Baseline leptin predicts response to metformin in adolescents with type 1 diabetes and increased body mass index (2023)

There is an increased prevalence of overweight and obesity among children and adults with type 1 diabetes (T1D).1-4 Further, the rate of overweight and obesity continues to be on the rise among people with T1D compared with general population trends.5 This is problematic because obesity-induced insulin resistance increases exogenous insulin needs, with a higher risk of hypoglycaemia, weight gain, chronic inflammation, dyslipidaemia and long-term cardiovascular complications.4, 6 Metformin is the first-line diabetes therapy in overweight and obese children and adults with type 2 diabetes. It improves insulin sensitivity and inhibits hepatic gluconeogenesis.7 In a multicentre, double-blind,8 placebo-controlled randomized clinical trial of 140 adolescents with overweight or obesity and T1D (NCT01881828) randomized to either metformin or placebo for 26 weeks, the use of metformin as an adjunct to insulin therapy failed to show a sustained effect on glycaemic control after 26 weeks of treatment compared with controls. Despite a small decrease in HbA1c at 13 weeks in the metformin group, mean HbA1c levels increased by 26 weeks in both groups. However, metformin therapy was associated with a reduction in weight, body mass index (BMI), percent body fat (%BF) and total daily insulin dose (TDID). Because the original trial did not show a sustained glycaemic response and effect, as measured by HbA1c, we selected reduction in TDID, which was a positive outcome of the trial, to determine predictors of response. Here, we aimed to determine predictors of response to metformin therapy; in particular, we selected percentage (%) change in TDID. We hypothesized that baseline phenotypic or metabolic predictors may help better identify youth with T1D with a higher reduction in %ΔTDID in response to metformin therapy. We analysed data from the T1D Exchange Clinic Network Metformin Randomized Clinical Trial8 (NCT01881828). Detailed methods for this study were previously described.8 For this analysis, data were extracted from the publicly available dataset as a text file, and Microsoft software was used to convert the data tables to usable .xlsx files. The data were then cleaned, and random checks were performed to ensure accuracy of the data conversion and extraction. Out of the 140 participants, 122 (61 in each of the placebo and the metformin arms) had available data on anthropometrics, physical examinations and laboratory evaluations at baseline, 13 and 26 weeks. Figure S1 shows the analysis flow at each time point and the outcome measures of interest. Statistical analyses were conducted using MATLAB (https://www.mathworks.com/, V.R2019b) and R software (RStudio: Integrated Development for R). Between-group comparisons were made using a Student's t-test while within-group comparisons across multiple time points were performed using a paired t-test. Correlation values were computed via the Spearman rank correlation method under the ‘corrplot’ package in R. Linear regression analyses were performed using the ‘fitlm’ function in MATLAB. TDID was quantified as units/kg of body weight/day. The primary outcome was the relative percentage change in TDID (%ΔTDID) and was calculated as % ΔTDID = 100 * TDI D 13 weeks or 26 weeks − TDI D baseline / TDI D baseline , at 13 and 26 weeks, respectively. The regression models adjusted for potential confounders included participant's age, diabetes duration, sex, race, BMI z-score and %BF at baseline. P values less than .05 were considered statistically significant. Comparisons for reduction in TDID were performed within each arm. Table S1 shows the baseline demographic and clinical data for individuals in each arm. We observed a statistically significant reduction in TDID at both 13 and 26 weeks compared with baseline (P < .0001 at both time points) in the metformin arm. In addition, we observed a reduction in TDID only at 26 weeks in the placebo arm (P < .001) (Figure S2 and Table S2). Linear regression models were then used to identify the association between baseline prognostic factors (which included intervention type, age, sex, race, BMI z-score, diabetes duration, waist circumference, %BF, leptin and adiponectin) and %ΔTDID in the entire cohort from baseline to 13 weeks and 26 weeks. First, using pairwise Spearman correlation, we observed that no pair of factors had a correlation value greater than a predefined threshold of 0.8 and, therefore, none were excluded from the linear regression analysis. We found that, indeed, intervention type was significant (P = .004), and the metformin group showed significantly more reduction in %ΔTDID than the placebo group. Baseline leptin was the only significant prognostic factor associated with %ΔTDID at 13 and 26 weeks in the entire cohort (P < .0001 and P = .0002, respectively) (Tables S3 and S4). When assessing by the intervention arm using multiple linear regression analysis to adjust for baseline variables, baseline leptin predicted larger negative %ΔTDID (i.e. larger relative reduction) at 13 (P = .0012) and 26 weeks (P = .007) in the metformin arm (Table 1). In the placebo arm, baseline leptin was negatively associated with %ΔTDID (i.e. larger relative reduction) at 13 (P = .043) but not at 26 weeks (P = .11) (Tables S5 and S6). Figure 1A,B show the regression slopes for association between baseline leptin and %ΔTDID in the metformin (A) and placebo (B) arms. To determine if simpler measures of insulin resistance could be used as predictors of %ΔTDID in response to metformin, we used each of triglyceride (TG) levels and HDL cholesterol levels in univariate and multivariate regression models, with and without leptin in the multivariate models. We found the TG levels were not predictive in the univariate model whether at 13 or 26 weeks (P = .083 and P = .146, respectively). HDL levels were also not predictive at either 13 or 26 weeks (P = .070 and P = .865, respectively). Interestingly, we did observe that for the same value of HDL, the percentage reduction in ΔTDID was slightly greater for the metformin arm than for the placebo arm (effect = 0.6%, P = .014). In multivariate regression models that included leptin, neither TG nor HDL levels were predictive at 13 or 26 weeks, while leptin levels remained strongly predictive of the percentage reduction in ΔTDID at both time points (effect = 1.1%, P < .0001 and effect = 1.0%, P = .001, respectively). When leptin was removed from the regression models, only HDL was predictive at 13 weeks (effect = 0.3%, P = .039). The results also suggest reduced explainability and predictive power if leptin is removed (drop in adjusted R-squared from 0.24 to 0.11 for 13 weeks, and from 0.15 to 0.05 for 26 weeks). The goal of this analysis was to identify predictors of %ΔTDID in response to metformin as adjuvant therapy in adolescents with T1D and obesity or overweight. We show that baseline leptin was a predictor of decrease in TDID in response to metformin at 13 and 26 weeks. In addition, baseline leptin was a prognostic factor of decrease in TDID in participants in the placebo arm, at 13 but not at 26 weeks. Leptin is an adipokine that is secreted by the adipose tissue to regulate obesity-induced inflammation,9 food intake, BMI and lipolysis, among other actions. It has been shown to be an indicator of insulin resistance, independent of body fat mass.10, 11 Therefore, it is possible that higher baseline leptin levels reflect higher insulin resistance and thus, predict more pronounced reduction in TDID in response to metformin. It is probable that the reduction in TDID is secondary to improved insulin sensitivity. Indeed, in a subsequent study that included hyperinsulinaemic euglycaemic clamps in a subgroup of participants from the original trial by Libman et al.,8 there was improved insulin sensitivity in those who received metformin.12 We additionally performed an analysis to evaluate if baseline leptin is positively correlated with reduction in HbA1c. We found a weak significant relationship at 13 weeks between baseline leptin and reduction in HbA1c (P = .047) that was not sustained at 26 weeks (data not shown). We assessed whether other simpler measures of insulin resistance, such as HDL and TG levels, could be used as predictors of response to metformin. We found that when including leptin in the multivariate models that also contained TG and HDL, leptin remained a strong predictor at both 13 and 26 weeks. Removing leptin reduced the explainability and predictive power of the model, and HDL was no longer a significant predictor of %ΔTDID at 26 weeks. This strongly suggests that leptin provides additional value compared with simpler measures such as TG and HDL, and that perhaps baseline HDL may be used to complement rather than replace baseline leptin to predict %ΔTDID. We did not observe a predictive value for baseline adiponectin on %ΔTDID, whether in the univariate or multivariate models, and whether we performed the analysis in the entire cohort or by study arm. Because lower adiponectin levels could reflect higher insulin resistance, we assessed for correlations between levels of adiponectin levels and available measures of insulin resistance and found a significant positive correlation of adiponectin with HDL levels and a negative correlation with TG levels (data not shown). However, in our predictive models using TG or HDL as surrogate measures for insulin resistance, we did not observe a predictive effect of these measures on %ΔTDID. Therefore, it appears that %ΔTDID is not affected by insulin resistance and therefore would not be influenced by adiponectin levels. Thus, it appears that leptin may have a predictive effect independent of insulin resistance. This analysis has some limitations, including the comparatively smaller sample size, although whether in the entire cohort or in each arm separately, univariate and multiple regression analyses appeared to show the same predictive and prognostic value of baseline leptin. Additionally, while we did not see a predictive effect of baseline %BF on %ΔTDID, and because leptin has been positively correlated with obesity and %BF, it is still possible that leptin as a predictor and independent of adiposity, has an effect on response to metformin, and that this effect is mediated by insulin sensitivity. Our study shows that baseline leptin may be a useful predictor of response to metformin therapy. Future larger studies are needed to verify whether leptin can serve as an adequate biomarker for treatment response in youth with T1D. MJR and HMI conceptualized the study. HMI, SB, JW and MJR analysed and interpreted the data, and wrote the manuscript. SB, JW, AS, MT, IL, FB and KN contributed to the design, interpreted the data and reviewed/edited the manuscript. Dr. Ismail's work was supported by Grant 2021258 from the Doris Duke Charitable Foundation through the COVID-19 Fund to Retain Clinical Scientists collaborative grant programme and was made possible through the support of Grant 62288 from the John Templeton Foundation. This publication was also supported by a Pilot and Feasibility Award within the CDMD NIH/NIDDK (Grant Number P30 DK097512) and the NIH/NIDDK (Grant Number K23DK129799). Dr. Redondo's work is supported by NIH grant numbers R01 DK124395 and R01 DK121843. Dr. Barua and Dr. Sabharwal were supported by the NSF PATHS-UP Engineering Research Center Award #1648451. The authors declare that there is no duality of interest associated with this manuscript. The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/dom.15218. The data were analysed and generated from a publicly available dataset and are available on request from the authors. Data S1. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.