A total of 1679 diabetic participants were recruited and 359 individuals were excluded from the analysis (ESM Fig. 1). Individuals who were excluded because of missing data did not significantly differ in terms of their baseline characteristics from those remaining in the study. Out of the 1320 individuals, 78% had eGFR >60 ml min−1 1.73 m−2 at baseline and their serum galectin-3 level was significantly lower than those individuals with eGFR between 30 and 60 ml min−1 1.73 m−2 (7.70 ± 2.38 vs 9.85 ± 2.90 ng/ml respectively, p < 0.01). To determine whether there were changes in serum galectin-3 levels in type 2 diabetic individuals with normal renal function, 270 participants with type 2 diabetes and eGFR >60 ml min−1 1.73 m−2 were randomly chosen and compared with a non-diabetic control group. Their clinical characteristics are shown in Table 1. The two groups were matched for age and sex, but the diabetic individuals had significantly higher BMI. Despite eGFR being similar in the two groups, the diabetic individuals had significantly higher serum galectin-3 concentration than the control group. The differences in galectin-3 levels remained significant even after adjusting for BMI. Hence, serum galectin-3 was increased in individuals with type 2 diabetes even when renal function was normal.
During prospective follow-up (mean duration 9 ± 5 years), the primary outcome of doubling of serum creatinine was observed in 270 individuals, of whom 77 individuals required renal replacement therapy (71 with dialysis and six with renal transplantation). In those individuals without doubling of serum creatinine, 139 progressed from normo- or microalbuminuria at baseline to macroalbuminuria. Total mortality in the whole cohort was 8.6%. The baseline clinical characteristics and serum galectin-3 levels are shown in Tables 2 and 3. Serum galectin-3 was significantly increased in the group with doubling of serum creatinine (Table 2) and in those with incident macroalbuminuria (Table 3). In the whole cohort, there were no sex differences in serum galectin-3, and galectin-3 correlated with age (r = 0.28, p < 0.01), eGFR (r = −0.37, p < 0.01), urine ACR (r = 0.16, p < 0.01) and, weakly, with HbA1c (r = 0.06, p = 0.04). No correlation was seen with BMI, systolic BP or duration of diabetes. There was a significant association between baseline galectin-3 levels and change in eGFR from baseline (r = −0.29, p < 0.01) (ESM Fig. 2) and in ACR (ρ = 0.24, p < 0.01) (ESM Fig. 3).
Kaplan–Meier analysis was performed to evaluate the association of serum galectin-3 with the hard renal endpoint of doubling of serum creatinine (Fig. 1). Baseline serum galectin-3 was stratified into quartiles and there was a graded association between increasing quartiles of galectin-3 and renal outcome (logrank test p < 0.001). Multivariable Cox regression analysis showed a significant association between serum galectin-3 level (analysed either as quartiles or as a continuous variable) and doubling of serum creatinine. The association remained significant even after adjustment for potential confounders, including baseline eGFR, albuminuria status, age, sex, BMI, duration of diabetes, HbA1c, smoking, systolic BP and ACE inhibitor (ACEI)/angiotensin II receptor blocker (ARB) therapy at baseline. Table 4 shows the results when galectin-3 was analysed as a continuous variable. In the fully adjusted model, when galectin-3 was analysed as quartiles, individuals in the third quartile (>7.68 to ≤9.73 ng/ml, HR 2.00, 95% CI 1.20, 3.34, p = 0.008) and the highest quartile of serum galectin-3 (>9.73 ng/ml, HR 4.07, 95% CI 2.47, 6.71, p < 0.001) had significantly elevated risk of deterioration of renal function compared with those in the lowest quartile. The AUC for the predictive model comprising traditional risk factors including baseline eGFR, albuminuria status, age, sex, BMI, duration of diabetes, HbA1c, smoking, systolic BP and ACEI/ARB therapy was 0.84 (95% CI 0.82, 0.86). The AUC significantly increased to 0.87 (95% CI 0.85, 0.89) when galectin-3 was added to the predictive model, with the mean difference between the two AUC values being 0.03 (95% CI 0.02, 0.05, p < 0.01) (ESM Fig. 4).
To evaluate the association between galectin-3 and progression to macroalbuminuria, 361 individuals with macroalbuminuria at baseline and/or those with doubling of serum creatinine on follow-up were excluded from the analysis. Overall, 139 individuals developed macroalbuminuria during follow-up and there was a significant association between serum galectin-3 and incident macroalbuminuria (HR 1.22, 95% CI 1.14, 1.30, p < 0.001). The association remained significant in the fully adjusted model (HR 1.20, 95% CI 1.12, 1.30, p < 0.001) (Table 5), and when serum galectin-3 was analysed as quartiles, individuals in the highest quartile had a threefold risk of incident macroalbuminuria (HR 3.23, 95% CI 1.84, 5.68, p < 0.001). The AUC for the predictive model comprising baseline eGFR, ACR, age, sex, BMI, duration of diabetes, HbA1c, smoking, systolic BP and ACEI/ARB therapy was 0.70 (95% CI 0.65, 0.75). There was no significant difference in AUC (0.73, 95% CI 0.68, 0.77) when galectin-3 was added to the model (ESM Fig. 5).