Meta-analysis of Alzheimer's disease risk with obesity, diabetes, and related disorders.
By L. Profenno, A. Porsteinsson, Stephen V. Faraone. Biological Psychiatry, 2010
693 citations recorded by the source corpus.
BACKGROUND Late-onset Alzheimer's disease (AD) is a multifactorial and heterogeneous disorder with major risk factors including advanced age, presence of an apolipoprotein E epsilon4 (APOE4) allele, and family history of AD. Other risk factors may be obesity and diabetes and related disorders, which are highly prevalent. METHODS We reviewed longitudinal epidemiological studies of body mass, diabetes, metabolic syndrome, and glucose and insulin levels on risk for AD. We conducted meta-analyses of the results from these studies. RESULTS For obesity assessed by body mass index, the pooled effect size for AD was 1.59 (95% confidence interval [CI] 1.02-2.5; z = 2.0; p = .042), and for diabetes, the pooled effect size for AD was 1.54 (95% CI 1.33-1.79; z = 5.7; p < .001). Egger's test did not find significant evidence for publication bias in the meta-analysis for obesity (t = -1.4, p = .21) or for diabetes (t = -.86, p = .42). Since these disorders are highly comorbid, we conducted a meta-analysis combining all studies of obesity, diabetes, and abnormal glucose or insulin levels, which yielded a highly significant pooled effect size for AD of 1.63 (95% CI 1.39-1.92; z = 5.9; p < .001). CONCLUSIONS Obesity and diabetes significantly and independently increase risk for AD. Though the level of risk is less than that with the APOE4 allele, the high prevalence of these disorders may result in substantial increases in future incidence of AD. Physiological changes common to obesity and diabetes plausibly promote AD.
Subfields: Endocrinology, Diabetes and Metabolism, Physiology, Public Health, Environmental and Occupational Health
View Paper: https://www.semanticscholar.org/paper/2896f1a80605d9034cce7f9a0c5365a9675a5c3a
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Citations in the corpus
- Summary of the evidence on modifiable risk factors for cognitive decline and dementia: A population‐based perspective (2015, neutral): “[20] Profenno LA, Porsteinsson AP, Faraone SV.”
- Understanding the somatic consequences of depression: biological mechanisms and the role of depression symptom profile (2013, background): “obesity, cognitive impairment and even cancer [7,19-21], thereby being a potential linking mechanism between depression and incident somatic conditions.”