Risk factors for cardiovascular disease in heterozygous familial hypercholesterolemia: A systematic review and meta-analysis

BACKGROUND: Current data from individual studies present conflicting evidence about the relationship between risk factors and cardiovascular disease (CVD) in heterozygous familial hypercholesterolemia (FH). OBJECTIVES: We conducted a systematic review and meta-analysis to quantify the association between various CVD risk factors and CVD in FH. METHODS: We searched MEDLINE, EMBASE, Global Health, the Cochrane Library, and PubMed for English-language studies reporting adjusted-associations between cardiovascular, behavioral, or clinical risk factors and CVD with >= 100 participants. We calculated pooled odds ratios (ORs) with 95% confidence intervals (CIs) for selected risk factors with random-effects meta-analysis, from which we derived attributable risk estimates. RESULTS: We identified 27 studies representing 41,831 unique participants and 6629 CVD events. Age (OR: 1.07; 95% CI: 1.03, 1.10), male sex (OR: 1.95; 95% CI: 1.68, 2.23), hypertension (OR: 2.11; 95% CI: 1.64, 2.58), diabetes (OR: 1.95; 95% CI: 1.33, 2.57), body mass index (OR: 1.04; 95% CI: 1.03, 1.05), smoking (OR: 1.71; 95% CI: 1.30, 2.12), elevated lipoprotein(a) (OR: 1.90; 95% CI: 1.10, 2.71), low high-density lipoprotein cholesterol (OR: 1.39; 95% CI: 1.24, 1.53), and a family history of CVD (OR: 1.83, 95% CI: 1.58, 2.07) were found to be significant CVD risk factors in FH. Smoking, hypertension, and diabetes accounted for more than a quarter of CVD risk in FH individuals, whereas low-density lipoprotein cholesterol > 4.0 mmol/L accounted for 1 in 3 CVD cases. Meta-regression analyses found associations between low-density lipoprotein cholesterol (P = cholesterol (P < .001) and CVD. Results were broadly consistent in sensitivity analyses. CONCLUSION: Several clinical risk factors are significantly and independently CVD risk in patients with FH and should be targeted for modification. These data the selection of variables for prediction models to aid in risk stratifying patients. (C) 2018 National Lipid Association. All rights reserved.