intro courses in R
quick-R - a "must see" by dr Kabacoff,
also preparing book "R in action"
nice tutorial on R- pdf. 104pp by Matloff - Uni Davis
Oxford course by Ruth Ripley incl. slides, r files ....
Aarhus introduction course XXX- recommended books, course materials,
by Ulrich Halekoh, Søren Højsgaard, Asger R. Pedersen incl. R in Few Hours tutorial :)
Intro to R XXX - quite extensive pdf, datamanipulations, graphics, statistics, up to datamining.
by Statgenetics ... UAB ...UofAlabamaATbirmingham by Lumley T /member of R core develepment team
site includes some other pdf of exercise etc, and even video /videos only for Uni-members
Using R for statistical analyses - Introduction by dr Gardener - nice layout
MAT 356 R Tutorial, Spring 2004 - concise, many examples, inclusive plotting options, very instructive
advanced statistics - Exploratory Data Analysis (EDA) and Regression from astrophysicist
- includes a lot of code for different types of regression (linear, polynomial, ridge, spline .....), but no plot, or other output
streda 28. októbra 2009
genetic statistics SOFTWARE
LINKAGE AND LINKAGE DISEQUILIBRIUM
GENETIC STATISTICS, STATISTICAL GENETICS SOFTWARE
Following are the category of programs we have available for the analysis:
Association and TDT Programs
Error Checking Programs
Genome Wide Association Programs
Haplotyping Programs
Linkage Analysis Programs
Pedigree Drawing Programs
Population Genetics Programs
Segregation Programs
Simulation Programs
Stratification Programs
Utility Programs
GENETIC STATISTICS, STATISTICAL GENETICS SOFTWARE
Following are the category of programs we have available for the analysis:
Association and TDT Programs
Error Checking Programs
Genome Wide Association Programs
Haplotyping Programs
Linkage Analysis Programs
Pedigree Drawing Programs
Population Genetics Programs
Segregation Programs
Simulation Programs
Stratification Programs
Utility Programs
linkage disequilibrium
linkage disequilibrium (LD)- basics, measures /D, Dprime, r_squared/, causes, of LD by Abecasis from U Michigan
- includes pros and cons and limitations of individual measures of LD !!!!
linkage disequilibrium - worked example of how to compute different measures, on causes etc.
"classical" brief note on LD - linkage vs linkage disequilibrium
paper on volume measures of LD - as part of intro describes calculation of D´and r2.
- includes pros and cons and limitations of individual measures of LD !!!!
linkage disequilibrium - worked example of how to compute different measures, on causes etc.
"classical" brief note on LD - linkage vs linkage disequilibrium
paper on volume measures of LD - as part of intro describes calculation of D´and r2.
nedeľa 18. októbra 2009
statistics resources
hyperstats - basics - distribution
SOCR - statistcal online computation resource -from UCLA
JAVA applets - distributions - analyses - plots - models - games
check out - links to wiki.
Dallal - Little Handbook of statistical practice
- comprehensible - text-based, includes figures
SOCR - statistcal online computation resource -from UCLA
JAVA applets - distributions - analyses - plots - models - games
check out - links to wiki.
Dallal - Little Handbook of statistical practice
- comprehensible - text-based, includes figures
piatok 28. augusta 2009
xanthomas
Cutaneous xanthomas associated with hyperlipidemia can be classified as
xanthelasma palpebrarum,
tendinous xanthomas,
eruptive xanthomas,
tuberous xanthomas, and
plane xanthomas.
xanthelasma palpebrarum,
tendinous xanthomas,
eruptive xanthomas,
tuberous xanthomas, and
plane xanthomas.
utorok 11. augusta 2009
low HDL syndromes
Tangier disease
Tangier - 9q
- orange tonsils, hepatosplenomegaly, polyneuropathy (syringomyelia-like), premature CVS
- hypoHDL
- ABCA1 mutation, loss-off-function,
- cellular cholesterol efflux impaired
primary familial hypoalfalipoproteinemia OMIM 604091
-French Canadian
- lowHDL, normal TG
- ABCA1 mutation 9q
LCAT deficiency
lecithin-cholesterol acyltransferase OMIM 606967
- soluble enzyme that converts cholesterol and phosphatidylcholines (lecithins) to cholesteryl esters and lysophosphatidylcholines on the surface of high density lipoproteins
'reverse cholesterol transport.' The enzyme is synthesized in the liver and circulates in blood plasma as a complex with components of high density lipoprotein (HDL).
- Cholesterol from peripheral cells is transferred to HDL particles, esterified through the action of LCAT on HDL, and incorporated into the core of the lipoprotein. The cholesterol ester is thereby transported to the liver (Jonas, 2000). A lack of LCAT activity would be expected to lead to accumulation of free cholesterol in the tissues. Most cholesterol esters present in plasma are the product of the reaction catalyzed by LCAT in which cholesterol is esterified with the sn-2 fatty acid of phosphatidylcholine. The resulting cholesteryl esters are packed into the hydrophobic core of lipoproteins.
In fish-eye disease (OMIM 136120), there is a specific inability of LCAT to esterify cholesterol in HDL, a deficiency of alpha-LCAT function. In Norum disease (245900), the deficient esterification is generalized (Kinoshita and Teramoto, 2001).
PATHOGENESIS
Carlson and Holmquist (1985) and Holmquist and Carlson (1987) demonstrated that the defect in fish-eye disease is deficiency of high density lipoprotein lecithin:cholesterol acyltransferase activity.
Alpha-LCAT, deficient in this condition, is specific for HDL, whereas beta-LCAT, also deficient in Norum disease (245900), is specific for combined VLDL and LDL (Carlson and Holmquist, 1985). Thus, fish-eye disease is one form of LCAT deficiency. In fish-eye disease, the HDL of plasma contains only about 20% cholesteryl esters relative to total cholesterol as compared to 75 to 80% in control HDL. In fish-eye disease plasma, however, there is a normal cholesteryl ester percentage as well as a normal plasma cholesterol esterification rate as a result of the activity of beta-LCAT.
Tangier - 9q
- orange tonsils, hepatosplenomegaly, polyneuropathy (syringomyelia-like), premature CVS
- hypoHDL
- ABCA1 mutation, loss-off-function,
- cellular cholesterol efflux impaired
primary familial hypoalfalipoproteinemia OMIM 604091
-French Canadian
- lowHDL, normal TG
- ABCA1 mutation 9q
LCAT deficiency
lecithin-cholesterol acyltransferase OMIM 606967
- soluble enzyme that converts cholesterol and phosphatidylcholines (lecithins) to cholesteryl esters and lysophosphatidylcholines on the surface of high density lipoproteins
'reverse cholesterol transport.' The enzyme is synthesized in the liver and circulates in blood plasma as a complex with components of high density lipoprotein (HDL).
- Cholesterol from peripheral cells is transferred to HDL particles, esterified through the action of LCAT on HDL, and incorporated into the core of the lipoprotein. The cholesterol ester is thereby transported to the liver (Jonas, 2000). A lack of LCAT activity would be expected to lead to accumulation of free cholesterol in the tissues. Most cholesterol esters present in plasma are the product of the reaction catalyzed by LCAT in which cholesterol is esterified with the sn-2 fatty acid of phosphatidylcholine. The resulting cholesteryl esters are packed into the hydrophobic core of lipoproteins.
In fish-eye disease (OMIM 136120), there is a specific inability of LCAT to esterify cholesterol in HDL, a deficiency of alpha-LCAT function. In Norum disease (245900), the deficient esterification is generalized (Kinoshita and Teramoto, 2001).
PATHOGENESIS
Carlson and Holmquist (1985) and Holmquist and Carlson (1987) demonstrated that the defect in fish-eye disease is deficiency of high density lipoprotein lecithin:cholesterol acyltransferase activity.
Alpha-LCAT, deficient in this condition, is specific for HDL, whereas beta-LCAT, also deficient in Norum disease (245900), is specific for combined VLDL and LDL (Carlson and Holmquist, 1985). Thus, fish-eye disease is one form of LCAT deficiency. In fish-eye disease, the HDL of plasma contains only about 20% cholesteryl esters relative to total cholesterol as compared to 75 to 80% in control HDL. In fish-eye disease plasma, however, there is a normal cholesteryl ester percentage as well as a normal plasma cholesterol esterification rate as a result of the activity of beta-LCAT.
sobota 4. júla 2009
dm2 prediction
Multiple biomarker prediction of type 2 diabetes.
Meigs JB.
van Hoek DM2 prediction in population study Rotterdam
Joint Effects of Common Genetic Variants on the Risk for Type 2 Diabetes in U.S. Men and Women of European Ancestry Marilyn C. Cornelis, PhD; Lu Qi, MD, PhD; Cuilin Zhang, MD, PhD; Peter Kraft, PhD; JoAnn Manson, MD, DPH; Tianxi Cai, PhD; David J. Hunter, MBBS, ScD; and Frank B. Hu, MD, PhD
21 April 2009 Volume 150 Issue 8 Pages 541-550
Conclusion: Although its discriminatory value is currently limited, a GRS that combines information from multiple genetic variants might be useful for identifying subgroups with a particularly high risk for type 2 diabetes.
annal of internal medicine- NURSES HEALTH STUDY
Comment in:
Lancet. 2009 Jun 27;373(9682):2178-9.
Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: an analysis from the Whitehall II study.
Tabák AG, Jokela M, Akbaraly TN, Brunner EJ, Kivimäki M, Witte DR.
INTERPRETATION: In this study, we show changes in glucose concentrations, insulin sensitivity, and insulin secretion as much as 3-6 years before diagnosis of diabetes. The description of biomarker trajectories leading to diabetes diagnosis could contribute to more-accurate risk prediction models that use repeated measures available for patients through regular check-ups.
Using the Optimal Receiver Operating Characteristic Curve to Design a Predictive Genetic Test, Exemplified with Type 2 Diabetes
Qing Lu1 and Robert C. Elston1
CF SERIES OF BASIC EPIDEMIOLOGY/STATISTCS BY AKOBENG
/DIAGNOSTIC TESTS/ UIN ACTA PAEDIATRICA
CALIBRATION STATISTICS
CF DIAGNOSTIC VS PROGNOSTIC MODELS - ROC
Cook NR. Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve. Clin Chem 2008;54:17-23. [Free Full Text]
Fine-tuning of prediction of isolated impaired glucose tolerance: a quantitative clinical prediction model.
Rambod M, Hosseinpanah F, Ardakani EM, Padyab M, Azizi F.
Diabetes Res Clin Pract. 2009 Jan;83(1):61-8. Epub 2008 Nov 13.
PMID: 19012984 [PubMed - indexed for MEDLINE]
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ARIC STUDY
Comment in:
Ann Intern Med. 2009 Jun 2;150(11):812-4.
Two risk-scoring systems for predicting incident diabetes mellitus in U.S. adults age 45 to 64 years.
Kahn HS, Cheng YJ, Thompson TJ, Imperatore G, Gregg EW.
Centers for Disease Control and Prevention, 4770 Buford Highway Northeast, Atlanta, GA 30341, USA.
CONCLUSION: Basic information identified adults at high risk for diabetes. Additional data from fasting blood tests better identified those at extreme risk.
RESULTS: The basic scoring system included waist circumference (10 to 35 points); maternal diabetes (13 points); hypertension (11 points); and paternal diabetes, short stature, black race, age 55 years or older, increased weight, rapid pulse, and smoking history (< or ="8" or ="7" href="http://www.bmj.com/cgi/content/full/338/mar17_2/b880?view=long&pmid=19297312">Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore
Different strategies for screening and prevention of type 2 diabetes in adults: cost effectiveness analysis
Clare L Gillies, Paul C Lambert, Keith R Abrams, Alex J Sutton, Nicola J Cooper, Ron T Hsu, Melanie J Davies, and Kamlesh KhuntiBMJ 2008 336: 1180-1185. [Abstract] [Full Text] [
Impaired fasting glucose cutoff value of 5.6 mmol/l combined with other cardiovascular risk markers is a better predictor for incident Type 2 diabetes than the 6.1 mmol/l value: Tehran lipid and glucose study.
Harati H, Hadaegh F, Tohidi M, Azizi F.
Diabetes Res Clin Pract. 2009 Jul;85(1):90-5. Epub 2009 May 2.
PMID: 19414206 [PubMed - in process]
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Serum uric acid levels improve prediction of incident type 2 diabetes in individuals with impaired fasting glucose: the Rancho Bernardo Study.
Kramer CK, von Mühlen D, Jassal SK, Barrett-Connor E.
Diabetes Care. 2009 Jul;32(7):1272-3. Epub 2009 Apr 14.
PMID: 19366963 [PubMed - in process]
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gattaca are we yet there????
Genome-wide association studies in type 2 diabetes.
McCarthy MI, Zeggini E.
Curr Diab Rep. 2009 Apr;9(2):164-71. Review.
PMID: 19323962 [PubMed - in process]
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Clinical translation of genetic predictors for type 2 diabetes.
Majithia AR, Florez JC.
Curr Opin Endocrinol Diabetes Obes. 2009 Apr;16(2):100-6. Review.
PMID: 19306524 [PubMed - indexed for MEDLINE]
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Construction of a prediction model for type 2 diabetes mellitus in the Japanese population based on 11 genes with strong evidence of the association.
Miyake K, Yang W, Hara K, Yasuda K, Horikawa Y, Osawa H, Furuta H, Ng MC, Hirota Y, Mori H, Ido K, Yamagata K, Hinokio Y, Oka Y, Iwasaki N, Iwamoto Y, Yamada Y, Seino Y, Maegawa H, Kashiwagi A, Wang HY, Tanahashi T, Nakamura N, Takeda J, Maeda E, Yamamoto K, Tokunaga K, Ma RC, So WY, Chan JC, Kamatani N, Makino H, Nanjo K, Kadowaki T, Kasuga M.
J Hum Genet. 2009;54(4):236-41. Epub 2009 Feb 27.
PMID: 19247372 [PubMed - in process]
Prediction of type 2 diabetes using alternate anthropometric measures in a multi-ethnic cohort: the insulin resistance atherosclerosis study.
MacKay MF, Haffner SM, Wagenknecht LE, D'Agostino RB Jr, Hanley AJ.
Diabetes Care. 2009 May;32(5):956-8. Epub 2009 Feb 5.
PMID: 19196891 [PubMed - in process]
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Prediction of type 2 diabetes using simple measures of insulin resistance: combined results from the San Antonio Heart Study, the Mexico City Diabetes Study, and the Insulin Resistance Atherosclerosis Study.
Hanley AJ, Williams K, Gonzalez C, D'Agostino RB Jr, Wagenknecht LE, Stern MP, Haffner SM; San Antonio Heart Study; Mexico City Diabetes Study; Insulin Resistance Atherosclerosis Study.
Diabetes. 2003 Feb;52(2):463-9. Erratum in: Diabetes. 2003 May;52(5):1306.
PMID: 12540622 [PubMed - indexed for MEDLINE]
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Use of alternative thresholds defining insulin resistance to predict incident type 2 diabetes mellitus and cardiovascular disease.
Rutter MK, Wilson PW, Sullivan LM, Fox CS, D'Agostino RB Sr, Meigs JB.
Circulation. 2008 Feb 26;117(8):1003-9. Epub 2008 Feb 4.
PMID: 18250267 [PubMed - indexed for MEDLINE]
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M. A. Abdul-Ghani, V. Lyssenko, T. Tuomi, R. A. DeFronzo,
and L. Groop
Fasting Versus Postload Plasma Glucose Concentration and the Risk for Future Type 2 Diabetes: Results from the Botnia Study Diabetes Care February 1, 2009 32:281-286
Abstract Full Text Full Text (PDF)
S. Prudente, D. Scarpelli, M. Chandalia, Y.-Y. Zhang, E. Morini, S. Del Guerra, F. Perticone, R. Li, C. Powers, F. Andreozzi, P. Marchetti, B. Dallapiccola, N. Abate, A. Doria, G. Sesti,
and V. Trischitta
The TRIB3 Q84R Polymorphism and Risk of Early-Onset Type 2 Diabetes Journal of Clinical Endocrinol January 1, 2009 94:190-196
Abstract
Is femur length the key height component in risk prediction of type 2 diabetes among adults?
Liu J, Tan H, Jeynes B.
Diabetes Care. 2009 Apr;32(4):739-40. Epub 2009 Jan 26.
PMID: 19171722 [PubMed - in process]
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Risk prediction of prevalent diabetes in a Swiss population using a weighted genetic score--the CoLaus Study.
Lin X, Song K, Lim N, Yuan X, Johnson T, Abderrahmani A, Vollenweider P, Stirnadel H, Sundseth SS, Lai E, Burns DK, Middleton LT, Roses AD, Matthews PM, Waeber G, Cardon L, Waterworth DM, Mooser V.
Diabetologia. 2009 Apr;52(4):600-8. Epub 2009 Jan 13.
PMID: 19139842 [PubMed - indexed for MEDLINE]
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Combined measurement of fasting plasma glucose and A1C is effective for the prediction of type 2 diabetes: the Kansai Healthcare Study.
Sato KK, Hayashi T, Harita N, Yoneda T, Nakamura Y, Endo G, Kambe H.
Diabetes Care. 2009 Apr;32(4):644-6. Epub 2009 Jan 8.
PMID: 19131461 [PubMed - in process]
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Use of HbA1c in predicting progression to diabetes in French men and women: data from an Epidemiological Study on the Insulin Resistance Syndrome (DESIR).
Droumaguet C, Balkau B, Simon D, Caces E, Tichet J, Charles MA, Eschwege E; DESIR Study Group.
Diabetes Care. 2006 Jul;29(7):1619-25.
PMID: 16801588 [PubMed - indexed for MEDLINE]
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Comparison of a clinical model, the oral glucose tolerance test, and fasting glucose for prediction of type 2 diabetes risk in Japanese Americans.
McNeely MJ, Boyko EJ, Leonetti DL, Kahn SE, Fujimoto WY.
Diabetes Care. 2003 Mar;26(3):758-63.
PMID: 12610034 [PubMed - indexed for MEDLINE]
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Fasting versus postload plasma glucose concentration and the risk for future type 2 diabetes: results from the Botnia Study.
Abdul-Ghani MA, Lyssenko V, Tuomi T, DeFronzo RA, Groop L.
Diabetes Care. 2009 Feb;32(2):281-6. Epub 2008 Nov 18.
PMID: 19017778 [PubMed - indexed for MEDLINE]
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SKIN AUTOFLUORESCENCE
Noninvasive type 2 diabetes screening: superior sensitivity to fasting plasma glucose and A1C.
Maynard JD, Rohrscheib M, Way JF, Nguyen CM, Ediger MN.
Diabetes Care. 2007 May;30(5):1120-4. Epub 2007 Mar 2.
PMID: 17337498 [PubMed - indexed for MEDLINE]
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HbA1c measurement improves the detection of type 2 diabetes in high-risk individuals with nondiagnostic levels of fasting plasma glucose: the Early Diabetes Intervention Program (EDIP).
Perry RC, Shankar RR, Fineberg N, McGill J, Baron AD; Early Diabetes Intervention Program (EDIP).
Diabetes Care. 2001 Mar;24(3):465-71.
PMID: 11289469 [PubMed - indexed for MEDLINE]
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Screening for type 2 diabetes and impaired glucose metabolism: the Australian experience.
Colagiuri S, Hussain Z, Zimmet P, Cameron A, Shaw J; AusDiab.
Diabetes Care. 2004 Feb;27(2):367-71.
PMID: 14747215 [PubMed - indexed for MEDLINE]
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Diagnostic and therapeutic implications of relationships between fasting, 2-hour postchallenge plasma glucose and hemoglobin a1c values.
Woerle HJ, Pimenta WP, Meyer C, Gosmanov NR, Szoke E, Szombathy T, Mitrakou A, Gerich JE.
Arch Intern Med. 2004 Aug 9-23;164(15):1627-32.
PMID: 15302632 [PubMed - indexed for MEDLINE]
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FINNISH DIABETES RISK SCORE
The Finnish Diabetes Risk Score is associated with insulin resistance and progression towards type 2 diabetes.
Schwarz PE, Li J, Reimann M, Schutte AE, Bergmann A, Hanefeld M, Bornstein SR, Schulze J, Tuomilehto J, Lindström J.
J Clin Endocrinol Metab. 2009 Mar;94(3):920-6. Epub 2008 Dec 23.
PMID: 19106274 [PubMed - indexed for MEDLINE]
NMR-determined lipoprotein subclass profile predicts type 2 diabetes.
Hodge AM, Jenkins AJ, English DR, O'Dea K, Giles GG.
Diabetes Res Clin Pract. 2009 Jan;83(1):132-9. Epub 2008 Dec 16.
PMID: 19091436 [PubMed - indexed for MEDLINE]
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Metabolic syndrome and risk of incident diabetes: findings from the European Prospective Investigation into Cancer and Nutrition-Potsdam Study.
Ford ES, Schulze MB, Pischon T, Bergmann MM, Joost HG, Boeing H.
Cardiovasc Diabetol. 2008 Dec 12;7:35.
PMID: 19077281 [PubMed - indexed for MEDLINE]
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A prediction model for type 2 diabetes risk among Chinese people.
Chien K, Cai T, Hsu H, Su T, Chang W, Chen M, Lee Y, Hu FB.
Diabetologia. 2009 Mar;52(3):443-50. Epub 2008 Dec 5.
PMID: 19057891 [PubMed - indexed for MEDLINE]
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TCF7L2 variant genotypes and type 2 diabetes risk in Brazil: significant association, but not a significant tool for risk stratification in the general population.
Marquezine GF, Pereira AC, Sousa AG, Mill JG, Hueb WA, Krieger JE.
BMC Med Genet. 2008 Dec 4;9:106.
PMID: 19055834 [PubMed - indexed for MEDLINE]
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Tools for predicting the risk of type 2 diabetes in daily practice.
Schwarz PE, Li J, Lindstrom J, Tuomilehto J.
Horm Metab Res. 2009 Feb;41(2):86-97. Epub 2008 Nov 19. Review.
PMID: 19021089 [PubMed - indexed for MEDLINE]
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Clinical risk factors, DNA variants, and the development of type 2 diabetes.
Lyssenko V, Jonsson A, Almgren P, Pulizzi N, Isomaa B, Tuomi T, Berglund G, Altshuler D, Nilsson P, Groop L.
N Engl J Med. 2008 Nov 20;359(21):2220-32.
PMID: 19020324 [PubMed - indexed for MEDLINE]
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159:
Genotype score in addition to common risk factors for prediction of type 2 diabetes.
Meigs JB, Shrader P, Sullivan LM, McAteer JB, Fox CS, Dupuis J, Manning AK, Florez JC, Wilson PW, D'Agostino RB Sr, Cupples LA.
N Engl J Med. 2008 Nov 20;359(21):2208-19. Erratum in: N Engl J Med. 2009 Feb 5;360(6):648.
PMID: 19020323 [PubMed - indexed for MEDLINE]
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Validating the Framingham Offspring Study equations for predicting incident diabetes mellitus.
Nichols GA, Brown JB.
Am J Manag Care. 2008 Sep;14(9):574-80.
PMID: 18778172 [PubMed - indexed for MEDLINE]
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Predicting diabetes: clinical, biological, and genetic approaches: data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR).
Balkau B, Lange C, Fezeu L, Tichet J, de Lauzon-Guillain B, Czernichow S, Fumeron F, Froguel P, Vaxillaire M, Cauchi S, Ducimetière P, Eschwège E.
Diabetes Care. 2008 Oct;31(10):2056-61. Epub 2008 Aug 8.
PMID: 18689695 [PubMed - indexed for MEDLINE]
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Metabolic syndrome and incident diabetes: current state of the evidence.
Ford ES, Li C, Sattar N.
Diabetes Care. 2008 Sep;31(9):1898-904. Epub 2008 Jun 30. Review.
PMID: 18591398 [PubMed - indexed for MEDLINE]
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The interaction between impaired acute insulin response and insulin resistance predict type 2 diabetes and impairment of fasting glucose: report from a 20-year follow-up in the Uppsala Longitudinal Study of Adult Men - ULSAM.
Zethelius B, Berglund L, Hänni A, Berne C.
Ups J Med Sci. 2008;113(2):117-30.
PMID: 18509807 [PubMed - indexed for MEDLINE]
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Validation of prediction of diabetes by the Archimedes model and comparison with other predicting models.
Stern M, Williams K, Eddy D, Kahn R.
Diabetes Care. 2008 Aug;31(8):1670-1. Epub 2008 May 28.
PMID: 18509203 [PubMed - indexed for MEDLINE]
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Insulin-like growth factor-binding protein-1 in the prediction and development of type 2 diabetes in middle-aged Swedish men.
Lewitt MS, Hilding A, Ostenson CG, Efendic S, Brismar K, Hall K.
Diabetologia. 2008 Jul;51(7):1135-45. Epub 2008 May 22.
PMID: 18496669 [PubMed - indexed for MEDLINE]
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305:
Additional contribution of emerging risk factors to the prediction of the risk of type 2 diabetes: evidence from the Western New York Study.
Stranges S, Rafalson LB, Dmochowski J, Rejman K, Tracy RP, Trevisan M, Donahue RP.
Obesity (Silver Spring). 2008 Jun;16(6):1370-6. Epub 2008 Mar 20.
PMID: 18356828 [PubMed - indexed for MEDLINE]
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Adipokines and incident type 2 diabetes in an Aboriginal Canadian [corrected] population: the Sandy Lake Health and Diabetes Project.
Ley SH, Harris SB, Connelly PW, Mamakeesick M, Gittelsohn J, Hegele RA, Retnakaran R, Zinman B, Hanley AJ.
Diabetes Care. 2008 Jul;31(7):1410-5. Epub 2008 Mar 13. Erratum in: Diabetes Care. 2008 Aug;31(8):1713.
PMID: 18339973 [PubMed - indexed for MEDLINE]
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The metabolic syndrome as a tool for predicting future diabetes: the AusDiab study.
Cameron AJ, Magliano DJ, Zimmet PZ, Welborn TA, Colagiuri S, Tonkin AM, Shaw JE.
J Intern Med. 2008 Aug;264(2):177-86. Epub 2008 Feb 20.
PMID: 18298479 [PubMed - indexed for MEDLINE]
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LIVER ENZYMES
Liver enzymes and risk of diabetes and cardiovascular disease: results of the Firenze Bagno a Ripoli (FIBAR) study.
Monami M, Bardini G, Lamanna C, Pala L, Cresci B, Francesconi P, Buiatti E, Rotella CM, Mannucci E.
Metabolism. 2008 Mar;57(3):387-92.
PMID: 18249212 [PubMed - indexed for MEDLINE]
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Determinants of new-onset diabetes among 19,257 hypertensive patients randomized in the Anglo-Scandinavian Cardiac Outcomes Trial--Blood Pressure Lowering Arm and the relative influence of antihypertensive medication.
Gupta AK, Dahlof B, Dobson J, Sever PS, Wedel H, Poulter NR; Anglo-Scandinavian Cardiac Outcomes Trial Investigators.
Diabetes Care. 2008 May;31(5):982-8. Epub 2008 Jan 30.
PMID: 18235048 [PubMed - indexed for MEDLINE]
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Can self-rated health scores be used for risk prediction in patients with type 2 diabetes?
Hayes AJ, Clarke PM, Glasziou PG, Simes RJ, Drury PL, Keech AC.
Diabetes Care. 2008 Apr;31(4):795-7. Epub 2008 Jan 9.
PMID: 18184900 [PubMed - indexed for MEDLINE]
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Impact of common type 2 diabetes risk polymorphisms in the DESIR prospective study.
Vaxillaire M, Veslot J, Dina C, Proença C, Cauchi S, Charpentier G, Tichet J, Fumeron F, Marre M, Meyre D, Balkau B, Froguel P; DESIR Study Group.
Diabetes. 2008 Jan;57(1):244-54. Epub 2007 Oct 31.
PMID: 17977958 [PubMed - indexed for MEDLINE]
Related Articles Free article at journal site
A simplified Finnish diabetes risk score to predict type 2 diabetes risk and disease evolution in a German population.
Bergmann A, Li J, Wang L, Schulze J, Bornstein SR, Schwarz PE.
Horm Metab Res. 2007 Sep;39(9):677-82.
PMID: 17846976 [PubMed - indexed for MEDLINE]
Related Articles
Meigs JB.
van Hoek DM2 prediction in population study Rotterdam
Joint Effects of Common Genetic Variants on the Risk for Type 2 Diabetes in U.S. Men and Women of European Ancestry Marilyn C. Cornelis, PhD; Lu Qi, MD, PhD; Cuilin Zhang, MD, PhD; Peter Kraft, PhD; JoAnn Manson, MD, DPH; Tianxi Cai, PhD; David J. Hunter, MBBS, ScD; and Frank B. Hu, MD, PhD
21 April 2009 Volume 150 Issue 8 Pages 541-550
Conclusion: Although its discriminatory value is currently limited, a GRS that combines information from multiple genetic variants might be useful for identifying subgroups with a particularly high risk for type 2 diabetes.
annal of internal medicine- NURSES HEALTH STUDY
Comment in:
Lancet. 2009 Jun 27;373(9682):2178-9.
Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: an analysis from the Whitehall II study.
Tabák AG, Jokela M, Akbaraly TN, Brunner EJ, Kivimäki M, Witte DR.
INTERPRETATION: In this study, we show changes in glucose concentrations, insulin sensitivity, and insulin secretion as much as 3-6 years before diagnosis of diabetes. The description of biomarker trajectories leading to diabetes diagnosis could contribute to more-accurate risk prediction models that use repeated measures available for patients through regular check-ups.
Using the Optimal Receiver Operating Characteristic Curve to Design a Predictive Genetic Test, Exemplified with Type 2 Diabetes
Qing Lu1 and Robert C. Elston1
CF SERIES OF BASIC EPIDEMIOLOGY/STATISTCS BY AKOBENG
/DIAGNOSTIC TESTS/ UIN ACTA PAEDIATRICA
CALIBRATION STATISTICS
CF DIAGNOSTIC VS PROGNOSTIC MODELS - ROC
Cook NR. Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve. Clin Chem 2008;54:17-23. [Free Full Text]
Fine-tuning of prediction of isolated impaired glucose tolerance: a quantitative clinical prediction model.
Rambod M, Hosseinpanah F, Ardakani EM, Padyab M, Azizi F.
Diabetes Res Clin Pract. 2009 Jan;83(1):61-8. Epub 2008 Nov 13.
PMID: 19012984 [PubMed - indexed for MEDLINE]
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ARIC STUDY
Comment in:
Ann Intern Med. 2009 Jun 2;150(11):812-4.
Two risk-scoring systems for predicting incident diabetes mellitus in U.S. adults age 45 to 64 years.
Kahn HS, Cheng YJ, Thompson TJ, Imperatore G, Gregg EW.
Centers for Disease Control and Prevention, 4770 Buford Highway Northeast, Atlanta, GA 30341, USA.
CONCLUSION: Basic information identified adults at high risk for diabetes. Additional data from fasting blood tests better identified those at extreme risk.
RESULTS: The basic scoring system included waist circumference (10 to 35 points); maternal diabetes (13 points); hypertension (11 points); and paternal diabetes, short stature, black race, age 55 years or older, increased weight, rapid pulse, and smoking history (< or ="8" or ="7" href="http://www.bmj.com/cgi/content/full/338/mar17_2/b880?view=long&pmid=19297312">Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore
Different strategies for screening and prevention of type 2 diabetes in adults: cost effectiveness analysis
Clare L Gillies, Paul C Lambert, Keith R Abrams, Alex J Sutton, Nicola J Cooper, Ron T Hsu, Melanie J Davies, and Kamlesh KhuntiBMJ 2008 336: 1180-1185. [Abstract] [Full Text] [
Impaired fasting glucose cutoff value of 5.6 mmol/l combined with other cardiovascular risk markers is a better predictor for incident Type 2 diabetes than the 6.1 mmol/l value: Tehran lipid and glucose study.
Harati H, Hadaegh F, Tohidi M, Azizi F.
Diabetes Res Clin Pract. 2009 Jul;85(1):90-5. Epub 2009 May 2.
PMID: 19414206 [PubMed - in process]
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Serum uric acid levels improve prediction of incident type 2 diabetes in individuals with impaired fasting glucose: the Rancho Bernardo Study.
Kramer CK, von Mühlen D, Jassal SK, Barrett-Connor E.
Diabetes Care. 2009 Jul;32(7):1272-3. Epub 2009 Apr 14.
PMID: 19366963 [PubMed - in process]
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gattaca are we yet there????
Genome-wide association studies in type 2 diabetes.
McCarthy MI, Zeggini E.
Curr Diab Rep. 2009 Apr;9(2):164-71. Review.
PMID: 19323962 [PubMed - in process]
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Clinical translation of genetic predictors for type 2 diabetes.
Majithia AR, Florez JC.
Curr Opin Endocrinol Diabetes Obes. 2009 Apr;16(2):100-6. Review.
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Construction of a prediction model for type 2 diabetes mellitus in the Japanese population based on 11 genes with strong evidence of the association.
Miyake K, Yang W, Hara K, Yasuda K, Horikawa Y, Osawa H, Furuta H, Ng MC, Hirota Y, Mori H, Ido K, Yamagata K, Hinokio Y, Oka Y, Iwasaki N, Iwamoto Y, Yamada Y, Seino Y, Maegawa H, Kashiwagi A, Wang HY, Tanahashi T, Nakamura N, Takeda J, Maeda E, Yamamoto K, Tokunaga K, Ma RC, So WY, Chan JC, Kamatani N, Makino H, Nanjo K, Kadowaki T, Kasuga M.
J Hum Genet. 2009;54(4):236-41. Epub 2009 Feb 27.
PMID: 19247372 [PubMed - in process]
Prediction of type 2 diabetes using alternate anthropometric measures in a multi-ethnic cohort: the insulin resistance atherosclerosis study.
MacKay MF, Haffner SM, Wagenknecht LE, D'Agostino RB Jr, Hanley AJ.
Diabetes Care. 2009 May;32(5):956-8. Epub 2009 Feb 5.
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Prediction of type 2 diabetes using simple measures of insulin resistance: combined results from the San Antonio Heart Study, the Mexico City Diabetes Study, and the Insulin Resistance Atherosclerosis Study.
Hanley AJ, Williams K, Gonzalez C, D'Agostino RB Jr, Wagenknecht LE, Stern MP, Haffner SM; San Antonio Heart Study; Mexico City Diabetes Study; Insulin Resistance Atherosclerosis Study.
Diabetes. 2003 Feb;52(2):463-9. Erratum in: Diabetes. 2003 May;52(5):1306.
PMID: 12540622 [PubMed - indexed for MEDLINE]
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Use of alternative thresholds defining insulin resistance to predict incident type 2 diabetes mellitus and cardiovascular disease.
Rutter MK, Wilson PW, Sullivan LM, Fox CS, D'Agostino RB Sr, Meigs JB.
Circulation. 2008 Feb 26;117(8):1003-9. Epub 2008 Feb 4.
PMID: 18250267 [PubMed - indexed for MEDLINE]
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M. A. Abdul-Ghani, V. Lyssenko, T. Tuomi, R. A. DeFronzo,
and L. Groop
Fasting Versus Postload Plasma Glucose Concentration and the Risk for Future Type 2 Diabetes: Results from the Botnia Study Diabetes Care February 1, 2009 32:281-286
Abstract Full Text Full Text (PDF)
S. Prudente, D. Scarpelli, M. Chandalia, Y.-Y. Zhang, E. Morini, S. Del Guerra, F. Perticone, R. Li, C. Powers, F. Andreozzi, P. Marchetti, B. Dallapiccola, N. Abate, A. Doria, G. Sesti,
and V. Trischitta
The TRIB3 Q84R Polymorphism and Risk of Early-Onset Type 2 Diabetes Journal of Clinical Endocrinol January 1, 2009 94:190-196
Abstract
Is femur length the key height component in risk prediction of type 2 diabetes among adults?
Liu J, Tan H, Jeynes B.
Diabetes Care. 2009 Apr;32(4):739-40. Epub 2009 Jan 26.
PMID: 19171722 [PubMed - in process]
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Risk prediction of prevalent diabetes in a Swiss population using a weighted genetic score--the CoLaus Study.
Lin X, Song K, Lim N, Yuan X, Johnson T, Abderrahmani A, Vollenweider P, Stirnadel H, Sundseth SS, Lai E, Burns DK, Middleton LT, Roses AD, Matthews PM, Waeber G, Cardon L, Waterworth DM, Mooser V.
Diabetologia. 2009 Apr;52(4):600-8. Epub 2009 Jan 13.
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Combined measurement of fasting plasma glucose and A1C is effective for the prediction of type 2 diabetes: the Kansai Healthcare Study.
Sato KK, Hayashi T, Harita N, Yoneda T, Nakamura Y, Endo G, Kambe H.
Diabetes Care. 2009 Apr;32(4):644-6. Epub 2009 Jan 8.
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Use of HbA1c in predicting progression to diabetes in French men and women: data from an Epidemiological Study on the Insulin Resistance Syndrome (DESIR).
Droumaguet C, Balkau B, Simon D, Caces E, Tichet J, Charles MA, Eschwege E; DESIR Study Group.
Diabetes Care. 2006 Jul;29(7):1619-25.
PMID: 16801588 [PubMed - indexed for MEDLINE]
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Comparison of a clinical model, the oral glucose tolerance test, and fasting glucose for prediction of type 2 diabetes risk in Japanese Americans.
McNeely MJ, Boyko EJ, Leonetti DL, Kahn SE, Fujimoto WY.
Diabetes Care. 2003 Mar;26(3):758-63.
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Fasting versus postload plasma glucose concentration and the risk for future type 2 diabetes: results from the Botnia Study.
Abdul-Ghani MA, Lyssenko V, Tuomi T, DeFronzo RA, Groop L.
Diabetes Care. 2009 Feb;32(2):281-6. Epub 2008 Nov 18.
PMID: 19017778 [PubMed - indexed for MEDLINE]
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SKIN AUTOFLUORESCENCE
Noninvasive type 2 diabetes screening: superior sensitivity to fasting plasma glucose and A1C.
Maynard JD, Rohrscheib M, Way JF, Nguyen CM, Ediger MN.
Diabetes Care. 2007 May;30(5):1120-4. Epub 2007 Mar 2.
PMID: 17337498 [PubMed - indexed for MEDLINE]
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HbA1c measurement improves the detection of type 2 diabetes in high-risk individuals with nondiagnostic levels of fasting plasma glucose: the Early Diabetes Intervention Program (EDIP).
Perry RC, Shankar RR, Fineberg N, McGill J, Baron AD; Early Diabetes Intervention Program (EDIP).
Diabetes Care. 2001 Mar;24(3):465-71.
PMID: 11289469 [PubMed - indexed for MEDLINE]
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Screening for type 2 diabetes and impaired glucose metabolism: the Australian experience.
Colagiuri S, Hussain Z, Zimmet P, Cameron A, Shaw J; AusDiab.
Diabetes Care. 2004 Feb;27(2):367-71.
PMID: 14747215 [PubMed - indexed for MEDLINE]
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Diagnostic and therapeutic implications of relationships between fasting, 2-hour postchallenge plasma glucose and hemoglobin a1c values.
Woerle HJ, Pimenta WP, Meyer C, Gosmanov NR, Szoke E, Szombathy T, Mitrakou A, Gerich JE.
Arch Intern Med. 2004 Aug 9-23;164(15):1627-32.
PMID: 15302632 [PubMed - indexed for MEDLINE]
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FINNISH DIABETES RISK SCORE
The Finnish Diabetes Risk Score is associated with insulin resistance and progression towards type 2 diabetes.
Schwarz PE, Li J, Reimann M, Schutte AE, Bergmann A, Hanefeld M, Bornstein SR, Schulze J, Tuomilehto J, Lindström J.
J Clin Endocrinol Metab. 2009 Mar;94(3):920-6. Epub 2008 Dec 23.
PMID: 19106274 [PubMed - indexed for MEDLINE]
NMR-determined lipoprotein subclass profile predicts type 2 diabetes.
Hodge AM, Jenkins AJ, English DR, O'Dea K, Giles GG.
Diabetes Res Clin Pract. 2009 Jan;83(1):132-9. Epub 2008 Dec 16.
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Metabolic syndrome and risk of incident diabetes: findings from the European Prospective Investigation into Cancer and Nutrition-Potsdam Study.
Ford ES, Schulze MB, Pischon T, Bergmann MM, Joost HG, Boeing H.
Cardiovasc Diabetol. 2008 Dec 12;7:35.
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A prediction model for type 2 diabetes risk among Chinese people.
Chien K, Cai T, Hsu H, Su T, Chang W, Chen M, Lee Y, Hu FB.
Diabetologia. 2009 Mar;52(3):443-50. Epub 2008 Dec 5.
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TCF7L2 variant genotypes and type 2 diabetes risk in Brazil: significant association, but not a significant tool for risk stratification in the general population.
Marquezine GF, Pereira AC, Sousa AG, Mill JG, Hueb WA, Krieger JE.
BMC Med Genet. 2008 Dec 4;9:106.
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Tools for predicting the risk of type 2 diabetes in daily practice.
Schwarz PE, Li J, Lindstrom J, Tuomilehto J.
Horm Metab Res. 2009 Feb;41(2):86-97. Epub 2008 Nov 19. Review.
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Clinical risk factors, DNA variants, and the development of type 2 diabetes.
Lyssenko V, Jonsson A, Almgren P, Pulizzi N, Isomaa B, Tuomi T, Berglund G, Altshuler D, Nilsson P, Groop L.
N Engl J Med. 2008 Nov 20;359(21):2220-32.
PMID: 19020324 [PubMed - indexed for MEDLINE]
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159:
Genotype score in addition to common risk factors for prediction of type 2 diabetes.
Meigs JB, Shrader P, Sullivan LM, McAteer JB, Fox CS, Dupuis J, Manning AK, Florez JC, Wilson PW, D'Agostino RB Sr, Cupples LA.
N Engl J Med. 2008 Nov 20;359(21):2208-19. Erratum in: N Engl J Med. 2009 Feb 5;360(6):648.
PMID: 19020323 [PubMed - indexed for MEDLINE]
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Validating the Framingham Offspring Study equations for predicting incident diabetes mellitus.
Nichols GA, Brown JB.
Am J Manag Care. 2008 Sep;14(9):574-80.
PMID: 18778172 [PubMed - indexed for MEDLINE]
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Predicting diabetes: clinical, biological, and genetic approaches: data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR).
Balkau B, Lange C, Fezeu L, Tichet J, de Lauzon-Guillain B, Czernichow S, Fumeron F, Froguel P, Vaxillaire M, Cauchi S, Ducimetière P, Eschwège E.
Diabetes Care. 2008 Oct;31(10):2056-61. Epub 2008 Aug 8.
PMID: 18689695 [PubMed - indexed for MEDLINE]
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Metabolic syndrome and incident diabetes: current state of the evidence.
Ford ES, Li C, Sattar N.
Diabetes Care. 2008 Sep;31(9):1898-904. Epub 2008 Jun 30. Review.
PMID: 18591398 [PubMed - indexed for MEDLINE]
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The interaction between impaired acute insulin response and insulin resistance predict type 2 diabetes and impairment of fasting glucose: report from a 20-year follow-up in the Uppsala Longitudinal Study of Adult Men - ULSAM.
Zethelius B, Berglund L, Hänni A, Berne C.
Ups J Med Sci. 2008;113(2):117-30.
PMID: 18509807 [PubMed - indexed for MEDLINE]
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Validation of prediction of diabetes by the Archimedes model and comparison with other predicting models.
Stern M, Williams K, Eddy D, Kahn R.
Diabetes Care. 2008 Aug;31(8):1670-1. Epub 2008 May 28.
PMID: 18509203 [PubMed - indexed for MEDLINE]
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Insulin-like growth factor-binding protein-1 in the prediction and development of type 2 diabetes in middle-aged Swedish men.
Lewitt MS, Hilding A, Ostenson CG, Efendic S, Brismar K, Hall K.
Diabetologia. 2008 Jul;51(7):1135-45. Epub 2008 May 22.
PMID: 18496669 [PubMed - indexed for MEDLINE]
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305:
Additional contribution of emerging risk factors to the prediction of the risk of type 2 diabetes: evidence from the Western New York Study.
Stranges S, Rafalson LB, Dmochowski J, Rejman K, Tracy RP, Trevisan M, Donahue RP.
Obesity (Silver Spring). 2008 Jun;16(6):1370-6. Epub 2008 Mar 20.
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Adipokines and incident type 2 diabetes in an Aboriginal Canadian [corrected] population: the Sandy Lake Health and Diabetes Project.
Ley SH, Harris SB, Connelly PW, Mamakeesick M, Gittelsohn J, Hegele RA, Retnakaran R, Zinman B, Hanley AJ.
Diabetes Care. 2008 Jul;31(7):1410-5. Epub 2008 Mar 13. Erratum in: Diabetes Care. 2008 Aug;31(8):1713.
PMID: 18339973 [PubMed - indexed for MEDLINE]
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The metabolic syndrome as a tool for predicting future diabetes: the AusDiab study.
Cameron AJ, Magliano DJ, Zimmet PZ, Welborn TA, Colagiuri S, Tonkin AM, Shaw JE.
J Intern Med. 2008 Aug;264(2):177-86. Epub 2008 Feb 20.
PMID: 18298479 [PubMed - indexed for MEDLINE]
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LIVER ENZYMES
Liver enzymes and risk of diabetes and cardiovascular disease: results of the Firenze Bagno a Ripoli (FIBAR) study.
Monami M, Bardini G, Lamanna C, Pala L, Cresci B, Francesconi P, Buiatti E, Rotella CM, Mannucci E.
Metabolism. 2008 Mar;57(3):387-92.
PMID: 18249212 [PubMed - indexed for MEDLINE]
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Determinants of new-onset diabetes among 19,257 hypertensive patients randomized in the Anglo-Scandinavian Cardiac Outcomes Trial--Blood Pressure Lowering Arm and the relative influence of antihypertensive medication.
Gupta AK, Dahlof B, Dobson J, Sever PS, Wedel H, Poulter NR; Anglo-Scandinavian Cardiac Outcomes Trial Investigators.
Diabetes Care. 2008 May;31(5):982-8. Epub 2008 Jan 30.
PMID: 18235048 [PubMed - indexed for MEDLINE]
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Can self-rated health scores be used for risk prediction in patients with type 2 diabetes?
Hayes AJ, Clarke PM, Glasziou PG, Simes RJ, Drury PL, Keech AC.
Diabetes Care. 2008 Apr;31(4):795-7. Epub 2008 Jan 9.
PMID: 18184900 [PubMed - indexed for MEDLINE]
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Impact of common type 2 diabetes risk polymorphisms in the DESIR prospective study.
Vaxillaire M, Veslot J, Dina C, Proença C, Cauchi S, Charpentier G, Tichet J, Fumeron F, Marre M, Meyre D, Balkau B, Froguel P; DESIR Study Group.
Diabetes. 2008 Jan;57(1):244-54. Epub 2007 Oct 31.
PMID: 17977958 [PubMed - indexed for MEDLINE]
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A simplified Finnish diabetes risk score to predict type 2 diabetes risk and disease evolution in a German population.
Bergmann A, Li J, Wang L, Schulze J, Bornstein SR, Schwarz PE.
Horm Metab Res. 2007 Sep;39(9):677-82.
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utorok 23. júna 2009
pondelok 8. júna 2009
GOODNESS OF FIT, lack of fit test, statistics
biostatistical genetics and genetic epidemiology by Elston
-- GOODNESS OF FIT
lack of fit test
XXXXX NIST
with examples in R in pdf
Model selection in ecology Johnson 2004 REVIEW PDF
-- GOODNESS OF FIT
lack of fit test
XXXXX NIST
with examples in R in pdf
Model selection in ecology Johnson 2004 REVIEW PDF
nedeľa 31. mája 2009
genetic models CLARIFICATIONS NEEDED
KEYWORDS TO BE CLARIFIED
allelic association, genotypic association (PLINK)
genotype relative risk
allele risk
genetic models = diallelic polymorphisms:
dominant
recessive
co-dominant
overdominant
additive (log-additive)
multiplicative
cf PLINK tutorial section Association
cf Moreno et al. SNPstats
cf Lewis 2002: Genetic association studies: design, analysis interpretation.
cf Handbook of Statistical genetics. Eds. Balding, Bishop ...: chapters 36, 37 by Clayton, and Morris/Cardon
cf SNPGWA tutorial - gives exact documentation for calculations. explains CochranArmitage TREND TEST for log-additive genetic/inheritance model. involves NEWTON-RAPHSON algorithm /iterative process/ simply --- complicated
cf summarize - assumptions of HWE for allelic multiplicative, genotypic tests??
-
can log-additive be computed in SPSS
cf coding recommendations by CLAYTON, Morris -- logistic reg model including ADDITIVE and DOMINANCE factor.
- furthermore - cf likelihood ratio tests for model selection, cf Moreno aIC, BIC...
cf MODELS for binary data /disease status/ and for quantitative phenotypes.
allelic association, genotypic association (PLINK)
genotype relative risk
allele risk
genetic models = diallelic polymorphisms:
dominant
recessive
co-dominant
overdominant
additive (log-additive)
multiplicative
cf PLINK tutorial section Association
cf Moreno et al. SNPstats
cf Lewis 2002: Genetic association studies: design, analysis interpretation.
cf Handbook of Statistical genetics. Eds. Balding, Bishop ...: chapters 36, 37 by Clayton, and Morris/Cardon
cf SNPGWA tutorial - gives exact documentation for calculations. explains CochranArmitage TREND TEST for log-additive genetic/inheritance model. involves NEWTON-RAPHSON algorithm /iterative process/ simply --- complicated
cf summarize - assumptions of HWE for allelic multiplicative, genotypic tests??
-
can log-additive be computed in SPSS
cf coding recommendations by CLAYTON, Morris -- logistic reg model including ADDITIVE and DOMINANCE factor.
- furthermore - cf likelihood ratio tests for model selection, cf Moreno aIC, BIC...
cf MODELS for binary data /disease status/ and for quantitative phenotypes.
streda 27. mája 2009
GENETIC EPIDEMIOLOGY BASICS
https://courses.washington.edu/b516/lectures/handout9.pdf
Genotypic and Allelic Association for Binary traits TUTORIAL IN STATA
ODDS RATIO IN association testing - includes worked example, incl. formula for 95CI calculation /Woolf
genetic ANALYSIS OF complex disease GOOGLEBOOK
GENETIC ASSOCIATION STUDIES EXPLAINED - A PRIMER
ELEMENTARY CLASSICAL GENETICS
/ASSOCIATION TESTING Rpackage by clayton
Genotypic and Allelic Association for Binary traits TUTORIAL IN STATA
ODDS RATIO IN association testing - includes worked example, incl. formula for 95CI calculation /Woolf
genetic ANALYSIS OF complex disease GOOGLEBOOK
GENETIC ASSOCIATION STUDIES EXPLAINED - A PRIMER
ELEMENTARY CLASSICAL GENETICS
/ASSOCIATION TESTING Rpackage by clayton
pondelok 20. apríla 2009
hardy weiberg equilibrium, population genetics
Online Encyclopedia for Genetic Epidemiology
nice resource but seems to lack updates since 2007 ___???
inclued tool for HWE calculation ˇ(but doesn t provide exact p values)
DORAK POPGEN XXXXXXXX ______XXXXXXX
extensive collection of text on genetics, genetic epidemiology, genetic statistics etc (as mentioned repeatedly in this blog)
slight disadvantage - layout is rather poor. lumps of text
- nice addition - music notes
Michael Court excel template for HWE calculation. simple. works fine
www.tufts.edu/~mcourt01/Documents/Court%20lab%20-%20HW%20calculator.xls
evolutionary statistics - evomaths HWE test principle explained
http://de.wikipedia.org/wiki/Hardy-Weinberg-Gleichgewicht DEUTSCH
includes letter by Hardy to Editor of Science: "I am reluctant to intrude in a discussion concerning matters of which I have no expert knowledge, and I should have expected the very simple point which I wish to make to have been familiar to biologists."""
eine schoene erläuterung von Hardy-Weinberg-Gleichgewicht DEUTSCH PPT
Hardy Weinberg EQUILIBRIUM explained .... from Kimballs Biology pages NICE
populační genetika CZECH vedecko populárny resp. pre gymnazia
HWE, theory _ calculation applet + explanation /danish author NICE . population genetics
aktualni genetika Šeda, Šedová Liška, - vybrané kapitoly z lékarskej genetiky, genomiky, biologie.
sim8 applet for allele frequencie, HWE, allele frequencies based on different evolutionary and biological factors -- COMPLICATED
nice resource but seems to lack updates since 2007 ___???
inclued tool for HWE calculation ˇ(but doesn t provide exact p values)
DORAK POPGEN XXXXXXXX ______XXXXXXX
extensive collection of text on genetics, genetic epidemiology, genetic statistics etc (as mentioned repeatedly in this blog)
slight disadvantage - layout is rather poor. lumps of text
- nice addition - music notes
Michael Court excel template for HWE calculation. simple. works fine
www.tufts.edu/~mcourt01/Documents/Court%20lab%20-%20HW%20calculator.xls
evolutionary statistics - evomaths HWE test principle explained
http://de.wikipedia.org/wiki/Hardy-Weinberg-Gleichgewicht DEUTSCH
includes letter by Hardy to Editor of Science: "I am reluctant to intrude in a discussion concerning matters of which I have no expert knowledge, and I should have expected the very simple point which I wish to make to have been familiar to biologists."""
eine schoene erläuterung von Hardy-Weinberg-Gleichgewicht DEUTSCH PPT
Hardy Weinberg EQUILIBRIUM explained .... from Kimballs Biology pages NICE
populační genetika CZECH vedecko populárny resp. pre gymnazia
HWE, theory _ calculation applet + explanation /danish author NICE . population genetics
aktualni genetika Šeda, Šedová Liška, - vybrané kapitoly z lékarskej genetiky, genomiky, biologie.
sim8 applet for allele frequencie, HWE, allele frequencies based on different evolutionary and biological factors -- COMPLICATED
utorok 31. marca 2009
MTNR1B rs10830962 PROKOPENKO
PROKOPENKO
STAIGER 2009
http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?showRare=on&chooseRs=all&locusId=4544&mrna=NM_005959.3&ctg=NT_008984.17&prot=NP_005950.1&orien=forward&refresh=refresh
http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&DbFrom=snp&Cmd=Link&LinkName=snp_pubmed_cited&IdsFromResult=12804291
STAIGER 2009
http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?showRare=on&chooseRs=all&locusId=4544&mrna=NM_005959.3&ctg=NT_008984.17&prot=NP_005950.1&orien=forward&refresh=refresh
http://www.ncbi.nlm.nih.gov/sites/entrez?Db=pubmed&DbFrom=snp&Cmd=Link&LinkName=snp_pubmed_cited&IdsFromResult=12804291
streda 21. januára 2009
sample size
intro I . in RCT
lancet epidemiology series on sample size samba
physiological statistics by D Sackett
clinical trial simulator
lancet epidemiology series on sample size samba
physiological statistics by D Sackett
clinical trial simulator
utorok 13. januára 2009
fibrinogen
FIBRINOGEN ASSAYS GUIDELINES UK 2003
FIBRINOGEN STANDARD 2ND
http://atvb.ahajournals.org/cgi/content/abstract/28/4/758
FIBRINOGEN HAPLOTYPES -NO CAD KOCH2008
GWAS FRAMINGHAM 2007
fibrinogen minutes 2006 XXXXX
ISTH 2008
FBG INTRAINDIVIDUAL VARIABILITY ROUVIER 2002
FIBRINOGEN STUDIES COLLABORATION JAMA 2005
Variability in fibrinogen measurements: an obstacle to cardiovascular risk stratification
rosenson 2001
http://www.clinchem.org/cgi/content/full/50/11/2150
Effect of Specimen Collection on Routine Coagulation Assays and D-Dimer Measurement LIPPI 2004
Among routine coagulation assays, fibrinogen and D-dimer measurements are thought to be more susceptible to variations introduced in the preanalytical phase (2)(3)(4). The term "D-dimer" usually refers to a heterogeneous class of end-stage degradation products of cross-linked fibrin that occur in vivo with a wide range of molecular weights and contain various numbers of the D-dimer motif (5). The strong heterogeneity of D-dimer in plasma might be reflected by most immunoassays (the monoclonal antibodies show variable reactivity with different molecular forms) because the various forms occur naturally in the plasma of patients. This might be particularly true when evaluating D-dimer after continuous blood-sampling because it is conceivable that different samples from the same patients might contain heterogeneous D-dimer isoforms. Some recommended procedures for acquiring samples for coagulation analysis, especially those involving the measurement of D-dimer and fibrinogen by the Clauss method, mandate that the first sample be discarded because tissue thromboplastin released by the trauma of venipuncture could interfere with coagulation assays by activating intrinsic pathway (2). However, in agreement with the earlier report by Rosenson et al. (6), our results demonstrate that a standardized procedure for blood collection does not influence the reliability of in vitro routine coagulation testing. This is particularly true for fibrinogen and D-dimer measurements, as it had been speculated previously that discrepancies might arise from different specimens collected simultaneously from the same patient (2)(6).
atorvastatin on fibrinogen 1998
Immunonephelometric determination of fibrinogen on citrated or heparinized plasma: comparison with functional Clauss method.
As a result, the immunonephelometric method shows reliable performance and clinical sample measurements are not affected by the method used, validating the use of heparinized plasma samples for fibrinogen antigen determination with Dade Behring reagents.
Kinetic Fibrinogen Assay (KFA), a method based on the kinetic reaction of the developing fibrin clot, was used to determine fibrinogen concentration in plasma. Two other methods employing different quantification principles were used for comparison: the von Clauss method and the procedure measuring protein concentration in an isolated and washed plasma clot (World Health Organization [WHO] method). All three methods quantified functional thrombin-coagulable fibrinogen. Tan Am J Clin Path 1995
Comparison of Clauss fibrinogen and fibrinogen measurement derived from aPTT and PT in diabetes mellitus and correlations with markers of glycemic equilibration
Abstract number: P0278
Sobas* F., Hanss† M., Dechavanne* M., Riou* J. P., Drai‡ J., Negrier* C.
‡Centre Hospitalier, Lyon, France *Hôpital Edouard Herriot, Lyon, France; †Hop Cardiologique Lyon, France;
The consequences of fibrinogen glycosylation on fibrinogen polymerization are controversial in the literature. We have compared fibrinogen levels determined with Clauss, prothrombin time derived measurement of fibrinogen (PTd) and activated partial prothrombin time-derived measurement of fibrinogen (aPTTd) in 32 diabetic patients (Sobas et al. Blood Coagul Fibrinolysis, 2002; 13: 61–68) (7 type II and 10 type I diabetic patients without clinical complication, 11 type II and 4 type I diabetic patients with clinical complications, all patients presenting normal range of PT, aPTT and fibrinogen level). We have also correlated the differences between Clauss and derived methods with markers of glycemic equilibration such as glycemia, capillary glucose value and levels of hemoglobin A1C and fructosamine. All coagulation assays (PT, aPTT, Clauss fibrinogen) were performed on an MDA II coagulometer (bioMérieux). To allow comparison of fibrinogen measurements in diabetic samples (Clauss vs. PTd and aPTTd) reference curves were constructed from 30 control plasmas with normal range of PT, aPTT and fibrinogen level. In all the diabetic patients, fibrinogen was lower when using PTd and aPTTd measurements vs. the Clauss method (paired t-test for PTd: mean of differences = -0.49, P < 0.0001; for aPTTd: mean of differences = -0.39, P < 0.0001). In the 18 type II diabetic patients, there are significant correlation between (PTd-Clauss) difference and Glycemia (P = 0.013); Capillary glucose value (P = 0.008) and Haemoglobin A1C (P = 0.031). In the 14 type I diabetic patients, no correlation is observed. In the 11 type II diabetic patients with complications, no correlation is observed. In the 7 type II diabetic patients without complication (PTd-Clauss) difference is correlated with Glycemia (P = 0.034), with Capillary glucose value (P < 0.001) and with hemoglobin A1C (P = 0.023). (aPTTd-Clauss) difference is correlated with fructosamine (P = 0.048) in the 7 type II diabetic patients without complication. The fibrinogen polymerization is abnormal in aPTT and PT tests in diabetic patients when using MDA II coagulometer. The absence of correlation between markers of glycemic equilibration and difference (Clauss fibrinogen-fibrinogen derived methods) may be an help in finding diabetic patients with clinical complications. To cite this abstract use the following format: Journal of Thrombosis and Haemostasis 2003; 1 Supplement 1 July: abstract number
http://www.biomedexperts.com/Abstract.bme/15843224/Interferences_in_coagulation_tests--evaluation_of_the_570-nm_method_on_the_Dade_Behring_BCS_analyser
2005: Junker Ralf; Käse Margit; Schulte Helmut; Bäumer Ruth; Langer Claus; Nowak-Göttl UlrikeInterferences in coagulation tests--evaluation of the 570-nm method on the Dade Behring BCS analyser.Clinical chemistry and laboratory medicine : CCLM / FESCC 2005;43(2):244-52.
The Dade Behring BCS is a coagulation analyser with optical reaction detection (standard 405 nm). The present study was conducted to evaluate measurement at 570 nm for analyses in interfering plasma samples. Prothrombin time (PT), activated partial thromboplastin time (aPTT), fibrinogen and D-dimer in normal (n=50), lipaemic (n=60), icteric (n=113), and haemolytic (n=58) samples were measured at 405 and 570 nm. As they are unaffected by the optical properties of the sample, the mechanical STAcompact analyser (Roche Diagnostics) and an ELISA technique were defined as the "comparison" methods. The percentage of valid PT results using the 570-nm method varied from 54% (lipaemic samples) to 97% (haemolytic samples). Valid aPTT measurements were found in 67% (lipaemic samples) up to 93% (icteric samples) of samples. Fibrinogen measurement revealed valid results in 58% (lipaemic samples) to 100% (haemolytic samples) of samples. The number of valid D-dimer results varied from 28% (lipaemic material) up to 100% (haemolytic material). Significant inter-method differences were found: aPTT in lipaemic (BCS 405 vs. 570 nm) and icteric samples (STAcompact vs. BCS 405 and 570 nm); fibrinogen in lipaemic (BCS 405 vs. 570 nm), icteric (BCS 405 vs. 570 nm; STAcompact vs. BCS 570 nm) and haemolytic samples (STAcompact vs. BCS 405 and 570 nm). Differences between the BCS 570-nm and the STAcompact methods were in most cases low and less pronounced than between the BCS 570- and 405-nm methods, making the BCS 570-nm method an alternative to measurement at 405 nm. Limitations have to be taken into account regarding lipaemic plasma.
FIBRINOGEN STANDARD 2ND
http://atvb.ahajournals.org/cgi/content/abstract/28/4/758
FIBRINOGEN HAPLOTYPES -NO CAD KOCH2008
GWAS FRAMINGHAM 2007
fibrinogen minutes 2006 XXXXX
ISTH 2008
FBG INTRAINDIVIDUAL VARIABILITY ROUVIER 2002
FIBRINOGEN STUDIES COLLABORATION JAMA 2005
Variability in fibrinogen measurements: an obstacle to cardiovascular risk stratification
rosenson 2001
http://www.clinchem.org/cgi/content/full/50/11/2150
Effect of Specimen Collection on Routine Coagulation Assays and D-Dimer Measurement LIPPI 2004
Among routine coagulation assays, fibrinogen and D-dimer measurements are thought to be more susceptible to variations introduced in the preanalytical phase (2)(3)(4). The term "D-dimer" usually refers to a heterogeneous class of end-stage degradation products of cross-linked fibrin that occur in vivo with a wide range of molecular weights and contain various numbers of the D-dimer motif (5). The strong heterogeneity of D-dimer in plasma might be reflected by most immunoassays (the monoclonal antibodies show variable reactivity with different molecular forms) because the various forms occur naturally in the plasma of patients. This might be particularly true when evaluating D-dimer after continuous blood-sampling because it is conceivable that different samples from the same patients might contain heterogeneous D-dimer isoforms. Some recommended procedures for acquiring samples for coagulation analysis, especially those involving the measurement of D-dimer and fibrinogen by the Clauss method, mandate that the first sample be discarded because tissue thromboplastin released by the trauma of venipuncture could interfere with coagulation assays by activating intrinsic pathway (2). However, in agreement with the earlier report by Rosenson et al. (6), our results demonstrate that a standardized procedure for blood collection does not influence the reliability of in vitro routine coagulation testing. This is particularly true for fibrinogen and D-dimer measurements, as it had been speculated previously that discrepancies might arise from different specimens collected simultaneously from the same patient (2)(6).
atorvastatin on fibrinogen 1998
Immunonephelometric determination of fibrinogen on citrated or heparinized plasma: comparison with functional Clauss method.
As a result, the immunonephelometric method shows reliable performance and clinical sample measurements are not affected by the method used, validating the use of heparinized plasma samples for fibrinogen antigen determination with Dade Behring reagents.
Kinetic Fibrinogen Assay (KFA), a method based on the kinetic reaction of the developing fibrin clot, was used to determine fibrinogen concentration in plasma. Two other methods employing different quantification principles were used for comparison: the von Clauss method and the procedure measuring protein concentration in an isolated and washed plasma clot (World Health Organization [WHO] method). All three methods quantified functional thrombin-coagulable fibrinogen. Tan Am J Clin Path 1995
Comparison of Clauss fibrinogen and fibrinogen measurement derived from aPTT and PT in diabetes mellitus and correlations with markers of glycemic equilibration
Abstract number: P0278
Sobas* F., Hanss† M., Dechavanne* M., Riou* J. P., Drai‡ J., Negrier* C.
‡Centre Hospitalier, Lyon, France *Hôpital Edouard Herriot, Lyon, France; †Hop Cardiologique Lyon, France;
The consequences of fibrinogen glycosylation on fibrinogen polymerization are controversial in the literature. We have compared fibrinogen levels determined with Clauss, prothrombin time derived measurement of fibrinogen (PTd) and activated partial prothrombin time-derived measurement of fibrinogen (aPTTd) in 32 diabetic patients (Sobas et al. Blood Coagul Fibrinolysis, 2002; 13: 61–68) (7 type II and 10 type I diabetic patients without clinical complication, 11 type II and 4 type I diabetic patients with clinical complications, all patients presenting normal range of PT, aPTT and fibrinogen level). We have also correlated the differences between Clauss and derived methods with markers of glycemic equilibration such as glycemia, capillary glucose value and levels of hemoglobin A1C and fructosamine. All coagulation assays (PT, aPTT, Clauss fibrinogen) were performed on an MDA II coagulometer (bioMérieux). To allow comparison of fibrinogen measurements in diabetic samples (Clauss vs. PTd and aPTTd) reference curves were constructed from 30 control plasmas with normal range of PT, aPTT and fibrinogen level. In all the diabetic patients, fibrinogen was lower when using PTd and aPTTd measurements vs. the Clauss method (paired t-test for PTd: mean of differences = -0.49, P < 0.0001; for aPTTd: mean of differences = -0.39, P < 0.0001). In the 18 type II diabetic patients, there are significant correlation between (PTd-Clauss) difference and Glycemia (P = 0.013); Capillary glucose value (P = 0.008) and Haemoglobin A1C (P = 0.031). In the 14 type I diabetic patients, no correlation is observed. In the 11 type II diabetic patients with complications, no correlation is observed. In the 7 type II diabetic patients without complication (PTd-Clauss) difference is correlated with Glycemia (P = 0.034), with Capillary glucose value (P < 0.001) and with hemoglobin A1C (P = 0.023). (aPTTd-Clauss) difference is correlated with fructosamine (P = 0.048) in the 7 type II diabetic patients without complication. The fibrinogen polymerization is abnormal in aPTT and PT tests in diabetic patients when using MDA II coagulometer. The absence of correlation between markers of glycemic equilibration and difference (Clauss fibrinogen-fibrinogen derived methods) may be an help in finding diabetic patients with clinical complications. To cite this abstract use the following format: Journal of Thrombosis and Haemostasis 2003; 1 Supplement 1 July: abstract number
http://www.biomedexperts.com/Abstract.bme/15843224/Interferences_in_coagulation_tests--evaluation_of_the_570-nm_method_on_the_Dade_Behring_BCS_analyser
2005: Junker Ralf; Käse Margit; Schulte Helmut; Bäumer Ruth; Langer Claus; Nowak-Göttl UlrikeInterferences in coagulation tests--evaluation of the 570-nm method on the Dade Behring BCS analyser.Clinical chemistry and laboratory medicine : CCLM / FESCC 2005;43(2):244-52.
The Dade Behring BCS is a coagulation analyser with optical reaction detection (standard 405 nm). The present study was conducted to evaluate measurement at 570 nm for analyses in interfering plasma samples. Prothrombin time (PT), activated partial thromboplastin time (aPTT), fibrinogen and D-dimer in normal (n=50), lipaemic (n=60), icteric (n=113), and haemolytic (n=58) samples were measured at 405 and 570 nm. As they are unaffected by the optical properties of the sample, the mechanical STAcompact analyser (Roche Diagnostics) and an ELISA technique were defined as the "comparison" methods. The percentage of valid PT results using the 570-nm method varied from 54% (lipaemic samples) to 97% (haemolytic samples). Valid aPTT measurements were found in 67% (lipaemic samples) up to 93% (icteric samples) of samples. Fibrinogen measurement revealed valid results in 58% (lipaemic samples) to 100% (haemolytic samples) of samples. The number of valid D-dimer results varied from 28% (lipaemic material) up to 100% (haemolytic material). Significant inter-method differences were found: aPTT in lipaemic (BCS 405 vs. 570 nm) and icteric samples (STAcompact vs. BCS 405 and 570 nm); fibrinogen in lipaemic (BCS 405 vs. 570 nm), icteric (BCS 405 vs. 570 nm; STAcompact vs. BCS 570 nm) and haemolytic samples (STAcompact vs. BCS 405 and 570 nm). Differences between the BCS 570-nm and the STAcompact methods were in most cases low and less pronounced than between the BCS 570- and 405-nm methods, making the BCS 570-nm method an alternative to measurement at 405 nm. Limitations have to be taken into account regarding lipaemic plasma.
štvrtok 8. januára 2009
ALFRED
The ALlele FREquency Database
A resource of gene frequency data on human populations supported by the U. S. National Science Foundation.
The ALlele FREquency Database
A resource of gene frequency data on human populations supported by the U. S. National Science Foundation.
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