Research Report
Relationship between dietary pattern and cognitive function in elderly patients with type 2 diabetes mellitus
Journal of International Medical Research 2015, Vol. 43(4) 506–517 ! The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0300060515581672 imr.sagepub.com
Mari Enomoto1, Hidenori Yoshii1, Tomoya Mita2,3, Haruna Sanke2, Ayako Yokota1, Keiko Yamashiro1, Noriko Inagaki1, Masahiko Gosho4, Chie Ohmura2, Kayo Kudo5, Hirotaka Watada2,4,6,7 and Tomio Onuma1
Abstract Objective: To analyse the relationships between dietary patterns and cognitive function in elderly patients with type 2 diabetes mellitus (T2DM). Methods: Patients with T2DM completed a 3-day dietary record and Mini-mental State Examination (MMSE). Dietary patterns were identified by factor analysis. Results: The study included 73 patients and identified five dietary patterns, one of which was characterized by high loading for vegetables and fish. A higher consumption of vegetables and fish was significantly associated with improved MMSE score (unadjusted model, model adjusted for age and sex, and model adjusted for age, sex, education, diabetic nephropathy and alcohol consumption), and decreased prevalence of suspected mild dementia (unadjusted model, model adjusted for age and sex). Conclusions: A high score in the vegetables and fish dietary pattern was associated with high MMSE score and low prevalence of suspected mild dementia in elderly patients with T2DM.
1
Department of Medicine, Diabetology and Endocrinology, Juntendo Tokyo Koto Geriatric Medical Centre, Tokyo, Japan 2 Department of Metabolism and Endocrinology, Juntendo University Graduate School of Medicine, Tokyo, Japan 3 Centre for Molecular Diabetology, Juntendo University Graduate School of Medicine, Tokyo, Japan 4 Department of Clinical Trials and Clinical Epidemiology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan 5 Department of Medicine, Nutritional Management Section, Juntendo Tokyo Koto Geriatric Medical Centre, Tokyo, Japan
6
Centre for Therapeutic Innovations in Diabetes, Tokyo, Japan 7 Sportology Centre, Juntendo University Graduate School of Medicine, Tokyo, Japan Corresponding author: Hidenori Yoshii, Department of Medicine, Diabetology & Endocrinology Juntendo Tokyo Koto Geriatric Medical Center, Shinsuna 3-3-20, Koto-ku, Tokyo 136-0075, Japan. Email: [email protected]
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Keywords Dietary pattern, suspected mild dementia, type 2 diabetes mellitus Date received: 9 December 2014; accepted: 20 March 2015
Introduction Patients with type 2 diabetes mellitus (T2DM) are between two and four times more likely to develop cognitive impairment and dementia than individuals without T2DM.1 Several factors (including age, sex, glycaemic control, disease duration, complications, hypertension, lipid metabolism, education, depression, physical activity and chronic infection) are known to correlate with cognitive impairment in patients with T2DM,2–6 and there are likely to be additional unrecognized contributing factors. Intake of certain types of foods (such as fish and vegetables) and nutrients (such as vitamins C, E, B6 and B12, folate, potassium, calcium, magnesium and unsaturated fatty acids) lowers the risk of cognitive impairment7,8 and/or Alzheimer’s disease,9 although the extent of protection varies among studies. Since people do not consume individual foods or single nutrients in real life, the dietary pattern should be taken into consideration as it reflects the complexity of dietary intake, where foods have interactive, synergistic and antagonistic effects.10 It has been suggested that dietary pattern may be more predictive of disease risks than specific food- and nutrient-based approaches.10 Adherence to the Mediterranean dietary pattern (characterized by high consumption of plant foods [vegetables, fruits, legumes and cereals], high intake of olive oil, moderate intake of fish, low-to-moderate intake of dairy products and low intake of saturated fats and meat11) is associated with slower cognitive decline12 and reduced Alzheimer’s disease risk.13 Although the Japanese dietary pattern differs widely from the Mediterranean diet, a diet characterized by high intake of soybeans,
vegetables, algae, dairy products and low intake of rice was associated with reduced risk of dementia in the general population of elderly Japanese individuals.14 Taken together, these data suggest that dietary pattern could contribute to the risk of cognitive decline and/or Alzheimer’s disease. Patients with T2DM have been found to consume higher quantities of fruit, vegetables and meat than healthy control subjects,15 but the relationship between dietary patterns and cognitive function in patients with T2DM remains largely unexplored. The aim of this cross-sectional study was to analyse these associations using data from elderly Japanese patients with T2DM.
Patients and methods Study population This ancillary study was a subanalysis of an original study that investigated the relationship between olfactory function and cognitive function.16 Patients with T2DM were enrolled from the Diabetes Outpatient Clinic of Juntendo Tokyo Koto Geriatric Medical Centre (Tokyo, Japan) and Juntendo University Hospital (Tokyo, Japan) between October 2012 and December 2013, as described.16 Inclusion criteria were patients with T2DM who were aged >65 years and free of clinically evident cognitive impairment. Exclusion criteria were: (i) severe infection within the preceding 2 weeks; (ii) any scheduled or performed surgery; (iii) severe trauma; (iv) current psychiatric disorders; (v) partial or complete olfactory dysfunction associated with sinusitis, allergic rhinitis or deviated nasal septum; (vi) history of brain tumour; (vii) MMSE (mini-mental state examination)
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score 0.5 were retained. For simplicity, individual metabolites with a factor loading >0.4 are reported as composing that factor. Factor scores for each dietary pattern and each subject were calculated by summing each dietary pattern score, weighted by their factor loadings. Estimated factor scores were categorized into tertiles. Trend association across tertiles was evaluated using linear regression analysis for continuous variables or logistic regression analysis for categorical variables, in an unadjusted model and/or model adjusted for age and sex. Spearman’s correlation coefficient was used to evaluate correlations between MMSE score and possible risk factors for cognitive impairment (BMI, estimated duration of diabetes, glycaemic control, presence of diabetic complications [retinopathy, nephropathy and neuropathy], education, depressive status, smoking, alcohol consumption, food intake, hypertension and hyperlipidaemia). Trend associations across MMSE score and suspected mild dementia tertiles were evaluated by linear regression analysis and logistic regression analysis, respectively. Regression models included statistically significant variables (P < 0.05) from the above Spearman’s correlation analysis, in addition to age and sex. Statistical analyses were performed using SASÕ software version 9.3 (SAS Institute, Cary, NC, USA), and two-sided Pvalues < 0.05 were considered statistically significant.
Results The study included 73 patients (40 male/33 female; mean age 72.4 5.1 years; age range 65–83 years). Demographic and clinical characteristics of the patients are shown in Table 1.
Table 1. Demographic and clinical characteristics of patients with type 2 diabetes mellitus included in a study investigating the relationship between cognitive function and dietary patterns (n ¼ 73). Characteristic
n
Age, years Male sex Body mass index, kg/m2 Estimated duration of diabetes, years Current smoker Education, years Beck Depression inventory II score20 Energy intake, kcal/day Alcohol consumption, g/day Diabetic retinopathy Diabetic nephropathy Diabetic neuropathy Hypertension Hyperlipidaemia Oral hypoglycaemic drugs Insulin therapy Hypertension medication Hyperlipidaemia medication Antiplatelet agents Systolic blood pressure, mmHg Diastolic blood pressure, mmHg Glycosylated haemoglobin, % Fasting blood glucose, mg/dl Total cholesterol, mg/dl High-density lipoprotein cholesterol, mg/dl Low-density lipoprotein cholesterol, mg/dl Triglyceride, mg/dl Mini-mental State Examination score18,19 Suspected cognitive impairment
72.4 5.1 40 (54.8) 23.9 3.3 15.5 9.5 6 (8.2) 11.8 2.8 9.6 7.2 1728 332 4.8 11.1 28 (38.4) 40 (54.8) 54 (72.0) 45 (61.6) 53 (72.6) 56 (76.7) 17 (23.3) 34 (46.6) 38 (52.1) 18 (24.7) 131.5 15.5 69.9 12.0 7.3 0.9 128 32 186 36 56 14 109 29 83 (63–106) 26.9 2.2 26 (35.6)
Data presented as mean SD, n (%) or median (interquartile range).
Factor analysis with varimax rotation identified five dietary patterns (Table 2): (i) oils, nuts, seeds, sugars and eggs (high loadings for fats and oils, nuts, seeds, sugars and eggs); (ii) cereals and meats (high loadings for cereals, meats, seasonings,
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Table 2. Dietary patterns of patients with type 2 diabetes mellitus, as determined by factor analysis with varimax rotation of food diary information (n ¼ 73). Dietary pattern Components and item labels
Oils, nuts, seeds, sugars, eggs
Cereals and meats
Fruits and potatoes
Vegetables and fish
Pulses
Cereals Potatoes and starch Sugars Pulses Nuts and seeds Green and dark yellow vegetables Leafy vegetables Fruits Mushrooms Algae Fish and seafood Meats Eggs Milk and milk products Fats and oils Snacks Beverages Seasonings and spices Cooking and processing Contribution, %
0.36 0.07 0.78 0.04 0.81 0.21 0.08 0.09 0.09 0.08 0.27 0.24 0.48 0.10 0.87 0.07 0.03 0.15 0.14 15
0.59 0.23 0.15 0.12 0.31 0.08 0.36 0.19 0.55 0.14 0.15 0.56 0.18 0.63 0.04 0.06 0.32 0.56 0.27 14
0.04 0.62 0.26 0.09 0.09 0.45 0.02 0.71 0.04 0.51 0.10 0.01 0.05 0.44 0.14 0.07 0.19 0.30 0.58 12
0.23 0.01 0.00 0.32 0.22 0.48 0.76 0.02 0.13 0.32 0.73 0.24 0.26 0.05 0.01 0.09 0.51 0.13 0.04 8
0.16 0.24 0.12 0.73 0.06 0.20 0.03 0.15 0.03 0.31 0.18 0.51 0.33 0.02 0.11 0.44 0.26 0.28 0.05 7
spices and mushrooms); (iii) fruits and potatoes (high loadings for fruits, potatoes, cooking and processing, algae, green and dark-yellow vegetables, and milk and milk products); (iv) vegetables and fish (high loadings for green/dark yellow leafy vegetables, fish and seafood, and beverages); (v) pulses (high loading for pulses). Overall, these five patterns accounted for 56% of the variance in food intake. Table 3 shows the nutrient intake for each dietary pattern. In the oils, nuts, seeds, sugars and eggs pattern, there was a significant trend across the tertiles for increased intake of carbohydrate, protein, fat, zinc, copper, cholesterol, salt, saturated fatty acids and omega 6 fatty acids (P < 0.05 for each comparison). Across the tertiles in the cereals and meats pattern, there were significant trends for increased consumption of
carbohydrate, zinc, copper, fibre and salt (P < 0.05 for each comparison). In the fruits and potatoes pattern, there were significant trends towards increased intake of carbohydrate, vitamin B6, vitamin C, folic acid, calcium, magnesium, zinc, copper and fibre (P < 0.05 for each comparison). In the vegetables and fish pattern, there were significantly higher intakes of protein, fat, vitamin B6, vitamin B12, niacin, vitamin C, vitamin E, folic acid, potassium, calcium, magnesium, zinc, copper, fibre, salt, monounsaturated fatty acids, omega 3 fatty acids and omega 6 fatty acids across the tertiles (P < 0.05 for each comparison). There was a significant trend towards increased cholesterol consumption in the pulses pattern (P < 0.05). Table 4 shows the demographic and clinical characteristics of study subjects
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23.2 6.2
15.1 3.6
2.90 1.26
12.11 2.73*
16.3 4.7
10.6 2.8
2.46 1.03
8.10 2.18
Tertile 3
Tertile 1
Tertile 3
Fruits and potatoes
3.00 1.81
14.9 6.2
21.7 8.5
10.15 3.41 11.88 4.62
2.91 1.52
13.1 4.6
20.7 7.5
2.86 1.34
14.0 3.9
20.1 5.8
10.98 4.74 11.11 2.91
3.07 1.85
14.1 6.3
20.6 9.4
201 53 243 42* 197 46 243 41*** 71.0 16.1 79.1 11.5 70.2 15.3 78.4 12.6 58.0 17.5 60.1 18.6 57.0 21.9 58.3 13.1 1.29 0.35 1.55 0.39 1.27 0.54 1.60 0.35** 8.46 4.53 8.10 4.15 9.59 7.00 8.12 4.55 30.0 8.8 33.6 4.9 30.1 9.0 33.5 6.1 113 61 134 58 91 57 152 50*** 23.2 6.4 25.6 9.5 23.8 9.3 25.1 6.4 366 137 413 148 341 146 428 121* 259 98 309 142 357 169 339 159 642 239 562 221 480 238 658 207** 275 70 319 82 262 81 334 80** 7.33 1.59 9.08 1.90** 7.57 1.64 8.87 1.98* 1.09 0.28 1.33 0.32* 1.13 0.25 1.34 0.33* 14.4 4.5 18.3 5.0* 13.3 4.4 18.7 5.0*** 296 112 345 133 335 149 303 109 9.32 2.23 11.50 1.81** 10.07 2.89 10.74 1.63 15.5 4.7 14.6 4.1 14.1 6.0 14.6 3.8
Tertile 1
Cereals and meats Tertile 3
Tertile 1
Pulses Tertile 3
3.74 1.94***
15.6 6.6**
22.7 9.8*
10.35 2.91 11.77 5.03*
2.24 0.74
12.6 3.4
19.9 6.1
2.83 1.41
14.0 4.2
18.7 6.5
10.26 4.61 11.15 3.08
2.87 1.83
13.2 6.2
22.6 8.5
220 44 221 42 219 48 221 45 67.0 14.2 81.0 10.8*** 72.9 12.2 75.5 14.3 56.6 15.2 62.6 21.9* 61.0 19.2 56.2 14.9 1.14 0.32 1.63 0.31*** 1.44 0.35 1.39 0.39 6.14 3.27 9.43 3.93* 8.17 3.90 9.09 7.27 26.9 6.3 35.3 5.5*** 32.2 5.7 30.5 7.3 97 34 153 62*** 132 51 118 63 22.6 6.4 26.6 9.5* 23.3 9.5 24.8 6.5 319 67 465 159*** 409 155 386 136 292 133 374 156* 288 133 366 153 466 160 676 218*** 564 231 644 209 250 73 337 67*** 283 75 314 82 7.85 2.26 8.52 1.43* 8.24 1.73 8.34 2.03 1.07 0.27 1.34 0.29*** 1.16 0.29 1.30 0.35 13.6 3.5 18.5 4.9*** 16.2 5.1 16.8 5.2 332 127 323 125 284 102 348 154* 9.59 2.46 11.00 1.90** 10.00 1.60 10.41 2.50 14.7 5.2 15.5 5.3 16.0 5.3 14.2 4.4
Tertile 1
Vegetables and fish
Data presented as mean SD before adjustment for age and sex. *P < 0.05, **P < 0.01, ***P < 0.001 across tertiles; linear regression analysis for continuous variables or logistic regression analysis for categorical variables, adjusted for age and sex.
250 45*** 80.5 11.1** 65.5 15.0* 1.50 0.54 9.26 4.62 33.3 7.8 123 74 25.8 6.1 393 147 318 106 622 244 303 80 8.99 1.80* 1.31 0.31* 16.8 4.7 384 138** 11.38 1.92** 17.0 5.4*
189 30 66.3 12.7 46.5 11.0 1.30 0.38 8.69 6.69 29.1 6.1 110 44 19.7 5.2 353 107 276 156 527 186 263 65 7.19 1.20 1.05 0.22 14.4 4.4 249 86 9.02 1.89 12.2 4.1
Carbohydrate, g Protein, g Fat, g Vitamin B6, mg Vitamin B12, mg Niacin, mg Vitamin C, mg Vitamin E, mg Folic acid, mg Potassium, mg Calcium, mg Magnesium, mg Zinc, mg Copper, mg Fibre, g Cholesterol, mg Salt, g Saturated fatty acids, g Monounsaturated fatty acids, g Polyunsaturated fatty acids, g Omega 3 fatty acid, g Omega 6 fatty acid, g
Tertile 3
Tertile 1
Parameter
Oils, nuts, seeds, sugars, eggs
Table 3. Nutrient intake associated with dietary patterns (identified by factor analysis with varimax rotation of food diary information) in patients with type 2 diabetes mellitus (n ¼ 73), stratified by tertile.
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2 (8.0) 12.0 3.5 9.3 8.2
1960 221*** 5.0 9.4 9 (36.0) 16 (64.0) 17 (68.0) 12 (48.0) 18 (72.0) 18 (72.0) 6 (24.0) 11 (44.0) 10 (40.0) 4 (16.0) 127.0 15.3 69.8 10.9 7.2 0.7 130 38 191 39 55 14
116 29*
78 (60–107)
1 (4.2) 11.7 2.5 8.4 5.3
1460 215 1.9 4.8 9 (37.5) 8 (33.3) 18 (75.0) 10 (41.7) 16 (66.7) 17 (70.8) 6 (25.0) 6 (25.0) 13 (54.2) 4 (16.7) 132.2 17.0 72.0 13.8 7.5 1.2 125 30 181 30 55 13
103 24
84 (73–96)
82 (60–95)
108 28
1667 402 8.3 16.7 12 (50.0) 10 (41.7) 20 (83.3) 16 (66.7) 18 (75.0) 21 (87.5) 4 (16.7) 13 (54.2) 13 (54.2) 4 (16.7) 135.5 17.0 69.2 12.3 7.4 0.8 135 29 192 33 61 15
1 (4.2) 12.8 3.0 9.4 7.5
72.1 4.8 8 (33.3) 24.4 3.1 16.0 8.9
Tertile 1
70 (57–86)
104 27
1863 277 3.6 6.8 7 (28.0) 16 (64.0) 21 (84.0) 15 (60.0) 16 (64.0) 18 (72.0) 9 (36.0) 11 (44.0) 14 (56.0) 9 (36.0) 128.6 15.6 70.6 11.9 7.4 0.8 127 39 177 36 52 13*
2 (8.0) 11.9 2.5 8.9 6.7
71.2 4.5 18 (72.0) 24.1 3.2 15.7 10.3
Tertile 3
Cereals and meats
103 31
1864 317** 7.7 15.9 4 (16.0)* 13 (52.0) 20 (80.0) 13 (52.0) 20 (80.0) 18 (72.0) 5 (20.0) 11 (44.0) 13 (52.0) 6 (24.0) 131.2 12.6 72.4 10.6 7.5 1.1 133 31 180 41 57 17
1 (4.0) 11.9 2.6 8.5 5.5
72.3 5.5 16 (64.0) 23.7 2.5 14.9 9.1
Tertile 3
104 (74–168) 83 (68–88)
114 27
1615 323 3.0 7.3 12 (50.0) 14 (58.3) 18 (75.0) 16 (66.7) 16 (66.7) 20 (83.3) 6 (25.0) 15 (62.5) 13 (54.2) 6 (25.0) 130.0 12.1 69.1 11.8 7.3 0.7 125 27 194 31 54 13
3 (12.5) 11.3 3.3 11.1 6.7
73.5 4.9 13 (54.2) 24.4 3.9 16.0 8.4
Tertile 1
Fruits and potatoes
110 29
1831 335** 8.7 15.6* 7 (28.0) 11 (44.0) 18 (72.0) 18 (72.0) 15 (60.0) 21 (84.0) 3 (12.0) 11 (44.0) 11 (44.0) 1 (4.0)* 134.8 13.9 73.4 12.3* 7.3 0.6 128 24 192 31 59 14
1 (4.0) 12.9 3.2 9.0 6.8
71.1 4.2 13 (52.0) 24.3 3.0 14.0 7.2
Tertile 3
75 (60–112) 83 (67–102)
102 35
1686 311 2.4 4.9 12 (50.0) 17 (70.8) 18 (75.0) 16 (66.7) 19 (79.2) 18 (75.0) 6 (25.0) 13 (54.2) 13 (54.2) 8 (33.3) 130.7 15.9 67.1 9.2 7.4 1.0 128 38 175 42 52 15
4 (16.7) 11.4 2.4 8.9 5.9
73.1 5.3 18 (75.0) 24.6 3.9 17.2 11.7
Tertile 1
Vegetables and fish
77 (64–87)
107 27
1746 340 3.8 8.9 8 (33.3) 11 (45.8) 19 (79.2) 13 (54.2) 17 (70.8) 19 (79.2) 4 (16.7) 10 (41.7) 13 (54.2) 5 (20.8) 128.6 12.7 67.5 10.5 7.3 1.3 126 31 185 35 58 14
2 (8.3) 11.8 3.0 10.1 8.0
72.4 4.8 14 (58.3) 23.6 3.3 15.1 9.4
Tertile 1
Pulses
82 (60–102)
107 28
1725 287 3.2 6.3 9 (34.6) 16 (61.5) 20 (76.9) 16 (61.5) 20 (76.9) 20 (76.9) 7 (26.9) 12 (46.2) 15 (57.7) 8 (30.8) 137.4 15.8* 77.1 11.2** 7.4 0.6 129 23 180 41 53 14
2 (7.7) 11.7 2.7 7.9 5.9
73.0 5.2 14 (53.8) 24.4 2.9 13.8 8.3
Tertile 3
Data presented as mean SD n (%) or median (interquartile range) before adjustment for age and sex. *P < 0.05, **P < 0.01, ***P < 0.001 across tertiles; linear regression analysis for continuous variables or logistic regression analysis for categorical variables, adjusted for age and sex.
73.7 5.0 22 (88.0) 23.5 3.6 16.4 10.0
72.5 5.7 7 (29.2) 24.8 2.8 14.8 8.9
Age, years Male sex Body mass index, kg/m2 Estimated duration of diabetes, years Current smoker Education, years Beck Depression inventory II score20 Energy, kcal/day Alcohol, g/day Diabetic retinopathy Diabetic nephropathy Diabetic neuropathy Hypertension Hyperlipidaemia Oral hypoglycaemic drugs Insulin therapy Hypertension medication Hyperlipidaemia medication Antiplatelet agents Systolic BP, mmHg Diastolic BP, mmHg Glycosylated haemoglobin, % Fasting blood glucose, mg/dl Total cholesterol, mg/dl High-density lipoprotein cholesterol, mg/dl Low-density lipoprotein cholesterol, mg/dl Triglyceride, mg/dl
Tertile 3
Tertile 1
Variable
Oils, nuts, seeds, sugars, eggs
Table 4. Demographic and clinical characteristics associated with dietary patterns (identified by factor analysis with varimax rotation of food diary information) in patients with type 2 diabetes mellitus (n ¼ 73), stratified by tertile.
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Table 5. Cognitive function in patients with type 2 diabetes mellitus (n ¼ 73), stratified by tertiles of dietary pattern (identified by factor analysis with varimax rotation of food diary information). Trend estimation Dietary pattern
Variable
MMSE score18,19 Suspected mild dementia MMSE score Suspected mild dementia Fruits and potatoes MMSE score Suspected mild dementia Vegetables and fish MMSE score Suspected mild dementia Pulses MMSE score Suspected mild dementia Oils, nuts, seeds, sugars, eggs Cereals and meats
Tertile 1
Tertile 2
Tertile 3
A
B
C
27.0 2.3 9 (37.5) 27.5 1.8 5 (20.8) 26.4 2.0 10 (41.7) 25.5 2.3 14 (58.3) 27.5 2.2 7 (29.2)
27.0 2.1 7 (29.2) 26.2 2.5 12 (50.0) 26.9 2.2 9 (37.5) 27.5 1.6 6 (25.0) 26.2 2.5 9 (39.1)
26.6 2.4 10 (40.0) 26.9 2.2 9 (36.0) 27.2 2.5 7 (28.0) 27.6 2.1 6 (24.0) 26.9 1.8 10 (38.5)
0.50 0.19 1.02 1.08 1.30 1.00 3.45*** 2.43* 0.94 0.67
0.54 0.62 0.50 0.73 1.43 1.08 3.08** 2.19* 0.99 0.72
0.44 0.20 0.37 0.17 0.95 0.45 2.17* 1.49 0.79 0.74
Data presented as mean SD or n (%) before adjustment for age, sex, education, presence of diabetic nephropathy and alcohol consumption. A, unadjusted model; B, adjusted for age and sex; C, adjusted for age, sex, education, presence of diabetic nephropathy and alcohol consumption. *P < 0.05, **P < 0.01, ***P < 0.001; linear regression analysis (MMSE score) or logistic regression analysis (suspected mild dementia). MMSE: Mini-mental State Examination.18
stratified by dietary pattern score. In the oils, nuts, seeds, sugars and eggs pattern, there was a significant trend towards increased calorie consumption and LDL cholesterol level (P < 0.05 for each comparison). In the cereals and meats pattern, there was a significant trend towards lower HDL cholesterol (P < 0.05). There were significant trends for increased calorie consumption and lower prevalence of diabetic retinopathy in the fruits and potatoes pattern (P < 0.05 for each comparison). Across the tertiles in the vegetables and fish pattern, there were significant trends towards increased calorie and alcohol consumption, higher diastolic blood pressure, and reduced likelihood of antiplatelet agent use (P < 0.05 for each comparison). There was a significant trend towards higher diastolic and systolic blood pressure in the pulses pattern (P < 0.05 for each comparison). Correlation analysis found significant relationships between MMSE score and education (r ¼ 0.38, P ¼ 0.001), presence of
diabetic nephropathy (r ¼ 0.29, P ¼ 0.013) and alcohol consumption (r ¼ 0.24, P ¼ 0.042). Data regarding cognitive function across the tertiles for each dietary pattern are shown in Table 5. A higher consumption of vegetables and fish was significantly associated with improved MMSE score (unadjusted model, P < 0.001; model adjusted for age and sex, P < 0.01; model adjusted for age, sex, education, diabetic nephropathy and alcohol consumption, P < 0.05) and decreased prevalence of suspected mild dementia (unadjusted model, P < 0.05; model adjusted for age and sex, P < 0.05). There were no other significant trends in cognitive function in any other dietary pattern.
Discussion The present study identified five dietary patterns, of which a high score in the vegetables and fish pattern was associated with a heightened MMSE score and a
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reduced risk of suspected mild dementia in elderly patients with T2DM. A high score in the vegetables and fish pattern was also associated with a heightened intake of various vitamins and minerals, in the present study. These are known to act as antioxidant agents, and oxidative stress is a substantial risk factor for agerelated cognitive decline.21 The antioxidant rich diet in the vegetables and fish pattern may account for the high MMSE score and low prevalence of mild dementia in these subjects. Consistent with our findings, others have demonstrated an association between a high intake of vegetables and a reduced risk of cognitive decline.22–24 In the present study, subjects with a high score in the fruits and potatoes pattern also consumed a diet that was high in vitamins and minerals, but there was no association between this dietary pattern and cognitive function. The effect of a high intake of fruits on the prevention of cognitive decline is unclear.24 A significant negative association has been identified between fructose intake and cognitive function among middle-aged and elderly subjects without T2DM,25 and it is possible that high fructose intake may attenuate the beneficial effects of vitamins and minerals from fruits. On the other hand, others have found that high fruit intake correlated with low incidence of diabetic retinopathy,26 suggesting that the antioxidative effect of vitamins may prevent its progression. This is consistent with the present finding of a low prevalence of diabetic retinopathy in the highest tertile of the fruits and potatoes pattern. Studies have found a correlation between high fish consumption and a reduced risk of cognitive decline,27–29 although reports are not consistent.30,31 Generally, fish contains high amount of omega 3 fatty acids in addition to vitamins and minerals. There was a positive correlation between the vegetables and fish pattern and omega 3 fatty acids in the present study, which may be
beneficial for brain health via their antiinflammatory, antioxidative and antithrombotic properties.32 The higher cognitive function and lower use of antiplatelet agents in subjects with a high vegetables and fish score, compared with other subjects in the present study, may be at least in part due to their high intake of omega 3 fatty acids. Several studies have demonstrated that light-to-moderate alcohol consumption is associated with high cognitive test score and/or risk reductions in the development of dementia,33–36 although other studies do not support such findings.37,38 While the mechanisms underlying the relationship between alcohol intake and cognitive function remain largely unknown, moderate alcohol intake may play a role in cardioprotection and/or neuroprotection through the activation of cellular survival pathways,39 leading to reductions in the risk of cardiovascular and/or cerebrovascular diseases,40,41 and, ultimately, a reduced risk of cognitive decline. Alcohol consumption was very modest in the present study, but correlated with MMSE score. In addition, patients with a high vegetables and fish score consumed significantly more alcohol than those in the lowest tertile. Thus, the consumption of moderate amounts of alcohol (8 g/day) in those subjects could also be associated with better cognitive function. It is interesting to note that the vegetables and fish pattern was associated with high MMSE score, even after adjustment for confounding factors including alcohol intake. The present study has several limitations. First, the study was a subanalysis of a small sample size study with a cross-sectional design. Such study design may result in selection bias and does not allow inference of a causal relationship between dietary patterns and cognitive function. In particular, it is possible that dietary patterns may have changed as a result of altered cognition; also, we cannot exclude the possibility
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that dietary patterns found in this study were influenced by unknown factors caused by a decline in cognitive function. Furthermore, some of the negative results may be related to the underpowered sample size. Another potential limitation is that we evaluated dietary patterns and cognitive function via self-reported questionnaires; this method has been widely used in studies, however. It is also necessary to acknowledge that the validity and reproducibility of dietary patterns identified in this study have not been confirmed, although the methods employed have been widely used. In addition, we did not include a group of control subjects without T2DM, and it is therefore impossible to determine whether our findings are general or specific to T2DM. Other lifestyle factors such as physical activity, sleep/wake pattern and metal health, were not evaluated, and we cannot exclude the possibility that subjects with higher scores for vegetables and fish may have had higher health-related literacy than other subjects. Finally, we could not fully consider possible confounding factors associated with cognitive function. In conclusion, a high score in the vegetables and fish dietary pattern was associated with high MMSE score and low prevalence of suspected mild dementia in elderly patients with T2DM. Declaration of conflicting interest T.M. received research funds from MSD, Takeda and Eli Lilly. M.G. has received lecture fees from Novartis Pharmaceuticals and travel fees from Takeda Pharmaceutical Co. H.W. has received lecture fees from Boehringer Ingelheim, SanofiAventis, Ono Pharmaceutical Co., Novo Nordisk Pharma, Novartis Pharmaceuticals, Eli Lilly, Sanwakagaku Kenkyusho, Daiichi Sankyo Inc., Takeda Pharmaceutical Co., MSD, Dainippon Sumitomo Pharm., Kowa Co. and research funds from Boehringer Ingelheim, Pfizer, Mochida Pharmaceutical Co., Sanofi-Aventis, Novo
Nordisk Pharma, Novartis Pharmaceuticals, Sanwakagaku Kenkyusho, Terumo Corp. Eli Lilly, Mitsubishi Tanabe Pharma, Daiichi Sankyo Inc., Takeda Pharmaceutical Co., MSD, Shionogi, Pharma, Dainippon Sumitomo Pharma, Kissei Pharma, and Astrazeneca. These funding sources were potentially related to the present study.
Funding This study was funded by a grant from the Ministry of Education, Sports and Culture of Japan to Chie Ohmura (grant number 23500858).
Acknowledgements We thank all patients who participated in this study and all the staff at Juntendo University Graduate School of Medicine, Department of Medicine, Metabolism and Endocrinology (Tokyo, Japan), and Juntendo Tokyo Koto Geriatric Medical Centre, Department of Medicine, Diabetology and Endocrinology (Tokyo, Japan).
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18. Folstein MF, Folstein SE and McHugh PR. ‘‘Mini-mental state’’. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12: 189–198. 19. Crum RM, Anthony JC, Bassett SS, et al. Population-based norms for the MiniMental State Examination by age and educational level. JAMA 1993; 269: 2386–2391. 20. Beck AT, Steer RA and Brown GK. Manual for the Beck Depression Inventory-II. San Antonio, TX: Psychological Corporation, 1996. 21. Berr C, Balansard B, Arnaud J, et al. Cognitive decline is associated with systemic oxidative stress: the EVA study. Etude du Vieillissement Arteriel. J Am Geriatr Soc 2000; 48: 1285–1291. 22. Chen X, Huang Y and Cheng HG. Lower intake of vegetables and legumes associated with cognitive decline among illiterate elderly Chinese: a 3-year cohort study. J Nutr Health Aging 2012; 16: 549–552. 23. Roberts RO, Geda YE, Cerhan JR, et al. Vegetables, unsaturated fats, moderate alcohol intake, and mild cognitive impairment. Dement Geriatr Cogn Disord 2010; 29: 413–423. 24. Loef M and Walach H. Fruit, vegetables and prevention of cognitive decline or dementia: a systematic review of cohort studies. J Nutr Health Aging 2012; 16: 626–630. 25. Ye X, Gao X, Scott T, et al. Habitual sugar intake and cognitive function among middleaged and older Puerto Ricans without diabetes. Br J Nutr 2011; 106: 1423–1432. 26. Tanaka S, Yoshimura Y, Kawasaki R, et al. Fruit intake and incident diabetic retinopathy with type 2 diabetes. Epidemiology 2013; 24: 204–211. 27. Kalmijn S, van Boxtel MP, Ocke M, et al. Dietary intake of fatty acids and fish in relation to cognitive performance at middle age. Neurology 2004; 62: 275–280. 28. Qin B, Plassman BL, Edwards LJ, et al. Fish intake is associated with slower cognitive decline in Chinese older adults. J Nutr 2014; 144: 1579–1585. 29. Morris MC, Evans DA, Bienias JL, et al. Consumption of fish and n-3 fatty acids and risk of incident Alzheimer disease. Arch Neurol 2003; 60: 940–946.
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Relationship between dietary pattern and cognitive function in elderly patients with type 2 diabetes mellitus
Journal of International Medical Research 2015, Vol. 43(4) 506–517 ! The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0300060515581672 imr.sagepub.com
Mari Enomoto1, Hidenori Yoshii1, Tomoya Mita2,3, Haruna Sanke2, Ayako Yokota1, Keiko Yamashiro1, Noriko Inagaki1, Masahiko Gosho4, Chie Ohmura2, Kayo Kudo5, Hirotaka Watada2,4,6,7 and Tomio Onuma1
Abstract Objective: To analyse the relationships between dietary patterns and cognitive function in elderly patients with type 2 diabetes mellitus (T2DM). Methods: Patients with T2DM completed a 3-day dietary record and Mini-mental State Examination (MMSE). Dietary patterns were identified by factor analysis. Results: The study included 73 patients and identified five dietary patterns, one of which was characterized by high loading for vegetables and fish. A higher consumption of vegetables and fish was significantly associated with improved MMSE score (unadjusted model, model adjusted for age and sex, and model adjusted for age, sex, education, diabetic nephropathy and alcohol consumption), and decreased prevalence of suspected mild dementia (unadjusted model, model adjusted for age and sex). Conclusions: A high score in the vegetables and fish dietary pattern was associated with high MMSE score and low prevalence of suspected mild dementia in elderly patients with T2DM.
1
Department of Medicine, Diabetology and Endocrinology, Juntendo Tokyo Koto Geriatric Medical Centre, Tokyo, Japan 2 Department of Metabolism and Endocrinology, Juntendo University Graduate School of Medicine, Tokyo, Japan 3 Centre for Molecular Diabetology, Juntendo University Graduate School of Medicine, Tokyo, Japan 4 Department of Clinical Trials and Clinical Epidemiology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan 5 Department of Medicine, Nutritional Management Section, Juntendo Tokyo Koto Geriatric Medical Centre, Tokyo, Japan
6
Centre for Therapeutic Innovations in Diabetes, Tokyo, Japan 7 Sportology Centre, Juntendo University Graduate School of Medicine, Tokyo, Japan Corresponding author: Hidenori Yoshii, Department of Medicine, Diabetology & Endocrinology Juntendo Tokyo Koto Geriatric Medical Center, Shinsuna 3-3-20, Koto-ku, Tokyo 136-0075, Japan. Email: [email protected]
Creative Commons CC-BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License (http://www.creativecommons.org/licenses/by-nc/3.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the atoriginal work is attributed as specified on 15, the2015 SAGE and Open Access page Downloaded from imr.sagepub.com UNIV CALIFORNIA SAN DIEGO on November (http://www.uk.sagepub.com/aboutus/openaccess.htm).
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Keywords Dietary pattern, suspected mild dementia, type 2 diabetes mellitus Date received: 9 December 2014; accepted: 20 March 2015
Introduction Patients with type 2 diabetes mellitus (T2DM) are between two and four times more likely to develop cognitive impairment and dementia than individuals without T2DM.1 Several factors (including age, sex, glycaemic control, disease duration, complications, hypertension, lipid metabolism, education, depression, physical activity and chronic infection) are known to correlate with cognitive impairment in patients with T2DM,2–6 and there are likely to be additional unrecognized contributing factors. Intake of certain types of foods (such as fish and vegetables) and nutrients (such as vitamins C, E, B6 and B12, folate, potassium, calcium, magnesium and unsaturated fatty acids) lowers the risk of cognitive impairment7,8 and/or Alzheimer’s disease,9 although the extent of protection varies among studies. Since people do not consume individual foods or single nutrients in real life, the dietary pattern should be taken into consideration as it reflects the complexity of dietary intake, where foods have interactive, synergistic and antagonistic effects.10 It has been suggested that dietary pattern may be more predictive of disease risks than specific food- and nutrient-based approaches.10 Adherence to the Mediterranean dietary pattern (characterized by high consumption of plant foods [vegetables, fruits, legumes and cereals], high intake of olive oil, moderate intake of fish, low-to-moderate intake of dairy products and low intake of saturated fats and meat11) is associated with slower cognitive decline12 and reduced Alzheimer’s disease risk.13 Although the Japanese dietary pattern differs widely from the Mediterranean diet, a diet characterized by high intake of soybeans,
vegetables, algae, dairy products and low intake of rice was associated with reduced risk of dementia in the general population of elderly Japanese individuals.14 Taken together, these data suggest that dietary pattern could contribute to the risk of cognitive decline and/or Alzheimer’s disease. Patients with T2DM have been found to consume higher quantities of fruit, vegetables and meat than healthy control subjects,15 but the relationship between dietary patterns and cognitive function in patients with T2DM remains largely unexplored. The aim of this cross-sectional study was to analyse these associations using data from elderly Japanese patients with T2DM.
Patients and methods Study population This ancillary study was a subanalysis of an original study that investigated the relationship between olfactory function and cognitive function.16 Patients with T2DM were enrolled from the Diabetes Outpatient Clinic of Juntendo Tokyo Koto Geriatric Medical Centre (Tokyo, Japan) and Juntendo University Hospital (Tokyo, Japan) between October 2012 and December 2013, as described.16 Inclusion criteria were patients with T2DM who were aged >65 years and free of clinically evident cognitive impairment. Exclusion criteria were: (i) severe infection within the preceding 2 weeks; (ii) any scheduled or performed surgery; (iii) severe trauma; (iv) current psychiatric disorders; (v) partial or complete olfactory dysfunction associated with sinusitis, allergic rhinitis or deviated nasal septum; (vi) history of brain tumour; (vii) MMSE (mini-mental state examination)
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score 0.5 were retained. For simplicity, individual metabolites with a factor loading >0.4 are reported as composing that factor. Factor scores for each dietary pattern and each subject were calculated by summing each dietary pattern score, weighted by their factor loadings. Estimated factor scores were categorized into tertiles. Trend association across tertiles was evaluated using linear regression analysis for continuous variables or logistic regression analysis for categorical variables, in an unadjusted model and/or model adjusted for age and sex. Spearman’s correlation coefficient was used to evaluate correlations between MMSE score and possible risk factors for cognitive impairment (BMI, estimated duration of diabetes, glycaemic control, presence of diabetic complications [retinopathy, nephropathy and neuropathy], education, depressive status, smoking, alcohol consumption, food intake, hypertension and hyperlipidaemia). Trend associations across MMSE score and suspected mild dementia tertiles were evaluated by linear regression analysis and logistic regression analysis, respectively. Regression models included statistically significant variables (P < 0.05) from the above Spearman’s correlation analysis, in addition to age and sex. Statistical analyses were performed using SASÕ software version 9.3 (SAS Institute, Cary, NC, USA), and two-sided Pvalues < 0.05 were considered statistically significant.
Results The study included 73 patients (40 male/33 female; mean age 72.4 5.1 years; age range 65–83 years). Demographic and clinical characteristics of the patients are shown in Table 1.
Table 1. Demographic and clinical characteristics of patients with type 2 diabetes mellitus included in a study investigating the relationship between cognitive function and dietary patterns (n ¼ 73). Characteristic
n
Age, years Male sex Body mass index, kg/m2 Estimated duration of diabetes, years Current smoker Education, years Beck Depression inventory II score20 Energy intake, kcal/day Alcohol consumption, g/day Diabetic retinopathy Diabetic nephropathy Diabetic neuropathy Hypertension Hyperlipidaemia Oral hypoglycaemic drugs Insulin therapy Hypertension medication Hyperlipidaemia medication Antiplatelet agents Systolic blood pressure, mmHg Diastolic blood pressure, mmHg Glycosylated haemoglobin, % Fasting blood glucose, mg/dl Total cholesterol, mg/dl High-density lipoprotein cholesterol, mg/dl Low-density lipoprotein cholesterol, mg/dl Triglyceride, mg/dl Mini-mental State Examination score18,19 Suspected cognitive impairment
72.4 5.1 40 (54.8) 23.9 3.3 15.5 9.5 6 (8.2) 11.8 2.8 9.6 7.2 1728 332 4.8 11.1 28 (38.4) 40 (54.8) 54 (72.0) 45 (61.6) 53 (72.6) 56 (76.7) 17 (23.3) 34 (46.6) 38 (52.1) 18 (24.7) 131.5 15.5 69.9 12.0 7.3 0.9 128 32 186 36 56 14 109 29 83 (63–106) 26.9 2.2 26 (35.6)
Data presented as mean SD, n (%) or median (interquartile range).
Factor analysis with varimax rotation identified five dietary patterns (Table 2): (i) oils, nuts, seeds, sugars and eggs (high loadings for fats and oils, nuts, seeds, sugars and eggs); (ii) cereals and meats (high loadings for cereals, meats, seasonings,
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Table 2. Dietary patterns of patients with type 2 diabetes mellitus, as determined by factor analysis with varimax rotation of food diary information (n ¼ 73). Dietary pattern Components and item labels
Oils, nuts, seeds, sugars, eggs
Cereals and meats
Fruits and potatoes
Vegetables and fish
Pulses
Cereals Potatoes and starch Sugars Pulses Nuts and seeds Green and dark yellow vegetables Leafy vegetables Fruits Mushrooms Algae Fish and seafood Meats Eggs Milk and milk products Fats and oils Snacks Beverages Seasonings and spices Cooking and processing Contribution, %
0.36 0.07 0.78 0.04 0.81 0.21 0.08 0.09 0.09 0.08 0.27 0.24 0.48 0.10 0.87 0.07 0.03 0.15 0.14 15
0.59 0.23 0.15 0.12 0.31 0.08 0.36 0.19 0.55 0.14 0.15 0.56 0.18 0.63 0.04 0.06 0.32 0.56 0.27 14
0.04 0.62 0.26 0.09 0.09 0.45 0.02 0.71 0.04 0.51 0.10 0.01 0.05 0.44 0.14 0.07 0.19 0.30 0.58 12
0.23 0.01 0.00 0.32 0.22 0.48 0.76 0.02 0.13 0.32 0.73 0.24 0.26 0.05 0.01 0.09 0.51 0.13 0.04 8
0.16 0.24 0.12 0.73 0.06 0.20 0.03 0.15 0.03 0.31 0.18 0.51 0.33 0.02 0.11 0.44 0.26 0.28 0.05 7
spices and mushrooms); (iii) fruits and potatoes (high loadings for fruits, potatoes, cooking and processing, algae, green and dark-yellow vegetables, and milk and milk products); (iv) vegetables and fish (high loadings for green/dark yellow leafy vegetables, fish and seafood, and beverages); (v) pulses (high loading for pulses). Overall, these five patterns accounted for 56% of the variance in food intake. Table 3 shows the nutrient intake for each dietary pattern. In the oils, nuts, seeds, sugars and eggs pattern, there was a significant trend across the tertiles for increased intake of carbohydrate, protein, fat, zinc, copper, cholesterol, salt, saturated fatty acids and omega 6 fatty acids (P < 0.05 for each comparison). Across the tertiles in the cereals and meats pattern, there were significant trends for increased consumption of
carbohydrate, zinc, copper, fibre and salt (P < 0.05 for each comparison). In the fruits and potatoes pattern, there were significant trends towards increased intake of carbohydrate, vitamin B6, vitamin C, folic acid, calcium, magnesium, zinc, copper and fibre (P < 0.05 for each comparison). In the vegetables and fish pattern, there were significantly higher intakes of protein, fat, vitamin B6, vitamin B12, niacin, vitamin C, vitamin E, folic acid, potassium, calcium, magnesium, zinc, copper, fibre, salt, monounsaturated fatty acids, omega 3 fatty acids and omega 6 fatty acids across the tertiles (P < 0.05 for each comparison). There was a significant trend towards increased cholesterol consumption in the pulses pattern (P < 0.05). Table 4 shows the demographic and clinical characteristics of study subjects
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23.2 6.2
15.1 3.6
2.90 1.26
12.11 2.73*
16.3 4.7
10.6 2.8
2.46 1.03
8.10 2.18
Tertile 3
Tertile 1
Tertile 3
Fruits and potatoes
3.00 1.81
14.9 6.2
21.7 8.5
10.15 3.41 11.88 4.62
2.91 1.52
13.1 4.6
20.7 7.5
2.86 1.34
14.0 3.9
20.1 5.8
10.98 4.74 11.11 2.91
3.07 1.85
14.1 6.3
20.6 9.4
201 53 243 42* 197 46 243 41*** 71.0 16.1 79.1 11.5 70.2 15.3 78.4 12.6 58.0 17.5 60.1 18.6 57.0 21.9 58.3 13.1 1.29 0.35 1.55 0.39 1.27 0.54 1.60 0.35** 8.46 4.53 8.10 4.15 9.59 7.00 8.12 4.55 30.0 8.8 33.6 4.9 30.1 9.0 33.5 6.1 113 61 134 58 91 57 152 50*** 23.2 6.4 25.6 9.5 23.8 9.3 25.1 6.4 366 137 413 148 341 146 428 121* 259 98 309 142 357 169 339 159 642 239 562 221 480 238 658 207** 275 70 319 82 262 81 334 80** 7.33 1.59 9.08 1.90** 7.57 1.64 8.87 1.98* 1.09 0.28 1.33 0.32* 1.13 0.25 1.34 0.33* 14.4 4.5 18.3 5.0* 13.3 4.4 18.7 5.0*** 296 112 345 133 335 149 303 109 9.32 2.23 11.50 1.81** 10.07 2.89 10.74 1.63 15.5 4.7 14.6 4.1 14.1 6.0 14.6 3.8
Tertile 1
Cereals and meats Tertile 3
Tertile 1
Pulses Tertile 3
3.74 1.94***
15.6 6.6**
22.7 9.8*
10.35 2.91 11.77 5.03*
2.24 0.74
12.6 3.4
19.9 6.1
2.83 1.41
14.0 4.2
18.7 6.5
10.26 4.61 11.15 3.08
2.87 1.83
13.2 6.2
22.6 8.5
220 44 221 42 219 48 221 45 67.0 14.2 81.0 10.8*** 72.9 12.2 75.5 14.3 56.6 15.2 62.6 21.9* 61.0 19.2 56.2 14.9 1.14 0.32 1.63 0.31*** 1.44 0.35 1.39 0.39 6.14 3.27 9.43 3.93* 8.17 3.90 9.09 7.27 26.9 6.3 35.3 5.5*** 32.2 5.7 30.5 7.3 97 34 153 62*** 132 51 118 63 22.6 6.4 26.6 9.5* 23.3 9.5 24.8 6.5 319 67 465 159*** 409 155 386 136 292 133 374 156* 288 133 366 153 466 160 676 218*** 564 231 644 209 250 73 337 67*** 283 75 314 82 7.85 2.26 8.52 1.43* 8.24 1.73 8.34 2.03 1.07 0.27 1.34 0.29*** 1.16 0.29 1.30 0.35 13.6 3.5 18.5 4.9*** 16.2 5.1 16.8 5.2 332 127 323 125 284 102 348 154* 9.59 2.46 11.00 1.90** 10.00 1.60 10.41 2.50 14.7 5.2 15.5 5.3 16.0 5.3 14.2 4.4
Tertile 1
Vegetables and fish
Data presented as mean SD before adjustment for age and sex. *P < 0.05, **P < 0.01, ***P < 0.001 across tertiles; linear regression analysis for continuous variables or logistic regression analysis for categorical variables, adjusted for age and sex.
250 45*** 80.5 11.1** 65.5 15.0* 1.50 0.54 9.26 4.62 33.3 7.8 123 74 25.8 6.1 393 147 318 106 622 244 303 80 8.99 1.80* 1.31 0.31* 16.8 4.7 384 138** 11.38 1.92** 17.0 5.4*
189 30 66.3 12.7 46.5 11.0 1.30 0.38 8.69 6.69 29.1 6.1 110 44 19.7 5.2 353 107 276 156 527 186 263 65 7.19 1.20 1.05 0.22 14.4 4.4 249 86 9.02 1.89 12.2 4.1
Carbohydrate, g Protein, g Fat, g Vitamin B6, mg Vitamin B12, mg Niacin, mg Vitamin C, mg Vitamin E, mg Folic acid, mg Potassium, mg Calcium, mg Magnesium, mg Zinc, mg Copper, mg Fibre, g Cholesterol, mg Salt, g Saturated fatty acids, g Monounsaturated fatty acids, g Polyunsaturated fatty acids, g Omega 3 fatty acid, g Omega 6 fatty acid, g
Tertile 3
Tertile 1
Parameter
Oils, nuts, seeds, sugars, eggs
Table 3. Nutrient intake associated with dietary patterns (identified by factor analysis with varimax rotation of food diary information) in patients with type 2 diabetes mellitus (n ¼ 73), stratified by tertile.
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2 (8.0) 12.0 3.5 9.3 8.2
1960 221*** 5.0 9.4 9 (36.0) 16 (64.0) 17 (68.0) 12 (48.0) 18 (72.0) 18 (72.0) 6 (24.0) 11 (44.0) 10 (40.0) 4 (16.0) 127.0 15.3 69.8 10.9 7.2 0.7 130 38 191 39 55 14
116 29*
78 (60–107)
1 (4.2) 11.7 2.5 8.4 5.3
1460 215 1.9 4.8 9 (37.5) 8 (33.3) 18 (75.0) 10 (41.7) 16 (66.7) 17 (70.8) 6 (25.0) 6 (25.0) 13 (54.2) 4 (16.7) 132.2 17.0 72.0 13.8 7.5 1.2 125 30 181 30 55 13
103 24
84 (73–96)
82 (60–95)
108 28
1667 402 8.3 16.7 12 (50.0) 10 (41.7) 20 (83.3) 16 (66.7) 18 (75.0) 21 (87.5) 4 (16.7) 13 (54.2) 13 (54.2) 4 (16.7) 135.5 17.0 69.2 12.3 7.4 0.8 135 29 192 33 61 15
1 (4.2) 12.8 3.0 9.4 7.5
72.1 4.8 8 (33.3) 24.4 3.1 16.0 8.9
Tertile 1
70 (57–86)
104 27
1863 277 3.6 6.8 7 (28.0) 16 (64.0) 21 (84.0) 15 (60.0) 16 (64.0) 18 (72.0) 9 (36.0) 11 (44.0) 14 (56.0) 9 (36.0) 128.6 15.6 70.6 11.9 7.4 0.8 127 39 177 36 52 13*
2 (8.0) 11.9 2.5 8.9 6.7
71.2 4.5 18 (72.0) 24.1 3.2 15.7 10.3
Tertile 3
Cereals and meats
103 31
1864 317** 7.7 15.9 4 (16.0)* 13 (52.0) 20 (80.0) 13 (52.0) 20 (80.0) 18 (72.0) 5 (20.0) 11 (44.0) 13 (52.0) 6 (24.0) 131.2 12.6 72.4 10.6 7.5 1.1 133 31 180 41 57 17
1 (4.0) 11.9 2.6 8.5 5.5
72.3 5.5 16 (64.0) 23.7 2.5 14.9 9.1
Tertile 3
104 (74–168) 83 (68–88)
114 27
1615 323 3.0 7.3 12 (50.0) 14 (58.3) 18 (75.0) 16 (66.7) 16 (66.7) 20 (83.3) 6 (25.0) 15 (62.5) 13 (54.2) 6 (25.0) 130.0 12.1 69.1 11.8 7.3 0.7 125 27 194 31 54 13
3 (12.5) 11.3 3.3 11.1 6.7
73.5 4.9 13 (54.2) 24.4 3.9 16.0 8.4
Tertile 1
Fruits and potatoes
110 29
1831 335** 8.7 15.6* 7 (28.0) 11 (44.0) 18 (72.0) 18 (72.0) 15 (60.0) 21 (84.0) 3 (12.0) 11 (44.0) 11 (44.0) 1 (4.0)* 134.8 13.9 73.4 12.3* 7.3 0.6 128 24 192 31 59 14
1 (4.0) 12.9 3.2 9.0 6.8
71.1 4.2 13 (52.0) 24.3 3.0 14.0 7.2
Tertile 3
75 (60–112) 83 (67–102)
102 35
1686 311 2.4 4.9 12 (50.0) 17 (70.8) 18 (75.0) 16 (66.7) 19 (79.2) 18 (75.0) 6 (25.0) 13 (54.2) 13 (54.2) 8 (33.3) 130.7 15.9 67.1 9.2 7.4 1.0 128 38 175 42 52 15
4 (16.7) 11.4 2.4 8.9 5.9
73.1 5.3 18 (75.0) 24.6 3.9 17.2 11.7
Tertile 1
Vegetables and fish
77 (64–87)
107 27
1746 340 3.8 8.9 8 (33.3) 11 (45.8) 19 (79.2) 13 (54.2) 17 (70.8) 19 (79.2) 4 (16.7) 10 (41.7) 13 (54.2) 5 (20.8) 128.6 12.7 67.5 10.5 7.3 1.3 126 31 185 35 58 14
2 (8.3) 11.8 3.0 10.1 8.0
72.4 4.8 14 (58.3) 23.6 3.3 15.1 9.4
Tertile 1
Pulses
82 (60–102)
107 28
1725 287 3.2 6.3 9 (34.6) 16 (61.5) 20 (76.9) 16 (61.5) 20 (76.9) 20 (76.9) 7 (26.9) 12 (46.2) 15 (57.7) 8 (30.8) 137.4 15.8* 77.1 11.2** 7.4 0.6 129 23 180 41 53 14
2 (7.7) 11.7 2.7 7.9 5.9
73.0 5.2 14 (53.8) 24.4 2.9 13.8 8.3
Tertile 3
Data presented as mean SD n (%) or median (interquartile range) before adjustment for age and sex. *P < 0.05, **P < 0.01, ***P < 0.001 across tertiles; linear regression analysis for continuous variables or logistic regression analysis for categorical variables, adjusted for age and sex.
73.7 5.0 22 (88.0) 23.5 3.6 16.4 10.0
72.5 5.7 7 (29.2) 24.8 2.8 14.8 8.9
Age, years Male sex Body mass index, kg/m2 Estimated duration of diabetes, years Current smoker Education, years Beck Depression inventory II score20 Energy, kcal/day Alcohol, g/day Diabetic retinopathy Diabetic nephropathy Diabetic neuropathy Hypertension Hyperlipidaemia Oral hypoglycaemic drugs Insulin therapy Hypertension medication Hyperlipidaemia medication Antiplatelet agents Systolic BP, mmHg Diastolic BP, mmHg Glycosylated haemoglobin, % Fasting blood glucose, mg/dl Total cholesterol, mg/dl High-density lipoprotein cholesterol, mg/dl Low-density lipoprotein cholesterol, mg/dl Triglyceride, mg/dl
Tertile 3
Tertile 1
Variable
Oils, nuts, seeds, sugars, eggs
Table 4. Demographic and clinical characteristics associated with dietary patterns (identified by factor analysis with varimax rotation of food diary information) in patients with type 2 diabetes mellitus (n ¼ 73), stratified by tertile.
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Table 5. Cognitive function in patients with type 2 diabetes mellitus (n ¼ 73), stratified by tertiles of dietary pattern (identified by factor analysis with varimax rotation of food diary information). Trend estimation Dietary pattern
Variable
MMSE score18,19 Suspected mild dementia MMSE score Suspected mild dementia Fruits and potatoes MMSE score Suspected mild dementia Vegetables and fish MMSE score Suspected mild dementia Pulses MMSE score Suspected mild dementia Oils, nuts, seeds, sugars, eggs Cereals and meats
Tertile 1
Tertile 2
Tertile 3
A
B
C
27.0 2.3 9 (37.5) 27.5 1.8 5 (20.8) 26.4 2.0 10 (41.7) 25.5 2.3 14 (58.3) 27.5 2.2 7 (29.2)
27.0 2.1 7 (29.2) 26.2 2.5 12 (50.0) 26.9 2.2 9 (37.5) 27.5 1.6 6 (25.0) 26.2 2.5 9 (39.1)
26.6 2.4 10 (40.0) 26.9 2.2 9 (36.0) 27.2 2.5 7 (28.0) 27.6 2.1 6 (24.0) 26.9 1.8 10 (38.5)
0.50 0.19 1.02 1.08 1.30 1.00 3.45*** 2.43* 0.94 0.67
0.54 0.62 0.50 0.73 1.43 1.08 3.08** 2.19* 0.99 0.72
0.44 0.20 0.37 0.17 0.95 0.45 2.17* 1.49 0.79 0.74
Data presented as mean SD or n (%) before adjustment for age, sex, education, presence of diabetic nephropathy and alcohol consumption. A, unadjusted model; B, adjusted for age and sex; C, adjusted for age, sex, education, presence of diabetic nephropathy and alcohol consumption. *P < 0.05, **P < 0.01, ***P < 0.001; linear regression analysis (MMSE score) or logistic regression analysis (suspected mild dementia). MMSE: Mini-mental State Examination.18
stratified by dietary pattern score. In the oils, nuts, seeds, sugars and eggs pattern, there was a significant trend towards increased calorie consumption and LDL cholesterol level (P < 0.05 for each comparison). In the cereals and meats pattern, there was a significant trend towards lower HDL cholesterol (P < 0.05). There were significant trends for increased calorie consumption and lower prevalence of diabetic retinopathy in the fruits and potatoes pattern (P < 0.05 for each comparison). Across the tertiles in the vegetables and fish pattern, there were significant trends towards increased calorie and alcohol consumption, higher diastolic blood pressure, and reduced likelihood of antiplatelet agent use (P < 0.05 for each comparison). There was a significant trend towards higher diastolic and systolic blood pressure in the pulses pattern (P < 0.05 for each comparison). Correlation analysis found significant relationships between MMSE score and education (r ¼ 0.38, P ¼ 0.001), presence of
diabetic nephropathy (r ¼ 0.29, P ¼ 0.013) and alcohol consumption (r ¼ 0.24, P ¼ 0.042). Data regarding cognitive function across the tertiles for each dietary pattern are shown in Table 5. A higher consumption of vegetables and fish was significantly associated with improved MMSE score (unadjusted model, P < 0.001; model adjusted for age and sex, P < 0.01; model adjusted for age, sex, education, diabetic nephropathy and alcohol consumption, P < 0.05) and decreased prevalence of suspected mild dementia (unadjusted model, P < 0.05; model adjusted for age and sex, P < 0.05). There were no other significant trends in cognitive function in any other dietary pattern.
Discussion The present study identified five dietary patterns, of which a high score in the vegetables and fish pattern was associated with a heightened MMSE score and a
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reduced risk of suspected mild dementia in elderly patients with T2DM. A high score in the vegetables and fish pattern was also associated with a heightened intake of various vitamins and minerals, in the present study. These are known to act as antioxidant agents, and oxidative stress is a substantial risk factor for agerelated cognitive decline.21 The antioxidant rich diet in the vegetables and fish pattern may account for the high MMSE score and low prevalence of mild dementia in these subjects. Consistent with our findings, others have demonstrated an association between a high intake of vegetables and a reduced risk of cognitive decline.22–24 In the present study, subjects with a high score in the fruits and potatoes pattern also consumed a diet that was high in vitamins and minerals, but there was no association between this dietary pattern and cognitive function. The effect of a high intake of fruits on the prevention of cognitive decline is unclear.24 A significant negative association has been identified between fructose intake and cognitive function among middle-aged and elderly subjects without T2DM,25 and it is possible that high fructose intake may attenuate the beneficial effects of vitamins and minerals from fruits. On the other hand, others have found that high fruit intake correlated with low incidence of diabetic retinopathy,26 suggesting that the antioxidative effect of vitamins may prevent its progression. This is consistent with the present finding of a low prevalence of diabetic retinopathy in the highest tertile of the fruits and potatoes pattern. Studies have found a correlation between high fish consumption and a reduced risk of cognitive decline,27–29 although reports are not consistent.30,31 Generally, fish contains high amount of omega 3 fatty acids in addition to vitamins and minerals. There was a positive correlation between the vegetables and fish pattern and omega 3 fatty acids in the present study, which may be
beneficial for brain health via their antiinflammatory, antioxidative and antithrombotic properties.32 The higher cognitive function and lower use of antiplatelet agents in subjects with a high vegetables and fish score, compared with other subjects in the present study, may be at least in part due to their high intake of omega 3 fatty acids. Several studies have demonstrated that light-to-moderate alcohol consumption is associated with high cognitive test score and/or risk reductions in the development of dementia,33–36 although other studies do not support such findings.37,38 While the mechanisms underlying the relationship between alcohol intake and cognitive function remain largely unknown, moderate alcohol intake may play a role in cardioprotection and/or neuroprotection through the activation of cellular survival pathways,39 leading to reductions in the risk of cardiovascular and/or cerebrovascular diseases,40,41 and, ultimately, a reduced risk of cognitive decline. Alcohol consumption was very modest in the present study, but correlated with MMSE score. In addition, patients with a high vegetables and fish score consumed significantly more alcohol than those in the lowest tertile. Thus, the consumption of moderate amounts of alcohol (8 g/day) in those subjects could also be associated with better cognitive function. It is interesting to note that the vegetables and fish pattern was associated with high MMSE score, even after adjustment for confounding factors including alcohol intake. The present study has several limitations. First, the study was a subanalysis of a small sample size study with a cross-sectional design. Such study design may result in selection bias and does not allow inference of a causal relationship between dietary patterns and cognitive function. In particular, it is possible that dietary patterns may have changed as a result of altered cognition; also, we cannot exclude the possibility
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that dietary patterns found in this study were influenced by unknown factors caused by a decline in cognitive function. Furthermore, some of the negative results may be related to the underpowered sample size. Another potential limitation is that we evaluated dietary patterns and cognitive function via self-reported questionnaires; this method has been widely used in studies, however. It is also necessary to acknowledge that the validity and reproducibility of dietary patterns identified in this study have not been confirmed, although the methods employed have been widely used. In addition, we did not include a group of control subjects without T2DM, and it is therefore impossible to determine whether our findings are general or specific to T2DM. Other lifestyle factors such as physical activity, sleep/wake pattern and metal health, were not evaluated, and we cannot exclude the possibility that subjects with higher scores for vegetables and fish may have had higher health-related literacy than other subjects. Finally, we could not fully consider possible confounding factors associated with cognitive function. In conclusion, a high score in the vegetables and fish dietary pattern was associated with high MMSE score and low prevalence of suspected mild dementia in elderly patients with T2DM. Declaration of conflicting interest T.M. received research funds from MSD, Takeda and Eli Lilly. M.G. has received lecture fees from Novartis Pharmaceuticals and travel fees from Takeda Pharmaceutical Co. H.W. has received lecture fees from Boehringer Ingelheim, SanofiAventis, Ono Pharmaceutical Co., Novo Nordisk Pharma, Novartis Pharmaceuticals, Eli Lilly, Sanwakagaku Kenkyusho, Daiichi Sankyo Inc., Takeda Pharmaceutical Co., MSD, Dainippon Sumitomo Pharm., Kowa Co. and research funds from Boehringer Ingelheim, Pfizer, Mochida Pharmaceutical Co., Sanofi-Aventis, Novo
Nordisk Pharma, Novartis Pharmaceuticals, Sanwakagaku Kenkyusho, Terumo Corp. Eli Lilly, Mitsubishi Tanabe Pharma, Daiichi Sankyo Inc., Takeda Pharmaceutical Co., MSD, Shionogi, Pharma, Dainippon Sumitomo Pharma, Kissei Pharma, and Astrazeneca. These funding sources were potentially related to the present study.
Funding This study was funded by a grant from the Ministry of Education, Sports and Culture of Japan to Chie Ohmura (grant number 23500858).
Acknowledgements We thank all patients who participated in this study and all the staff at Juntendo University Graduate School of Medicine, Department of Medicine, Metabolism and Endocrinology (Tokyo, Japan), and Juntendo Tokyo Koto Geriatric Medical Centre, Department of Medicine, Diabetology and Endocrinology (Tokyo, Japan).
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