Longitudinal association of inflammatory markers with markers of glycaemia and insulin resistance in European children

Subclinical systemic inflammation may lead to development of type 2 diabetes, but there has been no investigation into its relationship with early progression of glycaemic deterioration and insulin resistance, especially in younger population. In this study we assessed longitudinal associations of pro‐ and anti‐inflammatory markers with markers that evaluate glycaemia and insulin resistance.


| INTRODUCTION
Inflammation plays a significant role in the pathogenesis of diabetes. 1 In the adult population, increased concentrations of proinflammatory and reduced anti-inflammatory markers were significantly associated with the incidence of type 2 diabetes. [2][3][4] However, these associations need to be confirmed for causality, as Mendelian randomisation studies yielded inconsistent results for some inflammatory markers. [5][6][7][8] Previously, there have been prospective studies that have investigated associations between inflammatory markers and glycaemia or insulin resistance, measured at one-time point. 9,10 However, few studies have addressed longitudinal associations between inflammatory markers and glycaemic traits [11][12][13] and even fewer studies have investigated on how inflammatory markers act in combination. 14 Particularly, longitudinal studies investigating the association between low-grade systemic inflammation and markers of glycaemic deterioration/insulin resistance in children are missing. 7,8 Moreover, since higher HbA 1c and HOMA-IR are important indicators of vascular complications in prediabetic conditions 15 and have also been closely related to higher risk of cardiovascular disease and all-cause mortality in nondiabetic people, 16 better biological markers are required to identify the subjects at high risk in very early phases, such as prediabetes which may open new directions for early prevention. As inflammatory markers may be used to refine diabetes risk prediction and thus better target individuals for lifestyle interventions, we aimed to investigate longitudinal associations between pro-and anti-inflammatory markers (individually and combined) and markers of glycaemia (fasting glucose [FG], HbA 1c ), and insulin resistance (HOMA-IR) in European children.

| Study population
The study population was enroled in the pan-European, multi-centre, prospective IDEFICS/I.Family cohort of 16,229 children aged between 2 and 9.9 years at T 0 , from eight European countries (Belgium, Cyprus, Estonia, Germany, Hungary, Italy, Spain, and Sweden). The children were first examined in 2007 and 2008 with follow-up examinations conducted after two (T 1 ) and six (T 3 , I.Family study) years; the design of this cohort study has been described in detail elsewhere. 17,18 In the IDEFICS/I.Family study, risk factors of lifestylerelated outcomes were investigated in young children and anthropometric and clinical examinations were conducted at each survey wave. Blood samples were considered fasting if the last meal or drink (other than water) was consumed >8 h before drawing blood. Before children entered the study, parents provided written informed consent. Additionally, children aged 12 years and older gave simplified written consent. Younger children gave verbal assent for examinations and sample collection. Ethics approval was obtained from the institutional review boards of all eight study centres.

| Markers of glycaemia/insulin resistance
At T 0 , FG was assessed either with capillary blood from finger prick or with venous blood from venipuncture using a point-of-care ana-

| Inflammatory markers
Serum samples stored at −80°C were used to detect levels of C-reactive protein (CRP), interleukin-1 receptor antagonist (IL-1Ra), IL-6, 8, 15, interferon gamma inducible protein (IP-10), TNF-α, adiponectin and leptin were measured at T 0 and T 3 , by ELISA using electrochemiluminescent multiplex assay (using either single or MULTI-SPOT® Assay Systems, Meso Scale Discovery). The choice of inflammatory markers were based on their role in endothelial function via either direct or indirect mechanisms such as reducing nitric oxide production and stimulating inflammation-oxidative stress pathways. IL-6, IL-8, TNF-α, IP-10, IL-15 and IL-1Ra were run together on a 6-plex assay, insulin and leptin run together on a 2-plex assay, whereas adiponectin, and CRP on single-plex assays each. The combination of markers for the assays were decided based on the feasibility of combinations with the help of MSD customer support.

| Covariables
Based on the validated and reproducibility tested FFQ data, a Healthy Diet Adherence Score (HDAS) was developed for all the study regions, 19,20 as a proxy-indicator of children's adherence to healthy dietary guidelines including a high consumption of fruits and vegetables, wholemeal, fish consumption of 2-3 times per week and a reduced intake of refined sugars and fat. The HDAS was used for the present analyses as a continuous variable and ranged from 0 to 50. A higher score represented a higher adherence to healthy dietary guidelines. The pubertal status was self-reported by children in T 3 , and was defined as pre-pubertal or pubertal based on voice change in boys and age at menarche in girls. This definition of pubertal status has given similar results when compared to Tanner stage in this cohort previously. 21 We used the number of occasions reported for alcohol intake/cigarette smoking in lifetime to create binary indicator variables for alcohol intake and smoking of ever smokers/drinkers versus nonsmokers/non-drinkers. The alcohol and smoking questionnaire was completed at T 3 by study participants 12 years of age or older at the time of examination. Sports club membership (yes/no) as an indicator of physical activity 22 and daily TV, DVD, video, computer or games-console use in hours which were summed to obtain the total screen time for the whole week as a proxy for sedentary behaviour were reported by parents in T 0 . In T 3 , these proxy measures were reported by parents if the child was younger than 12 years, or self-reported if the child was 12 years or older.
Parents self-reported their history of diabetes which was categorised as positive (at least one parent with diabetes), negative (both parents without diabetes), or unknown (if diabetes status of mother and father were unknown). Parents reported medication use and medical history for their children by means of an interview based on the health and lifestyle questionnaire. Mothers were asked to retrospectively report starting and ending months of exclusive breast feeding and breast feeding combinations which were used to derive the total breast feeding duration. 23 Information on mother's height and weight assessed at cohort entry was used for calculating maternal BMI. A binary indicator for children delivered at term versus children born preterm (≤37th gestational week) and continuous variable for birthweight were derived from parental questionnaire data. As part of the standardised anthropometric examination protocol, waist circumference (WC; cm) was measured in an upright position with relaxed abdomen and feet together, midway between the lowest rib margin and the iliac crest to the nearest 0.1 cm (inelastic tape: Seca 200; Seca). Height (cm) of the children was measured to the nearest 0.1 cm with a calibrated stadiometer (Seca 225 stadiometer), body weight (kg) was measured in fasting state in light clothing on a calibrated scale accurate to 0.1 kg (Tanita BC 420 SMA, Tanita Europe GmbH). BMI was calculated as weight (kg) divided by height (m) squared.

| Analysis dataset
The present analysis used only T 0 and T 3 measurements as inflammatory markers were not measured at T 1 . Our analysis dataset included participants with measurements of at least one inflammatory marker from T 0 or T 3 (n = 7992). Children diagnosed with type 1 or type 2 diabetes at cohort entry (n = 9) or taking anti-diabetic drugs (ATC codes: A10), anti-inflammatory drugs (M01), or corticosteroids (H02) within the last 14 days of cohort entry or follow-up examination were excluded from the analysis (n = 560). Children with acute infection defined as CRP level ≥10 mg/l at T 0 or T 3 were also excluded (n = 886). Finally, for non-fasting blood samples the values of FG and HOMA-IR were set to missing, thus leading to a final study sample of 6537 children ( Figure S1).

| Statistical analysis
Data were expressed as mean � SD or median with an interquartile range as appropriate. According to previously described methods, [24][25][26][27] age-and sex-specific z-scores were derived for waistto-height ratio, WC, HbA 1c , HOMA-IR, triglycerides, SBP, and FG in children and adolescents using the data collected in the IDEFICS/I. Family cohort. Since the laboratory methods to measure FG changed between T 0 and T 3 , age-and sex-specific reference percentiles were estimated for T 0 and T 3 , separately, and were used to calculate the respective z-scores for the analysis. We used stata module STNDZXAGE for calculating z-scores of inflammatory markers by standardising its raw values (irrespective of their distribution with respect to the detection limits) over age, sex, and survey. 28 Since the children were newly recruited in all surveys (i.e. T 0 , T 1 and T 3 ; Figure S1), we henceforth use the word 'baseline' for cohort entry and follow-up time for representing difference between age at follow-up and age at cohort entry. The follow-up time was used as a continuous variable, as it was different for different study participants.
To model the association between inflammatory markers and markers of glycaemia/insulin resistance, a two-level growth model was used, where one level accounts for differences between individuals and the other level for changes over time within individuals. 29 Markers of inflammation (continuous variable) were the exposure variables and markers of glycaemia/insulin resistance NAGRANI ET AL. The description of the crude model is as follows: let y ij be j-th measurement of the i-th child (e.g., z-scores of HOMA-IR, HbA 1c , FG), M ij is an inflammatory marker (e.g., z-scores of CRP, IL-1Ra, IL-6, 8, 15, IP-10, TNF-α, adiponectin and leptin), time ij is the follow-up time since cohort entry and ϵ ij is the error term for individual i at follow-up time j, then the crude model without adjustment was specified as follows: where β 00 is the overall mean intercept, β 10 is the overall mean slope and u 0i and u 1i express how much the intercept and slope, respec- abdominal obesity (waist circumference z-scores < 0.1) and low to normal triglyceride levels (z-scores < 0.1) on DAG suggested model to rule-out the confounding effects of overall and central obesity.
All covariates were treated as time-varying to account for changes in lifestyle and anthropometric factors over time. The results were reported as regression coefficients and their 95% confidence intervals. Bonferroni correction was used to account for multiple testing, that is the statistical significance level was set to α = 0.05/ 10 = 0.005 (nine independent inflammatory markers and one dichotomised sum score were tested for FG, HbA 1c , HOMA-IR). All statistical tests were two-sided. Statistical analyses were performed using Stata 16 and R 4.0.3. Table 1 shows the characteristics of the study participants included in the analysis at T 0 and T 3 . The mean age of participants at T 0 was 6.17 years (SD = 1.75) and 48% were girls ( Table 1)

| Two-level growth models
Results of the two-level growth models for the association between inflammatory markers and HbA 1c and HOMA-IR are depicted in Table 2.  Table 2). We also observed weak association between leptin and FG (β = 0.04, 95% CI = 0.004 to 0.09; Table S1). Further, a significant interaction was observed between  (Table 2).   An inverse association between IL-15 levels and HOMA-IR was observed with significant interaction between IL-15 and follow-up time ( Figure 1; Table 2). An inverse association between IL-1Ra and HOMA-IR was observed in adjusted model, however the associations were no longer significant after Bonferroni correction for multiple comparisons (Table 2). TNF-α was associated with higher HbA 1c levels only at baseline (Figure 1). The association between most inflammatory markers of glycaemia/insulin resistance did not differ between boys and girls (Table S2). We also observed high sum score of the combined effect variable to be positively associated with HOMA-IR (Table 3).

| Sensitivity analyses
The sensitivity analysis, in which we additionally excluded study participants with HbA 1c , HOMA-IR, or FG levels >90th percentile at baseline, showed mostly very similar results to the main analyses concerning the direction of effect and the effect sizes ( Figure S4). The association of leptin and IL-15 persisted with HOMA-IR after including triglyceride levels, systolic blood pressure, antibiotic intake and additional covariates of early markers such as duration of breastfeeding, preterm birth, maternal obesity (Table S3).

| DISCUSSION
This study primarily focussed to evaluate the relationship between systemic inflammation and its association with markers of glycaemia and insulin resistance, to better understand the cause-effect rela- Additionally adjusted for study region, waist-to-height ratio, lifetime smoking and alcohol status, pubertal status, birthweight, healthy diet adherence score, family history of diabetes, membership in sport club, screen time/week and other inflammatory markers (minimal sufficient adjustment set).
IL-1Ra in the circulation is linked to an increased risk of type 2 diabetes. [38][39][40][41][42] This may be due to a counterregulation to proinflammatory and/or metabolic stimuli and can be interpreted as a futile response to the presence of multiple diabetes risk factors, thus not conferring a sufficient degree of protection against the onset of the disease. 43 However, when the cohort studies were pooled for a joint genotyping analysis along with gene expression, genetically raised levels of IL-1Ra seemed to protect against increased insulin resistance. 43 Similarly, our whereas they seem to rise during late adolescence. 45,46 Therefore the present study may need a longer follow-up to assess the association between CRP and markers of glycaemia/insulin resistance.
Though there have been studies showing protective effects of adiponectin on prediabetic markers, 3,7,47 we did not observe any protective association after Bonferroni correction for multiple comparisons with adiponectin which is consistent with results from Mendelian randomisation studies that did not support a causal role for reduced circulating adiponectin levels in type 2 diabetes. 6,48 Our results confirms the role of leptin as a proinflammatory marker for insulin resistance and adds to the existing evidence on protective role of IL-15 for insulin resistance in a large prospective cohort of children. We also observed an association with the combined effect of inflammatory markers which was largely driven by leptin (Table S5).
The development of such scores or a diabetes panel may prove to be beneficial in the identification of high-risk group individuals resulting in early diagnosis. There have been some studies that have targeted in preparation of such panels, however, more detailed studies are required. 14

| CONCLUSIONS
The associations observed in the present study provide observational evidence suggesting that systemic inflammation may potentially contribute to the aetiology of prediabetes. Our findings imply a potential clinical value of these inflammatory factors as early stage markers for type 2 diabetes. Particularly, leptin may hold the promise T A B L E 3 Association of the combined effect of inflammatory markers on HbA 1c and HOMA-IR  Additionally adjusted for study region, waist-to-height ratio, lifetime smoking and alcohol status, pubertal status, birthweight, healthy diet adherence score, family history of diabetes, membership in sport club and screen time/week. NAGRANI ET AL. analysis, and interpretation of data; writing the report; and did not impose any restrictions regarding the publication of the report.
Open access funding enabled and organized by Projekt DEAL.

CONFLICT OF INTEREST
None.

ETHICS STATEMENT
Ethical approval was obtained from the relevant local or national ethics committees by each of the study centres. We certify that all applicable institutional and governmental guidelines and regulations concerning the ethical use of human volunteers were followed during this research. Before children entered the study, parents provided written informed consent. Additionally, children aged 12 years and older gave simplified written consent. Younger children gave verbal assent for examinations and sample collection. for publication. Rajini Nagrani is the guarantor of this work and, as such, 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.

DATA AVAILABILITY STATEMENT
All data generated or analysed during this study are included in this published article or in the data repositories listed in References.