Temporal Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation

Natasha Wiebe; Feng Ye; Ellen T. Crumley; Aminu Bello; Peter Stenvinkel; Marcello Tonelli

doi:10.1001/jamanetworkopen.2021.1263

Temporal Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation

Wiebe, Natasha; Ye, Feng; Crumley, Ellen T.; Bello, Aminu; Stenvinkel, Peter; Tonelli, Marcello 2021-03-12 00:00:00 Key Points Question What are the temporal IMPORTANCE Obesity is associated with a number of noncommunicable chronic diseases and is associations among higher body mass purported to cause premature death. index (BMI) and chronic inflammation and/or hyperinsulinemia? OBJECTIVE To summarize evidence on the temporality of the association between higher body Findings In this systematic review and mass index (BMI) and 2 potential mediators: chronic inflammation and hyperinsulinemia. meta-analysis of 5603 participants in 112 cohorts from 60 studies, the association DATA SOURCES MEDLINE (1946 to August 20, 2019) and Embase (from 1974 to August 19, 2019) between period 1 (preceding) levels of were searched, although only studies published in 2018 were included because of a high volume of fasting insulin and period 2 (subsequent) results. The data analysis was conducted between January 2020 and October 2020. BMI was positive and significant: for every unit of SD change in period 1 STUDY SELECTION AND MEASURES Longitudinal studies and randomized clinical trials that insulin level, there was an ensuing measured fasting insulin level and/or an inflammation marker and BMI with at least 3 commensurate associated change in 0.26 units of SD in time points were selected. period 2 BMI. DATA EXTRACTION AND SYNTHESIS Slopes of these markers were calculated between time Meaning These findings suggest that points and standardized. Standardized slopes were meta-regressed in later periods (period 2) with adverse consequences currently standardized slopes in earlier periods (period 1). Evidence-based items potentially indicating risk of attributed to obesity could be attributed bias were assessed. to hyperinsulinemia (or another proximate factor). RESULTS Of 1865 records, 60 eligible studies with 112 cohorts of 5603 participants were identified. Most standardized slopes were negative, meaning that participants in most studies experienced Supplemental content decreases in BMI, fasting insulin level, and C-reactive protein level. The association between period 1 fasting insulin level and period 2 BMI was positive and significant (β = 0.26; 95% CI, 0.13-0.38; Author affiliations and article information are listed at the end of this article. I = 79%): for every unit of SD change in period 1 insulin level, there was an ensuing associated change in 0.26 units of SD in period 2 BMI. The association of period 1 fasting insulin level with period 2 BMI remained significant when period 1 C-reactive protein level was added to the model (β = 0.57; 95% CI, 0.27-0.86). In this bivariable model, period 1 C-reactive protein level was not significantly associated with period 2 BMI (β = –0.07; 95% CI, –0.42 to 0.29; I = 81%). CONCLUSIONS AND RELEVANCE In this meta-analysis, the finding of temporal sequencing (in which changes in fasting insulin level precede changes in weight) is not consistent with the assertion that obesity causes noncommunicable chronic diseases and premature death by increasing levels of fasting insulin. JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 Open Access. This is an open access article distributed under the terms of the CC-BY License. JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 1/17 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation Introduction Obesity is associated with a number of noncommunicable chronic diseases (NCDs), such as type 2 diabetes, coronary disease, chronic kidney disease, and asthma. Although obesity is also purported to cause premature death, this association fails to meet several of the Bradford Hill criteria for 1,2 3 causation. First, the putative attributable risk of death is small (<5%). Second, the dose-response gradient between body mass index (BMI) and mortality is U-shaped with overweight (and possibly obesity level I) as the minima. Third, evidence from animal models comes largely from mice that have been fed high-fat diets; unlike humans, these animals did not normally have fat as part of their typical diet, and thus the experiments are potentially not analogous to those in humans. Fourth, evidence that people with obesity live longer than their lean counterparts in populations with acute 4-16 or chronic conditions and older age is remarkably consistent. Therefore, it is possible that rather than being a risk factor for NCDs, obesity is actually a protective response to the development of disease. The putative links between obesity and adverse outcomes are often attributed to 2 potential mediators: chronic inflammation and hyperinsulinemia. These characteristics have been associated with several NCDs, including obesity as well as type 2 diabetes, cardiovascular disease, and chronic kidney disease. Existing data on the association of obesity with chronic inflammation and/or hyperinsulinemia are chiefly cross-sectional, making it difficult to confirm the direction of any causality. This systematic review and meta-analysis summarizes evidence on the temporality of the association between higher BMI and chronic inflammation and/or hyperinsulinemia. We hypothesized that changes in chronic inflammation and hyperinsulinemia would precede changes in higher BMI. Methods This systematic review and meta-analysis was conducted and reported according to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and Meta-analysis of Observational Studies in Epidemiology (MOOSE) reporting guidelines. Research ethics board approval was not required because this is a systematic review of previously published research. Data Sources and Searches We performed a comprehensive search designed by a trained librarian (E.T.C.) to identify all longitudinal studies and randomized clinical trials (RCTs) that measured fasting insulin and/or an inflammation marker and weight with at least 3 commensurate time points. We included only primary studies published in the English language as full peer-reviewed articles. MEDLINE (1946 to August 20, 2019) and Embase (1974 to August 19, 2019) were searched; however, only studies published in 2018 were retained because of the high volume of results. No existing systematic reviews were found. The specific search strategies are provided in eTable 1 in the Supplement.The abstracts were independently screened by 2 reviewers (including N.W.). The full text of any study considered potentially relevant by 1 or both reviewers was retrieved for further consideration. The data analysis was conducted between January 2020 and October 2020. Study Selection Each potentially relevant study was independently assessed by 2 reviewers (N.W. and F.Y.) for inclusion in the review using the following predetermined eligibility criteria. Longitudinal studies and RCTs with men and nonpregnant and not recently pregnant women (18 years of age) and at least 3 time points with 1 or more weeks of follow-up in which fasting insulin levels or a marker of inflammation and some measure of weight were included in this review. Disagreements were resolved by consultation. JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 2/17 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation Data Extraction and Risk of Bias Assessment Data from eligible studies were extracted by a single reviewer (N.W.). A second reviewer checked the extracted data for accuracy. The following properties of each study were recorded in a database: study characteristics (country, era of accrual, design, duration of follow-up, populations of interest, intervention where applicable, and sample size), age and sex of participants, and the measures of interest (numbers, means, and SDs) for all time points: (1) fasting insulin, the homeostatic model assessment index, or the quantitative insulin sensitivity check index; (2) concentrations of C-reactive protein (CRP), interleukin cytokines, or tumor necrosis factor; and (3) weight, BMI (calculated as weight in kilograms divided by height in meters squared), fat mass, or fat mass percentage. Risk of bias was assessed using items from Downs and Black : clear objective, adequate description of measures, sample size or power calculation, intention to treat study design (in those studies that assigned the intervention), adequate description of withdrawals, adequate handling of missing data, and adequate description of results. Source of funding was also extracted, given its potential to introduce bias. Statistical Analysis Data were analyzed using Stata software, version 15.1 (StataCorp LLC). Missing SDs were imputed using interquartile ranges or using another SD from the same cohort. Data were extracted from graphs if required. To determine a likely temporal sequencing of fasting insulin level or chronic inflammation with obesity, we compared the associations of period 2 insulin level or inflammation regressed on period 1 BMI and period 2 BMI regressed on period 1 insulin or inflammation. A stronger association would support a particular direction of effect. For each measure of interest, the change in means was calculated between adjacent time points and divided by the number of weeks between the measures. This slope or per week change in measure was then standardized by dividing it by the pooled SD, giving a standardized slope. Because of expected diversity among studies, we decided a priori to combine the standardized slopes using a random-effects models. Period 2 standardized slopes of weight measures were regressed onto period 1 standardized slopes of insulin or inflammation measures and vice versa. We regressed measures of insulin post hoc on measures of inflammation and vice versa. The type I error rate for meta-regressions was set at a 2-sided P < .05. Statistical heterogeneity 2 24 2 was quantified using the τ statistic (between-study variance) and the I statistic. Differences in standardized slopes (βs) along with 95% CIs are reported. We considered a number of sensitivity analyses. Because we included multiple standardized slopes at different intervals from the same studies (or same cohorts), we accounted for this nonindependence using a generalized linear model in which the family was gaussian and the link was identity, which allowed for nested random effects (results by intervals were nested within cohorts). To estimate between-study heterogeneity, the coefficients for the within-cohort SEs were constrained to 1. We also performed 2 subgroup analyses: whether the study population had undergone bariatric surgery and the numbers of weeks between time points (>12 vs12 weeks), reasoning that if the effects of one measure of interest acted quickly on the other, then shorter intervals might demonstrate stronger associations. We explored post hoc models with 2 measures of interest as period 1 independent variables. Results Quantity of Research Available The searches identified 1865 unique records identifying articles or abstracts published in 2018 (Figure 1). After the initial screening, the full texts of 813 articles were retrieved for detailed evaluation. Of these, 753 articles were excluded, resulting in 60 that met the selection criteria and 25-84 5603 enrolled participants (of whom 5261 were analyzed). We decided to exclude 12 studies of JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 3/17 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation children and adolescents post hoc because these studies used different BMI measures. Disagreements about the inclusion of studies occurred in 2% of the articles (κ = 0.87). Characteristics of Studies There were 26 RCTs, 4 nonrandomized clinical trials, 23 prospective cohort studies (3 nested within 25-84 an RCT), and 7 retrospective cohort studies (Table 1 ). Of the studies, 58% began data collection in the 5 years before publication. The earliest study accrued participants starting in 2000. The durations of follow-up ranged from 1 to 60 months (median, 12 months). A total of 21 studies were 25,27,41,54,61,62,65,72,81,82,84 25,27,41,54,61,62,65,72,81,82,84 from Western Europe, 11 from North America, 9 29,38,39,44,51,52,60,66,68 28,69,78,79,83 from East Asia, 5 from South America, 5 from Western 28,69,78,79,83 35,50,76 26,42,75 45,74,80 Asia, and 3 each from Africa, the Pacific, and Eastern Europe. A total of 90% of the studies were in populations with metabolic disease or conditions 25,32-34,37,41,43,45,47-49,52,57-60,62-64,67-74,76,77,79-83 associated with metabolic disease: obesity, diabetes 26,32,38,46,59 40 65 51 or prediabetes, hypertension, coronary artery disease, dyslipidemia, chronic 78 31,35 36 kidney disease, nonalcoholic fatty liver disease, Cushing disease, polycystic ovary 61 28,56,82 84 syndrome, breast cancer, and aging (ie, college students ). Of the patients in these 54 studies, 22 (41%) had undergone bariatric surgery as the studied intervention (n = 14) or as part of the required eligibility criteria (n = 8). Other populations were subjected to operations or therapies 27,42 39 that adversely cause lean mass loss and/or fat mass gain, such as prostate, esophageal, head 44 53 and neck squamous cell cancers, and psychosis, or where the disease course itself (ie, tuberculosis) causes lean mass loss and/or fat mass gain. The 60 studies included 112 cohorts: 40 cohorts contained participants who had undergone bariatric surgery, 33 cohorts contained participants who were receiving diet therapies (all except 65,84 2 designed for weight loss or weight maintenance), 16 cohorts contained participants who received a medication or supplement, 7 cohorts contained participants who were following exercise regimens, 14 cohorts contained participants who were followed up for other reasons (ie, prostate Figure 1. Flow Diagram of Studies 403 Records identified in MEDLINE 0 Records identified from 1725 Records identified in Embase references of included articles 1865 Records after duplicates removed 1865 Citations screened 1052 Records excluded 813 Full-text articles assessed for eligibilty 753 Excluded on the basis of full-text review of the article 386 <3 Measures of fat mass 277 Not full English-language article 32 <3 Insulin or inflammation 27 <1 Week of follow-up 14 Not original research 12 Pediatric or adolescent 2 Pregnancy 2 Not contemporaneous 1 No usable data 60 Studies included in systematic review JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 4/17 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation Table 1. Study and Study Population Characteristics Year of Follow-up, Enrolled/ Source Country study start Design mo Population Cohort(s) analyzed Mean age, y Male, % Abdel-Razik Egypt 2015 RCT 6 NASH Rifaximin and placebo 25 and 25 40 and 38 36 and 28 et al, 2018 Abiad Lebanon 2015 Prospective 12 BMI ≥40 or >35 with PCOS and control 6 and 19/16 24 and 28 0 et al, 2018 cohort a comorbidity and SG Arikawa US 2009 RCT 4 BMI ≥27 after breast CR diet plus exercise and 10/10 and 55 and 58 0 et al, 2018 cancer weight management 11/10 counseling Arnold US 2015 Prospective 3 BMI >30 Decreased added sugars, 15/14 59 0 et al, 2018 cohort increased fiber, and fish diet Asle Iran NR RCT 6 T2D Low-carbohydrate diet, 11, 11, 11, 47, 49, 45, 100 Mohammadi low-fat diet, high-fat diet, and 9 and 45 Zadeh and control et al, 2018 Baltieri Brazil 2015 Prospective 12 BMI ≥35 RYGB 19/13 37 0 et al, 2018 cohort Bulatova Jordan 2012 RCT 6 Prediabetes or T2D Metformin and control 42/26 and 51 and 51 22 and 3 et al, 2018 49/27 a a Carbone Italy 2007 Retrospective 36 RYGB or BPD with T2D T2D remission and no T2D 14 and 27 54 and 56 64 and 82 67 b et al, 2019 cohort remission 51 and 51 64 and 64 47 and 47 Chen China 2012 nRCT 12 T2D Saxagliptin and metformin et al, 2018 and acarbose and metformin a a Cheung Australia NR Prospective 36 Prostate cancer Cessation of androgen 34/27 and 68 and 71 100 et al, 2018 cohort deprivation therapy and 29/19 control Chiappetta Germany 2014 Retrospective 6 BMI ≥40 or ≥35 with a SG, 1-anastomosis GB, 241, 68, and 44 32 et al, 2018 cohort comorbidity and RYGB 159 Dardzińska Poland NR Prospective 12 BMI >35 with no diabetes Mini-GB, SG, and RYGB 12/9, 8/5, and 38 24 et al, 2018 cohort medication and no 11/9 cardiovascular events De Luis, Calvo Spain NR Prospective 36 BMI ≥40 or >35 with a CC rs266729 84 and 65 47 and 47 23 and 25 et al, 2018 cohort comorbidity after bariatric CG and GG rs266729 surgery De Luis, Izaola Spain NR Prospective 36 BMI ≥30 Mediterranean CR diet 335 50 25 et al, 2018 cohort then dietary counseling De Luis, Spain NR Prospective 36 BPD with no diabetes GG rs670 and GA 46 and 17 48 and 47 13 and 18 Pacheco cohort and BMI ≥40 or AA rs670 et al, 2018 De Paulo Brazil 2015 RCT 8 Aromatase inhibitor after Aerobic and resistance 18 and 18 63 and 67 0 et al, 2018 breast cancer training and stretching and relaxation exercises Demerdash Egypt 2011 Prospective 24 Obesity SG 92 43 30 et al, 2018 cohort Derosa Italy NR RCT 12 T2D and hypertension Canrenone and 92 and 90 53 and 53 52 and 49 et al, 2018 hydrochlorothiazide Dhillon US 2016 RCT 2 College students (no Almond snacks and cracker 38 and 35 18 and 18 42 and 46 et al, 2018 diabetes or prediabetes) snacks Di Sebastiano Canada NR Prospective 8 Prostate cancer Treated 9 71 100 et al, 2018 cohort Drummen Netherlands 2013 Prospective 24 BMI >25 and prediabetes High-protein diet and 12 and 13 58 and 54 50 and 58 et al, 2018 cohort moderate-protein diet (PREVIEW) nested in RCT Esquivel Argentina 2009 Prospective 12 BMI >40 or >35 with a SG 63/43 40 35 et al, 2018 cohort comorbidity Fortin Canada 2016 RCT 9 T1D and metabolic Mediterranean diet and 14 and 14 52 and 50 47 and 64 et al, 2018 syndrome low-fat diet Fuller Australia 2013 RCT 6 Prediabetes or T2D High-egg diet 72/66 and 60 and 61 50 and 42 et al, 2018 and low-egg diet 68/62 (DIABEGG) Gadéa France 2011 Prospective 6 Breast cancer Chemotherapy 52 60 0 et al, 2018 cohort Galbreath US NR RCT 3 BMI >27 or body fat >35% High-protein diet, 24/17, 66, 63, and 0 et al, 2018 high-carbohydrate diet, 24/18, and 66 and control 24/19 Goday Spain 2010 Retrospective 24 SG and Helicobacter pylori 49 and 44 and 37 and et al, 2018 cohort RYGB eradication and control 60 (SG) 46 (SG) and 22 (SG) and for SG and H pylori and 50 and 42 and 22 and eradication 70 (RYGB) 42 (RYGB) 16 (RYGB) control for RYGB (continued) JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 5/17 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation Table 1. Study and Study Population Characteristics (continued) Year of Follow-up, Enrolled/ Source Country study start Design mo Population Cohort(s) analyzed Mean age, y Male, % Guarnotta Italy 2013 Prospective 12 Cushing disease Pasireotide 12 40 17 et al, 2018 cohort Hady Poland 2012 RCT 12 Obesity 32F bougie size in SG, 40, 40, and 41, 43, and 38, 30, and et al, 2018 36F bougie size in SG, and 40 45 43 40F bougie size in SG Hanai Japan NR RCT 1 Surgery for head and neck EPA-enriched nutritional 13 and 14 62 and 66 62 and 57 et al, 2018 squamous cell carcinoma supplement and control Hattori, Japan 2016 RCT 12 SGLT2 inhibitors in T2D Empagliflozin and placebo 51 and 51 57 and 58 75 and 80 Kazemi Canada 2011 RCT 12 PCOS Low-glycemic index pulse- 47/31 and 27 and 27 0 et al, 2018 based diet and therapeutic 48/30 lifestyle change diet Keinänen Finland 2010 Prospective 12 First-episode psychosis Treated 95 25 68 et al, 2018 cohort Krishnan US 2015 RCT 2 BMI of 25-39.9 2010 American dietary 28/22 and 47 and 47 0 et al, 2018 guidelines diet and typical 24/22 American diet Lambert Brazil NR Retrospective 12 BMI >40 or BMI >35 with RYGB and BPD 108 44 42 et al, 2018 cohort comorbidity or BMI >30 and T2D Lee Singapore 2009 Retrospective 36 Prediabetes Bariatric surgery 44 and 25 43 and 50 34 and 12 et al, 2018 cohort and control Liang Taiwan 2008 Prospective 60 WC ≥90, MetS, no Low-calorie diet 40/18 46 100 et al, 2018 cohort diabetes Liaskos Greece NR nRCT 6 BMI >40 and no T2D SG and RYGB 43 and 28 38 and 38 21 and 25 et al, 2018 Liu China 2014 Retrospective 12 T2D and BMI ≥28 RYGB 45 44 100 et al, 2018 cohort Madjd Iran 2014 RCT 18 BMI of 27-40 Diet beverages and water 36 and 35 32 and 32 0 et al, 2018 Most US 2007 Prospective 24 BMI of 22-27.9 plus ≥5% 25% CR and control 47/34 and 40 and 39 29 and 37 et al, 2018 cohort weight loss in CR 25% and 26/19 (CALERIE 2) nested in <5% weight loss RCT in ad libitum Mraović Serbia 2014 RCT 10 BMI ≥35 20% CR diet, 50% CR diet, 37, 30, and 31, 32, and 0 et al, 2018 and alternating 70% and 30 32 30% CR diet Munukka Finland NR Prospective 1 BMI >27.5 with no major American College of Sports 19/17 37 0 et al, 2018 cohort comorbidity Medicine exercise program Nicoletto Brazil 2014 Prospective 12 CKD Kidney transplantation 46 49 59 et al, 2018 cohort Nilholm Sweden 2014 Prospective 6 T2D Okinawan-based Nordic diet 30 58 43 et al, 2018 cohort a a Nishino Japan 2011 RCT 1 Esophagectomy for Daikenchuto (TJ-100) 19 and 20 68 and 61 89 and 80 et al, 2018 esophageal cancer and control Patel United NR nRCT 18 BMI of 30-50 and T2D Duodenal-jejunal sleeve 45 50 49 et al, 2018 Kingdom bypass Rajan-Khan US 2011 RCT 4 BMI ≥25 Mindfulness-based stress 42 and 44 47 and 42 0 et al, 2018 reduction and health education Rubio- Spain 2000 Retrospective 60 Prediabetes or T2D and Prediabetes and T2D 57/38 and 48 17 Almanza cohort bariatric surgery 48/32 et al, 2018 Schübel Germany 2015 RCT 12 BMI of 25-39.9 5:2 intermittent CR diet, 49, 49, and 49, 51, and 51, 51, and et al, 2018 continuous CR diet, 52 51 48 (HELENA) and control a a Shah US 2014 RCT 2 Coronary artery disease Vegan diet and AHA diet 50 and 50 63 and 60 86 and 84 et al, 2018 (EVADE CAD) Sherf-Dagan Israel 2014 RCT 12 NAFLD after SG Probiotic and placebo 50/40 and 42 and 44 40 and 45 et al, 2018 50/40 Stolberg Denmark 2012 RCT 24 RYGB Moderate-intensity physical 32 and 28 43 and 43 34 and 25 et al, 2018 training and control van Dammen Netherlands 2009 RCT 6 BMI ≥29 and infertily Lifestyle intervention 290/289 and 30 and 30 0 et al, 2018 and control 287/285 van Rijn Netherlands 2014 Prospective 12 BMI of 30-50 and T2D Duodenal-jejunal 28 50 39 et al, 2018 cohort bypass liner nested in RCT (continued) JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 6/17 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation Table 1. Study and Study Population Characteristics (continued) Year of Follow-up, Enrolled/ Source Country study start Design mo Population Cohort(s) analyzed Mean age, y Male, % Wilson South 2005 Prospective 2 TB symptoms Treated for TB and control 295 and 93 34 56 et al, 2018 Africa cohort Witczak United NR Prospective 6 BMI >40 and T2D or IGT Bariatric surgery 20 51 35 et al, 2018 Kingdom cohort Wormgoor New 2015 RCT 9 T2D HIIT and moderate-intensity 12/11 and 11 52 and 53 100 et al, 2018 Zealand continuous training Yang China 2015 nRCT 12 BMI >35 or ≥30 with SG and RYGB 32/10 and 32 and 32 50 and 50 et al, 2018 a comorbidity 28/10 Zhang China 2015 RCT 6 Dyslipidemia Coenzyme Q10 and placebo 51 and 50 52 and 50 28 and 36 et al, 2018 Abbreviations: AHA, American Heart Association; BMI, body mass index (calculated as sleeve gastrectomy; SGLT2, sodium-glucose transport protein 2; T1D, type 1 diabetes; weight in kilograms divided by height in meters squared); BPD, biliopancreatic diversion T2D, type 2 diabetes; TB, tuberculosis; WC, waist circumference. surgery; CKD, chronic kidney disease; CR, calorie restriction; EPA, eicosapentaenoic acid; Median. GB, gastric bypass; HIIT, high-intensity interval training; IGT, intolerance glucose test; Published online in 2018. MetS, metabolic syndrome; NAFLD, nonalcoholic fatty liver disease; NASH, nonalcoholic AA, CC, CG, GA, and GG are alleles. steatohepatitis; NR, not reported; nRCT, nonrandomized clinical trial; PCOS, polycystic ovary syndrome; RCT, randomized clinical trial; RYGB, Roux-en-Y gastric bypass; SG, 27 78 34 32 cancer, kidney transplants, gene-associated obesity, diabetes vs prediabetes, polycystic 61 81 ovary syndrome, and mindfulness intervention ), and 21 cohorts contained control participants (of 31,35,51,66 which 4 cohorts contained participants who received placebo ). The size of the cohorts ranged from 5 to 335 participants (median, 32). The mean ages ranged from 18 to 71 years (median, 47 years). The percentage of men ranged from 0 to 100% (median, 35%). The mean BMIs of the patients ranged from 23 to 54 (median, 38) (eTable 2 in the Supplement). Similarly, mean weight (median, 94 kg; range, 50-156 kg), fat mass (median, 32 kg; range, 20-47 kg), and percentage of body fat (median, 41%; range, 27%-53%) were high compared with general populations. Mean fasting insulin level (median, 13.53 μIU/mL; range, 4.32-27.79 μIU/mL [to convert to picomoles per liter, multiply by 6.945]), and the homeostatic model assessment index (median, 3.3; range, 0.9-12.9) were also high. Most of the mean CRP levels corresponded to a low-grade inflammation (median, 0.52 mg/dL; interquartile range, 0.21-0.75 mg/dL; range, 0.06-5.62 mg/dL [to convert to milligrams per liter, multiply by 10]). Mean interleukin 6 level ranged from 1.3 to 19.8 pg/mL (median, 3.4 pg/mL) and mean tumor necrosis factor α levels from 3.1 to 19.2 pg/mL (median, 12.4 pg/mL). Risk of Bias Assessment Studies were largely rated as low risk for description of the objectives (96.7%), the outcome measures (90.0%), and the results (98.3%) (Figure 2). Approximately half the studies were high risk because they lacked a sample size or power calculation (51.7%), they (in those studies that assigned the interventions) did not take an intention-to-treat approach (47.2%), they had a withdrawal rate greater than 20% or they did not adequately describe their withdrawals (50.0%), or they did not adequately explore the effect of missing data (50.0%). In addition, 38.3% of studies had an industry source of funding. BMI and Fasting Insulin Level There were 90 pairs of standardized slopes from 56 cohorts and 35 studies that measured BMI and fasting insulin (Table 2). Most BMI and fasting insulin standardized slopes were negative (81% for BMI and 71% for fasting insulin), meaning that participants in most studies experienced decreases in BMI and insulin. The association between period 1 fasting insulin level and period 2 BMI was positive and significant (β = 0.26; 95% CI, 0.13-0.38; I = 79%) (Figure 3), indicating that for every unit of SD change in period 1 insulin, there was an associated change in 0.26 units of SD in period 2 BMI. The association between period 1 BMI and period 2 fasting insulin level was not significant (β = 0.01; 95% CI, –0.08 to 0.10; I = 69%) (Figure 3). The heterogeneities were large. JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 7/17 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation The associations between insulin level and BMI increased in magnitude when studies that reported findings at 12 weeks or less were isolated from those that reported findings at greater than 12 weeks (eTable 3 in the Supplement). The magnitude of association between period 1 fasting insulin level and period 2 BMI was greater at 12 weeks or less than at greater than 12 weeks (β = 0.61; 95% CI, 0.38-0.84 vs β = 0.17; 95% CI, 0.05-0.30; I =76%, P = .001). The association between period 1 fasting insulin level and period 2 BMI was present in participants who had undergone bariatric surgery but not in participants who had not undergone bariatric surgery (β = 0.31; 95% CI, 0.19-0.44 vs β = –0.12; 95% CI, –0.41 to 0.18; I =76%, P = .007) (eTable 4 in the Supplement). BMI and CRP There were 57 pairs of standardized slopes from 39 cohorts and 22 studies that measured both BMI and CRP levels (Table 2). Most standardized slopes for BMI and CRP were negative (81% for BMI and 68% for CRP), suggesting that participants in most studies experienced decreases in BMI and CRP level. The association between period 1 CRP level and period 2 BMI was not significant (β = 0.23; 95% CI, –0.09 to 0.55; I = 83%). The association between period 1 BMI and period 2 CRP level was positive and significant (β = 0.20; 95% CI, 0.04-0.36; I = 53%), suggesting that for every unit of SD change in period 1 BMI, there was an associated change of 0.20 units of SD in period 2 CRP level. However, both β coefficients were positive and had similar magnitudes, and the β coefficient for BMI had larger heterogeneity. The associations between BMI and CRP level increased in magnitude when the studies that reported findings at 12 weeks or less were isolated from those that reported findings at greater than Figure 2. Risk of Bias Assessment Low risk Moderate risk High risk Clear Adequate Sample size Intention Adequate Adequate Adequate Sources Except for 3 items (clear objective, adequate objective description or power to treat description handling of description of funding of measures calculation of withdrawals missing data of results description of measures, and adequate description of Risk of bias results), the assessments indicate several risks of bias. Table 2. Pooled Temporal Associations: Primary Analysis 2 2 Dependent (Period 2) Independent (Period 1) No. of cohorts β (95% CI) I /τ BMI and insulin ΔBMI ΔInsulin 90 0.26 (0.13 to 0.38) 79%/0.161 ΔInsulin ΔBMI 90 0.01 (–0.08 to 0.10) 69%/0.099 BMI and CRP Abbreviations: BMI, body mass index (calculated as ΔBMI ΔCRP 57 0.23 (–0.09 to 0.55) 83%/0.168 weight in kilograms divided by height in meters squared); CRP, C-reactive protein; Δ, change. ΔCRP ΔBMI 57 0.20 (0.04 to 0.36) 53%/0.048 Each row describes one model where change in a Insulin and CRP measure, specifically a standardized slope, of a later ΔInsulin ΔCRP 42 0.19 (–0.04 to 0.42) 49%/0.038 period (period 2) is regressed on a change in a ΔCRP ΔInsulin 42 0.29 (0.10 to 0.47) 36%/0.023 different measure of an earlier period (period 1). JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 8/17 Studies, % JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation 12 weeks, when period 2 BMI was regressed on period 1 CRP level (eTable 3 in the Supplement). Although not significantly so, the magnitude of the association between period 1 CRP level and period 2 BMI was greater at 12 weeks or less than at greater than 12 weeks (β = 0.72; 95% CI, 0.08- 1.37 vs β = 0.14; 95% CI, –0.18 to 0.47; I = 81%, P = .09). In addition, the association between period 1 CRP level and period 2 BMI was present in participants who underwent bariatric surgery but not in participants who had not undergone bariatric surgery (β = 0.43; 95% CI, 0.10-0.76 vs β = –0.40; 95% CI, –0.93 to 0.13; I = 81%, P = .005) (eTable 4 in the Supplement). Fasting Insulin and CRP There were 42 pairs of standardized slopes from 27 cohorts and 16 studies that measured both fasting insulin and CRP levels (Table 2). Most fasting insulin and CRP standardized slopes were negative (74% of fasting insulin slopes and 63% of CRP slopes), suggesting that participants in most studies experienced decreases in insulin and CRP levels. The association between period 1 CRP level and period 2 fasting insulin level was not significant (β = 0.19; 95% CI, –0.04 to 0.42; I = 49%). The association between period 1 fasting insulin level and period 2 CRP level was positive and significant (β = 0.29; 95% CI, 0.10-0.47; I = 36%), suggesting that for every unit of SD change in period 1 insulin level, there was an associated change of 0.29 units of SD in period 2 CRP level. There was moderate heterogeneity. The subgroups did not significantly modify the associations between fasting insulin and CRP levels (eTables 2 and 3 in the Supplement). Other Sensitivity Analyses When we considered related measures of BMI (weight, fat mass, and fat percentage), homeostatic model assessment index, and the other inflammatory markers (ie, interleukin 6 and tumor necrosis factor α), the associations among these variables were similar to those for BMI or could not be calculated (eTable 5 in the Supplement). The results when adjusting for nonindependence when available were similar (eTable 6 in the Supplement)—1 of the 6 models did not converge likely because of overly identified models (too few data for the number of model parameters). When we considered 2 measures as independent variables, the association of period 1 insulin level on period 2 BMI remained significant when the period 1 CRP level remained in the model (β = 0.57; 95% CI, 0.27-0.86 and β = –0.07; 95% CI, –0.42 to 0.29; I = 81%) (eTable 7 in the Supplement). Figure 3. Bubble Plot of Temporal Associations Between Period 1 and Period 2 Changes A Period 1 insulin and period 2 BMI B Period 1 BMI and period 2 insulin 3 3 –1 –1 –2 –2 –3 –3 –4 –5 –4 –3 –2 –1 0 1 2 3 –5 –4 –3 –2 –1 0 1 2 3 Insulin slope for period 1 BMI slope for period 1 A, Period 2 change in body mass index (BMI) (or standardized slope) is regressed onto BMI and period 2 change in insulin. The diagonal trend line in panel A supports a positive period 1 change in insulin. B, Period 2 change in insulin is regressed onto period 1 change and temporal association between period 1 change in insulin and period 2 change in BMI. in BMI. The flat trend line in panel B suggests no association between period 1 change in The size of the circles is based on the inverse of the SE of each cohort. JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 9/17 BMI slope for period 2 Insulin slope for period 2 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation Discussion This systematic review and meta-analysis suggests that decreases in fasting insulin are more likely to precede decreasing weight than are decreases in weight to precede decreasing levels in fasting insulin. After accounting for the association between preceding levels of fasting insulin and the subsequent likelihood of weight gain, there was no evidence that inflammation preceded subsequent weight gain (eTable 7 in the Supplement). This temporal sequencing (in which changes in fasting insulin precede changes in weight) is not consistent with the assertion that obesity causes NCDs and premature death by increasing levels of fasting insulin. Support From Other Studies In patients with type 2 diabetes, RCTs have found that introducing exogenous insulin and sulfonylureas (which increase endogenous insulin production) compared with lower doses or no drug 85,86 therapy produce increases in weight. Some patients with type 1 diabetes deliberately omit or reduce their insulin injections to lose weight. Similarly, reports after bariatric surgery consistently indicate that insulin levels decrease before weight decreases in patients undergoing bariatric surgery. Thus, the finding that changes in insulin levels tend to precede changes in weight rather than the other way around has been previously demonstrated in 3 different scenarios. To our knowledge, there is no clinical evidence demonstrating that weight gain or loss precedes increases or decreases in endogenous insulin. Importance of the Findings Obesity as a cause of premature death fails to meet several of the Bradford Hill criteria for causation: the strength of association is small ; the consistency of effect across older and/or ill populations 4-16 favors obesity ; and the biological gradient is U-shaped, with overweight and obesity level 1 associated with the lowest risk ; and if hyperinsulinemia is to be considered the mediator, then the temporal sequencing is incorrect. Insulin resistance, a cause and consequence of hyperinsulinemia, leads to type 2 diabetes and is associated with other adverse outcomes, such as myocardial infarction, chronic pulmonary 90,91 92 disease, and some cancers, and may also be indicated in diabetic nephropathy. Despite the 3 93-95 scenarios described earlier, it is commonly believed that obesity leads to hyperinsulinemia. If the converse is true and hyperinsulinemia actually leads to obesity and its putative adverse consequences, then weight loss without concomitant decreases in insulin (eg, liposuction) would not be expected to address these adverse consequences. In addition, weight loss would not address risk in people with so-called metabolically healthy obesity, that is, those without insulin resistance. Of interest, insulin resistance is also present in lean individuals, in particular men and individuals 97 98 of Asian descent. These 2 groups are at heightened risk for type 2 diabetes and cardiovascular disease, yet are more likely to be lean than women and individuals not of Asian descent. These observations are consistent with the hypothesis that hyperinsulinemia rather than obesity is driving adverse outcomes in this population. We speculate that the capacity to store the byproducts of excess glucose by increasing the size of fat cells (manifested as obesity) might delay the onset of type 2 diabetes and its consequences in some individuals, thus explaining the so-called obesity paradox of lower mortality among people with obesity. This idea, although not new, fits better with the emerging evidence. If this speculation is correct, assessing the capacity to store such by-products at the individual level may be a useful step toward personalized medicine. Although it is possible that hyperinsulinemia per se is not the causal agent that leads to adverse outcomes (but is rather a marker for another more proximate factor), this would not change the lack of support for recommending weight loss among people with obesity. Rather, other markers should be investigated that, although correlated with obesity, are more strongly associated with premature mortality because they also exist in lean individuals. Therapies that lower insulin levels (eg, moderate diets with fewer simple carbohydrates and metformin) may be sustainable if an intermediate marker JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 10/17 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation other than weight is targeted. Because the prevalence of obesity continues to increase worldwide, additional studies to confirm this hypothesis are urgently needed, not least because public health campaigns promoting weight loss are ineffective and lead to stigma among those with obesity. Limitations This study has limitations. First, the identified studies largely enrolled participants with chronic obesity undergoing weight loss interventions and measures of interest (eg, weight, insulin level, and CRP level) mostly decreased. The findings are limited to those individuals losing weight and, given the findings from the bariatric subgroup analysis, are likely driven by quick decreases in circulating insulin levels (eTable 4 in the Supplement). Second, the included populations mostly had baseline mean CRP levels between 1 and 10 mg/L (eTable 2 in the Supplement), suggesting a low grade of chronic inflammation normally associated with atherosclerosis and insulin resistance. A number of 90,101-104 studies have highlighted a group of people characterized by CRP levels consistently greater than 10 mg/L. Although this higher grade of chronic inflammation is associated with obesity, few participants had insulin resistance, suggesting a distinct grouping. Third, this meta-analysis used summary-level rather than individual patient–level data and is therefore vulnerable to the ecologic fallacy. A prospective cohort study designed for weight loss or gain with very frequent measurements in a diverse population would contribute a stronger form of evidence. Fourth, the review was limited to studies published in 2018, and many of the studies indicate a significant risk of bias with respect to their stated goals. However, none of the studies were designed to measure temporal associations between the measures of interest, so these limitations in study conduct would not necessarily have led to bias with respect to the findings. Although the search was limited to a single publication year (2018) to reduce the workload associated with this review, there is no reason to expect that data from this year would differ from data published earlier or later. Conclusions The pooled evidence from this meta-analysis suggests that decreases in fasting insulin levels precede weight loss; it does not suggest that weight loss precedes decreases in fasting insulin. This temporal sequencing is not consistent with the assertion that obesity causes NCDs and premature death by increasing levels of fasting insulin. This finding, together with the obesity paradox, suggests that hyperinsulinemia or another proximate factor may cause the adverse consequences currently attributed to obesity. Additional studies to confirm this hypothesis are urgently needed. ARTICLE INFORMATION Accepted for Publication: January 20, 2021. Published: March 12, 2021. doi:10.1001/jamanetworkopen.2021.1263 Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Wiebe N et al. JAMA Network Open. Corresponding Author: Natasha Wiebe, MMath, PStat, Department of Medicine, University of Alberta, 11-112Y Clinical Sciences Bldg, 11350 83 Ave NW, Edmonton AB, T6G 2G3 Canada (nwiebe@ualberta.ca). Author Affiliations: Department of Medicine, University of Alberta, Edmonton, Alberta, Canada (Wiebe, Ye, Bello); Department of Health, St Francis Xavier University, Antigonish, Nova Scotia, Canada (Crumley); Department of Renal Medicine M99, Karolinska University Hospital, Stockholm, Sweden (Stenvinkel); Department of Medicine, University of Calgary, Calgary, Alberta, Canada (Tonelli). Author Contributions: Ms Wiebe had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Wiebe. Acquisition, analysis, or interpretation of data: All authors. JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 11/17 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation Drafting of the manuscript: Wiebe. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Wiebe, Ye. Obtained funding: Tonelli. Administrative, technical, or material support: Wiebe. Supervision: Wiebe. Conflict of Interest Disclosures: Dr Bello reported receiving grants from the Canadian Institutes of Health Research (CIHR), University of Alberta, during the conduct of the study and personal fees from Janssen and grants from Amgen and CIHR outside the submitted work. Dr Stenvinkel reported receiving grants from AstraZeneca and Bayer and personal fees from Baxter, Reata, Pfizer, Astellas, and Fresenius Medical Care outside the submitted work. Dr Tonelli reported receiving grants from the Canadian Institutes of Health Care during the conduct of the study. No other disclosures were reported. Additional Contributions: Nasreen Ahmad, BSc, and Sophanny Tiv, BSc, University of Alberta, Edmonton, Alberta, Canada, provided reviewer support and Ghenette Houston, BA, University of Alberta, Edmonton, Alberta, Canada, provided administrative support. They received no other compensation besides their regular salary. REFERENCES 1. Hill AB. The environment and disease: association or causation? Proc R Soc Med. 1965;58:295-300. doi:10.1177/ 2. Fedak KM, Bernal A, Capshaw ZA, Gross S. Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology. 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Wideochir Inne Tech Maloinwazyjne. 2018;13(3):366-375. doi: 10.5114/wiitm.2018.75868 JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 15/17 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation 75. Wormgoor SG, Dalleck LC, Zinn C, Borotkanics R, Harris NK. High-intensity interval training is equivalent to moderate-intensity continuous training for short- and medium-term outcomes of glucose control, cardiometabolic risk, and microvascular complication markers in men with type 2 diabetes. Front Endocrinol (Lausanne). 2018;9 (Aug):475. doi:10.3389/fendo.2018.00475 76. Demerdash HM, Sabry AA, Arida EA. Role of serotonin hormone in weight regain after sleeve gastrectomy. Scand J Clin Lab Invest. 2018;78(1-2):68-73. doi:10.1080/00365513.2017.1413714 77. Goday A, Castañer O, Benaiges D, et al. Can Helicobacter pylori eradication treatment modify the metabolic response to bariatric surgery? Obes Surg. 2018;28(8):2386-2395. doi:10.1007/s11695-018-3170-7 78. Nicoletto BB, Pedrollo EF, Carpes LS, et al. Progranulin serum levels in human kidney transplant recipients: a longitudinal study. PLoS One. 2018;13(3):e0192959. doi:10.1371/journal.pone.0192959 79. Lambert G, Lima MMO, Felici AC, et al. Early regression of carotid intima-media thickness after bariatric surgery and its relation to serum leptin reduction. Obes Surg. 2018;28(1):226-233. doi:10.1007/s11695-017-2839-7 80. Mraović T, Radakovic S, Medic DR, et al. The effects of different caloric restriction diets on anthropometric and cardiometabolic risk factors in overweight and obese females. Vojnosanitetski Pregled. 2018;75(1):30-38. doi:10. 2298/VSP160408206M 81. Rajan-Kahn N, Agito K, Shah J, et al. Mindfulness-based stress reduction in women with overweight or obesity: a randomized clinical trial. Obesity. 2017;25:1349-1359. doi:10.1002/oby.21910 82. Arikawa AY, Kaufman BC, Raatz SK, Kurzer MS. Effects of a parallel-arm randomized controlled weight loss pilot study on biological and psychosocial parameters of overweight and obese breast cancer survivors. Pilot Feasibility Stud. 2018;4(1):17. doi:10.1186/s40814-017-0160-9 83. Baltieri L, Cazzo E, de Souza AL, et al. Influence of weight loss on pulmonary function and levels of adipokines among asthmatic individuals with obesity: one-year follow-up. Respir Med. 2018;145:48-56. doi:10.1016/j.rmed. 2018.10.017 84. Dhillon J, Thorwald M, De La Cruz N, et al. Glucoregulatory and cardiometabolic profiles of almond vs. cracker snacking for 8 weeks in young adults: a randomized controlled trial. Nutrients. 2018;10(8):E960. doi:10.3390/ nu10080960 85. Holman RR, Thorne KI, Farmer AJ, et al; 4-T Study Group. Addition of biphasic, prandial, or basal insulin to oral therapy in type 2 diabetes. N Engl J Med. 2007;357(17):1716-1730. doi:10.1056/NEJMoa075392 86. UK Prospective Diabetes Study (UKPDS) Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet. 1998;352(9131):837-853. doi:10.1016/S0140-6736(98)07019-6 87. Diabetes UK. Our position statement on diabulimia. 2017. Accessed September 29, 2020. https://www.diabetes. org.uk/resources-s3/2017-10/Diabulimia%20Position%20statement%20Mar%202017.pdf 88. Abbasi J. Unveiling the “magic” of diabetes remission after weight-loss surgery. JAMA. 2017;317(6):571-574. doi:10.1001/jama.2017.0020 89. Erion KA, Corkey BE. Hyperinsulinemia: a cause of obesity? Curr Obes Rep. 2017;6(2):178-186. doi:10.1007/ s13679-017-0261-z 90. Wiebe N, Stenvinkel P, Tonelli M. Associations of chronic inflammation, insulin resistance, and severe obesity with mortality, myocardial infarction, cancer, and chronic pulmonary disease. JAMA Netw Open. 2019;2(8): e1910456. doi:10.1001/jamanetworkopen.2019.10456 91. Crofts CAP, Zinn C, Wheldon MC, et al. Hyperinsulinemia: a unifying theory of chronic disease? Diabesity. 2015;1(4):34-43. doi:10.15562/diabesity.2015.19 92. Gurley SB, Coffman TM. An IRKO in the Podo: impaired insulin signaling in podocytes and the pathogenesis of diabetic nephropathy. Cell Metab. 2010;12(4):311-312. doi:10.1016/j.cmet.2010.09.007 93. Shulman GI. Ectopic fat in insulin resistance, dyslipidemia, and cardiometabolic disease. N Engl J Med. 2014; 371(12):1131-1141. doi:10.1056/NEJMra1011035 94. Samuel VT, Petersen KF, Shulman GI. Lipid-induced insulin resistance: unravelling the mechanism. Lancet. 2010;375(9733):2267-2277. doi:10.1016/S0140-6736(10)60408-4 95. Neeland IJ, Turer AT, Ayers CR, et al. Dysfunctional adiposity and the risk of prediabetes and type 2 diabetes in obese adults. JAMA. 2012;308(11):1150-1159. doi:10.1001/2012.jama.11132 96. Hinnouho GM, Czernichow S, Dugravot A, Batty GD, Kivimaki M, Singh-Manoux A. Metabolically healthy obesity and risk of mortality: does the definition of metabolic health matter? Diabetes Care. 2013;36(8): 2294-2300. doi:10.2337/dc12-1654 JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 16/17 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation 97. Pandit K, Mukhopadhyay P, Chatterjee P, Majhi B, Chowdhury S, Ghosh S. Assessment of insulin resistance indices in individuals with lean and obese metabolic syndrome compared to normal individuals: a population based study. J Assoc Physicians India. 2020;68(10):29-33. 98. Public Health Agency of Canada. The Canadian Diabetes Risk Questionnaire. 2011. Accessed October 6, 2020. https://health.canada.ca/apps/canrisk-standalone/pdf/canrisk-en.pdf 99. von Noorden KH. Diabetes mellitus. In: Clinical Treatises on the Pathology and Therapy of Disorders of Metabolism and Nutrition. Vol XII. John Wright & Co. 1905:1904-1910. 100. Freeman L. A matter of justice: “fat” is not necessarily a bad word. Hastings Cent Rep. 2020;50(5):11-16. 101. Aronson D, Bartha P, Zinder O, et al. Obesity is the major determinant of elevated C-reactive protein in subjects with the metabolic syndrome. Int J Obes Relat Metab Disord. 2004;28(5):674-679. doi:10.1038/sj.ijo. 102. Dhingra R, Gona P, Nam BH, et al. C-reactive protein, inflammatory conditions, and cardiovascular disease risk. Am J Med. 2007;120(12):1054-1062. doi:10.1016/j.amjmed.2007.08.037 103. Ishii S, Karlamangla AS, Bote M, et al. Gender, obesity and repeated elevation of C-reactive protein: data from the CARDIA cohort. PLoS One. 2012;7(4):e36062. doi:10.1371/journal.pone.0036062 104. Paepegaey AC, Genser L, Bouillot JL, Oppert JM, Clément K, Poitou C. High levels of CRP in morbid obesity: the central role of adipose tissue and lessons for clinical practice before and after bariatric surgery. Surg Obes Relat Dis. 2015;11(1):148-154. doi:10.1016/j.soard.2014.06.010 SUPPLEMENT. eTable 1. Search Strategies eTable 2. Study Population Characteristics eTable 3. Pooled Temporal Associations: Subgroup Group Analysis12 vs >12 Weeks eTable 4. Pooled Temporal Associations: Subgroup Group Analysis Bariatric vs Non-bariatric Patients eTable 5. Pooled Temporal Associations: Extended Analysis (All Measures) eTable 6. Pooled Temporal Associations: Sensitivity Analysis Adjusting for Non-independence eTable 7. Pooled Temporal Associations: Sensitivity Analysis Adjusting for Two Independent Variables JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 17/17 Supplementary Online Content Wiebe N, Ye F, Crumley ET, Bello A, Stenvinkel P, Tonelli M. Temporal associations among body mass index, fasting insulin, and systemic inflammation: a systematic review and meta-analysis. JAMA Netw Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 eTable 1. Search Strategies eTable 2. Study Population Characteristics eTable 3. Po eTable 4. Pooled Temporal Associations: Subgroup Group Analysis Bariatric vs Non- bariatric Patients eTable 5. Pooled Temporal Associations: Extended Analysis (All Measures) eTable 6. Pooled Temporal Associations: Sensitivity Analysis Adjusting for Non- independence eTable 7. Pooled Temporal Associations: Sensitivity Analysis Adjusting for Two Independent Variables This supplementary material has been provided by the authors to give readers additional information about their work. © 2021 Wiebe N et al. JAMA Network Open. eTable 1. Search Strategies Database Search Strategies Date and Coverage MEDLINE 1. C-Reactive Protein/ Ovid MEDLINE(R) 2. CRP.ti,ab. and Epub Ahead of 3. "C-reactive protein".ti,ab. Print, In-Process & 4. (fasting adj3 insulin).ti,ab. Other Non-Indexed 5. HOMA*.ti,ab. Citations and Daily 6. "homeostatic model assessment of insulin resistance".ti,ab. 1946 to August 20, 7. "homeostasis model assessment of insulin resistance".ti,ab. 8. exp interleukins/ 9. interleukin*.ti,ab. 10. QUIKI*.ti,ab. 11. QUICKI*.ti,ab. 12. "quantitative insulin sensitivity check index".ti,ab. 13. Tumor Necrosis Factor-alpha/ 14. mhr24.ti,ab. 15. "tumour necrosis factor*".ti,ab. 16. "tissue necrosis factor*".ti,ab. 17. "tumor necrosis serum*".ti,ab. 18. "tumour necrosis serum*".ti,ab. 19. TNFalpha.ti,ab. 20. TNF-alpha.ti,ab. 21. TNF alfa.ti,ab. 22. "tumor necrosis factor*".ti,ab. 23. Cachectin.ti,ab. 24. Cachetin.ti,ab. 25. or/1-24 26. body fat distribution/ 27. Body fat meter.ti,ab. 28. "fat distribut*".ti,ab. 29. body mass index/ 30. BMI.ti,ab. 31. "body mass index".ti,ab. 32. "Quetelet* Index".ti,ab. 33. ("fat mass" or "percentage body fat" or Skin fold thickness or "weight change*" or "weight gain" or "weight loss" or Waist circumference or "Waist circumference" or "waist size" or "skinfold measure*").ti,ab. 34. Waist-Hip Ratio/ or exp body weight changes/ or Body- Weight Trajectory/ or weight gain/ or weight loss/ or waist circumference/ or Skinfold thickness/ 35. (("waist to hip" or waist-hip or "hip to waist" or hip-waist) adj3 ratio).ti,ab. 36. (weight adj3 trajectory).ti,ab. 37. (weight adj3 reduc*).ti,ab. 38. or/26-37 39. 25 and 38 40. "randomized controlled trial".pt. 41. (random$ or placebo$ or single blind$ or double blind$ or triple blind$).ti,ab. © 2021 Wiebe N et al. JAMA Network Open. Database Search Strategies Date and Coverage 42. (retraction of publication or retracted publication).pt. 43. or/40-42 44. (animals not humans).sh. 45. ((comment or editorial or meta-analysis or practice- guideline or review or letter) not "randomized controlled trial").pt. 46. (random sampl$ or random digit$ or random effect$ or random survey or random regression).ti,ab. not "randomized controlled trial".pt. 47. 43 not (44 or 45 or 46) 48. controlled clinical trial.pt. 49. epidemiologic methods/ 50. epidemiologic studies/ 51. exp Case-Control Studies/ 52. (epidemiologic adj (study or studies)).ab,ti. 53. case control.ab,ti. 54. (cohort adj (study or studies)).ab,ti. 55. cohort analy$.ab,ti. 56. (follow up adj (study or studies)).ab,ti. 57. (observ$ adj3 (study or studies)).ab,ti. 58. or/48-57 59. 47 or 58 60. exp Cross-Sectional Studies/ 61. 59 not 60 62. 39 and 61 63. (change or reduc* or decreas* or increas* or loss or gain or trajectory or pre-post).mp. 64. 62 and 63 65. limit 64 to (english language and humans) 66. limit 65 to year=”2018” EMBASE 1. C-Reactive Protein.ti,ab. Embase 1974 to 404-2128 2. exp C reactive protein/ August 19, 2019 3. CRP.ti,ab. 4. exp insulin blood level/ 5. (fasting adj3 insulin).ti,ab. 6. exp homeostasis model assessment/ 7. homa*.ti,ab. 8. homeostatic model assessment of insulin resistance.ti,ab. 9. "homeostasis model assessment of insulin resistance".ti,ab. 10. exp interleukin derivative/ 11. interleukin*.ti,ab. 12. QUIKI*.ti,ab. 13. exp Quantitative Insulin Sensitivity Check Index/ 14. quicki.ti,ab. 15. "quantitative insulin sensitivity check index".ti,ab. 16. exp tumor necrosis factor/ 17. Tumor Necrosis Factor-alpha.ti,ab. 18. TNF-alpha.ti,ab. 19. TNF alfa.ti,ab. 20. TNFalpha.ti,ab. 21. TNFalfa.ti,ab. 22. tissue necrosis factor*.ti,ab. © 2021 Wiebe N et al. JAMA Network Open. Database Search Strategies Date and Coverage 23. "tumor necrosis factor*".ti,ab. 24. "tumour necrosis factor*".ti,ab. 25. "tumor necrosis serum*".ti,ab. 26. "tumour necrosis serum*".ti,ab. 27. mhr24.ti,ab. 28. Cachectin.ti,ab. 29. cachetin.ti,ab. 30. or/1-29 31. exp body fat distribution/ 32. exp body fat meter/ 33. Body fat meter.ti,ab. 34. "fat distribut*".ti,ab. 35. exp body mass/ 36. BMI.ti,ab. 37. "body mass index".ti,ab. 38. Body ban mass.ti,ab. 39. "Quetelet* Index".ti,ab. 40. ("fat mass" or "percentage body fat" or Skin fold thickness or "weight change*" or "weight gain" or "weight loss" or Waist circumference or "Waist circumference" or "waist size" or "skinfold measure*").ti,ab. 41. Waist-Hip Ratio/ or exp body weight change/ or body weight gain/ or body weight loss/ or waist circumference/ or Skinfold thickness/ 42. exp waist hip ratio/ 43. (("waist to hip" or waist-hip or "hip to waist" or hip-waist) adj3 ratio).ti,ab. 44. (weight adj3 trajectory).ti,ab. 45. (weight adj3 reduc*).ti,ab. 46. or/31-45 47. 30 and 46 48. (random$ or placebo$ or single blind$ or double blind$ or triple blind$).ti,ab. 49. RETRACTED ARTICLE/ 50. or/48-49 51. (animal$ not human$).sh,hw. 52. (book or conference paper or editorial or letter or review).pt. not exp randomized controlled trial/ 53. (random sampl$ or random digit$ or random effect$ or random survey or random regression).ti,ab. not exp randomized controlled trial/ 54. 50 not (51 or 52 or 53) 55. exp cohort analysis/ 56. exp longitudinal study/ 57. exp prospective study/ 58. exp follow up/ 59. cohort$.tw. 60. or/55-59 61. 54 or 60 62. 47 and 61 63. (change or reduc* or decreas* or increas* or loss or gain or trajectory or pre-post).ti,ab. 64. 62 and 63 65. exp Cross-Sectional Studies/ 66. 64 not 65 © 2021 Wiebe N et al. JAMA Network Open. Database Search Strategies Date and Coverage 67. limit 66 to (human and english language) 68. limit 67 to year=”2018” © 2021 Wiebe N et al. JAMA Network Open. eTable 2. Study Population Characteristics Study Cohort(s) N Mean Mean Mean Mean Mean Mean Mean Mean Mean BMI, weight, fat, fat, insulin, HOMA CRP, IL-6, TNF- kg/m kg kg % pmol/L mg/L pg/mL pg/mL 25 33 . . . 83 3.9 . 8.3 19.2 Rifaximin Abdel-Razik 25 33 . . . 82 3.7 . 8.1 19.0 et al, 2018 Placebo 6 41 111 . . 144 . 6.6 . . PCOS Abiad et 16 41 107 . . 111 . 10.0 . . al, 2018 Control 10 . 86 . . 54 1.8 6.0 2.7 . CR diet plus exercise Arikawa et Weight management 10 . 98 . . 116 5.4 5.2 2.8 . al, 2018 counselling Arnold et Decreased added sugars, 14 39 103 . . 96 . 7.4 1.3 . al, 2018 increased fiber and fish diet 11 . 100 . . 130 2.5 . 2.4 3.4 Low-carbohydrate diet Asle 11 . 108 . . 131 2.5 . 2.4 3.1 Low-fat diet Mohammadi 11 . 105 . . 137 2.6 . 2.6 3.2 High-fat diet Zadeh et 9 . 100 . . 130 2.5 . 2.1 3.5 al, 2018 Control Baltieri et 13 36 96 . . . . 8.0 19.8 15.8 al, 2018 RYGB 26 37 . . . . . . . . Metformin Bulatova et 27 40 . . . . . . . . al, 2018 Control 14 39 108 . . 97 2.3 . . . T2D remission Carbone et 67 a 27 31 93 . . 46 1.4 . . . al, 2019 No T2D remission 29 51 24 . . . . 2.6 . . . Saxagliptin and metformin Chen et al, 51 24 . . . . 2.6 . . . 2018 Acarbose and metformin Cessation of androgen 27 . . 27 . . 2.3 . . . deprivation therapy Cheung et 19 . . 27 . . 2.3 . . . al, 2018 Control 241 53 156 . . . . 11.7 . . SG One-anastomosis GB 68 49 144 . . . . 10.6 . . Chiappetta 159 45 128 . . . . 8.6 . . et al, 2018 RYGB 9 42 . . . 65 2.5 . . . Mini-GB 5 42 . . . 63 2.4 . . . et al, 2018 SG © 2021 Wiebe N et al. JAMA Network Open. Study Cohort(s) N Mean Mean Mean Mean Mean Mean Mean Mean Mean BMI, weight, fat, fat, insulin, HOMA CRP, IL-6, TNF- kg/m kg kg % pmol/L mg/L pg/mL pg/mL 9 45 . . . 73 2.7 . . . RYGB De Luis, 46 46 123 47 . 103 4.2 . . . CC rs266729 Calvo et 84 49 124 43 . 115 4.3 . . . al, 2018 CG or GG rs266729 De Luis, 335 35 92 37 . 81 3.3 5.2 . . Izaola et Mediterranean CR diet then al, 2018 dietary counseling De Luis, GG rs670 17 47 128 45 . 101 4.1 . . . Pacheco et 65 49 124 44 . 108 4.1 . . . al, 2018 GA or AA rs670 Aerobic and resistance 18 . 67 31 46 . . 5.0 . . training De Paulo et Stretching and relaxation 18 . 72 34 46 . . 7.0 . . al, 2018 exercises Demerdash 92 47 130 . . 51 2.6 . . . et al, 2018 SG 92 28 82 . . 103 5.5 . . . Canrenone Derosa et 90 28 80 . . 109 5.6 . . . al, 2018 Hydrochlorothiazide 38 . 72 20 27 34 1.4 . . . Almond snacks Dhillon et 35 . 71 20 28 40 1.8 . . . al, 2018 Cracker snacks Di 8 28 86 27 31 88 3.3 2.3 11.9 12.4 Sebastiano et al, 2018 Treated Drummen et 12 31 92 . 41 71 3.2 . . . High-protein diet al, 2018 13 31 94 . 41 89 4.1 . . . (PREVIEW) Moderate-protein diet Esquivel et 63 45 129 . . 94 3.9 . . . al, 2018 SG 14 32 91 . 39 . . 2.4 . . Mediterranean diet Fortin et 14 30 88 . 36 . . 2.1 . . al, 2018 Low-fat diet 66 . 97 . 30 . . 4.8 3.4 . High-egg diet © 2021 Wiebe N et al. JAMA Network Open. Study Cohort(s) N Mean Mean Mean Mean Mean Mean Mean Mean Mean BMI, weight, fat, fat, insulin, HOMA CRP, IL-6, TNF- kg/m kg kg % pmol/L mg/L pg/mL pg/mL Fuller et 62 . 91 . 30 . . 4.4 2.9 . al, 2018 (DIABEGG) Low-egg diet Gadéa et 38-50 . 69 26 . . 1.7 4.5 . . al, 2018 Chemotherapy 17 . 82 33 44 76 3.7 . . . High-protein diet 18 . 79 32 44 61 2.5 . . . High-carbohydrate diet Galbreath et 19 . 76 30 43 67 3.2 . . . al, 2018 Control Helicobacter pylori 49 43 . . . . 6.9 . . . eradication before SG 60 43 . . . . 6.2 . . . Control before SG Helicobacter pylori 50 43 . . . . 5.6 . . . eradication before RYGB Goday et 70 43 . . . . 6.3 . . . al, 2018 Control before RYGB Guarnotta et 12 36 98 . . 46 0.9 . . . al, 2018 Pasireotide 40 50 141 . . 193 9.6 9.5 . . 32 French bougie size in SG 45 40 48 136 . . 135 6.5 9.6 . . 36 French bougie size in SG Hady et al, 40 45 131 . . 126 5.6 7.5 . . 2018 40 French bougie size in SG EPA-enriched nutritional 13 . 50 . . . . 1.3 . . supplement Hanai et 14 . 51 . . . . 0.7 . . al, 2018 Control 51 31 . . . 65 2.6 1.3 . . Empagliflozin Hattori et 51 30 . . . 55 3.6 1.5 . . al, 2018 Placebo Low-glycemic index pulse- 16 30 82 32 . 81 . 3.2 . . based diet Kazemi et Therapeutic lifestyle change 16 35 96 43 . 88 . 5.2 . . al, 2018 diet Keinänen et 84-94 23 . . . 53 1.6 0.7 . . al, 2018 Treated © 2021 Wiebe N et al. JAMA Network Open. Study Cohort(s) N Mean Mean Mean Mean Mean Mean Mean Mean Mean BMI, weight, fat, fat, insulin, HOMA CRP, IL-6, TNF- kg/m kg kg % pmol/L mg/L pg/mL pg/mL 2010 American dietary 28 32 90 . . 103 4.2 . . . guidelines diet Krishnan et 24 33 90 . . 88 3.7 . . . al, 2018 Typical American diet Lambert et 109 39 102 41 39 119 5.2 7.7 . . al, 2018 RYGB and BPD 38 44 . 117 . . . . . . . Bariatric surgery Lee et al, 25-44 44 92 . . . 5.5 . . . 2018 Control Liang et al, 18 33 95 . . 119 5.2 2.3 . . 2018 Low-calorie diet 43 50 . . . 158 8.1 . . . SG Liaskos et 28 47 . . . 176 6.7 . . . al, 2018 RYGB Liu et al, 45 33 . . . 116 8.1 . . . 2018 RYGB 36 34 88 . . 79 3.1 . . . Diet beverages Madjd et 35 34 88 . . 82 3.2 . . . al, 2018 Water Most et al, 34 . 72 25 . . 1.1 . . . 25% CR 19 . 71 23 . . 1.3 . . . (CALERIE 2) Control 30 29 83 . 40 . . 4.8 . . 20% CR diet 30 31 86 . 41 . . 5.8 . . 50% CR diet et Alternating 70% and 30% CR 37 30 85 . 41 . . 6.2 . . al, 2018 diet Munukka et American College of Sports 17 32 90 39 44 55 12.9 . . . al, 2018 Medicine exercise program Nicoletto et 46 27 . . . 46 1.7 4.4 4.5 . al, 2018 Kidney transplantation Nilholm et 30 30 . . . 93 . . . 14.9 al, 2018 Okinawan-based Nordic diet 19 . 63 . . . . 1.0 . . Daikenchuto (TJ-100) Nishino et 20 . 60 . . . . 0.6 . . al, 2018 Control © 2021 Wiebe N et al. JAMA Network Open. Study Cohort(s) N Mean Mean Mean Mean Mean Mean Mean Mean Mean BMI, weight, fat, fat, insulin, HOMA CRP, IL-6, TNF- kg/m kg kg % pmol/L mg/L pg/mL pg/mL 57 b Patel et al, Duodenal-jejunal sleeve 41-45 40 116 . . 113 . . . . 2018 bypass Mindfulness-based stress 42 39 104 . . 30 3.7 9.7 . . reduction Raja Khan et 44 39 102 . . 32 4.0 10.7 . . al, 2018 Health education Rubio 57 50 126 . 52 158 6.7 . . . Prediabetes Almanza et 48 54 132 . 53 132 8.5 . . . al, 2018 T2D 49 . 96 . . . 2.7 4.2 . . 5:2 intermittent CR diet Schübel et 58 49 . 93 . . . 3.0 4.1 . . Continuous CR diet al, 2018 52 . 93 . . . 3.0 5.4 . . (HELENA) Control Shah et al, 50 31 . . . 65 . 1.3 . . Vegan diet 50 31 . . . 72 . 1.1 . . (EVADE CAD) AHA diet 40 42 . . . . 6.5 11.5 . . Probiotic Sherf-Dagan 40 42 . . . . 5.6 12.3 . . et al, 2018 Placebo Moderate-intensity physical 32-60 43 129 . . 173 . 6.1 3.8 . training Stolberg et 28 43 124 . . 143 . 6.1 3.8 . al, 2018 Control Van 289 36 104 . . 97 3.3 5.6 . . Lifestyle intervention Dammen et 285 36 103 . . 104 3.6 5.6 . . al, 2018 Control Van Rijn et 28 37 113 . . 129 . . . . al, 2018 Duodenal-jejunal bypass liner 265- . 57 . . . . 56.2 . . Treated for TB 270 Wilson et 82 . 60 . . . . 3.9 . . al, 2018 Control Witczak et 20 54 151 . . 112 2.4 . 9.5 . al, 2018 Bariatric surgery 12 39 . . . . . 1.8 . . HIIT © 2021 Wiebe N et al. JAMA Network Open. Study Cohort(s) N Mean Mean Mean Mean Mean Mean Mean Mean Mean BMI, weight, fat, fat, insulin, HOMA CRP, IL-6, TNF- kg/m kg kg % pmol/L mg/L pg/mL pg/mL Wormgoor Moderate-intensity continuous 11 35 . . . . . 2.1 . . et al, 2018 training 68 10 38 . . . 132 . . . . SG Yang et al, 10 39 . . . 127 . . . . 2018 RYGB 51 25 63 . . 57 2.5 1.1 . . Coenzyme Q10 Zhang et 50 25 65 . . 66 3.0 1.0 . . al, 2018 Placebo AHA American Heart Association, BMI body mass index, BPD biliopancreatic diversion surgery, CR calorie restriction, CRP C-reactive protein, DM diabetes mellitus, GB Gastric Bypass, HOMA homeostatic model assessment, HIIT high-intensity interval training, IGT intolerance glucose test, IL-6 interleukin-6, MetS metabolic syndrome, na non-applicable, NAFLD non-alcoholic fatty liver disease, NASH non-alcoholic steatohepatitis, PCOS polycystic ovary syndrome, RYGB roux-en-y gastric bypass, SG sleeve gastrectomy, SGLT2 sodium-glucose transport protein 2, T1D type 1 diabetes, T2D type 2 diabetes, TB tuberculosis, TNF- -alpha, WC waist circumference This study was published online in 2018. The number of measurements varied. AA, CC, CG, GA, GG are alleles. © 2021 Wiebe N et al. JAMA Network Open. eTable 3. Pooled Temporal Associations: Subgroup Group A Weeks 2 2 Dependent Independent Number N I /Tau (Period 2) (Period 1) of P interaction weeks All 90 0.26 (0.13,0.38) 79/0.161 33 0.61 (0.38,0.84) 76/0.133 57 0.17 (0.05,0.30) P=0.001 All 90 0.01 (-0.08,0.10) 69/0.099 33 0.56 (0.32,0.80) 62/0.073 57 -0.00 (-0.08,0.08) P<0.001 All 57 0.23 (-0.09,0.55) 83/0.168 27 0.72 (0.08,1.37) 81/0.153 30 0.14 (-0.18,0.47) P=0.09 All 57 0.20 (0.04,0.36) 53/0.048 27 0.22 (-0.03,0.48) 53/0.050 30 0.20 (0.03,0.36) P=0.82 All 42 0.19 (-0.04,0.42) 49/0.038 22 0.45 (-0.05,0.94) 48/0.036 20 0.14 (-0.10,0.38) P=0.25 All 42 36/0.023 22 0.20 (-0.02,0.42) 34/0.018 20 0.43 (0.16,0.70) P=0.14 coefficient/association, BMI body mass index, CI confidence interval, CRP C-reactive Each set of three rows describes two models. The first row describes one model and uses all available cohorts where change in a measure, specifically a standardized slope, of a later time period (Period 2) is regressed on a change in a different measure of an earlier time period (Period 1). The second and third row describe a second model with an additional interaction term for a subgroup. The result 2 2 confidence interval and two measures of between-cohort heterogeneity, I and Tau . The P-value for interaction indicates whether the subgrouping is significant or not. Results significant at P<0.05 are in boldface. © 2021 Wiebe N et al. JAMA Network Open. eTable 4. Pooled Temporal Associations: Subgroup Group Analysis Bariatric vs Non- bariatric Patients 2 2 Dependent Independent Bariatric N I /Tau (Period 2) (Period 1) or not P interaction All 90 0.26 (0.13,0.38) 79/0.161 Yes 52 0.31 (0. ,0.44) 76/0.145 No 38 -0.12 (-0.41,0.18) P=0.007 All 90 0.01 (-0.08,0.10) 69/0.099 Yes 52 0.01(-0.08,0.10) 68/0.099 No 38 -0.18 (-0.50,0.14) P=0.23 All 57 0.23 (-0.09,0.55) 83/0.168 Yes 26 0.43 (0.10,0.76) 81/0.142 No 31 -0.40 (-0.93,0.13) P=0.005 All 57 0.20 (0.04,0.36) 53/0.048 Yes 26 0.20 (0.04,0.36) 54/0.051 No 31 0.16 (-0.27,0.59) P=0.84 All 42 0.19 (-0.04,0.42) 49/0.038 Yes 15 0.22 (-0.03,0.46) 50/0.040 No 27 0.07 (-0.42,0.56) P=0.58 All 42 36/0.023 Yes 15 0.26 (0.06,0.45) 36/0.021 No 27 0.46 (0.05,0.87) P=0.34 coefficient/association, BMI body mass index, CI confidence interval, CRP C-reactive Each set of three rows describes two models. The first row describes one model and uses all available cohorts where change in a measure, specifically a standardized slope, of a later time period (Period 2) is regressed on a change in a different measure of an earlier time period (Period 1). The second and third row describe a second model with an additional interaction term for a subgroup. The result 2 2 confidence interval and two measures of between-cohort heterogeneity, I and Tau . The P-value for interaction indicates whether the subgrouping is significant or not. Results significant at P<0.05 are in boldface. © 2021 Wiebe N et al. JAMA Network Open. eTable 5. Pooled Temporal Associations: Extended Analysis (All Measures) 2 2 Dependent Independent N I /Tau (Period 2) (Period 1) 0.26 (0.13,0.38) 79/0.161 0.01 (-0.08,0.10) 69/0.099 88 0. (0.03,0.34) 84/0.202 88 0.01 (-0.05,0.08) 74/0.097 57 0.23 (-0.09,0.55) 83/0.168 57 0.20 (0.04,0.36) 53/0.048 -6 13 0.20 (-0.17,0.57) 29/0.043 -6 13 0.19 (-0.10,0.48) 20/0.038 - 9 -0.56 (-1.31,0.20) 0/0 - 9 -0.04 (-1.56,1.48) 86/0.755 91 0.16 (0.05,0.27) 74/0.118 91 0.04 (-0.08,0.17) 69/0.132 83 0.12 (-0.02,0.26) 79/0.152 83 0.01 (-0.12,0.13) 74/0.136 70 0.09 (-0.08,0.27) 75/0.093 70 -0.01 (-0.30,0.27) 71/0.144 -6 30 Could not calculate - numerical derivatives -6 30 0.21 (-0.01,0.44) 0/0 - 18 -0.01 (-0.56,0.53) 0/0 - 18 -0.01 (-0.88,0.87) 36/0.109 21 0.17 (-0.09,0.43) 80/0.085 21 0.08 (-0.28,0.44) 76/0.150 22 0.16 (-0.12,0.45) 81/0.100 22 -0.05 (-0.25,0.16) 81/0.129 12 0.66 (-0.08,1.39) 78/0.100 12 0.36 (-0.65,1.38) 58/0.144 -6 1 Insufficient observations -6 1 - 1 Insufficient observations - 1 16 0.23 (-0.01,0.47) 61/0.079 © 2021 Wiebe N et al. JAMA Network Open. 2 2 Dependent Independent N I /Tau (Period 2) (Period 1) 16 0.24 (-0.05,0.52) 72/0.160 16 0.25 (0.02,0.48) 53/0.061 percent 16 0.19 (-0.11,0.50) 70/0.174 11 0.54 (-0.01,1.09) 29/0.040 11 0.37 (-1.13,1.87) 64/0.187 -6 1 Insufficient observations -6 1 - 1 Insufficient observations - 1 42 0.19 (-0.04,0.42) 49/0.038 42 0. (0.10,0.47) 36/0.023 -6 32 0.15 (-0.26,0.56) 49/0.093 -6 32 0.12 (-0.07,0.31) 0/0.010 - 22 0.03 (-0.91,0.97) 64/0.351 - 22 -0.02 (-0.29,0.26) 26/0.039 39 -0.05 (-0.37,0.28) 72/0.059 39 -0.02 (-0.25,0.22) 64/0.083 -6 27 0.18 (-0.35,0.71) 29/0.042 -6 27 0.15 (-0.16,0.46) 0/0.008 - 21 0.20 (-0.53,0.93) 44/0.128 - 21 0.16 (-0.25,0.58) 21/0.008 -reactive homeostatic model assessment, IL-6 interleukin-6, TNF- necrosis factor-alpha Each row describes one model where change in a measure, specifically a standardized slope, of a later time period (Period 2) is regressed on a change in a different measure of an earlier time 2 2 its 95% confidence interval and two measures of between-cohort heterogeneity, I and Tau . Results significant at P<0.05 are in boldface. © 2021 Wiebe N et al. JAMA Network Open. eTable 6. Pooled Temporal Associations: Sensitivity Analysis Adjusting for Non- independence 2 2 Dependent Independent N I /Tau (Period 2) (Period 1) 90 0.28 (0.22,0.33) 84/0.239 90 -0.00 (-0.08,0.08) 69/0.102 57 0.37 (0.22,0.52) 84/0.198 57 58/0.053 42 - - 42 44/0.031 interval, CRP C-reactive Each row describes one model where change in a measure, specifically a standardized slope, of a later time period (Period 2) is regressed on a change in a different measure of an earlier time period (Period 2 2 its 95% confidence interval and two measures of between-cohort heterogeneity, I and Tau . These models nest cohorts within studies. One row has no results because the model did not converge. Results significant at P<0.05 are in boldface. © 2021 Wiebe N et al. JAMA Network Open. eTable 7. Pooled Temporal Associations: Sensitivity Analysis Adjusting for Two Independent Variables 2 2 Dependent Independent N I /Tau (Period 2) (Period 1) 38 0.57 (0.27,0.86) 81/0.109 -0.07 (-0.42,0.29) 38 0.27 (0.00,0.55) 54/0.041 -0.08 (-0.43,0.28) 38 0.26 (-0.01,0.54) 35/0.016 0.07 (-0.22,0.37) coefficient/association, BMI body mass index, CI confidence interval, CRP C-reactive Each row describes one model where change in a measure, specifically a standardized slope, of a later time period (Period 2) is regressed on changes in two different measures of an earlier time 2 2 its 95% confidence interval and two measures of between-cohort heterogeneity, I and Tau . Results significant at P<0.05 are in boldface. © 2021 Wiebe N et al. JAMA Network Open. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JAMA Network Open American Medical Association http://www.deepdyve.com/lp/american-medical-association/temporal-associations-among-body-mass-index-fasting-insulin-and-b3wiNSIuQb

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Publisher: American Medical Association
Copyright: Copyright 2021 Wiebe N et al. JAMA Network Open.
eISSN: 2574-3805
DOI: 10.1001/jamanetworkopen.2021.1263
Publisher site: See Article on Publisher Site

Abstract

Key Points Question What are the temporal IMPORTANCE Obesity is associated with a number of noncommunicable chronic diseases and is associations among higher body mass purported to cause premature death. index (BMI) and chronic inflammation and/or hyperinsulinemia? OBJECTIVE To summarize evidence on the temporality of the association between higher body Findings In this systematic review and mass index (BMI) and 2 potential mediators: chronic inflammation and hyperinsulinemia. meta-analysis of 5603 participants in 112 cohorts from 60 studies, the association DATA SOURCES MEDLINE (1946 to August 20, 2019) and Embase (from 1974 to August 19, 2019) between period 1 (preceding) levels of were searched, although only studies published in 2018 were included because of a high volume of fasting insulin and period 2 (subsequent) results. The data analysis was conducted between January 2020 and October 2020. BMI was positive and significant: for every unit of SD change in period 1 STUDY SELECTION AND MEASURES Longitudinal studies and randomized clinical trials that insulin level, there was an ensuing measured fasting insulin level and/or an inflammation marker and BMI with at least 3 commensurate associated change in 0.26 units of SD in time points were selected. period 2 BMI. DATA EXTRACTION AND SYNTHESIS Slopes of these markers were calculated between time Meaning These findings suggest that points and standardized. Standardized slopes were meta-regressed in later periods (period 2) with adverse consequences currently standardized slopes in earlier periods (period 1). Evidence-based items potentially indicating risk of attributed to obesity could be attributed bias were assessed. to hyperinsulinemia (or another proximate factor). RESULTS Of 1865 records, 60 eligible studies with 112 cohorts of 5603 participants were identified. Most standardized slopes were negative, meaning that participants in most studies experienced Supplemental content decreases in BMI, fasting insulin level, and C-reactive protein level. The association between period 1 fasting insulin level and period 2 BMI was positive and significant (β = 0.26; 95% CI, 0.13-0.38; Author affiliations and article information are listed at the end of this article. I = 79%): for every unit of SD change in period 1 insulin level, there was an ensuing associated change in 0.26 units of SD in period 2 BMI. The association of period 1 fasting insulin level with period 2 BMI remained significant when period 1 C-reactive protein level was added to the model (β = 0.57; 95% CI, 0.27-0.86). In this bivariable model, period 1 C-reactive protein level was not significantly associated with period 2 BMI (β = –0.07; 95% CI, –0.42 to 0.29; I = 81%). CONCLUSIONS AND RELEVANCE In this meta-analysis, the finding of temporal sequencing (in which changes in fasting insulin level precede changes in weight) is not consistent with the assertion that obesity causes noncommunicable chronic diseases and premature death by increasing levels of fasting insulin. JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 Open Access. This is an open access article distributed under the terms of the CC-BY License. JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 1/17 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation Introduction Obesity is associated with a number of noncommunicable chronic diseases (NCDs), such as type 2 diabetes, coronary disease, chronic kidney disease, and asthma. Although obesity is also purported to cause premature death, this association fails to meet several of the Bradford Hill criteria for 1,2 3 causation. First, the putative attributable risk of death is small (<5%). Second, the dose-response gradient between body mass index (BMI) and mortality is U-shaped with overweight (and possibly obesity level I) as the minima. Third, evidence from animal models comes largely from mice that have been fed high-fat diets; unlike humans, these animals did not normally have fat as part of their typical diet, and thus the experiments are potentially not analogous to those in humans. Fourth, evidence that people with obesity live longer than their lean counterparts in populations with acute 4-16 or chronic conditions and older age is remarkably consistent. Therefore, it is possible that rather than being a risk factor for NCDs, obesity is actually a protective response to the development of disease. The putative links between obesity and adverse outcomes are often attributed to 2 potential mediators: chronic inflammation and hyperinsulinemia. These characteristics have been associated with several NCDs, including obesity as well as type 2 diabetes, cardiovascular disease, and chronic kidney disease. Existing data on the association of obesity with chronic inflammation and/or hyperinsulinemia are chiefly cross-sectional, making it difficult to confirm the direction of any causality. This systematic review and meta-analysis summarizes evidence on the temporality of the association between higher BMI and chronic inflammation and/or hyperinsulinemia. We hypothesized that changes in chronic inflammation and hyperinsulinemia would precede changes in higher BMI. Methods This systematic review and meta-analysis was conducted and reported according to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and Meta-analysis of Observational Studies in Epidemiology (MOOSE) reporting guidelines. Research ethics board approval was not required because this is a systematic review of previously published research. Data Sources and Searches We performed a comprehensive search designed by a trained librarian (E.T.C.) to identify all longitudinal studies and randomized clinical trials (RCTs) that measured fasting insulin and/or an inflammation marker and weight with at least 3 commensurate time points. We included only primary studies published in the English language as full peer-reviewed articles. MEDLINE (1946 to August 20, 2019) and Embase (1974 to August 19, 2019) were searched; however, only studies published in 2018 were retained because of the high volume of results. No existing systematic reviews were found. The specific search strategies are provided in eTable 1 in the Supplement.The abstracts were independently screened by 2 reviewers (including N.W.). The full text of any study considered potentially relevant by 1 or both reviewers was retrieved for further consideration. The data analysis was conducted between January 2020 and October 2020. Study Selection Each potentially relevant study was independently assessed by 2 reviewers (N.W. and F.Y.) for inclusion in the review using the following predetermined eligibility criteria. Longitudinal studies and RCTs with men and nonpregnant and not recently pregnant women (18 years of age) and at least 3 time points with 1 or more weeks of follow-up in which fasting insulin levels or a marker of inflammation and some measure of weight were included in this review. Disagreements were resolved by consultation. JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 2/17 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation Data Extraction and Risk of Bias Assessment Data from eligible studies were extracted by a single reviewer (N.W.). A second reviewer checked the extracted data for accuracy. The following properties of each study were recorded in a database: study characteristics (country, era of accrual, design, duration of follow-up, populations of interest, intervention where applicable, and sample size), age and sex of participants, and the measures of interest (numbers, means, and SDs) for all time points: (1) fasting insulin, the homeostatic model assessment index, or the quantitative insulin sensitivity check index; (2) concentrations of C-reactive protein (CRP), interleukin cytokines, or tumor necrosis factor; and (3) weight, BMI (calculated as weight in kilograms divided by height in meters squared), fat mass, or fat mass percentage. Risk of bias was assessed using items from Downs and Black : clear objective, adequate description of measures, sample size or power calculation, intention to treat study design (in those studies that assigned the intervention), adequate description of withdrawals, adequate handling of missing data, and adequate description of results. Source of funding was also extracted, given its potential to introduce bias. Statistical Analysis Data were analyzed using Stata software, version 15.1 (StataCorp LLC). Missing SDs were imputed using interquartile ranges or using another SD from the same cohort. Data were extracted from graphs if required. To determine a likely temporal sequencing of fasting insulin level or chronic inflammation with obesity, we compared the associations of period 2 insulin level or inflammation regressed on period 1 BMI and period 2 BMI regressed on period 1 insulin or inflammation. A stronger association would support a particular direction of effect. For each measure of interest, the change in means was calculated between adjacent time points and divided by the number of weeks between the measures. This slope or per week change in measure was then standardized by dividing it by the pooled SD, giving a standardized slope. Because of expected diversity among studies, we decided a priori to combine the standardized slopes using a random-effects models. Period 2 standardized slopes of weight measures were regressed onto period 1 standardized slopes of insulin or inflammation measures and vice versa. We regressed measures of insulin post hoc on measures of inflammation and vice versa. The type I error rate for meta-regressions was set at a 2-sided P < .05. Statistical heterogeneity 2 24 2 was quantified using the τ statistic (between-study variance) and the I statistic. Differences in standardized slopes (βs) along with 95% CIs are reported. We considered a number of sensitivity analyses. Because we included multiple standardized slopes at different intervals from the same studies (or same cohorts), we accounted for this nonindependence using a generalized linear model in which the family was gaussian and the link was identity, which allowed for nested random effects (results by intervals were nested within cohorts). To estimate between-study heterogeneity, the coefficients for the within-cohort SEs were constrained to 1. We also performed 2 subgroup analyses: whether the study population had undergone bariatric surgery and the numbers of weeks between time points (>12 vs12 weeks), reasoning that if the effects of one measure of interest acted quickly on the other, then shorter intervals might demonstrate stronger associations. We explored post hoc models with 2 measures of interest as period 1 independent variables. Results Quantity of Research Available The searches identified 1865 unique records identifying articles or abstracts published in 2018 (Figure 1). After the initial screening, the full texts of 813 articles were retrieved for detailed evaluation. Of these, 753 articles were excluded, resulting in 60 that met the selection criteria and 25-84 5603 enrolled participants (of whom 5261 were analyzed). We decided to exclude 12 studies of JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 3/17 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation children and adolescents post hoc because these studies used different BMI measures. Disagreements about the inclusion of studies occurred in 2% of the articles (κ = 0.87). Characteristics of Studies There were 26 RCTs, 4 nonrandomized clinical trials, 23 prospective cohort studies (3 nested within 25-84 an RCT), and 7 retrospective cohort studies (Table 1 ). Of the studies, 58% began data collection in the 5 years before publication. The earliest study accrued participants starting in 2000. The durations of follow-up ranged from 1 to 60 months (median, 12 months). A total of 21 studies were 25,27,41,54,61,62,65,72,81,82,84 25,27,41,54,61,62,65,72,81,82,84 from Western Europe, 11 from North America, 9 29,38,39,44,51,52,60,66,68 28,69,78,79,83 from East Asia, 5 from South America, 5 from Western 28,69,78,79,83 35,50,76 26,42,75 45,74,80 Asia, and 3 each from Africa, the Pacific, and Eastern Europe. A total of 90% of the studies were in populations with metabolic disease or conditions 25,32-34,37,41,43,45,47-49,52,57-60,62-64,67-74,76,77,79-83 associated with metabolic disease: obesity, diabetes 26,32,38,46,59 40 65 51 or prediabetes, hypertension, coronary artery disease, dyslipidemia, chronic 78 31,35 36 kidney disease, nonalcoholic fatty liver disease, Cushing disease, polycystic ovary 61 28,56,82 84 syndrome, breast cancer, and aging (ie, college students ). Of the patients in these 54 studies, 22 (41%) had undergone bariatric surgery as the studied intervention (n = 14) or as part of the required eligibility criteria (n = 8). Other populations were subjected to operations or therapies 27,42 39 that adversely cause lean mass loss and/or fat mass gain, such as prostate, esophageal, head 44 53 and neck squamous cell cancers, and psychosis, or where the disease course itself (ie, tuberculosis) causes lean mass loss and/or fat mass gain. The 60 studies included 112 cohorts: 40 cohorts contained participants who had undergone bariatric surgery, 33 cohorts contained participants who were receiving diet therapies (all except 65,84 2 designed for weight loss or weight maintenance), 16 cohorts contained participants who received a medication or supplement, 7 cohorts contained participants who were following exercise regimens, 14 cohorts contained participants who were followed up for other reasons (ie, prostate Figure 1. Flow Diagram of Studies 403 Records identified in MEDLINE 0 Records identified from 1725 Records identified in Embase references of included articles 1865 Records after duplicates removed 1865 Citations screened 1052 Records excluded 813 Full-text articles assessed for eligibilty 753 Excluded on the basis of full-text review of the article 386 <3 Measures of fat mass 277 Not full English-language article 32 <3 Insulin or inflammation 27 <1 Week of follow-up 14 Not original research 12 Pediatric or adolescent 2 Pregnancy 2 Not contemporaneous 1 No usable data 60 Studies included in systematic review JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 4/17 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation Table 1. Study and Study Population Characteristics Year of Follow-up, Enrolled/ Source Country study start Design mo Population Cohort(s) analyzed Mean age, y Male, % Abdel-Razik Egypt 2015 RCT 6 NASH Rifaximin and placebo 25 and 25 40 and 38 36 and 28 et al, 2018 Abiad Lebanon 2015 Prospective 12 BMI ≥40 or >35 with PCOS and control 6 and 19/16 24 and 28 0 et al, 2018 cohort a comorbidity and SG Arikawa US 2009 RCT 4 BMI ≥27 after breast CR diet plus exercise and 10/10 and 55 and 58 0 et al, 2018 cancer weight management 11/10 counseling Arnold US 2015 Prospective 3 BMI >30 Decreased added sugars, 15/14 59 0 et al, 2018 cohort increased fiber, and fish diet Asle Iran NR RCT 6 T2D Low-carbohydrate diet, 11, 11, 11, 47, 49, 45, 100 Mohammadi low-fat diet, high-fat diet, and 9 and 45 Zadeh and control et al, 2018 Baltieri Brazil 2015 Prospective 12 BMI ≥35 RYGB 19/13 37 0 et al, 2018 cohort Bulatova Jordan 2012 RCT 6 Prediabetes or T2D Metformin and control 42/26 and 51 and 51 22 and 3 et al, 2018 49/27 a a Carbone Italy 2007 Retrospective 36 RYGB or BPD with T2D T2D remission and no T2D 14 and 27 54 and 56 64 and 82 67 b et al, 2019 cohort remission 51 and 51 64 and 64 47 and 47 Chen China 2012 nRCT 12 T2D Saxagliptin and metformin et al, 2018 and acarbose and metformin a a Cheung Australia NR Prospective 36 Prostate cancer Cessation of androgen 34/27 and 68 and 71 100 et al, 2018 cohort deprivation therapy and 29/19 control Chiappetta Germany 2014 Retrospective 6 BMI ≥40 or ≥35 with a SG, 1-anastomosis GB, 241, 68, and 44 32 et al, 2018 cohort comorbidity and RYGB 159 Dardzińska Poland NR Prospective 12 BMI >35 with no diabetes Mini-GB, SG, and RYGB 12/9, 8/5, and 38 24 et al, 2018 cohort medication and no 11/9 cardiovascular events De Luis, Calvo Spain NR Prospective 36 BMI ≥40 or >35 with a CC rs266729 84 and 65 47 and 47 23 and 25 et al, 2018 cohort comorbidity after bariatric CG and GG rs266729 surgery De Luis, Izaola Spain NR Prospective 36 BMI ≥30 Mediterranean CR diet 335 50 25 et al, 2018 cohort then dietary counseling De Luis, Spain NR Prospective 36 BPD with no diabetes GG rs670 and GA 46 and 17 48 and 47 13 and 18 Pacheco cohort and BMI ≥40 or AA rs670 et al, 2018 De Paulo Brazil 2015 RCT 8 Aromatase inhibitor after Aerobic and resistance 18 and 18 63 and 67 0 et al, 2018 breast cancer training and stretching and relaxation exercises Demerdash Egypt 2011 Prospective 24 Obesity SG 92 43 30 et al, 2018 cohort Derosa Italy NR RCT 12 T2D and hypertension Canrenone and 92 and 90 53 and 53 52 and 49 et al, 2018 hydrochlorothiazide Dhillon US 2016 RCT 2 College students (no Almond snacks and cracker 38 and 35 18 and 18 42 and 46 et al, 2018 diabetes or prediabetes) snacks Di Sebastiano Canada NR Prospective 8 Prostate cancer Treated 9 71 100 et al, 2018 cohort Drummen Netherlands 2013 Prospective 24 BMI >25 and prediabetes High-protein diet and 12 and 13 58 and 54 50 and 58 et al, 2018 cohort moderate-protein diet (PREVIEW) nested in RCT Esquivel Argentina 2009 Prospective 12 BMI >40 or >35 with a SG 63/43 40 35 et al, 2018 cohort comorbidity Fortin Canada 2016 RCT 9 T1D and metabolic Mediterranean diet and 14 and 14 52 and 50 47 and 64 et al, 2018 syndrome low-fat diet Fuller Australia 2013 RCT 6 Prediabetes or T2D High-egg diet 72/66 and 60 and 61 50 and 42 et al, 2018 and low-egg diet 68/62 (DIABEGG) Gadéa France 2011 Prospective 6 Breast cancer Chemotherapy 52 60 0 et al, 2018 cohort Galbreath US NR RCT 3 BMI >27 or body fat >35% High-protein diet, 24/17, 66, 63, and 0 et al, 2018 high-carbohydrate diet, 24/18, and 66 and control 24/19 Goday Spain 2010 Retrospective 24 SG and Helicobacter pylori 49 and 44 and 37 and et al, 2018 cohort RYGB eradication and control 60 (SG) 46 (SG) and 22 (SG) and for SG and H pylori and 50 and 42 and 22 and eradication 70 (RYGB) 42 (RYGB) 16 (RYGB) control for RYGB (continued) JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 5/17 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation Table 1. Study and Study Population Characteristics (continued) Year of Follow-up, Enrolled/ Source Country study start Design mo Population Cohort(s) analyzed Mean age, y Male, % Guarnotta Italy 2013 Prospective 12 Cushing disease Pasireotide 12 40 17 et al, 2018 cohort Hady Poland 2012 RCT 12 Obesity 32F bougie size in SG, 40, 40, and 41, 43, and 38, 30, and et al, 2018 36F bougie size in SG, and 40 45 43 40F bougie size in SG Hanai Japan NR RCT 1 Surgery for head and neck EPA-enriched nutritional 13 and 14 62 and 66 62 and 57 et al, 2018 squamous cell carcinoma supplement and control Hattori, Japan 2016 RCT 12 SGLT2 inhibitors in T2D Empagliflozin and placebo 51 and 51 57 and 58 75 and 80 Kazemi Canada 2011 RCT 12 PCOS Low-glycemic index pulse- 47/31 and 27 and 27 0 et al, 2018 based diet and therapeutic 48/30 lifestyle change diet Keinänen Finland 2010 Prospective 12 First-episode psychosis Treated 95 25 68 et al, 2018 cohort Krishnan US 2015 RCT 2 BMI of 25-39.9 2010 American dietary 28/22 and 47 and 47 0 et al, 2018 guidelines diet and typical 24/22 American diet Lambert Brazil NR Retrospective 12 BMI >40 or BMI >35 with RYGB and BPD 108 44 42 et al, 2018 cohort comorbidity or BMI >30 and T2D Lee Singapore 2009 Retrospective 36 Prediabetes Bariatric surgery 44 and 25 43 and 50 34 and 12 et al, 2018 cohort and control Liang Taiwan 2008 Prospective 60 WC ≥90, MetS, no Low-calorie diet 40/18 46 100 et al, 2018 cohort diabetes Liaskos Greece NR nRCT 6 BMI >40 and no T2D SG and RYGB 43 and 28 38 and 38 21 and 25 et al, 2018 Liu China 2014 Retrospective 12 T2D and BMI ≥28 RYGB 45 44 100 et al, 2018 cohort Madjd Iran 2014 RCT 18 BMI of 27-40 Diet beverages and water 36 and 35 32 and 32 0 et al, 2018 Most US 2007 Prospective 24 BMI of 22-27.9 plus ≥5% 25% CR and control 47/34 and 40 and 39 29 and 37 et al, 2018 cohort weight loss in CR 25% and 26/19 (CALERIE 2) nested in <5% weight loss RCT in ad libitum Mraović Serbia 2014 RCT 10 BMI ≥35 20% CR diet, 50% CR diet, 37, 30, and 31, 32, and 0 et al, 2018 and alternating 70% and 30 32 30% CR diet Munukka Finland NR Prospective 1 BMI >27.5 with no major American College of Sports 19/17 37 0 et al, 2018 cohort comorbidity Medicine exercise program Nicoletto Brazil 2014 Prospective 12 CKD Kidney transplantation 46 49 59 et al, 2018 cohort Nilholm Sweden 2014 Prospective 6 T2D Okinawan-based Nordic diet 30 58 43 et al, 2018 cohort a a Nishino Japan 2011 RCT 1 Esophagectomy for Daikenchuto (TJ-100) 19 and 20 68 and 61 89 and 80 et al, 2018 esophageal cancer and control Patel United NR nRCT 18 BMI of 30-50 and T2D Duodenal-jejunal sleeve 45 50 49 et al, 2018 Kingdom bypass Rajan-Khan US 2011 RCT 4 BMI ≥25 Mindfulness-based stress 42 and 44 47 and 42 0 et al, 2018 reduction and health education Rubio- Spain 2000 Retrospective 60 Prediabetes or T2D and Prediabetes and T2D 57/38 and 48 17 Almanza cohort bariatric surgery 48/32 et al, 2018 Schübel Germany 2015 RCT 12 BMI of 25-39.9 5:2 intermittent CR diet, 49, 49, and 49, 51, and 51, 51, and et al, 2018 continuous CR diet, 52 51 48 (HELENA) and control a a Shah US 2014 RCT 2 Coronary artery disease Vegan diet and AHA diet 50 and 50 63 and 60 86 and 84 et al, 2018 (EVADE CAD) Sherf-Dagan Israel 2014 RCT 12 NAFLD after SG Probiotic and placebo 50/40 and 42 and 44 40 and 45 et al, 2018 50/40 Stolberg Denmark 2012 RCT 24 RYGB Moderate-intensity physical 32 and 28 43 and 43 34 and 25 et al, 2018 training and control van Dammen Netherlands 2009 RCT 6 BMI ≥29 and infertily Lifestyle intervention 290/289 and 30 and 30 0 et al, 2018 and control 287/285 van Rijn Netherlands 2014 Prospective 12 BMI of 30-50 and T2D Duodenal-jejunal 28 50 39 et al, 2018 cohort bypass liner nested in RCT (continued) JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 6/17 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation Table 1. Study and Study Population Characteristics (continued) Year of Follow-up, Enrolled/ Source Country study start Design mo Population Cohort(s) analyzed Mean age, y Male, % Wilson South 2005 Prospective 2 TB symptoms Treated for TB and control 295 and 93 34 56 et al, 2018 Africa cohort Witczak United NR Prospective 6 BMI >40 and T2D or IGT Bariatric surgery 20 51 35 et al, 2018 Kingdom cohort Wormgoor New 2015 RCT 9 T2D HIIT and moderate-intensity 12/11 and 11 52 and 53 100 et al, 2018 Zealand continuous training Yang China 2015 nRCT 12 BMI >35 or ≥30 with SG and RYGB 32/10 and 32 and 32 50 and 50 et al, 2018 a comorbidity 28/10 Zhang China 2015 RCT 6 Dyslipidemia Coenzyme Q10 and placebo 51 and 50 52 and 50 28 and 36 et al, 2018 Abbreviations: AHA, American Heart Association; BMI, body mass index (calculated as sleeve gastrectomy; SGLT2, sodium-glucose transport protein 2; T1D, type 1 diabetes; weight in kilograms divided by height in meters squared); BPD, biliopancreatic diversion T2D, type 2 diabetes; TB, tuberculosis; WC, waist circumference. surgery; CKD, chronic kidney disease; CR, calorie restriction; EPA, eicosapentaenoic acid; Median. GB, gastric bypass; HIIT, high-intensity interval training; IGT, intolerance glucose test; Published online in 2018. MetS, metabolic syndrome; NAFLD, nonalcoholic fatty liver disease; NASH, nonalcoholic AA, CC, CG, GA, and GG are alleles. steatohepatitis; NR, not reported; nRCT, nonrandomized clinical trial; PCOS, polycystic ovary syndrome; RCT, randomized clinical trial; RYGB, Roux-en-Y gastric bypass; SG, 27 78 34 32 cancer, kidney transplants, gene-associated obesity, diabetes vs prediabetes, polycystic 61 81 ovary syndrome, and mindfulness intervention ), and 21 cohorts contained control participants (of 31,35,51,66 which 4 cohorts contained participants who received placebo ). The size of the cohorts ranged from 5 to 335 participants (median, 32). The mean ages ranged from 18 to 71 years (median, 47 years). The percentage of men ranged from 0 to 100% (median, 35%). The mean BMIs of the patients ranged from 23 to 54 (median, 38) (eTable 2 in the Supplement). Similarly, mean weight (median, 94 kg; range, 50-156 kg), fat mass (median, 32 kg; range, 20-47 kg), and percentage of body fat (median, 41%; range, 27%-53%) were high compared with general populations. Mean fasting insulin level (median, 13.53 μIU/mL; range, 4.32-27.79 μIU/mL [to convert to picomoles per liter, multiply by 6.945]), and the homeostatic model assessment index (median, 3.3; range, 0.9-12.9) were also high. Most of the mean CRP levels corresponded to a low-grade inflammation (median, 0.52 mg/dL; interquartile range, 0.21-0.75 mg/dL; range, 0.06-5.62 mg/dL [to convert to milligrams per liter, multiply by 10]). Mean interleukin 6 level ranged from 1.3 to 19.8 pg/mL (median, 3.4 pg/mL) and mean tumor necrosis factor α levels from 3.1 to 19.2 pg/mL (median, 12.4 pg/mL). Risk of Bias Assessment Studies were largely rated as low risk for description of the objectives (96.7%), the outcome measures (90.0%), and the results (98.3%) (Figure 2). Approximately half the studies were high risk because they lacked a sample size or power calculation (51.7%), they (in those studies that assigned the interventions) did not take an intention-to-treat approach (47.2%), they had a withdrawal rate greater than 20% or they did not adequately describe their withdrawals (50.0%), or they did not adequately explore the effect of missing data (50.0%). In addition, 38.3% of studies had an industry source of funding. BMI and Fasting Insulin Level There were 90 pairs of standardized slopes from 56 cohorts and 35 studies that measured BMI and fasting insulin (Table 2). Most BMI and fasting insulin standardized slopes were negative (81% for BMI and 71% for fasting insulin), meaning that participants in most studies experienced decreases in BMI and insulin. The association between period 1 fasting insulin level and period 2 BMI was positive and significant (β = 0.26; 95% CI, 0.13-0.38; I = 79%) (Figure 3), indicating that for every unit of SD change in period 1 insulin, there was an associated change in 0.26 units of SD in period 2 BMI. The association between period 1 BMI and period 2 fasting insulin level was not significant (β = 0.01; 95% CI, –0.08 to 0.10; I = 69%) (Figure 3). The heterogeneities were large. JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 7/17 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation The associations between insulin level and BMI increased in magnitude when studies that reported findings at 12 weeks or less were isolated from those that reported findings at greater than 12 weeks (eTable 3 in the Supplement). The magnitude of association between period 1 fasting insulin level and period 2 BMI was greater at 12 weeks or less than at greater than 12 weeks (β = 0.61; 95% CI, 0.38-0.84 vs β = 0.17; 95% CI, 0.05-0.30; I =76%, P = .001). The association between period 1 fasting insulin level and period 2 BMI was present in participants who had undergone bariatric surgery but not in participants who had not undergone bariatric surgery (β = 0.31; 95% CI, 0.19-0.44 vs β = –0.12; 95% CI, –0.41 to 0.18; I =76%, P = .007) (eTable 4 in the Supplement). BMI and CRP There were 57 pairs of standardized slopes from 39 cohorts and 22 studies that measured both BMI and CRP levels (Table 2). Most standardized slopes for BMI and CRP were negative (81% for BMI and 68% for CRP), suggesting that participants in most studies experienced decreases in BMI and CRP level. The association between period 1 CRP level and period 2 BMI was not significant (β = 0.23; 95% CI, –0.09 to 0.55; I = 83%). The association between period 1 BMI and period 2 CRP level was positive and significant (β = 0.20; 95% CI, 0.04-0.36; I = 53%), suggesting that for every unit of SD change in period 1 BMI, there was an associated change of 0.20 units of SD in period 2 CRP level. However, both β coefficients were positive and had similar magnitudes, and the β coefficient for BMI had larger heterogeneity. The associations between BMI and CRP level increased in magnitude when the studies that reported findings at 12 weeks or less were isolated from those that reported findings at greater than Figure 2. Risk of Bias Assessment Low risk Moderate risk High risk Clear Adequate Sample size Intention Adequate Adequate Adequate Sources Except for 3 items (clear objective, adequate objective description or power to treat description handling of description of funding of measures calculation of withdrawals missing data of results description of measures, and adequate description of Risk of bias results), the assessments indicate several risks of bias. Table 2. Pooled Temporal Associations: Primary Analysis 2 2 Dependent (Period 2) Independent (Period 1) No. of cohorts β (95% CI) I /τ BMI and insulin ΔBMI ΔInsulin 90 0.26 (0.13 to 0.38) 79%/0.161 ΔInsulin ΔBMI 90 0.01 (–0.08 to 0.10) 69%/0.099 BMI and CRP Abbreviations: BMI, body mass index (calculated as ΔBMI ΔCRP 57 0.23 (–0.09 to 0.55) 83%/0.168 weight in kilograms divided by height in meters squared); CRP, C-reactive protein; Δ, change. ΔCRP ΔBMI 57 0.20 (0.04 to 0.36) 53%/0.048 Each row describes one model where change in a Insulin and CRP measure, specifically a standardized slope, of a later ΔInsulin ΔCRP 42 0.19 (–0.04 to 0.42) 49%/0.038 period (period 2) is regressed on a change in a ΔCRP ΔInsulin 42 0.29 (0.10 to 0.47) 36%/0.023 different measure of an earlier period (period 1). JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 8/17 Studies, % JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation 12 weeks, when period 2 BMI was regressed on period 1 CRP level (eTable 3 in the Supplement). Although not significantly so, the magnitude of the association between period 1 CRP level and period 2 BMI was greater at 12 weeks or less than at greater than 12 weeks (β = 0.72; 95% CI, 0.08- 1.37 vs β = 0.14; 95% CI, –0.18 to 0.47; I = 81%, P = .09). In addition, the association between period 1 CRP level and period 2 BMI was present in participants who underwent bariatric surgery but not in participants who had not undergone bariatric surgery (β = 0.43; 95% CI, 0.10-0.76 vs β = –0.40; 95% CI, –0.93 to 0.13; I = 81%, P = .005) (eTable 4 in the Supplement). Fasting Insulin and CRP There were 42 pairs of standardized slopes from 27 cohorts and 16 studies that measured both fasting insulin and CRP levels (Table 2). Most fasting insulin and CRP standardized slopes were negative (74% of fasting insulin slopes and 63% of CRP slopes), suggesting that participants in most studies experienced decreases in insulin and CRP levels. The association between period 1 CRP level and period 2 fasting insulin level was not significant (β = 0.19; 95% CI, –0.04 to 0.42; I = 49%). The association between period 1 fasting insulin level and period 2 CRP level was positive and significant (β = 0.29; 95% CI, 0.10-0.47; I = 36%), suggesting that for every unit of SD change in period 1 insulin level, there was an associated change of 0.29 units of SD in period 2 CRP level. There was moderate heterogeneity. The subgroups did not significantly modify the associations between fasting insulin and CRP levels (eTables 2 and 3 in the Supplement). Other Sensitivity Analyses When we considered related measures of BMI (weight, fat mass, and fat percentage), homeostatic model assessment index, and the other inflammatory markers (ie, interleukin 6 and tumor necrosis factor α), the associations among these variables were similar to those for BMI or could not be calculated (eTable 5 in the Supplement). The results when adjusting for nonindependence when available were similar (eTable 6 in the Supplement)—1 of the 6 models did not converge likely because of overly identified models (too few data for the number of model parameters). When we considered 2 measures as independent variables, the association of period 1 insulin level on period 2 BMI remained significant when the period 1 CRP level remained in the model (β = 0.57; 95% CI, 0.27-0.86 and β = –0.07; 95% CI, –0.42 to 0.29; I = 81%) (eTable 7 in the Supplement). Figure 3. Bubble Plot of Temporal Associations Between Period 1 and Period 2 Changes A Period 1 insulin and period 2 BMI B Period 1 BMI and period 2 insulin 3 3 –1 –1 –2 –2 –3 –3 –4 –5 –4 –3 –2 –1 0 1 2 3 –5 –4 –3 –2 –1 0 1 2 3 Insulin slope for period 1 BMI slope for period 1 A, Period 2 change in body mass index (BMI) (or standardized slope) is regressed onto BMI and period 2 change in insulin. The diagonal trend line in panel A supports a positive period 1 change in insulin. B, Period 2 change in insulin is regressed onto period 1 change and temporal association between period 1 change in insulin and period 2 change in BMI. in BMI. The flat trend line in panel B suggests no association between period 1 change in The size of the circles is based on the inverse of the SE of each cohort. JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 9/17 BMI slope for period 2 Insulin slope for period 2 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation Discussion This systematic review and meta-analysis suggests that decreases in fasting insulin are more likely to precede decreasing weight than are decreases in weight to precede decreasing levels in fasting insulin. After accounting for the association between preceding levels of fasting insulin and the subsequent likelihood of weight gain, there was no evidence that inflammation preceded subsequent weight gain (eTable 7 in the Supplement). This temporal sequencing (in which changes in fasting insulin precede changes in weight) is not consistent with the assertion that obesity causes NCDs and premature death by increasing levels of fasting insulin. Support From Other Studies In patients with type 2 diabetes, RCTs have found that introducing exogenous insulin and sulfonylureas (which increase endogenous insulin production) compared with lower doses or no drug 85,86 therapy produce increases in weight. Some patients with type 1 diabetes deliberately omit or reduce their insulin injections to lose weight. Similarly, reports after bariatric surgery consistently indicate that insulin levels decrease before weight decreases in patients undergoing bariatric surgery. Thus, the finding that changes in insulin levels tend to precede changes in weight rather than the other way around has been previously demonstrated in 3 different scenarios. To our knowledge, there is no clinical evidence demonstrating that weight gain or loss precedes increases or decreases in endogenous insulin. Importance of the Findings Obesity as a cause of premature death fails to meet several of the Bradford Hill criteria for causation: the strength of association is small ; the consistency of effect across older and/or ill populations 4-16 favors obesity ; and the biological gradient is U-shaped, with overweight and obesity level 1 associated with the lowest risk ; and if hyperinsulinemia is to be considered the mediator, then the temporal sequencing is incorrect. Insulin resistance, a cause and consequence of hyperinsulinemia, leads to type 2 diabetes and is associated with other adverse outcomes, such as myocardial infarction, chronic pulmonary 90,91 92 disease, and some cancers, and may also be indicated in diabetic nephropathy. Despite the 3 93-95 scenarios described earlier, it is commonly believed that obesity leads to hyperinsulinemia. If the converse is true and hyperinsulinemia actually leads to obesity and its putative adverse consequences, then weight loss without concomitant decreases in insulin (eg, liposuction) would not be expected to address these adverse consequences. In addition, weight loss would not address risk in people with so-called metabolically healthy obesity, that is, those without insulin resistance. Of interest, insulin resistance is also present in lean individuals, in particular men and individuals 97 98 of Asian descent. These 2 groups are at heightened risk for type 2 diabetes and cardiovascular disease, yet are more likely to be lean than women and individuals not of Asian descent. These observations are consistent with the hypothesis that hyperinsulinemia rather than obesity is driving adverse outcomes in this population. We speculate that the capacity to store the byproducts of excess glucose by increasing the size of fat cells (manifested as obesity) might delay the onset of type 2 diabetes and its consequences in some individuals, thus explaining the so-called obesity paradox of lower mortality among people with obesity. This idea, although not new, fits better with the emerging evidence. If this speculation is correct, assessing the capacity to store such by-products at the individual level may be a useful step toward personalized medicine. Although it is possible that hyperinsulinemia per se is not the causal agent that leads to adverse outcomes (but is rather a marker for another more proximate factor), this would not change the lack of support for recommending weight loss among people with obesity. Rather, other markers should be investigated that, although correlated with obesity, are more strongly associated with premature mortality because they also exist in lean individuals. Therapies that lower insulin levels (eg, moderate diets with fewer simple carbohydrates and metformin) may be sustainable if an intermediate marker JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 10/17 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation other than weight is targeted. Because the prevalence of obesity continues to increase worldwide, additional studies to confirm this hypothesis are urgently needed, not least because public health campaigns promoting weight loss are ineffective and lead to stigma among those with obesity. Limitations This study has limitations. First, the identified studies largely enrolled participants with chronic obesity undergoing weight loss interventions and measures of interest (eg, weight, insulin level, and CRP level) mostly decreased. The findings are limited to those individuals losing weight and, given the findings from the bariatric subgroup analysis, are likely driven by quick decreases in circulating insulin levels (eTable 4 in the Supplement). Second, the included populations mostly had baseline mean CRP levels between 1 and 10 mg/L (eTable 2 in the Supplement), suggesting a low grade of chronic inflammation normally associated with atherosclerosis and insulin resistance. A number of 90,101-104 studies have highlighted a group of people characterized by CRP levels consistently greater than 10 mg/L. Although this higher grade of chronic inflammation is associated with obesity, few participants had insulin resistance, suggesting a distinct grouping. Third, this meta-analysis used summary-level rather than individual patient–level data and is therefore vulnerable to the ecologic fallacy. A prospective cohort study designed for weight loss or gain with very frequent measurements in a diverse population would contribute a stronger form of evidence. Fourth, the review was limited to studies published in 2018, and many of the studies indicate a significant risk of bias with respect to their stated goals. However, none of the studies were designed to measure temporal associations between the measures of interest, so these limitations in study conduct would not necessarily have led to bias with respect to the findings. Although the search was limited to a single publication year (2018) to reduce the workload associated with this review, there is no reason to expect that data from this year would differ from data published earlier or later. Conclusions The pooled evidence from this meta-analysis suggests that decreases in fasting insulin levels precede weight loss; it does not suggest that weight loss precedes decreases in fasting insulin. This temporal sequencing is not consistent with the assertion that obesity causes NCDs and premature death by increasing levels of fasting insulin. This finding, together with the obesity paradox, suggests that hyperinsulinemia or another proximate factor may cause the adverse consequences currently attributed to obesity. Additional studies to confirm this hypothesis are urgently needed. ARTICLE INFORMATION Accepted for Publication: January 20, 2021. Published: March 12, 2021. doi:10.1001/jamanetworkopen.2021.1263 Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Wiebe N et al. JAMA Network Open. Corresponding Author: Natasha Wiebe, MMath, PStat, Department of Medicine, University of Alberta, 11-112Y Clinical Sciences Bldg, 11350 83 Ave NW, Edmonton AB, T6G 2G3 Canada (nwiebe@ualberta.ca). Author Affiliations: Department of Medicine, University of Alberta, Edmonton, Alberta, Canada (Wiebe, Ye, Bello); Department of Health, St Francis Xavier University, Antigonish, Nova Scotia, Canada (Crumley); Department of Renal Medicine M99, Karolinska University Hospital, Stockholm, Sweden (Stenvinkel); Department of Medicine, University of Calgary, Calgary, Alberta, Canada (Tonelli). Author Contributions: Ms Wiebe had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Wiebe. Acquisition, analysis, or interpretation of data: All authors. JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 11/17 JAMA Network Open | Nutrition, Obesity, and Exercise Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation Drafting of the manuscript: Wiebe. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Wiebe, Ye. Obtained funding: Tonelli. Administrative, technical, or material support: Wiebe. Supervision: Wiebe. Conflict of Interest Disclosures: Dr Bello reported receiving grants from the Canadian Institutes of Health Research (CIHR), University of Alberta, during the conduct of the study and personal fees from Janssen and grants from Amgen and CIHR outside the submitted work. Dr Stenvinkel reported receiving grants from AstraZeneca and Bayer and personal fees from Baxter, Reata, Pfizer, Astellas, and Fresenius Medical Care outside the submitted work. Dr Tonelli reported receiving grants from the Canadian Institutes of Health Care during the conduct of the study. No other disclosures were reported. Additional Contributions: Nasreen Ahmad, BSc, and Sophanny Tiv, BSc, University of Alberta, Edmonton, Alberta, Canada, provided reviewer support and Ghenette Houston, BA, University of Alberta, Edmonton, Alberta, Canada, provided administrative support. They received no other compensation besides their regular salary. REFERENCES 1. Hill AB. The environment and disease: association or causation? Proc R Soc Med. 1965;58:295-300. doi:10.1177/ 2. Fedak KM, Bernal A, Capshaw ZA, Gross S. Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology. 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High levels of CRP in morbid obesity: the central role of adipose tissue and lessons for clinical practice before and after bariatric surgery. Surg Obes Relat Dis. 2015;11(1):148-154. doi:10.1016/j.soard.2014.06.010 SUPPLEMENT. eTable 1. Search Strategies eTable 2. Study Population Characteristics eTable 3. Pooled Temporal Associations: Subgroup Group Analysis12 vs >12 Weeks eTable 4. Pooled Temporal Associations: Subgroup Group Analysis Bariatric vs Non-bariatric Patients eTable 5. Pooled Temporal Associations: Extended Analysis (All Measures) eTable 6. Pooled Temporal Associations: Sensitivity Analysis Adjusting for Non-independence eTable 7. Pooled Temporal Associations: Sensitivity Analysis Adjusting for Two Independent Variables JAMA Network Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 (Reprinted) March 12, 2021 17/17 Supplementary Online Content Wiebe N, Ye F, Crumley ET, Bello A, Stenvinkel P, Tonelli M. Temporal associations among body mass index, fasting insulin, and systemic inflammation: a systematic review and meta-analysis. JAMA Netw Open. 2021;4(3):e211263. doi:10.1001/jamanetworkopen.2021.1263 eTable 1. Search Strategies eTable 2. Study Population Characteristics eTable 3. Po eTable 4. Pooled Temporal Associations: Subgroup Group Analysis Bariatric vs Non- bariatric Patients eTable 5. Pooled Temporal Associations: Extended Analysis (All Measures) eTable 6. Pooled Temporal Associations: Sensitivity Analysis Adjusting for Non- independence eTable 7. Pooled Temporal Associations: Sensitivity Analysis Adjusting for Two Independent Variables This supplementary material has been provided by the authors to give readers additional information about their work. © 2021 Wiebe N et al. JAMA Network Open. eTable 1. Search Strategies Database Search Strategies Date and Coverage MEDLINE 1. C-Reactive Protein/ Ovid MEDLINE(R) 2. CRP.ti,ab. and Epub Ahead of 3. "C-reactive protein".ti,ab. Print, In-Process & 4. (fasting adj3 insulin).ti,ab. Other Non-Indexed 5. HOMA*.ti,ab. Citations and Daily 6. "homeostatic model assessment of insulin resistance".ti,ab. 1946 to August 20, 7. "homeostasis model assessment of insulin resistance".ti,ab. 8. exp interleukins/ 9. interleukin*.ti,ab. 10. QUIKI*.ti,ab. 11. QUICKI*.ti,ab. 12. "quantitative insulin sensitivity check index".ti,ab. 13. Tumor Necrosis Factor-alpha/ 14. mhr24.ti,ab. 15. "tumour necrosis factor*".ti,ab. 16. "tissue necrosis factor*".ti,ab. 17. "tumor necrosis serum*".ti,ab. 18. "tumour necrosis serum*".ti,ab. 19. TNFalpha.ti,ab. 20. TNF-alpha.ti,ab. 21. TNF alfa.ti,ab. 22. "tumor necrosis factor*".ti,ab. 23. Cachectin.ti,ab. 24. Cachetin.ti,ab. 25. or/1-24 26. body fat distribution/ 27. Body fat meter.ti,ab. 28. "fat distribut*".ti,ab. 29. body mass index/ 30. BMI.ti,ab. 31. "body mass index".ti,ab. 32. "Quetelet* Index".ti,ab. 33. ("fat mass" or "percentage body fat" or Skin fold thickness or "weight change*" or "weight gain" or "weight loss" or Waist circumference or "Waist circumference" or "waist size" or "skinfold measure*").ti,ab. 34. Waist-Hip Ratio/ or exp body weight changes/ or Body- Weight Trajectory/ or weight gain/ or weight loss/ or waist circumference/ or Skinfold thickness/ 35. (("waist to hip" or waist-hip or "hip to waist" or hip-waist) adj3 ratio).ti,ab. 36. (weight adj3 trajectory).ti,ab. 37. (weight adj3 reduc*).ti,ab. 38. or/26-37 39. 25 and 38 40. "randomized controlled trial".pt. 41. (random$ or placebo$ or single blind$ or double blind$ or triple blind$).ti,ab. © 2021 Wiebe N et al. JAMA Network Open. Database Search Strategies Date and Coverage 42. (retraction of publication or retracted publication).pt. 43. or/40-42 44. (animals not humans).sh. 45. ((comment or editorial or meta-analysis or practice- guideline or review or letter) not "randomized controlled trial").pt. 46. (random sampl$ or random digit$ or random effect$ or random survey or random regression).ti,ab. not "randomized controlled trial".pt. 47. 43 not (44 or 45 or 46) 48. controlled clinical trial.pt. 49. epidemiologic methods/ 50. epidemiologic studies/ 51. exp Case-Control Studies/ 52. (epidemiologic adj (study or studies)).ab,ti. 53. case control.ab,ti. 54. (cohort adj (study or studies)).ab,ti. 55. cohort analy$.ab,ti. 56. (follow up adj (study or studies)).ab,ti. 57. (observ$ adj3 (study or studies)).ab,ti. 58. or/48-57 59. 47 or 58 60. exp Cross-Sectional Studies/ 61. 59 not 60 62. 39 and 61 63. (change or reduc* or decreas* or increas* or loss or gain or trajectory or pre-post).mp. 64. 62 and 63 65. limit 64 to (english language and humans) 66. limit 65 to year=”2018” EMBASE 1. C-Reactive Protein.ti,ab. Embase 1974 to 404-2128 2. exp C reactive protein/ August 19, 2019 3. CRP.ti,ab. 4. exp insulin blood level/ 5. (fasting adj3 insulin).ti,ab. 6. exp homeostasis model assessment/ 7. homa*.ti,ab. 8. homeostatic model assessment of insulin resistance.ti,ab. 9. "homeostasis model assessment of insulin resistance".ti,ab. 10. exp interleukin derivative/ 11. interleukin*.ti,ab. 12. QUIKI*.ti,ab. 13. exp Quantitative Insulin Sensitivity Check Index/ 14. quicki.ti,ab. 15. "quantitative insulin sensitivity check index".ti,ab. 16. exp tumor necrosis factor/ 17. Tumor Necrosis Factor-alpha.ti,ab. 18. TNF-alpha.ti,ab. 19. TNF alfa.ti,ab. 20. TNFalpha.ti,ab. 21. TNFalfa.ti,ab. 22. tissue necrosis factor*.ti,ab. © 2021 Wiebe N et al. JAMA Network Open. Database Search Strategies Date and Coverage 23. "tumor necrosis factor*".ti,ab. 24. "tumour necrosis factor*".ti,ab. 25. "tumor necrosis serum*".ti,ab. 26. "tumour necrosis serum*".ti,ab. 27. mhr24.ti,ab. 28. Cachectin.ti,ab. 29. cachetin.ti,ab. 30. or/1-29 31. exp body fat distribution/ 32. exp body fat meter/ 33. Body fat meter.ti,ab. 34. "fat distribut*".ti,ab. 35. exp body mass/ 36. BMI.ti,ab. 37. "body mass index".ti,ab. 38. Body ban mass.ti,ab. 39. "Quetelet* Index".ti,ab. 40. ("fat mass" or "percentage body fat" or Skin fold thickness or "weight change*" or "weight gain" or "weight loss" or Waist circumference or "Waist circumference" or "waist size" or "skinfold measure*").ti,ab. 41. Waist-Hip Ratio/ or exp body weight change/ or body weight gain/ or body weight loss/ or waist circumference/ or Skinfold thickness/ 42. exp waist hip ratio/ 43. (("waist to hip" or waist-hip or "hip to waist" or hip-waist) adj3 ratio).ti,ab. 44. (weight adj3 trajectory).ti,ab. 45. (weight adj3 reduc*).ti,ab. 46. or/31-45 47. 30 and 46 48. (random$ or placebo$ or single blind$ or double blind$ or triple blind$).ti,ab. 49. RETRACTED ARTICLE/ 50. or/48-49 51. (animal$ not human$).sh,hw. 52. (book or conference paper or editorial or letter or review).pt. not exp randomized controlled trial/ 53. (random sampl$ or random digit$ or random effect$ or random survey or random regression).ti,ab. not exp randomized controlled trial/ 54. 50 not (51 or 52 or 53) 55. exp cohort analysis/ 56. exp longitudinal study/ 57. exp prospective study/ 58. exp follow up/ 59. cohort$.tw. 60. or/55-59 61. 54 or 60 62. 47 and 61 63. (change or reduc* or decreas* or increas* or loss or gain or trajectory or pre-post).ti,ab. 64. 62 and 63 65. exp Cross-Sectional Studies/ 66. 64 not 65 © 2021 Wiebe N et al. JAMA Network Open. Database Search Strategies Date and Coverage 67. limit 66 to (human and english language) 68. limit 67 to year=”2018” © 2021 Wiebe N et al. JAMA Network Open. eTable 2. Study Population Characteristics Study Cohort(s) N Mean Mean Mean Mean Mean Mean Mean Mean Mean BMI, weight, fat, fat, insulin, HOMA CRP, IL-6, TNF- kg/m kg kg % pmol/L mg/L pg/mL pg/mL 25 33 . . . 83 3.9 . 8.3 19.2 Rifaximin Abdel-Razik 25 33 . . . 82 3.7 . 8.1 19.0 et al, 2018 Placebo 6 41 111 . . 144 . 6.6 . . PCOS Abiad et 16 41 107 . . 111 . 10.0 . . al, 2018 Control 10 . 86 . . 54 1.8 6.0 2.7 . CR diet plus exercise Arikawa et Weight management 10 . 98 . . 116 5.4 5.2 2.8 . al, 2018 counselling Arnold et Decreased added sugars, 14 39 103 . . 96 . 7.4 1.3 . al, 2018 increased fiber and fish diet 11 . 100 . . 130 2.5 . 2.4 3.4 Low-carbohydrate diet Asle 11 . 108 . . 131 2.5 . 2.4 3.1 Low-fat diet Mohammadi 11 . 105 . . 137 2.6 . 2.6 3.2 High-fat diet Zadeh et 9 . 100 . . 130 2.5 . 2.1 3.5 al, 2018 Control Baltieri et 13 36 96 . . . . 8.0 19.8 15.8 al, 2018 RYGB 26 37 . . . . . . . . Metformin Bulatova et 27 40 . . . . . . . . al, 2018 Control 14 39 108 . . 97 2.3 . . . T2D remission Carbone et 67 a 27 31 93 . . 46 1.4 . . . al, 2019 No T2D remission 29 51 24 . . . . 2.6 . . . Saxagliptin and metformin Chen et al, 51 24 . . . . 2.6 . . . 2018 Acarbose and metformin Cessation of androgen 27 . . 27 . . 2.3 . . . deprivation therapy Cheung et 19 . . 27 . . 2.3 . . . al, 2018 Control 241 53 156 . . . . 11.7 . . SG One-anastomosis GB 68 49 144 . . . . 10.6 . . Chiappetta 159 45 128 . . . . 8.6 . . et al, 2018 RYGB 9 42 . . . 65 2.5 . . . Mini-GB 5 42 . . . 63 2.4 . . . et al, 2018 SG © 2021 Wiebe N et al. JAMA Network Open. Study Cohort(s) N Mean Mean Mean Mean Mean Mean Mean Mean Mean BMI, weight, fat, fat, insulin, HOMA CRP, IL-6, TNF- kg/m kg kg % pmol/L mg/L pg/mL pg/mL 9 45 . . . 73 2.7 . . . RYGB De Luis, 46 46 123 47 . 103 4.2 . . . CC rs266729 Calvo et 84 49 124 43 . 115 4.3 . . . al, 2018 CG or GG rs266729 De Luis, 335 35 92 37 . 81 3.3 5.2 . . Izaola et Mediterranean CR diet then al, 2018 dietary counseling De Luis, GG rs670 17 47 128 45 . 101 4.1 . . . Pacheco et 65 49 124 44 . 108 4.1 . . . al, 2018 GA or AA rs670 Aerobic and resistance 18 . 67 31 46 . . 5.0 . . training De Paulo et Stretching and relaxation 18 . 72 34 46 . . 7.0 . . al, 2018 exercises Demerdash 92 47 130 . . 51 2.6 . . . et al, 2018 SG 92 28 82 . . 103 5.5 . . . Canrenone Derosa et 90 28 80 . . 109 5.6 . . . al, 2018 Hydrochlorothiazide 38 . 72 20 27 34 1.4 . . . Almond snacks Dhillon et 35 . 71 20 28 40 1.8 . . . al, 2018 Cracker snacks Di 8 28 86 27 31 88 3.3 2.3 11.9 12.4 Sebastiano et al, 2018 Treated Drummen et 12 31 92 . 41 71 3.2 . . . High-protein diet al, 2018 13 31 94 . 41 89 4.1 . . . (PREVIEW) Moderate-protein diet Esquivel et 63 45 129 . . 94 3.9 . . . al, 2018 SG 14 32 91 . 39 . . 2.4 . . Mediterranean diet Fortin et 14 30 88 . 36 . . 2.1 . . al, 2018 Low-fat diet 66 . 97 . 30 . . 4.8 3.4 . High-egg diet © 2021 Wiebe N et al. JAMA Network Open. Study Cohort(s) N Mean Mean Mean Mean Mean Mean Mean Mean Mean BMI, weight, fat, fat, insulin, HOMA CRP, IL-6, TNF- kg/m kg kg % pmol/L mg/L pg/mL pg/mL Fuller et 62 . 91 . 30 . . 4.4 2.9 . al, 2018 (DIABEGG) Low-egg diet Gadéa et 38-50 . 69 26 . . 1.7 4.5 . . al, 2018 Chemotherapy 17 . 82 33 44 76 3.7 . . . High-protein diet 18 . 79 32 44 61 2.5 . . . High-carbohydrate diet Galbreath et 19 . 76 30 43 67 3.2 . . . al, 2018 Control Helicobacter pylori 49 43 . . . . 6.9 . . . eradication before SG 60 43 . . . . 6.2 . . . Control before SG Helicobacter pylori 50 43 . . . . 5.6 . . . eradication before RYGB Goday et 70 43 . . . . 6.3 . . . al, 2018 Control before RYGB Guarnotta et 12 36 98 . . 46 0.9 . . . al, 2018 Pasireotide 40 50 141 . . 193 9.6 9.5 . . 32 French bougie size in SG 45 40 48 136 . . 135 6.5 9.6 . . 36 French bougie size in SG Hady et al, 40 45 131 . . 126 5.6 7.5 . . 2018 40 French bougie size in SG EPA-enriched nutritional 13 . 50 . . . . 1.3 . . supplement Hanai et 14 . 51 . . . . 0.7 . . al, 2018 Control 51 31 . . . 65 2.6 1.3 . . Empagliflozin Hattori et 51 30 . . . 55 3.6 1.5 . . al, 2018 Placebo Low-glycemic index pulse- 16 30 82 32 . 81 . 3.2 . . based diet Kazemi et Therapeutic lifestyle change 16 35 96 43 . 88 . 5.2 . . al, 2018 diet Keinänen et 84-94 23 . . . 53 1.6 0.7 . . al, 2018 Treated © 2021 Wiebe N et al. JAMA Network Open. Study Cohort(s) N Mean Mean Mean Mean Mean Mean Mean Mean Mean BMI, weight, fat, fat, insulin, HOMA CRP, IL-6, TNF- kg/m kg kg % pmol/L mg/L pg/mL pg/mL 2010 American dietary 28 32 90 . . 103 4.2 . . . guidelines diet Krishnan et 24 33 90 . . 88 3.7 . . . al, 2018 Typical American diet Lambert et 109 39 102 41 39 119 5.2 7.7 . . al, 2018 RYGB and BPD 38 44 . 117 . . . . . . . Bariatric surgery Lee et al, 25-44 44 92 . . . 5.5 . . . 2018 Control Liang et al, 18 33 95 . . 119 5.2 2.3 . . 2018 Low-calorie diet 43 50 . . . 158 8.1 . . . SG Liaskos et 28 47 . . . 176 6.7 . . . al, 2018 RYGB Liu et al, 45 33 . . . 116 8.1 . . . 2018 RYGB 36 34 88 . . 79 3.1 . . . Diet beverages Madjd et 35 34 88 . . 82 3.2 . . . al, 2018 Water Most et al, 34 . 72 25 . . 1.1 . . . 25% CR 19 . 71 23 . . 1.3 . . . (CALERIE 2) Control 30 29 83 . 40 . . 4.8 . . 20% CR diet 30 31 86 . 41 . . 5.8 . . 50% CR diet et Alternating 70% and 30% CR 37 30 85 . 41 . . 6.2 . . al, 2018 diet Munukka et American College of Sports 17 32 90 39 44 55 12.9 . . . al, 2018 Medicine exercise program Nicoletto et 46 27 . . . 46 1.7 4.4 4.5 . al, 2018 Kidney transplantation Nilholm et 30 30 . . . 93 . . . 14.9 al, 2018 Okinawan-based Nordic diet 19 . 63 . . . . 1.0 . . Daikenchuto (TJ-100) Nishino et 20 . 60 . . . . 0.6 . . al, 2018 Control © 2021 Wiebe N et al. JAMA Network Open. Study Cohort(s) N Mean Mean Mean Mean Mean Mean Mean Mean Mean BMI, weight, fat, fat, insulin, HOMA CRP, IL-6, TNF- kg/m kg kg % pmol/L mg/L pg/mL pg/mL 57 b Patel et al, Duodenal-jejunal sleeve 41-45 40 116 . . 113 . . . . 2018 bypass Mindfulness-based stress 42 39 104 . . 30 3.7 9.7 . . reduction Raja Khan et 44 39 102 . . 32 4.0 10.7 . . al, 2018 Health education Rubio 57 50 126 . 52 158 6.7 . . . Prediabetes Almanza et 48 54 132 . 53 132 8.5 . . . al, 2018 T2D 49 . 96 . . . 2.7 4.2 . . 5:2 intermittent CR diet Schübel et 58 49 . 93 . . . 3.0 4.1 . . Continuous CR diet al, 2018 52 . 93 . . . 3.0 5.4 . . (HELENA) Control Shah et al, 50 31 . . . 65 . 1.3 . . Vegan diet 50 31 . . . 72 . 1.1 . . (EVADE CAD) AHA diet 40 42 . . . . 6.5 11.5 . . Probiotic Sherf-Dagan 40 42 . . . . 5.6 12.3 . . et al, 2018 Placebo Moderate-intensity physical 32-60 43 129 . . 173 . 6.1 3.8 . training Stolberg et 28 43 124 . . 143 . 6.1 3.8 . al, 2018 Control Van 289 36 104 . . 97 3.3 5.6 . . Lifestyle intervention Dammen et 285 36 103 . . 104 3.6 5.6 . . al, 2018 Control Van Rijn et 28 37 113 . . 129 . . . . al, 2018 Duodenal-jejunal bypass liner 265- . 57 . . . . 56.2 . . Treated for TB 270 Wilson et 82 . 60 . . . . 3.9 . . al, 2018 Control Witczak et 20 54 151 . . 112 2.4 . 9.5 . al, 2018 Bariatric surgery 12 39 . . . . . 1.8 . . HIIT © 2021 Wiebe N et al. JAMA Network Open. Study Cohort(s) N Mean Mean Mean Mean Mean Mean Mean Mean Mean BMI, weight, fat, fat, insulin, HOMA CRP, IL-6, TNF- kg/m kg kg % pmol/L mg/L pg/mL pg/mL Wormgoor Moderate-intensity continuous 11 35 . . . . . 2.1 . . et al, 2018 training 68 10 38 . . . 132 . . . . SG Yang et al, 10 39 . . . 127 . . . . 2018 RYGB 51 25 63 . . 57 2.5 1.1 . . Coenzyme Q10 Zhang et 50 25 65 . . 66 3.0 1.0 . . al, 2018 Placebo AHA American Heart Association, BMI body mass index, BPD biliopancreatic diversion surgery, CR calorie restriction, CRP C-reactive protein, DM diabetes mellitus, GB Gastric Bypass, HOMA homeostatic model assessment, HIIT high-intensity interval training, IGT intolerance glucose test, IL-6 interleukin-6, MetS metabolic syndrome, na non-applicable, NAFLD non-alcoholic fatty liver disease, NASH non-alcoholic steatohepatitis, PCOS polycystic ovary syndrome, RYGB roux-en-y gastric bypass, SG sleeve gastrectomy, SGLT2 sodium-glucose transport protein 2, T1D type 1 diabetes, T2D type 2 diabetes, TB tuberculosis, TNF- -alpha, WC waist circumference This study was published online in 2018. The number of measurements varied. AA, CC, CG, GA, GG are alleles. © 2021 Wiebe N et al. JAMA Network Open. eTable 3. Pooled Temporal Associations: Subgroup Group A Weeks 2 2 Dependent Independent Number N I /Tau (Period 2) (Period 1) of P interaction weeks All 90 0.26 (0.13,0.38) 79/0.161 33 0.61 (0.38,0.84) 76/0.133 57 0.17 (0.05,0.30) P=0.001 All 90 0.01 (-0.08,0.10) 69/0.099 33 0.56 (0.32,0.80) 62/0.073 57 -0.00 (-0.08,0.08) P<0.001 All 57 0.23 (-0.09,0.55) 83/0.168 27 0.72 (0.08,1.37) 81/0.153 30 0.14 (-0.18,0.47) P=0.09 All 57 0.20 (0.04,0.36) 53/0.048 27 0.22 (-0.03,0.48) 53/0.050 30 0.20 (0.03,0.36) P=0.82 All 42 0.19 (-0.04,0.42) 49/0.038 22 0.45 (-0.05,0.94) 48/0.036 20 0.14 (-0.10,0.38) P=0.25 All 42 36/0.023 22 0.20 (-0.02,0.42) 34/0.018 20 0.43 (0.16,0.70) P=0.14 coefficient/association, BMI body mass index, CI confidence interval, CRP C-reactive Each set of three rows describes two models. The first row describes one model and uses all available cohorts where change in a measure, specifically a standardized slope, of a later time period (Period 2) is regressed on a change in a different measure of an earlier time period (Period 1). The second and third row describe a second model with an additional interaction term for a subgroup. The result 2 2 confidence interval and two measures of between-cohort heterogeneity, I and Tau . The P-value for interaction indicates whether the subgrouping is significant or not. Results significant at P<0.05 are in boldface. © 2021 Wiebe N et al. JAMA Network Open. eTable 4. Pooled Temporal Associations: Subgroup Group Analysis Bariatric vs Non- bariatric Patients 2 2 Dependent Independent Bariatric N I /Tau (Period 2) (Period 1) or not P interaction All 90 0.26 (0.13,0.38) 79/0.161 Yes 52 0.31 (0. ,0.44) 76/0.145 No 38 -0.12 (-0.41,0.18) P=0.007 All 90 0.01 (-0.08,0.10) 69/0.099 Yes 52 0.01(-0.08,0.10) 68/0.099 No 38 -0.18 (-0.50,0.14) P=0.23 All 57 0.23 (-0.09,0.55) 83/0.168 Yes 26 0.43 (0.10,0.76) 81/0.142 No 31 -0.40 (-0.93,0.13) P=0.005 All 57 0.20 (0.04,0.36) 53/0.048 Yes 26 0.20 (0.04,0.36) 54/0.051 No 31 0.16 (-0.27,0.59) P=0.84 All 42 0.19 (-0.04,0.42) 49/0.038 Yes 15 0.22 (-0.03,0.46) 50/0.040 No 27 0.07 (-0.42,0.56) P=0.58 All 42 36/0.023 Yes 15 0.26 (0.06,0.45) 36/0.021 No 27 0.46 (0.05,0.87) P=0.34 coefficient/association, BMI body mass index, CI confidence interval, CRP C-reactive Each set of three rows describes two models. The first row describes one model and uses all available cohorts where change in a measure, specifically a standardized slope, of a later time period (Period 2) is regressed on a change in a different measure of an earlier time period (Period 1). The second and third row describe a second model with an additional interaction term for a subgroup. The result 2 2 confidence interval and two measures of between-cohort heterogeneity, I and Tau . The P-value for interaction indicates whether the subgrouping is significant or not. Results significant at P<0.05 are in boldface. © 2021 Wiebe N et al. JAMA Network Open. eTable 5. Pooled Temporal Associations: Extended Analysis (All Measures) 2 2 Dependent Independent N I /Tau (Period 2) (Period 1) 0.26 (0.13,0.38) 79/0.161 0.01 (-0.08,0.10) 69/0.099 88 0. (0.03,0.34) 84/0.202 88 0.01 (-0.05,0.08) 74/0.097 57 0.23 (-0.09,0.55) 83/0.168 57 0.20 (0.04,0.36) 53/0.048 -6 13 0.20 (-0.17,0.57) 29/0.043 -6 13 0.19 (-0.10,0.48) 20/0.038 - 9 -0.56 (-1.31,0.20) 0/0 - 9 -0.04 (-1.56,1.48) 86/0.755 91 0.16 (0.05,0.27) 74/0.118 91 0.04 (-0.08,0.17) 69/0.132 83 0.12 (-0.02,0.26) 79/0.152 83 0.01 (-0.12,0.13) 74/0.136 70 0.09 (-0.08,0.27) 75/0.093 70 -0.01 (-0.30,0.27) 71/0.144 -6 30 Could not calculate - numerical derivatives -6 30 0.21 (-0.01,0.44) 0/0 - 18 -0.01 (-0.56,0.53) 0/0 - 18 -0.01 (-0.88,0.87) 36/0.109 21 0.17 (-0.09,0.43) 80/0.085 21 0.08 (-0.28,0.44) 76/0.150 22 0.16 (-0.12,0.45) 81/0.100 22 -0.05 (-0.25,0.16) 81/0.129 12 0.66 (-0.08,1.39) 78/0.100 12 0.36 (-0.65,1.38) 58/0.144 -6 1 Insufficient observations -6 1 - 1 Insufficient observations - 1 16 0.23 (-0.01,0.47) 61/0.079 © 2021 Wiebe N et al. JAMA Network Open. 2 2 Dependent Independent N I /Tau (Period 2) (Period 1) 16 0.24 (-0.05,0.52) 72/0.160 16 0.25 (0.02,0.48) 53/0.061 percent 16 0.19 (-0.11,0.50) 70/0.174 11 0.54 (-0.01,1.09) 29/0.040 11 0.37 (-1.13,1.87) 64/0.187 -6 1 Insufficient observations -6 1 - 1 Insufficient observations - 1 42 0.19 (-0.04,0.42) 49/0.038 42 0. (0.10,0.47) 36/0.023 -6 32 0.15 (-0.26,0.56) 49/0.093 -6 32 0.12 (-0.07,0.31) 0/0.010 - 22 0.03 (-0.91,0.97) 64/0.351 - 22 -0.02 (-0.29,0.26) 26/0.039 39 -0.05 (-0.37,0.28) 72/0.059 39 -0.02 (-0.25,0.22) 64/0.083 -6 27 0.18 (-0.35,0.71) 29/0.042 -6 27 0.15 (-0.16,0.46) 0/0.008 - 21 0.20 (-0.53,0.93) 44/0.128 - 21 0.16 (-0.25,0.58) 21/0.008 -reactive homeostatic model assessment, IL-6 interleukin-6, TNF- necrosis factor-alpha Each row describes one model where change in a measure, specifically a standardized slope, of a later time period (Period 2) is regressed on a change in a different measure of an earlier time 2 2 its 95% confidence interval and two measures of between-cohort heterogeneity, I and Tau . Results significant at P<0.05 are in boldface. © 2021 Wiebe N et al. JAMA Network Open. eTable 6. Pooled Temporal Associations: Sensitivity Analysis Adjusting for Non- independence 2 2 Dependent Independent N I /Tau (Period 2) (Period 1) 90 0.28 (0.22,0.33) 84/0.239 90 -0.00 (-0.08,0.08) 69/0.102 57 0.37 (0.22,0.52) 84/0.198 57 58/0.053 42 - - 42 44/0.031 interval, CRP C-reactive Each row describes one model where change in a measure, specifically a standardized slope, of a later time period (Period 2) is regressed on a change in a different measure of an earlier time period (Period 2 2 its 95% confidence interval and two measures of between-cohort heterogeneity, I and Tau . These models nest cohorts within studies. One row has no results because the model did not converge. Results significant at P<0.05 are in boldface. © 2021 Wiebe N et al. JAMA Network Open. eTable 7. Pooled Temporal Associations: Sensitivity Analysis Adjusting for Two Independent Variables 2 2 Dependent Independent N I /Tau (Period 2) (Period 1) 38 0.57 (0.27,0.86) 81/0.109 -0.07 (-0.42,0.29) 38 0.27 (0.00,0.55) 54/0.041 -0.08 (-0.43,0.28) 38 0.26 (-0.01,0.54) 35/0.016 0.07 (-0.22,0.37) coefficient/association, BMI body mass index, CI confidence interval, CRP C-reactive Each row describes one model where change in a measure, specifically a standardized slope, of a later time period (Period 2) is regressed on changes in two different measures of an earlier time 2 2 its 95% confidence interval and two measures of between-cohort heterogeneity, I and Tau . Results significant at P<0.05 are in boldface. © 2021 Wiebe N et al. JAMA Network Open.

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Temporal Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation

Temporal Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation

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Temporal Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation

Temporal Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation

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