Macronutrients, fatty acids, cholesterol and renal cell cancer risk



The role of selected macronutrients, fatty acids and cholesterol in the etiology of renal cell carcinoma (RCC) was analyzed using data from a case–control study conducted in 4 Italian areas between 1992 and 2004. Cases were 767 patients with incident, histologically confirmed RCC, admitted to major teaching and general hospitals of the study areas. Controls were 1,534 subjects admitted for acute, nonneoplastic conditions to the same hospitals. Information on dietary habits and nutrient intake was elicited using a validated food frequency questionnaire including 78 food groups and recipes. Odds ratios (OR) and 95% confidence intervals (CI) for increasing levels of nutrient intake were estimated after allowance for total energy intake and other potential confounding factors. A direct association with RCC was found for starch intake (OR = 1.9 for highest versus lowest quintile of intake; 95% CI: 1.4–2.6, p-value for trend = 0.001), while an inverse association was found for fats from vegetable sources (OR = 0.6; 95% CI: 0.5–0.8; p-value for trend = 0.002), unsaturated fatty acids (OR = 0.5; 95% CI: 0.4–0.7; p-value for trend = 0.0002), and polyunsaturated fatty acids (OR = 0.5; 95% CI: 0.4–0.7; p-value for trend = 0.001). Among polyunsaturated fatty acids, linoleic acid (OR = 0.5; 95% CI: 0.4–0.7; p-value for trend = 0.0001) and linolenic acid (OR = 0.7; 95% CI: 0.5–1.0; p-value for trend = 0.01) were inversely related to RCC. When 6 major macronutrients were included in the same model, the adverse effect of high intake of starch remained statistically significant, together with the protective effect of polyunsaturated fatty acids. Results were consistent in strata of age, body mass index, treated hypertension, energy intake, stage and family history of RCC. © 2008 Wiley-Liss, Inc.

A possible link between renal cell carcinoma (RCC), the major histologic type of kidney cancer, and diet has been suggested based on international differences in mortality rates and ecological studies with national average intakes of fats and proteins1, 2 or major dietary patterns.3–5 However, the epidemiological evidence on the relation between RCC and nutrient intake remains limited and largely unclear. Most studies examined the association between RCC and total fat intake4 rather than specific fats, i.e., fats from animal or vegetable sources and saturated or unsaturated fats. Some studies showed an adverse effect of saturated fat intake,6–8 and a protective effect of polyunsaturated fats.9 An international collaborative case–control study showed a protective association for polyunsaturated fatty acids and no effect for saturated and monounsaturated fats.9 Another study conducted within the Swedish mammography cohort suggested that consumption of fatty fish, rich in omega-3 fatty acids, but not lean fish, may reduce RCC in women.10

Less is known about other macronutrients such as proteins, carbohydrates or dietary cholesterol and RCC risk.

Our article provides further insight on the relation between RCC and intakes of 6 macronutrients, including various types of fats, selected fatty acids, and cholesterol using data from a large case–control study conducted in Italy.

Material and methods

The data were derived from a case–control study on RCC conducted between 1992 and 2004 in 4 Italian areas, including the greater Milan area and the provinces of Pordenone and Gorizia in northern Italy, the province of Latina in central Italy, and the urban area of Naples in southern Italy.11, 12 Cases were 767 patients (494 men and 273 women) under the age of 80 years (median age 62 years, range 24–79 years) with incident, histologically confirmed RCC (ICD-9: 189.0), admitted to the major teaching and general hospitals of the study areas. Cancers of the renal pelvis and ureter (ICD-9: 189.1–189.2) were not included. Controls were 1,534 subjects (988 men and 546 women) under age 80 years (median age 62 years, range 22–79 years) admitted to the same hospitals as cases for a wide spectrum of acute nonneoplastic conditions, unrelated to known or potential risk factors for RCC, and to long-term dietary modifications. Controls were matched with cases by study center, sex, and quinquennia of age, with a case:control ratio of 1:2. Twenty-six percent of controls were admitted for traumas (mostly fractures and sprains), 32% for other orthopedic disorders (such as low back pain and disc disorders), 14% for surgical conditions, and 27% for various other illnesses including eye, nose, ear, skin or dental disorders. Less than 5% of both cases and controls contacted refused to participate.

Centrally trained and supervised interviewers identified and questioned patients in hospitals using a standard structured questionnaire. Interviewing nurses were introduced to patients by attending clinical staff. The data were centrally checked for consistency.

The questionnaire included information on age, education and other socio-demographic characteristics, anthropometric measures, lifestyle habits such as tobacco smoking and alcohol drinking and history of selected diseases. A food frequency questionnaire (FFQ) was used to assess the usual diet during the 2 years before diagnosis or hospital admission for the controls. This FFQ included 78 foods, food groups or recipes divided into 7 sections: (i) bread, cereals and first courses; (ii) second courses (e.g., meat and other main dishes); (iii) side dishes (i.e., vegetables); (iv) fruits; (v) sweets, desserts and soft drinks; (vi) milk, hot beverages and sweeteners; (vii) alcoholic beverages. At the end of each section, 1 or 2 open questions were used to report foods not included in the questionnaire but eaten at least once a week. For 40 food items, the serving size was defined in “natural” units (e.g., 1 teaspoon of sugar, 1 egg), while for the remaining ones it was defined as small, average, or large with the help of pictures. Macronutrients, fatty acids, and cholesterol were computed using an Italian food composition database.13 The reproducibility and validity of the FFQ were satisfactory. With reference to reproducibility, the correlation coefficients were 0.6 for proteins, 0.7 for sugars, 0.6 for starch and between 0.5 and 0.6 for fatty acids and cholesterol14; corresponding values for validity were 0.6, 0.6, 0.7 and between 0.3 and 0.6.15

Statistical analysis

Odds ratios (OR), and their corresponding 95% confidence intervals (CI), for increasing levels of nutrient intake compared to the lowest one, were computed using multiple logistic regression models conditioned on study center, sex, and 5-year age groups.16 The models included terms for period of interview, years of education, tobacco smoking, alcohol drinking, body mass index (BMI), family history of kidney cancer in first-degree relatives, treated hypertension, and total energy intake. Adjustment for energy was made firstly using the residual model,17 and for comparative purposes, by means of a fully partitioned model, to allow for the mutual confounding effect of major macronutrients.18 When nutrients were entered in the model as quintiles of intake in the residual model, these were based on the distribution of cases and controls combined. The OR for an increase of 1 standard deviation among controls was also estimated, including each nutrient in the model as a continuous variable.


Table I shows the distribution of RCC cases and control subjects according to age, sex, center, education, and other selected variables. By design, cases and controls had similar age, sex, and center distribution. Cases tended to be more educated than controls, were more frequently heavy smokers, had a higher BMI, and reported more frequently a treated hypertension and a family history of kidney cancer.

Table I. Distribution of 767 Cases of Renal Cell Carcinoma and 1,534 Frequency Matched1 Controls and Crude Odds Ratios (OR) and Corresponding 95% Confidence Interval (95% CI), According to Study Center, Sex, Age and Selected Covariates. Italy, 1992–20042
 Cases (%)Controls (%)OR (95% CI)
  • 1

    Matched by study center, sex and age (controls having ± 2 years of cases).

  • 2

    The sum may not add up to the total because of some missing values.

  • 3

    Ex-smokers were subjects who had stopped smoking for at least 4 years.

  • 4

    First-degree relatives.

Age (years)
 <50123 (16.0)246 (16.0)
 50–59200 (26.1)407 (26.5)
 60–69281 (36.6)555 (36.2)
 70–79163 (21.3)326 (21.3)
 Range (median)24–79 (62)22–79 (62) 
 Male494 (64.4)988 (64.4)
 Female273 (35.6)546 (35.6)
Study center
 Pordenone/Gorizia346 (45.1)692 (45.1)
 Milano186 (24.3)372 (24.3)
 Napoli155 (20.2)310 (20.2)
 Latina80 (10.4)160 (10.4)
Education (years)
 <7372 (48.5)849 (55.3)1
 7–11212 (27.6)457 (29.8)1.06 (0.86–1.30)
 ≥12183 (23.9)228 (14.9)1.83 (1.46–2.30)
p-value < 0.001
Smoking habit
 Never smoker314 (41.1)640 (41.7)1
 Current smoker
  1–19 cigarettes/day109 (14.3)277 (18.1)0.80 (0.62–1.04)
  ≥20 cigarettes/day126 (16.5)189 (12.3)1.36 (1.04–1.77)
 Ex-smoker3215 (28.1)428 (27.9)1.03 (0.83–1.27)
p-value = 0.01
Alcohol consumption
 Never drinker131 (17.1)231 (15.1)1
 Ex-drinker63 (8.2)114 (7.4)0.97 (0.67–1.42)
 Current drinker   
  <21 drinks/week361 (47.1)720 (46.9)0.88 (0.69–1.13)
  ≥21 drinks/week212 (27.6)469 (30.6)0.80 (0.61–1.04)
p-value = 0.36
Body mass index (kg/m2)
 <25281 (36.8)561 (36.7)1
 25 to <30347 (45.4)750 (49.1)0.92 (0.76–1.12)
 ≥30136 (17.8)218 (14.2)1.25 (0.96–1.61)
p-value = 0.06
Treated hypertension
 No486 (63.4)1152 (75.1)1
 Yes281 (36.6)382 (24.9)1.74 (1.45–2.10)
p-value < 0.001
Family history of kidney cancer4
 No749 (97.7)1526 (99.5)1
 Yes18 (2.3)8 (0.5)4.58 (1.18–10.59)
p-value < 0.001
Family history of any cancer4
 No446 (58.1)1014 (66.1)1
 Yes321 (41.9)520 (33.9)1.40 (1.17–1.68)
p-value < 0.001   

Table II gives the mean daily intake among controls of 6 macronutrients, fatty acids and cholesterol, and the ORs of RCC according to quintile of intake. Proteins, sugars, fat from animal sources and saturated fatty acids were unrelated to RCC. High starch intake was significantly related to an increased risk of RCC cancer (OR = 1.9 for the highest versus the lowest quintile of intake; 95% CI: 1.4–2.6) with a significant linear trend in risk (p-value for trend = 0.001), while an inverse relation was found for fats from vegetable sources (OR = 0.6, 95% CI: 0.5–0.8; p-value for trend = 0.002), unsaturated fatty acids (OR = 0.5, 95% CI: 0.4–0.7; p-value for trend = 0.0002) and polyunsaturated fatty acids (OR = 0.5, 95% CI: 0.4–0.7; p-value for trend = 0.001). Among specific fatty acids, linoleic (OR = 0.5; 95% CI: 0.4–0.7; p-value for trend = 0.0001) and linolenic (OR = 0.7; 95% CI: 0.5–1.0; p-value for trend = 0.01) acids were inversely associated to RCC cancer risk. Other polyunsaturated fatty acids, whose intake was low, showed an inconsistent inverse relation (OR = 0.8, 95% CI: 0.6–1.1; p-value for trend = 0.04). Proteins (OR = 0.9, 95% CI: 0.7–1.2), saturated fatty acids (OR = 1.0, 95% CI: 0.7–1.3), monounsaturated fatty acids (OR = 0.7, 95% CI: 0.5–1.0) and cholesterol (OR = 0.8, 95% CI: 0.6–1.0) were unrelated to RCC cancer risk.

Table II. Odds Ratios (OR)1 of Renal Cell Carcinoma and Corresponding 95% Confidence Intervals (CI) According to Intake of Macronutrients, Selected Fatty Acids and Cholesterol. Italy, 1992–2004
NutrientMean2Standard deviationQuintile, OR (95% CI)3p-value of χ2 for trend
2345 (highest)
  • 1

    Estimates from conditional logistic regression, conditioned on center, sex and age, and adjusted for period of interview, education, tobacco smoking, alcohol drinking, treated hypertension, body mass index, family history of kidney cancer, and total energy intake, according to the residual model.

  • 2

    Among controls, per day.

  • 3

    First quintile: reference category.

Macronutrients (g)       
 Proteins91.025.71.0 (0.8–1.3)1.1 (0.8–1.5)0.9 (07–1.2)0.9 (0.7–1.2)0.34
 Sugars100.243.81.1 (0.9–1.5)1.2 (0.9–1.6)1.3 (1.0–1.7)1.1 (0.8–1.4)0.51
 Starch189.776.21.5 (1.1–2.0)1.6 (1.2–2.2)1.7 (1.2–2.3)1.9 (1.4–2.6)0.001
 Total fat84.934.10.7 (0.5–1.0)0.8 (0.6–1.0)0.9 (0.6–1.1)0.6 (0.4–0.8)0.01
 Fat from animal sources39.416.71.1 (0.8–1.4)1.0 (0.8–1.4)1.0 (0.8–1.4)0.9 (0.7–1.3)0.60
 Fat from vegetable sources45.523.90.9 (0.7–1.2)0.7 (0.5–0.9)0.7 (0.5–0.9)0.6 (0.5–0.8)0.002
 Saturated fatty acids26.510.61.2 (0.9–1.5)1.0 (0.8–1.4)1.1 (0.8–1.5)1.0 (0.7–1.3)0.66
 Unsaturated fatty acids53.423.40.7 (0.5–1.0)0.8 (0.6–1.0)0.7 (0.5–0.9)0.5 (0.4–0.7)0.0002
 Monounsaturated fatty acids39.718.10.9 (0.7–1.2)1.0 (0.7–1.3)1.0 (0.7–1.3)0.7 (0.5–1.0)0.09
 Polyunsaturated fatty acids13.99.10.7 (0.5–0.9)0.7 (0.5–0.9)0.5 (0.4–0.7)0.5 (0.4–0.7)0.001
Fatty acids and cholesterol       
 Oleic acid (g)37.417.50.9 (0.7–1.2)1.0 (0.7–1.3)0.9 (0.7–1.2)0.7 (0.5–1.0)0.07
 Linoleic acid (g) (0.6–1.0)0.7 (0.5–0.9)0.6 (0.4–0.8)0.5 (0.4–0.7)0.0001
 Linolenic acid (g) (0.7–1.3)0.8 (0.6–1.1)0.8 (0.6–1.1)0.7 (0.5–1.0)0.01
 Other polyunsaturated fatty acids (g) (0.8–1.4)0.8 (0.6–1.0)0.8 (0.6–1.0)0.8 (0.6–1.1)0.04
 Cholesterol (mg)291.5113.81.0 (0.7–1.3)1.1 (0.8–1.5)1.0 (0.8–1.4)0.8 (0.6–1.0)0.17

The relation between various nutrients and RCC cancer risk was further examined in separate strata of age (<60 and ≥60 years), BMI (<25 and ≥25 kg/m2), smoking habits (never/ever), treated hypertension (no/yes), energy intake (<2,300 and ≥2,300 kcal) and stage of disease (1–2 and 3–4) (data not shown). Although some differences in the estimated risks were observed across strata, these were compatible with the effect of random variation, since heterogeneity tests were not significant. When the 6 major macronutrients were also included in a fully partitioned model the ORs, per 100 kcal per day, were statistically significant for starch (OR = 1.10; 95% CI: 1.05–1.15) and polyunsaturated fatty acids (OR = 0.66; 95% CI: 0.54–0.79).


Our study, one of the largest case–control investigation of diet and RCC to date, showed that starch intake is directly associated with RCC risk in this southern European population. Moreover, our study further supports the existence of a different role of specific types of fats, rather than total fat itself, on RCC. Vegetable fats and in particular polyunsaturated fatty acids seemed to exert a favorable effect. We did not confirm a direct association with proteins.7, 19 Intakes of sugars, saturated fats, and dietary cholesterol were unrelated to risk.

A direct association between carbohydrates and RCC has been observed for women only in a Danish study.20 The Italian population has one of the highest intake of starches among western countries,21 and the main sources of starch, associated also with RCC risk in our dataset,12 were white bread and pasta.22 The mechanism of action of starch intake may be related to a reduced intake of beneficial micronutrients inversely related to RCC risk,12 or to a glycemic overload23 as in other common cancers.24 The effect of fat intake on RCC is still unclear.4, 8 Vegetable fats have been associated to a decreased RCC risk in some studies9 but not in others.25, 26 Unsaturated and polyunsaturated fatty acids, in particular linoleic and linolenic fatty acids, largely derive in Italian population from olive oil.22, 27 Previous studies found a protective effect of polyunsaturated fats9, 10 linked to inflammatory inhibition, influence on gene expression, or alteration of free radicals production.28 However, it is possible that specific fats are simply indirect markers of a diet rich in olive oil and possibly in vegetables rather than having an active role on RCC. Our study does not support a detrimental effect of proteins on RCC, consistently with other studies,25, 26 while a recent meta-analysis showed that various types of meat are associated with an increased risk of RCC.29 This apparent inconsistency may be explained by a selective reduction of protein-rich diets in subjects with chronic renal conditions that may predispose individuals to RCC,30 and/or to a relatively limited intake of proteins in this Italian population as compared to other populations mainly from the USA and northern Europe.

Potential recall and selection biases are possible, as in most case–control studies. Awareness about any particular dietary hypothesis in RCC etiology was still limited in the Italian population. With reference to selection bias, the catchment areas were comparable for cases and controls. Great attention was paid to exclude all diagnoses that might have been associated with or have determined long-term modifications of diet in controls. The comparability of recall between cases and controls was improved by interviewing all subjects in a hospital setting. Moreover, adjustment for total energy intake should have reduced potential bias due to differential over- or under-reporting of food intakes. Our findings are strengthened by the large dataset used, the nearly complete participation of identified cases and controls, the use of a validated FFQ,14, 15 the assessment of a broad range of nutrients and the geographically heterogeneous dietary habits over Italy, which increases study power to detect any significant or meaningful associations. A large proportion of cases were detected because of clinical symptoms, thus reducing the possibility of detection bias. Allowance was made for various known potential confounding factors, and the ORs were consistent when major categories of controls were separately used. For instance, OR in the highest versus the lowest quintile of starch intake was 1.9, 1.9 and 2.0 considering controls from surgery, orthopedic, and other wards, respectively. Additional allowance for family history of any cancer type did not meaningfully change the results.

In conclusion, the findings of our study add to the evidence that, in the Italian population, intakes of some macronutrients are related to the risk of RCC. This underlines the potential importance of diet and consequently of possible dietary changes to decrease the risk of this cancer.


The contributions of the Italian Association for Research on Cancer and the Italian League Against Cancer are gratefully acknowledged. The work of this study was undertaken while C.L.V. was a Senior Fellow at the International Agency for Research on Cancer. The authors thank Mrs O. Volpato for study coordination, and Mrs. L. Mei and Mrs. I. Calderan for editorial assistance.