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Obesity and risk factors for the metabolic syndrome among low-income, urban, African American schoolchildren: the rule rather than the exception?^1,2,3

¹ From the Department of Human Nutrition, University of Illinois at Chicago, Chicago, IL

² Supported by TAKEDA Pharmaceuticals America.

³ Address reprint requests to CL Braunschweig, Department of Human Nutrition, 1919 West Taylor Street, University of Illinois at Chicago, Chicago, IL 60612. E-mail: braunsch{at}uic.edu.

	ABSTRACT

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Background: Adult obesity is associated with the metabolic syndrome; however, the prevalence of the metabolic syndrome among young children has not been reported. Clinic-based screening efforts for the metabolic syndrome in low-income neighborhoods, where obesity is prevalent, are limited by minimal health insurance coverage and inadequate access to health care. School-based obesity screening programs may effectively target high-risk populations.

Objective: The objective was to describe the prevalence of overweight and features of the metabolic syndrome (defined as the presence of 3 of the following risk factors: HDL 40 mg/dL, triacylglycerol 110 mg/dL, and blood pressure or waist circumference at or above the 90th percentile) in a pilot, school-based screening program.

Design: A cross-sectional study of obesity and the metabolic syndrome was conducted in third- to sixth-grade, low-income, urban, African American children. Lipid and glucose concentrations were measured in fasting capillary finger-stick samples.

Results: Age- and sex-specific BMI percentiles were assessed in 385 students, 90 of whom were full participants in this study (participants) and 295 of whom had only height and weight measurements taken (other students). Risk factors of the metabolic syndrome were assessed in the 90 participants (23%). No significant differences in BMI percentiles were found between the participants and the other students. Overall, 44% of the participants had BMIs at or above the 85th percentile, and 59% had an elevated BMI or one metabolic syndrome risk factor. The metabolic syndrome was present in 5.6% of all participants, in 13.8% of participants with BMIs at or above the 95th percentile, and in 0% of participants with BMIs below the 95th percentile.

Conclusions: Most of the African American children attending 2 urban schools in low-income neighborhoods were overweight or had one or more risk factors for the metabolic syndrome. School-based screening programs in high-risk populations may provide an efficient venue for the screening of obesity and related risk factors.

Key Words: Obesity • risk factors • metabolic syndrome • African American schoolchildren

	INTRODUCTION

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In the United States, the prevalence of overweight among children—defined as a body mass index (BMI; weight in kilograms divided by height squared in meters) at or above the 95th percentile on age- and sex-specific growth charts—has tripled in the past 30 y (1–3). Current data indicate that 15% of US children between 6 and 11 y of age are overweight, and nearly 20% of non-Hispanic black children are overweight. Obesity in childhood is a public health concern because it increases the risk of developing hypertension, elevated cholesterol, type 2 diabetes, coronary heart disease, orthopedic disorders, and respiratory disease (4–6); has a significant negative effect on childhood emotional development (7, 8); has been shown to track to adult obesity; and increases the risk of adult mortality (9–11).

Insulin resistance and its associated metabolic characteristics have been proposed as a link between obesity and disease. The metabolic syndrome is a constellation of metabolic abnormalities aligned with insulin resistance that predicts premature coronary artery disease and type 2 diabetes; it affects 23% of the US adult population (12, 13). Among adolescents (12–19 y), Cook et al (14) defined this syndrome as the presence of 3 of the following risk factors: triacylglycerols > 110 mg/dL, HDL cholesterol 40 mg/dL, waist circumference (WC) at or above the 90th percentile, fasting glucose 110 mg/dL, and blood pressure (BP) at or above the 90th percentile. Using this definition to analyze data from the third National Health and Nutrition Examination Survey (NHANES III, 1988–1994), they estimated the overall prevalence among adolescents to be 4%. According to the National Cholesterol Education Program, the increasing rate of obesity and its association with insulin resistance and type 2 diabetes positions the metabolic syndrome to play a greater role in premature cardiac disease than tobacco use (12). To our knowledge, estimates for the metabolic syndrome among younger children have not been described, possibly because NHANES does not include the biochemical data needed for children aged <12 y.

Given the effect of obesity on a vast array of diseases that track into adulthood, efforts aimed at early detection, treatment, and monitoring of its risk factors are needed. Results from NHANES 1999–2000 suggest that low-income, African American grade school children likely have greater prevalence rates of obesity and elevated lipid and glucose concentrations and BP than the national average; however, studies in this population are lacking. Clinic-based screening programs in low-income neighborhoods, where these characteristics are highly prevalent, are plagued by minimal health insurance coverage, which limits routine access to health care. Detecting risk in individuals who might not otherwise be assessed is one of the objectives set forth by the National Heart Lung and Blood Institute and the American Heart Association for public cholesterol screening (15). Additionally, a small but growing body of literature suggests that the treatment of obesity tends to be underrecognized and undertreated by physicians, particularly in minority and low-income environments (16–18). A school-based universal health screening program that targets students in low-income minority neighborhoods may provide an optimal environment for early detection and surveillance of obesity and related risk factors.

The purpose of this study was to describe a pilot, school-based screening program in urban, low-socioeconomic, third- to sixth-grade African American schoolchildren for obesity and features of the "presumed" metabolic syndrome.

	SUBJECTS AND METHODS

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Subjects
All children in grades 3–6 enrolled in the 2002 academic year at 2 public elementary schools in Chicago who had their sex- and age-specific BMIs assessed as part of a school-wide health report card were eligible for participation. Letters inviting interested parents and guardians to provide phone numbers were mailed to the student's homes. Those who responded were given detailed information about the research project and participation requirements. The study protocol was approved by the Ethics Committee of the Institutional Review Board of the University of Illinois at Chicago. Informed consent was obtained from all parents or guardians, and participating students signed an assent agreement.

Methods
Data collection occurred before the start of the school day. Height and weight measurements were rotated and performed twice for each participant with the use of standardized, recommended techniques (19, 20). Participants were classified as underweight (<5th percentile), normal weight (5th and <85th percentile), at risk of overweight (85th and <95th percentile), or overweight (95th percentile) according to the 2000 Centers for Disease Control and Prevention growth charts (2).

The WC measurement was made at the narrowest observed point between the bottom of the rib cage and the umbilicus; if a point of least circumference was not apparent, measurements were obtained at the umbilicus to the nearest 0.1 cm. Measurements were made by using an inelastic 3/8-in (1 cm) measuring tape that was calibrated with a metal tape measure on a monthly basis (19). A WC at or above the 90th percentile for age, ethnicity, and sex according to the 2000 Centers for Disease Control and Prevention growth charts (2) was categorized as a risk factor for the metabolic syndrome.

BP measurements were taken twice, after the participant sat comfortably for 5 min, with an appropriately sized cuff on the right arm, which was slightly flexed at heart level. The second BP measurement was used for analysis (21). A systolic or diastolic BP at or above the 90th percentile on the basis of normative BP tables (22) for height, age, and sex was considered a risk factor for the metabolic syndrome.

Fasting finger-stick capillary samples were used to assess total cholesterol, LDL cholesterol, HDL cholesterol, triacylglycerols, glucose, and hemoglobin A_1C (Hb A_1c). The lipid and glucose concentrations were analyzed by using the Cholestech LDX Lipid Monitoring System (Cholestech Corp, Hayward, CA). This system correctly classified fasting individuals into the appropriate National Cholesterol and Education Program risk groups 95% of the time (23), and further, the CVs for between- and within-runs were higher than the National Cholesterol and Education Program standards for accuracy and precision (manufacturer's information). Hb A_1c was analyzed with the use of the DCA 2000 + Analyzer (Bayer Healthcare, Diagnostics Division, Tarrytown, NY). The DCA 2000 analyzer has a within-run reliability intraclass correlation CV of 2.8% and a between-run correlation coefficient of 0.996 (24). Quality control procedures for calibration of the Cholestech LDX and DCA 2000 were performed according to manufacturer guidelines and were recorded daily. Lipid concentrations were categorized on the basis of American Heart Association guidelines for children (25).

The metabolic syndrome was defined as the presence of 3 risk factors according to the guidelines developed by Cook et al (14) for adolescents. The 1998 guidelines of the American Academy of Pediatrics were used for the lipid guidelines (26). The criteria used for both lipid concentrations and metabolic syndrome are provided in Table 1. In November 2003, the American Diabetes Association changed the glucose categorization for impaired fasting glucose (IFG) from 110 mg/dL to 100 mg/dL (27). Despite this change, we retained the earlier cutoff guideline for IFG of 110 mg/dL to allow for comparisons between our population and that of Cook et al (14) and because, to our knowledge, it remains the only study that has defined the prevalence of the metabolic syndrome in a nationally representative sample of children. Because we used the more stringent cutoff, our estimates for elevated glucose should be viewed as a conservative estimate for the prevalence of elevated glucose. Glucose concentrations >125 mg/dL were categorized as indicating diabetes.

Statistical analysis
Standard descriptive statistics (mean, median, range, and SD) were assessed for continuous variables. Nonnormally distributed variables were transformed as necessary. Chi-square tests were used to examine whether the participants' distributions within the 4 BMI categories (<5th percentile, 5th to <85th percentile, 85th to 95th percentile, and 95th percentile) were similar to those of the other third- to sixth-grade students. Wilcoxon's rank-sum test was used to determine whether the participants' weights and BMIs were similar to those of the other third- to sixth-grade students. Student's t test was used to assess differences in age and height of the participants and other third- to sixth-grade students and to detect differences in risk factors between sexes. Prevalence and 95% CIs for individual features of the metabolic syndrome and different numbers of risk factors were calculated and used to assess differences in the prevalence of metabolic syndrome by sex and BMI. All data were entered and cleaned in EPIINFO 6.0 (1997; Centers for Disease Control and Prevention, Atlanta), and statistical analyses were performed by using SAS version 8.2 (SAS Institute, Cary, NC).

	RESULTS

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Overall, 23% (n = 90) of the 385 eligible students assessed for age- and sex- specific BMI measurements were recruited. All participants were African American and qualified for free school lunch; 28% had private medical insurance, 64% had Medicaid, and 8% had no insurance. Age- and sex-specific BMIs of the participants and the other third- to sixth-grade students are provided in Table 2. No participants were underweight; 1.4% of the other third- to sixth-grade students had BMIs below the 5th percentile. No significant differences in mean age, height, weight, BMI percentiles, or the distribution within the BMI categories were found between the participants and the other third- to sixth-grade students (Table 2). More than 40% of students were categorized as at or above the 85th percentile for BMI, and 23% were at or above the 95th percentile. The mean (±SD) WC of the participants was 64.8 ± 10.7 cm (range: 48.8–109.0 cm).

Table 3

Table 4

Table 5

	DISCUSSION

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Few studies have examined capillary finger-stick screening for lipid and glucose concentrations in conjunction with anthropometric measurements in a school-based setting, particularly in low-income, urban, African American children. Muratova et al (30) reported that nonfasting finger-stick capillary samples in rural, economically disadvantaged Appalachian fifth-grade students were more sensitive than was family history in predicting elevated blood cholesterol concentrations. The same group obtained measurements for BMI and nonfasting finger-stick samples in a school-based obesity screening in (n = 1413; 98% were white) and found that mean total cholesterol was 170 mg/dL, and 25% of the students were presumptively dyslipidemic, defined as a total cholesterol concentration >200 mg/dL or an HDL-cholesterol concentration <35 mg/dL (31). Overall, 15% of their sample had systolic or diastolic hypertension (95th percentile), and 45% had BMIs at or above the 85th percentile. Our mean values for total cholesterol and prevalence for elevated total cholesterol and BMI are quite similar to these (mean total cholesterol: 172.3 mg/dL; 19% with elevated total cholesterol, 44% with a BMI at or above the 85th percentile, and 9% with a BP at or above the 90th percentile), despite differences in the fasted state for capillary samples, age, and ethnicity between our studies. Far fewer of our participants had low HDL-cholesterol concentrations (1.1%), as would be expected with a comparison of African American with white populations.

More than 40% of our participants had a BMI at or above the 85th percentile. Very similar rates for BMI categories were found among the other third- to sixth-grade students, which suggests that our participants were a representative sample of the target group. We are currently involved in a larger study of low-income, predominantly African American fifth- to seventh-grade students at 4 other elementary schools in the Chicago area. Among the 500 students we have recruited thus far, >40% have BMIs at or above the 85th percentile. A recent survey of 3000 children in kindergarten through fifth grade in New York City public schools reported that 43% of the children had BMIs at or above the 85th percentile (19% at risk of overweight, 24% overweight), results that are strikingly similar to ours (32). The Sinai Health System in Chicago recently completed a door-to-door community health survey of 1700 scientifically selected households in 6 Chicago community areas that represented the racial, ethnic, and socioeconomic diversity of the city (33). On the basis of parental reports of heights and weights for 2–12-y-old children in 3 neighborhoods with the largest African American population (ranging from 47% to 98%), >60% had BMIs at or above the 85th percentile. In the poorest of these neighborhoods, this value increased to 68%. Nationally, 15.3% of 6–11-y-olds are at or above the 95th percentile for sex- and age-specific BMI (3) and among non-Hispanic black children, this value was 19.5%. Overweight and obesity among low-income, urban, African American children are greater than the national averages. Research focused on the reasons for this is needed to effectively design and tailor interventions to meet the needs of this population.

Abdominal obesity was not found in participants with BMIs below the 95th percentile; however, it occurred in almost 30% of students with BMIs at or above this level. All of these measurements were made by a person with extensive training in anthropometrics using quality assurance methods that were tightly defined; thus, we do not believe that our WC data are plagued with systematic errors. Our students were between 8 and 12 y of age and in the midst of pubertal development, a time when body proportions are known to vary among different races. In adults, central obesity is associated with insulin resistance and individual risk factors for the metabolic syndrome (34); however, this relation is not as definitive in younger children. Weiss et al (28) used a threshold BMI z score of 2.0 as a risk factor rather than WC in their definition for the metabolic syndrome because of concerns over the changes that occur in body proportions and risk assessment during pubertal development. No associations between waist-to-hip ratios and traditional cardiovascular disease risk factors were reported by Valle et al (35) in 6–9 y-old children. In 8–13-y-old Hispanic children with BMIs at or above the 85th percentile and a family history of type 2 diabetes, central obesity occurred in 62% of participants (36). This is more than twice the level observed among our overweight participants; however, we did not recruit based on a positive family history of type 2 diabetes, which is known to be highly associated with central adiposity. Because the prevalence of elevated WC was lower than expected, it would have diminished rather than inflated our prevalence rates for the metabolic syndrome. Further study is needed on the role of WC on disease risk in this age group.

The cross-sectional design of this study precludes any causal inferences and limits assumptions regarding duration for all of the risk factors. The definition used for the metabolic syndrome was devised for older children; thus, our findings can only be viewed as "presumable" metabolic syndrome rates. Fasting finger-stick capillary samples were used to assess the biochemical indexes; thus, individual concentrations may be higher or lower than plasma samples. However, the manufacturer's information indicates that these measurements are not affected by systematic error. To reduce error and overestimation of undesirable lipid and glucose concentrations, a second measurement was obtained at a later date in all children with values outside desirable ranges and the lower of the 2 values was used for analysis. Only 23% of the students in the third to sixth grades were included in our analysis for lipid abnormalities and features of the metabolic syndrome. This rate can be partially explained by the numerous independent steps required of the parents and guardians for student participation (5). Specifically, the parent or guardian had to 1) read the letter mailed home describing the study, 2) sign and return the enclosed consent form, 3) and bring their child to school 45 min before the beginning of the scheduled start of the school day in a fasted condition on the appointed day for finger-stick measurements. Despite this relatively low participation rate, the similarities in WC, BMI, and demographic backgrounds of our participants suggests that our findings can be generalized to the other third- to sixth-grade students in the 2 participating schools.

In conclusion, our findings confirm and expand the findings of a small but consistent number of studies that have reported excessive obesity and the metabolic syndrome in schoolchildren. These pilot data document for the first time that most of African American children (59%) attending the 2 low-income urban schools in the study were overweight, had one or more risk factors for the metabolic syndrome, or met both conditions. Additionally, 48% of the participants with BMIs at or above the 85th percentile had one or more risk factors for the metabolic syndrome. A school-based universal health screening program tailored to children in low-income minority neighborhoods may provide an efficient venue for prevention and tracking childhood obesity and its risk factors.

	ACKNOWLEDGMENTS

 
CLB developed the primary hypothesis and participated in the study design, data management, interpretation of results, and overall coordination of the project personnel. SG was the project coordinator, was responsible for personnel management, and participated in the subject recruitment, data collection, data entry, and manuscript preparation. HFL participated in the subject recruitment, data analysis, data entry, and interpretation of the results. KT participated in the subject recruitment, data collection, and manuscript preparation. BD was responsible for data collection, entry, cleaning, and preliminary analysis and for quality assurance. YW participated in the data analysis and was a scientific advisor. CB participated in the data analysis and manuscript preparation and was a scientific advisor. RL was a scientific advisor and assisted with study design development. There were no conflicts of interest.

	REFERENCES

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1. Kuczmarski RJ, Ogden CL, Grummer-Strawn LM. CDC growth charts: US—Advance data from Vital and Health Statistics, no. 314. Hyattsville, MD: National Center for Health Statistics, 2000. Internet: http://www.cdc.gov/growthcharts.htm (accessed 18 June 2003).
2. Centers for Disease Control and Prevention, National Center for Health Statistics. NHANES (1982-84) and NHANES (1963-65 and 1966-70), United States, 2003. Overweight children and adolescents 6-19 years of age, according to sex, age, race, and Hispanic origin: United States, selected years 1963-65 through 1999-2000. Internet: http://www.cdc.gov/nchs/products/pubs/pubd/hestats/overwght99.htm (accessed 18 June 2003).
3. Ogden CL, Flegal KM, Carroll MD, Johnson CL. Prevalence and trends in overweight among us children and adolescents, 1999–2000. JAMA 2002;288:1728–32.[Abstract/Free Full Text]
4. Micic D. Obesity in children and adolescents—a new epidemic? Consequences in adult life. J Pediatr Endocrinol 2001;14:1345–52.
5. Dietz WH. Obesity in infants, children, and adolescents in the United States. I. Identification, natural history, and after-effects. Nutr Res 1981;1:117–37.
6. Must A, Strauss RS. Risks and consequences of childhood and adolescent obesity. Int J Obes Relat Metab Disord 1999;23(suppl 2):S2–11.
7. Tershakovec AM, Weller SC, Gallagher PR. Obesity, school performance and behavior of black, urban elementary school children. Int J Obes Relat Metab Disord 1994;18:323–7.[Medline]
8. Hill AJ, Draper E, Stack J. A weight on children's minds: body shape dissatisfaction at 9-years old. Int J Obes Relat Metab Disord 1994;18:383–9.[Medline]
9. Berenson GS, Srinivasan SR, Bao W. Precursors of cardiovascular risk in young adults from a biracial (black vs. white) population: the Bogalusa Heart Study. Ann N Y Acad Sci 1997;817:189–98.[Medline]
10. Nicklas TA, von Duvillard SP, Berenson GS. Tracking of serum lipids and lipoproteins from childhood to dyslipidemia in adults: the Bogalusa Heart Study. Int J Sports Med 2002;23(S1):S39–43.
11. Whitaker RC. Understanding the complex journey to obesity in early adulthood. Ann Intern Med 2002;136:923–5.[Free Full Text]
12. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 2002;106:3143–421.[Free Full Text]
13. Ford ESG. Prevalence of the metabolic syndrome among US adults: findings from the Third National Health and Nutrition Examination Survey. JAMA 2002;287:356–9.[Abstract/Free Full Text]
14. Cook S, Weitzman M, Auinger P, Nguyen M, Dietz WH. Prevalence of a metabolic syndrome phenotype in adolescents. Arch Pediatr Adolesc Med 2003;157:821–7.[Abstract/Free Full Text]
15. National Heart, Lung, and Blood Institute. Recommendations regarding public screening for measuring blood cholesterol. Washington, DC: NHLBI, 1995. (NIH publication no. 95-3045.)
16. Honda K. Factors underlying variation in receipt of physician advice on diet and exercise: applications of the behavioral model of health care utilization. Am J Health Promot 2004;18:370–7.[Medline]
17. Galuska D, Will J, Serdula M, Ford E. Are health care professional advising obese patients to lose weight? JAMA 1999;282:1576–8.[Abstract/Free Full Text]
18. Stafford R, Kfarhat J, Mistr B, Schoenfeld D. National patterns of physician activities related to obesity management. Arch Fam Med 2000;9:631–8.[Abstract/Free Full Text]
19. Lohman TG, Roche AF, Martorell R, ed. Anthropometric standardization reference manual. Champaign, IL: Human Kinetics Books, 1988.
20. Lee RD, Nieman DC. Nutritional assessment. 3rd ed. New York, NY: McGraw-Hill, 2003.
21. Chobanian AV, Bakris GL, Black HR, et al. Seventh report of the joint national committee on the prevention, detection, evaluation and treatment of high blood pressure. Hypertension 2003;42:1206–52.[Abstract/Free Full Text]
22. Update on the 1987 Task Force Report on High Blood Pressure in Children and Adolescents: a working group report from the National High Blood Pressure Education Program. Pediatrics 1996;94(4):649–58.
23. Volles DF, McKenney JM, Miller WG, Ruffen D, Zhang D. Analytic and clinical performance of two compact cholesterol-testing devices. Pharmacotherapy 1998;18:184–92.[Medline]
24. Carter JS, Houston CA, Gilliland SS, et al. Rapid HbA1c testing in a community setting. Diabetes Care 1996;19:764–7.[Abstract]
25. Kavey RE, Daniels SR, Laurer RM, Atkins DL, Hayman LL, Taubert K. American Heart Association guidelines for primary prevention of atherosclerotic cardiovascular disease beginning in childhood. J Pediatr 2003;142:368–72.[Medline]
26. American Academy of Pediatrics, Committee on Nutrition. Cholesterol in childhood. Pediatrics 1998;101:141–7.[Abstract/Free Full Text]
27. The Expert Committee on Diagnosis and Classification of Diabetes Mellitus. Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 2003;26:3160–7.[Free Full Text]
28. Weiss R, Dziura J, Burget TS, et al. Obesity and the metabolic syndrome in children and adolescents. N Engl J Med 2004;350:2362–74.[Abstract/Free Full Text]
29. STOPP-T2D Collaborative Group. Obesity, diabetes and the metabolic syndrome in 8th grade children. Diabetes 2004;53:S2;A7.
30. Muratova VN, Islam SS, Demerath EW, Minor VE, Neal WA. Cholesterol screening among children and their parents. Prev Med 2001;33:1–6.[Medline]
31. Demerath E, Muratova V, Spangler E, Li J, Minor VE, Neal WA. School-based obesity screening in rural Appalachia. Prev Med 2003;37:553–60.[Medline]
32. Perez-Pena R. Obesity on the rise in New York public schools. New York Times 2003 July 9.
33. Whitman S, Williams C, Shah AM. Sinai Health Systems' Community Health Survey: Report I. Chicago, IL: Sinai Health System, 2004.
34. Kissebah AH, Vydelingum N, Murray R, et al. Relation of body fat distribution to metabolic complications of obesity. J Clin Endocrinol Metab 1982;54:254–60.[Abstract/Free Full Text]
35. Valle M, Gascon F, Martos R, et al. Metabolic cardiovascular syndrome in obese prepubertal children: the role of high fasting insulin levels. Metabolism 2002;51:423–8.[Medline]
36. Cruz ML, Weigensberg MJ, Huang TT, Ball G, Shaibi GQ, Goran MI. The metabolic syndrome in overweight Hispanic youth and the role of insulin sensitivity. J Clin Endocrinol Metab 2004;89:108–13.[Abstract/Free Full Text]

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