Dietary patterns and metabolic syndrome factors in a non-diabetic Italian population.
OBJECTIVE
To examine the relationship between dietary patterns and metabolic syndrome.


METHODS
Population-based cross-sectional study. The K-means clustering method was used to identify dietary patterns and logistic regression models were used to compare the adjusted prevalence rates of metabolic syndrome factors, stratifying by obesity status.


METHODS
The 1992-3 Italian Bollate Eye Study, a population-based survey carried out in the town of Bollate (Milan), Italy.


METHODS
A total of 1052 non-diabetic Italian subjects, 527 men and 525 women, aged 42-74 years.


RESULTS
Five dietary clusters were identified: common, animal products, starch, vegetal/fat and vitamin/fibre. After adjusting for potential confounders, the starch group showed the highest prevalence of metabolic syndrome (36%) followed by the animal products group (30%); the vitamin/fibre (20%) and vegetal/fat groups (19%) showed the lowest prevalence. The starch group had more dyslipidaemia (higher TAG and lower HDL cholesterol levels) and the animal products group had a higher prevalence of impaired fasting glucose. The vitamin/fibre group had the lowest prevalence of abdominal obesity. The beneficial effect of the vegetal/fat and vitamin/fibre dietary patterns seemed stronger among the obese.


CONCLUSIONS
Our results confirm the deleterious effect of a very-low-fat, high-carbohydrate diet and also of high intakes of animal products. The consumption of a diet high in vegetal fats or rich in fruits and vegetables is associated with a healthier metabolic profile. Reducing obesity is essential to prevent metabolic syndrome, but even among the obese dietary habits are important for preserving healthy lipid and glycaemic profiles.
Leite
Maria Léa Corrêa
ML
Department of Epidemiology and Medical Informatics, Institute of Biomedical Technologies, National Research Council, Via Fratelli Cervi 93, 20090 Segrate, Milan, Italy. lea.correa@itb.cnr.it
Nicolosi
Alfredo
A
D000328:Adult; D000368:Aged; D001786:Blood Glucose / Q000032:analysis; D016000:Cluster Analysis; D003430:Cross-Sectional Studies; D004032:Diet / Q000145:classification / Q000639:trends*; D018752:Diet, Fat-Restricted; D004040:Dietary Carbohydrates / Q000008:administration & dosage / Q000009:adverse effects / Q000378:metabolism; D050171:Dyslipidemias / Q000097:blood / Q000453:epidemiology / Q000209:etiology*; D005260:Female; D006801:Humans; D007558:Italy / Q000453:epidemiology; D016015:Logistic Models; D008297:Male; D008460:Meat; D024821:Metabolic Syndrome / Q000097:blood / Q000453:epidemiology / Q000209:etiology*; D008875:Middle Aged; D009765:Obesity / Q000097:blood / Q000150:complications* / Q000453:epidemiology; D015995:Prevalence; D012307:Risk Factors; D013213:Starch / Q000008:administration & dosage / Q000009:adverse effects / Q000378:metabolism; D014675:Vegetables
D001786:Blood Glucose; D004040:Dietary Carbohydrates; D013213:Starch