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      Is Open Access

      Quality of life in adults with congenital adrenal hyperplasia relates to glucocorticoid treatment, adiposity and insulin resistance: United Kingdom Congenital adrenal Hyperplasia Adult Study Executive (CaHASE)

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          Abstract

          Context

          Quality of life (QoL) has been variously reported as normal or impaired in adults with congenital adrenal hyperplasia (CAH). To explore the reasons for this discrepancy we investigated the relationship between QoL, glucocorticoid treatment and other health outcomes in CAH adults.

          Methods

          Cross-sectional analysis of 151 adults with 21-hydroxylase deficiency aged 18–69 years in whom QoL (assessed using the Short Form Health Survey), glucocorticoid regimen, anthropometric and metabolic measures were recorded. Relationships were examined between QoL, type of glucocorticoid (hydrocortisone, prednisolone and dexamethasone) and dose of glucocorticoid expressed as prednisolone dose equivalent (PreDEq). QoL was expressed as z-scores calculated from matched controls (14 430 subjects from UK population). Principal components analysis (PCA) was undertaken to identify clusters of associated clinical and biochemical features and the principal component (PC) scores used in regression analysis as predictor of QoL.

          Results

          QoL scores were associated with type of glucocorticoid treatment for vitality ( P=0.002) and mental health ( P=0.011), with higher z-scores indicating better QoL in patients on hydrocortisone monotherapy ( P<0.05). QoL did not relate to PreDEq or mutation severity. PCA identified three PCs (PC1, disease control; PC2, adiposity and insulin resistance and PC3, blood pressure and mutations) that explained 61% of the variance in observed variables. Stepwise multiple regression analysis demonstrated that PC2, reflecting adiposity and insulin resistance (waist circumference, serum triglycerides, homeostasis model assessment of insulin resistance and HDL-cholesterol), related to QoL scores, specifically impaired physical functioning, bodily pain, general health, Physical Component Summary Score ( P<0.001) and vitality ( P=0.002).

          Conclusions

          Increased adiposity, insulin resistance and use of prednisolone or dexamethasone are associated with impaired QoL in adults with CAH. Intervention trials are required to establish whether choice of glucocorticoid treatment and/or weight loss can improve QoL in CAH adults.

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          Most cited references15

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          Interpreting SF-36 summary health measures: a response.

          J E Ware, M Kosinski (2001)
          In response to questions raised about the "accuracy" of SF-36 physical (PCS) and mental (MCS) component summary scores, particularly extremely high and low scores, we briefly comment on: how they were developed, how they are scored, the factor content of the eight SF-36 subscales, cross-tabulations between item-level responses and extreme summary scores, and published and new tests of their empirical validity. Published cross-tabulations between SF-36 items and PCS and MCS scores, reanalyses of public datasets (N = 5919), and preliminary results from the Medicare Health Outcomes Survey (HOS) (N = 172,314) yielded little or no evidence in support of Taft's hypothesis that extreme scores are an invalid artifact of some negative scoring weights. For example, in the HOS, those (N = 432) with "unexpected" PCS scores worse than 20 (which, according to Taft, indicate better mental health rather than worse physical health) were about 25% more likely to die within two years, in comparison with those scoring in the next highest (21-30) category. In this test and in all other empirical tests, results of predictions supported the validity of extreme PCS and MCS scores. We recommend against the interpretation of average differences smaller than one point in studies that seek to detect "false" measurement and we again repeat our 7-year-old recommendation that results based on summary measures should be thoroughly compared with the SF-36 profile before drawing conclusions. To facilitate such comparisons, scoring utilities and user-friendly graphs for SF-36 profiles and physical and mental summary scores (both orthogonal and oblique scoring algorithms) have been made available on the Internet at www.sf-36.com/test.
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            Do SF-36 summary component scores accurately summarize subscale scores?

            C Taft, J. Karlsson, M. Sullivan (2001)
            Standard scoring algorithms were recently made available for aggregating scores from the eight SF-36 subscales in two distinct, higher-order summary scores: Physical Component Summary (PCS) and Mental Component Summary (MCS). Recent studies have suggested, however, that PCS and MCS scores are not independent and may in part be measuring the same constructs. The aims of this paper were to examine and illustrate (1) relationships between SF-36 subscale and PCS, MCS scores, (2) relationships between PCS and MCS scores, and (3) their implications for interpreting research findings. Simulation analyses were conducted to illustrate the contributions of various aspects of the scoring algorithm to potential discrepancies between subscale profile and summary component scores. Using the Swedish SF-36 normative database, correlation and regression analyses were performed to estimate the relationship between the two components, as well as the relative contributions of the subscales to the components. Discrepancies between subscale profile and component scores were identified and explained. Significant correlations (r = -0.74, -0.67) were found between PCS and MCS scores at their respective upper scoring intervals, indicating that the components are not independent. Regression analyses revealed that in these ranges PCS primarily measures aspects of mental health (57% of variance) and MCS measures physical health (65% of variance). Implications of the findings were discussed. It was concluded that the current PCS MCS scoring procedure inaccurately summarizes subscale profile scores and should therefore be revised. Until then, component scores should be interpreted with caution and only in combination with profile scores.
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              Quality of life in relation to overweight and body fat distribution.

              T S Han, M A Tijhuis, M E Lean … (1998)
              This study quantified the impairment of quality of life attributable to body fatness by using the standardized SF-36 Health Survey. Tertiles of waist circumference and body mass index (BMI) in 1885 men and 2156 women aged 20 to 59 years in the Netherlands in 1995 were compared. The odds ratios and 95% confidence intervals of subjects with the largest waist circumferences, compared with those in the lowest tertile, were 1.8 (1.3, 2.4) in men and 2.2 (1.7, 2.9) in women with difficulties in bending, kneeling, or stooping; 2.2 (1.4, 3.7) in men and 1.7 (1.2, 2.6) in women with difficulties in walking 500 m; and 1.3 (1.0, 1.9) in men and 1.5 (1.1, 1.9) in women with difficulties in lifting or carrying groceries. Anthropometric measures were less strongly associated with social functioning, role limitations due to physical or emotional problems, mental health, vitality, pain, or health change in 1 year. The relationship between quality of life measures and BMI were similar to those between quality of life measures and waist circumference. Large waist circumferences and high BMIs are more likely to be associated with impaired quality of life and disability affecting basic activities of daily living.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Eur J Endocrinol
                Journal ID (iso-abbrev): Eur. J. Endocrinol
                Journal ID (publisher-id): EJE
                Title: European Journal of Endocrinology
                Publisher: BioScientifica (Bristol )
                ISSN (Print): 0804-4643
                ISSN (Electronic): 1479-683X
                Publication date (Print): June 2013
                Publication date (Electronic preprint): 17 March 2013
                Volume: 168
                Issue: 6
                Pages: 887-893
                Affiliations
                [1]Department of Endocrinology , University College London Hospitals , London, UK
                [2]School of Clinical and Experimental Medicine, Centre for Endocrinology, Diabetes and Metabolism, University of Birmingham , Birmingham, UK
                [3]Society for Endocrinology , Bristol, UK
                [4]Endocrinology and Diabetes Unit, Department of Medicine I , University of Würzburg , Würzburg, Germany
                [5]Endocrinology Unit , Queen's Medical Research Institute, Centre for Cardiovascular Science, University of Edinburgh , Edinburgh, UK
                [6]Institute for Molecular and Experimental Medicine, Centre for Endocrine and Diabetes Sciences, Cardiff University , Cardiff, UK
                [7]Academic Unit of Diabetes, Endocrinology and Metabolism , University of Sheffield , Room 112, Floor M, Royal Hallamshire Hospital, Glossop Road, Sheffield, S10 2JF, UK
                Author notes
                (Correspondence should be addressed to R J Ross; Email: r.j.ross@ 123456sheffield.ac.uk )
                Article
                Publisher ID: EJE130128
                DOI: 10.1530/EJE-13-0128
                PMC ID: 3650754
                PubMed ID: 23520247
                SO-VID: d2d1fc5c-8eb7-4f40-96b2-26f3cf91c05d
                Copyright © © 2013 European Society of Endocrinology
                License:

                This work is licensed under a Creative Commons Attribution 3.0 Unported License

                History
                Date received : 8 February 2013
                Date accepted : 21 March 2013
                Categories
                Subject: Clinical Study

                ScienceOpen disciplines: Endocrinology & Diabetes
                Data availability:
                ScienceOpen disciplines: Endocrinology & Diabetes

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