively large sample size with both sexes. Certainly, these results should be examined prospectively and in different populations to establish the causal relationship between LBP, inflammation, and metabolic outcomes.

Our study indicates that LBP is significantly associated with obesity-related metabolic disorders in apparently healthy Chinese. These findings indicate the role of LPS-initiated innate immune mechanisms in metabolic diseases. Future prospective studies are needed to clarify whether LBP predicts future risk of metabolic disorders.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

This study was supported by research grants from the Chinese Academy of Sciences (KSCX2-YW-R-116), the Ministry of Science and Technology of China (2008DFA31960, 2008AA02Z315, 2009AA022704, and 2007AA027332), the Science and Technology Commission of Shanghai Municipality (075407001), the Shanghai Institutes for Biological Sciences, the Chinese Academy of Sciences (SIBS2008006), the National Natural Science Foundation of China (30930081), and the Novo Nordisk A/S. J.D. and K.C. also acknowledge the French National Agency for Research (ANR MicroObes program).

The sponsors were not involved in the study design, data collection, analysis, or interpretation.

No other potential conflicts of interest relevant to this article were reported.

L.S. contributed to the study design, researched data, contributed to discussion, and wrote the manuscript. Z.Y. contributed to the study design, researched data, contributed to discussion, and reviewed/edited the manuscript. X.Y., S.Z., and H.L. contributed to the study design, researched data, and reviewed/edited the manuscript. D.Y. and H.W. researched data and reviewed/edited the manuscript. Y.C. contributed to the study design and reviewed/edited the manuscript. J.D. reviewed/edited the manuscript. K.C. wrote the manuscript. F.B.H. and X.L. contributed to the conception and design of the study, contributed to discussion, reviewed/edited the manuscript.

We are grateful to An Pan, Qibin Qi, Ling Lu, Chen Liu, Geng Zhang, Geng Zong, Shaojie Ma, He Zheng, and the local Center for Disease Control and Prevention staff of Shanghai for their kind help at various stages of this study.

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