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Measurement Variability Related to Insulin Secretion and Sensitivity: Assessment and Implications in Epidemiological Studies
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

There is a growing interest in random measurement variability of biological variables. In regression models, such variability of the predictors yields biased estimators of coefficients (regression dilution bias). The objectives of this thesis were to develop an efficient method to correct for such bias, to reveal the relative importance of insulin sensitivity and insulin secretion, corrected for regression dilution bias, on glucose tolerance, and to explore the seasonal nature of the variability of insulin sensitivity.

A reliability study is often designed to randomly select subjects from the main study. Our idea was to collect replicates for subjects with extreme values on their first measurement. The extreme selection design, in combination with maximum likelihood estimation, resulted in an efficient estimator of a corrected regression coefficient in a simple linear regression model. Results were presented theoretically and with an application: The relation between insulin sensitivity and fasting insulin in Uppsala Longitudinal Study of Adult Men (ULSAM) where the extreme selection design decreased the standard error of the estimated regression coefficient with 28 per cent compared with the random sampling design.

We estimated the partial longitudinal effects of the predictors insulin sensitivity and insulin secretion, corrected for regression dilution bias, on glucose tolerance in ULSAM. The effects of the predictors, when corrected, were similar.

Insulin sensitivity in ULSAM increased during summer and decreased during winter and insulin secretion exposed opposite variation keeping glucose homeostasis nearly constant. Insulin sensitivity was related to outdoor temperature.

In summary, we developed a cost-efficient reliability design for correction for regression dilution bias. Insulin sensitivity and insulin secretion had similar longitudinal effects on glucose tolerance, which implies that interventions aimed at these targets are equally important. Further, we revealed the seasonal nature of variations of insulin sensitivity and insulin secretion. This result has implications on glycaemic control in diabetic patients.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis , 2009. , p. 67
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 440
National Category
Geriatrics
Research subject
Geriatrics
Identifiers
URN: urn:nbn:se:uu:diva-99636ISBN: 978-91-554-7469-0 (print)OAI: oai:DiVA.org:uu-99636DiVA, id: diva2:208447
Public defence
2009-04-29, Enghoffsalen, Akademiska sjukhuset, Uppsala, 13:15 (Swedish)
Opponent
Supervisors
Available from: 2009-04-07 Created: 2009-03-18 Last updated: 2012-07-26Bibliographically approved
List of papers
1. Correction for regression dilution bias using replicates from subjects with extreme first measurements
Open this publication in new window or tab >>Correction for regression dilution bias using replicates from subjects with extreme first measurements
2007 (English)In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 26, no 10, p. 2246-2257Article in journal (Refereed) Published
Abstract [en]

The least squares estimator of the slope in a simple linear regression model will be biased towards zero when the predictor is measured with random error, i.e. intra-individual variation or technical measurement error. A correction factor can be estimated from a reliability study where one replicate is available on a subset of subjects from the main study. Previous work in this field has assumed that the reliability study constitutes a random subsample from the main study.We propose that a more efficient design is to collect replicates for subjects with extreme values on their first measurement. A variance formula for this estimator of the correction factor is presented. The variance for the corrected estimated regression coefficient for the extreme selection technique is also derived and compared with random subsampling. Results show that variances for corrected regression coefficients can be markedly reduced with extreme selection. The variance gain can be estimated from the main study data. The results are illustrated using Monte Carlo simulations and an application on the relation between insulin sensitivity and fasting insulin using data from the population-based ULSAM study.In conclusion, an investigator faced with the planning of a reliability study may wish to consider an extreme selection design in order to improve precision at a given number of subjects or alternatively decrease the number of subjects at a given precision.

Keywords
regression dilution bias, reliability study, extreme selection, corrected regression coefficient, insulin sensitivity
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-10991 (URN)10.1002/sim.2698 (DOI)000245965200008 ()16969892 (PubMedID)
Available from: 2007-05-08 Created: 2007-05-08 Last updated: 2017-12-11Bibliographically approved
2. Maximum likelihood estimation of correction for dilution bias in simple linear regression using replicates from subjects with extreme first measurements
Open this publication in new window or tab >>Maximum likelihood estimation of correction for dilution bias in simple linear regression using replicates from subjects with extreme first measurements
Show others...
2008 (English)In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 27, no 22, p. 4397-4407Article in journal (Refereed) Published
Abstract [en]

The least-squares estimator of the slope in a simple linear regression model is biased towards zero when the predictor is measured with random error. A corrected slope may be estimated by adding data from a reliability study, which comprises a subset of subjects from the main study. The precision of this corrected slope depends on the design of the reliability study and estimator choice.Previous work has assumed that the reliability study constitutes a random sample from the main study. A more efficient design is to use subjects with extreme values on their first measurement. Previously, we published a variance formula for the corrected slope, when the correction factor is the slope in the regression of the second measurement on the first. In this paper we show that both designs improve by maximum likelihood estimation (MLE). The precision gain is explained by the inclusion of data from all subjects for estimation of the predictor's variance and by the use of the second measurement for estimation of the covariance between response and predictor. The gain of MLE enhances with stronger true relationship between response and predictor and with lower precision in the predictor measurements. We present a real data example on the relationship between fasting insulin, a surrogate market, and true insulin sensitivity measured by a gold-standard euglycaemic insulin clamp, and simulations, where the behavior of profile-likelihood-based confidence intervals is examined. MLE was shown to be a robust estimator for non-normal distributions and efficient for small sample situations.

Keywords
regression dilution bias, maximum likelihood estimation, reliability study, extreme selection, corrected regression coefficient
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-17699 (URN)10.1002/sim.3312 (DOI)000259550200002 ()18618419 (PubMedID)
Available from: 2008-08-15 Created: 2008-08-15 Last updated: 2017-12-08Bibliographically approved
3. Early Insulin Response and Insulin Sensitivity are Equally Important as Predictors of Glucose Tolerance after Correction for Measurement Errors
Open this publication in new window or tab >>Early Insulin Response and Insulin Sensitivity are Equally Important as Predictors of Glucose Tolerance after Correction for Measurement Errors
Show others...
2009 (English)In: Diabetes Research and Clinical Practice, ISSN 0168-8227, E-ISSN 1872-8227, Vol. 86, no 3, p. 219-224Article in journal (Refereed) Published
Abstract [en]

Aims: We estimated measurement error (ME) corrected effects of   insulin sensitivity (M/I), from euglycaemic insulin clamp, and insulin   secretion, measured as early insulin response (EIR) from oral glucose   tolerance test (OGTT), on fasting plasma glucose, HbA1c and type 2   diabetes longitudinally and cross-sectional.   Methods: : In a population-based study (n = 1128 men) 17 men made   replicate measurements to estimate ME at age 71 years. Effect of 1 SD   decrease of predictors M/I and EIR on longitudinal response variables   fasting plasma glucose (FPG) and HbA1c at follow-ups up to 11 years,   were estimated using uncorrected and ME-corrected (with the regression   calibration method) regression models.   Results: : Uncorrected effect on FPG at age 77 years was larger for M/I   than for EIR (effect difference 0.10 mmol/l, 95% CI 0.00;0.21), while   ME-corrected effects were similar (0.02 mmol/l, 95% CI -0.13;0.15   mmol/l). EIR had greater ME-corrected impact than M/I on HbA1c at age   82 years (-0.11%, -0.28; -0.01%).   Conclusions: : Due to higher ME effect of EIR on glycaemia is   underestimated as compared with M/I. By correcting for ME valid   estimates of relative contributions of insulin secretion and insulin   sensitivity on glycaemia are obtained.

National Category
Medical and Health Sciences
Research subject
Geriatrics
Identifiers
urn:nbn:se:uu:diva-99497 (URN)10.1016/j.diabres.2009.09.016 (DOI)000272521800010 ()19811847 (PubMedID)
Available from: 2009-03-18 Created: 2009-03-15 Last updated: 2017-12-13Bibliographically approved
4. Seasonal variations of insulin sensitivity from a euglycemic insulin clamp in elderly men
Open this publication in new window or tab >>Seasonal variations of insulin sensitivity from a euglycemic insulin clamp in elderly men
Show others...
2012 (English)In: Upsala Journal of Medical Sciences, ISSN 0300-9734, E-ISSN 2000-1967, Vol. 117, no 1, p. 35-40Article in journal (Refereed) Published
Abstract [en]

Introduction

Seasonal variations in hemoglobin-A1c have been reported in diabetic patients, but the underlying mechanisms have not been elucidated.

Aims

To study if insulin sensitivity, insulin secretion, and fasting plasma glucose showed seasonal variations in a Swedish population-based cohort of elderly men.

Methods

Altogether 1117 men were investigated with a euglycemic insulin clamp and measurements of fasting plasma glucose and insulin secretion after an oral glucose tolerance test. Values were analyzed in linear regression models with an indicator variable for winter/summer season and outdoor temperature as predictors.

Results

 During winter, insulin sensitivity (M/I, unit = 100 × mg × min-1 × kg-1/(mU × L-1)) was 11.0% lower (4.84 versus 5.44, P = 0.0003), incremental area under the insulin curve was 16.4% higher (1167 versus 1003 mU/L, P = 0.007). Fasting plasma glucose was, however, not statistically significantly different (5.80 versus 5.71 mmol/L, P = 0.28) compared to the summer season. There was an association between outdoor temperature and M/I (0.57 units increase (95% CI 0.29–0.82, P < 0.0001) per 10°C increase of outdoor temperature) independent of winter/summer season. Adjustment for life-style factors, type 2 diabetes, and medication did not alter these results.Read More:http://informahealthcare.com/doi/abs/10.3109/03009734.2011.628422

Conclusions

Insulin sensitivity showed seasonal variations with lower values during the winter and higher during the summer season. Inverse compensatory variations of insulin secretion resulted in only minor variations of fasting plasma glucose. Insulin sensitivity was associated with outdoor temperature. These phenomena should be further investigated in diabetic patients.

Keywords
Seasonal variations, insulin sensitivity, clamp, insulin secretion, temperature
National Category
Endocrinology and Diabetes Geriatrics
Identifiers
urn:nbn:se:uu:diva-165044 (URN)10.3109/03009734.2011.628422 (DOI)000300304000006 ()22066936 (PubMedID)
Available from: 2012-01-02 Created: 2012-01-02 Last updated: 2017-12-08Bibliographically approved

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