Body mass index (BMI), the prevalent indicator of obesity, is not easily grasped by patients nor physicians. Waist circumference (WC) is correlated to obesity, is better understood and has a stronger relationship to the metabolic syndrome. We compiled WC, complete blood count (CBC) parameters as well as other pertinent data of 6766 25-55-year-old US volunteers sampled in the US National Health and Nutrition Examination Survey, in the years 2005-2010. To determine reference intervals of typical US patients visiting their clinician, we used minimal exclusion criteria. We compiled hemoglobin, red blood cell count, hematocrit, mean corpuscular volume (MCV), mean corpuscular hemoglobin concentration, mean cell hemoglobin (MCH), red cell distribution width (RDW), platelet count, mean platelet volume, and counts of white blood cells (WBC), neutrophils, lymphocytes, monocytes, eosinophils, and basophils. In addition, we also compiled serum C reactive protein and serum iron. The three major US races were studied and reference interval diagrams were constructed for each CBC parameter plotted against WC. WBC count, RDW, lymphocyte, neutrophil, and red blood cell count increase with WC. Conversely, serum iron and MCH and MCV decrease. These relationships may be related to insulin resistance and chronic activation of the immune system and the resulting low-grade inflammatory state. WC is a strong predictor for many CBC parameters, suggesting that WC should be taken into account when evaluating blood count results. Clinicians who take care of obese patients should be aware of altered hematology and investigate and treat accordingly.

No abstract

Background Serial differences between intrapatient consecutive measurements can be transformed into Taylor series of variation vs time with the intersection at time = 0 (y0) equal to the total variation (analytical + biological + preanalytical). With small preanalytical variation, y0, expressed as a percentage of the mean, is equal to the variable component of the reference change value (RCV) calculation: (CVA2 + CVI2)1/2. Methods We determined the between-day RCV of patient data for 17 analytes and compared them to healthy participants’ RCVs. We analyzed 653 consecutive days of Dartmouth-Hitchcock Roche Modular general chemistry data (4.2 million results: 60% inpatient, 40% outpatient). The serial patient values of 17 analytes were transformed into 95% 2-sided RCV (RCVAlternate), and 3 sets of RCVhealthy were calculated from 3 Roche Modular analyzers’ quality control summaries and CVI derived from biological variation (BV) studies using healthy participants. Results The RCVAlternate values are similar to RCVhealthy derived from known components of variation. For sodium, chloride, bicarbonate calcium, magnesium, phosphate, alanine aminotransferase, albumin, and total protein, the RCVs are equivalent. As expected, increased variation was found for glucose, aspartate aminotransferase, creatinine, and potassium. Direct bilirubin and urea demonstrated lower variation. Conclusions Our RCVAlternate values integrate known and unknown components of analytic, biologic, and preanalytic variation, and depict the variations observed by clinical teams that make medical decisions based on the test values. The RCVAlternate values are similar to the RCVhealthy values derived from known components of variation and suggest further studies to better understand the results being generated on actual patients tested in typical laboratory environments.

No abstract