***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: 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 Summary Background Conflicting evidence exists regarding the association between saturated fatty acids ( SFAs ) and type 2 diabetes . In this longitudinal case-cohort study , we aim ed to investigate the prospect i ve associations between objective ly measured individual plasma phospholipid SFAs and incident type 2 diabetes in EPIC-InterAct participants . Methods The EPIC-InterAct case-cohort study includes 12 403 people with incident type 2 diabetes and a representative subcohort of 16 154 individuals who were selected from a cohort of 340 234 European participants with 3·99 million person-years of follow-up ( the EPIC study ) . Incident type 2 diabetes was ascertained until Dec 31 , 2007 , by a review of several sources of evidence . Gas chromatography was used to measure the distribution of fatty acids in plasma phospholipids ( mol% ) ; sample s from people with type 2 diabetes and subcohort participants were processed in a r and om order by centre , and laboratory staff were masked to participant characteristics . We estimated country-specific hazard ratios ( HRs ) for associations per SD of each SFA with incident type 2 diabetes using Prentice-weighted Cox regression , which is weighted for case-cohort sampling , and pooled our findings using r and om-effects meta- analysis . Findings SFAs accounted for 46 % of total plasma phospholipid fatty acids . In adjusted analyses , different individual SFAs were associated with incident type 2 diabetes in opposing directions . Even-chain SFAs that were measured ( 14:0 [ myristic acid ] , 16:0 [ palmitic acid ] , and 18:0 [ stearic acid ] ) were positively associated with incident type 2 diabetes ( HR [ 95 % CI ] per SD difference : myristic acid 1·15 [ 95 % CI 1·09–1·22 ] , palmitic acid 1·26 [ 1·15–1·37 ] , and stearic acid 1·06 [ 1·00–1·13 ] ) . By contrast , measured odd-chain SFAs ( 15:0 [ pentadecanoic acid ] and 17:0 [ heptadecanoic acid ] ) were inversely associated with incident type 2 diabetes ( HR [ 95 % CI ] per 1 SD difference : 0·79 [ 0·73–0·85 ] for pentadecanoic acid and 0·67 [ 0·63–0·71 ] for heptadecanoic acid ) , as were measured longer-chain SFAs ( 20:0 [ arachidic acid ] , 22:0 [ behenic acid ] , 23:0 [ tricosanoic acid ] , and 24:0 [ lignoceric acid ] ) , with HRs ranging from 0·72 to 0·81 ( 95 % CIs ranging between 0·61 and 0·92 ) . Our findings were robust to a range of sensitivity analyses . Interpretation Different individual plasma phospholipid SFAs were associated with incident type 2 diabetes in opposite directions , which suggests that SFAs are not homogeneous in their effects . Our findings emphasise the importance of the recognition of subtypes of these fatty acids . An improved underst and ing of differences in sources of individual SFAs from dietary intake versus endogenous metabolism is needed . Funding EU FP6 programme , Medical Research Council Epidemiology Unit , Medical Research Council Human Nutrition Research , and Cambridge Lipidomics Biomarker Research Initiative OBJECTIVE Acylcarnitines were suggested as early biomarkers even prior to insulin resistance in animal studies , but their roles in predicting type 2 diabetes were unknown . Therefore , we aim ed to determine whether acylcarnitines could independently predict type 2 diabetes by using a targeted metabolic profiling approach . RESEARCH DESIGN AND METHODS A population -based prospect i ve study was conducted among 2,103 community-living Chinese individuals aged 50–70 years from Beijing and Shanghai with a mean follow-up duration of 6 years .  Fasting glucose , glycohemoglobin , and insulin  were determined at baseline and in a follow-up survey . Baseline plasma acylcarnitines were profiled by liquid chromatography – t and em mass spectrometry . RESULTS Over the 6-year period , 507 participants developed diabetes .  A panel of acylcanitines , especially with long chain , was significantly associated with increased risk of type 2 diabetes . The relative risks of type 2 diabetes per SD increase of the predictive model score were 2.48 ( 95 % CI 2.20–2.78 ) for the conventional and 9.41 ( 95 % CI 7.62–11.62 ) for the full model including acylcarnitines , respectively . Moreover , adding selected acylcarnitines substantially improved predictive ability for incident diabetes , as area under the receiver operator characteristic curve improved to 0.89 in the full model compared with 0.73 in the conventional model . Similar associations were obtained when the predictive models were established separately among Beijing or Shanghai residents . CONCLUSIONS A panel of acylcarnitines , mainly involving mitochondrial lipid d 
    Output:
    
Results of meta- analysis in prospect i ve studies indicated that isoleucine , leucine , valine , tyrosine , phenylalanine , glutamate , alanine , valerylcarnitine ( C5 ) , palmitoylcarnitine ( C16 ) , palmitic acid and linoleic acid were associated with higher T2D risk .
Conversely , serine , glutamine and LPC C18:2 decreased risk of T2D .
Arginine and glycine increased risk of T2D in western countries subgroup , and betaine was negatively correlated with T2D in nested case-control subgroup .
In addition , slight improvements in T2D prediction beyond traditional risk factors were observed when adding these metabolites in predictive analysis .
Pathway analysis identified 17 metabolic pathways may alter in the process of T2D and metabolite-related genes were also enriched in functions and pathways associated with T2D .
Several metabolites and metabolic pathways associated with T2D have been identified , which provide valuable biomarkers and novel targets for prevention and drug therapy