Increased relative risk of subsequent affective disorders in patients with a hospital diagnosis of obesity

Thomsen, Anders Eller; Kvist, Tine K.; Andersen, Per Kragh; Kessing, Lars Vedel

doi:10.1038/sj.ijo.0803241

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…Specifically, we considered obesity admissions using the ICD-8 code 277.99 and the ICD-10 codes E65.0–E66.9. 26 We refer to this throughout the paper as obesity for short hand, but it should be noted that this is not based on a BMI>30, the typical definition of obesity, but most likely reflects a much more elevated BMI. We also obtained hyperlipidemia and hypercholesterolemia data from the NPR (ICD-8: 279.00 and ICD-10: E78.0E78.5).…”

Section: Methodsmentioning

confidence: 99%

Diabetes Mellitus, Obesity, and Diagnosis of Amyotrophic Lateral Sclerosis

et al. 2015

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IMPORTANCE Although prior studies have suggested a role of cardiometabolic health on pathogenesis of amyotrophic lateral sclerosis (ALS), the association with diabetes mellitus has not been widely examined. Amyotrophic lateral sclerosis is the most common motor neuron disorder. Several vascular risk factors have been associated with decreased risk for ALS. Although diabetes is also a risk factor for vascular disease, the few studies of diabetes and ALS have been inconsistent.OBJECTIVE To examine the association between diabetes and obesity, each identified through International Statistical Classification of Diseases, Eighth or Tenth Revision codes in a hospital registry, and ALS using data from the Danish National Registers. DESIGN, SETTING, AND PARTICIPANTSPopulation-based nested case-control study of 3650 Danish residents diagnosed as having ALS between January 1, 1982, and December 31, 2009, and 365 000 controls (100 for each ALS case) matched on age and sex. The analysis was conducted in September and October 2014. MAIN OUTCOMES AND MEASURESAdjusted odds ratio for ALS associated with diabetes or obesity diagnoses at least 3 years prior to the ALS diagnosis date. RESULTSWhen considering diabetes and our obesity indicator together, the estimated odds ratio for ALS was 0.61 (95% CI, 0.46-0.80) for diabetes and 0.81 (95% CI, 0.57-1.16) for obesity. We observed no effect modification on the association with diabetes by sex. We did find a significant modification by age at ALS diagnosis and age at first mention of diabetes in the hospital registers. The protective association was stronger with increasing age at ALS diagnosis (P = .01), and the odds ratio for first mention of diabetes was 1.66 (95% CI, 0.85-3.21) before age 40 years but 0.52 (95% CI, 0.39-0.70) for older ages. These results are consistent with different associations for type 1 vs type 2 diabetes. CONCLUSIONS AND RELEVANCEIn this Danish nationwide study to investigate the association between diabetes and ALS diagnosis, our findings are in agreement with previous reports of a protective association between vascular risk factors and ALS and suggest that type 2 diabetes, but not type 1, is protective for ALS.

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Section: Methodsmentioning

confidence: 99%

Diabetes Mellitus, Obesity, and Diagnosis of Amyotrophic Lateral Sclerosis

et al. 2015

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“…In other studies, one failure cause may be of primary interest, and focus is then on the analysis of that cause in the presence of competing risks. As such an example, Thomsen et al have studied the occurrence of affective disorders in patients with obesity (compared with patients with osteoarthritis). The authors presented an analysis not only focused on explanatory variables for the cause‐specific hazard of affective disorders but also predicted cumulative incidence curves for that event for various covariate patterns taking the competing risk of death into account.…”

Section: Multistate Modelsmentioning

confidence: 99%

Interpretability and importance of functionals in competing risks and multistate models

Andersen

Keiding

2011

Statistics in Medicine

178

193

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The basic parameters in both survival analysis and more general multistate models, including the competing risks model and the illness-death model, are the transition hazards. It is often necessary to supplement the analysis of such models with other model parameters, which are all functionals of the transition hazards. Unfortunately, not all such functionals are equally meaningful in practical contexts, even though they may be mathematically well defined. We have found it useful to check whether the functionals satisfy three simple principles, which may be used as criteria for practical interpretability.

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“…This is particularly relevant to studies like the nasopharyngeal trial 1 where DM was the event of interest, whereas LN relapse was accounted for as a competing risk in the analysis. Similarly, in a study on the occurrence of affective disorders, Thomsen et al 13 considered affective disorder as the main risk of interest and accounted for death as a competing risk in the analysis. Nevertheless, for more meaningful interpretation of the results in competing risks situation, a universal recommendation is that all competing risks should be analysed to provide a fuller picture of the inter-relationship between competing events.…”

Section: Discussionmentioning

confidence: 99%

Estimating sample size in the presence of competing risks – Cause-specific hazard or cumulative incidence approach?

Tai

Machin

2015

Stat Methods Med Res

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In designing randomised clinical trials involving competing risks endpoints, it is important to consider competing events to ensure appropriate determination of sample size. We conduct a simulation study to compare sample sizes obtained from the cause-specific hazard and cumulative incidence (CMI) approaches, by first assuming exponential event times. As the proportional subdistribution hazard assumption does not hold for the CMI exponential (CMI) model, we further investigate the impact of violation of such an assumption by comparing the results obtained from the CMI exponential model with those of a CMI model assuming a Gompertz distribution (CMI) where the proportional assumption is tenable. The simulation suggests that the CMI approach requires a considerably larger sample size when treatment reduces the hazards of both the main event, A, and the competing risk, B. When treatment has a beneficial effect on A but no effect on B, the sample sizes required by both methods are largely similar, especially for large reduction in the main risk. If treatment has a protective effect on A but adversely affects B, then the sample size required by CMI is notably smaller than cause-specific hazard for small to moderate reduction in the main risk. Further, a smaller sample size is required for CMI as compared with CMI. The choice between a cause-specific hazard or CMI model in competing risks outcomes has implications on the study design. This should be made on the basis of the clinical question of interest and the validity of the associated model assumption.

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Increased relative risk of subsequent affective disorders in patients with a hospital diagnosis of obesity

Cited by 7 publications

References 27 publications

Diabetes Mellitus, Obesity, and Diagnosis of Amyotrophic Lateral Sclerosis

Diabetes Mellitus, Obesity, and Diagnosis of Amyotrophic Lateral Sclerosis

Interpretability and importance of functionals in competing risks and multistate models

Estimating sample size in the presence of competing risks – Cause-specific hazard or cumulative incidence approach?

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