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- Danaei, Goodarz, et al.
(author)
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Effects of diabetes definition on global surveillance of diabetes prevalence and diagnosis: a pooled analysis of 96 population-based studies with 331288 participants
- 2015
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In: The Lancet Diabetes & Endocrinology. - 2213-8595 .- 2213-8587. ; 3:8, s. 624-637
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Journal article (peer-reviewed)abstract
- Background Diabetes has been defined on the basis of different biomarkers, including fasting plasma glucose (FPG), 2-h plasma glucose in an oral glucose tolerance test (2hOGTT), and HbA(1c). We assessed the effect of different diagnostic definitions on both the population prevalence of diabetes and the classification of previously undiagnosed individuals as having diabetes versus not having diabetes in a pooled analysis of data from population-based health examination surveys in different regions. Methods We used data from 96 population-based health examination surveys that had measured at least two of the biomarkers used for defining diabetes. Diabetes was defined using HbA(1c) (HbA(1c) >= 6 . 5% or history of diabetes diagnosis or using insulin or oral hypoglycaemic drugs) compared with either FPG only or FPG-or-2hOGTT definitions (FPG >= 7 . 0 mmol/L or 2hOGTT >= 11 . 1 mmol/L or history of diabetes or using insulin or oral hypoglycaemic drugs). We calculated diabetes prevalence, taking into account complex survey design and survey sample weights. We compared the prevalences of diabetes using different definitions graphically and by regression analyses. We calculated sensitivity and specificity of diabetes diagnosis based on HbA1c compared with diagnosis based on glucose among previously undiagnosed individuals (ie, excluding those with history of diabetes or using insulin or oral hypoglycaemic drugs). We calculated sensitivity and specificity in each survey, and then pooled results using a random-effects model. We assessed the sources of heterogeneity of sensitivity by meta-regressions for study characteristics selected a priori. Findings Population prevalence of diabetes based on FPG- or-2hOGTT was correlated with prevalence based on FPG alone (r= 0 . 98), but was higher by 2-6 percentage points at different prevalence levels. Prevalence based on HbA(1c) was lower than prevalence based on FPG in 42 . 8% of age-sex-survey groups and higher in another 41 . 6%; in the other 15 . 6%, the two definitions provided similar prevalence estimates. The variation across studies in the relation between glucose-based and HbA(1c)-based prevalences was partly related to participants' age, followed by natural logarithm of per person gross domestic product, the year of survey, mean BMI, and whether the survey population was national, subnational, or from specific communities. Diabetes defined as HbA(1c) 6 . 5% or more had a pooled sensitivity of 52 . 8% (95% CI 51 . 3-54 . 3%) and a pooled specificity of 99 . 74% (99 . 71-99 . 78%) compared with FPG 7 . 0 mmol/L or more for diagnosing previously undiagnosed participants; sensitivity compared with diabetes defined based on FPG-or-2hOGTT was 30 . 5% (28 . 7-32 . 3%). None of the preselected study-level characteristics explained the heterogeneity in the sensitivity of HbA(1c) versus FPG. Interpretation Different biomarkers and definitions for diabetes can provide different estimates of population prevalence of diabetes, and differentially identify people without previous diagnosis as having diabetes. Using an HbA(1c)-based definition alone in health surveys will not identify a substantial proportion of previously undiagnosed people who would be considered as having diabetes using a glucose-based test.
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- Huffman, Jennifer E., et al.
(author)
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Modulation of Genetic Associations with Serum Urate Levels by Body-Mass-Index in Humans
- 2015
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In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 10:3
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Journal article (peer-reviewed)abstract
- We tested for interactions between body mass index (BMI) and common genetic variants affecting serum urate levels, genome-wide, in up to 42569 participants. Both stratified genome-wide association (GWAS) analyses, in lean, overweight and obese individuals, and regression-type analyses in a non BMI-stratified overall sample were performed. The former did not uncover any novel locus with a major main effect, but supported modulation of effects for some known and potentially new urate loci. The latter highlighted a SNP at RBFOX3 reaching genome-wide significant level (effect size 0.014, 95% CI 0.008-0.02, P-inter= 2.6 x 10(-8)). Two top loci in interaction term analyses, RBFOX3 and ERO1LB-EDAR-ADD, also displayed suggestive differences in main effect size between the lean and obese strata. All top ranking loci for urate effect differences between BMI categories were novel and most had small magnitude but opposite direction effects between strata. They include the locus RBMS1-TANK (men, Pdifflean-overweight= 4.7 x 10(-8)), a region that has been associated with several obesity related traits, and TSPYL5 (men, Pdifflean-overweight= 9.1 x 10(-8)), regulating adipocytes-produced estradiol. The top-ranking known urate loci was ABCG2, the strongest known gout risk locus, with an effect halved in obese compared to lean men (Pdifflean-obese= 2 x 10(-4)). Finally, pathway analysis suggested a role for N-glycan biosynthesis as a prominent urate-associated pathway in the lean stratum. These results illustrate a potentially powerful way to monitor changes occurring in obesogenic environment.
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