Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
特定健診(2022 年度)の eGFR に 60 の一本の線を引くと、線の下の人は年齢でどれだけ違うか、その違いはどこから来るかを、第10回 NDB オープンデータの「eGFR」「血清クレアチニン」「CGA分類」「各項目の平均値」で確かめた。eGFR の式は Matsuo ほか(2009)の要旨の式(194 × クレアチニン^(−1.094) × 年齢^(−0.287)、女性は × 0.739)を使った。 40〜44 歳から 70〜74 歳へ、eGFR 60 未満の人は男性 3.60 → 32.73 %(9.08 倍)、女性 2.59 → 26.82 %(10.36 倍)に増える。表の数は式どおりに動いており(672 の升目で表の平均 ÷ 式は 1.0234〜1.0767)、男性の平均クレアチニンは 0.868 → 0.915 しか上がらないのに、平均 eGFR の対数の下がりのうち 0.829 は式の年齢の項の分だった。同じ 60 の線は、測ったクレアチニンでは 1.097(42 歳)から 0.952(72 歳)へ下がる線で、クレアチニン 1.0 以上の男性は 1.88 倍にしか増えない。線の下の人の 77.1〜91.4 % は尿蛋白(-)だった。分けるのは、線を引いたのが測った値か、年齢を入れて換算した値かである。 本稿は公開の集計データの数の形を述べる構造的解釈であり、医学的な助言・診断・治療の推奨ではない。著者は医師ではない。個人の健康に関する判断は医師・医療機関に相談すること。査読を経ていないプレプリントである。新しい数学定理も新しい法則も主張しない。60 の線の良し悪しや、年齢で線を変えるべきかは論じない。eGFR の年齢による下がりが見かけだとは言わない。eGFR のある人は特定健診を受けた人の一部で、その割合は年齢で違う。都道府県は比べない。出典:第10回 NDB オープンデータ(厚生労働省)を加工して作成。 作成にあたって:本稿の着想と内容は、著者自身の考察に基づくものです。文章の構成整理や英訳、数式の確認には AI(大規模言語モデル)の助力を得ました。最終的な内容の解釈や誤りがあれば、それらはすべて著者の責に帰します。お気づきの点があれば、ご教示いただければ幸いです。 ----- This paper checks how much the number of people below a single line of 60 on eGFR in the Specific Health Checkups (fiscal 2022) differs with age, and where that difference comes from, using the "eGFR," "serum creatinine," "CGA classification," and "mean of each item" tables of the 10th NDB Open Data. For eGFR, the equation in the abstract of Matsuo et al. (2009) was used (194 × creatinine^(−1.094) × age^(−0.287), × 0.739 for women). From 40–44 to 70–74, people with eGFR below 60 increase 3.60 → 32.73% (9.08-fold) for men and 2.59 → 26.82% (10.36-fold) for women. The tables follow the equation (table mean ÷ equation is 1.0234–1.0767 in 672 cells), and while men's mean creatinine rises only 0.868 → 0.915, 0.829 of the decline in mean eGFR on the log scale is the equation's age term. The same line of 60 is, in measured creatinine, a line that falls from 1.097 (age 42) to 0.952 (age 72), and men with creatinine of 1.0 or above increase only 1.88-fold. Of people below the line, 77.1–91.4% had urine protein (−). What separates them is whether the line is drawn on the measured value or on a value converted with age. This paper is a structural interpretation of the shape of numbers in public aggregate data; it is not medical advice, not a diagnosis, and not a treatment recommendation. The author is not a physician. Decisions about individual health should be discussed with a physician or a medical institution. This is a preprint that has not been peer reviewed. No new mathematical theorem and no new law are claimed. It does not argue whether the line of 60 is good or whether the line should change with age. It does not say that the decline of eGFR with age is only apparent. People with eGFR are part of those who attended the Specific Health Checkups, and that part differs with age. Prefectures are not compared. Source: processed from the 10th NDB Open Data (Ministry of Health, Labour and Welfare). On the making of this work: The ideas and content of this work stem from the author's own considerations. Assistance from an AI (a large language model) was used for structuring, English translation, and checking the algebra. Any remaining errors or misinterpretations are solely the author's. Feedback and corrections are sincerely appreciated.
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