Short term elevation in dietary protein intake does not worsen insulin resistance or lipids in older adults with metabolic syndrome: a randomized-controlled trial
© The Author(s). 2017
Received: 28 October 2016
Accepted: 29 March 2017
Published: 17 April 2017
There is a great deal of controversy as to whether higher protein intake improves or worsens insulin sensitivity in humans. The purpose of the study was to determine the influence of a short-term elevation in dietary protein on hepatic and peripheral insulin sensitivity in twelve older subjects (51–70 yrs) with metabolic syndrome.
Individuals were randomly assigned to one of the dietary groups: recommended protein intake (RPI, 10% of daily calorie intake) or elevated protein intake (EPI, 20% of daily calorie intake) for 4 weeks. Prior to and immediately following the dietary intervention, subjects were studied with primed continuous infusion of [6,6-2H2]glucose and [1-13C]glucose dissolved in drink during the dual tracer oral glucose tolerance test (DT OGTT) to determine hepatic and peripheral insulin sensitivity. Plasma lipids were measured pre- and post-dietary intervention.
In both intervention groups: 1) hepatic insulin sensitivity as assessed by the endogenous glucose rate of appearance (glucose Ra), 2) peripheral insulin sensitivity as assessed by the metabolic clearance rate of glucose normalized to plasma glucose concentration (MCR) and/or the rate of glucose utilization (Rd) or 3) glucose/insulin AUC were unaffected by the diets. Moreover, fasting lipid was not affected by RPI or EPI.
Our findings suggest that a short-term elevation in EPI with correspondingly higher branched chain amino acid (BCAA) contents has no detrimental impact on hepatic and peripheral insulin sensitivity or plasma lipid parameters in older adults with metabolic syndrome.
ClinicalTrials.gov Identifier: NCT02885935; This trial was registered retrospectively (Study start date, April 01, 2013, date of registration, August 26, 2016).
KeywordsStable isotope tracers Liver Muscle Insulin resistance Protein intake
It has been well demonstrated that dietary protein intake above the recommended dietary allowance (RDA) of 0.8 g protein/kg body weight/day which promotes reductions in body fat mass due in part to increased satiety and/or energy expenditure linked to feeding induced thermogenesis . A high protein diet has also been linked to improvements in lean body mass (reflecting muscle mass) via the stimulation of net protein synthesis [2, 3], which has also been accompanied by improved strength and function . Given that skeletal muscle is the largest organ and responsible for the majority of postprandial glucose disposal , an increase in protein intake may promote the preservation of skeletal muscle and lead to improvements in insulin sensitivity . Studies have also shown that increased protein intake may have favorable effects on circulating triglyceride concentrations . Indeed, elevations in protein intake have been linked to many improvements in what many consider the hallmarks of metabolic syndrome (i.e., hypertension, atherosclerosis, hyperlipidemia) . Considering these data, one would anticipate benefits in metabolic health by increasing the amount of protein and decreasing the amount of calories from fat (particularly, saturated fats) and highly processed carbohydrates.
Despite the potentially favorable influence of higher protein intake on metabolic health, controversy exists regarding the results (improved or worsen) from short- (<6 months) and long-term intervention studies and/or the phenotype of participants that includes those who are healthy, obese, insulin resistant, and/or have type 2 diabetes . Moreover, the term itself “high protein” can be used to indicate a slight elevation in protein intake or a diet comprised of only dietary fat and protein . Despite these wide variations in protein intake that obviously affect dietary intake of fat and carbohydrate, studies based on dietary intake data from food questionnaires have linked high dietary protein to deleterious alterations in glucose metabolism . Moreover, cross sectional studies have suggested that elevations in plasma branched chain amino acids are connected excess visceral adipose tissue and markers of insulin resistance [12, 13].
On the other hand, numerous studies over the past 10 years or so have demonstrated significant improvements in metabolic health with increased dietary protein intake [14–18]. In many of the cases where changes in dietary intake were implemented, it is difficult to ascertain whether the alterations were induced through dietary counseling, dietary assessment or metabolic feeding. Whereas the purpose of cross sectional studies are largely directed towards descriptive interpretation  and the limitations of dietary recall have been known for many years , conclusions drawn from these approaches still persist. In order to address the short-term impact of significant elevations in protein intake, we utilized a metabolic feeding approach that closely controlled dietary intake. In turn, we hypothesized that short-term (i.e., 4 weeks) changes in 1) elevated protein intake (EPI) that is 20% of daily calorie intake) compared to the 2) recommended level of protein intake (RPI) that is approximately 10% of daily calorie intake) would not have any measurable negative influence on hepatic and peripheral insulin sensitivity and plasma lipid profiles in older individuals with metabolic syndrome.
Group Characteristics and Medications
Recommended Protein Intake
Elevated Protein Intake
64.5 ± 3.0
60.2 ± 2.8
Total body mass, kg
108.5 ± 10.6
108.5 ± 10.5
106.0 ± 6.8
105.3 ± 6.8
Lean body mass, kg
59.0 ± 7.4
57.3 ± 6.0
58.1 ± 4.6
56.4 ± 3.8
Body mass index, kg/m2
37.4 ± 2.6
37.4 ± 2.6
37.6 ± 1.4
37.3 ± 1.4
Body fat (%)
41.3 ± 7.5
41.2 ± 3.4
Total cholesterol, mg/dl
198.3 ± 43.6
188.7 ± 37.9
185.3 ± 60.9
171.2 ± 41.5
HDL cholesterol, mg/dl
44.2 ± 10.1
41.5 ± 7.1
102.5 ± 23.1
99.2 ± 14.3
Blood pressure, mm Hg
Systolic blood pressure
143.0 ± 21.8
145.2 ± 17.2
Diastolic blood pressure
85.0 ± 9.5
82.8 ± 5.4
Medications (# of subjects)
Calcium channel blocker
For lipid or type 2 diabetes
Recommended Protein Intake
Elevated Protein Intake
Days of diet provision
27.7 ± 0.7
28.0 ± 0.7
Daily calories intake, kcal/day
2962 ± 293
2826 ± 208
77.2 ± 6.9
145.9 ± 11.6**
0.72 ± 0.03
1.37 ± 0.02**
10.3 ± 0.1
20.4 ± 0.3**
19.8 ± 4.1
49.4 ± 10.0**
9.1 ± 1.9
22.8 ± 4.6**
4.1 ± 0.9
10.2 ± 2.1**
117.2 ± 11.5
111.0 ± 8.2
10.5 ± 0.1
13.8 ± 0.1**
10.3 ± 0.5
9.7 ± 0.3*
7.1 ± 0.1
5.1 ± 0.1**
35.0 ± 0.1
34.9 ± 0.1
412.6 ± 41.9
320.2 ± 22.8
54.7 ± 0.2
44.8 ± 0.3**
32.9 ± 2.8
28.3 ± 1.2
Dual Tracer Oral Glucose Tolerance Test (DT OGTT)
Blood samples (t = −150, −140, and −130 min) were collected prior to the onset of the DT OGTT protocol, and serial blood samples (t = 30, 45, 60, 70, 80, 90, 105, and 120 min) were collected during the remainder of the study into ethylenediaminetetraacetic acid (EDTA)-containing tubes and centrifuged at 3500 rpm for 15 min at 4 °C. Plasma enrichments of glucose tracers were measured on the pentaacetate derivative with the use of gas-chromatography-mass spectrometry (models 7890A/5975; Agilent Technologies, Santa Clara, CA). Ions of mass-to-charge ratio of 331.1, 332.1, and 333.1 for glucose were monitored with chemical impact ionization and selective ion monitoring . Plasma glucose concentrations were measured spectrophotometrically on a Cobas c 111 analyzer (Roche, F. Hoffman-La Roche, Basel, Switzerland). Plasma insulin concentrations were measured by using a commercially available human insulin enzyme-linked immunosorbent assay (ELISA) kit (Alpco Diagnostics). The lipid panels were determined by Labcorp (Labcorp 7777 Forest Lane, Dallas TX) using enzymatic methodology.
Whole-body insulin sensitivity was estimated by the Insulin Sensitivity Index (ISI) = 10,000/square root of ([fasting glucose x fasting insulin] x [mean glucose x mean insulin during OGTT]) .
Two-tailed independent t-test was used to compare changes in whole body glucose kinetics and ISI from pre- to post-intervention between RPI and EPI. Two-factor analysis of variance (ANOVA) was used to evaluate the effect of group (RPI and EPI) and intervention (before and after the respective dietary intervention) on measures of whole body glucose kinetics and AUCs of plasma glucose, insulin, and lipids. Statistical significance was declared when the p-value was less than 5% level. All data were analyzed using the Graphpad Prism 6 for Mac (Graphpad Software, Inc. La Jolla CA) and presented as mean ± SEM.
Glucose kinetics and insulin sensitivity
Whole body glucose kinetics during the oral glucose tolerance test over 4-week dietary intervention
Recommended Protein Intake
Elevated Protein Intake
T × G
R a Total, mg/kg/min
3.39 ± 0.13
3.25 ± 0.17
3.75 ± 0.18
3.50 ± 0.19
R a Endo, mg/kg/min
1.20 ± 0.09
1.22 ± 0.11
1.08 ± 0.09
1.15 ± 0.10
R a Exo, mg/kg/min
2.19 ± 0.15
2.02 ± 0.22
2.67 ± 0.25
2.35 ± 0.20
R d, mg/kg/min
3.25 ± 0.17
3.06 ± 0.19
3.71 ± 0.18
3.42 ± 0.14
1.67 ± 0.16
1.68 ± 0.24
1.97 ± 0.36
1.83 ± 0.29
Plasma glucose and insulin responses
Plasma glucose and insulin responses during the oral glucose tolerance test and fasting lipids over 4-week dietary intervention
Recommended Protein Intake
Elevated Protein Intake
22504 ± 2062
21745 ± 2789
23286 ± 2615
23335 ± 3097
10022 ± 2122
8314 ± 1899
14831 ± 3026
12561 ± 2300
184.8 ± 12.9
176.7 ± 21.6
179.5 ± 13.2
170.2 ± 10.4
154.5 ± 19.3
175.3 ± 18.9
188.5 ± 18.2
192.0 ± 29.3
36.8 ± 3.3
34.3 ± 3.6
35.5 ± 8.3
34.8 ± 9.8
116.8 ± 11.5
108.3 ± 17.3
106.3 ± 12.9
97.7 ± 9.7
31.2 ± 3.9
35.3 ± 3.9
37.7 ± 8.7
38.5 ± 5.8
Fasting plasma lipids
There were no significant group-by-intervention interactions, group effects, and intervention effects of total, HDL, LDL, and VLDL cholesterol and triglyceride (for all, p > 0.05) (Table 4).
Consistent with our hypothesis regarding the short-term influence on glucose metabolism, we did not observe any adverse changes in hepatic and peripheral insulin sensitivity (as evaluated by glucose Ra, glucose Rd and MCR during the DT OGTT) and/or plasma lipid profiles in older individuals with metabolic syndrome following either the RPI (i.e., 0.72 g protein/kg body weight/day) or EPI (i.e., 1.37 g protein/kg body weight/day) after 4 weeks of respective dietary intervention. More specifically, glucose Ra, glucose Rd and MCR during the DT OGTT were not different between groups and there was no difference in glucose and/or insulin AUC following the respective interventions. ISI was increased in the RPI and did not change in EPI. This difference was largely influenced by outliers in two participants in each insulin data set that were reduced in RPI, and yet increased in EPI. There were no differences in fasting blood lipids between RPI and EPI. Unlike evidence from epidemiological studies that suggest a negative effect of increased dietary protein on glucose metabolism , direct short-term elevation of dietary protein intake (i.e., EPI) from dairy products does not have a negative effect on insulin resistance and/or lipid parameters in older adults with metabolic syndrome.
It has been suggested that high protein intake induces insulin resistance via leucine-mediated activation of the mechanistic target of rapamycin (mTOR) . This hypothesis is largely based upon a positive correlation between tissue or plasma concentrations of branched-chain amino acids (BCAA) and insulin resistance in obese individuals , and based upon data showing impaired insulin-mediated glucose uptake with in vitro leucine treatment via this mechanism [28, 29]. This postulation was further strengthened by the observations in humans that intravenous amino acid infusion reduced glucose uptake during hyperinsulinemic euglycemic clamp [30, 31], although the role for leucine in the pathogenesis of insulin resistance has been challenged . However, there are several issues to consider. It was not established whether the elevated BCAA concentrations in obese insulin resistant individuals were the cause or an effect of insulin resistance. Second, reduced glucose uptake as a result of amino acid infusion during the clamp (a non-physiological condition) may not reflect the glucose metabolism in the physiological circumstance of mixed meal intake with varying protein or BCAA amount. It is also possible that acute physiological responses may not reflect chronic responses to high protein or BCAAs, necessitating interventional studies and chronic response determination.
Despite significant interests in the effects of high protein diets on glucose metabolism, the study of these diets in humans under weight-stable conditions using controlled metabolic feeding has been surprisingly scarce [33–35]. In the present study, we observed no impairment in glucose disposal during a physiological condition (i.e., OGTT) following 4 weeks of EPI containing more than 2-fold higher leucine or BCAA contents than RPI. Consistent with our findings, Shiu et al. found that consumption of a high protein diet for 4 weeks did not alter plasma glucose/insulin concentrations or insulin sensitivity as assessed by an intravenous glucose tolerance test . In longer-term weight-stable studies where participants served as their own controls, and wash-out periods in between three different types of diets (including the Dietary Approaches to Stop Hypertension (DASH), protein rich diet or unsaturated fat rich diets), insulin sensitivity as assessed by the quantitative insulin sensitivity check index (QUICKI) and homeostasis model assessment of insulin resistance (HOMA-IR) was not different . Lastly, in studies comparing four different types of diet (Control, High cereal fiber, High Protein, and High Cereal Fiber/High Protein), the High Protein diet had no influence on markers of insulin sensitivity (i.e., QUICKI). In individuals with more profound insulin resistance, Gannon et al., demonstrated that higher protein intake (30% protein vs. 15% protein in total energy of the daily meals) for 5 weeks in type 2 diabetic patients resulted in a 40% reduction in the mean 24-h integrated glucose AUC (mean age: 61y, range: 39 – 79y) . Glycated hemoglobin also decreased significantly with 5 weeks of higher protein intake in this study providing additional evidence to the importance of this strategy in individuals with type 2 diabetes. On other hand, in studies where individuals had yet to be classified with type 2 diabetes [33–35], consumption of increased dietary protein was not sufficient to improve insulin sensitivity even when measured with methods that provided enhanced specificity .
Given the epidemiological evidence showing a significant inverse relation between dairy product intake and metabolic syndrome , improvements in peripheral insulin sensitivity with EPI can be expected, as EPI contained > 2-fold higher dairy products, compared to RPI. It is possible that to observe beneficial effects on peripheral insulin sensitivity, a higher relative protein intake (i.e., 30% of overall energy intake) is required. For example, the higher relative protein intake of Gannon study was not only 1/3 greater than ours, but was also lower in dietary carbohydrate. The combination of higher protein and lower carbohydrate intake may be required to elicit alterations in glucose metabolism. Alternatively, it may be likely that a longer time is required to realize the beneficial effects of increasing dairy protein intake in mixed meals with respect to insulin sensitivity.
As secondary outcomes in the present study, we determined lipid panels in the fasted state before and after the respective interventions. In many cases, studies that have demonstrated the effectiveness of increased dietary protein intake on improvements in blood lipids were also associated with weight loss [15, 38, 39]. Meta-analysis of high protein/weight loss studies has confirmed their preferential efficacy on the reduction of triglycerides in particular . In the case of a hypercaloric diet, increased protein intake is linked to a trend (i.e., p = 0.07) towards reduced triglycerides . In our study under conditions of weight balance and isocaloric dietary intake, we found no improvements in any of the lipid panels (Table 4). Consistent with our findings, Chiu et al. found no improvement in triglyceride and total-, LDL-, or HDL-cholesterol after 4 weeks of either 20% (as in the present study) or 30% of protein with either low or high saturated fat intake without weight loss in overweight and obese adults . Therefore, an elevation in dietary protein intake without weight loss seems to foster stable lipid parameters in individuals with the characteristics of metabolic syndrome.
A potential limitation of the present study is that we did not quantify habitual protein intake or dietary patterns of the subjects prior to study initiation. Thus, it is possible that subjects in the EPI group may not have consumed much more protein than their usual protein intake. In retrospect, study design may have been better served with a longer dietary run-in period to reduce the potential influences of subjects’ previous dietary pattern . If we assume that most individuals were eating the average American protein intake by NHANES (i.e., 1.1 g/kg/day; we had no vegetarians in the study), then the variance from habitual protein intake to that consumed between groups (0.72 g/kg/d vs. 1.37 g/kg/d) should only serve to magnify the response to changes in protein intake in this population, if indeed one exists. We have illustrated that elevation of dietary protein intake using dairy products does not have a negative influence on insulin sensitivity. While some studies have linked increased consumption of red meat to the development of insulin resistance , recent data from longitudinal feeding studies dispute this assertion . These findings indicate that reasonable elevations in protein intake do not alter glucose kinetics in subjects with metabolic syndrome, and also highlight the empirical nature of nature of epidemiological research .
In the present study, we found that 4 weeks of higher protein intake (i.e., EPI) containing a significant amount of dairy products and BCAAs did not improve nor worsen glucose metabolism as measured by isotopically measured glucose kinetics, and lipid parameters in individuals with the clinical characteristics of metabolic syndrome. Unfortunately, interpretation of the ISI was complicated by two outliers in each insulin data set for RPI and EPI. Future studies with a longer intervention period should be performed to ascertain whether increasing amount of “high quality” protein intake containing correspondingly high BCAA have positive or negative impact on the modulation of glucose and lipid parameters.
Recommended protein intake
Elevated protein intake
- DT OGTT:
Dual Tracer Glucose Tolerance Test
- Glucose Ra :
Glucose rate of appearance
- Glucose Rd :
Glucose Rate of Disappearance
Metabolic Clearance Rate of Glucose/Glucose Concentration
Branched chain amino acids
Recommended Daily Allowance
University of Arkansas for Medical Sciences
Reynolds Institute on Aging
Consolidated Standards of Reporting Trials
Area under the curves
Very low density lipoprotein
Enzyme-linked immunosorbent assay
Mole percent excess
Tracer to tracee ratio
Infusion rate of [6,6-2H2]glucose
Effective volume of distribution for glucose
- C1 and C2 :
Plasma glucose concentrations at times t1 and t2
- E1 and E2 :
Plasma enrichment of [6,6-2H2]glucose at times t1 and t2
- E D and E PL :
Tracer enrichments of [1-13C]glucose from the test drink and plasma
Mechanistic target of rapamycin
Analysis of variance
Dietary Approaches to Stop Hypertension
Quantitative insulin sensitivity check index
Homeostasis model assessment of insulin resistance
We thank the research subjects for their participation in the study. We also thank the research staffs/associates for their support in conducting isotope tracer infusion protocols and sample analyses: Cosby J. Lasley for coordinating study subjects and conducting the isotope infusion studies; Josh Spore for determination of blood chemistry; Rick Williams for gas-chromatography mass-spectrometry analysis and determination of blood chemistry. Lastly, we thank the study dietician Amanda M. Dawson for preparing study foods for subjects.
The project was supported by a grant #1165 from the National Dairy Council (Co-PI: Robert Coker and Arny Ferrando). This project was also supported by the National Institutes of Health Older American Independence Center Grant PG30-AG-028718 (Co-PI: Jeanne Wei and Robert R. Wolfe) and Award Number UL1-TR-000039 and KL2-TR-000063 from the National Center for Advancing Translations Sciences (NCATS). While the influence of dairy products on overall health is of interest to the National Dairy Council, they nor the other entities played a direct role in the study design, data collection/analysis and/or the writing of the manuscript.
Availability of data and materials
All data and research material were made available only to the medical staffs and the investigation team. Data will not be available or shared in the future outside of this publication and anther presentations. These data included demographic data, body composition data, screening laboratory data, and a full medical history (covered by HIPPA regulations). All data used and/or analyzed during the current study will be available upon request.
I-YK, RRW, AAF, and RHC conceived and designed the study; I-YK, SES, GA, RRW, AAF, and RHC analyzed data and interpreted results of experiments; I-YK performed calculations of glucose kinetics and statistical analysis; I-YK and RHC prepared figures and drafted manuscript; I-YK and SES performed experiments; GA provided medical supervision; I-YK, SES, GA, RRW, AAF, and RHC revised the manuscript and were responsible for the research conception and design of experiments. All authors read and approved the final manuscript.
Dr. Wolfe has received research grants and honoraria from the National Cattleman’s Beef Checkoff program. Drs. Coker and Wolfe are Managing Partners and Co-Owners of Essential Blends, LLC that have received funding from the National Institutes of Health to develop clinical nutrition products. The data presented in this manuscript are unrelated. Other authors have no potential competing interests.
Consent for publication
This manuscript does not contain any personal information about an individual person or persons.
Ethics approval and consent to participate
The study was approved by the Institutional Review Board (ie., ethics approval) at the University of Arkansas for Medical Sciences.
Written consent was obtained from all participants. No personal data, information or images was provided from the participants (not applicable).
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