analysis of 230 cohort studies with 3

AbstractObjectiveTo conduct a systematic review and meta analysis of cohort studies of body mass index (BMI) and the risk of all cause mortality, and to clarify the shape and the nadir of the dose response curve, and the influence on the results of confounding from smoking, weight loss associated with disease, and preclinical disease.Data synthesisSummary relative risks were calculated with random effects models. Non linear associations were explored with fractional polynomial models.Results230 cohort studies (207 publications) were included. The analysis of never smokers included 53 cohort studies (44 risk estimates) with >738144 deaths and >9976077 participants. The analysis of all participants included 228 cohort studies (198 risk estimates) with >3744722 deaths among 30233329 participants. The summary relative risk for a 5 unit increment in BMI was 1.18 (95% confidence interval 1.15 to 1.21; I2=95%, n=44) among never smokers, 1.21 (1.18 to 1.25; I2=93%, n=25) among healthy never smokers, 1.27 (1.21 to 1.33; I2=89%, n=11) among healthy never smokers with exclusion of early follow up, and 1.05 (1.04 to 1.07; I2=97%, n=198) among all participants. There was a J shaped dose response relation in never smokers (Pnon linearityConclusionOverweight and obesity is associated with increased risk of all cause mortality and the nadir of the curve was observed at BMI 23 24 among never smokers, 22 23 among healthy never smokers, and 20 22 with longer durations of follow up. The increased risk of mortality observed in underweight people could at least partly be caused by residual confounding from prediagnostic disease. Lack of exclusion of ever smokers, people with prevalent and preclinical disease, and early follow up could bias the results towards a more U shaped association.IntroductionThe prevalence of overweight and obesity has increased rapidly over the past decades throughout the world.1 This has raised serious public health concerns because of the association between overweight and obesity and increased risk of a wide range of chronic diseases, including cardiovascular diseases,2 type 2 diabetes,3 several types of cancer,4 5 6 7 8 gallbladder disease,imitation f��erie de van cleef & arpels,9 gout,10 osteoarthritis,11 and several other conditions, 11 12 13 as well as all cause mortality.2 14Though many studies have shown an increased risk of all cause mortality with greater adiposity as measured by body mass index (BMI),15 16 17 18 19 20 21 22 23 24 questions remain about the shape of the dose response relation. Several large scale prospective studies15 16 17 18 19 20 21 22 23 24 and pooled analyses (each with 900000 to 1.46 million participants)2 14 25 have reported increased risk of all cause mortality with greater BMI, and most of these found the lowest risk among participants with BMI in the range of 20 or 22.5 to 24.9. A large meta analysis of 97 cohort studies with 2.88 million participants and 270000 deaths, which used the WHO cut off points for overweight and obesity, however, found summary hazard ratios of 0.94 (95% confidence interval 0.90 to 0.97), 0.97 (0.90 to 1.04), and 1.34 (1.21 to 1.47) for a BMI of 25 26 That review, however, had several limitations for example,van cleef joaillerie replique, it excluded several large and some smaller studies including >5.4 million participants and >1.1 million deaths that used more refined categorisations of BMI than the WHO categorisations.15 16 18 19 20 24 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 Thus more deaths and participants were excluded than included in the analysis, and questions have been raised with regard to the validity of the findings.55 In addition, a large number of additional cohorts were either missed by the search or excluded from the analysis,56 57 58 59 60 61 62 63 64 65 and at least 53 additional studies have since been published, including >2.3 million deaths and >21.6 million participants.23 24 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 An updated analysis is therefore warranted.It is well known that smoking strongly increases risk of mortality and many specific causes of death,116 117 and there is therefore a great potential for residual confounding by smoking as it is typically also associated with lower weight.118 Indeed, many studies have reported a different shape of the dose response relation between BMI and mortality when the analysis is restricted to people who have never smoked or in comparisons between smokers and never smokers,2 14 15 16 17 18 19 20 21 22 24 77 but this was not adequately dealt with in the previous meta analysis.26 Furthermore, confounding by prevalent or undiagnosed illness could also have biased the results. It is well known that many chronic diseases (which increase the risk of death) lead to weight loss.119 Weight loss can precede a diagnosis of disease by many years and because of such preclinical weight loss the associations between low BMI and increased mortality might at least partly be caused by confounding by preclinical disease.120 Such bias might be avoided by the exclusion of people with prevalent disease at baseline, by exclusion of the early follow up period of the studies, and by stratifying studies by duration of follow up, but the most recent meta analysis did not conduct such subgroup or sensitivity analyses.26For these reasons we conducted a systematic review and dose response meta analysis of published cohort studies to clarify the strength and the shape of the dose response relation between BMI and all cause mortality,van cleef and arple replique, the potential confounding effects of smoking, and whether prevalent disease, exclusion of early follow up, or stratification by duration of follow up, or the quality of the studies influenced the association between BMI and all cause mortality. We used fractional polynomial models to assess the association between BMI and mortality, and this allowed for inclusion of all relevant studies reporting results for three or more categories of BMI and not only those reporting results using the WHO criteria for categorisation of BMI.MethodsSearch strategy and inclusion criteriaWe searched PubMed and Embase up to 23 September 2015 for eligible studies (DA, AS), using wide search terms (appendix 1). We followed standard criteria for conducting and reporting meta analyses.121 In addition, we searched the reference lists of a previous meta analysis26 for further studies. Study quality was assessed with the Newcastle Ottawa scale.122Patient involvementNo patients were involved in setting the research question or the outcome measures, nor were they involved in developing plans for design, or implementation of the study. No patients were asked to advise on interpretation or writing up of results. There are no plans to disseminate the results of the research to study participants or the relevant patient community.Study selectionWe included cohort studies of the association between BMI and risk of all cause mortality published in English language and excluded abstract only publications and grey literature. In each publication, adjusted relative risk estimates (hazard ratios or risk ratios) for three or more BMI categories had to be available, either with the 95% confidence intervals or with the information to calculate them. The dose response analysis, a quantitative measure of the exposure (BMI), also had to be available. Studies from populations living in the community were included, while studies that included only patients (for example, those with diabetes, stroke, heart disease, and cancer), nursing home residents, and disabled people were excluded. When multiple publications were published from the same study, in general we used the publication with the largest number of deaths. Exceptions to this rule were made when publications with smaller number of deaths provided more detailed analyses with restriction to never smokers, healthy people, and/or exclusion of early follow up than the publications with larger number of deaths. In the analysis of never smokers, the definition of never smokers was strict so we did not include data from studies that combined never smokers and former smokers who had quit for a long duration. When more detailed analyses (restricted to never smokers or other subgroups) were published in an overlapping publication but not in the publication used for the main analysis we used the information from the overlapping publication in the specific analysis, but each study was included only once in each analysis. Studies that reported only a continuous linear risk estimate were excluded as there is evidence that the association between BMI and mortality is non linear. A list of the excluded studies and reasons for exclusion is provided in table A in appendix 2.Statistical methodsWe used a random effects model to calculate summary relative risks and 95% confidence intervals for a 5 unit increment in BMI.123 For the primary analysis we used the model from each study that had the greatest degree of control for potential confounding, with the exception of studies that also adjusted mutually between BMI and waist circumference and waist to hip ratio or that adjusted for potentially intermediate variables such as diabetes, hypertension, and serum cholesterol, for which we used the multivariate model without such adjustment if available. If the alternative model was adjusted only for age and the multivariate model included other confounders as well, we chose the multivariate model with intermediates. We estimated the average of the natural logarithm of the relative risks and weighted the relative risk from each study according to the method of DerSimonian and Laird.123 A two tailed PWe used the method described by Greenland and Longnecker124 for the linear dose response analysis of BMI and mortality and calculated study specific slopes (linear trends) and 95% confidence intervals from the natural logs of the reported relative risks and confidence intervals across categories of BMI. When the reference category was not the lowest category (because, for example, of power issues) we excluded the categories below the reference category for the linear dose response analysis to model the association between higher BMI and mortality. The mean or median BMI level in each category was assigned to the corresponding relative risk for each study, and for studies that reported the exposures in ranges we used the midpoint of the upper and the lower cut off point. When upper and lower categories were open ended or had extreme upper or lower values, we used the width of the adjacent category to calculate an upper or lower bound. When studies reported analyses by the WHO categories of overweight and obesity we used a BMI of 15 as a lower bound for the underweight category (125 We determined the best fitting second order fractional polynomial regression model, defined as the one with the lowest deviance. A likelihood ratio test assessed the difference between the non linear and linear models to test for non linearity.125 For the non linear dose response analysis we included all categories of BMI (even the underweight categories) to model the association between BMI and mortality across the full BMI range and used the method of Hamling and colleagues to convert risk estimates when the lowest category was not the reference category.126 The analyses were re scaled so the reference category was a BMI of 23, which seemed to be the nadir of the curve among never smokers, so there was no loss of statistical power from these re calculations. The fractional polynomial method estimated a dose response curve for each study across the BMI values observed in the whole dataset (which was extrapolated across the full BMI range for studies with a limited BMI range), so all studies contributed to the pooled risk estimates across the full BMI range. The dose response curves for each of the individual studies were then pooled into an overall dose response curve, which are the curves showed in the non linear figures. The relative risk estimates in the tables were based on the non linear figures but show risk estimates for selected BMI values.We conducted subgroup and meta regression analyses to investigate potential sources of heterogeneity and heterogeneity between studies quantitatively assessed by the Q test and I2.127 Small study effects, such as publication bias, were assessed by inspection of the funnel plots for asymmetry and with Egger's test128 and Begg's test,129 with the results considered to indicate small study effects when P26 and in smokers in secondary analyses. Further subgroup analyses were conducted by sex, method of assessment of weight and height, duration of follow up, geographical location, number of deaths, study quality and adjustment for confounders, adjustment for mediators, and restriction to studies with appropriate adjustment for age, smoking, alcohol, and physical activity, but without adjustment for prevalent disease or intermediate factors. Because we did not have access to the original data and because not every study excluded early follow up we also conducted analyses stratified by duration of follow up to investigate the influence of undiagnosed disease on the results. As the number of deaths increases with increasing duration of follow up, the early follow up (when participants with undiagnosed disease most likely would have died) will account for a smaller and smaller proportion of the total deaths the longer the duration of follow up is. As preclinical weight loss can precede the diagnosis of disease by many years, stratification by duration of follow up can allow for assessments of the longer term impact of confounding by undiagnosed disease. We used Stata, version 12.0 (Stata Corp, College Station, TX) for the statistical analyses.ResultsFrom a total of 112173 records identified by the search we included 207 publications16 17 18 19 20 21 22 23 24 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 with 230 cohort studies including >3748549 deaths among 30361918 participants in the meta analysis of BMI and risk of all cause mortality (table B in appendix 2; fig 1). Table 1 summarises the main characteristics (number of studies, cases, and participants, geographical location, study size, and mean or median duration of follow up) of the studies included in the analysis of never smokers and among all participants. Some publications reported on or included data from more than one study (which were analysed as one combined dataset); one publication included data from nine studies,138 and another publication included eight cohort studies that were combined in one analysis,16 one publication reported results from six studies that were combined,95 five publications reported results from three studies that were combined,74 140 142 150 178 four publications reported results from two studies,163 170 189 191 which were included in the analysis. Four publications reported on men and women separately from the same two studies.30 31 131 132 Two duplicate publications were included only in subgroup analyses by sex229 235 as the main article provided only results for both sexes combined140 or because the duplicate publication had a longer follow up.235 That publication was not used for the main analysis as it reported only on women, while the main publication reported on both men and women.166 One publication was included only in the analysis of African Americans237 as the main publication reported results from the full population.17Fig 1 chart of study selection in systematic review and non linear dose response meta analysis of BMI and all cause mortalityThere was no heterogeneity in the analyses among never smokers when we stratified by sex, and, although there was heterogeneity when we stratified analyses of all participants by sex, this seemed to be due to no association among the studies of men and women combined, and when analysis was restricted to studies in either men or women there was no heterogeneity (tables C, D, and F in appendix 2, fig I in appendix 3). Although there was evidence of heterogeneity by geographical location in the linear dose response analysis of all participants (P=0.04), with a significant positive association observed only for Europe and North America (table F in appendix 2), there was no heterogeneity by geographical location in never smokers (P=0.91) and positive associations were observed in European, North American, Australian, and Asian studies (table C in appendix 2), although slight variations in the risk estimates from the non linear dose response analyses were observed (table H in appendix 2, fig J in appendix 3). There was evidence of heterogeneity between studies when we stratified by study quality scores in the analysis of all participants (P=0.03), with a significant association among studies with high study quality scores but not among the studies with medium study quality scores (table F in appendix 2). The non linearity was also more pronounced among the studies with medium study quality compared with the studies of high study quality (table I in appendix 2, figs K and L in appendix 3). There was, however, no heterogeneity by study quality scores in the subgroup analyses of never smokers (table C in appendix 2, figs M and N in appendix 3). There was evidence of heterogeneity when we stratified studies by the number of deaths in the analysis of all participants (PThe positive association between BMI and all cause mortality among never smokers persisted in subgroup analyses defined by sex, assessment of anthropometric measures, geographical location, number of deaths, and adjustment for confounding factors including age, education, alcohol, physical activity, height, dietary pattern, and intake of fat, fruit, and vegetables. There was little evidence of heterogeneity between any of these subgroups with meta regression analyses (table C in appendix 2). We observed no association among the few studies that adjusted for potential intermediate factors (diabetes, hypertension, cholesterol). In general, heterogeneity was high in most of the subgroup analyses.In the analysis of all participants there was no evidence of heterogeneity when we stratified studies by adjustment for age, education, socioeconomic status, alcohol, smoking status, pack years, years since quitting, physical activity, height, dietary pattern, fat intake, or fruit and vegetable intake. There was heterogeneity among studies when we stratified by adjustment for number of cigarettes smoked a day (PWhen we stratified studies by potential intermediates, there was heterogeneity by whether studies adjusted for diabetes, with no association among studies with such adjustment (table F in appendix 2). Although the test for heterogeneity was not significant, there was also no association among studies with adjustment for systolic blood pressure and hypertension. There was also heterogeneity by adjustment for prevalent coronary heart disease (P=0.003), stroke (P=0.07),van cleefs & arpels replique, and prevalent cancer (P=0.03), with no association among studies with such adjustment (table F in appendix 2). Although the test for heterogeneity between subgroups was not signi