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HbA1c is used in forensic toxicology to identify undiagnosed diabetes mellitus (DM) and those with poor glycemic control prior to death. HbA1c is typically measured in whole blood collected in tubes containing ethylenediaminetetraacetic acid (EDTA). The effect of other additives, including sodium fluoride (NaF), is unclear. Furthermore, the assessment of short- and long-term stability of HbA1c has produced conflicting results. In this study, we collected paired postmortem blood samples in EDTA and NaF tubes (n = 142) to assess their comparability for HbA1c measurement. Stability was assessed by measuring HbA1c at baseline, 2, 3, and 4 weeks postcollection (stored at 4°C) and at 2, 4, 6, and 12 months postcollection (stored at -20°C). We found no significant difference in HbA1c between the two preservatives at any of the time points indicating NaF is a suitable preservative for HbA1c measurement. We also determined that DM status, postmortem interval, and decomposition had no effect on stability.

Background Lysophosphatidic acid (LPA) receptor signaling has been implicated in cardiovascular and obesity-related metabolic disease. However, the distribution and regulation of LPA receptors in the myocardium and adipose tissue remain unclear. Objectives This study aimed to characterize the mRNA expression of LPA receptors (LPA1-6) in the murine and human myocardium and adipose tissue, and its regulation in response to obesity. Methods LPA receptor mRNA levels were determined by qPCR in i) heart ventricles, isolated cardiomyocytes, and perigonadal adipose tissue from chow or high fat-high sucrose (HFHS)-fed male C57BL/6 mice, ii) 3T3-L1 adipocytes and HL-1 cardiomyocytes under conditions mimicking gluco/lipotoxicity, and iii) human atrial and subcutaneous adipose tissue from non-obese, pre-obese, and obese cardiac surgery patients. Results LPA1-6 were expressed in myocardium and white adipose tissue from mice and humans, except for LPA3, which was undetectable in murine adipocytes and human adipose tissue. Obesity was associated with increased LPA4, LPA5 and/or LPA6 levels in mice ventricles and cardiomyocytes, HL-1 cells exposed to high palmitate, and human atrial tissue. LPA4 and LPA5 mRNA levels in human atrial tissue correlated with measures of obesity. LPA5 mRNA levels were increased in HFHS-fed mice and insulin resistant adipocytes, yet were reduced in adipose tissue from obese patients. LPA4, LPA5, and LPA6 mRNA levels in human adipose tissue were negatively associated with measures of obesity and cardiac surgery outcomes. This study suggests that obesity leads to marked changes in LPA receptor expression in the murine and human heart and white adipose tissue that may alter LPA receptor signaling during obesity.

Nearly 25% of normal weight individuals display abnormal metabolic profiles associated with obesity. As a wide range in body fat percentage (%BF) exists for BMI-defined normal weight individuals, we investigated whether elevated %BF (determined using DXA) was associated with cardiometabolic dysregulation among 977 normal weight subjects (192 men, 785 women) from the Canadian province of Newfoundland and Labrador.BMI and %BF were measured after a 12-h fasting period. Cardiometabolic abnormalities considered included elevated triglyceride, glucose and hsCRP levels, decreased HDL cholesterol, insulin resistance, and hypertension. Subjects were classified as metabolically healthy (0 or 1 cardiometabolic abnormality) or abnormal (≥2 cardiometabolic abnormalities) and divided into sex-specific %BF tertiles as follows: low (≤15.2% men, ≤29.7% women), medium (15.3-20.7%% men, 29.8-34.9%% women) and high (≥20.8% men, ≥35.0% women). The prevalence of the metabolically abnormal phenotype was higher among medium and high %BF subjects (12.0% and 19.5%, respectively) compared to the low group (7.4%; p 0.05). Furthermore, the odds of being metabolically abnormal were 1.61 (95% CI 0.94-2.77) for medium %BF subjects compared to the low group and nearly tripled for high %BF subjects (OR 2.73, 95% CI 1.63-4.86). ORs remained significant after further adjustment for waist circumference.Our findings indicate that those with elevated %BF are at increased risk of developing cardiometabolic disease despite having a normal BMI. Future development of adequate screening tools to identify these individuals is crucial to the prevention of obesity-associated disease.

The prevalence of obesity is increasing at an alarming rate and with it the occurrence of a number of comorbidities, including cardiovascular disease, type 2 diabetes, stroke, and certain types of cancer. Approximately one third of adults in the US population are currently classified as obese. Globally, >400 × 106 adults are classified as obese, a number that is expected to double by 2015. Consequently, obesity and its associated health problems are placing a large burden on an already overwhelmed healthcare system. The worldwide costs attributable to this disease are estimated to be between 0.7% and 2.8% of total healthcare expenditures. Furthermore, medical costs for obese individuals are substantially higher than for nonobese people. Obesity is a multifactorial disease caused by a chronic energy surplus in which energy intake exceeds energy expenditure, leading to the accumulation of excess adipose tissue. Regulation of energy homeostasis is a complex process, and that fact imparts a considerable challenge in trying to elucidate the pathogenesis of obesity. Although poor lifestyle choices, including inappropriate diets and lack of physical activity, undoubtedly play a large role, genetic susceptibility also puts an individual at increased risk. The majority of obesity therapies have been aimed at behavior modification and pharmacologic intervention, although to date these therapies have led to only modest weight loss. Although less common, bariatric surgery has led to substantial long-term weight loss in morbidly obese patients; this type of treatment, however, is both invasive and costly. Given the growing trend of obesity in both developed and developing countries, considerable importance has been placed on increasing awareness of the problem. In this article, 4 leaders in the field of obesity provide their opinions on obesity and discuss the latest advancements in pathogenesis, therapy, prevention, and the use of novel biomarkers to identify those at risk …

Nearly one-third of obese (OB) people are reported to be metabolically healthy based on BMI criteria. It is unknown whether this holds true when more accurate adiposity measurements are applied such as dual-energy X-ray absorptiometry (DXA). We compared differences in the prevalence of cardiometabolic abnormalities among adiposity groups classified using BMI vs. DXA criteria. A total of 1,907 adult volunteers from Newfoundland and Labrador participated. BMI and body fat percentage (%BF; measured using DXA) were measured following a 12-h fasting period. Subjects were categorized as normal weight (NW), overweight (OW), or OB based on BMI and %BF criteria. Cardiometabolic abnormalities considered included elevated triglyceride, glucose, and high-sensitivity C-reactive protein (hsCRP) levels, decreased high-density lipoprotein (HDL) cholesterol levels, insulin resistance, and hypertension. Subjects were classified as metabolically healthy (0 or 1 cardiometabolic abnormality) or abnormal (≥2 cardiometabolic abnormalities). We found low agreement in the prevalence of cardiometabolic abnormalities between BMI and %BF classifications (κ = 0.373, P < 0.001). Among NW and OW subjects, the prevalence of metabolically healthy individuals was similar between BMI and %BF (77.6 vs. 75.7% and 58.8 vs. 62.5%, respectively) however, there was a pronounced difference among OB subjects (34.0 vs. 47.7%, P < 0.05). Similar trends were evident using three additional definitions to characterize metabolically healthy individuals. Our findings indicate that approximately one-half of OB people are metabolically healthy when classified using %BF criteria which is significantly higher than previously reported using BMI. Caution should therefore be taken when making inferences about the metabolic health of an OB population depending on the method used to measure adiposity.

Testosterone, the most abundant androgen in men, is a steroid hormone that is synthesized predominantly by the testes. In women, minor amounts are synthesized in the ovaries. Androgen precursors are also produced and secreted from the adrenal glands in both sexes, where they undergo peripheral conversion to testosterone. Circulating concentrations are approximately 15-25 times higher in adult men compared to women. Maintenance of these levels is necessary for development and maintenance of secondary sexual characteristics, libido, growth, prevention of osteoporosis, and most importantly in men, spermatogenesis. Most testosterone circulates tightly bound to sex hormone-binding globulin (SHBG) or weakly bound to albumin. A minor amount circulates as free testosterone, and it is believed that this is the metabolically active fraction. Measurement of free testosterone is important in the diagnosis of many diseases, most importantly disorders of androgen deficiency in men (i.e., hypogonadism) and androgen excess in women (i.e., polycystic ovary syndrome and hirsutism). Many methodologies are available for free testosterone measurement including the reference methods (equilibrium dialysis and ultrafiltration), analog immunoassay, and calculated free testosterone based on measurement of total testosterone, SHBG, and albumin. Moreover, measurement of bioavailable testosterone, a combination of albumin-bound and free testosterone, also has clinical utility and can be measured by selective protein precipitation or calculation. In this review, the advantages and limitations of each of these methods will be discussed in the context of clinical utility and implementation into a routine hospital laboratory. Furthermore, up and coming methodologies for free testosterone measurement, including liquid chromatography-tandem mass spectrometry, will also be discussed.

Prenatal screening for chromosomal aneuploidies is a fundamental part of routine obstetric care in most countries. Typically, maternal age, weight, ethnicity, serum biomarkers (including pregnancy-associated plasma protein A, human chorionic gonadotropin, α-fetoprotein, inhibin A, and estriol), and sonographic features (i.e., nuchal translucency) are included in a risk algorithm to determine the probability of the fetus being affected. Pregnant women identified as at high risk according to the prenatal screen can then undergo invasive procedures, such as amniocentesis and chorionic villus sampling, to confirm the diagnosis. Current prenatal-screening methods are able to identify approximately 90% of pregnancies affected by trisomy 21 (Down syndrome) at a false-positive rate of approximately 5%. Given that the prevalence of chromosomal aneuploidies is generally quite low, a false-positive rate of 5% means that a large number of women with unaffected pregnancies undergo invasive procedures, putting the fetus at an unnecessary risk for miscarriage. The discovery of fetal cell-free DNA in the plasma of pregnant women 14 years ago opened up the possibility of identifying chromosomal abnormalities noninvasively, through a single blood sample. Approximately 10% of cell-free DNA in the maternal circulation is of fetal origin, and this property was initially exploited to determine rhesus D status and the sex of the unborn fetus. The advent of next-generation DNA sequencing, however, has allowed prenatal detection of chromosomal aneuploidies, including trisomy 21, from maternal blood. In brief, the proportion of chromosome 21 DNA molecules in maternal plasma is measured directly; an increase above a predetermined threshold is indicative of trisomy 21. The clinical performance of this noninvasive prenatal test has been promising, with recent clinical studies having shown a diagnostic sensitivity of 100% and a diagnostic specificity of 98%–99%, compared with full karyotyping by invasive means. When used as a second-tier screening procedure, this technology also has the …

Background Peptide YY (PYY), a gut hormone that inhibits appetite, has been linked to the development of obesity. Objective This study investigated the nutritional regulation of PYY after 7 d of overfeeding (70% above normal energy requirements) in normal-weight, overweight, and obese men. Design Sixty-nine men (aged 19-29 y) participated in the study. We analyzed the relation between fasting serum PYY before and after a 7-d overfeeding challenge in normal-weight, overweight, and obese men. In addition, we analyzed PYY with obesity-related phenotypes including weight, percentage body fat (measured by dual-energy X-ray absorptiometry), body mass index (BMI), total cholesterol, HDL, LDL, glucose, insulin, insulin resistance, and β cell function evaluated by the homeostasis model assessment of insulin resistance (HOMA-IR) and β cell function (HOMA-β) at baseline and in response to the energy surplus. Results Fasting serum PYY concentrations at baseline were not significantly different between the normal-weight, overweight, and obese subjects on the basis of dual-energy X-ray absorptiometry or BMI. Although the PYY concentration significantly increased due to overfeeding, no differences were observed between adiposity statuses. In addition, basal PYY was negatively correlated with the changes of total cholesterol, HDL, and LDL in normal weight. In addition, the increase in PYY after overfeeding was positively correlated with HDL cholesterol and glucose in normal-weight subjects. Conclusions Our findings suggest that fasting PYY concentrations are not associated with adiposity status. Moreover, the 7-d overfeeding challenge significantly increased fasting PYY, which is likely a protective response to the positive energy balance.

Retinol-binding protein 4 (RBP4) is a novel adipokine that likely contributes to systemic insulin resistance and dyslipidemia. The role of genetic variations in RBP4 on phenotypes of glucose and lipid metabolism is not clear in humans. The purpose of this study was to examine five single-nucleotide polymorphisms (SNPs) in the RBP4 gene to determine their relationship with markers of insulin resistance and serum lipids in the CODING Study. The CODING Study consists of 1,836 subjects recruited from the genetically homogeneous population of Newfoundland and Labrador (NL), Canada. Serum glucose, insulin, homeostasis model assessment of insulin resistance (HOMA(IR)), HOMA for beta cell function (HOMA(beta)), total cholesterol (Chol), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides were determined after a 12-h fast. Five SNPs within RBP4 (rs3758539, G/A 5' flanking region; rs61461737, A/G intron; rs10882280, C/A intron; rs11187545, A/G intron; and rs12265684, C/G intron) were genotyped using TaqMan validated or functionally tested SNP genotyping assays. After correcting for multiple testing, we observed a significant association between the minor allele of two noncoding SNPs (rs10882280 and rs11187545) and higher serum HDL-C (P = 0.043 and 0.042, respectively). No significant associations were observed with any other parameter related to lipid metabolism. We also found no significant association between any variant sites and markers of insulin resistance. Our results suggest that genetic variations in RBP4 may play a role in the differences in serum HDL-C levels in the NL population.

Although BMI is the most widely used measure of obesity, debate still exists on how accurately BMI defines obesity. In this study, adiposity status defined by BMI and dual-energy X-ray absorptiometry (DXA) was compared in a large population to evaluate the accuracy of BMI. A total of 1,691 adult volunteers from Newfoundland and Labrador participated in the study. BMI and body fat percentage (%BF) were measured for all subjects following a 12-h fasting period. Subjects were categorized as underweight (UW), normal weight (NW), overweight (OW), or obese (OB) based on BMI and %BF criteria. Differences between the two methods were compared within gender and by age-groups. According to BMI criteria, 1.2% of women were classified as UW, 44.2% as NW, 34.2% as OW, and 20.3% as OB. When women were classified according to %BF criteria, 2.2% were UW, 29.6% were NW, 30.9% were OW, and 37.1% were OB. The overall discrepancy between the two methods for women was substantial at 34.7% (14.6% for NW and 16.8% for OB, P < 0.001). In men, the overall discrepancy was 35.2% between BMI and DXA (17.6% for OW and 13.5% for OB, P < 0.001). Misclassification by BMI was dependent on age, gender, and adiposity status. In conclusion, BMI misclassified adiposity status in approximately one-third of women and men compared with DXA. Caution should be taken when BMI is used in clinical and scientific research as well as clinical practice.

Background: Retinol-binding protein 4 (RBP4) is a novel adipokine that induces insulin resistance in mice. Studies in humans have shown a correlation between serum RBP4 and insulin resistance in obese subjects and in subjects with type 2 diabetes. Few data are available regarding the nutritional regulation of RBP4. Objective: The study investigated the relation of RBP4 with phenotypes of glucose and lipid metabolism at baseline and in response to a 7-d overfeeding protocol in young men. Design: Sixty-five men participated in the study. Subjects were classified on the basis of body mass index (BMI; kg/m 2 ) as normal-weight (≤24.9) or as overweight or obese (≥25.0). Serum RBP4, interleukin-6, visfatin, glucose, insulin, total cholesterol, HDL cholesterol, and LDL cholesterol (calculated), and triacylglycerols were measured. Insulin resistance and (3 cell function were assessed by using the homeostasis model. Percentage body fat was measured by using dual-energy X-ray absorptiometry. Results: No significant differences were found in serum RBP4 between the 2 groups at baseline. Likewise, no significant differences were observed in fasting serum RBP4 in response to overfeeding. Baseline RBP4 was negatively correlated with the change in insulin resistance in normal-weight subjects, independent of age and BMI. No significant correlation was found between serum RBP4 and visfatin, interleukin-6, or any other variables measured. Conclusions: Short-term overfeeding did not induce significant changes in RBP4. Baseline RBP4 concentrations may predict insulin resistance when exposed to a positive energy challenge in normal-weight men.

As an essential nutrient, Selenium (Se) is involved in many metabolic activities including mimicking insulin function. Data on Se in various biological samples and insulin resistance are contradictory, moreover there is no large study available regarding the relationship of dietary Se intake with insulin resistance in the general population. To investigate the association between dietary Se intake and variation of insulin resistance in a large population based study, a total of 2420 subjects without diabetes from the CODING (Complex Diseases in the Newfoundland Population: Environment and Genetics) study were assessed. Dietary Se intake was evaluated from the Willett Food Frequency questionnaire. Fasting blood samples were used for the measurement of glucose and insulin. Insulin resistance was determined with the homeostasis model assessment (HOMA-IR). Body composition was measured using dual energy X-ray absorptiometry. Analysis of covariance showed that high HOMA-IR groups in both males and females had the lowest dietary Se intake (μg/kg/day) (p < 0.01), being 18% and 11% lower than low HOMA-IR groups respectively. Insulin resistance decreased with the increase of dietary Se intake in females but not in males after controlling for age, total calorie intake, physical activity level, serum calcium, serum magnesium, and body fat percentage (p < 0.01). Partial correlation analysis showed that dietary Se intake was negatively correlated with HOMA-IR after adjusting for the Se confounding factors in subjects whose dietary Se intake was below 1.6 μg/kg/day (r = -0.121 for males and -0.153 for females, p < 0.05). However, the negative correlation was no longer significant when dietary Se intake was above 1.6 μg/kg/day. Our findings suggest that higher dietary Se intake is beneficially correlated with lower insulin resistance when total dietary Se intake was below 1.6 μg/kg/day. Above this cutoff, this beneficial effect disappears.

Objective Visfatin is a novel adipokine initially reported to exhibit insulin-mimetic effects that increase insulin sensitivity. Further studies indicate it may also be associated with obesity, serum lipids, and systemic inflammation. At the current time, the role of genetic variation in the visfatin gene (NAMPT) on these parameters is not clear. In the present study, we examined the association between 10 SNPs in NAMPT and insulin resistance, obesity, serum lipids and hsCRP levels. Research design and methods A total of 1838 subjects (413 men, 1425 women) were recruited from the ongoing CODING Study. All subjects were from the genetically homogenous population of Newfoundland and Labrador, Canada. BMI, waist circumference, waist-to-hip ratio, and body fat percentage (determined using DXA) were measured for all subjects. Serum glucose, insulin, HOMAIR, HOMAβ, total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides and hsCRP were also determined after a 12-hour fast. Ten SNPs in NAMPT were genotyped using TaqMan validated or functionally tested SNP genotyping assays including rs7789066 (A > G 5′ flanking region), rs3801266 (A > G intron), rs6963243 (G > C intron), rs2058539 (A > C intron), rs6947766 (C > T intron), rs4730153 (G > A intron), rs10808150 (G > A intron), rs2098291 (C > T intron), rs10953502 (T > C intron), and rs10953501 (A > G 3′ UTR). Results We observed no significant associations between any of the variants sites and any parameter of insulin resistance, body composition, serum lipids or hsCRP under an additive model with age and gender included as covariates. This was also true when both dominant and recessive models were applied. Conclusions Our results do not support a significant role for variations in NAMPT with differences in the measured variables in the Newfoundland population.