Physical Fitness and Cardiovascular Risk Factors in Novel Diabetes Subgroups.

CONTEXT: Physical inactivity promotes insulin resistance and increases the risk of diabetes and cardiovascular disease. Recently introduced clustering based on simple clinical measures identified diabetes subgroups (clusters) with different risks of diabetes-related comorbidities and complications.
OBJECTIVE: This study aims to determine differences in physical fitness and cardiovascular risk between diabetes subgroups and a glucose-tolerant control group (CON). We hypothesized that the severe insulin-resistant diabetes (SIRD) subgroup would be associated with lower physical fitness and increased cardiovascular risk.
METHODS: The physical fitness and cardiovascular risk of 746 participants with recent-onset diabetes (diabetes duration of < 12 months, aged 18-69 years) and 74 CONs of the German Diabetes Study (GDS), a prospective longitudinal cohort study, were analyzed. Main outcome measures included physical fitness (VO2max from spiroerogometry), endothelial function (flow- and nitroglycerin-mediated dilation), and cardiovascular risk scores (Framingham Risk Scores for Coronary Heart Disease [FRS-CHD] and Atherosclerotic CardioVascular Disease [ASCVD] risk score).
RESULTS: VO2max was lower in SIRD than in CON, severe autoimmune diabetes (SAID) (both P < .001), and mild age-related diabetes (MARD) (P < .01) subgroups, but not different compared to severe insulin-deficient diabetes (SIDD) (P = .98) and moderate obesity-related diabetes (MOD) subgroups (P = .07) after adjustment for age, sex, and body mass index. Endothelial function was similar among all groups, whereas SAID had lower FRS-CHD and ASCVD than SIRD, MOD, and MARD (all P < .001).
CONCLUSION: Despite comparable endothelial function across all groups, SIRD showed the lowest physical fitness. Of note, SAID had the lowest cardiovascular risk within the first year after diabetes diagnosis compared to the other diabetes subgroups.

Diabetes mellitus confers an about doubled risk of cardiovascular outcomes, as documented by a meta-analysis of 102 prospective studies (1). Chronic hyperglycemia contributed to higher cardiovascular risk by impairment of vascular function (2). However, other abnormalities such as adipose tissue dysfunction and insulin resistance with subsequent dyslipidemia and hyperinsulinemia may be at least as important not only for the development of type 2 diabetes, but also for worsening cardiovascular risk (3). Thus, it is of clinical relevance to assess the roles of the various diabetes-related risk factors for differences in premature morbidity and mortality related to cardiovascular complications in order to contribute to the road map of precision in medicine (4).

In this context, clustering of people with diabetes recently allowed us to identify subgroups (clusters) with distinct clinical features (5, 6). Out of the 5 subgroups, 1 subgroup (severe autoimmune diabetes [SAID]) overlaps with type 1 diabetes, while the others (severe insulin-deficient diabetes [SIDD], severe insulin-resistant diabetes [SIRD], mild obesity-related diabetes [MOD], and mild age-related diabetes [MARD]) reflect the classical type 2 diabetes (5, 6). Specifically, the SIRD subgroup is associated with a higher prevalence of diabetic nephropathy (5) and nonalcoholic fatty liver disease (6), both of which are associated with greater risk of cardiovascular events (3, 7). In line with these findings, endothelial dysfunction, characterized by impaired flow-mediated vasodilation due to decreased nitric oxide bioavailability (8), is a cardiovascular risk factor, which is more prevalent in people with insulin resistance, dyslipidemia, and type 2 diabetes (9). Moreover, 2 previous studies reported an at least nominally increased cardiovascular risk in individuals with SIRD (5, 10), while another analysis suggested that high glycated hemoglobin A1c (HbA1c) and low body mass index (BMI), typical for SIDD, may better explain the elevated risk of major adverse cardiovascular events and death (11). Currently, there is no explanation for the observed differences between the subgroups and the findings from different studies.

Lifestyle is the key modifiable cardiovascular risk factor, as even small improvements in physical fitness, reflecting cardiorespiratory fitness (CRF), can reduce the risk of type 2 diabetes (12) and regular moderate physical activity decreases cardiovascular risk (13). Exercise training enhances physical fitness, that is, maximal oxidative capacity or oxidative capacity at the anaerobic threshold, and insulin sensitivity in type 2 diabetes (14), but even habitual physical activity of lower intensity can improve insulin sensitivity (15). In general, individuals with overt type 2 diabetes exhibit lower physical activity levels (16) and lower tolerance to physical exhaustion than glucose-tolerant humans (17). Interestingly, physical fitness is not affected by hyperglycemia in people with type 1 diabetes (18). However, little is known about physical fitness and activity behavior, endothelial function, and cardiovascular risk in the novel diabetes subgroups. Because of the higher risk of sedentary lifestyle for the development of insulin resistance (19), we hypothesized that particularly SIRD is associated with (i) lower physical fitness, (ii) possibly due to reduced physical activity, (iii) lower endothelial dysfunction, and (iv) with increased cardiometabolic risk. To this end, we analyzed the data from the multicentric prospective German Diabetes Study (GDS), which follows people with diabetes from the first year after diagnosis and glucose-tolerant humans (20) and recently allowed us to validate insulin sensitivity and secretion of the diabetes cluster concept (6).

Materials and Methods

Study Population

This study included all consecutive participants with diabetes mellitus (n = 746) at the first visit (diabetes duration of < 12 months, aged 18-69 years) and a lean, glucose-tolerant control group (CON, n = 74) of the GDS (20), who had complete data sets with regard to age at diagnosis, BMI, glycemia, homoeostasis model estimates (homeostatic model assessment of insulin resistance [HOMA-IR] and homeostatic model assessment of β-cell activity [HOMA-B]), calculated from C-peptide values, glutamate acid decarboxylase antibodies (GADAs), and spiroergometry data. They underwent comprehensive phenotyping after giving written informed consent to the study, which was registered at clinicaltrials.gov (identifier No. NCT01055093), approved by the ethics boards of the Medical Faculty of Heinrich Heine University Düsseldorf (reference No. 4508) and of the associated centers, and performed according to the Declaration of Helsinki (20). Diagnosis of diabetes is based on criteria of the American Diabetes Association (21). CONs had no first-degree relatives with known diabetes and underwent a 75-g oral glucose tolerance test to exclude dysglycemia (21). Further, they had a BMI lower than 25. Exclusion criteria for all participants comprised diabetes of other causes; pregnancy; acute or severe chronic heart, hepatic, renal or psychiatric diseases; or immunosuppressive treatment. Comprehensive phenotyping was performed as described previously (20). Blood pressure was measured 3 times by a blood pressure monitor (Omron M400, Omron Healthcare GmbH) in supine position on the left arm. A 12-lead electrocardiogram and resting heart rate (HR) were recorded under resting conditions.

Spiroergometry

This gold-standard test to predict CRF measures physical fitness, which is described by the capacity of the circulatory and respiratory systems to deliver oxygenated blood to skeletal muscles during exercise (14). Measurements were performed by an exhaustive exercise test on a cycle ergometer (Ergometrics 900, Ergoline) (20), with continuous monitoring of HR, electrocardiogram, blood pressure, respiratory gas exchange ratio (RER), and peak power. During exercising, work rate was continually increased every 15 seconds and the incremental part of the test lasted 8 to 12 minutes. During the entire exercise test, the breath-by-breath system continuously records every single breath and parameters from gas measures are then given as averaged data over 8 breaths. VO2max refers to the average of the last 8 breaths of exercising. VO2max and VCO2max were recorded at maximal exertion, whereby the maximal respiratory minute volume (VEmax) indicates the volume of gas inhaled/exhaled per minute at maximal exertion. During incremental exercise, the aerobic-anaerobic transition indicates the first lactate increase. The first ventilatory threshold (VT1) is the respiratory response to these metabolic changes and is defined as an increase in ventilation (VE) disproportionally relative to VO2. As result of the decrease in pH and partial pressure of carbon dioxide, ventilation increases (respiratory compensation of the lactate increase), which can be measured during spiroergometry, that is, VE/VO2 exhibited a systematic increase without a concomitant increase in VE/VCO2 (22). Oxygen pulse describes the maximal amount of oxygen absorbed per maximal heartbeat during spiroergometry. All individuals performed cycling spiroergometry until exhaustion, as defined by RERmax, exceeding VT1, HR ,and ventilation. Spiroerometry was not been performed under fasted condition.

Baecke Questionnaire

The Baecke questionnaire (23) was used to assess habitual physical activity scores in the areas of work, sports, and leisure. The Baecke index represents the sum of individual scores. Scores range from 0 to 15, and high scores reflect high physical activity levels. The German translation was validated against 14-day exercise protocols (24) in adults aged 20 to 65.

Bioimpedance Analysis

Bioimpedance analysis (BIA) was used for the estimation of fat mass and fat-free mass and to calculate the deriving percentage fat mass (BioElectrical Impedance Analyzer System, RJL Systems) (20).

Endothelial Function Test

Flow-mediated dilation (FMD), nitroglycerin (glycerol trinitrate)-mediated dilation (NMD), flow velocity, preocclusion, and postocclusion diameter were measured as described previously using a B-mode ultrasonography Logiq S8 GE device (GE Healthcare) (20, 25). Endothelium-independent dilation was measured after sublingual application of 0.4 mg glycerol trinitrate. Flow velocity was measured at the brachial artery at rest, 15 seconds after deflation as well as 120 seconds after deflation. Furthermore, increase in brachial artery diameter was taken as surrogate for pulse-wave velocity (26) to assess arterial stiffness (27).

FMD was analyzed using the established edge-detection Brachial Analyzer software (Medical Imaging Applications). FMD was calculated as the quotient between the arterial baseline diameter and the diameter post–cuff deflation (25). Results were expressed in percentage [(diameter “stress” − diameter baseline)/diameter baseline], as possible advantages of allometric scaling have been challenged by others (eg, relationship between FMD and CVD is weaker (28); ability to examine the statistical relevance of individually values are limited) (29).

Cardiovascular Risk Scores

The Framingham Risk score for Coronary Heart Disease (FRS-CHD) is used to assess the 10-year risk of an individual to suffer under CHD. FRS-CHD was calculated based on age, sex, systolic blood pressure (SBP), total and high-density lipoprotein (HDL) cholesterol, smoking status, and treatment for hypertension (30). The Framingham Risk Score for Cardiovascular Disease (FRS-CVD) estimates the 10-year risk of an individual for a cardiovascular event in regard to age, sex, SBP, total and HDL cholesterol, smoking status, treatment for hypertension, and diabetes mellitus (31). The Atherosclerotic CardioVascular Disease (ASCVD) risk score estimates the 10-year risk for atherosclerotic CVD events based on age, sex, SBP, total and HDL cholesterol, smoking status, treatment for hypertension, diabetes mellitus, and race (32). The Systematic COronary Risk Evaluation (SCORE) for populations at high risk offers the estimation of total fatal cardiovascular risk. SCORE was calculated based on age, sex, SBP, total cholesterol, and smoking status (33). The triglycerides (TGs):HDL ratio is used as surrogate both of insulin resistance and CVD risk (34). We used several risk scores because of their different specificity and sensitivity for certain cardiovascular abnormalities, as they might differently affect the results in the novel subgroups.

Laboratory Analyses

Serum and plasma samples were analyzed in the biomedical laboratory as described previously (20).

Cluster Analyses and Statistics

The cluster analysis was based on the 6 variables age at diagnosis, BMI, HbA1c, HOMA2-B, HOMA2-IR, and GADAs. By using the nearest centroid approach, each participant is assigned to a predefined subgroup (cluster), that is, insulin-deficient individuals with positive GADA are allocated to the SAID subgroup, whereas those without GADAs to the SIDD subgroup. While all other subgroups show lower insulin sensitivity, SIRD is characterized by the most pronounced insulin resistance and MOD by the highest BMI. Individuals in the MARD subgroup are identified by their older age (5, 6). Recent studies also show different patterns of blood cell counts and biomarkers of inflammatory pathways between the subgroups (eg, highest biomarker levels and leukocyte counts in SIRD, but lowest biomarker levels in SIDD and lowest leukocyte counts in SAID) (35, 36). These findings underline possible pathophysiological differences between the subgroups. Data are presented as mean (SD) or median (interquartile range) for continuous variables and percentages for categorical variables (6). Skewed data were log-transformed before analysis. To account for multiple group comparisons, the Tukey-Kramer correction was applied. P values less than .05 were considered to indicate statistically significant differences. Associations between parameters of anthropometric and clinical characteristics and each cluster variable were evaluated using Pearson partial correlation adjusted for all remaining cluster variables. Statistical significance levels were adjusted for multiple comparisons by applying Bonferroni correction. Statistical analyses were performed with SAS (version 9.4; SAS Institute). Figures were drawn using GraphPad Prism (version 8.4.3; GraphPad Software).

Results

Clinical Characteristics of the Study Population

Table 1 shows anthropometric and clinical data of the participants with diabetes from the first year after diagnosis stratified according to the cluster analysis as well as of CONs. CONs comprised 38 (51%) women and 36 (49%) men, who were more likely to be of a younger age, and had a lower BMI (< 25) and no metabolic abnormalities. Members of SAID, representing 32% of the total cohort, were younger than members of SIRD, MOD, and MARD (all P < .001), but had similar serum low-density lipoprotein and TGs (both P ≥ .999) levels as CONs. Further, the SAID subgroup had lower BMI than SIRD, MOD, and MARD (all P < .001). The SIDD subgroup, representing 2% of the cohort, exhibited similar features as SAID and had poorer glycemic control than other subgroups. SIRD members, comprising 5% of the cohort, had higher waist-to-hip ratio (WHR) compared to individuals in all other subgroups and CONs (all P < .001). They also featured higher BMI and percentage body fat than SAID, SIDD, MARD, and CON members (all P < .001). The MOD subgroup, representing 25% of the cohort, showed similar BMI (P = .96) and percentage body fat (P = .87), but younger age than SIRD (P < .001). Members of MARD, comprising 27% of all participants, were generally older than those of the SAID, SIDD, and MOD and CON groups (all P < .001) and showed only minor metabolic abnormalities (see Table 1).

Table 1.

Anthropometric and clinical characteristics of the study population

	CONs (n = 74)	SAID (n = 261)	SIDD (n = 19)	SIRD (n = 42)	MOD (n = 204)	MARD (n = 220)
Female, %	51	47	11	19	49	26
Age, y	35.7 ± 12.6	36.7 ± 11.9	42.7 ± 12.2	55.4 ± 9.2	45.3 ± 9.9	56.7 ± 7.3
BMI	22.6 ± 1.6	24.7 ± 4.1	26.5 ± 2.8	34.0 ± 4.0	34.7 ± 6.0	27.3 ± 3.7
Waist-to-hip ratio	0.83 ± 0.07	0.86 ± 0.09	0.94 ± 0.06	1.01 ± 0.08	0.95 ± 0.07	0.94 ± 0.07
Body fat, %	22.6 ± 6.1	25.4 ± 8.3	26.7 ± 7.6	36.3 ± 5.8	37.5 ± 8.4	30.2 ± 6.5
HbA1c, mmol/mol	32 ± 3	48 ± 10	72 ± 14	44 ± 8	47 ± 9	45 ± 7
HbA1c, %	5.1 ± 0.3	6.5 ± 0.9	8.8 ± 1.3	6.2 ± 0.1	6.4 ± 0.1	6.3 ± 0.7
FPG, mmol/L	4.83 ± 0.83	7.34 ± 2.05	10.66 ± 3.55	6.60 ± 1.89	7.27 ± 1.67	6.83 ± 1.28
LDL, mmol/L	2.82 ± 0.98	2.87 ± 0.85	3.34 ± 0.75	3.24 ± 1.01	3.44 ± 0.98	3.29 ± 0.93
HDL, mmol/L	1.79 ± 0.44	1.63 ± 0.47	1.35 ± 0.28	1.01 ± 0.23	1.17 ± 0.28	1.32 ± 0.39
TGs, mmol/L	0.9 (0.6-1.2)	0.9 (0.6-1.2)	1.2 (0.8-2.3)	2.4 (1.5-3.3)	1.6 (1.1-2.3)	1.3 (0.9-1.8)
TGs/HDL ratio	1.1 (0.8-1.7)	1.2 (0.8-2.1)	1.9 (1.2-3.4)	5.1 (2.9-10.3)	3.3 (2.1-5.0)	2.2 (1.5-3.7)
Oxygen pulse, mL/heartbeat	14.3 ± 9.8	12.7 ± 3.9	11.8 ± 3.2	13.3 ± 3.3	13.1 ± 3.6	12.6 ± 3.6
RERmax	1.18 ± 0.09	1.18 ± 0.11	1.14 ± 0.09	1.12 ± 0.09	1.14 ± 0.11	1.16 ± 0.1
Systolic BP, mm Hg	122 ± 14	128 ± 16	137 ± 14	140 ± 16	138 ± 16	140 ± 19
Diastolic BP, mm Hg	74 ± 9	78 ± 10	81 ± 8	84 ± 12	85 ± 10	82 ± 10
Heart rate, bpm	63 ± 10	64 ± 10	70 ± 12	70 ± 11	69 ± 10	67 ± 10

Data are shown as mean ± SD for normally distributed parameters, median (first quartile to third quartile) for log-normally distributed parameters, or n (%).

Abbreviations: BMI, body mass index; BP, blood pressure; bpm, beats per minute; CONs, lean, glucose-tolerant controls; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin A1c; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; MARD, moderate age-related diabetes; MOD, moderate obesity-related diabetes; RER, respiratory exchange ratio, SAID, severe autoimmune diabetes; SIDD, severe insulin-deficient diabetes; SIRD, severe insulin-resistant diabetes; TGs, triglycerides.

Physical Fitness and Physical Activity Behavior

Physical fitness, as assessed by VO2max, was higher in SAID (28.5 ± 7.8 mL min–1 kg–1, P < .05) than in all other subgroups (Fig. 1A). On the other hand, VO2max was lower in SIRD than in SAID and MARD (both P < .01), but not different from SIDD and MOD (P = .98; P = .07) after adjustment for age, sex, and BMI. Further, power output at VT1 was lower in SIRD than in SAID (P < .001), MOD, and MARD (both P < .05) (Fig. 1B), but equal to SIDD (P = .88) after adjustment for age, sex, and BMI. Likewise, peak power output was lower in SIRD (1.33 ± 0.39 W/kg) than in SAID (2.50 ± 0.71 W/kg, P < .001), MOD (1.61 ± 0.53 W/kg, P < .001), and MARD (1.97 ± 0.65 W/kg, P < .01), but not different from SIDD (P = .38) after adjustment for age, sex, and BMI. Even after adjustment for the main confounders, peak power output was statistical higher in SAID compared to SIRD, MOD, and MARD (all P < .001) and tended to be higher than in SIDD (P = .05) (Fig. 1C). As expected, CONs exhibited higher physical fitness (VO2max: 33.0 ± 7.7 mL min–1 kg–1, all P < .001), power output at VT1 (1.70 ± 0.50 W/kg, all P < .001), and peak power (3.06 ± 0.64 W/kg, all P < .001) than individuals with diabetes independent of cluster classification after adjustments for age, sex, and BMI.

Figure 1.

Physical fitness and physical activity of the study population. Physical fitness was assessed by A to E, cycling Spiroergometry, and physical activity was assessed by F, Baecke index, in individuals with newly diagnosed severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), moderate obesity-related diabetes (MOD), and moderate age-related diabetes (MARD) and glucose-tolerant controls (CONs). Box plot with median, first quartile to third quartile and whiskers for tenth to 90th percentile. #P less than .05 vs CON. *P less than .05 vs SAID. +P less than .05 vs SIDD. $P less than .05 vs SIRD. Statistical significance levels were adjusted for age, sex, and body mass index and Tukey-Kramer for 10 pairwise comparisons. VEmax, maximal respiratory minute volume; VT1, first ventilatory threshold.

Differences in maximal HR lost statistical significance after adjustment for age, sex, and BMI between subgroups and CONs (all P > .05) (Fig. 1D). Additionally, oxygen pulse was statistical higher in CONs than in SIDD, SIRD (both P < .01), and MARD (P < .05) and tended to be higher than in MOD (P = .05) but was equal to SAID (P = .4) after adjustment for age, sex, and BMI. There was no difference between subgroups with nonautoimmune diabetes (all P > .05) (see Table 1).

Finally, SAID presented with a higher VEmax (76 ± 27 L/min) compared to SIDD and SIRD (both P < .001), but not to MOD (P = .14), MARD (P = .08), and CON groups (P = .13), whereas SIDD had lower VEmax (60 ± 18 L/min) than SAID, MARD (both P < .001), and MOD (P < .01) after adjustment for age, sex, and BMI. There was no difference between SIDD and SIRD (P = .36). CONs showed a higher VEmax (92.6 ± 61.8 L/min) than SIDD, SIRD (both P < .001), MOD, and MARD (both P < .05) after adjustment for age, sex, and BMI (Fig. 1E).

Physical activity behavior, as assessed by the Baecke index, was similar between subgroups on adjustment for age, sex, and BMI, whereas CONs presented with higher habitual physical activity (9.4 ± 1.4) than SAID, SIRD, MOD (all P < .001), and MARD (all P < .01) (Fig. 1F).

Endothelial Function

There were no differences between subgroups and CONs in FMD after adjustment for age, sex, and BMI (P < .05) (Fig. 2A). Moreover, NMD was similar between subgroups and CONs after previous mentioned adjustments (Fig. 2B). At rest, flow velocity was similar between subgroups after adjustment for age, sex, and BMI (P > .05). CONs showed a higher flow velocity than individuals in the SAID (P < .05), SIRD, MOD, and MARD groups (all P < .005), but no difference compared with SIDD after adjustments for age, sex, and BMI (P = .47) (Fig. 2C). In line with these results, the subgroups had equal flow velocity 15 seconds after deflation after adjustment for the main confounders (P > .05), while in CONs flow velocity was higher than in all the diabetes subgroups after adjustments for age, sex, and BMI (P < .001) (Fig. 2D). Preocclusion and postocclusion, brachial artery diameter was similar between subgroups and glucose-tolerant individuals after adjustments for age, sex, and BMI (P > .05) (Fig. 2E-2F). Also, there were no differences in NMD response of brachial artery diameter between all groups after adjustment for age, sex, and BMI (P > .05) (data not shown).

Figure 2.

Endothelial function of the study population. Parameters of endothelial function and flow velocity in severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), moderate obesity-related diabetes (MOD), and moderate age-related diabetes (MARD) subgroups and glucose-tolerant controls (CONs) showing A, flow-mediated dilation (FMD); B, nitroglycerin-mediated dilation (NMD); C, flow velocity at rest; and D, 15 seconds after deflation (hyperemia); and E, brachial artery diameter at rest, and F, 15 seconds after deflation (hyperemia). Box plot with median, first quartile to third quartile and whiskers for tenth to 90th percentile. #P less than .05 vs CON. Statistical significance levels were adjusted for age, sex, and body mass index and Tukey-Kramer for 10 pairwise comparisons.

SBP was higher in the CON than in the MARD groups (P < .01) after adjustment for age, sex, and BMI, but there were no further differences between groups. Additionally, HR was higher in MARD than in CONs and SAID and lower in SIRD than in CONs (all P < .05) after adjustment for age, sex, and BMI. But again, there were no further differences between groups (see Table 1).

Cardiovascular Risk

Regarding the novel diabetes subgroups, all cardiovascular risk scores indicated the CVD risk to be highest for SIRD and lowest for SAID. According to the FRS, the SAID subgroup had the lowest 10-year risk for CHD (0.3% [0.02%-1.7%]) compared to all other subgroups (all P < .05) and a lower CVD (3.9% [1.6%-9.4%]) than SIRD (P < .05) and MOD (P < .001) even after adjustment for age, sex, and BMI. FRS-CVD presented a higher CVD risk for individuals with diabetes mellitus compared to CONs after adjustment for age, sex, and BMI (all P < .001). Along this line, FRS-CHD was lower in CONs than in the SIDD, SIRD, MOD, and MARD subgroups (all P < .001) after adjustments for the main confounders. However, there was no difference between the CON and SAID groups (P = .19) after the previous-mentioned adjustments (Fig. 3A-3B). Using the ASCVD score, SAID (1.04% [0.3%-3.9%]) had a lower CVD risk than SIRD (18.4% [12.4%-35.9%]), MOD (6.7% [2.5%-11.5%]), and MARD (15.1% [7.9%-26.7%]) (all P < .001) after adjustments for age, sex, and BMI. As for FRS-CVD, ASCVD showed a higher CVD risk for members of all diabetes subgroups compared to CONs after adjustments for age, sex, and BMI (all P < .001). (Fig. 3C). Finally, SCORE indicated a higher 10-year risk for major adverse cardiac events for participants in the SIRD (2.5% [1.3%-4.3%]) and MARD (2.3% [1.2%-4.6%]) subgroups compared to SIDD (P < .05; P < .001) and MOD (both P < .001) after adjustments for the main confounders. Furthermore, SCORE was similar between the CON and SAID (P = .06), SIRD (P = .96), and MARD groups (P = .98) after adjustments for age, sex, and BMI, but CONs had a lower SCORE than SIDD and MOD (both P < .001) (see Fig. 3A).

Figure 3.

Diabetes-related cardiovascular complications of study population. Calculation of the 10-year risk of heart disease in individuals with newly diagnosed severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), moderate obesity-related diabetes (MOD), and moderate age-related diabetes (MARD) and glucose-tolerant control (CON). The 10-year risk was estimated by A, Framingham Risk Score–Cardiovascular Disease (FRS-CVD); B, Framingham Risk Score–Coronary Heart Disease (FRS-CHD); C, Atherosclerotic CardioVascular Disease (ASCVD) risk score; and D, Systematic COronary Risk Evaluation (SCORE). Box plot with median, first quartile to third quartile and whiskers for tenth to 90th percentile. #P less than .05 vs CON. *P less than .05 vs SAID. +P less than .05 vs SIDD. §P less than .05 vs MOD. Statistical significance levels were adjusted for age, sex, and body mass index and Tukey-Kramer for 10 pairwise comparisons.

The overall higher risk of SIRD is supported by the greater TGs:HDL ratio compared to SAID (P < .001), MOD (P < .05), and MARD (P < .01) after adjustment for age, sex, and BMI (all P < .05) (see Table 1) and by the strong association between HOMA-IR and HDL cholesterol (r = –0.24; P < .001) and SBP (r = 0.16; P = .001) in volunteers with nonautoimmune diabetes (SIDD, SIRD, MOD, and MARD subgroups) of the present study (data not shown).

Discussion

This study detected differences in CRF and cardiovascular risk among novel diabetes subgroups, already within the first year after diabetes diagnosis. Further, the analyses show a lower physical fitness and activity in individuals with diabetes compared to glucose-tolerant humans, supporting the concept of a tight relationship between cardiovascular complications and diabetes. In detail, members of the SIRD subgroup have the lowest physical fitness, despite comparable physical activity levels among all subgroups, and SAID individuals have the lowest scores for cardiovascular complications within the first year after diabetes diagnosis. However, these results were not reflected by parallel alterations of endothelial function, as both FMD and NMD were comparable among all subgroups.

The insulin-resistant SIRD subgroup showed not only lowest VO2max, but also peak power at VT1 and peak power output. These findings confirm the close association between VO2max and insulin sensitivity (37, 38) as well as between CRF and muscular strength with the risk of type 2 diabetes (12). Among others, VO2max reflects mitochondrial function and density, as demonstrated by its positive relationship with muscle adenosine 5′-triphosphate synthesis in humans (39). Impairment in mitochondrial function may result from inherited and acquired factors, for example, sedentary lifestyle, age, and body fat mass, and their interaction with whole-body insulin sensitivity, possibly via lipotoxicity (40). These results were underlined by higher physical fitness levels of glucose-tolerant individuals compared to people with diabetes as shown in the present and previous studies (41). Interestingly, the lower physical fitness in SIRD was largely independent of age, sex, and BMI. Surprisingly, differences in physical fitness between the diabetes subgroups could not be attributed to physical activity, as habitual physical activity behavior from the Baecke index was similar between all subgroups after adjusting for age, sex, and BMI. However, glucose-tolerant people exhibited greater physical activity than individuals with diabetes. This is in line with previous studies as physical activity levels seem to be lower in long-standing type 1 (42) and type 2 diabetes compared to glucose-tolerant patients (16). Taken together, these data suggest the presence of distinct abnormalities in mitochondrial plasticity (39, 43) and/or other mechanisms (44), not simply resulting from aging, adiposity, or physical inactivity.

Recent studies indicate that cardiovascular autonomic neuropathy is associated with newly diagnosed diabetes, albeit more prominently with type 2 diabetes than type 1 diabetes, and is linked to insulin resistance and hyperglycemia (45). The present study supports this finding in that oxygen pulse was reduced in all nonautoimmune diabetes subgroups, classically summarized as type 2 diabetes, but not in SAID when compared to the CON group after adjustment for age, sex, and BMI. Thus, these findings may help to explain the slight differences in maximal HR observed between subgroups before adjustment for age, sex, and BMI (data not shown) suggesting impaired compensatory HR elevation during spiroergometry, specifically in SIRD.

Nevertheless, some of the findings may be explained by body fat mass, as assessed roughly by BMI and WHR in the present study. For example, VEmax, that is, the product of tidal volume and breathing rate, was comparable between the most obese MOD and the leanest SAID subgroups, which is likely due to the compensatory higher breathing rates for lower tidal volumes in the obese individuals (46). Also, VEmax was greater in the lowest (SAID) than in the highest (SIRD) WHR subgroup, suggesting an additional role for visceral adiposity aside from contributing to insulin resistance. This is supported by the inverse correlation between WHR and respiratory function reported for 9674 men and 11 876 women (47). It is tempting to speculate that the increased ventilation rates could cause breathing discomfort and premature exercise termination in insulin-resistant individuals with (visceral) obesity and type 2 diabetes. However, impaired pulmonary function is strongly related to type 2 diabetes, due to chronic glycemic exposure (48), which would be supported by the present study.

Endothelial function, as assessed by FMD, was not different between the novel diabetes subgroups and glucose-tolerant individuals after adjustment for the main confounders. These results cannot be attributed to confounders for FMD such as differences in blood flow, artery diameter, and/or arterial stiffness, as intergroup differences in flow velocity stayed stable between rest and 15 seconds after deflation, and preocclusion and postocclusion diameter were similar between all groups. Rather, specific features of the baseline GDS population, such as recent-onset diabetes, good glucometabolic control, and absence of clinically relevant diabetes-related complications or comorbidities, may explain these results. Additionally, the data in the present study are supported by larger cohort studies, which also detected no association between diabetes and FMD (49) or lower FMD only in people with type 2 diabetes, but not in those with impaired glucose metabolism (8). Finally, the present results for FMD% are within the range from about 3.6 ± 2.3% to 6.4 ± 3.6% in people with type 2 diabetes reported by previous studies (50). Thus, the present study supports data challenging the concept of a close, direct relationship between endothelial dysfunction and diabetes mellitus. Previous results from Scandinavian cohorts suggested an higher CVD risk in SIRD and MARD subgroups, which disappeared after adjusting for age and sex (5). Further, a recent meta-analysis indicated an association between higher CRF and a lower risk of all-cause mortality, mostly linked to decreased CVD/CHD risks (13). Along this line, the data from the GDS cohort first identify the lowest CVD/CHD risk in the glucose-tolerant control group. Second, the present study reveals differences in cardiovascular risk between diabetes subgroups as SAID individuals have the lowest CVD/CHD risk—based on FRS-CHD and ASCVD scores—which mostly remained low on adjustment for age, sex, and BMI. On the other hand, the SIRD and MARD subgroups showed higher CVD risk using ASCVD and SCORE, which were only partly retained after the adjustments. These findings underscore the importance of overweight and BMI (51) and diabetes per se(52) as drivers of increased cardiovascular risk. Nevertheless, this study confirms the strong inverse association between HOMA-IR and FRS-CHD, FRS-CVD, and ASCVD and pointing to a higher risk for CVD/CHD in insulin-resistant individuals. Of note, the present analyses of the novel diabetes subgroups rely mainly on short duration of disease and do not take into account cluster instability (6). Also, SIRD features additional possibly detrimental risk factors, which may determine long-term CVD risk, including dyslipidemia, progressive nonalcoholic fatty liver disease (6, 53), low-grade inflammation (35), as well as impaired CRF, as shown in the present study. Finally, this group also had the greatest TGs:HDL ratio, another surrogate both of insulin resistance (34) and CVD risk (54).

An additional recent analysis of three intervention trials suggested the highest risk of major adverse cardiovascular events and cardiovascular death for members of their cluster A, corresponding to the SIDD subgroup (11). The present study only found higher FRS-CHD score and SCORE in SIDD when compared to SAID, but not in other subgroups. Longer diabetes duration (14-16 years), worse HbA1c (96-98 mmol/mol (10.9-11.1%)) and proportion of participants at high CVD risk (80-85%) likely explains the observed difference.

The strengths of the present study are the large sample size of well-characterized individuals with a known diabetes duration of less than 1 year and the use of gold-standard methods to assess whole-body insulin sensitivity, physical fitness, and endothelial function. One limitation is the nominally small number of individuals in the SIDD and SIRD subgroups, although proportionally representative of the distribution shown in previous study populations (5, 6, 10). Consequently, some associations may not reach statistical significance in the present analysis. Also, other mediating factors, such as specific dietary patterns (20), were not assessed in the context of the present analysis, but may affect the results. Moreover, the glucose-tolerant CON group illustrated the variables in lean, healthy people but did not allow for direct comparison with each subgroup of the study. Further, there are also other factors that can affect physical fitness, such as the circulatory system, and left and right ventricle function. Of note, differences in resting HR, SBP, and age between groups could affect the results of FMD (55), a method with already very high intrinsic variability. Furthermore, resting HR and SBP were recorded before but not continuously monitored during FMD measurements.

Taken together, the novel diabetes subgroups (clusters) exhibit distinct differences in cardiovascular risk already within the first year after diabetes diagnosis. Specifically, the SAID subgroup has lower risk scores for CVDs among diabetes subgroups, whereas the SIRD subgroup exhibits the lowest physical fitness, despite similar physical activity and endothelial function compared to other groups.