Apolipoprotein E gene is related to mortality only in normal weight individuals : The Rotterdam study Objective To investigate the relationship between the apolipoprotein E -LRB- APOE -RRB- gene and the risk of mortality in normal weight , overweight and obese individuals . Methods and Results In a population-based study of 7,983 individuals aged 55 years and older , we compared the risks of all-cause and coronary heart disease -LRB- CHD -RRB- mortality by APOE genotype , both overall and in subgroups defined by body mass index -LRB- BMI -RRB- . We found significant evidence for interaction between APOE and BMI in relation to total cholesterol -LRB- p = 0.04 -RRB- and HDL cholesterol -LRB- p < 0.001 -RRB- . Overall , APOE * 2 carriers showed a decreased risk of all-cause mortality . Analyses within BMI strata showed a beneficial effect of APOE * 2 only in normal weight persons -LRB- adjusted hazard ratio -LRB- HR -RRB- 0.7 -LSB- 95 % CI 0.5 -- 0.9 -RSB- -RRB- . APOE * 2 was not associated with a lower risk of all-cause mortality in overweight or obese persons . The effect of APOE * 2 in normal weight individuals tended to be due to the risk of CHD mortality -LRB- adjusted HR 0.5 -LSB- 95 % CI 0.2 -- 1.2 -RSB- -RRB- . Conclusion The APOE * 2 allele confers a lower risk of all-cause mortality only to normal weight individuals . Apolipoprotein E is a plasma protein involved in the metabolism of cholesterol . The apolipoprotein E isoforms ε2 , ε3 and ε4 , determined by the three alleles of the apolipoprotein E gene -LRB- APOE * 2 , APOE * 3 and APOE * 4 -RRB- , differ in their binding affinity to serum cholesterol and hence in their ability to clear dietary fat from the blood -LSB- 1 -RSB- . Accordingly , total serum cholesterol levels differ between APOE genotypes , with APOE * 2 carriers having lower total serum cholesterol levels and APOE * 4 carriers having higher levels than APOE * 3 homozygotes -LSB- 1 -- 4 -RSB- . In line with the clear differences in total serum cholesterol levels associated with the APOE genotype , one would expect that APOE * 2 carriers have lower risks of cardiovascular morbidity and mortality and APOE * 4 carriers have higher risks . Yet , the evidence for this association is inconsistent . A recent meta-analysis showed that APOE * 4 was associated with a higher risk of coronary heart disease -LRB- CHD -RRB- , but found no association with APOE * 2 -LSB- 5 -RSB- . APOE * 2 has been associated with a lower risk of mortality -LSB- 6 -RSB- and APOE * 4 with a higher risk -LSB- 7 -- 11 -RSB- , but also these relationships were not found by others -LSB- 12 -- 19 -RSB- . A possible explanation for these inconsistencies may be found in the role of body weight . Several studies have demonstrated that the relationship between the APOE gene and lipids differs between normal weight , overweight and obese individuals -LSB- 20 -- 22 -RSB- . Total serum cholesterol levels were higher among obese than among non-obese APOE * 4 carriers -LSB- 20 -RSB- , and obese APOE * 2 carriers had higher serum triglycerides and higher LDL-cholesterol levels than non-obese APOE * 2 carriers -LSB- 20 , 21 -RSB- . While APOE * 2 carriers generally have the lowest cholesterol levels , those with high BMI were found to have similar total serum cholesterol levels compared to APOE * 3 homozygotes and APOE * 4 carriers -LSB- 20 -RSB- . These findings suggest that the adverse effects of high body weight may outweigh the beneficial effect of APOE * 2 status . If so , a protective effect of APOE * 2 on mortality may be largest among normal weight individuals . Despite this evidence for a moderating effect of body weight on the relation between the APOE gene and total serum cholesterol , no studies so far have performed a stratified analysis on the relationship between the APOE gene and the risk of mortality . The aim of the present study was to investigate the extent to which the relationship between APOE genotypes and the risk of mortality differs between normal weight , overweight and obese individuals . We examined this relationship for both all-cause and CHD mortality . Methods Study population and procedures The present analyses were performed in the Rotterdam Study , an ongoing population-based study on the determinants of disease and disability among persons 55 years and older . Details of this study have been described elsewhere -LSB- 23 -RSB- . Baseline data were collected between 1990 and 1993 . During home visits , a trained investigator obtained information on health status , medical history , medication use and lifestyle . Subsequently , participants were invited to the study center where they underwent an extensive clinical examination . The Medical Ethics Committee of the Erasmus Medical Center approved the study protocol and all participants provided written informed consent . From 10,275 eligible subjects , 7,983 -LRB- 78 % -RRB- individuals agreed to participate in the study and were examined at baseline . Data collection Clinical and laboratory assessments The clinical examination included weight , height , systolic and diastolic blood pressure , serum glucose , total serum cholesterol and high-density lipoprotein -LRB- HDL -RRB- cholesterol levels . BMI was computed as weight -LRB- kg -RRB- divided by height squared -LRB- m2 -RRB- . Participants were classified as underweight -LRB- BMI < 18.5 kg/m2 -RRB- , normal weight -LRB- 18.5 kg/m2 ≤ BMI < 25 kg/m2 -RRB- , overweight -LRB- 25 kg/m2 ≤ BMI < 30 kg/m2 -RRB- or obese -LRB- BMI ≥ 30 kg/m2 -RRB- -LSB- 24 -RSB- . The waist and hip circumferences were measured , and the waist-hip-ratio -LRB- WHR -RRB- was calculated as indirect assessment of abdominal fat . Systolic and diastolic blood pressures were measured twice in a sitting position after 5 min rest using a random-zero sphygmomanometer . The mean of the two measurements was used for the analyses . Hypertension was defined as systolic blood pressure higher than 160 mmHg , diastolic blood pressure higher than 100 mmHg , or the use of antihypertensive medication indicated to treat high blood pressure -LRB- hypertension grades 2 and 3 -RRB- -LSB- 25 -RSB- . Diabetes was diagnosed based on a random or post-load glucose level higher than 11.0 mmol/l and/or the use of anti-diabetic medication -LSB- 26 -RSB- . Total serum cholesterol and HDL-cholesterol were measured using an automated enzymatic procedure -LSB- 27 -RSB- . Hypercholesterolemia was defined as a total serum cholesterol level above 6.2 mmol/l -LSB- 28 -RSB- . The APOE genotype was determined on DNA samples using a polymerase chain reaction followed by enzymatic digestion using methods previously described -LSB- 29 -RSB- . The frequencies of APOE genotypes were APOE 2/2 0.8 % , APOE 2/3 12.8 % , APOE 2/4 2.7 % , APOE 3/3 58.4 % , APOE 3/4 22.9 % and APOE 4/4 2.4 % . The proportions of the APOE alleles and genotypes were in Hardy-Weinberg equilibrium -LRB- p = 0.71 -RRB- . Mortality data Information on the vital status of the participants was obtained at regular intervals from the municipal population registry . Causes of death were obtained from the general practitioners by means of a standardized questionnaire relating to the circumstances of death , most likely cause of death and time and place of death . Two independent research physicians coded all events according to the International Classification of Diseases , 10th edition -LRB- ICD-10 -RRB- -LSB- 30 -RSB- . CHD mortality was defined as death from diseases coded I20 -- I25 , I46 , I50 or R96 . Mortality data were available up to December 2003 . Statistical analyses Of the 7,983 individuals who participated at baseline , 5,817 -LRB- 73 % -RRB- had APOE genotyped successfully and had complete information on BMI , WHR and cholesterol levels . Because persons with an extremely low BMI may suffer from life-threatening diseases such as cancer , individuals with a BMI below 18.5 kg/m2 -LRB- n = 51 -RRB- were excluded from the analyses . Also , APOE 2/4 individuals were excluded from the analyses -LRB- n = 158 -RRB- to distinguish the effect of the APOE alleles unambiguously . Hence , data from 5,608 participants were available for the analyses . Differences in baseline characteristics by APOE genotypes were tested using the chi-squared statistic -LRB- categorical variables -RRB- or ANOVA -LRB- continuous variables -RRB- . P for trend was obtained by testing the linearity of the sum of squares from the ANOVA analyses . Multiple linear regression analysis was used to assess the interaction between APOE genotype and BMI on total serum cholesterol and HDL-cholesterol levels . Differences in survival probabilities were examined by comparing Kaplan-Meier survival curves and tested using the log rank test . Kaplan-Meier plots were constructed using age as the time scale to take proper account of the effect of age -LSB- 31 , 32 -RSB- . Risks of mortality were quantified as hazard ratios -LRB- HRs -RRB- using Cox proportional hazards analyses with age as the time scale . The most common genotype -LRB- APOE 3/3 -RRB- was used as the reference category . The proportionality assumption of all models was verified by testing the Schoenfeld residuals -LSB- 33 -RSB- . Because the proportionality assumption was not met , HRs were calculated for early and late mortality using 80 years as the cut-off age . This cut-off age was selected because it led to proportionality of the models below and above the cut-off age . HRs were calculated adjusted for gender , smoking status , education level , total serum cholesterol , HDL-cholesterol , waist to hip ratio hypertension and diabetes mellitus . Results The mean age at entry was 68.7 years -LRB- SD = 8.7 years -RRB- and 41.8 % of the participants were men . The mean follow up time was 11.1 years -LRB- SD = 3.8 years -RRB- . Baseline characteristics of APOE * 2 carriers , APOE * 3 homozygotes and APOE * 4 carriers are presented in Table 1 . As expected , cholesterol levels differed significantly between the genotype groups . The prevalence of hypercholesterolaemia was lowest among APOE * 2 carriers -LRB- 49 % -RRB- and highest among APOE * 4 carriers -LRB- 66 % -RRB- . Furthermore , there were statistically significant differences in BMI scores between the genotype groups , with APOE * 2 carriers having the highest mean BMI -LRB- 26.7 ± 3.6 kg/m2 -RRB- and APOE * 4 carriers the lowest -LRB- 26.1 ± 3.4 kg/m2 -RRB- . There were no statistically significant differences in WHR between the genotype groups . Table 1Baseline characteristics by APOE genotype groupsAPOE * 2 carriersAPOE 3/3APOE * 4 carrierspn7823 ,3691,457 Sex -LRB- men -RRB- 3842420.05 Age at entry -LRB- years -RRB- 68.8 -LRB- 8.8 -RRB- 68.9 -LRB- 8.7 -RRB- 68.3 -LRB- 8.5 -RRB- 0.14 Body mass index -LRB- kg/m2 -RRB- 26.7 -LRB- 3.6 -RRB- 26.4 -LRB- 3.6 -RRB- 26.1 -LRB- 3.4 -RRB- < 0.01 Normal weight343940Overweight494747 < 0.05Obese171413Waist-to-hip ratio0 .9 -LRB- 0.1 -RRB- 0.9 -LRB- 0.1 -RRB- 0.9 -LRB- 0.1 -RRB- 0.18 Systolic blood pressure -LRB- mmHg -RRB- 140 -LRB- 22 -RRB- 140 -LRB- 23 -RRB- 138 -LRB- 2 -RRB- 0.04 Diastolic blood pressure -LRB- mmHg -RRB- 74 -LRB- 11 -RRB- 74 -LRB- 12 -RRB- 73 -LRB- 11 -RRB- 0.06 Hypertension3634330 .25 Total serum cholesterol -LRB- mmol/l -RRB- 6.4 -LRB- 1.3 -RRB- 6.6 -LRB- 1.2 -RRB- 6.8 -LRB- 1.2 -RRB- < 0.001HDL-cholesterol -LRB- mmol/l -RRB- 1.4 -LRB- 0.4 -RRB- 1.3 -LRB- 0.3 -RRB- 1.3 -LRB- 0.4 -RRB- < 0.001 Cholesterol lowering medication3230 .71 Hypercholesterolemia496066 < 0.001 Smoking status -LRB- current -RRB- 3935330.02 Education level Lower434345 Intermediate4342400 .21 Higher141515Diabetes mellitus101090 .75 Values are means -LRB- standard deviations -RRB- for continuous variables and percentages for categorical variables . p values are obtained by ANOVA for continuous variables and by χ2 for categorical variables Figure 1 presents mean total serum cholesterol and HDL-cholesterol levels by APOE genotype in normal weight , overweight and obese persons . In all BMI strata , APOE * 2 carriers had the lowest and APOE * 4 carriers the highest total serum cholesterol -LRB- pfor trend < 0.01 -RRB- . The association between HDL-cholesterol and APOE genotype was in the opposite direction -LRB- pfor trend < 0.01 -RRB- . Note that even though APOE * 2 carriers had the lowest total serum cholesterol levels in each BMI group , still 50 % of the overweight and 57 % of the obese APOE * 2 carriers had hypercholesterolaemia compared to 41 % of the normal weight APOE * 2 carriers -LRB- p = 0.003 -RRB- . There was no significant evidence for interaction between APOE genotype and BMI in relation to total serum cholesterol -LRB- p = 0.26 -RRB- and HDL-cholesterol -LRB- p = 0.25 -RRB- . However , when BMI was analyzed as a continuous trait , then the p value for interaction was 0.04 for total serum cholesterol and less than 0.001 for HDL-cholesterol . Fig. 1Mean total serum cholesterol and HDL-cholesterol levels by APOE genotypes for normal weight , overweight and obese individuals . HDL : high-density lipoproteins A total of 1,918 deaths occurred during follow-up . Of those , 257 occurred in 782 APOE * 2 carriers -LRB- 33 % -RRB- , 1,160 in 3,369 APOE * 3 homozygotes -LRB- 33 % -RRB- and 501 in 1,457 APOE * 4 carriers -LRB- 34 % -RRB- . Differences between the Kaplan-Meier survival curves of the APOE * 2 carriers , APOE * 3 homozygotes and APOE * 4 carriers were small but statistically significant -LRB- p = 0.03 -RRB- with APOE * 2 carriers having slightly lower mortality risks -LRB- Fig. 2 -RRB- . After adjustment for other cardiovascular risk factors , APOE genotype was not associated with all-cause mortality before or after 80 years of age in the overall population -LRB- Table 2 -RRB- . The differences between the curves were only statistically significant in normal weight individuals -LRB- p = 0.04 ; Fig. 3 -RRB- . Normal weight carriers of the APOE * 2 allele had a significantly lower risk of mortality before age 80 years -LRB- HR -LSB- 95 % CI -RSB- = 0.7 -LSB- 0.5 -- 0.9 -RSB- -RRB- , but not after age 80 years -LRB- crude HR -LSB- 95 % CI -RSB- = 1.0 -LSB- 0.8 -- 1.4 -RSB- -RRB- . There were no significant differences in the risk of mortality between genotype groups among overweight or obese persons -LRB- Table 2 -RRB- . Fig. 2Kaplan Meier survival curves for all-cause mortality by APOE genotypesTable 2Incidence rates and hazard ratios for all-cause mortality by APOE genotypes and BMI categoriesFollow-upIncidence rateHazard ratioFollow-upIncidence rateHazard ratioDeaths -LRB- py -RRB- Deaths/1 ,000 py -LRB- 95 % CI -RRB- -LRB- 95 % CI -RRB- Deaths -LRB- py -RRB- Deaths/1 ,000 py -LRB- 95 % CI -RRB- -LRB- 95 % CI -RRB- Before 80 years of ageAfter 80 years of ageAllAPOE * 2 carriers1016 ,78214.9 -LRB- 12.3 -- 18.1 -RRB- 0.9 -LRB- 0.7 -- 1.1 -RRB- 15621,7647.2 -LRB- 6.1 -- 8.4 -RRB- 0.9 -LRB- 0.8 -- 1.1 -RRB- APOE 3/348128 ,70316.8 -LRB- 15.3 -- 18.3 -RRB- 67995,8087.1 -LRB- 6.6 -- 7.6 -RRB- APOE * 4 carriers19812 ,69315.6 -LRB- 13.6 -- 17.9 -RRB- 1.0 -LRB- 0.8 -- 1.2 -RRB- 30341,6377.3 -LRB- 6.5 -- 8.1 -RRB- 1.2 -LRB- 1.0 -- 1.4 -RRB- Total78048 ,17816.2 -LRB- 15.1 -- 17.4 -RRB- 1,138159,2097.1 -LRB- 6.7 -- 7.6 -RRB- Normal weightAPOE * 2 carriers282 ,37011.8 -LRB- 8.2 -- 17.1 -RRB- 0.7 -LRB- 0.5 -- 0.9 -RRB- 577,2897.8 -LRB- 6.0 -- 10.1 -RRB- 1.0 -LRB- 0.8 -- 1.4 -RRB- APOE 3/320611 ,31418.2 -LRB- 15.9 -- 20.9 -RRB- 25638,0456.7 -LRB- 6.0 -- 7.6 -RRB- APOE * 4 carriers785 ,08015.4 -LRB- 12.3 -- 19.2 -RRB- 1.0 -LRB- 0.7 -- 1.3 -RRB- 12717,3057.3 -LRB- 6.2 -- 8.7 -RRB- 1.3 -LRB- 0.9 -- 1.7 -RRB- Total31218 ,76416.6 -LRB- 14.9 -- 18.6 -RRB- 44062,6397.0 -LRB- 6.4 -- 7.7 -RRB- OverweightAPOE * 2 carriers593 ,30517.9 -LRB- 13.8 -- 23.0 -RRB- 1.1 -LRB- 0.9 -- 1.5 -RRB- 6511,0645.9 -LRB- 4.6 -- 7.5 -RRB- 0.9 -LRB- 0.7 -- 1.1 -RRB- APOE 3/321713 ,57316.0 -LRB- 14.0 -- 18.3 -RRB- 31045,3666.8 -LRB- 6.1 -- 7.6 -RRB- APOE * 4 carriers906 ,04714.9 -LRB- 12.1 -- 18.3 -RRB- 0.9 -LRB- 0.7 -- 1.2 -RRB- 13719,3987.1 -LRB- 6.0 -- 8.4 -RRB- 1.2 -LRB- 1.0 -- 1.5 -RRB- Total36622 ,92416.0 -LRB- 14.4 -- 17.7 -RRB- 51275,8276.8 -LRB- 6.2 -- 7.4 -RRB- ObeseAPOE * 2 carriers141 ,10712.6 -LRB- 7.5 -- 21.4 -RRB- 1.0 -LRB- 0.5 -- 1.8 -RRB- 343,41110.0 -LRB- 7.1 -- 14.0 -RRB- 1.0 -LRB- 0.6 -- 1.5 -RRB- APOE 3/3583 ,81615.2 -LRB- 11.7 -- 19.7 -RRB- 11312,3989.1 -LRB- 7.6 -- 11.0 -RRB- APOE * 4 carriers301 ,56619.2 -LRB- 13.4 -- 27.4 -RRB- 1.3 -LRB- 0.9 -- 2.0 -RRB- 394,9347.9 -LRB- 5.8 -- 10.8 -RRB- 1.1 -LRB- 0.8 -- 1.6 -RRB- Total1026 ,48915.7 -LRB- 12.9 -- 19.1 -RRB- 18620,7429.0 -LRB- 7.8 -- 10.4 -RRB- Hazard ratios adjusted for sex , waist-hip-ratio , smoking , education level , total serum cholesterol , HDL cholesterol hypertension and diabetes ; py : person-years ; CI : Confidence intervalFig . 3Kaplan Meier survival curves for all-cause mortality by APOE genotypes for normal weight , overweight and obese individuals To investigate whether the lower risk of mortality observed among normal weight APOE * 2 carriers was due to a lower risk of mortality from CHD , we calculated the HRs separately for CHD mortality . APOE genotype was not significantly associated with CHD mortality . Analysis within BMI strata showed that normal weight APOE * 2 carriers tended to have lower risks of CHD mortality -LRB- adjusted HR -LSB- 95 % CI -RSB- = 0.5 -LSB- 0.2 -- 1.2 -RSB- -RRB- . No differences in the risk of CHD mortality between genotype groups were found in overweight or obese participants -LRB- Table 3 -RRB- . Table 3Incidence rates and hazard ratios for CHD mortality by APOE genotypes and BMI categoriesFollow-upIncidence rateHazard ratioFollow-upIncidence rateHazard ratioDeaths -LRB- py -RRB- Deaths/1 ,000 py -LRB- 95 % CI -RRB- -LRB- 95 % CI -RRB- Deaths -LRB- py -RRB- Deaths/1 ,000 py -LRB- 95 % CI -RRB- -LRB- 95 % CI -RRB- Before 80 years of ageAfter 80 years of ageAllAPOE * 2 carriers266 ,7823.8 -LRB- 2.6 -- 5.6 -RRB- 1.0 -LRB- 0.6 -- 1.5 -RRB- 412,10319.5 -LRB- 14.4 -- 26.5 -RRB- 1.1 -LRB- 0.8 -- 1.5 -RRB- APOE 3/312628 ,7034.4 -LRB- 3.7 -- 5.2 -RRB- 1718,74419.6 -LRB- 16.8 -- 22.7 -RRB- APOE * 4 carriers5912 ,6934.6 -LRB- 3.6 -- 6.0 -RRB- 1.1 -LRB- 0.8 -- 1.4 -RRB- 663,47219.0 -LRB- 14.9 -- 24.2 -RRB- 1.1 -LRB- 0.8 -- 1.4 -RRB- Total21148 ,1784.4 -LRB- 3.8 -- 5.0 -RRB- 27814,31919.4 -LRB- 17.3 -- 21.8 -RRB- Normal weightAPOE * 2 carriers52 ,3702.1 -LRB- 0.9 -- 5.1 -RRB- 0.5 -LRB- 0.2 -- 1.2 -RRB- 1566322.6 -LRB- 13.6 -- 37.5 -RRB- 1.6 -LRB- 0.9 -- 2.9 -RRB- APOE 3/35611 ,3144.9 -LRB- 3.8 -- 6.4 -RRB- 483,07515.6 -LRB- 11.8 -- 20.7 -RRB- APOE * 4 carriers265 ,0805.1 -LRB- 3.5 -- 7.5 -RRB- 1.2 -LRB- 0.7 -- 1.9 -RRB- 211,28616.3 -LRB- 10.6 -- 25.1 -RRB- 1.1 -LRB- 0.6 -- 1.8 -RRB- Total8718 ,7644.6 -LRB- 3.8 -- 5.7 -RRB- 845,02416.7 -LRB- 13.5 -- 20.7 -RRB- OverweightAPOE * 2 carriers163 ,3054.8 -LRB- 3.0 -- 7.9 -RRB- 1.4 -LRB- 0.8 -- 2.4 -RRB- 1598915.2 -LRB- 9.1 -- 25.2 -RRB- 0.7 -LRB- 0.4 -- 1.3 -RRB- APOE 3/35113 ,5733.8 -LRB- 2.9 -- 4.9 -RRB- 924,03522.8 -LRB- 18.6 -- 28.0 -RRB- APOE * 4 carriers216 ,0473.5 -LRB- 2.3 -- 5.3 -RRB- 0.8 -LRB- 0.5 -- 1.4 -RRB- 351,61921.6 -LRB- 15.5 -- 30.1 -RRB- 1.0 -LRB- 0.7 -- 1.5 -RRB- Total8822 ,9243.8 -LRB- 3.1 -- 4.7 -RRB- 1426,64321.4 -LRB- 18.1 -- 25.2 -RRB- ObeseAPOE * 2 carriers51 ,1074.5 -LRB- 1.9 -- 10.8 -RRB- 1.1 -LRB- 0.4 -- 3.4 -RRB- 1145024.4 -LRB- 13.5 -- 44.1 -RRB- 1.4 -LRB- 0.7 -- 3.1 -RRB- APOE 3/3193 ,8165.0 -LRB- 3.2 -- 7.8 -RRB- 311,63419.0 -LRB- 13.3 -- 27.0 -RRB- APOE * 4 carriers121 ,5667.7 -LRB- 4.4 -- 13.5 -RRB- 1.6 -LRB- 0.8 -- 3.5 -RRB- 1056817.6 -LRB- 9.5 -- 32.7 -RRB- 1.1 -LRB- 0.5 -- 2.2 -RRB- Total366 ,4895.5 -LRB- 4.0 -- 7.7 -RRB- 522,65119.6 -LRB- 14.9 -- 25.7 -RRB- Hazard ratios adjusted for sex , waist-hip-ratio , smoking , education level , total serum cholesterol , HDL cholesterol hypertension and diabetes ; py : person-years ; CI : Confidence interval Discussion Our study shows that the APOE * 2 allele has a protective effect on overall mortality . This protective effect seems to be limited to mortality from CHD . Analyses within BMI strata demonstrated that this genetic advantage of APOE * 2 was observed only in normal weight individuals , but not in overweight and obese APOE * 2 carriers . The results of the present analyses may seem to contradict our earlier work in this population . We previously reported that the APOE gene was not significantly related to mortality -LSB- 12 -RSB- . While our findings come from the same population , the mean follow-up time of the participants has increased from 5.4 years to 11.1 years and the number of deaths increased by 53 % from 18 % to 34 % . This extended follow-up has improved the statistical power of our study to show a modest effect of APOE genotype on overall mortality . Research on the relationship between APOE and mortality has merely provided conflicting results -LSB- 6 -- 19 , 34 -RSB- . Three out of eleven studies showed a protective effect of APOE * 2 -LSB- 6 , 14 , 18 -RSB- . and three a deleterious effect of APOE * 4 -LSB- 8 -- 10 -RSB- . The latter effect may partly mediated by the increased risk of Alzheimer disease in association with APOE * 4 and mortality -LSB- 10 , 11 -RSB- . Note that our analysis showed small significant difference in the Kaplan-Meier survival curves , demonstrating a protective effect of APOE * 2 before age 80 . This may be due to the effect of age that becomes apparent when performing survival analysis with age as time scale , as we did in the present paper . Such finding is compatible with previous reports suggesting that genes affecting human lifespan might be age-specific -LSB- 35 -RSB- . Genetic and environmental interactions at older ages vary from those at early ages and therefore explain the differences in the association between APOE and mortality before and after 80 years of age . A previous report showed that among nonagenarians , APOE genotype has no effect on cognitive function , cognitive decline or survival -LSB- 36 -RSB- . In line with previous studies -LSB- 20 -- 22 -RSB- , we found that the total serum cholesterol levels associated with APOE genotype differed between normal weight , overweight and obese individuals . As expected , mean levels of total serum cholesterol increased with BMI and APOE * 2 carriers had the lowest mean total serum cholesterol in each BMI group . APOE * 4 carriers had higher cholesterol levels and lower HDL-cholesterol concentrations than APOE * 2 carriers despite the presence of a lower BMI and equal WHR . Previous reports showed that APOE genotype , BMI and WHR determine together the lipid levels -LSB- 20 -- 22 , 37 -RSB- . However , in our study abdominal fat , as measured by WHR , did not have any effect on the lipid levels by APOE genotype , while BMI was an important determinant . The significant interaction between APOE genotype and BMI in relation to total serum cholesterol found previously -LSB- 20 -RSB- , was also supported by this study . Although the mechanism of such interaction has not been yet elucidated , evidence suggests that obesity and abdominal fat increase low density lipoprotein cholesterol and therefore total cholesterol at a higher degree among APOE * 4 carriers -LSB- 20 -- 22 , 37 -RSB- . This is in line with our observation that the deleterious effect of increased BMI prevails over the beneficial effects of the APOE * 2 allele . Furthermore , previous research in the Rotterdam Study and the Dutch population indicated that among APOE * 2 homozygous carriers , the expression of Hyperlipoproteinemia type III is determined to a great extent by hyperinsulinemia and the presence of insulin resistance syndrome -LSB- 38 -RSB- , conferring and increased risk of CHD through accelerated atherosclerosis -LSB- 39 -RSB- . Although the association between high BMI and high cholesterol levels is well known and the APOE gene is associated with cholesterol levels as previously described , this is the first study that examined the effect of APOE genotype on mortality in BMI strata . We found that presence of the APOE * 2 allele was associated with a decreased risk of mortality only in normal weight individuals and that this lower risk was partly explained by lower risk of CHD mortality . These results were in line with the lower total cholesterol and higher HDL levels among normal weight APOE * 2 carriers . Our finding that the protective effect of APOE * 2 was only found in normal weight individuals suggests that the negative influences of increasing BMI outweigh the positive effects of genetic predisposition . When these results are confirmed by other population-based studies , a challenging question remains whether weight loss in overweight and obese APOE * 2 carriers restores the genetic advantage of their APOE status .

O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O B O O O O O O O O O B O B O O O O B O O O O O O O B O O O O O O O O B O O O O O O O O O B O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O B O O O B O O O O O O O B O O B O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O B O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O B O O O O O B O O O O O O O B O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O B O O O O B O O O O B O O O O B O O O O B O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O B O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O B O O B O O O O O O O B O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O B O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O B O O O O B O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O B O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O B O O O O O O O O O O B O B O O O O O O O B O O O B O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O B O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O B O O B O B O O O O O O O O O B O O B O O B O O O O O B O O O O O O B O O O O O O O O O B O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O B O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O B O O O O B O O O O O O O O O O O O O O O B O O B O B O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O


Prior outpatient antibiotic use as predictor for microbial aetiology of community-acquired pneumonia : hospital-based study Objective The causative micro-organism in community-acquired pneumonia -LRB- CAP -RRB- is often difficult to predict . Different studies have examined chronic morbidity and clinical symptoms as predictors for microbial aetiology of pneumonia . The aim of our study was to assess whether prior outpatient antimicrobial treatment is predictive for determining the microbial aetiology of CAP . Introduction Community-acquired pneumonia -LRB- CAP -RRB- remains a major reason for hospital admission and a common cause of death in developed countries -LSB- 1 , 2 -RSB- . The initial management of patients hospitalised with pneumonia consists mostly of empirical antimicrobial treatment -LSB- 3 , 4 -RSB- . Determination of the appropriate antimicrobial treatment is essential as inadequate antimicrobial treatment , generally defined as microbial ineffective therapy against the causative pathogen , can negatively influence patient outcome -LSB- 5 -RSB- . Because it is not always possible to identify the causative pathogen , especially during the first days following hospitalisation , many studies have focussed on other parameters suggestive of the causative pathogen . The most frequently studied parameters for this purpose are patient characteristics -LRB- age and co-morbidities -RRB- and clinical signs -LSB- 6 , 7 -RSB- , although nonresponsiveness to prior outpatient antimicrobial treatment has also been suggested as a predictor of the aetiology of pneumonia . However , to date , this latter parameter has not been comprehensively quantified and documented . The aim of the study reported here was to assess whether prior outpatient antimicrobial treatment is a predictor of microbial aetiology in patients admitted to hospital for CAP . Methods The study was conducted in the St. Antonius Hospital , a 600-bed teaching hospital in Nieuwegein , The Netherlands . Patient population This was a prospective observational study of patients with confirmed pneumonia admitted between October 1 , 2004 and August 1 , 2006 . Pneumonia was defined as a new or progressive infiltrate on a chest X-ray plus at least two of the following criteria : cough , sputum production , temperature > 38 °C or < 35 °C , ausculatory findings consistent with pneumonia , leucocytosis or leucopenia -LRB- > 10 g/l , < 4 g/l or > 10 % rods in leucocyte differentiation -RRB- , C-reactive protein > 3 times the upper reference value for normal . Patients who were immune compromised -LRB- systemic steroid use at admission -LRB- prednisone equivalent > 20 mg/daily for more than 3 days -RRB- , haematological malignancies and other immunosuppressive therapy -RRB- were excluded . The study was approved by the local Medical Ethics Committee , and informed consent was obtained from each patient . Microbial aetiology workup At least two blood cultures were performed , and sputum was taken for Gram-staining and culture and subsequently analysed by Taqman real-time PCR for Mycoplasma pneumoniae , Legionella pneumophila and Chlamydophyla psittaci -LSB- 8 -RSB- . Pharyngeal samples were taken for viral culture . Urine was sampled for antigen testing on Streptococcus pneumoniae and L.pneumophila -LRB- Binax NOW ; Binax , Scarborough , ME -RRB- -LSB- 9 , 10 -RSB- . In addition , serum samples taken on the day of admission and on day 10 were analysed in pairs for detection of a fourfold rise of antibodies to respiratory viruses , Coxiella burnetii , M. pneumoniae , and C. psittaci by complement fixation assay -LSB- 11 -RSB- . For each patient , the total workup was completed , and the microbiology department was blinded to data on outpatient antibacterial drug use . When both viruses and bacteria were identified in a patient , the search prevailed for a definite aetiology for the bacteria . Exposure to antimicrobial therapy Data on outpatient antimicrobial drug use were acquired through community pharmacy dispensing records that captured all drug exposures the year prior to hospital admission . A patient was considered to be exposed to an antimicrobial drug when a prescription was filled within 14 days prior to hospitalisation . The name , dosage and amount of antimicrobial drug dispensed were also ascertained . The prescribed antimicrobial drug was classified as appropriate or inappropriate in accordance with current Dutch guidelines on the initial treatment of patients with suspected pneumonia -LSB- 12 , 13 -RSB- . Co-morbidity assessment In addition to outpatient antimicrobial drug use , co-morbidities and other relevant patient characteristics were identified to address factors related with the aetiology of CAP . Co-morbidities were defined based on the presence of conditions for which the patient was under active medical supervision or was receiving treatment at the time of hospital admission . The co-morbidities evaluated were pulmonary diseases -LRB- chronic obstructive pulmonary disease or treated asthma -RRB- , congestive heart failure , diabetes -LRB- both type I and type II -RRB- , history of stroke and end-stage renal disease -LRB- serum creatinine > 150 μmol/l -RRB- . The patients were also classified according to the Pneumonia Severity Index -LRB- PSI -RRB- developed by Fine et al. -LSB- 14 -RSB- . This index classifies patients into five categories of predicted mortality risk -LRB- with the fifth category being that of highest mortality risk -RRB- . The outpatient use of oral corticosteroids and gastric acid-suppressing drugs was also ascertained . Statistical analysis The SPSS statistical package -LRB- ver . 12.0.1 for Windows ; SPSS , Chicago , IL -RRB- was used for the statistical analyses . Continuous data were expressed as the mean ± SD or as the median -LRB- interquartile range -RRB- where appropriate . To study the association between prior outpatient antimicrobial treatment and the aetiology of pneumonia , we applied multivariate logistic regression analyses . The analyses were conducted for overall aetiology and relevant pathogens separately . All baseline characteristics were considered potential predictors . Potential predictors were included in the multivariate model when they were retained after backward stepwise elimination . Significance was set at a p value < 0.05 . The model 's performance -LRB- goodness-of-fit and discriminative ability -RRB- was tested by performing the Hosmer and Lemeshow test and calculating the area under the receiver operator characteristic -LRB- ROC -RRB- curve . Results In total , 201 patients with pneumonia were included in the study . The mean age of the patients was 63 ± 17 years , and 124 patients were male . Three patients -LRB- 1 % -RRB- were admitted from a nursing home . Pneumonia severity as well as co-morbid illnesses are summarised in Table 1 . The overall median duration of the hospital stay was 10 days -LSB- 7 -- 14 -RSB- , and 21 patients were admitted to the intensive care ward . During the hospital stay , ten patients died , all due to pneumonia . The overall 28-day mortality rate was 5 % . Forty-seven patients -LRB- 23 % -RRB- had received antimicrobial treatment in the 14-day time-window prior to hospital admission . The antimicrobial drugs dispensed to these patients are summarised in Table 2 . Table 1Demographics , co-morbidities and pneumonia severity index of 201 patients with community-acquired pneumonia -LRB- CAP -RRB- Characteristicn -LRB- % -RRB- Age -LRB- Years -RRB- < 6074 -LRB- 37 -RRB- 60 -- 6939 -LRB- 19 -RRB- 70 -- 7950 -LRB- 25 -RRB- > 8038 -LRB- 19 -RRB- Gender Male124 -LRB- 62 -RRB- Female77 -LRB- 38 -RRB- Co-morbidities Pulmonary diseases71 -LRB- 35 -RRB- Heart failure18 -LRB- 9 -RRB- Diabetes35 -LRB- 17 -RRB- History of stroke17 -LRB- 9 -RRB- End-stage renal disease10 -LRB- 5 -RRB- Nursing home resident3 -LRB- 1 -RRB- Co-medication Oral corticosteroids58 -LRB- 29 -RRB- Gastric acid suppressing drugs61 -LRB- 30 -RRB- Fine score at admissiona I30 -LRB- 15 -RRB- II34 -LRB- 17 -RRB- III53 -LRB- 26 -RRB- IV56 -LRB- 28 -RRB- V28 -LRB- 14 -RRB- aFine et al. -LSB- 14 -RSB- Table 2Outpatient antibiotics utilization profile prior to hospitalisation for CAPType of antimicrobial drugNumber of users -LRB- % -RRB- aAppropriatebClavulanic acid18 -LRB- 38 -RRB- YesAmoxycillin12 -LRB- 26 -RRB- YesDoxycycline7 -LRB- 15 -RRB- YesClarithromycin5 -LRB- 11 -RRB- YesCo-trimoxazole4 -LRB- 8 -RRB- NoCiprofloxacin2 -LRB- 4 -RRB- NoNorfloxacin1 -LRB- 2 -RRB- NoAzithromycin1 -LRB- 2 -RRB- YesaTotal percentage exceeds 100 % because some patients -LRB- n = 3 -RRB- had two prescriptionsbBased on current Dutch guidelines NVALT and SWAB -LSB- 12 , 13 -RSB- The majority of the patients -LRB- 79 % -RRB- had their prescription filled within 4 days prior to hospital admission , and 85 % of the prescribed antimicrobial drugs complied with current Dutch guidelines -LSB- 12 , 13 -RSB- . A microbial aetiology could be determined in 128 -LRB- 64 % -RRB- of the patients . Table 3 shows the results of different tests used to determine the aetiological diagnosis of CAP . Table 3Results of of different tests used to determine the aetiology of CAP Sputum cultureSputum PCRAntigen testingBlood cultureSerologyViral cultureNumber of samples1487818318213088Number of positive samples781436193814Percentage of positive samples531820102916Streptococcus pneumoniae33 -- 3017 -- -- Gram-negative strain Heamophilus influenzae19 -- -- 0 -- -- Other10 -- -- 1 -- -- Atypical Mycoplasma pneumoniae -- 7 -- -- 8 -- Legionella spp. 156 -- 7 -- Other -- 2 -- -- 1 -- Viral -- -- -- -- 2214Other S. aureus6 -- -- 1 -- -- Gram-positive other2 -- -- 0 -- -- Fewer causative pathogens were found in the population hospitalised following prior outpatient antimicrobial treatment than in patients without prior antimicrobial treatment -LSB- 57 vs. 66 % ; crude odds ratio -LRB- OR -RRB- 0.71 , 95 % confidence interval -LRB- CI -RRB- 0.36 -- 1.38 -RSB- . In patients with prior beta-lactam treatment , aetiology in the group comprising atypical bacterial pathogens was more probable -LRB- eight of 29 cases , 28 % ; crude OR 4.66 , 95 % CI 1.73 -- 12.56 -RRB- . The aetiology of S. pneumoniae was less prevalent in patients with prior beta-lactam antimicrobial treatment -LRB- four of 29 cases , 14 % ; crude OR 0.33 , 95 % CI 0.11 -- 0.99 -RRB- . In multivariate analyses , these associations remained significant -LRB- OR 3.51 , 95 % CI 1.25 -- 9.99 and OR 0.30 , 95 % CI 0.10 -- 0.90 , respectively -RRB- -LRB- Table 4 -RRB- . Additional significant predictors included in these models were heart failure , pulmonary comorbidity , pneumonia severity index and the use of gastric acid suppressing drugs . The goodness-of-fit of both multivariate models was good , with a p value of 0.899 -LRB- Hosmer and Lemeshow test -RRB- for the model predicting pneumococcal pneumonia and a p value of 0.995 for the model predicting pneumonia of an atypical aetiology . The corresponding areas under the ROC curve were 0.62 and 0.67 , respectively . Table 4Odds ratios -LRB- OR -RRB- for aetiology and prior outpatient beta-lactam treatment in patients admitted to hospital for CAPAetiologyPrior outpatient beta-lactam treatment -LRB- n = no . of patients -RRB- OR -LRB- 95 % CI -RRB- Yes -LRB- % -RRB- No -LRB- % -RRB- Total no . of samples29 -LRB- 100 -RRB- 172 -LRB- 100 -RRB- Univariate Pneumococcal4 -LRB- 14 -RRB- 56 -LRB- 33 -RRB- 0.33 -LRB- 0.10 -- 0.99 -RRB- Atypical8 -LRB- 28 -RRB- 13 -LRB- 8 -RRB- 4.66 -LRB- 1.72 -- 12.56 -RRB- Viral2 -LRB- 7 -RRB- 14 -LRB- 8 -RRB- 0.84 -LRB- 0.18 -- 3.89 -RRB- Gram-negative strains2 -LRB- 7 -RRB- 21 -LRB- 12 -RRB- 0.53 -LRB- 0.12 -- 2.40 -RRB- Other1 -LRB- 3 -RRB- 7 -LRB- 4 -RRB- 0.84 -LRB- 0.10 -- 7.11 -RRB- Unidentified12 -LRB- 41 -RRB- 61 -LRB- 36 -RRB- 1.28 -LRB- 0.58 -- 2.87 -RRB- Multivariate Pneumococcal -- -- 0.30 -LRB- 0.10 -- 0.90 -RRB- a Atypical -- -- 3.51 -LRB- 1.25 -- 9.99 -RRB- bOR , Odds ratio ; CI , confidence IntervalaAdjusted for heart failure and use of gastric acid suppressing drugsbAdjusted for pulmonary diseases and pneumonia severity index In patients aged < 60 years without co-morbidities , the aetiology of atypical bacterial pathogens was more prevalent -LRB- OR 4.64 , 95 % CI 1.72 -- 12.56 -RRB- . Pulmonary co-morbidity was associated with the finding of S. pneumoniae and H. influenzae as causative pathogens -LRB- OR 1.87 , 95 % CI 1.00 -- 3.47 ; OR 3.72 , 95 % CI 1.20 -- 11.57 , respectively -RRB- . Patients who received macrolides prior to hospitalisation had an increased probability of viral pneumonia -LRB- two of five cases , 40 % -RRB- -LRB- crude OR 8.67 , 95 % CI 1.34 -- 56.23 -RRB- . Discussion The results of our study show that among patients admitted for CAP , a threefold decreased chance of having a S. pneumoniae infection and a threefold increased probability of having pneumonia of atypical aetiology were associated with the patient having received an initial beta-lactam treatment as an outpatient . These findings indicate that information on prior outpatient antimicrobial therapy has a predictive value in the diagnostic workup aimed at identifying the causative pathogen and planning the corresponding treatment in patients with pneumonia . The initial management of patients hospitalised with pneumonia has been under constant study in different settings during the past decades . The choice of antimicrobial treatment , time to first antimicrobial drug administration and route of administration have all appeared to be relevant factors associated with the outcome of pneumonia -LSB- 15 -- 17 -RSB- . A knowledge of the predominant microbial patterns in CAP is therefore essential when choosing an essential empirical antimicrobial treatment . Previous studies have found that S. pneumoniae , H. influenzae , Influenza virus A and B , Legionella spp. and C. pneumoniae are the most frequent pathogens in CAP -LSB- 13 -RSB- , which is in accordance with our results . Because S. pneumoniae is the most frequently appearing pathogen , the administration of beta-lactam antibiotics is the initial empirical antimicrobial treatment of choice in the treatment guidelines on CAP -LSB- 3 , 12 , 13 -RSB- . Beta-lactam antibiotics , however , do not cover Legionella spp. , C. pneumoniae and M. pneumoniae , the so-called atypical pathogens . Therefore , patients with pneumonia of atypical aetiology who are treated with beta-lactam antibiotics as an outpatient will probably not respond to treatment , with the possible consequence being a deterioration of the situation and subsequent hospital admission . Our finding of an increased prevalence of atypical pathogens in patients with prior outpatient beta-lactam treatment supports such an explanation , but also confirms what has already been suggested in the different guidelines for the management of community-acquired pneumonia in adults -LSB- 13 , 18 -RSB- . These guidelines state that following the failure of the initial empirical treatment with beta-lactam antibiotics , the microbiological examination should be reassessed with a view to excluding the less common pathogens , such as atypical pathogens , and that antimicrobial treatment covering atypical pathogens should be considered . Our study supports the choice of antimicrobial treatment covering atypical pathogens -LRB- e.g. macrolides -RRB- for all patients with CAP who are admitted to hospital after prior treatment with beta-lactam antibiotics . However , whether this protocol will result in improved clinical outcome should be subject to additional study . The observed reduction in the frequency of S. pneumoniae in patients who received prior outpatient antimicrobial treatment could also be due to a failure to detect the organisms in cultures . This could mask S. pneumoniae as the causative pathogen . However , we believe that this explanation is less plausible , especially since we also used antigen testing to identify the causative pathogen -LSB- 19 -RSB- . In addition , such a mechanism can not explain the finding of an increased probability of pneumonia caused by atypical pathogens . To the best of our knowledge , our study is the first to specifically document and quantify the failure of initial outpatient antibiotic treatment as a predictor of the microbial aetiology of CAP . This study was conducted in a single teaching hospital in The Netherlands , but we believe that the conclusions drawn will apply to other settings . First , the percentage of identified aetiology -LRB- 64 % in this study -RRB- is in agreement with that of other studies using similar microbiological techniques -LSB- 20 -- 22 -RSB- . Second , our patient characteristics comply to a great extent with a previous nationwide study on prior outpatient antibacterial therapy as a prognostic factor for mortality in patients hospitalised for pneumonia -LSB- 23 -RSB- . In that large database study , the percentage of patients hospitalised after initial outpatient antimicrobial treatment was almost identical to that observed in our study -LRB- 27 vs. 23 % , respectively -RRB- . In addition , age distribution , co-morbidities and the antibiotic utilisation profile of the outpatients were very similar as were the median duration of hospital stay and in-hospital mortality . Unfortunately , due to limited numbers , we were unable to study an association between prior outpatient antimicrobial treatment and mortality in our study . A very reassuring finding was that 85 % of all outpatient antibiotic prescriptions complied with national guidelines on the initial treatment of adults with suspected pneumonia . This reduces the possibility that the findings , rather than being associated with antibiotics , might reflect the diagnostic acumen of the physicians who saw the patients in primary care . However , we can not rule out the possibility that typical signs of infection with atypical pathogens may have been missed by primary care physicians . As well as finding a relation between prior antimicrobial treatment and aetiology , we also found an association between aetiology and age and pulmonary co-morbidity . Patients aged < 60 years without co-morbidities were more likely to have an aetiology comprising viral or atypical bacterial pathogens , and pulmonary co-morbidity was independently associated with S. pneumoniae and H. influenzae as causative pathogens . These findings confirm the results of previous studies on the impact of age and co-morbidity on the microbial aetiology of CAP -LSB- 6 -RSB- . A limitation of the present study , however , is that we were not able to adjust for smoking habits and alcohol intake of the patients . Previous studies on determinants for pneumonia aetiology found that these factors are significant predictors of pneumococcal infection -LSB- 6 , 7 -RSB- . On the other hand , we do not expect prior antimicrobial therapy and smoking and alcohol intake to coincide in such a way that this would result in a null effect when all the information is available . In conclusion , among patients admitted for pneumonia , whether or not a patient has received prior antimicrobial therapy as an outpatient provides relevant information in the diagnostic workup , in particular in terms of identifying the causative pathogen and planning the initial treatment at the time of hospital admission . This finding supports a further strengthening of the continuity of care at the interface between the extramural and hospitalised settings .

O O O O O O O O B O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

aetiology antibiotics diagnosis pneumonia


Data splitting as a countermeasure against hypothesis fishing : with a case study of predictors for low back pain There is growing concern in the scientific community that many published scientific findings may represent spurious patterns that are not reproducible in independent data sets . A reason for this is that significance levels or confidence intervals are often applied to secondary variables or sub-samples within the trial , in addition to the primary hypotheses -LRB- multiple hypotheses -RRB- . This problem is likely to be extensive for population-based surveys , in which epidemiological hypotheses are derived after seeing the data set -LRB- hypothesis fishing -RRB- . We recommend a data-splitting procedure to counteract this methodological problem , in which one part of the data set is used for identifying hypotheses , and the other is used for hypothesis testing . The procedure is similar to two-stage analysis of microarray data . We illustrate the process using a real data set related to predictors of low back pain at 14-year follow-up in a population initially free of low back pain . `` Widespreadness '' of pain -LRB- pain reported in several other places than the low back -RRB- was a statistically significant predictor , while smoking was not , despite its strong association with low back pain in the first half of the data set . We argue that the application of data splitting , in which an independent party handles the data set , will achieve for epidemiological surveys what pre-registration has done for clinical studies . Introduction The concept of statistical significance may be the single-most important mathematical invention for applied science . Its use has become so widespread and commonplace that many non-mathematical readers may not be actively aware of its true meaning . To briefly review , the statement `` X is correlated with Y at significance level alpha '' signifies , `` If no true correlation between X and Y exists , the probability of obtaining the observed correlation is less than alpha . '' The P-value of a test is therefore a measure of surprise ; the smaller the P-value , the greater the surprise . Standard practice has been to set alpha at 0.05 , which literally allows for a 5 % chance of erroneously reporting a significant finding -LRB- Type I error -RRB- . One can not interpret the P-value as a probability of having made a Type I error , so 5 % significance does not imply that the conclusion is correct with a 95 % probability . Such statements are meaningful only in a Bayesian context where one assigns a priori probabilities to hypotheses . The present article addresses non-Bayesian -LRB- frequentist -RRB- analysis , which is by far the most common in epidemiology . Ioannidis -LSB- 1 -RSB- purports that most scientific findings are likely to be false , despite being reported as statistically significant . One of his arguments , which we support , is that the pressure to publish creates an incentive for researchers to simultaneously address a large number of hypotheses and selectively report only `` significant '' results . This conduct is labelled hypothesis fishing or data dredging . -LRB- In older papers the term data mining has also been used , but the meaning of this has shifted toward discovery of valid patterns in databases . -RRB- Also , the investigator may run different statistical tests -LRB- e.g. parametric and non-parametric tests -RRB- for any given hypothesis , which is a less recognized form of hypothesis fishing . In an epidemiological and health services setting , complex data sets based on statistical surveys are commonplace , in which hundreds of variables are collected from thousands of people . Data collection for a survey requires enormous effort , with both individual and collective demands on the researchers and respondents . From a purely economic point of view , it appears logical to `` turn the data set upside down , '' searching for anything of interest buried in it . Therein lies the temptation to launch a large-scale fishing expedition for all potentially interesting hypotheses . Similar to real-life commercial ocean fishing , which requires adjusting the mesh size of the nets upward , researchers investigating multiple hypotheses need to adjust the level of statistical significance down in order to preserve the meaning of statistical significance . The general rule of thumb is to divide alpha by the number of hypotheses , referred to as a Bonferroni correction -LSB- 2 -RSB- . However , in accordance with Ioannidis -LSB- 1 -RSB- , we contend this method is rarely applied . The limited use of Bonferroni correction may result from uncertainty surrounding the exact number of hypotheses `` fished for , '' but some researchers may also be motivated to keep the `` catch . '' In epidemiological research , upwards of 100 possible hypotheses are common , and setting the alpha at 0.0005 eliminates much of the fun in a fishing expedition . As a consequence , researchers may be tempted to discuss only a handful of `` significant '' results , failing to mention the 95 that proved non-significant . Hypothesis fishing renders P-values almost completely meaningless , and we consider it to be a serious problem for epidemiological survey analysis . The present paper recommends a very simple countermeasure against hypothesis fishing , based on data splitting . The procedure is similar to two-stage analysis of microarray data . We argue that if our method could be applied for survey data in general , it could do for epidemiological studies what pre-experiment registration of RCTs has done for medical experiments . The rest of the article is laid out as follows : First we review some mathematical methods that might be considered useful for counteracting hypothesis fishing . Then we explain our method in detail , and compare it to two-stage analysis of microarray data , followed by a case study where the method is applied to an analysis of low back pain . The article ends with discussion and conclusion . Mathematical remedies -LRB- that fail to solve the problem -RRB- If one considers hypotheses fishing to be a mathematical problem , it is reasonable to look for mathematical solutions . There is a large mathematical literature that relates to model building and multiple hypotheses , and we will only try to point out the main themes . The simplest way of handling the problem of multiple hypotheses is to reduce the significance level through Bonferroni correction . It is also possible to use so-called false discovery rate -LRB- FDR -RRB- -LSB- 3 -RSB- , which is a way of controlling the expected percentage of rejected null-hypotheses -LRB- discoveries -RRB- that are falsely rejected . If all the null-hypotheses are true , FDR is equivalent to Bonferroni correction , but otherwise it is less strict . As we mentioned in the introduction , it can be hard to keep track of the number of hypotheses one is really addressing , which makes the use of Bonferroni or FDR cumbersome . Complex mathematical methods exist that adjust for the effect of model selection on inference -LRB- for example , see Madigan and Raftery -LSB- 4 -RSB- -RRB- . In order to utilize these methods , however , one must describe the model-building strategy in a formal mathematical way . Faraway -LSB- 5 -RSB- has analysed various data splitting approaches similar to the one we apply , and compares them to mathematical inference adjustment methods . Based on his simulation study , he concluded that data splitting costs more in reduced accuracy than it gains in `` honesty '' . This conclusion is to be expected , because adjustment methods utilize information that the splitting methods disregard . In our present setting , however , accurate information on modelling procedure is not available , so adjustment methods are not an option . The task of choosing a set of hypotheses is -- at least superficially -- related to the problem of choosing the set of predictors to include in a statistical model for the data . An overview of this field is given by Hastie et al. . -LSB- 6 -RSB- , and we only mention a few key concepts . An obvious goal in statistical modelling of a given set of data is to develop a model that fits the data well , and a model will always fit better when the set of predictors is increased . However , if one includes too many predictors , the model is prone to imitating random properties of the data set , which are not present in the underlying sampling distribution of the data . Therefore , one needs ways to make trade-offs between model fit and model size . This can be done directly through various information criteria , such as Akaike 's Information Criterion -LRB- AIC -RRB- , the Bayesian Information Criterion -LRB- BIC -RRB- , or the Divergence Information Criterion -LRB- DIC -RRB- . A different approach called cross validation is to split the data set in two parts , using one part for estimating model parameters , and the other for evaluating model fit . A problem with this method is that only half of the data is used for each task . A clever remedy is to leave one data point out at the time , estimate the model from the remaining data points , and measure model fit by taking the average fit on the points left out -LRB- leave-one-out cross validation -RRB- . We mention these methods mainly to point out that they are not very relevant for controlling hypotheses fishing . The use of information criteria or cross validation helps in trading model fit for model size , but does not produce adjustments for the total number of variables . The cross validation procedure of splitting the data set , using one part to estimate model parameters and the other for validation is deceptively similar to the method we apply . But the purpose of validating a model 's predictions is entirely different from our purpose of conducting sound hypothesis testing . In addition to the inherent difficulties related to mathematical ways of dealing with hypothesis fishing , we do not see the problem as mainly a mathematical one . Some researchers may not be aware of the fact that P-values lose their meaning when the hypotheses are chosen from a large pool of undocumented ones . Others may be vaguely aware of the problem , but choose not to address it unless reviewers demand it . Reviewers , on the other hand , may not feel inclined to insist on purity beyond what they have done in their own scientific work . The solution : data splitting We recommend splitting the data set randomly into two sections -LRB- Parts 1 and 2 -RRB- . This allows the investigators to identify hypotheses in Part 1 of the data set , while remaining blind to Part 2 until the hypotheses are specified . True hypothesis testing is then performed using only Part 2 of the data . At this point there is no second-guessing . If the alpha-level is set to 0.05 , and the P-value in Part 2 is 0.051 , the result is by definition not significant , even if it received a P-value of 0.001 in Part 1 . In such cases , there will be a temptation to `` make a compromise '' by computing the average of the P-values from Parts 1 and 2 , but this is not allowed . Because the data in Part 1 was used to construct the hypotheses , it is tainted , and can not take any part in the hypotheses testing . This procedure is strict with respect to identifying statistical significance . Once a hypothesis is supported , however , the entire data set is used for estimating the effect size . Thus , the purpose is to ensure the proper use of the term statistical significance . Once a significant finding is established , though , it is preferable to obtain the most accurate parameter estimates possible . Models : hypothesis variables , and confounders Epidemiological hypotheses are usually formulated within the framework of a model . Assume the hypothesis is that eating mushrooms increases the risk of cancer . To test this hypothesis , one would build a model with predictors like age , gender , smoking status , as well as mushroom habits , in order to control for these confounding factors . -LRB- If old people eat more mushrooms , excluding age from the model would give an incorrect positive association between mushroom eating and cancer . -RRB- From a purely computational point of view , no differences exist between predictors associated with hypotheses and confounders , yet the semantics are very different . The confounders are included only as a means of estimating the causal link between the cause -LRB- mushrooms -RRB- and its hypothesized effect -LRB- cancer -RRB- . Researchers often use P-values as a road map -LRB- in addition to literature reviews and general medical knowledge -RRB- , when deciding which variables should be included as confounders . A common piece of advice is to include confounders demonstrating an association at a P-value below 0.1 or 0.2 . Despite this rationale for including a confounding factor , no similar demands are made for inclusion of the confounder in Part 2 . The chosen set of confounders provides the framework within which the hypothesis is defined , and it is not the framework that is being tested . In the mushroom example , the hypothesis is that mushroom eating is associated with cancer when controlling for age , gender , and smoking , not that controlling for each of these factors is necessary . Size of Part 1 and Part 2 When deciding upon the relative size of Parts 1 and 2 , a trade-off exists between the need to identify hypotheses by exploration in Part 1 , and the need to achieve statistical significance in Part 2 . An even split may be reasonable in cases where the need for exploration is high , particularly if the data set is large , so that half of the data set is sufficient to achieve statistical significance for stronger effects . In cases where greater domain knowledge is available based on the existing literature , a smaller Part 1 is reasonable , especially when the sample size is small . Multiple hypotheses It is possible to investigate multiple hypotheses within our splitting regime , using Bonferroni corrections . Assume , in the mushroom-cancer example , that the analysis of Part 1 also provided strong support for the hypothesis that eating bananas protects against cancer . One might then choose to include both mushroom habits and banana habits as hypothesis variables , and consequently divide alpha by 2 -LRB- the number of hypotheses -RRB- . If either mushroom or banana habit fails the significance test in Part 2 , it will still be in the model , as a confounder . It might be the case that both banana and mushroom habits get P-values that fall between alpha/2 and alpha . In this situation , both hypotheses would have passed the significance test individually , but the choice to include two hypotheses resulted in failure of both variables to reach significance . Many non-statisticians would argue that scientific procedures and statistical analysis should be objective , with conclusions based on ` hard facts ' , independent of arbitrary choices of hypotheses . Unfortunately , this is not the case if we wish to claim statistical significance . The mushroom-banana example is a clear case in which investigators should reduce the alpha-level to account for multiple hypotheses . At the other extreme , if independent research groups investigating different research questions based on independent data sets , their combined effort is obviously not a case of ` multiple hypothesis testing ' . A grey area exists with partially overlapping data sets , hypotheses , and research groups , often making it difficult to decide whether Bonferroni corrections are called for . A pragmatic solution may be to view a published article as a unit , and apply Bonferroni corrections within each one . Relation to two-stage analysis in genetics Readers who are familiar with microarray analysis will recognize that our data splitting method is similar to two-stage analysis , as it is routinely performed in genetics -LSB- 7 -RSB- . There are a few differences , however . In a microarray context the set of possible hypotheses is given by the number of genes , and the FDR method is normally used to limit the number of incorrect findings . Rather than primarily counteracting hypothesis fishing , microarray two-stage analysis is usually motivated by cost effectiveness : By screening out promising candidates first , and then evaluating them , researchers can make a higher number of valuable discoveries for each monetary unit spent . In a microarray setting the procedure is also likely to be more automatic , as interesting genes are filtered out in two more or less mechanical steps of analysis . In our epidemiological application , on the other hand , there will be a man-in-the-loop , as the researcher builds a model with hypothesis variables and confounders based on a combination of his domain knowledge and Part 1 of the data . Case study of low back pain in the Ullensaker study Study sample and setting Data material consisted of adults enrolled in an epidemiological survey for musculoskeletal pain -LRB- MSP -RRB- in the Ullensaker municipality , 40 km northeast of Oslo in Norway . In 1990 , 4050 inhabitants born in 1918 -- 1920 , 1928 -- 1930 , 1938 -- 1940 , 1948 -- 1950 , 1958 -- 1960 and 1968 -- 1970 -LRB- age 20 -- 70 years -RRB- were sent a postal questionnaire about MSP . Of these people , 67 % responded . Individuals who reported low back pain -LRB- LBP -RRB- during the past year -LRB- 1990 -RRB- were excluded from this material -LRB- N = 1439 -RRB- , such that the original sample consisted of 1283 participants who were free of LBP in 1990 . In 2004 , a 14-year follow-up was conducted . A total of 763 participants -LRB- 59 % -RRB- responded and formed the present study sample . These 763 participants were randomly divided in two samples with n = 369 -LRB- Part 1 -RRB- and n = 394 -LRB- Part 2 -RRB- , respectively . Outcome measures To identify respondents with LBP , we used the answer to the question , `` During the past year , have you experienced pain or discomfort in your lower back ? '' . This item was based on the Standardised Nordic Pain Questionnaire -LSB- 8 -RSB- , which is a self-report questionnaire frequently used in Scandinavian epidemiological studies . Independent variables -LRB- potential risk factors -RRB- In 1990 , the survey questionnaire contained a number of socio-demographic and health-related factors , which could be included as risk factors in the present study . Socio-demographic variables were gender , age , marital status , and work status . Health-related variables were body mass index -LRB- BMI -RRB- , smoking status , number of MSP sites other than the low back , duration of previous MSP , use of medication due to MSP , having been examined by a health care provider due to MSP during the last year , comorbidity , family history of musculoskeletal problems , emotional distress , leisure physical activity , participation in competitive sports , sleeping problems , and self-perceived health . Model and hypotheses A logistic regression model was developed based on Part 1 of the data set and medical expertise . The number of pain sites and smoking status were included as independent variables . Smoking status was dichotomised as smoking and non-smoking . Number of pain sites was operationalized using participant responses on the Nordic Pain Questionnaire -LSB- 8 -RSB- . Specifically , respondents reported whether they had experienced any pain or discomfort from the following 10 areas during the previous year : head , neck , shoulder , elbow , hand/wrist , upper back , low back , hip , knee and ankle/foot -LRB- responses were `` yes/no '' -RRB- . The total number of pain sites was computed and categorized into the following four categories : no pain sites , 1 or 2 sites , 3 or 4 sites , and 5 or more pain sites . We also included age , which was categorized into values corresponding to the six birth cohorts : 1918 -- 1920 , 1928 -- 1930 , 1938 -- 1940 , 1948 -- 1950 , 1958 -- 1960 , and 1968 -- 1970 . In addition , gender and marital status -LRB- dichotomised into married/partnership versus living alone -RRB- were included . Results of the logistic regression model are presented in Table 1 . We hypothesized that smoking would be positively associated with LBP . Therefore , a 1-tailed hypothesis test was conducted . It was also hypothesized that individual pain sites would be positively associated with LBP . To limit the number of hypotheses , though , we hypothesized that the total number of pain sites would affect LBP probability , rather than run analyses for each level of the variable . Hypotheses testing The significance level alpha was set to the usual value of 0.05 . With two hypotheses , the critical P-value becomes 0.025 . Results for Part 2 of the data set are illustrated in Table 2 . The P-value of the pain sites variable -LRB- 0.015 -RRB- was below the critical value of 0.025 , and therefore it is concluded that the number of pain sites was significantly associated with LBP at the 14-year follow-up . Table 1Parameter estimates from Part 1 , controlling for age , gender , and marital statusPredictorOR estimate 95 % CI for ORP-valueNumber of pain sitesa0 .0121 or 2 pain sites2 .292 -LRB- 1.248 -- 4.208 -RRB- 0.0073 or 4 pain sites2 .690 -LRB- 1.406 -- 5.147 -RRB- 0.0035 or more pain sites2 .944 -LRB- 1.193 -- 7.262 -RRB- 0.019 Smoking2 .079 -LRB- 1.285 -- 3.363 -RRB- 0.003 aThe reference category for number of pain sites was no pain sitesTable 2Parameter estimates from Part 2 , controlling for age , gender , and marital statusPredictorOR estimate 95 % CI for ORP-valueNumber of pain sitesa0 .0151 or 2 pain sites1 .328 -LRB- 0.793 -- 2.224 -RRB- 0.2813 or 4 pain sites1 .598 -LRB- 0.857 -- 2.979 -RRB- 0.1415 or more pain sites3 .941 -LRB- 1.700 -- 9.136 -RRB- 0.001 Smoking0 .993 -LRB- 0.627 -- 1.571 -RRB- 0.487 * aThe reference category for number of pain sites was no pain sites * 1-sided P-value Smoking status received a 1-sided P-value of 0.487 , which exceeds the limit of 0.025 by a large margin , and this variable is thus deemed non-significant . This result may seem surprising , given the variable 's strong association with the dependent variable in Part 1 of the data set -LRB- 2-sided P-value = 0.003 -RRB- . This illustrates the dangers of hypothesis fishing : Our analysis suggests that the smoking status variable may only have been a `` lucky winner '' in the `` P-value lottery '' of Part 1 . Parameter estimates Having concluded that number of pain sites has a significant effect on low back pain at follow up , we estimated the magnitude of the effect from the full data set -LRB- Table 3 -RRB- . Because the hypothesis test failed to give significance for smoking status , it is included only as a confounder together with age , gender , and marital status . The 2-sided P-value for smoking status was 0.040 , so researchers following a hypothesis fishing procedure would probably have reported it as a statistically significant predictor . Table 3Parameter estimates of the hypotheses variable number of pain sites , from the complete data set controlling for age , gender , marital status and smoking statusPredictorOR estimate 95 % CI for ORP-valueNumber of pain sitesa0 .0001 or 2 pain sites1 .637 -LRB- 1.116 -- 2.400 -RRB- 0.0123 or 4 pain sites1 .983 -LRB- 1.285 -- 3.061 -RRB- 0.0025 or more pain sites3 .346 -LRB- 1.846 -- 6.067 -RRB- 0.000 aThe reference category for number of pain sites was no pain sites Discussion Our study was designed to illustrate a simple and straightforward data splitting method to counteract hypothesis fishing in large-scale epidemiological surveys . This method involves splitting the data set , where the first half is used to identify hypotheses , while the remaining data is used to test the hypotheses . The data splitting procedure was illustrated using data material collected for a population-based health survey administered in Norway in 1990 and 2004 . Results demonstrated that the number of pain sites -LRB- `` widespreadness '' of pain -RRB- was significantly associated with LBP following a 14-year follow-up . Smoking status was a strong predictor of LBP in Part 1 of the data set -LRB- hypothesis identification -RRB- , but did not achieve significance in Part 2 -LRB- hypothesis testing -RRB- . Therefore , this finding was dismissed as non-significant in our study . For the full data set , the P-value for smoking status was 0.040 , so the traditional way of analysing epidemiological data would have given a different conclusion . In this study , the investigators had free access to the entire data set prior to data splitting and during model development . However , any temptation to `` peak '' at the material was successfully avoided , as indicated by the discrepant results for smoking status . This indicates that the data splitting procedure can indeed function properly in the absence of strict external control of the data . Nevertheless , we recommend that the data set be handled by an independent party , so that researchers can document claims that only Part 1 of the data set was used for model and hypothesis development . Ideally , the establishment of an independent international body is recommended to manage splitting of survey data . A fixed date for releasing data for Part 2 would be agreed upon , so that only those hypotheses specified prior to the release date would undergo a true significance test . Although several challenges and practical issues would inevitably need resolution -LRB- i.e. , data collection , confidentiality , release of data -RRB- , such an organization should be feasible and acceptable to the scientific community . Conclusions Results demonstrated that the number of musculoskeletal pain sites significantly predicts low back pain at a 14-year follow-up , when controlling for age , gender , marital status , and smoking . The application of the data splitting method in our study indicates its potential as an effective and useful method to counteract hypothesis fishing in population surveys . In our opinion , systematic data splitting administered by an independent party would accomplish for statistical surveys what pre-registration has already done for clinical trials .

B I O O O O B I O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O

data splitting
hypothesis fishing
data dredging
low back pain