ࡱ > #` s bjbj5G5G W- W- B " J
J
J
J
J J J $ R R R P R S S F PT D V V V V \ d# ү ԯ ԯ ԯ ԯ ԯ ԯ $ h J , }[ \ , , J
J
V V
, @ J
p V J V , ү J < V DT `! R l , # 0 S 4 < J < s
| ^ S , , , , d= xK $ xK n J
J
J
J
J
J
Metabolic syndrome:a critical look from the viewpoints of causal diagrams and statistics
Eyal Shahar, MD, MPH
Address:
Eyal Shahar, MD, MPH
Professor
Division of Epidemiology and Biostatistics
Mel and Enid Zuckerman College of Public Health
The University of Arizona
1295 N. Martin Ave.
Tucson, AZ 85724
Email: HYPERLINK "mailto:Shahar@email.arizona.edu"Shahar@email.arizona.edu
Phone: 520-626-8025
Fax: 520-626-2767
Introduction
PubMed search for the words metabolic syndrome in the title of articles and letters has found 175 publications in 2002, 870 in 2005,and 1,431 in 2007. At the time of this writing, the trend might have reached a plateau, countingabout 700 titles by mid 2008. Undoubtedly, the term metabolic syndrome has found a place of honor on the pages of scientific and medical journals, but has it also survived numerous attacks by critical minds? ADDIN EN.CITE Federspil200621212117Federspil, G.Nisoli, E.Vettor, R.Clinica Medica 3, Department of Medical and Surgical Sciences, Padua University, Italy. giovanni.federspil@unipd.itA critical reflection on the definition of metabolic syndromePharmacol ResPharmacol Res449-56536HumansMetabolic Syndrome X/*classification/diagnosisTerminology as Topic2006Jun1043-6618 (Print)16632375http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16632375 engGale200528282817Gale, E. A.The myth of the metabolic syndromeDiabetologiaDiabetologiaDiabetologiaDiabetologiaDiabetologiaDiabetologia1679-83489Blood Glucose/metabolismHumans*Metabolic Syndrome X/classificationModels, Biological2005Sep0012-186X (Print)16025251http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16025251 engGoodman200810101017Goodman, E.Metabolic syndrome and the mismeasure of riskJ Adolesc HealthJ Adolesc Health538-404262008Jun1054-139X (Print)18486860http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18486860 engGreenland200527272717Greenland, P.Critical questions about the metabolic syndromeCirculationCirculationCirculationCirculationCirculationCirculation3675-611224Cardiovascular Diseases/*etiologyForecastingHumansMetabolic Syndrome X/*pathologyRisk Factors2005Dec 131524-4539 (Electronic)16344398http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16344398 engKahn200699917Kahn, R.The metabolic syndrome (emperor) wears no clothesDiabetes careDiabetes care1693-1696292006Kahn200788817Kahn, R.American Diabetes Association, 1701 N Beauregard St, Alexandria, VA 22311, USA. RKahn@diabetes.orgMetabolic syndrome: is it a syndrome? Does it matter?CirculationCirculationCirculationCirculationCirculationCirculation1806-10; discussion 181111513Cardiovascular Diseases/*etiologyCausalityDiabetes Mellitus, Type 2/etiologyDyslipidemias/complications/drug therapyHumansHydroxymethylglutaryl-CoA Reductase Inhibitors/therapeutic useHyperinsulinism/etiology/physiopathologyHypertension/complicationsInsulin ResistanceLife Style*Metabolic Syndrome X/complications/diagnosis/physiopathology/psychologyObesity/complications/physiopathologyRisk FactorsTerminology as Topic2007Apr 31524-4539 (Electronic)17404171http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17404171 engKahn200514141417Kahn, R.Buse, J.Ferrannini, E.Stern, M.American Diabetes Association, 1701 N. Beauregard St., Alexandria, Virginia 22311, USA. rkahn@diabetes.orgThe metabolic syndrome: time for a critical appraisal: joint statement from the American Diabetes Association and the European Association for the Study of DiabetesDiabetes CareDiabetes careDiabetes careDiabetes care2289-304289C-Reactive Protein/metabolismCardiovascular Diseases/epidemiologyHumansInsulin ResistanceMetabolic Syndrome X/*classification/epidemiology/*therapyModels, Biological2005Sep0149-5992 (Print)16123508http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16123508 engReaven200518181817Reaven, G. M.Division of Cardiovascular Medicine, Falk CVRC, Stanford Medical Center, 300 Pasteur Dr., Stanford, CA 94305, USA. greaven@cvmed.stanford.eduThe metabolic syndrome: requiescat in paceClin ChemClinical chemistryClin ChemClinical chemistryClin ChemClinical chemistry931-8516Body Weights and MeasuresHumansMetabolic Syndrome X/classification/*diagnosisPractice Guidelines as Topic2005Jun0009-9147 (Print)15746300http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15746300 engTikhonoff200720202017Tikhonoff, V.Casiglia, E.Metabolic syndrome: nothing more than a constellation?Eur Heart JEuropean heart journalEur Heart JEuropean heart journalEur Heart JEuropean heart journal780-1287Cardiovascular Diseases/*etiologyHumansMetabolic Syndrome X/*complications/diagnosisRisk Factors2007Apr0195-668X (Print)17400607http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17400607 eng1-9 I am not so sure. Moreover, it is difficult to recall another example of a newly discovered, prevalent syndrome whose very existence had to be defended, repeatedly. ADDIN EN.CITE Gale200829292917Gale, E. A.Diabetes and Metabolism, University of Bristol, Medical School Unit, Southmead Hospital, Bristol BS10 5NB. Edwin.Gale@bristol.ac.ukShould we dump the metabolic syndrome? YesBmjBMJ (Clinical research edBmjBMJ (Clinical research edBmjBMJ (Clinical research ed6403367645Body Mass IndexDiabetes Mellitus/diagnosisDiagnosis, DifferentialHumansMetabolic Syndrome X/complications/*diagnosisObesity/*complicationsTerminology as Topic2008Mar 221468-5833 (Electronic)18356231http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18356231 engGrundy200615151517Grundy, S. M.Department of Clinical Nutrition, Center for Human Nutrition, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Y3.206, Dallas, 75390-9052, USA. scott.grundy@utsouthwestern.eduDoes the metabolic syndrome exist?Diabetes CareDiabetes careDiabetes careDiabetes care1689-92; discussion 1693-6297Atherosclerosis/etiologyBlood PressureDiabetes Mellitus, Type 2/etiologyHumansInsulin ResistanceMetabolic Syndrome X/*classification/complicationsRisk2006Jul0149-5992 (Print)16801603http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16801603 engAlberti200849494917Alberti, K. G.Zimmet, P. Z.Department of Endocrinology and Metabolism, St Mary's Hospital and Imperial College, London. george.alberti@ncl.ac.ukShould we dump the metabolic syndrome? NoBmjBMJ (Clinical research edBmjBMJ (Clinical research edBmjBMJ (Clinical research ed6413367645Cardiovascular Diseases/etiologyDiabetes Mellitus/diagnosisHumansMetabolic Syndrome X/complications/*diagnosisRisk FactorsTerminology as Topic2008Mar 221468-5833 (Electronic)18356232http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18356232 eng10-12
In this article I analyze the term "metabolic syndrome" from two related viewpoints: causaland statistical. To shed a new light on the debate, I rely on a simple tool called causal diagrams, formally known as directed acyclic graphs (DAG). ADDIN EN.CITE Pearl20004343436Pearl, J.Causality: models, reasoning, and inference2000Cambridge, United KingdomCambridge University Press13 Causal diagrams encode causal assertions unambiguously; mercilessly expose foggy causal thinking; and create a bridge between causal reality and statistical associations. In epidemiology, for example, causal diagrams proved to be a unified method to explain the key categories of bias: confounding, ADDIN EN.CITE Greenland199911117Greenland, S.Pearl, J.Robins, J. M.Department of Epidemiology, UCLA School of Public Health, Los Angeles, CA 90095-1772, USA.Causal diagrams for epidemiologic researchEpidemiologyEpidemiology (Cambridge, MassEpidemiologyEpidemiology (Cambridge, MassEpidemiologyEpidemiology (Cambridge, Mass37-48101*Epidemiologic Methods*Models, Statistical1999Jan1044-3983 (Print)9888278http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9888278 eng14 selection bias, ADDIN EN.CITE Hernan200433317Hernan, M. A.Hernandez-Diaz, S.Robins, J. M.Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts 02115, USA. miguel_hernan@post.harvard.eduA structural approach to selection biasEpidemiologyEpidemiology (Cambridge, MassEpidemiologyEpidemiology (Cambridge, MassEpidemiologyEpidemiology (Cambridge, Mass615-25155Case-Control StudiesDisease/etiologyEnvironmental Exposure/adverse effectsEpidemiologic Methods*Epidemiologic Research DesignEvaluation Studies as TopicHumansLongitudinal StudiesModels, TheoreticalResearchResearch SubjectsRisk Factors*Selection Bias2004Sep1044-3983 (Print)15308962http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15308962 eng15 and information bias. ADDIN EN.CITE Hernan200522217Hernan, M. A.A structural approach to observation biasAmerican Journal of EpidemiologyAmerican Journal of EpidemiologyS1001612005Shahar2008 (in press)66617Shahar, E.Causal diagrams for encoding and evaluation of information biasJ Eval Clin PractJ Eval Clin PractJournal of evaluation in clinical practice2008 (in press)16, 17
Thearticle is divided into two parts:The first part lays essential theoretical foundation.In thesecond partI analyze various aspects of the new syndrome.
Part I: Theoretical Foundation
Causal diagrams
The essence is simple. We write down the names of variables and draw arrows to connect them such that each arrow emanates from a cause and points to an effect. For example, smoking status(lung cancer status encodes the statement smoking causes lung cancer. The sequence weight(insulin resistance(vital status encodes the statement weight affects survival through an intermediary variable called insulin resistance. HDL-cholesterol(gender(hemoglobin encodes the statement gender affects both HDL-cholesterol and hemoglobin. The variables in question may be binary, nominal, ordinal, or continuous, but they must be variables and not values of variables. For example, formally we should not write smoking(lung cancer because smoking and lung cancer are not variables. We may draw arrows, however, to connect smoking status or pack-years of smoking with lung cancer status.
Causal diagrams assume an underlying causal structure, which percolates up to create the familiar statistical associations between variables. ADDIN EN.CITE Pearl20004343436Pearl, J.Causality: models, reasoning, and inference2000Cambridge, United KingdomCambridge University Press13 For instance, we observe a statistical association between smoking status and incident lung cancer because smoking status(lung cancer status. Most statistical associations, however,do not reflect the cause-and-effect of interest. One key explanation for observing an association between two variables is their sharing of a common cause. For example, fasting blood glucose and resting blood pressure are associated, at least in part, because weight affects both. And in general: a crude association between two variables contains both the effect of one on the other (if any) and the contribution of their common causes (if any). In causal inquiry, these common causes are called confounders. Their contribution to the crude association is called confounding.
Natural variables and derived variables
Some variables are more natural than others in the sense that nature has created their values through various causal mechanisms, and we just try to measure those values. Fasting glucose leveland weight may be examples of natural variables (although their measured version already contains the influence of human measurement.) Trisomy 21(present, absent) is another example. At the other extreme we find human-made variables in the sense that we, rather than nature, are the ultimate reason for their existence. Body mass index (BMI), for instance, is not a natural variable because we create the content (values) of that variable from the measured version of two natural variables: weight and height. Stated differently, natural variables are measured, whereas their human-made counterparts are derived from natural variables (and sometimes from other derived variables.) The derivation could be carried out by an arithmetic expression (BMI=weight/height2) or by conditional statements (If fasting glucoseGreenland199546464617Greenland, S.Department of Epidemiology, UCLA School of Public Health 90095-1772, USA.Dose-response and trend analysis in epidemiology: alternatives to categorical analysisEpidemiologyEpidemiology (Cambridge, MassEpidemiologyEpidemiology (Cambridge, MassEpidemiologyEpidemiology (Cambridge, Mass356-6564Biometry/*methodsConfidence Intervals*Epidemiologic MethodsHIV Infections/epidemiologyHumansIncidenceLogistic ModelsLos Angeles/epidemiology*Models, Statistical*Risk Assessment1995Jul1044-3983 (Print)7548341http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7548341 eng18
Derived variables and causal diagrams
When we think about cause-and-effect, we usually think about the relation between two natural variables where the values of one affect the values of the other. Set weight to be 300lb, rather than 150lb, and chances are that fasting blood glucose will rise. But there is no reason to exclude derived variables from the domain of causal connections. In fact, their creation is a form of causation, just like the creation of fasting glucose by weight. Set the weight of a 5-foot person to be 300lb, rather than 150lb, and BMI will rise. The rules of causal diagrams, therefore, apply. We encode the expression BMI=weight/height2 just as we encode any other causal relation between two causes and their common effect: weight(BMI(height. Similarly, fasting glucose level(diabetes status encodes the derivation of a variable called diabetes status according to conditionals about fasting glucose and cutoff points.
Relation of causes to their effect
There is one important empirical difference, however, between causal relations among natural variables and causal relations that involve derived variables. No set of causal variables will enable us to know the fasting glucose level (a natural variable) of any patient, either due to unknown causal variables or because causation is inherently indeterministic. In contrast, we can always tell the patients diabetes status (a derived variable) from his or her level of fasting blood glucose because we set up a causal mechanismthe derivation ruleto link the two. Likewise, no set of causal variables will precisely tell us anybodys weight, but weight and height will precisely determine the value of BMI.
Which leads to the following key conclusion: the information that is contained in a derived variable cannot exceed the information that is already present in the variables from which it was derived. Therefore, from a statistical perspective, there is no reason to expect that a derived variable will predict something above and beyond its makers. In fact, in many cases a derived variableis not evena good substitute for the original information. ADDIN EN.CITE Atchley197850505017Atchley, W.R.Anderson, DRatios and the statistical analysis of biological dataSystematic ZoologySystematic Zoology71-78271978Raubenheimer199551515117Raubenheimer, DProblems with ratio analysis in nutritional studiesFunctional EcologyFunctional Ecology21-299199519, 20
Part II: Analysis
Deriving metabolic syndrome status
For some writers the metabolic syndrome was discovered; for others itwas defined; and for others it was made up of nothing. Technically, however, we should all agree that metabolic syndrome status is, undoubtedly,a derived variable. Actually, there are numerous derived variables that claim the titleas many as there are proposed definitions, or more correctly, as many as there are rules of derivation. ADDIN EN.CITE Alberti200623232317Alberti, K. G.Zimmet, P.Shaw, J.Department of Endocrinology and Metabolic Medicine, St Mary's Hospital, London, UK and International Diabetes Institute, Caulfield, Australia.Metabolic syndrome--a new world-wide definition. A Consensus Statement from the International Diabetes FederationDiabet MedDiabet Med469-80235Adipose Tissue/pathologyAdultConsensusDyslipidemias/complications/drug therapyFemaleHumansHypertension/complications/drug therapyInsulin ResistanceInternational CooperationMaleMetabolic Syndrome X/*diagnosis/etiology/therapyObesity/complications*Societies, Medical2006May0742-3071 (Print)16681555http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16681555 engBrietzke200737373717Brietzke, S. A.Division of Endocrinology, Department of Internal Medicine, MA406 UMHC, 1 Hospital Drive, Columbia, MO 65212, USA. brietzkes@health.missouri.eduControversy in diagnosis and management of the metabolic syndromeMed Clin North AmThe Medical clinics of North AmericaMed Clin North AmThe Medical clinics of North AmericaMed Clin North AmThe Medical clinics of North America1041-61, vii-viii916Coronary Disease/etiology/prevention & controlDiabetes Mellitus/etiology/prevention & controlDiagnosis, DifferentialExercise Therapy/*methodsHumansMetabolic Syndrome X/complications/*diagnosis/*therapy*Practice Guidelines as Topic2007Nov0025-7125 (Print)17964908http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17964908 engDay200733333317Day, C.Diabetes Research Group, School of Life and Health Sciences, Aston University, Birmingham, B4 7ET, UK. cday@mededuk.comMetabolic syndrome, or What you will: definitions and epidemiologyDiab Vasc Dis ResDiab Vasc Dis Res32-841Cardiovascular Diseases/etiologyDiabetes Mellitus/etiologyHumansMetabolic Syndrome X/*epidemiology/etiologyObesity/complicationsPrevalence2007Mar1479-1641 (Print)17469041http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17469041 engFord200839393917Ford, E. S.Li, C.Division of Adult and Community Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention , Atlanta, GA, USA.Defining the metabolic syndrome in children and adolescents: will the real definition please stand up?J PediatrThe Journal of pediatricsJ PediatrThe Journal of pediatricsJ PediatrThe Journal of pediatrics160-41522AdolescentChildDiagnosis, DifferentialHumansInsulin ResistanceMetabolic Syndrome X/*diagnosis/epidemiologyObesity/epidemiologyPediatrics/*methods/standardsPrevalenceRisk Factors2008Feb1097-6833 (Electronic)18206681http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18206681 engGrundy200432323217Grundy, S. M.Brewer, H. B., Jr.Cleeman, J. I.Smith, S. C., Jr.Lenfant, C.Definition of metabolic syndrome: Report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definitionCirculationCirculationCirculationCirculationCirculationCirculation433-81093Cardiovascular Diseases/etiologyDiabetes Mellitus/etiologyHumans*Metabolic Syndrome X/complications/diagnosis/etiology/therapyObesity/therapyPrognosisRisk Factors*Terminology as Topic2004Jan 271524-4539 (Electronic)14744958http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14744958 engMagliano200638383817Magliano, D. J.Shaw, J. E.Zimmet, P. Z.International Diabetes Institute, Melbourne, Australia. dmagliano@idi.org.auHow to best define the metabolic syndromeAnn MedAnnals of medicineAnn MedAnnals of medicineAnn MedAnnals of medicine34-41381Cardiovascular Diseases/epidemiology/etiologyDiabetes Mellitus, Type 2/epidemiology/etiologyHumansMetabolic Syndrome X/*classification/complications/epidemiologyPrevalenceRisk FactorsWorld Health20060785-3890 (Print)16448987http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16448987 engWubben200635353517Wubben, D. P.Adams, A. K.Department of Medicine, University of Wisconsin-Madison, 600 Highland Ave, Madison, WI 53714, USA. dpw@medicine.wisc.eduMetabolic syndrome: what's in a name?WmjWmj17-201055Cardiovascular Diseases/etiologyDiabetes Mellitus, Type 2/etiologyHumansInflammationInsulin ResistanceMetabolic Syndrome X/*classification/diagnosis/epidemiologyObesityPrognosisRisk FactorsTerminology as TopicWorld Health Organization2006Jul1098-1861 (Print)16933408http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16933408 engZimmet200530303017Zimmet, P.Magliano, D.Matsuzawa, Y.Alberti, G.Shaw, J.International Diabetes Institute, Melbourne, Australia. pzimmet@idi.org.auThe metabolic syndrome: a global public health problem and a new definitionJ Atheroscler ThrombJournal of atherosclerosis and thrombosisJ Atheroscler ThrombJournal of atherosclerosis and thrombosisJ Atheroscler ThrombJournal of atherosclerosis and thrombosis295-300126Cardiovascular Diseases/epidemiologyCholesterol, HDL/bloodDiabetes Mellitus, Type 2/epidemiologyHumansMetabolic Syndrome X/*epidemiology*Public Health*World Health20051340-3478 (Print)16394610http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16394610 eng21-28
Almost every proposed derivation of metabolic syndrome statusfollows the same format. ADDIN EN.CITE Goodman200810101017Goodman, E.Metabolic syndrome and the mismeasure of riskJ Adolesc HealthJ Adolesc Health538-404262008Jun1054-139X (Print)18486860http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18486860 eng3 Let V1, V2,,Vn denote a set of n continuous variables, either natural or derived. For each variable, decide on a cutoff point and derive a binary variable (0, 1) on the basis of that cutoff point and a conditional. Next, add up the values of these binary variables to derive a summation variable, say, SUM. Finally, derive metabolic syndrome status from SUM using a cutoff point and a conditional: if SUMFord200839393917Ford, E. S.Li, C.Division of Adult and Community Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention , Atlanta, GA, USA.Defining the metabolic syndrome in children and adolescents: will the real definition please stand up?J PediatrThe Journal of pediatricsJ PediatrThe Journal of pediatricsJ PediatrThe Journal of pediatrics160-41522AdolescentChildDiagnosis, DifferentialHumansInsulin ResistanceMetabolic Syndrome X/*diagnosis/epidemiologyObesity/epidemiologyPediatrics/*methods/standardsPrevalenceRisk Factors2008Feb1097-6833 (Electronic)18206681http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18206681 engFederspil200621212117Federspil, G.Nisoli, E.Vettor, R.Clinica Medica 3, Department of Medical and Surgical Sciences, Padua University, Italy. giovanni.federspil@unipd.itA critical reflection on the definition of metabolic syndromePharmacol ResPharmacol Res449-56536HumansMetabolic Syndrome X/*classification/diagnosisTerminology as Topic2006Jun1043-6618 (Print)16632375http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16632375 eng1, 24Rather than naming them after organizations that have endorsed them, it is better to use numerical subscripts to indicate the chronology of the proposed rules:metabolic syndrome status1, metabolic syndrome status2, metabolic syndrome status3, and so on. The sequence has no meaningfulorder other than chronology, and may continue indefinitely.
Clustering of risk factors
Almost every writer about the metabolic syndrome, whether a proponent or an opponent, mentions the clustering of risk factors as a key feature of the syndrome. For example, a group of proponents writes: "Five risk factors of metabolic origin (atherogenic dyslipidemia, elevated blood pressure, elevated glucose, a prothrombotic state, and a proinflammatory state) commonly cluster together". ADDIN EN.CITE Grundy200615151517Grundy, S. M.Department of Clinical Nutrition, Center for Human Nutrition, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Y3.206, Dallas, 75390-9052, USA. scott.grundy@utsouthwestern.eduDoes the metabolic syndrome exist?Diabetes CareDiabetes careDiabetes careDiabetes care1689-92; discussion 1693-6297Atherosclerosis/etiologyBlood PressureDiabetes Mellitus, Type 2/etiologyHumansInsulin ResistanceMetabolic Syndrome X/*classification/complicationsRisk2006Jul0149-5992 (Print)16801603http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16801603 eng11 Likewise, a group of opponents writes: "The term 'metabolic syndrome' refers to a clustering of specific cardiovascular disease (CVD) risk factors..." ADDIN EN.CITE Kahn200514141417Kahn, R.Buse, J.Ferrannini, E.Stern, M.American Diabetes Association, 1701 N. Beauregard St., Alexandria, Virginia 22311, USA. rkahn@diabetes.orgThe metabolic syndrome: time for a critical appraisal: joint statement from the American Diabetes Association and the European Association for the Study of DiabetesDiabetes CareDiabetes careDiabetes careDiabetes care2289-304289C-Reactive Protein/metabolismCardiovascular Diseases/epidemiologyHumansInsulin ResistanceMetabolic Syndrome X/*classification/epidemiology/*therapyModels, Biological2005Sep0149-5992 (Print)16123508http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16123508 eng7What is clustering, however? What statistical idea underlies that powerful, emotiveword, which invokes a sense of evilforces conspiringto cause harm?
A patient with high blood pressure is more likely to have a high level of blood glucose than a patient with low blood pressure, and a patient with low blood pressure is more likely to have a low level of blood glucose than a patient with high blood pressure. But no one would say that blood pressure and blood glucose cluster or"cluster together". We would say that these traits are correlated or associated. Even if we add a third variable, say plasma triglycerides, which correlates with both, we would still not use the word "cluster" because it is not used in the context of continuous variables. The word is reservedfor categorical variables, selectivelypointing to one aspect of a well-known statistical idea: association.
Let Binary V1, Binary V2,...,Binary Vn be a group of binary variables eachtaking the values of 1 ("bad, high risk") or 0 ("good, low risk"). Clustering is said to exist if patientswith a value of1 on any one variableare more likely to have a value of1 on all others (than patients with a value of 0 on that variable). If that is the case, however, zero values cluster, too: patients with a valueof 0 on any one variable are more likely to have a value of0 on all others (than patients with a value of 1 on that variable). Regardless of mechanism, patients who smoke are more likely to drink alcohol than patients who don't and vice versa (clustering of smoking and drinking). Likewise, patients who don't smoke are more likelyto not drink than patients who do and vice versa (clustering of no smoking and no drinking). In short, clustering is a word to describe a group ofcategorical variables, usually binary, where each variable is associated with all others. Of course, the latter descriptiondoes not have the rhetorical power of "clustering of risk".
The phrase"clustering of risk" or clustering of high risk may be rhetorically helpful, butitis nonetheless poor scientific terminology for several reasons: First,why talk about clustering of one value of variables when the underlying statisticalphenomenon is an association between variables? Second, the complement, favorable clustering of the other value ("low risk") is conveniently ignoredhardly an objective representation of statistical reality. Third, there is a better, coredescription of the phenomenon behind the metabolic syndrome: several natural, continuous variables are associated with each other (for reasons that will be discussed later).
Indeed, opponents of the syndrome have already reduced the "clustering" into common statistical jargon: "...certain 'metabolic' factors tend to associate with each other..." ADDIN EN.CITE Kahn200699917Kahn, R.The metabolic syndrome (emperor) wears no clothesDiabetes careDiabetes care1693-16962920065 Similar, though less clear,expression may also be found in the writing of aproponent:"multiple risk factors that are metabolically interrelated". ADDIN EN.CITE Grundy200615151517Grundy, S. M.Department of Clinical Nutrition, Center for Human Nutrition, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Y3.206, Dallas, 75390-9052, USA. scott.grundy@utsouthwestern.eduDoes the metabolic syndrome exist?Diabetes CareDiabetes careDiabetes careDiabetes care1689-92; discussion 1693-6297Atherosclerosis/etiologyBlood PressureDiabetes Mellitus, Type 2/etiologyHumansInsulin ResistanceMetabolic Syndrome X/*classification/complicationsRisk2006Jul0149-5992 (Print)16801603http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16801603 eng11 Surprisingly, however, numerous writers from both camps have also adopted a pseudo-statistical ideathat the observed clustering exceeds the clustering that would be expected by chance alone. Although we can estimate the magnitude of an association between variablesand perhaps gather evidence against the claim of no association, no statistical computation can tell ushow strong of an association is expected by chance alone. (Chance alone could account for any association.) That erroneous idea can probably betracedto prevailing misinterpretation of a P-value as "the probability of observing this result by chance alone".
Possible causal mechanisms behind "clustering"
Having reduced the "clustering phenomenon" into multiple associations among derived, binary variables, we may now explore the scientific questions of interest: Why Binary V1, Binary V2,...,Binary V5are all associated with each other? Which causal mechanisms have created these multiple associations? Why are they "interrelated" or plainly related?
As you may recall, two mechanisms contribute to an association between two variables:1) one variablecauses the other; 2)they both share at least one common cause. (There is a third mechanism, which will be mentioned later.) As we see inFigure 1, the first mechanism does not operate in that diagram: no causal arrow emanates from any binary variableand points to anotherand rightly so. The only immediate causes of a derived variableare the variables from which it was derived. We may therefore conclude thatthe observed associations among the binary variables in Figure 1 must be attributed to their sharing ofat least one common cause, which is missing from the figure. The causal diagramin Figure 1 must be incomplete.
Figures 2-4 show minimal causal structures that would create an association between each of the five binary variables behind metabolic syndrome status and the other four. To check the claim, we just need to verify that each pair shares at least one common cause. Indeed, if we pick any two binary variables and follow their arrows upstream to their causes, we will always end up in a common cause. In Figure 2, the common cause is U; in Figure 3 it is U, too (as well as V4 for the pair Binary V4 and Binary V5); and in Figure 4 it is V1. Notice that in each case, the explanation for the associations among the binary variables has nothing to do with these variables per se; everything happened between natural variables at earlier stages of causation.
According to Figure 2 or Figure 3, one U will explain the clustering of metabolic risk factors, and it is not too difficult to name at least two candidates: age and maybe the amount of abdominal fat. As for Figure 4, one of the five continuous variables should assume the role of a common cause of all other. That variable may also be abdominal fat, whenever it is partof thederivation of metabolic syndrome status. The clustering mystery is finally and trivially solved.
Causal structures that cannot cause "clustering"
Figures 5-8 show examples of causal diagrams with no shared cause of all five binary variables. For reasons that are well-established in the theorems of causal diagrams, ADDIN EN.CITE Greenland199911117Greenland, S.Pearl, J.Robins, J. M.Department of Epidemiology, UCLA School of Public Health, Los Angeles, CA 90095-1772, USA.Causal diagrams for epidemiologic researchEpidemiologyEpidemiology (Cambridge, MassEpidemiologyEpidemiology (Cambridge, MassEpidemiologyEpidemiology (Cambridge, Mass37-48101*Epidemiologic Methods*Models, Statistical1999Jan1044-3983 (Print)9888278http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9888278 engPearl20004343436Pearl, J.Causality: models, reasoning, and inference2000Cambridge, United KingdomCambridge University Press13, 14 these causal structures will not create an association between every pair of the five binary variables. For example,Binary V3 andBinary V4 would not be associated in Figure 5;Binary V2 andBinary V4 would not be associated in Figure 6;Binary V3 andBinary V5 would not be associated in Figure 7; andBinary V1 andBinary V5 would not be associated in Figure 8.
Proponents of the metabolic syndrome state that no common cause is needed. Needed for what? No common cause is needed to derive any variable, including a variable called metabolic syndrome status, but a causal structure with no common cause could not have created that clustering, which was the motivation for deriving metabolic syndrome status in the first place. Moreover, we have already realized that at least one common cause does exist (age) and maybe there are others.
Spurious clustering
Two variables that do not cause each other, nor share a common cause, may still be associated due to a third, less well-known mechanism called selection bias. ADDIN EN.CITE Hernan200433317Hernan, M. A.Hernandez-Diaz, S.Robins, J. M.Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts 02115, USA. miguel_hernan@post.harvard.eduA structural approach to selection biasEpidemiologyEpidemiology (Cambridge, MassEpidemiologyEpidemiology (Cambridge, MassEpidemiologyEpidemiology (Cambridge, Mass615-25155Case-Control StudiesDisease/etiologyEnvironmental Exposure/adverse effectsEpidemiologic Methods*Epidemiologic Research DesignEvaluation Studies as TopicHumansLongitudinal StudiesModels, TheoreticalResearchResearch SubjectsRisk Factors*Selection Bias2004Sep1044-3983 (Print)15308962http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15308962 eng15 In brief, selection bias arises fromunnecessary manipulation ofa common effect of the variables of interest. For example, two variables that are not associated at all will be associated within at least one stratum of a third variable, if that variable is their common effect. ADDIN EN.CITE Greenland199911117Greenland, S.Pearl, J.Robins, J. M.Department of Epidemiology, UCLA School of Public Health, Los Angeles, CA 90095-1772, USA.Causal diagrams for epidemiologic researchEpidemiologyEpidemiology (Cambridge, MassEpidemiologyEpidemiology (Cambridge, MassEpidemiologyEpidemiology (Cambridge, Mass37-48101*Epidemiologic Methods*Models, Statistical1999Jan1044-3983 (Print)9888278http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9888278 eng14 The association we would observe has no interesting causal meaning: it reflects neither cause-and-effect,nor confounding (a common cause).
As shown in Figures 1-8, "metabolic syndrome status" is a common effect of all preceding variables. Therefore, stratifying on this variable might create associations between components of the syndrome among patients who are classified as having the syndrome. Stated in the "clustering" jargon, part of the clustering of metabolic risk factorsthat we observe among patients who carry the label "metabolic syndrome" is likely spurious, attributable tostratification on the variable we havederived. Therefore, we should never look at the clustering in patients who received the label, or in patients who did not.
"The combined effect is more than the sum"
This loose idea shows up occasionally, usually referring to "summation" versus "multiplication" of the effects of the natural variables from which metabolic syndrome status is derived.The underlying concept is called interactionby statisticians, or effect modification by epidemiologists, and like other methodological topics, it is muchdeeper andmore subtle than is usually appreciated. ADDIN EN.CITE Greenland199345454517Greenland, S.Department of Epidemiology, UCLA School of Public Health 90024-1772.Basic problems in interaction assessmentEnviron Health PerspectEnvironmental health perspectivesEnviron Health PerspectEnvironmental health perspectivesEnviron Health PerspectEnvironmental health perspectives59-66101 Suppl 4Bias (Epidemiology)Dose-Response Relationship, DrugEnvironmental Exposure/*analysisEnvironmental Monitoring/methodsHumansModels, BiologicalModels, StatisticalRisk Factors1993Dec0091-6765 (Print)8206043http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8206043 engRothman198044444417Rothman, K. J.Greenland, S.Walker, A. M.Concepts of interactionAm J EpidemiolAmerican journal of epidemiologyAmerican Journal of Epidemiology467-701124*Epidemiologic MethodsHumans*Models, BiologicalPublic HealthRisk*Statistics as Topic1980Oct0002-9262 (Print)7424895http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=7424895 engShahar200712121217Shahar, E.Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ 85724, USA. shahar@email.arizona.eduEstimating causal parameters without target populationsJ Eval Clin PractJournal of evaluation in clinical practiceJ Eval Clin PractJournal of evaluation in clinical practiceJ Eval Clin PractJournal of evaluation in clinical practice814-6135*CausalityConfounding Factors (Epidemiology)Data Interpretation, StatisticalHumans*Research Design2007Oct1356-1294 (Print)17824877http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17824877 engVanderWeele200740404017VanderWeele, T. J.Robins, J. M.Department of Health Studies, University of Chicago, Chicago, Illinois 60637, USA. vanderweele@uchicago.eduFour types of effect modification: a classification based on directed acyclic graphsEpidemiologyEpidemiology (Cambridge, MassEpidemiologyEpidemiology (Cambridge, MassEpidemiologyEpidemiology (Cambridge, Mass561-8185Causality*Computer Graphics*Data Interpretation, Statistical*Effect Modifiers (Epidemiology)*Models, Statistical2007Sep1044-3983 (Print)17700242http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17700242 eng29-32 For example, the phenomenon depends on the scale on which we choose to measure associations and almost always exists on some scale. ADDIN EN.CITE Rothman20024747476Rothman, K. J.Epidemiology: an introduction169-1702002New YorkOxford University PressWeinberg200742424217Weinberg, C. R.National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709, USA. Weinber2@niehs.nih.govCan DAGs clarify effect modification?EpidemiologyEpidemiology (Cambridge, MassEpidemiologyEpidemiology (Cambridge, MassEpidemiologyEpidemiology (Cambridge, Mass569-72185Causality*Computer Graphics*Effect Modifiers (Epidemiology)*Epidemiologic Methods*Models, Statistical2007Sep1044-3983 (Print)17700243http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17700243 engRothman20024747476Rothman, K. J.Epidemiology: an introduction169-1702002New YorkOxford University Press33, 34
Regardless, the metabolic syndrome isneither the sum nor "more than the sum" of its components. Thereis no theoretical basis for the claim thatderiving abinary variable from five continuous variables will somehow capture their combined effect, or a complex structure ofmultiplicative or additive interactions among them. Interactions among variablesare modeled by interaction terms, not by reducing five continuous variables to one binary variable through cutoff points and derivation rules.
Apredictor or a risk factor? Both or neither?
One contentious topic has been theability of the metabolic syndrome to predict outcomes, or more precisely, the ability of a derived variable called "metabolic syndrome status" to predict outcome status,above and beyond components of the syndrome. Discussing stroke as a possibleoutcome, one writer has summarized the issue in two questions: ADDIN EN.CITE Kurth200811111117Kurth, T.Logroscino, G.The metabolic syndrome: more than the sum of its components?StrokeStroke; a journal of cerebral circulationStrokeStroke; a journal of cerebral circulationStrokeStroke; a journal of cerebral circulation1068-9394HumansMetabolic Syndrome X/*epidemiologyPredictive Value of TestsRisk FactorsStroke/*epidemiology2008Apr1524-4628 (Electronic)18323482http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18323482 eng35 Is the metabolic syndrome a risk factor for stroke? Does metabolic syndrome status help topredict stroke?
Figure9 is a revision of Figure 2, adding stroke status as an outcome variable. The arrows pointing from V1, V2, V3, V4, and V5 tostroke statuscorrespond to prevailing theories: components of the metabolicsyndrome are causes of stroke (or risk factors, in another jargon). Notice,however,that no arrow emanates from metabolic syndrome status and points to stroke status, because aderived variable cannot be a cause of a natural variable; it can only be a cause of another derived variable. Therefore, "metabolic syndrome status", in any of its versions, is not a risk factor for stroke. Any association between this variable and any outcome is due to confounding by their common causesfor example by V1, V2, V3, V4, V5, and U (Figure 9).
Moreover, even if someone entertains a causal arrow between the two variables ADDIN EN.CITE Kurth200811111117Kurth, T.Logroscino, G.The metabolic syndrome: more than the sum of its components?StrokeStroke; a journal of cerebral circulationStrokeStroke; a journal of cerebral circulationStrokeStroke; a journal of cerebral circulation1068-9394HumansMetabolic Syndrome X/*epidemiologyPredictive Value of TestsRisk FactorsStroke/*epidemiology2008Apr1524-4628 (Electronic)18323482http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18323482 eng35 (which is wrong, in my view), metabolic syndrome statuswouldsimply be an intermediary variable on five causal pathways from natural variables to stroke status.All of its "effect" on strokeis already contained in the effects of its causes. In fact, only part of their effects is captured by the metabolic syndrome because each of the five variablesis also connected to stroke status through another causalpathway (Fig 9).
The terms "predictor" and "cause" (or "risk factor") are not synonyms, butthe syndrome fails the prediction test, too. As we realized earlier, all the information carried bya derived variable is already contained in the variables from which it was derived, and therefore, it should not predict anything above and beyond its makers, as found empirically. ADDIN EN.CITE Iribarren200777717Iribarren, C.Division of Research, Northern California Kaiser Permanente, Oakland, CA 94612, USA. Carlos.Iribarren@kp.orgThe metabolic syndrome is no better than its componentsMinerva CardioangiolMinerva cardioangiologicaMinerva CardioangiolMinerva cardioangiologicaMinerva CardioangiolMinerva cardioangiologica487-9554California/epidemiologyCoronary Artery Disease/epidemiology/etiologyHumansMetabolic Syndrome X/complications/*diagnosis/*epidemiology/mortalityPredictive Value of TestsRisk FactorsSurvival AnalysisSweden/epidemiology2007Aug0026-4725 (Print)17653024http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17653024 eng36Components of the metabolic syndromemight predict stroke statusbetter if we model interactions among them, butonly a miracle would allow a single derived binary variable to reflect those interactions.
To sum up,a derived variable called metabolic syndrome status is not a risk factor for anything, nor should it predict anything above and beyond its makers, let alone astheir substitute. Predictive information is lost atevery step of the derivation chain which begins in several continuous natural variables and ends ina single binary variable.
Circular causation
The issue of circular causationis minor, but worth explaining, since it might be used to criticize the diagrams I presented here. Causal diagrams are formally called directed acyclic graphs because a cycle ofcausation is not permissible. We may not draw a diagram in which we can make a full cycle along a causal chain, returning to the causefrom which we started. Self-causation does not exist because a future effectcannot be a cause of its cause in the past.
Although controversial, some writers raise the possibility of vicious cycles among components of the metabolic syndrome. For example, abdominal fat(insulin resistance(abdominal fat( insulin resistance, and so on. Nonetheless, the second showing of thevariables "abdominal fat" and "insulin resistance" in that chain are new variables, and therefore the sequence requires subscripts to denote time-dependent variables: abdominal fat1(insulin resistance2(abdominal fat3( insulin resistance4, and so on. The so-called circular causation is not circular at all: abdominal fat at time 1 affects insulin resistance at time 2 which affects abdominal fat at time 3, not abdominal fat at time 1. Measurements of time-dependent variables are often taken in longitudinal studies, allowingus toestimate effects in such causal chains. ADDIN EN.CITE Robins200041414117Robins, J. M.Hernan, M. A.Brumback, B.Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.Marginal structural models and causal inference in epidemiologyEpidemiologyEpidemiology (Cambridge, MassEpidemiologyEpidemiology (Cambridge, MassEpidemiologyEpidemiology (Cambridge, Mass550-60115Anti-HIV Agents/therapeutic use*CausalityConfounding Factors (Epidemiology)*Epidemiologic MethodsHIV Infections/drug therapy/mortalityHumans*Models, StatisticalRisk FactorsTime FactorsZidovudine/therapeutic use2000Sep1044-3983 (Print)10955408http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10955408 eng37
What is a syndrome?
Thus, if the metabolic syndrome is defined as multiple risk factors that are metabolically interrelated, then the syndrome certainly exists. ADDIN EN.CITE Grundy200615151517Grundy, S. M.Department of Clinical Nutrition, Center for Human Nutrition, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Y3.206, Dallas, 75390-9052, USA. scott.grundy@utsouthwestern.eduDoes the metabolic syndrome exist?Diabetes CareDiabetes careDiabetes careDiabetes care1689-92; discussion 1693-6297Atherosclerosis/etiologyBlood PressureDiabetes Mellitus, Type 2/etiologyHumansInsulin ResistanceMetabolic Syndrome X/*classification/complicationsRisk2006Jul0149-5992 (Print)16801603http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16801603 eng11
This argument is linguistically clever: the syndrome exists because it is defined as "multiple risk factors that are metabolically interrelated", which empirically exist. In other words, if something exists and I give it a name, then the name I just gave itexists, too. But the question remains: what is a syndrome?
As might be expected, online dictionaries offer numerous explanatory phrases. Most of the explanations, however, seem to share one key idea:a syndrome is more than a collection of symptoms, signs,or physiological traitsmore than "metabolically interrelated" variables. Both dictionaries andcommon medical usage require that components of a syndromewould "indicate","characterize", or"becharacteristic of" a disease, a medical condition, a particular abnormality, and the like. It is not correlated components per se that make up a syndrome, as implied in the quote above, nor their sharing of acommon effect. It is a meaningfully deeper abnormality which has caused them.
Sometimes, that deeper abnormality is a well-established cause of the syndrome(e.g., trisomy 21 behind Down syndrome; HIV infection behind AIDS). Other times the cause isa general pathological descriptor(acute myocardial ischemia behind acute coronary syndrome). In many instances no causal pathway or pathways areknown yet, but even thenwe assume that some "interesting"causal mechanism has generated the syndrome and we hope to discoverit some day. For this reason, we don't call every setofaging related medical conditions, such as dementia, osteoporosis, and atherosclerosisthe "aging syndrome".
This cardinal feature of a syndrome is lacking in the so-called metabolic syndrome. Furthermore, an articulate proponent has stated thatthe term does not commit toa particular pathogenesis" (and proposed an ambiguous causal distinction between underlying causes and exacerbating factors). ADDIN EN.CITE Grundy200615151517Grundy, S. M.Department of Clinical Nutrition, Center for Human Nutrition, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Y3.206, Dallas, 75390-9052, USA. scott.grundy@utsouthwestern.eduDoes the metabolic syndrome exist?Diabetes CareDiabetes careDiabetes careDiabetes care1689-92; discussion 1693-6297Atherosclerosis/etiologyBlood PressureDiabetes Mellitus, Type 2/etiologyHumansInsulin ResistanceMetabolic Syndrome X/*classification/complicationsRisk2006Jul0149-5992 (Print)16801603http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16801603 eng11 Theoretically, he would not dump the name even if age alone underlies the associations amongthe various components. A name is just a name,of course, but the metabolic syndrome seems to proposea new meaning for the term "medical syndrome". Neither the "metabolic syndrome" nor the "behavioral syndrome" I coined earlier fit contemporary meaning of the term.
The metabolic syndrome vis--vis the insulin resistance syndrome
The originofthe metabolic syndrome is often traced to the insulin resistance syndrome, which was postulated long ago. ADDIN EN.CITE Reaven198848484817Reaven, G. M.Department of Medicine, Stanford University Medical Center, California.Banting lecture 1988. Role of insulin resistance in human diseaseDiabetesDiabetesDiabetesDiabetesDiabetesDiabetes1595-6073712Coronary Disease/physiopathologyDiabetes Mellitus, Type 2/*physiopathologyHumansHypertension/physiopathology*Insulin Resistance1988Dec0012-1797 (Print)3056758http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=3056758 engReaven199319191917Reaven, G. M.Department of Medicine, Stanford University School of Medicine, California 94305.Role of insulin resistance in human disease (syndrome X): an expanded definitionAnnu Rev MedAnnual review of medicineAnnu Rev MedAnnual review of medicineAnnu Rev MedAnnual review of medicine121-3144Diabetes Mellitus, Type 2/physiopathologyHumansHyperinsulinism/physiopathologyHyperlipidemias/physiopathologyHypertension/physiopathologyInsulin Resistance/*physiologySyndrome19930066-4219 (Print)8476236http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8476236 eng38, 39 The idea was both clever and simple:perhaps resistance to the action of insulin is one of the determinants (causes) of several continuous physiological traits, such asglucose tolerance, blood pressure, plasma triglycerides, and HDL-cholesterol. That does not mean, of course,that every patient with unfavorable levels of these variables suffersfrom insulin resistance, but it does raise the possibility that some patients do. Unlike the metabolic syndrome, the name was not merely a reduction of natural continuous variables to a derived binary variable, nor was it a means for labeling patients. It was a scientific hypothesis to be tested, corroborated, refuted,or perhaps revised. ADDIN EN.CITE Unger200813131317Unger, R. H.Touchstone Center for Diabetes Research, University of Texas Southwestern Medical Center, and Veterans Affairs Medical Center, Dallas, Texas 75390-8854, USA. roger.unger@utsouthwestern.eduReinventing type 2 diabetes: pathogenesis, treatment, and preventionJamaJama1185-729910Diabetes Mellitus, Type 2/etiology/*prevention & control/therapyHumansHyperglycemia/physiopathology/*prevention & controlInsulin Resistance/*physiologyLipogenesis/*physiology2008Mar 121538-3598 (Electronic)18334695http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18334695 eng40 If true, we have enriched our understanding ofthe pathogenesis of several risk factors for cardiovascular disease.
In contrast, deriving the metabolic syndrome variable offers nothing scientifically newneither fresh insight into pathogenesis nora new daringhypothesis. Not surprisingly, much of the debate centers on tangential matters: In what sense does the syndrome exist?What shouldwe call it? What are the cutoff points? Whose definition will prevail?How do we promoteanother "worldwide definition? ADDIN EN.CITE Alberti200534343417Alberti, K. G.Zimmet, P.Shaw, J.Department of Endocrinology and Metabolism, St Marys Hospital, London W2 1NY, UK. George.Alberti@newcastle.ac.ukThe metabolic syndrome--a new worldwide definitionLancetLancetLancetLancetLancetLancet1059-623669491Asia, Southeastern/ethnologyCardiovascular Diseases/complicationsChina/ethnologyDiabetes Mellitus, Type 2/complicationsEthnic GroupsFemaleHumansJapan/ethnologyMaleMetabolic Syndrome X/classification/complications/*diagnosis/ethnologyRisk Factors2005Sep 24-301474-547X (Electronic)16182882http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16182882 engAlberti200623232317Alberti, K. G.Zimmet, P.Shaw, J.Department of Endocrinology and Metabolic Medicine, St Mary's Hospital, London, UK and International Diabetes Institute, Caulfield, Australia.Metabolic syndrome--a new world-wide definition. A Consensus Statement from the International Diabetes FederationDiabet MedDiabet Med469-80235Adipose Tissue/pathologyAdultConsensusDyslipidemias/complications/drug therapyFemaleHumansHypertension/complications/drug therapyInsulin ResistanceInternational CooperationMaleMetabolic Syndrome X/*diagnosis/etiology/therapyObesity/complications*Societies, Medical2006May0742-3071 (Print)16681555http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16681555 engZimmet200831313117Zimmet, P.Alberti, G.The metabolic syndrome: progress towards one definition for an epidemic of our timeNat Clin Pract Endocrinol MetabNature clinical practiceNat Clin Pract Endocrinol MetabNature clinical practiceNat Clin Pract Endocrinol MetabNature clinical practice23945Cardiovascular Diseases/epidemiologyDiabetes Mellitus, Type 2/epidemiologyDisease OutbreaksHumansMetabolic Syndrome X/*diagnosis/*epidemiology/etiologyTerminology as Topic2008May1745-8374 (Electronic)18427543http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=18427543 engZimmet200530303017Zimmet, P.Magliano, D.Matsuzawa, Y.Alberti, G.Shaw, J.International Diabetes Institute, Melbourne, Australia. pzimmet@idi.org.auThe metabolic syndrome: a global public health problem and a new definitionJ Atheroscler ThrombJournal of atherosclerosis and thrombosisJ Atheroscler ThrombJournal of atherosclerosis and thrombosisJ Atheroscler ThrombJournal of atherosclerosis and thrombosis295-300126Cardiovascular Diseases/epidemiologyCholesterol, HDL/bloodDiabetes Mellitus, Type 2/epidemiologyHumansMetabolic Syndrome X/*epidemiology*Public Health*World Health20051340-3478 (Print)16394610http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=16394610 eng21, 28, 41, 42
The merit of the derivation
What is left of the term? Are there any benefits to deriving that binary variable and deciding whether a patient "has it"?
Proponents argue that the label would motivate patientsto change risky behaviors and cause doctorsto pay greater attention to risk factor modification, certainly a reasonablehypothesis. Opponents argue that labeling would change nothing and thatother patients, who missed the labeling, would have a sense of complacency and might do less to change their risk factor profileanother reasonable hypothesis. Unfortunately, it is difficult to imagine a study that would allow us to estimate the neteffect.
I might propose two other merit-relatedquestions for which no empirical answer is possible either: First, how many of the 1,431 publicationsin 2007 that contained the words "metabolic syndrome" in their titles would have been published if the term did not exist? Second, how much less we would have known today?
I do not dare to share my guesses.
References
ADDIN EN.REFLIST 1. Federspil G, Nisoli E, Vettor R. A critical reflection on the definition of metabolic syndrome. Pharmacol Res 2006;53(6):449-56.
2. Gale EA. The myth of the metabolic syndrome. Diabetologia 2005;48(9):1679-83.
3. Goodman E. Metabolic syndrome and the mismeasure of risk. J Adolesc Health 2008;42(6):538-40.
4. Greenland P. Critical questions about the metabolic syndrome. Circulation 2005;112(24):3675-6.
5. Kahn R. The metabolic syndrome (emperor) wears no clothes. Diabetes care 2006;29:1693-6.
6. Kahn R. Metabolic syndrome: is it a syndrome? Does it matter? Circulation 2007;115(13):1806-10; discussion 11.
7. Kahn R, Buse J, Ferrannini E, Stern M. The metabolic syndrome: time for a critical appraisal: joint statement from the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care 2005;28(9):2289-304.
8. Reaven GM. The metabolic syndrome: requiescat in pace. Clinical chemistry 2005;51(6):931-8.
9. Tikhonoff V, Casiglia E. Metabolic syndrome: nothing more than a constellation? European heart journal 2007;28(7):780-1.
10. Gale EA. Should we dump the metabolic syndrome? Yes. BMJ (Clinical research ed 2008;336(7645):640.
11. Grundy SM. Does the metabolic syndrome exist? Diabetes Care 2006;29(7):1689-92; discussion 93-6.
12. Alberti KG, Zimmet PZ. Should we dump the metabolic syndrome? No. BMJ (Clinical research ed 2008;336(7645):641.
13. Pearl J. Causality: models, reasoning, and inference. Cambridge, United Kingdom: Cambridge University Press; 2000.
14. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology (Cambridge, Mass 1999;10(1):37-48.
15. Hernan MA, Hernandez-Diaz S, Robins JM. A structural approach to selection bias. Epidemiology (Cambridge, Mass 2004;15(5):615-25.
16. Hernan MA. A structural approach to observation bias. American Journal of Epidemiology 2005;161:S100.
17. Shahar E. Causal diagrams for encoding and evaluation of information bias. Journal of evaluation in clinical practice 2008 (in press).
18. Greenland S. Dose-response and trend analysis in epidemiology: alternatives to categorical analysis. Epidemiology (Cambridge, Mass 1995;6(4):356-65.
19. Atchley WR, Anderson D. Ratios and the statistical analysis of biological data. Systematic Zoology 1978;27:71-8.
20. Raubenheimer D. Problems with ratio analysis in nutritional studies. Functional Ecology 1995;9:21-9.
21. Alberti KG, Zimmet P, Shaw J. Metabolic syndrome--a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med 2006;23(5):469-80.
22. Brietzke SA. Controversy in diagnosis and management of the metabolic syndrome. The Medical clinics of North America 2007;91(6):1041-61, vii-viii.
23. Day C. Metabolic syndrome, or What you will: definitions and epidemiology. Diab Vasc Dis Res 2007;4(1):32-8.
24. Ford ES, Li C. Defining the metabolic syndrome in children and adolescents: will the real definition please stand up? The Journal of pediatrics 2008;152(2):160-4.
25. Grundy SM, Brewer HB, Jr., Cleeman JI, Smith SC, Jr., Lenfant C. Definition of metabolic syndrome: Report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation 2004;109(3):433-8.
26. Magliano DJ, Shaw JE, Zimmet PZ. How to best define the metabolic syndrome. Annals of medicine 2006;38(1):34-41.
27. Wubben DP, Adams AK. Metabolic syndrome: what's in a name? Wmj 2006;105(5):17-20.
28. Zimmet P, Magliano D, Matsuzawa Y, Alberti G, Shaw J. The metabolic syndrome: a global public health problem and a new definition. Journal of atherosclerosis and thrombosis 2005;12(6):295-300.
29. Greenland S. Basic problems in interaction assessment. Environmental health perspectives 1993;101 Suppl 4:59-66.
30. Rothman KJ, Greenland S, Walker AM. Concepts of interaction. Am J Epidemiol 1980;112(4):467-70.
31. Shahar E. Estimating causal parameters without target populations. Journal of evaluation in clinical practice 2007;13(5):814-6.
32. VanderWeele TJ, Robins JM. Four types of effect modification: a classification based on directed acyclic graphs. Epidemiology (Cambridge, Mass 2007;18(5):561-8.
33. Rothman KJ. Epidemiology: an introduction. New York: Oxford University Press; 2002.
34. Weinberg CR. Can DAGs clarify effect modification? Epidemiology (Cambridge, Mass 2007;18(5):569-72.
35. Kurth T, Logroscino G. The metabolic syndrome: more than the sum of its components? Stroke; a journal of cerebral circulation 2008;39(4):1068-9.
36. Iribarren C. The metabolic syndrome is no better than its components. Minerva cardioangiologica 2007;55(4):487-9.
37. Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology (Cambridge, Mass 2000;11(5):550-60.
38. Reaven GM. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes 1988;37(12):1595-607.
39. Reaven GM. Role of insulin resistance in human disease (syndrome X): an expanded definition. Annual review of medicine 1993;44:121-31.
40. Unger RH. Reinventing type 2 diabetes: pathogenesis, treatment, and prevention. Jama 2008;299(10):1185-7.
41. Alberti KG, Zimmet P, Shaw J. The metabolic syndrome--a new worldwide definition. Lancet 2005;366(9491):1059-62.
42. Zimmet P, Alberti G. The metabolic syndrome: progress towards one definition for an epidemic of our time. Nature clinical practice 2008;4(5):239.
Draft-1
PAGE 1
] ` C K L x y z
i
j
`9 a9 d9 e9 9 9 弳tlt`tX h CJ aJ h h CJ H*aJ h5 | CJ aJ j h CJ UaJ h CJ aJ hV h 8 CJ aJ hV h)S CJ aJ h 8 h)S 5OJ QJ h)S 5OJ QJ hH 5OJ QJ h 8 5OJ QJ
h)S 0J j h)S Uj h)S U h 8 h)S h)S h 8 h)S CJ$ aJ$ h@"c CJ$ aJ$ h 8 ! ] ^ _ ` v w x 1 C D d gd_c) gd 8 $a$ $
^ a$ B d r 9 : : : :
: oL pL uL vL jM kM O O O O P P qV rV tV uV V V ] ] ] ] ] ] >c ?c Ec Fc c c d d d d ѽݱɱݱɱݱɱݱɱݦq h), h)S 5CJ OJ QJ aJ hH OJ QJ ^J h), h)S OJ QJ ^J h), h)S 5OJ QJ ^J hH 5OJ QJ ^J j h CJ UaJ hfB hfB CJ H*aJ h5 | CJ aJ j h CJ UaJ hV h)S CJ aJ h
?O CJ aJ h CJ aJ h~a CJ aJ ' xL yL Hc Kc c c d #d $d g g xm {m ~m m m r r t t v v d gd": gd_c) d gdH d gd_c) d !d #d $d d d d e e e &e (e )e =e >e Pe Qe ue e e e e e e e e f f f 6f 8f 9f f f f f f f f f f 3g 4g 2h 3h sj tj vj wj ܽܫԠܫܽܽܔ hfB hfB CJ H*aJ h5 | CJ aJ j h-VB CJ UaJ j h?5> CJ aJ hH 6CJ aJ h 6CJ aJ hV h)S 6CJ aJ j h?5> CJ aJ h?5> CJ aJ hV h)S CJ aJ hH h)S hK< h)S 5CJ OJ QJ aJ 0wj xj j j j ck dk k k &l Fl l l 3m 4m ~m m m m m m Tn Un n n n o o o op pp p p p p p p q q nq oq q q r ͽw hY% h)S CJ H*aJ h@"c CJ aJ hV h)S 6CJ aJ hY% CJ aJ hK< hH CJ aJ h)S CJ aJ hK< h)S CJ aJ hK< h)S 5CJ OJ QJ aJ h 5CJ OJ QJ aJ hH CJ aJ j h-VB CJ aJ hV h)S CJ aJ h-VB CJ aJ +r r Ls Ms 6t 7t t t t t u u 6u >u ?u Wu Xu %v &v Pv vv wv xv w w w x \x ]x x x :y ;y y y 2z Jz Kz u h)S 5CJ OJ QJ aJ hK< h)S 5CJ OJ QJ aJ hfB hfB CJ H*aJ h5 | CJ aJ j h3M CJ UaJ h CJ aJ h3M CJ aJ hWOl CJ aJ hV h0 CJ aJ h2 CJ aJ h0 CJ aJ hY% CJ aJ hV h)S CJ aJ h_& CJ aJ , ܁ o p ׂ - . ] ^ ȃ փ ݃ Ԅ Մ ք ׄ s t @ A
d e ԵԪԵ䚏|th j hab CJ UaJ h' CJ aJ hK< hH CJ aJ h)S CJ aJ hK< h)S CJ aJ hK< h)S 5CJ OJ QJ aJ j hNck CJ aJ j hNck CJ aJ h5J CJ aJ hNck h)S CJ H*aJ hNck CJ aJ h('] CJ aJ hV h)S CJ aJ hK< hH 5CJ OJ QJ aJ & ք ׄ ؎ َ v w d gdd d gdH d gdm{ d gd": e ֎ ؎ َ V W ŏ _ r s t տ|ti^V^J^V^>j h), CJ UaJ hm{ h)S 6CJ aJ h), CJ aJ hm{ h)S CJ aJ hK< hH CJ aJ h)S CJ aJ hK< h)S CJ aJ hK< h)S 5CJ OJ QJ aJ hH hH CJ OJ QJ ^J aJ h)S OJ QJ ^J h), h)S OJ QJ ^J h), h)S 5OJ QJ ^J ht
CJ aJ hV h)S CJ aJ hab hab CJ H*aJ j hab CJ UaJ h5 | CJ aJ , - y z ^ _ & ' t u мܱ܉܁܁umm h.; CJ aJ h h)S 6CJ aJ h" CJ aJ h), CJ aJ hm{ hR 6CJ aJ hR CJ aJ h), hR CJ H*aJ hm{ hR CJ aJ h/ h/ CJ H*aJ h5 | CJ aJ j h/ CJ UaJ hm{ h)S CJ aJ hab hab CJ H*aJ j h), CJ UaJ ' ' ( ; Q R S b c h i % & ( ) * C D F G H a b d p q Žű흩흩흩퐀u hK< h)S CJ aJ hK< h)S 5CJ OJ QJ aJ hH 5CJ OJ QJ aJ hV h)S CJ H*aJ h
u CJ aJ h/ h/ CJ H*aJ h5 | CJ aJ j hV CJ UaJ hm{ h)S 6CJ aJ hr CJ aJ hV CJ aJ hm{ h)S CJ aJ h@"c CJ aJ - * J W s t u 0 L M G [ s t ~ ( 8 c i ƾƲ⪞~~~rr hm{ h)S 6CJ aJ h h)S CJ H*aJ h CJ aJ h h CJ H*aJ j h CJ UaJ h CJ aJ hv hv CJ H*aJ h5 | CJ aJ j hv CJ UaJ hv CJ aJ h` CJ aJ hm{ h)S CJ aJ hK< hH CJ aJ h)S CJ aJ , f g 6 7 O P 3 6 / 0
C
D
d gda d d gdm{ d gdd g h e f g h m ; < > ? h x u v Žű퉁qf hK< h)S CJ aJ hK< h)S 5CJ OJ QJ aJ h8V CJ aJ hRra CJ aJ hb CJ aJ hq hq CJ H*aJ j hq CJ UaJ hs# hs# CJ H*aJ h5 | CJ aJ j hs# CJ UaJ hs# CJ aJ hm{ h)S 6CJ aJ h CJ aJ hm{ h)S CJ aJ h CJ aJ $ 9 : > ? o u
*
+
8
9
S
T
V
W
& h i {
A
v hK< h)S 5CJ OJ QJ aJ h CJ aJ hm CJ aJ hN h)S 6CJ aJ hN h)S CJ H*aJ h` CJ aJ hi CJ aJ hN CJ aJ h CJ aJ hRra CJ aJ h h)S CJ H*aJ ha h)S CJ aJ hK< h` CJ aJ h)S CJ aJ ,A
B
C
D
& ' ( - 4 5 6 \ c d e j q r s d e ˷ףחףחףףףףףףׂz h? CJ aJ h CJ H*aJ h A } ŏ ٤ ܤ d gdd d gda p p p p p p p p q q
q q q #r /r or pr Yy Zy \y ]y by dy ey xy yy zy {y |y ֶ֔qcR hK< hk CJ OJ QJ ^J aJ h)S CJ OJ QJ ^J aJ #hK< h)S 5CJ OJ QJ ^J aJ hK< h)S CJ OJ QJ ^J aJ h2 CJ OJ QJ ^J aJ hab hab CJ H*aJ h5 | CJ aJ j h7= CJ UaJ h CJ aJ h7= h)S CJ H*aJ ha h)S CJ aJ j h7= CJ aJ h7= h7= CJ H*aJ h7= CJ aJ |y z z
z E F H I J K P o ق 0 6 z # $ . 4 = > E j k l m q x y { · χ Ї ч
וyy j h5 | CJ UaJ h8V CJ aJ h\[0 CJ aJ h]n CJ aJ h[ CJ aJ hn{ CJ aJ hr CJ aJ ha ha CJ aJ ha h)S CJ aJ h7= h7= CJ H*aJ h5 | CJ aJ j h7= CJ UaJ h7= CJ aJ ha h)S 6CJ aJ *
! # * . 2 3 r z ď ŏ < = ݛ ޛ ^ _ a b ܼܬyqyeyVqVeV j ha hV CJ UaJ hab hab CJ H*aJ h5 | CJ aJ j hrJ5 CJ UaJ hK< h)S CJ aJ hk 5CJ OJ QJ aJ hn{ 5CJ OJ QJ ^J aJ hK< h)S 5CJ OJ QJ aJ hDh CJ aJ hWx? CJ aJ h CJ aJ h]n CJ aJ ha h)S CJ aJ j h5 | CJ UaJ h5 | h5 | CJ H*aJ 5 [ \ B C E F M ` a k l m n u v w t u v 흒tldY ha hb CJ aJ hr CJ aJ hF CJ aJ ha ha CJ aJ hK< h CJ aJ h)S CJ aJ hK< h)S CJ aJ hK< h)S 5CJ OJ QJ aJ hab hab CJ H*aJ h5 | CJ aJ j hrJ5 CJ UaJ hWx? CJ aJ h CJ aJ hDh CJ aJ h CJ aJ ha h)S CJ aJ hrJ5 CJ aJ " v w u v 4 d W 2 r j Z gd5 | d gdd d gda 9 : < = > ? @ [ \ ] x y z ʸΒunununc j h9A h9A U h% h`l j h% h% U h`l h`l j h`l h`l Uh`l h5 | j hV UhV #hF hV 5CJ OJ QJ ^J aJ #hF h)S 5CJ OJ QJ ^J aJ hF h)S h6 CJ aJ h|R CJ aJ ha h)S CJ aJ ha hb CJ aJ h)S CJ aJ & ~ \ C ^ 7 ` + 4 - 8 9 ; 0^`0gd5 | gd5 | ; < = > @ A B C D E F G H I J K L M N O P Q R S T U V W gd`l 0^`0gd5 | W X Y Z [ ] ^ _ ` a b c d e f g h i j k l m n o p q r s t 0^`0gd5 | t u v w x z { | } ~ 0^`0gd5 | 0^`0gd5 | 0^`0gd5 | 0^`0gd5 | 0^`0gd5 | , - . B C E F H I K L N W X ^ _ ` a c d s hC 0J mH nH u
h`l 0J j h`l 0J UhC j hC U hx h`l j hx hx U hM% h`l j hM% hM% Uh`l h9A h`l
! " 0^`0gd5 | " # $ % & ' ( ) * + , . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? 0^`0gd5 | ? @ A B D E G H J K M N V W b c d e f g h i j k l m n $a$gd7 0^`0gd5 | n o p q r s 0^`0gd5 | 5 &P :p 8 ;0 / =!"#$% 22 8 &P 0:p`l ;0 = /!"#$% 22 `! / r(a| O w /h|7]=g1Dsc sA"H - NcW 1ckR1|g ǧ< 8|4w f |okjhNQEUl6fiꚬc[23V>X[^RSxcw=3cdz1C{>:wc(bX0okxDVjVzo,,oOw{7# R)"P:ػ{W&ޙ5)\
=ٳ"]"ZgjDR5^%M
_J ˈ5%O
|] / Ѱ9EQ#>3hp\hmo
țD=o EF4jqWV^!A'_iR|E]EFM#}C|?
3zA
Gv~h-Ua| F߇9{.LjK6" r_tM_Ո`F'O I+{&bDhH5)F"2N?tWT~??"3faV j pt#dZ-|g5`AV auq݊X~R#[8=xc ػJ^ ҤNG)oL&7gb1eTo&S ]XHf}h.3f 6Kl6gQ$]f8cfql`_ uȝUf1k9PgVzp Ҥ,
hF LAay<r^Aj `dq/jw"UP; ,} bEY<̝Kӂu8"j]!m>݈XDNs]+eJb+fThBamKFMӭ;Bk^Q!38-*N2C/a\ Wa^+D\.:p~/Қ(`MJb+{rQT~)-
^fZ4êV=EZne5A8
@ .< bUEY۩AFԂP%S|_iyN` w#*4)FGy,G"_.gV ]L~[|b>!i> Ab (B,+7T-<%`f9r'#E[i>yXADė!yJb+d42Ӕ':#P{Dn5icJ$ i1Pc_)/@;VJu]/)Ct(@ANynHwrJ7)Û$-LκgyrJ]2wNd{f3222 T(qx
=-WewˎǹEb}X++]rXzBԿÁ晛p/H͜NJz2nܦ݃L_?nDK/싕)myFZ{-!"l!Hx<#bzӰTk3;#[ݞgR-xF F˟pwFjܞgd;#-/~)/qЉ%ʺTOi̟u1Xæ9V3o`Y/X=;%p"v `Ul?dXal7{ePհwX
#4s#`b3pdVks%bD̝
K27|ד# ~ϲ"{ @.@Qd7o"b#"֓"
^#b K
@ U]d;rMTz^E$SW^~ƶ,l&F֓, `~=`7b jEN\4Oh͞,L lbhkV][A4m9ZQeFg\N+jrٹd:`qiȇC}g.y0yәG97Hlq 'i_"^|LM&H}oM t# o>Knzv7h'jst8B(uG+ A#DSCѫ
h7 &i.8@r~?|N?&S > wI?!; nZy̐/wRU2IDF2Rn #zr/F 1$'.x ˋ@1(Hnc6NoeF[hCV;A `~'bݍּ=v =75#T˒?10m_ &sD_@1
EoX2JDO㤸8%2Nn Q*@.8!E\1o,r(Š|Q !w+r5X _R_y%aj2G}\yL\8O [n5?ůOXr]r"pPl 9OdP@ *{A'wٝw DF|4cPgj2GbZ|^̒kT-Q3}.D\,&ɋbJub,+"D1=L!GȯEy1Xր*ؕ" :]f ])PCnr˭B9Q,nza_!|
Y7tDQ eD#`! jDpʒkYO2l" O w ( a
hm @0 ӖֶX;DDpG""a !! #iնͷ;fs ״ MtMkYM-LlΩiMk SVa.Sc$unݴd:TOѩG ֲ Z. sl `kh9[E
%Cz=*$8ԌEh0Āh=J]͢0W$ܑikRh\a}}}
ʃ#ws3>PVhMꛩ}=:F Wwqb~b1C՞L6jZ``[9 NgYF gRTK4>'i\C>46[{_ml)"DMKLkiZ x-֍jkKOs?vJz,ٶ UmN3
uMr?R2}Y n@uE ?鶢
EvZlt7;̛Ox}933Ξy}YތLu]Zg- ~vg;gہ88;v>wx;ߝmǿ/a߿
; _w^Zorߗf7vf=.q'\
HKNeNgg{ss|3SRg냄i3YjKu_qWcU.oNI|پW^ nG&qTHSex
SNfZ8}|
5}v5K g$[zSnw&ao32Sako3#)Hn4,1|jHf<#7)7g3g$
xFX7lHư5$_J%>a<:X[J-3/kT:6%bղ\@5b>Vr.Vvb| wYjv*&
VÞ\2Q9`x@~3Ubj\I3 s'A֤^zwdG蚈W,OMqňl A45Ev8b6D +N;笗;Ak\WO$+% ?NbVa!V|>?͎30/gx+MT c1GCul/ըeیp \ Ոc[
(ge%Qr0ClqB
eQzx ݃P3 `X 9ƨd1G,9GbkkR܈ȡz!,.Iױ2Q/Ѫep \ Ոc
*gaVk
ZX,c0buQí*[Q1_Ul}
O?=SO3&Ewd3;6L?f;po&Y'z5iWziz)8
Nb!^HZ~$
_^~¶l3x `I.@ '{:|v;y;pvN$hQKZ[)B
w(moXKJb+0n,#.@ԻU
R^Et!W@q T3.q>@u璷1no܇V(.$ p
爗"_:I t-2ڝnws!o;KNH 4ՎWVjA+XMqtTs15e+%
iW $i.Gr^{?>|J?$Нc
> A?"۩Ҥh,:Zmk Q(zԙs¼^6ҋpz Aϛ` 8KD'Q:\NO~g{5ϣv)YV^Z9}sm0 Р4)# !:КŻX)V>z >~H&>+{g2S0ƃx<91ZkVg-0j[[xՂByG+u!oݬ;=6v-WMjd˟XE3^ϗCY+_
D/HE넸:)nN3U""f cik[Yk-%eQ @yr8$_ "W
%?'gҤgMy 5uS; $ 7\kQ0KGnw4N,' x_eO>@6 %V~1G">
ѨSMߑXc(3Z1H2eȐDyQ8Y#+bRĂY'Z1(@}+Wg{wM 3\?aUUǓx.$7jgҹ*8k{+-%i?덭CF5Ho;4کE.I1za
(g!C3'}F>GG3ٵ=3יQc?[k5D4L =53ƤNJF ^QFgX7^[jagCKK<ǘP5eL]] MOWFRFf@@ӿCՊ)P6$[ΈM <6#y?ilXܬBZԉSL'&e'MgexЈ0@4.`зqG|0aCF$C`lh="/뵯znbRzjqiI)' N <_U+ P>wJJOO :?&9czrrVrZjRJ?u?cb|JVLʔ$ij a̤Lw<]
Ź ڍ{wu/^$7.ZmEjQ]Dq/O2$,g;ؘ kUs
kP.:7X4:{DX/&{$m^m#;
kG~=>k6A
|#{| P=R_G:bnHg7# kG"fĻ ?h5KϿ(zXbJ1W$Z;0 . %.ςQ*ΉqSJI2q s
Q(s[Ks_ ρEr|6{^sl:4y\^71"*e*j
_}.>ꜸGDXVEOU$c*]@쎠=rm F}/Z,ryBD(>XX 'E,d984y|ES}=i`o+E__5 ۾b}bT+ouSu!W&Vb+
my Dlrcp ~ÚΈ~#{`
M 7"uWM::{Y4u/<8iq[.nvKE#D4 uS" c(b[&l̑nh; m]b>->e]*>%bۢf}{y.Q#NMAcwb[KbUEN_USG@|:1Gwz}Nt]@gvs B9g\sJ)㰏˃Nwap9$ q؞&=\
7ASuOh%a:U'aq (Ca1`Xot}7bEo:bn<M`_߆aPiy0L慕a@ Eai[x'0c`{z~ ,zfwx8
2{^ه 7cxُ 3`0!f/"x 5_+jk_[uc7ɣf*RV]8T))䗘,a1a-4?ao|sbf~K8 &rV4-泃{|jpk6bZ Lk5[dm`kw.v~PY?ޒd8ϒW / :]f#r=+^e{[X5ͰfV6eevV&Jgu4ƬiG1
<>mV,fOYlͺ *5׳7ml1sY7(k`fWsN._,m|%M,And` _nf+.GvLjk>M YoWYY~3jH6pME<7ux[ɣf:KHS)Ul4g/|)9
2cYّ=iVu;&gKYB(b*> b &ҧ+tXJw@̥??Rm͠wXio+>h,k]eeңV6lͤIRΧ}4I.3jHߔ/bG:O۫6P *=ڱ=lJ`j";f0~E[Y}=m l/lkcO`xVzBՆ}nak-T? UN PiަvP6
/wzЉ2v26AZdOw[8c"];AFXzB-+>&/
:5 Ҷb/џc_ȍ6/Y6;?mSϱglOGtrNN.aM:çy[,^?a5IO)꘩V3F`9 oY,!< 9nn&WUXJ:Ťx'fQq2E 3t#b*Y/&!HB$*1ZH$`'CdJV[Ydd'M_"Yr3Y)woArUV*yPե T MϪLoHF|'7WX{=:ulo/m{GQvKZu>SNj'j=ɗZCfYdL#5Z ukyH=D @~IL
g"l1, 7 /ԥxtFXAΚoG] YJfUdYƛh7363GQ̦b_Jjn66f |9k3ɽ{ zSz#
W슳U:eg .9`'1GJ9l?u_ }/ ?wL`ÿt\ʿ=|;&g NLw{~K՝'$y@HM~GȽ:Q~0C@̋up ~[7E鲎ƲHsn}ȭ+ov&n8CG y;h֓w"m($=M 5_[ouU;O5=?_/U]ɪZ{Dt U ='m 4-R7!=ߩx>)j"A# 䃃z:ש}*WQU`%xrė!/*OǍr1AgD@߫[tl$O43K<
A&biMDJ'KXzAΒzl@GVxh-떲n! 2Z7wf; A/0Q
%gDZ%F<_*]P%_鿩XG?vW}YuA>Z0?JSt(]^ו*,`-ћ7*gkaU *+SO5TK`! & : aL =Pf% $ O w dž pM3Ej9.Vا2X4sj1
%Slq4muC@TwCL5g|os\GQT%ZxYlAgZulbwUQ,r}np(oOsXU+ET٢ 5{*_dK¸AYj @
bzS++s4 2^Q+KYVEjf XFfjGa Fl 9@kh
Ĝ;MM1X acc#^O /Z~KccGW-+ʐXg+vhy^܋P=TyN&*j=gKS9-Vk簙) h甠J6'~ j6{ʨ_kScE f+951.6Rݠ$* |@X7^m
G Qɉ( m%ZƷx[Ju3 M֩G-E6Z)!i.GGTl+2pFΌęuD';b )13b NG3-HIMNLۥ>)a&e~f4ԴX fޝ w03玅~nKSC31#1-.1vGd˙r4 ]8 z+qO2?b Lp8SR#c .W5ݕ<ӑMv$NsƸ b ScOwB8Wʵ5R(Ξ+.+ztN7ր;'k&3oܗ =fMcubB<g8G)7᤺~~z5#}ptMFZ\o _#q=SCo`\#i|o ƭ!]9H F*45mx~5׃kqs {x7!?.ۘ1Be*w6fw# )<֞nǥ6_" /*P"VF3`^BSt /A 쾠7r=y)xvUVӴг-cŴ.B |VF4U V.&ɵ4{ޑ$m1W-M
t666n@-j;OѪV
¸ uZ%]UZ5Ђl)y! r&~0 E, ]#0G9aabj܈ABpEDx
7Эb NF=ͨA<ը
(mA+ 1ݨ-PÌjj3l'DyOTҽ~,.| v ]Tw]C蛢ԁz 5Iێ4=f}ǵ-6_W"k[Rj>b~!Yø3ȷl9XFWI{ԳH [B} JQs-%gX:)c+H)[Hl/@&l Y3}A$ G@C,d㨑$ ֬N:Z.28"Zb %p^E v>by0G? ~?\?
n]r ,Gbz8ǎe4S^\! X
A~Ӱ&IwdhKw-;qO _#Y(^j%$>ۇca<~/ϵa߃Xr/j.>6 ev~۫1ygdrpv)b%ȟd"v Y&IwdqDhߐx=]dy[;@i_]SCw!P>yK{loG|VA jIj$F{L^".m1' -!$L{ܯ-#iГDw>ݴRUu::JIZB
brB-"Ej!9 'j G B )T"/I bZHYeu=X V%J2Bu9Y giRWa5dIR;ۚ[=
]WYXւXIJ
rZ,'D:9)^#8ȃx *rH!E&9 =]dY v #<)9b _` $sa&ɍx
d2i:*:N-xôԂK _
-} cvJ5 j>
- ka@l:rIFm1M>]Ck
朂@$il.Wd#b,hf`̡H.J` I6$:r%_~ Wn:^!K@9Bn&jf6
$ W rw \0 saIr<.k-el3Ygcjϳxxg
ӑ_sR.5I#2ZdYH?Rtg)b1yL2/ "bsٿ 0!\DFD<6Z &X/ 7 ;-;X`0? ȏ@$ihIb1|6E>F\E %#7<> =@ U+x8Tu.v%qx+b+oԷM`#
[ZWs"V>&(a`! B/Tq'q?% O w dž T3}v\7)"OEQhEf(mL$CLEVwH3M(bƤ|f n&e:V?Մz(a 0syæghݏ^us=ܚbikrFs 1\QF$c _vd&T# p
~|GdRs8s5*d 6G'y=#+ȭ98b1j頻]%ިa6{SV3bnT 4^ ֯˨-),$->*)!oT5$F]ۼ!J&dt+ 5TlhhnvJPLtj66LqDi${,[16-Ҟl%$FR S 2dolNxm0ކuw Gz{y`}lU6^L]ofyZ
VN/+ r)l HY%c
]$s z;摨
3ia jRopXwk
tV5P eK;JCƺZ]LfhdcMQĄ!6La ƞc+t r!W r"w j2jFG0s !<Nn@A jaL֫]z%WЎ~zbm EmjͧezPT~**` QMw+]KQA5Iێw|s(> b1U?2ߐjVKY%d-; ߰`!-$lkeت@E|gT* lcf
cvf*vdGI: VH2XbdM L~]\,Elh5[F2^
!V` y ~2 㹖`}|x ?eû t\-WXTK,r|W
,ŬRJ@+ ˚$-ߑQmUGƥ^a=͂<yk0/g 1:Ho B~6|yu b f
uVd
52+V|k
y?Ê4YKX] !˚$-ߑGL2OUt(*K}J=G
rRj))UK Bρ)Q$E j yNm"Klk2u5ldU
UW3qZE櫲&Ik3/Y4c=z#A˱zփ:p
v KZI
z ~1:sa_
bo,fb(zk "3#PݨI nZBof$YBhf`ͣI>I
fMN
"zr#_! s DBoe |M zWS
SD;!\8;. ΎXC#MY
XVJY}f2/t]Y;3kwd ގݥ{n'7/z `8 h N$iipY|I ,I: n_1<F> ȧ"ԓ* &e A9Dd8BGI!O\y B#k}Ek \UtSwMo:#{y9y)}<ȹw 1_" vmmuqDx6I:א|5IK|%Ot
gU$<Ǯ%x5IZ.$AI$ &Ij^3L'|{-״&5Mk &v]ІYMS-jzivNpشE10=1Baڨ=LGNM뮏'aX~ Ԃ4b'
-R-1q>b_P~Gڽ`
a
E. A }9{'i"f֎K .a}}3]ϼ5 `Q;Mkw9*kAy]9
P*5=Pi5Z/`Wi9&VIפKgJiߢwц\54F[ /jz&9Z 'y;1a {eȕ"g /vRPaئ&ȝMu)(w6m+Lu
8X u
G\Q } b@_> E".6!>b)uBW*?K\e /w
W GaL#oҝu4]Wz\R gʽOQM%r'ʯ`;؊f/7f#ʏi@y` K>4K~Olz^.bzVflIO zR.@~ ?.cM(M. ZAnߑ{m:;-Jz[*X/VtT Z-#*tHeAJ~P |E{bڭ.M;E"橏i@sb&6 m3;`jr&)y>dQ˙_`chw:,8 հ }ɪ`;ƶb܌Fvm@GEe\*jQ&GaXvkf 5kcci2iľ'l% gz.TրUW\VP>˦=l9d+SA7
ȅ=
)MFM:j@
Hx5kzYLeG\\>5@ݘ n kxOc6ߧ<@A {}|w
CnE"5Cqm`#mx3on-ь_mFꂠ#_:MȇWd.Եdn[_EOc(( rj 7
q 1*DkZEKhcZFp3DcPE9(%U@)"O=j9X
D<P.LM&ߑv9^M |g?Xu)enWOq]=ɛ'x55
d䒸M%rRy x 5W* t]T[Ag :n*y{x/ Bɤ;2@%G-F1)jΉ7.u^ QߊA;1 SD_vOU'# .Q Έ)PZu rS'@N4Q
zGhjw?8Ws@Pː_"H-B}
\)xYk)bM['`#؇4YO=J)b =
`Nj&Z b9$ҍb1,^,ĚډckZLj|3%%lr|QI.ynٶ8M{ !]>Ir˙MΠ0ENΠ#P; C8,E+* # .FȑIEXByeǑM `%X5r&#/9!o`!) Zmnu`Lq% O w dž pd;`(.}* Od p˥4a%KEqE\K1Ӵ
DD\XT "mb~>T3}vwpELVD+ fb1Tj.3{j|Ӥ( r父pMϢ aRFc
&I{t5M֍2'b@kX&UD2[O:Z
X֥IYD+hv"6A/A9+]+ =; M:kswtg ?rOc[{ֶzP:"HQz%:+w{ΣF/\? P(T'#uP~*lJr3 M H9On$wu} ih ?k524<_ v8&% k1{,lJ|TRB%P5$AWC0^Gʄƴ&l ?Eц 锐 IN{@;=*6⌲;"Dڣ 1䘄Iv=1!1ޞ4!2E?.)W ã#3 QkDC
ە } a]P<+*
3rXkx~?9_^w~wzq'%L e 2t=2Hq\C))ut3w2?cJUzr xXg$3LbOt&Sbq':bxg5R(v#s e'WN ?:רޯH_\ m1f=k# O% gl8CZpuֻ=odžnkc=|ٸ4tܾGƋ
ܸG{rL-KC=rHun#{s=ٍ{Ks \{H+##4 qlTCC,_cc
_ixr"B >lY+ g !H ,.
Y7 \Q X[lMp\9/fe +E"?BV
a |.?N/eټ \WtnJsw_ O$+Qi~ FxUu&많2Ca;jPJ.r]-e[^bX#N-ckPJ`j9
)ĞD D6 <!+?zW_}!Фs7&'{
U%u&.G/amհjPJ.rvVʚi% h]bX fʘ5\`f-|Z
vP^fd/K٧G`b;e9{O^AJM^c[e5@פNK2}x}[WjJMϘu?zt=_[-r/&5
R|!*a_F \KFդfl Z~JY"!Əc K)tA}B[O^Qd'
EȃXȶ^"S t"Yv'E+IAs
϶,k)ďYσsy5Zsy>( l]NOO*z{D-No E@Ql E _"[ C,9qEko3h :?esm2ϱpv1bEȟy~ mȖσuM:?ͦ
~GLNFGȻ!CT$]셿6ކ uj)yR" r.KH&AB. 0<@,B]D$L}<.%4I'2(!S)hS 4C's$4Wfr=X V r%)+9i\F)?,NU\C rH"d{|w2O&`.f#6S~B^~2] i+
dfChDRPD19dc{ 48 M:?-ȶtI4Xrm'9l+{#$z2Q bkKpg+o/{|nE tĦ"75P;$D8xbqFqDo'434+ˊ^Huej6Vt[-Fw^'g1PC_^]e(]*q_La Kr0"Zu{ r-+Q.x ?Y4R̢y t;Rl-i ғ. `"7c XB,Ke LgZb Xm GMkI-q`)e-G. 5Pd` 6" ܌6
&e"/?*˱`! fA;_ *X
7 O a c [ V x͚ |E'Ɦj% HH& 9"w84$I \\TP<@E.V.QD tMޯA{>w}UOzf2LX~=X|˾Thw~-'rGӯ jmyX.f$Y5p_EX,(`D:KC 0(@. _6GE tK wLȅK|?8O"KBBv (D.$OD!r!"(D.$
ȅ2ՓlBltb6lv2-yخfm+hDԚU,NeHb`9X
X\,GN wv's">=1N9f
ƒדG y]^m
ذT^1k} |Ιc~O&աm8;J8 _s {w _>_9Q?3,KUUW5k=ȽeOʧ=ڕJGs} V= Η,37r\ܳZ+}Uޣ@=Ur۳o} zh^%סb|i ]HT] 5# ZT+. Y kSr!b5}NiYk$~tkUD\#CukaU㽋ҿ7\_N@XD %%9` Q\Bl"JKȏD r V,Q\Bމ/ȥ`Q\Jl JKD)r) ( H!JK@>Q\J!JKw31<\>f#V"$o z+WӉ+tk\a|[x ߵrBP< ~̙0?LB>c f2
LùӬ$דO~/SБ D 9` Ml"ɏD4r4KC@" "sr| @Cy" "" r H!B!4OC@ r|7 C .G ǐQ`: CN d]l6
MA"jMd '{Ʋl${X wLme4 ;//8yA4߀D<"IMpDh&?-@+z})N1߉ 8`1 y>yGGOl"#'?.EE@<!ȗ
Dr~p(B.""ω"ہ<
EE1#!T Q\L&GD1r1y;,bF-@SZщrH5
^KN|I+t0 y j_* 00<8v
qL9$דc ~2A| <#ʐWMDrp(C.#73K!w22L0eKrrݒ8FO"NvrZ 6NhDԚ:N2F7:VF>.udΑU,S r{Tܥ6+țTc6&rJuhד?+^r/xy: K廌$|;(>b%iFr$0,]N`]e,YG KB ZLLfr#ڥ8~TL`E;$&1iɣr ޢ+Gժ˅DATjA GG']=(7Du'f999A w}L9[2mnM`*
X
u_y^O ETX q=ֲȰ O Le_3UcgY"1 e)af,)V¾grv-ϰ)lهl!; c`7 ߎVǞ`דG_as 6Rb**lPtnUX?5Uj:YMc7d
j&AuPSss16TfV {̨Lm,v0 &`?A1vs2W}}<*E S c=e+柪b*S]QTRc_n ?XY?Rɍ_ Y=D_j^zC Se :|c-MޚY uL[(됞lg[[맭tO?I.+Vmy9l1hSfE&0xs-oaQ1hMo~iKonH>d f(3b|) dU|y?57?f]f y{Ww}T_ ɣ̍Wmq nOpN|˝@>m,r;NTwӄ+;V(w#p0~7wE5vtnQسD_7@=
E7R^#bPFTrCb4~'ʹ^/ ?]
OTT
P)χ*CYjVȧ;*Y=GX'Tsj6U: U]
Uک Th uKWJ
Di6@#4ys}O S:%0D'
yE` :cNyJ}yCHE? Џ8g"^E~ROq.юms.&ũa::S 'ohf\6;5̾8=3yf=,mfdo0/k=>l;L&~3MOwe ضې{V6tI^7s7Nn}Cvwu
Y{=#`2Rwˡ`0 >8is`y;G 뮓 A5Y]#]UiR+E<1PO s7ZWGw2uUMSF_PU ~Iu Wu#z(wu6A7Uه8×Te TOz9럀ǻ|w=/vgj7~Χi|{S< 'l 2PbX f[ 5>]ۻxh}7sW <}'
_C} ݀yoדG_? <̋Rx[/5 <Ex]?ۂ揼y77xS(O4E<, yS: Ɨ`R_'ʿ" &F 3OHoYofO< !Ԣ0v
\mN4g>y5j0$RLHe yƇ2Z O;3F<a&LOדoqEk:+ҍ{8wxQOF{#. kp sz
^!3z8gSz8A!=]"
ndzحw}mzت?[~p E? }*>^O EcVhN="QuPMWt5yAlσs5 !OZ+? H2>S te2ܴ~^ Fw%pW+pA$?ţ'B0p'
D y K y K @ m a i l t o : S h a h a r @ e m a i l . a r i z o n a . e d u z @ z N o r m a l ( V V V V *$1$ ]V ^V ` + B* CJ OJ PJ QJ ^J _H aJ mH sH tH ^ @!2^ H e a d i n g 1 @&
& F
& F 5CJ OJ PJ QJ \^J aJ T !2T H e a d i n g 2 @&
& F
& F 56CJ \]aJ N !2N H e a d i n g 3 @&
& F
& F 5CJ \aJ T !2T H e a d i n g 4 @&
& F
& F 56CJ \]aJ N !2N H e a d i n g 5 @&
& F
& F 5CJ \aJ N !2N H e a d i n g 6 @&
& F
& F 5CJ \aJ D A@ D D e f a u l t P a r a g r a p h F o n t V i V T a b l e N o r m a l :V 4 4
l a ( k ( N o L i s t < < E n d n o t e C h a r a c t e r s > > F o o t n o t e C h a r a c t e r s 6 U@6 H y p e r l i n k B* ph >*N O 2N H e a d i n g
$ CJ OJ PJ QJ ^J aJ N B@ 2N B o d y T e x t " ] ^ ` $ / 1B$ L i s t D " RD C a p t i o n
x x $ 6CJ ]aJ * b* I n d e x $ ^ 2^ H o r i z o n t a l L i n e " $d %d &d 'd CJ F % F E n v e l o p e R e t u r n
6 8 18 T a b l e C o n t e n t s 6 @ 6 F o o t e r
(% $ 6 @ 6 H e a d e r
(% $ h h
Q u o t a t i o n s : 77 V $d%d&d'd]7^7` N N
L i s t C o n t e n t s V 7 ]V ^7` L L L i s t H e a d i n g V ]V ^ ` . )@ . 7 P a g e N u m b e r
s
< s 2 72 s ] ^ _ ` v w x 1 C D N W t $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ $ ] ^ _ ` v w x 1 C D xD yD H[ K[ [ [ \ #\ $\ _ _ xe {e ~e e e j j l l n n x x y y | | | | ؆ ن Ż ƻ v w f g 6 7 O P 3 6 / 0 C D T W " " " " " F F H H I I oP rP aS bS Z [ c c De Ee Ye Ze 0g 3g _q bq cq dq zq {q Jx Lx y y >| A| ~ ~ } Ň ٜ ܜ ڸ v w u v ټ ۼ ӽ 4 d W 2 r j Z ~ \ C ^ 7 ` + 4 - 8 9 ; < = > @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z [ ] ^ _ ` a b c d e f g h i j k l m n o p q r s t u v w x z { | } ~
! " # $ % & ' ( ) * + , . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B D E G H J K M N V W b c d e f g h i j k l m n o p q t 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 @ 0 0 0 @ 0 0 0 @ 0 0 0 @ 0 0 0 @ 0 @ 0 @ 0 @ 0 0 0 # 0 00 00 00 00 00 00 00 00
00 00 00 0 D P\ xD yD H[ _ _ xe n n x ن Ż ƻ v w 6 7 D T " " F I I oP rP Z [ c 0g 3g _q bq {q Jx Ň ٜ ܜ t 0 0
\ 0 0 0 0 00 0 0 00
00 0 0 00
+ 00 0 0 0 0 ' 0 0 % 0 0 0 0
0 0 0 0 00 # 00 00
1 00 0 0 00
p1 00 0 0 00 `d 00 00 00
t1 00 0 0 00
ȼ1 00 l 00 0 0 0 0
0 0 0 0 0 0
T^ 0 0 0 0 0&0 0 0 0 0 0 0#0 $ U^ 0#0 00 0&0 ' U^ 0&0 00 00 ] 00 p .
0,0 0
0 0 ! ! ! ! ! ! ! ! ! $ 9 d wj r e A
* X Pa p |y
s Ym ; W t " ? n s
r K y `1 d1 2 oD uD jE G G H qN tN N U U U >[ E[ 2` sb vb Jr x x d s » տ R b h t L e g ; >
+ y$ < < < F F ]J O O S LY OY f\ c c oj Yq \q r Ex Hx < ݓ ^ a m Ÿ Ը 9 s XQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQ $ ! r s 2 $ / r(a| q2 2 $ p}:4X K; 2 $ jDpʒkYO2 F 2 $ qY۪GG3v 5 tR 2 $ & : aL =Pf% a 2 $ B/Tq'q? " `m 2 $ 尡F !Hi y 2 $ Zmnu`Lq 1 E 2 $ fA;_ *X v @
r (
<
j
# A <
k
# A <
l
# A <
m
# A <
n
# A <
o
# A <
p
# A <
q
# A <
r
# A B
S ? > [ x , s j 52 t k 2 t l 4 -" t m 93 7! t n z3 t o .4 c! t p 4 ! t q l4 ! t r 4 |# t x u l n i 9 l - n 6 4 u l n i 8 l - n 6 3 u l n i 7 u l n i 6 u l n i 1 3 l - n 6 6 u l n i 1 2 l - n 6 5 u l n i 1 1 u l n i 1 0 u l n i 1 7 l - n 6 8 u l n i 1 6 l - n 6 7 u l n i 1 5 u l n i 1 4 u l n i 2 1 l - n 6 1 0 u l n i 2 0 l - n 6 9 u l n i 1 9 u l n i 1 8 u l n i 2 5 l - n 6 1 2 u l n i 2 4 l - n 6 1 1 u l n i 2 3 u l n i 2 2 u l n i 2 9 l - n 6 1 4 u l n i 2 8 l - n 6 1 3 u l n i 2 7 u l n i 2 6 u l n i 3 3 l - n 6 1 6 u l n i 3 2 l - n 6 1 5 u l n i 3 1 u l n i 3 0 u l n i 3 7 l - n 6 1 8 u l n i 3 6 l - n 6 1 7 u l n i 3 5 u l n i 3 4 u l n i 4 1 l - n 6 2 0 u l n i 4 0 l - n 6 1 9 u l n i 3 9 u l n i 3 8 u l n i 4 5 l - n 6 2 2 u l n i 4 4 l - n 6 2 1 u l n i 4 3 u l n i 4 2 u l n i 4 9 l - n 6 2 4 u l n i 4 8 l - n 6 2 3 u l n i 4 7 u l n i 4 6 l - n 6 2 5 u l n i 5 1 u l n i 5 0 u l n i 5 5 l - n 6 2 7 u l n i 5 4 l - n 6 2 6 u l n i 5 3 u l n i 5 2 l - n 6 2 8 u l n i 5 7 u l n i 5 6 l - n 6 2 9 u l n i 5 9 u l n i 5 8 l - n 6 3 0 u l n i 6 1 u l n i 6 0 l - n 6 3 1 u l n i 6 3 u l n i 6 2 l - n 6 3 2 u l n i 6 5 u l n i 6 4 l - n 6 3 3 u l n i 6 7 u l n i 6 6 l - n 6 3 4 u l n i 6 9 u l n i 6 8 u l n i 7 3 l - n 6 3 6 u l n i 7 2 l - n 6 3 5 u l n i 7 1 u l n i 7 0 l - n 6 4 0 u l n i 7 8 l - n 6 3 9 u l n i 7 7 l - n 6 3 8 u l n i 7 6 l - n 6 3 7 u l n i 7 5 u l n i 7 4 u l n i 8 1 u l n i 8 0 u l n i 7 9 l - n 6 4 1 u l n i 8 3 u l n i 8 2 l - n 6 4 2 u l n i 8 5 u l n i 8 4 u l n i 8 9 l - n 6 4 4 u l n i 8 8 l - n 6 4 3 u l n i 8 7 u l n i 8 6 u l n i 9 5 u l n i 9 4 l - n 6 4 7 u l n i 9 3 l - n 6 4 6 u l n i 9 2 l - n 6 4 5 u l n i 9 1 u l n i 9 0 u l n i 9 9 l - n 6 4 9 u l n i 9 8 l - n 6 4 8 u l n i 9 7 u l n i 9 6 l - n 6 5 0 u l n i 1 0 1 u l n i 1 0 0 u l n i 1 0 5 l - n 6 5 2 u l n i 1 0 4 l - n 6 5 1 u l n i 1 0 3 u l n i 1 0 2 l - n 6 5 3 u l n i 1 0 7 u l n i 1 0 6 l 5 l 1 i v r i g 4 a j e s h 3 0 e s h 3 l - n 6 5 5 u l n i 1 1 0 l - n 6 5 4 u l n i 1 0 9 u s y - u l n i 1 1 1 u s y - 2 u s y - 1 u s y - 0 k 0 c z 2 k 0 c z 1 u l n i 1 2 1 l - n 6 6 0 u l n i 1 2 0 l - n 6 5 9 u l n i 1 1 9 u l n i 1 1 8 l - n 6 7 2 u l n i 1 3 4 l - n 6 7 1 u l n i 1 3 3 l - n 6 7 0 u l n i 1 3 2 l - n 6 6 9 u l n i 1 3 1 l - n 6 6 8 u l n i 1 3 0 l - n 6 6 7 u l n i 1 2 9 l - n 6 6 6 u l n i 1 2 8 l - n 6 6 5 u l n i 1 2 7 l - n 6 6 4 u l n i 1 2 6 l - n 6 6 3 u l n i 1 2 5 l - n 6 6 2 u l n i 1 2 4 l - n 6 6 1 u l n i 1 2 3 u l n i 1 2 2 u l n i 1 3 5 u l n i 1 3 6 u l n i 1 3 7 u l n i 1 3 8 u l n i 1 3 9 u l n i 1 4 3 l - n 6 7 4 u l n i 1 4 2 l - n 6 7 3 u l n i 1 4 1 u l n i 1 4 0 l - n 6 7 6 u l n i 1 4 6 l - n 6 7 5 u l n i 1 4 5 u l n i 1 4 4 l - n 6 7 8 u l n i 1 4 9 l - n 6 7 7 u l n i 1 4 8 h 0 % 3 A 5 h 0 % 3 A 5 0 u l n i 1 5 0 h 0 % 3 A 5 1 u l n i 1 5 6 u l n i 1 5 5 l - n 6 8 1 u l n i 1 5 4 l - n 6 8 0 u l n i 1 5 3 l - n 6 7 9 u l n i 1 5 2 u l n i 1 5 1 u l n i 1 6 0 l - n 6 8 3 u l n i 1 5 9 l - n 6 8 2 u l n i 1 5 8 u l n i 1 5 7 l - n 6 8 6 u l n i 1 6 4 l - n 6 8 5 u l n i 1 6 3 l - n 6 8 4 u l n i 1 6 2 u l n i 1 6 1 u l n i 1 6 5 p 0 l i u l n i 1 6 9 l - n 6 8 8 u l n i 1 6 8 l - n 6 8 7 u l n i 1 6 7 u l n i 1 6 6 l - n 6 8 9 u l n i 1 7 1 u l n i 1 7 0 u l n i 1 7 5 l - n 6 9 1 u l n i 1 7 4 l - n 6 9 0 u l n i 1 7 3 u l n i 1 7 2 l - n 6 9 2 u l n i 1 7 7 u l n i 1 7 6 u l n i 1 8 1 l - n 6 9 4 u l n i 1 8 0 l - n 6 9 3 u l n i 1 7 9 u l n i 1 7 8 l - n 6 9 5 u l n i 1 8 3 u l n i 1 8 2 u l n i 1 8 7 l - n 6 9 7 u l n i 1 8 6 l - n 6 9 6 u l n i 1 8 5 u l n i 1 8 4 u l n i 1 9 3 u l n i 1 9 2 l - n 6 1 0 0 u l n i 1 9 1 l - n 6 9 9 u l n i 1 9 0 l - n 6 9 8 u l n i 1 8 9 u l n i 1 8 8 u l n i 1 9 7 l - n 6 1 0 2 u l n i 1 9 6 l - n 6 1 0 1 u l n i 1 9 5 u l n i 1 9 4 l - n 6 1 0 6 u l n i 2 0 2 l - n 6 1 0 5 u l n i 2 0 1 l - n 6 1 0 4 u l n i 2 0 0 l - n 6 1 0 3 u l n i 1 9 9 u l n i 1 9 8 l - n 6 1 0 7 u l n i 2 0 4 u l n i 2 0 3 u l n i 2 1 2 u l n i 2 1 1 u l n i 2 0 5 n e s t 2 u l n i 2 1 4 u l n i 2 1 3 l - n 6 1 0 9 u l n i 2 1 6 u l n i 2 1 5 f x u 4 u l n i 2 1 8 u l n i 2 1 7 f x u 4 0 u l n i 2 2 0 u l n i 2 1 9 j z e l u l n i 2 2 2 u l n i 2 2 1 r q _ x 0 r q _ x l - n 6 1 1 3 u l n i 2 2 6 l - n 6 1 1 2 u l n i 2 2 5 l - n 6 1 1 1 u l n i 2 2 4 u l n i 2 2 3 n d y 5 c n 7 m 1 c n 7 m 0 c n 7 m u l n i 2 3 2 l - n 6 1 1 5 u l n i 2 3 1 l - n 6 1 1 4 u l n i 2 3 0 u l n i 2 2 9 l - n 6 1 1 7 u l n i 2 3 5 l - n 6 1 1 6 u l n i 2 3 4 u l n i 2 3 3 u l n i 2 3 6 u l n i 2 4 0 l - n 6 1 1 9 u l n i 2 3 9 l - n 6 1 1 8 u l n i 2 3 8 u l n i 2 3 7 l - n 6 1 2 4 u l n i 2 4 6 l - n 6 1 2 3 u l n i 2 4 5 l - n 6 1 2 2 u l n i 2 4 4 l - n 6 1 2 1 u l n i 2 4 3 l - n 6 1 2 0 u l n i 2 4 2 u l n i 2 4 1 u l n i 2 4 7 u l n i 2 4 9 u l n i 2 4 8 l - n 6 1 2 6 u l n i 2 5 2 l - n 6 1 2 5 u l n i 2 5 1 u l n i 2 5 0 c l h f l - n 6 1 3 0 u l n i 2 5 8 n _ q t u l n i 2 5 7 h % 2 5 3 A x d u l n i 2 5 6 u l n i 2 5 5 u l n i 2 6 0 u l n i 2 5 9 t v 8 o u l n i 2 6 8 l - n 6 1 1 0 u l n i 2 6 7 i y v _ 0 u l n i 2 6 6 i y v _ u l n i 2 6 5 j p 7 m u l n i 2 6 4 s q 2 h u l n i 2 6 3 c c g s u l n i 2 6 2 u l n i 2 6 1 u l n i 2 6 9 u l n i 2 7 0 u l n i 2 7 2 u l n i 2 7 1 u l n i 2 7 7 u l n i 2 7 6 u l n i 2 7 3 u l n i 2 7 9 u l n i 2 7 8 u 5 t w u l n i 2 8 4 h 0 j 2 u l n i 2 8 3 z 3 6 g u l n i 2 8 2 u f n r u l n i 2 8 1 u l n i 2 8 0 u l n i 2 8 6 u l n i 2 8 5 a e % 2 5 3 A p u l n i 2 8 9 p x x y u l n i 2 8 8 u l n i 2 8 7 u l n i 2 9 1 u l n i 2 9 0 k s h b u l n i 2 9 7 t y _ v u l n i 2 9 6 s n . y u l n i 2 9 5 l - n 6 1 2 6 1 u l n i 2 9 4 l - n 6 1 2 5 1 u l n i 2 9 3 u l n i 2 9 2 u l n i 2 9 9 u l n i 2 9 8 a 4 j a u l n i 3 0 4 q r k 9 u l n i 3 0 3 o h i 7 0 u l n i 3 0 2 o h i 7 u l n i 3 0 1 u l n i 3 0 0 u l n i 3 0 5 t 0 r w i 7 e 1 i 7 e 1 0 u l n i 3 0 8 u l n i 3 0 7 a s 3 r u l n i 3 1 4 f % 2 5 3 A 6 j 0 u l n i 3 1 3 f % 2 5 3 A 6 j u l n i 3 1 2 c 1 _ h u l n i 3 1 1 e m d 0 u l n i 3 1 0 u l n i 3 0 9 b b 5 f u l n i 3 1 5 u l n i 3 1 7 u l n i 3 1 6 u l n i 3 2 2 u l n i 3 2 1 f % 2 5 3 A 6 j 1 u l n i 3 2 0 j b r . u l n i 3 1 9 u l n i 3 1 8 u l n i 3 2 4 u l n i 3 2 3 m 8 p g 0 u l n i 3 3 0 m 8 p g 1 u l n i 3 2 9 m 8 p g u l n i 3 2 8 l - n 6 1 2 6 2 u l n i 3 2 7 l - n 6 1 2 5 2 u l n i 3 2 6 u l n i 3 2 5 u l n i 3 3 2 u l n i 3 3 1 v l 3 2 u l n i 3 3 5 c 9 d d 0 u l n i 3 3 4 u l n i 3 3 3 u l n i 3 3 6 u l n i 3 3 7 u l n i 3 3 9 u l n i 3 3 8 u l n i 3 4 0 u l n i 3 4 2 u l n i 3 4 1 u l n i 3 4 5 u l n i 3 4 4 u l n i 3 4 3 u l n i 3 4 7 u l n i 3 4 6 u l n i 3 4 8 u l n i 3 4 9 u l n i 3 5 1 u l n i 3 5 0 u l n i 3 5 2 i q g j 0 u l n i 3 6 4 a 7 z d u l n i 3 6 3 a 7 z d 0 u l n i 3 6 2 g c d 0 u l n i 3 6 1 g c d 0 0 u l n i 3 6 0 g n 4 k u l n i 3 5 9 g n 4 k 0 u l n i 3 5 8 n g 7 d u l n i 3 5 7
f % 2 5 3 A 6 j 0 1 u l n i 3 5 6 f % 2 5 3 A 6 j 2 u l n i 3 5 5 c 1 _ h 1 u l n i 3 5 4 q y 5 7 m 7 _ t u l n i 3 4 5 1 u l n i 3 4 4 1 u l n i 3 4 3 1 i 1 c e q g s j u y y a j o h h 0 p 7 o a e g 0 x u l n i 3 6 8 u l n i 3 6 7 q y 5 7 1 c d j w 0 y f _ q s 2 - g s 2 - g 0 t a k 5 n 7 7 f 1 n 7 7 f 0 n 7 7 f q 8 3 y 0 q 8 3 y n 7 7 f 2 z o 7 f e t d m f 1 g 2 0 r w j . e s p i n y 1 j n y 1 j 0 q 4 0 9 g m x _ 0 g m x _ z % 3 A _ 2 z % 3 A _ 2 0 z % 3 A _ 2 1 v k l h p r z y p r z y 0 z % 3 A _ 2 2 z % 3 A _ 2 3 k j p f 0 k j p f l i p 3 0 u l n i 3 9 0 u i r c x 9 8 u r x t w k - 6 d a 2 8 b j r f 1 s 1 y 8 j z j a w m w 5 w m w 5 0 k r 9 z q 4 0 9 2 q 4 0 9 1 w 0 d d q s b a 1 i i z j 0 r 9 u 6 r 9 u 6 0 j m 6 - u l n i 4 2 8 u l n i 4 2 7 t v 8 o 0 u l n i 4 2 6 x 4 6 4 u l n i 4 2 5 p % 2 5 3 A 8 d 1 u l n i 4 2 4 p % 2 5 3 A 8 d 0 u l n i 4 2 3 p % 2 5 3 A 8 d u l n i 4 2 2 u l n i 4 2 1 a a b b 0 a a b b u l n i 4 2 9 r 1 - o 0 r 1 - o r 1 - o 1 y 3 7 2 0 y 3 7 2 c 2 . s 0 m i m q 0 m i m q l b 0 j 2 l b 0 j 1 u l n i 4 8 5 h e 9 . u 2 % 3 A y ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ^ ^ ^ ^ ^ ^ _ _ _ _ _ _ ` ` ` ` ` ` v v v v v v w w w w w w w w w w w w x x x 1 1 1 C C C C C C D D D D D D D D D K K K xD xD xD xD xD xD yD yD yD H[ K[ [ [ [ [ [ [ [ [ \ \ \ \ \ \ "\ "\ "\ "\ "\ "\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ ] u] ^ ^ ^ _ _ _ _ _ _ _ _ _ _ _ xe xe xe xe xe {e }e }e ~e ~e ~e ~e ~e ~e ~e ~e ~e e e e e e e e e e e e e e f h j j j j j j j j j l l l l l l l l l n n n n n n n n n x x x x x x x x x x x x x x x y y y y y y y y y y y y y y y | | | | | | | | | | | | ׆ ׆ ׆ ׆ ׆ ׆ ن ن ن ن ن Ż Ż Ż Ż Ż Ż ƻ ƻ ƻ ƻ ƻ ƻ ƻ ƻ ƻ ƻ ƻ v v w w w w w w w w w w w w w w w f f g g g g g g g g g g g c 6 6 7 7 7 7 7 7 7 7 7 7 7 u O O P P P P P 3 6 / / 0 D D D
T W " " " " " " " F F H H H H H H I I oP rP Z [ c c Ee Ee Ee Ee Xe Ze f 0g 3g _q bq eq eq yq {q |q Jx Lx y y 0| >| A| ~ ~ } Ň ٜ ܜ ڸ ڸ ڸ ڸ ڸ ڸ ڸ ڸ ڸ ڸ ڸ ڸ ڸ u u w t t v ټ ټ ټ t
! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z [ \ ] ^ _ ` a b c d e f g h i j k l m n o p q r s t u v w x y z { | } ~
! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z [ \ ] ^ _ ` a b c d e f g h i j k l m n o p q r s t u v w x y z { | } ~
! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z [ \ ] ^ _ ` a b c d e f g h i j k l m n o p q r s t u v w ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ] ^ ^ ^ ^ ^ ^ _ _ _ _ _ _ ` ` ` ` ` ` v v v v v v w w w w w w w w w w w w x x x 1 1 1 C C C C C C D D D D D D D D D K K K xD xD xD xD xD xD yD yD yD H[ K[ [ [ [ [ [ [ [ [ \ \ \ \ \ \ "\ "\ "\ "\ "\ "\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ $\ ] u] ^ ^ ^ _ _ _ _ _ _ _ _ _ _ _ xe xe xe xe xe {e }e }e ~e ~e ~e ~e ~e ~e ~e ~e ~e e e e e e e e e e e e e e f h j j j j j j j j j l l l l l l l l l n n n n n n n n n x x x x x x x x x x x x x x x y y y y y y y y y y y y y y y | | | | | | | | | | | | ׆ ׆ ׆ ׆ ׆ ׆ ن ن ن ن ن Ż Ż Ż Ż Ż Ż ƻ ƻ ƻ ƻ ƻ ƻ ƻ ƻ ƻ ƻ ƻ v v w w w w w w w w w w w w w w w f f g g g g g g g g g g g c 6 6 7 7 7 7 7 7 7 7 7 7 7 u O O P P P P P 3 6 / / 0 D D D
T W " " " " " " " F F H H H H H H I I oP rP Z [ c c Ee Ee Ee Ee Xe Ze f 0g 3g _q bq eq eq yq {q |q Jx Lx y y 0| >| A| ~ ~ } Ň ٜ ܜ ڸ ڸ ڸ ڸ ڸ ڸ ڸ ڸ ڸ ڸ ڸ ڸ ڸ u u w t t v ټ ټ ټ t 5B
5B
5B 5B ԩ! 5B } 5B - 5B $ 5B 5B 5B
L 5B $ 5B *urn:schemas-microsoft-com:office:smarttags
PostalCode 8 *urn:schemas-microsoft-com:office:smarttagsCity 9 *urn:schemas-microsoft-com:office:smarttagsState : *urn:schemas-microsoft-com:office:smarttagsStreet ; *urn:schemas-microsoft-com:office:smarttagsaddress =
*urn:schemas-microsoft-com:office:smarttags PlaceType =
*urn:schemas-microsoft-com:office:smarttags PlaceName 9 *urn:schemas-microsoft-com:office:smarttagsplace > *urn:schemas-microsoft-com:office:smarttags
PersonName ~
f f /~ >~ ' 4 B Q | |
a j o s w Z ` [ ] ` g l r G M " $ ) - 7 E M 8 > 1 8 = C B B D D E E G H J K M N W a c d q t ] ] { { H H H H dq yq ܜ x " ` s v ý ƽ % ( C G F I $ ' b f \ _ ) O X p r S X b
5 8 N Q ' + 7 } R U " % O - / B B D D E E G H J K M N W a c d q t 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 Z [ Ee Ze dq xq {q < 9 B B D D E E G H J K M N U c d t B B D D E E G H J K M N c d t ^ ` ^ ` ^ ` ^ ` ^ ` ^ ` ^ ` ^ ` ^ ` f e 7= m t
x X _& b 7 Dh s# R) _c) ), \[0 U2 rJ5 L6 Z6 K< ?5> Wx? ?B -VB fB H 5J NL
?O P |R Sz\ ('] ~a Rra ab @"c < E n a b l e d > 1 < / E n a b l e d > < S c a n U n f o r m a t t e d > 1 < / S c a n U n f o r m a t t e d > < S c a n C h a n g e s > 1 < / S c a n C h a n g e s > < / E N I n s t a n t F o r m a t > U < E N L i b r a r i e s > < L i b r a r i e s > < i t e m > M e t a b o l i c S y n d r o m e . e n l < / i t e m > < / L i b r a r i e s > < / E N L i b r a r i e s > @ lc s p @ U n k n o w n G z T i m e s N e w R o m a n 5 S y m b o l 3& z A r i a l ; W i n g d i n g s [
T h o r n d a l e T i m e s N e w R o m a n k H G M i n c h o L i g h t J T i m e s N e w R o m a n I& ? ? A r i a l U n i c o d e M S A& A l b a n y A r i a l B h dž džf E P E P ! 4 d T T 2Q HP ? 8 [ M e t a b o l i c s y n d r o m e : a c r i t i c a l l o o k f r o m t h e v i e w p o i n t s o f c a u s a l d i a g r a m s a n d s t a t i s t i c s S h a h a r S h a h a r Oh +'0 , @ P \
|
\ Metabolic syndrome: a critical look from the viewpoints of causal diagrams and statistics Shahar Normal.dot Shahar 2 Microsoft Office Word @ @ ? @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z [ \ ] ^ _ ` a b c d e f g h i j k l m n o p q r s t u v w x y z { | } ~
! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M N O P Q R S T V W X Y Z [ \ ^ _ ` a b c d e f g h i j k l m n o p q r s t u v w x y z { | } ~ R o o t E n t r y F P! D a t a
U 1 T a b l e ] W o r d D o c u m e n t S u m m a r y I n f o r m a t i o n ( D o c u m e n t S u m m a r y I n f o r m a t i o n 8 C o m p O b j q
F Microsoft Office Word Document
MSWordDoc Word.Document.8 9q