***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: OBJECTIVE 1,5-Anhydroglucitol ( 1,5AG ) is a major circulating polyol arising primarily from ingestion and excreted competitively with glucose . Japanese studies have demonstrated reduced concentrations of 1,5AG in serum in hyperglycemic patients in comparison with euglycemic subjects and a gradual normalization of 1,5AG values for patients responding to antihyperglycemic therapies . In this first U.S. study , we assessed the ability of 1,5AG measurements to monitor glycemic control in a cohort of 77 patients with diabetes ( 22 with type 1 diabetes , 55 with type 2 diabetes ) who presented with suboptimal glycemic control at baseline ( defined as HbA(1c ) > or=7 % ) . RESEARCH DESIGN AND METHODS Each patient received therapies consisting of combinations of diabetes education , nutritional counseling , and addition or dose adjustment of various insulins or oral antihyperglycemic medications . Therapy was targeted to reduce mean HbA(1c ) by > or=1.0 % over the monitoring period .  1,5AG , HbA(1c ) , fructosamine , and r and om glucose measurements  were performed at baseline and at 2 , 4 , and 8 weeks after the initiation of therapy . RESULTS 1,5AG , fructosamine , and glucose values progressed significantly toward euglycemia by week 2 of monitoring ( Wilcoxon 's signed-rank test , P < 0.05 ) , with median changes of 93 , -7 , and -13 % for 1,5AG , fructosamine , and glucose , respectively . In contrast , HbA(1c ) values did not respond significantly to therapy until week 4 . On an individual patient basis , 89.6 % of patients displayed longitudinal changes of 1,5AG from baseline to week 8 in concordance with HbA(1c ) . 1,5AG was also highly correlated with HbA(1c ) and fructosamine ( Spearman rho = -0.6459 and -0.6751 , respectively ; both P < 0.0001 ) . CONCLUSIONS We conclude that 1,5AG responds sensitively and rapidly to changes in glycemia and monitors glycemic control in accordance with established markers Prognosis studies are investigations of future events or the evaluation of associations between risk factors and health outcomes in population s of patients ( 1 ) . The results of such studies improve our underst and ing of the clinical course of a disease and assist clinicians in making informed decisions about how best to manage patients . Prognostic research also informs the design of intervention studies by helping define subgroups of patients who may benefit from a new treatment and by providing necessary information about the natural history of a disorder ( 2 ) . There has recently been a rapid increase in the use of systematic review methods to synthesize the evidence on research questions related to prognosis . It is essential that investigators conducting systematic review s thoroughly appraise the method ologic quality of included studies to be confident that a study 's design , conduct , analysis , and interpretation have adequately reduced the opportunity for bias ( 3 , 4 ) . Caution is warranted , however , because inclusion of method ologically weak studies can threaten the internal validity of a systematic review ( 4 ) . This follows abundant empirical evidence that inadequate attention to biases can cause invalid results and inferences ( 5 - 9 ) . However , there is limited consensus on how to appraise the quality of prognosis studies ( 1 ) . A useful framework to assess bias in such studies follows the basic principles of epidemiologic research ( 10 , 11 ) . We focus on 6 areas of potential bias : study participation , study attrition , prognostic factor measurement , confounding measurement and account , outcome measurement , and analysis . The main objectives of our review of review s are to describe methods used to assess the quality of prognosis studies and to describe how well current practice s assess potential biases . Our secondary objective is to develop recommendations to guide future quality appraisal , both within single studies of prognostic factors and within systematic review s of the evidence . We hope this work facilitates future discussion and research on biases in prognosis studies and systematic review s. Methods Literature Search and Study Selection We identified systematic review s of prognosis studies by search ing MEDLINE ( 1966 to October 2005 ) using the search strategy recommended by McKibbon and colleagues ( 12 ) . This strategy combines broad search terms for systematic review s ( systematic review .mp ; meta- analysis .mp ) and a sensitive search strategy for prognosis studies ( cohort , incidence , mortality , follow-up studies , prognos * , predict * , or course ) . We also search ed the reference lists of included review s and method ologic papers to identify other relevant publications . We restricted our search to English- language publications . One review er conducted the search and selected the studies . Systematic review s , defined as review s of published studies with a comprehensive search and systematic selection , were included if they assessed the method ologic quality of the included studies by using 1 or more explicit criteria . We excluded studies if they were meta-analyses of independent patient data only , if their primary goal was to investigate the effectiveness of an intervention or specific diagnostic or screening tests , or if they included studies that were not done on humans . Data Extraction and Synthesis Individual items included in the quality assessment of the systematic review s were recorded as they were reported in the publication ( that is , the information that would be available to readers and future review ers ) . We review ed journal Web sites and contacted the authors of the systematic review s for additional information when authors made such an offer in their original papers . When review s assessed different study design s by using different sets of quality items , we extracted only those items used to assess cohort studies . We constructed a comprehensive list of distinct items that the review s used to assess the quality of their included studies . The full text of each review was screened . All items used by the review authors to assess the quality of studies were extracted into a computerized spreadsheet by 1 review er . Two experienced review ers , a clinical epidemiologist and an epidemiologist , independently synthesized the quality items extracted from the prognosis review s to determine how well the systematic review s assessed potential biases . We did this in 3 steps : 1 ) identified distinct concepts or domains addressed by the quality items ; 2 ) grouped each extracted quality item into the appropriate domain or domains ; and 3 ) identified the domains necessary to assess potential biases in prognosis studies . We then used this information to assess how well the review s ' quality assessment included items from the domains necessary to assess potential biases . After completing each of the first 3 steps , the review ers met to attempt to reach a consensus . The consensus process involved each review er presenting his or her observations and results , followed by discussion and debate . A third review er was available in cases of persistent disagreement or uncertainty . In the first step , all domains addressed by the quality items were identified . The first review er iteratively and progressively defined the domains as items were extracted from the included review s. The second review er defined domains from a r and om list of all extracted quality items . Limited guidance was provided to the review ers so that their assessment s and definitions of domains would be independent . The review ers agreed on a final set of domains that adequately and completely defined all of the extracted items . In the second step , review ers independently grouped each extracted item into the appropriate domains . Review ers considered each extracted item by asking , What is each particular quality item addressing ? or What are the review 's authors getting at with the particular quality assessment item ? . Items were grouped into the domain or domains that best represented the concepts being addressed . For example , the extracted items at least 80 % of the group originally identified was located for follow-up and follow-up was sufficiently complete or does n't jeopardize validity were each independently classified by both review ers as assessing the domain completeness of follow-up adequate , whereas the extracted item quantification and description of all subjects lost to follow-up was classified as assessing the domain completeness of follow-up described . In the third step , we identified the domains necessary to assess potential biases . Each review er considered the ability of the identified domains to adequately address , at least in part , 1 of the following 6 potential biases : 1 ) study participation , 2 ) study attrition , 3 ) prognostic factor measurement , 4 ) confounding measurement and account , 5 ) outcome measurement , and 6 ) analysis . Domains were considered to adequately address part of the framework if information garnered from that domain would inform the assessment of potential bias . For example , both review ers judged that the identified domain study population represents source population or population of interest assessed potential bias in a prognosis study , whereas the domain research question definition did not , although the latter is an important consideration in assessing the inclusion of studies in a systematic review . Finally , on the basis of our previous ratings , we looked at whether each review included items from the domains necessary to assess the 6 potential biases . We calculated the frequency of systematic review s by assessing each potential bias and the number of review s that adequately assessed bias overall . From this systematic synthesis , we developed recommendations for improving quality appraisal in future systematic review s of prognosis studies . We used Microsoft Access and Excel 2002 ( Microsoft Corp. , Redmond , Washington ) for data management and SAS for Windows , version 9.1 ( SAS Institute , Inc. , Cary , North Carolina ) for descriptive statistics . Role of the Funding Sources The funding sources , the Canadian Institutes of Health Research , the Canadian Chiropractic Research Foundation , the Ontario Chiropractic Association , and the Ontario Ministry of Health and Long Term Care , did not have a role in the collection , analysis , or interpretation of the data or in the decision to su bmi t the manuscript for publication . Results We identified 1384 potentially relevant articles . Figure 1 shows a flow chart of studies that were included and excluded . Figure 2 shows the number of review s identified by year of publication . We excluded 131 systematic review s of prognosis studies that did not seem to include any quality assessment of the included studies ; this represented 44 % of prognosis review s. We included 163 review s of prognosis studies in our analysis ( 13 - 175 ) . The most common topics were cancer ( 15 % ) , musculoskeletal disorders and rheumatology ( 13 % ) , cardiovascular ( 10 % ) , neurology ( 10 % ) , and obstetrics ( 10 % ) . Other review s included a wide range of health and health care topics . Sixty-three percent of the review s investigated the association between a specific prognostic factor and a particular outcome ; the remainder investigated multiple prognostic factors or models . The number of primary studies included in each systematic review ranged from 3 to 167 ( median , 18 [ interquartile range , 12 to 31 ] ) . A complete description of the included review s is available from the authors on request . Figure 1 . Flow diagram of inclusion and exclusion criteria of systematic review s. Figure 2 . Number of systematic review s of prognosis studies identified over time . Quality Items One hundred fifty-three review s provided adequate detail to allow extraction of quality items . Eight hundred eighty-two distinct quality items were extracted from the review s. Most review s developed their own set of quality items , with only a few applying criteria from previous review s. Most quality items OBJECTIVE Branched-chain and aromatic amino acids are associated with the risk for future type 2 diabetes ; however , the underlying mechanisms remain elusive . We tested whether amino acids predict insulin resistance index in healthy young adults . RESEARCH DESIGN AND METHODS Circulating isoleucine , leucine , valine , phenylalanine , tyrosine , and six additional amino acids were quantified in 1,680 individuals from the population -based Cardiovascular Risk in Young Finns Study ( baseline age 32 ± 5 years ; 54 % women ) . Insulin resistance was estimated by homeostasis model assessment ( HOMA ) at baseline and 6-year follow-up . Amino acid associations with HOMA of insulin resistance ( HOMA-IR ) and glucose were assessed using regression models adjusted for established risk factors . We further examined whether amino acid profiling could augment risk assessment of insulin resistance ( defined as 6-year HOMA-IR > 90th percentile ) in early adulthood . RESULTS Isoleucine , leucine , valine , phenylalanine , and tyrosine were associated with HOMA-IR at baseline and for men at 6-year follow-up , while for women only leucine , valine , and phenylalanine predicted 6-year HOMA-IR ( P < 0.05 ) . None of the other amino acids were prospect ively associated with HOMA-IR . The sum of branched-chain and aromatic amino acid concentrations was associated with 6-year insulin resistance for men ( odds ratio 2.09 [ 95 % CI 1.38–3.17 ] ; P = 0.0005 ) ; however , including the amino acid score in prediction models did not improve risk discrimination . CONCLUSIONS Branched-chain and aromatic amino acids are markers of the development of insulin resistance in young , normoglycemic adults , with most pronounced associations for men . These findings suggest that the association of branched-chain and aromatic amino acids with the risk for future diabetes is at least partly mediated through insulin resistance Plasma concentrations of amino acids are frequently elevated in insulin-resistant states , and a protein-enriched diet can impair glucose metabolism . This study examined effects of short-term plasma amino acid ( AA ) elevation on whole-body glucose disposal and cellular insulin action in skeletal muscle . Seven healthy men were studied for 5.5 h during euglycemic ( 5.5 mmol/l ) , hyperinsulinemic ( 430 pmol/l ) , fasting glucagon ( 65 ng/l ) , and growth hormone ( 0.4 microg/l ) somatostatin clamp tests in the presence of low ( approximately 1.6 mmol/l ) and increased ( approximately 4.6 mmol/l ) plasma AA concentrations . Glucose turnover was measured with D-[6, 
    Output:
    
Carbohydrate ( glucose and fructose ) , lipid ( phospholipids , sphingomyelins , and triglycerides ) , and amino acid ( branched-chain amino acids , aromatic amino acids , glycine , and glutamine ) metabolites were higher in individuals with type 2 diabetes compared with control subjects .
Prospect i ve studies provided evidence that blood concentrations of several metabolites , including hexoses , branched-chain amino acids , aromatic amino acids , phospholipids , and triglycerides , were associated with the incidence of prediabetes and type 2 diabetes .
In studies using high-throughput metabolomics , several blood amino acids appear to be consistently associated with the risk of developing type 2 diabetes