المؤلفون: Martinez, Maria Månsson, Spiliopoulos, Lampros, Salami, Falastin, Agardh, Daniel, Toppari, Jorma, Lernmark, Åke, Kero, Jukka, Veijola, Riitta, Tossavainen, Päivi, Palmu, Sauli, Lundgren, Markus, Borg, Henrik, Katsarou, Anastasia, Larsson, Helena Elding, Knip, Mikael, Maziarz, Marlena, Törn, Carina, Ramelius, Anita, Jönsson, Ida, Bennet, Rasmus, Sjöberg, Birgitta, Wimar, Åsa, Melin, Jessica, Ask, Maria, Wallin, Anne, Hansen, Monika, Hyberg, Susanne, Ottosson, Karin, Bremer, Jenny, Carlsson, Ulla-Marie, Ulvenhag, Ulrika, Sjöberg, Anette, Lindström, Marielle, Fransson, Lina, Johansen, Fredrik, Rahmati, Kobra, Mestan, Zeliha, Tekum-Amboh, Evelyn, Jovic, Silvija, Gerardsson, Joanna, Ericson-Hallström, Emelie, Åberg, Sofie, Sibthorpe, Sara, Mäntymäki, Elina, Vainionpää, Sini, Romo, Minna, Othmani, Zhian, Varjonen, Eeva, Jokipuu, Sanna, Ruohonen, Satu

المساهمون: juvenile diabetes research foundation united states of america, foundation for the national institutes of health, vetenskapsrådet, stiftelsen för strategisk forskning, Lund University

المصدر: Clinical Diabetes and Endocrinology ; volume 7, issue 1 ; ISSN 2055-8260

مصطلحات موضوعية: General Medicine

الوصف: Background Individuals with multiple islet autoantibodies are at increased risk for clinical type 1 diabetes and may proceed gradually from stage to stage complicating the recruitment to secondary prevention studies. We evaluated multiple islet autoantibody positive subjects before randomisation for a clinical trial 1 month apart for beta-cell function, glucose metabolism and continuous glucose monitoring (CGM). We hypothesized that the number and type of islet autoantibodies in combination with different measures of glucose metabolism including fasting glucose, HbA1c, oral glucose tolerance test (OGTT), intra venous glucose tolerance test (IvGTT) and CGM allows for more precise staging of autoimmune type 1 diabetes than the number of islet autoantibodies alone. Methods Subjects ( n = 57) at 2–50 years of age, positive for two or more islet autoantibodies were assessed by fasting plasma insulin, glucose, HbA1c as well as First Phase Insulin Response (FPIR) in IvGTT, followed 1 month later by OGTT, and 1 week of CGM ( n = 24). Results Autoantibodies against GAD65 (GADA; n = 52), ZnT8 (ZnT8A; n = 40), IA-2 (IA-2A; n = 38) and insulin (IAA; n = 28) were present in 9 different combinations of 2–4 autoantibodies. Fasting glucose and HbA1c did not differ between the two visits. The estimate of the linear relationship between log2-transformed FPIR as the outcome and log2-transformed area under the OGTT glucose curve (AUC) as the predictor, adjusting for age and sex was − 1.88 (− 2.71, − 1.05) p = 3.49 × 10–5. The direction of the estimates for all glucose metabolism measures was positive except for FPIR, which was negative. FPIR was associated with higher blood glucose. Both the median and the spread of the CGM glucose data were significantly associated with higher glucose values based on OGTT, higher HbA1c, and lower FPIR. There was no association between glucose metabolism, autoantibody number and type except that there was an indication that the presence of at least one of ZnT8(Q/R/W) A was associated with ...

الإتاحة: https://doi.org/10.1186/s40842-021-00135-6Test

المؤلفون: Johnson, Randi K., Lernmark, Åke, Andrén Aronsson, Carin, Ask, Maria, Bremer, Jenny, Cilio, Corrado, Ericson-Hallström, Emelie, Björne Fors, Annika, Fransson, Lina, Gard, Thomas, Bennet, Rasmus, Hansen, Monica, Hyberg-Karlsson, Suzanne, Jisser, Hanna, Johansen, Fredrik, Jónsdóttir, Berglind, JOVIC, SILVIJA, Elding Larsson, Helena, Lindström, Marielle, Lundgren, Markus, Månsson Martinez, Maria, Markan, Maria, Melin, Marie Jessica, Mestan, Zeliha, Nilsson, Caroline N, Ottosson, Karin, Rahmati, Kobra, Ramelius, Anita, Salami, Falastin, Sjöberg, Anette, Sjöberg, Birgitta, Törn, Carina, Wallin, Anne, Wimar, Åsa, Åberg, Sofie, Norris, Jill M

المصدر: Scientific Reports EXODIAB: Excellence of Diabetes Research in Sweden. 9(1)

مصطلحات موضوعية: Medicin och hälsovetenskap, Klinisk medicin, Endokrinologi och diabetes, Medical and Health Sciences, Clinical Medicine, Endocrinology and Diabetes, Hälsovetenskap, Näringslära, Health Sciences, Nutrition and Dietetics

الوصف: The role of diet in type 1 diabetes development is poorly understood. Metabolites, which reflect dietary response, may help elucidate this role. We explored metabolomics and lipidomics differences between 352 cases of islet autoimmunity (IA) and controls in the TEDDY (The Environmental Determinants of Diabetes in the Young) study. We created dietary patterns reflecting pre-IA metabolite differences between groups and examined their association with IA. Secondary outcomes included IA cases positive for multiple autoantibodies (mAb+). The association of 853 plasma metabolites with outcomes was tested at seroconversion to IA, just prior to seroconversion, and during infancy. Key compounds in enriched metabolite sets were used to create dietary patterns reflecting metabolite composition, which were then tested for association with outcomes in the nested case-control subset and the full TEDDY cohort. Unsaturated phosphatidylcholines, sphingomyelins, phosphatidylethanolamines, glucosylceramides, and phospholipid ethers in infancy were inversely associated with mAb+ risk, while dicarboxylic acids were associated with an increased risk. An infancy dietary pattern representing higher levels of unsaturated phosphatidylcholines and phospholipid ethers, and lower sphingomyelins was protective for mAb+ in the nested case-control study only. Characterization of this high-risk infant metabolomics profile may help shape the future of early diagnosis or prevention efforts. © 2019, The Author(s).

الوصول الحر: https://lup.lub.lu.se/record/4093762d-1d72-4627-ab46-e0e9a6d8f362Test
http://dx.doi.org/10.1038/s41598-019-51251-4Test

المؤلفون: Lundgren, Markus, Steed, Leigh Johnson, Tamura, Roy N., Jonsdottir, Berglind, Gesualdo, Patricia, Crouch, Claire Cowen, Sjöberg, Maija, Hansson, Gertie, Hagopian, William A., Ziegler, Anette-G, Rewers, Marian J., Lernmark, Åke, Toppari, Jorma, She, Jin-Xiong, Akolkar, Beena, Krischer, Jeffrey P., Haller, Michael J., Elding Larsson, Helena, Bautista, Kimberly, Baxter, Judith, Bedoy, Ruth, Felipe-Morales, Daniel, Driscoll, Kimberly, Frohnert, Brigitte I., Hoffman, Michelle, Karban, Rachel, Liu, Edwin, Norris, Jill, Samper-Imaz, Adela, Steck, Andrea K, Waugh, Kathleen, Wright, Hali, Simell, Olli G., Adamsson, Annika, Ahonen, Suvi, Hyöty, Heikki, Ilonen, Jorma, Jokipuu, Sanna, Kallio, Tiina, Karlsson, Leena, Kähönen, Miia, Knip, Mikael, Kovanen, Lea, Koreasalo, Mirva, Kurppa, Kalle, Latvaaho, Tiina, Lönnrot, Maria, Mäntymäki, Elina, Multasuo, Katja, Mykkänen, Juha, Niininen, Tiina, Niinistö, Sari, Nyblom, Mia, Rajala, Petra, Rautanen, Jenna, Riikonen, Anne, Riikonen, Mika, Rouhiainen, Jenni, Romo, Minna, Simell, Tuula, Simell, Ville, Stenius, Aino, Leppänen, Maria, Vainionpää, Sini, Varjonen, Eeva, Veijola, Riitta, Virtanen, Suvi M, Vähä-Mäkilä, Mari, Åkerlund, Mari, Lindfors, Katri, Schatz, Desmond, Hopkins, Diane, Thomas, Jamie, Adams, Janey, Silvis, Katherine, Gardiner, Melissa, McIndoe, Richard, Sharma, Ashok, Williams, Joshua W, Young, Gabriela, Anderson, Stephen W., Jacobsen, Laura Mary, Beyerlein, Andreas, Bonifacio, Ezio, Hummel, Michael, Hummel, Sandra, Foterek, Kristina, Janz, Nicole, Kersting, Mathilde, Knopff, Annette, Koletzko, Sibylle, Peplow, Claudia, Roth, Roswith, Scholz, Marlon, Stock, Joanna, Warncke, Katharina, Wendel, Lorena, Winkler, Christiane, Agardh, Daniel, Aronsson, Carin Andrén, Ask, Maria, Bremer, Jenny, Carlsson, Ulla Marie, Cilio, Corrado, Ericson-Hallström, Emelie, Fransson, Lina, Gard, Thomas, Gerardsson, Joanna, Bennet, Rasmus, Hansen, Monica, Hyberg, Susanne, Johansen, Fredrik, Lindström, Marielle, Månsson-Martinez, Maria, Markan, Maria, Melin, Jessica, Mestan, Zeliha, Ottosson, Karin, Rahmati, Kobra, Ramelius, Anita, Salami, Falastin, Sibthorpe, Sara, Sjöberg, Birgitta, Swartling, Ulrica, Amboh, Evelyn Tekum, Törn, Carina, Wallin, Anne, Wimar, Åsa, Åberg, Sofie, Michael Killian, Killian, Skidmore, Jennifer, Carson, Josephine, Dalzell, Maria, Dunson, Kayleen, Hervey, Rachel, Johnson, Corbin, Lyons, Rachel, Meyer, Arlene, Mulenga, Denise, Tarr, Alexander, Uland, Morgan, Willis, John, Becker, Dorothy, Franciscus, Margaret, Ellen, Mary, Smith, Mary Ellen Dalmagro Elias, Daftary, Ashi, Klein, Mary Beth, Yates, Chrystal, Abbondondolo, Michael, Austin-Gonzalez, Sarah, Avendano, Maryouri, Baethke, Sandra, Brown, Rasheedah, Burkhardt, Brant R., Butterworth, Martha, Clasen, Joanna, Cuthbertson, David, Christopher Eberhard, Eberhard, Fiske, Steven W., Garcia, Dena, Garmeson, Jennifer, Gowda, Veena, Heyman, Kathleen, Laras, Francisco Perez, Lee, Hye-Seung, Liu, Shu, Liu, Xiang, Lynch, Kristian, Malloy, Jamie, McCarthy, Cristina, Meulemans, Steven, Parikh, Hemang, Shaffer, Chris, Smith, Laura, Smith, Susan, Sulman, Noah, Uusitalo, Ulla, Vehik, Kendra, Vijayakandipan, Ponni, Wood, Keith, Yang, Jimin, Lori Ballard, R. D., Hadley, David, Mcleod, Wendy, Yu, Liping, Miao, Dongmei, Bingley, Polly J, Williams, Alistair, Chandler, Kyla, Rokni, Saba, Williams, Claire L., Wyatt, Rebecca, George, Gifty, Grace, Sian, Erlich, Henry, Mack, Steven J., Fear, Anna Lisa, Ke, Sandra, Mulholland, Niveen, Rich, Stephen, Chen, Wei-Min, Onengut-Gumuscu, Suna, Farber, Emily, Pickin, Rebecca Roche, Davis, Jordan, Gallo, Dan, Bonnie, Jessica, Campolieto, Paul, Bourcier, Kasia, Briese, Thomas, Johnson, Suzanne Bennett, Triplett, Eric W

المصدر: BMC Pediatrics EXODIAB: Excellence of Diabetes Research in Sweden. 17(1)

مصطلحات موضوعية: Analgesics, Islet autoimmunity, Prospective studies, Type 1 diabetes, Medicin och hälsovetenskap, Klinisk medicin, Endokrinologi och diabetes, Medical and Health Sciences, Clinical Medicine, Endocrinology and Diabetes

الوصف: Background: The use of analgesic antipyretics (ANAP) in children have long been a matter of controversy. Data on their practical use on an individual level has, however, been scarce. There are indications of possible effects on glucose homeostasis and immune function related to the use of ANAP. The aim of this study was to analyze patterns of analgesic antipyretic use across the clinical centers of The Environmental Determinants of Diabetes in the Young (TEDDY) prospective cohort study and test if ANAP use was a risk factor for islet autoimmunity. Methods: Data were collected for 8542 children in the first 2.5 years of life. Incidence was analyzed using logistic regression with country and first child status as independent variables. Holm's procedure was used to adjust for multiplicity of intercountry comparisons. Time to autoantibody seroconversion was analyzed using a Cox proportional hazards model with cumulative analgesic use as primary time dependent covariate of interest. For each categorization, a generalized estimating equation (GEE) approach was used. Results: Higher prevalence of ANAP use was found in the U.S. (95.7%) and Sweden (94.8%) compared to Finland (78.1%) and Germany (80.2%). First-born children were more commonly given acetaminophen (OR 1.26; 95% CI 1.07, 1.49; p = 0.007) but less commonly Non-Steroidal Anti-inflammatory Drugs (NSAID) (OR 0.86; 95% CI 0.78, 0.95; p = 0.002). Acetaminophen and NSAID use in the absence of fever and infection was more prevalent in the U.S. (40.4%; 26.3% of doses) compared to Sweden, Finland and Germany (p < 0.001). Acetaminophen or NSAID use before age 2.5 years did not predict development of islet autoimmunity by age 6 years (HR 1.02, 95% CI 0.99-1.09; p = 0.27). In a sub-analysis, acetaminophen use in children with fever weakly predicted development of islet autoimmunity by age 3 years (HR 1.05; 95% CI 1.01-1.09; p = 0.024). Conclusions: ANAP use in young children is not a risk factor for seroconversion by age 6 years. Use of ANAP is widespread in young children, and significantly higher in the U.S. compared to other study sites, where use is common also in absence of fever and infection.

الوصول الحر: https://lup.lub.lu.se/record/6a59c413-936c-4908-8bed-26b4774b1f84Test
http://dx.doi.org/10.1186/s12887-017-0884-yTest

المؤلفون: Norris, Jill M, Lee, Hye-Seung, Frederiksen, Brittni, Erlund, Iris, Uusitalo, Ulla, Yang, Jimin, Lernmark, Åke, Simell, Olli, Toppari, Jorma, Rewers, Marian, Ziegler, Anette-G, She, Jin-Xiong, Onengut-Gumuscu, Suna, Chen, Wei-Min, Rich, Stephen S, Sundvall, Jouko, Akolkar, Beena, Krischer, Jeffrey, Virtanen, Suvi M, Hagopian, William, TEDDY Study Group, Bautista, Kimberly, Baxter, Judith, Bedoy, Ruth, Felipe-Morales, Daniel, Driscoll, Kimberly, Frohnert, Brigitte I, Gesualdo, Patricia, Hoffman, Michelle, Karban, Rachel, Liu, Edwin, Norris, Jill, Samper-Imaz, Adela, Steck, Andrea, Waugh, Kathleen, Wright, Hali, Simell, Olli G, Adamsson, Annika, Ahonen, Suvi, Hyöty, Heikki, Ilonen, Jorma, Jokipuu, Sanna, Kallio, Tiina, Karlsson, Leena, Kähönen, Miia, Knip, Mikael, Kovanen, Lea, Koreasalo, Mirva, Kurppa, Kalle, Latva-Aho, Tiina, Lönnrot, Maria, Mäntymäki, Elina, Multasuo, Katja, Mykkänen, Juha, Niininen, Tiina, Niinistö, Sari, Nyblom, Mia, Rajala, Petra, Rautanen, Jenna, Riikonen, Anne, Riikonen, Mika, Rouhiainen, Jenni, Romo, Minna, Simell, Tuula, Simell, Ville, Sjöberg, Maija, Stenius, Aino, Leppänen, Maria, Vainionpää, Sini, Varjonen, Eeva, Veijola, Riitta, Vähä-Mäkilä, Mari, Åkerlund, Mari, Lindfors, Katri, Schatz, Desmond, Hopkins, Diane, Steed, Leigh, Thomas, Jamie, Adams, Janey, Silvis, Katherine, Haller, Michael, Gardiner, Melissa, McIndoe, Richard, Sharma, Ashok, Williams, Joshua, Young, Gabriela, Anderson, Stephen W, Jacobsen, Laura, Ziegler, Anette G, Beyerlein, Andreas, Bonifacio, Ezio, Hummel, Michael, Hummel, Sandra, Foterek, Kristina, Janz, Nicole, Kersting, Mathilde, Knopff, Annette, Koletzko, Sibylle, Peplow, Claudia, Roth, Roswith, Scholz, Marlon, Stock, Joanna, Warncke, Katharina, Wendel, Lorena, Winkler, Christiane, Agardh, Daniel, Aronsson, Carin Andrén, Ask, Maria, Bremer, Jenny, Carlsson, Ulla-Marie, Cilio, Corrado, Ericson-Hallström, Emelie, Fransson, Lina, Gard, Thomas, Gerardsson, Joanna, Bennet, Rasmus, Hansen, Monica, Hansson, Gertie, Hyberg, Susanne, Johansen, Fredrik, Jonsdottir, Berglind, Larsson, Helena Elding, Lindström, Marielle, Lundgren, Markus, Månsson-Martinez, Maria, Markan, Maria, Melin, Jessica, Mestan, Zeliha, Ottosson, Karin, Rahmati, Kobra, Ramelius, Anita, Salami, Falastin, Sibthorpe, Sara, Sjöberg, Birgitta, Swartling, Ulrica, Amboh, Evelyn Tekum, Törn, Carina, Wallin, Anne, Wimar, Åsa, Åberg, Sofie, Hagopian, William A, Killian, Michael, Crouch, Claire Cowen, Skidmore, Jennifer, Carson, Josephine, Dalzell, Maria, Dunson, Kayleen, Hervey, Rachel, Johnson, Corbin, Lyons, Rachel, Meyer, Arlene, Mulenga, Denise, Tarr, Alexander, Uland, Morgan, Willis, John, Becker, Dorothy, Franciscus, Margaret, Dalmagro-Elias Smith, MaryEllen, Daftary, Ashi, Klein, Mary Beth, Yates, Chrystal, Krischer, Jeffrey P, Abbondondolo, Michael, Austin-Gonzalez, Sarah, Avendano, Maryouri, Baethke, Sandra, Brown, Rasheedah, Burkhardt, Brant, Butterworth, Martha, Clasen, Joanna, Cuthbertson, David, Eberhard, Christopher, Fiske, Steven, Garcia, Dena, Garmeson, Jennifer, Gowda, Veena, Heyman, Kathleen, Laras, Francisco Perez, Liu, Shu, Liu, Xiang, Lynch, Kristian, Malloy, Jamie, McCarthy, Cristina, Meulemans, Steven, Parikh, Hemang, Shaffer, Chris, Smith, Laura, Smith, Susan, Sulman, Noah, Tamura, Roy, Vehik, Kendra, Vijayakandipan, Ponni, Wood, Keith, Bourcier, Kasia, Briese, Thomas, Johnson, Suzanne Bennett, Triplett, Eric, Yu, Liping, Miao, Dongmei, Bingley, Polly, Williams, Alistair, Chandler, Kyla, Rokni, Saba, Williams, Claire, Wyatt, Rebecca, George, Gifty, Grace, Sian, Salminen, Irma, Leiviskä, Jaana, Kangas, Nina, Arohonka, Petra, Erlich, Henry, Mack, Steven J, Fear, Anna Lisa, Ke, Sandra, Mulholland, Niveen, Farber, Emily, Pickin, Rebecca Roche, Davis, Jordan, Gallo, Dan, Bonnie, Jessica, Campolieto, Paul

المساهمون: Klinik und Poliklinik für Kinder- und Jugendmedizin

مصطلحات موضوعية: info:eu-repo/classification/ddc

الوصف: We examined the association between plasma 25-hydroxyvitamin D [25(OH)D] concentration and islet autoimmunity (IA) and whether vitamin D gene polymorphisms modify the effect of 25(OH)D on IA risk. We followed 8,676 children at increased genetic risk of type 1 diabetes at six sites in the U.S. and Europe. We defined IA as positivity for at least one autoantibody (GADA, IAA, or IA-2A) on two or more visits. We conducted a risk set sampled nested case-control study of 376 IA case subjects and up to 3 control subjects per case subject. 25(OH)D concentration was measured on all samples prior to, and including, the first IA positive visit. Nine polymorphisms in , and were analyzed as effect modifiers of 25(OH)D. Adjusting for HLA-DR-DQ and ancestry, higher childhood 25(OH)D was associated with lower IA risk (odds ratio = 0.93 for a 5 nmol/L difference; 95% CI 0.89, 0.97). Moreover, this association was modified by rs7975232 (interaction = 0.0072), where increased childhood 25(OH)D was associated with a decreasing IA risk based upon number of minor alleles: 0 (1.00; 0.93, 1.07), 1 (0.92; 0.89, 0.96), and 2 (0.86; 0.80, 0.92). Vitamin D and may have a combined role in IA development in children at increased genetic risk for type 1 diabetes.

العلاقة: https://mediatum.ub.tum.de/1453079Test

الإتاحة: https://doi.org/10.2337/db17-0802Test
https://mediatum.ub.tum.de/1453079Test

المؤلفون: Köhler, Meike, Beyerlein, Andreas, Vehik, Kendra, Greven, Sonja, Umlauf, Nikolaus, Lernmark, Åke, Hagopian, William A, Rewers, Marian, She, Jin-Xiong, Toppari, Jorma, Akolkar, Beena, Krischer, Jeffrey P, Bonifacio, Ezio, Ziegler, Anette-G, TEDDY study group, Bautista, Kimberly, Baxter, Judith, Bedoy, Ruth, Felipe-Morales, Daniel, Driscoll, Kimberly, Frohnert, Brigitte I, Gesualdo, Patricia, Hoffman, Michelle, Karban, Rachel, Liu, Edwin, Norris, Jill, Samper-Imaz, Adela, Steck, Andrea, Waugh, Kathleen, Wright, Hali, Simell, Olli G, Adamsson, Annika, Ahonen, Suvi, Hyöty, Heikki, Ilonen, Jorma, Jokipuu, Sanna, Kallio, Tiina, Karlsson, Leena, Kähönen, Miia, Knip, Mikael, Kovanen, Lea, Koreasalo, Mirva, Kurppa, Kalle, Latva-Aho, Tiina, Lönnrot, Maria, Mäntymäki, Elina, Multasuo, Katja, Mykkänen, Juha, Niininen, Tiina, Niinistö, Sari, Nyblom, Mia, Rajala, Petra, Rautanen, Jenna, Riikonen, Anne, Riikonen, Mika, Rouhiainen, Jenni, Romo, Minna, Simell, Tuula, Simell, Ville, Sjöberg, Maija, Stenius, Aino, Leppänen, Maria, Vainionpää, Sini, Varjonen, Eeva, Veijola, Riitta, Virtanen, Suvi M, Vähä-Mäkilä, Mari, Åkerlund, Mari, Lindfors, Katri, Schatz, Desmond, Hopkins, Diane, Steed, Leigh, Thomas, Jamie, Adams, Janey, Silvis, Katherine, Haller, Michael, Gardiner, Melissa, McIndoe, Richard, Sharma, Ashok, Williams, Joshua, Young, Gabriela, Anderson, Stephen W, Jacobsen, Laura, Hummel, Michael, Hummel, Sandra, Foterek, Kristina, Janz, Nicole, Kersting, Mathilde, Knopff, Annette, Koletzko, Sibylle, Peplow, Claudia, Roth, Roswith, Scholz, Marlon, Stock, Joanna, Warncke, Katharina, Wendel, Lorena, Winkler, Christiane, Agardh, Daniel, Aronsson, Carin Andrén, Ask, Maria, Bremer, Jenny, Carlsson, Ulla-Marie, Cilio, Corrado, Ericson-Hallström, Emelie, Fransson, Lina, Gard, Thomas, Gerardsson, Joanna, Bennet, Rasmus, Hansen, Monica, Hansson, Gertie, Hyberg, Susanne, Johansen, Fredrik, Jonsdottir, Berglind, Larsson, Helena Elding, Lindström, Marielle, Lundgren, Markus, Månsson-Martinez, Maria, Markan, Maria, Melin, Jessica, Mestan, Zeliha, Ottosson, Karin, Rahmati, Kobra, Ramelius, Anita, Salami, Falastin, Sibthorpe, Sara, Sjöberg, Birgitta, Swartling, Ulrica, Amboh, Evelyn Tekum, Törn, Carina, Wallin, Anne, Wimar, Åsa, Åberg, Sofie, Killian, Michael, Crouch, Claire Cowen, Skidmore, Jennifer, Carson, Josephine, Dalzell, Maria, Dunson, Kayleen, Hervey, Rachel, Johnson, Corbin, Lyons, Rachel, Meyer, Arlene, Mulenga, Denise, Tarr, Alexander, Uland, Morgan, Willis, John, Becker, Dorothy, Franciscus, Margaret, Smith, MaryEllen Dalmagro-Elias, Daftary, Ashi, Klein, Mary Beth, Yates, Chrystal, Abbondondolo, Michael, Austin-Gonzalez, Sarah, Avendano, Maryouri, Baethke, Sandra, Brown, Rasheedah, Burkhardt, Brant, Butterworth, Martha, Clasen, Joanna, Cuthbertson, David, Eberhard, Christopher, Fiske, Steven, Garcia, Dena, Garmeson, Jennifer, Gowda, Veena, Heyman, Kathleen, PerezLaras, Francisco, Lee, Hye-Seung, Liu, Shu, Liu, Xiang, Lynch, Kristian, Malloy, Jamie, McCarthy, Cristina, Meulemans, Steven, Parikh, Hemang, Shaffer, Chris, Smith, Laura, Smith, Susan, Sulman, Noah, Tamura, Roy, Uusitalo, Ulla, Vijayakandipan, Ponni, Wood, Keith, Yang, Jimin, Ballard, Lori, Hadley, David, McLeod, Wendy, Yu, Liping, Miao, Dongmei, Bingley, Polly, Williams, Alistair, Chandler, Kyla, Rokni, Saba, Williams, Claire, Wyatt, Rebecca, George, Gifty, Grace, Sian, Erlich, Henry, Mack, Steven J, Ke, Sandra, Mulholland, Niveen, Bourcier, Kasia, Briese, Thomas, Johnson, Suzanne Bennett, Triplett, Eric

مصطلحات موضوعية: info:eu-repo/classification/ddc, stat, demo

الوصف: The onset of clinical type 1 diabetes (T1D) is preceded by the occurrence of disease-specific autoantibodies. The level of autoantibody titers is known to be associated with progression time from the first emergence of autoantibodies to the onset of clinical symptoms, but detailed analyses of this complex relationship are lacking. We aimed to fill this gap by applying advanced statistical models.We investigated data of 613 children from the prospective TEDDY study who were persistent positive for IAA, GADA and/or IA2A autoantibodies. We used a novel approach of Bayesian joint modeling of longitudinal and survival data to assess the potentially time- and covariate-dependent association between the longitudinal autoantibody titers and progression time to T1D.For all autoantibodies we observed a positive association between the titers and the T1D progression risk. This association was estimated as time-constant for IA2A, but decreased over time for IAA and GADA. For example the hazard ratio [95% credibility interval] for IAA (per transformed unit) was 3.38 [2.66, 4.38] at 6 months after seroconversion, and 2.02 [1.55, 2.68] at 36 months after seroconversion.These findings indicate that T1D progression risk stratification based on autoantibody titers should focus on time points early after seroconversion. Joint modeling techniques allow for new insights into these associations.

العلاقة: http://mediatum.ub.tum.de/node?id=1453062Test

الإتاحة: http://mediatum.ub.tum.de/node?id=1453062Test

المؤلفون: Haghighi, Mona, Johnson, Suzanne Bennett, Qian, Xiaoning, Lynch, Kristian F, Vehik, Kendra, Huang, Shuai, TEDDY Study Group, Rewers, Marian, Bautista, Kimberly, Baxter, Judith, Bedoy, Ruth, Felipe-Morales, Daniel, Driscoll, Kimberly, Frohnert, Brigitte I, Gesualdo, Patricia, Hoffman, Michelle, Karban, Rachel, Liu, Edwin, Norris, Jill, Samper-Imaz, Adela, Steck, Andrea, Waugh, Kathleen, Wright, Hali, Sharma, Ashok, Hopkins, Diane, Young, Gabriela, She, Jin-Xiong, Williams, Joshua, Silvis, Katherine, Steed, Leigh, Gardiner, Melissa, McIndoe, Richard, Schatz, Desmond, Thomas, Jamie, Adams, Janey, Jacobsen, Laura, Haller, Michael, Triplett, Eric, Anderson, Stephen W, Mykkänen, Juha, Lindfors, Katri, Adamsson, Annika, Jokipuu, Sanna, Kallio, Tiina, Karlsson, Leena, Mäntymäki, Elina, Rajala, Petra, Riikonen, Mika, Rouhiainen, Jenni, Romo, Minna, Leppänen, Maria, Vainionpää, Sini, Vähä-Mäkilä, Mari, Stenius, Aino, Toppari, Jorma, Simell, Olli G, Simell, Tuula, Sjöberg, Maija, Varjonen, Eeva, Hyöty, Heikki, Knip, Mikael, Kurppa, Kalle, Lönnrot, Maria, Niininen, Tiina, Nyblom, Mia, Ahonen, Suvi, Kovanen, Lea, Koreasalo, Mirva, Riikonen, Anne, Virtanen, Suvi M, Åkerlund, Mari, Ilonen, Jorma, Kähönen, Miia, Latva-Aho, Tiina, Multasuo, Katja, Veijola, Riitta, Niinistö, Sari, Rautanen, Jenna, Ziegler, Anette G, Hummel, Michael, Hummel, Sandra, Janz, Nicole, Knopff, Annette, Peplow, Claudia, Roth, Roswith, Scholz, Marlon, Stock, Joanna, Warncke, Katharina, Wendel, Lorena, Winkler, Christiane, Beyerlein, Andreas, Bonifacio, Ezio, Koletzko, Sibylle, Foterek, Kristina, Kersting, Mathilde, Lernmark, Åke, Agardh, Daniel, Andrén Aronsson, Carin, Ask, Maria, Bremer, Jenny, Carlsson, Ulla-Marie, Cilio, Corrado, Ericson-Hallström, Emelie, Fransson, Lina, Gard, Thomas, Gerardsson, Joanna, Bennet, Rasmus, Hansen, Monica, Hansson, Gertie, Hyberg, Susanne, Johansen, Fredrik, Jonsdottir, Berglind, Elding Larsson, Helena, Lindström, Marielle, Lundgren, Markus, Månsson-Martinez, Maria, Markan, Maria, Melin, Jessica, Mestan, Zeliha, Ottosson, Karin, Rahmati, Kobra, Ramelius, Anita, Salami, Falastin, Sibthorpe, Sara, Sjöberg, Birgitta, Swartling, Ulrica, Tekum Amboh, Evelyn, Törn, Carina, Wallin, Anne, Wimar, Åsa, Åberg, Sofie, Hagopian, William A, Killian, Michael, Cowen Crouch, Claire, Skidmore, Jennifer, Carson, Josephine, Dalzell, Maria, Dunson, Kayleen, Hervey, Rachel, Johnson, Corbin, Lyons, Rachel, Meyer, Arlene, Mulenga, Denise, Tarr, Alexander, Uland, Morgan, Willis, John, Becker, Dorothy, Franciscus, Margaret, Smith, MaryEllen Dalmagro-Elias, Daftary, Ashi, Beth Klein, Mary, Yates, Chrystal, Krischer, Jeffrey P, Abbondondolo, Michael, Austin-Gonzalez, Sarah, Avendano, Maryouri, Baethke, Sandra, Brown, Rasheedah, Burkhardt, Brant, Butterworth, Martha, Clasen, Joanna, Cuthbertson, David, Eberhard, Christopher, Fiske, Steven, Garcia, Dena, Garmeson, Jennifer, Gowda, Veena, Heyman, Kathleen, Perez Laras, Francisco, Lee, Hye-Seung, Liu, Shu, Liu, Xiang, Malloy, Jamie, McCarthy, Cristina, Meulemans, Steven, Parikh, Hemang, Shaffer, Chris, Smith, Laura, Smith, Susan, Sulman, Noah, Tamura, Roy, Uusitalo, Ulla, Vijayakandipan, Ponni, Wood, Keith, Yang, Jimin, Akolkar, Beena, Bourcier, Kasia, Briese, Thomas

المساهمون: Klinik und Poliklinik für Kinder- und Jugendmedizin

مصطلحات موضوعية: info:eu-repo/classification/ddc

العلاقة: https://mediatum.ub.tum.de/1453064Test

الإتاحة: https://doi.org/10.1038/srep39723Test
https://mediatum.ub.tum.de/1453064Test

المؤلفون: Köhler, Meike, Beyerlein, Andreas, Vehik, Kendra, Greven, Sonja, Umlauf, Nikolaus, Lernmark, Åke, Hagopian, William A, Rewers, Marian, She, Jin-Xiong, Toppari, Jorma, Akolkar, Beena, Krischer, Jeffrey P, Bonifacio, Ezio, Ziegler, Anette-G, TEDDY study group, Bautista, Kimberly, Baxter, Judith, Bedoy, Ruth, Felipe-Morales, Daniel, Driscoll, Kimberly, Frohnert, Brigitte I, Gesualdo, Patricia, Hoffman, Michelle, Karban, Rachel, Liu, Edwin, Norris, Jill, Samper-Imaz, Adela, Steck, Andrea, Waugh, Kathleen, Wright, Hali, Simell, Olli G, Adamsson, Annika, Ahonen, Suvi, Hyöty, Heikki, Ilonen, Jorma, Jokipuu, Sanna, Kallio, Tiina, Karlsson, Leena, Kähönen, Miia, Knip, Mikael, Kovanen, Lea, Koreasalo, Mirva, Kurppa, Kalle, Latva-Aho, Tiina, Lönnrot, Maria, Mäntymäki, Elina, Multasuo, Katja, Mykkänen, Juha, Niininen, Tiina, Niinistö, Sari, Nyblom, Mia, Rajala, Petra, Rautanen, Jenna, Riikonen, Anne, Riikonen, Mika, Rouhiainen, Jenni, Romo, Minna, Simell, Tuula, Simell, Ville, Sjöberg, Maija, Stenius, Aino, Leppänen, Maria, Vainionpää, Sini, Varjonen, Eeva, Veijola, Riitta, Virtanen, Suvi M, Vähä-Mäkilä, Mari, Åkerlund, Mari, Lindfors, Katri, Schatz, Desmond, Hopkins, Diane, Steed, Leigh, Thomas, Jamie, Adams, Janey, Silvis, Katherine, Haller, Michael, Gardiner, Melissa, McIndoe, Richard, Sharma, Ashok, Williams, Joshua, Young, Gabriela, Anderson, Stephen W, Jacobsen, Laura, Ziegler, Anette G, Hummel, Michael, Hummel, Sandra, Foterek, Kristina, Janz, Nicole, Kersting, Mathilde, Knopff, Annette, Koletzko, Sibylle, Peplow, Claudia, Roth, Roswith, Scholz, Marlon, Stock, Joanna, Warncke, Katharina, Wendel, Lorena, Winkler, Christiane, Agardh, Daniel, Aronsson, Carin Andrén, Ask, Maria, Bremer, Jenny, Carlsson, Ulla-Marie, Cilio, Corrado, Ericson-Hallström, Emelie, Fransson, Lina, Gard, Thomas, Gerardsson, Joanna, Bennet, Rasmus, Hansen, Monica, Hansson, Gertie, Hyberg, Susanne, Johansen, Fredrik, Jonsdottir, Berglind, Larsson, Helena Elding, Lindström, Marielle, Lundgren, Markus, Månsson-Martinez, Maria, Markan, Maria, Melin, Jessica, Mestan, Zeliha, Ottosson, Karin, Rahmati, Kobra, Ramelius, Anita, Salami, Falastin, Sibthorpe, Sara, Sjöberg, Birgitta, Swartling, Ulrica, Amboh, Evelyn Tekum, Törn, Carina, Wallin, Anne, Wimar, Åsa, Åberg, Sofie, Killian, Michael, Crouch, Claire Cowen, Skidmore, Jennifer, Carson, Josephine, Dalzell, Maria, Dunson, Kayleen, Hervey, Rachel, Johnson, Corbin, Lyons, Rachel, Meyer, Arlene, Mulenga, Denise, Tarr, Alexander, Uland, Morgan, Willis, John, Becker, Dorothy, Franciscus, Margaret, Smith, MaryEllen Dalmagro-Elias, Daftary, Ashi, Klein, Mary Beth, Yates, Chrystal, Abbondondolo, Michael, Austin-Gonzalez, Sarah, Avendano, Maryouri, Baethke, Sandra, Brown, Rasheedah, Burkhardt, Brant, Butterworth, Martha, Clasen, Joanna, Cuthbertson, David, Eberhard, Christopher, Fiske, Steven, Garcia, Dena, Garmeson, Jennifer, Gowda, Veena, Heyman, Kathleen, PerezLaras, Francisco, Lee, Hye-Seung, Liu, Shu, Liu, Xiang, Lynch, Kristian, Malloy, Jamie, McCarthy, Cristina, Meulemans, Steven, Parikh, Hemang, Shaffer, Chris, Smith, Laura, Smith, Susan, Sulman, Noah, Tamura, Roy, Uusitalo, Ulla, Vijayakandipan, Ponni, Wood, Keith, Yang, Jimin, Ballard, Lori, Hadley, David, McLeod, Wendy, Yu, Liping, Miao, Dongmei, Bingley, Polly, Williams, Alistair, Chandler, Kyla, Rokni, Saba, Williams, Claire, Wyatt, Rebecca, George, Gifty, Grace, Sian, Erlich, Henry, Mack, Steven J, Ke, Sandra, Mulholland, Niveen, Bourcier, Kasia, Briese, Thomas, Johnson, Suzanne Bennett, Triplett, Eric

المساهمون: Klinik und Poliklinik für Kinder- und Jugendmedizin

مصطلحات موضوعية: info:eu-repo/classification/ddc

الوصف: The onset of clinical type 1 diabetes (T1D) is preceded by the occurrence of disease-specific autoantibodies. The level of autoantibody titers is known to be associated with progression time from the first emergence of autoantibodies to the onset of clinical symptoms, but detailed analyses of this complex relationship are lacking. We aimed to fill this gap by applying advanced statistical models.We investigated data of 613 children from the prospective TEDDY study who were persistent positive for IAA, GADA and/or IA2A autoantibodies. We used a novel approach of Bayesian joint modeling of longitudinal and survival data to assess the potentially time- and covariate-dependent association between the longitudinal autoantibody titers and progression time to T1D.For all autoantibodies we observed a positive association between the titers and the T1D progression risk. This association was estimated as time-constant for IA2A, but decreased over time for IAA and GADA. For example the hazard ratio [95% credibility interval] for IAA (per transformed unit) was 3.38 [2.66, 4.38] at 6 months after seroconversion, and 2.02 [1.55, 2.68] at 36 months after seroconversion.These findings indicate that T1D progression risk stratification based on autoantibody titers should focus on time points early after seroconversion. Joint modeling techniques allow for new insights into these associations.

العلاقة: https://mediatum.ub.tum.de/1453062Test

الإتاحة: https://doi.org/10.1007/s00592-017-1033-7Test
https://mediatum.ub.tum.de/1453062Test

المؤلفون: Haghighi, Mona, Johnson, Suzanne Bennett, Qian, Xiaoning, Lynch, Kristian F, Vehik, Kendra, Huang, Shuai, TEDDY Study Group, Rewers, Marian, Bautista, Kimberly, Baxter, Judith, Bedoy, Ruth, Morales, Daniel-Felipe, Driscoll, Kimberly, Frohnert, Brigitte I, Gesualdo, Patricia, Hoffman, Michelle, Karban, Rachel, Liu, Edwin, Norris, Jill, Samper-Imaz, Adela, Steck, Andrea, Waugh, Kathleen, Wright, Hali, Sharma, Ashok, Hopkins, Diane, Young, Gabriela, She, Jin-Xiong, Williams, Joshua, Silvis, Katherine, Steed, Leigh, Gardiner, Melissa, McIndoe, Richard, Schatz, Desmond, Thomas, Jamie, Adams, Janey, Jacobsen, Laura, Haller, Michael, Triplett, Michael, Anderson, Stephen W, Mykkänen, Juha, Lindfors, Katri, Adamsson, Annika, Jokipuu, Sanna, Kallio, Tiina, Karlsson, Leena, Mäntymäki, Elina, Rajala, Petra, Riikonen, Mika, Rouhiainen, Jenni, Romo, Minna, Leppänen, Maria, Vainionpää, Sini, Vähä-Mäkilä, Mari, Stenius, Aino, Toppari, Jorma, Simell, Olli G, Simell, Tuula, Sjöberg, Maija, Varjonen, Eeva, Hyöty, Heikki, Knip, Mikael, Kurppa, Kalle, Lönnrot, Maria, Niininen, Tiina, Nyblom, Mia, Ahonen, Suvi, Kovanen, Lea, Koreasalo, Mirva, Riikonen, Anne, Virtanen, Suvi M, Akerlund, Mari, Ilonen, Jorman, Kähönen, Miia, Larva-aho, Tiina, Multasuo, Katja, Veijola, Riita, Niinistö, Sari, Rautanen, Jenna, Ziegler, Anette G, Hummel, Michael, Hummel, Sandra, Janz, Nicole, Knopff, Annette, Peplow, Claudia, Roth, Roswith, Scholz, Marlon, Stock, Joanna, Warncke, Katharina, Wendel, Lorena, Winkler, Christiane, Beyerlein, Andreas, Bonifacio, Ezio, Koletzko, Sibylle, Foterek, Kristina, Kersting, Mathilde, Lernmark, Åke, Agardh, Daniel, Aronsson, Carin Andrén, Ask, Maria, Bremer, Jenny, Carlsson, Ulla-Marie, Cilio, Corrado, Ericson-Hallström, Emelie, Fransson, Lina, Gard, Thomas, Gerardsson, Joanna, Bennet, Rasmus, Hansen, Monica, Hansson, Gertie, Hyberg, Susanne, Johansen, Fredrik, Jonsdottir, Berglind, Larsson, Helena Elding, Lindström, Marielle, Lundgren, Markus, Månsson-Martinez, Maria, Markan, Maria, Melin, Jessica, Mestan, Zeliha, Ottoson, Karin, Rahmati, Kobra, Ramelius, Anita, Salami, Falastin, Sibthorpe, Sara, Sjöberg, Birgitta, Swartling, Ulrica, Amboh, Evelyn Tekum, Törn, Carina, Wallin, Anne, Wimar, Åsa, Åberg, Sofie, Hagopian, William A, Killian, Michael, Crouch, Claire Cowen, Skidmore, Jennifer, Carson, Josephine, Dalzell, Maria, Dunson, Kayleen, Hervey, Rachel, Johnson, Corbin, Lyons, Rachel, Meyer, Arlene, Mulenga, Denise, Tarr, Alexander, Uland, Morgan, Willis, John, Becker, Dorothy, Franciscus, Margaret, Dalmagro-Elias Smith, MaryEllen, Daftary, Ashi, Klein, Mary Beth, Yates, Chrystal, Krischer, Jeffrey P, Abbondondolo, Michael, Austin-Gonzalez, Sarah, Avendano, Maryouri, Baethke, Sandra, Brown, Rasheedah, Burkhardt, Brant, Butterworth, Martha, Clasen, Joanna, Cuthbertson, David, Eberhard, Christopher, Fiske, Steven, Garcia, Dena, Garmeson, Jennifer, Gowda, Veena, Heyman, Kathleen, Perez Laras, Francisco, Lee, Hye-Seung, Liu, Shu, Liu, Xiang, Malloy, Jamie, McCarthy, Cristina, Meulemans, Steven, Parikh, Hemang, Shaffer, Chris, Smith, Laura, Smith, Susan, Sulman, Noah, Tamura, Roy, Uusitalo, Ulla, Vijayakandipan, Ponni, Wood, Keith, Yang, Jimin, Akolkar, Beena, Bourcier, Kasia, Briese, Thomas

مصطلحات موضوعية: info:eu-repo/classification/ddc, demo, stat

الوصف: Regression models are extensively used in many epidemiological studies to understand the linkage between specific outcomes of interest and their risk factors. However, regression models in general examine the average effects of the risk factors and ignore subgroups with different risk profiles. As a result, interventions are often geared towards the average member of the population, without consideration of the special health needs of different subgroups within the population. This paper demonstrates the value of using rule-based analysis methods that can identify subgroups with heterogeneous risk profiles in a population without imposing assumptions on the subgroups or method. The rules define the risk pattern of subsets of individuals by not only considering the interactions between the risk factors but also their ranges. We compared the rule-based analysis results with the results from a logistic regression model in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Both methods detected a similar suite of risk factors, but the rule-based analysis was superior at detecting multiple interactions between the risk factors that characterize the subgroups. A further investigation of the particular characteristics of each subgroup may detect the special health needs of the subgroup and lead to tailored interventions.

العلاقة: http://mediatum.ub.tum.de/node?id=1364807Test

الإتاحة: http://mediatum.ub.tum.de/node?id=1364807Test

المؤلفون: Haghighi, Mona, Johnson, Suzanne Bennett, Qian, Xiaoning, Lynch, Kristian F, Vehik, Kendra, Huang, Shuai, TEDDY Study Group, Rewers, Marian, Bautista, Kimberly, Baxter, Judith, Bedoy, Ruth, Morales, Daniel-Felipe, Driscoll, Kimberly, Frohnert, Brigitte I, Gesualdo, Patricia, Hoffman, Michelle, Karban, Rachel, Liu, Edwin, Norris, Jill, Samper-Imaz, Adela, Steck, Andrea, Waugh, Kathleen, Wright, Hali, Sharma, Ashok, Hopkins, Diane, Young, Gabriela, She, Jin-Xiong, Williams, Joshua, Silvis, Katherine, Steed, Leigh, Gardiner, Melissa, McIndoe, Richard, Schatz, Desmond, Thomas, Jamie, Adams, Janey, Jacobsen, Laura, Haller, Michael, Triplett, Michael, Anderson, Stephen W, Mykkänen, Juha, Lindfors, Katri, Adamsson, Annika, Jokipuu, Sanna, Kallio, Tiina, Karlsson, Leena, Mäntymäki, Elina, Rajala, Petra, Riikonen, Mika, Rouhiainen, Jenni, Romo, Minna, Leppänen, Maria, Vainionpää, Sini, Vähä-Mäkilä, Mari, Stenius, Aino, Toppari, Jorma, Simell, Olli G, Simell, Tuula, Sjöberg, Maija, Varjonen, Eeva, Hyöty, Heikki, Knip, Mikael, Kurppa, Kalle, Lönnrot, Maria, Niininen, Tiina, Nyblom, Mia, Ahonen, Suvi, Kovanen, Lea, Koreasalo, Mirva, Riikonen, Anne, Virtanen, Suvi M, Akerlund, Mari, Ilonen, Jorman, Kähönen, Miia, Larva-aho, Tiina, Multasuo, Katja, Veijola, Riita, Niinistö, Sari, Rautanen, Jenna, Ziegler, Anette G, Hummel, Michael, Hummel, Sandra, Janz, Nicole, Knopff, Annette, Peplow, Claudia, Roth, Roswith, Scholz, Marlon, Stock, Joanna, Warncke, Katharina, Wendel, Lorena, Winkler, Christiane, Beyerlein, Andreas, Bonifacio, Ezio, Koletzko, Sibylle, Foterek, Kristina, Kersting, Mathilde, Lernmark, Åke, Agardh, Daniel, Aronsson, Carin Andrén, Ask, Maria, Bremer, Jenny, Carlsson, Ulla-Marie, Cilio, Corrado, Ericson-Hallström, Emelie, Fransson, Lina, Gard, Thomas, Gerardsson, Joanna, Bennet, Rasmus, Hansen, Monica, Hansson, Gertie, Hyberg, Susanne, Johansen, Fredrik, Jonsdottir, Berglind, Larsson, Helena Elding, Lindström, Marielle, Lundgren, Markus, Månsson-Martinez, Maria, Markan, Maria, Melin, Jessica, Mestan, Zeliha, Ottoson, Karin, Rahmati, Kobra, Ramelius, Anita, Salami, Falastin, Sibthorpe, Sara, Sjöberg, Birgitta, Swartling, Ulrica, Amboh, Evelyn Tekum, Törn, Carina, Wallin, Anne, Wimar, Åsa, Åberg, Sofie, Hagopian, William A, Killian, Michael, Crouch, Claire Cowen, Skidmore, Jennifer, Carson, Josephine, Dalzell, Maria, Dunson, Kayleen, Hervey, Rachel, Johnson, Corbin, Lyons, Rachel, Meyer, Arlene, Mulenga, Denise, Tarr, Alexander, Uland, Morgan, Willis, John, Becker, Dorothy, Franciscus, Margaret, Dalmagro-Elias Smith, MaryEllen, Daftary, Ashi, Klein, Mary Beth, Yates, Chrystal, Krischer, Jeffrey P, Abbondondolo, Michael, Austin-Gonzalez, Sarah, Avendano, Maryouri, Baethke, Sandra, Brown, Rasheedah, Burkhardt, Brant, Butterworth, Martha, Clasen, Joanna, Cuthbertson, David, Eberhard, Christopher, Fiske, Steven, Garcia, Dena, Garmeson, Jennifer, Gowda, Veena, Heyman, Kathleen, Perez Laras, Francisco, Lee, Hye-Seung, Liu, Shu, Liu, Xiang, Malloy, Jamie, McCarthy, Cristina, Meulemans, Steven, Parikh, Hemang, Shaffer, Chris, Smith, Laura, Smith, Susan, Sulman, Noah, Tamura, Roy, Uusitalo, Ulla, Vijayakandipan, Ponni, Wood, Keith, Yang, Jimin, Akolkar, Beena, Bourcier, Kasia, Briese, Thomas

المساهمون: Kinderklinik und Poliklinik

مصطلحات موضوعية: info:eu-repo/classification/ddc

الوصف: Regression models are extensively used in many epidemiological studies to understand the linkage between specific outcomes of interest and their risk factors. However, regression models in general examine the average effects of the risk factors and ignore subgroups with different risk profiles. As a result, interventions are often geared towards the average member of the population, without consideration of the special health needs of different subgroups within the population. This paper demonstrates the value of using rule-based analysis methods that can identify subgroups with heterogeneous risk profiles in a population without imposing assumptions on the subgroups or method. The rules define the risk pattern of subsets of individuals by not only considering the interactions between the risk factors but also their ranges. We compared the rule-based analysis results with the results from a logistic regression model in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Both methods detected a similar suite of risk factors, but the rule-based analysis was superior at detecting multiple interactions between the risk factors that characterize the subgroups. A further investigation of the particular characteristics of each subgroup may detect the special health needs of the subgroup and lead to tailored interventions.

العلاقة: https://mediatum.ub.tum.de/1364807Test

الإتاحة: https://doi.org/10.1038/srep30828Test
https://mediatum.ub.tum.de/1364807Test

المؤلفون: Krischer, Jeffrey P, Liu, Xiang, Vehik, Kendra, Akolkar, Beena, Hagopian, William A, Rewers, Marian J, She, Jin-Xiong, Toppari, Jorma, Ziegler, Anette-G, Lernmark, Åke

المساهمون: Agardh, Daniel, Creator, Andrén Aronsson, Carin, Creator, Ask, Maria, Creator, Bremer, Jenny, Creator, Ericson-Hallström, Emelie, Creator, Björne Fors, Annika, Creator, Fransson, Lina, Creator, Gard, Thomas, Creator, Bennet, Rasmus, Creator, Hyberg, Susanne, Creator, Jisser, Hanna, Creator, Johansen, Fredrik, Creator, Jónsdóttir, Berglind, Creator, JOVIC, SILVIJA, Creator, Elding Larsson, Helena, Creator, Lindström, Marielle, Creator, Lundgren, Markus, Creator, Månsson Martinez, Maria, Creator, Markan, Maria, Creator, Melin, Marie Jessica, Creator, Mestan, Zeliha, Creator, Nilsson, Caroline N, Creator, Ottosson, Karin, Creator, Rahmati, Kobra, Creator, Ramelius, Anita, Creator, Salami, Falastin, Creator, Sjöberg, Anette, Creator, Sjöberg, Birgitta, Creator, Törn, Carina, Creator, Wallin, Anne, Creator, Wimar, Åsa, Creator, Åberg, Sofie, Creator

المصدر: Diabetes Care EXODIAB: Excellence of Diabetes Research in Sweden. 42(6):1051-1060

مصطلحات موضوعية: Medicin och hälsovetenskap, Klinisk medicin, Endokrinologi och diabetes, Medical and Health Sciences, Clinical Medicine, Endocrinology and Diabetes

الوصف: OBJECTIVE: Assessment of the predictive power of The Environmental Determinants of Diabetes in the Young (TEDDY)-identified risk factors for islet autoimmunity (IA), the type of autoantibody appearing first, and type 1 diabetes (T1D).RESEARCH DESIGN AND METHODS: A total of 7,777 children were followed from birth to a median of 9.1 years of age for the development of islet autoantibodies and progression to T1D. Time-dependent sensitivity, specificity, and receiver operating characteristic (ROC) curves were calculated to provide estimates of their individual and collective ability to predict IA and T1D.RESULTS: HLA genotype (DR3/4 vs. others) was the best predictor for IA (Youden's index J = 0.117) and single nucleotide polymorphism rs2476601, in PTPN22, was the best predictor for insulin autoantibodies (IAA) appearing first (IAA-first) (J = 0.123). For GAD autoantibodies (GADA)-first, weight at 1 year was the best predictor (J = 0.114). In a multivariate model, the area under the ROC curve (AUC) was 0.678 (95% CI 0.655, 0.701), 0.707 (95% CI 0.676, 0.739), and 0.686 (95% CI 0.651, 0.722) for IA, IAA-first, and GADA-first, respectively, at 6 years. The AUC of the prediction model for T1D at 3 years after the appearance of multiple autoantibodies reached 0.706 (95% CI 0.649, 0.762).CONCLUSIONS: Prediction modeling statistics are valuable tools, when applied in a time-until-event setting, to evaluate the ability of risk factors to discriminate between those who will and those who will not get disease. Although significantly associated with IA and T1D, the TEDDY risk factors individually contribute little to prediction. However, in combination, these factors increased IA and T1D prediction substantially.

الوصول الحر: https://lup.lub.lu.se/record/d4a847df-2957-40fc-ad4a-d9c751d6e20dTest
http://dx.doi.org/10.2337/dc18-2282Test