Nutritional Intervention Improves all Components of Metabolic Syndrome

This paper was submitted as part of Dr. Pennings's application for ACOFP Fellowship, which recognizes exceptional national, state, and local service through teaching, authorship, research, or professional leadership. Visit the ACOFP Fellows page to learn more about fellowship and the nomination process.

ABSTRACT

Objective: The metabolic syndrome is a collection of conditions of risk factors associated with insulin resistance and an increased risk of developing cardiovascular disease (CVD) and type 2 diabetes (T2D). While lifestyle modification and weight loss are common recommendations for metabolic syndrome, there are few reports on the impact of dietary interventions on the components of metabolic syndrome. This study examines the impact of a low calorie, high protein diet combined with individualized coaching on a participant’s criteria for metabolic syndrome.

Methods: 95 subjects over age 18 were longitudinally enrolled between 2014 and 2017. A retrospective chart review was conducted with data points collected at initial enrollment and at 3- month follow up visits from electronic medical records.

Results: We found the percentage of participants meeting the criteria for metabolic syndrome declined 66.3% to 34.7% (p < 0.001). There was statistically significant weight loss of 24.45 lbs. (p < 0.001) and improvement on all five parameters of metabolic syndrome following nutritional intervention. A 50% reduction in insulin resistance was also recognized.

Conclusions: Participants engaged in a low calorie, high protein diet, with coaching support, for 3 months showed improvement in all 5 components of metabolic syndrome and insulin resistance. This study provides further evidence for the benefits of dietary intervention on the treatment of insulin resistance and metabolic syndrome.

INTRODUCTION

The metabolic syndrome is a collection of conditions of risk factors associated with an increased risk of developing coronary heart disease - the leading cause of death in the United States for the past 80 years.¹ Insulin resistance is identified as a key component of metabolic syndrome as well as a risk factor for cardiovascular disease (CVD) and type 2 diabetes (T2D). ^2,3 Obesity, especially when associated with central adiposity, is also associated with metabolic syndrome.⁴

Since Reaven first described insulin resistance and metabolic syndrome in 1988,⁵ numerous definitions of metabolic syndrome have been advanced. The NCEP/ATP III criteria is commonly utilized and includes blood pressure, fasting glucose, triglyceride, high-density lipoprotein (HDL) cholesterol, and waist circumference to identify metabolic syndrome.⁶ The most recent criteria refined the components to account for medication use and racial differences in waist circumference thresholds for metabolic risk (Table 1). ⁷

Each component of metabolic syndrome is associated with health risks. Elevated BP increases the risk for serious cardiovascular complications such as: angina, myocardial infarction (MI), heart failure (HF), stroke, peripheral arterial disease, and abdominal aortic aneurysm (AAA).⁸ Weight loss is the first-line treatment for hypertension. Lifestyle modification, such as diet and exercise, is recommended for all patients, including those on pharmacologic therapies.⁸

Waist circumference is a reflection of visceral adiposity, metabolic disease risk and mortality.^9,10 In patients with obesity, significant reductions in waist circumference is associated with improvement in components of metabolic syndrome.¹¹

Atherogenic dyslipidemia refers to elevated levels of triglycerides and small-dense low- density lipoprotein, as well as low levels of high-density lipoprotein cholesterol. ¹² Plasma triglyceride and HDL cholesterol levels are independently associated with insulin resistance and are independent predictors of cardiovascular disease. ¹³

Hyperglycemia in the prediabetic or diabetic range is associated with increased risk of CVD. ¹⁴ While the benefits of improving glycemic control with intensive lifestyle intervention in CVD is controversial, ¹⁵ lifestyle interventions improve glycemic control in T2D and, in some cases, can result in remission. ¹⁶ Hyperglycemia is typically a later consequence of metabolic syndrome since it normally results from insulin resistance.

Insulin resistance is defined as resistance to insulin stimulated glucose uptake in target cells such as adipose, muscle, and liver cells, resulting in increased circulating glucose levels.¹⁷

Variations in glucose uptake are thought to be due to adiposity, fitness, and genetic origin.¹⁸ Hyperinsulinemia and/or insulin resistance appears to be a syndrome that is associated with an array of metabolic and physiologic disorders, including weight gain, obesity, non-insulin- dependent diabetes mellitus, hypertension, lipid abnormalities, and atherosclerotic cardiovascular disease. ^20-22 Studies reveal that weight loss has a significant effect on the degree of improvement in insulin sensitivity in patients with obesity who participated in low calorie, low carbohydrate, high protein diets. ^23,24

To this date, there have been limited studies investigating the effect of specific dietary interventions on components of metabolic syndrome, insulin levels or insulin resistance. Our goal is to analyze the impact of a reduced calorie, high protein diet on parameters of metabolic syndrome and insulin resistance in patients with an overweight BMI or obesity.

MATERIALS AND METHODS

This is a longitudinal study using data from patients during the initial three months on a low-calorie meal replacement plan (Optavia, Baltimore MD) combined with individualized coaching at a single University clinic site located in a rural and underserved county. Dietary intervention consisted of 5 meal replacements consumed approximately every 3 hours, plus one self-prepared, lean protein and vegetable meal. Total calorie count was 800 to 1000 calories per day, with meals composed of approximately 40% protein, 40% carbohydrate, and 20% fat.

Individualized coaching included regular contact by phone or text. Daily contact was made for the first 5 days of the program, then weekly for the remainder of the 3-month trial. Patients also had monthly office encounters with a health care provider. Data was used for any appointment made +/- 1 month of this target. Data points were collected by review of the electronic medical record at initial enrollment in the program, and at follow-up visits scheduled 3 months from the original start date.

Patients were longitudinally enrolled between the years of 2014 and 2017. All participants in this study were 18 years of age or older. Patients were excluded if they had no follow-up appointment data found in the electronic medical record chart review. If patients had partial data, it was included in final statistical analysis using pairwise exclusion for each variable missing. A total of 94 subjects were included overall in this study.

Statistical Analysis

Frequencies and percentages were used to describe categorical variables, while means and standard deviations were used to describe continuous variables. Paired samples t-tests were used to examine the difference in mean values between the continuous variables within the same patient. Pairwise exclusion was utilized to preserve the data we collected during a follow-up visit, while accounting for missing blood draw and laboratory information. All analyses were performed using IBM SPSS Statistics for Macintosh, Version 21.0 (IBM Corp., Armonk, NY). All analyses were deemed significant at α = 0.05.

Weight Change (lbs.)

Participant weight was measured on a Tanita SC-331S body composition analyzer at both the initial visit and 3-month follow-up. The difference between these two weights was used to generate our weight change variable.

HOMA-IR

Insulin resistance was calculated from fasting insulin and fasting glucose (Labcorp, Burlington, NC) values collected at both the initial visit and 3-month follow-up visits using the following formula:

Insulin Resistance (HOMA-IR) = [Fasting Insulin (µU/mL) * Fasting Glucose (mg/dL)] / 405.²⁵

Metabolic Syndrome

For the purposes of this study, the criteria established by a 2009 published joint statement has been used to diagnose metabolic syndrome.⁷ A patient was considered to have the criteria for metabolic syndrome if >3 of the following were met: elevated waist circumference (using AHA/NHLBI guidelines of > 102cm for men, > 88cm for women), ⁶ elevated triglycerides (>150mg/dL, or currently treated with medication), low HDL-C (<40mmol/L in men,  <50mmol/L in women, or currently treated with medication), hypertension (as defined by systolic blood pressure > 130, diastolic blood pressure > 85, or currently treated with medication), and elevated fasting glucose (>100mg/dL, or currently treated with medication).

Covariates

Age, BMI, gender, and race were evaluated during this study. BMI was categorized as either normal (< 25 kg/m²), overweight (25-29.99 kg/m²), or obese (> 30 kg/m²). The study was IRB approved through the Institutional Review Board of Campbell University.

RESULTS

On average, the study population was 50.1 years of age (20-81, SD=12.88), with a 33.7% male and 66.3% female gender distribution. Race distribution was 88.4% non-Hispanic white, 1.1% non-Hispanic black, and 10.5% unreported. Patients with obesity made up 84.2% of the patient population, overweight 13.7%, and normal 1.1% (Table 2).

Table 2. Study population demographics and metrics.

*indicates statistically significant result at p<0.005.

Effect of Weight Loss on the Components and Diagnosis of Metabolic Syndrome (Table 3)

Table 3. Impact of weight loss intervention on participants meeting criteria for metabolic syndrome.

*indicates statistically significant result at p<0.005.

Figure 1

66.3% of patients met criteria for the diagnosis of metabolic syndrome at the start of the study, while only 34.7% met criteria at the end of the study (t = 6.587, df = 94, p < 0.001) (Figure 1). At the beginning of the trial, the average weight of the participants was 236.33lbs. After the 3-month intervention, the average weight decreased to 211.88lbs (p < 0.001). 47.4% of patients were considered hypertensive, with 42.1% on at least one antihypertensive medication. After the intervention, only 27.4% were considered to be hypertensive (t = 3.019, df = 94, p = 0.003), with 33.7% still on at least one antihypertensive medication (t = 2.940, df = 94, p = 0.004). 63.2% of patients were considered hyperglycemic, with 17.9% on at least one medication for glucose control. After the intervention, only 37.9% were considered hyperglycemic (t = 4.688, df = 94, p < 0.001), with no significant decrease in medication use.

25.3% of patients were considered to have high triglycerides, with 5.3% on medication to treat it. After the intervention, only 7.4% of patients were considered to have hypertriglyceridemia (t = 4.233, df = 94, p < 0.001) with no significant reduction in medication use. There was no significant increase in HDL levels after intervention. There was however a statistically significant decrease in the number of participants meeting the standards for a low HDL level according to the metabolic syndrome criteria. 40% were considered to have a low HDL, with no patient on HDL increasing medications. After the intervention, 22.1% had a low HDL (t = 3.609, df = 94, p < 0.001) with no change in HDL increasing medications. 78.9% were considered to have an elevated waist circumference initially, with only 57.9% after the intervention (t = 3.918, df = 94, p < 0.001)

 (Figure 2).

Effect of Weight Loss on Insulin Resistance

Overall, there was a statistically significant decline in fasting blood sugar in the study population (t = 3.191, df = 66, p = 0.002). On average, patients began with a fasting blood glucose of 121.03 (SD = 44.70) and ended three months later with an average of 106.21 (SD = 32.80). Fasting insulin levels also saw a statistically significant decline (t = 5.813, df = 50, p < 0.001). On average, patients began with an insulin level of 27.1 (SD = 15.0) and ended with a level of 15.8 (SD = 11.3). Insulin resistance was calculated using HOMA-IR (described above). A statistically significant decline in insulin resistance was seen (t = 6.224, df = 50, p < 0.001).

On average, patients began with HOMA-IR values of 8.03 (SD = 5.08) and ended with values of 4.03 (SD = 3.29).

DISCUSSION

The metabolic syndrome is becoming common worldwide and is highly associated with an increased risk of developing atherosclerotic cardiovascular disease (ASCVD) and type 2 diabetes (T2D).²⁶ According to one NHANES study, it was estimated that the prevalence of metabolic syndrome in U.S. adults in 2011-2016 was 34.7%, affecting 35.1% of women and 34.3% of men. ²⁷ This was a significant increase from 1988-1994 where the prevalence was 28.4% in women and 16.8% in men. ²⁸ Early identification of the parameters of metabolic syndrome in the clinical setting and intensive lifestyle modifications for these individuals has become increasingly important.

In previous studies and reviews, there has been discrepancy identifying the pathophysiology of the metabolic syndrome, which has made it difficult to prevent or improve the syndrome with one technique. Historically, the typical clinical management of metabolic syndrome was to treat each component of the disease separately. ²⁹ Pharmaceutical therapies are targeted to lower blood pressure, blood glucose, and triglycerides, as well as address the prothrombic state, which produces a piecemeal treatment approach without addressing the underlying cause. ³⁰

Lifestyle intervention through dietary restriction, increased physical activity, and behavioral modification is frequently touted as first-line therapy for the treatment of metabolic syndrome. ^31-33 Much of the support for lifestyle intervention has centered around individual components with limited data on the impact of intensive lifestyle or nutritional intervention on metabolic syndrome as a whole. ³⁰ Root analyzed the OMNI Heart dataset examining 4 of the 5 components of metabolic syndrome utilizing a DASH-like diet with weight-neutral effects. The study found overall improvement in metabolic syndrome based on the measured components, absent waist circumference, especially for participants on a high protein, high fat diet. ³⁴

This study is unique in that it examined the impact of lifestyle modification with resultant weight loss on not just the individual parameters of metabolic syndrome but the number of participants meeting criteria for metabolic syndrome. The percentage of participants meeting at least 3 of the 5 components of metabolic syndrome declined 66.3% to 34.7%, or a 48% reduction in participants meeting criteria for metabolic syndrome. There was a statistically significant average weight loss of 24.45lbs per participant during the 3-month intervention (p<0.001).

Metabolic improvements from normalizing components of metabolic syndrome may benefit individuals by decreasing their risk for developing CVD and diabetes.

We also found statistically significant improvement (p<0.001 elevated waist circumference, elevated triglycerides, elevated fasting glucose, reduced HDL-C; p=0.003 elevated blood pressure) in the percentage of participants meeting criteria for each individual parameter of metabolic syndrome following nutritional intervention.

In addition to a reduction in metabolic syndrome, a 50% reduction in insulin resistance was also recognized in this study. Metabolic syndrome is associated with impaired insulin- dependent vasodilatation, and lifestyle intervention has been shown to reverse vascular dysfunction, with potential improvement in vascular consequences associated with CVD and diabetes. ³⁵

Generalizability of this study may be limited due to our patient population. This study included 66.3% women, and overall 88.4% Caucasian individuals. Different populations, ethnicities, and nationalities have different distributions of norms for body weight and waist circumference. ²⁹ In this particular study, though each patient received a similar diet plan and individualized coaching, individual exercise routines were not quantified during the study.

Funding: No grants, equipment, drugs, or reagents were used in this trial.

Author Contributions: AG participated in data collection, data analysis, and document writing. NP participated in research design, data collection, data analysis, and document writing. All authors read and approved the final manuscript.

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