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回顧反方觀點：對低碳水化合物飲食增大死亡風險研究評論

09-22
回顧反方觀點：對低碳水化合物飲食增大死亡風險研究評論

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KellyWeaver 寫過一篇科普文章〈哈佛研究：低碳水化合物飲食增大死亡風險〉，本文主要摘錄其他相關領域專家，對發表在《Lancet Public Health》 「Dietary carbohydrate intake and mortality: a prospective cohort study and meta-analysis」 研究相關的質疑與評論(文章觀點可能有爭議性)。

聲明：文章主要摘錄整理反方觀點，不做相關評論與判斷。個案或相關研究疑問，請找流行病學、公共衛生或相關專業領域醫師諮詢。

反方質疑主要論點

  1. FFQs (Food Frequency Questionnaires) 有效性和基於記憶方法不可靠
  2. 5組 Carbohydrate ranges 分類以相對大小樣本進行比較
  3. 其他混雜因素 (飲食建議更動、酒精使用)

先看看 FFQs (Food Frequency Questionnaires) 是什麼樣子

一、FFQs (Food Frequency Questionnaires) 有效性和基於記憶方法不可靠

人們能正確的回想和評估吃了什麼嗎

How is anyone supposed to recall what was eaten as many as 12 months prior? Most people can』t remember what they ate three days ago. Note that 「I don』t know」 or 「I can』t remember」 or 「I gave up dairy in August」 are not options; you are forced to enter a specific value. Some questions even require that you do math to convert the number of servings of fruit you consumed seasonally into an annual average—absurd. These inaccurate guesses become the 「data」 that form the foundation of the entire study. Foods are not weighed, measured, or recorded in any way. [1]

KellyWeaver針對「受訪者的記憶不可靠」這個疑問回答，以研究作者之一於1985年針對 Reproducibility and validity 發表研究作為響應：

WILLETT, W. et al. REPRODUCIBILITY AND VALIDITY OF A SEMIQUANTITATIVE FOOD FREQUENCY QUESTIONNAIRE. American Journal of Epidemiology 122, 51-65 (1985).

Nutritional Epidemiologic 研究遭遇的挑戰

John P. A. Ioannidis 於JAMA 發表一篇 「The Challenge of Reforming Nutritional Epidemiologic Research」 他認為：

Assuming the meta-analyzed evidence from cohort studies represents life span–long causal associations, for a baseline life expectancy of 80 years, nonexperts presented with only relative risks may falsely infer that eating 12 hazelnuts daily (1 oz) would prolong life by 12 years (ie, 1 year per hazelnut),1 drinking 3 cups of coffee daily would achieve a similar gain of 12 extra years,2 and eating a single mandarin orange daily (80 g) would add 5 years of life.1 Conversely, consuming 1 egg daily would reduce life expectancy by 6 years, and eating 2 slices of bacon (30 g) daily would shorten life by a decade, an effect worse than smoking.1 Could these results possibly be true? Absolute differences are actually smaller, eg, a 15% relative risk reduction in mortality with 12 hazelnuts would correspond to 1.7 years longer life, but are still implausibly large. Authors often use causal language when reporting the findings from these studies (eg, 「optimal consumption of risk-decreasing foods results in a 56% reduction of all-cause mortality」).1 Burden-of-disease studies and guidelines endorse these estimates. Even when authors add caveats, results are still often presented by the media as causal.

These implausible estimates of benefits or risks associated with diet probably reflect almost exclusively the magnitude of the cumulative biases in this type of research, with extensive residual confounding and selective reporting.3 Almost all nutritional variables are correlated with one another; thus, if one variable is causally related to health outcomes, many other variables will also yield significant associations in large enough data sets. With more research involving big data, almost all nutritional variables will be associated with almost all outcomes. Moreover, given the complicated associations of eating behaviors and patterns with many time-varying social and behavioral factors that also affect health, no currently available cohort includes sufficient information to address confounding in nutritional associations.

Proponents of the status quo may maintain that the true associations are even larger than what are reported because of attenuation from nondifferential misclassification. Indeed, self-reported data have error,6 but there is no guarantee it is nondifferential. Nevertheless, if error is nondifferential and estimated effects are attenuated, reported results become even more implausible: eating 12 hazelnuts daily would increase life expectancy by 3 to 4 years, not just 1.7 years.

Individuals consume thousands of chemicals in millions of possible daily combinations. For instance, there are more than 250?000 different foods and even more potentially edible items, with 300?000 edible plants alone. Seemingly similar foods vary in exact chemical signatures (eg, more than 500 different polyphenols). Much of the literature silently assumes disease risk is modulated by the most abundant substances; for example, carbohydrates or fats. However, relatively uncommon chemicals within food, circumstantial contaminants, serendipitous toxicants, or components that appear only under specific conditions or food preparation methods (eg, red meat cooking) may be influential. Risk-conferring nutritional combinations may vary by an individual』s genetic background, metabolic profile, age, or environmental exposures. Disentangling the potential influence on health outcomes of a single dietary component from these other variables is challenging, if not impossible.

問卷和分析方法局限性

Georgia Ede 以上述 John P. A. Ioannidis 觀點，總結研究局限性

The entire FFQ used contained only 66 questions, yet the typical modern diet contains thousands of individual ingredients. It would be nearly impossible to design a questionnaire capable of capturing that kind of complexity, and even more difficult to mathematically analyze the risks and benefits of each ingredient in any meaningful way. [1]

缺失數據 (missing data) 問題

」 Dietary carbohydrate intake and mortality: a prospective cohort study and meta-analysis」 研究中，只有實施兩次 FFQ：Visit 1 (1987–89) and Visit 3 (1993–95)，意即研究假設人們的飲食習慣在 FFQ 調查完後的數十年之間 (1996-2017) 沒有變動。

Missing Data.

Between 1987 and 2017, researchers met with subjects enrolled in the study a total of six times, yet the FFQ was administered only twice: at the first visit in the late 1980s and at the third visit in the mid-1990s. Yes, you read that correctly. Did the researchers assume that everyone in the study continued eating exactly the same way from the mid-1990s to 2017? Popular new products and trends surely affected how some of them ate (Splenda, kale chips, or cupcakes, anyone?) and drank (think Frappucinos, juice boxes, and smoothies). Why was no effort made to evaluate intake during the final 20-plus years of the study? Even if the FFQ method were a reliable means of gathering data, the suggestion that what individuals reported eating in the mid-1990s would be directly responsible for their deaths more than two decades later is hard to swallow. [1]

Food Frequency Questionnaires (FFQs) and other Memory-Based

進一步針對正反雙方的爭議與辯論，可參見底下4篇文章：

1.Archer, E., Marlow, M. & Lavie, C. Controversy and Debate: Memory based Methods Paper 1: The Fatal Flaws of Food Frequency Questionnaires and other Memory-Based Dietary Assessment Methods. Journal of Clinical Epidemiology (2018). doi:10.1016/j.jclinepi.2018.08.003

2.Martín-Calvo, N. & Martínez-González, M. Controversy and Debate: Memory based Methods Paper 2: Why epidemiologic cohorts using Food Frequency Questionnaires are providing valid answers?. Journal of Clinical Epidemiology (2018). doi:10.1016/j.jclinepi.2018.08.005

3.Archer, E., Marlow, M. & Lavie, C. Controversy and Debate: Memory Based Methods Paper 3: Nutritions 『Black Swans』: Our reply. Journal of Clinical Epidemiology (2018). doi:10.1016/j.jclinepi.2018.07.013

4.Martín-Calvo, N. & Martínez-González, M. Controversy and Debate: Memory based Methods Paper 4: Please, no more idle talk on Memory-Based Methods in science. Journal of Clinical Epidemiology (2018). doi:10.1016/j.jclinepi.2018.08.004

二、5組 Carbohydrate ranges 分類以相對大小樣本進行比較

先上一張直觀圖

再上一張較抽象的數據表格

這個評論著重於低碳水化合物組 (<30%) 與參考組 (50-55%)，兩者樣本量大小有差異進行比較會有問題：

<30% of calories as carbohydrate (N = 315)

30-40% of calories as carbohydrate

40-50% of calories as carbohydrate

50-55% of calories as carbohydrate (N = 3026) ← Reference

55-65% of calories as carbohydrate

>65% of calories as carbohydrate

The small comparator group issue.

We now come on to the single biggest issue in my view – indeed it may be fair to call it a manipulation. It』s what I call the 「small comparator group issue.」 I have explained this here. It is summarised here again for completeness:

The characteristics table in the main paper split the 15,428 people into equal groups (of 3,085-3,086) from the lowest to the highest carb intake. This is the objective way to review data, because there is no argument that you drew the line in a particular place to bias the finding. The appendix revealed what had been done to produce the U-shaped finding that grabbed the headlines. The numbers were extracted in Table 1 above – repeated here for convenience: [2]

(註：此段敘述的圖片見上圖)

The groups have been subjectively chosen – not even the carb ranges are even. Most covered a 10% range (e.g. 40-50%), but the range chosen for the 『optimal』 group (50-55%) was just 5% wide. This placed as many as 6,097 people in one group and as few as 315 in another. The subjective group divisions introduced what I call 「the small comparator group issue.」

If 20 children go skiing – 2 of them with autism – and 2 children die in an avalanche – 1 with autism and 1 without – the death rate for the non-autistic children is 1 in 18 (5.5%) and the death rate for the autistic children is 1 in 2 (50%). Can you see how bad (or good?) you can make things look with a small comparator group?

To then get the media headlines about life expectancy, the researchers applied a statistical technique (called Kaplan-Meier estimates) (Ref 12) to try to estimate when people would die and conversely life expectancy. This is purely a statistical exercise – we don』t know when people will die. We just know how many have died so far.

This exercise resulted in the claim 「we estimated that a 50-year-old participant with intake of less than 30% of energy from carbohydrate would have a projected life expectancy of 29·1 years, compared with 33·1 years for a participant who consumed 50–55% of energy from carbohydrate… Similarly, we estimated that a 50-year-old participant with high carbohydrate intake (>65% of energy from carbohydrate) would have a projected life expectancy of 32·0 years, compared with 33·1 years for a participant who consumed 50–55% of energy from carbohydrate.」

Do you see how both of these claims have used the small comparator group extremes to make the reference group look better? [2]

三、其他混雜因素(飲食建議更動、酒精使用)

飲食建議可能更動

There was a more serious assumption. The paper reported: 「We did not update carbohydrate intake exposures of participants that developed heart disease, diabetes, and stroke before Visit 3, to reduce potential confounding from changes in diet that could arise from the diagnosis of these diseases.」 This means, if Fred is in the lower carb group at baseline and he develops diabetes and goes to see a dietician who tells him to eat 『healthy』 whole grains, he will be more likely to die (from diabetes), but this will be a death attributed to the lower carb group. Conversely, however, if Sally is in the moderate carb group at baseline and she develops heart disease and decides to cut her carb intake, she will be more likely to die (from heart disease), but this will be a death attributed to the moderate carb group. Ostensibly, this appears to be a reasonable assumption. In this paper it isn』t, for two reasons:

i) Conventional dietary advice is to consume approximately 55% of one』s diet in the form of carbohydrate. If someone in the ARIC study developed cardiovascular disease or diabetes between the two questionnaires (i.e. between 1987-89 and 1993-95), they would be in the healthcare system. As a result, they would be advised to consume c. 55% of their diet in the form of carbohydrate. This period pre-dated the literature, which has grown in the past decade, showing the benefit of very low carbohydrate diets for obesity, diabetes and chronic conditions (Ref 9) and hence no individual would have been advised to cut their carbohydrate intake following a health diagnosis. People who developed a life threatening chronic condition would, therefore, have wrongly stayed assigned to a lower carbohydrate group rather than being reassigned to a higher carbohydrate group. The converse would almost certainly not have happened.

ii) We』ll come on to the small comparator group issue soon, but it has an impact with this assumption too. You can see in Table 1 above that the <30% carb group is by far the smallest – just 315 people. Because it is so small, it is far more sensitive to small changes. It would take only 68 people to be reallocated from the <30% carb group (if they were diagnosed with a condition and advised to increase their carb intake and then assigned to their new carb group) for this group to have the same (unadjusted) death rate as the 『optimal』 carb group (down from 51.7% to 38.4%). If, conversely, 68 people from the 『optimal』 carb group changed their carb intake and were reallocated from that group, the death rate in this group would barely drop a percentage point (from 38.4% to 37%).

The assumption not to revise the carb group for those diagnosed with a serious condition was highly unfavourable to the lowest carb intake group. [2]

酒精因素沒有納入考慮

Failure to adjust for a serious confounder (alcohol).

Many thanks to George Henderson (who tweets as @puddleg – well worth a follow), for spotting that there was no mention of alcohol in the main paper at all. This means that alcohol was not accounted for or adjusted for. This is also despite the fact that the Food Frequency Questionnaire used in the ARIC study did include questions about alcohol (beer, wine and liquor to be precise). Maybe alcohol accounted for the missing calories in the total energy intake numbers? The whole alcohol issue is a major error.

There is also a confounder of this error in that, we know from the characteristics table in the main paper that those in the lower carb intake group were more likely to be smokers. There is a positive association between smoking and drinking: smokers are more likely to be drinkers (of coffee and alcoholic drinks). This unexplained omission was highly unfavourable to the lower carb intake group. [2]

其他關點和影響

「Dietary carbohydrate intake and mortality: a prospective cohort study and meta-analysis」 研究一發表，有些人指出他們以這項研究攻擊低碳水化合物、生酮飲食療法，忽視基於臨床研究短期療法之下的安全性、有效性。

twitter.com/JeffStanley

twitter.com/BrianLenzke

相關臨床研究彙整

Evidence that Low Carbohydrate Diets are Both Safe and Effective

Low Carb Diet Studies.xlsx

相關推文討論串

twitter.com/janvyjidak/

twitter.com/DrDuaneRD/s

摘錄文章觀點主要引用自

1.Ede, G. Latest Low-Carb Study: All Politics, No Science. Psychology Today (2018). at <psychologytoday.com/us/>

2.Harcombe, Z. Low carb diets could shorten life (really?!) – Zo? Harcombe. Zoeharcombe.com (2018). at <zoeharcombe.com/2018/08>

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