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The Sangre Por Salud Biobank: Facilitating Genetic Research in an Underrepresented Latino Community

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Background/Aims: The Sangre Por Salud (Blood for Health; SPS) Biobank was created for the purpose of expanding precision medicine research to include underrepresented Latino patients. It is the result of a unique collaboration between Mayo Clinic and Mountain Park Health Center, a federally qualified community health center in Phoenix, Arizona. This report describes the rationale, development, implementation, and characteristics of the SPS Biobank. Methods: Latino adults (ages 18-85 years) who were active patients within Mountain Park Health Center's internal medicine practice in Phoenix, Ariz., and had no history of diabetes were eligible. Participants provided a personal and family history of chronic disease, completed a sociodemographic, psychosocial, and behavioral questionnaire, underwent a comprehensive cardiometabolic risk assessment (anthropometrics, blood pressure and labs), and provided blood samples for banking. Laboratory results of cardiometabolic testing were returned to the participants and their providers through the electronic health record. Results: During the first 2 years of recruitment into theSPS Biobank, 2,335 patients were approached and 1,432 (61.3%) consented to participate; 1,354 (94.5%) ultimately completed all requisite questionnaires and medical evaluations. The cohort is primarily Spanish-speaking (72.9%), female (73.3%), with a mean age of 41.3 ± 12.5 years. Most participants were born outside of the US (77.9%) and do not have health insurance (77.5%). The prevalence of overweight (35.5%) and obesity (45.0%) was high, as was previously unidentified prediabetes (55.9%), type 2 diabetes (7.4%), prehypertension (46.8%), and hypertension (16.2%). The majority of participants rated their health as good to excellent (72.1%) and, as a whole, described their overall quality of life as high (7.9/10). Conclusion: Collaborative efforts such as the SPS Biobank are critical for ensuring that underrepresented minority populations are included in precision medicine initiatives and biomedical research that seeks to improve human health and reduce the burdens of disease.

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Most cited references 21

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A new initiative on precision medicine.

President Obama has announced a research initiative that aims to accelerate progress toward a new era of precision medicine, with a near-term focus on cancers and a longer-term aim to generate knowledge applicable to the whole range of health and disease.
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Mortality prediction with a single general self-rated health question. A meta-analysis.

Health planners and policy makers are increasingly asking for a feasible method to identify vulnerable persons with the greatest health needs. We conducted a systematic review of the association between a single item assessing general self-rated health (GSRH) and mortality. Systematic MEDLINE and EMBASE database searches for studies published from January 1966 to September 2003. Two investigators independently searched English language prospective, community-based cohort studies that reported (1) all-cause mortality, (2) a question assessing GSRH; and (3) an adjusted relative risk or equivalent. The investigators searched the citations to determine inclusion eligibility and abstracted data by following a standardized protocol. Of the 163 relevant studies identified, 22 cohorts met the inclusion criteria. Using a random effects model, compared with persons reporting "excellent" health status, the relative risk (95% confidence interval) for all-cause mortality was 1.23 [1.09, 1.39], 1.44 [1.21, 1.71], and 1.92 [1.64, 2.25] for those reporting "good,"fair," and "poor" health status, respectively. This relationship was robust in sensitivity analyses, limited to studies that adjusted for co-morbid illness, functional status, cognitive status, and depression, and across subgroups defined by gender and country of origin. Persons with "poor" self-rated health had a 2-fold higher mortality risk compared with persons with "excellent" self-rated health. Subjects' responses to a simple, single-item GSRH question maintained a strong association with mortality even after adjustment for key covariates such as functional status, depression, and co-morbidity.
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A1C Versus Glucose Testing: A Comparison

Diabetes was originally identified by the presence of glucose in the urine. Almost 2,500 years ago it was noticed that ants were attracted to the urine of some individuals. In the 18th and 19th centuries the sweet taste of urine was used for diagnosis before chemical methods became available to detect sugars in the urine. Tests to measure glucose in the blood were developed over 100 years ago, and hyperglycemia subsequently became the sole criterion recommended for the diagnosis of diabetes. Initial diagnostic criteria relied on the response to an oral glucose challenge, while later measurement of blood glucose in an individual who was fasting also became acceptable. The most widely accepted glucose-based criteria for diagnosis are fasting plasma glucose (FPG) ≥126 mg/dL or a 2-h plasma glucose ≥200 mg/dL during an oral glucose tolerance test (OGTT) on more than one occasion (1,2). In a patient with classic symptoms of diabetes, a single random plasma glucose ≥200 mg/dL is considered diagnostic (1). Before 2010 virtually all diabetes societies recommended blood glucose analysis as the exclusive method to diagnose diabetes. Notwithstanding these guidelines, over the last few years many physicians have been using hemoglobin A1C to screen for and diagnose diabetes (3). Although considered the “gold standard” for diagnosis, measurement of glucose in the blood is subject to several limitations, many of which are not widely appreciated. Measurement of A1C for diagnosis is appealing but has some inherent limitations. These issues have become the focus of considerable attention with the recent publication of the Report of the International Expert Committee that recommended the use of A1C for diagnosis of diabetes (4), a position that has been endorsed (at the time of writing) by the American Diabetes Association (ADA) (1), the Endocrine Society, and in a more limited fashion by American Association of Clinical Endocrinologists/American College of Endocrinology (5). This review will provide an overview of the factors that influence glucose and A1C testing. FACTORS CONTRIBUTING TO VARIATION IN RESULTS Before addressing glucose and A1C, it is important to consider the factors that impact the results of any blood test. While laboratory medicine journals have devoted some discussion to the sources of variability in results of blood tests, this topic has received little attention in the clinical literature. Factors that contribute to variation can conveniently be divided into three categories, namely biological, preanalytical, and analytical. Biological variation comprises both differences within a single person (termed intraindividual) and between two or more people (termed interindividual). Preanalytical issues pertain to the specimen before it is measured. Analytical differences result from the measurement procedure itself. The influence of these factors on both glucose and A1C results will be addressed in more detail below. GLUCOSE MEASUREMENT FPG Measurement of glucose in plasma of fasting subjects is widely accepted as a diagnostic criterion for diabetes (1,2). Advantages include inexpensive assays on automated instruments that are available in most laboratories worldwide (Table 1). Nevertheless, FPG is subject to some limitations. One report that analyzed repeated measurements from 685 fasting participants without diagnosed diabetes from the Third National Health and Nutrition Examination Survey (NHANES III) revealed that only 70.4% of people with FPG ≥126 mg/dL on the first test had FPG ≥126 mg/dL when analysis was repeated ∼2 weeks later (6). Numerous factors may contribute to this lack of reproducibility. These are elaborated below. Table 1 FPG for the diagnosis of diabetes Advantages  • Glucose assay easily automated  • Widely available  • Inexpensive  • Single sample Disadvantages  • Patient must fast ≥8 h  • Large biological variability  • Diurnal variation  • Sample not stable  • Numerous factors alter glucose concentrations, e.g., stress, acute illness  • No harmonization of glucose testing  • Concentration varies with source of the sample (venous, capillary, or arterial blood)  • Concentration in whole blood is different from that in plasma  • Guidelines recommend plasma, but many laboratories measure serum glucose  • FPG less tightly linked to diabetes complications (than A1C)  • Reflects glucose homeostasis at a single point in time Biological variation Fasting glucose concentrations vary considerably both in a single person from day to day and also between different subjects. Intraindividual variation in a healthy person is reported to be 5.7–8.3%, whereas interindividual variation of up to 12.5% has been observed (6,7). Based on a CV (coefficient of variation) of 5.7%, FPG can range from 112–140 mg/dL in an individual with an FPG of 126 mg/dL. (It is important to realize that these values encompass the 95% confidence interval, and 5% of values will be outside this range.) Preanalytical variation Numerous factors that occur before a sample is measured can influence results of blood tests. Examples include medications, venous stasis, posture, and sample handling. The concentration of glucose in the blood can be altered by food ingestion, prolonged fasting, or exercise (8). It is also important that measurements are performed in subjects in the absence of intercurrent illness, which frequently produces transient hyperglycemia (9). Similarly, acute stress (e.g., not being able to find parking or having to wait) can alter blood glucose concentrations. Samples for fasting glucose analysis should be drawn after an overnight fast (no caloric ingestion for at least 8 h), during which time the subject may consume water ad lib (10). The requirement that the subject be fasting is a considerable practical problem as patients are usually not fasting when they visit the doctor, and it is often inconvenient to return for phlebotomy. For example, at an HMO affiliated with an academic medical center, 69% (5,752 of 8,286) of eligible participants were screened for diabetes (11). However, FPG was performed on only 3% (152) of these individuals. Ninety-five percent (5,452) of participants were screened by random plasma glucose measurements, a technique not consistent with ADA recommendations. In addition, blood drawn in the morning as FPG has a diurnal variation. Analysis of 12,882 participants aged 20 years or older in NHANES III who had no previously diagnosed diabetes revealed that mean FPG in the morning was considerably higher than in the afternoon (12). Prevalence of diabetes (FPG ≥126 mg/dL) in afternoon-examined patients was half that of participants examined in the morning. Other patient-related factors that can influence the results include food ingestion when supposed to be fasting and hypocaloric diet for a week or more prior to testing. Glucose concentrations decrease in the test tube by 5–7% per hour due to glycolysis (13). Therefore, a sample with a true blood glucose value of 126 mg/dL would have a glucose concentration of ∼110 mg/dL after 2 h at room temperature. Samples with increased concentrations of erythrocytes, white blood cells, or platelets have even greater rates of glycolysis. A common misconception is that sodium fluoride, an inhibitor of glycolysis, prevents glucose consumption. While fluoride does attenuate in vitro glycolysis, it has no effect on the rate of decline in glucose concentrations in the first 1 to 2 h after blood is collected, and glycolysis continues for up to 4 h in samples containing fluoride (14). The delay in the glucose stabilizing effect of fluoride is most likely the result of glucose metabolism proximal to the fluoride target enolase (15). After 4 h, fluoride maintains a stable glucose concentration for 72 h at room temperature (14). A recent publication showed that acidification of the blood sample inhibits glycolysis in the first 2 h after phlebotomy (16), but the collection tubes used in that study are not commercially available. Placing tubes in ice water immediately after collection may be the best method to stabilize glucose initially (2,16), but this is not a practical solution in most clinical situations. Separating cells from plasma within minutes is also effective, but impractical. The nature of the specimen analyzed can have a large influence on the glucose concentration. Glucose can be measured in whole blood, serum, or plasma, but plasma is recommended by both the ADA and World Health Organization (WHO) for diagnosis (1,2). However, many laboratories measure glucose in serum, and these values may differ from those in plasma. There is a lack of consensus in the published literature, with glucose concentrations in plasma reported to be lower than (17), higher than (16,18,19), or the same as (20) those in serum. Importantly, glucose concentrations in whole blood are 11% lower than those in plasma because erythrocytes have a lower water content than plasma (13). The magnitude of the difference in glucose between whole blood and plasma changes with hematocrit. Most devices (usually handheld meters) that measure glucose in capillary blood use whole blood. While the majority of these report a plasma equivalent glucose value (21), this result is not accurate in patients with anemia (22) (unless the meter measures hematocrit). The source of the blood is another variable. Although not a substantial problem in the fasting state, capillary glucose concentrations can be 20–25% higher (mean of 30 mg/dL) than venous glucose during an OGTT (23). This finding has practical implications for the OGTT, particularly because the WHO deems capillary blood samples acceptable for the diagnosis of diabetes (2). Analytical variation Glucose is measured in central laboratories almost exclusively using enzymatic methods, predominantly with glucose oxidase or hexokinase (24). The following terms are important for understanding measurement: accuracy indicates how close a single measurement is to the “true value” and precision (or repeatability) refers to the closeness of agreement of repeated measurements under the same conditions. Precision is usually expressed as CV; methods with low CV have high precision. Numerous improvements in glucose measurement have produced low within-laboratory imprecision (CV 12% of patients (4). Similarly, inspection of a College of American Pathologists (CAP) survey comprising >5,000 laboratories revealed that one-third of the time the results among instruments for an individual measurement could range between 141 and 162 mg/dL (26). This variation of 6.9% above or below the mean reveals that one-third of the time the glucose results on a single patient sample measured in two different laboratories could differ by 14%. OGTT The OGTT evaluates the efficiency of the body to metabolize glucose and for many years has been used as the “gold standard” for diagnosis of diabetes. An increase in postprandial glucose concentration usually occurs before fasting glucose increases. Therefore, postprandial glucose is a sensitive indicator of the risk for developing diabetes and an early marker of impaired glucose homeostasis (Table 2). Published evidence suggests that an increased 2-h plasma glucose during an OGTT is a better predictor of both all-cause mortality and cardiovascular mortality or morbidity than the FPG (27,28). The OGTT is accepted as a diagnostic modality by the ADA, WHO/International Diabetes Federation (IDF) (1,2), and other organizations. However, extensive patient preparation is necessary to perform an OGTT. Important conditions include, among others, ingestion of at least 150 g of dietary carbohydrate per day for 3 days prior to the test, a 10- to 16-h fast, and commencement of the test between 7:00 a.m. and 9:00 a.m. (24). In addition, numerous conditions other than diabetes can influence the OGTT (24). Consistent with this, published evidence reveals a high degree of intraindividual variability in the OGTT, with a CV of 16.7%, which is considerably greater than the variability for FPG (6). These factors result in poor reproducibility of the OGTT, which has been documented in multiple studies (29,30). The lack of reproducibility, inconvenience, and cost of the OGTT led the ADA to recommend that FPG should be the preferred glucose-based diagnostic test (1). Note that glucose measurement in the OGTT is also subject to all the limitations described for FPG (Table 1). Table 2 OGTT for the diagnosis of diabetes Advantages  • Sensitive indicator of risk of developing diabetes  • Early marker of impaired glucose homeostasis Disadvantages  • Lacks reproducibility  • Extensive patient preparation  • Time-consuming and inconvenient for patients  • Unpalatable  • Expensive  • Influenced by numerous medications  • Subject to the same limitations as FPG, namely, sample not stable, needs to be performed in the morning, etc. A1C MEASUREMENT A1C is formed by the nonenzymatic attachment of glucose to the N-terminal valine of the β-chain of hemoglobin (24). The life span of erythrocytes is ∼120 days, and consequently A1C reflects long-term glycemic exposure, representing the average glucose concentration over the preceding 8–12 weeks (31,32). Both observational studies (33) and controlled clinical trials (34,35) demonstrate strong correlation between A1C and retinopathy, as well as other microvascular complications of diabetes. More importantly, the A1C value predicts the risk of microvascular complications and lowering A1C concentrations (by tight glycemic control) significantly reduces the rate of progression of microvascular complications (34,35). Biological variation Intraindividual variation of A1C in nondiabetic people is minimal (36) (Table 3), with CV 15% or if a large change in A1C coincides with a change in laboratory A1C method (53). In these situations, hemoglobin electrophoresis should be performed. It is important to emphasize that, like any other test, A1C results that are inconsistent with the clinical presentation should be investigated. PERSPECTIVE Notwithstanding the use of glucose (FPG and/or the OGTT) as the “gold standard” for the diagnosis of diabetes for many years, glucose testing suffers from several deficiencies. The requirement that the subject be fasting at the time the blood is drawn is a considerable inconvenience. While our ability to measure glucose has improved, inherent biological variability can produce very large differences within and among individuals. In conjunction with lack of sample stability, which is difficult to overcome in clinical practice, these factors results in lack of reproducibility of glucose testing. A1C, which reflects chronic blood glucose values, is routinely used in monitoring glycemic control and guiding therapy. The significant reduction in microvascular complications with lower A1C and the absence of sample lability, combined with several other advantages (Table 3), have led to the recommendation by some organizations that A1C be used for screening and diagnosis of diabetes (1). Accumulating evidence suggests that racial differences in A1C values may be present, and the possible clinical significance of this needs to be determined. Importantly, A1C cannot be measured in certain conditions. Despite these caveats, A1C can be measured accurately in the vast majority of people. A comprehension of the factors that influence A1C values and the conditions where it should not be used will produce accurate and clinically meaningful results. The convenience of sampling at any time without regard to food ingestion makes it likely that measurement of A1C will result in the detection of many of the millions of people with diabetes who are currently undiagnosed.

Author and article information

aCenter for Health Promotion and Disease Prevention, College of Nursing and Health Innovation, Arizona State University, and bMountain Park Health Center, Phoenix, Ariz., cDivision of Endocrinology, Department of Internal Medicine, and dCenter for Individualized Medicine, Mayo Clinic Arizona, Scottsdale, Ariz., eDepartment of Health Sciences Research, fBiomedical Ethics Research Program, and gDepartment of Laboratory Medicine and Pathology, Mayo Clinic Rochester, Rochester, Minn., and hCenter for Disparities in Diabetes, Obesity, and Metabolism, Division of Endocrinology, Department of Medicine, University of Arizona, Tucson, Ariz., USA
Public Health Genomics
Public Health Genomics
Public Health Genomics
S. Karger AG (Basel, Switzerland )
August 2016
05 July 2016
: 19
: 4
: 229-238
© 2016 S. Karger AG, Basel

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