Intern Emerg Med DOI 10.1007/s11739-015-1352-z
IM - ORIGINAL
Clustering of immunological, metabolic and genetic features in latent autoimmune diabetes in adults: evidence from principal component analysis Giovanni Mario Pes1 • Alessandro Palmerio Delitala1 • Alessandra Errigo1 Giuseppe Delitala1 • Maria Pina Dore1
•
Received: 22 July 2015 / Accepted: 7 November 2015 Ó SIMI 2015
Abstract Latent autoimmune diabetes in adults (LADA) which accounts for more than 10 % of all cases of diabetes is characterized by onset after age 30, absence of ketoacidosis, insulin independence for at least 6 months, and presence of circulating islet-cell antibodies. Its marked heterogeneity in clinical features and immunological markers suggests the existence of multiple mechanisms underlying its pathogenesis. The principal component (PC) analysis is a statistical approach used for finding patterns in data of high dimension. In this study the PC analysis was applied to a set of variables from a cohort of Sardinian LADA patients to identify a smaller number of latent patterns. A list of 11 variables including clinical (gender, BMI, lipid profile, systolic and diastolic blood pressure and insulin-free time period), immunological (anti-GAD65, anti-IA-2 and anti-TPO antibody titers) and genetic features (predisposing gene variants previously identified as risk factors for autoimmune diabetes) retrieved from clinical records of 238 LADA patients referred to the Internal Medicine Unit of University of Sassari, Italy, were analyzed by PC analysis. The predictive value of each PC on
the further development of insulin dependence was evaluated using Kaplan–Meier curves. Overall 4 clusters were identified by PC analysis. In component PC-1, the dominant variables were: BMI, triglycerides, systolic and diastolic blood pressure and duration of insulin-free time period; in PC-2: genetic variables such as Class II HLA, CTLA-4 as well as anti-GAD65, anti-IA-2 and anti-TPO antibody titers, and the insulin-free time period predominated; in PC-3: gender and triglycerides; and in PC-4: total cholesterol. These components explained 18, 15, 12, and 12 %, respectively, of the total variance in the LADA cohort. The predictive power of insulin dependence of the four components was different. PC-2 (characterized mostly by high antibody titers and presence of predisposing genetic markers) showed a faster beta-cells failure and PC3 (characterized mostly by gender and high triglycerides) and PC-4 (high cholesterol) showed a slower beta-cells failure. PC-1 (including dislipidemia and other metabolic dysfunctions), showed a mild beta-cells failure. In conclusion variable clustering might be consistent with different pathogenic pathways and/or distinct immune mechanisms in LADA and could potentially help physicians improve the clinical management of these patients. Keywords Latent autoimmune diabetes in adults Autoimmune diabetes Type 1 diabetes mellitus Clinical heterogeneity Principal component analysis
G. M. Pes and A. P. Delitala contributed equally to this work. & Maria Pina Dore
[email protected] Giovanni Mario Pes
[email protected] 1
Dipartimento di Medicina Clinica e Sperimentale, University of Sassari, Viale San Pietro 8, 07100 Sassari, Italy
Abbreviations LADA Latent autoimmune diabetes in adults PC Principal component HDL High density lipoprotein cholesterol BMI Body mass index INS Insulin gene CTLA-4 Cytotoxic T lymphocyte-associated protein-4
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Introduction Latent autoimmune diabetes in adults (LADA) is a common form of diabetes accounting for more than 10 % of all cases occurring in adulthood [1, 2]. Despite having been described more than 20 years ago, the pathogenesis, clinical features and treatment strategies of LADA are still controversial [3]. Apart from the conventional diagnostic criteria, i.e., onset after age 30 and no requirement for insulin treatment at least in the first 6 months, its hallmark is the presence of several antibodies against islet-cell antigens [2]. Those having the greater diagnostic value are anti-glutamic acid decarboxylase-65 (anti-GAD65), antiinsulinoma associated antigen IA-2 (anti-IA-2) and antizinc transporter 8 (ZnT8) antibodies [4–6]. Although in the past it was considered as an intermediate form between type 1 and 2 diabetes, more recently LADA is classified as a variant of late-onset type 1 diabetes with a delayed progression [7]. However, this assumption has been criticized by some authors [3]. Gale argues that this form of diabetes should not be regarded as a distinct entity from the usual type 1 diabetes occurring in infancy [8]. On the other hand, Palmer et al. emphasize that LADA patients show a greater insulin resistance and often do not require early insulin treatment compared to the more common early onset type 1 diabetes patients [9]. A number of genes playing a role in type 1 diabetes have also been associated with LADA, such as genetic components located in the HLA class II locus (primarily the HLA-DRB1, -DQA1, and -DQB1 alleles), as well as the gene encoding insulin (INS), the gene encoding cytotoxic T-lymphocyte associated protein 4 (CTLA-4), and a myriad of other gene variants with a weaker effect size [10–15]. Many authors recognize the substantial clinical and immunological heterogeneity of LADA, and their efforts are directed toward identification of patient subgroups with more homogeneous characteristics [16–18]. Since the LADA phenotype encompasses a high number of potential interrelated variables, the use of statistical techniques able to reduce the total variability, makes easier the identification of homogeneous subgroups of patients. Principal component (PC) analysis is a multivariate statistical technique allowing a set of correlated variables to be reduced into relatively few uncorrelated components made up of linear combinations of the original variables [19]. PC analysis has previously been applied to study diabetes but never to the specific LADA form [20]. This approach appears attractive, and may potentially be able to identify subgroups of patients with shared pathophysiology and a comparable risk to develop long-term complications. These subgroups may benefit from specific therapeutic interventions based upon an accurate risk stratification [21]. In addition to identifying independent prognostic
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factors for the long-term disease outcome, this approach could shed light on different pathogenic mechanisms. In this study, the PC analysis was applied to a set of correlated variables from a cohort of LADA patients from Sardinia, to identify a smaller number of latent patterns potentially useful in the clinical setting.
Materials and methods Patient selection The study was conducted by examining the clinical records of 238 adult patients with a definitive diagnosis of LADA. Patients were of Sardinian origin for at least 2 generations, and were recruited in the period between November 2005 and December 2010 from an open access Diabetic Unit, Department of Internal Medicine, University of Sassari, Italy. The demographic, immunological, and clinical features of LADA patients have been previously reported [22]. The LADA diagnosis was established in accordance with the onset after age 35, the presence of circulating antiGAD65 or anti-IA-2 antibodies in patients showing a type 2 diabetic phenotype, and no requirement for insulin within at least 8 months after onset [7, 22]. Patients who had severe liver or kidney disease were excluded from the analysis. C-peptide was available only in a limited number of patients. For this reason this variable was excluded from the analysis. The study is approved by the Ethics Committee of Sassari (Prot. no. 44 of 12/02/1997). Variables selection All variables available in the charts had been collected at diagnosis, which in most cases, was made within 2 years from onset. Body weight was measured in light clothing without shoes, and height using a wall-mounted stadiometer. Body mass index (BMI) was calculated by the formula: weight (kg)/height (m)2. Blood pressure was measured 3 times in a sitting position after resting for 10 min. Total cholesterol, triglycerides and high density lipoprotein (HDL) cholesterol were assessed after an overnight fast. Low density lipoprotein (LDL) cholesterol was calculated by the Friedewald formula: LDL-C = Total Cholesterol ((0.46 9 TG) ? HDL-C). The presence of anti-GAD65 and anti-IA-2 antibodies was evaluated by using a radio binding assay with in vitro-translated [35S] methionine labeled GAD65 or IA-2 [23]. Anti-TPO antibodies were detected by RIA using commercial kits (Medipan, Berlin, Germany). When any of the following conditions occurred: (1) ketoacidosis; (2) postprandial glucose level above 180 mg/dl despite maximum allowed dose of oral hypoglycemic agents,
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and (3) significant body weight loss with no change in dietary calorie intake, insulin therapy was initiated. The duration of the insulin-free period up to 4 years was known in all participants [22]. Genotypes or alleles previously associated with the susceptibility to autoimmune diabetes, such as HLA class II, insulin (INS) promoter and CTLA-4 were also included in the analysis. More specifically, HLA class II profile was ranked as high (at least two high-risk haplotypes), intermediate (one high-risk and one permissiveneutral haplotype), or low risk for diabetes (two copies of negatively associated haplotypes or combinations of negatively associated and neutral haplotypes). Patients were classified according to the presence of one or two risk alleles of insulin gene variable number of tandem repeats (INSVNTR). In addition, the G6230A (rs3087243) functional polymorphism within the CTLA-4 gene, exhibiting regulatory properties on immune effector T cells, had also been genotyped, and patients carrying at least one G allele were classified as high-risk, whereas patients with no G alleles were considered at low risk [24]. PTPN22 gene variants were excluded from the analysis since their association with autoimmune diabetes is very weak in Sardinia [25]. An ordinal cumulative genetic score (CGS) was calculated combining HLA, INS and CTLA–4, by giving 0–2 points to low, intermediate or high–risk genotypes. The sum of partial scores resulting from all these multiple combinations gave a CGS ranging from 0 to 6. Statistical analysis A standard PC analysis was performed by using SPSS 16.0 (SPSS Inc., Chicago, IL, USA) with the aim of grouping variables from LADA patients into clusters based upon their similarities. PC analysis is used to identify ‘‘hidden patterns’’ in a dataset. The analysis allows a correlation of variables into linear combinations, to retain most of the original system information in a simplified frame (cluster) [19]. Only 11 variables among those collected from patients charts were included in the analysis, namely: gender, BMI, total cholesterol and triglycerides, systolic and diastolic blood pressure, anti-GAD65, anti-IA-2, antiTPO antibodies, duration of the insulin-free time period, and the CGS (class II HLA, INS gene and CTLA-4 related variants). Excluded variables were (1) age at diagnosis (because it is poorly related to the age of onset), (2) LDL and HDL (they are excessively related to total cholesterol); (3) waist circumference (strongly related to BMI), and finally (4) fasting glucose and glycated hemoglobin (as they could have been modified by previous treatment). The optimal number of PCs explaining different parts of the total variance of dataset was determined by retaining only factors with eigenvalues Cto 1.0. Variables displaying an absolute factor loading C0.4 were considered
representative of each PCs. Patients were classified according to their dominant PC, and the corresponding Kaplan–Meier (KM) curves related to the progression toward insulin dependence for the resulting clusters were plotted. The log-rank test was performed to detect differences between PCs.
Results Basic characteristics of the 238 LADA patients included in this study are reported in Table 1. The proportion of LADA patients who required insulin therapy within 4 years of diagnosis was 37.4 % [22]. The 11 variables selected for PC analysis are shown in Table 2. The analysis was able to identify four principal components. The PC-1, -2, -3 and -4 explained individually 18, 15, 12, and 12 % of the variation within the LADA dataset and together reached 58.6 %. Table 2 also shows the stronger or weaker influence of each original variable on each PCs. In PC-1 the dominant variables were: BMI, triglycerides, systolic and diastolic blood pressure, and duration of the insulin-free period. Titers of both anti-GAD65 and anti-IA-2 islet cell antibodies and antiTPO auto-antibodies were the dominant variables in PC-2. Table 1 Basic characteristics of 238 Sardinian patients diagnosed with latent autoimmune diabetes in adults Variables
Absolute or mean values ± SD
Number of cases
238
Gender (M:F)
110:128
Mean age at diagnosis (years)
54.0 ± 10.9
Anti-GAD65 titer (arbitrary units)
0.77 ± 0.85
Anti-IA-2 titer (arbitrary units)
0.015 ± 0.017
Anti-TPO titer (IU/ml)
118.0 ± 409.6
BMI (kg/m2)
28.4 ± 4.6
Waist circumference (cm)
96.0 ± 10.6
Total cholesterol (mg/ml)
209.7 ± 43.3
HDL cholesterol (mmol/l)
55.4 ± 16.9
LDL cholesterol (mmol/l)
129.3 ± 38.7
Triglycerides (mmol/l)
115.2 ± 66.3
Fasting glucose (mg/dl) HbA1c (%)a
a
131.7 ± 32.8 7.9 ± 2.3
Systolic blood pressure (mmHg)
132.4 ± 18.6
Diastolic blood pressure (mmHg)
81.8 ± 10.3
Cumulative genetic scoreb
3.6 ± 1.2
Insulin-free period (months)
36.7 ± 15.8
a
These metabolic parameters merely reflect the average levels during follow-up and are not indicative of metabolic status at diagnosis
b
The cumulative genetic score is an integer ranging from 0 to 6, calculated by summing up the number of major genetic risk haplotypes/alleles related to HLA class II, INS-VNTR and CTLA-4 (see ‘‘Materials and methods’’)
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Intern Emerg Med Table 2 Eleven immunological, metabolic and genetic LADA variables included in PCA analysis
Variable selected for PCA analysis
Attribute namea
Extracted componentsb PC-1
PC-2
PC-3
PC-4
Gender
SEX
-0.008
-0.219
0.687
Body mass index
BMI
0.454
-0.387
-0.421
0.011
Total cholesterol
TC
0.269
0.192
0.445
-0.752
Triglycerides
TG
0.579
-0.035
0.502
0.003
0.410
Sistolic blood pressure
SBP
0.796
0.209
-0.180
0.184
Diastolic blood pressure
DBP
0.817
0.253
-0.126
0.012
-0.346
0.624
0.041
-0.038
0.022
0.567
0.073
-0.090
Anti-GAD65
GAD
Anti-IA-2
IA2
Anti-TPO
TPO
-0.077
0.764
-0.234
0.147
Cumulative genetic score
CGS
0.092
0.347
0.289
0.459
Insulin-free period
IFP
0.493
-0.457
-0.005
0.273
The factor-loading matrix for four principal components, after Varimax rotation, illustrates the stronger or weaker influence of each variable on each component a
The attribute name is the same reported in Fig. 1
b
Variables displaying an absolute factor loading C0.4 (in bold) were considered representative of each PCs. A positive sign in the factor indicates that the PC was influenced by higher values of the original variable, while a negative sign indicates the influence of lower values (with regard to gender, a negative sign is associated to women)
Discussion There are discordant opinions regarding the pathogenesis of LADA, basically due to the extreme heterogeneity of this condition, and there is no general agreement among physicians as to the right timing for insulin replacement therapy to prolong pancreatic b-cell life [17, 18, 26]. The present study is a preliminary attempt to dissect the heterogeneity of the disease by using PC analysis to reduce
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1,0
TPO GAD IA2 CGS
PC-2 scores
The variability associated to gender and triglycerides level was mainly represented in the PC-3, while PC-4 accounted for the majority of the variation in total cholesterol and the genetic score. Figure 1 illustrates the loading plot with the relative position of each variable in the orthogonal plane generated by the first two PCs. Two patterns were clearly evident: lipid parameters and blood pressure mainly on the first PC while autoantibody titers and genetic risk mainly on the second PC. BMI and duration of insulin-free period loaded equally in PC-1 and PC-2 (Fig. 1). Figure 2 represents the KM curves for disease progression in LADA patients stratified according to the dominant PC. Patients belonging to PC-2 (mostly characterized by genetic markers) showed a significantly faster insulin dependence (P = 0.001), whereas PC-3 (characterized mostly by triglycerides variability) and PC4 (cholesterol variability) showed the slowest progression to beta-cells failure. PC-1 (associated with dislipidemia and other metabolic dysfunctions), showed an intermediate, mild beta-cells failure.
SBP
TC 0,0
DBP TG
SEX
BMI IFP
-1,0 -1,0
0,0
1,0
PC-1 scores Fig. 1 Score plot shows the relationship between the original eleven variables and the two first principal components of the LADA data set, providing a map of how these variables aggregate into coherent groups (the 3 circles) suggesting distinct pathogenic pathways. SEX gender, BMI body mass index, TC total cholesterol, TG triglycerides, SBP systolic blood pressure, DBP diastolic blood pressure, GAD antibody anti-GAD65, IA2 antibody anti-IA-2, TPO antibody antiTPO, CGS cumulative genetic score, IFP insulin-free period
the dimensionality of the original LADA variables. PC analysis identifies 4 PCs (variable clusters) in our LADA dataset explaining 57 % of the total variance. PC-1, which accounts for most of the dataset variance, could be
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Proporon of insulin-free LADA paents
1.0
0.8
0.6
0.4
0.2
0.0
0
PC-1
PC-2
PC-3
PC-4
10
20
30
40
50
Follow-up (months)
Fig. 2 Kaplan–Meier curves showing the probability to develop insulin dependence in LADA patients subdivided according to principal components
considered as the ‘‘metabolic’’ component of LADA, since BMI, blood pressure and triglycerides, are the dominant variables represented by coefficients (i.e., loadings) with the largest absolute values. It could be speculated that this component reflects mainly the adipose tissue accumulation occurring in LADA, which exacerbates insulin resistance, leading in turn to hyperinsulinaemia and hypertension. Notably, the PC analysis reveals that this metabolic component explains only 18 % of the variance in LADA, suggesting that obesity and hypertriglyceridemia are not per se sufficient to characterize the entire metabolic impairment. Lohmann et al. observe that patients with lower BMI show a more aggressive disease, which can lead to earlier ß-cell exhaustion [16]. Besides, in our previous report we show that LADA patients with high BMI and metabolic syndrome tend to have a longer duration of residual insulin-secreting function, which is associated with a longer insulin-free time [22]. PC-2 is modeled by a cluster of variables including the cumulative genetic risk, the titer of pancreatic (anti-GAD65, anti-IA-2) and extrapancreatic (anti-TPO) antibodies. It is tempting to speculate that this PC would estimate the influence of gene variants driving the escape from immune tolerance as well as the expression of the concomitant antibody response. Our results support the findings of Falorni et al. that antiGAD65 titers are influenced by predisposing gene haplotypes [27]. Moreover, the covariation of this component with gender is weak; suggesting that the faster deterioration of ß-cell function observed in women with LADA is mediated mostly by their metabolic profile (PC-1) rather than by predisposing genetic factors. PC-3 and PC-4, which are partially overlapping, collect the remaining variability
associated to metabolic parameters such as cholesterol and triglycerides. However, these components reflect also the variability due to gender that is not present in the PC-1. On the other hand, although in the PC-4 cluster the cholesterol variable (metabolic profile) is predominant for those patients, the disease progression is lower likely underlining a different pathway. Despite being uncorrelated by definition with PC1, they may represent another aspect of the LADA metabolic phenotype that could be mediated by a different pathway. The identification of variable clusters in LADA patients may have a clinical relevant implication for physician. According to the prevalent pattern, physicians could tailor the right treatment for an individual patient. For instance, in patients where the PC-2 is dominant, who tend to develop insulin dependence relatively quickly, an early insulin therapy to prevent long-term complications might be beneficial according to some intervention studies [28]. Alternatively these patients may benefit from a diet rich in fatty fish that is associated with a decreased risk of LADA probably via the alterations in gene expression on the immune system mediated by EPA and DHA [29, 30]. On the contrary, LADA patients within ‘‘metabolic’’ clusters such as PC-1, -2 and -3 would benefit from treatment aimed mainly at reducing BMI through low-calorie diets, and a systematic program of physical activity in addition to the use of dipeptidyl peptidase 4 inhibitors [31].
Strength and limitations In the present study PC analysis has been used for the first time to examine the variability in metabolic and immunological profile of patients with LADA. The strength of this study is the analysis of a large cohort of patients from a very homogeneous geographical area, and the very robust diagnostic criteria adopted (age at onset C35; insulin-free period C8 months) to ensure an unequivocal phenotype. For the cohort studied, a high number of variables and a long time follow up (at least 4 years) were available making the PC analysis suitable and reliable for the purpose. A possible limitation is the subjective interpretation of the PC analysis findings, although unavoidable. In addition, data concerning long term clinical complications of our LADA cohort are lacking.
Conclusion The results of our study suggest that LADA heterogeneity could be, at least partially, unraveled by taking into account variables that cluster into relatively homogeneous subgroups. The identification of variable clusters in LADA
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patients may have clinically relevant implications. According to the prevalent pattern, physicians could tailor the right treatment and follow up for an individual patient. Compliance with ethical standards Conflict of interest of interest.
The authors declare that they have no conflict
Statement of human and animal rights All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the author. Informed consent Informed consent was obtained from all individual participants included in the study.
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