Browsing by Subject "FASTING PLASMA-GLUCOSE"

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  • Awad, Susanne F.; Dargham, Soha R.; Toumi, Amine A.; Dumit, Elsy M.; El-Nahas, Katie G.; Al-Hamaq, Abdulla O.; Critchley, Julia A.; Tuomilehto, Jaakko; Al-Thani, Mohamed H. J.; Abu-Raddad, Laith J. (2021)
    We developed a diabetes risk score using a novel analytical approach and tested its diagnostic performance to detect individuals at high risk of diabetes, by applying it to the Qatari population. A representative random sample of 5,000 Qataris selected at different time points was simulated using a diabetes mathematical model. Logistic regression was used to derive the score using age, sex, obesity, smoking, and physical inactivity as predictive variables. Performance diagnostics, validity, and potential yields of a diabetes testing program were evaluated. In 2020, the area under the curve (AUC) was 0.79 and sensitivity and specificity were 79.0% and 66.8%, respectively. Positive and negative predictive values (PPV and NPV) were 36.1% and 93.0%, with 42.0% of Qataris being at high diabetes risk. In 2030, projected AUC was 0.78 and sensitivity and specificity were 77.5% and 65.8%. PPV and NPV were 36.8% and 92.0%, with 43.0% of Qataris being at high diabetes risk. In 2050, AUC was 0.76 and sensitivity and specificity were 74.4% and 64.5%. PPV and NPV were 40.4% and 88.7%, with 45.0% of Qataris being at high diabetes risk. This model-based score demonstrated comparable performance to a data-derived score. The derived self-complete risk score provides an effective tool for initial diabetes screening, and for targeted lifestyle counselling and prevention programs.
  • Kettunen, Jarno L. T.; Tuomi, Tiinamaija (2020)
    The last decade has revealed hundreds of genetic variants associated with type 2 diabetes, many especially with insulin secretion. However, the evidence for their single or combined effect on beta-cell function relies mostly on genetic association of the variants or genetic risk scores with simple traits, and few have been functionally fully characterized even in cell or animal models. Translating the measured traits into human physiology is not straightforward: none of the various indices for beta-cell function or insulin sensitivity recapitulates the dynamic interplay between glucose sensing, endogenous glucose production, insulin production and secretion, insulin clearance, insulin resistance-to name just a few factors. Because insulin sensitivity is a major determinant of physiological need of insulin, insulin secretion should be evaluated in parallel with insulin sensitivity. On the other hand, multiple physiological or pathogenic processes can either mask or unmask subtle defects in beta-cell function. Even in monogenic diabetes, a clearly pathogenic genetic variant can result in different phenotypic characteristics-or no phenotype at all. In this review, we evaluate the methods available for studying beta-cell function in humans, critically examine the evidence linking some identified variants to a specific beta-cell phenotype, and highlight areas requiring further study. (C) 2020 The Authors. Published by Elsevier Ltd.
  • Mahajan, Anubha; Sim, Xueling; Ng, Hui Jin; Manning, Alisa; Rivas, Manuel A.; Highland, Heather M.; Locke, Adam E.; Grarup, Niels; Im, Hae Kyung; Cingolani, Pablo; Flannick, Jason; Fontanillas, Pierre; Fuchsberger, Christian; Gaulton, Kyle J.; Teslovich, Tanya M.; Rayner, N. William; Robertson, Neil R.; Beer, Nicola L.; Rundle, Jana K.; Bork-Jensen, Jette; Ladenvall, Claes; Blancher, Christine; Buck, David; Buck, Gemma; Burtt, Noel P.; Gabriel, Stacey; Gjesing, Anette P.; Groves, Christopher J.; Hollensted, Mette; Huyghe, Jeroen R.; Jackson, Anne U.; Jun, Goo; Justesen, Johanne Marie; Mangino, Massimo; Murphy, Jacquelyn; Neville, Matt; Onofrio, Robert; Small, Kerrin S.; Stringham, Heather M.; Syvanen, Ann-Christine; Trakalo, Joseph; Abecasis, Goncalo; Bell, Graeme I.; Blangero, John; Cox, Nancy J.; Duggirala, Ravindranath; Isomaa, Bo; Tuomi, Tiinamaija; Tuomilehto, Jaakko; Groop, Leif; T2D-GENES Consortium; Go T2D Consortium (2015)
  • Wheeler, Eleanor; Leong, Aaron; Liu, Ching-Ti; Hivert, Marie-France; Strawbridge, Rona J.; Podmore, Clara; Li, Man; Yao, Jie; Sim, Xueling; Hong, Jaeyoung; Chu, Audrey Y.; Zhang, Weihua; Wang, Xu; Chen, Peng; Maruthur, Nisa M.; Porneala, Bianca C.; Sharp, Stephen J.; Jia, Yucheng; Kabagambe, Edmond K.; Chang, Li-Ching; Chen, Wei-Min; Elks, Cathy E.; Evans, Daniel S.; Fan, Qiao; Giulianini, Franco; Go, Min Jin; Hottenga, Jouke-Jan; Hu, Yao; Jackson, Anne U.; Kanoni, Stavroula; Kim, Young Jin; Kleber, Marcus E.; Ladenvall, Claes; Lecoeur, Cecile; Lim, Sing-Hui; Lu, Yingchang; Mahajan, Anubha; Marzi, Carola; Nalls, Mike A.; Navarro, Pau; Nolte, Ilja M.; Rose, Lynda M.; Rybin, Denis V.; Sanna, Serena; Shi, Yuan; Stram, Daniel O.; Salo, Perttu; Kivimaki, Mika; Groop, Leif; Tuomilehto, Jaakko; EPIC-CVD Consortium; EPIC-InterAct Consortium; Lifelines Cohort Study (2017)
    Background Glycated hemoglobin (HbA1c) is used to diagnose type 2 diabetes (T2D) and assess glycemic control in patients with diabetes. Previous genome-wide association studies (GWAS) have identified 18 HbA1c-associated genetic variants. These variants proved to be classifiable by their likely biological action as erythrocytic (also associated with erythrocyte traits) or glycemic (associated with other glucose-related traits). In this study, we tested the hypotheses that, in a very large scale GWAS, we would identify more genetic variants associated with HbA1c and that HbA1c variants implicated in erythrocytic biology would affect the diagnostic accuracy of HbA1c. We therefore expanded the number of HbA1c-associated loci and tested the effect of genetic risk-scores comprised of erythrocytic or glycemic variants on incident diabetes prediction and on prevalent diabetes screening performance. Throughout this multiancestry study, we kept a focus on interancestry differences in HbA1c genetics performance that might influence race-ancestry differences in health outcomes. Methods & findings Using genome-wide association meta-analyses in up to 159,940 individuals from 82 cohorts of European, African, East Asian, and South Asian ancestry, we identified 60 common genetic variants associated with HbA1c. We classified variants as implicated in glycemic, erythrocytic, or unclassified biology and tested whether additive genetic scores of erythrocytic variants (GS-E) or glycemic variants (GS-G) were associated with higher T2D incidence in multiethnic longitudinal cohorts (N = 33,241). Nineteen glycemic and 22 erythrocytic variants were associated with HbA1c at genome-wide significance. GS-G was associated with higher T2D risk (incidence OR = 1.05, 95% CI 1.04-1.06, per HbA1c-raising allele, p = 3 x 10-29); whereas GS-E was not (OR = 1.00, 95% CI 0.99-1.01, p = 0.60). In Europeans and Asians, erythrocytic variants in aggregate had only modest effects on the diagnostic accuracy of HbA1c. Yet, in African Americans, the X-linked G6PD G202A variant (T-allele frequency 11%) was associated with an absolute decrease in HbA1c of 0.81%-units (95% CI 0.66-0.96) per allele in hemizygous men, and 0.68%-units (95% CI 0.38-0.97) in homozygous women. The G6PD variant may cause approximately 2% (N = 0.65 million, 95% CI0.55-0.74) of African American adults with T2Dto remain undiagnosed when screened with HbA1c. Limitations include the smaller sample sizes for non-European ancestries and the inability to classify approximately one-third of the variants. Further studies in large multiethnic cohorts with HbA1c, glycemic, and erythrocytic traits are required to better determine the biological action of the unclassified variants. Conclusions As G6PD deficiency can be clinically silent until illness strikes, we recommend investigation of the possible benefits of screening for the G6PD genotype along with using HbA1c to diagnose T2D in populations of African ancestry or groups where G6PD deficiency is common. Screening with direct glucose measurements, or genetically-informed HbA1c diagnostic thresholds in people with G6PD deficiency, may be required to avoid missed or delayed diagnoses.
  • Charvat, Hadrien; Goto, Atsushi; Goto, Maki; Inoue, Machiko; Heianza, Yoriko; Arase, Yasuji; Sone, Hirohito; Nakagami, Tomoko; Song, Xin; Qiao, Qing; Tuomilehto, Jaakko; Tsugane, Shoichiro; Noda, Mitsuhiko; Inoue, Manami (2015)
    Aims/IntroductionTo provide age- and sex-specific trends, age-standardized trends, and projections of diabetes prevalence through the year 2030 in the Japanese adult population. Materials and MethodsIn the present meta-regression analysis, we included 161,087 adults from six studies and nine national health surveys carried out between 1988 and 2011 in Japan. We assessed the prevalence of diabetes using a recorded history of diabetes or, for the population of individuals without known diabetes, either a glycated hemoglobin level of 6.5% (48mmol/mol) or the 1999 World Health Organization criteria (i.e., a fasting plasma glucose level of 126mg/dL and/or 2-h glucose level of 200mg/dL in the 75-g oral glucose tolerance test). ResultsFor both sexes, prevalence appeared to remain unchanged over the years in all age categories except for men aged 70years or older, in whom a significant increase in prevalence with time was observed. Age-standardized diabetes prevalence estimates based on the Japanese population of the corresponding year showed marked increasing trends: diabetes prevalence was 6.1% among women (95% confidence interval [CI] 5.5-6.7), 9.9% (95% CI 9.2-10.6) among men, and 7.9% (95% CI 7.5-8.4) among the total population in 2010, and was expected to rise by 2030 to 6.7% (95% CI 5.2-9.2), 13.1% (95% CI 10.9-16.7) and 9.8% (95% CI 8.5-12.0), respectively. In contrast, the age-standardized diabetes prevalence using a fixed population appeared to remain unchanged. ConclusionsThis large-scale meta-regression analysis shows that a substantial increase in diabetes prevalence is expected in Japan during the next few decades, mainly as a result of the aging of the adult population.
  • Haljas, Kadri; Hakaste, Liisa; Lahti, Jari; Isomaa, Bo; Groop, Leif; Tuomi, Tiinamaija; Räikkönen, Katri (2019)
    Background: Seasonal variation in glucose metabolism might be driven by changes in daylight. Melatonin entrains circadian regulation and is directly associated with daylight. The relationship between melatonin receptor 1B gene variants with glycemic traits and type 2 diabetes is well established. We studied if daylight length was associated with glycemic traits and if it modified the relationship between melatonin receptor 1B gene rs10830963 variant and glycemic traits. Materials: A population-based sample of 3422 18-78-year-old individuals without diabetes underwent an oral glucose tolerance test twice, an average 6.8 years (SD = 0.9) apart and were genotyped for rs10830963. Daylight data was obtained from the Finnish Meteorological Institute. Results: Cross-sectionally, more daylight was associated with lower fasting glucose, but worse insulin sensitivity and secretion at follow-up. Longitudinally, individuals studied on lighter days at follow-up than at baseline showed higher glucose values during the oral glucose tolerance test and lower Corrected Insulin Response at follow-up. GG genotype carriers in the rs10830963 became more insulin resistant during follow-up if daylight length was shorter at follow-up than at baseline. Conclusions: Our study shows that individual glycemic profiles may vary according to daylight, MTNR1B genotype and their interaction. Future studies may consider taking daylight length into account.Key messages In Western Finland, the amount daylight follows an extensive annual variation ranging from 4 h 44 min to 20 h 17 min, making it ideal to study the associations between daylight and glycemic traits. Moreover, this allows researchers to explore if the relationship between the melatonin receptor 1B gene rs10830963 variant and glycemic traits is modified by the amount of daylight both cross-sectionally and longitudinally. This study shows that individuals, who participated in the study on lighter days at the follow-up than at the baseline, displayed to a greater extent worse glycemic profiles across the follow-up. Novel findings from the current study show that in the longitudinal analyses, each addition of the minor G allele of the melatonin receptor 1B gene rs10830963 was associated with worsening of fasting glucose values and insulin secretion across the 6.8-year follow-up. Importantly, this study shows that in those with the rs10830963 GG genotype, insulin sensitivity deteriorated the most significantly across the 6.8-year follow-up if the daylight length on the oral glucose tolerance testing date at the follow-up was shorter than at the baseline. Taken together, the current findings suggest that the amount of daylight may affect glycemic traits, especially fasting glucose and insulin secretion even though the effect size is small. The association can very according to the rs10830963 risk variant. Further research is needed to elucidate the mechanisms behind these associations.