Insulin-related Dietary Indices Predict 24-hour Urinary C-peptide in Adult Men

Abstract

Dietary insulin index directly estimates the postprandial insulin secretion potential of foods, whereas empirical dietary index for hyperinsulinemia (EDIH) assesses insulinemic potential of usual diets based on fasting plasma C-peptide, and is primarily reflective of insulin resistance. It is unknown whether these insulin-related indices are predictive of an integrated measure of insulin secretion. We conducted a cross-sectional analysis that included 293 non-diabetic men with 24-hour urinary C-peptide data from the Men’s Lifestyle Validation Study. EDIH, dietary insulin index, and dietary insulin load were calculated using validated food frequency questionnaires. We conducted multivariable-adjusted linear regression to estimate relative and absolute concentrations of 24-hour urinary C-peptide. In multivariable-adjusted models, we found a significant positive association between all three insulin-related dietary indices and 24-hour urinary C-peptide (P<0.05). Relative concentrations of 24-hour urinary C-peptide per 1-standard deviation increase in insulin-related dietary indices were: 1.12 (95% confidence interval (CI), 1.02, 1.23) for EDIH, 1.18 (95% CI, 1.07, 1.29) for dietary insulin index and 1.16 (95% CI, 1.06, 1.27) for dietary insulin load. When we further adjusted for body mass index (BMI), the association was attenuated for EDIH, to 1.07 (95% CI, 0.98, 1.16), and remained unchanged for dietary insulin index and dietary insulin load. In conclusion, EDIH, dietary insulin index, and dietary insulin load were predictive of integrated insulin secretion assessed by 24-hour urinary C-peptide. Findings after adjustment for BMI appear to confirm the relation of EDIH to insulin resistance and dietary insulin index/load to insulin secretion; the respective constructs of the two dietary indices.

Introduction

Insulin resistance and hyperinsulinemia have been hypothesized as important underlying mechanisms linking diet and several major chronic diseases including type 2 diabetes⁽¹⁾ and obesity-related cancers.^{(2; 3)} Several studies have shown that carbohydrate-stimulated insulin secretion is related to weight gain⁽⁴⁾ and higher risk of obesity.⁽⁵⁾ Other dietary components, such as protein, influence acute insulin secretion. Dietary insulin index has been developed as a measure of postprandial insulin response of foods including both carbohydrate-containing foods and non-carbohydrate factors including protein.⁽⁶⁾ Although dietary insulin index predicted higher triglycerides and low high-density lipoprotein cholesterol levels,⁽⁷⁾ unexpectedly, higher dietary insulin index scores appeared to be related to lower fasting plasma C-peptide and lower body mass index (BMI) levels.⁽⁷⁾

Fasting plasma C-peptide may be more valid as a measure of insulin resistance than insulin secretion. Recently, we developed an empirical dietary index for hyperinsulinemia (EDIH) based on how foods and food groups comprehensively predicted fasting C-peptide levels.⁽⁸⁾ Although designed based on fasting plasma C-peptide, EDIH was also a better predictor of non-fasting plasma C-peptide than dietary insulin index or dietary insulin load.⁽⁹⁾ Since C-peptide is ultimately secreted in the urine, a 24-hour urine sample of C-peptide would provide an integrated measure of insulin secretion.⁽¹⁰⁾

In the current study, we collected C-peptide concentrations in 24-hour urine samples from the Men Lifestyle Validation Study (MLVS) to conduct a cross-sectional analysis of the association of EDIH, dietary insulin index, and dietary insulin load with 24-hour urinary C-peptide. Moreover, we examined interactions between these insulin-related dietary indices, physical activity and BMI in relation to 24-hour urinary C-peptide. We hypothesized that all three insulin-related dietary indices are positively associated with integrated insulin secretion assessed by 24-hour urinary C-peptide, and the positive association is stronger among overweight/obese individuals.

Methods

Study population

The MLVS was initiated as a validation study of diet and physical activity questionnaires used in the Health Professionals Follow-up Study (HPFS), a large prospective US cohort study.⁽¹¹⁾ The MLVS included a subset of men from the Health Professionals Follow-up Study and the Harvard Pilgrim Health Care insurance plan. Participants with a history of major diseases including coronary heart disease, stroke, cancer or major neurological disease were not included in the MLVS. The data collection was performed between 2011 and 2013. For this study, we selected individuals who had completed the 2010 food frequency questionnaire (FFQ) from the HPFS and donated a 24-hour urine sample. We excluded participants who were diabetic or over 70 years old. Participants with diabetes were identified using a validated questionnaire^{(12; 13)} which asked about diabetes-related symptoms, medications use, and diagnostic tests following the National Diabetes Data Group.^{(14; 15)} The final sample included 293 men. The study protocol was approved by the institutional review boards of the Brigham and Women’s Hospital and Harvard T.H. Chan School of Public Health.

Dietary assessment and calculation of the insulin-related dietary indices

Diet was assessed using a semiquantitative FFQ in 2010. Participants were asked to report their usual dietary intake of a standard portion of over 130 food items in the past year. Previous validation studies showed high reproducibility and validity of the dietary questionnaire.^{(16; 17)}

EDIH

The development and validation of EDIH have been described previously.⁽¹⁸⁾ Briefly, EDIH was developed in a sample of 5,812 women from the Nurses’ Health Study with a goal to create an empirical score to assess insulinemic potential of whole diets defined using food groups. Based on 39 predefined food groups from FFQs, stepwise linear regression models were used to identify a dietary pattern most predictive of plasma C-peptide, a marker of insulin secretion. EDIH is a weighted sum of 18 food groups that were shown to be statistically significant predictors. Higher (more positive) scores indicate hyperinsulinemic diets and lower (more negative) scores indicate low insulinemic diets. The food groups positively associated with EDIH score are: red meat, processed meat, poultry, non-fatty fish, margarine, butter, cream soup, low-fat dairy products, eggs, high-energy beverages, low-energy beverages, tomatoes, and French fries. The food groups inversely associated with EDIH score are: wine, coffee, leafy-green vegetables, whole fruits, and full-fat dairy products.

We also calculated empirical lifestyle index for hyperinsulinemia (ELIH) using the stepwise linear regression model but further including two lifestyle factors (BMI and physical activity).⁽¹⁸⁾ ELIH is a weighted sum of 12 food groups plus BMI and physical activity. Similarly, higher (more positive) scores indicate hyperinsulinemic lifestyles and lower (more negative) scores indicate low insulinemic lifestyles. For each participant, we used the self-administered FFQs to calculate EDIH and ELIH scores.

Dietary insulin index and insulin load

The insulin index values for individual food that appeared in the FFQs were obtained either from previously published estimates (31 foods)^{(6; 19)} or provided by Dr. Brand-Miller at University of Sydney, Sydney, Australia (73 foods). US food samples were shipped to the laboratory in Sydney for testing as previously described.⁽⁶⁾ Briefly, test subjects consumed various test foods on separate days, with insulin measured every 15 minutes for 2 hours after consumption. The insulin index for each food was calculated by dividing the area under the insulin response curve for 1000 kj of a test food by the area under the insulin response curve for 1000 kj of the reference food (glucose). The insulin index value for each food represented the mean responses of 11–13 subjects.

Using these insulin index values, we calculated the dietary insulin load for each participant by multiplying the insulin index value of each food by the total calories contributed by the food, and summing the values for all foods reported. We calculated the dietary insulin index by dividing dietary insulin load by total calories.

24-hour urinary C-peptide assessment

C-peptide was measured in 24-hour urine samples by a competitive electrochemiluminescence immunoassay on the Roche Cobas 6000 system (Roche Diagnostics, Indianapolis, IN). Briefly, a biotinylated C-peptide antibody and a C-peptide derivative labeled with ruthenium were mixed with the urine sample. The C-peptide in the sample and the ruthenium-labeled C-peptide compete for binding sites on the biotinylated antibody and form the respective immune complexes. Streptavidin-coated magnetic microparticles were then added to the reaction mixture to bind the biotinylated antibody. These immune complexes were magnetically entrapped on an electrode and the unbound reagents and sample were washed away. A chemiluminescent reaction was then electrically stimulated to generate light, the intensity being indirectly proportional to the amount of C-peptide present in the sample. This assay is approved by the U.S. Food and Drug Administration for clinical use. The lowest detection limit of this assay is 0.01 ng/mL and the day-to-day imprecision values at concentrations of 1.82, 5.69, 16.7 and 24.1 ng/mL are 5.0, 2.2, 3.8 and 1.8%, respectively.

Covariates assessment

Information on covariates such as age, height, body weight, physical activity, smoking status, nonsteroidal anti-inflammatory drug (NSAID) use and history of comorbidities were collected from a self-reported questionnaire in 2010. In addition, predicted lean body mass and fat mass were calculated based on previously validated anthropometric prediction equations⁽²⁰⁾ using age, race, height, weight, and waist circumference collected from a self-reported questionnaire in 2012.

Statistical analysis

Descriptive statistics according to the insulin-related dietary indices were presented as mean and standard deviation (SD) for continuous variables and as percentages for categorical variables. Spearman correlation was used to evaluate the relationship between the insulin-related dietary indices, BMI, physical activity and 24-hour urinary C-peptide, and among the insulin-related dietary indices.

Generalized linear models were used to examine the association of EDIH, dietary insulin index and dietary insulin load with 24-hour urinary C-peptide concentrations. All insulin-related dietary indices were adjusted for total calorie intake using the residual method,⁽²¹⁾ and then categorized into tertiles. We also used a continuous variable of the insulin-related dietary indices with unit of 1-SD increment. Multivariable-adjusted models included age (continuous), physical activity (continuous), comorbidity score (continuous), NSAID use (yes or no), and smoking status (never, past or current). We conducted an additional model further including BMI (continuous) to examine whether the association between the insulin-related dietary indices and 24-hour urinary C-peptide mediates through adiposity.

We examined stratified analysis of EDIH, dietary insulin index and dietary insulin load with 24-hour urinary C-peptide by BMI (<25 or ≥25 kg/m²), physical activity (<median or ≥median), and a combination of the two variables (lean and active, lean and sedentary, overweight/obese and active or overweight/obese and sedentary). Test for interaction was conducted (Wald test) by including a cross-product term of each insulin-related dietary index and stratification variable in the models. We further examined the joint association of EDIH (tertile) and dietary insulin index (tertile) with 24-hour urinary C-peptide concentrations.

As a sensitivity analysis, instead of using a single FFQ closest to 24-hour urine collection, we used two FFQs, assessed in 2010 and 2012, to examine the association of average EDIH, dietary insulin index and dietary insulin load with 24-hour urinary C-peptide concentrations. Because insulin sensitivity may partly mediate through muscle mass, we conducted an additional multivariable-adjusted model further adjusting for predicted lean body mass. Given that BMI may not be a perfect measure of adiposity, we also ran a multivariable-adjusted model using predicted fat mass. Lastly, we examined the association between ELIH and 24-hour urinary C-peptide to see if ELIH, a more comprehensive score of lifestyle factors including diet, BMI and physical activity, is more strongly associated with 24-hour urinary C-peptide.

All statistical analyses were performed using SAS, version 9.4 (SAS Institute Inc). All tests were two-sided and P-value<0.05 was considered statistically significant.

Results

Age-adjusted characteristics of participants by tertiles of insulin-related dietary indices (i.e., EDIH, dietary insulin index, and dietary insulin load) are presented in Table 1. The mean age, BMI and physical activity levels were 65.9 years, 25.5 kg/m² and 50.0 metabolic equivalent task-hour/week (Supplementary Table 1). Participants in the highest tertile of EDIH had higher BMI and lower physical activity levels. In contrast, BMI and physical activity levels were similar across tertile of dietary insulin index and dietary insulin load. Moreover, participants with higher scores on all three insulin-related dietary indices were more likely to be never-smokers. Compared to the main HPFS cohort, our study samples from the MLVS were younger and more active and had less comorbidities (Supplementary Table 1). The Spearman correlation of EDIH with dietary insulin index and dietary insulin load were 0.21 and 0.18, respectively (Supplementary Table 2).

Table 1.

Age-adjusted characteristics of participants by empirical dietary index for hyperinsulinemia (EDIH), dietary insulin index, and dietary insulin load in the Men’s Lifestyle Validation Study (n=293)

	Dietary patterns
	Tertile 1	Tertile 2	Tertile 3
EDIHa	(−0.8 to −0.2)	(−0.2 to 0.1)	(0.1 to 1.9)
Age, year	65.9 (1.7)	65.7 (1.8)	66.1 (1.9)
BMI, kg/m2	24.6 (2.9)	25.9 (3.6)	26.1 (3.1)
Physical activity, MET-hour/week	58.4 (40.7)	51.1 (47.1)	42.3 (32.8)
Comorbidity	0.8 (0.9)	0.8 (0.8)	0.8 (0.9)
Calorie intake, kcal/day	2210 (600)	2049 (574)	2142 (672)
NSAID use, %	70.4	62.3	73.9
Smoking status, %
Never	52.2	57.1	61.8
Past	46.9	42.9	34.8
Current	0.9	0.0	3.4
BMI & physical activity combination
Lean and active	36.5	21.8	16.9
Lean and sedentary	20.4	21.2	17.8
Overweight/obese and active	26.5	20.0	28.6
Overweight/obese and sedentary	16.6	36.9	36.7
Dietary insulin indexa	(−17 to −1.8)	(−1.8 to 2.4)	(2.4 to 14)
Age, year	66.1 (1.8)	65.9 (1.8)	65.7 (1.8)
BMI, kg/m2	25.2 (3.0)	26.1 (3.5)	25.2 (3.2)
Physical activity, MET-hour/week	49.2 (43.4)	49.0 (40.7)	50.5 (35.1)
Comorbidity	0.9 (0.9)	0.9 (0.9)	0.7 (0.8)
Calorie intake, kcal/day	2127 (652)	2170 (552)	2132 (653)
NSAID use, %	65.1	70.4	72.3
Smoking status, %
Never	42.3	63.0	66.6
Past	53.4	37.0	33.4
Current	4.3	0.0	0.0
BMI & physical activity combination
Lean and active	26.7	19.2	27.3
Lean and sedentary	23.5	18.8	18.7
Overweight/obese and active	18.9	26.9	27.7
Overweight/obese and sedentary	30.9	35.1	26.3
Dietary insulin loada	(−358 to −31)	(−31 to 49)	(49 to 353)
Age, year	66.1 (1.8)	65.7 (1.9)	65.8 (1.8)
BMI, kg/m2	25.3 (3.0)	25.9 (3.3)	25.2 (3.4)
Physical activity, MET-hour/week	49.5 (43.2)	47.2 (43.2)	52.1 (34.3)
Comorbidity	0.8 (0.9)	0.9 (0.9)	0.7 (0.8)
Calorie intake, kcal/day	2170 (638)	2031 (561)	2221 (648)
NSAID use, %	64.9	67.9	72.3
Smoking status, %
Never	41.6	63.0	65.7
Past	54.0	37.0	34.3
Current	4.4	0.0	0.0
BMI & physical activity combination
Lean and active	26.8	19.3	28.6
Lean and sedentary	21.1	21.6	18.8
Overweight/obese and active	19.7	21.8	29.8
Overweight/obese and sedentary	32.3	37.3	22.8

Data are presented as mean (SD) for continuous variables and percentage for categorical variables.

Abbreviation: BMI, body mass index, MET, metabolic equivalent task.

^aAdjusted for calorie intake.

In Table 2, EDIH, dietary insulin index and dietary insulin load showed similar correlations with 24-hour urinary C-peptide but only EDIH, not dietary insulin index and dietary insulin load, was correlated with BMI. Moreover, higher EDIH, dietary insulin index, and dietary insulin load scores were associated with higher levels of 24-hour urinary C-peptide concentrations (Table 3). Compared to participants in the lowest tertile of EDIH, dietary insulin index, and dietary insulin load, those in the highest tertile had significantly higher levels of 24-hour urinary C-peptide after adjusting for potential confounders. Relative concentrations (or relative ratio) of 24-hour urinary C-peptide per 1-SD increase in insulin-related dietary index was 1.12 (95% CI, 1.02, 1.23) for EDIH, 1.18 (95% CI, 1.07, 1.29) for dietary insulin index and 1.16 (95% CI, 1.06, 1.27) for dietary insulin load, respectively. When we further adjusted for BMI, the relative concentrations of C-peptide for EDIH attenuated to 1.07 (95% CI, 0.98, 1.16) while the relative concentrations for dietary insulin index and dietary insulin load remained consistent. Additional adjustment for predicted lean body mass or fat mass did not change the results (data not shown).

Table 2.

Spearman correlations of empirical dietary index for hyperinsulinemia (EDIH), dietary insulin index, and dietary insulin load with 24-hour urinary C-peptide and body mass index (BMI) (n=293)

	24-hour urinary C-peptidea	BMI
EDIH (single, 2010)	0.215	0.237
EDIH (average of 2010 and 2012)	0.220	0.233
Dietary insulin index (single, 2010)	0.249	0.018
Dietary insulin index (average of 2010 and 2012)	0.262	0.021
Dietary insulin load (single, 2010)	0.213	0.050
Dietary insulin load (average of 2010 and 2012)	0.224	0.043
BMI	0.267	-
Physical activity	−0.097	−0.202

^a24-hour urinary C-peptide was log transformed.

Correlations were adjusted for calorie intake.

Table 3.

Association of empirical dietary index for hyperinsulinemia (EDIH), dietary insulin index, and dietary insulin load with 24-hour urinary C-peptide in the Men’s Lifestyle Validation Study (n=293)^a

	EDIH
Mean (95% CI)	Tertile 1	Tertile 2	Tertile 3	1-SD increasec	P-trend
Age-adjusted	35.2 (30.2, 41.2)	45.5 (39.0, 53.1)*	48.6 (41.7, 56.8)*	1.14 (1.05, 1.25)	0.003
Multivariable adjustedb	36.1 (30.9, 42.2)	45.7 (39.2, 53.4)	47.3 (40.4, 55.2)*	1.12 (1.02, 1.23)	0.01
Multivariable adjustedb + BMI	38.1 (32.8, 44.3)	44.9 (38.7, 52.1)	45.6 (39.3, 53.0)	1.07 (0.98, 1.16)	0.17
	Dietary insulin index
Mean (95% CI)	Tertile 1	Tertile 2	Tertile 3	1-SD increasec
Age-adjusted	35.3 (30.2, 41.1)	43.0 (36.9, 50.1)	51.4 (44.1, 60.0)*	1.18 (1.08, 1.29)	<.001
Multivariable adjustedb	35.6 (30.4, 41.7)	42.7 (36.7, 49.8)	51.3 (43.9, 59.9)*	1.18 (1.07, 1.29)	<.001
Multivariable adjustedb + BMI	36.8 (31.7, 42.8)	41.0 (35.4, 47.4)	51.7 (44.7, 60.0)*	1.17 (1.07, 1.27)	<.001
	Dietary insulin load
Mean (95% CI)	Tertile 1	Tertile 2	Tertile 3	1-SD increasec
Age-adjusted	35.3 (30.3, 41.3)	43.8 (37.6, 51.2)	50.3 (43.1, 58.7)*	1.17 (1.07, 1.28)	<.001
Multivariable adjustedb	35.8 (30.6, 41.9)	43.3 (37.1, 50.5)	50.3 (43.1, 58.7)*	1.16 (1.06, 1.27)	0.001
Multivariable adjustedb + BMI	36.7 (31.6, 42.7)	42.0 (36.3, 48.7)	50.5 (43.6, 58.5)*	1.15 (1.05, 1.25)	0.002

Values are absolute 24-hour urinary C-peptide and its 95% CI (ng/mL).

Abbreviation: BMI, body mass index, CI, confidence interval.

^aEDIH, dietary insulin index, and dietary insulin load were adjusted for calorie intake using residual method.

^bMultivariable-adjusted models included age (continuous), physical activity (continuous), comorbidity score (continuous), NSAID use (yes or no), and smoking status (never, past or current). Multivariable-adjusted + BMI models further included BMI (continuous).

^cRelative concentrations (or relative ratio) with 95% CI of 24-hour urinary C-peptide per 1-SD increase in dietary index.

^*Significantly different from tertile 1 (P<0.05).

In stratified analyses of insulin-related dietary indices and 24-hour urinary C-peptide by BMI and physical activity, we found no significant interactions by BMI and physical activity (or their combination) (P-interaction>0.05) (Table 4). However, the positive associations for all insulin-related diet indices tended to be stronger among overweight/obese and/or active participants. In Figure 1, we examined the joint association of EDIH and dietary insulin index with 24-hour urinary C-peptide. Compared to participants in both lowest tertiles of EDIH and dietary insulin index, those in both highest tertiles had significantly higher levels of 24-hour urinary C-peptide (31.9 vs. 55.0 ng/mL). Additional adjustment of BMI slightly attenuated the association.

Table 4.

Stratified analyses of empirical dietary index for hyperinsulinemia (EDIH), dietary insulin index, and dietary insulin load with 24-hour urinary C-peptide in the Men’s Lifestyle Validation Study (n=293)^a,b

	EDIH	Dietary insulin index	Dietary insulin load
Relative concentration (95% CI)c	1-SD increase	1-SD increase	1-SD increase
BMI
<25 (n=133)	0.99 (0.83, 1.17)	1.13 (0.98, 1.30)	1.12 (0.98, 1.29)
≥25 (n=160)	1.12 (1.02, 1.23)	1.20 (1.08, 1.33)	1.18 (1.06, 1.31)
P-interaction	0.32	0.57	0.63
Physical activity
<median (n=148)	1.00 (0.88, 1.15)	1.09 (0.96, 1.24)	1.06 (0.93, 1.21)
≥median (n=145)	1.15 (1.03, 1.30)	1.24 (1.11, 1.38)	1.21 (1.09, 1.35)
P-interaction	0.29	0.18	0.15
BMI & physical activity combination
Lean and active (n=73)	1.10 (0.91, 1.33)	1.24 (1.06, 1.45)	1.23 (1.05, 1.44)
Lean and sedentary (n=60)	0.88 (0.66, 1.18)	0.98 (0.76, 1.26)	0.96 (0.76, 1.22)
Overweight/obese and active (n=72)	1.20 (1.04, 1.38)	1.27 (1.09, 1.48)	1.22 (1.05, 1.41)
Overweight/obese and sedentary (n=88)	1.06 (0.93, 1.21)	1.16 (1.00, 1.33)	1.16 (0.99, 1.36)
P-interaction	0.69	0.84	0.73

Values are absolute difference in 24-hour urinary C-peptide and its 95% confidence interval (ng/mL) associated with 1-SD increase in EDIH, insulin index or insulin load.

Abbreviation: BMI, body mass index, CI, confidence interval.

^aEDIH, dietary insulin index, and dietary insulin load were adjusted for calorie intake using residual method.

^bAll models included age (continuous), physical activity (continuous), comorbidity score (continuous), NSAID use (yes or no), smoking status (never, past or current) and BMI (continuous).

^cRelative concentrations (or relative ratio) with 95% CI of 24-hour urinary C-peptide per 1-SD increase in dietary index.

Figure 1. Joint association of EDIH and dietary insulin index with 24-hour urinary C-peptide.

Values are mean 24-hour urinary C-peptide with 95% confidence interval (ng/mL). Figure 1A. adjusted for the same covariates as the multivariable models in Table 2. Figure 1B. further adjusted for body mass index. EDIH, empirical dietary index for hyperinsulinemia, T1, tertile 1 of dietary insulin index, T2, tertile 2 of dietary insulin index, T3, tertile 3 of dietary insulin index.

* Significantly different from participants in both lowest tertiles of EDIH and dietary insulin index (P<0.05).

In a sensitivity analysis using two repeated measures of FFQs, we found the consistent positive associations between the average of EDIH, dietary insulin index and dietary insulin load with 24-hour urinary C-peptide (Supplementary Table 3). Moreover, overall results did not change when we used the residual method for BMI adjustment (Supplementary Table 4). Lastly, when we used ELIH, instead of EDIH, we found a stronger positive association with 24-hour urinary C-peptide. The relative concentration of 24-hour urinary C-peptide per 1-SD increase in ELIH was 1.26 (95% CI, 1.16, 1.38).

Discussion

Long-term average insulin exposure may be an important predictor of diseases such as cancer. Two indices have been used to assess the influence of diet on insulin exposure in epidemiological studies. Dietary insulin index directly measures the effect of a particular food on insulin secretion immediately after a meal, independent of the state of insulin resistance.⁽⁶⁾ In contrast, EDIH is an empirical hypothesis-oriented dietary index based on usual diets that are predictive of fasting plasma C-peptide, which is more reflective of insulin resistance than insulin secretion.⁽⁸⁾ However, it is unknown how these two different insulin-related dietary indices predict long-term average insulin secretion, which may be more directly assessed with 24-hour urinary C-peptide.^{(22; 23)} To address this knowledge gap, in a cross-sectional study of 293 men, we found that all insulin-related indices including EDIH, dietary insulin index, and dietary insulin load were significantly positively associated with 24-hour urinary C-peptide.

In a previous cross-sectional study, we found that EDIH but not dietary insulin index nor dietary insulin load was strongly predictive of both fasting and non-fasting plasma C-peptide.⁽²⁴⁾ Plasma insulin (C-peptide) concentration is determined both by insulin secretion and insulin clearance.^{(25; 26)} In the insulin resistance state, insulin clearance decreases physiologically as a compensatory mechanism, preserving beta-cell function and insulin levels.⁽²⁷⁾ 24-hour urinary C-peptide may not be a good measure of insulin clearance or insulin resistance but is a more comprehensive and integrated measure of insulin secretion. Our findings suggest that both EDIH and dietary insulin index are good measures of dietary factors that stimulate long-term average insulin secretion. Both factors associated with insulin resistance (e.g., EDIH) and insulin secretion directly (e.g., dietary insulin index) should ultimately increase 24-hour urinary C-peptide.

In a person with high insulin sensitivity (e.g., lean, physically active, low EDIH diet), a diet with high dietary insulin index and dietary insulin load will stimulate secretion of insulin. This may increase 24-hour urinary C-peptide, but the circulating insulin will remain relatively low because it is rapidly cleared. Insulin resistance will increase circulating insulin levels but in part or mostly by reducing clearance rate. Interestingly, prospective cohort studies have shown that EDIH was predictive of weight gain,⁽²⁸⁾ type 2 diabetes, and cancers such as digestive system cancers^{(29; 30)} and multiple myeloma⁽³¹⁾, while dietary insulin index and dietary insulin load were not predictive of diseases that are associated with hyperinsulinemia (e.g. type 2 diabetes, colorectal,⁽³²⁾ and pancreatic cancers⁽³³⁾). These findings may suggest that these diseases may be more associated with diets reflective of insulin resistance (high circulating insulin) than with diets that stimulate insulin secretion, because the insulin may be rapidly cleared in a highly insulin sensitive person.

Because dietary insulin index is a different construct and weakly correlated with EDIH, the two indices provide complementary information for the 24-hour urinary C-peptide. For example, overall, men in the high tertile of dietary insulin index have a 39% higher C-peptide level compared to those in the low tertile of dietary insulin index. However, men in the high tertiles of both dietary insulin index and EDIH had a 72% higher C-peptide level than those in the low tertiles of both. Thus, men in the high or low tertiles of dietary insulin index can be further classified based on their EDIH. Taking into account both measures may optimally predict total insulin secretion.

Our analysis showed correlation coefficients of 0.24 for EDIH, 0.02 for dietary insulin index and 0.05 for dietary insulin load in relation to BMI, which is a strong predictor of insulin resistance. Adjusting for BMI, a potential mediator, attenuated the observed positive association between EDIH, but not dietary insulin index/insulin load, and 24-hour urinary C-peptide. This finding suggests that increased BMI may be a potential mediator in part linking EDIH to insulin resistance.

In stratified analyses, we found no significant interactions by BMI or physical activity. However, the positive associations (relative concentrations) between insulin-related dietary indices and 24-hour urinary C-peptide appeared to be stronger among overweight/obese or active individuals. Yet, we observed substantially higher absolute 24-hour urinary C-peptide concentrations across all tertiles among overweight/obese individuals, compared to those among lean individuals. Although we found stronger positive associations among active individuals, compared to sedentary individuals, active individuals with low insulinemic diets had relatively lower absolute 24-hour urinary C-peptide concentrations, compared to sedentary individuals with low insulinemic diets. These findings are in line with a previous study that showed a lower concentration of fasting C-peptide among lean and/or active individuals with low EDIH scores.⁽⁸⁾

Our study has several limitations. First, we used single measure of 24-hour urinary C-peptide and thus there could be measurement error. It would be ideal to have multiple measures of 24-hour urinary C-peptide to reduce measurement error and to assess long-term insulin excretion. Second, diets and other lifestyle factors were measured using self-reported questionnaire and thus measurement error is inevitable. However, previous validation studies of diet,^{(16; 17)} physical activity,⁽³⁴⁾ and anthropometric measures⁽³⁵⁾ showed reasonably high validity and reproducibility. Third, the observational design of the current study limits causal interpretation of our findings. Lastly, our cohort predominantly included relatively healthy, white health professionals which may limit the generalizability of our findings, though the accurate reporting of this population of highly educated health professionals strengthens the internal validity of the study.

In conclusion, we found that higher intakes of diets with high insulinemic potential, indicated by higher EDIH, dietary insulin index, and dietary insulin load scores, were associated with higher 24-hour urinary C-peptide concentrations. Our study suggests that EDIH, dietary insulin index, and dietary insulin load are useful dietary indices that predict long-term integrated measure of insulin secretion. Moreover, findings from the BMI adjustment further confirm the underlying constructs of EDIH (insulin resistance) and dietary insulin index/insulin load (insulin secretion), suggesting that these biological dimensions of diet may play different roles in the etiology and/or prognosis of diseases.