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- Baekkeskov, S, et al.
(författare)
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Antibodies to a 64,000 Mr human islet cell antigen precede the clinical onset of insulin-dependent diabetes
- 1987
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Ingår i: Journal of Clinical Investigation. - 0021-9738. ; 79:3, s. 34-926
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Tidskriftsartikel (refereegranskat)abstract
- Antibodies in sera from newly diagnosed insulin-dependent diabetes mellitus (IDDM) patients are directed to a human islet cell protein of relative molecular mass (Mr) 64,000. Since IDDM seems to develop after a prodromal period of beta-cell autoimmunity, this study has examined whether 64,000 Mr antibodies could be detected in 14 individuals who subsequently developed IDDM and five first degree relatives who have indications of altered beta-cell function. Sera were screened by immunoprecipitation on total detergent lysates of human islets and positive sera retested on membrane protein preparations. Antibodies to the 64,000 Mr membrane protein were consistently detected in 11/14 IDDM patients, and in all 5 first degree relatives. 10 IDDM patients were already positive in the first samples, obtained 4-91 mo before the clinical onset of IDDM, whereas 1 patient progressed to a high 64,000 Mr immunoreactivity, at a time where a commencement of a decline in beta-cell function was detected. 64,000 Mr antibodies were detected before islet cell cytoplasmic antibodies (ICCA) in two patients. In the control groups of 21 healthy individuals, 36 patients with diseases of the thyroid and 5 SLE patients, the 64,000 Mr antibodies were detected in only one individual, who was a healthy sibling to an IDDM patient. These results suggest that antibodies against the Mr 64,000 human islet protein are an early marker of beta-cell autoimmunity and may be useful to predict a later development of IDDM.
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| 2. |
- Haghighi, Mona, et al.
(författare)
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A Comparison of Rule-based Analysis with Regression Methods in Understanding the Risk Factors for Study Withdrawal in a Pediatric Study
- 2016
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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