赤身肉の摂取増加で糖尿病リスク1.5倍

red meatとはハムやソーセージのような加工肉のことを指しているのでは無く、「哺乳類の肉」全てを指しています。決して「脂身の少ない赤身の肉」のことではありません。

魚、両生類、爬虫類、鶏肉はRed meatではありませんが、鯨はRed meatです。

米ハーバード公衆衛生大学院のAn Pan氏らは，米国の大規模コホート研究Health Professionals Follow-up Study（HPFS），Nurses' Health Study（NHS），NHSⅡの参加者約15万人を調べたところ，赤身肉の摂取が4年間に1日0.5サービング超増加していた場合，摂取量が変化しなかった場合と比べて，その後4年間の糖尿病発症リスクが約1.5倍になっていたことをJAMA Intern Med 2013年6月17日オンライン版に報告した。同氏らは「長期にわたる赤身肉の摂取制限が糖尿病予防に有効であるというエビデンスが追加された」としている。日本からも同様の研究が発表されたばかりで，赤身肉が糖尿病の新しい危険因子としてがぜん注目される。

4年間の赤身肉摂取の変化とその後4年間の2型糖尿病発症の関連を調査

　赤身肉摂取が2型糖尿病リスクに関わることは一貫して報告されており，日本人においても最近，赤身肉の摂取量が多い男性は糖尿病リスクが約1.5倍高いという大規模研究の結果が示されている（Br J Nutr2013年5月7日オンライン版）。

　米国の医療従事者を対象とした3つの大規模コホート研究， HPFS（1986～2006年）， NHS（1986～2006年），NHSⅡ（1991～2007年）では，4年置きに食事摂取頻度質問票，2年置き主要調査票，症状に関する追加質問票を実施。Pan氏らは，赤肉摂取量の変化が2型糖尿病のリスクに関連するのかどうかという点に着目し，それぞれに参加した男性2万6,357人，女性4万8,709人，女性7万4,077人について，各4年間の赤身肉摂取の変化と，その後4年間の2型糖尿病発症の関連を調べた。

　赤身肉摂取量を時間依存型共変量とするCox比例ハザード回帰分析により糖尿病発症のハザード比（HR）を求めた。その際，年齢，糖尿病家族歴，人種，婚姻状況，初期の赤身肉摂取，喫煙，高血圧，高コレステロール血症，生活習慣（身体活動，飲酒，総エネルギー摂取，食事の質）の変化などの潜在的交絡因子を共変量として調整した。

赤身肉の制限は長期的なリスクを低下させる

　196万5,824人・年の追跡中，7,540人が2型糖尿病を発症した。多変量調整モデルを用いた分析で，4年間，赤身肉の摂取レベルがほとんど変わらなかった場合と比べ，0.15～0.50サービング増加，0.50サービング超増加と，増加するに伴い，それぞれのコホート，および3つのコホート全体で糖尿病リスクは有意に上昇していた（傾向性のP値は全て＜0.001）。

　赤身肉の摂取レベルが変わらなかった場合と比べると，1日0.50超増加した場合のHRは1.48（95％CI 1.37～1.59）で，大幅に上昇していた。当初のBMIおよび4年間の体重増加を調整すると，HRは1.30（同1.21～1.41）となり，関連は減弱した。

　一方，赤身肉の減少と2型糖尿病リスクの間には有意な関連は認められなかった。ただし，ベースラインから4年間の赤身肉の減少が全追跡期間（HPFSとNHSは16年，NHSⅡは12年）の糖尿病リスクに関連するかどうかを調べたところ，0.50サービング超の減少でHRは0.86（同0.80～0.93）と低下しており，長期的な糖尿病リスクの減少につながることが示唆された。

低→高レベルの摂取でリスク2倍に

　さらに，週当たりの赤身肉摂取量を2サービング未満（低レベル），2～6サービング（中レベル），7サービング以上（高レベル）に分け，初期と4年後の摂取カテゴリーが低→低レベルの場合と比較したところ，低→高レベルではHR 1.99（95％CI 1.53～2.58）で，糖尿病リスクは2倍に高まった（図）。その他のHRは初期に中レベルだった場合，→高レベル1.87，→中レベル1.37，→低レベル1.19であった。初期に高レベルだった場合は，→高レベル2.10，→中レベルで1.69，→低レベルで1.78であった。赤身肉摂取変化の影響は，BMIおよび同期間の体重変化を調整後減弱した。

　糖尿病リスクに関して，初期のBMIと赤身肉摂取の変化の間には，有意な交互作用が認められた。赤身肉の摂取が変わらなかった場合と比べて，0.50サービング超増加した場合のHRは非肥満で1.65（95％CI 1.48～1.84）だったが，肥満では1.14（同1.02～1.27）であった。

　以上の結果から，赤身肉の摂取増加は糖尿病リスク上昇と関連し，その影響は部分的にBMIや体重の変化によって媒介されていることが示唆された。肥満者では関連が弱かったが，Pan氏らは，肥満者は初期において赤身肉の摂取が多く，糖尿病のリスクが既に上昇していた可能性があると指摘。赤身肉摂取に関連する糖尿病の絶対リスクは，非肥満より肥満の方でさらに大きく，肥満者においても赤身肉を制限することは有益であると考察している。

Changes in Red Meat Consumption and Subsequent Risk of Type 2 Diabetes MellitusThree Cohorts of US Men and Women

An Pan, PhD1,2; Qi Sun, MD, ScD1,3; Adam M. Bernstein, MD, ScD1,4; JoAnn E. Manson, MD, DrPH5,6; Walter C. Willett, MD, DrPH1,3,5; Frank B. Hu, MD, PhD1,3,5

JAMA Intern Med. 2013;173(14):1328-1335. doi:10.1001/jamainternmed.2013.6633.

July 22, 2013, Vol 173, No. 14

ABSTRACT

Importance

Red meat consumption has been consistently associated with an increased risk of type 2 diabetes mellitus (T2DM). However, whether changes in red meat intake are related to subsequent T2DM risk remains unknown.

Objective

To evaluate the association between changes in red meat consumption during a 4-year period and subsequent 4-year risk of T2DM in US adults.

Design and Setting

Three prospective cohort studies in US men and women.

Participants

We followed up 26 357 men in the Health Professionals Follow-up Study (1986-2006), 48 709 women in the Nurses’ Health Study (1986-2006), and 74 077 women in the Nurses’ Health Study II (1991-2007). Diet was assessed by validated food frequency questionnaires and updated every 4 years. Time-dependent Cox proportional hazards regression models were used to calculate hazard ratios with adjustment for age, family history, race, marital status, initial red meat consumption, smoking status, and initial and changes in other lifestyle factors (physical activity, alcohol intake, total energy intake, and diet quality). Results across cohorts were pooled by an inverse variance–weighted, fixed-effect meta-analysis.

Main Outcomes and Measures

Incident T2DM cases validated by supplementary questionnaires.

Results

During 1 965 824 person-years of follow-up, we documented 7540 incident T2DM cases. In the multivariate-adjusted models, increasing red meat intake during a 4-year interval was associated with an elevated risk of T2DM during the subsequent 4 years in each cohort (all P < .001 for trend). Compared with the reference group of no change in red meat intake, increasing red meat intake of more than 0.50 servings per day was associated with a 48% (pooled hazard ratio, 1.48; 95% CI, 1.37-1.59) elevated risk in the subsequent 4-year period, and the association was modestly attenuated after further adjustment for initial body mass index and concurrent weight gain (1.30; 95% CI, 1.21-1.41). Reducing red meat consumption by more than 0.50 servings per day from baseline to the first 4 years of follow-up was associated with a 14% (pooled hazard ratio, 0.86; 95% CI, 0.80-0.93) lower risk during the subsequent entire follow-up through 2006 or 2007.

Conclusions and Relevance

Increasing red meat consumption over time is associated with an elevated subsequent risk of T2DM, and the association is partly mediated by body weight. Our results add further evidence that limiting red meat consumption over time confers benefits for T2DM prevention.

Red meat consumption has been consistently related to an elevated risk of type 2 diabetes mellitus (T2DM). For example, 3 recent meta-analyses1- 3 of prospective cohort studies reported positive associations. However, most previous studies measured red meat consumption only at baseline with limited follow-up information. In real life, a person’s eating behavior changes over time, and secular trends in red meat intake are also changing dramatically across the globe.4 Because a measurement at a single time point does not capture the variability of red meat intake during follow-up, it is important to evaluate whether changes in red meat intake over time alter the risk of developing T2DM. Therefore, we analyzed data from 3 Harvard cohort studies: the Health Professionals Follow-up Study (HPFS), the Nurses’ Health Study (NHS), and the Nurses’ Health Study II (NHS II), in which we collected repeated measurements of red meat intake every 4 years, as well as other dietary components, lifestyle factors, and medical history with up to 20 years of follow-up. These repeated measures and long duration of follow-up allow us to investigate the association between dynamic changes in red meat intake and subsequent risk of T2DM. We conducted 2 sets of change analysis. In the first analysis, we examined 4-year change in red meat intake in relation to T2DM incidence in the next 4 years of follow-up. In the second analysis, to examine long-term effects of meat intake on T2DM, we analyzed changes in red meat intake from baseline to the first 4-year follow-up with T2DM incidence in the subsequent 12 (NHS II) and 16 (NHS and HPFS) years of follow-up.

METHODS

Study Population

The HPFS was initiated in 1986 when 51 529 US male health professionals, aged 40 to 75 years, returned a baseline questionnaire about detailed medical history, as well as lifestyle and usual diet. The NHS consists of 121 700 registered female nurses, aged 30 to 55 years, who completed a baseline questionnaire about lifestyle and medical history in 1976. The NHS II, established in 1989, comprises 116 671 younger female registered nurses, aged 25 to 42 years, who responded to a baseline questionnaire similar to that of the NHS. Detailed descriptions of the cohorts have been introduced elsewhere.3,5 In all cohorts, questionnaires were administered at baseline and biennially thereafter to collect and update information on lifestyle practices (eg, smoking and physical activity) and the occurrence of new-onset diseases. The cumulative follow-up of the 3 cohorts exceeds 90% of potential person-times.

In the current analysis, we used 1986 for the HPFS and NHS and 1991 for the NHS II as the baseline when we assessed detailed information on diet and lifestyle factors. Because we used the changes in red meat consumption every 4 years as the exposure to predict the subsequent 4-year T2DM risk, we excluded men and women who had a history of diabetes mellitus (including type 1 diabetes mellitus, T2DM, and gestational diabetes), cardiovascular disease, or cancer 4 years after baseline (ie, 1990 for the HPFS and NHS and 1995 for the NHS II). In addition, we excluded participants who left more than 10 blank food items on the baseline food frequency questionnaire (FFQ), reported unusual total energy intake levels (ie, <800 or >4200 kcal/d for men and <500 or >3500 kcal/d for women), or did not report meat consumption. After exclusions, data from 26 357 HPFS men, 48 709 NHS women, and 74 077 NHS II women were available. Participants who were excluded because of missing baseline FFQ data were similar in age and body mass index (BMI) compared with those included in the analysis (data not shown). The study protocol was approved by the institutional review boards of Brigham and Women’s Hospital and the Harvard School of Public Health.

Assessment of Meat Consumption

Dietary information, collected by a validated FFQ in 1986 for the HPFS and NHS and in 1991 for the NHS II, was updated every 4 years with similar FFQs. In all FFQs, we asked participants how often, on average, they consumed each food of a standard portion size. Frequency responses ranged from never or less than once per month to 6 or more times per day. Questionnaire items on unprocessed red meat (85 g or 3 oz) included beef, pork, or lamb as main dish; hamburger; and beef, pork, or lamb as a sandwich or mixed dish. Items on processed red meat included bacon (2 slices, 13 g), hot dogs (1 hot dog, 45 g), and sausage, salami, bologna, and other processed red meats (1 piece, 28 g). The reproducibility and validity of FFQs have been demonstrated in detail elsewhere.6- 8Correlation coefficients between FFQs and multiple diet records ranged from 0.38 to 0.70 for various red meat items.7

Assessment of Covariates

In the follow-up questionnaires, we obtained updated information on risk factors for T2DM, such as body weight, cigarette smoking, physical activity, and a history of hypertension and hypercholesterolemia. We also ascertained menopausal status and postmenopausal hormone use in women. Alcohol intake was asked on the FFQ and updated every 4 years. We also collected information on a family history of T2DM, race, and marital status. To assess overall diet quality, we calculated a diet score based on the 2010 Alternative Healthy Eating Index,9 which was designed to reflect food choices and nutrients associated with reduced noncommunicable disease risk. For the current analysis, we constructed the Alternative Healthy Eating Index score without the meat and alcohol components because they were included separately in the models.

Assessment of T2DM

Incident T2DM cases were identified by self-report on the main questionnaires every 2 years and confirmed by a validated supplementary questionnaire regarding symptoms, diagnostic tests, and treatment. The diagnosis was confirmed if at least 1 of the following was reported according to the National Diabetes Data Group10 criteria: (1) 1 or more classic symptoms (excessive thirst, polyuria, weight loss, or hunger) plus fasting glucose levels 140 mg/dL or higher or random glucose levels 200 mg/dL or higher (to convert to millimoles per liter, multiply by 0.0555), (2) at least 2 elevated glucose concentrations on different occasions (fasting glucose levels ≥140 mg/dL, random glucose levels ≥200 mg/dL, and/or concentrations ≥200 mg/dL after 2 hours or more by oral glucose tolerance testing) in the absence of symptoms, or (3) treatment with hypoglycemic medication (insulin or oral hypoglycemic agent). For cases diagnosed in 1998 and later, the fasting glucose threshold was lowered to 126 mg/dL according to the American Diabetes Association11 criteria.

The validity of the supplementary questionnaire for the diagnosis of T2DM has been documented previously: of 59 cases in the HPFS and 62 cases in the NHS confirmed by supplementary questionnaires, 57 (97%) and 61 (98%) cases, respectively, were reconfirmed by medical records.12,13 In another substudy to assess the prevalence of undiagnosed T2DM cases in the NHS, only 1 of 200 randomly selected women had elevated fasting glucose or fructosamine levels barely above the diagnostic cutoffs.14 We excluded false-positive cases and included only incident cases confirmed by the supplemental questionnaires.

Statistical Analysis

We calculated each individual’s person-years from the date of returning the baseline questionnaire to the date of T2DM diagnosis, death, or the end of the follow-up (January 31, 2006, for the HPFS; June 30, 2006, for the NHS; and June 30, 2007, for the NHS II), whichever came first. We used change in red meat consumption updated every 4 years as a time-varying exposure, and time-dependent Cox proportional hazards regression was used to estimate the hazard ratio (HR) for T2DM risk in the subsequent 4 years. For example, we used changes in red meat consumption between the 1986 and 1990 questionnaires to predict T2DM risk from 1990 through 1994, changes between the 1990 and 1994 questionnaires to predict T2DM risk from 1994 through 1998, and so forth. In the multivariate analysis, in addition to age and calendar time, we simultaneously controlled for various potential confounding factors, including race (white or nonwhite), family history of T2DM (yes or no), marital status (with spouse, yes or no; updated every 4 years), history of hypertension and hypercholesterolemia (yes or no; updated every 4 years), and simultaneous changes in other lifestyle factors: smoking status (never to never, never to current, past to past, past to current, current to past, current to current, or missing indicator), as well as initial and changes (all in quintiles) in alcohol intake, physical activity, total energy intake, and diet quality (Alternative Healthy Eating Index score). In the NHS and NHS II, we also adjusted for postmenopausal status and menopausal hormone use. It has been reported that increasing red meat consumption was related to weight gain in the 3 cohorts5; therefore, body weight and weight gain could be mediators. We adjusted for initial BMI (calculated as weight in kilograms divided by height in meters squared) (<23, 23-24.9, 25-29.9, 30-34.9, and ≥35) and changes in body weight (quintiles) in each 4-year period as time-varying covariates in an additional model. We also analyzed processed and unprocessed red meat separately.

In the second analysis, to examine long-term effects of red meat intake on T2DM, we analyzed changes in intake from baseline to the first 4-year follow-up and T2DM incidence in the subsequent follow-up years. Specifically, we used changes in red meat consumption between 1986 and 1990 to predict T2DM risk from 1990 through 2006 for the NHS and HPFS, as well as between 1991 and 1995 to predict T2DM risk from 1995 through 2007 for the NHS II.

To minimize missing values during follow-up, we replaced them with carried-forward values for continuous variables and added a missing indicator for categorical variables. Stratified analyses were performed a priori by initial BMI categories (<30.0 and ≥30.0), and the interaction was tested by including cross-product terms in the models. An inverse variance–weighted, fixed-effect meta-analysis was used to combine the results across cohorts because no significant heterogeneity was found.

We conducted a series of sensitivity analyses to test the robustness of our results: we stopped updating the dietary information after self-report of incident cardiovascular disease or cancer during the follow-up, censored participants when they did not answer FFQs during the follow-up, and used a multiple imputation procedure with 20 rounds of imputation and included all covariates to account for missing dietary and covariate data. All analyses were performed using SAS software, version 9.2 (SAS Institute), at a 2-tailed P value of .05.

RESULTS

We documented 7540 incident T2DM cases during the follow-up (1561 in the HPFS, 3482 in the NHS, and 2497 in the NHS II). Table 1　 describes the distribution of baseline characteristics according to change in total red meat consumption. Compared with people with relatively stable intake, individuals who decreased or increased their intake were generally younger, had higher BMI levels, had a lower diet quality score, and were more likely to be smokers. Those who decreased intake were also more likely to report a diagnosis of hypertension or hypercholesterolemia. As expected, increasing red meat intake was related to concurrent weight gain, increases in total energy intake, and decreases in diet quality scores, while the associations with decreasing red meat intake were in the opposite direction.