Abstract
Obstructive sleep apnea (OSA) is a common condition strongly linked to increased cardiovascular risk and poor glycemic control. Little is known about OSA, cardiovascular risk, and glycemia in maturity-onset diabetes of the young (MODY), an inherited form of diabetes, which is different than both type 1 and type 2 diabetes. We assessed OSA, resting heart rate (RHR), an important prognostic marker of cardiovascular disease, and glycemic variability among the most common subtypes of MODY, glucokinase (GCK)-MODY, and transcription factor (TF)-related MODY (HNF1A, HNF4A, and HNF1B). Adults with GCK-MODY (n = 63) and TF-related MODY (n = 60) and control adults without diabetes (n = 65) were screened for OSA by home sleep test. Glycemic variability (continuous glucose monitoring) and RHR (wearable sleep-activity tracker) were concomitantly assessed for 2 weeks at home. Data from 188 individuals (2,853 recorded days) were analyzed. Individuals with TF-related MODY, compared with those with GCK-MODY or control individuals, had more OSA (48.3%, 27.0%, and 30.8%, respectively; P = 0.033), higher RHR (72.8 ± 10.8, 65.2 ± 7.9, and 67.3 ± 7.7 bpm, respectively; P < 0.001), and higher glycemic variability (coefficient of variation of glucose 31.6 ± 6.0%, 17.3 ± 4.5%, and 17.5 ± 4.0%, respectively; P < 0.001). Greater severity of OSA and higher RHR were associated with higher glycemic variability. These findings may have important clinical implications for cardiovascular risk assessment in MODY.
Article Highlights
Obstructive sleep apnea (OSA) has been strongly linked to increased cardiovascular risk and poor glycemic control in the general population.
Resting heart rate (RHR) is a prognostic marker of cardiovascular morbidity and mortality and has been linked to dysglycemia.
Little is known about OSA, RHR, and glycemia in maturity-onset diabetes of the young (MODY), an inherited form of diabetes with discrete clinical features.
Adults with transcription factor–related MODY (HNF1A, HNF4A, and HNF1B) had more OSA and higher RHR and greater glycemic variability compared with those with glucokinase-MODY or control adults without diabetes, which may have important clinical implications for future cardiovascular risk.
Graphical Abstract
Introduction
Obstructive sleep apnea (OSA) is a common condition characterized by repetitive upper airway collapses that occur during sleep, leading to hypoxemia, sleep fragmentation by arousals, and poor sleep quality. OSA has been strongly linked to increased cardiovascular risk and poor glycemic control in the general population, and particularly in individuals with type 1 or type 2 diabetes (1,2). Little is known about OSA, cardiovascular disease risk, and glycemia in maturity-onset diabetes of the young (MODY), an inherited form of diabetes with discrete clinical features, which is different than both type 1 and type 2 diabetes (3). The two most common subtypes of MODY are glucokinase (GCK)-MODY and transcription factor (TF)-related MODY (herein referred to as HNF1A, HNF4A, and HNF1B) with distinct clinical presentation and cardiovascular risk profile. GCK-MODY presents with mild fasting hyperglycemia, normal blood pressure and lipid profiles, and low incidence of cardiovascular complications, whereas TF-related MODY typically shows progressive hyperglycemia, hypertension, hyperlipidemia, and diabetes-related complications (4,5).
Increased sympathetic activity, resulting from hypoxemia and arousals, is a hallmark of OSA and contributes to increased cardiovascular disease risk (1,6). Patients with OSA typically have higher resting heart rate (RHR), which reduces with treatment (7). RHR is a prognostic marker of cardiovascular morbidity and mortality and has been linked to insulin resistance and diabetes-related complications (8). Yet, little is known about these links in MODY. We therefore assessed OSA, RHR, and glycemic variability among individuals with the most common subtypes of MODY and control individuals without diabetes.
Research Design and Methods
Adults with MODY, GCK-MODY, and TF-related MODY (HNF1A, HNF4A, and HNF1B) were recruited from the University of Chicago’s Monogenic Diabetes Registry (9) from June 2022 to July 2023 after approval from the Institutional Review Board (IRB protocols 16935B, 6858, 15617B). Control participants without diabetes were recruited from nonaffected relatives, via social media (Kovler Diabetes Center–University of Chicago channels) and posting flyers at the academic medical center. Exclusion criteria included age <18 years, pregnancy, neurodevelopmental disorders, and diagnosed or treated sleep disorders; the same exclusion criteria applied for control participants who were defined as self-reporting normal glucose without a diagnosis of diabetes. All assessments were conducted in the participants’ homes and included concomitant 2-week wearable tracker (Fitbit Inspire 2) that collected sleep and RHR data, continuous glucose monitoring (CGM) (Free Style Libre Pro), and one night of home sleep apnea screening test.
Statistical Analysis
Statistical analysis was performed using Stata version 18 (StataCorp LLC, College Station, TX). No imputation for missing values was performed. While no formal adjustment for multiple comparisons was made to the P values reported, statistical significance was defined as P < 0.01. Further methodological details are provided in the Supplementary Material.
Data and Resource Availability
The data and resources underlying this article will be shared on reasonable request.
Results
A total of 123 adults with MODY and 65 control individuals without diabetes completed the study (Table 1 and Supplementary Fig. 1). MODY subtypes were GCK-MODY (51.2%) and TF-related MODY (48.8%; HNF1A, HNF4A, and HNF1B carriers). MODY participants were predominantly non-Hispanic White premenopausal females, reflecting the composition of the University of Chicago Monogenic registry. There was a greater percentage of Asians and Hispanics among control participants compared with participants with GCK-MODY and TF-related MODY (overall P < 0.001). Over 50% of participants in the three groups had normal weight and were premenopausal women. Participants with GCK-MODY were slightly older. Participants with TF-related MODY showed higher rates of hyperlipidemia compared with those with GCK-MODY and control participants. Insulin use was higher in participants with TF-related MODY than GK-MODY. Participants’ characteristics were otherwise similar between groups (Table 1).
Table 1.
Demographic and clinical characteristics of study participants
| Characteristic | GCK-MODY | TF-related MODY | Control | Overall P value |
|---|---|---|---|---|
| n | 63 | 60 | 65 | |
| Age, years* | 43.6 (14.2) | 39.1 (11.9) | 37.8 (12.5) | 0.030 |
| Sex, n (%) | ||||
| Female | 49 (77.8) | 42 (70.0) | 45 (69.2) | 0.509 |
| Male | 14 (22.2) | 18 (30.0) | 20 (30.8) | |
| Race and ethnicity | a | a | ||
| Non-Hispanic White | 56 (88.9) | 56 (93.3) | 29 (44.6) | <0.001 |
| Hispanic | 2 (3.2) | 1 (1.7) | 17 (26.2) | |
| Asian | 2 (3.2) | 1 (1.7) | 13 (20.0) | |
| Black or African American | 0 (0.0) | 2 (3.3) | 3 (4.6) | |
| More than one race | 3 (4.8) | 0 (0.0) | 3 (4.6) | |
| Menopausal status, n (%) | ||||
| Premenopausal | 35 (55.6) | 36 (60.0) | 38 (58.5) | 0.377 |
| Postmenopausal | 14 (22.2) | 6 (10.0) | 8 (12.3) | |
| None (male) | 14 (22.2) | 18 (30.0) | 19 (29.2) | |
| BMI, kg/m2 | 24.3 (3.5) | 25.6 (5.0) | 25.9 (5.2) | 0.132 |
| Body habitus, n (%) | ||||
| Normal weight | 38 (60.3) | 34 (56.7) | 33 (50.8) | 0.241 |
| Overweight | 20 (31.7) | 14 (23.3) | 19 (29.2) | |
| Obesity | 5 (7.9) | 12 (20.0) | 13 (20.0) | |
| Comorbidities, n (%) | ||||
| Hypertension† | ||||
| Yes | 5 (7.9) | 9 (15.0) | 3 (4.7) | 0.145 |
| No | 58 (92.1) | 51 (85.0) | 61 (95.3) | |
| Hyperlipidemia‡ | a , b | |||
| Yes | 11 (17.5) | 24 (40.0) | 4 (6.2) | <0.001 |
| No | 52 (82.5) | 36 (60.0) | 60 (93.8) | |
| Thyroid disease | ||||
| Yes | 12 (19.0) | 5 (8.3) | 5 (7.7) | 0.109 |
| No | 51 (81.0) | 55 (91.7) | 60 (92.3) | |
| HbA1c, % | 6.3 (0.4)a | 6.5 (1.0)a | 5.0 (0.5) | <0.001 |
| Duration of diabetes, years | 17.9 (12.0) | 20.5 (12.7) | NA | 0.253 |
| Regular exercise, n (%)§ | ||||
| Yes | 46 (73.0) | 36 (60.0) | 46 (70.8) | 0.265 |
| No | 17 (27.0) | 24 (40.0) | 19 (29.2) | |
| Work status, n (%) | ||||
| Employed full time | 37 (58.7) | 37 (61.7) | 41 (63.1) | 0.930 |
| Employed part time | 16 (25.4) | 12 (20.0) | 13 (20.0) | |
| Retired | 5 (7.9) | 3 (5.0) | 4 (6.2) | |
| Student | 4 (6.3) | 3 (5.0) | 4 (6.2) | |
| Unemployed | 1 (1.6) | 4 (6.7) | 3 (4.6) | |
| Did not respond | 0 (0.0) | 1 (1.7) | 0 (0) | |
| Continuous glucose monitoring, n (%) | ||||
| Users | 3 (4.8) | 30 (50.0) | NA | <0.001 |
| Nonusers | 60 (95.2) | 30 (50.0) | NA | |
| Diabetes treatment, n (%) | <0.001 | |||
| Nonpharmacologic (diet and exercise only) | 53 (84.1) | 5 (8.3) | NA | |
| Noninsulin (oral, weekly injectables or a combination) | 7 (11.1) | 30 (50.0) | NA | |
| Insulin (pump or injections)ǁ | 3 (4.8) | 25 (41.7) | NA | |
| Diabetes complications, n (%) | ||||
| Retinopathy | 3 (4.8) | 4 (6.7) | NA | 0.713 |
| Nephropathy | 0 (0) | 3 (5.0) | NA | 0.113 |
| Peripheral neuropathy | 8 (12.7) | 5 (8.3) | NA | 0.561 |
| Coronary artery disease | 1 (1.6) | 1 (1.7) | NA | 1 |
| Peripheral vascular disease | 0 (0) | 1 (1.7) | NA | 0.488 |
| Stroke | 0 (0) | 0 (0) | NA | — |
Data are shown as mean (SD) unless otherwise specified. TF-related MODY (compromises HNF1A, HNF4A, and HNF1B). NA, not applicable. P values are from ANOVA for continuous variables and Fisher exact test for categorical variables. Normal weight, BMI 18.5–24.9 kg/m2; overweight, BMI 25–29.9 kg/m2; obesity, BMI ≥30 kg/m2.
*Age, BMI (self-reported weight and height), race and ethnicity, and hypertension/hyperlipidemia were obtained after consent for the current study. HbA1c is from the most recent, self-reported value after consent for the current study.
†One control subject did not respond.
‡One control subject did not respond.
§Regular exercise was self-reported as engaging in voluntary exercise more than twice and accumulating at least 90 min of moderate or 40 min of vigorous exercise in an average week.
ǁInsulin therapy: four participants reported to be on insulin pump, and 24 on basal-bolus insulin (± oral antidiabetics or weekly injectables).
aP < 0.01 vs. control subject.
bP < 0.01 vs. GCK-MODY subject.
OSA prevalence was higher in participants with TF-related MODY (48.3%) compared with those with GCK-MODY (27.0%) or control participants (30.8%) (Table 2). Based on apnea-hypopnea index (AHI), in participants with TF-related MODY, 40.0% had mild OSA and 8.3% had moderate-to-severe OSA; in participants with GCK-MODY, 22.2% had mild OSA and 4.8% moderate-to-severe OSA; and, in control participants, 12.3% had mild OSA and 18.5% had moderate-to-severe OSA (Fig. 1A). Average sleep duration was similar across the groups, and, compared with age- and sex-based normative data for sleep duration, 43% of all participants were at or below the reference 50th percentile (Table 2 and Supplementary Fig. 2).
Table 2.
Obstructive sleep apnea and resting heart rate in GCK-MODY, TF-related MODY, and control participants
| Variable | GCK-MODY | TF-related MODY | Control | Unadjusted P value | Adjusted P value |
|---|---|---|---|---|---|
| n | 63 | 60 | 65 | ||
| Number of days | 13.1 (1.9) | 13.0 (1.9) | 12.8 (2.0) | 0.605 | 0.705* |
| Sleep duration, h | 7.1 (0.6) | 7.1 (0.8) | 6.8 (0.8) | 0.043 | 0.519* |
| Resting heart rate, bpm | 65.2 (7.9) | 72.8 (10.8)a,b | 67.3 (7.7) | <0.001 | <0.001* |
| Resting heart rate category, n (%) | a , b | <0.001 | <0.001* | ||
| Low (<70 bpm) | 48 (77.4) | 26 (43.3) | 42 (66.7) | ||
| High (≥70 bpm) | 14 (22.6) | 34 (56.7) | 21 (33.3) | ||
| Moderate to vigorous physical activity, min | 237.1 (95.3) | 241.0 (104.1) | 235.5 (87.1) | 0.947 | 0.868* |
| Obstructive sleep apnea, n (%) | 0.033 | 0.039‡ | |||
| Yes | 17 (27.0) | 29 (48.3) | 20 (30.8) | ||
| No | 46 (73.0) | 31 (51.7) | 45 (69.2) |
Data are mean (SD) unless otherwise specified. TF-related MODY (HNF1A, HNF4A, and HNF1B). Unadjusted P values are from ANOVA for continuous variables and logistic regression for categorical variables. Adjusted P values are from ANCOVA or a logistic regression.
*Adjusted values for race and AHI.
‡Adjusted for BMI category. Sleep duration, resting heart rate, and physical activity data are from a wearable sleep-activity tracker (Fitbit) based on 2-week monitoring in each subject using a total of 2,438 technically valid recorded nights. Sleep duration (hours) is the sum of all epochs scored as sleep during the total time spent in bed (for reference, the recommended sleep duration for adults is 7–9 h). Resting heart rate is a measure of the average heart bpm while the body is in a state of complete rest. Moderate to vigorous activity is the number of minutes in an activity that burns three times as many calories as one does at rest (≥3.0 metabolic equivalents). Obstructive sleep apnea is assessed by one night of home sleep apnea test and defined as AHI ≥5.
aP < 0.01 vs. control participant.
bP < 0.01 vs. GCK-MODY participant.
Figure 1.
OSA severity, resting heart rate, and glycemic control in GCK-MODY, TF-related MODY, and control participants. A: Apnea hypopnea index among participants with GCK-MODY, participants with TF-related MODY, and control participants. The following results were found for each group: GCK-MODY, 73.0% normal, 22.2% mild, 1.6% moderate, and 3.2% severe; TF-related MODY, 51.7% normal, 40.0% mild, 3.3% moderate, and 5.0% severe; control participants, 69.2% normal, 12.3% mild, 10.8% moderate, and 7.7% severe. B: Resting heart rate among GCK-MODY, TF-related MODY, and control participants. The following results were found for each group: GCK-MODY, 77.4% with resting heart rate below 70 bpm and 22.6% at or above; TF-related MODY, 43.3% with resting heart rate below 70 bpm and 56.7% at or above; control participants, 66.7% with resting heart rate below 70 bpm and 33.3% at or above. Resting heart rate was significantly higher in participants with TF-related MODY compared with control participants and participants with GCK-MODY (mean resting heart rate of 73 vs. 67 vs. 65 bpm, respectively; overall P < 0.001). The dashed line at 70 bpm marks the resting heart rate level above which high cardiovascular risk has been reported. C: Daily average glucose, glycemic variability (percent coefficient of variation of glucose), and resting heart rate over 14-day monitoring period in GCK-MODY, TF-related MODY, and control participants. The dashed line at 70 bpm marks the resting heart rate level above which high cardiovascular risk has been reported.
RHR was significantly higher in participants with TF-related MODY compared with those with GCK-MODY or control individuals (Table 2 and Fig. 1B and C). The percentage of participants with RHR at or above 70 bpm was approximately twofold higher in those with TF-related MODY (56.7%) compared with those with GCK-MODY (22.6%) or control individuals (33.3%) (Table 2). The differences in RHR remained significant after adjusting for race and the severity of OSA using AHI or oxygen desaturation index. Sensitivity analyses among non-Hispanic White individuals only (Supplementary Table 1), or among those who were not on insulin (Supplementary Table 2) or those without neuropathy, showed similar magnitude and pattern of differences between groups for RHR.
Participants with TF-related MODY and GCK-MODY had similar average glucose values, with TF-related MODY showing higher glucose variability (percent coefficient of variation or SD of glucose) compared with those with GCK-MODY or control participants (Table 3 and Fig. 1C). Participants with TF-related MODY had significantly less time in range, more time above range, and more time below range compared with those with GCK-MODY or control participants (Table 3).
Table 3.
Glucose profiles among GCK-MODY, TF-related MODY, and control participants
| GCK-MODY | TF-related MODY | Control | Overall P value | |
|---|---|---|---|---|
| n | 63 | 59 | 64 | |
| Number of days | 13.5 (1.6) | 13.8 (1.0) | 13.3 (1.9) | 0.298 |
| Average glucose, mg/dL | ||||
| Overall | 130.6 (14.8)a | 129.2 (37.8)a | 95.3 (10.7) | <0.001 |
| Daytime | 135.7 (15.3)a | 137.3 (38.3)a | 98.2 (11.4) | <0.001 |
| Nighttime | 119.9 (15.6)a | 111.8 (40.8)a | 89.3 (14.6) | <0.001 |
| Time in range (70–180 mg/dL), % | ||||
| Overall | 94.4 (5.8) | 76.7 (17.8)a,b | 93.3 (8.7) | <0.001 |
| Daytime | 94.3 (7.2) | 76.6 (19.1)a,b | 95.4 (8.2) | <0.001 |
| Nighttime | 96.6 (4.3)a | 75.8 (23.1)a,b | 86.7 (19.0) | <0.001 |
| Time above range (>180 mg/dL), % | ||||
| Overall | 4.8 (5.9) | 15.0 (19.0)a,b | 0.3 (1.0) | <0.001 |
| Daytime | 5.6 (7.2) | 18.3 (20.4)a,b | 0.4 (1.0) | <0.001 |
| Nighttime | 2.2 (4.3) | 8.7 (18.6)a,b | 0.3 (1.9) | <0.001 |
| Time below range (<70 mg/dL), % | ||||
| Overall | 0.7 (1.5)a | 8.3 (11.1)b | 6.4 (8.8) | <0.001 |
| Daytime | 0.1 (0.3)a | 5.1 (9.6)b | 4.3 (8.3) | <0.001 |
| Nighttime | 1.2 (1.9)a | 15.5 (19.8)b | 13.0 (19.2) | <0.001 |
| Coefficient of variation | ||||
| Overall | 17.3 (4.5) | 31.6 (6.0)a,b | 17.5 (4.0) | <0.001 |
| Daytime | 15.1 (4.1) | 28.7 (5.6)a,b | 16.6 (4.4) | <0.001 |
| Nighttime | 14.7 (5.0) | 27.5 (8.9)a,b | 14.8 (4.4) | <0.001 |
| SD | ||||
| Overall | 22.7 (7.1)a | 40.6 (13.2)a,b | 16.6 (4.2) | <0.001 |
| Daytime | 20.7 (7.3)a | 39.3 (13.4)a,b | 16.3 (4.9) | <0.001 |
| Nighttime | 17.8 (7.6) | 31.0 (14.9)a,b | 13.3 (5.1) | <0.001 |
Data are shown as mean (SD). TF-related MODY (HNF1A, HNF4A, and HNF1B). P values are from ANOVA.
aP < 0.01 vs. control participants.
bP < 0.01 vs. GCK-MODY participants.
Among MODY participants, greater severity of sleep apnea (R2 = 0.059, P = 0.007 using AHI) and higher resting heart rate (R2 = 0.017, P = 0.024) correlated with higher glucose variability measured by SD or log-transformed coefficient of variation, respectively. For every 5-bpm increase in RHR, there was a 3% increase in glucose variability, which remained significant in AHI- and race-adjusted models and when examined separately during the daytime or nighttime. Additionally, these results persisted when the whole cohort including control participants was analyzed, and the magnitude of these associations was similar in further multivariable analyses (Supplementary Table 3).
Discussion
We found that adults with TF-related MODY had more OSA and higher RHR compared with those with GCK-MODY or control individuals. It is well recognized that OSA and RHR are strongly associated with adverse cardiovascular outcomes (1,8). Thus, our findings may have important clinical implications for cardiovascular risk assessment and disease prevention in adults with MODY, particularly those with TF-related MODY.
Overall, 37% of all MODY participants had OSA (48.3% of those with TF-related MODY and 27.0% of those with GCK-MODY) despite their clinical characteristics that do not represent traditional risk factors for OSA (e.g., predominantly premenopausal females and normal weight), which warrants future investigations on OSA pathophysiology in MODY. It is noteworthy that OSA has been reported in lean individuals (10). In an earlier study of 24 adults, we also reported that OSA was highly prevalent (58%) in those with MODY (11). These findings are consistent with prior reports on type 1 or type 2 diabetes that OSA is a common comorbidity of diabetes (2). Our current data suggest that adults with MODY, particularly those with TF-related MODY, may need screening for OSA for cardiovascular risk assessment and prevention. Future studies are needed to elucidate underlying mechanisms and factors that were unmeasured in our study (e.g., family history of OSA, altered upper airway structure), which may contribute to higher prevalence of OSA in people with TF-related MODY.
In our study, RHR was, on average, ∼5–7 bpm higher in participants with TF-related MODY compared with those with GCK-MODY or control participants, and there was a greater proportion of those with TF-related MODY (56.7%) with an RHR ≥70 bpm. These findings have substantial clinical implications for cardiovascular risk stratification and prevention given that strong epidemiologic evidence indicate that higher RHR is an independent predictor of cardiovascular and all-cause mortality (8,12). For example, in one study, every beat increase in heart rate was associated with a 3% higher risk for all-cause death, 1% higher risk for cardiovascular disease, and 2% higher risk for coronary artery disease (13). Given that RHR is a readily accessible, noninvasive clinical measure, it can serve as a simple, at-home tool to interrogate the cardiovascular health in those with MODY and to learn about their future risk for cardiovascular disease and mortality (14). Interventions such as intensive diabetes management are associated with lower RHR in type 1 diabetes, and exercise training improves cardiac autonomic neuropathy in type 2 diabetes (15,16).
Despite similar average glucose levels, participants with TF-related MODY had higher glycemic variability, more hypoglycemia, and less time in range compared with those with GCK-MODY or control participants. This is expected based on distinct clinical features of these subtypes of MODY such that, in GCK-MODY, glucose homeostasis is maintained at a higher set point resulting in mild, asymptomatic fasting hyperglycemia and that those with TF-related MODY generally show higher glycemic variability, and these individuals are more frequently on insulin therapy, whereas most individuals with GCK-MODY do not require glucose-lowering therapy (17). There are only a few prior reports on CGM profiles in GCK-MODY and TF-related MODY, which warrants future research (18,19).
We found that greater severity of OSA and higher RHR were associated with higher glycemic variability in MODY. These modest, but statistically significant, associations between OSA, RHR, and glycemic variability warrant future investigations with larger sample sizes to study the clinical significance. Further research is also needed to examine other clinical markers that may correlate with glycemic variability. OSA severity has been associated with glucose variability in the general population (20) and is thought to be mediated by sympathetic overactivation (21). Although the relationship between elevated RHR and glucose is not yet well understood, some studies have reported associations between higher RHR and increased risk of developing dysglycemia (22). Additionally, higher glycemic variability among MODY participants was associated with presence of hypertension or hyperlipidemia, higher HbA1c levels, longer duration of diabetes, and use of glucose-lowering medication (Supplementary Tables 4 and 5).
Our study was not designed to examine underlying mechanisms, but one possibility for higher RHR in TF-related MODY is the higher prevalence of OSA that we observed in this subtype (23). Interestingly, in our adjusted models, RHR remained higher even after controlling for the severity of OSA using AHI or oxygen desaturation index. Other putative explanations for higher RHR in TF-related MODY include dysfunctional autonomic nervous system (e.g., autonomic neuropathy or orthostatic hypotension), as individuals with TF-related MODY show hyperglycemia from a relatively young age, or underlying underdiagnosed coronary artery disease, which might be disproportionally higher in TF-related MODY compared with other MODY subtypes, as it has also been seen in type 2 diabetes (8). It is also possible that there is a difference in RHR between GCK-MODY and TF-related MODY because GCK-expressing neurons are in regions of the brain that regulate vagal activity, which, in turn, may influence RHR (24). Previous studies have shown differences in cardiovascular risk for MODY, with GCK-MODY having a lower risk of coronary artery disease compared with type 2 diabetes (25) and a lower risk of diabetes-related complications overall, except for in individuals who could have superimposed type 2 diabetes (26). Those with HNF1A-MODY, on the other hand, have cardiovascular complications at higher rates compared with those with type 1 diabetes (27).
The strengths of this study include a relatively large sample of genotyped MODY participants, inclusion of control participants without diabetes, and concomitant and objective assessments of RHR, sleep-activity patterns, and glycemic variability for 2 weeks in the participants’ homes throughout day and night in a real-world setting, as well as evaluating for OSA using validated home testing.
Our study also has important limitations. There were racial and ethnic differences between MODY and control participants, with the majority of MODY participants consisting of non-Hispanic White individuals and with more Hispanic and Asian control participants. To address this potential limitation, our analyses on OSA and RHR among participants with MODY subtypes and control participants adjusted for race. Additionally, sensitivity analysis among non-Hispanic White individuals only (Supplementary Table 1) revealed similar conclusions. These racial and ethnic differences that we observed between MODY and control participants could also be related to a generally higher prevalence of non-Hispanic White individuals in MODY populations (9,28). It is important to note that, unfortunately, racial and ethnic minorities may be underrepresented, as the great majority of individuals with MODY remain undiagnosed. We did not collect EKG data but objectively captured heart rate via wearable tracker (Fitbit) in a real-world setting, while participants continued their daily activities. Control participants showed normoglycemia and CGM profiles that were consistent with individuals without diabetes, but they were not tested for MODY. Insulin use, which can affect glycemic variability, was higher in participants with TF-related MODY than those with GCK-MODY, but our sensitivity analysis among those who were not on insulin yielded similar conclusions (Supplementary Table 2).
In conclusion, we have shown that those with TF-related MODY, compared with those with GCK-MODY or control participants, had more OSA, higher RHR, and higher glycemic variability. Greater severity of OSA and higher RHR were associated with higher glycemic variability. These findings may have important clinical implications for cardiovascular risk assessment and prevention in MODY.
This article contains supplementary material online at https://doi.org/10.2337/figshare.30400291.
Article Information
Acknowledgments. The authors acknowledge the help of those on the Monogenic Diabetes team at the University of Chicago and Lisa Letourneau-Freiberg and Tiana Bowden at the Kovler Diabetes Center, Department of Medicine, Section of Adult and Pediatric Endocrinology, Diabetes and Metabolism, The University of Chicago, Chicago, IL.
Duality of Interest. No potential conflicts of interest relevant to this article were reported.
Author Contributions. M.A. contributed to conception and design of the work, data collection, drafting of the article, critical revision of the article, and final approval of the version to be published. K.C. contributed to data collection, drafting of the article, critical revision of the article, and final approval of the version to be published. M.V.S. contributed to drafting of the article and final approval of the version, to be published. D.R. contributed to data collection, drafting of the article, critical revision of the article and final approval of the version to be published. R.N.N. contributed to conception and design of the work, drafting of the article, critical revision of the article, and final approval of the version to be published. K.W. contributed to drafting of the article, statistical analysis, and final approval of the version to be published. E.T. contributed to conception and design of the work, drafting of the article, critical revision of the article, and final approval of the version to be published. L.H.P. contributed to the conception and design of the work, drafting of the article, critical revision of the article, and final approval of the version to be published. M.A. 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.
Prior Presentation. Parts of this study were presented as a poster presentation at the 82nd Scientific Sessions of the American Diabetes Association, New Orleans, LA, 3–7 June 2023 and the Endocrine Society meeting, Chicago, IL, 15–18 June 2023.
Funding Statement
This research was funded by Doris Duke Foundation grant 20222028 (M.A.); National Institute of Diabetes and Digestive and Kidney Diseases grants R01DK104942 and P30 DK020595 (L.H.P.); National Institutes of Health grants R01HL146127, R01DK120312, P30DK020595, R01DK136214, and R01HL174685 (E.T.); National Institute of Diabetes and Digestive and Kidney Diseases grant R01DK104942 (R.N.N.); Clinical Therapeutics Training grant T32GM00719 (M.V.S.); and grant NCT04234217 (K.W.).
Supporting information
References
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