Impact of fat and protein on postprandial glycemia in gestational diabetes: A randomized pilot crossover trial

Abstract

Background

The first‐line treatment for gestational diabetes mellitus (GDM) is dietary modification, but there is little evidence on the impact of fat and protein on glycemia in pregnancy.

Objective

To examine the effect of fat and protein on postprandial glycemia in individuals with GDM and to determine if this response differed in GDM characterized by insulin resistance versus insulin deficiency.

Methods

We conducted a double‐blind randomized crossover pilot study using test meals with similar caloric (390–411 kcal) and carbohydrate content (45%), but with either high‐fat (41%) or high‐protein (35%) content in diet‐controlled GDM (N = 16). We used fasting glucose and insulin levels to classify participants as insulin‐resistant or insulin‐deficient GDM. The primary outcome was peak postprandial glucose within 3 h following the test meals measured by a continuous glucose monitor (CGM). Secondary outcomes included glucose area under the curve (AUC) and glycemic excursion (peak postprandial glucose minus pre‐meal glucose). We used t‐tests to evaluate the relationship between meal type and outcomes.

Results

The high‐fat meal resulted in a higher mean (standard deviation [SD]) peak postprandial glucose (146.4 [19.7] mg/dL) than the high‐protein meal (137.2 [21.1] mg/dL, p = 0.03) (mean difference, 9.2 [15.9] mg/dL; 95% confidence interval [CI], 0.78, 148.4). The glucose AUC (high fat: 26,236 [3220] mg/dL*min, high protein: 24,788 [3567] mg/dL*min, p = 0.03) (mean difference, 1448 [2462] mg/dL*min; 95% CI, 136.0, 2760) and the glycemic excursion (high fat: 68.3 [23.3] mg/dL, high protein: 59.7 [22.3] mg/dL, p = 0.03) (mean difference, 8.6 [14.9] mg/dL; 95% CI, 0.7, 16.5) were higher after the high‐fat meal. There was no differential peak postprandial glucose response to high‐fat versus high‐protein in insulin‐deficient versus insulin‐resistant GDM (mean difference, −0.25 mg/dL; 95% CI, −24.1, 23.5, p = 0.98).

Conclusion

In GDM, a high‐protein meal leads to lower CGM‐measured postprandial glucose compared to a high‐fat meal, when calories and carbohydrate content are held constant.

Trial Registration

ClinicalTrials.gov identifier: NCT04187521

1. INTRODUCTION

Gestational diabetes mellitus (GDM) impacts 8% of pregnancies in the United States and is associated with adverse fetal and maternal outcomes [1]. Successful management of GDM hinges on nonpharmacological approaches including glucose monitoring, dietary changes, and physical activity. These approaches improve glycemia in approximately 70%–85% of cases, so that individuals can avoid insulin use, which can be burdensome and costly [2, 3]. Nutrition therapy is the first‐line therapy for GDM, yet the optimal diet for people with this condition remains elusive [4, 5, 6].

Studies evaluating nutritional strategies in GDM have mainly focused on carbohydrate quantity and quality [4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]. Carbohydrate restriction has been associated with both lower postprandial glucose and less macrosomia among infants born to individuals with GDM [8]. However, lower carbohydrate diets may increase the risk of ketosis (which may adversely affect fetal neurodevelopment) and lead to higher fat intake (potentially associated with increased maternal insulin resistance and fetal fat accumulation) [9].

Current nutrition‐related guidelines in GDM vary: all address dietary carbohydrate and nearly all recommend consultation with a dietitian, yet the recommendations for intake of other macronutrients, protein and fat, are inconsistent [2, 17, 18, 19]. Although carbohydrate is often thought of as the main macronutrient driving glycemia, studies outside of pregnancy have shown that protein and fat also impact glucose and insulin physiology [20, 21, 22, 23]. These macronutrients have not been well studied in pregnancy or in GDM. Current nutrition guidelines also treat GDM as one disease despite the fact that hyperglycemia in these individuals can be driven predominantly by either insulin resistance, insulin deficiency, or both [24]. Studies outside of pregnancy have suggested that postprandial responses to the same meal can differ widely between individuals [25].

In a proof‐of‐concept pilot double‐blind randomized crossover trial, we aimed to assess the impact of protein and fat on glycemia in GDM and to determine if individuals with different GDM physiologic subtypes respond to protein and fat differently.

2. METHODS

Heterogeneity Informed Nutrition Therapy for Gestational Diabetes Mellitus (HINT‐GDM) (NCT04187521) was a double‐blind randomized crossover pilot study, evaluating the impact of fat and protein on postprandial glucose in GDM. The study was 7 days in length. Patients were allocated to the two different meal sequences (high‐fat meal day 2, high‐protein meal day 3 or high‐protein meal day 2, high‐fat meal day 3) in a 1:1 ratio. A crossover trial was chosen given its feasibility and because carry‐over effect was not expected. The time between meals was approximately 24 h, which was expected to be an adequate wash‐out period when assessing the impact of a meal on postprandial glucose.

2.1. Participants

Pregnant individuals, age 18 and older, with GDM were included. Participants were diagnosed with GDM after either an abnormal 1‐h glucose loading test and abnormal diagnostic 3‐h oral glucose tolerance test (OGTT) using Carpenter–Coustan Criteria or after an abnormal 2‐h OGTT using the International Association of the Diabetes and Pregnancy Study Group diagnostic criteria [26]. Exclusion criteria included: known diabetes prior to pregnancy, use of medications known to affect glucose, multiple gestation, history of bariatric surgery, food allergies to test meal components, inability to wear a continuous glucose monitor (CGM), and working overnight during the days when test meals were consumed. Participants were recruited from the obstetric practices at Massachusetts General Brigham (MGB) and through advertisements to the community between 2020 and 2022.

The COVID‐19 pandemic impacted our recruitment and funding resources; thus, we decided to complete analyses for pilot data to facilitate possibly procuring additional funding for a full trial, depending on the results. The sample size for this pilot study was determined by feasibility. The MGB Human Research Committee (Institutional Review Board) approved the study (October 10, 2019), and all participants gave written informed consent.

2.2. Study procedures

Some participants were recruited prior to their diagnostic OGTT, and a study visit occurred during the participant's clinical 3‐h OGTT visit, where glucose and insulin were collected. If diagnosed with GDM, the participant was invited to the trial. Some participants were recruited to the trial after their diagnostic OGTT and had fasting blood drawn on day 1 of the trial. During the study (day 1), participants had a masked CGM placed and were provided with two breakfast test meals to eat at home on consecutive days (day 2 and day 3) in a randomly assigned order. Height and weight were measured at the visit, and participants completed surveys. The remainder of the study took place at home. Participants were instructed to fast for 8 h prior to their breakfast test meals, check their glucose with a glucometer prior to eating the test meal, consume the test meal within 1 h of waking up, and check a 1‐h postprandial glucose following the test meals. They were instructed to eat the meal in 5–15 min and to not eat or engage in exercise for 3 h after the meal. No special instructions for physical activity in the days preceding the test meals were provided. Participants followed standard of care recommendations per their clinical care team for GDM. After eating the meal, participants were instructed to take a photo of their plate to ensure that the meal was completely consumed. Participants were called by study staff team members on days 2 and 3 to assess adherence to the study protocol and review meal photos. After wearing the CGM for 7 days, participants returned the CGM along with study logs.

2.3. Randomization

We used a random number generator to assign each participant a meal order. Each meal was given a unique ID, which identified the meal as high fat or high protein. Participants, investigators, and study staff (other than a single team member who performed the randomization and the dietician who prepared the meals, neither of whom interacted with participants) were not aware of meal order during the study. The primary outcome (peak postprandial CGM glucose) was ascertained and recorded before unblinding the meals.

2.4. Exposure: Meals

The isocaloric and identical‐appearing test breakfast meals were prepared in the metabolic kitchen of the hospital's Translational and Clinical Research Center. The high‐protein meal contained 35% protein, 45% carbohydrate, and 20% fat. The high‐fat meal contained 15% protein, 45% carbohydrate, and 41% fat (Table 1). The meals consisted of a specially formulated waffle with butter, syrup, and milk (see Appendix S1 for additional details). Participants could drink tea, coffee, or water with the meal. Contents of the test meal were slightly changed in the middle of the study because of the availability of ingredients, but meal macronutrient content remained the same.

TABLE 1.

Dietary composition of breakfast test meals.

	High‐fat breakfast	High‐protein breakfast
Calories	411 kcal	390 kcal
Protein	15 g (15%)	35 g (35%)
Fat	19 g (41%)	9 g (20%)
Carbohydrates	46 g (45%)	45 g (45%)

Note: Percent represents the percent of total calories for the meal.

2.5. Outcomes

CGM data were collected using the professional FreeStyle Libre (14‐day CGM). CGM values were masked to participants, study staff, and investigators. The primary outcome of the study was the peak postprandial CGM glucose level within 3 h following the test meal. Secondary CGM outcomes included glucose area under the curve (AUC), calculated using trapezoidal rule for the 3 h following the meal, and glycemic excursion (pre‐meal glucose subtracted from the peak postprandial glucose). Secondary glucometer outcomes included the 1‐h after test meal glucose and glycemic excursion (fasting glucose subtracted from the 1‐h after test meal glucose).

2.6. Lab analyses

On the morning of the OGTT or the day of CGM placement, blood samples for glucose were collected in sodium fluoride tubes. Mimicking clinical practice, samples were sent promptly to the hospital's core clinical laboratory for analysis. Glucose was measured using the hexokinase method with the Cobas 8000 modular analyzer at the Massachusetts General Hospital Core Lab. Frozen insulin samples were assayed using the Beckman Coulter Access Ultrasensitive Insulin Assay at Quest Diagnostics (see Appendix S1 for additional details).

2.7. GDM subtypes

We defined insulin‐resistant and insulin‐deficient GDM subtypes with the homeostasis model assessment (HOMA2), a method for assessing insulin sensitivity and β‐cell function from fasting glucose and insulin levels [27, 28]. As previously described, we first calculated HOMA2S (a measure of insulin sensitivity) and HOMA2B (a measure of insulin secretion) scores [29]. We examined fasting insulin and glucose levels in a separate group of pregnant participants with normal OGTT glucose (N = 81), defining the 50th percentile for each HOMA2 index. Our initial intent was to use a 25th percentile threshold, but after data collection, publication of a related paper [29], and before unblinding, the threshold was changed to the 50th percentile in order to minimize the number of participants that were unclassified. We defined insulin‐deficient GDM as GDM with a HOMA2B score less than the 50th percentile (99%) and a HOMA2S score above the 50th percentile (154%); we defined insulin‐resistant GDM as GDM with a HOMA2S score below the 50th percentile (154%) and a HOMA2B score above the 50th percentile (99%). Individuals with GDM had a mixed defect if both the HOMA2S and HOMA2B scores were less than the 50th percentile.

2.8. Statistical analyses

We used paired t‐tests to evaluate the relationship between meal type and several different outcomes, including the primary outcome (peak postprandial CGM glucose), glucose AUC, glycemic excursion after each meal measured by CGM, 1‐h after test meal measured by glucometer, and glycemic excursion measured by glucometer. The assumption of normality was confirmed using the Shapiro–Wilk test. We used a two‐sample t‐test to assess a difference in the differential response to the two test meals by GDM subtype. We used linear regression to conduct a sensitivity analysis adjusting for gestational age. We also tested for a difference in time (minutes) to peak glucose between the meals using a Wilcoxon matched‐pairs signed rank test due to non‐normality of this outcome. In a sensitivity analysis, we excluded individuals who did not fully complete the meals. Using CGM data obtained during the days of the test meals, we calculated the mean glucose, percent of time spent with a glucose ≥120 mg/dL, and percent of time spent with a glucose ≥140 mg/dL on the days of the test meals; we compared these values using paired t‐tests. We used a paired t‐test to evaluate the difference in total calories, fat, protein, and carbohydrate intake following the test meals. To visualize the longitudinal trajectory of CGM data after both test meals were consumed, we used the locally estimated scatterplot smoothing (LOESS) technique with geom_smooth from the ggplot2 package in R with default options, which generates 95% confidence intervals (CIs) based on Student's t‐distribution. We excluded the participants who had CGM failures from the CGM analysis. Statistical analyses were conducted in STATA/IC version 16 (College Station, TX) and R Version 4.3.0.

3. RESULTS

3.1. Participant characteristics

We screened 260 pregnant individuals. A total of 19 cis‐gendered women completed the study, and 16 women had complete primary outcome data. Two enrolled women had failed CGM devices, and one was started on insulin in the middle of the study protocol (Figure 1). Of the women with complete primary outcome data, nine were randomized to the high‐fat/high‐protein sequence, and seven were randomized to the high‐protein/high‐fat sequence. We received 36 of 38 plate photos after meal completion from our participants. All participants reported finishing the meals. Three meal photos showed partially consumed butter and syrup.

FIGURE 1.

Consort diagram.

Participants were on average 36.2 years old with a body mass index (BMI) of 26.5 kg/m² (Table 2). Diagnosis of GDM by OGTT was at a median of 27.8 weeks’ gestation (Interquartile range [IQR] 26.8, 28.6 weeks’ gestation). Median gestational age on day 1 of the study was 31.2 weeks’ gestation (IQR 29.8, 33.3 weeks’ gestation). Of the 16 participants with complete primary outcome data, 8 had insulin‐resistant GDM, 4 had insulin‐deficient GDM, and 4 had mixed physiology (Table 2). Table S1 contains baseline characteristics for participants who have complete glucometer data (N = 18) and Table S2 lists baseline characteristics for participants based on sequence assignment.

TABLE 2.

Baseline characteristics of participants by GDM subtype.

	All participants with primary outcome (N = 16) Median (IQR) Frequency (%)	Insulin resistant (N = 8) Median (IQR) Frequency (%)	Insulin deficient (N = 4) Median (IQR) Frequency (%)	Mixed defect (N = 4) Median (IQR) Frequency (%)
Age	36.2 (33.5, 38.6)	37.2 (33.4, 38.5)	33.7 (32.5, 37.1)	37.0 (35.4, 38.7)
Family history of DM	5 (31)	2 (25)	1 (25)	2 (50)
Race and ethnicity a
Hispanic	2 (13)	1 (12.5)	0 (0)	1 (25)
Non‐Hispanic White	9 (56)	5 (62.5)	3 (75)	1 (25)
Non‐Hispanic Asian	4 (25)	2 (25)	0 (0)	2 (50)
Multiracial	3 (19)	1 (12.5)	1 (25)	1 (25)
Education
Completed graduate school	11 (69)	5 (63)	3 (75)	3 (75)
Gestational age at diagnosis (weeks)	27.8 (26.8, 28.6)	27.8 (27.0, 29.2)	26.6 (25.9, 27.8)	27.7 (27.1, 28.4)
Gestational age at study visit (weeks)	31.2 (29.8, 33.3)	30.0 (29.8, 33.5)	32.3 (29.8, 34.3)	31.6 (30.6, 31.9)
BMI at study visit (kg/m2)	26.5 (24.2, 30.0)	28.7 (24.0, 31.5)	24.9 (23.3, 28.4)	26.4 (24.7, 28.1)

Abbreviations: BMI, body mass index; GDM, gestational diabetes mellitus; IQR, interquartile range.

^aRace and ethnicity were self‐reported on questionnaires.

3.2. Primary outcome

In participants with primary outcome data (N = 16), the high‐fat meal resulted in a higher mean (standard deviation [SD]) peak postprandial glucose (146.4 [19.7] mg/dL) when compared to the high‐protein meal (137.2 [21.1] mg/dL) (p = 0.03) as measured by CGM (mean difference, 9.2 [15.9] mg/dL; 95% CI, 0.78, 148.4) (Table 3, Figure 2). There was no differential peak postprandial glycemic response to the meals by CGM in insulin‐resistant (N = 8; mean difference between fat and protein meals 9.5; 95% CI, −1.3, 20.3 mg/dL) versus insulin‐deficient (N = 4, mean difference between fat and protein meals 9.3; 95% CI, −30.5, 49.0 mg/dL) participants (difference −0.25 mg/dL; 95% CI, −24.1, 23.5) (p = 0.98). These results did not change with adjustment for gestational age. The median timing of test meal consumption was similar between test days (high‐fat diet: 7:58 a.m. vs. high‐protein diet: 7:55 a.m.). When we excluded the three individuals who did not complete their meals (per protocol analysis), the difference in peak glucose between meals was similar, but no longer statistically significant (high‐fat peak glucose: 148.1 [20.5] mg/dL; high‐protein peak glucose: 140.8 [20.0] mg/dL) (mean difference 7.4 [16.1] mg/dL, 95% CI, −1.9, 16.6) (p = 0.11). Although the average postprandial glucose was higher after the high‐fat meal, a minority of participants (n = 5) had higher postprandial glucose after the high‐protein meal; the baseline characteristics of these participants were similar to characteristics of those who had higher postprandial glucose after the high‐fat meal (n = 11) (Table S3). Figure 3 depicts CGM data after each test meal type was consumed, from meal start (time 0) to 3 h after meal start, smoothed using loess regression technique. Averages and standard deviations before smoothing are shown in Figure S1.

TABLE 3.

Glucose outcomes by meal type.

	High‐fat meal Mean (SD) (95% confidence interval)	High‐protein meal Mean (SD) (95% confidence interval)	p value
Peak postprandial glucose (CGM) (mg/dL) (N = 16)	146.4 (19.7) (135.9, 156.9)	137.2 (21.1) (125.9, 148.4)	0.03
Glucose area under the curve after test meal (CGM) (mg/dL*min) (N = 16)	26236 (3220) (24520, 27951)	24788 (3567) (22887, 26688)	0.03
Glycemic excursion a (CGM) (mg/dL) (N = 16)	68.3 (23.3) (55.9, 80.7)	59.7 (22.3) (47.8, 71.6)	0.03
1‐h postprandial test meal glucose (glucometer) (mg/dL) (N = 18)	163.0 (31.1) (147.6, 178.4)	149.4 (18.6) (140.1, 158.6)	0.08
Glycemic excursion b (glucometer) (mg/dL) N = 17 c	79.5 (30.7) (63.7, 95.3)	63.8 (16.3) (55.5, 72.2)	0.07

Note: The numbers in this table represent the mean across N individuals. Paired t‐tests were used to test the difference in each outcome for all participants for each meal type.

Abbreviations: CGM, continuous glucose monitor; SD, standard deviation.

^aDifference between peak postprandial glucose and pre‐meal glucose.

^bDifference between 1‐h postprandial test meal glucose and fasting glucose.

^cOne participant was excluded due to missing fasting glucometer glucose for one meal.

FIGURE 2.

Peak postprandial glucose following test meals. This figure depicts the peak glucose after both test meals for each participant as measured by a continuous glucose monitor. The pink line represents the average glucose response to each test meal.

FIGURE 3.

Glucose following test meals. This figure depicts the glucose as measured by a continuous glucose monitor from the start of the meal (time 0) to 180 min after smoothing using loess regression. Shaded areas indicate standard errors for predicted smoothed values.

3.3. Secondary outcomes

Table 3 lists secondary glycemic outcomes by meal type. Glucose AUC following the high‐fat meal was higher than AUC following the high‐protein meal (mean difference, 1448 [2462] mg/dL*min; 95% CI, 136.0, 2760) (p = 0.03). Glycemic excursion by CGM with the high‐fat meal was significantly larger than with the high‐protein meal (p = 0.03) (mean difference, 8.6 [14.9] mg/dL; 95% CI, 0.7,16.5). The 1‐h postprandial test meal glucose (measured by glucometer) (mean difference, 13.1 [7.3] mg/dL; 95% CI, −1.8, 29.1) and the glycemic excursion (measured by glucometer) (mean difference, 15.6 [8.0] mg/dL; 95% CI −1.2, 32.5) were higher after the high‐fat meal compared to the high‐protein meal, but these differences were not statistically significant (p = 0.08 and p = 0.07, respectively). The time to peak glucose by CGM was not significantly different between the two meals (high‐fat meal: median 49.5 min [IQR 40, 63], high‐protein meal: median 49.0 min [IQR 40.0, 62.5]) (p = 0.60).

The mean glucose on the day of the test meals did not differ (high‐fat mean glucose = 81.9 [11.8] mg/dL; high‐protein mean glucose = 80.7 [12.1] mg/dL) (p = 0.37). The percent of time spent ≥120 mg/dL (high fat: 8.1 [8.6]%, high protein: 6.6 [9.3]%) (p = 0.02) and ≥140 mg/dL (high fat: 3.1 [3.2] %, high protein: 1.9 [2.6]%) (p < 0.01) was significantly higher on day of the high‐fat meal compared to the day of the high‐protein meal. The caloric intake and macronutrient intake (fat, protein, and carbohydrate) were similar after the test meals when comparing consumption after the high‐fat meal and high‐protein meal on the day of the test meals. There was a trend toward higher fat and higher caloric intake on the day that the higher fat meal was consumed; however, these differences were not statistically significant.

4. DISCUSSION

In a randomized crossover double‐blind pilot study utilizing isocaloric test meals with the same carbohydrate content, individuals with GDM had improved glycemic outcomes following a high‐protein meal as compared to a high‐fat meal. Peak postprandial glucose, glucose AUC, and glucose excursion, measured by CGM, were higher after the high‐fat meal when compared to the high‐protein meal. Glucometer measures, which included 1‐h postprandial glucose and the difference between the 1‐h postprandial glucose and fasting glucose, appeared higher after the high‐fat meal when compared to the high‐protein meal, but these differences were not statistically significant. GDM subtype (insulin resistant vs. insulin deficient) did not influence the impact of meal type on glycemic outcomes.

While the data among pregnant individuals are lacking, studies among non‐pregnant individuals have shown that fat and protein impact glycemic physiology, even when carbohydrate intake is held constant. An interventional study evaluating people with type 2 diabetes ingesting carbohydrate and protein versus carbohydrate alone showed that protein ingestion increased insulin levels while reducing blood glucose levels, suggesting an effect of protein on insulin secretion [21]. Additionally, increasing dietary protein over weeks to months in patients with type 2 diabetes reduced hemoglobin A1c and improved insulin secretion [22]. In a study evaluating the impact of dietary fat on glucose and insulin in people with type 2 diabetes, addition of dietary fat (butter) to carbohydrate (potato) did not significantly alter glucose, but did lead to significantly higher insulin levels, suggesting an increase in insulin resistance [23].

Few trials have addressed non‐carbohydrate macronutrients (fat and protein) among individuals with GDM. Sarathi et al. randomized women with GDM (n = 62) to a high‐fiber complex carbohydrate diet versus a vegetable‐based protein‐rich diet (25% of cereal part in high fiber, complex carbohydrate diet replaced by soya food) at 25 weeks’ gestation [30]. After 1 week of the prescribed diets, postprandial glucose levels were significantly lower in the protein‐rich group when compared to the complex carbohydrate group. Fewer individuals on the protein‐rich diet were prescribed insulin after 1 week on the diet and at delivery. However, carbohydrate, fat, and protein were not quantified [30]. In another small trial assessing varying types of protein, soy protein, when substituted for animal and plant protein, was found to improve glycemia in women with GDM [31]. Other small interventional trials have evaluated diets varying in fat and carbohydrate and have not found differences in glycemia, measured using intermittent intravenous glucose tolerance tests or limited venous glucose blood draws [32, 33]. In a randomized crossover trial, Hernadez et al. manipulated dietary fat and carbohydrate and found that the participants randomized to the higher carbohydrate/lower fat diet spent more time with glucose levels > 120 as measured by CGM and had higher nocturnal glucose area under the curve than those randomized to the lower carbohydrate/higher fat diet: newborn adiposity was the same [34]. Our study, unlike these prior studies, was designed to evaluate the unique impact of fat and protein on glycemia while keeping carbohydrate and total calories constant in the short term. We sought to assess the acute glycemic response to these macronutrients using test meals; this allowed us to account for the macronutrients and calories consumed. Unlike many of these studies, which assessed glycemia based on limited venous glucose draws or in response to OGTT, we assessed glycemic response to fat and protein using both CGM and glucometer, which are the devices used in clinical practice.

We also set out to determine if a more personalized nutritional approach may improve glycemic outcomes in GDM [25, 35, 36]. Our previous research has shown increased risk of large for gestational age birthweight and other adverse pregnancy outcomes among hyperglycemic pregnant individuals with insulin resistance when compared to hyperglycemic pregnant individuals with insulin deficiency [24, 29]. In the present study, we compared individuals with insulin‐resistant GDM to individuals with insulin‐deficient GDM and did not find a difference in how these individuals responded to the meals: both had lower postprandial glucose levels following the high‐protein meal on average.

Based on the findings of our study, replacing a high‐protein meal for a high‐fat meal in individuals with diet‐controlled GDM may improve postprandial glucose levels. We hypothesize that this is due to enhanced insulin secretion with subsequent reduction in blood glucose when consuming the high‐protein meal compared to the high‐fat meal [20, 21, 22, 23]. A high‐fat meal may also enhance insulin resistance, leading to greater postprandial glucose [23]. A larger study should be performed to further support our findings. Our findings did not support our initial hypothesis that individuals with insulin‐resistant and insulin‐deficient GDM would respond differently to the meals, albeit our sample size for this analysis was limited.

Future research should determine the underlying reasons for inter‐individual glycemic variability. There are likely factors other than insulin physiology that determine inter‐individual variation in glycemic response to macronutrients in GDM, though we were likely underpowered to fully investigate the impact of GDM subtype on postprandial glycemic response to diet.

Our study has several strengths. We utilized a randomized crossover design, which minimizes confounders since each participant serves as their own control. Our study was double blinded: investigators and research participants were not aware of the order of the meals or the CGM glucose readings during the study. Participants were not aware of how the meals differed in content. We used CGM and glucometer testing to assess glycemic outcomes.

Limitations do exist. This study was a proof‐of‐concept trial that was affected by the COVID‐19 pandemic, resulting in a limited sample size and power to detect differences between GDM subtypes. Additionally, our population was restricted to diet‐controlled GDM, so it is unclear if the glycemic response to meals would differ in more severe cases of GDM. Due to the at‐home nature of our study and despite assessing adherence, deviations from the study procedures were possible. Also, we were unable to gather additional data on the test meal days, such as insulin and glucagon levels, which would have been helpful to improve understanding of the etiology of the glycemic response to the test meals. Additional research studies are required to evaluate this phenomenon in a larger population of people with GDM.

Nutrition therapy is the first‐line treatment for GDM as it reduces the need for insulin and has also been associated with improved maternal and fetal outcomes [4, 37]. While all individuals with GDM should receive nutritional therapy, the optimal diet to control hyperglycemia remains unknown. Specifically, there is a scarcity of literature evaluating the impact of macronutrients, protein and fat, on glycemia in individuals with GDM. Our data demonstrate that a high‐protein meal may lead to lower peak postprandial glucose when compared to a high‐fat meal, but that GDM physiologic subtype does not influence this response. Therefore, replacing fat with protein in GDM could be a strategy to enhance control of postprandial glucose levels. If validated by future research, our findings might influence the nutritional management of GDM.

AUTHOR CONTRIBUTIONS

All included co‐authors participated in substantial contributions to conception and design, acquisition of data, and/or analysis and interpretation of data, drafting the article or reviewing it critically, and approving the final version.

CONFLICT OF INTEREST STATEMENT

Camille E. Powe is an Associate Editor of Diabetes Care, receives payments from Wolters Kluwer for UpToDate chapters on diabetes in pregnancy, and has received payments for consulting and speaking from Mediflix Inc. Camille E. Powe has also received research funding from Dexcom through Massachusetts General Hospital. All other authors declare no conflicts of interest.

Supporting information

Supporting information

DATA AVAILABILITY STATEMENT

Data described in the manuscript, code book, and analytic code will not be made available because of IRB and institutional restrictions.