American Association for Clinical Chemistry
Better health through laboratory medicine


Excerpts from the Literature

Articles of interest compiled by the editorial board. Please welcome our new member of the editorial board, Van Leung Pineda, Ph.D, DABCC.

Whole genome and exome sequencing using archived neonatal dried blood spot samples (UG)

Hollegaard, M, Grauholm J, Nielsen R, Grove J, Mandrup S and Hougaard DM.

Mol Gen Metabol 110 (2013): 65-72

Dried blood spot (DBS) is the most commonly used sample in newborn screening. Commonly used screening markers are biochemical pathways intermediates, and less commonly used markers are proteins and DNA. With the availability of next-generation sequencing coupled with advances in data handling and analysis at a reasonable prices per sample, this technology is becoming widely used in clinical genetics, and entering into newborn screening. Since DNA is very stable and most newborn screening programs have repositories, DBS provide access to large cohorts of well-characterized patients and healthy controls. The authors previously demonstrated that DNA extracted from archived DBS can be whole genome amplified (wgaDNA) and used for accurate array genotyping. In this paper the authors demonstrated that wgaDNA from DBS can be used for accurate whole genome sequencing (WGS) and exome sequencing (WES). The results of DBS (archived and fresh) were compared with DNA from whole blood. The overall performance of the archived DBS was similar to the whole blood reference sample. The study demonstrates the use of neonatal DBS in genetics research, diagnostics and screening projects.

Low prepregnancy adiponectin concentrations are associated with a marked increase in risk for development of gestational diabetes mellitus. (JS)

Hedderson MM, Darbinian J, Havel PJ, Quesenberry CP, Sridhar S, Ehrlich S, Ferrara A. Diabetes Care. 2013 Aug 29. [Epub ahead of print]

The prevalence of gestational diabetes mellitus (GDM; elevated blood sugar concentrations during pregnancy in an individual not previously diagnosed with diabetes) has increased sharply in the past 20 years. While most discussions of the increased incidence of diabetes are centered on the obesity epidemic, up to 50% of women who develop GDM are not classified as overweight or obese. Women with GDM are at increased risk of both maternal and fetal morbidity, as well as developing type 2 DM at some point after the pregnancy. Additionally, their children are at increased risk of becoming obese and developing DM themselves. Identifying those at risk is therefore an important step to provide timely treatment for the eventual prevention of GDM.

Adiponectin is a hormone produced by adipocytes that plays a role in modulating metabolic responses and increasing insulin sensitivity. It is (paradoxically) inversely associated with body fat, it decreases during pregnancy and it is found in lower concentrations in type 2 DM patients. This article by Hedderson et al sought to determine if prepregnancy adiponectin concentrations might be predictive of GDM.

Samples collected up to 6 years prior to pregnancy were used to address this question. 256 women who went on to develop GDM were matched with 497 control women that did not develop GDM during pregnancy. Results were adjusted for differences in body mass index, number of pregnancies, race/ethnicity, smoking, glucose and insulin concentrations, fasting status, and family history of diabetes. After eliminating these differences, the authors observed increasing risk for developing GDM with decreasing adiponectin. Compared with the highest adiponectin quartile, the odds ratios increased from 1.5 to 3.7 to 5.2 in the lowest quartile. In other words, women with the lowest adiponectin concentrations had a more than 5-fold increased risk for developing GDM. The combination of adiponectin concentrations below the median and an overweight or obese BMI increased the odds ratio for developing GDM to 6.8.

The authors concluded that low prepregnancy concentrations of adiponectin may identify women at higher risk for developing GDM. This data may help target individuals for early therapies or intervention. It also highlights the importance of the preconception period for an eventual healthy pregnancy and may identify individuals that would benefit from increasing their health status prior to conception.

Obesity and diabetes related plasma amino acid alterations (VP)

Yong Zhou et al.  Clin Biochem 46(2013)1447-1452

This article describes using a test familiar to biochemical genetics laboratories to compare a normal population to diabetic patients. Although the population studied was not pediatric, their pathologies are becoming well known in the pediatric population.     

In this study, Zhou et al. analyzed and compared the amino acid profiles of normal individuals to individuals diagnosed with Type 2 Diabetes. In addition, within each group the authors also subdivided them in the categories of lean vs. obese participants. Fasting plasma specimens were obtained from study participants and analyzed for 42 amino acids using LC-MS/MS. Furthermore, the specimens were also tested for glucose, HbA1c, cholesterol, lipid profile and insulin. The patient population studied was 100 normal subjects, of which 80 were obese, and 126 type 2 diabetics, of which 31 patients were obese.

The authors found that when lean individuals were compared to obese individuals in the normal group, the obese subgroup 19 elevated amino acids, 15 of which are essential. This suggested to the authors that essential amino acids were not metabolized efficiently in obese subjects and led to plasma accumulation. In contrast, in the diabetic group, obese persons only had 3 amino acids increased when compared to lean diabetic patients, suggesting that the differences in obese vs. lean in terms of amino acid metabolism were less than in the normal group. When comparing normal vs. diabetic, the diabetic group showed increases in 16 amino acids and decreases in 11 amino acids. Multivariate regression revealed certain associations between changes in amino acid concentrations to alterations in the metabolism of diabetics. For example, changes in glycine, proline and sarcosine were related to HbA1c alterations.

Limitations of the study included the relatively small sample size and the age difference between the normal subjects (mean age early 30s) and the diabetic population (mean age early 60s). However, it is a good example of the expansion of an established biochemical genetic test to pathologies different than the classical inborn errors of metabolism. In this case amino acid profiling could provide prognostic and monitoring information for diabetic patients.