Natural Antibiotics and Insulin Sensitivity

The Role of Bactericidal/Permeability-Increasing Protein

  1. Carme Gubern1,
  2. Abel López-Bermejo1,
  3. Josefina Biarnés1,
  4. Joan Vendrell2,
  5. Wifredo Ricart1 and
  6. José Manuel Fernández-Real1
  1. 1Section of Diabetes, Endocrinology and Nutrition, Institut d’Investigació Biomédica de Girona, Girona, Spain
  2. 2Research Unit, University Hospital of Tarragona “Joan XXIII,” Institut Pere Virgili, Tarragona, Spain
  1. Address correspondence and reprint requests to J.M. Fernández-Real, MD, PhD, Unit of Diabetes, Endocrinology and Nutrition, Hospital de Girona “Dr. Josep Trueta,” Ctra. França s/n, 17007 Girona, Spain. E-mail: uden.jmfernandezreal{at}htrueta.scs.es

Abstract

The innate immune system can immediately respond to microorganism intrusion by helping to prevent further invasion. Bactericidal/permeability-increasing protein (BPI) is a major constituent of neutrophils that possesses anti-inflammatory properties. Inflammation is increasingly recognized as a component of the metabolic syndrome. We hypothesized that the production of BPI could be linked to insulin sensitivity and glucose tolerance. We studied circulating BPI across categories of glucose tolerance. We also studied whether these cross-sectional associations were of functional importance. For this reason, we investigated circulating bioactive lipopolysaccharide and the effects of changing insulin action—after treatment with an insulin sensitizer (metformin)—on circulating BPI in subjects with glucose intolerance. Finally, we tested whether a 3′-untranslated region (UTR) BPI polymorphism led to differences in BPI and insulin action among nondiabetic subjects. Age- and BMI-adjusted circulating BPI was significantly lower among patients with type 2 diabetes. Circulating BPI correlated negatively with fasting and postload glucose and insulin concentrations. In subjects with glucose intolerance, BPI was also linked to BMI, waist-to-hip ratio, and age- and BMI-adjusted insulin sensitivity. Bioactive lipopolysaccharide was negatively correlated with circulating BPI (r = −0.57, P < 0.0001) and positively with plasma lipopolysaccharide-binding protein (r = 0.54, P = 0.002). In parallel to improved insulin sensitivity, plasma BPI significantly increased in the metformin group but not in the placebo group. A 3′-UTR BPI polymorphism was simultaneously associated with plasma BPI concentration, waist-to-hip ratio, fasting and postload insulin concentration, fasting plasma triglycerides, and insulin sensitivity. These findings suggest that this component of the innate immune system is associated with metabolic pathways.

The first lines of defense against invasion by microbial agents are the physical barriers represented by the skin and mucosas. These physical barriers are susceptible to injuries that allow the entry of opportunistic microbial agents. The innate immune system can immediately respond to this intrusion by helping to prevent further invasion. This immune response includes phagocytosis by neutrophils and macrophages and their production of reactive oxygen intermediates that kill microbial agents (1).

A number of endogenous antimicrobial proteins produced by the neutrophils have been shown to play an integral part in innate immunity. Bactericidal/permeability-increasing protein (BPI) is a major constituent of neutrophils (from 0.5 to 1% of total protein) (2). This single-chain cationic protein (molecular weight 55 kDa) is located mainly in the primary granules but is also found on the neutrophil surface (3). Additionally, BPI has been found on the surface of monocytes, in eosinophils, and as a product of mucosal and skin epithelial cells (3,4). In addition to its well-known antimicrobial function against gram-negative bacteria, BPI also possesses anti-inflammatory properties (3).

Impressive evidence has accumulated over the past decade that the metabolic syndrome is linked to inflammatory pathways (59). The molecular mechanisms leading to development of the metabolic syndrome (the clustering of central obesity, alterations of glucose and lipid metabolism, and arterial hypertension) are not fully understood. However, it has become clear that chronic subclinical inflammation, increased levels of inflammation-sensitive plasma proteins, and alterations in the function of the innate immune system are intrinsic to the metabolic syndrome (59). Insulin resistance is central to the pathophysiology of all these alterations, which run together with the accumulation of fat and the presence of specific genetic components of the inflammatory cascade (9).

By virtue of its anti-infective and neutralizing properties, BPI is a proximal anti-inflammatory effector. BPI’s crystal structure reveals a boomerang-shaped bipartite molecule that includes a cationic lysine-rich NH2-terminal half containing the antibacterial and lipopolysaccharide (endotoxin)-neutralizing activities of the molecule and a COOH-terminal half that contributes to the opsonic activity of BPI (10). Plasma lipopolysaccharide-binding protein (LBP) is a product of the liver that has a net anionic charge and serves to greatly amplify responses to lipopolysaccharide by delivering lipopolysaccharide monomers to a monocyte receptor complex containing CD14, MD2, and Toll-like receptor 4 (11,12).

BPI competes with LBP for the binding of endotoxin, but BPI-lipopolysaccharide complexes (in contrast to LBP and lipopolysaccharide) do not activate macrophages and other lipopolysaccharide-responsive host cells. Thus, BPI and LBP are functionally antagonistic (3). In fact, the affinity of LBP for lipopolysaccharide is ∼70-fold lower than that of BPI and may explain why LBP does not exhibit any antibacterial activity (3).

We and others have previously reported that subtle deficiencies in proteins of the innate immune system (soluble CD14 and mannose-binding lectin) were associated with alterations of glucose metabolism and atherosclerosis. These deficiencies lead to enhancement of the inflammatory cascade, resulting in a worsening of insulin resistance (13,14). We thus hypothesized that the production of BPI could be linked to insulin sensitivity and glucose tolerance. To this aim we studied circulating BPI in healthy subjects, in patients with glucose intolerance, and in patients with type 2 diabetes. We also studied whether these cross-sectional associations were of functional importance. For this reason, we investigated whether circulating bioactive lipopolysaccharide was linked to BPI in healthy volunteers. We also aimed to evaluate the effects of changing insulin action—after treatment with an insulin sensitizer (metformin)—on circulating BPI in subjects with glucose intolerance. Finally, we hypothesized that there were differences in insulin sensitivity according to the capacity of BPI production. To this end, we tested whether the rs1131847 BPI polymorphism at the 3′-untranslated region (UTR) led to differences in BPI and insulin action among nondiabetic subjects.

RESEARCH DESIGN AND METHODS

We recruited and studied 174 Caucasian subjects, including analysis of glucose tolerance and insulin sensitivity, within an ongoing study on nonclas sical cardiovascular risk factors. All normotolerant subjects (n = 114) had fasting plasma glucose <7.0 mmol/l and 2-h postload plasma glucose <7.8 mmol/l after a 75-g oral glucose tolerance test. Glucose intolerance was diagnosed in 60 subjects according to American Diabetes Association (ADA) criteria (postload glucose between 7.8 and 11.1 mmol/l). Inclusion criteria were 1) BMI <40 kg/m2, 2) absence of systemic disease, and 3) absence of infection within the previous month. None of the control subjects were under medication or had evidence of metabolic disease other than obesity. Alcohol and caffeine were withheld within 12 h of performing the insulin sensitivity test. Smokers were defined as any person consuming at least one cigarette a day in the previous 6 months. Resting blood pressure was measured as previously reported (14). Liver disease and thyroid dysfunction were specifically excluded by biochemical workup.

Patients with type 2 diabetes, according to American Diabetes Association criteria, were prospectively recruited from diabetes outpatient clinics based on stable metabolic control in the previous 6 months, as defined by stable HbA1c (A1C) values. Exclusion criteria included the following: 1) clinically significant hepatic, neurological, endocrinologic, or other major systemic disease, including malignancy; 2) history or current clinical evidence of hemochromatosis; 3) history of drug or alcohol abuse, defined as >80 g/day in men and >40 g/day in women, or serum transaminase activity more than twice the upper limit of normal; 4) elevated serum creatinine concentration; 5) acute major cardiovascular event in the previous 6 months; 6) acute illness and current evidence of acute or chronic inflammatory or infective disease; and 7) mental illness rendering the subject unable to understand the nature, scope, and possible consequences of the study. Pharmacological treatment for these patients was as follows: insulin: 44.8%; oral hypoglycemic agents: 72.9%; statins: 38.0%; fibrates: 10.6%; blood pressure–lowering agents: 61.5%; aspirin: 42.7%; and allopurinol: 4.2%. All subjects gave written informed consent after the purpose of the study was explained to them. The institutional review board of the institution approved the protocol.

Anthropometric and clinical measurements.

BMI was calculated as the weight (in kilograms) divided by the square of height (in meters). The subjects’ waists were measured with a soft tape midway between the lowest rib and the iliac crest. The hip circumference was measured at the widest part of the gluteal region. The waist-to-hip ratio was then calculated. Fat mass and percent fat mass were calculated using bioelectric impedance (body composition analyzer; Holtain, Crosswell, Crymych, Dyfed, Wales, U.K.).

Study of insulin sensitivity.

Insulin sensitivity and glucose effectiveness were measured using the frequently sampled intravenous glucose tolerance test. In brief, the experimental protocol started between 8:00 and 8:30 a.m. after an overnight fast. A butterfly needle was inserted into an antecubital vein, and patency was maintained with a slow saline drip. Basal blood samples were drawn at −30, −10, and −5 min, after which glucose (300 mg/kg body wt) was injected over 1 min starting at time 0, and insulin (0.03 units/kg; Actrapid, Novo Nordisk, Denmark) was administered at time 20. Additional samples were obtained from a contralateral antecubital vein up to 180 min, as previously described (14).

Functional studies. BPI in association with bioactive lipopolysaccharide.

Lipopolysaccharide was measured by a limulus amebocyte lysate test (Pyrochrome; Cape Cod, Falmouth, MA), as recommended by the manufacturer. In brief, plasma was collected in nonpyrogenic EDTA tubes and frozen at −20°C until assay. All procedures were performed under nonpyrogenic conditions. Plasma was diluted 1:20 or 1:40 and heat inactivated at 75°C for 10 min. The reaction was read using kinetics, i.e., measuring the time to reach a given absorbance at 405 nm. Recovery of spiked lipopolysaccharide was between 50 and 200%. Sensitivity of the assay was 0.005 Ehrlich units/ml (0.5 pg/ml).

Study of the effects of an insulin sensitizer (metformin) on BPI.

Patients were recruited from the Diabetes and Endocrinology Unit at Hospital de Girona “Dr. Josep Trueta.” They were offered participation in the study if they were between 30 and 65 years old and fulfilled all the inclusion criteria. Men and women were both included, but women were included only when surgical sterility was documented, when they were postmenopausal, or when a reliable method of contraception was used. The inclusion criteria were as follows: 1) BMI between 22 and 35 kg/m2, 2) impaired glucose tolerance by an oral glucose tolerance test performed 2 months before the beginning of the study or a fasting glucose level between 6.11 and 7.8 mmol/l, and 3) stability of diet and physical exercise within the past 2 months. All subjects signed a written informed consent. Exclusion criteria were 1) type 2 diabetes by American Diabetes Association criteria; 2) pregnant or nursing women; 3) patients with renal impairment defined as plasma creatinine values ≥1.5 mg/dl for men and ≥1.4 mg/dl for women; 4) patients affected by cardiac or respiratory insufficiency likely to cause central hypoxia or reduced peripheral perfusion; 5) past history of lactic acidosis; 6) noncontrolled hypertension; 7) acute or chronic infection; 8) liver disease, including alcoholic liver disease as demonstrated by abnormal liver-function tests or alcohol abuse; 9) patients taking drugs that could modify glucose tolerance; 10) participants in another clinical trial within the last 30 days; and 11) legal incapacity as a study patient.

The clinical study was performed in accordance with the Declaration of Helsinki (revised version, Hong Kong, 1989) as well as with the European Community Note for Guidance on Good Clinical Practice for Studies on Medicinal Products in the European Community. It was approved by the local ethics committee and the Spanish health department (clinical assay no. 97/337).

We randomized 31 patients either to placebo or metformin, using randomization tablets (Lipha), according to the computer program Rancode +3.1. A dietitian with the aim of assuring a stable weight gave general diabetic dietary advice at the beginning of the study. At 2 months after the first visit (visit 2), subjects started taking metformin or placebo, one tablet per day (850 mg) for the 1st week and then two tablets per day (one after breakfast and one after dinner) for the next 11 weeks. Drug compliance was checked by tablet counts, and any side effect was recorded at visit 3 (6 weeks after randomization) and at visit 4 (at the end of the study).

Insulin sensitivity was evaluated by homeostasis model assessment (HOMA), using basal (mean of three samples obtained at 5-min intervals) glucose and insulin (15,16). β-Cell function was calculated by continuous infusion of glucose with model assessment (CIGMA) from achieved C-peptide and glucose values (17). The CIGMA test consists of a continuous intravenous infusion of 5 mg glucose · kg ideal body wt−1 · min−1, using a 10 g/dl glucose solution with model assessment of glucose, insulin, and C-peptide (radioimmunoassay; Byk-Sangtec Diagnostica, Dietzenbach, Germany) values obtained before (basal value, mean value of three samples obtained at 5-min intervals) and at the end (achieved value, mean value of samples obtained at 50, 55, and 60 min) of the test. C-peptide detection level was 0.1 ng/ml and had intra- and interassay coefficients of variation (CVs) of 2.6 and 4.4%, respectively. It shows 25% cross-reactivity with proinsulin but not with insulin.

Analytical methods.

Serum glucose concentrations were measured in duplicate by the glucose oxidase method, using a Glucose Analyzer II (Beckman Instruments, Brea, CA). Total serum cholesterol was measured through the reaction of cholesterol esterase with cholesterol oxidase and peroxidase. Total serum triglycerides were measured through the reaction of glycerol-phosphate-oxidase and peroxidase. Uric acid was measured by routine laboratory tests. A1C was measured by high-performance liquid chromatography (Bio-Rad, Muenchen, Germany) and a Jokoh HS-10 autoanalyzer. Intra- and interassay CVs were <4% for all of these tests.

Serum insulin levels were measured in duplicate by monoclonal immunoradiometric assay or enzyme-amplified sensitivity immunoassay (Medgenix Diagnostics, Fleunes, Belgium). Intra- and interassay CVs were similar to those previously reported (14).

Enzyme-linked immunosorbent assay of BPI and LBP.

Plasma EDTA BPI concentrations were measured by a sandwich enzyme-linked immunosorbent assay (ELISA; human BPI ELISA kit; HyCult Biotechnology, Uden, the Netherlands) according to the manufacturer’s instructions. The assay has a sensitivity of 250 pg/ml. Intra- and interassay CVs were <5%. Serum LBP levels were determined with a commercially available human LBP ELISA kit (HyCult Biotechnology, Uden, the Netherlands). Serum samples were diluted at least 1,000 times and assayed according to the manufacturer’s instructions. The assay has a sensitivity of 1 ng/ml and a measurable concentration range of 0.8–50 ng/ml. Intra- and interassay CVs were between 5 and 10%.

Study of the effects of a 3′-UTR BPI gene polymorphism on circulating BPI and insulin action among nondiabetic subjects.

Genomic DNA was extracted from peripheral blood leukocytes according to standard procedures (QIAamp DNA Blood Mini Kit; Qiagen, Hilden, Germany).

For the detection of the polymorphism rs1131847 (NCBI [National Center for Biotechnology Information]), G-to-A transition at 3′-UTR, we used a method based on TaqMan technology suitable for allelic discrimination (ABI Prism 7000 sequence detection system; Applied Biosystems, Darmstadt, Germany). The samples were genotyped with an Applied Biosystems Taqman assay (assay-on-demand C_308491_1_), using minor-groove binding reporter probes (VIC-labeled for the A allele and FAM-labeled for the G) and an end-read protocol. The PCR conditions were as recommended by the manufacturer, and a sample containing water instead of DNA, as a negative control, was used for each PCR run.

Statistical methods.

Descriptive results of continuous variables are the means ± SD. Before statistical analysis, normal distribution and homogeneity of the variances were evaluated using Levene’s test, and then variables were given a log transformation if necessary. These parameters (insulin sensitivity index [Si], glucose effectiveness index [SG], triglycerides, LBP, and BPI) were analyzed on a log scale and tested for significance on that scale. The anti–log-transformed values of the means (geometric mean) are reported in the Tables. Relationships between variables were tested using Pearson’s test and stepwise multiple linear regression analysis. We used χ2 test for comparisons of proportions and unpaired t tests for comparisons of quantitative variables. A general linear model for repeated measures with Bonferroni’s correction was used to compare BPI levels pre- and posttreatment (metformin study). A general linear model was also used to calculate circulating BPI values after adjusting for age and BMI. The statistical analyses were performed using the SPSS program (version 11.0).

RESULTS

Comparison of circulating BPI across categories of glucose tolerance.

BPI was significantly lower among patients with type 2 diabetes (Table 1). Median (95% CI) age- and BMI-adjusted BPI values were 11.42 (9.24–14.12) in type 2 diabetic patients, 16.98 (12.7–22.6) in subjects with glucose intolerance, and 17.53 (14.35–21.47) in control subjects (P = 0.006 type 2 diabetes versus control, P = 0.028 type 2 diabetes versus glucose intolerance). For any given medication, BPI concentrations did not differ between treated and untreated patients.

Metabolic studies in nondiabetic subjects.

Characteristics of the subjects and the comparisons with type 2 diabetic subjects are shown in Table 1. Subjects with glucose intolerance were significantly older, heavier, and showed lower insulin sensitivity and glucose effectiveness than normotolerant subjects. Circulating BPI correlated negatively with LBP (r = −0.31, P < 0.0001). This relationship was especially significant among normotolerant subjects (r = −0.35, P < 0.0001) but not in subjects with glucose intolerance (r = −0.18, P = 0.16).

When all nondiabetic subjects were considered as a whole, circulating BPI was significantly associated with age (Table 2). This was caused by the older age and higher plasma BPI levels of glucose-intolerant subjects. BPI correlated negatively with fasting and postload glucose, fasting and postload insulin, and A1C. After stratifying by glucose tolerance, all of these associations were observed only among subjects with glucose intolerance (Fig. 1), in whom BPI was also linked to BMI, waist-to-hip ratio, and to age- and BMI-adjusted insulin sensitivity (Tables 2 and 3 and Fig. 2A). Circulating BPI was also positively associated with HDL cholesterol and soluble fraction of tumor necrosis factor-α receptor 2 (sTNFR2), and these associations were attributable to the findings in normotolerant subjects (Table 3). BPI was also positively associated with white blood cell and neutrophil counts (Table 3).

LBP was negatively associated with age but only among normotolerant subjects (Table 2). As a possible reflection of its source, LBP was positively associated with fat-free mass and C-reactive protein (Tables 2 and 3). The remaining findings regarding plasma LBP were specular to those with BPI. Plasma LBP was positively associated with several components of the metabolic syndrome, such as BMI and diastolic blood pressure (Table 2), fasting and postload glucose concentrations, fasting insulin, A1C, and fasting triglycerides (Table 3) and negatively with insulin sensitivity (Table 3 and Fig. 2B) in subjects with glucose intolerance. In addition, LBP was negatively associated with glucose effectiveness when all subjects were considered as a whole (Table 3).

We performed a multiple linear regression analysis to predict circulating BPI. We considered as independent variables those with significant association on univariant analysis. When all subjects were considered as a whole, sTNFR2 (P = 0.003), neutrophil count (P = 0.003), and HDL cholesterol (P = 0.01) contributed to 19% of BPI variance (7, 6, and 6%, respectively) after controlling for the effects of age, BMI, waist-to-hip ratio, fasting glucose, and insulin sensitivity. Regarding LBP, fat-free mass alone contributed to 11% of LBP variance (P = 0.02).

Functional studies. BPI in association with bioactive lipopolysaccharide.

Bioactive lipopolysaccharide was negatively correlated with circulating BPI (r = −0.57, P < 0.0001) (Fig. 3) and positively with LBP (r = 0.54, P = 0.002).

Effects of insulin sensitizer (metformin) on BPI.

Of 118 preselected subjects, 31 were randomized for the study (16 to metformin and 15 to placebo). Six patients (three on metformin and three on placebo) withdrew early, and two noncompliant patients were excluded (both in metformin group). Three patients in the metformin group were excluded because no plasma EDTA was available for BPI measurement. We finally analyzed 20 patients (8 on metformin and 12 on placebo).

Table 4 shows the baseline characteristics of the analyzed patients. They were of similar age, BMI, and sex. Fasting glucose, insulin, and C-peptide levels were similar. Insulin sensitivity and β-cell function were also not significantly different. After a 12-week treatment, fasting and 2-h glucose decreased in the metformin group (P = 0.003 and P = 0.045, respectively). Fasting insulin (P = 0.005), fasting C-peptide (P = 0.01), achieved insulin (P = 0.013), and achieved C-peptide (P = 0.02) levels were also statistically lower after metformin treatment (Table 4). Insulin sensitivity by HOMA (P < 0.0001) improved after metformin treatment and was not modified by placebo. β-Cell function was not modified in either group (Table 4). In parallel to improved insulin sensitivity, plasma BPI significantly increased only in the metformin group in a general linear model for repeated measures with Bonferroni’s correction (P = 0.023) (Table 4).

Given that we have used different methods to assess the degree of insulin resistance in the subjects studied, we performed a Bland-Altman plot between log-HOMA–and log-Si–derived insulin sensitivity and between log-HOMA and log-CIGMA. The comparisons showed that the methods did not differ significantly.

BPI polymorphism and insulin sensitivity.

Given the associations between BPI and BMI, we screened 373 control subjects. GG homozygotes and A/− carriers showed no significant difference in BMI (27.5 ± 3.4 vs. 28.12 ± 4.1). In a sample of 174 consecutive subjects, we performed a more detailed study concerning insulin sensitivity. Interestingly, GG homozygotes showed significantly lower waist-to-hip ratio, lower fasting and postload insulin concentration, lower plasma triglycerides, and higher Si and plasma BPI concentration than A/− subjects (Table 5, Fig. 4).

DISCUSSION

According to previous reports, an underlying activation of the immune system leads to a higher white blood cell count, mainly neutrophil count, and to insulin resistance in apparently healthy subjects (18). In this study we substantiated this finding at the molecular level, providing evidence that this particular defense against infection (BPI) runs in parallel to insulin sensitivity among healthy subjects. First, we found that circulating BPI concentration was significantly different across categories of glucose tolerance: BPI was significantly lower in patients with type 2 diabetes. In subjects with glucose intolerance, we found the strongest associations between plasma BPI and central obesity, glucose metabolism, insulin sensitivity, and components of the metabolic syndrome. Interestingly, the associations between these components and LBP were specular to those of BPI.

Second, we tested the functional significance of these findings. Bioactive lipopolysaccharide was significantly associated with both BPI and LBP. In fact, the latter two proteins were also negatively associated. These findings suggest that, with decreasing BPI, the ability to buffer lipopolysaccharide is impaired, leading to increased LBP synthesis by the liver. Again, the specular relationships of BPI and LBP with several anthropometrical and metabolic variables support their antagonistic function.

Third, we further substantiated our hypothesis by studying the effects of an insulin sensitizer, metformin, on circulating BPI. In patients receiving metformin, but not in those receiving placebo, we observed improved insulin sensitivity and raised circulating BPI concomitantly.

How can all of these associations be explained? Insulin action may lead to increased plasma BPI concentration. A recent study has demonstrated that insulin is a strong regulator of the main neutrophil functions in nondiabetic healthy subjects (19). The cellular functions of human neutrophils, including bactericidal activity, require energy derived from glucose. Although insulin does not stimulate hexose transport in this immune cell, previous reports have clearly shown that this hormone is able to regulate glucose metabolism in neutrophils (20,21). Impaired neutrophil function would lead to decreased BPI exocytosis and impaired ability to buffer circulating lipopolysaccharide. The consequence would be increased liver C-reactive protein and LPB synthesis and, through enhancement of the inflammatory cascade, decreased insulin action in a vicious cycle. Metformin would help to reverse this situation.

Finally, we found that the 3′-UTR BPI gene polymorphism studied was associated with both increased BPI and raised insulin sensitivity concomitantly. This polymorphism may encompass mRNA destabilizing signals, thus affecting mRNA stability and leading to a reduction of BPI abundance in polymorphonuclear leukocytes. In this sense, the sequence of events would be precipitated in these individuals with poor ability to produce BPI. Interestingly, in animal models, the insulin signaling pathway modulates both inherent longetivity and pathogen resistance, increasing resistance to infection, to affect overall survival (22). Nevertheless, these results should be interpreted with caution, given the relatively small sample size. Furthermore, the BPI gene polymorphism may possibly represent a marker for another susceptibility gene in this region that could be the basis for the observed associations. In this sense, MODY1 (23) and agouti (24) genes are close to this region.

We cannot exclude that BPI could lead to increased insulin action. The selective and potent action of BPI against gram-negative bacteria and their lipopolysaccharides is fully manifest in biological fluids, including plasma, serum, and whole blood (25). In multiple animal models of gram-negative sepsis and/or endotoxemia, administration of BPI congeners is associated with improved outcome (26). Recombinant NH2-terminal proteins derived from BPI have demonstrated potent antiendotoxic activity in phase II/III trials (2729). BPI has been demonstrated to improve hyperglycemia and other metabolic events after lipopolysaccharide administration in animal models (30). In addition, the administration of erythromycin, and of other members of this family of antibiotics, has been reported to increase both insulin action (31) and BPI exocytosis (32). Insulin secretion and BPI exocytosis might be interrelated events reflecting common cellular pathways. In addition, BPI is capable of inhibiting all of the many proinflammatory activities of lipopolysaccharide, including induction of cytokine release (27,33). As a reflection of its source, plasma BPI was significantly associated with neutrophil count. However, BPI was not significantly associated with eosinophil count, possibly reflecting the minor relative importance of this cellular type.

In summary, our findings suggest that components of innate immunity, such as BPI and LBP, are not only linked to inflammatory pathways but also seem to be associated with insulin action. These molecules seem particularly important among patients with glucose intolerance and type 2 diabetes.

FIG. 1.

Relationship between fasting insulin (A) and A1C (B) with plasma BPI concentration among subjects with glucose intolerance.

FIG. 2.

Relationship between plasma BPI concentration (A) and LBP (B) with insulin sensitivity among subjects with glucose intolerance.

FIG. 3.

Relationship between bioactive lipopolysaccharide and plasma BPI concentration in healthy subjects.

FIG. 4.

Insulin sensitivity according to 3′-UTR BPI gene polymorphism in nondiabetic subjects.

TABLE 1

Anthropometrical and biochemical variables of study subjects

TABLE 2

Correlationships between circulating BPI, LBP, and clinical variables in nondiabetic subjects

TABLE 3

Correlationships between circulating BPI, LBP, and biochemical variables in nondiabetic subjects

TABLE 4

Study of the effects of an insulin sensitizer (metformin) on circulating BPI in subjects with glucose intolerance

TABLE 5

BPI 3′-UTR polymorphism and insulin sensitivity in nondiabetic subjects

Acknowledgments

This work was supported by research grants from the Ministerio de Educación y Ciencia (BFU2004-03654) and the Instituto de Salud Carlos III (RCMN C03/08, RGDM G03/212, and RGTO G03/028).

A.L.-B. is an investigator of the Fund for Scientific Research “Ramon y Cajal” (Ministerio of Education and Science, Spain). We thank Maria Garcia for her help in statistical analyses.

Footnotes

  • 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.

    • Accepted October 6, 2005.
    • Received August 25, 2005.

REFERENCES

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