White Blood Cells and the Metabolic Syndrome – A Factor Analysis in Elderly from the General Population
John Ohrvik
Medicine, Karolinska Institutet, Stockholm, Sweden

Background: Factor analysis aims at ascertains whether the interrelation between a set of directly measurable variables are explicable in terms of a smaller number of underlying unobservable variables termed factors.

High blood concentrations of inflammatory markers, including white blood cell (WBC) count, are closely related to the metabolic syndrome (MetS). Both conditions predict dismal survival. We determined prospective associations between mortality and factors derived by a factor analysis of WBC count and the basic components of MetS.

Methods: We performed a factor analysis of WBC count and the continuous components of MetS in 196 men and 200 women comprising 65% of all 75-year-olds from the Swedish city Västerås. Principle component analysis was used to identify an initial set of uncorrelated factors. Skewed variables were log-transformed and variables known to be negatively associated with mortality were inverted prior to the analysis. Varimax rotation, which results in factors with high factor loadings for a few variables and near zero loadings for the remaining, was applied to get more easily interpretable solutions. Nonparametric bootstrap was used to assess the consistency and accuracy of the factor analysis and calculate confidence intervals.

Prospective associations of the factors with all-cause mortality (median follow-up 10.6 years) were assessed by Cox proportional hazard regression. A best subset approach, using Bayesian information criterion (BIC) as performance measure, was used to identify an optimal set of significant confounders.

The predictive ability of the original components and the derived factors was assessed by the time dependent area under the ROC curve (AUCt).

Results: The analysis consistently revealed three factors in men and two in women. Factor1 included fasting glucose, HDL-cholesterol, triglycerides and waist in men and in addition WBC count in women. Factor2 included diastolic and systolic blood-pressure in both genders. In men factor3 included fasting glucose and WBC count. These factors explained in median 66% (factor1 28%; factor2 23%; factor3 15%) of the total variation in men and 57% (factor1 34%; factor2 23%) in women.

During follow-up 91 (46%) men and 58 (29%) women died. Factor1 was significantly related to 10-year mortality in men; Hazard Ratio (HR)=1.22/SD-unit (95%CI:1.06-1.41, p=0.007) and women; HR=1.25/SD-unit (95%CI: 1.06-1.48, p=0.010). In a pooled analysis the HR for factor1 was 1.16/SD-unit (95%CI: 1.04-1.29, p=0.010) adjusting for gender, previous myocardial infarction, known hypertension and current smoking, the only significant confounders using BIC. The AUCt after 10 year increased from 0.58 to 0.70 adding factor1 to the confounders. In men factor3 significantly related to increased mortality; HR=1.29/SD-unit (95%CI: 1.07-1.56, p=0.009).

Conclusions: A metabolic-inflammatory and a blood-pressure factor were identified. In men the former split into a metabolic and an inflammatory factor. Factors including metabolic and inflammatory components were significantly related to 10-year mortality a relation which remained after adjusting for confounders.

Keywords: Bootstrap; Factor Analysis; Metabolic Syndrome; Survival Analysis

Biography: John Ohrvik is working at the Department of Medicine, Karolinska Institutet Stockholm. His current research is mainly focused on modeling risk patterns for developing cardiovascular diseases and type 2 diabetes.