Original Article | Published:

Berry meals and risk factors associated with metabolic syndrome

European Journal of Clinical Nutrition volume 64, pages 614621 (2010) | Download Citation

Abstract

Background/Objectives:

Nonalcoholic fatty liver disease is commonly associated with obesity, insulin resistance, dyslipidemia and type 2 diabetes, and can thus be regarded as the hepatic manifestation of metabolic syndrome. In this study we compared the effects of lifestyle intervention with and without industrial berry products, on risk factors associated with metabolic syndrome on slightly overweight women.

Subjects/Methods:

Sixty-one female volunteers (average age 42.9 years) were recruited and randomized for a 20-week dietary intervention trial with two parallel treatment groups, one lifestyle intervention group with berry products equaling with an average daily dose of 163 g of northern berries (berry group, diet 1, N=31, of which 28 completed the study) and the other group with lifestyle intervention only (control group, diet 2, N=30, of which 22 completed the study).

Results:

Increased berry consumption as part of the normal daily diet was the only lifestyle difference between the two intervention groups. The major effects achieved by diet 1 were changes in the levels of alanine aminotransferase (ALAT) and adiponectin (at P-values <0.001 and 0.002, respectively). A statistically significant difference between the two intervention groups was the higher decrease in the ALAT value in the berry group (P=0.003).

Conclusions:

The 23% decrease in the ALAT value, from 20.29 to 15.66 U/l in the berry group may be regarded as nutritionally significant by enhancing the liver function. This may contribute positively to the low-grade systemic inflammation in body and decrease the risk of cardiovascular diseases.

Introduction

The prevalence of obesity and type II diabetes has increased at an alarming rate in developed countries (Mokdad et al., 2001). Metabolic syndrome and type II diabetes have acquired a lot of attention and aroused discussion worldwide, but fatty liver disease (nonalcoholic fatty liver disease, NAFLD) has remained a relatively unrecognized manifestation of the syndrome among general population. However, it seems in light of current knowledge that metabolic syndrome may not develop at all without NAFLD (Kotronen et al., 2007), and NAFLD is estimated to be as common as metabolic syndrome in the Western population (23% occurrence; Seppälä-Lindroos et al., 2002). NAFLD is commonly associated with obesity, insulin resistance, dyslipidemia and type II diabetes, and can thus be regarded as the hepatic manifestation of metabolic syndrome (Angulo, 2002; Bugianesi et al., 2005).

Berries are known to contain high amounts of vitamins, phenolic compounds, trace elements and fiber, as well as beneficial fatty acid composition (Kalt et al., 1999; Kallio et al., 2002; Määttä-Riihinen et al., 2004; Bere, 2007). A daily amount of 155 g of mixed berries has been shown to improve some factors associated with cardiovascular health and thus possibly to reduce the risk of vascular problems (Erlund et al., 2008). Sea buckthorn (Hippophae rhamnoides sp. Ljubitelskaja) lowers the sensitive C-reactive protein (hs-CRP) (Larmo et al., 2008) and sea buckthorn seed oil reduces the velocity and total amount of platelet aggregation (Johansson et al., 2008). An 8-week intervention of mixed berries resulted in partial resistance of low-density lipoprotein (LDL) to oxidation (Marniemi et al., 2000). Cranberry consumption has been shown to decrease the risk of urinary track infections in many clinical trials (Jepson and Craig, 2008).

Approximately 70–80% of patients with type II diabetes have NAFLD (Angulo, 2002; Targher et al., 2007), and those obese subjects who tend to accumulate abdominal fat have an increased risk of cardiovascular diseases (Lakka et al., 2002). This phenomenon can be explained by the impaired insulin sensitivity of fatty liver. Insulin-resistant liver overproduces glucose (Ryysy et al., 2000; Marchesini et al., 2001; Seppälä-Lindroos et al., 2002) and higher very low density lipoprotein (Adiels et al., 2006, 2007) increasing triglyceride concentration in blood and reducing the level of high-density lipoprotein (HDL) levels (Adiels et al., 2007). Liver with high fat content also overproduces inflammatory markers (Greco et al., 2008; Kotronen and Yki-Järvinen, 2008).

Liver markers are an effective way to assess accumulation of fat in the liver, and by far the most commonly used of these methods. Serum alanine aminotransferase (ALAT) values correlate positively with liver fat proportions (Greco et al., 2008). In persons having normal ALAT values, the levels of enzymes are higher in individuals having higher body mass index (BMI), and the upper limit of normal ALAT values has been impugned recently (Greenfield et al., 2008). It has also been shown that increased activity of ALAT predicts type II diabetes independently from obesity (Vozarova et al., 2002; Hanley et al., 2004; Sattar et al., 2004).

In this study we compared the effects of a conventional lifestyle intervention and a lifestyle intervention combined with industrial products containing four northern berries, on cardiovascular risk factors associated with metabolic syndrome on slightly overweight women.

Materials and methods

Study subjects

Sixty-one female volunteers (35–52 years, average age 42.9 years) were recruited through local advertisements. Normal health status and suitability for the study were checked by an interview and by biochemical laboratory tests of the first basal blood sample. A total of 162 subjects were briefly interviewed, and 102 of those not excluded were tested for their weight, waistline and blood pressure, and from plasma fasting lipid, glucose and insulin values, ALAT value (liver function), hemoglobin (anemia), creatinine (kidney function) and thyroid-stimulating hormone (TSH, thyroid function). Inclusion criteria were blood pressure <160/99 mm Hg, blood hemoglobin >120 g/l, fP-glucose <6 mmol/l, fP-insulin <25 mU/l, fP-cholesterol 4–8 mmol/l, fP-LDL-cholesterol >2.2 mmol/l, fP-triacylglycerol <4 mmol/l, P-TSH 0.3–4.2 mU/l, P-ALAT<60 U/l and fP-creatinine <115 μmol/l. Exclusion criteria were pregnancy, menopause, regular smoking, previously diagnosed diabetes (other than gestational), thyroid, renal, hematological or hepatic dysfunction, previous myocardial infarction, cardiovascular medication, treatment with lipid-lowering drugs and ongoing inflammatory disease. Baseline characteristics of subjects are presented in Table 1.

Table 1: Baseline characteristics of the study subjects

The aim of this study was to create a study design in which the only difference between groups would be the amount of berries in the diets and power estimation based on the results of Erlund et al. (2003) aimed to assure the statistically different intake of berry constituents in the intervention groups. P-value of 0.05 was applied, which gave f(αβ)=7.9 at 80% probability, and thus n=(2s.d.2)/(ka1−ka2)2 × 7.9=56.5. Study subjects in berry group were instructed to consume 163 g of berries daily according to a specific calendar and product consumption flow. According to diaries, the implemented total berry consumption among the berry group was 151 g daily and among the control group 27 g. There was a statistically significant difference in the berry intake between the groups (P<0.001), and the objective of the study design was fulfilled.

This study was conducted according to the guidelines laid down in the World Medical Association Declaration of Helsinki (2000) and all procedures involving human subjects were approved by the ethics committee of the hospital district of southwest Finland). Written informed consent was obtained from all the subjects. The study products used in the study were well-tolerated, traditional berry products.

Berry meals and study design

The study was conducted as a randomized 20-week dietary intervention trial with two parallel treatment groups; lifestyle intervention with berry products (berry group, diet 1, N=31) or lifestyle intervention (control group, diet 2, N=30). Physical measurements were carried out and blood samples were analyzed from both groups both before and after the intervention. The participants were randomly assigned to the two groups after stratification with LDL-cholesterol and BMI. All the subjects reported to have the same nutrition and physical activity guiding at the beginning of the study period as well as at week 10. The only difference between the two groups was that the berry group was advised to consume three portions of berry products daily (Table 2) equaling to 163 g of fresh berries (1138 g per week), and a weekly dose of 3.5 g of berry oils (equaling to 245 g of berries). Daily berry products were selected from 18 berry products according to a specific product use cycle, and consumption was recorded in a diary. The berry products were designed to replace other snack products in the diet. The berries used as ingredients were lingonberry (Vaccinium vitis-idaea), sea buckthorn berry (Hippophaë rhamnoides, ssp. mongolica, var. Ljubitelskaja), bilberry (V. myrtillus) and black currant (Ribes nigrum). The berry products were processed by five Finnish food enterprises on their commercial production lines.

Table 2: Characteristics of the berry products consumed by study subjects in the berry group

In the beginning of the intervention period, the overall health and lifestyle of the study subjects were estimated based on a questionnaire modified from the FINRISK Physical Activity Questionnaire (Laatikainen et al., 2007). In addition, the study subjects were requested to write down all subjective health symptoms for 1 week before each sample collection day, and to keep a 3-day food diary before, in the middle of and after the intervention period. Caloric and main nutrient intake during the intervention period in both groups was estimated based on the 3-day food records collected in the middle of the intervention period. For the whole duration of the study, the subjects filled a diary in which they wrote down diseases and medication used, berries consumed (both test products and other berries) and physical activity.

Blood samples

Blood samples (two basal samples, as well as samples drawn at 10, 19 and 20 weeks) were taken after an overnight fast (12 h) from each study subject. The study subjects were instructed to avoid alcohol and all medication for 3 days before sample collection. In addition, 2 h glucose tolerance tests (75 g of glucose consumed as liquid, capillary blood samples drawn at 0, 30, 60, 90 and 120 min) were performed before and after the intervention period. Plasma, serum and glucose tubes were centrifuged at 2200 g for 10 min, and the supernatant was stored at −80 °C in microcentrifuge tubes until analyzed further.

Physical measurements and clinical analyses

Body composition was determined by bioelectrical bioimpedance analysis. Body weight of barefoot subjects wearing light indoor clothing was recorded to the nearest 0.1 kg by a calibrated weighing scale (Inbody 3.0; Sunborn Saga Oy, Finland) and total body fat mass, fat-free mass and fat percentage were calculated from impedance values. Body height was recorded to the nearest 0.5 cm. Waist circumference was measured midway between spina iliaca superior and the lower rib margin. Blood pressure was measured in duplicate using the automated Omron M4-I device (Omron, Normomedical Oy, Helsinki, Finland) in accordance with standard procedures.

Alanine aminotransferase, GT, cholesterol, triacylglycerols and creatinine were analyzed from plasma collected into lithium heparin tubes, TNF-α, adiponectin, intercellular adhesion molecule (ICAM), vascular cell adhesion molecule (VCAM) and E-selectin from plasma collected into EDTA tubes, and TSH, insulin and hs-CRP were analyzed from serum.

Serum total cholesterol, HDL-cholesterol, LDL-cholesterol and triglycerides were measured from plasma samples by enzymatic photometric methods with commercial kits (Thermo Clinical Labsystems Oy, Espoo, Finland) using the Konelab20i analyzer (C.V. 2.2, 3.4, 3.1 and 3.2%) as described by Juutilainen et al. (2000). Plasma glucose was analyzed by enzymatic photometric method using Konelab Glucose HK (CV 2.6%) as reagent. ALAT, γ-glutamyltransferase, glycated hemoglobin and serum high-sensitive CRP (CV 4.3, 3.1, 4.6 and 5.3%) were analyzed with routine standardized methods, all with a Konelab20i analyzer (Thermo Clinical Labsystems Oy, Konelab, Finland; Tiikkainen et al., 2003) and TSH (CV 6.0%) was analyzed using Axsym (Axsym; Abbot Diagnostics, Espoo, Finland).

Serum insulin (CV 7.3%) was analyzed by chemiluminescence-immunoassay with Immulite 1000 Analyzer (Siemens Medical Solutions, Espoo, Finland). Hematological parameters were measured with CellDyn analyzer.

Soluble adhesion molecules sICAM-1 (CV 6.6%), sVCAM-1 (CV 6.7%) and adiponectin (CV 3.3%) were simultaneously measured from the plasma samples with Human CVD1-kit (HCVD1-67AK) and TNF-α (HCYTO-60K) with singleplex kit (both from Millipore, Billerica, MA, USA) using Bio-Rad Bio-Plex 200 System (Espoo, Finland).

Oxygen radical absorbing capacity (ORAC) was analyzed from serum using a multiwell plate reader according to the methods previously described (Venojärvi et al., 2008). Hemoglobin A1c (HbA1c) and blood cell count analysis was performed from fresh blood collected into EDTA tubes by immunoturbidimetric method (CV 4.6%).

Finger-prick capillary blood samples were collected to MiniCollect 0.5 ml lithium heparin tubes and glucose, insulin and glucose-dependent insulinotropic peptide were analyzed from plasma samples using enzymatic photometric method.

Statistical analyses

Group comparison analyses were calculated with unpaired t-test when data were abnormally distributed and with Mann–Whitney nonparametric test when data were normally distributed. Two-sided tests and significance levels of 0.05 were used throughout. Correlations between relative changes of measured parameters and berry consumption were calculated with the Spearman's correlation coefficient. All analyses were performed with SPSS software (SPSS Inc., Chicago, IL, USA), version 14.0.

Results

There was no difference in adherence to lifestyle intervention between groups, as during the study period the reported physical activity and the caloric intake did not differ between groups (Table 3), although both groups slightly reduced the caloric intake during the intervention period. In the berry group reduction in daily caloric intake was 177 calories (NS) and in control group 53 calories (NS). Fiber (P=0.02) and vitamin C (P<0.001) intakes were higher, and calcium intake was slightly lower (NS) in berry group during the intervention. PUFA and vitamin D intakes were also slightly lower (NS) in berry group. Compliance to alcohol and medication avoidance as well as adherence to fasting was good, and fasting was followed as advised.

Table 3: Nutrient and total energy intake of both groups during the intervention period, estimation based on 3-day food records and physical activity recorded in the diaries during intervention

Total berry consumption (study products and other berries consumed) among the berry group was 1054 g berries per week (151 g per day) and 192 g of berries per week (27 g per day) in control group. Difference between the groups was statistically significant (P<0.001). Total caloric intake was intended to be the same in both groups, and also this was succeeded as the caloric intake during the intervention period did not differ statistically between the groups (P=0.893).

Results of all measured parameters from fasting blood samples in the beginning and at the end of both interventions are presented in Table 4. No statistically significant changes in either groups were recorded regarding BMI, waist circumference, fasting plasma insulin levels, fasting plasma cholesterol or triacylglycerol levels, hs-CRP, TNF-α or ORAC. There was also no difference in the calculated HOMA-IR in neither group (0.32±0.60 in berry group, 0.42±0.73 in control group). A statistically significant, but small increase in fasting plasma HDL-cholesterol in control group and fasting plasma glucose, HbA1C, LDL-cholesterol, VCAM and ICAM in both groups was observed. Likewise, there was a statistically significant decrease in systolic and diastolic blood pressures as well as fasting plasma GT in both groups. Although statistical differences between the groups were not achieved, the observed changes in systolic (Δpressure −6.68 mm Hg, P=0.001) and diastolic (Δpressure −4.45 mm Hg, P=0.002) blood pressures in the berry group tended to be larger than in the control group. Also in ORAC values berry group had a statistically insignificant but larger positive trend than control group.

Table 4: All measured parameters and their changes in the fasting blood samples from both groups during the 20-week intervention

In the glucose tolerance test insulinotropic peptide, insulin and glucose area under the curves increased during the intervention in both groups and there was no statistical difference between the groups in any of the parameters, although insulinotropic peptide increased slightly more in control group (+2770.7 pg/ml in control group, +747.4pg/ml in berry group).

In the berry group, plasma ALAT decreased and plasma adiponectin increased (P<0.001 and 0.002), but there was no statistically significant change in the lifestyle group. The changes in plasma ALAT differed statistically significantly (P=0.003) between the groups (Figure 1). As there was a slight decreasing trend in the adiponectin in the control group (Δ0.56 μg/ml in control group, Δ 1.61 μg/ml in berry group), the difference between the groups did not reach the level of significance (Figure 2).

Figure 1
Figure 1

Changes in plasma alanine aminotransferase (ALAT) levels in berry group (−4.6, P<0.001) and in control group (−0.2, P>0.05) during the intervention period.

Figure 2
Figure 2

Changes in plasma adiponectin levels in berry group (+1.6, P=0.002) and in control group (+0.6, P>0.05) during the intervention period.

Changes in ALAT activities of the individual study subjects in the berry group showed mainly decreasing trend in 24 subjects out of 28 (Figure 3) whereas changes in the lifestyle group were random (data not shown). This supports the conclusions drawn from the statistical results. Also, there was a correlation (P=0.037, R2=0.156) between baseline ALAT and the change of ALAT indicating that those subjects with larger initial values benefited the most from the intervention.

Figure 3
Figure 3

Changes in alanine aminotransferase (ALAT) levels during intervention period of individual study subjects in berry group.

Because ALAT is affected by alcohol consumption, the average intake of alcohol of the study subjects was estimated from food diaries and compared between the groups. There was no difference in the average alcohol consumption (P=0.973), or in the compliance to alcohol restriction before the sample collection, between the groups.

Changes of adiponectin and ALAT correlated negatively and statistically significantly (P=0.049, R=0.08) when all the study subjects were taken into account. Within the berry group there was a significant correlation between berry consumption and both adiponectin increase (P=0.021, R=0.204) and ALAT decrease (P=0.039, R=0.159). Slight variation in berry consumption within the berry group explains 20.4% of the adiponectin increase and 15.9% of ALAT decrease during intervention, although berry intake in this group was already very high. Within the lifestyle group there was no correlation between the reported berry consumption and ALAT and adiponectin changes.

Discussion

Fasting plasma ALAT concentrations decreased in the berry group statistically significantly and the difference between the groups was statistically significant. ALAT is traditionally known as a common liver disease marker (NAFLD), and it has recently been suggested that metabolic syndrome does not develop at all without NAFLD (Kotronen et al., 2007).

In the late 1980s ALAT increase significantly predicted diabetes in Swedish men (Ohlson et al., 1988). Recently, the connection between ALAT and the risk of diabetes has been shown in Pima Indians (Sattar et al., 2004), and non-Hispanic blacks and whites in the United States (Hanley et al., 2004). Only one study, performed with Japanese subjects, reported no correlations between ALAT and the risk of diabetes (Nakanishi et al., 2004). According to this study, ALAT stands out as an early indicator of the metabolic state. Thus it represents a valuable marker both in diagnostics and clinical interventions.

As the slight variation in berry consumption within the berry group explained 20.4% of the adiponectin increase and 15.9% of ALAT decrease during intervention, it can be stated that berries probably caused the decrease of ALAT values and the increase of the adiponectin values in the berry group. This study showed that the daily consumption of more than 150 g of northern berries in various forms as part of the normal diet had a positive affect on ALAT and adiponectin levels, but the small amount of berries consumed as part of normal diet in lifestyle group was not enough to evoke such an impact.

Decrease in plasma ALAT values within the normal range can be regarded as beneficial because normal ALAT values do not exclude NAFLD, and in particular women with elevated serum ALAT levels have an increased risk of diabetes (Ekstedt et al., 2006). Further, patients with type II diabetes with NAFLD form a subgroup within diabetes patients with higher risk of future cardiovascular disease events (Targher et al., 2007). Present study results indicate common northern berries and berry products as an effective component of lifestyle modifications aimed at decreasing development of metabolic syndrome and subsequent complications.

Some indications of beneficial effects of berries and berry seed oils on serum lipid profile (Tahvonen et al., 2005), and infection marker CRP (Larmo et al., 2008) have been illustrated in human trials. However, to the best of our knowledge, this is the first human trial conducted to investigate the effects of berries on liver function and components of metabolic syndrome, although protective effects of bilberry on liver damage have been earlier postulated in an animal trial (Bao et al., 2008).

Some indications exist that the association between antioxidant-rich food and liver function could be explained other than antioxidative mechanism. Valtueña et al. (2008) found that the total antioxidant capacity of food correlated with the liver function whereas plasma total antioxidant capacity did not change. Hepatic inflammation seems to contribute to the low-grade systemic inflammation associated with the metabolic syndrome (Valtueña et al., 2008). In our study, the plasma antioxidant capacity measured as ORAC did not change statistically significantly in either group, although there was an increasing trend, and the trend was stronger in the berry group.

It has also been shown that sea buckthorn has a positive influence on CRP (Larmo et al., 2008). However, in our study, inflammation marker hs-CRP did not change statistically significantly, although there was a slight decrease in both the groups. This might be due to smaller vitamin D intake in berry group as even slight deficiency has a negative influence on inflammation (Overbergh et al., 2000). Also, low intake of vitamin D might have had negative effect on plasma glucose (Pittas et al., 2007).

The results of the study indicate the importance of innovative product research and development in enterprises to produce healthy industrial berry products. Further research is needed to investigate which berries and/or which parts of the berries serve as most healthy ingredients.

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Acknowledgements

We thank all the volunteers involved in the study. We are grateful for Finnish Funding Agency for Technology and Innovation as well as our industrial partners for financial support.

Author information

Affiliations

  1. Department of Biochemistry and Food Chemistry, University of Turku, Turku, Finland

    • H-M Lehtonen
    • , J-P Suomela
    • , J Vaarno
    •  & H Kallio
  2. MTT Agrifood Research Finland, Jokioinen, Finland

    • R Tahvonen
  3. Medical Laboratory Technology, Turku University of Applied Sciences, Turku, Finland

    • M Venojärvi
  4. Department of Medicine, Turku University Hospital, University of Turku, Turku, Finland

    • J Viikari

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DOI

https://doi.org/10.1038/ejcn.2010.27

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