LABORATORIO DE URGENCIAS

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

                                                   Natasha Wiebe; Fen Ye; Ellen T. Crumley et al

                                            JAMA Netw Open. 2021 (4(3):e211263. doi:10.1001) - Review

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 causation:^1,2 
* First, the putative attributable risk of death is small (<5%).³ 
* 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.³ 
* 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 or chronic conditions and older age is remarkably consistent.^4-16 
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,¹⁷ and chronic kidney disease.¹⁸ 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.

METHOD

This systematic review and meta-analysis was conducted and reported according to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA)¹⁹ and Meta-analysis of Observational Studies in Epidemiology (MOOSE)²⁰ 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 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.
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.²³ 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 was quantified using the τ² statistic (between-study variance)²⁴ and the I² 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 vs ≤12 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. 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 5603 enrolled participants (of whom 5261 were analyzed).^25-84 
We decided to exclude 12 studies of 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 an RCT), and 7 retrospective cohort studies. 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 from Western Europe,^{25,27,41,54,61,62,65,72,81,82,84} 11 from North America,^{25,27,41,54,61,62,65,72,81,82,84} 9 from East Asia,^{29,38,39,44,51,52,60,66,68} 5 from South America,^{28,69,78,79,83} 5 from Western Asia,^{28,69,78,79,83} and 3 each from Africa,^35,50,76 the Pacific,^26,42,75 and Eastern Europe.^45,74,80
A total of 90% of the studies were in populations with metabolic disease or conditions associated with metabolic disease: obesity,^{25,32-34,37,41,43,45,47-49,52,57-60,62-64,67-74,76,77,79-83} diabetes or prediabetes,^{26,32,38,46,59} hypertension,⁴⁰ coronary artery disease,⁶⁵ dyslipidemia,⁵¹ chronic kidney disease,⁷⁸ nonalcoholic fatty liver disease,^31,35 Cushing disease,³⁶ polycystic ovary syndrome,⁶¹ breast cancer,^28,56,82 and aging (ie, college students⁸⁴). 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 that adversely cause lean mass loss and/or fat mass gain, such as prostate,^27,42 esophageal,³⁹ head and neck squamous cell⁴⁴ cancers, and psychosis,⁵³ or where the disease course itself (ie, tuberculosis) causes lean mass loss and/or fat mass gain.⁵⁰
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 2^65,84 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 cancer,²⁷ kidney transplants,⁷⁸ gene-associated obesity,³⁴ diabetes vs prediabetes,³² polycystic ovary syndrome,⁶¹ and mindfulness intervention⁸¹), and 21 cohorts contained control participants (of which 4 cohorts contained participants who received placebo^31,35,51,66). 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), 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).
BMI and Fasting Insulin Level
There were 90 pairs of standardized slopes from 56 cohorts and 35 studies that measured BMI and fasting insulin. 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; I² = 79%), 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; I² = 69%). The heterogeneities were large.
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. 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; I² = 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; I² = 76%, P = .007).
BMI and CRP
There were 57 pairs of standardized slopes from 39 cohorts and 22 studies that measured both BMI and CRP levels. 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; I² = 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; I² = 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 12 weeks, when period 2 BMI was regressed on period 1 CRP level. 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;I² = 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; I² = 81%, P = .005).
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. 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; I² = 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; I² = 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.).
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. The results when adjusting for nonindependence when available were similar 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

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. 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.

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 small³; the consistency of effect across older and/or ill populations favors obesity^4-16; and the biological gradient is U-shaped, with overweight and obesity level 1 associated with the lowest risk³; and if hyperinsulinemia is to be considered the mediator, then the temporal sequencing is incorrect.
Insulin resistance, a cause and consequence of hyperinsulinemia,⁸⁹ leads to type 2 diabetes and is associated with other adverse outcomes, such as myocardial infarction, chronic pulmonary disease, and some cancers,^90,91 and may also be indicated in diabetic nephropathy.⁹² Despite the 3 scenarios described earlier, it is commonly believed that obesity leads to hyperinsulinemia.^93-95 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.⁹⁶
Of interest, insulin resistance is also present in lean individuals, in particular men and individuals of Asian descent.⁹⁷ These 2 groups are at heightened risk for type 2 diabetes⁹⁸ 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,⁹⁹ 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 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 stigma¹⁰⁰ among those with obesity.

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.

NOTE: References (104), tables and graphs in the original work